Advances in Agronomy, Volume 77

Agronomy D V A N C E S I N VOLUME 77 Advisory Board Martin Alexander Ronald Phillips Cornell University Univers...

Author: Donald L. Sparks

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Agronomy

D V A N C E S I N

VOLUME

77

Advisory Board Martin Alexander

Ronald Phillips

Cornell University

University of Minnesota

Kenneth J. Frey

Kate M. Scow

Iowa State University

University of California, Davis

Larry P. Wilding Texas A&M University

Prepared in cooperation with the American Society of Agronomy Monographs Committee Lisa K. Al-Almoodi David D. Baltensperger Warren A. Dick Jerry L. Hatﬁeld John L. Kovar

Diane E. Stott, Chairman David M. Kral Jennifer W. MacAdam Matthew J. Morra Gary A. Pederson John E. Rechcigl

Diane H. Rickerl Wayne F. Robarge Richard Shibles Jeffrey Volenec Richard E. Zartman

Agronomy

DVANCES IN

VOLUME

77

Edited by

Donald L. Sparks Department of Plant and Soil Sciences University of Delaware Newark, Delaware

Amsterdam Boston London New York Oxford Paris San Diego San Francisco Singapore Sydney Tokyo

This book is printed on acid-free paper.

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C 2002, Elsevier Science (USA). Copyright

All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the Publisher. The appearance of the code at the bottom of the ﬁrst page of a chapter in this book indicates the Publisher’s consent that copies of the chapter may be made for personal or internal use of speciﬁc clients. This consent is given on the condition, however, that the copier pay the stated per copy fee through the Copyright Clearance Center, Inc. (222 Rosewood Drive, Danvers, Massachusetts 01923), for copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Law. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale. Copy fees for pre-2002 chapters are as shown on the title pages. If no fee code appears on the title page, the copy fee is the same as for current chapters. 0065-2113/2002 $35.00 Explicit permission from Academic Press is not required to reproduce a maximum of two ﬁgures or tables from an Academic Press chapter in another scientiﬁc or research publication provided that the material has not been credited to another source and that full credit to the Academic Press chapter is given.

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Academic Press 32 Jamestown Road, London NW1 7BY, UK http://www.academicpress.com International Standard Book Number: 0-12-000795-9 PRINTED IN THE UNITED STATES OF AMERICA 02 03 04 05 06 07 SB 9 8 7 6

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Contents CONTRIBUTORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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DESERTIFICATION AND ITS RELATION TO CLIMATE VARIABILITY AND CHANGE Daniel Hillel and Cynthia Rosenzweig I. II. III. IV. V. VI. VII.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concepts and Deﬁnitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study: The Sahel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monitoring Desertiﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Climatic Variability and Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 3 5 16 20 21 31 35

FATE AND TRANSPORT OF VIRUSES IN POROUS MEDIA Yan Jin and Markus Flury I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Characteristics of Viruses Relevant for Subsurface Fate and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Virus Sorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Protein Sorption and Denaturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Virus Survival . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. The Role of the Gas–Liquid Interface in Protein/ Virus Inactivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Transport of Viruses in Porous Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII. Indicators for Human Enteroviruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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40 43 45 57 64 67 70 86 88 91

vi

CONTENTS

CURRENT CAPABILITIES AND FUTURE NEEDS OF ROOT WATER AND NUTRIENT UPTAKE MODELING Jan W. Hopmans and Keith L. Bristow I. II. III. IV. V. VI. VII. VIII. IX. X. XI.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Water Transport in Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linking Plant Transpiration with Assimilation. . . . . . . . . . . . . . . . . . . . . . . . . . Transport of Water and Nutrients in the Plant Root . . . . . . . . . . . . . . . . . . . Nutrient Uptake Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flow and Transport Modeling in Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Root Water Uptake. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutrient Uptake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coupled Root Water and Nutrient Uptake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comprehensive Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prognosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

104 109 115 120 126 132 135 145 152 162 169 175

MICRONUTRIENTS IN CROP PRODUCTION N. K. Fageria, V. C. Baligar, and R. B. Clark I. II. III. IV. V. VI.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Status in World Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Factors Affecting Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Factors Associated with Supply and Acquisition . . . . . . . . . . . . . . . . . . . . . . . . Improving Supply and Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

186 188 195 206 227 246 247

SOIL SCIENCE IN TROPICAL AND TEMPERATE REGIONS—SOME DIFFERENCES AND SIMILARITIES Alfred E. Hartemink I. II. III. IV. V. VI.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Science in Temperate Regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Science in Tropical Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diametrically Opposite Interests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of Soil Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

270 271 274 282 285 286 287

CONTENTS

vii

RESPONSES OF AGRICULTURAL CROPS TO FREE-AIR CO2 ENRICHMENT B. A. Kimball, K. Kobayashi, and M. Bindi I. II. III. IV. V.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results and Discussion of Crop Responses to Elevated CO2 . . . . . . . . . . Compendium and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

294 295 326 350 359 360

THE AGRONOMIC AND ECONOMIC POTENTIAL OF BREAK CROPS FOR LEY/ARABLE ROTATIONS IN TEMPERATE ORGANIC AGRICULTURE M. C. Robson, S. M. Fowler, N. H. Lampkin, C. Leifert, M. Leitch, D. Robinson, C. A. Watson, and A. M. Litterick I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Crop Rotations as the Central Management Tool in Organic Farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Break Crops for Nutrient Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Break Crops for Improving Soil Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Break Crops for Weed Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Break Crops for Pest and Disease Management . . . . . . . . . . . . . . . . . . . . . . . . VII. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

370 371 391 403 409 411 416 417

INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

V. C. BALIGAR (185), Alternate Crops and Systems Research Laboratory, Beltsville Agricultural Research Center, USDA-ARS, Beltsville, Maryland 20705 M. BINDI (293), Department of Agronomy and Land Management, University of Florence, 50144 Florence, Italy K. L. BRISTOW (103), CSIRO Land and Water/CRC Sugar, Townsville Qld 4814, Australia R. B. CLARK (185), Appalachian Farming Systems Research Center, USDA-ARS, Beaver, West Virginia 25813 N. K. FAGERIA (185), National Rice and Bean Research Center of EMBRAPA, Santo Antˆonio de Goi´as-GO, 75375-000, Brazil M. FLURY (39), Department of Crop and Soil Sciences, Washington State University, Pullman, Washington 99164 S. M. FOWLER (369), Welsh Institute of Rural Studies, University of Wales, Aberystwyth, SY23 3AL, United Kingdom A. E. HARTEMINK (269), International Soil Reference and Information Center (ISRIC), 6700 AJ Wageningen, The Netherlands D. HILLEL (1), Columbia University Center for Climate Systems Research and NASA Goddard Institute for Space Studies, New York, New York 10025 J. W. HOPMANS (103), Hydrology Program, Department of Land, Air and Water Resources, University of California, Davis, California 95616 Y. JIN (39), Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19717 B. A. KIMBALL (293), U.S. Water Conservation Laboratory, USDA, Agricultural Research Service, Phoenix, Arizona 85040 K. KOBAYASHI (293), National Institute of Agro-Environmental Sciences, Tsukuba, Ibaraki 305-8604, Japan N. H. LAMPKIN (369), Welsh Institute of Rural Studies, University of Wales, Aberystwyth, SY23 3AL, United Kingdom C. LEIFERT (369), Tesco Centre for Organic Agriculture, University of Newcastle, Newcastle upon Tyne, NE1 7RU, United Kingdom M. LEITCH (369), Welsh Institute of Rural Studies, University of Wales, Aberystwyth, SY23 3AL, United Kingdom A. M. LITTERICK (369), Land Management Department, SAC, Craibstone Estate, Bucksburn, Aberdeen AB21 9YA United Kingdom

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CONTRIBUTORS

D. ROBINSON (369), Department of Plant and Soil Science, Aberdeen University, Aberdeen, AB24 5UA, United Kingdom M. C. ROBSON (369), Department of Plant and Soil Science, Aberdeen University, Aberdeen, AB24 5UA, United Kingdom C. E. ROSENZWEIG (1), Columbia University Center for Climate Systems Research and NASA Goddard Institute for Space Studies, New York, New York 10025 C. A. WATSON (369), Land Management Department, SAC, Craibstone Estate, Bucksburn, Aberdeen AB21 9YA, United Kingdom

Preface Volume 77 contains seven excellent reviews that should be of great interest to crop, soil, and environmental scientists. Chapter 1 is a timely review on desertiﬁcation and its relation to climate variability and change that includes discussions on processes, use of the Sahel as a case study, maintaining desertiﬁcation, and future climatic variability and change. Chapter 2 is a comprehensive review on a very timely topic—fate and transport of viruses in porous media. Topics that are covered include characteristics of viruses, virus sorption, protein sorption and denaturation, survival of viruses, inactivation of viruses, and their transport. Chapter 3 discusses the current capabilities and future needs of root water and nutrient uptake modeling including water transport and uptake in plants, nutrient uptake mechanisms, and ﬂow and transport modeling in soils. Chapter 4 reviews past and present developments in understanding the chemistry and fertility of micronutrients and their role in crop production. Topics that are covered include status of micronutrients in world soils, and factors affecting and ways to improve micronutrient supply and availability. Chapter 5 is an interesting review on the comparisons and contrasts between tropical and temperate region soils. Chapter 6 is an informative review on the response of agricultural crops to free-air CO2 enrichment. Comprehensive discussions are included on methodologies and plant responses to elevated CO2 along with effects on soil processes. Chapter 7 provides a thorough treatment on the agronomic and economic potential of break crops for ley/arable rotations in temperate organic agriculture. The use of break crops in nutrient management, soil structure improvement, weed management, and pest and disease management is discussed. Many thanks to the authors for their superb contributions. DONALD L. SPARKS

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DESERTIFICATION IN RELATION TO CLIMATE VARIABILITY AND CHANGE Daniel Hillel and Cynthia Rosenzweig Columbia University Center for Climate Systems Research and NASA Goddard Institute for Space Studies New York, New York 10025

I. Introduction II. Concepts and Deﬁnitions III. Processes A. Drought B. Primary Production and Carrying Capacity C. Soil Degradation D. Water Resources E. Social Factors IV. Case Study: The Sahel V. Monitoring Desertiﬁcation VI. Future Climatic Variability and Change VII. Prospects References

Ecosystems in semiarid regions appear to be undergoing degradation processes commonly described as desertiﬁcation. We review the concepts, deﬁnitions, and processes pertinent to the problem. Focusing on the long-term drought in the African Sahel as a case study, we analyze the relationships among climatic, biophysical, and social factors. Hypotheses related to the causation and persistence of drought involve the roles of land–surface change, atmospheric dust, and ocean– atmosphere dynamics. Remote sensing techniques have made possible monitoring ecosystem changes on a regional scale. Where fresh water resources are available, irrigation can be an effective way to stabilize and intensify agricultural production, but water resource development needs to be accompanied by water conservation and salinity control. Key social factors include land tenure, institutional structures, and population growth. Projections derived from global climate models suggest that drought conditions in the Sahel may worsen in the coming decades. Given challenges facing semiarid countries, vulnerability to the intertwined effects of degradation and climate change appears to be high. Improvements of scientiﬁc understanding of climate phenomena and their interconnections over space and time offer opportunities for controlling destructive land-use practices, augmenting carbon sinks through better soil management, and enhancing C 2002 Elsevier Science (USA). resilience.

1 Advances in Agronomy, Volume 77 Copyright 2002, Elsevier Science (USA). All rights reserved. 0065-2113/02 $35.00

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I. INTRODUCTION Ecosystems in semiarid and arid regions around the world appear to be undergoing various processes of degradation commonly described as desertiﬁcation. According to UNEP (1992), all regions in which the ratio of total annual precipitation to potential evapotranspiration (P/ET) ranges from 0.05 to 0.65 should be considered vulnerable to desertiﬁcation. Such regions constitute some 40% of the global terrestrial area, which totals about 130 million km2 (13 billion ha). Dregne (1983) calculated that the arid, semiarid, and dry subhumid regions of the world occupy 12.1, 17.1, and 9.9% of the world’s total land area. Relatively dry areas cover much of northern Africa, southwestern Africa, southwestern Asia, central Asia, northwestern India and Pakistan, southwestern United States and Mexico, western South America, and much of Australia (Fig. 1, see color insert). Arid and semiarid regions cover over a fourth of the world’s land area, and are home to nearly one-sixth of the world’s population (WRI, 2000). The total population of the world has doubled in the last four decades, resulting in the current total of about 6 billion. As of 1998, some 80% of humanity resided in the so-called developing countries, which contain only 58% of the total land area and 54% of the total cropped area. Moreover, many of the developing countries are located in semiarid regions that are most vulnerable to degradation. According to a report published by the World Resources Institute (WRI, 1998), the total area of land under cropping has increased by some 25% since 1950. In the same period, the world’s population has more than doubled, so the area of cropland per capita has been reduced by nearly a half. At present, the annual growth rate of cropland (0.2%) is only one-seventh the growth in population (Lal, 1997), so the decline in arable land per capita is continuing. That decline is most severe in the developing countries, which are expected to increase their populations most rapidly and will therefore be most in need of increased food production. In sub-Saharan Africa, for instance, the per capita area of arable land, which was 1.6 ha in 1990, is projected to fall to 0.63 ha by 2025 (Scherr, 1999). The lands still available for the expansion of farming are, in large part, marginal lands of relatively low productivity and high vulnerability. Desertiﬁcation is an emotive term, conjuring up the specter of a tide of sand swallowing fertile farmland and pastures. The United Nations Environmental Programme (UNEP) sponsored projects in the early 1980s to plant trees along the edge of the Sahara, with the aim of warding off the invading sands. While there are places where the edge of the desert can be seen encroaching on fertile land, the more pressing problem is the deterioration of the land due to human abuse in regions well outside the desert. The latter problem emanates not only from the desert but also from the centers of population; not only from the spread of the sand dunes but also from the spread of people and their mismanagement of the land (Hillel, 1992). Therefore, protecting the front line may do nothing to halt the degradation

DESERTIFICATION: CLIMATE VARIABILITY AND CHANGE

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behind it. The true challenge is not so much to stop the desert at the edge of a semiarid region as to protect the entire region from internal abuse of its vegetation and of its soil and water resources. A vicious cycle is already operating in many areas: as the land degrades, it is worked ever more intensively so its degradation accelerates; and as the returns from “old” land diminish, “new” land is brought under cultivation or grazed by encroachment onto marginal or submarginal areas. But attempts to encapsulate these complex problems in the catchall term “desertiﬁcation” may have obscured its true character and confused the search for its amelioration. In this paper, we review the concepts, deﬁnitions, and processes pertinent to desertiﬁcation, and offer an alternative, more inclusive term, namely, “semi-arid ecosystem degradation.” We use the long-term drought in the Sahelian region of Africa as a case study for analyzing the complex set of climatic, biophysical, and social factors that interweave to create the process of semiarid ecosystem degradation, and we evaluate current monitoring techniques, including remote sensing. We next consider the potentialities and hazards of irrigation development as a possible means to improve agricultural production in semiarid regions. We then ask the question, “How might global climate change affect the Sahelian region of Africa?” and analyze a set of recent projections derived from global climate change scenarios, in light of the region’s vulnerabilities. Finally, we offer our views on prospects for sustaining semiarid ecosystems and agroecosystems in the future.

II. CONCEPTS AND DEFINITIONS Desertiﬁcation is a single word used to cover a wide variety of effects involving the actual and potential biological productivity of ecosystems in semiarid and arid regions. The term desertiﬁcation (or desertization) was apparently coined by the French ecologist LeHouerou (1977) to characterize what was perceived to be a northward advance of the Sahara in Tunisia and Algeria. It gained currency following the severe drought that afﬂicted the Sud region of Africa in the early 1970s, and again in the 1980s, during which the Sahara was reported to be advancing southward into the Sahelian zone as well. For example, Lamprey (1975) estimated that during the period from 1958 to 1975, while mean annual rainfall diminished by nearly 50%, the boundary between the Sahara and the Sahel had shifted southward by nearly 100 km. As deﬁned in recent dictionaries, desertiﬁcation is the process by which an area becomes (or is made to become) desert-like. The word “desert” itself is derived from the Latin desertus, being the past participle of deserere, meaning to desert, to abandon. The clear implication is that a desert is an area too barren and desolate to support human life. An area that was not originally desert may come to resemble a desert if it loses so much of its formerly usable resources that it can no longer

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provide adequate subsistence to humans. This is a very qualitative deﬁnition, since not all deserts are the same. An area’s resemblance to a desert does not make it a permanent desert if it can recover from its damaged state, and, in any case, the modes of human subsistence and levels of consumption differ greatly from place to place. The United Nations Conference on Desertiﬁcation (UNCOD) was held in Nairobi in 1977. It was convened in response to the severe drought that had befallen the Sahel from the late 1960s through most of the 1970s. Its report deﬁned desertiﬁcation as “the diminution or destruction of the biological potential of land that can lead ultimately to desert-like conditions . . . under the combined pressure of adverse and ﬂuctuating climate and excessive exploitation.” That statement leaves open several questions, such as the deﬁnition of the land’s “biological potential,” the type and degree of damage to the land that can be considered “destruction,” and the exact meaning of “desert-like” conditions. Mainguet (1994) characterized desertiﬁcation as the “ultimate step of land degradation to irreversible sterile land.” This deﬁnition ignores the complex set of processes that progress gradually (and, for a time, reversibly) at different rates. Rather, it conﬁnes the term to the ﬁnal condition that is the extreme culmination of those various processes. An alternative approach would be to deﬁne the processes themselves and characterize the degree of degradation at which their separate or combined effects may be considered to have become irreversible. In recent years, the very term desertiﬁcation has been called into question as being too vague, and the processes it purports to describe too ill-deﬁned. Some critics have even suggested abandoning the term, in favor of what they consider to be a more precisely deﬁnable term, namely, “land degradation” (e.g., Dregne, 1994). However, desertiﬁcation has already entered into such common usage that it can no longer be recalled or ignored (Glantz and Orlovsky, 1983). It must therefore be clariﬁed and qualiﬁed so that its usage may be less ambiguous. The United Nations has since modiﬁed its deﬁnition of desertiﬁcation as follows: “Land degradation in arid, semiarid, and dry subhumid areas resulting from various factors, including climate variations and human activities” (Warren, 1996). That deﬁnition still does not either clarify the relative importance of the two potential causes or imply the possibility that they may be interactive. It merely shifts the issue to the deﬁnition of “land degradation.” Does the latter pertain to the soil, and, if so, to just what qualities or attributes of the soil (physical, chemical, and/or biological)? Does it also pertain to the vegetation present on the land, and, if so, to what attributes of the vegetation (biomass, photosynthesis, respiration, transpiration, growth rate, ground coverage, species diversity, etc.)? And what of the animal life associated with the land? “Land degradation” itself is a vague term, since the land may be degraded with respect to one function and not necessarily with respect to another. For example, a tract of land may continue to function hydrologically—to regulate inﬁltration, runoff generation, and groundwater recharge—even if its vegetative cover is

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changed artiﬁcially from a natural species-diverse community to a monoculture, and its other ecological functions may be interrupted. Rather than “land degradation,” we prefer the term “semiarid ecosystem degradation.” A semiarid ecosystem encompasses the diverse biotic community living in this given domain. Included in this community is the host of plants, animals, and microorganisms that share the habitat and that interact with one another through such modes as competition or symbiosis, predation, and parasitism. It also includes the complex physical and chemical factors that condition the lives of those organisms and are in turn inﬂuenced by them. A semiarid ecosystem may be a more or less natural one, relatively undisturbed by humans, or it may be an artiﬁcially managed one, such as an agroecosystem. Each ecosystem performs a multiplicity of ecological functions. Included among these are photosynthesis, absorption of atmospheric carbon and its incorporation into biomass and the soil, emission of oxygen, regulation of temperature and the water cycle, as well as the decomposition of waste products and their transmutation into nutrients for the perpetuation of diverse interdependent forms of life. Integrated ecosystems may thus play a vital role in controlling global warming and in absorbing and neutralizing pollutants that might otherwise accumulate to toxic levels. An agroecosystem is a portion of the landscape that is managed for the economic purpose of agricultural production. The transformation of a natural ecosystem into an agroecosystem is not necessarily destructive, if the latter is indeed managed sustainably and if it coexists harmoniously alongside natural ecosystems that continue to maintain biodiversity and to perform vital ecological functions. In too many cases, however, the requirements of sustainability fail, especially where agricultural systems expand progressively at the expense of the remaining more or less natural ecosystems. The appropriation of ever-greater sections of the remaining native habitats, impelled by the increase of population as well as by the degradation of farmed or grazed lands due to overcultivation or overgrazing, decimates those habitats and imperils their ecological functions. In the initial stages of degradation, the deteriorating productivity of an agroecosystem can be masked by increasing the inputs of fertilizers, pesticides, water, and tillage. Sooner or later, however, if such destructive effects as organic matter loss, erosion, leaching of nutrients and salination continue, the degradation is likely to reach a point at which its effects are difﬁcult to overcome either ecologically or economically.

III. PROCESSES Key processes related to desertiﬁcation include drought, primary production and carrying capacity, soil degradation, and water resources. The role of social factors is also important.

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A. DROUGHT A typical feature of arid regions is that the mode (the most probable) amount of annual rainfall is generally less than the mean; i.e., there tend to be more years with a below-average rainfall than years in which the rainfall is above average, simply because a few unusually rainy years can skew the statistical average well above realistic expectations for rainfall in most years. More than 90% of the total variation in annual rainfall can generally be encompassed within a range between one-half and twice the mean. The variability in biologically effective rainfall is yet more pronounced, as years with less rain are usually characterized by greater evaporative demand, so the moisture deﬁcit is greater than that indicated by the reduction of rainfall alone. Timing and distribution of rainfall also play crucial roles. Below-average rainfall, if well distributed, may produce adequate crop yields, whereas average or even above-average rainfall may fail to produce adequate yields if the rain occurs as just a few large storms with long dry periods between them. In semiarid agricultural regions, “drought,” like desertiﬁcation, is a broad, somewhat subjective term that designates years in which cultivation becomes an unproductive activity, crops fail, and the productivity of pastures is signiﬁcantly diminished. Drought is a constant menace, a fact of life with which rural dwellers in arid regions must cope if they are to survive. The occurrence of drought is a certainty, sooner or later; only its timing, duration, and severity are ever in doubt. It is during a drought that ecosystem degradation in the form of devegetation and soil erosion occurs at an accelerated pace. Any management system that ignores the certainty of drought and fails to provide for it ahead of time is doomed to fail in the long run. That provision may take the form of grain or feed storage (as in the Biblical story of Joseph in Egypt), or of pasture tracts kept in reserve for grazing when the regular pasture is played out, or of the controlled migration of people and animals to other regions able to accommodate them for the period of the drought. There has been a prolonged period of drought in the Sahelian region of Africa since the early 1970s (Fig. 2). Various hypotheses involving both natural and

Figure 2 Rainfall ﬂuctuations 1901–1998, expressed as a regionally averaged standard deviation (departure from the long-term mean divided by the standard deviation) for the Sahel. (Source: IPCC WG II, 2001).

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anthropogenic factors have been advanced to explain the persistence of this drought. 1. Atmospheric Dust One hypothesis is that the recent droughts are due to a cooling of the land masses of the Northern Hemisphere by about 0.3◦ C between 1945 and the early 1970s, owing to an increase in atmospheric dust from drylands, as well as from air pollution and volcanic eruptions. The cooling may have changed the patterns of air mass movement (Tegen et al., 1996). Evidence in support of this hypothesis seems to be contradicted by the heavy rains that occurred in the Sahel during the 1950s when the Northern Hemisphere cooled, and by the severe Sahel drought that occurred during the early 1980s when the Northern Hemisphere experienced a warming. 2. Ocean–Atmosphere Dynamics Another hypothesis links drought in the Sahel to changes in ocean–atmosphere dynamics, speciﬁcally changes in sea–surface temperatures (SSTs) in the world’s oceans. Such changes might tend to reduce the northward penetration of the Intertropical Convergence Zone (ITCZ)—the great band of equatorial clouds whose shifting pattern brings monsoonal rain to the humid tropics as well as to the Sahel (Nicholson, 1986). Many studies have linked interannual variation of SSTs and seasonal precipitation variability in the region (e.g., Druyan, 1987; 1989; Folland et al., 1986; Lough, 1986; Rowell et al., 1995). Droughts in the Sahel tend to be coincident with positive SST anomalies in Southern Hemisphere oceans and the Indian Ocean, and negative SSTs in the Northern Hemisphere oceans, especially the subtropical North Atlantic Ocean. Abundant rain in the Sahel is often, but not always, linked with SSTs of the opposite sign in the Atlantic and other oceans (Lamb and Peppler, 1991, 1992). The interhemispheric SST gradient in the Atlantic Ocean appears to be a key mechanism for precipitation in the Sahelian latitudes (Fontaine and Janicot, 1996; Ward, 1998). Warmer than normal SSTs in the tropical Paciﬁc related to the El Ni˜no/Southern Oscillation (ENSO) phenomenon have similarly been linked with droughts in Australasia, India, South America, and Southern Africa, though these droughts typically do not persist for more than one or two seasons. The Sahelian region of Africa, on the other hand, has had many dry years that are not correlated with Paciﬁc SSTs, so the persistence of the Sahelian drought sets it apart from droughts in other parts of the world. There does appear to be some ENSO-driven teleconnection to drought in West Africa (e.g., Fontaine and Janicot, 1996), but Janicot et al. (1996) show that the strength of the correlation of Sahel rainfall with the Southern Oscillation Index (SOI) is quite variable. Hunt (2000) proposes a mechanism by which tropical Paciﬁc SSTs inﬂuence Sahel rainfall by modulating

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the North Atlantic Oscillation (NAO) via the Paciﬁc–North America oscillation. Druyan and Hall (1996) suggest that extreme Paciﬁc Ocean SST anomalies inﬂuence climate variability in the Sahel through wave disturbances of the tropical easterly jet, with associated effects on convergence, humidity, and precipitation. These and other ocean–atmosphere relationships are being used to forecast seasonal rainfall in the region (Nnaji, 2001; Ward, 1998). 3. Land–Surface Change Still another hypothesis is that droughts can be caused or worsened by largescale changes in the land surface of Africa, and speciﬁcally by the deforestation and overall denudation of the land (Charney, 1975; Sud and Molod, 1988). A process may thus have started whereby the drought can become self-reinforcing. According to the theory of “biophysical feedback,” losses of vegetative cover resulting from the drought as well as from overcultivation, overgrazing, and deforestation, along with the consequent increase of the dust content of the air, combine to enhance the area’s reﬂectivity to incoming sunlight. That reﬂectivity, called “albedo,” may rise from about 25% for a well-vegetated area to perhaps 35% or more for bare, bright, sandy soil. As a larger proportion of the incoming sunlight is reﬂected skyward rather than absorbed, the surface becomes cooler, and so the air in contact with the surface has less tendency to rise and condense its moisture so as to yield rainfall. An additional effect of denudation is to decrease interception of rainfall by vegetation and inﬁltration, while increasing surface runoff, thereby reducing the amount of soil moisture available for evapotranspiration. Crops and grasses, which have shallower roots than trees and in any case transpire less than the natural mixed vegetation of the savanna, transpire even less when deprived of moisture during a drought. The meteorological consequences of such changes have been explored in modeling studies (Xue and Shukla, 1993). The hypothesis is that such changes may have some effect on regional precipitation, since in many continental areas rainfall is derived in signiﬁcant part from water evaporated regionally. It proposes that the biophysical and physical processes interact, as lower rainfall leads in turn to more overgrazing, less regrowth of biomass, and further reduction in reevaporated rain owing to the decline in soil moisture. Thus, the feedback hypothesis offers its own explanation as to why the drought in the Sahel has tended to persist for so long. There is still no conclusive evidence, however, that even large-scale changes in land surface conditions do actually affect regional-scale climate (Nicholson et al., 1998; Nicholson, 2000). Key components in semiarid ecosystem degradation processes are increased surface albedo (the reﬂectance of solar radiation) and increased generation of dust, both of which are consequences of the exposure of bare, dry ground following removal of the original vegetative cover.

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The albedo of a bare soil depends on the organic matter content and the mineral composition of the topsoil. It also depends on the moisture content of the soil surface. A moist soil is generally less reﬂective (i.e., “darker”) than a dry soil (Hillel, 1998). Thus, Nicholson et al. (1998) found that near the southern edge of the African Sahel (at a latitude of 15 degrees north), where the rainfall was 450 mm, the albedo was about 30%. However, near the northern boundary of the Sahel, where the mean annual rainfall was only 200 mm, the surface albedo was about 43%. Albedo is also affected, to some degree, by the smoothness or roughness of the surface. Above all, however, it is affected by the vegetative cover and its above-ground residues. A widely cited hypothesis, promulgated by Charney (1975), Charney et al. (1975), and Otterman (1974, 1977, 1981), suggested a feedback mechanism between land use and climate change. Speciﬁcally, they raised the possibility that an increase in albedo resulting from anthropogenic denudation of the land can in turn cause a diminution of rainfall. The mechanistic reasoning underlying this hypothesis is that an increase in surface reﬂectivity implies a reduction in the absorption of solar energy, which entails a reduction in soil surface temperature and a consequent reduction in sensible heating of the atmospheric layer in contact with the soil. Proponents of the Charney hypothesis speculated that because a more highly reﬂective surface should tend to be cooler, it should enhance the subsidence of warm dry air and hence exacerbate the area’s aridity. This, in turn, reduces the upward convective rise of warm air that normally results in condensation of vapor and the formation of clouds. If the rise in albedo occurs over a large enough area, it might thus reduce the regionally generated rainfall. Hence, so the reasoning goes, surface denudation—which is the common effect of humans attempting to survive with their livestock during a drought—is a self-reinforcing process that exacerbates the very drought that initially induced it. Lare and Nicholson (1994) imply that if desertiﬁcation (i.e., denudation) is extreme, it could indeed evoke the sort of feedback originally postulated by Charney. A striking example of the albedo difference between grazed and ungrazed land can be seen along the border between the western Negev of Israel and northeastern Sinai of Egypt. The two contiguous areas of this arid region had been grazed to the same degree until 1948, after which the newly established State of Israel restricted grazing on its own side of the border. Consequently, the area within Israel developed a relatively dense vegetative cover that appears much darker on aerial and satellite photographs than the neighboring area on the Egyptian side. According to Otterman (1977, 1981), the protected area of the Negev had an albedo of 12% in the visible light and 24% in the infrared range, whereas the corresponding values on the overgrazed Sinai side were as high as 40 and 53%. Recent studies have shown, however, that the darkening is due not only to the shrubs and grasses growing in the area but also to a biological crust (consisting of algae, fungi, and cyanobacteria) that developed on the surface of the sandy soil.

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A vegetated area, though it appears darker in aerial photographs, may not be warmer than a bare area, as long as the plants are actively transpiring. The process of transpiration involves the absorption of latent heat and therefore tends to cool the foliage. During the dry season, however, many of the indigenous plants curtail transpiration so that they, along with the area as a whole, may indeed become warmer than it would be if it were bare of vegetation. Otterman and Tucker (1985) reported radiometric ground temperatures (evidently made in the summer season) of about 40◦ C in Sinai and about 45◦ C in the Negev. More recently, Otterman et al. (2001) reported that measurements made by NOAA satellites have consistently shown the Negev to be warmer than Sinai by about 4.5◦ C during the generally dry period of May to October. In contrast, Balling (1988) and Bryant et al. (1990) found that the surface temperatures on the darker (more densely vegetated) U.S. side of the Mexican border were 2 to 4◦ cooler than on the overgrazed and lighter-colored Mexican side. The latter measurements may well have been made during a period when the vegetation was actively transpiring, and hence produced a cooling effect despite its lower albedo. The persistent presence of dust in the atmosphere itself has an effect on an area’s radiation balance (Fouquart et al., 1987). It tends to scatter and reﬂect a fraction of the solar (shortwave) radiation, while absorbing longwave radiation emitted from the Earth. In some cases, a turbid atmosphere may actually warm the air near the ground, while in other cases it may do the opposite, depending on such variables as its density as well as its reﬂective or absorptive properties. Recent studies on the potential effects of aerosols on rainfall have advanced another feedback hypothesis. Denudation of an area’s vegetation is usually associated with biomass burning, which releases smoke into the air. In addition, denudation also results in deﬂation of the soil surface by wind erosion, which in turn creates a “dust bowl” effect. Rosenfeld and Farbstein (1992), Rosenfeld (1999, 2000) and Rosenfeld et al. (2001) have presented evidence that concentrations of such aerosols in the troposphere can suppress rainfall signiﬁcantly. The postulated mechanism is that moisture condensed on the dust particles forms small droplets that do no coalesce sufﬁciently to generate rainfall. The detrimental impact of dust on rainfall is less than that caused by smoke from biomass burning, but the abundance of desert dust in the atmosphere renders it important. The reduction of rainfall affected by desert dust can cause drier soil, which raises still more dust, thus creating a feedback loop to further reduce rainfall.

B. PRIMARY PRODUCTION AND CARRYING CAPACITY The biological productivity of any ecosystem is due to its primary producers (known as autotrophs), which are the green plants growing in it. They alone are

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able to create living matter from inorganic raw materials. They do so by combining atmospheric carbon dioxide with soil-derived water, thus converting radiant energy from the Sun into chemical energy in the process of photosynthesis. Green plants also respire, which is the reverse of photosynthesis, and in so doing they utilize part of the energy to power their own growth. The net primary production then becomes available for the myriad of heterotrophs, which subsist by consuming (directly or indirectly) the products of photosynthesis. A stable ecosystem is one in which production and consumption, synthesis and decomposition, are in balance over an extended period of time. When humans enter into an ecosystem and appropriate some of its products for themselves, they normally do so in competition with, and at the expense of, other potential consumers. Historically, in the hunter-gatherer phase of subsistence, humans merely selected the most readily obtainable and useful (or desirable) plant and animal products, leaving the remainder more or less intact. As their population increased, humans began to manage the ecosystem so as to promote the production of the goods they desired, and to suppress the species that competed for those products. At a still later stage, humans tended to take over sections of the ecosystem entirely, aiming to eradicate all species that did not serve them directly, and to plant (and harvest) only the plants and animals they chose to domesticate. In the process, the ecosystem’s biodiversity and natural productivity were profoundly affected (Hillel, 1992). As long as the tracts dominated by humans consist of small enclaves within a large and continuous ecological domain, the ecosystem as a whole is not seriously affected. However, as population grows progressively and human management becomes both more extensive and more intensive, the ecological integrity of entire regions is threatened. Especially affected are areas within the semiarid and arid regions, which, because of the paucity of water and the fragility of the soil (typically deﬁcient in organic matter, structurally unstable, and highly erodible) are most vulnerable and least resilient. The term “carrying capacity” has been used to characterize an area’s productivity in terms of the number of people or grazing animals it can support per unit area on a sustainable basis (Cohen, 1995). However, the productive yield obtainable from an area—and hence the number of people deriving their livelihood from it, at whatever standard of life—depends on how the area is being used. Under the hunter-gatherer mode of subsistence, an area may be able to carry only, say, 1 person per square kilometer, whereas under shifting cultivation it may carry 10, and under intensive agriculture perhaps 100. The more intensive forms of utilization also involve inputs of capital, energy, and materials, such as fertilizers and pesticides, that are brought in from the outside to enhance an area’s productivity. As the usable productivity is affected by the availability of water (i.e., by seasonal rainfall), it varies from year to year and from decade to decade, and a long-term average

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(as well as variability) is difﬁcult to determine, especially given the prospect of climate change. It is therefore doubtful that any given regions can be assigned an intrinsic and objectively quantiﬁable “carrying capacity.” Human pressure on the meager resources of arid ecosystems arises primarily because of increasing population and the trend toward sedentarization of formerly nomadic people. What typically follows includes the cutting down of wooded plants for fuel, overcultivation, and overgrazing by livestock (especially in the immediate peripheries of water supply centers such as wells, cisterns, or surfacewater impoundments). The denuded and pulverized soil surface then falls prey to erosion by wind (during the dry season) and by water (during the rainy season). Wind erosion blows away the fertile topsoil and greatly increases the content of dust in the atmosphere. Water erosion also scours away the topsoil and often cuts into the soil to produce deep gullies. During fallow periods, rainfall may also leach away soluble nutrients. The net result can be an overall reduction in biological productivity. Over a long period of time (say, centuries), and in the absence of human intervention, even a severely eroded soil can recover. However, on the time scale of years to a few decades, especially if humans continue to overgraze and/or overcultivate the land, soil erosion may be, in effect, irreversible. One problem is to measure the productivity of an area and its gradual change from year to year or from decade to decade. Quite another problem is to assess the recoverability (or resiliency) of an area following a partial loss of productivity, and the rate of potential recovery, i.e., the time pattern of gradual restoration of productivity and the period needed for its completion (Dregne, 1994). Desertiﬁcation from anthropogenic and climatic factors in Senegal caused a fall in standing wood biomass of 26 kg C ha− 1 y−1 in the period 1956–1993, releasing carbon at the rate of 60 kg C cap− 1 y− 1 (Gonzalez, 1997). The signiﬁcance of these quantities in the global balance may be small, but perhaps important nonetheless (Bouwman, 1992; Lal, 2001).

C. SOIL DEGRADATION An important criterion of soil degradation (itself a major component of land and ecosystem degradation) is the loss of soil organic matter. Compared to soils in more humid regions, those in arid regions tend to be inherently poor in organic matter content, owing to the relatively sparse natural vegetative cover and to the rapid rate of decomposition. The organic matter present is, however, vitally important to soil productivity. Plant residues over the surface protect the soil from the direct erosive impact of raindrops and from deﬂation by wind and help to conserve soil moisture by minimizing evaporation. Plant and animal residues that are partially decomposed and that are naturally incorporated into the topsoil help to stabilize its

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structural aggregates, which in turn enhance inﬁltrability, minimize water loss by runoff, and enable seed germination and root growth. The organic matter present also contributes to soil fertility by releasing nutrients. When the natural vegetative cover is removed, and especially when the soil is tilled repeatedly, there follows a rapid process of organic matter decomposition and depletion. Accelerated erosion also removes the layer of topsoil that is richest in organic matter. Consequently, the destabilized soil tends to form a surface crust that further inhibits inﬁltration. Water losses by both runoff and evaporation increase. Moreover, the soil loses an important source of nutrients. These destructive processes can be countered or ameliorated by methods of conservation management, including minimum or zero tillage, maintenance of crop residues, the periodic inclusion of green manures in the crop rotation (ASA, 1983; USDA, 1991), and agroforestry (Nair, 1993). The destructive processes induced by soil mismanagement, and—in contrast— the constructive processes induced by conservation management, though seemingly local, may have—when practiced on a regional scale—an impact on climate. Soils subject to accelerated decomposition of organic matter tend to release carbon dioxide and thus contribute to the enhanced greenhouse effect. Conversely, soils that are being enriched with organic matter can absorb and sequester quantities of carbon that are extracted from the atmosphere in photosynthesis (Bouman, 1992; Lal, 2001).

D. WATER RESOURCES Where fresh water resources are available and can be utilized economically, irrigation can be an effective way to intensify and stabilize production in semiarid or arid regions. Irrigation is the supply of water to agricultural crops by artiﬁcial means, designed to permit farming in arid regions and to offset drought in semiarid regions. Even in areas where total seasonal rainfall is adequate on average, it may be poorly distributed during the year and variable from year to year. Wherever traditional rain-fed farming is a high-risk enterprise owing to scarce or uncertain precipitation, irrigation can help to ensure stable production. Irrigation has long played a key role in feeding expanding populations and is expected to play a still greater role in the future. It not only raises the yields of speciﬁc crops but also prolongs the effective crop-growing period in areas with dry seasons, thus permitting multiple cropping (two, three, or even four, crops per year) where only a single crop could be grown otherwise. With the security provided by irrigation, additional inputs needed to intensify production further (pest control, fertilizers, improved varieties, and better tillage) become economically feasible. Irrigation reduces the risk of these expensive inputs being wasted by crop failure resulting from lack of water.

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Although irrigated land amounts to only some 17% of the world’s cropland, it contributes well over 30% of the total agricultural production. That vital contribution is even greater in arid regions, where the supply of water by rainfall is least, even as the demand for water imposed by the bright Sun and the dry air is greatest. The practice of irrigation consists of applying water to the part of the soil proﬁle that serves as the root zone, for the immediate and subsequent use of the crop. Inevitably, however, irrigation also entails the addition of water-borne salts. Many arid-zone soils contain natural reserves of salts, which are also mobilized by irrigation. Underlying groundwater in such zones may further contribute salts to the root zone by capillary rise. Finally, the roots of crop plants typically extract water from the soil while leaving most of the salts behind, thus causing them to accumulate. The problem is age-old. From its earliest inception in the Fertile Crescent, some six or more millennia ago, irrigated agriculture, especially in ill-drained river valleys, has induced processes of degradation that have threatened its sustainability. The artiﬁcial application of water to the land has ipso facto caused the water table to rise, which in turn induced the self-destructive twin phenomena of waterlogging and salination (Fig. 3). Some investigators include the degradation of irrigated lands, generally by waterlogging and salination, in the category of desertiﬁcation (Dregne and Chou, 1993). Though the processes taking place differ fundamentally from those in

Figure 3 Waterlogging and salination. The rising water-table in poorly drained land saturates the soil, impedes aeration, and infuses the root zone with salts. (Source: Hillel, 1998).

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rainfed lands, the damage done to injudiciously irrigated lands is indeed in the category of ecosystem degradation (Hillel, 2000). Processes occurring off-site (upstream as well as downstream of the irrigated area) strongly affect the sustainability of irrigation. For example, denudation of upland watersheds by forest clearing, cultivation, and overgrazing induces erosion and the consequent silting of reservoirs and canals, thereby reducing the water supply. The construction of reservoirs often causes the submergence of natural habitats as well as of valuable scenic and cultural sites. Concurrently, the downstream disposal of drainage from irrigated land tends to pollute aquifers, streams, estuaries, and lakes with salts, nutrients, and pesticides. Finally, the irrigation system itself may harbor and spread water-borne diseases, thus endangering public health. So the very future of irrigation is threatened by land degradation as well as by dwindling water supplies and deteriorating water quality. In the last few decades, even as great investments have been made in the development of new irrigation projects, the total area under irrigation has hardly expanded. That is because large tracts of irrigated land have degenerated to the point of being rendered uneconomic to cultivate, or—in extreme cases—have become totally sterile. The dilemma of land deterioration is not exclusive to the less developed nations, where it has caused repeated occurrences of famine. It applies to an equal extent to such technologically advanced countries as Australia, the United States, and the central Asian regions of the former Soviet Union. So pervasive and inherent are the problems that some critics doubt whether irrigation can be sustained in any one area for very long—and they have much evidence to support their pessimism. Irrigated agriculture can be sustained, albeit at a cost. The primary cost is effective salinity control, along with the prevention of upstream, on-site, and downstream environmental damage. Although there will be cases where the costs of continued irrigation (especially if severe damage has already occurred) may be prohibitive in practice, in most instances the cost is indeed well worth bearing. Investing in the maintenance of irrigation can result in improved economic and social well-being as well as in a healthier environment. Developing and implementing an effective salinity control program require an understanding of complex interrelationships with multiple causes, effects, and feedbacks, operating at different scales of space and time. Except in the most problematic locations, irrigation can be maintained, provided that water supplies of adequate quality can be assured, the salt balance and hence the productivity of the land can be maintained, the drainage efﬂuent can be disposed of safely, and the economic returns can justify the costs. The sine qua non of ensuring the sustainability of irrigation is the timely installation and continuous operation of a drainage system to dispose safely of excess salts. All too often, drainage creates an off-site problem, beyond the on-site cost of installation and maintenance, since the discharge of briny efﬂuent can degrade

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the quality of water along its downstream route. Where access to the open sea is feasible, solving the problem is likely to be easier than in closed basins or in areas far from the sea. In those cases, the disposal terminus (whether a lake or an aquifer) may eventually become unﬁt for future use, hence the importance of reducing the volume and salinity of efﬂuents. Much can be achieved by improving the efﬁciency of water use. Modern irrigation technology offers the opportunity to conserve water through reduced transport and application losses coupled with increased efﬁciency of utilization (Hillel, 1997).

E. SOCIAL FACTORS Social factors are necessarily involved in both semiarid ecosystem conservation and its inverse degradation. Farmers who do not have tenure to the land are not likely to invest in its conservation or improvement (Syers et al., 1996). Neither are communities that lack stable institutional structure likely to establish and maintain essential infrastructure and services to enable, encourage, and coordinate farmers’ efforts to implement land improvement and conservation measures (especially on communal lands). And no effective action at all may be possible in the absence of a proactive governmental policy, including the provision of credit or subsidies, professional guidance and training, as well as the preparation and implementation of national and regional drought contingency plans for both farmers and herders (Jolly and Torrey, 1993). The conservation of land resources is a collective societal concern, not merely a private concern of the people utilizing the land directly (Sen, 1981). Finally, there is the most difﬁcult, yet inescapable issue of population numbers. No system of management, however efﬁcient, can be sustained if the population continues to grow without limit. A crucial aspect of population control is the empowerment of women, through education and equal rights (social, political, and economic), as full participants in the management of their societies’ physical, biological, and human resources (Arizpe et al., 1994).

IV. CASE STUDY: THE SAHEL The debate over desertiﬁcation has tended to focus upon a particular region of Africa south of the Sahara called the Sahel. The word sahil in Arabic means a plain, a coast, or a border. Used geographically, the term refers to a band of territory approximately 200–400 km wide, centered on latitude 15◦ N, lying just south of the Sahara and stretching across most of the width of Africa. The Sahel

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covers well over 2 million km2 and constitutes signiﬁcant portions of Senegal, Gambia, Mauritania, Mali, Burkina Faso, Niger, Chad, and the Sudan. By some deﬁnitions, the Sahel covers a wider latitudinal belt that extends roughly between 10 and 20◦ N into parts of the Ivory Coast, Ghana, Benin, Togo, Nigeria, Cameroon, and Ethiopia. For our climate change analysis, we utilize the broader designation. The mean annual temperature of the more broadly deﬁned Sahel region ranges from 15 to 30◦ C, while rainfall varies from about 100 mm in the north to about 1000 mm in the south (Fig. 4, see color insert). The climatic regime depends on the excursions of the Intertropical Convergence Zone (ITCZ) and the African jetstream and is highly variable. The rainstorms are erratic and occasionally violent, and their variability increases from south to north. The rainy season, lasting 3 to 5 months, alternates with an extended, unrelieved, dry season. The periodic occurrence of drought is an inherent feature of this harsh climate and successive years of drought may be followed by years with torrential rains. The soils of the Sahel are generally of low fertility, particularly poor in phosphates and nitrogen, structurally unstable, with low humus content and low water retention. Hardened layers, laterization, and vulnerability to wind and water erosion are common features. Water for irrigation is available in some places from streams and rivers (Senegal, Niger, and Chari-Logone), and possibly from groundwater aquifers, but the area under irrigation is rather small and the irrigation potential has not been fully developed. The vegetation is a mixed stand of trees, shrubs, and perennial and annual grasses, typical of savannas and steppes. In the African Sahel, and similarly in other regions, the establishment and consolidation of European colonial rule in the 19th century brought about fundamental changes that subsequently were to modify the relation of indigenous societies to their environment. After an initial period of warfare, the area was stabilized and security conditions improved. So did medical and veterinary facilities including vaccination services. These interventions allowed human and animal populations to increase rapidly during favorable periods. At the same time, traditional patterns of land utilization and tenure, and of migration and transhumance, were disrupted by arbitrary boundaries and by imposed political and economic structures. Although the available historical records are rather meager, they suggest that similar major droughts, lasting 12–15 years, evidently occurred in the 1680s, the mid-1700s, the 1820s and 1830s, and the 1910s. In the ﬁrst half of the 19th century, the level of Lake Chad apparently declined for 2 or 3 decades, to about where it was during the drought of the mid-1980s. The geological record shows several similar falls of the lake level in the past 600 years (Rind et al., 1989). On the other hand, we know that the Sahel has also gone through much wetter periods in the 9th through the 13th centuries, and 16th through the 18th centuries; also from 1870 to 1895, and during the 1950s. The area near Timbuktu, which now has only 100 mm of annual rainfall, was humid enough in the late 19th century for wheat to be grown.

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The fortuitous occurrence of favorable weather conditions during most of the 20th century, and particularly during the abnormally wet period of 1950–1965 following the attainment of independence by the region’s states, obscured the effects of the changes imposed earlier. Given good rains, freshly cleared lands produced good harvests even in areas that normally would have been considered ill-suited for cultivation. Instead of deliberately keeping areas underpopulated and providing for eventual drought, the authorities in some cases encouraged farmers to move into marginally arable lands. Pastoral tribes were then pushed further into even more marginal grazing lands, where they were provided with water by means of mechanically powered tubewells. Inevitably, drought struck. As access to the wells was free to all, traditional control over management of pastures was eliminated. The overall result has been an increase in herd numbers, a decrease in pasture through more widespread cropping, and an abandonment of traditional range management mechanisms (Hillel, 1992). The Sahel region seems to have undergone a general decline of rainfall since the late 1960s (see Fig. 2). There have been several unusually prolonged and severe droughts since then, in marked contrast with the preceding 20 relatively wet years (Rind et al., 1989). At each drought, people may remain on the land in the hope that the rains might soon return, and while waiting, they do what they can to save their herds of goats, sheep, cattle, and camels. When the grass plays out, they may try to increase their animals’ intake of browse by lopping trees already weakened by lack of soil moisture. They also continue to collect ﬁrewood from the sparse shrubs and trees. When many months elapse without rain, the vegetation dies out, while the soil—desiccated, pulverized, and trampled—begins to blow away in the wind. And when a sudden rainstorm visits the area, it scours and gullies the erodible topsoil. Finally, the people are left with no choice but to abandon their traditional homes and villages and migrate to the cities, where they seek employment or relief assistance. The drought of 1968–1973 highlighted the basic problems that had been too long ignored. Family and tribal structures and their autonomous traditional practices of resource management and land tenure had been broken down, so the local population was now unable to cope with the drought on its own. The plight of the Sahel was exacerbated by the drought’s recurrence, in even more severe form, during the early 1980s. Consequently, sections of the region were almost emptied of inhabitants, as thousands of people migrated from their villages to refugee camps and overcrowded cities. Semiarid ecosystem degradation has been linked to migrations that may have displaced 3% of the population of Africa since the 1960s (Westing, 1994). Some of the Sahel’s problems have been compounded by ill-conceived development efforts. Some planners seem to have misunderstood the logic of traditional production systems, and have underestimated the difﬁculty of improving them. They also failed to foresee the potentially negative consequences of intended

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improvements brought in under the imprimatur of “technology transfer.” In many cases, they seem to have neglected the fundamental signiﬁcance of rainfall variability the probability of drought, and the principle of risk avoidance. Some of the traditional production systems were based on probable outcomes and were therefore better able to contend with droughts (though, of course, no production system can cope with a severe drought prolonged over several successive years). The population of the western African regions of the Sahel and the regions lying south of it, called the Sahelo-Sudanian and Sudanian zones, was estimated at 31 million in 1980. Though the population density is still fairly low throughout, varying from fewer than 2 per square kilometer in Mauritania to nearly 60 in Gambia, it has been increasing steadily. In recent decades, population growth rates have been close to 3% per annum. The area has reached 54 million inhabitants by the year 2000 (75% more than in 1980, and almost three times as many as in 1961). The urban population, incidentally, has been swelling at rates exceeding 5% per year, in large part from the inﬂux of people driven off the land because of drought. Gonzalez (2001) has measured declines in forest species richness and tree density in the West African Sahel in the last half of the 20th century. Such changes have apparently shifted vegetation zones in Northwest Senegal towards areas of higher rainfall at an average annual rate of 500–600 m. Xerophytic Sahel species have expanded in the north, while mesic Sudan and Guinean species have retracted to the south. Rainfall and temperature are identiﬁed as the most signiﬁcant factors explaining tree and shrub distribution. The changes have also decreased human carrying capacity below actual population densities. The rural population of 45 people per square kilometer exceeded the 1993 carrying capacity of ﬁrewood from shrubs of 13 people per square kilometer. Gonzalez advocates the traditional practice of regeneration of local species over the planting of exotic species. In the practice of native regeneration, farmers select small trees in their ﬁeld, protect them, and prune them to promote rapid growth of the apical meristem. The continued destruction of the rural environment is likely to result in further urbanization. As the demand for food, other agricultural products, and ﬁrewood continues to mount, it is likely to generate greater exploitation of the region’s meager resources. Policy options include social and educational programs that foster reduced population growth rates and improvements in rural productivity. The latter can be achieved by intensifying the use and conservation of favorable lands, developing the irrigation potential, improving management of range lands, reforesting marginal lands, and raising the efﬁciency of household energy use so as to curtail the burning of ﬁrewood. Above all, adequate provision must be made for the possible occurrence of drought in the future. Fortunately, the land itself exhibits a remarkable resilience. It had suffered many droughts in the past, and when the rains subsequently returned, so eventually did much of the vegetation. In large measure, the recent damage was temporary, and the land can recover if it is rehabilitated, or at least left undisturbed for a sufﬁcient time.

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V. MONITORING DESERTIFICATION The techniques of remote sensing have made possible the monitoring of changes to ecosystems on a regional scale (Fig. 5, see color insert) (e.g., Justice, 1986). Studies based on the remote sensing of the African Sahel were reported by Nicholson et al. (1998). The authors state that there has been no progressive change of either the Saharan boundary or of vegetation cover in the Sahel during the 16-year period of the study, nor has there been a systematic reduction of productivity as assessed by the water-use efﬁciency of the vegetation cover. In principle, statistical criteria designed to test the probability levels of differences (between sites or between successive measurements on the same site) should not be used to “prove” the opposite, namely that there are no differences. In this case, absence of evidence of change by one criterion or another is not in itself evidence of absence of any change. Measurements (partly indirect) made at various times on large areas may have obscured subtle local changes that may have occurred in speciﬁc sites. Generality may tend to ignore speciﬁcity. The authors themselves report that while their data “showed little change in surface albedo during the years analyzed, a change in albedo of up to 0.10% since the 1950s is conceivable.” (The ﬁgure 0.10% is apparently a misprint of what may have been a 1% or a 10% change in albedo). NDVI is the ratio between the red and near-red infrared reﬂectance bands, obtained from advanced high-resolution radiometer data from the polar-orbiting satellite of the National Oceanic and Atmospheric Administration (Tucker et al., 1991). In arid and semiarid regions, NDVI evidently correlates with the density of the vegetative cover and its biomass, as well as with its “leaf area index” (Nicholson et al., 1998) and photosynthetic activity (Prince, 1991). Another criticism is in order regarding the use of NDVI (the Normalized Difference Vegetation Index) as a measure of net primary production. That index may indeed indicate the activity of the vegetative cover at the time of measurement, but it is oblivious to the amount of vegetation harvested by humans and/or their animals prior to the time of measurement. Taken to be a general indicator of the “greenness” of an area, NDVI has also been conjectured to correlate with biological productivity, but that correlation may not necessarily hold. In principle, the amount of vegetation present per unit of area should depend on the amount produced in situ, minus the amount removed from it. Therefore, the relation between an area’s productivity and its vegetative biomass at any time must depend on whether the vegetation has been or is being “harvested” (e.g., grazed by livestock, or cut and carried away by humans). An area could be quite productive yet relatively bare, if it had been harvested just prior to the NDVI measurement.

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A more serious caveat is in order: even if there is no discernible change in the density of an area’s overall vegetative cover, there might well be a considerable change in the composition of the vegetation (i.e., in its biodiversity, ecological function, and feed value). For instance, an overgrazed area may exhibit a proliferation of less nutritious plants at the same time that it loses the most palatable species of grasses and legumes that had contributed to the area’s original carrying capacity. Evidence of this effect was demonstrated by Gonzalez (2001). Nicholson et al. (1998) noted that the interannual ﬂuctuations of the desert boundary, as assessed from NDVI, were indeed considerable, with a displacement as great as 3◦ latitude (roughly 300 km) back and forth. These ﬂuctuations corresponded to the variations of the region’s rainfall. However, the investigators could discern no progressive “march” of the desert over West Africa during the period of their study (1980 to 1995). Furthermore, they reported that the ratio of NDVI to rainfall, which they took to represent the rain-use efﬁciency of the vegetation, indicated little interannual variability and no discernible decline during the 13 years of their analysis. A criterion used by Tucker et al. (1991) to delineate the boundary between the Sahara and the Sahel is the mean annual rainfall contour (isohyet) of 200 mm. Malo and Nicholson (1990) found that this boundary corresponds approximately to an annually integrated NDVI of 1. However, the density of the vegetative cover must depend not only on rainfall but also on whether and to what extent that vegetation is being utilized. As seen in Fig. 2, the annual precipitation in the Sahel has ﬂuctuated widely, but the amounts for the last 3 decades of the 20th century are generally lower than those of the preceding decades. And although the trend in recent years appears to be an upward one, the annual amounts of rainfall are still low relative to the century’s earlier decades. Clearly, an analysis based on any particular short period may be misleading.

VI. FUTURE CLIMATIC VARIABILITY AND CHANGE Climate in arid and semiarid regions is likely to be even more inﬂuenced in the future by human activity due to the phenomenon known as global climate change. Emissions of greenhouse gases, among them carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), and aerosols due to human activities are altering the atmosphere in ways that are expected to warm the climate. The warming trend, or enhanced greenhouse effect, is attributed to the release into the atmosphere of radiatively active trace gases, which have the property of trapping a growing proportion of the heat emitted by the earth’s surface. The atmospheric concentration

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HILLEL AND ROSENZWEIG Table I Observed and Projected Changes in Extreme Weather and Climate Events Related to Temperature and Precipitationa

Conﬁdence in observed changes (latter half of the 20th century) Likely

Very likely

Very likely Likely Likely, over many Northern Hemisphere mid- to high-latitude land areas Likely, in a few areas

a

Changes in phenomenon Higher maximum temperatures and more hot days over nearly all land areas Higher minimum temperatures, fewer cold days and frost days over nearly all land areas Reduced diurnal temperature range over most land areas Increase of heat index over land areas More intense precipitation events

Increased summer continental drying and associated risk of drought

Conﬁdence in projected changes (during the 21st century) Very likely

Very likely

Very likely Very likely, over most areas Very likely, over many areas

Likely, over most mid-latitude continental interiors (lack of consistent projections in other areas)

Source: IPCC WGI (2001).

of CO2 has increased by ∼30% since 1750, mostly due to fossil fuel burning and partially due to land-use change, especially deforestation. The present CO2 concentration has not been exceeded during the past 420,000 years, and the rate of increase is unprecedented during the past 20,000 years (IPCC, 2001). One of the more insidious manifestations of global climate change may be an increase of climate instability (Rosenzweig and Hillel, 1998). In a warmer world, climatic phenomena are likely to intensify. Thus, episodes or seasons of anomalously wet conditions (violent rainstorms of great erosive power) may alternate with severe droughts, in an irregular and unpredictable pattern. Table I presents the IPCC assessment of conﬁdence in observed changes in extremes of weather and climate during the latter half of the 20th century and projected changes during the 21st century. Nearly all land areas are very likely to experience higher maximum and higher minimum temperatures and more intense precipitation events. A more unstable climatic regime will make it harder to devise and more expensive to implement optimal land use and agricultural production practices, including drought-contingency provisions. Failure to prepare for such contingencies may exacerbate the consequences of such extreme events as ﬂoods and

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droughts, to the effect of worsening land degradation and periods of severe food shortages. Working Group I of the Intergovernmental Panel on Climate Change (IPCC) has found that an increasing body of observations reveals that warming at the global scale is already underway (IPCC, 2001). The global average surface temperature has increased over the 20th century by 0.6◦ C +/−0.2◦ C. Most of the warming has occurred during two periods: 1910–1945 and 1976–2000. Since 1975, the Sahelian region has experienced warming of up to 1.5◦ C (Fig. 6, see color insert). The IPCC further ﬁnds that the frequency and the intensity of droughts in parts of Africa have increased in recent decades; in particular, there has been a decrease in rainfall over large portions of the Sahel (IPCC, 2001). Working Group II of the Intergovernmental Panel on Climate Change on Impacts, Adaptation, and Vulnerability ﬁnds that Africa is highly vulnerable to climate change (IPCC WGII, 2001). Sectors of concern include water resources, food security, natural resources and biodiversity, human health, and desertiﬁcation (Table II). Global climate models (GCMs) are mathematical formulations of the processes that comprise the climate system, including radiation, energy transfer by winds, cloud formation, evaporation and precipitation of water, and transport of heat by ocean currents (Fig. 7). GCMs are used to simulate climate by solving the fundamental equations for conservation of mass, momentum, energy, and water. For boundary conditions relevant to the Earth’s geographic features and with the relevant parameters, the equations of the GCMs are solved for the atmosphere, land surface, and oceans over the entire globe. GCMs project global climate responses at relatively coarse-scaled resolutions (2.5 to 3.75◦ latitude by ∼3.75◦ longitude).

Table II Sectors Vulnerable to Climate Change in Africaa Sector Water resources Food security Natural resources and biodiversity Human health Desertiﬁcation

a

Projected impacts Dominant impact is predicted to be a reduction in soil moisture in the subhumid zones and a reduction in runoff. There is wide consensus that climate change, through increased extremes, will worsen food security in Africa. Climate change is projected to exacerbate risks to already threatened plant and animal species, and fuelwood. Vector-borne and water-borne diseases are likely to increase, especially in areas with inadequate health infrastructure. Changes in rainfall, increased evaporation, and intensiﬁed land use may put additional stresses on arid, semiarid, and dry ubhumid ecosystems.

Source: IPCC WG II (2001).

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Figure 7

The climate system. (Source: WMO, 1985).

The models are used to simulate the climate system’s future responses to additional greenhouse gases and sulfate aerosols emitted into the atmosphere by human activities. Temperature and precipitation changes for the Sahel region of Africa in the 2050s projected by two global climate models (GCMs) are shown in Figs. 8 and 9 (see color inserts). The global climate models are the United Kingdom Hadley Centre (HC) and the Canadian Centre for Climate Modeling and Analysis (CC) (Flato et al., 1997; Johns et al., 1997). There are two types of scenarios for each GCM: the ﬁrst accounts for the effects of greenhouse gases on the climate (GG), and the second accounts for the effect of greenhouse gases and sulfate aerosols (GS). The GCM simulations for the 21st century are forced with a 1% per year increase of equivalent carbon dioxide (CO2) concentration in the atmosphere. These simulations are based on “businessas-usual” greenhouse gas emission scenarios of the Intergovernmental Panel on Climate Change and account for changes in other greenhouse gases besides CO2 (IPCC, 1996). Sulfate aerosols are emitted as by-products of industrial activities and create a cooling effect as they reﬂect and scatter solar radiation. Thus, the scenarios that incorporate both greenhouse gases and sulfate aerosols tend to be somewhat cooler than those with greenhouse gas forcing alone. Simulated annual temperature and precipitation were linearly interpolated across the GCM gridboxes in the Sahel region.

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The scenarios vary in the magnitude of the projected temperature changes, but they all project a warming trend for the Sahel region. The GCM models project temperature changes ranging from 2 to 7◦ C in the 2050s. The Canadian Centre for Climate Modeling and Analysis (CC) scenario consistently projects higher temperatures for the region than the United Kingdom Hadley Centre (HC), while the scenarios that combine greenhouse gases and sulfate aerosols (GS) are consistently cooler than those with the greenhouse gases alone (GG). Precipitation projections of the two global climate models show different patterns for the 2050s, indicating uncertainty regarding future hydrological conditions. Changes in precipitation range from −40% to +40% in the 2050s. At three sites across the Sahel (Fig. 10), an analysis was done to project the potential for future drought in the region. Mean monthly temperature and precipitation for Bamako, Mali; Kano, Nigeria; and Kosti, Sudan for the period of record are shown in Fig. 11. (Data were available from 1945 to 1988 in Bamako, Mali; from 1947 to 1965 in Kano, Nigeria; and from 1943 to 1979 in Kosti, Sudan). Mean annual temperature is 28.2, 26.3, and 27.3◦ C for Bamako, Kano, and Kosti, respectively. Mean annual precipitation is low at the Mali (1014 mm y−1) and Nigeria (859 mm y−1) sites, and extremely low at the Sudan site (400 mm y−1), with highest rainfall occurring in August.

Figure 10 Study sites for analysis of future droughts in the Sahel.

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Figure 11 Monthly mean temperature and precipitation for Sahel study sites (Bamako, Mali 1945–1988; Kano, Nigeria 1947–1965; and Kosti, Sudan 1943–1979) (Source: NASA GISS).

For the coming decades, both GCMs project signiﬁcant warming at all three sites (between ∼4 and 8◦ C by the 2080s) (Fig. 12). Precipitation projections, on the other hand, are mixed, with the Hadley Centre GCM simulating declines up to 30% in Bamako, Mali, in the 2080s, and increases of more than 20% in Kano, Nigeria, in the 2050s (Fig. 13). We explored the potential for drought in the Sahelian region further by calculating potential evaporation (PET) with the Penman–Monteith (Monteith, 1980) equation and the Thornthwaite (1948) equation and then using these formulas to calculate the Palmer Drought Stress Index (Palmer, 1965). The PDSI compares anomalous dry and wet years to normal years and is used to identify relative droughts and ﬂoods at particular places (Table III). It uses a water balance approach to calculate inﬁltration, runoff, and potential and actual evaporation. Inputs are monthly mean temperature and precipitation, soil water capacities, and Thornthwaite (1948) parameters, which are a function of the mean temperature and latitude.

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Figure 12 Projected annual change in temperature for the Sahel study sites for the Hadley Centre (HC) and Canadian Centre (CC) climate change scenarios with greenhouse gases alone (GG) and with greenhouse gases and sulfate aerosols (GS).

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Figure 13 Projected annual change in precipitation for the Sahel study sites for the Hadley Centre (HC) and Canadian Centre (CC) climate change scenarios with greenhouse gases alone (GG) and with greenhouse gases and sulfate aerosols (GS).

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Utilizing the Penman–Monteith equation, the base PET is higher than the Thornthwaite (5.89 compared to 4.85 mm day−1, respectively) and the projected changes in PET are smaller (∼10–15% increases calculated with Penman–Monteith compared to ∼20–25% increases calculated with Thornthwaite) (Fig. 14). When these PET formulations are used to calculate projected changes in the PDSI, the Thornthwaite PDSI projected greater changes than the Penman– Monteith PDSI (∼−6 compared to ∼−4) for the 2080s (Fig. 15). According to the deﬁnition of PDSI classes, indices = /<−4 are classiﬁed as extreme drought conditions (Table III). Finally, we utilized the Hadley Centre GCM results to see how other climate variables besides temperature and precipitation that affect potential evaporation may

Figure 14 Projected annual change in potential evapotranspiration (PET) calculated with the Thornthwaite and Penman–Monteith formulas for Bamako, Mali for the Hadley Centre (HC) and Canadian Centre (CC) climate change scenarios with greenhouse gases alone (GG) and with greenhouse gases and sulfate aerosols (GS).

Table III PDSI Classes for Wet and Dry Periodsa Class

Description

>4.00 3.00–3.99 2.00–2.99 1.00–1.99 0.50–0.99 0.49– −0.49 −0.50– −0.99 −1.00– −1.99 −2.00– −2.99 −3.00– −3.99 <−4.00

Extremely wet Very wet Moderately wet Slightly wet Incipient wet spell Near normal Incipient drought Mild drought Moderate drought Severe drought Extreme drought

a

Palmer (1965).

Figure 15 Projected change in Palmer Drought Stress Index (PDSI) calculated with the Thornthwaite and Penman–Monteith potential evapotranspiration (PET) formulas for Bamako, Mali for the Hadley Centre (HC) and Canadian Centre (CC) climate change scenarios with greenhouse gases alone (GG) and with greenhouse gases and sulfate aerosols (GS).

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Table IV Annual Changes in Climate Variables (a) and Penman–Monteith Potential Evapotranspiration (PET) (b) for the Sahel Region Utilizing Projections of the Hadley Centre GCM with Greenhouse Gases Alone (HCGG) 2020s (a) Climate variable (%) Solar radiation Windspeed Relative humidity

.6 1.5 −4.2

(b) Potential evapotranspiration (mm day−1) (%) PET (temperature)a PET (temperature, solar radiation, windspeed, relative humidity) a

2050s

1.9 3.1 −12.7

6.16 (4.6) 6.27 (6.5)

6.43 (9.2) 6.79 (15.3)

2080s

3.1 6.2 −19.6

6.74 (14.4) 7.35 (24.8)

Base PET (temperature) = 5.89 mm day−1.

change, namely, solar radiation, windspeed, and relative humidity. For each of these variables, as projected by the Hadley Centre GCM, direction of change is toward greater potential evaporation. Solar radiation and wind speed increase, while relative humidity declines (up to ∼20% in the 2080s). When the Penman–Monteith PET is calculated with GCM projections for the four variables (temperature, solar radiation, wind speed, and relative humidity), the percentage of change in PET increased in all decades compared to PET changes calculated with temperature projections alone (Table IV).

VII. PROSPECTS Climate change appears likely to cause further semiarid ecosystem degradation through alteration of spatial and temporal patterns in temperature, rainfall, solar insolation, winds, and humidity. Our analyses point toward a prolongation and worsening of drought conditions in the Sahel under climate change conditions. The global climate models, potential evapotranspiration formulations, and the suite of variables tested all project the potential for exacerbated drought in the region. In turn, desertiﬁcation may aggravate CO2-induced climate change through the release of CO2 from cleared, burned, and dead vegetation, and the reduction of the carbon sequestration potential of degraded semiarid lands.

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Projecting climate change due to anthropogenic greenhouse gas forcing at regional scales is an exploratory, albeit important, exercise. There is still considerable uncertainty regarding how fast and by how much the climate of the Sahel may change in the future, on how different subregions within the Sahel may experience the change, or on how the variability (as well as the mean values) of climatic parameters may change. Gradual changes may be punctuated by increasing frequencies of intense precipitation events. It is still difﬁcult, if not impossible, to ascribe probabilities to any of the various climate change scenarios, owing to uncertainties regarding future emissions of radiatively active trace gases and tropospheric aerosols and the potential response of the climate system to those emissions. Scenarios are also uncertain because global climate models lack realism in their simulation of current climate processes, especially regional hydrology. Regardless of future climate change, the pressures generated by growing populations and intensiﬁed land use are evidently causing a progressive degradation of arid and semiarid ecosystems in the Sahel and elsewhere. To deﬁne and quantify the nature, degree, and extent of the degradation, national and international programs are working to build consistent monitoring systems of monitoring. These consist not only of remote sensing from above but also of quantitative ground-based observations in both “natural” (relatively undisturbed) and managed ecosystems. Both national and international efforts should be strengthened, especially in such vulnerable regions as the Sahel. In these programs, the interacting inﬂuences of human interventions and of potential climate-change effects should be deﬁned, since the possibility exists that the two factors may interact, especially given the current trends of warming and drying in the region. To redress or rehabilitate degraded ecosystems, vulnerable countries are beginning to institute appropriate policies and programs. These include keeping reserve areas to protect biodiversity, avoiding over-grazing on managed lands, reseeding of pastures, implementing soil and water conservation measures, and—in the social arena—land tenure, family planning, and contingencies for droughts. Determining the availability of fresh water resources (surface water, renewable groundwater, and nonrenewable groundwater) and planning their careful utilization are important components of such programs. Inappropriate patterns of management may lead to a downward spiral of semi-arid ecosystem degradation, as illustrated in Figs. 16 and 17, whereas appropriate measures of soil and water conservation hold the promise of sustainable development in the contexts of both rainfed and irrigated systems. Given the range and magnitude of development constraints and challenges facing many countries in Africa and other nations with large areas of semiarid ecosystems, vulnerability to the intertwined effects of continued semiarid ecosystem degradation and to climate change appears to be high and the overall capacity to adapt low. However, the prospect of a changing global climate also offers some

DESERTIFICATION: CLIMATE VARIABILITY AND CHANGE

Figure 16 ecosystems.

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The upward and downward spirals of sustainable versus unsustainable rainfed agro-

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Figure 17 ecosystems.

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The upward and downward spirals of sustainable versus unsustainable irrigated agro-

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35

opportunities. Improving scientiﬁc understanding of global climate change, reducing land-use practices that release greenhouse gases (such as land clearing and biomass burning, augmenting carbon sinks through agroforestry and improved soil management, and developing resilience to unavoidable changes all help to build new development pathways and national and international cooperation. The goal is to reverse present-day semiarid ecosystem degradation, even as we address the global climate change issue.

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Glantz, M. H., and Orlovsky, N. (1983). Desertiﬁcation: A review of the concept. Desert. Control Bull. 9, 15–22. Gonzalez, P. (1997). “Dynamics of Biodiversity and Human Carrying Capacity in the Senegal Sahel.” Ph.D. Dissertation, University of California, Berkely. Gonzalez, P. (2001). Desertiﬁcation and a shift of forest species in the West African Sahel. Climate Res., in press. Hillel, D. (1992). “Out of the Earth: Civilization and the Life of the Soil.” University of California Press, Berkely. Hillel, D. (1997). “Small-scale Irrigation for Arid Zones: Principles and Options.” United Nations Food and Agriculture Organization, Rome, Italy. Hillel, D. (1998). “Environmental Soil Physics.” Academic Press, San Diego, CA. Hillel, D. (2000). “Salinity Management for Sustainable Irrigation.” The World Bank, Washington, DC. Hunt, B. (2000). Naural climatic variability and Sahelian rainfall trends. Glob. Planet. Change 24, 107–131. IPCC WG I (2001). “Summary for Policymakers.” IPCC Working Group I Third Assessment Report, Cambridge University Press, Cambridge. IPCC WG II (2001). Chapter 10 Africa. IPCC Working Group II “Third Assessment Report.” Cambridge University Press, Cambridge. Janicot, S., Moron, V., and Fontaine, B. (1996). Sahel droughts and ENSO dynamics. Geophys. Res. Lett. 23, 515–518. Johns, T. E., Carnell, R. E., Crossley, J. F., Gregory, J. M., Mitchell, J. F. B., Senior, C. A., Tett, S. F. B., and Wood, R. A. (1997). The second Hadley Centre coupled ocean-atmosphere GCM: Model description, spinup and validation. Climate Dynamics 13, 103–134. (http://ipccddc.cru.uea.ac.uk/dkrz/dkrz index.html) Jolly, C. L., and Torrey, B. B. (Eds.) (1993). “Population and Land Use in Developing Countries.” National Academy Press, Washington, DC. Justice, C. D. (Ed.) (1986). “Monitoring the Grassland of Semi-arid Africa Using NOAA-AVHRR Data.” Taylor and Francis, London, UK. Lal, R. (1997). Soil quality and sustainability. In “Methods for Assessment of Soil Degradation.” CRC Press, Boca Raton, FL. Lal, R. (2001). Potential of desertiﬁcation control to sequester carbon and mitigate the greenhouse effect. Climatic Change 51, 35–72. Lamb, P., and Peppler, R. (1991). West Africa. In “Teleconnections: Linkages Between ENSO, Worldwide Climate Anomalies, and Societal Impacts” M. H. Glantz, R. W. Katz, and N. Nicholls, (Eds.), pp. 121–189. Cambridge University Press, Cambridge. Lamb, P., and Peppler, R. (1992). Further case studies of tropical Atlantic surface atmospheric and oceanic patterns associated with sub-Saharan drought. J. Climate 5, 476–488. Lamprey, H. F. (1975). Report on the desert encroachment reconnaissance in northern Sudan. Desert. Control Bull. 17, 1–7. Lare, A. R., and Nicholson, S. E. (1994). Contrasting conditions of surface water balance in wet years and dry years as a possible land surface-atmosphere feedback mechanism in the West African Sahel. J. Climate 7, 653–668. Le Houerou, H. N. (1977). Biological recovery versus desertization. Econ. Geogr. 63, 413–420. Letey, J. (1994). Is irrigated agriculture sustainable? In “Soil and Water: Keys to Understanding Our Global Environment.” SSSA Special Publication 41, Madison, WI. Lough, J. (1986). Tropical Atlantic SST and rainfall variations in Subsaharan Africa. Month. Weather Rev. 114, 561–570. Mainguet, M. (1994). “Desertiﬁcation: Natural Background and Human Mismanagement.” Springer Verlag, Berlin.

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Malo, A. R., and Nicholson, S. E. (1990). A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index. J. Arid Environ. 19, 1–24. Monteith, J. L. (1980). The development and extension of Penman’s evaporation formula. In “Applications of Soil Physics” (D. Hillel, Ed.). Academic Press, New York. Nair, P. K. R. (1993). “An Introduction to Agroforestry.” Kluwer Academic, Dordrecht, The Netherlands. Nicholson, S. E. (1986). The spatial coherence of African rainfall anomalies: Interhemispheric teleconnections. J. Climate Appl. Meteorol. 25, 1365–1381. Nicholson, S. (2000). Land surface processes and Sahel climate. Rev. Geophys. 38 (1), 117–139. Nicholson, S. E., Tucker, C. J., and Ba, M. B. (1998). Desertiﬁcation, drought, and surface vegetation: An example from the West African Sahel. Bull. Am. Meteorol. Soc. 79, 815–829. Nnaji, A. O. (2001). Forecasting seasonal rainfall for agricultural decision-making in northern Nigeria. Agric. For. Meteorol. 107, 193–205. Otterman, J. (1974). Baring high-albedo soils by overgrazing: A hypothesized desertiﬁcation mechanism. Science 186, 531–533. Otterman, J. (1977). Anthropogenic impact on the albedo of the Earth. Climatic Change 1, 137– 157. Otterman, J. (1981). Satellite and ﬁeld studies of man’s impact on the surface in arid regions. Tellus 33, 68–77. Otterman, J., Karnieli, A., and Pielke, R., Sr. (2001). “Review of the Desertiﬁcation-by-Overgrazing Hypothesis: Recent Satellite Observations of Turbulent-Heat Transfer.” Land-Atmosphere-Ocean Research, NASA Goddard Space Flight Center. Greenbelt, MD. Otterman, J., and Tucker, C. J. (1985). Satellite measurements of surface albedo and temperatures in semi-desert. J. Climate Appl. Meteorol. 24, 228–234. Palmer, W. C. (1965). Meteorological Drought. Research Paper 45. U.S. Weather Bureau. Washington, DC. Prince, S. D. (1991). A model of regional primary production for use with coarse resolution satellite data. Int. J. Rem. Sens. 12, 1313–1330. Rind, D., Rosenzweig, C., and Peteet, D. (1989). African drought: History, possible causes, prognosis, In “Africa Beyond Famine.” Tycooly, London, UK/New York. Rosenfeld, D. (1999). TRMM (Tropical Rainfall Measuring Mission) observed ﬁrst direct evidence of smoke from forest ﬁres inhibiting rainfall. Geophys. Res. Lett. 26, 3105–3108. Rosenfeld, D. (2000). Suppression of rain and snow by urban and industrial air pollution. Science 287, 1793–1796. Rosenfeld, D., and Farbstein, E. (1992). Possible inﬂuence of desert dust on seedability of clouds in Israel. J. Appl. Meteorol. 31, 722–731. Rosenfeld, D. Y., Rudick, R., and Lahav, A. (2001). Desert dust suppressing precipitation: A possible desertiﬁcation feedback loop. Proc. U.S. Nat. Acad. Sci. 98, 5975–5980. Rosenzweig, C., and Hillel, D. (1998). “Climate Change and the Global Harvest: Potential Impacts of the Greenhouse Effect on Agriculture.” Oxford University Press, New York. Rowell, D., Folland, C., Maskell, K., Ward, J., and Ward, M. (1995). Variability of summer rainfall over tropical north Africa (1906–1992): Observations and modeling. Quart. J. Roy. Meteorol. Soc. 121, 669–704. Scherr, S. J. (1999). “Soil Degradation: A Threat of Developing Country Food Security by 2020?” Discussion Paper 27, International Food Policy Research Institute, Washington, DC. Sen, A. (1981). “Poverty and Famines: An Essay on Entitlement and Deprivation.” Clarendon Press, Oxford, UK. Sud, Y. C., and Molod, A. (1988). A GCM simulation study of the inﬂuence of Saharan evapotranspiration and surface-albedo anomalies on July circulation and rainfall. Month. Weath. Rev. 116, 2388–2400.

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Syers, J. K., Lingard, J., Pieri, C., Ezcurra, E., and Faure, G. (1996). Sustainable land management for the semiarid and subhumid tropics. Ambio 25, 484–491. Tegen, I., Lacis, A. A., and Fung, I. (1996). The inﬂuence of mineral aerosols from disturbed soils on the global radiation budget. Nature 380, 419–422. Thornthwaite, C. W. (1948). An approach toward a rational classiﬁcation of climate. Geogr. Rev. 38, 55–89. Tucker, C. J., Dregne, H. E., and Newcomb, W. W. (1991). Expansion and contraction of the Sahara desert from 1980 to 1990. Science 253, 299–301. UNEP (1992). “World Atlas of Desertiﬁcation.” Edward Arnold, Seven Oaks, UK. USDA (1991). “Agriculture and the Environment: The 1991 Yearbook of Agriculture.” Washington, DC. Ward, N. (1998). Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa at interannual and multidecadal timescales. J. Climate 11, 3167–3191. Warren, A. (1996). Desertiﬁcation. In “The Physical Geography of Africa.” Oxford University Press, New York. Westing, A. H. (1994). Population, desertiﬁcation, and migration. Environ. Conserv. 21, 109–114. WRI (1998). “World Resources: A Guide to the Global Environment.” WRI, UNEP, UNDP, and World Bank, New York. WRI (2000). “World Resources: People and Ecosystems, The Fraying Web of Life.” Elsevier Science, Oxford, UK. Xue, Y., and Shukla, J. (1993). The inﬂuence of land surface properties on Sahel climate. Part I: Desertiﬁcation. J. Climate 6, 2232–2245.

FATE AND TRANSPORT OF VIRUSES IN POROUS MEDIA Yan Jin1 and Markus Flury 2 1

Department of Plant and Soil Sciences University of Delaware Newark, Delaware 19717 2 Department of Crop and Soil Sciences Washington State University Pullman, Washington 99164

I. Introduction II. Characteristics of Viruses Relevant for Subsurface Fate and Transport III. Virus Sorption A. Mechanisms B. Modeling of Virus Sorption IV. Protein Sorption and Denaturation A. Mechanisms B. Modeling V. Virus Survival A. Factors Inﬂuencing Virus Survival B. Modeling of Virus Inactivation VI. The Role of the Gas–Liquid Interface in Protein/Virus Inactivation VII. Transport of Viruses in Porous Media A. Mechanisms B. Modeling of Virus Transport VIII. Indicators for Human Enteroviruses IX. Concluding Remarks References

Microbiological contaminants (e.g., bacteria, protozoa, and viruses) pose one of the greatest risks in water resources. About 70% of the waterborne microbial illness outbreaks in the United States has been associated with groundwater. Although viruses are not the only pathogens known to contaminate groundwater, they are much smaller in size than bacteria or protozoan cysts and are not ﬁltered out to the same extent in the porous soil matrix. Knowledge of the factors that inﬂuence the fate and transport of viruses in soil and aquifers is critical to making accurate determinations of groundwater vulnerability and to developing regulations that are protective of public health. In this paper, we review the current state of knowledge on fate and transport of viruses in porous media which include (i) mechanisms and

39 Advances in Agronomy, Volume 77 Copyright 2002, Elsevier Science (USA). All rights reserved. 0065-2113/02 $35.00

40

JIN AND FLURY modeling of virus sorption, (ii) virus survival and factors affecting virus inactivation in the natural environment, and (iii) mechanisms of virus transport in porous media and available modeling approaches. Because viruses are surrounded by a protein capsid and are expected to behave similarly to proteins, an overview on the mechanisms of protein sorption and denaturation is also provided. Factors such as solution chemistry, virus properties, soil properties, temperature, association with solid particles, and water content have been found to inﬂuence virus sorption, survival, and transport in porous media. A review of protein literature provides some insights as to what mechanism might be involved in virus sorption that have so far not been studied. Some needs for future research are suggested. C 2002 Elsevier Science (USA).

I. INTRODUCTION Viruses that are present in septic tanks, sewage sludges, wastewater, and other sources can be transported into ground and surface waters. About 70% of the waterborne illness outbreaks in the United States has been associated with groundwater (Craun, 1991; Herwaldt et al., 1992). In 1990, the U.S. Environmental Protection Agency (USEPA) Science Advisory Board cited drinking-water contamination as one of the highest ranking remaining environmental risks (USEPA, 1990). The Science Advisory Board reported that microbiological contaminants (e.g., bacteria, protozoa, and viruses) are likely to be the greatest remaining health riskmanagement challenge for drinking-water suppliers. These risks are most likely associated with groundwater. However, whereas stringent regulations to control microbial contaminants apply to drinking-water systems using surface water, only limited regulations apply to systems using groundwater (Macler and Merkle, 2000). Although viruses are not the only pathogens known to contaminate groundwater, they are much smaller in size than bacteria or protozoan cysts and are not ﬁltered out to the same extent in the porous soil matrix, thereby may move better through the subsurface. Knowledge of the factors that inﬂuence the fate and transport of viruses in soils and aquifers is critical to making accurate determinations of groundwater vulnerability and to developing regulations that are protective of public health. During passage through soil and aquifer systems, viruses are removed from the water by attachment and inactivation processes, and both these processes depend upon a number of factors (Table I). Natural soils and aquifers may serve as disinfection media. “Natural disinfection” is an essential component of the soil-aquifer treatment process for removing viruses released from various sources in the subsurface. Determination of disinfection efﬁciency and groundwater vulnerability requires accurate prediction of virus fate and transport in the subsurface. However,

Table I Factors Inﬂuencing Virus Fate in the Subsurfacea Factor

Inﬂuence on survival

Temperature

Viruses persist longer at low temperatures.

Microbial activity

Some viruses are inactivated more readily in the presence of certain microorganisms, while sorption to the surface of bacteria can be protective. Most viruses survive longer in moist soils and even longer under saturated conditions; unsaturated soil may inactivate viruses at the air–water interface. Most enteric viruses are stable over a pH range of 3–9; however, survival may be prolonged at near-neutral pH values Certain cations may prolong survival depending upon the type of virus.

Moisture content

pH

Salt species and concentration

Virus association with soil

Viral association with soil generally increases survival, although attachment to certain mineral surfaces (e.g., oxides and hydroxides) may cause inactivation.

Soil properties

Effects on survival are probably related to the degree of virus sorption, either prolonged or shortened depending on the properties of soil particles. Different virus types vary in their susceptibility to inactivation by physical, chemical, and biological factors. Organic matter may prolong survival by competitively binding to air–water interfaces where inactivation can occur; organic matter may also retard viral infectivity.

Virus type

Organic matter

Hydraulic conditions

A moving air–water interface may inactivate hydrophobic viruses.

Inﬂuence on migration Viruses migrate farther when inactivation is smaller; higher temperature tends to increase sorption to soils. Unknown

Virus migration usually increases under saturated ﬂow conditions as compared to unsaturated conditions; the air–water interface can sorb viruses, thereby decreasing migration. Low pH typically increases virus sorption to soils; high pH causes desorption thereby facilitating greater mobility. Increasing ionic strength of the surrounding medium will generally increase attachment to soils thus decrease mobility. Viruses interacting with soil particles are inhibited from migrating through the soil matrix.

Greater migration in coarse-textured soils; ﬁne-textured soils, especially clays, tend to sorb more viruses. Virus sorption to soils is related to physicochemical differences in capsid surface structure and amino acid sequence. Soluble organic matter competes with viruses for adsorption on soil particles which may result in increased virus migration; bonded organic matter may provide hydrophobic binding sites for viruses which may decrease virus migration. Virus migration generally increases at higher hydraulic loads and ﬂow rates.

a Adapted and expanded from Yates and Yates (1988). Reprinted with permission from Crit. Rev. Environ. Control 17, 307–344. Copyright CRC Press, Boca Raton, Florida.

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JIN AND FLURY

quantitative information on the basic factors controlling virus fate is insufﬁcient to allow model predictions. Speciﬁcally, the use of models for regulatory purposes has been questioned, particularly because uncertainties about input parameter values may lead to large errors in model outputs (Yates, 1995; Yates and Jury, 1996). The uncertainty of model parameters arises in part from insufﬁcient knowledge of virus sorption and inactivation characteristics during transport in porous media. Regulatory agencies, such as the USEPA, have the task to protect drinkingwater resources from contamination by pathogenic organisms. Effective policy making and the establishment of disinfection rules concerning viruses in drinking water require a thorough understanding of the fate and transport of viruses in subsurface waters. For instance, the question whether, and under what conditions, viruses released from a septic tank system may pose a threat to a drinking-water well need to be answered. The need to better understand the factors affecting and limiting microbial contamination of groundwater sources has been reemphasized recently (Macler and Merkle, 2000). The outstanding issues identiﬁed include the hydrogeological properties affecting groundwater vulnerability to contamination and the physical and chemical properties governing fate and transport of viruses in the subsurface including the unsaturated zone, the capillary fringe, and the saturated zone (Fig. 1). Viruses are microorganisms that are composed of RNA or DNA that is surrounded by a protein capsid. Fate and transport of viruses in the environment are largely determined by this protein coating. With respect to sorption and transport processes, viruses and proteins may therefore be regarded as “macromolecules”

Figure 1 Schematic of virus transport from septic tank system to a drinking-water well, indicating the pathways through the vadose zone, the capillary fringe, and the groundwater.

SUBSURFACE VIRUS FATE AND TRANSPORT

43

with similar characteristics. Protein adsorption at solid surfaces has been an active area of research. This is motivated by the importance of protein adsorption for both fundamental biochemical and biophysical processes and a variety of medical applications, including biomaterials, extracorporeal therapy, drug delivery, and solidphase diagnostics (Malmsten and Veide, 1996). Despite the similarities between viruses and proteins, virus and protein sorption has been treated separately in the literature, probably due to the isolation between the different disciplines engaged in protein and virus research. In this paper, we review the current state of knowledge on fate and transport of viruses in porous media. We also present an overview of the mechanisms of virus and protein sorption and transport and compare the different concepts used to analyze virus and protein sorption.

II. CHARACTERISTICS OF VIRUSES RELEVANT FOR SUBSURFACE FATE AND TRANSPORT Viruses are obligate intracellular parasites that are incapable of replication outside of a host cell. Their structure consists of a protein capsid enclosing a nucleic acid genome of RNA or DNA (Harrison, 1985). The capsid is made up of multiple protein subunits, each of which is a single folded polypeptide chain. Viruses are either enveloped or nonenveloped. A nonenveloped virus consists of a capsid and associated nucleic acids, together termed the nucleocapsid. An enveloped virus has a similar nucleocapsid, which is enclosed by an envelope consisting of both glyco- and lipoproteins. Fate and transport of viruses in the environment are largely determined by properties of the protein coating. The most relevant properties of a virus with respect to subsurface fate and transport are morphological characteristics, including size, shape, and density, and the physicochemical properties, including electrophoretic mobility, net charge, and hydrophobicity. The coating of viruses, whether enveloped or nonenveloped, contains polar, nonpolar, and ionic regions (e.g., Mix, 1974). The charge distribution originating from the ionic regions is heterogeneous, with pH-dependent positive and negative charges occurring simultaneously at different locations. Electrophoretic mobility is a key parameter to characterize the charge of a virus. Often the electrophoretic mobility as a function of pH is not documented in the literature, but rather the isoelectric point (pHIEP); i.e., the pH where the electrophoretic mobility vanishes, is reported. Although the pHIEP alone does not provide detailed information about charge densities, it provides an important benchmark for charge reversal relevant to an electric ﬁeld and is as such a key characteristic for electrokinetic phenomena.

Table II Basic Properties of Selected Viruses and Bacteriophages

Virus (strain)

Diameter (nm)

Density (g cm−3)

Shape

Envelope

Nucleic acid

Isoelectric point (pHIEP)

References

Reovirus 3 (Dearing) Poliovirus 1 (Mahoney) Poliovirus 1 (LSc) Poliovirus 2 (Sabin T2) Echo 1 (5 strains) Coxsackie A21

81 28–30 28–30 28–30 27 27

1.36 1.34 1.34 1.34 1.34 1.34

Human pathogens Icosahedral, spikes No Icosahedral No No Icosahedral No Icosahedral Icosahedral No Icosahedral No

ds-RNAa ss-RNAb ss-RNA ss-RNA ss-RNA ss-RNA

3.9 8.2 6.6 4.5, 6.5c 5.0–6.4 4.8, 6.1c

Floyd and Sharp (1978) Floyd and Sharp (1978) Zerda (1982) Murray and Parks (1980) Zerda (1982) Murray and Parks (1980)

T2 MS-2

60 24–26

n/a 1.422

Tailed Icosahedral

Bacteriophages No No

ds-DNA ss-RNA

4.2 3.5, 3.9c

Qβ

24–26

1.439

Icosahedral

No

ss-RNA

5.3

φX174

25–27

1.43

Icosahedral, spikes

No

ss-DNA

6.6

PRD-1 R17

62 26

n/a 1.46

Icosahedral, spikes Tailed or nontailed

No No

ds-DNA ss-RNA

3-4 3.9

PM2

60

n/a

Isometric, spikes

No

ds-DNA

7.3

rNorwalk Virus SJC3

∼27 nm 8 × 900 nm

1.33–1.40 n/a

Icosahedral Filamentous

No No

None ss-DNA

5 <2.5

Sharp et al. (1946) Overby et al. (1966), Zerda (1982), Penrod et al. (1995) Overby et al. (1966), Ackermann and Dubow (1987) Ackermann and Dubow (1987) Loveland et al. (1996) Ackermann and Dubow (1987) Ackermann and Dubow (1987) Redman et al. (1997) Redman et al. (1997)

a

ds = double stranded. ss = single stranded. c Values measured for two different conformational states of the same virus. b

SUBSURFACE VIRUS FATE AND TRANSPORT

45

Table II summarizes some relevant data for viruses that are mentioned in this manuscript, and four different viruses are depicted in Fig. 2 (see color insert). The typical size for viruses is in the range of 20 to 100 nm in diameter (Powelson and Gerba, 1995). There is considerable variability in the pHIEP among different viruses.

III. VIRUS SORPTION A. MECHANISMS The mechanisms of virus sorption to solid surfaces have been summarized in several review articles (Bitton, 1975; Duboise et al., 1979; Gerba, 1984; Gerba and Bitton, 1984; Mix, 1974; Schijven and Hassanizadeh, 2000) and will only be brieﬂy discussed here. Sorption of viruses has been studied with various types of viruses and sorbents (Table III). Since virus particles fall into the size range of colloids, theories that describe colloidal behavior have been applied to describe the behavior of viruses (Gerba, 1984). Although viruses carry an electrical charge, a colloidal system in its entirety remains electrically neutral. This phenomenon can be described by the diffuse double-layer theory, as reviewed by Gerba (1984), in its relation to virus sorption properties. Colloid stability is controlled by the balance between repulsive double-layer interactions and attractive van der Waals forces, best described by the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory of colloid stability (Verwey and Overbeek, 1948). Murray and Parks (1980) conducted sorption experiments of poliovirus to a variety of metal oxides and found that free energies agreed with potentials evaluated from the DLVO theory. Others have also used this theory to explain their experimental observations (Loveland et al., 1996). Chattopadhyay and Puls (1999) recently proposed that the total force leading to adhesion of virus particles to solid surfaces can be divided into three groups: (1) electrostatic (EL) interactions; (2) Lifshitz–van der Waals electrodynamic forces (LW), which include van der Waals–Keesom or orientation forces, van der Waals–Debye or induction forces, and van der Waals–London or dispersion forces; and (3) polar forces or acid–base interactions (AB). Both LW and AB forces depend on interfacial tensions, which are in turn determined by hydrophobicity of the sorbate and sorbent surfaces. Thermodynamic calculations revealed a dominant effect of hydrophobicity of sorbates and sorbents on sorption (Chattopadhyay and Puls, 1999). Virus sorption usually increases with increasing cation concentration in solution, particularly in the presence of divalent cations (Bales et al., 1991; Bitton et al., 1976; Drewry and Eliassen, 1968; Lipson and Stotzky, 1983; Moore et al., 1975). The enhanced sorption is attributed to the decrease of the thickness of the electric

Table III Virus Sorption to Soil and Aquifer Material Studied in Batch Systems Sorbent

Resultsa

MS-2

Hydrophilic and hydrophobic silica (pH 5 and 7)

Sorption conformed to linear Freundlich isotherm; strong sorption to hydrophobic silica at pH 5 and 7; no sorption to hydrophilic silica at pH 7

Bales et al. (1991)

Poliovirus 1

Magnetite

Adsorption enhanced by presence of cations; no effect of pH between pH 5 and 9, less sorption at pH 4; sorption conformed to nonlinear Freundlich isotherm; saturation-limited behavior

Bitton et al. (1976)

φX174

5 soils (pH 6–7.2)

Sorption conformed to nonlinear Freundlich but not Langmuir isotherm; no signiﬁcant correlation of K with pH, CEC, OC, and speciﬁc surface area; amount of viruses sorbed related to square root of time

Burge and Enkiri (1978a)

T2, MS-2, φX174

Clays (hectorite, saponite, kaolinite, Norman clay)

Total free energy (δG) assumed to be the sum of δGH (δG due to hydrophobic interactions) and δGEL (δG due to electrostatic interactions); hydrophobic interactions dominated during sorption of the selected bacteriophages on the selected clays

Chattopadhyay and Puls (1999)

Bacteriophages T1, T2, f2

9 soils (pH 4.7–6.3)

Sorption conformed to nonlinear Freundlich isotherm; decreased sorption when pH increased from 7 to 9; increased sorption with increased cation concentration for some soils only; increased sorption with increased CEC, clay content, and OC.

Drewry and Eliassen (1968)

Poliovirus 1

Loamy sand soil

Sorption conformed to nonlinear Freundlich isotherm.

Gerba and Lance (1978)

Virus

References

46

9 soils

Sorption is strain dependent; viruses grouped according their sorption characteristics, Group I, weakly sorbed, affected by pH, OC; Group II, strongly sorbed, not affected by soil characteristics; exception f2 which adsorbed least to all of the soils

Gerba et al. (1981)

9 soils (pH 4.5–8.2)

Sorption is strain dependent, pH most signiﬁcant soil characteristic inﬂuencing sorption; strong sorption when pH less than 5. Poliovirus 1 and coliphage T4 sorbed better than all other viruses. Echiovirus 1 and coliphage f2 sorbed least.

Goyal and Gerba (1979)

Ottawa sand (pH 7)

Sorption of φX174 conformed to linear Freundlich isotherm; no sorption of MS-2.

Jin et al. (1997)

Reovirus

Kaolinite (K), montmorillonite (M) (pH 9.5)

More reovirus was adsorbed by M than by K. Adsorption was essentially immediate and correlated with CEC of the clays. The addition of cations, especially divalent cations enhanced virus adsorption. Constant partition-type adsorption isotherm obtained.

Lipson and Stotzky (1983)

Reovirus, T1

Kaolinite (K) and montmorillonite (M) (pH 7)

Reovirus type 3 and coliphage T1 did not share common adsorption sites on K and M. Compounds in the minimal essential medium blocked T1 adsorption to K and M under some experimental conditions. Results indicate there was a speciﬁcity in adsorption sites for mixed virus population.

Lipson and Stotzky (1984b)

Poliovirus 1

6 silicate minerals

Organic matter competes with virus sorption and desorbs viruses, different sorption on different silicates.

Lo and Sproul (1977)

Poliovirus 1; echovirus 1 isolates Farouk, V212, V239, and V248; coxsackievirus B3 and B4 isolates V216, V240; rotavirus SA11; bacteriophages MS-2, φX174, T2, T4, f2 Poliovirus 1, 2, 3; echovirus 1–8, 11–13, 24–27, 29, 31, and isolates; coxsackievirus B1–B6, rotavirus SA11, bacteriophages MS-2, φX174, T2, T4, f2 MS-2, φX174 47

continues

Table III—continued Sorbent

Resultsa

PRD-1

Quartz, ferric oxyhydroxide-coated quartz

Attachment behavior changes at a pH value about 2.5–3.5 pH units above the pHIEP, below this pH value the attachment is irreversible, above this pH value attachment is reversible; attachment described by DLVO potential theory.

Loveland et al. (1996)

Poliovirus 1, bacteriophage T2, T7, f2

Bentonite, kaolin, organic particulates

T2, f2 more readily sorbed in presence of divalent cations. Poliovirus and T7 sorb equally well to organic and inorganic particulates; viruses remain infective in sorbed state.

Moore et al. (1975)

Poliovirus 2

34 soils (pH 7)

Sorption conformed to nonlinear Freundlich isotherm at low saturation, to Langmuir isotherm at high concentrations; saturation-limited behavior, most effective sorbents were magnetite and hematite.

Moore et al. (1981)

Reovirus

30 soils, minerals, rocks (pH 7)

Adsorption increases with presence of divalent cations and increasing available surface area; adsorption decreases with presence of soluble organic matter; adsorption inversely correlated with capacity to adsorb cationic polyelectrolytes and with electrophoretic mobility.

Moore et al. (1982)

Poliovirus 1

Different oxides (pH 7)

Sorption conformed to nonlinear Freundlich isotherm; adsorption and desorption points coincide, no sorption hysteresis; application of DLVO theory.

Murray and Parks (1980)

Virus

References

48

MS-2 sorption to both types of material adequately described by Freundlich isotherm; the amount of MS-2 sorption was controlled by surface area and the shape of the activated carbons used. Sorption conformed to linear Freundlich isotherm; sorption increased in the order MS-2, PRD-1, poliovirus. More T7 adsorbed to M than T1, but the same amount sorbed to K. Adsorption conﬁrmed to equilibrium isotherm. Mechanisms involved varied between the viruses and the clay minerals.

Powell et al. (2000)

Activated carbons (granular Calgon F-400, activated carbon ﬁber composite (ACFC)) (pH 7.4)

Poliovirus 1, MS-2, PRD-1

Sandy soil (pH 7.3)

T1 and T7

Kaolinite (K) and montmorillonite (M) (pH 7.1–7.4)

Poliovirus 2

Ottawa sand, montmorillonite, 3 soils

Sorption is consistent with pH-dependent charge properties of virus and sorbent; there is a critical pH region where sorption changes from strong to weak; effect of electrolytes on sorption was only signiﬁcant at pH above the critical region.

Taylor et al. (1981)

MS-2, φX174

Ottawa sand (pH 7.5)

Sorption of φX174 conformed to linear Freundlich isotherm; no sorption of MS-2.

Thompson et al. (1998)

MS-2, φX174

Loamy sand, sandy loam (pH 7.5)

Sorption conformed to nonlinear Freundlich isotherm; different apparent sorption observed in glass and polypropylene test tubes.

Thompson et al. (1998)

Poliovirus 1

Ottawa sand, montmorillonite (pH 7.3)

Sorption to sand described by Langmuir isotherm; saturation-limited behavior with low fractional coverage f of sand particles (f = 0.01); sorption to montmorillonite described by nonlinear Freundlich isotherm.

Vilker et al. (1983)

49

MS-2

a

K, sorption coefﬁcient; OC, organic carbon, CEC, cation-exchange capacity.

Powelson and Gerba (1994)

Schiffenbauer and Stotzky (1981)

50

JIN AND FLURY

double layer (Gerba, 1984). Solution pH determines the net charge of viruses and sorbents and is therefore a dominant factor (Bales et al., 1991; Drewry and Eliassen, 1968; Goyal and Gerba, 1979). Hydrophobic effects may also play a major role. Bales et al. (1991) showed that sorption of MS-2, which is a relatively hydrophobic virus, was dominated by hydrophobic factors. They also demonstrated increased sorption with increased temperature. This temperature effect might be due to the endothermic unfolding of protein structures at the interface or due to increased sorption rate at higher temperatures (Bales et al., 1991). Although virus sorption has been studied extensively, no standard procedure or systematic approach has been developed and followed. As a result, experimental conditions differ from study to study, which makes it difﬁcult to draw general conclusions from the available data about the extent as well as the mechanisms of virus sorption. Nevertheless, key variables inﬂuencing virus sorption have been identiﬁed, which include pH and ionic strength of the solution, presence of compounds competing for sorption sites (e.g., organic materials), properties of the virus (mainly isoelectric point and hydrophobicity), and properties of the sorbent. Sorption is favored when viruses and sorbents have opposed electric charge, a situation which usually occurs in natural porous media when the solution pH is lower than the pHIEP of the virus. Increased ionic strength compresses the electric double layer and results in increased virus sorption. Organic matter may provide hydrophobic sorption sites for viruses, although organic matter in dissolved form competes with viruses for the available sorption sites. A detailed review on how different factors affect adsorption of viruses to soil can be found in Schijven and Hassanizadeh (2000).

B. MODELING OF VIRUS SORPTION 1. Equilibrium Sorption of viruses is usually determined in a batch experiment, where a virus solution is mixed with a sorbent and shaken for a certain amount of time to let the system equilibrate. The time to reach equilibrium is usually considered to be less than 24 h. The equilibrium times used in sorption experiments range from 20 min to 24 h. Sorption data have usually been analyzed by the Langmuir or Freundlich isotherm models (Table IV). The Langmuir and Freundlich isotherms are given by Langmuir :

Smax K L C 1 + KLC

(1)

S = K Cn,

(2)

S=

Freundlich :

Table IV Freundlich Sorption Isotherms for Viruses Reported in the Literature Chemistry of background electrolyte

Equilibrium time and temperature

K and Kd value (mL g−1)

20 min, 25◦ C 18 h, 26◦ C 18 h, 26◦ C 18 h, 26◦ C 18 h, 26◦ C 18 h, 26◦ C 1 h, 4◦ C

K = 553000, n = 0.736 K = 72.5, n = 1.058 K = 161, n = 1.101 K = 457, n = 0.806 K = 4.61, n = 1.092 K=0 K = 505, n = 1.2

Bitton et al. (1976) Burge and Enkiri (1978b)

60 min, 24◦ C 60 min, 4◦ C 60 min, 4◦ C 60 min, 4◦ C 60 min, 4◦ C 3 h, 6–9◦ C 3 h, 6–9◦ C 3 h, 6–9◦ C 3 h, 6–9◦ C 3 h, 6–9◦ C 3 h, 6–9◦ C

φX174

Loamy sand

pH 7.5, Phosphate buffer salinea

3 h, 6–9◦ C

φX174

Sand loam

pH 7.5, Phosphate buffer salinea

3 h, 6–9◦ C

Kd = 580 Kd = 270 Kd = 0 Kd = 6.6 Kd = 8300 Kd = 0.74 Kd = 0 Kd = 0 Kd = 0.076 Kd = 0.44 Kd = 0.44b K = 0.74, n = 0.88c K = 2.9, n = 1.09b K = 1.01, n = 1.16c Kd = 6.5b Kd = 11, n = 1.06c

Bales et al. (1991)

φX174 MS-2 MS-2 MS-2 MS-2 φX174

Hydrophilic silica Hydrophilic silica Hydrophilic silica Hydrophilic silica Hydrophobic silica Ottawa sand Ottawa sand Ottawa sand Loamy sand Sandy loam Ottawa sand

1610 mg L−1 CaCl2 pH 6.9, 0.03 M NaCl pH 6.2, 0.02 M NaCl pH 6.0, 0.02 M NaCl pH 6.8, 0.02 M NaCl pH 7.2, 0.02 M NaCl pH 7, 1mM CaCl2, 1.25 mM NaHCO3 Ca phosphate buffer, pH 5 Ca phosphate buffer, pH 5 Ca-free phosphate buffer, pH 7 Ca-free phosphate buffer, pH 5 Ca-free phosphate buffer, pH 5 and 7 pH 7.5, Phosphate buffer salinea pH 7.5, Phosphate buffer salinea pH 7.5, Phosphate buffer salinea pH 7.5, Phosphate buffer salinea pH 7.5, Phosphate buffer salinea pH 7.5, Phosphate buffer salinea

Virus Poliovirus 1 φX174

Poliovirus 1 MS-2

a

Sorbent Magnetite Clay loam Silt loam Silt loam Silt loam Loamy sand Ottawa sand

0.1 M NaCl, 0.003 M KCl, 0.02 M Na2HPO4. In glass tubes. c In polypropylene tubes. b

References

Moore et al. (1981)

Jin et al. (1997) Thompson et al. (1998)

52

JIN AND FLURY

where S and C are sorbed and solution concentrations, respectively; Smax is the maximal sorbed concentration; and KL, K, and n are constants. In the Langmuir isotherm, the sorbed concentration reaches the asymptotic value Smax when the concentrations of dissolved chemicals or microbes are large (C 1). For small concentrations (C 1), the Langmuir isotherm reduces to the linear Freundlich isotherm, S = K d C,

(3)

where Kd is usually denoted as the distribution coefﬁcient. Empirical laws such as the Langmuir and Freundlich isotherms do not yield any information about the particular sorption mechanisms. The constants K, Kd, and KL embrace all the possible sorption mechanisms. For example, the distribution coefﬁcient Kd may be written as (Schwarzenbach et al., 1993) Kd =

Som f om + Smin A + Sie σie A + Srxn σrxn A , C

(4)

where Som, Smin, Sie, and Srxn are sorbed concentrations associated with organic matter, mineral surfaces, electrostatic forces, and reversible chemical reactions, respectively; fom is the fraction of organic matter; A is the surface area of minerals; σ ie and σ rxn are the concentrations of charged and reactive sites on the solid surface. For a charged, organic macromolecule such as a virus, all the mechanisms in Eq. (4) play a role in the sorption process. Depending on the type of virus and sorbent and the chemistry of the solution, some are more important than others. Given the large variability in experimental conditions used to study virus sorption, it is therefore not surprising that the Kd or K values reported vary considerably among various studies (Table IV). 2. Kinetics Kinetic models for virus sorption have almost exclusively been used in connection with transport models. Basically two different approaches are used, based on either ﬁrst-order sorption kinetics or ﬁltration theory. First-order kinetics is formulated as dS = κ1 C − κ2 S, dt

(5)

where κ 1 and κ 2 are the rate coefﬁcients. This type of kinetics has often been used to describe virus sorption (e.g., Sim and Chrysikopoulos, 1995). Grant et al. (1993) proposed a compartment model that includes sorption and inactivation of viruses. In this model, viruses can (i) inactivate in solution, (ii) adsorb reversibly and irreversibly, (iii) inactivate after reversible and irreversible adsorption, (iv) and transform from reversibly sorbed status to irreversibly sorbed status (Fig. 3).

SUBSURFACE VIRUS FATE AND TRANSPORT

53

Figure 3 Compartmental model for virus inactivation and sorption. Solid lines indicate an active virus; dashed lines denote an inactive virus. Ellipses represent an irreversibly sorbed virus. Adapted from Grant et al., 1993. Kinetic analysis of virus adsorption and inactivation in batch experiment. Water Resour. Res. 29, 2067–2085. Copyright [1993] American Geophysical Union. Reproduced/modiﬁed by permission of American Geophysical Union.

All sorption and inactivation reactions in this model are assumed to be of ﬁrst-order kinetics. Other types of rate laws have been proposed for bacteria, and have recently been applied to viruses (e.g., Chu et al., 2001; Sim and Chrysikopoulos, 2000). Such rate laws include Langmuir-type and second-order kinetics (Bengtssen and Lindqvist, 1995; Tan et al., 1994), expressed respectively as ∂S = κ1 (Smax − S)C − κ2 S ∂t ∂S = κ1 (Smax − S)C − κ2 C S. ∂t

(6) (7)

3. Aggregation and Filtration Viruses have often been considered to be of colloidal nature, and the concepts used in colloid aggregation and ﬁltration have been applied to describe virus retention and transport through porous media (Gerba, 1984; Loveland et al., 1996; Murray and Parks, 1980). A colloid is considered to be a particle with a diameter less than 2 to 10 μm. Colloids do not dissolve and remain suspended in waters because their gravitational setting is less than 10−4 m s−1 (Stumm and Morgan, 1981). Colloids may aggregate when they contact each other in a solution under favorable chemical conditions. The kinetics of colloid aggregation in monodisperse, static suspension due to collisions by Brownian motion can be represented as a second-order process (Stumm and Morgan, 1981), dN = −k p N 2 , dt

(8)

54

JIN AND FLURY

where N is the number of particles in solution and kp is the rate coefﬁcient for colloid aggregation. According to von Smoluchowski (1917), the rate coefﬁcient kp can be expressed as k p = α p 4Dπ d,

(9)

where D is the Brownian diffusion coefﬁcient and d is the particle diameter. The collision efﬁciency factor α p describes the fraction of collisions that lead to permanent aggregation. When the diffusion coefﬁcient is expressed by the Einstein–Stokes relation D = kT/(3πηd), then Eq. (8) can be written as dN 4kT 2 = −α p N , dt 3η

(10)

where k is the Boltzmann constant, T is the absolute temperature, and η is the dynamic viscosity. In a moving ﬂuid, particles may have more chances to collide and aggregation may increase. The rate of aggregation due to ﬂuid motion caused by a uniform velocity gradient dv/dz may be expressed as (Stumm and Morgan, 1981) 4 dv dN = − α0 d 3 N 2 , (11) dt 3 dz where the parameter α 0 is analogous to α p. If the volume of particles remains constant during aggregation, Eq. (11) can be written as 4 dN dv = − α0 ϕ N , dt π dz

(12)

where

π 3 d N0 (13) 6 is the total volume of particles in suspension. The overall rate of aggregation may then be given by adding collisions due to Brownian motion (Eq. 10) and due to ﬂuid motion (Eq. 12), ϕ=

4kT 2 4 dv dN = −α p N − α0 ϕ N . dt 3η π dz

(14)

The ﬁrst term is more important than the second term for particles with a diameter smaller than 1 μm (Stumm and Morgan, 1981). Aggregation of colloids is closely related to ﬁltration processes. The kinetic equation for particle removal by ﬁltration can be expressed as (Tien, 1989) dC = −λC, (15) dz where λ is the ﬁltration coefﬁcient. The ﬁltration coefﬁcient is not constant because the properties of the porous media that acts as a ﬁlter changes during proceeding

SUBSURFACE VIRUS FATE AND TRANSPORT

55

ﬁltration. Because the particles attached to the ﬁlter medium have a certain size, the porosity of the ﬁlter medium usually decreases. The ﬁltration coefﬁcient may be written as a function of the number of attached particles (Tien, 1989), λ = λ0 F(S)

(16)

and F(S) = 1

for

S = 0,

(17)

where λ0 is the initial value of the ﬁltration coefﬁcient and F(S) is a functional relationship that accounts for the changing ﬁltration properties of the medium. Various empirical relationships for F(S) have been proposed; e.g., F = 1 + bS, where b is an adjustable parameter (see Table 2.1 in Tien, 1989). Using the concept of the single-collector efﬁciency, the kinetic equation for particle removal by spherical ﬁlter grains may be expressed as (Stumm and Morgan, 1981) 31− dC =− αηs C, (18) dz 2 a where is the porosity, a is the diameter of a ﬁlter grain, ηs is the single-collector efﬁciency that reﬂects the rate of contact between suspended particles and ﬁlter grains, and a is the diameter of the ﬁlter grain. The parameter α represents the probability that a particle that contacts the ﬁlter grain will be permanently attached, thus α is analogous to α 0 for aggregation. The ﬁltration coefﬁcient in Eq. (15) is related to the single-collector efﬁciency by 31− αηs . (19) 2 a Various relationships have been postulated for the estimation of the single-collector efﬁciency, ηs. The single-collector efﬁciency, ηs, depends on the ﬁltration velocity, water viscosity, density of suspended particles, and grain size (Rajagopalan and Tien, 1976; Yao et al., 1971). Equations (15) and (18) for ﬁltration are analogous to Eq. (12) for aggregation. The rate of particle removal by ﬁltration and aggregation depends on the dimensionless product of variables shown in (Stumm, 1977) λ=−

dv t (20) dz L packed bed ﬁltration ξ = α(1 − )ηs , (21) a where L denotes the length of the ﬁlter bed. The contact probability of particles with each other in aggregation depends on (dv/dz)t, and the contact probability in ﬁltration is a function of ηsL/a. The time in the aggregation model is related to the travel distance in the ﬁltration model. Assuming steady-state ﬂow through aggregation by ﬂuid motion

ξ = α0 ϕ

56

JIN AND FLURY

a saturated column ﬁlter, the transport equation can be written as an advection– dispersion process ∂ C ∂ 2C ∂C ∂ρ S + = DDisp 2 − v (22) ∂t ∂t ∂z ∂z where DDisp is the hydrodynamic dispersion coefﬁcient. Assuming steady state for the liquid concentration (d C/dt = 0) and that dispersion can be neglected, Eq. (22) simpliﬁes to ∂ρ S ∂C = −v . ∂t ∂z Combing Eqs. (15) and (23) yields

(23)

dρ S = λv C. (24) dt Usually the ﬁltration process is assumed to be irreversible; i.e., a particle cannot detach from the collector surface. When detachment is considered, Eq. (24) can be written as (Tien, 1989) dS = ka C − kd S, (25) dt where ka and kd are attachment and detachment rates, respectively. If the coefﬁcients ka and kd are constant, the ﬁltration kinetics is formally identical to reversible sorption (Eq. 5). For ﬁltration, however, the rate coefﬁcients are not constant, but a function of velocity, porosity, and ﬁltration coefﬁcient. Nevertheless, sorption and ﬁltration are similar processes in terms of the underlying phenomena. The major difference between sorption and ﬁltration is caused by the different sizes of molecules and particles, respectively. The distinction between sorption and ﬁltration disappears when colloidal particles become small. More details on colloid aggregation and ﬁltration theory can be found in several textbooks (e.g., Elimelech et al., 1995; Stumm and Morgan, 1981; Tien, 1989) and review articles (e.g., O’Melia, 1987; Ottewill, 1977; Ryan and Elimelech, 1996). The collision efﬁciency factors, α p, α 0, and α, can be described by the DLVO potential theory of colloid stability (Verwey and Overbeek, 1948). The DLVO theory quantiﬁes the effects of repulsive and attractive forces that act between two solid surfaces. The sole factors responsible for the particle–particle interaction are van der Waals forces and electrostatic forces. The DLVO theory is a standard tool to analyze colloid aggregation, and it has also been applied to describe virus sorption onto surfaces. Murray and Parks (1980) found that adsorption of poliovirus 1 onto oxide surfaces was in agreement with the DLVO theory. The DLVO theory corresponded excellently with experimentally determined adsorption-free energies. Loveland et al. (1996) used the DLVO theory to analyze PRD-1 sorption onto quartz and ferric hydroxide-coated quartz and found good agreement between theory and

SUBSURFACE VIRUS FATE AND TRANSPORT

57

experimental results. Murray and Parks (1980) postulated that the DLVO theory describes the principle mechanisms of virus sorption on various inorganic surfaces. Despite many agreements, discrepancies have been observed between experimental results and theory of particle attachment to collectors (Elimelech and O’Melia, 1990a, 1990b). These discrepancies were attributed to the inadequacy of the DLVO theory to quantitatively describe the interaction between particle and collector (Elimelech and O’Melia, 1990b). Factors not included in the DLVO theory, such as hydrodynamic interactions, variability in surface potentials, and surface roughness, are presumed to have a signiﬁcant effect on the particle–collector interactions (Elimelech and O’Melia, 1990b; Kretzschmar et al., 1999).

IV. PROTEIN SORPTION AND DENATURATION Viruses consist of RNA or DNA surrounded by a protein capsid. It can therefore be anticipated that sorption of viruses is similar to sorption of proteins. Protein and polymer sorption is an active research area and has been reviewed extensively (Andrade and Hlady, 1986; Haynes and Norde, 1994; Kleijn and Norde, 1995; Norde, 1995; Stuart et al., 1986). A brief overview on the mechanisms and modeling approaches of protein sorption is provided in the following.

A. MECHANISMS Proteins are complex macromolecules consisting of charged, polar, and hydrophobic domains. These domains give rise to the electrostatic, hydrophobic, and hydrogen-bonding interactions, all of which are usually present to some degree among protein molecules in aqueous solutions. Proteins interact therefore in a complex manner with a surface interface (Fig. 4). Protein sorption differs in many ways from sorption of other organic chemicals like pesticides. The protein molecules form numerous contacts with the solid surface, and the molecules orient themselves in a way to optimize the contact with the solid surface. A wide variety of experimentally observed adsorption behaviors reﬂect the complexity of proteins (Andrade and Hlady, 1986; Haynes and Norde, 1994; Norde, 1986). With respect to sorption, “hard” and “soft” proteins are distinguished. Adsorption of hard proteins is determined by electrostatic interactions and dehydration of protein and sorbent. The protein structure remains unchanged. Soft proteins undergo structural changes during the sorption process, and the protein structure partially breaks down. This process is termed unfolding of the protein structure and results in an increase of conformational energy (Kleijn and Norde, 1995). The adsorption to the solid surface becomes stronger over time because

58

JIN AND FLURY

Figure 4 Schematic view of protein interaction with a solid surface. Adapted with permission from Andrade, J. D. and Hlady, V. (1986). Protein adsorption and material biocompatibility: A tutorial review and suggested hypothesis. Adv. Polym. Sci. 79, 1–63. Copyright Springer-Verlag GmbH & Co. KG.

more sorption sites become active during unfolding of the protein, and the desorption becomes less likely (Fig. 5). During adsorption, various parts of the protein molecule attach to the solid surface. To desorb the molecule from the surface, all of these attachment sites have to be released simultaneously, which requires a considerable amount of energy (Norde, 1995). Diluting the aqueous phase as in the standard desorption procedure usually does not desorb proteins from a surface very well. Desorption may be increased by changing pH, ionic strength of the solution, or by adding compounds, such as other proteins, that compete for sorption sites. It is also likely that in some cases the desorbed molecules differ from their original structure, and it has been found that in many cases the adsorption is not completely reversible (Norde, 1995). It is also known that in the case of irreversible adsorption, the amount of proteins adsorbed increases with increasing bulk concentration (Ramsden, 1995b). There is a controversy in the literature about the explanation for this ﬁnding. Hypotheses are that (i) proteins adsorb in two or more distinct orientations at the surface,

SUBSURFACE VIRUS FATE AND TRANSPORT

59

Figure 5 Schematic view of sorption of a soft protein. (1) Adsorption/desorption of native protein. (2) Structural rearrangement at the surface interface. (3) Adsorption/desorption of conformationally changed molecule. (4) Transformation to native state. Adapted from Kleijn and Norde (1995). The adsorption of proteins from aqueous solution on solid surfaces. Heterog. Chem. Rev. 2, 157–172. Copyright C John Wiley & Sons Limited. Reproduced with permission.

(ii) proteins form an ordered two-dimensional crystal on the surface, and (iii) proteins denature at the surface (Ramsden, 1995b). As discussed earlier, several types of interactions (e.g., electrostatic, hydrophobic, and hydrogen-bonding) are involved in the sorption of protein and other colloidal particles. The magnitudes of the various interactions determine the thermodynamic aspects of the sorption behavior in colloid and protein systems. Strong interactions between particles and the surface, often leading to irreversible sorption, have been observed with polymer microspheres (Johnson and Elimelech, 1995; Johnson and Lenhoff, 1996; Semmler et al., 1998) and proteins (Andrade and Hlady, 1986; Kondo and Mihara, 1996; Norde and Lyklema, 1978; Soderquist and Walton, 1980). In many protein systems, however, protein–surface interactions are relatively weak and are affected by changing solution conditions (Yuan et al., 2000). In general, the extent and mechanisms of protein sorption depend upon the balance between particle–surface attraction and particle–particle repulsion. In their review, Yuan et al. (2000) outlined the following possible types of protein sorption reactions. 1. Irreversible sorption. When the attraction between a charged particle and an oppositely charged surface is very strong, the particle is likely to maintain a stable position at the surface. Such an irreversible sorption behavior cannot be captured accurately with equilibrium models. Rather, random sequential adsorption (RSA)-type models have been found to be more appropriate. 2. Reversible sorption at low surface coverage. The particles may not bind permanently to any speciﬁc segment of the solid surface when particle–surface

60

JIN AND FLURY

interaction is relatively weak. Instead, they may attach to various parts of the surface or undergo desorption, in which case equilibrium sorption models can be used to describe such behavior. Whether an irreversible or a reversible sorption reaction occurs is determined by the balance between particle–surface and particle–particle interactions, both of which are inﬂuenced by solution pH and ionic strength as well as particle size (Oberholzer and Lenhoff, 1999; Yuan et al., 2000). Based on energetic considerations, irreversible sorption is more frequent for large particles, whereas reversible sorption is more frequent for smaller particles (Yuan et al., 2000).

B. MODELING 1. Equilibrium Protein sorption has usually been described with the Langmuir isotherm. The assumptions made for the Langmuir isotherm to be applicable are that (i) adsorption occurs in a monolayer, (ii) the surface is homogeneous, (iii) adsorption does not affect the energy status of other molecules, (iv) only one adsorbing species is present, and (v) sorption is reversible. Recent evidence, however, has shown mechanisms different from those of Langmuir-type behavior for protein sorption. Alternative approaches to describe protein sorption kinetics are discussed in the following sections. 2. Kinetics Many different approaches have been proposed to describe kinetics of protein sorption. The basic model is the kinetic Langmuir model, dS = κ1 φC − κ2 S, dt

(26)

φ = Smax − S.

(27)

where

In this model, the sorption process is completely reversible. The parameter φ represents the number of available sorption sites. For equilibrium conditions, Eqs. (26) and (27) reduce to the Langmuir isotherm (Eq. 1). To account for the speciﬁc characteristics of protein sorption, such as the timedependent conformational changes, the basic Langmuir model has been extended by adding additional compartments. The reactions between the compartments are assumed to be reversible, irreversible, or time-dependent. Andrade and Hlady (1986) give an overview over these types of models and propose a general model as shown in Fig. 6. This model assumes that the desorption is time dependent and

SUBSURFACE VIRUS FATE AND TRANSPORT

61

Figure 6 A general kinetic model for protein sorption in case of reversible denaturation. Adapted with permission from Andrade and Hlady (1986). Protein adsorption and material biocompatibility: A tutorial review and suggested hypothesis. Adv. Polym. Sci. 79, 1–63. Copyright Springer-Verlag GmbH & Co. KG.

that denaturated proteins quickly regain their native state. The return to the native state is justiﬁed when no covalent bonds have been formed during the denaturation process (Andrade and Hlady, 1986). This general model differs slightly from the concept shown in Fig. 5, where denaturated proteins may sorb reversibly and denaturation is considered to be more likely irreversible. Speciﬁcs on how to model time-dependent reactions are given in Soderquist and Walton (1980). These authors proposed that adsorption and desorption rates can be written as dS = κ1,i C(ti ) − κ2,i S(ti ), dt

(28)

where the rate constants κ 1,i and κ 2,i change with time. The kinetic Langmuir model implies that sorption is reversible and takes place at deﬁnite sites, and the sorbed molecules do not interfere with each other. However, at most surfaces sorption takes place at randomly selected positions. For large molecules, this random sorption on the surface may lead to gaps between sorbed molecules that cannot be ﬁlled anymore. It has also been observed that the adsorption is in many cases irreversible. The random adsorption leads to inaccessible sorption sites, and the available fraction of sites φ is actually smaller than Smax − S (Widom, 1966). This surface-exclusion effect has been formulated mathematically by sequentially placing particles of given size randomly on a line or plane and is often referred to as the “parking problem”(Feder and Giaever, 1980; Feder, 1980). Figure 7 shows a one-dimensional schematic of a Langmuir and parking problem process. Schaaf and Talbot (1989a, 1989b) presented the ﬁrst kinetic analysis of the surfaceexclusion process, and they write the kinetic expression in a generalized Langmuir form, d = κ1 φ()C − κ2 , dt

(29)

62

Figure 7

JIN AND FLURY

Schematic of a one-dimensional Langmuir sorption process and “parking problem.”

where is the surface coverage given by = S/Smax, and the surface exclusion effect is represented by the function φ(). This surface exclusion can be formulated in different ways, depending on the processes occurring during sorption. Random sequential adsorption theory (RSA) was developed to model the irreversible deposition of colloidal particles. In the RSA process it is assumed that (i) the particles cannot overlap on the surface, (ii) adsorption is irreversible (κ 2 = 0 in Eq. 29), and (iii) sorbed particles do not diffuse on the surface. In this case, and assuming spherical particles, the function φ() is given by (Schaaf and Talbot, 1989a) √ 40 176 6 3 2 + √ − (30) 3 + O( 4 ). φ = 1 − 4 + π 3π 2 3π The RSA model is a two-dimensional simulation of the adsorption process in which a disk is placed at a random location on a planar surface. The placement continues until no more disks can ﬁt on the surface without overlap with others previously placed on the surface. The jamming limit, given as the fraction of the total surface area covered by disks, is found empirically to be max = 0.547. Equation (30) is valid for < 0.3. At higher surface coverage, the kinetics can be approximated with (Schaaf and Talbot, 1989b) φ = 8.98(max − )3 .

(31)

Only steric interactions between adsorbed particles are accounted for in the basic RSA model (Yuan et al., 2000). Deviations from RSA deposition have been found in several experimental studies of charged particle adsorption where the maximum surface coverage was dependent upon ionic strength (Johnson and Elimelech, 1995; Johnson and Lenhoff, 1996; Semmler et al., 1998). The results of these studies indicate that the effective jamming limit was signiﬁcantly lower than the 54.7% predicted by the basic RSA model at low ionic strength and increased to approach the 54.7% maximum coverage with increasing ionic strength. Therefore, a modiﬁed version of the RSA model has been developed to include particle–particle electrostatic repulsion. Such modiﬁcation requires the adsorbing particle to avoid overlap with particles already adsorbed as well as to avoid adsorbing in close

SUBSURFACE VIRUS FATE AND TRANSPORT

63

proximity to each other when electrostatic repulsion is large. Taking into account the effect of the electrostatic particle–particle repulsion, the maximum coverage can be expressed as (Semmler et al., 1998) max = 0.547(a/aeff )2 ,

(32)

where a is the actual radius of a charged particle and aeff is the radius of the effective area excluded by the charged particle. A variety of other extensions and generalizations of the basic RSA model have been developed in recent years. Among these are the consideration of the relative importance of diffusion and sedimentation prior to particle deposition (Lavalle et al., 1999) and the application of RSA to particle deposition during ﬂow in porous media (Ko and Elimelech, 2000; Ko et al., 2000). During transport of charged particles in porous media, hydrodynamic as well as electrostatic interactions cause deviations from standard RSA behavior, and a modiﬁed blocking function taking into account particle size, ﬂow velocity, and electrolyte concentrations has been developed (Ko et al., 2000). Figure 8 shows the effect of the dynamic blocking function on the rate of adsorption in a ﬂowthrough reactor. We consider a ﬂowthrough reactor where the concentration in the aqueous phase is constant, and plot the rate of adsorption versus the amount sorbed on the sorbent. The solid line denotes an irreversible Langmuir kinetics (Eqs. 26, 27 with κ 2 = 0), the dashed line represents RSA (Eqs. 29–31). The parameter κ 1 has been chosen as 1. Langmuir kinetics shows a linear dependence, whereas in RSA the rate of adsorption is initially faster and decreases with increasing surface coverage.

Figure 8 Rate of surface coverage as a function of surface coverage for inrreversible Langmuir and RSA type of kinetics in a ﬂowthrough reactor.

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JIN AND FLURY

The RSA kinetics has been experimentally veriﬁed in some cases and was found to be superior to the Langmuir kinetics (Adamczyk et al., 1992; Kurrat et al., 1994; Ramsden, 1995a). Many other theoretical approaches of the RSA type have been developed (Adamczyk et al., 1992, 1999; Adamczyk, 2000; Evans, 1993; Kurrat et al., 1994; Oberholzer et al., 1997), but most of them have not yet been experimentally tested.

V. VIRUS SURVIVAL A. FACTORS INFLUENCING VIRUS SURVIVAL Many factors may inﬂuence virus survival in the subsurface environment (Table I). Virus inactivation is deﬁned as a loss of viral titer with time due to disruption of coat proteins and degradation of nucleic acid (Gerba, 1984). A large body of evidence indicates that temperature is the primary inﬂuential factor in the survival of viruses in soil and groundwater (Straub et al., 1992; Yahya et al., 1992; Yates et al., 1985). Leﬂer and Kott (1974) found that it took 42 days for 99% inactivation of poliovirus in sand at 25◦ C, whereas for the same degree of inactivation more than 175 days were required at 18◦ C. Poliovirus was found to persist for more than 180 days in saturated sand and sandy loam soils at 4◦ C, whereas no viruses could be recovered from the soils incubated at 37◦ C after 12 days (Yeager and O’Brien, 1979). Hurst et al. (1980) studied the survival of poliovirus at three temperatures: 1, 23, and 37◦ C in a loamy sand. They found that the inactivation rate was signiﬁcantly correlated with incubation temperature, noting faster inactivation rates at the highest temperatures. Generally, viruses seem to survive longer under moist as compared to dry conditions. Bagdasaryan (1964) observed that several enteroviruses, including poliovirus 1, coxsackievirus B3, and echoviruses 7 and 9, could survive for 60 to 90 days in soil with 10% moisture as compared with only 15 to 25 days in airdried soils. Ninety-nine percent inactivation of poliovirus occurred in 1 week as the soil moisture content was reduced from 13 to 0.6%; however, 7 to 8 and 10 to 11 weeks were required for the same amount of inactivation in soils with 25 and 15% moisture content, respectively (Sagik et al., 1978). Hurst et al. (1980) found that the poliovirus inactivation rate increased as the moisture content was increased from 5 to 15%, then decreased as more liquid was added. When viruses are sorbed to a solid surface, such as a clay particle, they are generally protected from inactivation. Sobsey et al. (1986) compared the rate of inactivation of hepatitis A virus in ﬁve different soil types including a clay soil, a clay loam, a loamy sand, a sand, and an organic muck. Survival was the greatest in the clay soils, in which at least 8 weeks were required to inactivate 99% of the infectious viruses. This may be due to virus sorption to the soils. Reductions in virus

SUBSURFACE VIRUS FATE AND TRANSPORT

65

inactivation rates have been reported for virus adsorbed to soil (Hurst et al., 1980), to clay minerals (Babich and Stotzky, 1980; Lipson and Stotzky, 1984a; Taylor et al., 1980), to estuarine sediments (Bitton, 1974; Gerba and Schaiberger, 1975; Liew and Gerba, 1980; Smith et al., 1978), and to sewage solids (Gerba et al., 1978). The protective effect of virus association with particulate matter or other surfaces includes protection from proteolytic enzymes or other substances which inactivate viruses, increased stability of the viral capsid, prevention of virus aggregation formation, and blocking of ultraviolet radiation (Gerba, 1984). Protection of viruses against thermoinactivation has been suggested from the results of Bitton et al. (1976), Liew and Gerba (1980), and Stotzky et al. (1981). Although virus association with solids in natural environments has generally been observed to be protective, the association with metal and metal oxide surfaces has been found to enhance virus inactivation. Bacteriophages MS-2 and f2 were inactivated in ﬂuids that had been in contact with Al3+, Zn2+, and Mg2+ (Yamamoto et al., 1964). Atherton and Bell (1983) observed degradation of MS-2 into small fragments after trichloroethylene elution from magnetite at pH 10. Poliovirus readily adsorbed to several metal oxide surfaces and was inactivated at the surface of MnO2 and CuO (Murray and Laband, 1979). Studies also have shown that Cu2+ and Fe3+ can cause virus inactivation, and viruses that are enveloped or contain RNA are more sensitive to inactivation to Cu2+ than those that are nonenveloped or contain DNA (Sagripanti 1992; Sagripanti et al., 1993). The presence of iron oxides (mainly goethite) was found to be responsible for the sorption and inactivation of MS-2; however, φX174 was not affected by the metal oxides (Chu et al., 2000). These results indicate that the degree of inactivation due to metals and metal oxides is virus speciﬁc. Other factors such as UV radiation (Battigelli et al., 1993), dissolved oxygen level (Scheuerman et al., 1991), organic matter concentration and pH (Campos et al., 2000), certain cations (Quignon et al., 1998; Sagripanti et al., 1993; Siegl et al., 1984; Wallis et al., 1962), and the presence of other microorganisms (Hurst, 1988; Yates et al., 1990) have also been found to inﬂuence virus survival in the subsurface environment. Because viruses can be quite persistent and mobile in soil and groundwater, there is a need to better understand and quantify the factors that inﬂuence virus survival behavior. A more detailed discussion on the factors affecting virus inactivation can be found in Schijven and Hassanizadeh (2000).

B. MODELING OF VIRUS INACTIVATION Virus inactivation is often assumed to follow ﬁrst-order kinetics (Sim and Chrysikopoulos, 1996; Yates and Yates, 1988), although deviations from this rate law have been noted for many years (Chang, 1966). In particular, the interplay of different factors affecting virus survival will likely cause deviations from simple ﬁrst-order kinetics.

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Several types of regression equations have been used by Hurst et al. (1992) to relate virus inactivation to environmental parameters. Among eight equations tested, log10

Nt = log10 β0 + β1 log10 X 1 + · · · + βn log10 X n + βt log10 t N0

(33)

was found to be most suitable (Hurst et al., 1992), where N0 is the titer at time zero, Nt is the titer at some subsequent observation time t, and X1 through Xn represent independent variables, i.e., water conductivity, water turbidity, incubation time, incubation temperature, and ability to support bacterial growth. The values β 0 through β t represent the respective coefﬁcients assigned to the independent variables X1 through Xn. Other possible causes of non-ﬁrst-order inactivation include natural variations in the lability of individual virus particles (virions) in a viral population (Pollard and Solosko, 1971; Yamagishi and Ozeki, 1972), the association of viruses with nonviral colloidal material (Babich and Stotzky, 1980; Bitton, 1974; Gerba and Schaiberger, 1975), or the presence of viral aggregates in the inactivating suspension (Sobsey, 1966). Grant (1995) developed a kinetic model for the inactivation of viruses that exhibit a range of initial aggregate sizes. Virus aggregation is a concern mainly because it affects the biological activity of viruses in the following ways: (i) if viral aggregates consist of many virions that are individually infective, the overall level of infectivity of an aggregated suspension is reduced relative to the dispersed state; and (ii) aggregates may be more resistant to inactivation than single virions (Floyd and Sharp, 1978). In his model, Grant (1995) assumed that viral aggregates are more resistant to inactivation than a single virion because all virions within an aggregate must be inactivated before the aggregate as a whole is considered inactive, and undamaged components of inactive virions within an aggregate may recombine to cause host–cell infection (multiplicity reactivation (MR)). Grant proposed three different inactivation laws for three different systems: (i) monodispersed and completely homogeneous, (ii) aggregated viruses without, and (iii) aggregated viruses with MR. Survival curves calculated from the model compared well with experimental inactivation data. There is evidence that virus inactivation rate coefﬁcients may change over time (Grant et al., 1993; Hurst et al., 1980; Parkinson and Huskey, 1971; Shah and McCamish, 1972). When a virus population consists of different subpopulations with different resistances against inactivation, the overall inactivation rate coefﬁcients will decrease with time (Grant et al., 1993; Parkinson and Huskey, 1971). The time dependence of the inactivation rate coefﬁcient can be modeled either explicitly as a function of time (Sim and Chrysikopoulos, 1996) or as a function of residence time (Flury and Jury, 1999). In batch systems, the two approaches are identical; however, in a ﬂow system, time and residence-time-dependent processes are different depending on the boundary conditions (Flury and Jury, 1999).

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67

VI. THE ROLE OF THE GAS–LIQUID INTERFACE IN PROTEIN/VIRUS INACTIVATION The effects of the air–water interface (AWI) on virus survival were ﬁrst encountered by investigators studying the fate of airborne viruses in aerosols. It was suggested that virus adsorption and inactivation at the AWI may be very similar to those for individual proteins (Adams, 1948; Trouwborst et al., 1974). Shaklee and Meltzer (1909) observed that shaking a pepsin solution in the presence of an AWI resulted in degradation (i.e., loss of biological activity) of pepsin, whereas in the absence of the AWI, i.e., when the bottles were completely ﬁlled with liquid, no degradation of pepsin occurred. They also observed that bubbling air or CO2 through the solution results in denaturation of pepsin and that the denaturation rate increased with decreasing pH of the solution. These early observations have been conﬁrmed and extended in many other studies (Table V). The mechanisms of denaturation or inactivation is closely related to sorption of viruses and proteins at the AWI. Studies have shown that proteins in solution diffuse to the AWI and adsorb, and subsequently may undergo conformational changes from their native state to a state of minimal interfacial tension (van del Vegt et al., 1996). Such a conformational change causes unfolding of a protein’s three-dimensional conﬁguration and results in its denaturation (Adams, 1948; Augenstine and Ray, 1957; Donaldson et al., 1980; Tronin et al., 1996). The unfolding of the protein molecular structure is an endothermic process (Zittle, 1953). Whether or not denaturation of a protein occurs at an AWI is partially determined by its structural characteristics, which are the cases for the distinction between “hard” and “soft” proteins. Hard proteins are structurally stable, possibly due to the presence of greater numbers of intrachain disulﬁde bonds, and generally resist denaturation upon adsorption (Arai and Norde, 1990; Norde and Favier, 1992; Tripp et al., 1995). Soft proteins are much less structurally stable and are more likely to undergo conformational change and lose their biological activities (Tripp et al., 1995). When proteins desorb from the interface, their native state generally cannot be regained. Due to the signiﬁcant change in physicochemical properties of the molecule, protein sorption at the AWI has been considered to be an irreversible process (MacRitchie, 1987; Neurath and Bull, 1938; Quinn and Dawson, 1970). Although a single protein is much less complex, it is reasonable to consider a virus as a large conglomerate of distinct proteins. The hydrophobic regions of a virus protein partition to the gas phase at an AWI via conformational change and may render the virus inactive (Trouwborst et al., 1974). However, studies have shown that the extent of inactivation by the air–water interfacial forces is a virus-dependent phenomenon. Trouwborst et al. (1974) demonstrated that during shaking, the EMC virus was not inactivated at the AWI, while phages T3 and T5 were partially inactivated, and T1, MS-2, and Semliki Forest viruses were rapidly

TABLE V Virus Inactivation and Protein Denaturation at the Gas–Liquid Interface Compound Pepsin, renin, trypsin

Egg albumin Proteins Proteins Proteins

Inﬂuenza A virus Equine encephalitis virus Bacteriophages T1, . . .,T7 Enzymes, proteins Proteins Viruses

MS-2 Protein

Proteins Bacteriophages P22H5 and T7 Protein MS-2, φX174

Observation Shaking results in ﬁrst-order denaturation; no denaturation in absence of interface; denaturation increases with increasing acidity of solution; no denaturation in full bottles; in absence of air–water interface; denaturation not caused by oxidation; different degree of denaturation for different proteins. Vigorous shaking results in zero-order denaturation. Protein unfold at interface and form monomolecular ﬁlm; stirring creates new interface area. Spreading of protein at gas–liquid interface results in denaturation of protein; proteins become insoluble. Proteins form surface ﬁlm; coagulum formed when solution is shaken; rate of denaturation dependent on size of bottle, shaking intensity but not concentration. Inactivation by bubbling air laden with certain vapors through virus solution. Inactivation by shaking in buffered saline solution and by bubbling gas through solution; inactivation increased as pH reduced from 7 to 5. Inactivation by shaking and bubbling air through solution; inactivation dependent on pH; presence of gelatin prevents inactivation. Unfolding of macromolecules largely determined by interfacial energy; sorption follows Langmuir isotherm. Irreversible binding of proteins to interface. EMC virus not affected, bacteriophages T3,T5 only little affected, T1, MS-2, and Semliki Forest virus inactivated by bubbling air or nitrogen gas through solution; inactivation prevented by adding peptone and apolar carboxylic acids; rate of inactivation dependent on salt concentration, more sorption at higher salt concentration. Inactivation of MS-2 at air–water interface. Denaturation at interface depends on gas–liquid contact time and surface regeneration rate; denaturation reduced in presence of surfactants. Denaturation at interface due to unfolding; insoluble coagulum formed when solution is shaken. Denaturation of T7 at the interface due to partial unfolding of protein structure in aeration and shaking experiments; P22H5 much more stable than T7. Denaturation at the interface due to partial unfolding of protein structure. Inactivation in polypropylene bottles, but not in glass bottles; inactivation occurs at the triple-phase interface boundary air–water–solid.

References Shaklee and Meltzer (1909)

Bull (1938) Gorter (1938) Langmuir and Waugh (1938) Neurath and Bull (1938)

Grubb et al. (1947) McLimas (1947)

Adams (1948)

James and Augenstein (1966) Quinn and Dawson (1970) Trouwborst et al. (1974)

Trouwborst and de Jong (1973) Donaldson et al. (1980)

MacRitchie (1987) ˇ sko (1992) Bricelj and Siˇ

Tronin et al. (1996) Thompson et al. (1998)

SUBSURFACE VIRUS FATE AND TRANSPORT

69

Figure 9 Water droplets resting on hydrophobic and hydrophilic surfaces. Adapted with permission from Thompson et al. (1998). Role of the air-water-solid interface in dynamic batch systems. Appl. Environ. Microbiol. 65, 1186–1190. Copyright American Society of Microbiology.

inactivated. In column transport experiments under unsaturated ﬂow conditions, Jin et al. (2000a) showed that MS-2 retained in the columns could not be recovered with beef extract solution, possibly due to inactivation at the AWI, whereas retained φX174 stayed viable. Thompson et al. (1998) and Thompson and Yates (1999) suggested that forces associated with the air–water–solid (where the solid is a hydrophobic surface) interface or triple-phase boundary (TPB), not the AWI alone, led to the inactivation of MS-2 particles. In batch experiments Thompson et al. (1998) found that whereas φX174 was resistant toward interfacial inactivation, MS-2 was inactivated at the air–water–polypropylene interface, but not at the air–water–glass interface (Thompson et al., 1998). Schematic examples of two TPB systems are shown in Fig. 9. Thompson et al. (1998) suggested that as a virus particle adsorbs at the AWI, hydrophobic domains of the virus protein capsid partition out of the solution and into the more nonpolar gas phase, and such exposed domains are susceptible to forces at the TPB that are not present at the AWI. The balance of the forces present at the TPB is inﬂuenced by the surface properties of the solid (e.g., polypropylene vs glass), mainly the contact angle against water. They suggest that virus particles partitioned at the TPB experience destructive forces as a result of the reconﬁguration of water molecules near the hydrophobic polypropylene surface. To explain the different behavior between MS-2 and φX174 observed at the TPB, Thompson (1997) postulated that a greater number of cysteine residues and disulﬁde bonds within the coat protein of φX174 might give the phage particle

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increased stability over MS-2 (hard vs soft proteins). Another possible explanation is that φX174 would not be attracted to the AWI or would be only weakly attracted because it is largely hydrophilic (Thompson and Yates, 1999). In a previous study, where animal viruses and bacteriophages were ranked on the basis of relative hydrophobicity (Shields and Farrah, 1987), it was determined that φX174 was the most hydrophilic, while MS-2 was the most hydrophobic of 15 viruses tested. The interaction of a virus particle with an AWI is strongly inﬂuenced by the virus’ amphipathicity, the result of localized hydrophobic and hydrophilic regions on the surfaces of the capsid proteins. Amphipathic molecules accumulate at AWIs with the hydrophobic end orienting into the nonpolar air phase, while the hydrophilic end remains in the aqueous phase (Stumm and Morgan, 1981). This suggests that φX174, because of its dominant hydrophilicity, will not readily accumulate at AWIs, while MS-2 will. Solution ionic strength has been found to inﬂuence virus survival at the AWI. Thompson and Yates (1999) demonstrated a clear relationship between the inactivation of MS-2 and R17 and solution salt concentration in their batch experiments. MS-2 and R17 both underwent greater inactivation as solution ionic strength increased. This type of behavior has been observed for various colloidal particles, such as viruses, bacteria, clay, and polystyrene and latex particles (Trouwborst et al., 1974; Wan and Wilson, 1992, 1994; Williams and Berg, 1992), as well as for individual proteins (Song and Damodaran, 1991). As the ionic strength of the solution increases, the particles are increasingly attracted to the AWI because of a compression of the electrostatic double layer (Trouwborst et al., 1974; Wan and Wilson, 1994; Williams and Berg, 1992). Thompson and Yates (1999) also conclusively showed that viruses must reach the AWI before being inactivated by relating the amount of surfactant Tween 80 present at the AWI to the level of phage inactivation. In other words, organic solutes, such as Tween 80, within an aqueous system compete with virus particles to accumulate at the AWI and result in decreased virus inactivation. This is because the solution surface tension decreases in proportion to the amount of organic solutes at the interface (Thompson and Yates, 1999). Other investigators have demonstrated a protective inﬂuence from peptone, amino acids, and various surfactants against phage inactivation upon exposure to AWIs (Adams, 1948; Trouwborst et al., 1972, 1974).

VII. TRANSPORT OF VIRUSES IN POROUS MEDIA Virus transport studies have been conducted at both ﬁeld and laboratory scales. The focus of the ﬁeld studies conducted in the last 25 years has been on the transport of enteric viruses, as they were thought to have the greatest potential to be transported due to their relatively small size. There is evidence that certain

SUBSURFACE VIRUS FATE AND TRANSPORT

71

viruses can move considerable distances through the vadose as well as through the saturated zones (Rossi et al., 1994; Sinton et al., 1997; Yates and Yates, 1988). Such ﬁndings have raised concerns about groundwater contamination. Regulations regarding drinking-water well disinfections (USEPA, 2000) have led to increased efforts in past years to elucidate the mechanisms of virus transport through soils and aquifers. Different aspects of virus transport through soil and groundwater have been reviewed (Gerba and Goyal, 1981; Keswick and Gerba, 1980; Melnick and Gerba, 1980; Schijven and Hassanizadeh, 2000; Yates and Yates, 1988).

A. MECHANISMS The movement of viruses, as well as other microorganisms, through the subsurface is dominated by advection and dispersion and has been modeled using various forms of the advection–dispersion equation (ADE). Virus transport through porous media is inﬂuenced by adsorption, desorption, and inactivation processes. These processes are in turn affected by the various factors listed in Table I, many of which have been the focus of virus transport studies and are discussed in the following. Column studies and experimental conditions are listed in Table VI. 1. Soil Properties Virus retention and transport have been found to be affected by soil properties. In a set of saturated column experiments, the behavior of three viruses (poliovirus 1, reovirus 3, and φX174) was studied in several different soils (Funderburg et al., 1981). A high poliovirus concentration measured in the column percolates correlated most favorably with low soil cation-exchange capacity and high organic carbon and clay content, whereas a high percolate concentration of φX174 was related to low soil organic carbon content and residence time of liquid within a column in combination with either high soil pH or percentage of clay. As with poliovirus, the detection of reovirus in the soil column percolates was negatively correlated with soil cation-exchange capacity. These experiments were conducted with ﬁeld soils in laboratory columns, which were packed in such a manner that they simulated the vertical proﬁle and bulk density of the soil as found in the ﬁeld. However, the procedures used for saturating the columns and applying input solution were not well controlled, so that complete saturation and constant steady-state ﬂow conditions were not guaranteed. In addition, because of the complex nature of the soils used, such experiments do not allow careful evaluation of speciﬁc mechanisms. Because of these limitations, results from most of the earlier studies are difﬁcult to interpret and quantify (Drewry and Eliassen, 1968; Gerba and Lance, 1978; Landry et al., 1979).

Table VI Transport of Viruses in Columns Column

Porous material

Virus

Condition

Background electrolyte

Diameter (cm)

Length (cm)

MS-2

Sat

Groundwater (pH 8.1)

5

105

Fractured tuff

f2

Sat

Groundwater (pH 8.1)

6.5

25

Silica beads

MS-2, PRD-1

Sat

0.9

Silica beads

MS-2, poliovirus 1

Sat

Ottawa sand

MS-2, φX174

Sat

Accusand (water-washed) Accusand (oxide-removed) Aquifer material

MS-2, φX174

Sat/unsat

MS-2, φX174

Sat/unsat

MS-2, PRD-1, Qβ, φX174, PM2

Sat

Phosphate-buffered NaCl (pH 5) Phosphate-buffered NaCl (pH 5.7–8.2, ISa 0.5 M) Phosphate-buffered saline (pH 7.5, IS 0.16 M, 0.002 M) Artiﬁcial groundwater (pH 7.5, IS 0.002 M) Phosphate-buffered saline (pH 7.5, IS 0.16 M) Phosphate-buffered saline (pH 7.5, IS 0.16 M) Groundwater (pH 7.1)

References Bales et al. (1989)

15

48.6 22.8, 28.2 187 225 13.3

0.9

15

19.6–28.1

Bales et al. (1993)

7.6

10.5

2.8–3.4

Chu et al. (2000)

7.6

10.5

3.4, 4.8, 16.1

Chu et al. (2001)

7.6

10.5

3.3, 23.3, 13.9

Chu et al. (2001)

5

76

ns

Dowd et al. (1998)

72

Sand

Pore water velocity (cm h−1)

Bales et al. (1991)

T1, T2

Sat

nsb

2.86

45–50

0.08–0.33

Loamy sand

Poliovirus 1

Sat

sec

5.6

19.5

ns

Eight soils

φX174

Sat

se (pH 7.2)

10

33, 66, 100

ns

Loamy sand

Poliovirus 1 Reovirus 3 Poliovirus 1

Sat se

ns

10

250

6.3

Ottawa sand

MS-2, φX174

Sat

9.2

10.5, 20

2.5, 10

Ottawa sand

MS-2

Sat

7.6

10.5

2.6

Jin et al. (2000b)

Ottawa sand

MS-2, φX174

Sat/unsat

7.6

10.5

3.8–18.4

Jin et al. (2000a)

Sand, sandy loam Sand

Coxsackievirus B3 MS-2, PRD-1

Sat/unsat Sat

ns 2.7

30, 100 10.6, 14.8

ns 8.6–11.2

Sand

Poliovirus 1 Adenovirus 1

Sat/unsat Unsat

Phosphate-buffered saline (pH 7.5, IS 0.16 M) Phosphate-buffered saline (pH 7.5, IS 0.16 M) Phosphate-buffered saline (pH 7.5, IS 0.16 M) 4.7, 7.5 NaCl (pH 5.7–8.0, IS 0.1–0.2 M) se ns

Gerba and Lance (1978) Jin et al. (1997)

10 ns

250 100

3.3–10 ns

Jorgensen (1985) Kinoshita et al. (1993) Lance and Gerba (1984)

73

Five soils

Drewry and Eliassen (1968) Duboise et al. (1976) Funderburg et al. (1981)

continues

Table VI—continued Column

Porous material Sand, gravel

Virus

Condition

Background electrolyte

Diameter (cm)

Length (cm)

Pore water velocity (cm h−1)

Sat

se (pH 4.4, 7–8.3)

4.3

12.5

∼200

Landry et al. (1979)

Sat

se (pH 7.0)

2.5

16.5

∼6.5

Lo and Sproul (1977) Powelson et al. (1990) Powelson et al. (1991) Powelson and Gerba (1994) Redman et al. (1997) Redman et al. (1999) Teutsch et al. (1991)

Silicate

Poliovirus 1, 3 Coxsackievirus B3, Echovirus 1, 6 Poliovirus 1

Sand Sand

Poliovirus 1 MS-2

Sat/unsat

Groundwater (pH 8.1)

5.2

105

1–1.3

Flushing Meadows soil Sand

MS-2

Unsat

Groundwater (pH 8.1)

5.2

105

1.125

MS-2, PRD-1

Sat/unsat

Se (pH 7.3)

5

100

30–84

Unimin sand

MS-2, Norwalk

Sat

0.01 M NaCl (pH 5 or 7)

1.6

16.9–18.4

90

Sand

SJC3

Sat

ns

19

90

Sand and gravel

T4, MS-2, φX174, Poliovirus 1, Rotavirus SA11

ns

NaCl, CaCl2, or MgCl2 (pH 7, IS 0.0003 M) ns

ns

100

8.3–12.5 or 29.2–33.3

a

IS, ionic strength. ns, not speciﬁed or not available. c Sewage efﬂuent. b

References

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2. Solution Chemistry The effect of solution chemistry (e.g., pH and ionic strength) has been the focus of many virus transport experiments. Viruses have variably electrically charged surfaces, therefore their sorption and transport in porous media are affected by pH. In general, since porous materials and viruses are both negatively charged under most natural conditions, increase in pH increases the electrostatic repulsion between them, thus virus sorption is decreased. In column studies, higher pH of efﬂuent solution usually results in higher column-outﬂow concentration; i.e., fewer viruses are retained in the column. Bales et al. (1991) reported that a change from pH 5.5 to 8.0 in the eluent solution produced a large bacteriophage pulse in the outﬂow, suggesting that increase in pH was responsible for the large virus desorption pulse during transport. The role of electrostatic reaction in virus sorption was demonstrated by Bales et al. (1993). The authors found that the attachment of poliovirus 1 (pHIEP 6.6) to silica sand was comparable to the attachment of MS-2 (pHIEP 3.9) at pH 5.5, but greater at pH 7 relative to MS-2. A moderate pH dependence was observed on MS-2 detachment with increasing pH (Kinoshita et al., 1993). In ﬁeld experiments carried out in a sandy aquifer, introduction of a pulse of high-pH water at the injection well caused detachment of viruses (Bales et al., 1995, 1997). The effect of pH on virus attachment/detachment and transport has been conﬁrmed by others (Loveland et al., 1996; Penrod et al., 1996; Redman et al., 1997; Ryan et al., 1999). Another important factor affecting virus transport is the ionic strength and composition of the background electrolyte solution. Ionic strength affects the thickness of the electrical-double-layer surrounding viruses as well as soil particles. An increase in ionic strength shrinks the double layer and provides a closer proximity between viruses and solid surfaces and therefore enhances virus attachment and retards transport. Duboise et al. (1976) found that a burst of poliovirus released from soil columns coincided with a drop in electrical conductivity of the percolate. The release of MS-2 and poliovirus during transport through silica columns was enhanced upon changing the eluent ionic strength from 0.5 to 0.005 M (Bales et al., 1993). The deposition rates of both MS-2 and λ are sensitive to the ionic strength of the suspending ﬂuid, with more rapid ﬁltration occurring at higher salt concentrations (Penrod et al., 1996). The study by Redman et al. (1999) examined the inﬂuence of pore water chemistry on the ﬁltration and physiochemical properties of a male-speciﬁc ﬁlamentous bacteriophage SJC3. Using a model ﬁltration system consisting of packed columns of quartz sand, they found that the ﬁltration of this virus was strongly dependent on the concentration and valence of the dominant cation in the pore ﬂuid. In one set of experiments, virus retention in the column increased from 0 to almost 100% when the electrolyte composition of the pore ﬂuid changed from 10 mM NaCl to 10 mM CaCl2.

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The composition of both cation and anions also inﬂuences virus retention and transport. Divalent cations were found to be more effective than monovalent cations in promoting adsorption of viruses to soil materials and wastewater solids (Duboise et al., 1976; Funderburg et al., 1981; Lance and Gerba, 1984). Anions such as NO3−, SO42−, and H2PO4− were found to be more effective than Cl−1 in promoting virus adsorption (Lance and Gerba, 1984). Ryan et al. (1999) showed that adsorbed phosphate might hinder attachment of PRD-1 and silica colloids to iron oxide coatings of aquifer materials based on calculations of zeta potentials. The effects of ionic strength and composition of several buffer solutions on the inactivation and sorption of two bacteriophages (MS-2 and φX174) were systematically evaluated in a series of experiments conducted using saturated sand columns (Chu et al., 2000). Changes in ionic strength and composition did not affect the behavior of φX174, whereas MS-2 was largely removed from a high ionic strength phosphate buffer solution during transport but moved through the columns in a low ionic strength phosphate buffer and in an artiﬁcial groundwater. The results indicate that the effects of ionic strength on virus sorption and transport are strongly virus dependent. Therefore, caution should employed when applying laboratory results obtained under ideal chemical conditions and using indicator viruses to the ﬁeld where pathogens are the concern. 3. Soil Water Content Soil water content has been found to play a signiﬁcant role in virus movement in porous media. Studies have shown that viruses are usually removed more extensively during unsaturated transport than saturated transport (Bitton et al., 1984; Hurst et al., 1980; Jin et al., 2000a, Jorgensen, 1985; Lance and Gerba, 1984; Poletika et al., 1995; Powelson et al., 1990; Powelson and Gerba, 1994; Yeager and O’Brien, 1979). Although the mechanisms by which the water content affects virus sorption and inactivation during transport are unclear at present, several possibilities have been proposed in the literature, which are summarized in the following. Bitton et al. (1984) and Jorgensen (1985) postulated that the limited virus movement under unsaturated conditions was due to the increased sorption of viruses to the solid surfaces. Electrostatic and hydrophobic interactions as well as van der Waals forces are believed to be responsible for virus sorption to the solid–water interface (Preston and Farrah, 1988). However, Powelson et al. (1990) dismissed this possibility based on their calculations of the size of water-ﬁlled pores that apparently were much larger than the sizes of viruses. Instead, they concluded that the strong removal of MS-2 in their unsaturated column experiments was caused by inactivation, presumably due to the presence of the air–water interface. Since the sorption of colloids (including hydrophobic and hydrophilic particles of

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clay and polystyrene latex as well as bacteria) to the surface of air bubbles was directly visualized (Wan and Wilson, 1992, 1994), the presence of AWI has been suggested by more and more researchers as the dominant mechanism responsible for the increased removal of colloidal particles, including viruses and bacteria, in unsaturated systems (Jewett et al., 1999; Jin et al., 2000a; Poletika et al., 1995; Powelson and Mills, 1996; Sch¨afer et al., 1998). The ﬁlm-straining theory, introduced by Wan and Tokunaga (1997), proposes that transport of suspended colloids can be retarded due to physical restrictions imposed by thin water ﬁlms in partially saturated porous media. Film straining becomes effective at a “critical matric potential” and “critical saturation” at which thick ﬁlm interconnections between pendular (capillary) rings are broken (Wan and Tokunaga, 1997). This model predicts that the magnitude of colloid transport through water ﬁlms depends on the ratio of particle size (dp) to ﬁlm thickness (h0) and on ﬂow velocity. Experiments were conducted using uniform sand and various sizes of latex particles at different velocities and various water saturation levels, and results agreed well with model predictions. Experimental results on the effect of the dp/h0 ratio on the motion of spherical particles in a stable liquid ﬁlm ﬂowing down an inclined ﬂat surface were recently reported by Veerapaneni et al. (2000). They found that (i) at low dp/h0 values, particle velocity increased almost linearly with increasing particle size; (ii) in the range of dp/h0 = 0.7–1, particle velocity decreased rapidly with increasing particle size; (iii) in the range of dp/h0 = 1– 1.75, particles ceased to move, and (iv) at dp/h0 > 1.7, particle velocity again increased with increasing particle size. The authors acknowledged the need to incorporate the effect of short-range forces (e.g., van der Waals and double-layer interactions) and the effect of Brownian motion in future work to verify their reported ﬁndings. Experimental evidence to verify the previously mentioned mechanisms is lacking. Most studies on virus transport through unsaturated porous media have been conducted under unsteady-state ﬂow conditions (e.g., inﬁltration experiments), or in systems with nonuniform water distribution. As such, it is very difﬁcult to identify and examine the mechanisms responsible for the increased virus removal in unsaturated systems. It should also be noted that previous studies that demonstrated the importance of the AWI on the retention of colloidal particles in unsaturated porous media have mostly been conducted under conditions that are highly favorable for sorption at AWI, such as by using nonreactive solid materials. In an unsaturated porous medium, it is difﬁcult to isolate the local reactions of viruses to the solid–water and air–water interfaces. An attempt to differentiate between the two interfacial reactions has been reported by Chu et al. (2001). These authors used nonreactive (metal oxides removed) and reactive sands in column experiments under saturated and unsaturated ﬂow conditions. The removal of oxides from the sand grains rendered the sand inert with respect to virus sorption and

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inactivation. The experimental data and a sequential modeling approach showed that two different viruses (φX174 and MS-2) preferentially sorbed to the solid– water than to the air–water interface. Virus retardation and removal in unsaturated columns were mainly controlled by sorption/inactivation at the solid–water interface rather than at the air–water interface. Figure 10 shows this behavior for φX174, indicating that the lower the water saturation in the columns, the bigger the difference of virus transport between nonreactive (metal oxides removed) and reactive sands. Under 100% saturation, φX174 behaved as a conservative tracer in the nonreactive sand, whereas an irreversible sorption/inactivation occurred in the reactive sand. Under unsaturated conditions, some sorption to the water–gas interface was observed, but sorption to the solid–water interface was by far more dominant. 4. Virus Type The protein capsids of viruses typically contain ionizable amino acids such as glutamic acid, aspartic acid, histidine, and tyrosine (Gerba, 1984). Depending on the pH of the surrounding environment, individual carboxyl and amino groups will ionize giving the capsid a net electrical charge. Viruses vary in their isoelectric point. The isoelectric point has been used as one of the most important characteristics in evaluating virus sorption to various solid surfaces. Viruses also differ in their surface hydrophobicity (Shields and Farrah, 1987). Both electrostatic and hydrophobic interactions between virus particles and solid surfaces are believed to control virus adsorption processes (Schijven and Hassanizadeh, 2000), and hence transport behavior. Dowd et al. (1998) conducted a study to identify the inﬂuence of viral isoelectric point on viral adsorption onto and transport through a sandy aquifer sediment. Five different spherical bacteriophages (MS-2, PRD-1, Qβ, φX174, and PM2) having different isoelectric points (pH 3.9, 4.2, 5.3, 6.6, and 7.3) were used in laboratory viral transport studies. Conventional batch ﬂowthrough columns, as well as a continuously recirculating column, in which the outﬂow is connected to the inﬂow, were used. In a 0.78-m batch ﬂowthrough column, the smaller phages (MS-2, Qβ, and φX174), which had similar diameters, exhibited maximum efﬂuent concentration /initial concentration values that correlated exactly with their isoelectric points. The amount of viruses sorbed was negatively correlated with the isoelectric points of the viruses. The data suggest that the isoelectric point of a virus is the predetermining factor controlling viral adsorption within aquifers for relatively small viruses (Dowd et al., 1998). However, such a dependence on the pHIEP was not observed for viruses with a diameter larger than 60 nm (Dowd et al., 1998). The ﬁltration behavior of a recombinant Norwalk virus (rNV) and the bacteriophage MS-2, a common surrogate for waterborne viral pathogens, were studied

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Figure 10 φX174 breakthrough curves in water-washed (ﬁlled circles) and oxide-removed (open circles) sand under different water saturations. The solid lines are model simulations considering virus sorption at solid–water and air–water interfaces. Adapted from Chu et al. (2001). Mechanisms of virus removal during transport in unsaturated porous media. Water Resour. Res. 37, 253–263. Copyright [2001] American Geophysical Union. Reproduced/modiﬁed by permission of American Geophysical Union.

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in a set of columns packed with pure quartz sand (Redman et al., 1997). The authors found that in contrast to MS-2, the surface charge of rNV particles and their ﬁltration through the columns are strongly inﬂuenced by pore water pH over the environmentally important range of pH 5–7 although MS-2 has a lower isoelectric point. Therefore, MS-2 may not be a good model for the subsurface ﬁltration of Norwalk virus in natural systems (Redman et al., 1997). Different behavior between MS-2 and φX174 has also been found repeatedly in several studies (Chu et al., 2000; Jin et al., 2000a; Jin et al., 1997). The two viruses not only have different isoelectric points (Table II) but also differ signiﬁcantly in surface hydrophobicity, with MS-2 being the more hydrophobic and φX174 the more hydrophilic bacteriophage. As pointed out by Schijven and Hassanizadeh (2000), virus removal by soil passage will be virus dependent because of the differences in electrical charge and hydrophobicity that exist between different types of viruses, and even between different strains of the same virus type. 5. Size Exclusion Due to their ﬁnite size, macromolecules, viruses, bacteria, and other colloidal particles are excluded from a certain pore space in a porous medium. Two mechanisms can be considered to be responsible for size exclusion. 1. Particles can be excluded from pores with diameters smaller than the suspended particle. The particles cannot diffuse into the microporous portion of the medium and are conﬁned to the larger pores. The microporous region is often a stagnant phase, so that the size exclusion acts between a mobile and a stationary phase. This type of exclusion has been denoted as “gel permeation chromatography” (Casassa, 1971). 2. The center of mass of particles in a single pore is excluded from the immediate neighborhood of the pore walls due to steric effects. Since the water velocity is larger in the center of the pore than close to the pore wall, the particle’s center of mass experiences a larger mean velocity compared to the bulk ﬂuid. This phenomenon has been referred to as “hydrodynamic chromatography” or “separation by ﬂow” (DiMarzio and Guttman, 1970; Small, 1974). In either case, the particles are excluded from regions with no or lower advective velocities, and it is not really operationally necessary to differentiate between the two mechanisms (Casassa, 1971). In the absence of chemical reactions, the size exclusion leads to different mean velocities for particles of different size, with larger particles usually traveling faster than smaller ones. This phenomenon is used in preparative and analytical biochemistry to separate polymers of different size and is commonly known as size-exclusion chromatography (Giddings et al.,

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1968; Yau et al., 1979), without distinguishing between the two different exclusion mechanisms. Size exclusion is also known to accelerate the movement of colloids through soils, sediments, and rocks (de Marsily, 1987; Ginn, 2000; Kretzschmar et al., 1999). For instance, microorganisms have been found to migrate at velocities greater than that of the bulk soil water, demonstrating early microbial breakthrough with respect to a conservative tracer (Crondin, 1987; Harvey, 1988; Harvey et al., 1989; Mawdsley et al., 1996; McKay et al., 1993). In a microplot ﬁeld study, movement of microbial particles such as Cryptosporidium parvum oocysts mainly occurred through macropores (Mawdsley et al., 1996). Since size exclusion is used to separate proteins in chromatography columns, viruses may be affected by this phenomenon as well. In a porous medium, viruses can therefore migrate faster than a conservative tracer, provided there is no sorption that would counteract the increased mean travel velocities. The conservative tracer experiences the entire spectrum of the advective velocities and will therefore have a smaller mean travel velocity than a size-excluded particle. The magnitude of the size-exclusion effect depends on the pore size distribution of the medium: the larger the fraction of the inaccessible pores, the faster the mean transport of the excluded particle. There is some experimental evidence that size exclusion can accelerate virus movement in laboratory sand columns (Bales et al., 1989), in sandy and gravelly soils (Powelson et al., 1993), and in fractured aquifers (McKay et al., 1993). The acceleration can be substantial: migration velocities for MS-2 and PRD1 in a fractured clay till were up to two orders of magnitude larger than those for the conservative tracer bromide (McKay et al., 1993). In addition to the steric size-exclusion phenomenon, viruses may also be excluded from portions of the pore space by electrostatic repulsion. At the natural pH of most soil water systems, viruses possess a net negative surface charge and may be repelled by the negatively charged soil particles, a phenomenon known as anion exclusion. Consequently, viruses will tend to move in the center of soil pores where the water velocity is greater than near the surface of the soil particle. 6. Colloid-Facilitated Virus Transport Studies have shown that mobile colloidal particles are generated in soils and aquifers in situ when changes in solution chemistry occur (Grolimund et al., 1998; McCarthy and Degueldre, 1993; Ryan and Elimelech, 1996; Ryan and Gschwend, 1994). Suspended colloids can serve as carriers of sorbed contaminants and can facilitate the transport of strongly sorbing chemicals (Kretzschmar et al., 1999; McCarthy and Zachara, 1989). Viruses have been found to sorb to mineral surfaces (Gerba, 1984; Lipson and Stotzky, 1984b), and while associated with suspended particles they tend to survive longer in the environment (Babich and Stotzky, 1980;

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Hurst et al., 1980; Liew and Gerba, 1980). In addition, most viruses are already present in a colloid-bound form in septic tank liquors and wastewaters before being released into the subsurface (Sobsey et al., 1991). However, information on the possible effect and the extent of mobile colloids on virus transport in porous media is limited. In a recent study, Jin et al. (2000b) examined the inﬂuence of several mineral colloids [kaolinite, Ca-montmorillonite (Ca-M), and Na-montmorillonite (Na-M)] on the movement of a bacteriophage MS-2 through sand columns under saturated ﬂow conditions. Results from this study provided direct experimental evidence for colloid-facilitated virus transport through saturated sand. As demonstrated in this study, facilitated MS-2 transport in the presence of colloids can be a signiﬁcant pathway (Fig. 11) when viruses become associated with colloids and when the colloids are mobile. The results also indicate that the extent of colloid-facilitated virus transport depends on many factors such as the type and size of colloids, as well as the extent of virus sorption to the colloids.

B. MODELING OF VIRUS TRANSPORT Several mathematical models have been developed to describe virus transport in porous media. These models are all based on the advection–dispersion equation (ADE) combined with speciﬁc local reaction processes accounting for sorption or ﬁltration, and for inactivation. Different levels of sophistication have been used to describe sorption/ﬁltration and inactivation. While many models used to analyze virus transport have been developed for solutes in general, and were later applied to viruses, other models have incorporated virus-speciﬁc reaction processes. Due to their small size, viruses have often been considered to be solutes, and consequently interactions with solids have been described with sorption theory. Often it was assumed that the sorption process is at equilibrium, and linear (Grosser, 1985; Tim and Mostaghimi, 1991; Yates and Ouyang, 1992), Freundlich (Corapcioglu and Haridas, 1984, 1985; Pekdeger and Matthess, 1983), and Langmuir isotherms (Teutsch et al., 1991; Vilker and Burge, 1980) have been used. The equilibrium sorption assumption for viruses has subsequently been relaxed to nonequilibrium sorption, employing ﬁrst-order kinetics (Bales et al., 1991; Corapcioglu and Choi, 1996; Sim and Chrysikopoulos, 1995) and Langmuir-type kinetics (Chu et al., 2001; Vilker et al., 1978). Random sequential adsorption, as proposed for proteins, has not yet been applied to virus sorption. As opposed to linear Freundlich and Langmuir sorption kinetics, RSA would result in earlier virus breakthrough through a soil column or deeper virus penetration into a soil proﬁle (Fig. 12). When viruses are considered to be colloidal particles, solid–liquid phase interactions have been described by ﬁltration theory. It has been shown that there is a slow detachment of viruses that will give rise to a long tail in virus breakthrough

B

A Figure 11

Transport of (a) mineral colloids and (b) MS-2 in absence and presence of mineral colloids, through sand columns (adapted from Jin et al., 2000b).

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Figure 12 Breakthrough curves and depth proﬁles of a solute or particle subject to different sorption kinetics assuming steady-state ﬂow according to the ADE. Sorption is assumed to be irreversible. The column Peclet number in these simulations is Pe = V L/D = 10.

curves (Schijven et al., 1999), so that the ﬁltration process is best modeled with an attachment and detachment process (Corapcioglu and Haridas, 1984; Sim and Chrysikopoulos, 1995). Attachment and detachment lead to ﬁrst-order reactions, similar to a kinetic sorption process. Whether the solid–liquid interaction is assumed a sorption or ﬁltration process depends on whether viruses are considered to be solutes or colloids. Conceptually, sorption and ﬁltration should therefore not be used simultaneously (Bouwer and Rittman, 1992). To describe virus movement under transient ﬂow conditions, numerical models have been developed in which water ﬂow (Richards equation) is combined with virus transport (ADE with reaction terms). Examples of such models are VIROTRANS (Tim and Mostaghimi, 1991) and VIRTUS (Yates and Ouyang, 1992), where the latter model also solves the heat ﬂow equation and considers temperature-dependent virus inactivation rate coefﬁcients for viruses in both liquid and sorbed phases. Another virus transport model, developed for steady-state, unsaturated ﬂow conditions is VIRALT (Park et al., 1992). In all these models, the viruses are assumed not to interact with the air–water interface. However, under unsaturated conditions, experiments have shown that virus sorption and inactivation occur at the air–water interface. Only recently has the air–water interface been explicitly considered as a reactive interface in transport models. The available surface area for sorption at the air–water interface is not constant, but rather changes with the water content of the medium. The drier the soil, the larger the air–water interfacial area, and the more sorption sites will be available. Corapcioglu and Choi (1996) presented a model for transport of nondecaying colloidal particles in an unsaturated porous

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Figure 13 Conceptual model of virus sorption and inactivation in an unsaturated porous medium. Solid circles denote viable viruses, open circles denote inactivated viruses, and arrows indicate directions of sorption (k’s) and inactivation (λ’s) reactions. Adapted from Chu et al. (2001). Mechanisms of virus removal during transport in unsaturated porous media. Water Resour. Res. 37, 253–263. Copyright [2001] American Geophysical Union. Reproduced/modiﬁed by permission of American Geophysical Union.

medium. In this model, the reactions of colloids to the solid–water interface were described with a ﬁrst-order process, and reactions to the air–water interface were of second order, assuming a Langmuir-type reaction process where the available sorption sites decrease with increasing surface coverage of colloids. This type of model has been extended explicitly for virus transport by including ﬁrst-order inactivation in different phases (Sim and Chrysikopoulos, 2000) and by considering that inactivated viruses can compete with viable viruses for sorption sites (Chu et al., 2001). A conceptual model for the general reactions in a unsaturated porous medium is depicted in Fig. 13. The different interfacial reactions have been formulated as ﬁrst-order reactions (Sim and Chrysikopoulos, 2000) or second-order reactions, assuming that sorbed particles cause a blocking of sorption sites (Chu et al., 2001). A complete set of governing equations for this conceptual model is given as (Chu et al., 2001) Ctot + Ctot,in = θ(C + Cin ) + ρ(S + Sin ) + ζ (U + Uin )

(34)

∂Ctot ∂ 2C ∂C = θ DDisp 2 − θ V − λl θC − λs ρ S − λu ζ U, ∂t ∂z ∂z

(35)

and

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where Ctot and Ctot,in are the total concentrations of viable and inactivated viruses, respectively; θ is the volumetric water content; ρ is the bulk density, ζ is the volumetric air content; C is the liquid phase concentration; S is the sorbed phase concentration; U is the mass of viruses sorbed at the air–water interface per unit volume of air; and the subscript “in” denotes inactivated viruses, λl, λs, and λu are ﬁrst-order inactivation coefﬁcients for the liquid phase, the solid–water and air–water interfaces, respectively. The interfacial reactions can be written as ∂S = k1 φ1 (S, Sin )θC − k2 ρ S − λs ρ S ∂t ∂U = k3 φ2 (U, Uin )θC − k4 ζ U − λu ζ U ζ ∂t (36) ∂ Sin = k5 φ1 (S, Sin )θCin − k6 ρ Sin + λs ρ S ρ ∂t ∂Uin = k7 φ2 (U, Uin )θCin − k8 ζ Uin + λu ζ U, ζ ∂t where the ki’s are the ad- and desorption rate coefﬁcients, and φ 1 and φ 2 represent blocking functions for sorption sites at the solid–water and the air–water interfaces. The blocking functions were assumed to be of a Langmuir type as proposed by Corapcioglu and Choi (1996), for example, 0 if X i > X i,max φ(X i ) = , (37) 1 − X i / X i,max otherwise ρ

where X1 = S + Sin for the solid–water interface, X2 = U + Uin for the air–water interface, and Xi,max represents the maximum sorption capacity at the respective interface. This model has been successfully applied to analyze transport of φX174 through unsaturated sand columns (Fig. 10). Under unsaturated ﬂow conditions in the subsurface, it appears that an adequate description of virus transport requires consideration of solid–water as well as air– water interfacial interactions. These interactions are relevant for both sorption and inactivation processes. Since viruses, as living organisms, are affected by a variety of environmental variables, the modeling of virus transport in the subsurface remains a challenging task.

VIII. INDICATORS FOR HUMAN ENTEROVIRUSES Because detection and enumeration of human enteroviruses (HEV) are difﬁcult and time-consuming, many studies on virus sorption and transport have been conducted using the so-called “indicator” viruses (e.g., MS-2, φX174, and

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PRD-1). In addition, some common disease-causing viruses (hepatitis A virus, rotaviruses, and Norwalk virus) cannot as yet be detected practically, and techniques available for the recovery and identiﬁcation of human enteric viruses often have limited sensitivity. Use of “indicator” organisms to assess HEV behavior in subsurface medium is necessary and has been practiced for almost a century. Characteristics of coliphages and their suitability to serve as indicator have been reviewed by Snowdon and Cliver (1989). An ideal indicator of viral contamination of groundwater should possess the following particular properties (IAWPRC Study Group on Health Related Water Microbiology, 1991; Snowdon and Cliver, 1989): 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

be applicable in all types of groundwater be unable to reproduce in contaminated water relate speciﬁcally to contamination by human feces have a density in contaminated water that directly relates to the degree of fecal pollution enable rapid detection and unambiguous identiﬁcation be nonpathogenic to humans be present whenever HEV are present, and in greater numbers have physical properties similar to HEV be similar to HEV in adsorption to soils and transport through groundwater have a survival time as long as the most persistent HEV

The suitability of a particular virus as an indicator is evaluated based on relative insensitivity to inactivation (Yates et al., 1985) and its ecological and morphological similarities to human pathogens (Havelaar et al., 1993). Coliphages, particularly RNA-phage, have been proposed as suitable indicators for HEV (Snowdon and Cliver, 1989). Among the coliphages, MS-2 has been suggested to be the most suitable indicator (Springthorpe et al., 1993; Yates et al., 1985). From the hydrological point of view, a worst-case indicator for transport and fate of HEV does not need to include all the criteria listed previously. We propose that such an indicator should 1. be unable to reproduce in contaminated media (soil, water) 2. show similar or less sorption and retention than HEV in porous media under identical conditions 3. be at least as resistant to inactivation under natural conditions as HEV 4. be nonpathogenic to humans and other animals (only if used as tracer in the ﬁeld) We may call this type of indicator a transport indicator, to differentiate from the pollution indicator that indicates viral pollution of groundwater. Because of the extremely complex sorption and transport mechanisms of viruses, recent studies have raised doubts regarding the applicability of any single

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indicator’s ability to mimic the behavior of HEV. MS-2 has been shown to be relatively easily inactivated in unsaturated systems and in the presence of metal oxides (Chu et al., 2000; Jin et al., 2000a). Penrod et al. (1996) compared the deposition kinetics of bacteriophages MS-2 and λ and found that even subtle differences in viral surface structures could signiﬁcantly inﬂuence the rate at which viruses were removed from the water phase by inﬁltration. Taking a different approach, Redman et al. (1997) used recombinant Norwalk virus (rNV) particles as a model system to study the ﬁltration behavior of Norwalk virus (NV), the human pathogen. The biochemical procedure used to created the rNV particles is given in Redman et al. (1997). The resulting rNV particles are morphologically and antigenically similar to the native NV but lack the genetic material (i.e., RNA) so they are harmless and cannot infect humans. Such rNV particles may be ideal to be used as a model system for transport studies because they can be grown to high concentration, and their noninfectious character implies that experiments at the ﬁeld scale may be possible (Redman et al. 1997). A comparison of the behavior between MS-2 and rNV indicates that MS-2 is not a suitable surrogate for NV. Redman et al. (1997) also pointed out that the rNV particle system is not well suited to simulate the inactivation behavior of the real NV. Considering the complex sorption and retention mechanisms of viruses, it is unlikely that any single compound or microorganism will be able to adequately represent the transport behavior of different HEV in porous media (Penrod et al., 1996). Caution should be used when extrapolating results from studies conducted with indicator microorganisms or other types of colloidal particles to the behavior of HEV, as such indicators are likely inadequate to represent human pathogens.

IX. CONCLUDING REMARKS There is evidence that large-scale virus transport occurs in the subsurface environment. The USEPA estimates that annually in the United States16 people are at risk of death and 168,000 people are at risk of viral illness from consuming groundwater contaminated with pathogenic viruses (USEPA, 2000). Development of effective regulations to protect public health from microbial contamination relies on a thorough understanding of key processes governing virus survival and transport in the natural environment. Considerable knowledge has been accumulated from research conducted over the last 20 to 30 years. The inﬂuence of the factors affecting virus sorption and inactivation have been extensively studied and well documented. Solution chemistry (pH and ionic strength), virus properties (isoelectric point and surface

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characteristics), soil properties (organic matter content, CEC, presence of metal oxides, etc.) have been found to affect virus sorption to various degrees, while temperature, association with solid particles, and water content are among the factors identiﬁed that affect virus survival. Laboratory and ﬁeld experiments have revealed many factors and processes that attenuate virus transport through porous media. As presented in this article, results from protein research provide some insights as to what mechanisms might be involved in virus sorption that have so far not been studied extensively. For example, the sorption mechanism seems to be affected largely by particle size, and there might be a transition between reversible and irreversible sorption for particles in the size range of viruses. Such information is essential for identifying the appropriate models to describe virus sorption behavior. Also lacking is information on reaction kinetics involved in virus sorption/desorption processes, especially under conditions that are closely related to ﬁeld conditions. Considering that one single virus can cause infection, the possible slow desorption kinetics of viruses needs detailed investigation. Carefully designed ﬁeld scale studies are needed to investigate the extent that chemical and physical heterogeneities of natural porous materials affect virus retention and transport. An even more challenging task of future research is to effectively apply fundamental theories from laboratory studies to the ﬁeld. Some research needs are summarized as follows. r Examine virus sorption mechanisms using both macroscopic and microscopic techniques and identify/develop appropriate models. r Study kinetics of virus sorption/desorption, and develop quantitative descriptions of these processes. r Investigate the inﬂuence of physical and chemical heterogeneity on virus transport and retention in natural porous media. r Elucidate mechanisms of virus inactivation during transport in unsaturated systems, and develop appropriate models to quantify these processes. r Study the role and extent of colloids in facilitating virus transport behavior and their effect on virus survival in natural media. r Systematically compare the behavior of the commonly used model viruses with that of the representative pathogens to identify more reliable surrogates for the pathogens. In summary, viruses and other pathogenic microorganisms may be one of the greatest health risks and management challenges for our drinking water resources. Viruses pose a public health threat at a very low level; e.g., the USEPA states a limit of 2 virus particles per 107 L of water to achieve an annual infection risk of less than 10−4 (USEPA, 1994). Such a drinking-water limit can only be achieved through a more thorough understanding of virus fate and transport in the subsurface.

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Symbol

Description

a a aeff A ADE AWI C C Ctot Ctot,in d D DDisp DLVO fom h0 K Kd KL k ka kd kp k1 , . . . , k 8 L N N0 n pHIEP RSA S S Sin Smax Smin Sie X1, . . . , Xn Som Srxn TPB t v U Uin

Diameter of ﬁlter grain Radius of ﬁlter grain Effective radius of ﬁlter grain Area of mineral surfaces per mass of solid Advection–dispersion equation Air–water interface Concentration of dissolved chemical Concentration of viable viruses in liquid phase Total concentration of viable viruses Total concentration of inactivated viruses Diameter of particle Brownian diffusion coefﬁcient Dispersion coefﬁcient Derjaguin–Landau–Verwey–Overbeek Fraction of organic matter Water-ﬁlm thickness Constant in Freundlich isotherm Distribution coefﬁcient Constant in Langmuir isotherm Boltzmann’s constant (1.3805 × 10−23 J K−1) Attachment rate Detachment rate Rate coefﬁcient in colloid aggregation Ad- and desorption rate coefﬁcients Length of ﬁlter bed or space coordinate Number of particles per unit suspension volume Initial number of particles per unit suspension volume Constant in Freundlich isotherm isoelectric point Random Sequential Adsorption Concentration of sorbed chemical Concentration of sorbed viable viruses Concentration of sorbed inactivated viruses Maximal concentration of sorbed chemical in Langmuir isotherm Concentration of sorbed chemical associated with mineral surfaces Concentration of sorbed chemical bonded by electrostatic forces Various factors affecting virus inactivation Concentration of sorbed chemical associated with organic matter Concentration of sorbed chemical bonded by reversible reaction Triple-phase boundary Time Pore water velocity Concentration of viable viruses at air-water interface Concentration of inactivated viruses at air-water interface

Dimension [L] [L] [L] [L2 M−1] [ML−3] [ML−3] [ML−3] [ML−3] [L] [L2 T−1] [L2 T−1] [−] [L] [L3 M−1] [L3 M−1] [L3 M−1] [L3 M−1 T−1] [T−1] L3 T−1] [T−1] [L] [L−3] [L−3] [−] [−] [MM−1] [MM−1] [MM−1] [MM−1] [ML−2] [ML−2] [Variable] [MM−1] [MM−1] [T] [LT−1] [ML−3] [ML−3] continues

91

SUBSURFACE VIRUS FATE AND TRANSPORT APPENDIX—continued Symbol z α α0 αp β 1, . . . ,β n βt η ηs

max κi λ λ0 λl λs λu φ ρ σ ie σ rxn θ ϕ ξ ζ

Description Spatial coordinate Collision efﬁciency factor for spherical collector model Collision efﬁciency factor for colloidal aggregation under uniform gradient ﬂow Collision efﬁciency factor for colloidal aggregation in static solution Coefﬁcients assigned to variables X1, . . . , Xn Coefﬁcient assigned to time t Dynamic viscosity Single-collector efﬁciency Porosity Surface coverage Jamming limit in RSA Rate coefﬁcients Filtration coefﬁcient Initial ﬁltration coefﬁcient First-order inactivation coefﬁcients for the liquid phase First-order inactivation coefﬁcients for the solid phase First-order inactivation coefﬁcients for the air-water interface Blocking factor in surface excusion models Bulk density Concentration of charged sites on solid surface Concentration of reactive sites on solid surface Volumetric water content Total volume of particles per unit volume suspension Effectiveness of particle removal Volumetric air content

Dimension [L] [−] [−] [−] [−] [−] [MT−1 L−1] [−] [−] [−] [−] [Variable] [L−1] [L−1] [T−1] [T−1] [T−1] [Variable] [ML−3] [ML−2] [ML−2] [L3 L−3] [L3 L−3] [−] [L3 L−3]

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CURRENT CAPABILITIES AND FUTURE NEEDS OF ROOT WATER AND NUTRIENT UPTAKE MODELING Jan W. Hopmans1,∗ and Keith L. Bristow2 1

Department of Land, Air and Water Resources University of California Davis, California 95616 2 CSIRO Land and Water/CRC∗ Sugar Townsville Qld 4814, Australia

I. Introduction II. Water Transport in Plants A. Soil–Plant–Atmosphere Continuum B. Water Potential C. Cavitation D. Commentary III. Linking Plant Transpiration with Assimilation A. Integrating Root Uptake Processes B. Transpiration Coefficient C. Commentary IV. Transport of Water and Nutrients in the Plant Root A. Plant Root Structure B. Apoplastic versus Symplastic Pathway C. Commentary V. Nutrient Uptake Mechanisms A. Active versus Passive Nutrient Uptake B. Michaelis–Menten Description of Nutrient Uptake C. Commentary VI. Flow and Transport Modeling in Soils A. Soil Water Flow B. Solute Transport C. Commentary VII. Root Water Uptake A. Macroscopic Water Uptake B. Root Water Uptake Types I and II C. Other Aspects Affecting Water Uptake D. Commentary

∗ To

whom correspondence should be addressed. [email protected] 103 Advances in Agronomy, Volume 77 Copyright 2002, Elsevier Science (USA). All rights reserved. 0065-2113/02 $35.00

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HOPMANS AND BRISTOW VIII. Nutrient Uptake A. Nutrient Transport in Soils B. Nutrient Transport in the Root C. Nitrate Uptake D. Commentary IX. Coupled Root Water and Nutrient Uptake A. Mechanistic Formulations B. Other Considerations C. Multidimensional Approach D. Commentary X. Comprehensive Example XI. Prognosis References

The importance of root function in water and nutrient transport is becoming increasingly clear, as constraints on agricultural resources are imposed due to water limitations and environmental concerns. Both are driven by the increasing need to expand global food production. However, the historical neglect of consideration of water and nutrient uptake processes below ground has created a knowledge gap concerning the plant responses of nutrient and water limitations to crop production. The review includes sections on (i) notation and deﬁnitions of water potential, (ii) the physical coupling of plant transpiration and plant assimilation by way of the principles of diffusion of water vapor and carbon dioxide, (iii) apoplastic and symplastic water and nutrient pathways in plants, (iv) active and passive nutrient uptake, and (v) a discussion of the current state-of-the-art in multidimensional soil water ﬂow and chemical transport modeling. The subsequent review of water uptake, nutrient uptake, and simultaneous water and nutrient uptake addresses shortcomings of current theory and modeling concepts. The review concludes with an example illustrating a possible multidimensional approach for simultaneous water and nutrient uptake modeling. Speciﬁc recommendations identify the need for coupling water and nutrient transport and uptake, including salinity effects on root water uptake and the provision of simultaneous passive and active nutrient uptake. It considers the requirement for multidimensional dedicated root water and nutrient uptake experiments to validate and calibrate hypothesized coupled root uptake C 2002 Elsevier Science (USA). models.

I. INTRODUCTION Comprehensive reviews of water and nutrient uptake concepts have been written by Molz (1981), Boyer (1985), Passioura (1988), Baker et al. (1992), van Noordwijk and van de Geijn (1996), and others. However, upon reading these

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reviews one will notice that while they aspire to address mechanistic description of mass transport in plant–soil water systems, their focus is mostly on either the plant or the soil. There are only a few reviews of the functional interactions between these two subsystems. Also, there has been relatively little progress in the advancement of the basic understanding of transport processes in plants, speciﬁcally regarding their control by interfacial ﬂuxes at either the root–soil or leaf–atmosphere interfaces. Both these observations may be a consequence of the way that scientists conduct their research. That is, after being thoroughly taught our scientiﬁc discipline of choice, we conduct our research business within its usual narrow disciplinary boundaries without really wondering too much about other closely related disciplines. Venturing too far outside one’s own strictly deﬁned area is usually discouraged for fear of discrediting yourself as being a generalist, and ending up knowing a little about everything. Much more credit is usually given to addressing fundamental issues in narrow disciplines. Moreover, large-scale funding to support all investigators in multidisciplinary research projects is sparse, whereas publication of research ﬁndings with multiple authors is challenging and perhaps less appreciated. Alternatively, one could argue that the quantitative plant physiology of plant water transport has been lagging behind, relative to the environmental ﬂuid mechanics studies of soil physical and atmospheric processes. The small-scale processes of atmospheric gas and soil water movement are believed to be well understood from a physical/hydrodynamic point of view. However, their connection with the plant at the interfaces is not. Undoubtedly, this is a complex and complicated area of research. Accordingly, ﬂuxes at the interfaces (plant–soil and plant– atmosphere) are mostly empirically derived, rather than mechanistically, as might be preferred. In part, this is likely caused by the increasing complexity of biological systems, with their functions and mechanisms of internal transport of water and nutrients (xylem) and assimilates (phloem) less well understood. Consequently, water and nutrient uptake in plant growth and soil water ﬂow models is mostly described in an empirical way, lacking a sound physiological or biophysical basis. This is unfortunate, as the exchange of water and nutrients is the unifying linkage between the plant root and the surrounding soil environment. The simpliﬁed sink approach was adequate for non-stress-plant-growth conditions and may work adequately for uniform soil conditions. However, it has become increasingly clear that a different approach is required if water and/or nutrient resources become limited in part of the root zone. Increasingly, recommended irrigation water and soil management practices tactically allocate both water and fertilizers, thereby maximizing their application efﬁciency and minimizing fertilizer losses through leaching toward the groundwater. For example, there has been the rise of new water and nutrient management techniques such as the simultaneous microirrigation and fertilization, or fertigation (Bar-Yosef, 1999), drip irrigation, regulated deﬁcit irrigation (RDI), partial root zone drying (PRD; Lovey et al., 1997; Stoll et al., 2000), and band application of fertilizers. It has been suggested that the rhizosphere might also

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be responsible for accelerated breakdown of organic chemicals by biodegradation (Walton and Anderson, 1990) or extraction of contaminants by photoremediation. As pointed out by van Noordwijk and van de Geijn (1996) in their review of processoriented crop growth models, the “new” agriculture will be directed at minimizing yield losses and crop quality, while keeping environmental side effects at acceptable levels. We suggest that the effectiveness of these practices regarding their effects on crop production and groundwater quality requires a thorough understanding of plant–soil interactions and the plant’s regulatory functions in managing stresses. This includes knowledge of the crops’ responses to the availability of spatially distributed soil water and plant-available nutrients, using a multidimensional modeling approach. It is our objective to integrate principles of soil and plant sciences, by way of reviewing the soils and plant literature on water and nutrient uptake and transport concepts and processes, within the soil–plant system. In doing so, most of the atmospheric–plant interaction literature is excluded, because we assume that the potential transpiration rate is a priori known by prediction from independent measurements. However, there is no doubt about the importance of stomatal conductance and its control on plant transpiration and assimilation and the importance of the stomatal physiological response to changing atmospheric, soil, and plant environmental conditions. Excellent contributions in this ﬁeld have been presented by Jarvis and McNaughton (1986), Leuning (1995), and Wang and Leuning (1998). The focus of the presented analysis is mostly on the description of the physical mechanisms, likely overlooking some of the basic biological concepts. Indeed, we admit that our background in plant biology is restricted to ﬂow and transport within the soil–plant–atmosphere continuum (SPAC). However, we strived to integrate our understanding of the pertinent biological processes with physical principles. Although we will direct the focus of this review toward spatially distributed root functioning and integration of soil–plant interactions, this treatise does not discuss the fundamental physiological and biogeochemical processes occurring in the rhizosphere. Although it is becoming increasingly clear that rhizosphere processes play a major role in root water and nutrient uptake and plant stress responses, their general understanding is often incomplete, thereby making it difﬁcult to integrate rhizosphere processes in the macroscopic modeling of plant growth and associated root water and nutrient uptake. For example, the root is considered the sensing organ of the soil environment and communicates with the shoot by chemical signals by transport of speciﬁc nutrients (e.g., calcium) or plant hormones to the shoot (L¨auchli and Epstein, 1990). As a result, root signals play a major role in mediating soil water and salinity stress. Speciﬁcally, root and shoot hormone levels of abscisic acid (ABA) have been shown to increase as a response to water and salinity stress (Davies et al., 2000; Stoll et al., 2000) and induce stomatal closure, whereas ethylene production is suggested to be related to drought resistance (Amzallag, 1997; Kirkham, 1990). Also, differences in soil microbial populations

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and chemical and physical properties between the rhizoshpere and the bulk soil are not speciﬁcally treated; however, it is realized that plant growth, water and nutrient uptake, and availability can be largely determined by the local environment in the rhizosphere, including root–soil contact. Hence, the measurement and modeling of processes in the bulk soil may not reﬂect the environment experienced by the root system. Examples of the inﬂuence of the rhizosheath on root growth and uptake processes were presented by Pierret et al. (1999) and Watt et al. (1994). The importance of soil structure and biopores on root and plant growth and nutrient uptake was considered by Passioura (1991), Volkmar (1996), and Pierret et al. (1999). Their examples show that rhizosphere properties and root functioning are different between the macropore and the bulk soil, speciﬁcally related to differences in microbiological heterogeneity and root–soil contact. In addition, this review largely ignores the role of mycorrhizae and their inﬂuence on plant water and nutrient uptake, particularly regarding phosphorus adsorption (Krikun, 1991). The trend toward the understanding of increasingly greater complexities of root uptake processes will warrant their integration in predictive crop growth modeling in the near future, as new experimental tools and better measurement methods are becoming more available. The developments and applications of innovative measurement techniques were documented by Clothier and Green (1997) and Mmolawa and Or (2000), regarding the measurement of multidimensional plant root–soil interactions, and by Asseng et al. (2000) and Clausnitzer and Hopmans (2000), who demonstrated the application of noninvasive measurement techniques to infer soil transport processes and plant root water uptake at spatial scales of less than 1 mm. This review of root water and nutrient uptake is cast within the context of crop and soil water modeling. This is because simulation models are now almost solely the universally accepted translation mechanism allowing communication and understanding among basic and applied scientists. The choice of computer models as a means to integrate state-of-the-art knowledge in root uptake mechanisms is especially advantageous when considering the integrated and interdisciplinary approach required to conceptualize the complex interactions between subsystems within SPAC. Moreover, simulation models may allow keen interpretation of experimental results, and they can be a useful tool to help understand and quantify uptake and transport processes (Whisler et al., 1986). Despite the usefulness of computer models, their development and application have limitations, as has been highlighted by Passioura (1973, 1996), Whisler et al. (1986), and Philip (1991). A major drawback of computer models is their apparent insatiable appetite for complexities, thereby providing the computer programmer with the opportunity to increase the number of a priori unknown parameters without limitations, and thereby giving the user the “false” appearance of mechanistic understanding of the simulated system. In addition, Philip (1991) forewarned that the increasing application of computer models might eventually substitute for experimentation, thereby preventing their real-word application. It is in this regard that inverse

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modeling may prove to be a more effective simulation tool. This process requires the combination of accurate experimentation with mechanistic modeling to yield appropriate measures of parameters, along with their uncertainties. Applications of such parameter estimation techniques are presented in Hopmans et al. (2002) for soil hydraulic characterization and in Vrugt et al. (2001) for characterizing multidimensional root–water uptake. Before considering root uptake mechanisms a number of related issues will be clariﬁed in the ﬁrst part of this review. First, there appears to have been a general and widespread confusion about the nature of the driving forces for water transport in plants. Even over the past 10 years, there has been a lively debate as to “how water moves through plants.” Although this difﬁculty, regarding ﬂow of water and solutes between and across plant cells, is understandable, we interpret this confusion to be also an indication of the current usage of different terminologies and notations. This has led to misunderstandings and confusion between soil and plant scientists. Speciﬁcally, when considering water ﬂow, one must clearly distinguish between water potential and water pressure. Second, we argue at the outset that there must be a clear understanding that the processes of plant transpiration (driving root–water uptake) and plant assimilation (driving nutrient uptake) are physically connected by the concurrent diffusion of water vapor and carbon dioxide through the stomata. In theory, assimilation and transpiration processes must be directly linked under both nonstressed and stressed soil environmental conditions. Clearly, this link can be achieved by introducing the notion of transpiration efﬁciency, deﬁned as the mass of biomass produced per unit of water transpired (Hsiao, 1993). It has been shown that this relationship between assimilation and transpiration, although plant speciﬁc, is linear and can be applied to both stressed and nonstressed conditions. Third, a review of the analogies of water and nutrient pathways in plants between apoplastic—along cell walls—and symplastic—between cells—is needed. These will deﬁne and allow interpretation of the various plant resistances and control of the driving forces to be considered. It appears that both pathways may occur simultaneously, in parallel, and that some reference to partitioning between these two pathways is needed. Fourth, a general review and deﬁnition of active and passive uptake and their differences are needed. In particular, the literature generalizes these two uptake processes without really describing their differences. Their deﬁnition arises from thermodynamic considerations, describing transport in terms of phenomenological transport equations. Finally, although short, we review the current state of the art in modeling soil water ﬂow and chemical transport, so that dynamic linkages with plant systems across multiple spatial dimensions can be better understood. After an introduction that elaborates on the research of the preceding ﬁve issues, reviews of water uptake, nutrient uptake, and simultaneous water and nutrient uptake will be followed by an example, summarizing a possible multidimensional approach, and a section summarizing the ﬁndings, including a synopsis on future

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research needs in root–water and nutrient uptake. It must be pointed out that notation and symbolism used here may not be familiar to everybody, as our backgrounds will vary. In the end, we introduce various alternative uptake models that are consistent with the current state-of-the-art mechanics that describe water and nutrient uptake by roots. These do not add much additional complexity and data requirements to currently used crop growth and soil water ﬂow models.

II. WATER TRANSPORT IN PLANTS A. SOIL–PLANT–ATMOSPHERE CONTINUUM Water is transported through the soil into the roots and plant xylem toward the plant canopy where it eventually transpires into the atmosphere. In a macroscopic sense, water transport within this SPAC can occur only if water is continuous between the soil rooting zone and the plant atmosphere, an assumption that generally triggers little debate. Conceptually, water transport is mathematically described by an Ohm’s law type of relationship, expressing the ﬂux or mass ﬂow rate of water (M L−2 T−1) as a function of a driving force (water potential per unit distance), and a proportionality factor that deﬁnes the ability of the transmitting medium to conduct water. In soil science, this relationship is known as Darcy’s law (Darcy, 1856), and its modiﬁed form is widely accepted as a means to predict water ﬂow in unsaturated soils from (Buckingham, 1907) Jw = −K

ψt , x

(1)

where Jw denotes water ﬂux density (L T−1); ψt /x is deﬁned as the total water potential gradient (L L−1), and K is known as the unsaturated hydraulic conductivity (L T−1), if ψt is expressed on a per unit weight basis. In plant science a similar expression was stated by van der Honert (1948) to deﬁne water ﬂow in plants by Q=

ψrs − ψx , Rr

(2)

where Q denotes the rate of volumetric water ﬂow through the plant (L3 T−1), ψr s and ψx denote the total water potential at the root surface (rs) and in the root xylem (x), both expressed in units of atm by van der Honert (1948), and Rr describes the overall root resistance to water ﬂow (dimension depends on units used for Q and ψ). These mathematical expressions are based on the assumption that ﬂow of water is steady and that the gradient is constant. Therefore, Eqs. (1) and (2) state that the water ﬂow rate is constant with time at any spatial location within

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SPAC; i.e., ﬂow must be at some kind of dynamic equilibrium. In contrast, ﬂow is most often transient, or water ﬂuxes change with time. Nevertheless, the steadystate expression can still be applied as long as the time period over which it is used is short compared to the rate at which the changes in time occur. The relation between ﬂux and volumetric ﬂow rate is determined by the cross-sectional area of the bulk soil over which ﬂow occurs. Although this area may be well deﬁned for soils, the actual ﬂow area in plants is much more difﬁcult to determine. Therefore, in plants it is much straightforward to use volumetric ﬂow rates on a per unit plant or on a per unit leaf area basis. However, in soil water ﬂow models, plant transpiration is deﬁned by dividing the volumetric ﬂow rate by the area of the soil surface represented by the plant. Also, the deﬁnition of the proportionality factor is different between plant and soil systems and is caused by the difference in physical size of the water-transmitting medium. A soil system is usually deﬁned by the bulk soil, without consideration of the size and geometry of the individual ﬂow channels or pores. Therefore, the hydraulic conductivity (K ) describes the ability of the bulk soil to transmit water and is expressed in dimensions of L3 L−2 T−1 (volume of water ﬂowing per unit area of bulk soil per unit time). However, in plants one may be more concerned with the conductive ability of a single membrane or organ, where the dimensions of the system are uncertain. Consequently, the water conduction is expressed by resistance, R = x/K, or conductance C = 1/R, with dimensions determined by the units of water potential. Rather loosely, the conductance term is deﬁned as a permeability coefﬁcient, likely derived from the terminology used in irreversible thermodynamics (Slayter, 1967).

B. WATER POTENTIAL When considering ﬂow in a soil–plant system it is imperative that the overall concepts and notation are well deﬁned and universally applied. Flow mechanisms can be then be understood from the same basic principles (see also Oertli, 1996). Recently, the cohesion theory (CT) of water transport in plants has been questioned, in part because of the lack of general consensus about notation and physical principles. The CT was introduced by Dixon and Joly (1895), who suggested that water moved as a continuous stream of water through the plant, driven by the capillary pressure in the leaf canopy, allowing water to move up through tall trees against gravity (as reviewed by Canny, 1977). Recent studies have either questioned this general concept or proposed alternative mechanisms (Canny, 1995; Steudle, 1995; Wei et al., 2000) that were fueled by recent developments allowing direct xylem water potential measurement (Balling and Zimmerman, 1990; Tyree et al., 1995). Most controversies have centered on the origin of the driving force and the sustainability of water transport under low water potentials without the onset of cavitation (see Section II.C.). The analogy of ﬂow between plants and soils is drawn because

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of their similarity in pore size ranges. For example, in plants water is transported upwards through water-conducting elements in the xylem. There are two kinds of such vessels: the tracheids which are spindle shaped and up to 5 mm long and 30 μm in diameter and other vessels that are formed by coalescing rows of cells, creating structures from a few centimeters to meters in length, and varying in diameter from 20 to 700 μm (Kramer and Boyer, 1995). Water movement within the plant is facilitated by pits or narrow pore-wall spaces between xylem vessels. Moreover, water ﬂow in cell walls occurs through pores in the nanometer range (see Section IV.A). In SPAC, the driving force for water to ﬂow is the gradient in total water potential (ψt). Soil water potential is formally deﬁned as (Aslyng, 1963) “the amount of work that must be done per unit quantity of pure water in order to transport reversibly (independent of path taken) and isothermally to the soil water at a considered point, an inﬁnitesimal quantity of water from a reference pool. The reference pool is at the elevation, the temperature, and the external gas pressure of the considered point, and contains a solution identical in composition to the soil water at the considered point.” In other words, the water potential is decreased if the water is at a lower elevation, lower temperature, lower pressure, or for water solutions with increasing solute concentrations. Adapting the Gibbs free energy concept, Nitao and Bear (1996) and Passioura (1980) demonstrated, by using the thermodynamic treatment of Bolt and Frissel (1960), that this formal deﬁnition can be extended to include surface forces acting on the surrounding liquid. As a result of this formal deﬁnition, mechanical equilibrium requires both chemical and thermal equilibrium. Moreover, the total potential of bulk soil and plant water can then be written as the sum of all possible component potentials, so that the total water potential (ψt ) is equal to the sum of osmotic (ψo ), matric (ψm ), gravitational (ψg ), and hydrostatic pressure potential (ψp), or ψt = ψo + ψm + ψg + ψ p .

(3)

This additive property of water potential assumes that water is in thermal equilibrium and that physical barriers within SPAC behave as perfect semipermeable membranes with a reﬂection coefﬁcient equal to 1 (see Section IV.A.). Moreover, it makes no distinction between water solution and water as a component of the solution (Corey and Klute, 1985). The negative water potential is effectively the result of suction forces on the water solution toward the solid soil or plant cell surface, so it is often conveniently denoted by a positive suction force. Whereas in physical chemistry, the chemical potential is usually deﬁned on a molar or mass basis, the macroscopic treatment of plants and soils expresses potential with respect to a unit volume of water, thereby giving pressure units (Pascal, Pa). When expressed per unit weight of water, the potential unit denotes the equivalent height of a water column (L). Likely, the common practice to measure water potential by water or mercury column height justiﬁes expressing water potential in pressure terms, such

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as osmotic pressure, capillary pressure, and hydrostatic pressure. However, this notation can lead to misinterpretation of the physical meaning of water potential, since gauge pressure is deﬁned relative to atmospheric pressure. Atmospheric pressure is caused by the weight of the air at the Earth’s surface, and is roughly 1 bar (about 1033 cm of water column, or about 100 kPa = 0.1 MPa) at sea level. Thus in the true sense of pressure, the absolute water pressure can never be smaller than −1 bar relative to atmospheric pressure, or zero absolute pressure. Nevertheless, internal forces within the water can create suction forces that correspond to water potentials much lower than −1 bar. With the introduction of pressure transducers, it is now physically possible to measure these forces that correspond with negative water potentials, much smaller than the pressure equivalent of −1 bar. For example, Steudle and Heydt (1988) and Ridley and Burland (1999) demonstrated the application of pressure transducers to directly measure osmotic and matric potentials in soils down to −0.7 and −1.5 MPa (−7 and −15 bar, respectively) for prolonged times. These negative water potential measurements are only possible if cavitation is prevented. Contributions to the driving force for soil water ﬂow may arise not only from gravity and capillary forces, but total water potential may include osmotic and surface forces. Flow by gravitation is caused by differences in vertical elevation, whereas osmotic potential is caused by a nonzero solution concentration of the bulk soil solution outside the diffuse double layer (ddl). The ddl is deﬁned by the thickness of the water ﬁlm, in which the ion distribution varies with distance to a charged surface, as a consequence of a balance between diffusive and adsorptive forces. Osmotic potential is effective only when solutes are constrained relative to water mobility, such as by a semipermeable membrane in plant roots. Hence, without such membranes, the total driving force for water ﬂow should exclude the osmotic potential; however, its magnitude will depend on the leakiness or reﬂection coefﬁcient of the membrane. Whereas the osmotic and gravitational components of the total water potential are generally well understood, the deﬁnition of matric and hydrostatic pressure potentials and their distinction require further attention. The matric potential (ψm ) is caused by a combination of capillary and surface forces, resulting in a capillary (ψcap) and surface force component to the total water potential. The following explanation of matric potential considers the various forces with corresponding potentials within the water ﬁlm around a soil particle, hence considers a microscopic view point. The capillary forces are caused by surface cohesion forces at the air–water interface, combined with the solid–water adhesion forces, creating a concave interfacial curvature and subsequent lowering of the water potential for an air–water interface. The surface forces become important when liquid ﬁlms are covering the entire solid surface, and they can be composed of various component forces that are (i) molecular-short-range London–van der Waals forces, (ii) electrostatic, and (iii) osmotic. Except for the molecular forces, the other two adsorptive forces

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are a consequence of a charged solid surface. The electrostatic forces are due to the dipolar nature of water molecules that orient themselves because of electrical forces in the ddl of the water solution near the charged soil or plant cell surface. These molecular and electrostatic forces combined create a negative water potential, deﬁned as the adsorptive potential (ψa ), that is, they are most negative at the solid surface and increase toward zero at the end of the diffuse double layer, which is about 1 μm or smaller. The third force acting on water molecules in the double layer is a result of the increasing ion concentration toward the solid surface, resulting in a negative osmotic potential (ψo,ddl ) that is caused by the constrained ions in the double layer. The resulting osmotic potential due to ions in the ddl in excess to those in the bulk soil solution decreases from the pore water solution inward. To attain mechanical equilibrium, the adsorptive and osmotic potentials combined are compensated by an increasing pressure potential toward the soil surface, ψ P , or ψm = ψo,ddl + ψa + ψ P . For a clariﬁcation of this concept, a hypothetical water potential distribution within a truncated ddl and a concave air–water interface (ψcap < 0) is presented in Fig. 1, where the various water potential components are shown as a function of distance to the soil particle surface. For a truncated ddl, the water ﬁlm thickness is smaller than the spatial extend of a fully developed ddl. The disjoining pressure concept (Derjaguin et al., 1957; Tuller et al., 1999) can be included in this concept by deﬁning the pressure potential as the sum of capillary and disjoining pressure (ψd p ), or ψ P = ψcap + ψd p . Its value is maximum at the soil surface and decreases toward the air–water interface or half-distance between solid surfaces for a saturated soil pore (see Fig. 1). It is this disjoining pressure that results

Figure 1 Spatial distribution of water potential components in a truncated diffuse double layer (adapted from Koorevaar et al., 1983).

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in repulsive forces, causing clays to expand upon wetting. Additional explanations of the underlying concepts and deﬁnition and application of matric potential were presented in Koorevaar et al. (1983) and Dane and Hopmans (2002). Finally, the last term of Eq. (3) to consider is the macroscopic hydrostatic pressure potential (ψ p ). It is included separately to distinguish its positive value from the other negative matric water potentials (ψm ). In soils, the hydrostatic pressure potential originates from the hydrostatic pressure caused by saturated soil conditions, whereas in plant cells the hydrostatic component is represented by the turgor pressure.

C. CAVITATION Cavitation starts when gas or vapor bubbles are formed in water under tension. Those create embolisms by exceeding the tensile strength of water and disrupting the hydraulic continuity of the conducting soil pore or xylem vessels and tracheids. They prevent the xylem water from sustaining the low water potentials required to drive a given transpiration stream. Vapor bubbles can be triggered at gaseous or other hydrophobic surfaces and by gas seeds already present on the pore surface. Water normally cavitates when the absolute water pressure is slightly smaller than its vapor pressure. However, higher tensions can be sustained if the radii of cavitation nuclei are sufﬁciently reduced (Guan and Fredlund, 1997; Tyree, 1997). The critical water pressure for cavitation (P∗ ) to occur is controlled by the radius of the seed bubble (r∗ ), as determined from Pbubble − Pxylem =

0.15 2σair−water ≈ , r r

(4)

or the capillary presssure equation of Youngs and Laplace (Pbubble < Pxylem); where σ denotes the temperature-dependent surface tension of water in contact with air, and P and r are expressed in centimeter units. Cavitation by gas bubble growth may occur if the xylem water pressure, Pxylem on the left-hand side of Eq. (4), is less than P∗ for that speciﬁc size bubble with radius r = r∗ (Pbubble ≈ 0, when equal to vapor pressure of water). For example, if the gas seed has a radius r∗ = 0.21 μm, cavitation will be triggered only if the xylem water potential is more negative than −0.7 MPa. Subsequently, if Pxylem becomes larger than P∗ , the bubble will reduce in size or collapse. Because of the metastable state of water, conducting pores with r < r∗ will remain conductive for Pxylem > P∗ (Tyree, 1997). Applying this theory to unsaturated soils may lead to situations of cavitation as well, resulting in changes in entrapped air phase and unsaturated hydraulic conductivity in soils, thereby affecting the unsaturated water ﬂow regime. For example, Or and Tuller (2002) suggest that bubble formation can signiﬁcantly affect the drainage branch of the soil water retention curve, depending on whether the soil is drained by positive gas pressure or under tension. In addition to being formed from small-sized seeds

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already in the xylem system, gas bubbles can also move into the water-conducting vessels by air seeding from neighboring conduits through pore walls (Tyree, 1997) or by temperature ﬂuctuations. However, air access is prevented if these pore radii are small enough (r < r∗ ), or if their air-entry value is not exceeded. Consequently, although cavitation is likely to occur to some degree in xylem vessels at low water potentials, it will disrupt ﬂow only in the larger vessels, which will reduce the xylem hydraulic conductivity. However, this is not such a surprise, knowing that transpiration rates may be signiﬁcantly reduced and be close to zero anyway at sufﬁciently low xylem water potentials.

D. COMMENTARY In summary, the driving force for water ﬂow in plants is the total water potential gradient as it is in soils. However, in contrast to soils, the osmotic component must always be considered for ﬂow through the roots, since water can move through cell membranes as a result of osmotic potential gradients. However, water movement along osmotic potential gradients is by diffusion, and ﬂow paths will likely be different from those followed by water driven by matric potential gradients, with each ﬂow path characterized by its own speciﬁc hydraulic conductance. Flow can be even more complex as water diffusion through membranes by osmotic gradients in one direction might cause matric potential and/or hydrostatic pressure potential gradients in the opposite direction. Within the xylem vessels and tracheids, water and solute ﬂow is likely by advection, so that osmotic gradients will not have to be considered. However, it is speciﬁcally in the xylem system that the gravitational component must be included. For example, to move water up a 25-m-tall tree, the total water potential change in the xylem from the roots to the tree canopy must be equal to or larger than 2.5 bar. For conditions of low water potentials, cavitation may cause embolisms in the xylem, thereby decreasing the axial conductance of water ﬂow through plants. However, water can bypass cavitated parts of the xylem by lateral movement to other water-conducting vessels. Moreover, as in soils, water can move through water ﬁlms along the xylem cell walls by surface forces, creating adsorption potential gradients (Amin, 1980; Canny, 1977).

III. LINKING PLANT TRANSPIRATION WITH ASSIMILATION A. INTEGRATING ROOT UPTAKE PROCESSES Within the general framework of crop growth modeling, one must take the broad plant–soil–atmosphere approach with linkages between individual system

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components. In the past this approach was limited when crop production research was viewed from the plant perspective only. Rather, there was the development of empirical relationships between yield and water and/or nutrient application (see Viets, 1962). Empirical relationships were considered adequate for soil water and nutrient management, even in the 1970s, when plant productivity was still the main driver and justiﬁcation for agronomic research. Crop water use research was mostly driven by the need for arid-region-irrigated agriculture where water is a scarce resource (Tanner and Sinclair, 1983). However, the need to integrate plant physiology with environmental sciences such as soil physics, micrometeorology, and agronomy was noted by Slayter in 1967. He justiﬁed this by acknowledging the control of plant cell water status on the plant’s environmental surroundings by water exchanges. Moreover, there is increasing evidence that photosynthesis is better correlated with soil water potential than leaf water potential status, indicating that roots respond to stressed soil conditions by transmission of hormonal signals to the shoot (Davies et al., 1994; Johnson et al., 1991; Passioura, 1996). Although much progress was reported in the seminal review of plant responses to water stress by Hsiao (1973), still much more research is needed to improve feedback mechanisms in soil and crop growth modeling (van Noordwijk and van de Geijn, 1996). In part, the historical neglect of consideration of water and nutrient uptake processes below ground has led to a knowledge gap between plant responses to nutrient and water limitations and crop production. The importance of root function in water and nutrient transport becomes increasingly clear, as constraints on agricultural resources are imposed due to water limitations and environmental concerns such as those caused by groundwater contamination. Both of these are driven by the ever-increasing need to expand global food production while taking better care of the environment. Contemporary agriculture is directed toward minimizing yield losses and limiting the degradation of soil and water resources, so as to keep environmental effects of crop production within acceptable levels (van Noordwijk and van de Geijn, 1996). This current state of sustainable agricultural systems justiﬁes the increasing need for combining soil knowledge with plant expertise, in particular as related to root development and functioning. This development may result in a better understanding of water and nutrient stress on crop productivity, in relation to heterogeneous soils with spatial and temporal variations in nutrient and water availability in combination with spatially distributed rooting systems. As was also clearly stated by Clothier and Green (1997), roots serve as big movers of water and chemicals in soils, and a much better understanding of root functioning and uptake mechanisms of roots is needed to establish sustainable crop production protocols. Soil scientists have paid much attention to water movement and chemical transport in the absence of roots, but much less to soil processes that are inﬂuenced by root development and function. In part, root systems are neglected because they

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are hidden below the ground, and their extensive branching makes description difﬁcult. For the plant physiologist, it is mostly the above ground portion of the plant that has been the most intriguing. It is here where photosynthesis takes place, and the leaves can be seen! Root growth and root systems nonetheless play critical roles in providing water and basic nutrients for leaf and shoot growth and development (Hoogenboom, 1999). Our physiological knowledge of root water and nutrient uptake and root–shoot interactions lacks a basic understanding, especially when soil water or nutrients are limiting. Consequently, both crop simulation and water ﬂow models tend to treat root uptake mostly by empirically means, thereby limiting their general application. As stated earlier, the need for crop simulation models originally arose from the wish to maximize crop productivity. In a mechanistic sense, the driving force for these crop growth models was generally the radiation use efﬁciency (RUE) or biomass produced per unit of photosynthetic active radiation (PAR). This has been coupled with plant canopy coverage or leaf area index (LAI), and then extension growth was largely determined by thermal time and leaf N content (van Keulen and Seligman, 1987). Simulation models that focus on crop growth simplify soil water ﬂow and transport and water and nutrient uptake. They ignore the dynamics of soil water and nutrient availability and uptake. In most models, relatively simple algorithms determine crop or soil control of nutrient uptake by a switch, depending on values of cumulative uptake versus demand. Examples of these model types are CERES (Godwin and Jones, 1991; Ritchie and Godwin, 2000), APSIM (Keating et al., 1999; McCown et al., 1996), and NutriAce (GRAZPLAN Project, 1997). Potential crop nitrogen demand is determined by growth-stage-dependent plant N concentration and biomass production. Water and nitrogen stress are quantiﬁed by “zero-to-unity” water or nitrogen supply factors that are computed from soil availability to reduce RUE and LAI accordingly (van Keulen and Seligman, 1987). The continued development of soil water modeling has traditionally been justiﬁed from the water management point of view considering irrigation and groundwater table management. However, this has been extended because water is the key transport vehicle for dissolved chemicals in soils. In either case, plant growth is considered secondary, although plant evapotranspiration (ET) is among the most important driving forces for water ﬂow in soils. Soil water ﬂow models compute ET from atmospheric variables such as net radiation, air humidity, and wind velocity. These include soil evaporation (Ritchie, 1972) and consider crop-speciﬁc transpiration using reference crop ET using growth-stage-dependent crop coefﬁcients (Allen et al., 1998; Doorenbos and Pruitt, 1977). Uncertainties in water ﬂow modeling mostly result from inherent spatial and temporal variability in soil physical properties, and they often lead to preferential transport of water and associated chemicals at much faster rates than predicted. Dynamic water ﬂow models, however, almost exclusively ignore crop growth processes and associated mechanics of water and nutrient uptake. The inﬂuence of the plant is included in the water ﬂow

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equation by way of a distributed root water uptake or sink term. The magnitude of this form depends on root distribution and ET. Also, water ﬂow models generally apply “zero-to-one” stress factors to mimic the inﬂuence of water shortage, and/or salinity buildup. The exception is those that include soil and plant resistances for water ﬂow, thereby allowing iterative computation of water uptake as controlled by plant water status. Nutrient uptake is either absent or simply coupled to the water uptake term, with an additional “zero-to-one” factor to account for nutrientspeciﬁc mechanisms other than by passive uptake. Examples of these types of ˇ unek et al., 1999) and SWIMv2.1 (Verburg et al., models are HYDRUS2D (Sim˚ 1996). In either case, crop simulation or soil water ﬂow modeling, simpliﬁed empirical expressions are applied to simulate the effects of soil water and nutrient stress on ET, RUE, and leaf growth rate.

B. TRANSPIRATION COEFFICIENT When combining plant and soil water simulation models, it is essential that net radiation provide the driving force for both biomass production and evapotranspiration. It allows the combined model to be calibrated using independently measured biomass and ET data. Although plant species speciﬁc, this ratio of transpiration to assimilation has been shown to be fairly constant (Hsiao, 1993). Despite that only about 60% of all assimilates being used for biomass production, with the remainder lost by respiration, about 95% of total water uptake is lost by transpiration. The transpiration to assimilation ratio (TAR) may vary between 30 and 150 kg/kg depending on meteorological conditions and plant species. van Noordwijk and van de Geijn (1996) introduced the water utilization efﬁciency (WUTE), deﬁned as the dry weight production per unit volume of transpired water, reporting a range of values between 3 and 7 g/kg. Alternatively, one can deﬁne water use efﬁciency (WUE) or a transpiration coefﬁcient (TRC), both denoting the mass of water transpired per unit biomass produced (Hsiao, 1993). This constant ratio was already introduced by de Wit (1958), when he presented crop-speciﬁc, unique relationships between crop yield and plant transpiration, after correction for evaporative demand through division of actual transpiration by potential ET. This almost constant ratio, even under water or nitrogen stress conditions can be explained by the sharing of transport pathways by CO2 and water vapor as they pass between the atmosphere and the intercellular leaf space. Also, this is in response to the dominant control of leaf-intercepted radiation on both assimilation and transpiration, although assimilation only uses the PAR part of total radiation (Hsiao, 1993). Variations in TRC occur between plant species as a result of differences between C3 versus C4 plants, the types of stomatal control, and the size and number of leaf stomata. Also, changes in environmental conditions, such as those caused by variations in CO2 (by elevated CO2 levels in atmosphere) and water vapor

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concentration gradients (by changes in plant leaf temperature), affect the magnitude of TRC (van Keulen and van Laar, 1986; Hsiao, 1993). The inclusion of the TRC concept in crop simulation modeling under stressed soil water conditions was ﬁrst introduced by van Keulen and Seligman (1987). Their suggestion was to multiply the potential assimilation rate (radiation limited for nonstressed conditions) with the actual to potential transpiration ratio. However, current crop growth or water ﬂow simulation models that incorporate radiation control of both biomass production and transpiration are few. Exceptions are the SWAP model (van Dam et al., 1997) and RZWQM (Ahuja et al., 1999). The SWAP model combines a ﬁeld-scale water ﬂow and nutrient transport model with a universal crop-growth simulator (Spitters et al., 1989). In this combined model, plant transpiration is computed from potential ET and a crop-stage-dependent LAI, whereas potential photosynthesis is controlled by RUE and LAI. Both ET and photosynthesis are then reduced by water and/or salinity stress factors that are computed from the decreased root water uptake as computed from the water ﬂow model. RZWQM is an integrated physical, biological, and chemical one-dimensional process model, simulating crop growth and movement of water, nutrients, and pesticides over and through the root zone. The model includes a generic crop growth simulator, estimates soil evaporation and plant transpiration, and links total root water and nutrient extraction to atmospheric demand.

C. COMMENTARY For crop growth modeling purposes, there must be a clear and intuitive understanding that plant transpiration and plant assimilation are physically connected by the concurrent diffusion of water vapor and carbon dioxide between the plant canopy and surrounding atmosphere through leaf stomata. Conceptually, assimilation and transpiration processes must be directly linked under both nonstressed and stressed soil environmental conditions. This is achieved in crop growth modeling by introduction of a WUE parameter, such as the transpiration coefﬁcient (TRC). A ﬁrst attempt to a mechanistic, multidimensional root growth and root uptake modeling approach was presented by Somma et al. (1998), by linking a threedimensional transient ﬂow and nutrient transport model to a root growth simulator. The simulation domain was discretized into a grid of ﬁnite elements in which the soil physical properties are distributed. Solute transport modeling included nutrient transport in the soil domain by both convection or mass ﬂow and diffusion. Root water uptake was computed as a function of matric and osmotic potential, whereas absorption of nutrients by the roots was calculated as a result of both passive and active uptake mechanisms. Genotype-speciﬁc and environment-dependent root growth processes were described using empirical functions. The most comprehensive modeling level included simulation of root and shoot growth, as inﬂuenced

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by soil water and nutrient status, TRC, temperature, and the dynamic allocation of assimilates to root and shoot. However, the extreme complexity of the model has precluded the expected application for plant growth simulations. Moreover, the physiological basis for biomass production and allocation as is generally included in crop growth simulation models was lacking. Nevertheless, the Somma et al. (1998) model included the essential features required for an integrated plant growth–soil water simulation model.

IV. TRANSPORT OF WATER AND NUTRIENTS IN THE PLANT ROOT A. PLANT ROOT STRUCTURE Although variable in size between monocotyledons and dicotyledons, the general structure of root apices is broadly similar for many plants (Russell, 1977). They contain the vascular stele and root cortex (Fig. 2). The inner center contains the stele, which includes the xylem and phloem, which are surrounded by the

Figure 2 Diagrammatic cross-sectional area of the apical zone of a plant root. The stele includes xylem and phloem elements, surrounded by pericycle.

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pericycle. The cortex consists of the inner endodermis, cortex, and hypodermis and is bounded by an outer layer of epidermal cells from where root hairs develop. Some roots will include an exodermis (Peterson, 1989), which is a specialized form of the hypodermis. If present, it can also be a major barrier of transport of water and nutrients through suberization of cell walls and presence of a Casparian band, as occurs in the endodermis. Roots are in contact with the surrounding soil by a ﬁlm on its surfaces or mucigel which can also play a controlling role on water and nutrient absorption by the plant. The radial pathways for water and nutrients in roots are either intracellular (apoplastic) and/or intercellular (symplastic pathway). The separation of both pathways is controlled by the plasmalemma. The protoplasm of plant cells are connected through plasmadesmata, which form continuous pathways between plant cells, allowing water and solutes to move along the symplastic pathway between cells. The apoplastic pathway occurs through cell walls that are constructed from bundles of cellulose molecules (microﬁbrils), surrounded by other polymers with a combined size of 3–30 nm, providing pore spaces of 4- to 8-nm-diameter pores (Fig. 3). Within this matrix, water and solutes can move freely within the cell wall solution, unless prohibited by the physical size of large, high-molecular-weight molecules. The second kind of pore space within the cell wall is much larger, about 50 nm, and forms a connection between plant cells by plasmodesmata,

Figure 3 Diagram of apoplast (shaded) of a plant cell and an enlarged view of cell wall. [Reproduced from P. J. Kramer and J. S. Boyer (1995). “Water Relations of Plant and Soils.” Copyright 1995. Academic Press, San Diego, with written permission from Harcourt.]

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providing a low resistance pathway for water and solute movement between plant cells. The plasmodesmata are lined by the plasmalemma and contain protoplasm of cell material, thereby providing opportunity for symplastic transport. Their frequency of presence appears to be well correlated with nutrient ﬂuxes, with a high abundance indicating dominant symplastic transport. The cell walls at a distance of about 1–2 mm from the root tip characteristically include an endodermis, which consists of only one cell layer. However, it plays a major function in the conduction of water and nutrients through the root. This functional aspect of the endodermis is caused by the development of the Casparian band. This is a thickening of the radial walls along the plasmalemma. The Casparian band is impregnated with suberin and lignin between the microﬁbrils of the cell wall, thereby making the endodermal cell wall hydrophobic and greatly reducing the porosity and permeability of their radial walls. Since the only effective way to move from the cortex to the stele is through the endodermal protoplast, the endodermis provides a major barrier to water ﬂow and acts as a selective membrane for solute transport. When present, the endodermis completely blocks water movement, thereby requiring water to move through the plasmalemma before returning to the walls of the stele cells. Further away from the root tip, some 1–20 cm from the tip, a secondary deposition of suberin lamellae forms over the entire endodermal wall and creates an additional layer of hydrophobic material, preventing exchange of water between cell walls and cytoplasm. This completely blocks the apoplastic pathway, including the wall-to-cell ﬂow route (Epstein, 1966). Consequently, it is believed that the dominant pathways for water uptake occurs directly behind the root tip, where the second layer of suberization is still lacking. However, in certain places suberization may be less well developed, and the effectiveness of the endodermal barrier may be reduced (Slayter, 1967), thereby opening the apoplastic pathways. Cell walls are negatively charged by dissociated carboxyl groups, thereby creating a diffuse double layer, as occurs in soils, along the cell wall. Therefore, the apoplast tends to exclude anions and preferentially absorbs cations such as Ca and K. In addition, ionic interactions within the cell wall slow down diffusion and affect active ion transport by carrier proteins (Clarkson, 1996). A schematic diagram showing ﬂow from the cortex, through the endodermis to the stele, is presented in Fig. 4. One may distinguish at least three different pathways with differences between ﬂow routes determined by the type and number of membrane crossings.

B. APOPLASTIC VERSUS SYMPLASTIC PATHWAY As one might expect, water ﬂow through the cortex is mostly apoplastic, but includes symplastic ﬂow through the endodermis, as ﬂow is diverted because of

Figure 4 Schematic representation of pathways for water and nutrients across root cells from the cortex (left) through the endodermis (center) toward the stele (right).

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the presence of the Casparian band. Approaching the endodermis, water ﬂow may either (i) move through the Casparian band by osmotic gradients or (ii) bypass the endodermis, moving through the cell wall and plasmalemma into the symplastic pathway, returning back to the apoplast once the Casparian band has been passed. In either case, considering water uptake across the whole plant, hydraulic equilibrium requires that the total water potential in the apoplast and symplast be the same (Kramer and Boyer, 1995). However, component potentials may differ, with generally much smaller osmotic potentials in the symplast, resulting in positive hydrostatic water potential, whereas the high osmotic potentials in the apoplast correspond with negative matric pressures in the apoplast. The transport of solutes may occur by active transport (see Section V), such as by ion channels and ion carriers (Russell, 1977) within the endodermis, so that plant nutrients can effectively bypass the Casparian strip as well. In part, the question regarding the contribution of the symplastic and apoplastic pathways to total transport has remained unanswered because transport appears to be dependent on plant species and ion type. Moreover, increasing experimental evidence (e.g., Weatherley, 1963) suggests that cell walls offer an important pathway for water movement by mass ﬂow, possibly because of the occurrence of osmotically driven water ﬂow across the Casparian band or by the occasional absence or incomplete development of the Casparian band. Molz and Ikenberry (1974) and Molz (1981) presented a mathematical development for parallel water transport across roots by symplastic and apoplastic movement. The physical–mathematical treatment of ﬂow of water and solutes across roots for steady mass ﬂuxes of water (Jwater) and solute (Jsolute) can be described by (Dalton et al., 1975; Fiscus, 1975; Steudle, 1994; Zimmerman and Steudle, 1978) Jwater = L (P − σ )

(5)

Jsolute = ω + (1 − σ ) Csoil Jwater + J ∗ .

(6)

and

In this approach, the soil and root system is simpliﬁed to a two-compartment (soil solution or apoplast, and cell solution or symplast) system. The compartments are separated by a single effective semipermeable membrane with a reﬂection coefﬁcient, σ , representing the effectiveness of the membrane complex (plasmalemma and Casparian band) for water ﬂow by a concentration gradient. Thus, if σ = 0, the membrane is fully permeable to both water and solutes. In this situation, the membrane cannot function as a means of driving water by a concentration difference, c, between the comparments. The concentration is here expressed by osmotic pressure, or = RTc. The parameter, L, reﬂects the effective permeability of the membrane to water, sometimes also called the ﬁltration coefﬁcient. Hence, in this formulation both apoplastic and symplastic pathways for water ﬂow are

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combined into a single equivalent membrane. Solute transport may occur by diffusion, with ω denoting the effective diffusion coefﬁcient or solute permeability of the membrane (ω = 0, if σ = 1), effectively allowing osmotic adjustment of the symplast to water stress conditions (low matric potential in apoplast), or by advection (Jwater) or solute drag, or by active uptake, J∗ (see Section V.A). Although these transport equations allow for a simple mechanistic description of ﬂow and nutrient transport by roots, the combined expressions (5) and (6) fail to recognize that ﬂow and transport may occur by different pathways, with pathwayspeciﬁc permeabilities and reﬂection coefﬁcients. Nevertheless, the adaptation of the two-compartment model with a single membrane can be justiﬁed (Steudle, 1994; Steudle et al., 1987). Moreover, the proposed physical–mathematical model of Dalton et al. (1975) that will be discussed in Section IX.A predicts that the value of the root permeability is dependent on transpiration rate, a ﬁnding that has been experimentally conﬁrmed by many investigators (Fiscus, 1983). Steudle et al. (1987) stated that effective root permeability, L, depends on the contribution of the various root-conducting parts to overall water transport, since different root tissues may have different hydraulic resistances. Consequently, root permeability is expected to be plant species dependent and is a function of the developmental stage of the plant. Moreover, it was postulated that ﬂow paths are different depending on whether concentration (osmotic driving force) or water pressure (matric pressure driving force) gradients are induced across the plant root. To investigate water transport in plant roots, a root pressure probe was developed (Balling and Zimmerman, 1990; Steudle et al., 1987) to measure directly root xylem water pressure. In the experiments of Steudle et al. (1987), controlled gradients of water and osmotic pressure were established to study the inﬂuence of different driving force types (osmotic or matric pressure) on root conductivity. They concluded that the driving force effect was plant species dependent and that it is determined by differences in ﬂow path mechanisms between species. More speciﬁcally, it was shown for maize roots that water ﬂow induced by matric pressure gradients is mainly apoplastic, whereas a major contribution to osmoticinduced ﬂow is the cell-to-cell or symplastic pathway. The small contribution of the apoplastic pathway was caused by the low reﬂection coefﬁcient value of the endodermis, causing a low permeabililty of the apoplast as induced by a concentration gradient in Eq. (5). Measured hydraulic conductivities between pathways differed by one order of magnitude or more. This new composite transport model with parallel transport of water between plant cells along the symplastic pathway, and through cell walls following the apoplastic pathway, was further reﬁned in Steudle (1994). In their work, the simplicity of the two-compartment plant root system was maintained; however, the effective root membrane reﬂection coefﬁcient was computed from fractional contributions of cross-sectional areas of apoplastic and symplastic pathways and their respective permeability values (see Section VII.C).

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C. COMMENTARY Water and nutrient transport in the root is mechanistically described by a set of coupled transport equations describing the simultaneous uptake of water and nutrient into the roots. In this approach, the soil and root system is simpliﬁed by a two-compartment, system, separated by a single effective semipermeable membrane, separating the soil solution or apoplast from the cell solution or symplast. The driving force for water ﬂow in plants is the total water potential gradient. However, in contrast to soils, the osmotic component must always be considered for ﬂow through the plant roots as cell walls act as a semipermeable membrane. However, water movement by osmotic potential gradients occurs by diffusion, so that water ﬂow paths used as a result of matric potential gradients are likely different from those driven by osmotic potential gradients. For example, it was shown for maize roots that water ﬂow induced by matric potential gradients is mainly apoplastic, whereas a major contribution to osmotic-induced ﬂow is the cell-tocell or symplastic pathway. Measured hydraulic conductances between pathways can differ by one order of magnitude or more. Therefore, the mechanistic description of water ﬂow and nutrient transport through plant roots should consider this parallel transport through symplastic and apoplastic pathways. Also, discrimination between mechanisms of transport in the roots between water and nutrients may dictate differences between the spatial distribution of the main water and nutrient uptake sites within a rooting system and their variation in time.

V. NUTRIENT UPTAKE MECHANISMS Using Eq. (6) in Section IV.B, it is demonstrated that nutrient uptake and transport within the root can occur by three different mechanisms. First, transport is driven by concentration gradients, causing nutrient movement by diffusion, and is generally driven by electrochemical gradients. Second, nutrients move into and through the root by mass transport when dissolved in water. This mechanism is generally designated as the convective transport component of nutrient transport. It is computed from the product of nutrient concentration and water ﬂux density. Third, active uptake occurs by nutrient ﬂows against concentration or electrochemical gradients. It is this third component of nutrient uptake that is sometimes referred to as “magic uptake,” and therefore requires separate treatment.

A. ACTIVE VERSUS PASSIVE NUTRIENT UPTAKE As the plant solution concentration of many macronutrients may be larger than that in the soil solution (Epstein, 1960), their uptake may require specialized

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ion-speciﬁc uptake mechanisms against an electrical or concentration gradient. Active transport is by deﬁnition a process in which energy, provided by respiration, is expended in moving ions from a zone of lower to higher electrochemical potential or concentration. Energy demand for ion uptake can be large and can consume as much as 35% of the total respiratory energy (Marschner, 1995). The fundamental difference between passive and active transport is determined by the description of coupled ﬂow of water, solute, heat, and electrical charge, using the general theory of irreversible thermodynamics. The resulting set of phenomenological equations deﬁnes the ﬂux of each physical unit as a linear function of all possible forces operating in the system. Transport is deﬁned as passive if the ﬂux is the result of any of the gradients included in these coupled transport equations. If, on the other hand, ﬂux occurs irrespective of the presence of the formulated forces, transport is deﬁned as active. This theory is applied in soil physics to describe the simultaneous transport of heat and water in soils, allowing both water and heat transport by water potential and temperature gradients (Taylor and Cary, 1964). When considering the transport of water and solutes in soil–plant systems, this theory leads to the coupled Eqs. (5) and (6), neglecting the inﬂuence of temperature on mass transport, with the cross or phenomenological coefﬁcients deﬁning the inﬂuence of water potential gradients on solute transport and concentration gradients on water ﬂow. Plant root water uptake is generally considered as passive only, although some active water movement may occur by electro-osmosis and other physiochemical mechanisms (Dainty, 1963; Slayter, 1967). However, the distinction between passive and active uptake is not so clear and depends on which driving forces are considered in describing total mass transport. The differences between “passive or physical” and “active or metabolic” nutrient adsorption were introduced by Epstein (1960). The two different mechanisms lead to transport “down a gradient” and “against a gradient,” respectively. Passive transport occurs in the root’s free space (cell walls) and is kinetically controlled by diffusion and mass ﬂow, with ion exchange occurring between the solution and the negatively charged cell walls. Since diffusion across the plasmalemma or the tonoplast (see Fig. 4) may be severely limited, active transport mechanisms to move speciﬁc ions into the cytoplasm, across the plasmalemma, and vacuole, across the tonoplast, are required. Speciﬁcally, the transport of water and nutrients is impeded by the presence of the Casparian band in the endodermis. The active ion transport across the plasmalemma and tonoplast is driven by speciﬁc energy-driven ion carriers or through ion channels embedded in slowly permeable, hydrophilic lipids within the cell membrane. Cell membranes control transport of nutrients from the apoplast (cell walls) to the symplast (cytoplasm and vacuole) and subsequently into the xylem. Their capability of transport and their regulation are closely related to their chemical composition and molecular structure. These membranes dominantly consist of hydrophobic polar lipids, which are combined by extrinsic proteins on the outside of the membrane with hydrogen

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Figure 5 Generalized model of a plasma membrane structure. [Reproduced from H. Marschner (1995). “Mineral Nutrition of Higher Plants,” second edition. Copyright 1995. Academic Press, San Diego, with written permission from Harcourt.]

bonds (see Fig. 5) to provide hydrophillic sections. In this way, active ion transport is mediated across the membrane; however, ion movement is by a diffusion type of transport. Alternatively, intrinsic proteins may be integrated into the membrane, allowing movement of hydrated nutrients through small open spaces or voids (<0.4 nm) (Clarkson, 1974; Marschner, 1995), such as by ion pumping. In addition, protein channels within the membrane can provide pathways for speciﬁc ion movement across the membrane. A possible generalized plasma membrane model with an approximate thickness of 5–10 nm is presented in Fig. 5 (Marschner, 1995). The energy required for active nutrient transport is metabolically driven by reduction of ATP to ADP through ATPase enzymes. This causes transport of ions across membranes from the apoplast to the symplast, from the cytoplasm into the vacuole, or in opposite directions. Speciﬁcally, ATP-driven proton pumps provide a major ion pathway through transport of H+ from within the cell to the apoplast, thereby creating pH and electropotential gradients by which both cations and anions can move across respective membranes by ion channels or carriers (Marschner, 1995). Thus, these proton pumps provide the driving force for energized transport of ions along electrochemical gradients across either the tonoplast or the plasmalemma. Hence, proton pumps provide for active transport of protons, thereby creating the necessary downhill electropotential gradients for passive nutrient transport. Charge separation by metabolically driven proton pumps across the tonoplast can be described by ATPase

nH+ cytoplasm + ATP −−−→ nH+ vacuole + ADP + phosphate.

(7)

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The resulting transport of protons causes a membrane potential difference and an electrochemical gradient, which is changed or dissipated by resulting ion ﬂuxes through passive diffusion, thereby carrying the electrons. Hence, active nutrient uptake does not only depends on concentration but is also primarily controlled by available energy and transport kinetics. The movement of ions of one sign by this process can cause ions of the opposite sign to move against a concentration gradient, but down an electrochemical potential gradient. For example, proton pumping allows downhill transport of cations along an electrical potential gradient, across the plasmalemma into the cytoplasm in uniport (by carriers or ion channels) or symport (co-transport) by returning protons. Alternatively, the generated H gradients by proton pumping may move anions from the apoplast into the symplast through a proton–anion co-transport mechanism. Thermodynamically, no work is required to move these ions; hence, it might be classiﬁed as passive. However, their diffusion is metabolically driven, because it requires ion pumping ﬁrst and is therefore deﬁned as an active transport. Thus, passive transport of one ion by diffusion is controlled by the active transport of another. In addition to the proton pump, many other ion-speciﬁc pumps may be active, as illustrated in Fig. 6 for an ion pump, exchanging cations C+ and M+ between the inside and the outside of a hypothetical membrane (Clarkson, 1974). The rate of transport is controlled by the ﬂipping rate of the turning proteins, as while opening and closing a valve. This particular ion pump is neutral, but others can be electrogenic, causing charge separation across the membrane. In addition to ion pumps, the presence of immobile negatively charged proteins within the cytoplasm can result in electrochemical gradients, causing passive ion diffusion across the plasmalemma. However, even the formation of these proteins requires metabolic energy, so that this passive movement can be interpreted as active transport as well!

Figure 6 Schematic of a neutral ion-exchange pump. [Reproduced from D. T. Clarkson (1974). “Ion Transport and Cell Structure in Plants,” Copyright 1974. McGraw-Hill, London, with written permission from McGraw–Hill.]

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The passive diffusion along electrochemical gradients, established by metabolically driven ion pumps, occurs by both ion carriers and ion channels. A review on ion channels and ion carriers was presented by Hedrich and Schroeder (1989). Carrier-mediated co-transport occurs by transport proteins or carriers that bind the speciﬁc ion, move it across the membrane, and subsequently release it. This transport dissipates the electrochemical potential by the return of these protons or other carrier ions coupled to speciﬁc plant nutrients such as nitrate, potassium, calcium, phosphate, etc. Carrier-mediated transport is highly ion speciﬁc. The role of ion channels in active nutrient uptake was reviewed by Tester (1990) and Tyerman and Schachtman (1992). Speciﬁcally, ion channels maintain electrochemical gradients via control of membrane potential using metabolically driven ion pumps, thereby facilitating passive ion movement. Nutrient transport through ion channels can be through co-transport systems, with driver ions and coupled solutes if opposite charges move in the same direction, and by counter-transport systems when driver ions and nutrients are of equal valence and move in opposite directions (Sanders et al., 1984). Ion channels can be cation or anion selective, however, much less so than carrier transport. They move ions either inward or outward, at order-of-magnitude larger ion ﬂuxes than through ion carriers. Active nutrient uptake may be up to 10 orders of magnitude larger than simple diffusion. Nissen (1996) hypothesized that active nutrient uptake at low concentrations is dominated by carrier-like properties at relatively low uptake rates, whereas active uptake has channel-like properties at high uptake rates and large soil solution concentrations. Maximum transport rates for a carrier protein are in the order of 104–105 ions per second, whereas an ion channel can pass more than 106 ions per second.

B. MICHAELIS–MENTEN DESCRIPTION OF NUTRIENT UPTAKE The active rate of uptake and transport within the plant, and its ion selectivity, is regarded as a kinetic process equivalent to that described by Michaelis–Menten (MM)-type kinetics, used for the description of ion-speciﬁc enzyme-catalyzed reactions. As shown by Sanders et al. (1984), who developed an algebraic model of facilitated ion transport kinetics across membranes, the inﬂuence of concentration and concentration gradients of carriers on substrate transport can be well characterized by the MM parameters, Km and Jmax. A single uptake model such as the MM model may characterize active uptake (J∗ ) for a wide range of conditions. In general, MM kinetics are described by J∗ =

J ∗ max (c − cmin ) , K m + (c − cmin )

(8)

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Figure 7 Characteristics of Michaelis–Menten description of active nutrient uptake by plant roots.

where J∗ max is the maximum uptake rate, and Km denotes the Michaelis constant, whose magnitude is inversely related to binding energy between substrate and enzyme, and denotes the concentration where J∗ = 0.5 J∗ max. The concentration, cmin, allows for inclusion of a minimum nutrient concentration where inﬂux becomes operational (Fig. 7). The dimensions of J∗ may vary depending on how nutrient uptake is measured, whether by mass of nutrient/mass of root or by mass of nutrient/root area. MM parameters vary with plant species, plant age, nutrient type, nutritional status of plant, and other conditions. Many different variations of Eq. (8) were introduced (Nissen, 1996) and include the addition of a linear term to (8) to account for a diffusion term at high concentrations (Kochian and Lucas, 1982). Other similar uptake models include different active uptake mechanisms that may occur in parallel or selectively, depending on supply concentration. For example, the presence of multiple plateaus in measured nutrient uptake curves led to the introduction of the multicarrier system concept (Epstein and Rains, 1966). In contrast, the multi step relationship between uptake rate and external concentration was interpreted by Nissen (1986) as evidence of a multiphasic uptake model. This is caused by a single active uptake mechanism with changing kinetic characteristics of increasing Km and vmax values at increasing concentrations established by discrete external concentration levels.

C. COMMENTARY Root nutrient uptake and transport through the roots can occur by (i) diffusion, (ii) advection, and (iii) active uptake. The active ion transport across the

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plasmalemma and tonoplast of root cells is driven by speciﬁc energy-driven carriers and ion channels. Both mechanisms require the creation of electrochemical gradients across membranes by metabolically driven ion pumps. The active rate of uptake and transport within the plant and its ion selectivity is regarded as a kinetic process, equivalent to that described by Michaelis–Menten type of kinetics. Knowledge of the concentration dependency of nutrient uptake is especially useful when optimizing N fertilization while minimizing environmental effects (Bar-Yosef, 1999). Moreover, the intrinsic difference in uptake mechanisms between passive and active uptake leads to different nutrient concentrations in the soil solution. Speciﬁcally, passive nutrient uptake by convective water ﬂow does not alter the soil solution concentration, whereas active uptake reduces the average nutrient concentration in the soil. Moreover, a better understanding of ion-speciﬁc active root uptake is key to the development of effective strategies for the success of heavy metal removal in soils by phytoremediation.

VI. FLOW AND TRANSPORT MODELING IN SOILS Before moving on to comprehensive descriptions of root water and nutrient uptake mechanisms, it is pertinent to review what has been achieved in the modeling of water ﬂow and solute transport in soils. This review is rather short, but is intended to appreciate the progress made in the physical–mathematical description and modeling of ﬂows and transport in soils toward plant root–soil interfaces.

A. SOIL WATER FLOW Numerous studies have been published addressing different issues in the modeling water ﬂow in the unsaturated zone using the Richards (1931) equation. In short, the dynamic water ﬂow equation is a combination of the steady-state Darcy expression and a mass balance formulation. Using various solution algorithms, the soil region of interest in discretized in ﬁnite-size elements that can be one, two or three dimensional. In three dimensions, this volume element can be deﬁned as a voxel. Numerical solution requires that mass balance be maintained within each small volume element within the soil domain at all times. A comprehensive review of unsaturated soil water ﬂow modeling was published Milly (1988), whereas Mari˜no and Tracy (1988) offered an in-depth review of root-water uptake modeling in combination with solving the Richards equation. The model by Clausnitzer and Hopmans (1994) uses a ﬁnite-element, Picard time-iterative numerical scheme

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ˇ unek et al., 1999) to solve the Richards equation in multiple (Vogel, 1987; Sim˚ dimensions ∂ψm ∂θ ∂ = K K ij A − S(x j , ψm , ψo ), + K i3 A (9) ∂t ∂ xi ∂x j where ψm is the soil water matric head (L) (see Section II.B), θ (L3 L−3) is volumetric soil water content, t is time, K (LT−1) is the unsaturated soil hydraulic conductivity, KijA is the generic component of the dimensionless anisotropy tensor for the unsaturated conductivity (i, j = 1, 2, 3), xi is the spatial coordinate, and S (L3 L−3 T−1) is the sink term, accounting for root water uptake. Boundary conditions can be included to allow for speciﬁed soil water potentials and ﬂuxes at the soil surface, and the bottom boundary of the soil domain; whereas user-speciﬁed initial conditions and time-varying source/sink volumetric ﬂow rates can usually be speciﬁed. Richards’ equation is typically a highly nonlinear partial differential equation and is therefore extremely difﬁcult to solve numerically because of the largely nonlinear dependencies of both water content and unsaturated hydraulic conductivity on the soil water matric potential (ψm ). Both the soil water retention and the unsaturated hydraulic conductivity relationships must be known a priori to solve the unsaturated water ﬂow equation. Speciﬁcally, it will need the slope of the soil water retention curve, or water capacity C(ψm ), deﬁned as C(ψm ) =

dθ . dψm

(10)

C(ψm ) is always larger than zero, since a decreasing matric pressure head will reduce θ for any soil as corresponding smaller-sized water-ﬁlled pores will drain. The water retention curve is very dependent on the soil particle size distribution and soil texture and the geometric arrangement of the solid particles and soil structure. Although soil water retention measurements are time-consuming, unsaturated hydraulic conductivity data are even much more difﬁcult to obtain from measurements (see Klute and Dirksen, 1986). Functional unsaturated hydraulic conductivity models, based on pore size distribution, pore geometry, and connectivity require integration of soil water retention models to obtain analytical expressions for the unsaturated hydraulic conductivity. The resulting expressions relate the relative hydraulic conductivity Kr, which is deﬁned as the ratio of the unsaturated hydraulic conductivity K to the saturated hydraulic conductivity Ks, to the effective saturation to yield a macroscopic hydraulic conductivity expression. The solution of the Richards equation provides values for water content, soil water matric potential, and water ﬂuxes at any predetermined point in the soil domain, usually with a temporal resolution of hours or less.

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B. SOLUTE TRANSPORT A general transport model has been developed to solve the three-dimensional form of the convection–dispersion equation (CDE) for solute concentration c ˇ unek et al. (1999), or, (ML−3), as fully described in Sim˚ ∂c ∂c ∂c ∂ θDi j − Jw,i − S , (11) (θ + ρk) = ∂t ∂ xi ∂x j ∂ xi where ρ (ML−3) is the soil bulk density, k (L3 M−1) is the linear adsorption coefﬁcient, Di j (L2 T−1 ) is the generic component of the dispersion coefﬁcient tensor, Jw,i (L T−1) is the Darcy water ﬂux density component in the ith direction, and S (T −1) is the sink term to account for root nutrient uptake. Many more rate constants can be added to the CDE, for example, to allow for reactions of the solute in the dissolved or adsorbed phase such as microbial degradation, volatilization, and precipitation. Hence, the CDE allows for nutrient adsorption to the solid phase (left-hand term), diffusion and dispersion, and mass ﬂow (ﬁrst and second terms on right side of (11), respectively) of the nutrient. The solution of Eq. (11) yields the spatial and temporal distribution of nutrient concentration and ﬂuxes at the same time resolution as Eq. (9), when solved simultaneously. Expanded reviews of solute transport in soils can be found in Bear (1972), Jury et al. (1991), Fogg et al. (1995), and Kramer and Cullen (1995).

C. COMMENTARY It must be pointed out that the solution of Eqs. (9) and (11) yields macroscopic quantities; i.e., values for matric potential, concentration, or ﬂux density denote voxel-representative values, with voxel sizes usually much larger than root diameter and root spacing. Moreover, because interpolation between simulated values is time consuming and prone to errors, the selection of a grid spacing (onedimensional) or voxel size and geometry (three-dimensional) is usually made a priori with the same grid spacings used throughout the simulation. Consequently, voxel geometries cannot be adjusted so that they coincide with root–soil interfaces. The integration of the Richards equation with root water uptake to solve for the macroscopic soil water potential within a continuum domain has been presented by Gardner (1960), Molz (1981), and Somma et al. (1998) for one, two, and three spatial dimensions, respectively. Moreover, some water ﬂow models incorporate the concept of Nimah and Hanks (1973) to allow for iterative solution of effective plant water potential when computing water stress effects on plant transpiration (Verburg et al., 1996). However, in all cases, integration with plant growth has been limited. In conclusion, it is anticipated that the next step in soil water ﬂow

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and chemical transport modeling for soil–plant systems must be to include the integration of soil water movement and nutrient transport with plant water and nutrient uptake in multiple spatial dimensions.

VII. ROOT WATER UPTAKE When considering root water uptake, we accept the continuum approach as presented in van den Honert (1948), assuming that ﬂow through the SPAC is at steady state for an unspeciﬁed time period, and that water potential across the SPAC is continuous and determined by the cohesion theory (CT). Hence, an Ohm’s law analog between water ﬂow and electrical current is valid, so that water ﬂow within each section of the SPAC pathway is determined by the ratio of water potential gradient and ﬂow resistance within each section. Speciﬁc sections may include “soil to root cortex,” “root cortex to xylem,” and “xylem to leaf.” In this approach, the overall resistance is deﬁned as the series combination of all resistances in SPAC (Campbell, 1985), so that the steady-state transpiration rate is controlled by the largest resistance. Consequently, the volumetric water uptake rate (Q) can be computed from [in analogy to Eq. (2)] Q=

ψm − ψ x , Rs + Rr

(12)

where Rs and Rr denote the soil and root resistance to ﬂow, respectively, and ψs and ψx deﬁne representative values for the soil matrix and xylem water potential. Although diffusion of water vapor into the air will generally be the largest resistance term within SPAC (certainly in nonstressed soil water conditions), it is excluded in Eq. (12). This can be done if the potential transpiration rate is assumed known from atmospheric demand. Equation (12) is generally used to quantify water transport across a single root in a microscopic approach, where Q denotes the volumetric uptake rate per unit length of root or per unit root surface area. An excellent example of such an approach was demonstrated by Molz (1981), where a similar form to Eq. (12) was used for a mechanistic description of water ﬂow between plant cells using parallel pathways of symplastic and apoplastic ﬂows. However, the Ohmtype approach can be equally applied to the macroscopic ﬂow of water across a complete rooting system (Gardner and Ehlig, 1962). In this approach, volumetric water uptake rate is expressed in water volume transpired per unit soil surface area, so that the dimension of Q in Eq. (12) is L T−1. Application of the van den Honert concept assumes that water ﬂow caused by heat and/or solutes is insigniﬁcant, and that the osmotic component of soil water potential is not contributing to water ﬂow into the root (Passioura, 1984). As will become clear later, this latter assumption may not hold. Finally, the electrical analog theory assumes that water ﬂow occurs

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through a simple series of constant, time-independent resistances; however, in reality the plant system is much more complex, resembling more a series-parallel network of ﬂow paths, each characterized by different resistances. Plant resistance is also likely to vary with transpiration rate (Passsioura, 1988; Slayter, 1967; Steudle et al., 1987; Weatherley, 1963) and water potential gradients, e.g., due to reduced plant conductance by cavitation (Section II.C). A thorough review of the simpliﬁcations and implications of Eq. (12) was presented by Philip (1966).

A. MACROSCOPIC WATER UPTAKE The steady-state assumption when using Eq. (12) is valid at small time scales, but is less likely to apply at time scales larger than a day. Nevertheless, the steadystate ﬂow assumption was used by Gardner (1960) to characterize ﬂow toward a single cylindrical root. Assuming radial ﬂow, an analytical solution was obtained, elucidating the inﬂuence of soil resistance on plant transpiration and the soil water matric potential distribution around the root. However, although Gardner’s studies were insightful and stimulating, the single-root approach is not practical when a whole rooting system with complex geometries must be considered. Moreover, ﬂow processes in the SPAC can be highly dynamic, thereby requiring a transient formulation of root water uptake. Consequently, later studies of water extraction by plants roots have considered the macroscopic rather than the microscopic approach. In the macroscopic approach, a sink term, representing the water extraction by plant roots is included in Richards’ Eq. (9) (Clausnitzer and Hopmans, 1994, Molz and Remson, 1970; Verburg et al., 1996; Whisler et al., 1968). When simpliﬁed to one spatial dimension (vertical z direction), this equation is written as ∂θ ∂ψt ∂ = K (ψm ) − S(z, t), (13) ∂t ∂z ∂z where the sink term S (L3 L−3 T−1, volumetric uptake rate per unit bulk soil volume and time) is a function of soil depth and time, and when integrated over the root zone (RZ) is equal to the actual transpiration rate (Tact). One-dimensional numerical ﬂow models to solve Eq. (13) compartmentalize the root zone in layers, z i , (i = 1, . . , Nl), solving the ﬂow equation and soil water extraction for each layer i, so that Nl Tact = Sdz = Si z i , (14) RZ

i=1

with the relation between potential (Tpot) and actual transpiration determined by a reduction factor (RED), and Tact = RED(ψm , ψx , Rr , Rs )Tpot ,

(15)

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where RED describes the inﬂuence of water stress on plant transpiration, as caused by local or total root system changes in soil and root water potential, and ﬂow resistances. The value of Tpot is solely deﬁned by atmospheric conditions (evaporative demand) and needs to be corrected for soil evaporation (Allen, 1998). For nonstressed conditions, the extraction term for each soil layer (Si) is deﬁned by Smax,i; for example, Smax,i = Tpot,i RDFi ,

(16a)

where Tpot,i represents the nonstressed water extraction rate (maximum) for the ith soil layer, and RDFi denotes the normalized active root distribution function (RDF ) for layer i (L−1). It characterizes the depth distribution of potential root water uptake sites and must be equal to 1 when integrated over the whole rooting zone, so that Tpot =

Nl

Smax,i z i .

(16b)

i=1

Hence, RDFi distributes the water uptake according to the relative presence of roots. Traditionally, one would use root length density (RLD) distribution to represent RDF; however, studies have shown that the root surface area rather than root length controls water uptake and that root water uptake is predominantly within 30 cm from the root tip (Varney and Canny, 1993). Moreover, active root distribution is not constant, but varies with time as roots grow and decay, and new soil volumes are explored. Consequently, the modeling study of Clausnitzer and Hopmans (1994) characterized temporal changes in RDF using dynamic simulations of three-dimensional root-tip distribution. Various empirical one-dimensional expressions have been developed to describe Smax or RDF, of which many are listed in Molz (1981) and Hoffman and van Genuchten (1983). Other speciﬁc active root water uptake models include those reported by Hoogland et al. (1981) and Raats (1974). In addition, multidimensional root density distribution functions have recently been developed by Coelho and Or (1996) and Vrugt et al. (2001). For example, Vrugt, van Wijk et al. (2001) introduced the three-dimensional root water uptake model X m Ym βi , RDFi = X m Ym Z m 0 0 0 βi d xd ydz where

(17a)

p xi yi zi − px |x ∗ −xi |+ Ymy |y ∗ −yi |+ Zpmz |z ∗ −z i | . 1− 1− e Xm βi = 1 − Xm Ym Zm (17b)

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Xm, Ym, and Zm denote maximum root exploration in directions of x, y, and z, respectively. With empirical parameters px, py, pz, x∗ , y∗ , and z∗ , this single expression was shown to simulate a wide variety of water uptake patterns.

B. ROOT WATER UPTAKE TYPES I AND II In general, two different approaches have been used to compute the time-variable root water uptake needed to solve for spatial distributions of soil water content and soil water matric potential by the numerical solution of Eq. (13). The ﬁrst approach (type I) was introduced by Nimah and Hanks (1973), and was further reﬁned by Campbell (1985, 1991). In either case, Smax,i is computed from the solution of Eq. (12) for each soil layer, zi, when combined with the steady-state equation of radial water ﬂow to a root (Cowan, 1965; Gardner, 1960) to estimate depth-dependent soil resistances as a function of the depth-speciﬁc unsaturated soil hydraulic conductivity. An effective xylem water potential (ψx ) is computed if the total estimated plant transpiration is larger than Tpot. For example, using the Campbell (1985) approach, plant transpiration is estimated from (Verburg et al., 1996) Tact =

i

Ti =

ψm,i − ψx , Rs,i + Rr,i i

(18)

where RDFi is included in both Rs,i and Rr,i, so that Si = Ti/zi. If the computed xylem water potential is lower than an a priori known minimum allowable value, a reduced actual plant transpiration value (Tact) is calculated using that minimum xylem water potential value. This then results in a reduction factor (RED) value smaller than 1. Applications (Verburg et al., 1996) exclude the possibility of return ﬂow from the root into the soil, if the computed xylem potential is larger than the soil water matric potential. The advantage of this approach is that it is mechanistic and results in effective time-dependent xylem water potential values. Moreover, this approach allows for compensation of water stress in one soil layer by increased water uptake in other nonstressed soil layers. Osmotic contributions can be included by adding the osmotic term to the soil water matric potential in Eq. (18). The second approach is much more empirical (type II) and was introduced by Feddes (1976). It assumes a priori knowledge of the so-called stress–response function, α (ψm), deﬁned by Si = αi (ψm )Smax,i .

(19)

The stress–response function, α(ψm ), is deﬁned by ﬁve critical matric potential values (Fig. 8), describing plant stress due to dry (ψ3l , ψ3h , and ψ4 ) and wet soil conditions (ψ1 and ψ2 ). Representative values for various crops are listed in

ROOT WATER AND NUTRIENT UPTAKE

Figure 8

139

Stress–response function (after Feddes et al., 1978).

van Dam et al. (1997), with ψ3 values varying between −200 and −1000 cm, depending on crop and Tpot. Speciﬁcally, the water potential threshold at which water stress initiates reduction in root water uptake is determined by Tpot, with water stress occurring earlier at a less negative value (ψ3h ), if Tpot is high. This type of functional dependence allows for less favorable water-supplying soil moisture conditions with increasing plant transpiration (van Dam et al., 1997). Similar functional forms as shown in Fig. 8 were experimentally determined by Gardner and Ehlig (1962) and presented by Cowan (1965) from a numerical solution in Gardner’s (1960) model investigating the inﬂuence of evaporative demand and water supply on plant transpiration. In this empirical approach, Eq. (19) is applied to each soil layer, substituting the same known Tpot−value for each Tpot,i to compute Smax,i from Eq. (16a), so that water stress in one layer cannot be compensated for by larger water uptake in nonstressed layers. The empirical water extraction function inherently assumes that only soil resistance reduces plant transpiration for ψm < ψ3 (Fig. 8). Although plant resistance may be larger than the soil resistance for ψm > ψ3 , the resulting decreasing xylem water potential does not affect Tpot. Osmotic stress can be included by multiplication of the right-hand term of Eq. (19) by a salinity stress response function, as demonstrated by van Dam et al. (1997) and Homae (1999) in Si = αi (ψm ) αi (ψo ) Smax,i ,

(20)

where α(ψo) deﬁnes the salinity stress reduction function, also with values between zero and one. Using the analogy of stress and crop yield (de Wit, 1958), an example of an osmotic stress response function is presented in Fig. 9, where soil salinity is

140

Figure 9

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Stress–response function for salinity stress (adapted from Van Dam et al., 1997).

expressed by electrical conductivity (EC) of the soil saturation extract (ECext), as deﬁned by Maas and Hoffman (1977). An alternative stress response function was presented by van Genuchten (1987), or, α (ψm ) =

1+

1 ψm,i ψm,50

p ,

(21)

where ψm,50 deﬁnes the soil water matric potential at which α(ψm ) = 0.5. This model is analogous to the expression introduced by van Genuchten and Hoffman (1984) that included osmotic effects on plant water stress by adding the osmotic potential to the power term in the denominator. Both root water extraction types I and II were examined by Cardon and Letey (1992) to investigate their sensitivity to salinity stress. It was concluded that the mechanistic approach of the type I models, while including the osmotic potential in Eq. (18), was insensitive to salinity with little reduction in Tpot for irrigation water salinities up to 6 dS/m. Moreover, the type I approach occasionally resulted in abrupt changes of plant transpiration, from Tpot to zero, particularly under saline conditions. For such conditions, Shani and Dudley (1996) proposed a combinational approach, using the type I model (Nimah and Hanks, 1973) to account for soil water matric stresses, α(ψm ), in combination with a type II model (van Genuchten, 1987) to account for osmotic stress, α(ψo ), on plant transpiration and crop yield by replacing ψm in Eq. (21) by ψo . Using this combinational approach, the effects of the osmotic and matric potential on crop yield were multiplicative rather than additive. This approach is similar to the one suggested by van Dam (1997) using Eq. (20).

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C. OTHER ASPECTS AFFECTING WATER UPTAKE The effect of soil salinity on water stress can be better understood by considering the uptake expressions (Slayter, 1965) Jwater = L (P − σ )

(22a)

Jwater = L(ψm + σ ψo ),

(22b)

or

which are routinely used when considering ﬂow of water and solutes across the plasmalemma and tonoplast. The matric potential component may instead be replaced by a hydrostatic pressure component if plant water pressure is positive, such as when turgor pressure is considered for transport of water between vacuoles and the soil. The parameter L denotes the effective hydraulic conductance of the root, and σ is the effective reﬂection coefﬁcient of all water-transporting root membranes combined (Section IV.B). The difference in adopted notation between L and L merely reﬂects the distinction in dimensions between the applied driving forces P (Eq. 22a) or ψm (Eq. 22b). The reﬂection coefﬁcient value varies between one and zero. Its value is an indication of the effectiveness of the osmotic potential as a driving force for water ﬂow across roots. Using a value of 1, the osmotic potential gradient is equally effective as a matric potential gradient. This is the case for a perfect semipermeable membrane, such as occurs in a well-developed endodermis. In contrast, a reﬂection coefﬁcient of zero describes a completely leaky membrane where osmotic potentials are not effective in moving water through the roots, such as is the case within the xylem and across cell walls. The true value of the reﬂection coefﬁcient is a function of solute and plant species, with some values presented in Table 3.2 of Kramer and Boyer (1995). Whereas the formulation in Eq. (22) regards the root as a simple conduit for water transfer, more recent research has demonstrated (see also Section IV.B) that there may be a number of different ﬂow paths for water to move through the root. Speciﬁcally, these are the apoplastic and symplastic pathway, each characterized by their permeability and reﬂection coefﬁcient (see also Weatherley, 1963). Moreover, using detailed pressure probe measurements, it was demonstrated by Steudle et al. (1987) and Steudle (1994) that matric potential gradients move water predominantly through the apoplast with a reﬂection coefﬁcient close to zero, and that this is possible because the local endodermis is not fully developed with an imperfect Casparian band. Moreover, Steudle (1994) determined from experiments in maize roots that the symplastic root conductance was about 1–2 orders smaller than the apoplastic conductance. It was hypothesized that the osmotic component drives water mainly through the symplast or cell-to-cell pathway, with a reﬂection

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coefﬁcient close to 1, across the plasmalemma or tonoplast. Hence, this composite transport model allows for a driving-force-dependent water ﬂow pathway. For such a system of two parallel pathways, Steudle (1994) deﬁned a composite reﬂection coefﬁcient (σ = σ c), which is a function of the fractional contribution of each pathway (f) to the total effective root area, or fapo = Aapo/A and fsym = Asym / A, so that L sym L apo σsym + f apo σapo . (23a) σc = f sym L L In his formulation, the composite root conductance, L, is deﬁned by L = f apo L apo + f sym L sym ,

(23b)

where the subscripts sym and apo refer to the symplastic and apoplastic component of conductance (L), reﬂection coefﬁcient (σ ), and fractional area of ﬂow ( f ). Accordingly, the composite reﬂection coefﬁcient is a weighted mean of the reﬂection coefﬁcients of the two parallel pathways that each contribute according to their individual conductance. After substitution of Eqs. (23a) and (23b) in Eq. (22b), the new formulation predicts that in the apoplastic pathway the effective osmotic driving force is low when osmotic gradients are applied to the root, despite its large hydraulic conductance, because σ apo is close to zero. A close inspection of the ﬁnal attained composite expression after the stated substitutions will also show that there is no differentiation between apoplastic and symplastic pathways if the Casparian band is fully developed everywhere. In that case all ﬂow must pass through the low conductive plasmalemma with conductance L. Hence, the composite approach assumes that differentiation in ﬂow paths and variability in root water uptake within the rooting system is determined by the presence of undeveloped Casparian bands (Dumbroff and Persion, 1971) or their complete absence. The composite ﬂow theory might also explain the dependency of the total hydraulic conductance on plant species, which is a function of the development of the endodermis and/or presence of suberization of cell walls and Casparian band (Steudle et al., 1987). The composite or three-compartment approach of Steudle (1994) may explain the nonlinear behavior of ﬂow into roots, as inferred from apparent transpirationdependent root conductances (Fiscus, 1975; Passioura, 1984). Speciﬁcally, the dominance of the high-resistance symplastic component for low ﬂow uptake conditions causes a relatively low conductance, whereas the osmotic component is obscured when ﬂow is largely controlled by matric potential gradients, resulting in a high ﬂow conductance. The apparent high ﬂow resistance at low uptake rates is accordingly explained by the active transport of solutes into the root stele, thereby causing high-resistance osmotically induced water uptake (Dalton et al., 1975; Fiscus, 1975). After partitioning of the absorbed water into water used for expansive growth and transpiration, the analytical work of Fiscus et al. (1983) showed

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the inﬂuence of this partitioning on the nonlinear whole plant water transport behavior. Even more so, selective uptake of water by the roots for conditions when σ c is relatively large may accumulate nutrients at the root–soil interface or apoplast, causing reverse ﬂow of water from the root into the soil by exudation. This is possibly counteracted by diffusion into the roots (Canny, 1990; Stirzaker and Passioura, 1996). However, as stated by Passioura (1984), this buildup of nutrients should increase with transpiration rate, thereby increasing the apparent root conductance. Using a three-compartment numerical model, the effect of changing the driving force on root resistance, causing nonlinear ﬂow behavior and exudation of water by roots, was also demonstrated by Katou and Taura (1989). Another aspect deserving attention is the ﬂow of water from the plant and roots into the surrounding soil, as may occur for dry topsoil conditions with deeper wet root zones or for wet-top and dry deeper soil moisture conditions (Smith et al., 1999). This phenomenon is deﬁned as hydraulic lift (Caldwell and Richards, 1989) and can lead to the accumulation of xylem nutrients and xylem osmotic potential leading to root water pressure buildup (Steudle, 1994). The reverse ﬂow mechanism was experimentally conﬁrmed by Molz and Peterson (1976); however, they determined that the resistance of the reversed ﬂow was much higher. In the general Ohm-type root water uptake formulation, the soil–root resistance is neglected, although it has been demonstrated from experimental work that soil and root shrinking and contact resistance can signiﬁcantly increase total water ﬂow resistance (Bristow et al., 1984; Herkelrath et al., 1977; Passioura, 1988). Thus, ﬁtted water extraction parameters represent effective values that may not be appropriate for conditions outside the experimental range. In general, one must always use caution when applying this inverse type of approach where experimental data are ﬁtted to a physical model. In addition to the radial root resistance, the longitudinal or axial root resistance in the xylem vessels may also contribute to the total root resistance. Various experimental studies (e.g., Frensch and Steudle, 1989) have shown that axial resistance is generally low. However, it is also intuitively clear that axial resistance might be important under dry soil conditions when cavitation in the xylem vessels can signiﬁcantly reduce its conductance (Boyer, 1985; Tyree and Sperry, 1989), or when the number of xylem containing roots is limited (Passioura, 1988). Much research has been conducted to understand the relative contribution of soil and plant resistance to root water uptake. In general, it is found from both experimental and modeling studies that plant resistance is larger than soil resistance, at least until the soil’s hydraulic conductivity becomes limiting (Gardner and Ehlig, 1962; Landsberg and Fowkes, 1978; Reicosky and Ritchie, 1976; Rowse et al., 1978). A comprehensive review of root resistance including a discussion on the root–soil interface resistance, axial root resistance, and measurement techniques was presented by Moreshet et al. (1996). Although it is generally accepted that apoplastic and symplastic water moves through pores, the exact biophysical mechanisms of water transport in the root

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was not as evident until the discovery of aquaporins (Maurel, 1997; Tyerman et al., 1999). These water channel proteins within cell membranes facilitate the passive movement of water across membranes by both pressure and osmotic gradients in either direction, thereby increasing their conductance. Aquaporins may function like ion channels or ion carriers; however, water transport does not carry charge along with its movement. The presence of aquaporins may explain the symplastic transport of water across the endodermis, bypassing the Casparian band. Moreover, they can help explain the leakiness of semipermeable membranes, as indicated in Tyerman et al. (1999), and support the composite theory of water transport along parallel pathways (Steudle, 1994, 2000). Finally, it was speculated by Steudle (2000) that water channels may be more operative under conditions of water shortage, thereby allowing ABA signaling from the root to the shoots. However, the molecular basis of water channel selectivity and its regulatory functioning is yet unknown (Tyerman et al., 1999).

D. COMMENTARY Root water uptake has been described at both the microscopic and the macroscopic levels. The microscopic approach requires details about root geometry and soil heterogeneity that are generally not available. In the macroscopic approach, a sink term representing water extraction by plant roots is included in the dynamic water ﬂow equation, allowing spatially and temporally variable uptake as controlled by soil moisture and plant demand. Water stress is determined by either computing effective leaf water potential (type I) or introducing a zero-to-one stress response function (type II). Within the macroscopic approach it is possible to differentiate between apoplastic and symplastic ﬂow using the composite approach, implying pathway-dependent conductance and reﬂection coefﬁcient values. Moreover, in this composite approach, a distinction is made between water uptake by matric and osmotic water potential gradients. Within the general framework of the SPAC, we might have to reconsider the signiﬁcance of the plant–root resistance in relation to the atmospheric and soil resistances. Under wet-soil conditions, the largest hydraulic resistance occurs in the leaf with water vapor diffusion into the surrounding air controlled by atmospheric conditions. Under these conditions, plant transpiration is at its potential rate, independent of the ﬂow resistance of the plant, root, or soil. Transpiration is demand controlled rather than supply controlled. As the soil is depleted of water, its ﬂow resistance increases, as controlled by the decreasing unsaturated soil hydraulic conductivity and possibly by the decreasing root–soil contact. At a certain point the soil resistance becomes the dominant factor controlling plant transpiration. Consequently, the potential transpiration rate is decreased by a factor RED. In either case, the plant or root resistance was not considered. Likely, the root

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resistance may be important to determine the timing of the transition from potential to reduced plant transpiration, as it is applied in the type I approach of Nimah and Hanks (1973) or Campbell (1985). In these cases its value may be needed for the accurate modeling of crop growth.

VIII. NUTRIENT UPTAKE As was already established in the previous section, the routes along which both water and nutrients enter into the plant through the roots are likely to be different. Both water and nutrients enter the plant root freely through the apoplast, but their pathways and mechanisms of transport diverge when moving into the symplast. When addressing plant nutrient uptake, we must distinguish between the soil and the plant root transport mechanisms, so that we can determine whether nutrient uptake is either supply controlled or demand controlled. Demand-controlled nutrient uptake is regulated by plant parameters, whereas nutrient supply to the roots is determined by soil nutrient transport.

A. NUTRIENT TRANSPORT IN SOILS Excellent reviews on soil transport and uptake mechanisms of nutrients are presented in Nye and Tinker (1977) and Barber (1984). Nutrient movement toward the root surface occurs by the parallel transport of convective ﬂow and diffusion, with the latter mechanism including dispersion. Nutrient transport by convection describes movement by the water as it moves through the soil. Hence, its magnitude is determined by the solution of Richards’ Eq. (9) and is a function of soil water potential gradients, the unsaturated hydraulic conductivity of the soil, and root water uptake. Larger water ﬂow rates as, for example, those induced by irrigation will provide increased access of dissolved nutrients to the roots, whereas small water ﬂow velocities tend to create depletion of nutrients near the roots. However, increasingly it is suggested that the dominant process of water ﬂow in soils is by preferential ﬂow. This general phenomenon causes accelerated transport of water and dissolved chemicals through the root zone, thereby bypassing large portions of the soil matrix and associated root surfaces. Dispersion and diffusion are caused by nutrient concentration differences near the roots, which may occur because of active nutrient uptake. Alternatively, preferential water uptake may cause accumulation of nutrients near the roots, resulting in their diffusing back into the surrounding soil. The dynamics of nutrient transport in soils can be described by the convective diffusion Eq. (11), including nutrient uptake from which nutrient concentrations can be computed at any time

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within any spatial location of the rooting system. Ion-speciﬁc nutrient diffusion is highly dependent on soil water content, with diffusion rates decreasing as the water content decreases. The transport equation includes a linear adsorption coefﬁcient (k), characterizing the adsorption of the speciﬁc nutrient to the soil’s solid surface, thereby largely inﬂuencing the proportion of total ion content available for transport. Nutrient adsorption is generally described by the adsorption isotherm, characterizing the amount of nutrient adsorbed in equilibrium with the dissolved concentration, and is related to the buffering power or buffer coefﬁcient of Nye (1966), Nye and Tinker (1977), and Claassen and Barber (1976). Although mass ﬂow in general is not ion speciﬁc, differences in diffusion and adsorption coefﬁcients between ions result in differences in the soil transport rate and root supply between nutrients. Since nutrient uptake rates can be ion speciﬁc, nutrient concentrations at the soil–root interface can be either accumulating or depleting. In addition to soil transport, nutrient uptake is controlled by the spatial distribution of roots, as inﬂuenced by its architecture, morphology, and presence of active sites of nutrient uptake, including root hairs. For nutrients that are immobile (e.g., phosphorus) or slowly mobile (ammonium), a root system must develop so that it has access to the nutrients, by increasing their exploration volume. Alternatively, the roots may increase their exploitation power for the speciﬁc nutrient by local adaptation of the rooting system, allowing for increased uptake efﬁciency of the nutrient. In the case of nonadsorbing nutrients, nutrient uptake is controlled by mass ﬂow, as is the case of nitrate–nitrogen, which is hardly adsorbed by the soil. If the nutrient uptake rate or root absorbing power is supply controlled, mechanistic, analytical, and numerical solutions of nutrient transport that include the various transport mechanisms in soils can be used. Examples are presented in Olsen and Kemper (1968) and were reviewed by Jungk (1996). However, most, if not all of these solutions, have severe limitations regarding the portion of dynamics of ﬂow and transport within the soil rooting system. For example, the proposed analytical model for nutrient uptake by growing roots given by Cushman (1979), which is based on Nye and Marriott (1969), assumes that moisture content is constant; and the Yanai (1994) nutrient uptake model assumes steady-state water ﬂow with dynamic root growth. Under these commonly used nutrient supplylimiting conditions (as in Claassen and Barber, 1976), the soil supply is assumed to be equal to the nutrient uptake rate per unit root, which is illustrated schematically in Fig. 10 (from Jungk, 1996). An excellent review of the available mechanistic nutrient uptake models is presented in Silberbush (1996). When considering the many complications and soil–root–nutrient interactions, the predictive ability of these supply-limiting mechanistic nutrient uptake models has been remarkably good (Silberbush and Barber, 1984). This suggests that there is a reasonable level of understanding of the dominant physical and chemical processes of nutrient transport in soils.

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Figure 10 Schematic representation of the mechanistic description of soil-limiting nutrient uptake (adapted from Jungk, 1996).

B. NUTRIENT TRANSPORT IN THE ROOT The differences between transport processes in the roots become clear when considering that solution concentrations are generally much different from the xylem concentrations, with transpiration stream concentration factors (TSCF) larger than 1 (Russell, 1977). This indicates that nutrients have been moved against their concentration gradient. This type of transport is deﬁned as an active, metabolically driven transport. An active transport is very much ion and plant species speciﬁc and can move ions in either direction. For example, plant species growing in seawater have Na and Ca concentrations in the cell sap that are much smaller than those in solution. Clearly, when the hydrophobic Casparian band is absent, as is the case for the young root cells near the root apex, water and nutrients can move through the cell walls toward the xylem by the apoplastic pathway. However, the differences in transport mechanisms become evident when approaching the endodermis if the impermeable Casparian band is present. Water and nutrient pathways converge again after both have reached the parenchyma cells of the xylem, moving simultaneously upward toward the plant leaves. However, speciﬁc nutrients can diffuse across most parts of the rooting system, independent of water-transporting pathways and age. This is possibly related to the presence of passage cells in the secondary suberized plasmalemma of the endodermis (Clarkson, 1996). When available at the root–soil interface, nutrients must diffuse through the secreted mucigel, the restricted unstirred water layer around the roots of the rhizophere, and across the epidermis to arrive into the free space or apoplast (Clarkson, 1996). Because of their large surface area, young root hairs provide for metabolically driven active uptake through proton pumping across their plasma membranes.

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When in the apoplast, pores are sufﬁciently large (3–4 nm) to permit unrestricted entry of water, hydrated inorganic ions, and small organic molecules. The net negative charge of the cell walls tends to exclude anions from the narrower pores of the apoplast, thereby likely reducing anion concentrations near the plasmalemma. This subsequently affects magnitudes of active uptake between anions and cations. Once accumulated in the inner spaces, active uptake mechanisms provide for transport across the plasmalemma and other protoplasmic membranes (Epstein, 1960). It is kinetically controlled by cell metabolism, the number of binding sites or nutrienttransporting carriers, the external nutrient concentration, and other environmental factors such as temperature and pH. Moreover, active transport is ion speciﬁc. In principle, nutrients can be adsorbed into the symplast by the peripheral cell layers of the cortex, or they can move across the plasmalemma in the endodermis, bypassing the Casparian band. An example of this was presented by Clarkson (1996), where Ca was transported into the cytoplasm by calcium channels and returned into the cell wall by calcium pumping (Fig. 11). Once past the endodermis and

Figure 11 Possible transport mechanism of calcium through the sympast and apoplast, bypassing the Casparian band. [Reprinted from Clarkson, D. T. (1996). Root structure and sites of ion uptake. In “Plants Roots: The Hidden Half,” p.439, by courtesy of Marcel Dekker, Inc.]

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in the symplast, the main pathway for the nutrients is through the cytoplasm and plasmodesmata of neighboring parenchyma cells in the stele, thereby providing a low resistance pathway for both water and dissolved nutrients toward the xylem. The provision of energy for active nutrient transport occurs via cell metabolism by the reduction of ATP. This reaction is an enzymatic reaction, so that the kinetics of active nutrient uptake is traditionally described by Michaelis and Menten enzyme kinetics (see Section V.B). Values for uptake parameters for a large group of crops are listed in Table 10.1 of Clarkson (1974). Generally, a distinction is made between mechanisms I and II, with the kinetic parameters of system I typical for carrier-type transport with the carrier having a high afﬁnity for the moving nutrient (Epstein and Rains, 1966). The type II mechanism of uptake is operative in the higher concentration range and is much less ion speciﬁc and faster. These higher uptake rates would be typical for an ion-channel type of uptake and almost act as passive diffusion down an electrochemical potential gradient, since solution concentrations are usually larger than xylem concentrations. Instead of this dualcarrier system, Barber (1972) proposed the dual-isotherm hypothesis, suggesting that phosphate uptake in the low concentration range (mechanism I) was by active uptake, with the external nutrient concentration lower than the tissue concentration. In contrast, he hypothesized that phosphate uptake in the high concentration range of the external solution was passive by diffusion when the concentration gradient was reversed. Most recently, Nissen (1996) suggested that nutrient uptake was universally active across the whole external concentration range and proposed the multiphasic model with different MM parameters for each stage. Also Nissen (1996) proposed that uptake in the high concentration range was increasingly less active and operated more like a diffusion process. In summary, these results indicate that nutrient uptake is dominant by active uptake at low transpiration rates with the xylem nutrient concentration relatively high, whereas passive uptake is likely favored at high transpiration rates when the xylem nutrient concentration is low. Macroscopic models of nutrient uptake for a whole rooting system use a macroscopic sink term S (mass of nutrient taken up per unit bulk volume and time; M L−3 T−1), which when combined with the one-dimensional form of Eq. (11) predicts nutrient uptake for each soil layer, zi, for example, Si = RDFi Jsolute,i ,

(24)

where RDFi denotes the spatial distribution of active nutrient uptake area roots per unit bulk soil volume (L2 L−3) and Jsolute,i deﬁnes the nutrient uptake per unit root area (M L−2 T−1) for each soil layer. The total nutrient uptake is computed from integration of Eq. (24) over the whole rooting zone (e.g., Ran et al., 1994). For soil supply-limited conditions, the resulting total nutrient uptake may be compared with plant demand, with the resulting ratio deﬁned as a nutrient stress factor with a value between zero and 1. This nutrient stress factor then characterizes the effect

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of plant nutrient stress on crop biomass production. The plant nutrient demand, for example, expressed in mass of nitrate required per unit mass dry matter produced, can be computed from the nutrient use efﬁciency using known values of the biomass produced per unit nutrient taken up (NUTE). This efﬁciency parameter can be computed from plant tissue concentrations between the various plant organs for unlimited nutrient supply conditions (van Noordwijk and van de Geijn, 1996). The total actual nutrient uptake must be distributed across the rooting zone according to the spatial distribution of nitrate supply rate and active root uptake area. In addition to the presence of roots, it has also been demonstrated that plant root growth responds to local variations in nutrient supply (Robinson, 1994). For example, as was experimentally determined by Drew and Saker (1975), localized proliferation of root growth can occur if part of the rooting zone is supplied with an enhanced supply of nitrate, with nonlimiting other soil environmental conditions. The local high concentration of nitrate was able to offset the limited nutrient supply available to other parts of the rooting system.

C. NITRATE UPTAKE In general, it has been found that NO3− uptake is independent of transpiration, except for conditions when the transpiration and, hence, water uptake rates were small. Under these conditions, nitrate levels in the xylem were high, caused by active nitrate uptake in xylem, inhibiting continued active root uptake of nitrate (Shaner and Boyer, 1976). Their results demonstrated that the the nitrate xylem concentration varied inversely with the transpiration rate, and that nitrate uptake is mostly a function of metabolic rate rather than transpiration rate. Active nitrate uptake is considered to occur via NO3−/H+ co-transport, or NO3−/H+ countertransport via carriers (Haynes, 1986), with the electrochemical gradient generated by proton pumping. Increased values of Km with increasing nitrogen application rates have been related to a corresponding increase in the number of active nitrate carriers in the plasmalemma (Oscarson et al., 1989), whereas Lee and Drew (1986) determined an increased uptake response as quantiﬁed by MM parameters as a result of increased nitrate application after nitrate starvation. In another study, Pinton et al. (1999) determined from experiments with young maize root seedlings that certain humic substances could increase root nitrate uptake by enhanced production of the H+-ATPase. Van den Honert and Hooymans (1955) experimentally showed a decrease in nitrate uptake by increasing the pH from 5 to 8, which can be explained by the requirement of electron neutrality in the root cells, resulted in an efﬂux of OH− into the rhizosphere in proportion to nitrate uptake (Haynes, 1986). In addition, it has been hypothesized that the nitrogen metabolism rate through its reduction to nitrite and ammonia might control uptake. That is, active uptake

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rate is controlled by nitrate efﬂux from the symplast through nitrate reduction (Deane-Drummond, 1984). Although not strictly proven, it is generally proposed that active uptake dominates in the low supply concentration range and under stress conditions, whereas passive uptake and diffusion become more important at higher soil solution concentrations. When considering the rhizosphere dynamics of water and nutrient uptake, many more mechanisms may have to be considered, including rhizosphere acidiﬁcation and nitrogen mineralization. Acidiﬁcation of the rhizosphere occurs because of a cation–anion imbalance of root uptake, resulting in the efﬂux or excretion of protons by the roots in the surrounding soil, for example, by root uptake of ammonium (Pierre and Banwart, 1973), with degree of acidiﬁcation variations between plant species, fertilizer type, and contribution from nitrogen ﬁxation and ash alkalinity (Jarvis and Robson, 1983; Tang et al., 1997).

D. COMMENTARY While reviewing the general literature on nutrient uptake by roots, it is indeed perplexing that uptake has been considered in so many different and occasionally opposing ways. Crop growth models (e.g., APSIM, CERES) generally assume little, or no, dynamics in nutrient uptake, considering the changes in the total available nutrient pool of the rooting zone without discriminating between active and passive uptake. In contrast, dynamic water ﬂow and solute transport models (e.g., HYDRUS2D, RZWQM, SWAP) track spatial and temporal changes in water content, solute concentration, and water and solute ﬂuxes. However, these model types regard nutrient uptake solely as a passive process, computing nutrient uptake ﬂuxes from the product of water ﬂux density and soil solution concentration within predeﬁned small root zone volume elements with spatially distributed root densities. In this approach, the nutrient uptake rate is corrected by multiplying the passive nutrient uptake ﬂux by a correction factor, to match predicted with observed total plant nutrient uptake. This correction factor can be both smaller or larger than 1, depending on the magnitude of active uptake, relative to total nutrient uptake. Finally, comprehensive plant nutrient uptake models (Barber, 1984; Nye and Tinker, 1977), although dynamic in root growth and nutrient uptake and transport through the soil, generally assume steady-state water ﬂow with time-independent water content and water ﬂuxes and describe nutrient uptake by active uptake only. Moreover, if passive water uptake through the apoplastic pathway is dominant, as is generally assumed, passive nutrient uptake must occur simultaneously, possibly in parallel with active transport, and most prominently for conditions of high transpiration rates.

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IX. COUPLED ROOT WATER AND NUTRIENT UPTAKE A. MECHANISTIC FORMULATIONS Among the ﬁrst to study the inﬂuence of water uptake on plant nutrient uptake was Brouwer (1956). He found that ion uptake increased with transpiration rate when transpiration was low, but that the coupling disappeared at the higher transpiration rates. Using calcium chloride solutions, he concluded that about 70–80% of total ion uptake was by metabolism-dependent active uptake. A seminal contribution to a better understanding of the coupled uptake of water and nutrients to a single root for steady-state water ﬂow conditions was presented by Dalton et al. (1975), showing that (i) solute ﬂux is related to the water ﬂux, even when active uptake is dominant; and (ii) a nonlinear relationship existed between transpiration rate (or water ﬂux through the plant) and water pressure gradient. Applying the theory of irreversible thermodynamics to ﬂow in plant roots (Dainty, 1963), Dalton et al. (1975) solved the coupled ﬂow Eqs. (5) and (6), repeated in the following for convenience: Jwater = L (P − σ )

(25a)

Jsolute = ω + (1 − σ ) C1 Jwater + J ∗ .

(25b)

and

Using the van ‘t Hoff expression to relate concentration to osmotic pressure ( = RTC) for dilute solutions, the selectivity coefﬁcient (Se) was deﬁned as Se =

σ − RTJ∗ /(1 Jwater ) , 1 + ω RT/Jwater

(26)

where Se = 1, if σ = 1, J∗ = 0, and ω = 0 (perfect semipermeable membrane), and where Se = 0, if σ = 0 and J∗ = 0. Using subscripts 1 and 2 to denote nutrient and xylem concentrations, it was subsequently shown that C2 = (1 − Se)C1

and

Jsolute = (1 − Se)C1 Jwater .

(27)

Equation (27) also shows that the selectivity coefﬁcient, Se, is negative, if the xylem concentration is larger than the solution concentration, leading to a correction factor larger than 1, if total nutrient uptake is estimated from the product of solution concentration and root water uptake (see Section VII.D). Using this twocompartment model it was demonstrated that the xylem concentration decreased and the solute uptake rate increased as the transpiration rate increased, even for a reﬂection coefﬁcient as high as 0.975. Moreover, it was shown that there exists a

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nonlinear relationship between water ﬂux and pressure gradient for nonzero active uptake. This relation approached linearity if transpiration rates (Jwater) were high. Using a similar approach, but assuming that nutrient uptake was by active uptake only and that the semipermeable membrane was perfect, Fiscus (1975) arrived at similar conclusions from J∗ Jwater , (28) + RT C2 − P = L Jwater and their computation of the total resistance to water ﬂow (slope of Jw in Fig. 10) equal to 1 RTJ∗ d(P) = + 2 . d(Jwater ) L J water

(29)

The general results are shown in Fig. 12, which was adapted from Fig. 2 of Fiscus (1975), with an external solution osmotic potential of 1 = 1.0. From either expression it is clear that the resistance to ﬂow is nonlinear, becoming linear as Jwater increases, as caused by the changing driving forces rather than a changing ﬂow resistance. Figure 10 also shows that there is nonzero uptake, even when P is zero. This ﬂow is caused by the osmotic contribution, which decreases as transpiration rate increases because of the reduction of the osmotic component

Figure 12 Qualitative relationship between applied pressure, water uptake, and internal osmotic pressure (adapted from Fiscus, 1975).

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with decreasing xylem concentration. As is evident from the Eq. (27), linear relationships between water and solute uptake ﬂuxes are expected if the active uptake term is zero. Moreover, Fiscus (1975) pointed out that the positive relation between water and solute ﬂux is controlled by the relative magnitude of the diffusive and convective components of Eq. (25b). A similar two-compartment system was presented by Zimmerman and Steudle (1978); however, they split the osmotic terms into two components, whereby i and p are the osmotic pressure differences of the impermeable (i) and permeable (p) solutes for which σ < 1, or Jwater = L((P − i ) − σ p ),

(30a)

so that the effective osmotic pressure effect on water ﬂow is less than that of the total solute concentration (see also Dainty, 1963). The corresponding solute uptake ﬂux equation then allows for diffusive transport for the permeable solute fraction only, or Jsolute = ω p + (1 − σ )C1 Jwater + J ∗ .

(30b)

Rather than a two-compartment model, a three-compartment model could possibly more realistically describe the cortex–symplast–stele pathway, with the compartments separated by two distinct membranes with different membrane transport properties. These membranes could be arranged in either series, or in parallel, from which composite membrane conductance and permeability values are determined. This was done by Celentano et al. (1988) and Zimmerman and Steudle (1978). The three-compartment concept was also suggested by Passioura (1988) to account for nonzero uptake at a zero pressure gradient, allowing for active solute uptake into the stele, thereby generating osmotically driven water ﬂow. Also, Katou and Taura (1989) used the three-compartment approach by applying their double-canal model as a means to explain nonlinear water ﬂow as caused by osmotic gradients.

B. OTHER CONSIDERATIONS A major limitation of current nutrient uptake models, when integrated with dynamic soil water ﬂow models, is their general omission of the inﬂuence of soil salinity on nutrient uptake. Speciﬁcally, salinity may reduce plant growth by its osmotic effect and/or through toxic effects (Maas and Grattan, 1999; Pasternak, 1987) and reduce water permeability of root cell membranes (Mansour, 1997). Whereas the solute effect on root water uptake is considered to be a function of the total solute concentration or osmotic potential of the soil solution, uptake of speciﬁc nutrients will depend on the speciﬁc ion concentration in solution, but can be a function of total salinity as well. Moreover, solute interactions can occur in the soil through the soil’s cation-exchange capacity (CEC), making speciﬁc nutrient availability functionally dependent on other ions in solution. Mathematical models describing such interactions as between K+ and Na+ and Ca2+ and K+ were

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developed by Bouldin (1989) and Silberbush et al. (1993). Whereas Bouldin (1989) emphasized the importance of ion-exchange processes and the control of partial soil CO2 pressure on cation uptake, Silberbush et al. (1993) proposed a theoretical model for K+ uptake in saline soils, considering soil chemical ion-exchange and ion-speciﬁc uptake mechanisms, both active and passive, depending on ion concentrations while maintaining total ion charge neutrality. Another factor that requires attention is the apparent accumulation of salts at the root–soil interface, resulting in rhizophere salt concentrations much higher than those in the bulk soil. The salt accumulation or ﬁltering is caused by salt transport toward the roots by mass ﬂow through the soil. This is followed by preferential adsorption of speciﬁc nutrients by active uptake, thereby excluding most other salts at the root–soil interface or in the root apoplast. This salt buildup is expected to increase with transpiration rate, but is moderated by back diffusion into the soil or into the roots. Experimental evidence of salt accumulation was presented by Hamza and Aylmore (1992) from X-ray computed tomography and sodium microelectrode measurements around lupin and radish roots. The salinity buildup in the rhizosphere can lead to large osmotic pressure gradients across the roots, thereby effectively reducing root water uptake. We hypothesize that this rhizosphere effect may explain the failure of the additive stress concept. Speciﬁcally, it has been determined (Section VII.B.) that salinity stress cannot be predicted by simply adding the osmotic component to the soil water matric potential component in Eq. (21). To describe the salinity buildup and its effect on nutrient uptake, it is imperative that uptake be considered as a nutrient-speciﬁc process, and that the distinction is made between root uptake of the speciﬁc nutrient and total salinity. It is of further interest to note that nutrients and water may be taken up by different parts of the root system, so that salt accumulation may occur only at the active water uptake sites, while nutrients are taken up elsewhere within the rooting system (Stirzaker and Passioura, 1996).

C. MULTIDIMENSIONAL APPROACH Although many models (see Sections VI and VII) have been developed to simulate root growth and its interactions with soil water and nutrients, most of these models use simpliﬁed forms of the governing equations of soil water ﬂow and solute transport; most notably they are limited to one spatial dimension and assume steady-state ﬂow of water. Moreover, root uptake dynamics is usually related to measured distributions of root length density, ignoring uptake control by root surface area and root age. Consequently, these models will likely fail in predicting spatial variations and the dynamics of soil water–nutrient–plant growth interactions. An alternative is to characterize root water and nutrient uptake by a coupled dynamic approach, linking nutrient extraction to water uptake, controlled by the transient and locally variable supply of water and nutrients to the roots.

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As an example of this type of approach, van Noordwijk and van de Geijn (1996) speciﬁcally addressed the need for detailed root water and nutrient uptake models that include root growth and its response to changing local soil conditions such as water content, nutrient status, and mechanical impedance. They related water and nutrient stress to water use and biomass production. Local variations in water and nutrients occur naturally because of inherent large soil heterogeneity, but can also be imposed when using pressurized irrigation systems for fertigation purposes (Hagin and Lowengart, 1996). For such conditions, we must better understand the dynamics of changing patterns of nutrient and water availability and uptake. For example, roots can adjust their uptake patterns, thereby compensating for local stress conditions by enhanced or preferential uptake in other regions of the rooting zone with less stressful conditions. As a result, plants can temporarily deal with local stress and may be more effective in using water and nutrient resources under such conditions. Moreover, an improved understanding of these dynamic processes may provide guidelines in hot spot removal of speciﬁc toxic ions from soils as for bioremediation purposes (e.g., Ben-Asher, 1994). Preferential root uptake may minimize spatial variations in water and nutrients, thereby reducing drainage losses and chemical leaching below the rooting zone toward the groundwater. Mmolawa and Or (2000) pointed out that drip irrigation has an enormous potential to improve water and nutrient efﬁciency but that improper management may compound salinity problems and pollute groundwater resources. The main consideration in the management of pressurized irrigation systems is a priori knowledge of the interactions of irrigation method, soil type, crop root distribution, and uptake patterns and rates of water and nutrients or solutes. During water inﬁltration and redistribution, soil water content varies both spatially and temporarily, affecting soil solution concentration, composition, and spatial distribution by its control on mass ﬂow and diffusion of solutes, soil-exchange processes, and chemical reactions. Excellent contributions to the signiﬁcance of multidimensional treatment of water and nutrient transport in soils have been presented by Clothier and Sauer (1988),Green and Clothier (1995), and Clothier and Green (1997). The transport theory of Clothier and Sauer (1988) showed the prediction of ammonium and nitrate fronts, relative to the water fronts when using fertigation by a drip irrigation system. They also showed the negative consequences with prediction of a pH drop in the wetting zone under the emitter. The interaction of root water uptake and soil moisture and their spatial variations within the root zone of a kiwi fruit vine was demonstrated in Green and Clothier (1995). It was shown experimentally that following irrigation, preferential uptake of water shifted to the wetter parts of the soil within periods of days, away from the deeper drier parts of the root zone. Upon rewetting, plant roots recovered and showed enhanced activity by new root growth. A similar shifting of root water uptake patterns was observed by Andreu et al. (1997), using three-dimensional soil water content measurements around a dripirrigated almond tree. The derived three-dimensional water uptake for a 1-week period following irrigation is shown in Fig. 13. The water and chemical trapping

Figure 13 Three-dimensional root water uptake distribution during a 1-week drying period around an almond tree. [Reprinted from Agricultural Water Management 35, J. W. Andreu, J. W. Hopmans, and L. J. Schwanki, Spatial and temporal distribution of soil water balance for a drip-irrigated almond tree, 123–146, Copyright 1997, with permission from Elsevier Science.]

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Figure 14 Diagram linking spatial variations of active root water uptake sites to plant transpiration (Q). [Full credit (Kluwer Academic Publishers, Plant and Soil, Vol. 162 (No.8), p. 539, Roots: The big movers of water and chemical in soil, B. E. Clothier and S. R. Green, Fig, 2, Copyright 1997) is given to the publication in which the material was originally published, with kind permission from Kluwer Academic Publishers.]

mechanisms by roots were illustrated in Clothier and Green (1997), designating roots as “the big movers of water and chemical in soil.” In this uniquely wellwritten justiﬁcation for root–soil research, their Fig. 2 is reproduced in our Fig. 14. It shows that the overall functioning of the plant and its transpiration are controlled by the complicated variations in root water uptake rates along supply-active root segments within the whole root system. The challenge then is to integrate local uptake variations to total plant uptake, which requires a better understanding of the link between root architecture and morphology and the functioning of root water and nutrient uptake. Based on the analysis so far we conclude that a multidimensional approach should be developed to allow for analysis of the inﬂuence of multidimensional distribution of root water and nutrient uptake sites within the root zone on crop growth. In part, nutrient and water supply rates to the roots are controlled by diffusion and mass ﬂow induced by both spatial and temporal variations in soil water and nutrient status within the root zone. However, the extent and shape of the rooting system and their changes with time also play major roles in determining uptake patterns. Therefore, along with the characteristics of the soil nutrient supply,

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it is important to understand root growth dynamics and activity (van Noordwijk and de Willigen, 1991) as well as their spatial variability. This is caused by differences in root adsorption within the rooting zone as caused by root length or root area variations within and between soil layers, spatial variations in root–soil contact due to local soil moisture changes, and variations in root uptake as caused by root age and branching order. 1. Example of Multidimensional Approach It is only recently that multidimensional root water uptake models have been introduced (Coelho and Or, 1996; Vrugt, Hopmans et al., 2001). In the past few years, computing capabilities have signiﬁcantly improved the effectiveness of multidimensional soil water ﬂow models to study spatial and temporal patterns of root water uptake. A multidimensional approach in root water uptake is needed if uptake is varying in space thereby allowing a more accurate quantiﬁcation of spatial variability of the soil water regime, including water ﬂux densities below the rooting zone. As an example, Fig. 15 shows the predicted three-dimensional soil water content and root water uptake rates applying the three-dimensional root water uptake model in Eq. (17) of Section VII.A to measured time changes in water content for a sprinkler-irrigated almond tree (Koumanov et al., 1997), providing data similar to that presented in Fig. 13. Corresponding root water uptake parameters (as deﬁned in Eq. (17)) were obtained from inverse modeling (Vrugt, van Wijk et al., 2001), minimizing the residuals of measured and simulated water content values around the almond tree. Simulated water content values were obtained using the transient threeˇ unek et al., 1995) from which drainage dimensional HYDRUS-3D model (Sim˚ ﬂuxes below the rooting zone were computed. The effect of multidimensional root water uptake in an otherwise uniform soil can be illustrated by considering the resulting spatial variation in drainage ﬂux, when calibrated to the almond tree soil moisture data of Andreu et al. (1997). For example, Fig. 16 shows a detailed two-dimensional contour plot of the spatial variability of cumulative ﬂux density (mm) during the monitoring period of the data. Evidently, spatial variability of the drainage rate is large, with values increasing as corresponding root water uptake values decrease. Also, a variability analysis showed (Vrugt, van Wijk et al., 2001) that the spatial variation in drainage rate and root water uptake decreased signiﬁcantly when simplifying multidimensional soil water ﬂow and root water uptake to decreasing spatial dimensions. The increasing accurate spatial description of root water uptake and soil water ﬂow with increasing spatial dimension is essential to improve model predictions of water ﬂuxes and contaminant transport through the vadose zone. Moreover, the total chemical load to the groundwater will depend on local concentration and ﬂuxes and their spatial variability. Speciﬁcally, the actual chemical load can be much larger than

Figure 15 Simulated three-dimensional volumetric water content and potential root water uptake distributions at three times during the monitoring period (Vrugt, van Wijk et al., 2001).

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Figure 16 Two-dimensional contour plot of spatial variability in cumulative drainage at a soil depth of 0.55 m during the monitoring period (Vrugt, van Wijk et al., 2001).

the average chemical load, when computed from average ﬂux and concentration values using strictly one-dimensional simulations. For example, this whould be the case if the local regions in Fig. 14 with high drainage rates corresponded with high nutrient concentration values.

D. COMMENTARY In summary, it is clear that root transport is the result of various root membranes with distinct transport properties that can be nutrient and plant species dependent. Moreover, the formulation of an effective composite membrane allows one to capture the essential membrane characteristics that have been demonstrated under different experimental conditions. This coupled formulation allows the prediction of the experimentally measured decrease in xylem nutrient concentration with increased transpiration rate. It also considers the effect of active ion uptake on the hydraulic pressure gradient required for a given transpiration rate and accounts for

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the experimental evidence of the effects of nutrient concentration, active uptake, and transpiration rate on plant nutrient uptake. The nonlinear relationship between xylem matric potential and transpiration rate allows for temperature effects on active nutrient uptake (Baker et al., 1992). Root water uptake may lead to salt accumulation at the root–soil interface, resulting in rhizophere salt concentrations much higher than those in the bulk soil. This salt accumulation is caused by salt transport toward the roots by mass ﬂow through the soil, followed by the preferential adsorption of speciﬁc nutrients by active uptake, thereby excluding most other salts at the root–soil interface or in the root apoplast. The salinity buildup can lead to large osmotic pressure gradients across the roots with corresponding high salinity stress, thereby effectively reducing root water uptake much more than originally believed. To describe such salinity buildup and its effect on water and nutrient uptake, a distinction must be made between nutrient-speciﬁc concentration and total salinity. The coupled transport approach of water and nutrients is certainly more complicated than the much simpler uncoupled and passive uptake approach, but is necessary if we intend to progress our understanding and ability to improve predictive capabilities of crop growth models. Although its extrapolation to the whole three-dimensional root zone scale is yet to be fully tested and conﬁrmed, the coupling of water ﬂow with nutrient transport is needed to simulate plant response to stresses in water, nutrients, and salinity, and to predict the space and time distribution of soil solute concentrations that are controlled by the contribution of active nutrient uptake to total uptake. At the same time, the results of these multidimensional studies can be used to develop “simpler” models that capture the effective uptake behavior more correctly for their application in crop management and decision models.

X. COMPREHENSIVE EXAMPLE What follows now are suggestions of the types of water and nutrient uptake modeling that are needed to help us better understand soil–plant interactions, especially under conditions of limited water and nutrient supply. The presented example can be found in two of our research papers (Clausnitzer and Hopmans, 1994; Somma et al., 1998) and is extensively described in Somma et al. (1997). The ﬁnal result was a transient model for the simultaneous dynamic simulation of water and solute transport, root growth, and root water and nutrient uptake in three dimensions. The model includes formulation of interactions between plant growth and nutrient concentration, thus providing a tool for studying the dynamic relationships between changing soil–water, nutrient status, temperature, and root activity. The model presented offers the most comprehensive approach to date in

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Figure 17 Concept of comprehensive SPAC modeling. [Full credit (Kluwer Academic Publishers, Plant and Soil, Vol. 164, p. 309, Simultaneous modeling of transient three-dimensional root growth and soil water ﬂow, V. Clausnitzer and J. W. Hopmans, Fig. 7, Copyright 1994, with kind permission from Kluwer Academic Publishers.]

the modeling of the dynamic relationship between root architecture and the soil domain. The essential components of the soil–crop model are presented in Fig. 17. The convection–dispersion equation used for the simulation of nutrient transport was considered in its comprehensive form, thus allowing a realistic description of solute fate in the soil domain. Soil–water uptake was computed as a function of matric and osmotic potential, whereas absorption of nutrients by the roots was calculated as a result of passive and active uptake mechanisms. Uptake and respiration activities varied along the root axis and among roots as a result of root age. Genotype-speciﬁc and environment-dependent root growth processes such as soil moisture, nutrient concentration, and soil temperature were included using empirical functions. The water ﬂow and solute transport model used for the transient three-dimensional ﬂow and transport was described in Section VI.B. Here, we are mainly concerned with the dynamic growth of roots and the resulting water and nutrient uptake distributions. In concept, the modeling approach followed the requirement that plant transpiration and assimilation are directly coupled through

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the water use efﬁciency term (see Section III). The root and soil parameters of root length, surface area, age, soil water content, temperature, and nutrient concentration are computed within a priori selected volume elements at any desired temporal resolution. Root water and nutrient uptake was computed at the same time and space scales and were dynamically controlled by root and soil parameters under both unstressed and stressed conditions (soil resistance, temperature, water, and nutrient stress). In order to solve the ﬂow and transport Eqs. (9) and (11) in Section VI, the soil domain was discretisized into a rectangular grid of ﬁnite elements, each deﬁned by eight nodes, with the element size deﬁning the spatial resolution of the soil environment. Root growth, architecture, and age are simulated starting from a germinating seed that “grows” at user-deﬁned time intervals with new segments added to the apex of each growing root. The ﬂow and transport model was integrated with the root growth model (Fig. 18), allowing soil–plant–root interactions through water and nutrient uptake as a function of root properties (size and age) and soil properties (water content and nutrient concentration). Moreover, soil water

Figure 18 Discretization of soil domain. [Full credit (Kluwer Academic Publishers, Plant and Soil, Vol. 164, p. 300, Simultaneous modeling of transient three-dimensional root growth and soil water ﬂow, V. Clausnitzer and J. W. Hopmans, Fig. 1, Copyright 1994, with kind permission from Kluwer Academic Publishers.]

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content, resistance, nutrient concentration, and temperature affected root growth and architecture directly. The model tracks each segment by recording its topological position within the root system and its spatial location within the model domain, as well as its age, mass, and surface area. Root growth was simulated as a function of mechanical soil strength, soil temperature, and solute concentration. Root axes were generated at user-deﬁned times. Branching time and spacing were described by user-deﬁned functions of root age and branching order. A root growth impedance factor was calculated for each growing root apex as a function of the local soil strength, nutrient and temperature conditions at each time t to reduce the length of the growing segment from its potential (unimpeded) value. The impedance factor varies linearly between zero and unity (unimpeded growth). Consequently, root growth rates were unaffected by nutrient availability, as long as the latter were maintained within an optimal concentration range. Because the optimal range and minimum and maximum concentrations are both genotype and nutrient speciﬁc, nutrientconcentration effects were simulated using a piecewise linear impedance function, varying linearly between zero (c ≤ cmin or cmax ≤ c, no growth) and unity (optimal concentration range). In a similar manner, other impedance functions were deﬁned to simulate the effects of soil strength and soil temperature on local root growth. The sink term S(xj, ψm, ψo, t) in Eq. (9) describing root water uptake was computed at each time step from S(x j , ψm , ψo , t) = α (ψm , ψo , t) RDF(x, y, z, t) Tpot ,

(31)

where RDF(x, y, z, t) denotes the normalized nodal distribution of water uptake sites, as derived from root length or root area distribution or from the spatial distribution of root apices. When integrated over the root zone domain (RZ), its value is equal to 1, or R D F(x, y, z, t) =

β(x, y, z, t) . RZ β(x, y, z, t)

(32)

The localized form of the water-extraction function α (ψm, ψo, t), accounting for the local inﬂuence of soil water potential on root water uptake rate, included both the effects of soil water osmotic and matric potential on root water uptake, and uses van Genuchten (1987) α(x, y, z, t) =

[1 + (ψm /ψm,50

) p1 ]

1 , ∗ [1 + (ψ0 /ψ0,50 ) p2 ]

(33)

where ψm,50 and ψ 0,50 denote the soil water matric head and the osmotic head at which the uptake rate is reduced by 50%, respectively, and p1 and p2 are ﬁtting parameters, here both assumed to be 3 (van Genuchten and Gupta, 1993).

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Alternatively, if osmotic effects do not need to be considered, a simpliﬁed form of Eq. (33) such as the stress response function of Fig. 8 can be used. The distribution of potential root water uptake sites within the soil domain was lumped into nodal values of a function β(x, y, z, t). At any time, the β value of a particular node increases as the number of active root segments and their respective lengths within its neighboring elements increase, and the distances from the element nodes to the centerpoints of those segments decrease. To account for root-age effects on water uptake, piecewise linear weighting functions were deﬁned, which allow for variations in root water uptake for each branch segment depending on age and branching order. Depending on this weighting factor, whose values vary between unity and zero, each segment in the root system can fully contribute to uptake or is partially or totally excluded. Root nutrient uptake was lumped into nodal values of the sink term S (x, y, z, t) of Eq. (11), or, S = f 1 S c + f 2 A,

(34)

where f1 and f2 are partitioning coefﬁcients that distribute total nutrient uptake between passive and active uptake (terms S and A, respectively). Active nutrient uptake was considered to be described by the sum of MM uptake and a linear, diffusive uptake component term (Kochian and Lucas, 1982), or Jmax c + χ Rd , (35) A= Km + c where Jmax (ML−2 T−1) is the maximum nutrient uptake rate, Km (ML−3) is the Michaelis–Menten constant, Rd (L2 L−3) is the root area as computed from the cumulative root segment surface area within each volumetric element, and χ (LT−1) is the ﬁrst-order rate coefﬁcient allowing for a linear/diffusive uptake component. Little is known about the relative magnitudes of the partitioning between passive and active uptake ( f1 and f2); however, it is expected that they are plant and ion speciﬁc, whereas their values might depend on nutrient availability and plant nutrient demand or deﬁcit. For example, the active uptake contribution may be low if crop demand is low, whereas the contribution of active uptake may increase if either nutrient concentration in soil solution or transpiration rate is low. Therefore, instead of the Somma et al. (1998) approach, using a partitioning factor to quantify active nutrient uptake, we may choose to deﬁne potential active root nutrient uptake, Apot (M T−1), as Apot = J ∗, (36) RZ

so that the local maximum active uptake (Amax,i) is computed from Amax,i = RDFAi Apot

(37)

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where RDFAi deﬁnes the spatial distribution function of active nutrient uptake sites (L−3) between elements within the root zone. It is deﬁned as the RDF for water uptake in Eq. (32), but the relative spatial distribution of the nodal values may be different between water and nutrient uptake. Similarly, as was done for root water uptake, a reduced local active nutrient uptake (M L−3 T−1) can then be deﬁned, or Ai = α(?)i Amax,i ,

(38)

where the reduction function α(?) may be a function of soil temperature, pH, or other local environmental condition, and is plant and ion speciﬁc. Finally, by integration over the whole root zone domain, the total actual active nutrient uptake (Aact) is obtained. An example of the possible inﬂuence of NO3–N concentration on root growth is presented in Fig. 19, which was taken from Somma et al. (1998), assuming passive nitrate uptake only. Both water and NO3–N were supplied through a dripper at the soil surface. Figures 19a and 19b show the simulated root system grown under nonlimiting and deﬁcient NO3–N supply, respectively, at the end of the growth period (25 days). In both cases the soil water content was such that soil strength did not limit root growth. Root density is presented to the left of each root system, with the NO3–N concentration proﬁle is shown on the right. In the example of Fig. 19a, NO3–N was applied continuously with the irrigation water throughout the growth period (nonlimiting N case). The predicted N concentration was higher in the upper part of the soil domain. Similarly, the predicted root density decreased with increasing depth. In Fig. 19b, NO3–N was applied only during a limited time interval at the beginning of the growth period (deﬁcient N case), with the total amount applied equal to the nonlimiting case. Once N application stopped, the subsequent irrigations by the dripper moved the N plume downward, causing a greater root density in the central part of the root zone where the NO3–N content was higher. Indeed, the higher predicted root density in the center of the root system was fostered by the higher N amounts transported downward earlier, thus explaining the slight offset between root density and soil N. The downward movement of the N plume promoted root development at increasing depth, but resulted in a smaller average root density than for the nonlimiting N case. Complementary simulations that included water and nutrient uptake, for both the nonlimiting and N-limited case, also showed clearly the concomitant leaching of nitrate for the N-limited case. This was a result of the single early application of nitrate thereby limiting nutrient availability and potential nutrient uptake in the subsequent growing period. Although the multidimensional and mechanistic modeling approach appears attractive, it is limited by the need of much more additional soil and plant parameters. It is therefore that dedicated experiments, such as those presented in Andreu et al. (1997), are needed. Such data can then be effectively used to obtain soil and plant parameters for conceptual uptake models, as was done in Vrugt, van Wijk et al. (2001) by indirect estimation of three-dimensional

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root water uptake parameters using inverse modeling. It is suggested that a similar inverse approach may be used to improve the mechanistic description of nutrient uptake by roots, using dedicated facilities as the Wageningen Rhizolab (van de Geijn et al., 1994), or large fully instrumented lysimeters. With nitrates being the most problematic and widespread among potential groundwater contaminants (Canter, 1997; Keeney, 1989) in crop production, their uptake in relation to availability is especially important. The fate of NO3–N in cropping systems is determined by the interplay between nitriﬁcation, plant uptake, immobilization, denitriﬁcation, and mineralization, and is controlled by availability of soil microbes and soil organic carbon and their spatial distribution within the root zone. Most of the transformation processes of nitrogen compounds are fairly rapid and must be considered when nitrogen fate is studied. Since the degree of soil saturation and its variability partly govern these microbial processes, nitrate availability and leaching can be accurately predicted only if soil moisture processes are taken into consideration. However, the nitrogen cycle is a complex system, and simpliﬁcations in the experimental designs will be needed to accurately quantify nitrate uptake, its partitioning between passive and active uptake, and its spatial variability as determined by soil moisture, temperature, solution concentration, and root distribution.

XI. PROGNOSIS This ﬁnal section is a summary, speciﬁcally addressing the major ﬁndings and recommendations. In general, we found that water and nutrient uptake in plant growth and soil water ﬂow models is mostly described by empirical means, lacking a sound physiological or biophysical basis. This is unfortunate, as the exchange of water and nutrients is the unifying linkage between the plant root and surrounding soil environment. In part, the historical neglect of consideration of water and nutrient uptake processes below ground has led to a knowledge gap between plant responses to nutrient and water limitations and crop production, especially for conditions when soil water or nutrients are limiting.

Figure 19 Simulated three-dimensional root architecture with corresponding root density and nitrate concentration distribution for (a) nonlimiting and (b) deﬁcient nitrogen supply conditions. [Full credit (Kluwer Academic Publishers, Plant and Soil, Vol. 202, No. 2, p. 286, Transient threedimensional modeling of soil water and solute transport with simultaneous root growth, root water and nutrient uptake, F. Somma, J. W. Hopmans, and V. Clausnitzer, Fig. 2, Copyright 1998, with kind permission from Kluwer Academic Publishers.]

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r The simpliﬁed approach for description of water and nutrient uptake was adequate for unstressed plant growth conditions and may work adequately for uniform soil conditions. However, it has become increasingly clear that a different approach is required if water and/or nutrient resources become limited in part of the rootzone. Increasingly, recommended irrigation water and soil management practices tactically allocate both water and fertilizers, thereby maximizing their application efﬁciency and minimizing fertilizer losses through leaching toward the groundwater. Likely, sustainable agriculture will be directed at minimizing yield losses and crop quality, while keeping environmental side effects at acceptable levels. This current state of sustainable agricultural systems justiﬁes the increasing need for combining soil knowledge with plant expertise, in particular as related to root development and functioning. r We suggest that the effectiveness of these practices regarding their effects on crop production and groundwater quality requires a thorough understanding of plant–soil interactions and the plant’s regulatory functions in managing stresses. This includes knowledge of the crops responses to the availability of spatially distributed soil water and plant-available nutrients, using a multidimensional modeling approach. For crop growth modeling purposes, there must be a clear and intuitive understanding that plant transpiration and plant assimilation are physically connected by the concurrent diffusion of water vapor and carbon dioxide between the plant canopy and surrounding atmosphere through leaf stomata. Conceptually, assimilation and transpiration processes must be directly linked in both nonstressed and stressed soil environmental conditions. r This is achieved in crop growth modeling by introduction of a water use efﬁciency parameter, such as the transpiration coefﬁcient (TRC), deﬁned as the mass of water transpired per unit biomass produced. The driving force for water ﬂow in both soils and plants is the total water potential gradient, as caused by matric, gravity, and hydrostatic pressure forces. However, in contrast to soils, the osmotic component must always be considered for ﬂow through the roots, since water can move through cell membranes as a result of osmotic potential gradients. r For conditions of low water potentials, cavitation may cause embolisms in the xylem, thereby decreasing the axial conductance of water ﬂow through plants. However, water can bypass cavitated parts of the xylem by lateral movement to other water-conducting vessels. Moreover, as in soils, water can move through water ﬁlms along the xylem cell walls by surface forces, creating adsorption potential gradients.

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Water and nutrient transport in the root is mechanistically described by a set of coupled transport equations, describing simultaneous uptake of water and nutrient into the roots. In this approach, the soil and root system is simpliﬁed by a twocompartment system, separated by a single effective semipermeable membrane, separating the soil solution or apoplast from the cell solution or symplast. It has been shown in maize roots that water ﬂow induced by matric potential gradients is mainly apoplastic, whereas a major contribution to osmotic-induced ﬂow is the cell-to-cell or symplastic pathway. Measured hydraulic conductances between pathways can differ by 1 order of magnitude or more. Flow can be even more complex as water diffusion through membranes by osmotic gradients in one direction might cause matric potential and/or hydrostatic pressure potential gradients in the opposite direction. Within the xylem vessels and tracheids, water and solute ﬂow is likely by advection only, so that osmotic gradients will not have to be considered. r The mechanistic description of water ﬂow and nutrient transport through plant roots should consider this parallel transport through symplastic and apoplastic pathways. Also, discrimination between mechanisms of transport in the roots between water and nutrients may dictate differences between the spatial distribution of the main water and nutrient uptake sites within a rooting system, and their variation in time. Root water uptake has been described both at the microscopic and macroscopic levels. The microscopic approach requires details about root geometry and soil heterogeneity that is generally not available. In the macroscopic approach, a sink term, representing water extraction by plant roots is included in the dynamic water ﬂow equation, allowing spatially and temporally variable uptake as controlled by soil moisture and plant demand. r In this macroscopic approach it is possible to differentiate between apoplastic and symplastic ﬂow using the composite approach, implying pathwaydependent conductance and reﬂection coefﬁcient values. Moreover, in this composite approach, a distinction is made between water uptake by matric and osmotic water potential gradients. The biophysical mechanisms of water transport in roots include the role of aquaporins. These water channel proteins within cell membranes facilitate the passive movement of water across membranes by both pressure and osmotic gradients, thereby increasing their hydraulic conductance. r The presence of aquaporins in roots may explain the symplastic transport of water across the endodermis and the leakiness of semipermeable membranes. Moreover, they support the composite theory of water transport along parallel pathways.

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Within the general framework of the SPAC, we might have to reconsider the signiﬁcance of the plant-root resistance in relation to the atmospheric and soil resistances. Under wet-soil conditions, the largest hydraulic resistance occurs in the leaf with water vapor diffusion into the surrounding air controlled by atmospheric conditions. Under these conditions, plant transpiration is at its potential rate, independent of the ﬂow resistance of the plant, root, or soil. Transpiration is demand-controlled, rather than supply-controlled. As the soil is depleted of water, its ﬂow resistance increases, as controlled by the decreasing unsaturated soil hydraulic conductivity and possibly by the decreasing root–soil contact. r Hence, while under wet-soil conditions the maximum resistance for plant transpiration occurs in the leaf–atmosphere, the soil resistance becomes the dominant factor controlling plant transpiration under dry soil conditions. In either case, the plant or root resistance is not considered. Crop growth models generally assume little, or no, dynamics in nutrient uptake, considering changes in the total available nutrient pool of the rooting zone without discriminating between active and passive uptake. In contrast, dynamic water ﬂow and solute transport track spatial and temporal changes in water content, solute concentration, and water and solute ﬂuxes. However, these model types regard nutrient uptake solely as a passive process, computing nutrient uptake ﬂuxes from the product of water ﬂux density and soil solution concentration within predeﬁned small root zone volume elements with spatially distributed root densities r While reviewing the general literature on nutrient uptake by roots, it is indeed perplexing that uptake has been considered in so many different and occasionally opposing ways. Nutrient uptake by the roots can occur by diffusion, advection, and active uptake. Prediction of the relative contribution of the advective component requires knowledge of the partitioning between apoplastic and symplastic water uptake components of root water uptake. Active nutrient uptake is driven by speciﬁc energy-driven carriers and ion channels and requires the creation of electrochemical gradients across membranes by metabolically driven ion pumps. r In the macroscopic approach, active nutrient uptake and transport within the roots is considered a kinetic process, equivalent to that characterized by Michaelis– Menten type of enzyme kinetics. Also nutrient transport in roots is the result of various root membranes with distinct transport properties that can be nutrient and plant species dependent. The formulation of a single effective composite membrane allows one to capture the essential membrane characteristics that have been demonstrated under different experimental conditions.

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r Speciﬁcally, the coupled formulation of water and nutrient uptake accounts for the experimental evidence of the effects of nutrient concentration, active uptake, and transpiration rate on plant nutrient uptake. The coupled transport approach of water and nutrients is certainly more complicated than the much simpler uncoupled and passive uptake approach, but is necessary if we intend to progress our understanding and ability to improve predictive capabilities of crop growth models. Root water uptake may lead to salt accumulation at the root–soil interface, resulting in rhizosphere salt concentrations much higher than those in the bulk soil. This salt accumulation is caused by salt transport toward the roots by mass ﬂow through the soil, followed by preferential adsorption of speciﬁc nutrients by active uptake, thereby excluding most other salts at the root–soil interface or in the root apoplast. The salinity buildup can lead to large osmotic pressure gradients across the roots with corresponding high salinity stress, thereby effectively reducing root water uptake much more than originally believed. r To describe such salinity buildup and its effect on water and nutrient uptake, a distinction must be made between nutrient-speciﬁc concentration and total salinity. Knowledge of the concentration dependency of nutrient uptake is especially useful when optimizing N fertilization while minimizing environmental effects. Moreover, the intrinsic difference in uptake mechanisms between passive and active uptake leads to different nutrient concentrations in soil solution. r Moreover, a better understanding of ion-speciﬁc active root uptake is key to the development of effective strategies for the success of heavy metal removal in soils by phytoremediation. Although many models have been developed to simulate root growth and its interactions with soil water and nutrients, most of these are limited to one spatial dimension, and assume steady-state ﬂow of water. Moreover, root uptake dynamics is usually related to measured distributions of root-length density, ignoring uptake control by root surface area and root age. r Consequently, these models will likely fail in predicting spatial variations and the dynamics of soil water–nutrient–plant growth interactions. An alternative is to characterize root water and nutrient uptake by a coupled dynamic approach, linking nutrient extraction to water uptake, controlled by the transient and locally variable supply of water and nutrients to the roots. r Although the extrapolation of the coupled uptake to the whole three-dimensional root zone scale is yet to be fully tested and conﬁrmed, the coupling of water ﬂow with nutrient transport is needed to simulate plant response to stresses in water, nutrients, and salinity and to predict the space and time distribution of soil solute

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concentrations that is controlled by the contribution of active nutrient uptake to total uptake. At the same time, the results of these multidimensional studies can be used to develop “simpler” models that capture the effective uptake behavior more correctly for their application in crop management and decision models. In part, nutrient and water supply rates to the roots are controlled by diffusion and mass ﬂow induced by both spatial and temporal variations in soil water and nutrient status within the root zone. However, also the extent and shape of the rooting system and their changes with time play a major role in determining uptake patterns. Moreover, it has been shown that multidimensional root water uptake in an otherwise uniform soil can cause large drainage rate variability, with local values increasing as corresponding root water uptake values decrease. Variability analysis has demonstrated that the spatial variation in drainage rate and root water uptake decreased signiﬁcantly when simplifying multidimensional soil water ﬂow and root water uptake to decreasing spatial dimensions. r The increasing accurate spatial description of root water uptake and soil water ﬂow with increasing spatial dimension is essential to improve model predictions of water and contaminant ﬂuxes and total chemical load of plant nutrients to the groundwater. Although the multidimensional and mechanistic modeling approach appears attractive, it is limited by the need of much more additional soil and plant parameters. It is therefore that dedicated experiments are conducted. Such data can then be effectively used to obtain soil and plant parameters for mechanistic uptake models. r As was demonstrated, estimates of three-dimensional root water uptake parameters can successfully be obtained using inverse modeling. It is suggested that a similar approach may be used to improve the mechanistic description of nutrient uptake by roots, using dedicated facilities such as large fully instrumented lysimeters.

ACKNOWLEDGMENTS This research was made possible through a fellowship of the Land and Water Resources Research and Development Corporation (LWRRDC) and CSIRO Land and Water, Davies Laboratories in Townsville, Australia. We especially thank the reviewers, who collectively have given structure to the paper, thereby greatly improving its readability. They are Brent Clothier, Dani Or, and John Passioura. Also, ad hoc discussions on water potential concepts with Jacob Dane, Ken Kosugi, Dani Or, Ken Shackel, and Alex Globus helped to formulate Section II.B.

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Steudle, E. (1995). Tree under tension. Nature 378, 663–664. Steudle, E. (2000). Water uptake by roots: effects of water deﬁcit. J. Exp. Bot. 51, 1531–1542. Steudle, E., Oren, R., and Schulze, E-D. (1987). Water transport in Maize Roots. Plant Physiol. 84, 1220–1232. Steudle, E., and Heydt, H. (1988). An artiﬁcial osmotic cell: a model system for simulating osmotic processes and for studying phenomena of negative pressure in plants. Plant Cell Environ. 11, 629–637. Stirzaker, R. J., and Passioura, J. B. (1996). The water relations of the root-soil interface. Plant Cell Environ. 19, 201–208. Stoll, M., Loveys, B., and Dry, P. (2000). Hormonal changes induced by partial rootzone drying of irrigated grapevine. J. Exp. Bot. 51, 1627–1634. Tang, C., McLay, C. D. A., and Barton, L. (1997). A comparison of proton excretion of twelve pasture legumes grown in nutrient solution. Austr. J. Exp. Agric. 37, 563–570. Tanner, C. B., and Sinclair, T. R. (1983). Efﬁcient water use in crop production: Research or Re-Search. In “Limitations to Efﬁcient Water Use in Crop Production” (H. M. Taylor, W. R. Jordan, and T. R. Sinclair, Eds.), pp. 1–25. American Society of Agronomy, Madison, WI. Taylor, S. A., and Cary, J. W. (1964). Linear equations for the simultaneous ﬂow of matter and energy in a continuous soil system. Proc. Soil Sci. Soc. Am. 28, 167–172. Tester, M. (1990). Plant ion channels: whole cell and single-channel studies. New Phytol. 114, 305–340. Tuller, M., Or, D., and Dudley, L. M. (1999). Adsorption and capillary condensation in porous media: liquid retention and interfacial conﬁgurations in angular pores. Water Resour. Res. 35, 1949–1964. Tyerman, S. D., Bohnert, H. J., Maurel, C., Steudle, E., and Smith, J. A. C. (1999). Plant aquaporins: Their molecular biology, biophysics, and signiﬁcance for plant water relations. J. Exp. Bot. 50, 1055–1071. Tyerman, S. D., and Schachtman, D. P. (1992). The role of ion channels in plant nutrition and prospects for their genetic manipulation. Plant Soil 146, 137–144. Tyree, M. T. (1997). The cohesion-tension theory of sap ascent: current controversies. J. Exp. Bot. 48, 1753–1765. Tyree, M. T., Pantino, S., Bennink, J., and Alexander, J. (1995). Dynamic measurements of root hydraulic conductance using a high-pressure ﬂow meter in the laboratory and ﬁeld. J. Exp. Bot. 46, 83–94. Tyree, M. T., and Sperry, J. S. (1989). Vulnerability of xylem to cavitation and embolism. Annu. Rev. Plant Phys. Mol. Biol. 40, 19–38. van Dam, J. C., Huygen, J., Wesseling, J. G., Feddes, R. A., Kabat, P., van Walsum, R. E. V., Groenendijk, P., and van Diepen, C. A. (1997). “Theory of SWAP Version 2.0,” SC-DLO, Wageningen Agricultural University, Report 71. Department of Water Resources, The Netherlands. van de Geijn, S. C., Vos, J., Groenwold, J., Goudriaan, J., and Leffelaar, P. (1994). The Wageningen Rhizolab—A facility to study soil-root-shoot-atmosphere interactions in crops. I. Description of main functions. Plant Soil 161, 275–288. van den Honert, T. H., and Hooymans, J. J. M. (1955). On the absorption of nitrate by maize in water culture. Acta Botan. Neerlandica 4, 376–384. van der Honert, T. H. (1948). Water transport as a catenary pocess. Faraday Soc. Discuss. 3, 146–153. van Genuchten, M. Th. (1987). “A Numerical Model for Water and Solute Movement in and below the Root Zone,” Research Report No 121. U.S. Salinity Lab, ARS USDA, Riverside, CA. van Genuchten, M. Th., and Hoffman, G. J. (1984). Analysis of crop salt tolerance data. In “Soil Salinity under Irrigation. Ecological. Studies” (I. Shainberg and J. Shalhevet, Eds.), pp. 258–271. Springer-Verlag, Berlin/New York. van Genuchten, M. Th., and Gupta, S. K. (1993). A reassessment of the crop tolerance response function. Indian Soc. Soil Sci. 4, 730–737.

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van Keulen, H., and Seligman, N. G. (1987). “Simulation of Water Use, Nitrogen Nutrition and Growth of a Spring Wheat Crop.” Pudoc. Wageningen, The Netherlands. van Keulen, H., and van Laar, H. H. (1986). The relation between water use and crop production. In “Modeling of agricultural production: Weather, Soils and Crops” (H. van Keulen, and J. Wolf, Eds.), Pudoc. Wageningen, The Netherlands. van Noordwijk, M., and de Willigen, P. (1991). Root function in agricultural systems. In “Plant Roots and Their Environment” (B. L. McMichael, and H. Persson, Eds.), pp. 381–395. Elsevier Science, Oxford, UK. van Noordwijk, M., and van de Geijn, S. C. (1996). Root, shoot and soil parameters required for process-oriented models of crop growth limited by water or nutrients. Plant Soil 183, 1–25. Varney, G. T., and Canny, M. J. (1993). Rates of water uptake into the mature root system of maize plants. New Phytolog. 123, 775–786. Verburg, K, Ross, P. J., and Bristow, K. L. (1996). “SWIMv2.1 User Manual,” Divisional Report No. 130. Division of Soils, CSIRO, Australia. Viets, F. G. (1962). Fertilizers and the efﬁcient use of water. Adv. Agron. 14, 223–264. Vogel, T. (1987). “SWM II—Numerical Model of Two-Dimensional Flow in a Variably Saturated Porous Medium,” Research Report No. 87. Wageningen Agricultural Univ., The Netherlands. Volkmar, K. M. (1996). Effects of biopores on the growth and N-uptake of wheat at three levels of soil moisture. Can. J. Soil Sci. 76, 453–458. Vrugt, J. A., Hopmans, J. W., and Simunek, J. (2001). Two dimensional root water uptake model for a sprinkler-irrigated almond tree. Soil Sci. Soc. Am. J. 65, 1027–1037. Vrugt, J. A., van Wijk, M. T., Hopmans, J. W., and Simunek, J. (2001). Comparison of one, two, and three-dimensional root water uptake functions for transient water ﬂow modeling. Water Resour. Res. 37, 2457–2470. Walton, B. T., and Anderson, T. A. (1990). Microbial degradation of trichloroethylene in the rhizosphere: Potential application to biological remediation of waste sites. Appl. Environ. Biol. 56, 1012–1016. Wang, Y. P., and Leuning, R. (1998). A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy. I. Model description. Agric. For. Meteorol. 91, 89–111. Watt, M., McCully, M. E., and Canny, M. J. (1994). Formation and stabilization of rhizosheaths of Zea mays L. Plant Physiol. 106, 179–186. Weatherley, P. E. (1963). The pathway of water movement across the root cortex and leaf mesophyll of transpiring plants. In “The Water Relations of Plants” (A. J. Rutter, and F. H. Whitehead, Eds.), pp. 85–100. A symposium of the Britsh Ecological Society, London, 5–8 April, 1961. Blackwell Scientiﬁc, London. Wei, C., Steudel, E., and Tyree, M. T. (2000). Reply—Water ascent in plants. Trends Plant Sci. 5, 146–147. Whisler, F. D., Acock, B., Baker, D. N., Fye, R. E., Hodges, H. F., Lambert, J. R., Lemmon, H. E., McKinion, J. M., and Reddy, V. R. (1986). Adv. Agron. 40, 141–208. Whisler, F. D., Klute, A., and Millington, R. J. (1968). Analysis of steady state evapotranspiration from a soil column. Soil Sci. Soc. Am. Proc. 32, 167–174. Yanai, R. D. (1994). A steady-state model of nutrient uptake accounting for newly grown roots. Soil Sci. Soc. Am. J. 58, 1562–1571. Zimmerman, U., and Steudle, E. (1978). Physical aspects of water relations of plant cells. Adv. Bot. Res. 6, 45–117.

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MICRONUTRIENTS IN CROP PRODUCTION N. K. Fageria,1 V. C. Baligar,2 and R. B. Clark3 1

National Rice and Bean Research Center of EMBRAPA ˆ Santo Antonio de Goi´as-GO, 75375-000, Brazil 2 Alternate Crops and Systems Research Laboratory Beltsville Agricultural Research Center, USDA-ARS Beltsville, Maryland 20705 3 Appalachian Farming Systems Research Center, USDA-ARS Beaver, West Virginia 25813

I. Introduction II. Status in World Soils III. Soil Factors Affecting Availability A. pH B. Organic Matter C. Temperature, Moisture, and Light IV. Factors Associated with Supply and Acquisition A. Deﬁciencies and Toxicities B. Supply and Uptake C. Oxidation and Reduction D. Rhizosphere E. Interactions with Other Elements V. Improving Supply and Acquisition A. Soil Improvement B. Soil and Foliar Fertilization C. Plant Improvement D. Microbial Associations E. Improved Disease and Insect Resistance and Tolerance VI. Conclusion References

The essential micronutrients for ﬁeld crops are B, Cu, Fe, Mn, Mo, and Zn. Other mineral nutrients at low concentrations considered essential to growth of some plants are Ni and Co. The incidence of micronutrient deﬁciencies in crops has increased markedly in recent years due to intensive cropping, loss of top soil by erosion, losses of micronutrients through leaching, liming of acid soils, decreased proportions of farmyard manure compared to chemical fertilizers, increased purity of chemical fertilizers, and use of marginal lands for crop production. Micronutrient deﬁciency problems are also aggravated by the high demand of modern crop cultivars. Increases in crop yields from application of micronutrients have been reported in many parts of the world. Factors such as pH, redox potential, biological 185 Advances in Agronomy, Volume 77 Copyright 2002, Elsevier Science (USA). All rights reserved. 0065-2113/02 $35.00

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FAGERIA et al. activity, SOM, cation-exchange capacity, and clay contents are important in determining the availability of micronutrients in soils. Plant factors such as root and root hair morphology (length, density, surface area), root-induced changes (secretion of H+, OH−, HCO3−), root exudation of organic acids (citric, malic, tartaric, oxalic, phenolic), sugars, and nonproteinogenic amino acids (phytosiderophores), secretion of enzymes (phosphatases), plant demand, plant species/cultivars, and microbial associations (enhanced CO2 production, rhizobia, mycorrhizae, rhizobacteria) have profound inﬂuences on plant ability to absorb and utilize micronutrients from soil. The objectives of this article are to report advances in research on the micronutrient availability and requirements for crops, in correcting deﬁciencies and toxicities in soils and plants, and in increasing the ability of plants to acquire needed amounts C 2002 Elsevier Science (USA). of micronutrient elements.

I. INTRODUCTION Essential nutrients may be deﬁned as those without which plants cannot complete their life cycle, irreplaceable by other elements, and directly involved in plant metabolism. Based on the quantity required, nutrients are divided into macro- and micronutrients. Macronutrients are required in large quantities by plants compared to micronutrients. Micronutrients have also been called minor or trace elements, indicating that their concentrations in plant tissues are minor or in trace amounts relative to the macronutrients (Mortvedt, 2000 ). The essential micronutrients for ﬁeld crops are B, Cu, Fe, Mn, Mo, and Zn. The accumulation of these micronutrients by plants generally follows the order of Mn > Fe > Zn > B > Cu > Mo. This order may change among plant species and growth conditions (e.g., ﬂooded rice). Other mineral nutrients at low concentrations considered essential to the growth of some plants are Ni and Co. Convincing evidence exists to indicate that Ni is essential for certain plants (Brown et al., 1987; Eskew et al., 1983). Even though Co stimulates growth of certain plants, it is not considered essential according to the Arnon and Stout (1939) deﬁnition of essentiality. Cobalt is essential for the ﬁxation of N2 by bacteria, but is not required by higher plants (Ahmed and Evans, 1960; Marschner, 1995; Needham, 1983). Rhizobia and other N2-ﬁxing microorganisms have absolute Co requirements whether growing inside or outside root nodules regardless of N source (N2 ﬁxation or mineral N) (Marschner, 1995). Even so, Co is essential for animal nutrition as a component of vitamin B12 (Needham, 1983). Chlorine and Si have often been referred to as micronutrients, even though their concentrations in plant tissue are often equivalent to those of macronutrients. Chlorine will be considered in this article, but since recent reviews have appeared about Si (Epstein, 1994, 1999; Savant et al.,1997, 1999), this element will not be

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considered. Possibly, other essential micronutrients will be discovered in the future because of the recent advances in solution culture techniques and the availability of highly sensitive analytical instruments. Based on physicochemical properties, the essential plant micronutrients are metals except for B and Cl. Even though micronutrients are required in small quantities by ﬁeld crops, their inﬂuence is as important as the macronutrients in crop production. Except for B and Cl, the micronutrients are commonly constituents of prosthetic groups that catalyze redox processes by electron transfer such as with the primary transition elements Fe and Mn and to some extent Cu and Mo. Micronutrients normally form enzyme–substrate complexes (Fe and Zn) and/or enhance enzyme reactions by inﬂuencing molecular conﬁgurations between enzymes and substrates (Zn) (R¨omheld and Marschner, 1991). Micronutrient deﬁciencies in crop plants are widespread because of (i) increased micronutrient demands from intensive cropping practices and adaptation of high yielding cultivars which may have higher micronutrient demand, (ii) enhanced production of crops on marginal soils that contain low levels of essential nutrients, (iii) increased use of high analysis fertilizers with low amounts of micronutrient contamination, (iv) decreased use of animal manures, composts, and crop residues; (v) use of soils that are inherently low in micronutrient reserves, and (vi) involvement of natural and anthropogenic factors that limit adequate plant availability and create element imbalances. Plant acquisition of micronutrients is affected by numerous soil, plant, microbial, and environmental factors. Parent material, minerals containing micronutrients, and soil formation processes inﬂuence micronutrient availability to plants. Solid-phase materials are important in determining solubility relationships of nutrients in soils (Lindsay, 1991). Available micronutrients in soil are derived from weathering of underlying parent materials, natural processes (e.g., gases from volcanic eruption, rain/snow, marine aerosols, continental dust, forest ﬁres), and anthropogenic processes (industrial and automobile discharges, addition of fertilizers, lime, pesticides, manures, sewage sludges). Soil micronutrients exist in solid phases like primary minerals, secondary precipitates, and adsorbed on clay surfaces (Lindsay, 1991; Shuman, 1991). Soil adsorption reactions are important in determining the bioavailability of B, Cu, Mo, and Zn. Micronutrients in solid phases are not immediately available to plants. Only about 10% of micronutrients in soil are soluble and/or in exchangeable forms for plant acquisition (Lake et al., 1984). Fluctuating temperatures, moisture, and anthropogenic factors change micronutrient concentrations, forms, and distribution among various phases in soil. Soil pH, redox potential, and soil organic matter (SOM) profoundly affect the bioavailability of micronutrients (Stevenson, 1986; Tate, 1987). For most soils, soil SOM contains the largest pool of labile micronutrients in soil and inﬂuences micronutrient cycling, distribution of naturally occurring organic ligands, speciation and form (organic or inorganic) of elements in soil solution, and nature

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and stability of micronutrient complexes with humic and fulvic acids, especially with microbe conversion of SOM (Stevenson, 1991). The importance of SOM for inﬂuencing micronutrient retention follows the sequence of Cu > Zn > Mn (McGrath et al., 1988). Most metallic micronutrients in soil are complexed by both inorganic and organic ligands. Organic ligands act as carriers to plant roots (Lindsay, 1979), and Cu, Zn, and Mn form stable complexes, especially with carboxyl and phenolic groups, to make these minerals less available to plants (Stevenson, 1991). Organic substances like humic and fulvic acids formed in SOM degradation and transformation are also important in micronutrient cycling (Stevenson, 1986). Plant factors such as root and root hair morphology (length, density, surface area), root-induced changes (secretion of H+, OH−, HCO3−), root exudation of organic acids (citric, malic, tartaric, oxalic, phenolic), sugars, and nonproteinogenic amino acids (phytosiderophores), secretion of enzymes (phosphatases), plant demand, plant species/cultivars, and microbial associations (enhanced CO2 production, rhizobia, mycorrhizae, rhizobacteria) have profound inﬂuences on plant ability to absorb and utilize micronutrients from soil (Barber, 1995; Baligar and Fageria, 1997; Marschner, 1995). Macro- and micronutrients have long been recognized as being associated with changes in plant susceptibility or tolerance and resistance to diseases and pests (Engelhard, 1990; Graham and Webb, 1991). Even though research information on the mineral nutrition of plants has advanced signiﬁcantly in recent years, most of the advances have been associated with macronutrients. Reasons for this may have been that micronutrients are required in such small amounts, and their deﬁciencies have not been systematically veriﬁed under ﬁeld conditions. The objectives of this article are to report advances in research on the micronutrient availability and requirements for crops, in correcting deﬁciencies and toxicities in soils and plants, and in increasing the ability of plants to acquire needed amounts of micronutrient elements.

II. STATUS IN WORLD SOILS The amounts and distribution of micronutrients in soils are inﬂuenced by parent materials, levels and form of SOM, pH, Eh (oxidizing conditions), mineralogy, particle size distribution, soil horizon, soil age, soil formation processes, drainage, vegetation, and microbial, anthropogenic, and natural processes (Baligar et al., 1998; Stevenson, 1986; Tate, 1987). Micronutrient concentrations are generally higher in surface soil horizons (Ap) and decrease with soil depth. In spite of the relatively high total concentrations of micronutrients reported in soils on a global basis, micronutrient deﬁciencies have been frequently reported on many crops

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grown in various parts of the world (Cakmak, Sari et al., 1996; Fageria, 2000a; Galr˜ao, 1999; Graham et al., 1992; Grewal and Graham, 1999; Mandal and Mandal, 1990; Martens and Lindsay, 1990). It has been estimated that 3.95 billion ha of the world’s ice-free land area is subject to mineral stresses for plants, with 14% of this area being subject to potential micronutrient stresses (Gettier et al., 1985). The reasons for micronutrient deﬁciencies are that these elements have not usually been applied regularly to soils through fertilization. Furthermore, increased crop yields, loss of micronutrients through leaching, liming of soils, decreased use of manures compared to chemical fertilizers, and increased purity of chemical fertilizers without micronutrient additions have contributed to accelerated exhaustion of available micronutrients in soils. Hidden micronutrient deﬁciencies may be more widespread than has generally been suspected. Potential micronutrient deﬁciencies/toxicities associated with major soil groups (Table I), common soil

Table I Potential Micronutrient Deﬁciencies or Toxicities Associated with Major Soil Groupsa Element problem Soil order Andosols (Andepts) Ultisols Ultilsols/Alﬁsols Spodosols (Podsols) Oxisols Histosols Entisols (Psamments) Entisols (Fluvents) Mollisols (Aqu), Inceptisols, Entisols, etc. (poorly drained) Mollisols (Borolls) Mollisols (Ustolls) Mollisols (Aridis) (Udolls) Mollisols (Rendolls) (shallow) Vertisols Aridisols Alﬁsols/arid Entisols Alﬁsols/Utisols (Albic) (poorly drained) Alﬁsols/Aridisols/Mollisols (Natric) (high alkali) Aridisols (high salt)

Soil group

Deﬁciency

Andosol Acrisol Nitosol Podsol Ferralsol Histosol Arensol Fluvisol

B, Mo Most micronutrients

Gleysol Chernozem Kastanozem Phaeozem Rendzina Vertisol Xerosol Yermosol

Mn Fe, Mn, Zn Cu, Mn, Zn Fe, Mn, Zn Fe Fe, Zn Co, Fe, Zn

Planosol

Most micronutrients

Solenetz Solonchak

Cu, Fe, Mn, Zn

Most micronutrients Mo Cu Cu, Fe, Mn, Zn

Toxicity

Fe, Mn Mn Fe, Mn

Fe, Mn Fe, Mo

Mo

B, Cl

a Modiﬁed from Baligar and Fageria (1999); Clark (1982); Dudal (1976); S. W. Buol, North Carolina State University, Raleigh; H. Eswaren, USDA-NRCS, Washington, DC.

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FAGERIA et al. Table II Major Soil Minerals Containing Micronutrientsa

Element B

Cl

Cu

Fe

Mn

Mo

Zn

Ni

Co

a

Type Borates (hydrous) Borates (anhydrous) Complex borosilicates Sylvite Kainite Langbeinite Carbonates Oxides Simple sulﬁdes Complex sulﬁdes Carbonates Oxides Sulﬁdes Sulfates Carbonates Simple oxides Complex oxides Silicates Oxides Molybdates Sulﬁdes Carbonates Sulﬁdes Silicates Pentlandite Awaruite Cohenite Haxonite Nickel Cobaltite Skutterudite Erythrite

Mineral Borax—Na2B4O7·10H2O; Kernite—Na2B4O7·4H2O; Colemanite—Ca2B6O11·5H2O; Ulexite—NaCaB5O9·4H2O Ludwigite—Mg2FeBO5; Kotoite—Mg3(BO3)2 Tourmaline; Axinite KCl KCl; MgSO4·3H2O K2SO4·2MgSO4 Malachite—Cu2(OH)2CO3; Azurite—Cu3(OH)2(CO3)2 Cuprite—Cu2O; Tenorite—CuO Chalcocite—Cu2S; Covellite—CuS Chalcopyrite—CuFeS2; Bornite—Cu3FeS4; Digenite—Cu9S5; Enargite—Cu3AsS4; Tetrjedrote—Cu12Sb4S13 Siderite—FeCO3 Hematite—Fe2O3; Goethite—FeOOH; Magnetite—Fe3O4 Pyrite—FeS2; Pyrrhotite—Fe1–xS Jarosite—KFe3(OH)6(SO4)4 Rhodochrosite—MnCO3 Pyrolusite—MnO2; Hausmannite—Mn3O4; Manganite—MnOOH Braunite—(Mn, Si)2O3; Psilomelane—BaMg9O18·2H2O Rhodanate—MnSiO3 Ilsemanite—Mo3O8·8H2O Wulﬂenite—PbMoO4; Powellite—CaMoO4; Ferrimolybdite—Fe2(MoO4)·8H2O Molybdenite—MoS2 Smithsonite—ZnCO3 Sphalerite—ZnS Hemimorphite—Zn4(OH)2Si2O7·H2O (Fe, Ni)9S8 Ni3Fe (Fe,Ni)3C (Fe,Ni)23C6 Ni CoAsS CoAs2–3 Co3(AsO4)·8H2O

From Chesworth (1991), Dana and Dana (1997), Krauskopf (1972), and Mortvedt (2000)

minerals containing various micronutrient elements (Table II), and concentration ranges of micronutrients in soils and plants (Table III) have been provided to help deﬁne where micronutrient problems might occur. Concentrations of B in soils range from about 2 to 100 mg kg−1 (mean of 10 mg kg−1) and generally occurs as H3BO3/B(OH)3 (Goldberg, 1993). Soils

Table III Essential Micronutrients for Plant Growth, Principal Forms Absorbed, Concentration Ranges in Plants and Soils, and Persons Demonstrating Essentiality in Plants Concentration range in plantsa (mg kg−1)

Concentration in soilb,c (mg kg−1)

Element

Form absorbed

Critical

Sufﬁcient

Toxic

B Cl Cu

H3BO3; BO3−; B4O72− Cl− Cu2+

<10 <2000 3–5

10–100 2000–20000 5–20

50–200 >20000 20–100

Fe Mn Mo Zn Ni Co

Fe2+; Fe3+ Mn2+ MoO42− Zn2+ Ni2+ Co2+

<50 10–20 <0.1 15–20 1.0–5 <0.2

50–250 20–300 0.1–0.5 20–100 0.1–5 0.2–0.5

>1000 300–500 10–50 100–400 10–100 15–50

a

Bennett (1993) and McBride (1995). Alloway (1995a) (critical level above which toxicity is likely). c Kabata-Pendias and Pendias (1992). d Marschner (1995). b

Normal 2–150 20–900 2–250 200–500,000 7–10,000 0.1–40 1–900 0.4–1000 0.1–70

Critical total

Demonstration of essentialityd

15–25 70–200 60–125

K. Warington(1923) T. C. Broyer et al. (1954) A. L. Sommers, C. B. Lipman, and G. MacKinney (1931) J. Sachs (1860) Knop. J. S. McHargue (1922) D. I. Arnon and P. R. Stout (1939) A. L. Sommers and C. B. Lipman (1926) P. H. Brown et al. (1987)

1000–3000 2–13 70–400 100 25–50

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FAGERIA et al.

formed from igneous rock contain less B than soils formed from marine sediments. Soils derived from granite, igneous, and acidic rocks and metamorphic sediments are often poor in B (Gupta, 1979). Low B soils are usually strongly weathered (Acrisols, Podzols, Ferralsols), coarse textured (Arenosols), and shallow (Lithosols) (Shorrocks, 1997). In acidic rocks and metamorphic sediments, B occurs in tourmaline minerals and is not readily available to plants. Boron adsorption usually increases with increasing soil solution pH, temperature, ionic strength, and nature of adsorbed cations (Goldberg, 1993, 1997). The amount of B adsorbed in ﬁne-textured soils usually increases with enhanced clay contents. For example, montmorillonitic clays normally adsorb greater amounts of B than illitic clays (Goldberg and Glaubig, 1986). Competitive anion effects on B adsorption increased in the order of P > Mo > S even though the competitive effect was low, indicating that B adsorption sites are generally speciﬁc for B (Goldberg, 1997; Goldberg, Forster, and Lesch et al., 1996). The B-adsorbing surfaces in soils are commonly Al and Fe oxides, Mg hydroxides, clay minerals, Ca carbonates, and organic matter (OM). The distribution of B between soil solution and adsorption surfaces is affected by clay mineral types, content, and speciﬁc surface areas, mineralogy of sand/silt fractions, sesquioxides, SOM content, pH, ions on exchange sites, and salinity (Elrashidi and O’Conner, 1982; Evans and Sparks, 1983; Gupta et al., 1985). These soil factors also affect the retention of B in soils (Gupta, 1993). The availability of B is commonly reduced in soils high in Al oxides (Bingham et al., 1971) as well as in volcanic ash soils (Sillanp¨aa¨ and Vlek, 1985). In soil, B is normally present as nonionized molecules and easily lost by leaching. In arid and semiarid regions particularly, B toxicity can be of major concern (Gupta, 1979). Chlorine is ubiquitous in soils and occurs in aqueous solutions such as Cl−. Soil Cl is not tightly held by soil-exchange sites and is readily leached. Chlorine is commonly added to soil with manures and fertilizers(KCl), rainfall, sea spray, and irrigation waters (Needham, 1983). Copper is mostly found in silt and clay fractions of soil and usually present in carbonate fractions in alkaline soils and in Fe oxide fractions in acid soils (Shuman, 1991). Concentrations of Cu in soils range from about 2 to 100 mg kg−1 (mean of 30 mg kg−1) (Mortvedt, 2000). Crops grown in soils developed from sand, sandstones, acid igneous rocks, and calcareous materials often exhibit Cu deﬁciency, but deﬁciencies are not generally found on plants grown in clays and in soils formed from basic rocks (Jarvis, 1981a). In the United States, soils formed from weathered bed rocks have high Cu, whereas soils formed in the lower Atlantic coastal plains have low Cu (Kubota, 1983). Organic, peat, and muck soils generally have low amounts of labile Cu (Oplinger and Ohlrogge, 1974). When histosols are brought under cultivation, plants commonly exhibit Cu deﬁciency, which has been termed as a “reclamation disease” (Welch et al., 1991). Iron is the most abundant of the micronutrients in the lithosphere (Mortvedt, 2000). Soil concentrations of Fe range from 7000 to 500,000 mg kg−1 (mean of

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38,000 mg kg−1 or 3.8% in soil) (Lindsay, 1979). Most Fe in the Earth’s crust is in the form of ferromagnesium silicate. Iron is precipitated as Fe oxides or hydroxides during weathering, and small fractions of Fe are incorporated into secondary silicate materials (Schwertmann and Taylor, 1977). Iron deﬁciency occurs commonly on plants grown in calcareous and noncalcareous coarse-textured soils, especially in arid/semiarid regions. However, Fe deﬁciency can also occur on plants grown in acid soils. About 4.8 million ha of land west of the Mississippi river in the United States (intermountain region) is prone to Fe deﬁciency in “Fe-inefﬁcient” crops (Mortvedt, 1975). Alkaline, calcareous, and acidic sandy soils in Florida have also been prone to Fe deﬁciency on citrus (Welch et al., 1991). Iron deﬁciency has also been closely related to Ca carbonate equivalency and soluble salts in soil (Franzen and Richardson, 2000). High soil pH, SOM, CaCO3, HCO3−, and Ca contents have also been related to decreased Fe acquisition in some plants (K¨oseoglu, 1995). Iron deﬁciency also occurs in various regions of Europe, east India, Bangladesh, and in most Mediterranean and west African countries (Welch et al., 1991). Low Fe soils and Fe-deﬁcient crops have been reported for certain areas of Malta, Turkey, Zambia, and Mexico (Sillanp¨aa¨ , 1982), Indonesia (Katyal and Vlek, 1985), several Central and South American countries (Leon et al., 1985), and in south Australia, Victoria, and western Australia (Donald and Prescott, 1975). Excess Fe (toxicity) has been reported on rice grown under ﬂooded conditions in acid soils of China, Vietnam, Thailand, Burma, Bangladesh, Sri Lanka, Malaysia, Phillippines, and Indonesia (Vose, 1982). Kang and Osiname(1985) also reported Fe toxicity on plants grown in the acid soil belt of equatorial Africa, which includes Senegal, Gambia, Liberia, and Sierra Leone. Manganese is the 10th most abundant element in the Earth’s crust. Soil Mn concentrations range from about 20 to 3000 mg kg−1 (mean of 600 mg kg−1) (Lindsay, 1979). Soil Mn appears in primary and secondary minerals, is sorbed onto mineral and OM surfaces, and incorporated into soil organisms and in soil solution. Soils derived from crystalline shales and acid igneous rocks have low reducible Mn, and soils derived from basalt, limestone, and shale commonly have high Mn (Glinski and Thai, 1971). High extractable Mn has been reported for Inceptisols and Vertisols and low extractable Mn has been reported for Ultisols and Oxisols (Lombin, 1983). Labanouskas (1966) and Reuter et al. (1988) grouped the world soils with less than adequate levels of available Mn as (i) shallow, peaty, marsh, and alluvial soils developed from calcareous parent materials; (ii) calcareous soils with poor drainage and high OM, calcareous black sands, and calcareous grassland soils recently brought into cultivation; (iii) soils occurring over limed and reclaimed acid heath soils; and (iv) sandy acid soils containing low native Mn. Manganese deﬁciency has been reported for plants grown in coarse-textured and poorly drained coastal plains soils of the United States (Reuter et al., 1988) and in soils of Central America, Bolivia, and Brazil (Leon et al., 1985). In Europe, Mn deﬁciency has been reported for plants grown in peaty (England and Denmark),

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coarse-textured (Sweden and Denmark), coarse/ﬁne-textured (Netherlands), and podzolic and brown forest (Scotland) soils (Welch et al., 1991). Manganese deﬁciency has also been reported on plants grown in semiarid regions of China, India, southeast and western Australia, Congo, Ivory Coast, Nigeria, and other western African countries. Manganese toxicity on crop plants grown in many parts of the world has been reported to be more important than Mn deﬁciency (Foy, 1984; Welch et al., 1991). Molybdenum is the least abundant of the micronutrients in the lithosphere (Mortvedt, 2000), and soil concentrations range from about 0.2 to 5 mg kg−1 (mean of 2 mg kg−1). Plants exhibiting Mo deﬁciency usually occur on plants grown in broad areas of acid well-drained soils and in soils formed from parent materials low in Mo. In Australia, Mo deﬁciency occurred on crops grown in soils derived from sedimentary rocks, basalts, and granites (Anderson, 1970). Peaty, alkaline, and poorly drained soils commonly have high Mo. Iron oxides adsorb more Mo than Al oxides (Jones, 1957), and Mo adsorption on clays followed the sequence of montmorillonite > illite > kaolinite (Goldberg, Forster et al., 1996). Hydrous ferric oxides or ferric oxide molybdate complexes and insoluble ferric molybdates may form in well-aerated soils so that Mo solubility and availability to plants are low (Welch et al., 1991). In poorly drained soils, formation of soluble ferrous molybdates or molybdites may lead to high Mo availability to plants. Plants grown in high Mo soils of the intermountain valleys of western United States have been reported to accumulate high Mo which has induced “molybdenosis” (Cu deﬁciency) in cattle (Welch et al., 1991). Zinc deﬁciency is a worldwide nutritional constraint for crop production. About 50% of soils used for cereal production in the world contain low levels of plantavailable Zn, which reduces not only grain yield but also nutritional grain quality (Graham and Welch, 1996). Total Zn concentrations in soils range from about 10 to 300 mg kg−1 (mean of 50 mg kg−1)(Lindsay, 1979). Zinc-deﬁcient soils occur in both tropical and temperate regions, but are widespread in Mediterranean countries like Turkey (Cakmak et al., 1997), and in New South Wales, Queensland, and western and south Australia (Donald and Prescott, 1975; Sillanp¨aa¨ and Vlek, 1985). In China, Zn deﬁciency has been reported on plants grown in calcareous, desert, and paddy soils along the Yangtze river (Takkar and Walker, 1993). In Africa, Zn deﬁciency has been observed on plants grown in Alﬁsols and Ultisols (Cottenie et al., 1981) and in low Zn soils of Niger, Guinea, Ivory Coast, Sierra Leone, Sudan, and Zimbabwe, which has often been induced by lime additions to increase soil pH to near 7. In Asia, Zn deﬁciency is common for plants grown in arid and semiarid soils (Katyal and Vlek, 1985; Welch et al., 1991). Zinc deﬁciency in the United States has occurred mostly in plants grown in sandy, well-drained acid soils, and in soils formed from phosphate rock parent materials of the southeast. In the Cerrado soils of Brazil (Oxisols and Ultisols), Zn deﬁciency is widespread (Fageria, 2000b; Lopes and Cox, 1977).

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Serpentine (ultramaﬁc) soils are usually high in Ni, Co, Fe, and Mg, but low in Ca. Nickel levels in soils are usually adequate to provide plant needs. No evidence of Ni deﬁciency for soil-grown plants has been reported (Dalton et al., 1985), but Ni toxicity has been of concern for plants grown in soils receiving industrial wastes (sewage sludges, by-products) (Marschner, 1995). Cobalt deﬁciency has been reported for ruminant animals grazing forages grown in soils low in Co such as New Zealand, south and western Australia, The Netherlands, and the United States (Michigan and northeastern states) (Miller et al., 1991). Cobalt is adsorbed on Mn oxides, and liming tends to reduce Co availability to plants.

III. SOIL FACTORS AFFECTING AVAILABILITY Many soil factors such as pH, SOM, temperature, and moisture affect the availability of micronutrients to crop plants. The effects of these factors vary considerably from one micronutrient to another as well as in their relative degree of effectiveness. The availability of micronutrients is largely controlled by the same soil factor(s) where good correlations exist between plant concentrations of two or more micronutrients. The relationships associated with each of the many soil factors are complicated, even though correlations between many factors can be explained with relatively high certainty. A good example of this is the highly signiﬁcant negative correlation between Mo and Mn. The availability of both Mo and Mn is so strongly affected by soil pH that the other factors are of limited value. While Mn in plants decreases extensively with increasing soil pH, Mo increases, and deﬁciencies of both Mn and Mo are not expected or do not usually occur in the same soil. Manganese deﬁciency is often combined with excess Mo and vice versa (Sillanp¨aa¨ , 1982). Copper, Mn, and Zn were predominantly in organically bound forms in Spodosols of Florida, whereas these elements were organically bound and associated with Mn oxides and amorphous forms in Alﬁsols and Entisols (Zhang et al., 1997a). Available concentrations of Co, Cu, Ni, and Zn increased with increased amounts of clay (Lee et al., 1997).

A. pH Soil pH inﬂuences solubility, concentration in soil solution, ionic form, and mobility of micronutrients in soil, and consequently acquisition of these elements by plants (Fageria, Baligar and Edwards, 1990; Fageria, Baligar, and Jones, 1997). As a rule, the availability of B, Cu, Fe, Mn, and Zn usually decreases, and Mo increases as soil pH increases. These nutrients are usually adsorbed onto sesquioxide soil surfaces. Table IV summarizes important changes in micronutrient concentrations

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FAGERIA et al. Table IV Inﬂuence of Soil pH on Micronutrient Concentrations in Soil and Plant Uptakea

Element B Cl

Inﬂuence on concentration/uptake Increasing soil pH favors adsorption of B. This element generally becomes less available to plants. Availability and uptake of B decrease dramatically at pH > 6.0. Chloride is bound tightly by most soils in mildly acid to neutral pH soils and becomes negligible to pH 7.0. Appreciable amounts can be adsorbed with increasing soil acidity, particularly by Oxisols and Ultisols, which are dominated by kaolinitic clay. Increasing soil pH generally increases Cl uptake by plants.

Cu

Solubility of Cu2+ is very soil pH dependent and decreases 100-fold for each unit increase in pH. Plant uptake also decreases.

Fe

Ferric (Fe3+) and ferrous (Fe2+) activities in soil solution decrease 1000-fold and 100-fold, respectively, for each unit increase in soil pH. In most oxidized soils, uptake of Fe by crop plants decreases with increasing soil pH.

Mn

The principal ionic Mn species in soil solution is Mn2+, and concentrations decrease 100-fold for each unit increase in soil pH. In extremely acid soils, Mn2+ solubility can be sufﬁciently high to induce toxicity problems in sensitive crop species.

Mo

Above soil pH 4.2, MoO42− is dominant. Concentration of this species increases with increasing soil pH and plant uptake also increases. Water-soluble Mo increases sixfold as pH increases from 4.7 to 7.5. Replacement of adsorbed Mo by OH− is responsible for increases in water-soluble Mo as soil pH increases. Zinc solubility is highly soil pH dependent and decreases 100-fold for each unit increase in pH, and uptake by plants decreases as a consequence.

Zn Ni

Co

a

Ni2+ is relatively stable over wide ranges of soil pH and redox conditions. However, availability is usually higher in acidic than in alkaline soils. At pH 7 and higher, retention and precipitation increase. Increasing the pH of serpentine soils through liming from 4 to 7 reduced Ni in plant tissue. Solubility and availability of Co decrease with extreme soil pH. Presence of CaCO3, and high Fe, Mn, SOM, and moisture.

Adriano (1986), Fageria, Baligar, and Jones (1997), and Tisdale et al. (1985).

as inﬂuenced by soil pH and consequent acquisition by plants. Table V has been provided to show acquisition of Cu, Fe, Mn, and Zn by rice grown at various soil pH values. Boron is the only micronutrient to exist in solution as a nonionized molecule over soil pH ranges suitable for the growth of most plants. Increasing soil pH decreases B availability by increasing B adsorption onto clay and Al and Fe hydroxyl surfaces, especially at high soil pH (Keren and Bingham, 1985). The highest availability of B was at pH 5.5–7.5, and the availability decreased below or above this pH range. In other studies, B adsorption increased from pH 3 to 8 on kaolinite,

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MICRONUTRIENTS IN CROP PRODUCTION Table V Inﬂuence of Soil pH on Acquisition of Cu, Fe, Mn, and Zn by Upland Rice Grown in an Oxisol of Brazila Soil pH

Cu (μg plant−1)

Fe (μg plant−1)

Mn (μg plant−1)

Zn (μg plant−1)

4.6 5.7 6.2 6.4 6.6 6.8

75 105 78 64 61 51

4540 1860 1980 1630 1660 1570

11,160 5,010 4,310 3,610 2,760 2,360

1090 300 242 262 163 142

r2

0.89b

0.97c

0.99c

0.98c

a

Fageria (2000c). P < 0.05. c P < 0.01. b

montmorillonite, and two arid zone soils with peak adsorption at pH 8–10 and decreases from pH 10 to 12 (Goldberg, Forster, Lesch et al., 1996). Reduced B availability occurs from liming (called “B ﬁxation”)(Fleming, 1980) as CaCO3 acts as an adsorption surface. As such, B deﬁciency may occur in plants grown in limed acid soils. Chloride is bound only lightly by most soil-exchange sites in acid to neutral soils and becomes negligible to pH 7.0. Chloride is easily leached from soil. Considerable soil Cu is speciﬁcally adsorbed as pH increases. For example, increasing the pH from 4 to 7 increased Cu adsorption (Cavallaro and McBride, 1984), and Cu was adsorbed on inorganic soil components and occluded by soil hydroxide and oxides (Martens and Westermann, 1991). Increases in soil pH above 6.0 induces hydrolysis of hydrated Cu which can lead to stronger Cu adsorption to clay minerals and OM. Readily soluble sources of Cu (exchangeable or sorbed) were highly toxic to citrus, and Cu concentrations decreased considerably with soil pH increases above 6.5 (Alva et al., 2000). Over-liming acid soils may also lead to Cu deﬁciency. SOM is a primary constituent for Cu adsorption and readily complexes Cu. As the pH increases, the sizes of organic colloids of high molecular weight diminish, thus increasing the surfaces where Cu can be adsorbed (Geering and Hodgson, 1969). The solubility of Fe decreases by ∼1000-fold for each unit increase of soil pH in the range of 4 to 9 compared to ∼100-fold decreases in the activity of Mn, Cu, and Zn (Lindsay, 1979). Iron exists in Fe0 (metallic), Fe2+ (ferrous), and Fe3+ (ferric) forms. Under acidic conditions, Fe0 readily oxidizes to Fe2+, and Fe2+ oxidizes to Fe3+ as the pH increases above 5. Ferric Fe (Fe3+) is reduced to Fe2+ and is readily

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available to plants in acidic soils, but precipitates in alkaline soils. Iron oxides are dominant in governing Fe solubility in soils. Minimum Fe solubility occurs between pH 7.5 and 8.5, which is the pH range of many calcareous soils (Lindsay, 1991). The increases in soil pH or Eh shift Fe from exchangeable organic forms to water-soluble and Fe oxide forms. The solubility of Fe in well-aerated soils is controlled by dissolution and precipitation of Fe3+ (Moraghan and Mascagni, 1991). Decreasing rhizosphere pH with added N (NH4–N) and/or K (KCl and/or K2SO4) was effective for increasing Fe uptake by plants (Barak and Chen, 1984). Applying FeSO4 with acid-forming fertilizer also increased Fe availability to plants (Moraghan and Mascagni, 1991). Soil pH affects solubility, adsorption, desorption, oxidation of Mn, and reduction of Mn oxides in soil. As the pH decreases, Mn is mobilized from various fractions and increases Mn soil solution concentrations and availability. Exchangeable Mn (plant available form) was high at low soil pH (<5.2), while organic and Fe oxide fractions of Mn (low availability form) were high at high pH (Sims, 1986). In sandy soil, increasing pH also increased organic fractions of Mn (Shuman, 1991). Increasing soil pH with Mg applications on peanut decreased Mn toxicity and leaf and stem Mn concentrations (Davis, 1996). The reduction of Mn4+ to Mn2+ is greatest at low soil pH, and acid soil conditions (<5) lead to Mn toxicities for many sensitive plant species (Mortvedt, 2000). In addition, high-molecular-weight organic colloids diminish as soil pH increases to increase surfaces where Mn as well as Cu and Fe can be adsorbed (Geering and Hodgson, 1969). Soil solution Mn increased 1.6-fold for each unit decrease in pH in a well-drained Mollisol acidiﬁed with high N fertilizer, indicating that soil acidity and aeration are important for Mn availability (Fageria and Gheyi, 1999). Manganese, Cu, and Fe are generally more available under conditions of restricted drainage or in ﬂooded soils (Ponnamperuma, 1972). Molybdenum is the only micronutrient whose availability normally increases with increases in soil pH. The active form of Mo is normally MoO42−, which tends to polymerize when in solution. This condition is enhanced by acidiﬁcation which could partially explain the low availability of Mo in some acid soils (KabataPendias and Pendias, 1984). The solubility of CaMoO4 and H2MoO4 (molybdic acid) increases with increases in soil pH. Molybdenum sorption on Fe oxides increased with decreases in soil pH in the range of 7.8 to 4.5 (Hodgson, 1963). Adsorption of Mo on Al and Fe oxides was maximum at pH <5, and decreased as the pH increased >5 with little or no adsorption at pH 8 (Goldberg, Forster, and Godfrey, 1996). Soil pH had pronounced effects on Mo adsorption between 3 and 10.5 with virtually no adsorption at pH 8 (Goldberg and Foster, 1998). Adsorption of Mo on hydrous Fe and Al oxides decreased as soil pH increased, and the addition of lime to soil normally increased Mo solubility and a cquisition by plants (Williams and Thornton, 1972). In addition, maximum Mo adsorption on Al and Fe oxides was at pH 4–5, but adsorption was maximum at pH 3.5 with

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humic acid and decreased as soil pH increased (Biback and Borggaard, 1994). Different mechanisms were apparent for Mo adsorption with humic acid compared to Al/Fe oxides, which involved complex formation between carboxyl and phenolic groups. Harmful effects occasionally arise for legumes grown in acid soils, as Mo deﬁciency may be more dominant than Al toxicity (Bohn et al., 1979). In some cases, both lime and Mo applications may be needed to provide adequate Mo to plants (Lindsay, 1991). Soil pH is more important than any other single property for controlling Zn mobility in soils (Anderson and Christensen, 1988). Increasing soil pH generally decreased Zn availability to plants (Saeed and Fox, 1977), and such decreases were usually due to higher adsorption of Zn. As soil pH increases above pH 5.5, Zn is adsorbed on hydrous oxides of Al, Fe, and Mn (Moraghan and Mascagni, 1991). However, the extent to which Zn is retained on Fe and Al hydrous oxides is inﬂuenced by the nature of clay minerals, surface conditions, and pH (Harter, 1991). In some cases, a soil pH higher than 7 may increase soil solution Zn due to solubilization of OM and also forms Zn(OH)+ and increased complexation of Zn with a lower positive charge (Barber, 1995). Gradual decreases in Zn activity as soil pH increases have been attributed to increased cation-exchange capacity (Stahl and James, 1991). Thirtyfold decreases in Zn concentration in acid soil have been reported for each unit increase in soil pH between 5 and 7 (McBride and Blasiak, 1979). Zinc was preferentially adsorbed over Cu on exchange sites indicating that chemisorption of hydrolyzed Zn occurs. Zinc adsorption is a major factor contributing to low concentrations of solution Zn in Zn-deﬁcient soils. Soil pH affected Zn adsorption either by changing the number of sites available for adsorption or by changing the concentration of Zn species that is preferentially absorbed by plants (Barrow, 1986). Over-liming of soil may induce Zn deﬁciency and decrease Zn availability, especially at a high soil pH. Zinc absorption by wheat decreased as H+ concentrations increased, presumably because of the direct effects of H+ toxicity and the indirect effects of competition between Zn2+ and H+ for uptake sites on root surfaces (Chairidchai and Ritchie, 1993). The effect of pH may also be modiﬁed by organic ligands, and these ligands may decrease Zn uptake by plants as soil pH increases. Zinc deﬁciency may be expected in slightly acid and particularly in alkaline soils where inorganic Zn in equilibrium with soil Zn decreases between 10−8 and 10−10 M (Lindsay, 1991). Chemisorption of Ni on oxides, noncyrstalline alumino silicates, and layer silicate clays is favored at soil pH > 6, but exchangeable and soluble Ni 2+ is favored under lower pH conditions (McBride, 1994). The mobility of Ni is moderate in acid soils and becomes low in neutral and alkaline soils. Cobalt solubility decreases with increases in soil pH because of increased chemisorption on oxides and silicate clay, complexation by OM, and possible precipitation of Co(OH)2 (McBride, 1994). Cobalt is somewhat mobile in acid soils, but reduces as soil pH approaches neutrality.

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B. ORGANIC MATTER Soil OM may be grouped into water-insoluble (humic acids or humin) and water-soluble (fulvic acids and small molecular weight microbial products) compounds. Humic acids contain many anionic oxygen groups (phenolic hydroxyl and carboxyl, aliphatic carboxyl, alcoholic hydroxyl), which may interact with metal cations (Tate, 1987). Predominant reactions between humic acids and metals are ionic-bonding or complexation reactions. The increases in humiﬁcation of OM increased these reactive groups and enhanced the potential for reaction with metallic cations (Stevenson, 1986). Metal complexation with humic substances normally forms strong metal complexes, while ionic bonding with low-molecular- weight organic acids (acetic, citric, malic) form relatively weak bonds. Both types of bonding normally result in the enhancement of metal mobility and/or plant availability (Tate, 1987), but some complexes are not readily available to plants (Harmsen and Vlek, 1985). Chemical reactions involved with SOM and metals have been reviewed (Stevenson, 1982). Native soil B was signiﬁcantly and positively correlated with organic C (Elrashidi and O’Connor, 1982). Soil OM adsorbs B by ligand exchange and such adsorption is vital to B availability (Goldberg, 1997). Organic matter is the main source of B in acid soil, as relatively little B is adsorbed on mineral fractions at low pH. Born adsorption by soil-composed OM increases with increased SOM content and with increased soil pH (Yermiyahu et al., 1995). Even though reactions of B with OM are not well understood, B may be involved in reactions with hydroxyl groups on organic complexes (Ofﬁah and Axley, 1993). Boron complexes with dihydroxyl compounds in OM, and these compounds retain considerable amounts of B (Marzadori et al., 1991). Soil OM also appears to be responsible for occluding important adsorption sites and reduces possible hysteretic reactions (confers reversibility characteristics) with adsorption sites (Marzadori et al., 1991). Because B is so closely associated with OM, it is usually more available in surface compared to subsurface soils because of higher amounts of OM in surface soil (Tisdale et al., 1985). Chloride bioavailability does not complex with and is not related to OM content in soil (Mortvedt, 2000). Copper is tightly bound to compounds in SOM, even more so than the other micronutrients, and is generally unavailable to plants (Mathur and Levesque, 1983). Much of the Cu in soil solution is also associated with OM (Kline and Rust, 1966). Low Cu levels in soil and Cu complexation into insoluble forms when soils have high OM lead to Cu deﬁciency in some plants (Moraghan and Mascagni, 1991). The major portions of total Cu were organically bound in an acid sandy soil, but precipitated when soil pH was high (Alva et al., 2000). The solubility of Cu in soil is usually decreased by complexation with clay–humus particles and/or formation of insoluble humic complexes (Stevenson and Fitch, 1981). In Cu-deﬁcient soils,

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humic and fulvic acids probably form highly stable complexes with Cu to reduce its availability. Complexation of Cu with OM occurs mainly at solution pH values above 6.5 (Barber, 1995), and increased Cu complex formation usually occurs with increased pH, decreased ionic strength, and increased OM/Cu ratios (Sanders and Bloomﬁeld, 1980). Inorganic Cu commonly complexes with hydroxyls and carbonates when soil solution pH is >7.0 (McBride, 1981). The breakdown of crop residues by soil microbes may release signiﬁcant amounts of Cu, but natural complexing substances produced during OM decomposition could complex Cu into unavailable forms (Moraghan and Mascagni, 1991). Iron forms stable complexes with organic compounds that occur in both soil and solid phases (Barber, 1995). Organic acids such as citric, malic, oxalic, and phenolic that form soluble Fe complexes are released when OM decomposes. These Fe complexes enhance the mobility and bioavailability of Fe (Lindsay, 1991). Even though Fe complexes with OM, Fe bioavailability is affected more by soil pH than by OM content. Fulvic and humic ligands form the most stable complexes with Fe compared to the other transition metals, and the effectiveness of these complexes increases with increasing pH because of the enhanced dispersion and ionization of surface ligands (Stevenson, 1991). The formation of soluble Fe complexes by naturally occurring chelating ligands may also increase Fe solubility in soil. The addition of OM to soil leads to reducing conditions, and Fe is changed from less soluble to exchangeable and organic forms under these conditions (Shuman, 1991).The biological degradation of OM also releases electrons or other reducing agents to lower soil redox potentials and signiﬁcantly increases the solubility of Fe (Lindsay, 1991). Increases in oxalate-extractable Fe (and Al) occurred after decomposition of OM, and Fe and Al oxide adsorption sites became coated or occluded with OM and were active only after removal of OM (Marzadori et al., 1991). In addition, Fe availability improved with the addition of OM in drained and water-logged soils (Tisdale et al., 1985). Soil OM content has been related to increased, decreased, and no effects on Mn availability to crop plants (Reisenauer, 1988). Within soil fractions, exchangeable and organically bound forms of Mn are important to plant availability. The higher accumulation of Mn in surface soil horizons has been reported to indicate that Mn may be closely associated with OM (McDaniel and Buol, 1991). Positive correlations between OM and Mn indicate that Mn has a strong afﬁnity for OM, and higher Mn concentrations in surface soil compared to lower layers are likely due to higher OM in surface horizons (Zhang et al., 1997b). Sites of Mn retention have also been associated not only with OM but also with CaCO3 in pH 8 calcareous soils (Karimian and Gholamalizadeh Ahangar, 1998). Mn2+ especially forms complexes with fulvic and humic acids and humins, and with organic ligands such as organic, amino, and sugar acids, hydroxamates, phenolics, siderophores, and other organic compounds produced by various organisms in soil solution (Marschner, 1995; Stevenson, 1986; Tate, 1987). Hydrated Mn2+ forms complexes with carboxyl

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groups of OM, which helps explain observations that Mn binds weakly to OM compared to Fe, Cu, and Zn (Bloom, 1981). Manganese availability in soils high in OM may also decrease because of the formation of unavailable Mn complexes. Unavailable Mn complexes form in peaty or muck soils. Soil OM appears to have smaller effects on the availability of Mo than does soil pH. Molybdenum availability under acid soil conditions is primarily affected through the adsorption of MoO42− onto inorganic soil components. However, evidence exists that Mo is ﬁxed by OM (Moraghan and Mascagni, 1991). In southeastern United States soils, adsorbed Mo increased with increases in SOM and Fe–oxide contents (Karimian and Cox, 1978). Organic matter may also potentially increase the mobilization of Mo under conditions of impeded drainage. Soil OM appears to affect the availability of Zn by (i) increasing the solubility of Zn through the formation of complexes with organic, amino, or fulvic acids; (ii) forming insoluble Zn–organic complexes that decrease the solubility of Zn; (iii) roots releasing exudates and ligands that may complex Zn in the rhizosphere; and (iv) microbes immobilizing and mineralizing decreased or increased soilavailable Zn (Lindsay, 1972). Increased levels of OM increase exchangeable and organic fractions of Zn and decrease oxide fractions of Zn in soil because of reducing conditions to enhance Zn bioavailability. A widespread Zn deﬁciency in lowland rice in Asia was related to high soil pH, low available soil Zn, and OM content (Yoshida et al., 1973). The decomposition of OM releases OH−, HCO3−, and organic ligands that tend to immobilize Zn in the root rhizosphere (Yoon et al., 1975). In practice, ﬁne-textured soils and soils with horizons containing high levels of OM had higher Zn sorption capacities than sandy-textured, low OM soils (Stahl and James, 1991). Adsorption of organic anions may also increase negative charges on particle surfaces to enhance Zn adsorption. On the other hand, organic ligands in solution may decrease Zn adsorption by competing with surface sites for Zn. Zinc adsorption onto clays and hydrous oxides may be increased or decreased with organic ligands (Chairidchai and Ritchie, 1990). High SOM levels in Ni-rich soils can solubilize Ni2+ as organic complexes at high soil pH (McBride, 1994). At high soil pH, Co complexes with SOM, and Co bioavailability increases when it is complexed with SOM (McBride, 1994).

C. TEMPERATURE, MOISTURE, AND LIGHT Temperature and moisture are important factors affecting the availability of micronutrients in soils (Cooper, 1973; Fageria, Baligar, and Jones, 1997). The availability of most micronutrients tends to decrease at low temperatures and moisture contents because of reduced root activity and low rates of dissolution and diffusion of nutrients. In soils with low moisture, colloidal particles may become immobilized as a result of micronutrient adsorption on surfaces of soil particles (Harmsen and Vlek, 1985). Light affects mostly metabolic processes of plants.

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Boron and Cl uptake are inﬂuenced more than any other mineral for plants grown under hot and dry conditions. For example, increased temperatures in nutrient solutions enhanced B concentrations in shoots of plants by increasing B uptake with increased transpiration (Moraghan and Mascagni, 1991; Vlamis and Williams, 1970). On the other hand, turnip was B deﬁcient when grown in soil with <0.3 mg B kg−1 of hot water-extractable B, but became B deﬁcient when grown in the ﬁeld with 0.5–0.6 mg kg−1 hot water-extractable B during a dry summer (Batey, 1971). The availability of B decreases under drought conditions most likely because of the reduced mobility of B by mass ﬂow to roots (Barber, 1995). Boron can move relatively long distances by mass ﬂow and diffusion to roots. Soil drying reduces B diffusion by reducing the mobility of soil solution and increasing the diffusion path length (Scott et al., 1975). Boron deﬁciency in crops commonly occurs during drought periods because of restricted water ﬂow to roots, and B deﬁciency may also restrict root growth to reduce acquisition of water and intensify drought stress effects (Bouma, 1969). The lack of soil moisture reduces the rate of transpiration, thereby reducing B transport to shoots (Lovatt, 1985). Wetting and drying cycles and increasing soil temperature (e.g., 25 to 45◦ C) also increased B ﬁxation by montmorillonitic and kaolinitic clays (Biggar and Fireman, 1960). Low temperature in spring and fall seasons of temperate regions reduced availability of B to forage legumes, while increased temperature enhanced B concentrations for sugarcane (Gupta, 1993). Another aspect of drought-induced B deﬁciency involves moisture stress that may restrict the mineralization and availability of organically bound soil B (Evans and Sparks, 1983; Flannery, 1985). High light intensity may also induce B deﬁciency and reduce B toxicity (Moraghan and Mascagni, 1991). Temperature can affect mobilization/immobilization reactions to decrease/ increase solubility of organically bound soil Cu and its acquisition by plants (Moraghan and Mascagni, 1991; Stevenson and Fitch, 1981). For example, increasing temperature from 8 to 20◦ C increased Cu uptake by carrot grown in acid organic soil (MacMillan and Hamilton, 1971). Soil moisture had no consistent effect on Cu levels available to alsike clover (Kubota et al., 1963), but Cu availability to annual ryegrass increased when roots had access to subsoil water (Nambiar, 1977). Flooding soil also decreased Cu availability to rice (Beckwith et al., 1975). Low Cu acquisition by plants was attributed to low soil moisture conditions in the zone of Cu application (Mortvedt, 2000). Iron deﬁciency, which occurs predominantly in calcareous and alkaline soils, is commonly enhanced by low soil temperature and high water (wet) and/or poorly aerated conditions (Marschner, 1995). Low soil temperatures reduce root growth and metabolic activity and increase HCO3− levels in the soil solution to increase the severity of Fe deﬁciency with the increased solubility of CO2 in soil solutions (Inskeep and Bloom, 1986). On the other hand, high soil temperature may decrease Fe acquisition by increasing the microbial decomposition of organic materials to stimulate microbial activity and CO2 production to increase the severity of Fe

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deﬁciency (Inskeep and Bloom, 1986; Moraghan and Mascagni, 1991). High aerial or soil temperatures may also stimulate relative growth rates to enhance the induction of Fe deﬁciency (Inskeep and Bloom, 1986). High soil temperature may also increase P uptake to enhance P-induced Fe deﬁciency (Moraghan and Mascagni, 1991). Since absorption of Fe by plant roots is largely restricted to actively growing root tips, restricted root growth in dry surface layers (zone with highest amount of available Fe) may partially explain the appearance of Fe deﬁciency for some plants grown under hot, dry conditions (Moraghan and Mascagni, 1991). Soil temperature generally has less effect on Fe deﬁciency in Strategy II (root release of phytosiderophores) than in Strategy I (root release of organic acids and increased root reducing power) plants (R¨omheld and Marschner, 1986). Increasing light intensity enhanced the release of phytosiderophores by cereal roots, which could increase uptake of both Fe and Zn (Cakmak et al., 1998). Drying and ﬂooding of acid sulfate soils increase the risk of Fe toxicity (Sahrawat, 1979), since water-logging enhances the accumulation of high amounts of soluble Fe2+, especially in acid soils. Good soil drainage increases oxidation, and Fe toxicity in rice is commonly reduced (Gunawardena et al., 1982). The ratio of Fe2+/(Fe2+ + Mn2+ + Ca2+ + Mg2+) in the soil solution, rather than the activity of Fe2+ alone, controlled Fe acquisition by ﬂooded rice grown in the acid sulfate soils of Thailand (Moore and Patrick, 1989). High levels of Mn, Ca, and Mg also reduced the likelihood of Fe toxicity for plants grown in acid soils. Low soil temperature may induce Mn deﬁciency. For example, Mn deﬁciency of ﬁeld-grown soybean was more severe at low temperature despite having high Mn concentrations in shoot tissue (Ghazali and Cox, 1981). Critical Mn concentrations in leaves are often lower at low than at high soil temperatures (Rufty et al., 1979). High soil temperatures may increase the solubility of soil Mn and enhance the Mn availability, and air drying often combined with high temperature increased extractable and exchangeable Mn, which sometimes has led to Mn toxicity (Moraghan and Mascagni, 1991). Soil temperature increases of 10 to 25◦ C approximately tripled Mn accumulation in shoots of barley grown in organic soil (Reid and Racz, 1985), while soybean grown at 16, not 24◦ C, developed severe Mn toxicity symptoms when grown in calcareous soil (Moraghan et al., 1986). Plant tolerance to Mn toxicity increased in tobacco and soybean with increased temperatures despite higher Mn absorption, which was attributed to faster plant growth to provide larger leaf vacuoles to sequester potentially toxic Mn (Heenan and Carter, 1976; Rufty et al., 1979). Excess moisture favors Mn-reducing conditions and water-logging, even for relatively short periods of time, and enhanced Mn accumulation could possibly induce Mn toxicity (Moraghan and Mascagni, 1991; Siman et al., 1974). Excess soil moisture can restrict diffusion of O2 within soils and favor Mn reduction. At lower soil redox potentials, high levels of Fe2+ may also be formed which could lead to Mn–Fe antagonisms (Vlamis and Williams, 1962, 1964). Manganese deﬁciency

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has rarely been observed in rice grown under ﬂooded conditions, and Mn toxicity was aggravated in alfalfa grown under hot dry conditions in Australia (Siman et al., 1974). Manganese deﬁciency on various crops in Sweden disappeared after a heavy rainfall following a dry period (Stahlberg and Sombatpanit, 1974). High and low light intensities may intensify Mn deﬁciency and toxicity symptoms on plants (Hewitt, 1966). High intensity stimulated Mn absorption and accentuated the severity of Mn toxicity (El-Jaoual and Cox, 1998; Horiguchi, 1998). The Mn-induced chlorosis symptoms on leaves with high light intensity were attributed to the oxidation of chlorophyll (El-Jaoual and Cox, 1998). On the other hand, decreased light intensity appeared to lower Mn concentrations in leaves through reduced water transport and increased the leaf area to dilute internal Mn (Campbell and Nable, 1988). Molybdenum is associated with N2 ﬁxation, and low temperatures will suppress this process and lower Mo requirements (Anderson, 1956). The temperature had little effect on plant incidence or severity of Mo deﬁciency (Moraghan and Mascagni, 1991). The adsorption of Mo increased when the temperature increased from 10 to 40◦ C (Goldberg and Forster, 1998). The acquisition of Mo decreased in plants grown under dry conditions (Gupta and Sutcliffe, 1968). Submerged acid soils had increased soluble Mo fractions because of decreased MoO42− adsorption (Ponnamperuma, 1972). Temperatures lower than optimum normally decrease Zn acquisition by crop plants. Zinc deﬁciency symptoms were relatively severe at low soil temperature, but Zn concentrations increased when the temperature was increased (Martin et al., 1965). Added P also induced Zn deﬁciency at low soil temperature. Cool and wet conditions induced Zn deﬁciency, which were related to reduced mineralization of Zn and reduced root growth (Moraghan and Mascagni, 1991). Spring-seeded crops like maize, edible bean, and potato grown in western United States soils exhibited early season Zn deﬁciency symptoms, which did not appear in newer growth later in the season (Viets, 1967). The detrimental effects of low root temperatures on Zn accumulation by maize grown in nutrient solution were partially due to decreased translocation from roots to shoots (Edwards and Kamprath, 1974). Since Zn moves to roots mainly by diffusion, Zn deﬁciency is common also in crops grown under dry conditions (Warncke and Barber, 1972). Mycorrhizae associated with roots enhances uptake of Zn (Clark and Zeto, 2000), and low soil temperatures may severely reduce root colonization with mycorrhizae and induce Zn deﬁciency (Hayman, 1974). Zinc deﬁciency was less severe for plants grown under low light and cool conditions compared to optimum growth conditions (Moraghan and Mascagni, 1991). Zinc deﬁciency frequently associated with ﬂooded soil may be the result of Zn reactions with free sulﬁde (Sajwan and Lindsay, 1988). Under ﬂooded conditions, Zn may precipitate as ZnS or possibly form organic–Zn complexes which can lead to reduced availability. Zinc may also react with sesquioxides under ﬂooded

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conditions to enhance Zn deﬁciency (Sajwan and Lindsay, 1986). Added OM also suppressed Zn acquisition due to redox processes and buildup of Fe2+ (Giordano et al., 1974). Flooding–drying and alternating wetting–drying decreased Zn adsorption, whereas preﬂooding increased Zn adsorption, and the addition of OM increased Zn adsorption under these water treatments (Mandal and Hazra, 1997). Under reducing conditions Ni2+ is incorporated into sulﬁdes that restrict its mobility to very low levels, and strong oxidizing soil conditions favor adsorption of Co (McBride, 1994).

IV. FACTORS ASSOCIATED WITH SUPPLY AND ACQUISITION Sufﬁcient concentrations and/or available forms of micronutrients must be at or near root surfaces to meet plant acquisition needs. Nutrient supplies to plants are governed by such factors as concentrations inside plants and in soil solution, supply and chemistry at root surfaces or in the rhizosphere, and interactions of one nutrient with another. At any given time, concentrations of nutrients in the solution immediately adjacent to roots appear to be one of the best measures for assessing absorption potential, although plant and rhizosphere factors may inﬂuence the rates of absorption (Fageria, Baligar, and Wright, 1997). Information about mechanisms and processes associated with mineral nutrient uptake and translocation are not discussed here, since many review articles are available on the subject (Barber, 1995; Clarkson and Hanson, 1980; Fageria, Baligar, and Jones, 1997; Glass, 1989; Kochian, 1991; Marschner, 1995; Moore, 1972; Tifﬁn, 1972). This article will focus on supply and general acquisition processes.

A. DEFICIENCIES AND TOXICITIES For plants to obtain micronutrients for proper physiological and biochemical functioning (Table VI), these mineral nutrients need to be at appropriate concentrations. Micronutrient deﬁciencies and toxicities are widespread and have been documented in various soils throughout the world. The deﬁciency of essential micronutrients induces abnormal pigmentation, size, and shape of plant tissues, reduces leaf photosynthetic rates, and leads to various detrimental conditions (Masoni et al., 1996). Speciﬁc deﬁciency symptoms appear on all plant parts, but discoloration of leaves is most commonly observed. Deﬁciency symptoms of low mobile nutrients (Fe, B, Mn, Zn, and Mo) appear initially and primarily on upper leaves or leaf tips, while symptoms of mobile nutrients (N, P, K, and Mg) appear primarily on lower leaves. Deﬁciency and toxicity symptoms may be confused

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Table VI Functions of Micronutrients in Plantsa Element B Cl Cu

Fe

Mn Mo Zn Ni Co

Function Activates certain dehydrogenese enzymes. Involved in carbohydrate metabolism. Synthesis of cell wall components. Essential for cell division and development. Essential for photosynthesis and as an activator of enzymes. Involved in splitting water. Functions in osmoregulation of plants growing on saline soils. Constituent of a number of important oxidase enzymes including cytochrome oxidase, ascorbic acid oxidase, and lactase. Important in photosynthesis and protein and carbohydrate metabolism. Important in chlorophyll formation and an essential component of several peroxidase, catalase, and cytochrome oxidase enzymes. Found in key metabolic functions such as N2 ﬁxation, photosynthesis, and electron transfer. Activates decarboxylase, dehydrogenese, and oxidase enzymes. Involved in photosynthesis, N metabolism, and assimilation. An essential component of nitrate reductase and N2-ﬁxation enzymes and required for normal assimilation of N. Essential component of several dehydrogenase, proteinase, and peptidase enzymes. Promotes growth hormones, starch formation, and seed maturation. Component of urease enzyme. Participates in redox reactions. Improves hydrogenase acitivity, urea hydrolysis. Stimulates germination and growth. Nodule development, rhizobium infection, N2 ﬁxation, component of coenzyme cobalamin (vitamin B12).

a From Brady and Weil (1996), Fageria, Baligar, and Jones (1997), Marschner (1995), and Stevenson (1986).

with drought, disease, insect, and other damage, so correct diagnosis may be difﬁcult without experience. Critical concentration ranges of micronutrients in soil for important ﬁeld crops (Table VII) and some description of deﬁciency and toxicity symptoms associated with many crop plants (Table VIII and Table IX) have been provided. Boron deﬁciency is common for plants grown in arid, semiarid, and heavy rainfall areas in calcareous, sandy, light textured, acid, and low OM soils (Bradford, 1966; Gupta, 1993). Soils supplied with high amounts of municipal compost, sludge, and biosolids tend to accumulate high amounts of B which may result in B toxicity. Boron toxicities are commonly associated with crops receiving irrigation water containing high B. Differences between B sufﬁciency and toxicity are narrow (Marschner, 1995). Chlorine deﬁciencies under ﬁeld conditions have been reported for oil palm, sugarcane, hard red spring wheat, and potato (Martens and Westermann, 1991). Soybean grown in Atlantic coastal plain soils with added KCl developed Cl

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FAGERIA et al. Table VII Critical Micronutrient Concentrations (mg kg−1) in Soil for Some Field Cropsa Critical concentration Element B

Crop

Cl

Alfalfa, sugar beet, cotton, maize, peanut Wheat, barley, oat

Cu

Maize and small grains Barley and oat Rice Maize, soybean and wheat

Fe Mn

Mo

Zn

Sorghum and soybean Sorghum Soybean Small grains Maize Soybean Forage legumes, Soybean, Cauliﬂower Bean (common), maize, rice, sorghum, ﬂax Maize Maize Rice

Extracting solution

Range

Mean

Hot water

0.1–2

0.8

Water 0.01 M Ca(NO3)2 0.05 M K2SO4 CaO NH4HCO3–DTPA Mehlich-1 0.05 M EDTA 0.05 M HCl Mehlich-1 NH4HCO3–DTPA Mehlich-3 NH4HCO3–DTPA DTPA–TEA Mehlich-1 NH4HCO3–DTPA Mehlich-3 Mehlich-3 NH4–oxalate

NH4HCO3–DTPA Mehlich-1 0.1 M HCl DTPA–TEA 0.05 M HCl

>22

0.12–2.5 0.1–10

2.5–5 4–8 1–2

0.8 3 1.1 0.1 0.26 0.53 0.37 4.8 4.5 7 1.4 3 3.9

0.1–0.3

0.25–2 0.5–3 2–10

0.8 1.1 5 0.86 1

a

From Cox (1987), Martens and Lindsay (1990), Sims (2000), and Sims and Johnson (1991).

toxicity (Parker et al., 1983). Crops that are grown in salt-affected soils and receive irrigation (sprinkler) often have enhanced symptoms of Cl toxicity. Copper deﬁciency is often observed on plants grown in soils inherently low in Cu (coarse-textured and calcareous soils) and in soils high in OM, where Cu is readily complexed (Alloway and Tills, 1984). Higher than normal Cu supplies usually inhibit root growth more than shoot growth (Lexmond and Vorm, 1981). The use of Cu-containing fungicides and antihelminthic compounds (insecticides) in agriculture has resulted in Cu toxicity in some plants, but naturally occurring Cu toxicity is relatively uncommon (Welch et al., 1991).

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Table VIII Micronutrient Deﬁciency Symptoms in Crop Plantsa Element B Cl Cu Fe Mn

Mo

Zn

Ni Co

Symptoms Death of growing points of shoot and root. Failure of ﬂower buds to develop. Blackening and death of tissues, especially inner tissue of brassica plants. Reduced leaf size. Yellowing, bronzing and necrosis on leaves. Roots reduced in growth and without hairs. Yellowing of young leaves. Rolling and dieback of leaf tips. Leaves are small. Tillering is retarded. Growth is stunted. Interveinal yellowing of younger leaves with distinct green veins. Entire leaves become dark yellow or white with severe deﬁciency, and leaf borders turn brown and die. Interveinal tissue becomes light green with veins and surrounding tissue remaining green on dicots (Christmas tree design) and long interveinal leaf streaks on cereals. Develop necrosis in advanced stages. Mottled pale appearance in young leaves. Bleaching and withering of leaves and sometimes tip death. Legumes suffering Mo deﬁciency have pale green to yellowish leaves. Growth stunted. Seed production is poor. Deep yellowing of whorl leaves (cereals). Dwarﬁng (rosette) and yellowing of growing points of leaves and roots (dicots). Rusting in strip on older leaves with yellowing in mature leaves. Leaf size reduced. Main vein of leaf or vascular bundle tissue becomes silver-white, and marked stripes appear in middle of leaf. Chlorosis of newest leaves. Ultimately leads to necrosis of meristems. Reduced germination and seedling vigor (low seed viability). Diffuse yellowing in leaves. Young shoots and older leaves have severe localized marginal scorching.

a From Baligar et al. (1998), Bennett (1993), Bould et al. (1983), Brown et al. (1987), Clark and Baligar (2000), and Fageria, Baligar, and Jones (1997).

Iron deﬁciency is a worldwide problem and occurs in numerous crops (Korcak, 1987; Marschner, 1995; Vose, 1982). Iron deﬁciency occurs not because of Fe scarcity in soil but because of various soil and plant factors that affect Fe availability to inhibit its absorption or impair its metabolic use (Marschner, 1995; Welch et al., 1991). In the majority of soils, the total concentration of soluble Fe in the rhizosphere is nearly always far below the level required for adequate plant growth (Marschner, 1995). Induced Fe-deﬁcient chlorosis is widespread and is a major concern for plants growing on calcareous or alkaline soils due to their high pH and low Fe (Korcak, 1987). Bicarbonate, nitrate, and environmental factors inﬂuence the occurrence of Fe-deﬁcient chlorosis in plants, which occurs in young leaves due to inhibited chloroplast chlorophyll syntheses as a consequence of the low Fe nutrition status of plants (Lucena, 2000). Plant species that commonly become Fe deﬁcient are apple, peach, citrus, grape, peanut, soybean, sorghum, and upland

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FAGERIA et al. Table IX General Description of Mineral Toxicity Symptoms on Plantsa

Element B

Cl Cu

Fe

Symptoms High B may induce some interveinal necrosis, and severe cases turn leaf margins straw color (dead) with distinct boundaries between dead and green tissue. Roots appear relatively normal. High Cl results in burning leaf tips or margins, reduced leaf size, sometimes yellowing, resembles K deﬁciency, and root tips die. High Cu may induce Fe deﬁciency (chlorosis). Light colored leaves with red steaks along margins. Plants become stunted with reduced branching, and roots are often short or barbed (like wire). Laterals may be dense and compact. Excess Fe is a common problem for plants grown in ﬂooded acidic soil. May induce P, K, and Zn deﬁciencies. Bronze or blackish-straw colored leaves extending from margins to midrib. Roots may be dark red and slimy.

Mn

Excess Mn may cause leaves to be dark green with extensive reddish-purple specks before turning bronze yellow, especially interveinal tissue. Uneven distribution of chlorophyll. Margins and leaf tips turn brown and die. Sometimes Fe deﬁciency appears, and main roots become stunted with increased number and density of laterals.

Mo

Excess Mo induces symptoms similar to P deﬁciency (red bands along leaf margins), and roots often have no abnormal symptoms. Excess Zn may enhance Fe deﬁciency. Leaves become light colored with uniform necrotic lesions in interveinal tissue, sometimes damping off near tips. Roots may be dense or compact and may resemble bared wire. High Ni results in white interveinal banding alternating with green semichlorotic areas with irregular oblique streaking, dark green veins, longitudinal white stripes, and brown patches. Yellowing of leaves may resemble Fe or Mn deﬁciency. Distortion of young leaﬂets (peg-like or hook type).

Zn

Ni

Co

Pale green leaves with pale longitudinal stripes.

a From Baligar et al. (1998); Bould et al. (1983); Clark and Baligar (2000), and Fageria, Baligar, and Jones (1997).

rice. Iron toxicity (bronzing) can be a serious disorder for the production of crops in water-logged soils. For wetland rice, Fe toxicity is the second most severe yield-limiting mineral disorder after P deﬁciency. Audebert and Sahrawat (2000) reported that the application of P, K, and Zn with N to an iron-toxic lowland soil in the Ivory Coast reduced Fe toxicity symptoms and increased lowland rice yields. Manganese toxicity is probably more of a problem than Mn deﬁciency throughout the world. Manganese deﬁciency occurs on plants grown in organic, alkaline, calcareous, poorly drained, slightly acid soils, and coarse-textured sandy soils (Martens and Westermann, 1991). Over-liming of acid soils may induce Mn deﬁciency. Manganese toxicity is a major factor for reduced production of crops grown in acid soils, as is Al toxicity. Plant ability to tolerate Mn toxicity is affected by plant genotype, concentration of Si in soils, temperature, light intensity,

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and physiological age of leaves (Horst, 1988). The conditions that lead to the buildup of high levels of Mn in soil solution are high levels of total Mn, soil pH below 5.5, high soluble Mn relative to Ca, reduction of Mn under low oxygen caused by poor drainage, soil compaction, and excess water from irrigation or rainfall (Reisenauer, 1988). Molybdenum deﬁciency is widespread in legumes, maize, and cauliﬂower grown in acid mineral soils containing high amounts of iron oxides and hydroxides. Copper/Mo ratios <2 will normally reduce Mo deﬁciency in plants (Miltmore and Mason, 1971). The appearance of Mo toxicity is rare, but high levels of Mo in forages may induce Cu deﬁciency in animals. Molybdenum concentrations >5 to 10 mg kg−1 dry wt in forage tissue have induced toxicity in ruminants (“molybdenosis or teart”) (Marschner, 1995). Such disorders of Cu occur in forage grown in poorly drained and high organic soils. Zinc deﬁciency in plants is widespread throughout the world (Bould et al., 1983; Viets, 1966). Increasing pH due to liming reduces plant available Zn. High clay and P supply and low soil temperatures are also known to promote Zn deﬁciency (Marschner, 1995). Lowland rice grown in limed or calcareous soils often exhibit Zn deﬁciency (Ponnamperuma, 1972). Chaney (1993) indicated that after “natural” phytotoxicity from Al or Mn in strongly acid soils, Zn phytotoxicity is the next most extensive micronutrient phytotoxicity compared to Cu, Ni, Co, Cd, or other trace element toxicities. As soil pH decreases, Zn solubility and uptake increase, and the potential for Zn phytotoxicity increases. At comparable soil pH and total Zn contents, Zn phytotoxicity is more severe on plants grown in light-textured than in heavy-textured soils. This is mainly because of the differences in the speciﬁc Zn adsorption capacities of soil. Continued applications of Zn to alkaline sandy soils low in OM and clay tend to develop Zn toxicity in plants, even though the occurrence of Zn toxicity is relatively rare under ﬁeld conditions (Rattan and Shukla, 1984). Liming was effective in overcoming Zn toxicity on peanut (Keisling et al., 1977). Even though no clear evidence exists for Ni deﬁciency in plants, Ni toxicity is of concern for plants grown in soil receiving sewage sludge and industrial by-products. Nickel as well as Co toxicity may also be found on plants grown in soils formed from serpentinite or other ultrabasic rocks (McBride, 1994). Cobalt deﬁciency may occur on plants grown in highly leached sandy soils derived from acid igneous rocks and in calcareous or peaty soils (Martens and Westermann, 1991) and in coarse-textured, acid-leaching alkaline or calcareous soils and humic rich soils (McBride, 1994).

B. SUPPLY AND UPTAKE Micronutrient uptake by roots depends on nutrient concentrations at root surfaces, root absorption capacity, and plant demand. Micronutrient acquisition includes dynamic processes in which mineral nutrients must be continuously

212

FAGERIA et al. Table X Estimated Proportions of Micronutrients Potentially Supplied by Mass Flow, Diffusion, and Root Interception to Maize Roots Grown in a Fertile Alﬁsola Estimated percentage of total uptake Micronutrient

Mass ﬂow

Diffusion

Root interception

B Cu Fe Mn Zn

1000 219 66 22 230

29 0 21 35 0

29 6 13 43 43

a

From Barber (1966).

replenished in soil solution from the soil solid phase and transported to roots as uptake proceeds. Mineral nutrient transport to roots, absorption by roots, and translocation from roots to shoots occur simultaneously, which means that rate changes of one process will ultimately inﬂuence other processes involved in uptake (Fageria, Baligar, and Jones, 1997). In soil systems, mineral nutrients move to plant roots by mass ﬂow, diffusion, and root interception (Barber, 1995). Mass ﬂow is the passive transport of minerals to roots as water moves through soil and occurs when solutes are transported to roots with convective ﬂow of water (soil solution) from soil. The amount of minerals supplied to roots depends on the rates of water ﬂow to roots and the average mineral content of the water. The amounts of mineral nutrients reaching roots by this process depend on the concentrations of nutrients in soil solution and the rates of water transport to and into roots. Diffusion and mass ﬂow could meet plant micronutrient requirements for B, Cu, and Zn, provided sufﬁcient nutrient concentrations are in soil solution. Table X provides estimates of nutrients supplied to maize roots by mass ﬂow, diffusion, and root interception in a fertile Alﬁsol. Diffusion is deﬁned as the movement of nutrients from regions of high concentration to regions of low concentration. When the nutrient supply to root surfaces is not sufﬁcient to satisfy plant demands by mass ﬂow and root interception, concentration gradients develop and nutrients move by diffusion (Barber, 1966, 1995). Considerable quantities of B, Mn, and Fe move by diffusion. Root interception is another process by which roots obtain minerals. As roots grow in soil, they push soil particles aside and root surfaces come in direct contact with mineral nutrients. Mineral interception by roots depends on soil volume occupied by roots, root morphology, and concentrations of minerals in the soil volume occupied by roots. On average, soil volume occupied by roots of crop

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213

plants is about 0.7 to 0.9% (Fageria, Baligar, and Wright, 1997). Root interception can provide signiﬁcant amounts of plant requirements for B, Zn, and Mn. The interaction of soil and plant factors inﬂuences the processes of mineral ﬂux in soil. The major soil factors that inﬂuence mineral ﬂux are concentrations of mineral ions on exchange sites and in solution, soil buffer capacity, diffusion coefﬁcient, type of clay, soil structure, nature of OM, water content, and temperature. Soil capacity to adsorb mineral nutrients is important in mineral transport to roots. If soil ion-exchange capacity is low, ions are usually freely mobile in solution. In addition, diffusion coefﬁcients of Cu, Mn, and Zn decrease ∼10-fold for various clays in the order of kaolinite > illite > montmorillonite > vermiculite (Lindsay, 1979). The major plant factors that contribute to mineral ﬂuxes are root and root hair density and length, plant demand for mineral nutrients and water, and plant modiﬁcation of the rhizosphere (Fageria and Baligar, 1993, 1997a,b). The amount of minerals in soil, concentration in soil solution, and transport to roots are key factors inﬂuencing mineral uptake by roots. Since B, Mn, and Fe move to plant roots primarily by diffusion, soil properties that affect diffusion govern micronutrient availability to plant roots. Mineral nutrient supply, whether at adequate or toxic levels, can strongly inﬂuence root growth, morphology, and distribution of root systems in soil (Baligar et al., 1998; Barber, 1995; Marschner 1995). As most micronutrients may be supplied by diffusion, the size of roots has profound effects on plant ability to acquire required mineral concentrations. Toxic levels of Al, Mn, and H in acid soils and the presence of H2CO3, Na2CO3, B, Na, Mo, SO4–S, and Cl in alkaline or high-salt soils can directly reduce root growth and inhibit ability of roots to explore large soil volumes for minerals and water. Soil weathering, anthropogenic activities, addition of agricultural amendments (fertilizers, organic manures, lime, slags, sewage sludge), and pesticides have contributed to increased levels of essential micronutrients and nonessential trace elements in soil (Baligar et al., 1998). The mobility and bioavailability of these minerals in soil are inﬂuenced by pH, temperature, redox potential, cation exchange, anion ligand formation, and composition and quantity in soil solution (Alloway, 1995a,b; Baligar et al., 1998). At any given pH, the relative mobility of some micronutrients in acid soil decreases in the order of B > Ni > Zn > Mn > Cu. Mineral nutrient deﬁciencies and excesses affect growth (dry mass, root : shoot ratio) and morphology (length, thickness, surface area, density) of roots and root hairs (Baligar et al., 1998). Nutrient deﬁciencies usually lead to ﬁner roots and trace element toxicities stimulate initiation and growth of second- and third-order lateral roots, while tap roots and ﬁrst-order laterals (seminal/basal) become suppressed (Hagemeyer and Breckle, 1996). Additional information about toxicity and constraints of micronutrients and trace elements on root growth is available (Baligar et al., 1998). Changes in root growth and morphology affect plant ability to absorb minerals from soil to meet plant demands. Mineral uptake involves selectivity [where certain minerals are absorbed preferentially over others

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(discrimination or exclusion)], accumulation (where minerals accumulate at higher concentrations in cell sap than in external soil solution), and genotype (where distinct differences exist among plant species and within species) (Marschner, 1995). A detailed discussion and reviews of plant and soil factors that affect micronutrient uptake, transport, and utilization in plants are available (Barber, 1995; Chen and Hadar, 1991; Graham et al., 1988; Gupta, 1993; Marschner, 1995; Mengel and Kirkby, 1982; Mortvedt et al., 1991; Robson, 1993; Sumner, 2000; Welch, 1995). Micronutrient cations in soil solution also commonly form organic complexes of varying stability, size, and charge (Tifﬁn, 1972). Kochian (1991) stated that to understand the overall mechanisms of micronutrient cation uptake in plants there is a need to consider the form of metal chelates in the root rhizosphere at the root–cell plasma membrane, forms of micronutrient cations transported into plant cells, and the nature of the metal chelate complexes, both within cells and involved in long-distance transport. A detailed discussion of the processes associated with mineral uptake and transport is provided in several review articles (Epstein, 1972; Kochian, 1991; Marschner, 1995; Moore, 1972; Mengel and Kirkby, 1982; Tifﬁn, 1972). Boron is absorbed by roots as undissociated boric acid [B(OH)3 or H3BO3], and it is not clear whether uptake is active or passive (Marschner, 1995; Mengel and Kirkby, 1982). Nevertheless, B uptake by rice appeared to be passive under normal B supplies and active under low B supplies (Yu and Bell, 1998) and was the result of passive assimilation of undissociated boric acid (Hu and Brown, 1997). At high B supplies, passive uptake and active excretion of B were also noted (Yu and Bell, 1998). Boron as well as Cl distribution in plant tissue appear to be primarily governed by transpiration, since B and Cl in soil are highly mobile and move with water. Boron is supplied to roots primarily by mass ﬂow. The factors affecting B uptake include soil type, B content, soil pH, amount of water soil receives, and plant species (Welch et al., 1991). Soil pH affects B absorption kinetics of roots, adsorption on soil particles, and maintenance of B concentrations in soil solution (Barber, 1995). The absorption of B by monocotyledonous plants was less than that by dicotyledonous plants and was passive (Shelp, 1993). Long-distance transport of B from roots to shoots occurs in the xylem and is related to the rates of transpiration (Brown and Shelp, 1997). Copper uptake is an active process (Dokiya et al., 1964) and is inﬂuenced by plant species, growth stage, plant part, various soil properties, and added amendments. Copper is relatively immobile in soil, so that large portions of Cu are derived from root interception in soils low in labile Cu (Oliver and Barber, 1966). The exploitation of soil by roots (root volume, density) inﬂuenced the Cu absorbed by roots (Barber, 1995). Soil pH did not affect Cu uptake extensively because the soil maintained sufﬁcient levels of Cu, even when free Cu2+ had been reduced with increased soil pH (Barber 1995). Mycorrhizal associations with roots improved Cu uptake by 53 to 62% in white clover (Li et al., 1991).

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In soil solution, Fe3+ dominates and forms organic complexes with degraded OM (fulvic acid) or siderophores (Fe-complexing compounds released by soil microbes and/or plant roots) (Powell et al., 1982). In well-aerated soils, complexed Fe3+ is the major form of Fe. Higher plants use nonspeciﬁc and speciﬁc processes to increase the solubility and uptake of Fe from the rhizosphere. Uptake of cations over anions is one of the most important nonspeciﬁc processes that results in pH decreases in the rhizosphere to increase Fe availability and uptake (R¨omheld and Marschner, 1986). The factors that interfere with ionic balances in plants and contribute to Fe uptake are N source, K supply, plant P status, and genotypic differences (Zaharieva and R¨omheld, 1991). Strategy I processes used by dicotyledons and nongrass monocotyledons (nongraminaceous species) in responding to Fe deﬁciency are to excrete protons (acidiﬁcation of rhizosphere) and increase reductase activity at the root–soil interphase. The iron deﬁciency in dicotyledonous plants is reduced by lowering the rhizosphere pH from the root H+ excretion (proton excretion), root exudation of organic acids (mainly phenolics), enhanced root reduction of Fe3+ to Fe2+, and activated root-reducing capacity at cell plasma membranes. Increased medium acidiﬁcation and Fe3+ reduction are brought about by plasmalemma-linked H+: ATPase and NADH:Fe3+ reductase activities (Dell’Orto et al., 2000). Organic anions such as citrate and oxalate exudated from the roots contribute to the Fe mobilization in soil, and such a response appears to be the factors under P deﬁciency for species such as rape or lupin. (Hinsinger, 1998; Jones et al., 1996). In Strategy I plants, reduction activity at the root–soil interface appears to play a dominant role in Fe aquisiton (Bertrand and Hinsinger, 2000; Brown, 1978; Chaney et al., 1972). In Strategy I, plant response to Fe deﬁciency is the increased capacity of the roots to reduce ferric chelates (Bienfait, 1988), which is affected by HCO3−, Fe, and other metals (Alc´antara et al., 2000). Many monocotyledonous plants, especially those of Poaceae (grasses), transport Fe3+-phytosiderophores (root-derived chelates) across root cells (Strategy II plants), which is an important mechanism by which Fe is acquired by these plants. Strategy II processes are used by graminaceous species, which excrete several types of phytosiderophores as adaptive mechanisms to Fe deﬁciency (Kanazawa et al., 1993; Takagi et al., 1984). Phytosiderophores are low-molecular-weight polydentate (nonproteinogenic amino acids) ligands which bind Fe3+ to facilitate transport (Kochian, 1991; Marschner, 1995; R¨omheld, 1991; R¨omheld, and Marschner, 1986). Overall, the high pH, redox state, pH buffer (HCO3−, active lime, OM), nitrate, and Fe mineral types affect Fe uptake by plants (Lindsay, 1994; Lucena, 2000; Marschner, 1995; R¨omheld and Marschner, 1986). The rate of phytosiderophore release in cereals under Fe deﬁciency greatly differs between species, and these differences are positively correlated with the resistance of cereals to Fe deﬁciency (Marschner et al., 1986; R¨omheld and Marschner, 1990). ¨ urk, Besides Fe, phytosiderophores also mobilize Zn, Mn, and Cu (Cakmak, Ozt¨ et al., 1996; Hopkins et al., 1998; R¨omheld, 1991).

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Manganese uptake is metabolically mediated, and uptake increases from pH 4 to 6 (Maas et al., 1969). Above pH 6, oxidation of Mn2+ to Mn4+ occurs, and Mn2+ uptake is reduced. Soil pH and redox potentials control the Mn supply to roots by mass ﬂow and diffusion. Deﬁciency of Mn usually occurs when soil pH is >6.2, but Mn2+ may be sufﬁcient in some soils, even though the pH is ≥7.5 (Barber, 1995). The prevailing source of Mn at root surfaces is Mn2+. Manganese forms complexes with organic compounds (trihydroxamic acid, sideramines) of microbial and plant origin, which increases the Mn mobility in soil (Clarkson, 1988). The three major sources of Mn in soils that are primarily responsible for the Mn supply to roots are exchangeable Mn, organically complexed Mn, and Mn oxides (Marschner, 1988). The proportion of these Mn forms vary with soil type, soil pH, and OM. As the soil pH decreases, the proportion of exchangeable Mn increases dramatically, while the proportions of Mn oxides and Mn bound to Mn and Fe oxides decrease. In soils low in available Fe, root reductase activity is stimulated because of acidiﬁcation of the rhizosphere and may lead to higher Mn mobility and uptake. Greater ranges in foliage Mn were noted for different species of plants growing in the same soil compared to Cu, Fe, or Zn (Gladstones and Loneragan, 1970). These differences were attributed to species ability to acidify soil in the rhizosphere rather than to the Mn requirement. Molybdenum is absorbed as an anion (MoO42−) and is energy dependent; S can interfere, and P enhances Mo uptake (Barber, 1995; Mengel and Kirkby, 1982). Mass ﬂow and diffusion supply Mo to roots in soil (Table X). Zinc is absorbed primarily as a divalent cation (Zn2+) and may be absorbed at high soil pH as a monovalent cation (ZnOH+). It is not clear whether Zn uptake is active or passive, even though Mengel and Kirkby (1982) indicated that Zn was actively absorbed. Zinc is not reduced or oxidized as are Mn, Fe, and Cu. The low availability of Zn in high pH calcareous soils is due to the adsorption of Zn on clay or CaCO3 (Trehan and Sekhon, 1977). In addition, high concentrations of HCO3− inhibit Zn uptake and translocation (Dogar and van Hai, 1980). Zinc uptake is ¨ urk et al., 1996; Hopkins et al., also enhanced by phytosiderophores (Cakmak, Ozt¨ 1998).

C. OXIDATION AND REDUCTION Oxidation–reduction reactions occur when electrons are transferred from a donor to an acceptor. The donor loses electrons to increase in oxidation number, and the acceptor gains electrons to decrease in oxidation number. Redox reactions with various forms of Mn (Mn2+ and Mn4+), Fe (Fe2+ and Fe3+), and Cu (Cu+ and Cu2+) are common in soils (Lindsay, 1979), but Fe and Mn redox reactions are considerably more important than Cu because of their higher concentrations in soil. The primary source of electrons for biological redox reactions in soil is OM,

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but aeration, pH, and root and microbial activities also inﬂuence these reactions. Redox reactions in soil can also be inﬂuenced by organic metabolites produced by roots and microorganisms. Certain forms of micronutrients are more available to plants than others, and concentrations of each mineral form depend on soil conditions affecting redox. The most water-soluble and available forms to plants are Mn2+, Fe2+, and Cu2+, and these may be altered greatly depending on redox conditions. In general, a high pH favors oxidation and a low pH favors reduction of these minerals. The availability of Fe and Mn increases, and sometimes they become toxic to plants grown under highly reducing conditions (ﬂooding). Redox of Mn is thermodynamically favored at relatively higher redox potentials compared to Fe at given pH values. For example, the critical redox potential at which Fe2+ appeared was 100 mV and Mn2+ appeared at 200 mV in a Crowley silt loam soil at pH 6.5 (Patrick and Jugsujinda, 1992). As a result, demonstrated spatial relationships between Mn and Fe precipitation in horizontal sand columns relative to increased redox potentials were observed (Collins and Buol, 1970). Iron precipitated at relatively lower redox potentials compared to Mn, which did not precipitate until reaching more oxidized portions in columns. Liming soil to pH > 5.6 increased oxidation processes and reduced or prevented Mn toxicity (Kamprath and Foy, 1985). Increased reduction of Mn oxides occurred with increased soil temperature (Ross and Bartlett, 1981; Sparrow and Uren, 1987). Hence, warm soils may induce Mn toxicity more readily than cooler soils. Flooding (reducing conditions) had no inﬂuence on B concentrations in soils, and B did not undergo redox reactions (Ponnamperuma, 1972). Increasing soil Eh values (oxidation) redistributed Cu from exchangeable and organic fractions to Fe oxide fractions, thereby reducing Cu availability to plants (Shuman, 1991). Under ﬂooded conditions, Cu was adsorbed onto surfaces of reduced Mn and Fe oxides (Iu et al., 1981). Reducing conditions in soil mobilized Fe oxide fractions, which became associated with exchangeable, organic, and Mn oxide fractions to make Fe more available to plants (Shuman, 1991). Increases in Eh or soil pH shifted Fe from exchangeable and organic forms to water-soluble and Fe oxide fractions. Under alternate wetting and drying conditions, adding OM led to reducing conditions and enhanced Fe availability (Shuman, 1988). As redox potentials and/or soil pH increase, the plant availability of Fe decreases due to the insolubility of Fe3+ oxides. The critical redox potential for Fe3+ was −100 mV at pH 8, +100 mV at pH 7, and +300 mV at pH 6 (Gotoh and Patrick, 1974). Water-logging resulted in a decreased redox potential, and a low pH led to increased water-soluble and exchangeable Fe. Excess water in calcareous soil increased the buildup of HCO3−, which reduced soluble Fe3+ and induced Fe deﬁciency (Moraghan and Mascagni, 1991). Soil pH and redox potential are responsible for Mn transformation from insoluble to water-soluble and extractable forms. Under reducing conditions, Mn

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was released from organic and oxide forms into water-soluble and exchangeable fractions (Sims and Patrick, 1978). Low Eh values (reducing conditions) increased exchangeable Mn to mobilize Mn into more plant-available fractions (Shuman, 1991). In poorly drained soils, organic Mn and Mn oxides dominate compared to well-drained soils. Molybdenum does not appear to be directly involved in redox reactions in soil. However, the increases in soil pH and the reduction of Fe oxides under reducing conditions (low redox values) may increase the solubility of MoO4 (Moraghan and Mascagni, 1991). Zinc is not reduced under low redox conditions, but soil submergence tends to decrease Zn concentrations in soil solution (Ponnamperuma, 1972). Neither Zn nor Cu is affected by redox reactions which occur under most soil conditions. Submergence of soil caused Eh to decrease and pH to increase to enhance solubility and release oxide metals (Shuman, 1991). In ﬂooded rice soils, decreased concentration and mobility of Zn was due to Zn adsorption on surfaces of hydrated Mn oxides (Singh and Bollu, 1983).

D. RHIZOSPHERE The rhizosphere is deﬁned as the zone of soil immediately adjacent to plant roots in which the kinds, numbers, and/or activities of microorganisms differ from those of the bulk soil (SSSA, 1996). This zone usually contains fungi, bacteria, root and microorganism secretions, sloughed off or dead materials from microorganisms and roots, and chemical properties that are markedly different from the bulk soil. The chemistry of the rhizosphere has pronounced effects on the availability of micronutrients. An example of rhizosphere activity is mycorrhizae. Mycorrhizae associated with crop plants are primarily arbuscular mycorrhizal fungi (AMF). The AMF form beneﬁcial symbioses with roots to allow plants to grow considerably better than would be expected under relatively harsh mineral stress conditions. These fungi are ubiquitous in most soils, and about 90% of plants are mycorrhizal. The AMF improve host plant nutrition by improving the acquisition of P and other minerals, especially the low mobile micronutrients Zn, Cu, and Fe (Marschner, 1991a). The AMF accomplish this primarily by extension of root geometry. That is, AMF hyphae are smaller (average diameter = 3–4 μm) than roots and/or root hairs (diameter = >10 μm) and can make contact with soil particles and/or explore pores/cavities that roots would not otherwise contact (Clark and Zeto, 2000). Hyphae also extend away from roots and explore greater volumes of soil than roots themselves. The AMF may also protect plants from excessive uptake of some toxic minerals (Brady and Weil, 1996; Clark and Zeto, 2000). Root colonization with

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AMF can decrease the risk of plants to Mn, Fe, B, and Al toxicity in acid soils (Clark and Zeto, 2000; Marschner, 1991a). Toxicity factors may be reduced by inhibiting the acquisition of toxic minerals and/or from root/hyphae exudations to decrease reactions in the rhizosphere like Mn reduction (Marschner, 1991a). In addition to mycorrhizae, noninfecting rhizosphere microorganisms may affect mineral nutrition of plants through their inﬂuence on growth and morphology of roots, physiology and development of roots and shoots, availability of nutrients, and nutrient acquisition (Marschner, 1995). Whether high microbial activity in the rhizosphere leads to increases or decreases in micronutrient availability depends on the conditions. For instance, if root exudates consist mainly of organic acids or complexing compounds with high activity toward mobilizing Mn or Fe, utilization of these organic acids by rhizosphere microorganisms may decrease the acquisition of Mn and Fe. The positive effects of rhizosphere microorganisms on micronutrient availability have generally been noted when sugars are released in root exudates (Marschner, 1991b). Noninfecting rhizosphere microorganisms may also be responsible for oxidation of Mn2+ in bulk and rhizosphere soils and may immobilize (oxidize) or mobilize (reduce) Mn (Marschner, 1995). Roots also induce chemical and microbial changes in the rhizosphere that affect micronutrient availability. The rhizosphere pH may differ by as many as 2–3 units from that bulk of soil (Marschner, 1995). The net excretion of H+,OH−, and HCO3− from roots associated with cation/anion uptake induces pH changes in the rhizosphere, which have been related to soil buffer capacity and source of N. Root excretion of H+ at root surfaces is an effective mechanism for enhancing Zn uptake compared to excretion of complexing agents (Bar-Yosef et al., 1980). Acidiﬁcation of the rhizosphere generally improves availability of micronutrients, even in calcareous soils, to enhance micronutrient mobilization. This has been noted especially for Fe. Enhanced reducing activity at root surfaces has been noted as root-induced responses to Fe deﬁciency in dicotyledonous and nongraminaceous monocotyledonous plants (Marschner, 1995). Modiﬁcation of rhizosphere properties by roots is important in micronutrient acquisition by plants and plant ability to adapt to adverse mineral stress soil conditions (Marschner, 1995). Plant roots release or secrete low- and high-molecular-weight root exudates. Low-molecular-weight exudates include organic, amino, and phenolic acids (including phytosiderophores) and sugars. These low-molecular-weight exudates released from roots mobilize micronutrients in the rhizosphere and assist roots in acquiring less available minerals. The effectiveness with which root exudates dissolve sparingly soluble micronutrients depends on rhizosphere pH, N form, mineral deﬁciency-induced H+ excretion, and/or microbial acid production (Marschner, 1988). The major components of high-molecular-weight substances released to the rhizosphere are mucilages and ectoenzymes. These substances contribute to

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rhizodeposition (deposition of organic C). High-molecular-weight organic C exudates released into the rhizosphere serve as substrates for microorganisms around roots and may indirectly affect the solubility and availability of micronutrients (Curl and Truelove, 1986; Marschner, 1995). Microorganisms in the rhizosphere can beneﬁt plant growth by enhancing nutrient availability (mineralization, root morphology, fauna activity), increasing nonsymbiotic N2 ﬁxation, improving symbiotic root relationships with other microorganisms (rhizobia, mycorrhizae), enhancing plant responses to microbial metabolites, and decreasing plant pathogen activity and diseases (Curl and Truelove, 1986). Considerable amounts of C may be released by plants into the rhizosphere. On average, 30–60% of the net photosynthetic C is allocated to roots, and appreciable proportions of this C (14 to 40% of ﬁxed C) are released as organic C into the rhizosphere (Marschner, 1995). The amount of C released depends on plant age and growing conditions such as plant water status, soil aeration, soil strength, and nutritional status of plants (Whipps and Lynch, 1986). Rhizosphere deposition of organic C normally increases when various forms of stress such as mechanical impedance, anaerobiosis, drought, and mineral deﬁciencies occur (Lynch and Whipps, 1990; Whipps and Lynch, 1986). Soil microbes mineralize SOM, thereby releasing large amounts of essential mineral nutrients. Microorganisms at root surfaces may also affect root morphology (main root and root hair density, surface area), and subsequently enhance or reduce mineral absorption (Curl and Truelove, 1986). The release of root exudates increased soluble Cu concentrations (Nielson, 1976), and the dissociation of Cu2+ from organic ligands occurred prior to plant uptake (Goodman and Linehan, 1979). Reducing processes near roots can increase available Fe3+ from dissociation of Fe3+–chelates (R¨omheld and Marschner, 1986). Organic acids may also be responsible for the mobilization of sparingly soluble Fe (Fe3+) in the rhizosphere. Plant responses to Fe deﬁciency may increase the exudation of phenolic and amino acids, especially phytosiderophores, so that plants may acquire Fe (Marschner, 1995). Root exudation from Fe-deﬁcient barley grown in calcareous soil mobilized considerable amounts of Fe, Zn, Mn, and Cu (Treeby et al., 1989). Organic compounds such as hydroxy-carboxylates released from roots enhanced the Mn availability by reducing Mn4+ oxides and complexing Mn2+ (Godo and Reisenauer, 1980). Such effects of root exudates are particularly important in soils at pH < 5.5. Acquisition of Mn by rice grown in aerobic soil apparently was inﬂuenced by Fe uptake and soil pH (Jugsujinda and Patrick, 1977). Increased solubility of MnO2 by root exudates resulted mainly from organic acids (Uren and Reisenauer, 1988). For example, exuded organic, amino, and phenolic acids may directly enhance dissolution of sparingly soluble Mn compounds in soil. The effectiveness of root exudates for dissolution (reduction) of Mn oxides is favored at low and inhibited at high rhizosphere pH.

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E. INTERACTIONS WITH OTHER ELEMENTS The understanding of micronutrient interactions between and among the various mineral nutrients is important for balancing nutrient supplies to plants, improving growth and yields of plants, and eliminating deﬁciencies and toxicities imposed on plants. Mineral interactions are generally measured in terms of growth responses and changes in mineral nutrient concentrations in plants. An excellent review of the many interactions micronutrients have with other elements has been provided by Olsen (1972), and our article discusses mostly information since that review.

1. Boron The ability of anions to leach adsorbed B from Fe and Al oxides in soil increased in the order of Cl < S P (Metwally et al., 1974). Magnesium hydroxides also adsorb B (Rhoades et al., 1970). Normal B concentrations in plant tissue usually range from 10 to 50 mg kg−1 dry wt, but some plants like alfalfa require considerably more than others (Mengel and Kirkby, 1982). Positive relations have also been noted between B and K and N fertilizers for improving crop yields (Hill and Morrill, 1975; Moraghan and Mascagni, 1991). High B supplies resulted in low uptake of Zn, Fe, and Mn, but increased uptake of Cu. High pH, Ca, Mg, and N in soil may also reduce B in plants. In low B soil, high N induced B deﬁciency in plants (Gupta, 1993). However, the effects of P, K, and S on uptake of B are not clear, and these minerals had positive, negative, and/or no effects on B uptake (Gupta, 1993). Zinc deﬁciency enhanced B accumulation (Graham et al., 1987), and Zn fertilization reduced B accumulation and toxicity on plants grown in soils containing adequate B (Graham et al., 1987; Moraghan and Mascagni, 1991; Swietlik, 1995). Boron deﬁciency reduced uptake of P by faba bean (Robertson and Loughman, 1974) and reduced uptake of Mn and Zn by cotton (Ohki, 1975). Boron became toxic to maize when grown under P deﬁciency conditions, and P applications alleviated B toxicity (G¨unes and Alpaslan, 2000). Calcium translocation to shoots was inhibited because of the relatively high xylem sap pH, which was improved by applying B (Singaram and Prabha, 1997). Root Ca concentrations decreased while B concentrations increased, but B in shoots and fruit did not change, indicating that B translocation was not hindered by Ca in plants grown in calcareous soil. On the basis of equivalent Ca/B ratios, both foliar and soil applications of B insured adequate B to shoots and alleviated excess Ca uptake from soil (Moraghan and Mascagni, 1991). Even though the role of B in plants is not clearly understood, B is important in membrane structure, transport across membranes, metabolism of cellular N and P compounds, and viability of seeds (Kastori et al., 1995; Rerkasem et al., 1997). These processes

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would indirectly affect uptake of not only B but also other minerals. Uptake and transport of various mineral nutrients in plants are sensitive to B concentrations in the growth media (Mozafar, 1989). 2. Chlorine Only limited information is available on interactions of Cl with other nutrients. Chlorine is highly mobile in soil, and excessive concentrations can be leached by excess irrigation and/or rainfall. High concentrations of Cl in soil solution may depress mineral nutrient activities and produce abnormal Na/Ca, Na/K, Ca/Mg, and Cl/NO3–N ratios. As a result, plants may become susceptible to osmotic injury as well as nutritional disorders that could reduce plant yield and quality (Grattan and Grieve, 1999). Chloride is often added with K fertilizers, which are added at relatively high rates compared to other micronutrients. Increased levels of Cl reduced NO3–N (Inal et al., 1995) as Cl competes with NO3–N during uptake processes (Mengel and Kirkby, 1982). Evidence exists that if Cl rather than SO4–S is dominant in saline soils, Ca deﬁciency can be alleviated, and Cl may increase Ca uptake independent of Ca addition (Curtin et al., 1993). Chloride enhancement of Ca may also be related to increases in cation activity from Cl in soil solution or from co-transport resulting in neutralization of positive charges during cation uptake (Marschner, 1995). Ranges of Cl concentrations normal for tissue are high even though amounts needed for plant activity are relatively low (Mengel and Kirkby, 1982). 3. Copper Copper uptake is metabolically mediated and strongly inhibited by other divalent cations, especially Zn2+ (Mengel and Kirkby, 1982). Applications of relatively high levels of N and P fertilizers have induced Cu deﬁciency on plants grown in low Cu soils. Even though N and Cu interact, no signiﬁcant effects of NO3–N or NH4–N on Cu uptake have been noted (Kochian, 1991). However, transport of Cu was related to supply and transport of N, and Cu translocation increased with increasing N supplies (Jarvis, 1981b). Increased soil P induced Cu deﬁciency, but was related to dilution effects from increased growth and depressing effects of P on Cu absorption (Reuter et al., 1981). Copper toxicity has also been noted in P-deﬁcient plants (Wallace, 1984), and K also decreased Cu uptake in sunﬂower (Graham, 1979). Plants grown in coarse-textured soils with low available P and Fe and high in Cu exhibited Cu toxicity (Moraghan and Mascagni, 1991). Added Fe ameliorated Cu toxicity in spinach (Ouzounidou et al., 1998), and Cu toxicity has induced Fe deﬁciency in plants (Bowen, 1969). Increased Cu in the growth media, decreased Zn, and increased P levels in soil resulted in a reduced exploration of soil by mycorrhizal roots, which led to low Cu availability and low Cu concentrations

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in plant tissue (Moraghan and Mascagni, 1991). Since Zn and Cu are absorbed by the same carrier, each of these mineral nutrients competitively inhibits uptake of each other (Giordano et al., 1974). Microbial immobilization and antagonistic effects of increased concentrations of Fe and Mn reduced soil-available Cu. 4. Iron High soil levels of several minerals (Ca, P, N, Mn, and Cu) may contribute to the induction of Fe deﬁciency in many plants (Madero et al., 1993). On the other hand, low soil Fe may also inhibit or promote absorption of other minerals. Of the nutrients that interfere with Fe nutrition, minerals with the greatest effects followed the sequence of P > K > Mg > N > Ca (Luo et al., 1997). High Fe may also reduce uptake of these minerals. Different concentrations of Fe inhibited mineral uptake by rice grown in nutrient solution and uptake of P, K, Ca, Mg, and S by alfalfa, wheat, rice, and red clover also decreased with increased levels of Fe (Fageria, Baligar, and Edwards, 1990; Fageria and Rabelo, 1987). Similarly, uptake of Mn, Zn, and Cu in alfalfa, red clover, and wheat decreased when Fe concentrations increased. Increasing Cu in the growth medium decreased not only Fe but also Zn and Mn (Alva and Chen, 1995). However, the effect on Fe was more pronounced than that on Zn and Mn. Negative interactions between Fe and Mn have also been reported for other crop plants (Moraghan, 1985; Zaharieva, 1986). Soils low in Zn may enhance Fe uptake, especially when soil pH is >7.0 (Fageria and Gheyi, 1999). The effects of high soil P on decreasing plant Fe concentrations because of immobilization of soil Fe are well documented (Olsen, 1972), and high soil P levels decreasing plant Fe concentrations may also be related to inhibition of Fe absorption by roots, subsequent transport to shoots, and inactivation of Fe in plants (Moraghan and Mascagni, 1991). Nitrogen, especially NO3–N, can aggravate Fe deﬁciency by raising soil pH (Aktas and Van Egmond, 1979; Wallace et al., 1976) and release of HCO3− in the rhizosphere (Chen and Barak, 1982). With or without N fertilizer, the application of Fe resulted in increased N, P, K, Mg, Zn, and Cu concentrations in leaf blades of peanut, but decreased Ca and Mn (Ali et al., 1998). Manganese decreased Fe uptake and adversely affected Fe metabolism (Zaharieva et al., 1988) and increased Mo-decreased Fe uptake (Olsen and Watanabe, 1979). This latter interaction may be important in alkaline soils where Fe availability is low and soluble MoO42− concentrations may be high. Iron toxicity is common for rice grown in ﬂooded soils because of enhanced reducing conditions (Fe3+ to Fe2+), and Fe concentrations in solution and plants increase (Fageria, Baligar, and Wright, 1990). The nutritional status of rice is commonly related to Fe toxicity. When Fe toxicity occurs in rice, Fe concentrations in leaf blades may exceed 300 mg kg−1 dry wt (Fageria, Baligar, and Wright, 1990; Yoshida, 1981). In addition, P, K, Ca, Mg, and Mn deﬁciencies decrease the capacity of rice roots to exclude Fe, and Fe toxicity may result. In soils where

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problems of Fe toxicity exist, P and K deﬁciencies appeared before uptake of Mn, Zn, and Cu was reduced. Adequate concentrations of K in soil solution also decreased Fe toxicity in rice (Fageria, Baligar, and Edwards, 1990). Zinc deﬁciency may accentuate Fe uptake and lead to the accumulation of toxic levels of Fe in plants (Adams and Pearson, 1967).The addition of MnO2 increased soil redox potential and reduced concentrations of Fe2+ and organic reducing products (Fageria, Baligar, and Wright, 1990). Iron toxicity is more severe for plants grown on heavy-textured soils compared to light-textured soils. 5. Manganese The anionic minerals P, S, NO3–N, and Cl and the cationic minerals K and NH4–N affect solubility, mobility, and/or availability of Mn to crop plants (Norvell, 1988). Studies on the interactions between Mn and divalent minerals are also common (Bowen, 1969; Chinnery and Harding, 1980). Manganese uptake is considered to be active and may be inhibited by Ca, Mg, and Zn (Maas et al., 1969; Robson and Loneragan, 1970). Relatively high concentrations of Fe were noted in leaves of soybean grown with low Mn, and Mn concentrations in soybean shoots decreased with increased Fe levels in solution (Chinnery and Harding, 1980). Free CaCO3, high Fe, and strongly alkaline conditions may also induce Mn deﬁciency in plants. The application of Fe may reduce concentrations of Mn in plants. Plants grown with Fe applications had high plant growth and low shoot Mn concentrations, even to deﬁciency levels, because of dilution (Romero, 1988). The antagonistic effects of FeEDDHA on Mn accumulation were reported in white lupin, but these effects occurred mainly when relatively high amounts of P were added (Moraghan, 1992). Relatively low levels of Fe (4 mg kg−1 soil) in the absence of added P had only slight negative effects on Mn and even increased Mn concentrations. In contrast, marked depressing effects of FeEDDHA on Mn concentrations were noted for plants grown with high P (120 mg kg−1 soil). Problems associated with Fe–Mn interactions have been related mainly to chemical interactions at the root–soil interface (Kochian, 1991). Increased rhizosphere acidity from plant responses to Fe deﬁciency may also enhance Mn4+ reduction to Mn2+, and increase Mn2+ solubility (Marschner, 1988). Increased levels of soil P both increased and decreased Mn toxicity in plants, and applications of Zn or Mo fertilization reduced Mn uptake (Moraghan and Mascagni, 1991). Increasing concentrations of Fe (also Ca or Mg) in the growth medium may also decrease Mn toxicity (Marschner, 1995). Excess Mn-induced Fe deﬁciency in potato and leaves had Mn/Fe ratios of 18 or higher (Lee, 1972). The high Al availability counteracted these effects by increasing Fe in plants and decreasing Mn/Fe ratios. Plants with Fe deﬁciency had lower Mn/Fe ratios, and plants with higher ratios developed Mn toxicity (Lee, 1972). Manganese toxicity and Fe deﬁciency symptoms are different in rice, and the range at which Fe toxicity can be remedied by Mn application is narrow (Tanaka

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and Navasero, 1966). High shoot Fe concentrations of Fe-inefﬁcient, Mn-sensitive soybean accentuated Mn toxicity, and high shoot K concentrations of Mn-tolerant soybean alleviated the harmful effects of high internal Mn concentrations (Brown and Jones, 1977). The increased Ca levels in the growth medium decreased Mn uptake and toxicity (Heenan and Carter, 1976). Phosphorus detoxiﬁed Mn by precipitating it within plant roots (Heintze, 1968). Soluble sources of Si in the growth medium can also protect plants against Mn toxicity (Foy et al., 1978). Plants low in Si, P, Ca, Mg, and Fe often accumulate high Mn and are susceptible to Mn toxicity (El-Jaoual and Cox, 1998). Silicon may also decrease excessive uptake of Mn and Fe (Foy et al., 1978). Excess Mn can interfere with absorption, translocation, and utilization of P, Ca, Mg, and Fe (Clark, 1982) and reduce concentrations of Si, K, Zn, and Cu (Clark and Baligar, 2000). Increasing Mn concentrations in nutrient solution triggered synergistic effects on Ca, Mg, Na, P, and Cu uptake, but displayed antagonistic action on K and Zn in rice (Lidon, 1999). Translocation of Fe was also inhibited. Increasing Mn levels delayed rice maturation and the concentrations of the minerals accumulated. However, concentrations of potentially toxic minerals in grain were lower than those in vegetative tissues. Concentrations of Ca, K, Na, P, and Zn interacted with increasing Mn concentrations, mostly in shoots, but different patterns were noted for Mg, Cu, and Fe. Manganese acquisition was reduced with the application of Zn (Haldar and Mandal, 1981) and Mo fertilizers (Sims et al., 1975). Interactions of Mn with other elements, particularly Fe and Si, may be extensive (El-Jaoual and Cox, 1998). 6. Molybdenum Sulfur, P, and NH4–N applications may decrease Mo concentrations in plants and accentuate Mo deﬁciency (Anderson, 1956; Gupta and MacLeod, 1975; Ray et al., 1986). Soil application of Mo increased Mo and N uptake by legumes at soil pH 5 (Mortvedt, 1981). High Fe and Al oxides and good soil aeration (drainage) also reduced Mo availability. Sulfur has been used to decrease Mo uptake and reduce Mo toxicity in plants through decreasing soil pH (Chatterjee et al., 1992). Increased B and decreased K, Mn, and Cu were noted in barley grown with high Mo (Brune and Dietz, 1995). High Mo may also induce Cu deﬁciency in cattle (“molybdenosis”) (Miller et al., 1991). Although Mo is essential to higher plants, its concentration in tissue is low (usually < 1 mg kg−1 dry wt) and crucial in N metabolizing enzymes (nitrate reductase) (Beevers and Hageman, 1969; Yu et al., 1999). 7. Zinc Zinc interactions with other elements are many and include Zn–P, Zn–N, Zn–K, Zn–Mn, Zn–Fe, and Zn–Cu (Moraghan and Mascagni, 1991; Olsen, 1972). Under

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some conditions, Co and Na may also inhibit Zn absorption (Loneragan and Webb, 1993). The most widely reported interaction with Zn is that of P. High P applied to low Zn soils enhanced the plant accumulation of P thereby increasing the internal plant Zn requirement because of Zn precipitation (Robson and Pitman, 1983). High applications of P fertilizer can induce Zn deﬁciency (P-induced Zn deﬁciency) and increase plant requirements for Zn (Robson and Pitman, 1983). Inappropriately high P applications have induced Zn deﬁciency in plants most likely because of increased P uptake and higher shoot growth, which has led to decreased Zn in shoots because of dilution (Loneragan et al., 1979; Marschner, 1993). Zinc-deﬁcient plants may also have high and potentially toxic P concentrations, and P toxicity symptoms have sometimes been mistaken for Zn deﬁciency (Fageria and Gheyi, 1999). Nevertheless, Zn-deﬁciency-induced P toxicity may be an artifact caused by high P concentrations (Loneragan and Webb, 1993). High levels of P have also resulted in increased absorption and retention of Zn in roots and decreased translocation to leaves (Iorio et al., 1996). The processes involved with P–Zn interactions and the subsequent low acquisition of Zn by plants include high P in soil decreasing Zn solubility, reduced root growth, cations added with and H+ generated by P salts to inhibit Zn absorption, and suppressed root colonization by mycorrhizae (Loneragan and Webb, 1993; Robson and Pitman, 1983). Plants with reduced mycorrhizal root colonization had lower Zn concentrations (Lambert et al., 1979), and mycorrhizal plants commonly have higher Zn concentrations than nonmycorrhizal plants (Clark and Zeto, 2000). In certain soils, added P tended to enhance the adsorption of Zn on soil particles rich in hydrated Fe and Al oxides with subsequent inducement of Zn deﬁciency on plants (Barber, 1995). Many interactions of Zn with macronutrients other than P have been noted. Both monovalent and divalent cations can inhibit Zn uptake, and the importance of these were NH4–N > Rb > K > Cs > Na > Li for monovalent minerals and Mg > Ba > Sr = Ca for divalent minerals (Chaudhry and Loneragan, 1972a,b). The application of gypsum to sodic soils and the addition of manures have also helped alleviate Zn deﬁciency (Takkar and Walker, 1993). Alkaline soils and soils high in CaCO3, N, and P and low in SOM normally have reduced Zn availability. High levels of H+ also competitively reduced Zn absorption (Barber, 1995). With increased supplies of S, increased Zn was translocated from roots to shoots (Fontes and Cox, 1998a). High levels of Zn decreased uptake of Cu and Mn in upland rice grown in an Oxisols in central Brazil. Zinc interactions with other micronutrients include enhanced B concentrations in Zn-deﬁcient plants (Singh et al., 1990) and B toxicity being reduced with Zn applications (Graham et al., 1987; Singh et al., 1990). Mutually competitive interactions occur between Cu and Zn (Barber, 1995; Loneragan and Webb, 1993). Zinc–Cu interactions affected plant nutrition because Zn strongly depressed Cu absorption, Zn and Cu competitively inhibited each other, and Cu affected

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redistribution of Zn within plants (Loneragan and Webb, 1993). Enhanced Zn supplies improved the growth of Fe-deﬁcient soybean (Fontes and Cox, 1998a), and Fe applications overcame many soybean Zn toxicity effects (Fontes and Cox, 1998b). Increased soil Zn increased translocation of Mn to soybean shoots to induce Mn toxicity (crinkle leaf), and Zn and Mn interfered with Fe utilization in leaves to reduce chlorophyll synthesis (Foy et al., 1978) . In addition, Zn deﬁciency enhanced uptake of Mn so that Mn concentrations reached phytotoxic levels (Robson and Pitman, 1983). Increased concentrations of Mn, B, and Mo were also noted when barley received Zn applications (Brune and Dietz, 1995). 8. Nickel and Cobalt Maize grown in calcareous soil with Ni applications enhanced Zn and decreased P concentrations (Karimian, 1995), and high levels of Ni increased B, Mn, and Mo in barley (Brune and Dietz, 1995). Simultaneous supplies of NO3–N and NH4–N reduced Ni toxicity in sunﬂower, and growth was enhanced from added Ni (Zornoza et al., 1999). Low Ni plants became N deﬁcient from lack of urease activity with a high accumulation of urea but low tissue N (Gerendas and Sattelmacher, 1997). Cobalt availability was decreased in soils containing high CaCO3 and high Fe, Mn, SOM, and moisture. Added Co to growth media increased N, P, Ca, and Cu, but had no enhancement effects on K, Mg, Na, and Zn in tomato (Moreno-Caselles et al., 1997). Calcium and Mg noncompetitively inhibited Ni uptake, whereas Cu, Zn, and Co competitively inhibited Ni absorption (Korner et al., 1987).

V. IMPROVING SUPPLY AND ACQUISITION A. SOIL IMPROVEMENT Production potentials of many soils in the world are decreased by low supplies of micronutrients from adverse soil physical and chemical constraints (Baligar and Duncan, 1990; Baligar and Fageria, 1997; Dudal, 1976; Fageria, 1992; Fageria and Baligar, 1997a; Fageria, Baligar, and Edwards, 1997; Fageria, Baligar, and Wright, 1997; Foy, 1984). Major chemical (salinity, acidity, elemental deﬁciencies and toxicities, low SOM) and physical (bulk density, hardpan layers, structure and texture, surface sealing and crusting, water holding capacity, water-logging, drying, aeration) constraints affect transformation (mineralization, immobilization), ﬁxation (adsorption, precipitation), and leaching or surface runoff of indigenous and added micronutrients (Baligar and Bennett, 1986a,b; Baligar and Fageria, 1997). In tropical regions, common soil micronutrient problems in rainfed systems affecting crop production include Fe toxicity and Zn deﬁciency (Baligar and

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Fageria, 1997; Fischer, 1998). Acid soils present special micronutrient nutritional problems for plants because of the high availability of Mn and Fe and the reduced availability of Zn and Mo (Baligar and Fageria, 1997; Fageria, Baligar, and Edwards, 1990; Fageria, Baligar, and Wright, 1990; Sumner et al., 1991). In addition, factors enhancing acidiﬁcation not only lead to micronutrient toxicities/deﬁciencies but also to soil degradation (Baligar and Ahlrichs, 1998; Baligar et al., 1998; Dudal, 1976; Sumner et al., 1991). Micronutrients commonly occurring in toxic concentrations in salt-affected soils-include Mo and B (Gupta and Abrol, 1990). In recent years, the addition of toxic trace elements like Cd, Cr, Ni, Pb, Cu, Zn, As, Co, and Mn (some of which are considered micronutrients) to agricultural soils has increased from enhanced anthropogenic activity (burning fossil fuels, application of sewage, industrial, mine, municipal products), use of amendments (fertilizers, manures, lime), application of pesticides, and deposition of atmospheric particles (Adriano, 1986; Alloway, 1995a,b; Kabata-Pendias and Pendias, 1992). Excessive levels of trace elements pose phytotoxicities to plants and may reduce growth and acquisition of micronutrients (Baligar et al., 1998; KabataPendias and Pendias, 1992; Marschner, 1995). Temperature, pH, redox potentials, anion ligand formation, and composition and quantity of solution greatly inﬂuence the mobility and bioavailability of micronutrients and other trace elements in soil (Alloway, 1995b). The bioavailability of most trace elements is high at low soil pH. Adverse soil physical properties affect longitudinal and radial root growth, root distribution, morphological (stunting, thickening, reduction of lateral roots) and anatomical changes (Bennie, 1996; Russell, 1977; Taylor et al., 1972). High mechanical impedance leads to the loss of root caps and the reduction of root thickening, primarily due to short and wide cells of the same cortex volume (Camp and Lund, 1964) and thick cortex cells (Baligar et al., 1975). Mechanical impedance may also cause changes in the structure of the endodermis and pericycle cells (Baligar et al., 1975; Bennie, 1996). Such changes in root size and internal and external morphology will inﬂuence root ability to explore large soil volumes for micronutrients. Excessive or deﬁcient micronutrients also affect morphology (length, thickness, surface areas, density) and growth (dry mass, root : shoot ratio) of roots and root hairs (Baligar et al., 1998; Bennett, 1993; Hagemeyer and Breckle, 1996; Fageria, Baligar, and Jones, 1997; Fageria, Baligar, and Wright, 1997; Foy, 1992; Kafkaﬁ and Bernstein, 1996; Marschner, 1995). Maize root : shoot ratios increased when Zn was decreased and decreased when Mn and Cu were decreased (Clark, 1970). Organic matter helps maintain good soil aggregation, increases water holding capacity and exchangeable ions, leaching of nutrients, and Mn and Fe toxicities (Baligar and Fageria, 1997; Fageria, 1992; von Uexkull, 1986). The addition of crop residues, green manures, composts, animal manures, growing cover crops,

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using reduced tillage, and avoiding elimination (burning) of crop residues can signiﬁcantly improve SOM levels and eventually lead to improved plant growth and acquisition of micronutrients. Liming has also been effective in correcting soil chemical constraints (Adams, 1984) and has improved the availability of Mo and decreased the availability of Mn, Fe, B, Zn, and Cu, and reduced Mn toxicity (von Uexkull, 1986). Liming also improves root growth to increase plant ability to absorb micronutrients. In addition, liming improves soil capacity to supply needed micronutrients to plants (Baligar and Fageria, 1997; Fageria, 1992; Fageria et al., 1995).Since lime has low mobility in soil, surface-applied lime has little or no effect on improving problems in subsurface soil. However, the tendency for downward movement of Ca from surface-applied gypsum (CaSO4) is high (Farina and Channon, 1988; Farina et al., 2000; Ritchey et al., 1980, 2000) and has long-term positive effects on plant growth (Farina et al., 2000; Toma et al., 1999). The downward movement of Ca in soil improved the rooting depth and increased the levels of micronutrients for maize grown in Cerrado acid soils of Brazil (Sousa et al., 1992). The reduction of subsoil acidity problems usually leads to deeper rooting and improves micronutrient uptake by plants.

B. SOIL AND FOLIAR FERTILIZATION The sources of micronutrients may be inorganic, synthetic chelates, and/or natural organic complexes. The potential exists for creating toxic levels of micronutrient in soil by misapplication, since only small amounts are leached from soil (except B) or small quantities are absorbed by plants (Martens and Westermann, 1991). Micronutrient toxicities are undesirable as they lower yields and product quality, and excessive levels may enter the food chain. The remediation of soils with high levels of micronutrients is relatively difﬁcult. The factors inﬂuencing availability and plant acquisition of micronutrients have been discussed in earlier sections. Both organic and inorganic micronutrient sources are used to correct deﬁciencies in soil. Soil application includes band or broadcast applications before planting or foliar sprays during vegetative growth. Micronutrients are usually blended with or coated onto granular N, P, and K fertilizers or mixed with ﬂuid fertilizers (Mortvedt, 1991, 2000). To prevent chemical alteration of micronutrients, blending should occur relatively soon before application (Mortvedt, 1991). Foliar applications are used to supply micronutrients more rapidly for correction of severe deﬁciencies commonly induced during the early stages of growth, and are temporary solutions to the problem. Several problems associated with foliar applications include low penetration rates in thick leaves, run-off from hydrophobic surfaces or being washed off by rain, rapid drying of spray solution, limited

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translocation from uptake site to other plant parts, limited amounts of nutrients that can be supplied and often do not meet plant demands, and leaf damage/burn (Marschner, 1995). Reducing the pH of spray solutions may reduce leaf damage. The addition of Si-based surfactants appears to reduce leaf damage and increase spray effectiveness (Horesh and Leavy, 1981). The disadvantages of foliar application are maximum yields which may not be possible if spraying is delayed until deﬁciency symptoms appear and residual effects from foliar sprays are little, thus multiple sprays may be required for season-long correction (Mortvedt, 2000). However, foliar fertilization has many advantages which include: rates applied are considerably lower than soil applications; uniform applications are possible; crop response to applied micronutrient is almost immediate so that deﬁciency can be corrected relatively rapidly; problems often associated with inactivation of soil-applied micronutrients may be overcome (Mortvedt, 2000). Plant (leaf age, species, nutritional status and requirements), climatic (light, temperature, humidity), and chemical (form, carrier, adjuvant) factors affect foliar spray effectiveness (Kannan, 1990). Greater absorption by leaves is favored under low light, optimum temperature, and high humidity conditions. Young leaves are metabolically more active than older leaves and are more effective with absorption. Hygroscopic compounds keep micronutrients in solution longer, thereby helping plants absorb these elements more effectively than nonhygroscopic compounds. To increase the effectiveness of foliar uptake, wetting agents are usually added to sprays. These chemicals are neutral nonionic compounds which reduce surface tension and increase wetting of leaf surfaces to enable larger amounts of solution to be absorbed (Kannan, 1990). 1. Correcting Deﬁciencies The measures for correcting micronutrients are summarized in Table XI. This information includes concentrations of nutrients for soil and foliar spray applications. The concentrations listed are approximate and may vary depending on original soil level, crop species/cultivar, crop yield desired, and climatic conditions. Issues related to soil and foliar fertilization of micronutrients and correcting their deﬁciencies in soil and plants have been discussed (Martens and Westermann, 1991; Mortvedt, 1991, 2000). Crop recovery of micronutrients is relatively low (5 to 10%) compared to that of macronutrients (10 to 50%) because of poor distribution from low rates applied, fertilizer reactions with soil to form unavailable products, and low mobility in soil (Mortvedt, 1994). The principal sources of micronutrient fertilizers used have been listed in Table XII. Boron is usually applied at 0.25 to 3 kg ha−1, and higher rates are required for broadcast than for band application or foliar sprays (Mortvedt and Woodruff, 1993). Legumes and certain root crops require 2 to 4 kg B ha−1, while lower rates are usually necessary for maximum yields of other crops (Martens and Westermann,

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Table XI Methods of Correcting Micronutrient Deﬁcienciesa Corrective measure Element B Cl Cu

Fe

Soil applicationb 0.25–7 kg borax ha−1 (soil application preferred) 20–50 kg KCl ha−1 1–20 kg CuSO4 ha−1 (every 5–10 years)

Zn

30–100 kg FeSO4 or FeEDDHA ha−1 (need annual treatment of 0.5–10 kg ha−1) 5–50 kg Mn source ha−1 (soil application not recommended) 0.01–1 kg Mo source ha−1 (0.3 Na or NH4 molybdate ha−1) or lime to pH 6.5 0.5–35 kg ZnSO4 or ZnEDTA ha−1

Ni Co

Usually not needed 1–6 kg Co source ha−1 (broadcast)

Mn Mo

Foliar applicationc 0.1–0.25% B solution or 1–10 kg B ha−1 Unknown 0.1–0.2% solution CuSO4·5H2O or 0.1–4.0 kg Cu ha−1 as CuCl2·2H2O, CuSO4·5H2O, or CuO 2% FeSO4·7H2O or 0.02–0.05% FeEDTA solution (several sprays needed) 0.1% MnSO4·H2O solution or 0.3–6 kg Mn ha−1 0.07–0.1% Na or NH4 molybdate (100 g Mo ha−1) 0.1–0.5% ZnSO4·7H2O solution (0.17–1.5 kg ha−1) May be applied as spray 500 mg Co L−1 solution or 500 mg Co kg−1 seed treatment

a From Bould et al. (1983), Fageria, Baligar, and Jones (1997), and Martens and Westermann (1991). b Lower values for soil applications are applicable for band application and higher values are for broadcast applications. c 400 liters of solution is sufﬁcient to spray 1 ha of ﬁeld crop.

1991). Using the concept of Ca/B ratios, the application of foliar (0.3%) or soil (10 kg ha−1) B ensured adequate B (Moraghan and Mascagni, 1991). Borax or other soluble borates are usually applied to soil before planting. Boron fertilizer should not be placed in contact with seeds or at levels that may be toxic to crops. Boron availability commonly decreases during drought and when acid soils are limed (Martens and Westermann, 1991). Even though Cl has been recognized as essential to plants, comparatively little attention has been given to Cl as a fertilizer because soil levels from inputs and rain are considered adequate to meet crop requirements. Chlorine may become limiting for high yields in intensive production practices. Positive yield responses were noted for application of 400 kg Cl ha−1 for maize (Heckman, 1995). Winter wheat yields were also increased with Cl applications at seven of nine experimental sites (Engel et al., 1994). Only a few land areas are deﬁcient in Cl, and crops grown

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FAGERIA et al. Table XII Principal Sources of Micronutrient Fertilizers to Correct Deﬁcienciesa

Element B

Cl

Cu

Fe

Mn

Mo

Zn

Source

Formula

Boric acid Borax Na borate (anhydrous) Na pentaborate Na tetraborate Boron frits K chloride Zn chloride Ca chloride Mn chloride

H3BO3 [B(OH)3] Na2B4O7·10H2O Na2B4O7 Na2B10O16·10H2O Na2B4O7·5H2O Fritted glass KCl ZnCl2 CaCl2 MnCl2

Cu sulfate (monohydrate) Cu sulfate (pentahydrate) Cu chloride Cuprous oxide Cupric oxide Cu chelate Cu chelate Ferrous sulfate (monohydrate) Ferrous sulfate (heptahydrate) Ferrous ammonium sulfate Ferric sulfate Fe chelate Fe chelate Fe chelate Fe chelate Fe frits Mn sulfate (anhydrous) Mn sulfate (tetrahydrate) Mn chloride Mn carbonate Mn oxide Mn chelate Mn frits Na molybdate Ammonium molybdate Mo trioxide Molybdic acid Mo frits Zn sulfate (monohydrate) Zn sulfate (heptahydrate) Zn chloride Zn oxide Basic Zn sulfate

CuSO4·H2O CuSO4·5H2O CuCl2 Cu2O CuO Na2CuEDTA NaCuHEDTA FeSO4·H2O FeSO4·7H2O (NH4)2SO4·FeSO4·6H2O Fe2(SO4)3·4H2O NaFeEDTA NaFEHEDTA NaFeEDDHA NaFEDTPA Fritted glass MnSO4 MnSO4·4H2O MnCl2 MnCO3 MnO Na2MnEDTA Fritted glass Na2MoO24·2H2O (NH4)6Mo7O24·4H2O MoO3 H2MoO4·H2O Fritted glass ZnSO4·H2O ZnSO4·7H2O ZnCl2 ZnO ZnSO4·4Zn(OH)2

Element (%)

Solubilitya

17 11 20 18 14 1.5–2.5 48 52 64 44 35 25 47 89 75 13 9

Soluble Soluble Soluble Soluble Soluble Sl. solubleb Soluble Soluble Soluble Soluble Soluble Soluble Soluble Insoluble Insoluble Soluble Soluble

33 19 14 23 5–14 5–9 6 10 2–6

Soluble Soluble Soluble Soluble Soluble Soluble Soluble Soluble Sl. soluble

23–28 26–28 17 31 41–68 5–12 2–10

Soluble Soluble Soluble Insoluble Insoluble Soluble Sl. soluble

39 54 66 53 0.1–0.4

Soluble Soluble Sl. soluble Soluble Sl. soluble

36 23 48–50 50–80 55

Soluble Soluble Soluble Insoluble Sl. soluble continues

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Table XII—continued Element

Ni

Co

a b

Source Zn chelate Zn chelate Zn frits Ni chloride Ni nitrate Ni oxide Co sulfate Co nitrate

Formula Na2ZnEDTA NaZnEDTA Fritted glass NiCl2·6H2O Ni(NO3)2·6H2O NiO CoSO4·7H2O Co(NO3)2·6H2O

Element%

Solubilitya

14 9 4–9 25 20 79 21 20

Soluble Soluble Sl. soluble Soluble Soluble Insoluble Soluble Soluble

From Mortvedt (1991, 2000), and Martens and Westermann (1991). Slightly soluble.

on salt-affected soils often exhibit symptoms of Cl toxicity. Seed germination may be inhibited with high concentrations of Cl, so Cl fertilizers need to be applied in advance of planting (Bould et al., 1983). Copper deﬁciency can generally be corrected by applying 3.3 to 14.5 kg Cu ha−1 as broadcast CuSO4 (Martens and Westermann, 1991). The rates of banded CuSO4 required to correct Cu deﬁciency have been as low as 1.1 kg ha−1 for vegetables and as high as 6.6 kg Cu ha−1 for alfalfa, oat, and wheat. Copper deﬁciency can be corrected by banding or broadcasting Cu to soil or as foliar sprays. Lower rates of Cu application are required to correct Cu deﬁciency with banded compared to broadcast CuSO4. Foliar sprays are emergency measures, as Cu deﬁciency is most frequently corrected by soil applications (Murphy and Walsh, 1972) which are more effective than foliar sprays (Solberg et al., 1993). Soil application of CuSO4 is usually more effective than CuO, and Cu might need frequent applications when problems persist (Karamanos et al., 1986). The differences in the rates of Cu required to correct Cu deﬁciency vary with soil properties, crop requirement, and concentrations of extractable soil Cu. In semiarid regions, drying of top soil reduces Cu availability. Iron deﬁciency is corrected mainly by foliar sprays because soil applications are generally ineffective unless very high rates are applied. Typical Fe compounds used for foliar application to crops are FeSO4, Fe(NO3)2, and FeDTPA, and a 200 kg ha−1 FeSO4 rate was required to obtain maximum yields of annual crops (Mortvedt, 1991). More than one foliar spray and often three to four are needed during vegetative growth periods to obtain optimum production of crops like sorghum, soybean, and rice. Tree injection with ferric ammonium citrate (8% Fe) and seed treatment with FeEDDHA have had limited success in correcting Fe deﬁciency. Inorganic Fe sources applied to soils are rapidly converted to unavailable forms (oxidation of Fe2+ to Fe3+) in well-aerated soils, especially as soil pH increases. In Oxisols from central Brazil, Fe deﬁciency on upland rice was frequently reported where soil had been limed to pH ∼ 6 for the production of common bean

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and soybean in rotations (Fageria et al., 1994). Synthetic Fe chelates are generally the most effective Fe sources for soil and foliar applications, but their cost may be prohibitive. A common source of Fe applied to annual crops is FeSO4, but Fe chelates may be cost-effective if crops are of high value (fruits and berries). Fritted materials are sometimes used in acid soils to maintain Fe for plants (Martens and Westermann, 1991). A common source of Mn applied to soils and as foliar sprays is MnSO4. Soybean and rice commonly develop Mn deﬁciency during their growth on many soils. Optimum soybean yields were obtained with MnSO4 broadcast (14 kg ha−1) and band (3 kg ha−1) applied, and Mn deﬁciency was corrected by broadcasting MnSO4 (11 kg ha−1) or banding at half that rate or by timely foliar applications (1–2 kg ha−1) (Hatﬁeld and Hickey, 1981). In other studies, 10 to 40 kg MnSO4 ha−1 was required to achieve maximum soybean yields (Anderson and Mortvedt, 1982). Manganese deﬁciency on soybeans grown in a Brazilian Cerrado Oxisol at pH 6.7 was corrected with applications of 15 mg MnSO4 kg−1 soil (Novais et al., 1989). Manganese deﬁciency on rice grown in a drained Histosol at pH ∼ 7 was alleviated with soil applications of ∼15 kg MnSO4 (Snyder et al., 1990). Seed applications of Mn also prevented Mn deﬁciency and provided near-maximum grain yields, and banded MnSO4 with seed has been equally as effective as sprayed Mn. Soil applications of Mn with acid-forming macronutrient fertilizers in neutral to high pH soils generally increase Mn effectiveness, and Mn deﬁciencies on plants grown in acid soils may be avoided by not over-liming. Both MnSO4 and MnO were effective as sources of Mn at rates of 20 kg Mn kg−1 for correcting Mn deﬁciency on soybeans grown in an Oxisol at pH 6.9 (Abreu et al., 1996). Chelated Mn (MnEDTA), MnSO4, and mangasol were equally effective for alleviating Mn deﬁciency on lupine (Brennan, 1996). Foliar applications of MnSO4 are effective for small grain cereals grown in calcareous and alkaline soils, which tend to dry during the growing season (Reuter et al., 1973). Soybean receiving 1.12 kg MnSO4 foliar sprays during early growth stages (V6) and again during late growth stages (R1) had higher yields than plants receiving single early sprays (Gettier et al., 1985). Multiple applications of foliar MnSO4 are usually superior to single applications on soybean (Cox, 1968). Molybdenum deﬁciency can be corrected by soil and foliar applications and by seed treatments. Since the availability of Mo increases as soil pH increases, liming acid soils to pH 6.5–7.0 will frequently prevent or correct Mo deﬁciencies (Martens and Westermann, 1991). The application of 0.01 to 0.5 kg Mo ha−1 will generally correct Mo deﬁciency. Sodium and/or ammonium molybdates are suitable sources for soil applications. Foliar applications of Mo have usually been more effective than soil applications for crops grown under dry conditions (Martens and Westermann, 1991). Foliar applications of 40 g Mo ha−1 increased bean growth and shoot N concentrations (Viera et al., 1998). High rates of seed-treated Mo

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could be toxic to rhizobia or my induce seedling injury (Sedberry et al., 1973). Even though excess Mo applications could lead to Cu deﬁciencies in animals (“molybdenosis”), this hazard is low since most Mo becomes relatively insoluble in well-drained soils (Martens and Westermann, 1991). Zinc deﬁciency can be corrected by either foliar or soil applications of ZnSO4 or ZnEDTA (Martens and Westermann, 1991). Foliar Zn is usually applied in emergencies to salvage crops when Zn deﬁciencies appear, and one foliar application is usually not adequate for correcting moderate to severe Zn deﬁciency. Maximum grain yields were obtained with foliar applications of ∼1 mg Zn kg−1 during the third and fourth weeks after plant emergence for maize grown in an Oxisol in central Brazil (Galr˜ao, 1994, 1996) and with 6 mg Zn kg−1 soil for upland rice grown in a greenhouse (Barbosa Filho et al., 1990). Applications of Zn either by broadcast or band usually are more effective than foliar applications (Murphy and Walsh, 1972). Zinc deﬁciency is common on land where subsoils have been exposed after land leveling, and these normally receive applications of farmyard manure to alleviate deﬁciencies and improve soil conditions (Martens and Westermann, 1991). Nickel is ubiquitous in soils, and most P fertilizers contain sufﬁcient Ni for plant productivity, so Ni is not usually applied to soils. However, foliar applications have corrected Ni deﬁciency (Chamel and Newmann, 1987). Cobalt deﬁciency is usually controlled by soil broadcast applications (0.4 to 6 kg Co ha−1), foliar applications (500 mg Co L−1), and seed treatments (500 mg Co kg−1) (Raj, 1987; Reddy and Raj, 1975). Both sulfate and nitrate salts of Co have been used as fertilizers. 2. Residual Effects Knowledge concerning residual effects of applied micronutrient fertilizers is important to make sound and economic recommendations for succeeding crops. Micronutrient fertilizers have longer residual effects in high silt and clay than in sandy soils. Slightly soluble materials also have longer residual effects than highly soluble materials. Crop yields also determine residual micronutrient effects in soil. Information about long-term micronutrient effects is limited. Since crop recovery of micronutrients is relatively low, long-term residual effects might be expected. Broadcast applications of 2 kg B ha−1 as Borate-65 to a loam soil provided sufﬁcient B for alfalfa and red clover for 2 years (Gupta, 1993). Recommendations for correcting Cu deﬁciency indicated a relatively high residual availability of applied Cu. For example, residual Cu was effective for 5 to 8 years after application for several crops (Martens and Westermann, 1991). Soil applications of Fe sources usually have no or only limited residual effects, since Fe2+ is rapidly converted to Fe3+ in aerated soils. Band applications of Fe at relatively high rates may be effective for more than 1 year provided tillage operations do not mix fertilizer

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with surrounding soil (Martens and Westermann, 1991). Manganese applied at 20 to 40 kg ha−1 to a sandy loam soil produced maximum soybean yields, but this Mn was insufﬁcient to alleviate deﬁciency the next year (Gettier et al., 1984). However, optimum soybean yields occurred 2 years after broadcasting 30 kg Mn ha−1 on a clay loam soil (Mascagni and Cox, 1985). Residual effects have usually been higher for MnSO4 than for MnO (Abreu et al., 1996). The results regarding residual effects of Mo fertilization showed that effectiveness decreased ∼50% per year in some soils (Barrow et al., 1985). Broadcast applications of 34 kg ZnSO4 ha−1 were adequate to correct Zn deﬁciency on maize for 4 to 5 years, but banded Zn had to be applied at 6.6 kg ha−1 for ∼5 years to assure adequate residual Zn (Frye et al., 1978). Economical and long-term residual effects were also obtained for soil applications of Zn on wheat (Yilma et al., 1997).

C. PLANT IMPROVEMENT The steady increases in yields of major crops during the last half-century have been achieved through genetic improvement and improved management practices. The selection of improved genotypes adapted to wide ranges of climatic differences has contributed greatly to the overall gain in crop productivity during this time. In spite of these advances, mean yields of major crops are normally two- to fourfold below recorded maximum potentials (Baligar and Fageria, 1997). Newly developed genotypes of rice, maize, wheat, and soybean have been more efﬁcient in the absorption and utilization of micronutrients compared to older cultivars (Clark and Duncan, 1991; Fageria, 1992). (See Table XV for scientiﬁc names of plant species.) The accumulation of micronutrients varies among plant species and cultivars/ genotypes within species (Marschner, 1995; Welch, 1986). Such differences among plant species/cultivars have been attributed to genetics, physiological/biochemical mechanisms, responses to climate variables, tolerance to pest and diseases, and responses to agronomic management practices. Genetic variations in plant acquisition of micronutrients have been reviewed (Brown et al., 1972; Duncan, 1994, Duncan and Carrow, 1999; Gerloff and Gabelman, 1983; Graham, 1984; Marschner, 1995). The development of genotypes/cultivars effective in the acquisition and use of micronutrients and with the desired agronomic characteristics is vital for improving yields and achieving genotypic adaptation to diversiﬁed environmental conditions and increased resistance to pests (Baligar and Fageria, 1997; Duncan, 1994; Graham, 1984). Plant and external factors affecting micronutrient use by plants and mechanisms and processes inﬂuencing genotypic differences in micronutrient efﬁciency have been summarized (Table XIII and Table XIV). Plant species differ considerably for B requirements and tolerance to deﬁcient and toxic levels of B in soil (Fixen, 1993; Gupta, 1979; Rerkasem and Loneragan,

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Table XIII Plant and External Factors Affecting Micronutrient Use by Plantsa Plant factors

External factors

Genetic control Species/cultivar/genotype

Agronomic management practices Liming Crop rotation Incorporate crop residue, cover crops Soil Aeration/reducing conditions pH Organic matter levels and forms Temperature Moisture Status Texture/structure Compaction Fertilizers Source Timing, depth, method of placement, and application Use slow release form Elements Toxicities in acid (Al, Mn, pH) and saline (B, Cl) soils Deﬁciencies in acid (Cu, Zn, Mo) and alkaline (Zn, Fe, Mn, Cu) soils Others Arbuscular mycorrhizae, beneﬁcial soil microbes Control weeds, diseases, and insects

Physiological Root length, density of main, laterals, and root hairs Higher shoot yield, harvest index, internal demand Higher physiological efﬁciency Higher nutrient uptake and utilization Excretion of H+, OH−, and HCO3− Biochemical Enzymes: rhodotorulic acid (Fe), ferroxamine b (Fe), ascorbic acid oxidase (Cu), carbonic acid anhydrase (Zn) Metallothionein (trace elements) Proline, aspharagine pinitol (salinity) Abscisic acid, proline (drought). Root exudates (citric, malic, transaconitic acids) Phytosiderophores Others Tolerance to stress (drought, acidity, alkalinity) Tolerance/resistance to diseases/pests Arial temperature, light quality, humidity

a

Baligar and Bennett (1986a,b), Baligar and Fageria (1997), Duncan (1994), and Fageria (1992).

1994). Plants with high requirements for B are alfalfa, apple, red beet, turnip, cabbage, and cauliﬂower (NRC, 1980). Genotypic differences for tolerance to high B have been observed in wheat, barley, annual medic, and ﬁeld peas (Nable and Paull, 1991; Paull et al., 1992). Such differences sometimes are related to restricted B uptake and transport. For example, the susceptibility to B deﬁciency in tomato was due to the lack of plant ability to transport B from roots to shoots (Brown et al., 1972). The genetic variability for B uptake and leaf concentration was noted for maize (Gorsline et al., 1968). Sensitivity to high Cl concentrations varies widely among plant species and cultivars (Eaton, 1966), but Cl toxicity is more extensive worldwide than Cl deﬁciency,

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FAGERIA et al. Table XIV Soil and Plant Mechanisms and Processes and Other Factors Inﬂuencing Genotypic Differences in Micronutrient Efﬁciency in Plants Grown under Mineral Stressesa

Nutrient acquisition Diffusion and mass ﬂow in soil: buffer capacity, ionic concentration and properties, tortuosity, moisture, bulk density, temperature Root morphological factors: number, length, extension, density, root hair density Physiological: root/shoot ratio, root microorganisms (rhizobia, azotobacter, mycorrhizae), nutrient status, water uptake, nutrient inﬂux and effux, nutrient transport rates, afﬁnity for uptake (Km), threshold concentration (Cmin) Biochemical: enzyme secretion (phosphatases), chelating compounds, phytosiderophores, proton exudate, organic acid exudates (citric, malic, trans-aconitic, malic) Nutrient movement in root Transfer across endodermal cells and transport in roots Compartmentalization/binding within roots Rate of nutrient release to xylem Nutrient accumulation and remobilization in shoots Demand at cellular level and storage in vacuoles Retransport from older to younger leaves and from vegetative to reproductive tissues Rate of chelation in xylem transport Nutrient utilization and growth Nutrient metabolism at reduced tissue concentrations Lower element concentrations in supporting structures, particularly stems Elemental substitution (Fe for Mn, Mo for P, Co for Ni) Biochemical: peroxidase for Fe efﬁciency, ascorbic acid oxidase for Cu, carbonic anhydrase for Zn, metallothionein for metal toxicities Other factors Soil factors Soil solution: ionic equilibria, solubility, precipitation, competing ions, organic ions, pH, phytotoxic ions Physiochemical properties: organic matter, pH, aeration, structure, texture, compaction, moisture Environmental effects Intensity and quality of light (solar radiation) Temperature Moisture (rainfall, humidity, drought) Plant diseases, insects, and allelopathy a

From Baligar and Fageria (1997), Baligar et al. (1990), Duncan and Baligar (1990), Fageria (1992), and Gerloff (1987).

particularly in arid and semiarid regions. Plant tolerance to Cl has reported strawberry and pea to be very sensitive; lettuce, onion, maize, apple to be moderately sensitive; potato, cabbage, cauliﬂower, wheat, and ryegrass to be slightly tolerant; and red beet, spinach, rape and barley to be highly tolerant (Marschner, 1995). The genotypic differences in tolerance to Cu and other heavy metals are well known in certain species and ecotypes of natural vegetation (Woolhouse and

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Table XV Common and Scientiﬁc Names of Plant Species Mentioned in Text Alfalfa Amaranth, purple Apple Avocado Banana Barley Bean, broad/common/navy Bean, faba Bean, mung Beet, red and sugar Bluestem, big Cabbage Carrot Cauliﬂower Celery Chickpea Citrus Clover, red Clover, subterranean Clover, white Cotton Cowpea Cucumber Fescue, red Grape Lentil Lettuce Lupine, white Maize Mango Medic, annual (black) Millet, pearl Oat Onion Orchard grass Palm, oil Pea, common/ﬁeld Peach Peanut (groundnut) Pear Pecan Pepper Potato, white Potato, sweet Radish

Medicago sativa L. Amaranthus cruentus L. Malus domestica Borkh. Persea americana Miller Musa paradisiaca L. Hordeum vulgare L. Phaseolus vulgaris L. Vicia faba L. Vigna radiata L. Beta vulgaris L. Andropogon gerardii Vitman Brassica oleracea var. capitata L. Daucus carota Hoffm. Brassica oleracea var. botrytis L. Apium graveolens L. Cicer arietinum L. Citrus spp. Trifolium pratense L. Trifolium subterraneum L. Trifolium repens L. Gossypium hirsutum L. Vigna unguiculata L. Walp. Cucumis sativus L. Festuca rubra L. Vitus vinifera L. Lens culinaris Medikus Lactuca sativa L. Lupinus albus L. Zea mays L. Mangifera indica L. Medicago spp. (Medicago lupulina L.) Pennisetum glaucum L. R. Br. Avena sativa L. Allium cepa L. Dactylis glomerata L. Elaeis oleifera Kunth Pisum sativum L. Prunus persica L. Arachis hypogaea L. Pyrus communis L. Carya illinoensis Wangenh. Capsicum annuum L. Solanum tuberosum L. Ipomoea batatas L. Raphanus sativus L. continues

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Rape Rice Rutabaga/swede Rye Ryegrass, annual Sorghum Soybean Spinach Sugarcane Sunﬂower Swede/rutabaga Tobacco Tomato Turnip Wheat

Brassica napus L. Oryza sativa L. Brassica napus var. napobrassica Secale cereale L. Lolium multiﬂorum Lam. Sorghum bicolor (L.) Moench Glycine max (L.) Merr. Spinacia oleracea L. Saccharum ofﬁcinarum L. Helianthus annuus L. Brassica napus var. napobrassica Nicotiana tabacum L. Lycopersicon lycopersicum (L.) Karsten Brassica rapa L. Triticum aestivum L.

Walker, 1981). It has been known for a long time that special ﬂora (metallophytes) with a high tolerance to metals, including Cu, develop on outcrops of many contaminated mining sites (Marschner, 1995). The differences among plant species/cultivars for resistance to Fe deﬁciency and toxicity are extensive (Clark and Gross, 1986). Some plant species sensitive to Fe deﬁciency are apple, avocado, banana, citrus, grape, peach, pecan, bean, peanut, potato, sorghum, and soybean (Chen and Hadar, 1991). The differences among genotypes for Fe deﬁciency occur because of many physiological and biochemical differences. The recent classiﬁcation of plants for differences in resistance to Fe deﬁciency has been categorized as Strategy I or Strategy II plants (R¨omheld and Marschner, 1986). That is, genotypes possessing Strategy I responses increase Fe solubility and uptake from the rhizosphere by enhanced reduction of Fe3+ to Fe2+, increased root H+ efﬂux and ATPase pumps to lower pH, increased root release of reductants capable of reducing Fe3+ to Fe2+, and increased production of organic acids, particularly citric and phenolics (Hughes et al., 1992). Most dicotyledonous and monocotyledonous plants, except those of the Poaceae (grass) family, exhibit these Fe deﬁciency stress traits. Genotypes of Poaceae exhibit Strategy II responses which are characterized by the production and release of Fe-solubilizing compounds (phytosiderophores) which complex sparingly soluble Fe3+ and make it available to plants (Hughes et al., 1992). Brown and Jolley (1988) and Jolley et al. (1996) extensively addressed mechanisms affecting Fe availability in different species of crops and plant physiological responses for genotypic evaluation of Fe efﬁciency associated with Strategy I and Strategy II plants. Selecting and breeding plants with resistance to Fe deﬁciency have been important for adapting plants for production on many Fe-deﬁcient soils (Chen and Barak, 1982; Clark and Duncan, 1991, 1993; Clark et al., 1990; Rodriquez de

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Cianzio, 1991). The control of Fe deﬁciency is complicated in some plant species (multiple genes) and relatively simple in others (two genes), and good progress of achieving Fe deﬁciency resistance in some plant species has been made (Clark and Duncan, 1991, 1993; Clark et al., 1990). Improved germplasm for Fe deﬁciency has been released for bean, soybean, oat, and sorghum, with considerable progress being achieved with peanut, clover, bluestem grass, pepper, citrus, mango, and avocado (Rodriquez de Cianzio, 1991). Audeber and Sahrawat (2000) reported that the Fe-tolerant lowland rice cultivar “CK4” owed its superior performance under Fe-toxic conditions partly to avoidance (less Fe accumulation in leaves) and tolerance (superior photosynthetic potential in the presence of absorbed Fe in the leaves). Further, they stated that these mechanisms can be enhanced further through the application of P, K, and Zn to soil. Genotypic differences to Mo deﬁciency/toxicity have been noted (Marschner, 1995), and Mo toxicity tolerance has been closely related to the differences in translocation of Mo from roots to shoots (Marschner, 1995). Plant species and cultivars within species differ considerably in susceptibility to Mn deﬁciency when grown in low Mn soils (Marschner, 1995). Mechanisms responsible for cultivar differences for resistance to Mn deﬁciency are not known, but Marschner (1995) speculated that Mn oxidation/reduction reactions in the rhizosphere by roots and microorganisms were involved. Root exudates enhance the reduction of Mn oxides (Godo and Reisenauer, 1980). Both Mn deﬁciency and toxicity are common among plant species, and wide differences among plant species for resistance/tolerance to low and high Mn have been reported (Foy et al., 1988; Martens and Westermann, 1991; Reuter et al., 1988). Maize and rye are very susceptible to Mn deﬁciency, but oat, wheat, soybean, and peach are not (Reuter et al., 1988). The genotypic ability to tolerate Mn deﬁciency has been associated with root geometry, root excretion of substances (H+, reductants, Mn-binding ligands, microbial stimulants) to mobilize insoluble Mn, rates of Mn absorption at low soil Mn levels (low Km and high Vmax values), internal redistribution of Mn, and internal utilization or lower functional Mn requirements (Graham, 1984). Greater ranges of Mn in foliage of different plant species growing in the same soil were noted compared to Cu, Zn, and Fe, and these differences were attributed to species ability to acidify rhizosphere soil rather than with an internal Mn requirement (Gladstone and Loneragan, 1970). Genotypic differences were related to Mn acquisition from soil by rye and wheat (Marschner, 1988) and to geographic origin for barley (Graham, 1984), and not to differences in plant internal utilization and requirement. Some plant species grown in acid soils are more sensitive to Mn toxicity than others, and species differences to Mn toxicity have been reported for subterranean clover, bean, rice, tobacco, orchard grass, cotton, cowpea, apple, amaranth, and red fescue (Foy et al., 1988). Plants that are sensitive to Mn toxicity include cotton, ﬁeld beans, alfalfa, cabbage, small grains, sugar beets, and pineapple (Martens

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and Westermann, 1991). Differential tolerance for Mn toxicity in plants has been associated with the oxidizing power of roots, uptake and rate of translocation from roots to shoots, entrapment of Mn in nonmetabolic centers, high internal tolerance to excess Mn, and distribution of Si, Cu, and Fe in tissue (Foy et al., 1988). Plant species/genotypes vary widely in resistance/tolerance to Zn-deﬁcient or toxic soils (Graham and Rengel, 1993; Parker, 1997; Rashid and Fox, 1992; Takkar, 1993). The susceptibility of plants to Zn deﬁciency is high in cotton, bean, maize, and apple compared to pea, wheat, and oat. Maize, rice, lentil, chickpea, pea, and citrus are more sensitive to Zn deﬁciency than oilseed and cereal crops (Tiwari and Dwivedi, 1990). The differential responses among genotypes for Zn deﬁciency have also been reported for wheat, barley, oat, maize, sorghum, pearl millet, navy bean, potato, spinach, and soybean (Cakmak et al., 1997; Graham and Rengel, 1993; Takkar, 1993; Takkar and Walker, 1993). The differences in rice cultivars for Zn deﬁciency, especially those growing in high pH soil, were associated with the differences in susceptibility to HCO3− (Forno et al., 1975). Bicarbonate concentrations of 5 to 10 mM inhibited root growth of a “Zn-inefﬁcient” rice cultivar, but stimulated root growth of “Zn-efﬁcient” cultivars (Yang et al., 1994). Greater Zn acquisition in rice was casually related to the high HCO3− tolerance of roots (Yang et al., 1994). The differential susceptibility of common bean and soybean to Zn deﬁciency was associated with restricted translocation of Zn from roots to shoots (Ibrikci and Moraghan, 1993). The genotypic differences for “Zn efﬁciency” have been related to the effectiveness of absorption and translocation capacity of roots, ability of plants to avoid P toxicity when Zn deﬁciency occurs, root productivity of Zn mobilizing phytosiderophores, and production of seeds with high Zn contents (Graham and Rengel, 1993). Zinc deﬁciency is known to enhance the release of phytosideophores from roots of graminaceous species, and the release of phytosideophores by roots appears to be an adaptive response to Zn deﬁciency (Erenoglu et al., 2000). The rate of phytosiderophores released in triticale, rye, and bread wheat genotypes was not related to Zn efﬁciency or inefﬁciency. However, phytosiderophores had a role in Zn efﬁciency in barley cultivars, and it appears that phytosiderophores have a role in the solubility and mobility of Zn in the rhizosphere and within plant tissue (Erenoglu et al., 2000). The mechanisms associated with the differences for Zn deﬁciency operate in the soil as well as in the plant. Soil mechanisms for differential Zn at low levels include the differential ability of roots to sustain mycorrhizal infections, Zn mobilization and utilization, and Zn extraction from soil. Plant mechanisms include changes in rhizosphere pH, root uptake kinetics and transport, and root exudation of ion complexing and mobilization compounds (phytosiderophores). Crops also differ in susceptibility to toxic levels of Zn. In acid soils, most grasses (monocotyledons) are more tolerant than most dicotyledons, but this order is reversed for plants grown in alkaline soils, and leafy vegetables, legumes, and

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beet family plants are sensitive to high Zn while many dicotyledons tolerate toxic levels of Zn (Chaney, 1993). Bean and soybean cultivars also differ in tolerance to phytotoxic Zn (Chaney, 1993). Sugar beet and spinach are very susceptible to Ni toxicity, while barley, wheat, ryegrass, and broad bean are fairly resistant to Ni toxicity (Hewitt, 1983). The genotypic differences in tolerance to Co concentrations in shoots have been reported (Marschner, 1995).

D. MICROBIAL ASSOCIATIONS Beneﬁcial soil microorganisms such as rhizobia, diazotrophic bacteria, and mycorrhizae may improve growth by enhancing atmospheric N2 ﬁxation, suppressing pathogens, producing phytohormones, enhancing root surface areas to facilitate uptake of less mobile micronutrients, and mobilizing and solubilizing unavailable organic and inorganic mineral nutrients (Cattlelan et al., 1999; Marschner, 1995). Legumes would be unable to ﬁx N2 without microorganisms like rhizobia, which have essential Co requirements (Ahmed and Evans, 1960). Many microorganisms produce siderophores, especially when grown under Fe deﬁciency conditions, which may enhance the plant acquisition of Fe (Crowley et al., 1987). Siderophores are large organic molecules [e.g., hydroxamates (amide functional groups) produced by fungi and bacteria and catecholates (aeromatic functional groups) produced by bacteria] that strongly and speciﬁcally bind metals, especially Fe3+ (Crowley et al., 1987; Germida and Siciliano, 2000; Lynch, 1990). Rhizosphere microorganisms may also be associated with differences among cultivars in their effectiveness to grow with low levels of some minerals. For example, a “Mn-efﬁcient” wheat cultivar (high growth under Mn deﬁciency conditions) had a higher colonization of soil pseudomonads than “Mn-inefﬁcient” cultivars, and a “Zn-efﬁcient” cultivar had a higher colonization of nonpseudomonads than “Zn-inefﬁcient” cultivars (Rengel et al., 1998). Mycorrhizal colonization of roots increases root surface areas to enhance root exploration of large soil volumes compared to uninfected roots and increases mineral nutrient uptake and plant tolerance to soil chemical constraints (acidity, alkalinity, salinity), toxic elements, and drought (Marschner, 1995). Mycorrhizal fungi and/or mycorrhizal roots have particularly increased acquisition of Cu, Fe, Mn, and Zn in plants grown under deﬁciency conditions (usually in alkaline soils) and decreased B, Fe, and Mn in plants grown under conditions where these minerals are excessive (usually in acidic soils) (Clark and Zeto, 2000; Marschner and R¨omheld, 1996). Mycorrhizae are also involved in the biological control of root pathogens and in nutrient cycling (solubilization, mineralization) (Marschner, 1995). Microbial interactions may also inﬂuence micronutrient mobility. Micronutrients react with microbial products (CO2, siderophores, organic compounds) and

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form microbial-mediated alterations in physical and chemical (pH, redox potentials) environments (Tate, 1987). Iron, Mn, and sometimes Cu are directly reduced by soil microbes or by soil humic acids (Tate, 1987). Several microbes involved in redox reactions in soil have been identiﬁed (Mullen, 1998). For example, Thiobacillus, Geobacter, Desulfovibrio, Pseudomonas, and Thiobacillus bacteria are involved in the oxidation of Fe2+ to Fe3+; Arthobacter, Leptothrix, Pseudomonas, and several other bacteria and fungi enhance oxidation of Mn2+ to Mn4+; and Bacillus, Geobacter, and Pseudomonas bacteria enhance Mn4+ reduction to Mn2+ (Paul and Clark, 1989). Noninfecting rhizosphere microorganisms may enhance plant micronutrient nutrition by improving growth and morphology of roots, physiology and development of plants, and micronutrient uptake processes by roots (Bowen and Rovira, 1991). Large numbers of microorganisms may enhance plant disease and insect infestations to reduce crop yields (Fageria, 1992; Lyda, 1981). Soilborne pathogens such as actinomycetes, bacteria, fungi, nematodes, and viruses lead to pathogenic stress and change the morphology and physiology of roots and shoots (Fageria, 1992; Fageria, Baligar, and Jones, 1997; Lyda, 1981). Such changes reduce plant ability to absorb and use micronutrients effectively. Diseases and insects mostly infect plant leaves (site of photosynthesis), and reduced photosynthetic activity results in a lower utilization of absorbed micronutrients (Fageria, 1992). Plant diseases are also greatly inﬂuenced by micronutrient deﬁciencies and/or toxicities (Huber, 1980). The severity of obligate and facultative parasites on plants is inﬂuenced by many micronutrients (Engelhard, 1990; Graham and Webb, 1991; Huber, 1980). The lack of Zn, B, Mn, Mo, Ni, Cu, and Fe in plant tissue can enhance various diseases on plants (Engelhard, 1990; Fageria, Baligar and Jones, 1997; Graham and Webb, 1991; Huber, 1980).

E. IMPROVED DISEASE AND INSECT RESISTANCE AND TOLERANCE Plant nutrition has always been an important component of disease control (Huber and Wilhelm, 1988). Mineral nutrients in plant tissue increase resistance by maximizing the inherent resistance of plants, facilitating disease escape through increased nutrient availability or stimulated plant growth, and altering external environments to inﬂuence survival, germination, and penetration of pathogens. Micronutrient concentrations in plants are important in host ability to resist or tolerate infectious pathogens. The tolerance of host plants to diseases is measured by the ability to maintain growth and/or yield in spite of infections (Turdgill, 1986). The resistance of the host plants is determined by plant ability to limit penetration, development, and/or reproduction of invading pathogens, and the resistance varies with species or genotype of the two organisms, plant age, and changes in the environment (Graham and Webb, 1991).

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The deﬁciencies of Cu, Fe, Mn, Mo, and Zn reduced growth and sporulation of the fungus Fusorium oxyspoum (Jones et al., 1990), while increased levels of soil Fe, Mn, and Zn beneﬁtted the growth and sporulation of the pathogen. Take-all diseases of small grains from Gaeumannomyces graminis responded dramatically to the differences in micronutrient nutrition (Huber, 1990). Chlorine, Cu, Fe, Mn, and Zn in plants reduced take-all severity, while Mo increased disease severity. Adequate Cu and Mn could control white potato common scab caused by Streptomyces scabies, and Fe, Zn, and B had beneﬁcial effects on reducing scab (Keinath and Loria, 1990). Boron sufﬁciency in plants reduced the incidence and severity of diseases, while B deﬁciency enhanced them. For example, brown-heart (water core) in radish and rutabaga roots, heart rot of beets, brown-heart rot of cauliﬂower, internal brown spot of sweet potato, and cracked stem of celery were enhanced when plants had insufﬁcient B (Gupta, 1993). Boron sufﬁciency also reduced the incidence of club root in swede and other crucifers, fusarium in bean, tomato, and cotton, rhizoctonia infection in mung bean, pea, and cowpea, tobacco mosaic virus in bean and tomato, and yellow leaf curl virus in tomato (Graham and Webb, 1991). Chlorine tends to reduce the incidence of disease on many plants (Fixen, 1993; Marschner, 1995). For example, Cl particularly controlled stalk rot and northern leaf blight on maize, stripe rust and take-all on wheat, downy mildew on millet, and root rot on barley (Graham and Webb, 1991; Heckman, 1998). Powdery mildew and leaf rust diseases were suppressed in winter wheat with Cl applications at seven of nine experimental locations (Engel et al., 1994). Copper has been used extensively over time as a fungicide and suppresses many soilborne diseases. Soil applications of Cu decreased many fungal and bacterial diseases, including mildew on wheat and ergot on rye and barley (Graham and Webb, 1991). Iron decreased rust and smut infections on wheat and reduced Colletotrichum musae infections on banana, and foliar Fe sprays enhanced the resistance of apple and pear to Sphaeropsis malorum and tolerance of cabbage to Olpidium brassicae (Graham and Webb, 1991). Manganese increased the resistance and tolerance of plants to both root and foliar fungal and bacterial diseases. The effects of Mn on disease resistance occur over both Mn deﬁciency and sufﬁciency ranges of host plants (Graham and Webb, 1991). Manganese concentrations in host tissue commonly decrease as the incidence of disease increases, and the incidence of disease may be related to the reduced absorptive capacity of roots by pathogens and the immobilization of Mn by oxidation. Manganese availability in the rhizosphere and Mn concentrations in roots are important for manifestation of take-all severity. For example, increases in soil pH or using NO3–N versus NH4–N decreased the Mn availability and increased the take-all severity (Huber, 1990). Take-all on wheat was also reduced when seeds contained high compared to low Mn (McCay-Buis et al., 1995). Manganese was

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also effective in controlling other soilborne diseases such as potato common scab and verticillium wilt (Verticillum dahaliac) (Graham and Webb, 1991). Kleb wilt in cotton grown in acidic soil may be due to toxicity of Mn, Al, and possibly other acid soluble micronutrients (Bell, 1990). Even though the speciﬁc roles of Mo in protecting plants from diseases are unknown, the indications have been that Mo suppresses verticillium wilt in tomato (Graham and Webb, 1991). Zinc had decreased, increased, and no effects on plant susceptibility to diseases (Graham and Webb, 1991). Nickel salts were effective as fungicides against leaf and stem rusts on wheat (Graham and Webb 1991). The factors by which plants resist pests include physical (surface properties, hairs, color), mechanical (ﬁbers, silicon), and chemical and/or biochemical (stimulants, toxins, repellants) properties (Marschner, 1995). Mineral nutrients can affect these factors to some degree. High amino acids in plants encourage the incidence of sucking parasites. Zinc deﬁciency can reduce protein synthesis which may lead to the high accumulation of amino acids. Negative relationships were noted between B contents in leaves of oil palm seedlings and attack by red spider mites (Rajaratnam and Hock, 1975). Boron was required for biosynthesis of cyanidin and was related to polyphenol production, which is involved in resistance against some insects (Marschner, 1995). Although Si has not been discussed as a micronutrient, high or adequate Si can restrict fungal and insect penetration of plant cells (and alleviate many diseases) to alleviate many insect and disease problems on plants (B´elanger et al., 1995; Epstein, 1994, 1999; Menzies and B´elanger, 1996; Savant et al., 1997, 1999). Silicon in epidermal cell walls acts as a mechanical barrier to insect and fungal attacks. The importance of Si in insect and disease resistance has been studied extensively in rice, sugarcane, and cucumber (B´elanger et al., 1995; Menzies and B´elanger, 1996; Savant et al., 1997, 1999).

VI. CONCLUSION The incidence of micronutrient deﬁciencies in crops has increased markedly in recent years due to intensive cropping, loss of top soil by erosion, losses of micronutrients through leaching, liming of acid soils, decreased proportions of farmyard manure compared with chemical fertilizers, increased purity of chemical fertilizers, and use of marginal lands for crop production. Micronutrient deﬁciency problems are also aggravated by a high demand of modern crop cultivars. Increases in crop yields from application of micronutrients have been reported in many parts of the world. Factors such as pH, redox potential, biological activity, SOM, cation-exchange capacity, and clay contents are important in determining the availability of micronutrients in soils. Further, root-induced changes in the rhizosphere affect the availability of micronutrients to plants. Major root-induced

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changes in the rhizosphere are pH, reducing capacity, redox potentials, and root exudates that mobilize sparingly soluble mineral nutrients. Root exudates may make elements like Fe more available, but they may also produce water-soluble metal chelating agents which reduce metal activity with roots. Compared to macronutrients, micronutrients are required for crop growth in lower amounts and serve mainly as constituents of prosthetic groups in metalloproteins and/or as activators of enzyme reactions. Micronutrients in crop production are important, and micronutrients deserve consideration similar to that of macronutrients. Micronutrient application rates range from 0.2 to 100 kg ha−1, depending on the micronutrient, crop requirement, and method of application. Higher rates are required for broadcast than for banded applications on soil or as foliar sprays. Because recommended application rates of micronutrients are low, most micronutrient sources are combined with macronutrient fertilizers for application to soil. This practice assures uniform micronutrient application. The development micronutrient-efﬁcient and/or tolerant-resistant genotypes appears promising for improving future crop production. Additional information is needed to improve micronutrient recommendations, especially for determining long-term availability, and to evaluate macronutrient fertilizer effects on micronutrient availability. Considerable information about critical deﬁciency levels of micronutrients is available, but information about critical toxic levels is limited. Information about the interactions of micronutrients with other minerals is also needed.

ACKNOWLEDGMENTS The authors are grateful to Drs. V. D. Jolley, L. M. Shuman, D. C. Martens, G.Ba˜nuelos, and C. D. Foy for their critical review and valuable suggestions for the manuscript. We also thank Dr. L. W. Zelazny for providing information on major soil minerals containing micronutrients.

REFERENCES Abreu, C. A., Van Raij, B., and Tanaka, R. T. (1996). Manganese sources for soybean and their effects on soil analysis. R. Bras. Ci. Solo 20, 91–97. Adams, F. (1984). “Soil Acidity and Liming.” Agronomy Spec. Publ., No. 12, American Society of Agronomy, Madison, WI. Adams, F., and Pearson, R. W. (1967). Crop response to lime in the southern United States and Puerto Rico. In “Soil Acidity and Liming” (R. W. Pearson and F. Adams, Eds.), pp. 161–126. Agronomy Monogr. No. 12, American Society of Agronomy, Madison, WI. Adriano, D. C. (1986). “Trace Elements in the Terrestrial Environment.” Springer-Verlag, New York. Ahmed, S., and Evans, H. J. (1960). Cobalt: A micronutrient element for growth of soybean plants under symbiotic conditions. Soil Sci. 90, 205–210.

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SOIL SCIENCE IN TROPICAL AND TEMPERATE REGIONS—SOME DIFFERENCES AND SIMILARITIES Alfred E. Hartemink International Soil Reference and Information Centre (ISRIC) 6700 AJ Wageningen, The Netherlands

I. Introduction II. Soil Science in Temperate Regions A. After the Second World War B. Funding and Scope III. Soil Science in Tropical Regions A. First Theories B. After the Second World War C. Inorganic Fertilizer Use D. Important Themes E. Number of Publications and Soil Scientists F. Myths about Soils in the Tropics IV. Diametrically Opposite Interests A. Soil Acidity B. Soil Nutrients V. Impact of Soil Science VI. Concluding Remarks References

Little has been written about geographical differences in the progress and development of soil science, whereas such information is of interest for determining research priorities and for an improved understanding of the impact of soil science in various parts of the globe. This paper reviews some of the differences and similarities in soil science of the temperate and tropical regions. It is largely based on Anglo–Dutch literature and focuses on soil fertility research. The range of conditions under which soils are formed is as diverse in the tropical as in the temperate regions, but soil science has a different history and focus in the two regions. In densely populated western Europe soil fertility research started because there was little spare land, whereas in the Russian Empire and the United States land was amply available and soil survey developed. Since the second World War, soil science has greatly beneﬁted from new instrumentation and developments in other sciences. Many subdisciplines and specializations have been formed, and soil science has broadened its scope in the temperate regions. Currently, much research 269 Advances in Agronomy, Volume 77 Copyright 2002, Elsevier Science (USA). All rights reserved. 0065-2113/02 $35.00

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ALFRED E. HARTEMINK is externally funded and has a problem-solving character. Soil research in tropical regions started later, and its scope has not changed much. The feeding of the everincreasing population, land degradation, and maintenance of soil fertility are still important research themes. The amount of research in environmental protection, soil contamination, and ecosystem health is relatively small. More is known about the soil resources in the temperate regions than in the tropical regions despite the fact that one-third of the soils of the world are in the tropics, and these support more than three-quarters of the world population. Some of the common interests are the development of sustainable land management systems and appropriate land quality indicators, quantiﬁcation of soil properties and processes, ﬁne tuning of models, the sequestration of C in agricultural soils, and the optimum use of agricultural inputs to minimize environmental degradation and maximize proﬁt. Nutrient surplus is a major concern in many temperate soils under agriculture, whereas the increase of soil fertility is an important research topic in many tropical regions. From a soil nutrient perspective it appears that soil fertility research in tropical regions is all about alleviating poverty, whereas in the temperate regions it is mainly about alleviating abundance and wealth. Although efforts have been undertaken to promote soil science to a wider audience, the impact of soil science on the society has been poorly quantiﬁed, and this applies to both temperate and tropical C 2002 Elsevier Science (USA). regions.

I. INTRODUCTION The world would have been different if soil science had not emerged in the 19th century. It grosso modo applies to many—if not all—of the sciences, but for soil science its impact on society and the world at large has been poorly quantiﬁed. This is understandable, as it would be almost impossible to unravel the effect of different factors on the state of the world. Besides there are large regional differences. Soil studies are conducted in every agroecological region of the world, but soil science has mostly developed in the temperate regions. In tropical regions, soil science has followed its own path based on different needs and processes affecting soil conditions and plant growth. Sanchez and Buol (1975) summarized some of the differences and similarities between soils and their forming factors in tropical and temperate regions. Aside from the lack of a difference between summer and winter temperatures, the range of conditions under which soils are formed is as diverse in the tropics as in the temperate regions. Similar rock types occur, and also erosional and depositional patterns are similar. In both tropical and temperate regions the time of soil formation may range from very recent on alluvial plains or volcanic deposits to very old on stable geomorphic surfaces. Arid and humid as well as warm and cold climates occur in both temperate and tropical regions. Nevertheless the extent of

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certain soil types is very different. Pleistocene glaciation and wind erosion have had a great impact on the soils in the temperate region, whereas many more soils in the tropics have intensively weathered and are often derived from Precambrian parent materials. Although the extent of recent volcanic ash deposits is greater in the tropics, there is a larger proportion of relatively young soils in the temperate regions. Generalizations beyond these statements begin to lose accuracy (Sanchez and Buol, 1975), and generalizations have done much harm in the advancement of soil science in tropical regions (Lal and Sanchez, 1992). There have been several papers focusing on the developments in soil science in tropical or temperate regions (e.g., Greenland, 1991; Lal, 2000; Theng, 1991; Yaalon, 1997). Little has been written on a comparison of soil science in the temperate and tropical regions, whereas such information is of interest for determining research priorities and for an improved understanding of the impact of soil science in various parts of the world. This paper aims to partly ﬁll the gap, and its objectives are (i) to compare some of the differences and similarities in soil science conducted in tropical and temperate regions, (ii) to give an overview of some recent trends in soil science of the temperate and tropical regions, and (iii) to discuss the impact of soil research in tropical regions. The review is largely based on an analysis of Anglo–Dutch literature and focused mainly on soil fertility aspects. The paper does not aim to present a detailed and historical review of soil science in the tropical and temperate regions, but highlights the main developments and some of the striking differences and similarities.

II. SOIL SCIENCE IN TEMPERATE REGIONS Practitioners of soil science could be roughly divided into those who made maps (pedologists, surveyors) and those who made graphs (the others). Such time has long gone, but the division had clear historical roots. At the beginning of the 20th century there were scientists studying soils in the ﬁeld (agrogeologists), and there was a group studying soils in the laboratory who were often named agrochemists (van Baren et al., 2000). These groups were found in different parts of the world. In western Europe, there were limited possibilities for extending the agricultural area because the population was relatively dense. Research focused on the improvement of soil conditions in existing ﬁelds, e.g., the maintenance of soil fertility under continuous cropping. As a result, agricultural chemistry and the fertilizer industry developed in Europe. In other parts of the temperate region (United States and the Russian Empire) there were large areas of soils that could be used for agricultural expansion, and questions were centered on ﬁnding out what soils they had, how to select those responsive to management, and how to avoid wasted effort

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in farm development (Kellogg, 1974). There was a clear need for soil mapping and a better understanding of the concepts of the soils which resulted in the development of soil survey and soil genesis as subdisciplines of soil science. In the United States, soil science and in particular soil fertility research had a slower start than in Europe, as there was no urgency for maintaining the fertility and productivity of the soil—it was easier to move west (Viets, 1977).

A. AFTER THE SECOND WORLD WAR Early experiments with inorganic fertilizers were conducted in the mid-19th century at Rothamsted in England and in some other European countries. Acidulated phosphate rock and guano were mainly used, but in general, inorganic fertilizers were scarce in the 19th century. Inorganic fertilizers became widely used after the Haber–Bosch process had developed in Germany (Smil, 1999). It made fertilizers costs lower, and in addition new products were developed like nitriﬁcation inhibitors, new N compounds, coated fertilizers, and synthetic chelates (Viets, 1977). Inorganic fertilizer use in some selected European countries and in the United States is shown in Table I. In the Netherlands inorganic fertilizer use was already high at the beginning of the 20th century, but increased to almost 800 kg N, P2O5, and K2O per hectare in the mid-1980s. The rate of increase in fertilizer consumption in Germany and the UK was similar, but inorganic fertilizer consumption in the United States has been low compared to European countries. It should be borne in mind that these are national averages and that inorganic fertilizer use between states and agricultural sectors may vary greatly. A major development in soil fertility research took place after the second World War. Radioactive and heavy isotopes became available, and this was accompanied by the development of instrumentation like ﬂame and atomic absorption spectrometers, emission and mass spectrographs, X-ray diffractometers and ﬂuorescence, colorimeters, spectrophotometers, column and gas chromatographs, and Table I Inorganic Fertilizer Use in Some Selected European Countries and the United States in Different Periodsa

Germany Netherlands United Kingdom United States

1913

1936

1986

47 146 26 6

64 320 44 8

427 784 356 94

a Modiﬁed after Knibbe (2000). Values in kg nutrients (N, P2O5, K2O) per hectare y−1.

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computers (Viets, 1977). Advances in instrumentation allowed improved soil and plant tissue testing for better guidance of fertilizer use. Other developments which greatly aided soil fertility research were advances in statistical theory and designs of ﬁeld experiments, theories on ion transport from the solid phase to the root surface, and the increased understanding of soil chemical and biological properties and processes. Traditionally, soil science in the temperate regions was concerned with agricultural production (Cooke, 1979). The feeding of the post-second-World-War baby boom demanded a large increase in agricultural production, which resulted indirectly in a leap in soil knowledge. In the 1960s food production exceeded demand, and surplus production followed; and at the height of the cold war the optimism and positivism of the 1950s gradually vanished. Conservationists and environmental groups drew attention to the widespread deterioration of the environment (e.g., Meadows et al., 1972). It brought about changes in the way the public and politicians looked upon agriculture and the environment. Since the 1970s rates of population growth have been declining in most temperate countries. Currently, the focus of attention is more on the problem of aging than on population growth per se (Tuljapurkar, 1997). Moreover overweight of the human population is a problem in many countries. The shift of attention meant new opportunities for soil science (Tinker, 1985), and soil scientists became involved in studies of nonagricultural land use, nature conservation, pollution, contamination, environment protection, soil remediation, and soils in urban environments. An increased emphasis was placed on the relationship between soil processes and water quality, and soil scientists became caught up in global and regional environmental issues (Wild, 1989) and learned to interact with ecologists, economists, and sociologists (Bouma, 1993). Consequently, the focus of soil science was broadened in the temperate regions resulting in the development of various subdisciplines and specializations. By its very nature soil science is an outdoor science, but with the introduction of the microcomputer, soil science has also become an ofﬁce science where deskwork has increased, and this has occurred sometimes at the expense of laboratory and ﬁeld work (Hartemink et al., 2001). An emphasis is placed on the use of previously collected data in combination with functional or mechanistic modeling and the development of risk scenarios. Field work concentrates on advanced realtime measurements of soil properties as required for the development of precision agriculture, which is likely to have a large impact (Schepers and Francis, 1998), although its potential in Europe is still under debate (Sylvester-Bradley et al., 1999). Invasive and noninvasive measuring techniques of soil properties require time before they will be fully developed, but progress has been made, particularly in the United States and Australia (Viscarra Rossel and McBratney, 1998). In western Europe there is perhaps more expertise in the environmental aspects and nonagricultural applications of soil science. Another major theme in the temperate regions

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is the role of soils as a sink and source of carbon in relation to global climate change (Lal, Kimble, Follet, and Stewart, 1998) and the development of quantitative techniques in soil science (McBratney et al., 2000; McBratney and Odeh, 1997).

B. FUNDING AND SCOPE Throughout past decades funding opportunities for fundamental soil research have been reduced (Mermut and Eswaran, 1997), and much soil research is externally funded with a strong problem-solving character. With this trend soil science has returned to where it started: little fundamental research and a main focus on adaptive research. There is some fear that this means that soil science will lose its dynamism and independence (Ruellan, 1997). Bouma (1998) ﬁnds, however, that the external funding trend should not be rigidly opposed, and he advocates research procedures where applied and basic research logically ﬁt together in so-called research chains. Current soil fertility issues are integrated nutrient management systems aiming to minimize environmental pollution through leaching and denitriﬁcation. In a broader sense, research in soil fertility focuses on a reduction of the environmental impact of farming by reducing losses and conservation of fossil fuel energy. Other important factors are the breeding of cultivars tolerant to less favorable soil conditions or heavy polluted soil. Also mine site rehabilitation, bioremediation, and precision agriculture have become important in soil fertility research in temperate regions. Since the mid-1970s, modeling has become a major tool in the advancement of soil fertility research. There is growing interest in biological farming in many western European countries, and although it may have the potential to reduce the environmental impact of farming, it is generally perceived that biological farming cannot feed a rapidly growing population. There are large challenges ahead for soil science and in particular for soil fertility research in the temperate regions, e.g., the development of nutrient management systems, which are both environmental friendly and cost-effective. This need is the same for soil science and soil fertility research in the tropical regions, although the research focus is distinctly different.

III. SOIL SCIENCE IN TROPICAL REGIONS Little was known about tropical soils some 100 years ago. Travelers saw landscapes and vegetation that was never observed in any of the temperate regions, and many tried to comprehend the differences. Between the wars, signiﬁcant soil research took place in, for example, Trinidad (F. Hardy), East Africa (G. Milne),

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and India (H. H. Mann). A useful overview of early investigations in tropical regions is given by Hilgard (1906). Considerable soil research was conducted in Indonesia (e.g., by E. C. J. Mohr) which included the mapping, chemistry, and formation of tropical soils. Systematic research started after the second World War following rapid developments in soil surveying and soil chemistry, and an overall increased interest ocurred in the natural resources of the tropics. The interest was mainly pedological, and many tropical soil science books were not concerned with the soil as a medium for plant growth (Moss, 1968; NAS, 1972; Nye and Greenland, 1960). Soil fertility was mainly the research terrain of the agronomist.

A. FIRST THEORIES The theory on the fertility of tropical soils has gone through a number of stages. In the late 1800s and early 1900s it was assumed that soil fertility in the humid tropics must be very high because it supports such abundant vegetation such as the rain forest. In the 1890s, the Deutsch Ost-Afrika Gesellschaft based their research station in Amani in the East Usambara mountains (Tanzania), as they thought that underneath the rain forest there must be abundantly productive soils (Conte, 1999). The point of view was fairly popular by tropical agriculturists and was prominently mentioned in the book of J. C. Willis (Willis, 1909), which ran through several editions during the ﬁrst two decades of the 1900s. The American soil scientist E. W. Hilgard together with V. V. Dokuchaev, founder of modern pedology (Jenny, 1961), thought that soils of the humid tropics were rich in humus because of the abundant vegetation supplying plant material (Hilgard, 1906). Continuous and rapid rock and soil decomposition was thought to be high under the prevailing climatic condition, hence providing a constant supply of minerals for plant growth (Hilgard, 1906). Also Shantz and Marbut (1923) stated that the soil under the tropical rain forest is relatively fertile. It is not surprising that such views existed, since virtually nothing was known about tropical soils at the beginning of the 1900s, and generalizations existed widely. For example, it was thought there were four major soil types which occupied the cultivated area in India, although Hilgard (1906) mentioned that “. . . it is hardly to be expected that so large an area as that of India . . .could be even thus brieﬂy characterized.” The high fertility theory was dispelled when the forest was cut and crops were planted, and it was discovered that yields were disappointingly low. In the subsequent period it was emphasized that soil fertility in the tropics was uniformly low and easily lost by cultivation (Jacks and Whyte, 1939). Travelers in the tropics noted that soils were lighter in color, and hence assumed that such soils had lower organic matter contents and chemical fertility. It is likely that these ideas about lower organic matter contents and soil chemical fertility are an aftermath of the 19th century humus theory, which was dispelled by Baron Justus von Liebig in the 1840s.

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B. AFTER THE SECOND WORLD WAR After the second World War, research emphasis was placed on the improvement of soil fertility by the judicious application of inorganic fertilizers. A very large number of inorganic fertilizer experiments were conducted from the 1950s onward (Greenland, 1994; Singh and Goma, 1995; Traore and Harris, 1995). These experiments focused on the search for balanced nutrition, the economics of fertilizers, credit, subsidies, and marketing of fertilizers, and fertilizer training programs and extension. Attention was focused more on the rate and balance of fertilizer application than on the identiﬁcation of nutrient disorders. Following the food production decline in the 1960s, FAO launched in 1961 the Freedom From Hunger Campaign (FFHC) which was partly ﬁnanced by the world fertilizer industry. The FFHC’s main target was to encourage the use of fertilizers by small-scale farmers through education and effective means of distribution and credit. The overall idea was that agricultural production cannot be signiﬁcantly increased in the developing countries of the world without improving the nutrient status of most soils (Olson, 1970).

C. INORGANIC FERTILIZER USE The increased use of inorganic fertilizers in tropical regions was deemed necessary (i) to increase production per unit of land in the face of a growing shortage of arable land in many developing countries, (ii) to increase marketed food supplies or exports, and (iii) to raise incomes and return to labor (FAO, 1987). Furthermore inorganic fertilizers were needed to make full use of the new high-yielding varieties. The combined package of new crop varieties, pests and disease control, and the use of inorganic fertilizers caused a dramatic increase in crop yields in many parts of the tropics. There is no better summary than the “Fertilizer Guide for the Tropics and Subtropics” published in 1967 and 1973 containing over 5000 references to fertilizer trials throughout the tropics (de Geus, 1973). Locally it was noted that inorganic fertilizers had little or no effect due to crop husbandry practices (poor seedbed preparation, improper seeding, delay in sowing, etc.) or because of wrong fertilizer placement, unbalanced nutrient application, incorrect identiﬁcation of nutrient limitations, or weed and insect problems. Obviously these factors were eliminated when inorganic fertilizer trials were conducted on a research station, but surfaced when fertilizers were used by subsistence farmers. As an overall result, inorganic fertilizers gave a poor proﬁtability which affected the widespread use. Some of the inorganic fertilizers being used in the tropics were given as aid by the United States and western European countries. On the one hand this was meant to stimulate the use of fertilizers in tropical regions and increase crop production on the other hand European countries could maintain their fertilizer industry which

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suffered from the declining use of fertilizers by European farmers. It also meant that many of the aid funds were retained in Europe. In the 1970s an 1980s environmental concerns about inorganic fertilizers were rising. Excessive use of inorganic fertilizers can have devastating effects on water quality, and a well-known example is the proliferate growth of algae following enrichment with phosphates. In the Netherlands this was, however, mainly due to the use of phosphate in washing detergents and not so much due to the use of excessive amounts of P fertilizers. A second concern is the nitrate content of drinking water which is said to create health hazards for humans under speciﬁc conditions (Addiscott et al., 1991). Inorganic fertilizers have also been associated with the destruction of the ozone layer, as nitrous oxides resulting from denitriﬁcation can give rise to products which catalyze ozone destruction (Bouwman, 1998). In other words, inorganic fertilizers were regarded as environmentally damaging. Part of the public opinion was probably exaggerated and excessive as was the use of inorganic fertilizers by some farmers in western Europe. The negative image of inorganic fertilizers in the temperate regions probably had some effects on the use of fertilizers in the tropical regions, although the environmental consequences of the continued low use of fertilizers are more devastating than those anticipated from increased fertilizer use in the tropics (Dudal and Byrnes, 1993). The FFHC, which was replaced in the late 1970s by the FAO’s Fertilizer Programme, gradually ceased in the 1990s, and currently FAO has no such program. With few exceptions, large-scale and widespread inorganic fertilizer trials are no longer conducted. Instead of advocating the use of inorganic fertilizers, studies in the late 1980s and early 1990s focused on new arguments to justify the use of inorganic fertilizers. This was the case when nutrient balances were reintroduced as a research tool and widespread soil fertility decline and nutrient mining were being reported, particularly for sub-Saharan Africa (Smaling, 1993). Inorganic fertilizers are not only being advocated to correct the negative nutrient balance, but, integrated nutrient management is also advocated to improve the overall negative nutrient balance and the efﬁciency of nutrient use (Sanchez, 1994). Fertilizer use in some selected Asian countries is given in Table II. Although the consumption of inorganic fertilizer use is much lower than that in some European countries (Table I), the data show that the rate of increase has been high in Asian countries. The increase in inorganic fertilizers runs parallel with the increase in food production. It is interesting to note that inorganic fertilizer use in Asian countries is on average higher than that in the United States. Inorganic fertilizer use in sub-Saharan Africa countries is lower than 15 kg ha−1. Summarizing the soil fertility paradigms in tropical regions, it can be noted that in the late 1800s and early 1900s it was perceived that tropical soils were uniformly rich. This was followed by a period in which it was believed that tropical soils were of inherent low fertility and quickly lost by cultivation. After the second World War, research efforts largely focused on the use of inorganic fertilizers to overcome low

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ALFRED E. HARTEMINK Table II Inorganic Fertilizer Use in Some Selected Asian Countries in Different Periodsa

India Indonesia Bangladesh Thailand Vietnam Pakistan

1968–1970

1983–1985

1993–1995

16 16 12 7 36 19

61 111 49 20 62 79

105 135 93 70 170 124

a Modiﬁed after Hossain and Singh (2000) based on FAO databases. Values in kg nutrients (N, P2O5, K2O) per hectare y−1.

soil fertility, and a large number of trials were conducted. In the period that followed it was found that inorganic fertilizers, were not widely used, and as a result, soil fertility is being mined leading to a declining agricultural productivity, which particularly applies to sub-Sahara Africa.

D. IMPORTANT THEMES In tropical regions, important soil science themes have not changed much in past decades, and soil science is still closely linked to agriculture and society at large. The feeding of the ever-increasing population, the decreasing food production per capita in some African countries, and soil degradation are as worthy themes today as they were 20 to 30 years ago. About 95% of the current population growth takes place in tropical regions, and a continuing increase in food production is required. Recently, some emphasis has been placed on nature conservation, in particular in relation to rain forests (biodiversity) and dry areas (desertiﬁcation), but less in savannah areas. Increased contamination of soil and water environment is of particular concern in developing countries where both local industries and often foreign investors have shown a general lack of appreciation of the environment (Naidu, 1998). The amount of research in environmental protection, soil contamination, and ecosystem health is relatively small. Overall there has been an increase in process-oriented research, but the absolute amount is by no means comparable to that conducted in the temperate regions. Soil fertility research in tropical regions has, however, greatly beneﬁted from developments in instrumentation and analytical techniques (Viets, 1977). More is known about soil resources in temperate regions than in tropical regions, despite the fact that one-third of the soils of the world are in the tropics (Eswaran et al., 1992), and these support more than three-quarters of the world population (Fischer and Heilig, 1997). There are a number of reasons that are discussed later,

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but ﬁrst we will attempt to quantify the differences. Currently about 10,000 publications on soils appear in international and national journals each year (Hartemink, 1999). These are the publications in English only, but many more are written in other major languages in books, conference proceedings, and reports. In the late 1940s and 1950s there were about 1000 to 2000 soil science publications—so the number of soil science publications has greatly increased. This is due to the increase in the number of soil scientists (van Baren et al., 2000), an increase in the number of soil science and agronomic journals (Hartemink, 2000), and an increased pressure to publish, which also resulted in the recycling of ideas and manuscripts. Above all, it demonstrates the enormous increase in soil science knowledge, which is also reﬂected, for example, in the development of the book—“Soil conditions and Plant Growth” (Greenland, 1997) and the extensive “Handbook of Soil Science”(Sumner, 2000).

E. NUMBER OF PUBLICATIONS AND SOIL SCIENTISTS How many of journal publications deal with the tropics? Arvanitis (1994) estimated from French databases that about 22% of soil publications originate from the tropics. Yaalon (1989) mentioned that the share of all the Third World countries in soil research increased from 9 to 11% in 21 years. Searches through ISI’s databases showed that more publications appear on Australia than on the whole of Africa. On average there are ﬁve times more publications on the Netherlands than on Tanzania, whereas the population of Tanzania is twice as large as that of the Netherlands. Three times more publications originate from Europe as compared to Africa. On average there are 30 to 40 times more publications on cancer than on poverty, and twice as many publications on cancer than on soils. There is, however, a clear increasing trend in the number of publications about soil. The increase is on average 5% per year, which was also noted by Yaalon (1989), and found when other literature databases were analyzed (Hartemink, 1999). The difference in the number of publications on tropical soil research compared to soil research in the temperate regions is because, with some exceptions, soil research in the tropics started several decades later than in the temperate regions, and there are (and have been) fewer soil scientists with less advanced research facilities in tropical regions. Educational opportunities are also more limited in these regions. The amount of research funds differs largely between tropical and temperate regions, although exact ﬁgures are not available. In Africa the allocation of funds for agricultural research grew rapidly in the 1960s, moderately in the 1970s, and in general stagnated in the 1980s in most countries (Noor, 1998). Currently, developed countries spend on average about $200 a year per farmer on research and extension, whereas developing countries spend $4 (Young, 1998). Most developing countries face reduced funding and a wave of redundancies in the international research centers. There are no signs that the funding situation is

280

ALFRED E. HARTEMINK Table III Number of International Society of Soil Science Members for Different Continents in 1974 and 1998a 1974

Western Europe Eastern Europe +USSR/CIS Middle East Africa Asia Australia + New Zealand Latin America + Caribbean North America

1316 351 104 278 280 348 171 1110

Total

3958

a b

1998 (33)b (9) (3) (7) (7) (9) (4) (28)

2481 379 233 454 881 364 597 1653 7042

Difference 1974–1998(%) (35) (5) (3) (6) (13) (5) (8) (23)

+89 +8 +124 +63 +215 +6 +249 +49 +78

After van Baren et al. (2000) based on ISSS statistics. Percentage of total members is in parentheses.

improving, and, for example, the European Union reduced its contribution to the CGIAR system by U$16 million for the year 2000. The number of soil scientists has greatly increased in the past century, although regional differences are large (Table III). Between 1974 and 1998, the total number of members of the International Society of Soil Science (ISSS) increased by 78%, whereas over the same period the world population increased by 42%, from 4.14 to 5.86 billion. More than half of the ISSS members are based in western Europe and North America. Large increases in ISSS members were found in the Middle East, Asia and Latin America, and the Caribbean, in which the number of members tripled between 1974 and 1998. Few changes in membership were registered in eastern Europe/CIS. The total number of members in Australia increased from 243 to 312 between 1974 and 1998, but the number in New Zealand decreased from 105 to 52 over the same period (van Baren et al., 2000). There is a difference in the number of agricultural and soil scientists between tropical and temperate regions. In the 1960s, the number of research workers per 100,000 farm workers was about 1.0 in Cameroon, 1.2 in India, but 60 in Japan, and 133 in The Netherlands (Olson, 1970). In 1998, there were per 1000 km2 agricultural land about 0.5 soil scientists in India, 1.2 in Brazil compared to 2.8 in The Netherlands and 55.1 in Japan (Table IV). A large number of soil scientists are found in China, the United States, Brazil, and Japan. However, the number of soil scientists per million inhabitants was highest in New Zealand, Australia, Israel, and Spain. With some exceptions the data show that the total number of soil scientists as well as the number of soil scientists per million inhabitants or hectare agricultural land are commonly lower in tropical regions than in temperate regions. A criticism is that developed countries have paid little attention to the education of local soil scientists in tropical regions (Muchena and Kiome, 1995). With time

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Table IV Soil Scientists per Million Habitants and Agricultural Land in 1998 in Some Selected Countriesa

Country Australia Brazil Canada China, P.R. of France Germany India Israel Italy Japan Mexico Netherlands New Zealand South Africa South Korea Spain Thailand Turkey UK United States a

Total number of soil scientists

Soil scientists per million inhabitants

Soil scientists per 1000 km2 agricultural land

1,000 2,900 320 10,200 900 2,500 900 250 300 2,800 700 450 430 270 930 1,450 500 225 1,000 6,050

53.7 17.1 10.4 8.2 15.3 30.5 0.9 44.3 5.3 22.2 7.1 28.6 118.6 6.3 20.0 37.1 8.3 3.5 17.0 22.4

0.2 1.2 0.4 1.9 3.0 14.4 0.5 43.1 1.9 55.1 0.6 22.8 2.6 0.3 49.7 4.7 2.4 0.6 5.8 1.4

Modiﬁed after van Baren et al. (2000) based on ISSS statistics and agricultural databases.

the difference in the number of soil scientists may level out, as the number is declining in most countries of the temperate region. Changes in the number of soil scientists is of course directly related to the level of government funding. Arvanitis and Chatelin (1994) mentioned that the number of soil scientists in a country is probably inversely proportional to the pressures exerted on them. Soil scientists in the tropics are often required to conduct applied research in areas of direct national interest such as self-sufﬁciency and education, or they are even asked to participate actively in politics (Arvanitis and Chatelin, 1994).

F. MYTHS ABOUT SOILS IN THE TROPICS In addition to the quantitative aspects of the number of soil scientists and publications, there are other causes which have restricted the advancement of soil science in tropical regions. Overgeneralizations about soil in tropical regions have led to many misconceptions about its potential (Lal and Sanchez, 1992; Sanchez and Buol, 1975). There have been a number of myths, and the myth of rapid

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laterization under cultivation is probably best known. Up to the 1930s it was thought that the tropics were covered by laterite crust and lateritic soils, because a number of often-quoted writers on laterite had never been in the tropics (Prescott and Pendleton, 1952). Research in Indonesia and East Africa dispelled the theory, but it took many decades before it was fully dispelled from soil science literature (Lal and Sanchez, 1992). Other myths were that soils in the rain forest were extremely rich and able to support the abundance of vegetation, that shifting cultivation was a backward type of agriculture (FAO-Staff, 1957) accelerating the formation of laterite (Vine, 1968), that all soils in the tropics were highly erodible (Jacks and Whyte, 1939), that tropical soils were very low in organic matter (Ruthenberg, 1972), very old, and intensively weathered due to year-round high rainfall and temperatures. These misconceptions were largely eliminated by the works of, among others, Mohr and van Baren (1959), Nye and Greenland (1960), Kellogg (1963), Sombroek (1966), Sanchez (1976), Sanchez et al. (1982), and Greenland et al. (1992). Some misconceptions are hard to eliminate. For example, the concept of zonality introduced by the Russian school of pedology is still being used in some standard texts on tropical forests (Burnham, 1985) and tropical agriculture (Webster and Wilson, 1980; Wrigley, 1982) despite its abandonment in the 1940s (Smith, 1983). The lack of a universally used soil classiﬁcation system also retarded the advancement of soil knowledge in tropical regions. For example, Latosols has a different meaning to different soil scientists, as it was used in both the national soil classiﬁcation systems of Brazil and Indonesia. A tremendous effort has been made to develop soil classiﬁcation systems, but it is unfortunate that the efforts have not resulted in something widely used and understood by nonsoil scientists or even nonpedologists. The World Reference Base for soil resources, which was presented at the 16th World Congress of Soil Science as the international soil classiﬁcation system, might change the situation.

IV. DIAMETRICALLY OPPOSITE INTERESTS There are a number of common interests in soil research in temperate and tropical regions. In both regions it is recognized that sustainable land management systems need to be developed (Eger et al., 1996), and there is a search for appropriate land quality indicators (Doran and Parkin, 1996; Eijsackers, 1998). Another common interest is the sequestration of C in agricultural and forest soils (Lal, Kimble, and Follet, 1998) and the problems associated with global climate change. Tools and techniques developed in the temperate region are therefore of direct interest to soil science in the tropical regions, and some consider that soil science in developing countries should focus on soil technology adoption only (Yaalon, 1996).

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Nevertheless, it sometimes appears that soil science in temperate and tropical regions has diametrically opposite interests, and two striking examples are discussed here.

A. SOIL ACIDITY In upland soils in tropical regions soil acidity is a major problem which can have pedogenetic (parent material, age) or anthropogenic causes (ammonia-N fertilizers). The upland soils are nevertheless considered the largest remaining potential for future agricultural development (Theng, 1991; Von Uexk¨ull and Mutert, 1995). Several strategies to manage soil acidity have been developed in order to increase and sustain food production on these soils (Myers and de Pauw, 1995; Sanchez and Salinas, 1981). Research has focused not only on methods to increase the pH but also on the development of acid-tolerant crop cultivars (Sanchez and Benites, 1987). In temperate regions, it has been recognized since before Roman times that chalk or marl spread on acid soils improved their fertility, and this was widely used during the 18th century by the pioneers of the English agricultural revolution (Bridges and de Bakker, 1997). This practice lapsed when agricultural lime became available in the 19th century. So the soil acidity problem in the temperate regions was largely overcome through application of pH increasing substances over decades or even centuries. Research interest in soil acidity increased in the 1970s because of the problems associated with acid rain (Reuss and Johnson, 1986). Acid rain studies made many people aware that environmental problems cut across national borders. With falling emission and deposition of N and S (Jenkins, 1999), interest in soil and surface water acidiﬁcation decreased, and climate change became the new focus of attention. Currently there is renewed interest in soil acidity because of the set-aside policy whereby agricultural land is taken out of production and restored to heathland or forest. In some soils in Scotland restoration to heathland meant that the pH, which was increased through many years of lime applications, had to be reduced by 2 to 3 units for which heavy applications of elemental sulfur were used (Owen et al., 1999). Set-aside problems are unknown in tropical regions where the need for more land has increased because of the growing population (Harris and Kennedy, 1999; Krautkraemer, 1994; Seidl and Tisdell, 1999). The only example from the tropics is the use of elemental sulfur in neutral soils at tea plantations, since tea requires a strongly acid soil (TRFK, 1986). Another example for the renewed interest in soil acidity comes from The Netherlands, where about 25,000 ha or 1% of the total area under agriculture was taken out of production between 1993 and 1996. When sandy soils previously under intensive horticulture with heavy applications of biocides were set aside and

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not cultivated, these soils naturally acidiﬁed. As a result mobile Cd originating from the biocides increased, and regular lime applications are needed to these soils to reduce the Cd solubility and mobility (Boekhold, 1992). It is an interesting example how nature restoration—not agriculture—brings to surface the so-called chemical time bomb.

B. SOIL NUTRIENTS Nutrient enrichment, particularly N and P, has occurred in many agricultural soils of western Europe, and nutrient management is a topic of major political interest (de Walle and Sevenster, 1998; Kuipers and Mandersloot, 1999). In most intensive crop and livestock production systems, the input of nutrients exceeds the output resulting in considerable mineral surpluses in the soil. Inorganic fertilizers are relatively cheap, and there is a large import of nutrients with stock feed resulting in more manure than can be spread on the land. Many of the problems in the intensive agricultural systems of western Europe are therefore structural rather than local and cannot easily be solved by transport of manure to other regions (de Walle and Sevenster, 1998). In the 1980s and 1990s, evidence has accumulated that nutrient depletion is a problem in many tropical soils (Dudal, 1982; Greenland, 1981; Lal, 1987; Pieri, 1989; Sanchez et al., 1997). The major cause is the drain of nutrients with the crop yield, erosion, and losses through leaching or denitriﬁcation, while little or no inorganic fertilizers are being used. Also the use of manure is insufﬁcient to cover the drain of nutrients, and this shortage is further aggravated as livestock numbers generally decrease with increasing population. Thus, where the soil scientist in the temperate region is concerned with N leaching causing groundwater contamination and eutrophication of surface waters, soil scientists in tropical regions are concerned with leaching because of the loss of N for crop production. There is a common interest in reduction of nutrient losses, although the motives are diametrically opposed. Where in the temperate soils under intensive agriculture P saturation is a concern, the low levels in many tropical soils warrant a similar level of interests in the complex chemistry of soil P. And where the soil scientist in the temperate regions is interested in soil changes when the land is deliberately taken out of production and not cultivated, a key question in the tropics is how the soil can be kept productive when continuously cultivated, and what needs to be done to make, and keep, marginally suitable soils productive. The soil nutrient situation is even more deplorable if it is realized that in the intensive livestock production systems of the temperate region soils are being used as a dumping ground for nutrients, whereas some of these nutrients originate from tropical countries where many soils are chemically poor and few inorganic

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fertilizers are being used (Bouwman and Booij, 1998; van Diest, 1986). From a soil nutrient perspective it appears that soil fertility research in tropical regions is all about alleviating poverty, whereas in the temperate regions it is mainly about alleviating abundance and wealth. The soil appears as a ﬁtting metaphor for the economic differences between the two regions.

V. IMPACT OF SOIL SCIENCE The understanding and knowledge of soils kept pace with the dramatic increase in population and enormous changes in global land use of the past 100 years. Despite this success, the general public has never been widely interested in soils, and there is a deep concern about the public proﬁle and appreciation of soil science (White, 1997). It was noted that soil science goes through a period of reduced funding and public interest, and several conferences and committees were dedicated to the question of how soil scientists should cope with this situation (Mermut and Eswaran, 1997; Sposito and Reginato, 1992; Wagenet and Bouma, 1996). Most authors are optimistic and positive; for example, Mermut and Eswaran (1997) stated that “. . . we believe that the future of soil science is stronger than before and the demand for soil scientists will be greater than before.” Largely absent in these forward-looking publications is the future development of soil science in tropical regions. That is particularly unfortunate as less is known about tropical soils, and evident problems are evolving because of population pressure (Young, 1998). It is in the tropics where soil scientists can have the largest impact on society and where there is incomplete understanding of the soil and a paucity of hard information (Theng, 1991). Although it is generally accepted that soil science is of great importance, very little has been written about the contribution to knowledge and, hence, to society, arising from the scientiﬁc study of the soil (Greenland, 1991). This particularly concerns the impact of soil science in tropical regions, and much more is known about agricultural research and the role it has played in the advancement of agriculture and land use in Europe (Porceddu and Rabbinge, 1997). Many soil scientists are concerned by the lack of impact, and authoritative knowledge about soils has failed to reach many government administrators, ﬁnancial organizations, planners, educational authorities, and land users who would most beneﬁt from the knowledge (Bridges and Catizzone, 1996). Such impact is of course hard to measure directly, but Lal (1995) mentioned that it can be judged from agricultural and food production trends and from the use of science-based input. Much of the credit for the agricultural production increase has deservedly been given to the plant breeders, but demonstration of the importance of proper nutrient management and of the potential to intensify cropping systems and develop new lands was due to soil

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scientists. If it were not for soil scientists, Thomas Malthus would have been right according to Greenland (1991). The situation is different in different continents. In large parts of Asia agricultural productivity has increased largely due to new crop cultivars and other products from the Green Revolution (Table II). Food production in some African countries has been falling (Greenland, 1997; Pinstrup-Andersen, 1998), which could be because the Green Revolution had fewer inroads (Lappe et al., 1998). Or does it imply that soil scientists had limited impact in Africa? We do not know; but quite likely there would have been many more East African Groundnut Schemes if soil science had ignored Africa, although the failure of the scheme was an important stimulus to the use of soil surveys in development projects (Young, 1976). Muchena and Kiome (1995) discussed the role of soil science in agricultural development in East Africa and concluded that it has played a modest role. Unfortunately this role goes largely unquantiﬁed. They conclude that despite the activities of numerous foreign experts, there is still inadequate expertise in some key disciplines such as soil physics, land evaluation, and water management. More research is needed. However, a convincing plea for the increasing need for soil research in the tropics should not be based on areas where expertise is inadequate but on a quantitative analysis of the impact of soil science. That may be much needed since donors are less eager to fund soil research in the tropics, and large international organizations like FAO essentially stopped collecting soil data because of the lack of funds from the UNDP and bilaterals for ﬁeld projects. In past decades, many national soil science institutes in tropical regions have emerged, but the need remains to maintain an active international soil science network for effective exchange of information and to cut costs. The developed world is reducing its willingness to contribute to the development of science in the tropical regions, and this may hinder the advancement of soil science in the tropical regions. A possible option to reverse this trend is to quantify the impact of soil science on development in tropical regions. There have been a number of initiatives to actively promote soil science, but too few studies have quantiﬁed the impact of soil science, and that, unfortunately, applies to both tropical and temperate regions.

VI. CONCLUDING REMARKS More is known about soil resources in temperate regions than in tropical regions, despite the fact that one-third of the soils of the world are in the tropics and support more than three-quarters of the world population. In addition, 95% of the population growth takes place in tropical regions. Therefore it is in the tropics that soil scientists can have a large impact on society, because there is an incomplete understanding of the soil and insufﬁcient hard information.

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In temperate regions, the focus of attention is currently shifting to population aging, whereas in tropical regions the increasing population and the associated need to increase food production remain important subjects for soil science. Most attention needs to be given to yield increases, as there is limited potential for an expansion of the agricultural area in most tropical countries. Also environmental soil science in tropical regions needs to be further developed. Some of the common research interests in the temperate and tropical region are the development of sustainable land management systems and appropriate land quality indicators, quantiﬁcation of soil properties and processes, ﬁne tuning of models, sequestration of C in agricultural soils, and optimum use of agricultural inputs to minimize environmental degradation and maximize proﬁt. Close cooperation on these subjects is of interest for soil science in both temperate and tropical regions. However, it seems that the developed world is reducing its willingness to contribute to the development of science in tropical regions, and this may hinder the advancement of soil science in tropical regions.

ACKNOWLEDGMENTS I am greatly indebted to Professor D. J. Greenland and Mr. J. H. V. van Baren, Mr. J. H. Kauffman, and Dr. W. G. Sombroek for comments on the draft of this paper.

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RESPONSES OF AGRICULTURAL CROPS TO FREE-AIR CO2 ENRICHMENT B. A. Kimball,1,∗ K. Kobayashi,2 and M. Bindi3 1

U.S. Water Conservation Laboratory, USDA, Agricultural Research Service Phoenix, Arizona, 85040 2 National Institute of Agro-Environmental Sciences Tsukuba, Ibaraki 305-8604, Japan 3 Department of Agronomy and Land Management University of Florence 50144 Florence, Italy

I. Introduction II. Methodology III. Results and Discussion of Crop Responses to Elevated CO2 A. Photosynthesis B. Water Relations C. Peak Leaf Area Index D. Biomass Accumulation E. Radiation-Use Efﬁciency F. Speciﬁc Leaf Area G. Chemical Composition Changes H. Phenology I. Soil Changes IV. Compendium and Conclusions V. Summary References

Over the past decade, free-air CO2 enrichment (FACE) experiments have been conducted on wheat, perennial ryegrass, and rice, which are C3 grasses; sorghum, a C4 grass; white clover, a C3 legume; potato, a C3 forb with tuber storage; and cotton and grape, which are C3 woody perennials. Elevated CO2 increased photosynthesis, biomass, and yield substantially in C3 species, but little in C4. It decreased stomatal conductance in both C3 and C4 species and greatly improved water-use efﬁciency in all crops. Growth stimulations were as large or larger under water stress compared to well-watered conditions. At low soil N, stimulations of nonlegumes were reduced, whereas elevated CO2 strongly stimulated the growth of the clover legume ∗

To whom correspondence should be addressed. Phone: 602-437-1702 x-248. Fax: 602-437-5291. E-mail: [email protected]. 293 Advances in Agronomy, Volume 77 Copyright 2002, Elsevier Science (USA). All rights reserved. 0065-2113/02 $35.00

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KIMBALL et al. at both ample and low N conditions. Roots were generally stimulated more than shoots. Woody perennials had larger growth responses to elevated CO2, but their reductions in stomatal conductance were smaller. Tissue N concentrations went down, while carbohydrate and some other carbon-based compounds went up, with leaves being the organs affected most. Phenology was accelerated slightly in most but not all species. Elevated CO2 affected some soil microbes greatly but not others, yet overall activity was stimulated. Detection of statistically signiﬁcant changes in soil organic carbon in any one study was nearly impossible, yet combining results from several sites and years, it appeared that elevated CO2 did increase sequestration of soil carbon. Comparisons of the FACE results with those from earlier chamber-based results were consistent, which gives conﬁdence that conclusions C 2002 Elsevier Science (USA). drawn from both types of data are accurate.

I. INTRODUCTION The increasing CO2 concentration of Earth’s atmosphere and associated predictions of global warming (IPCC, 1996) have stimulated research programs to determine the likely effects of the future elevated CO2 levels on agricultural productivity and on the functioning of natural ecosystems (e.g., Dahlman et al., 1985). However, even predating the global change concerns, the effects of atmospheric CO2 enrichment have been studied for more than a century in greenhouses, controlled-environment chambers, open-top chambers, and other enclosures to conﬁne the CO2 gas around the experimental plants (e.g., Drake et al., 1985; Enoch and Kimball, 1986; Schulze and Mooney, 1993). The results of these many chamber-based experiments have been reviewed by Kimball (1983, 1986, 1993), Morison (1985), Cure (1985), Cure and Acock (1986), Kimball and Idso (1983), Poorter (1993), Idso and Idso (1994), Ceulemans and Mousseau (1994), Wullschleger et al. (1997), Cotrufo et al. (1998), Norby et al. (1999), Nakagawa and Horie (2000), Curtis and Wang (1998), and Wand et al. (1999) (although the latter two also included a few observations from recent nonchamber open-ﬁeld experiments). However, the environment inside enclosures is not generally like that outside (e.g., Kimball et al., 1997; McLeod and Long, 1999); thus, there have been many concerns that the results from such enclosure-based CO2-enrichment experiments might not be representative of future open ﬁelds and forests. Therefore, various attempts were made to develop techniques which could maintain the CO2 concentrations over open-ﬁeld plots at elevated levels despite the challenges imposed by open-ﬁeld winds causing rapid dispersal of the CO2 (Allen, 1992; Norby et al., 2001). Eventually, engineers from Brookhaven National Laboratory (Upton, New York) working cooperatively with scientists from the

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U.S. Department of Agriculture, Agricultural Research Service, and from Tuskegee University, as well as others, were able to adapt a “vertical vent pipe” technology that could adequately maintain the desired high levels of CO2 over open-ﬁeld plots all growing-season long (Hendrey, 1993; Norby et al., 2001). The ﬁrst such experiment with publishable biological data was conducted on cotton in 1989 at Maricopa, Arizona. After success with cotton, the Brookhaven group moved their engineering efforts to the forest, and they were able to increase the scale of the apparatus to accommodate 14-m-tall trees (e.g., Delucia et al., 1999). Once it was demonstrated that such free-air CO2 enrichment (FACE) experiments were feasible, several other research groups also initiated similar experiments in both managed and natural ecosystems. To date, there are about 30 active or planned FACE sites (http://cdiac.esd.ornl.gov/programs/FACE/face.html; http://www.face.bnl.gov/; http://gcte-focus1.org/co2.html). The purposes of this paper are (i) to compile the available data from the FACE experiments on agricultural crops; (ii) to determine the relative responses of the several crops to elevated CO2 with regard to their physiology, growth, yield, water relations, and soil processes; (iii) to search for similarities and differences among species and plant functional types; and (iv) to compare these FACE results with those from prior chamber-based studies.

II. METHODOLOGY We have extracted data from papers and manuscripts generated by our agricultural-crop-oriented free-air CO2 enrichment (FACE) projects at Maricopa, Arizona; Shizukuishi, Iwate, Japan; and Rapolano, Terme, Italy, as well as from the grassland project at Eschikon, Switzerland. The experimental protocols and site characteristics for the several experiments are listed in Table I. From the absolute crop response values, we computed the relative increases (or decreases, which are listed as negative increases) due to the FACE treatment with respect to their corresponding control treatments at ambient CO2, as listed in Table II. The number of FACE experiments is relatively small, so conclusions cannot be as deﬁnitive as desired. On the other hand, the relatively large plot size of the FACE projects produces enough plant material to support the research of a large number of cooperating scientists from several disciplines, so the number of processes for which measurements were obtained is comparatively large. The crops include wheat (Triticum aestivum L.), rice (Oryza sativa L.), perennial ryegrass (Lolium perenne), sorghum (Sorghum bicolor (L.) M¨oench), potato (Solanum tuberosum L.), white clover (Trifolium repens), lucerne or alfalfa (Medicago sativa L.), cotton (Gossypium hirsutum L.), and grape (Vitis vinifera L.). These crops are representative of several functional types of plants. Speciﬁcally, wheat, rice, and ryegrass are all C3 grasses, and sorghum is a C4 grass.

Table I Experimental Protocols and Site Characteristics for Several Agricultural FACE Experiments FACE experiment (experiment ID)a Parameter Location Species or ecosystem

Latitude (deg, min) Longitude (deg, min) Elevation (m) Experiment start dateb 296 Experiment end dateb

Growing season startc

Growing season endc

FACE startd

FACE endd Solar rad.e(MJ m−2 day−1) Max. air temp.f(◦ C)

MCCot89–91

MCWht93–94

MCWht96–97

Maricopa, Arizona Cotton (Gossypium hirsutum L.)

Maricopa, Arizona Wheat (Triticum aestivum L.)

Maricopa, Arizona Wheat (Triticum aestivum L.)

33◦ 4 N 111◦ 59 W 358 17-04-1989 23-04-1990 16-04-1991 17-09-1989 17-09-1990 16-09-1991 17-04-1989 17-04-1990 16-04-1991 17-09-1989 17-09-1990 16-09-1991 19-05-1989 04-05-1990s 26-04-1991 17-09-1989 17-09-1990 16-09-1991 Avg. 25.1 Avg. 46.3

33◦ 4 N 111◦ 59 W 358 15-12-1992 08-12-1993

MCSor98–99

Swiss93–98

RiceFACE98–99

Eschikon, Switzerland Grassland; ryegrass (Lolium perenne) and white clover (Trifolium repens) 47◦ 27 N 8◦ 41 E 550 31-5-1993

Shizukuishi, Iwate, Japan Rice (Oryza sativa L.)

33◦ 4 N 111◦ 59 W 358 15-12-1995 15-12-1996

Maricopa, Arizona Sorghum [Sorghum bicolor (L.) M¨oench] 33◦ 4 N 111◦ 59 W 358 16-07-1998 15-06-1999

24-05-1993 01-06-1994

29-05-1996 28-05-1997

21-12-1998 26-10-1999

Continuing

29-09-1998 24-09-1999

15-12-1992 08-12-1993

15-12-1995 15-12-1996

16-07-1998 15-06-1999

Temp > 5◦ C

21-05-1998 20-05-1999

24-05-1993 01-06-1994

29-05-1996 28-05-1997

21-12-1998 26-10-1999

Temp < 5◦ C

29-09-1998 24-09-1999

01-01-1993 28-12-1993

01-01-1996 03-01-1997

31-07-1998 01-07-1999

1-6-1993

03-06-1998 20-05-1999

16-05-1993 18-05-1994

15-05-1996 12-05-1997

21-12-1998 26-10-1999

Continuing

29-09-1998 24-09-1999

Avg. 18.7 Avg. 38.5

Avg. 19.9 Avg. 40.9

Avg. 21.6 Avg. 44.2

∼14.2 25

Avg. 13.8 24.5

39◦ , 38 N 140◦ , 57 E 200 21-05-1998 20-05-1999

Min. air temp.f(◦ C) Plot diameterg(m) No. of replicatesh No. of CO2 levelsi Predilution of the CO2? j Set point or increment?k FACE CO2 conc.(s)l Daily enrichment timem “No-enrichment” criterian Add’nal treat. #1 nameo

297

Avg. −2.8 20 4 2 Yes Set point 550 24 h day− 1 None Water

Avg. −3.7 20 4 2 + Ambient Yes increment +200 24 h day− 1 None Nitrogen

Avg. 2.0 21 4 2 Yes increment +200 24 h day− 1 None Water

−5 18 3 2 Yes Set point 600 Daylight Winter Nitrogen

15.9 10 4 2 No 200 ppm increment 589 (at the ring center) 24 h na Nitrogen

Main or split?p Level 1q (dry or low-N)

Avg. 7.6 18 4 2 Yes Set point 550 Daylight None Water in 1990 and 1991 Split Avg. 1009 mm

Split Avg. 335 mm

Split Avg. 483 mm

Split 140 kg ha− 1 y− 1

Split Low (40 kg N/ha)

Level 2q (wet or high-N)

Avg. 1202 mm

Avg. 679 mm

Split 70 kg N ha− 1 in 1996; 15 kg N ha− 1 in 1997 350 kg N ha− 1

Avg.1133 mm

560 kg ha− 1 y− 1

Standard [80 (1998) or 90 (1999) kg N/ha] High [120 (1998) or 150 (1999) kg N/ha]

1120 kg ha− 1 y− 1

Level 3q (very high-N) Add’nal treat. #2 nameo Main or split?p Level 1q Level 2q Add’nal treat. #3 Main or Split?p Level 1q Level 2q Level 3q Reference(s)r

Pinter et al. (1994), Mauney et al. (1994), Lewin et al. (1994)

Hunsaker et al. (1996), Kimball et al. (1999), GCTE (1996)

Kimball et al. (1999)

Ottman et al. (2001)

Cutting frequency Split 4 cuts y− 1 8 cuts y− 1, then 5 Sward type Split Lolium perenne Trifolium repens Mixture Jongen et al. (1995), Hebeisen et al. (1997), Daepp et al. (2000)

Kim et al. (2001), Kobayashi et al. (2001)

continues

Table I—continued FACE experiment (experiment ID)a Parameter

CLAIRE 94–95

CLIVARA 96–97

Location Species or ecosystem

Rapolano, Terme, Italy Grape (Vitis vinifera cv. Sangiovese)

Rapolano, Terme, Italy Grape (Vitis vinifera cv. Sangiovese)

Latitude (deg, min) Longitude (deg, min) Elevation (m) Experiment start dateb

50◦ 32 N 8◦ 41.3 E 172 01-05-1994 19-4-1995 04-10-1994 17-10-1995 01-05-1994 19-04-1995 04-10-1994 17-10-1995 01-15-1994 02-05-1995 04-10-1994 17-10-1995 Avg. 21.3 35.8 0.9 8 × 1.5 3 2 Yes Set point 700 Daylight

50◦ 32 N 8◦ 41.3 E 172 22-04-1996 20-04-1997 30-09-1996 07-10-1997 22-04-1996 20-04-1997 30-09-1996 07-10-1997 06-05-1996 11-05-1997 01-10-1996 07-10-1997 Avg. 20.9 Avg. 32.4 Avg. −1.3 8 × 1.5 2 3 Yes Set point 550, 700 Daylight

Experiment end dateb 298

Growing season startc Growing season endc FACE startd FACE endd Solar rad.e(MJ m− 2 day− 1) Max. air temp.f(◦ C) Min. air temp.f(◦ C) Plot diameter g(m) No. of replicatesh No. of CO2levelsi Predilution of the CO2? j Set point or increment?k FACE CO2conc.(s)l Daily enrichment timem

POTATO95 Rapolano, Terme, Italy Potato (Solanum tuberosum cv. Primura) 50◦ 32 N 8◦ 41.3 E 172 27-05-1995 05-09-1995 10-06-1995 05-09-1995 10-06-1995 04-05-1995 21.5 35.8 2.9 8 1 4 Yes Set point 460,560,660 Daylight

CHIP98–99 Rapolano, Terme, Italy Potato ( Solanum tuberosum cv. Bintje) 50◦ 32 N 8◦ 41.3 E 172 20-05-1998 05-05-1999 18-08-1998 17-08-1999 28-05-1998 26-05-1999 18-08-1998 17-08-1999 28-05-1998 27-05-1999 18-08-1998 17-08-1999 Avg. 21.4 Avg. 37.8 Avg. 5.1 8 3 2 + Ambient Yes Set point 560 Daylight

“No-enrichment” criterian Reference(s)r

a

None Bindi et al. (1995a, 1995b), Raschi et al. (1996), Giuntoli (2000)

None Bindi et al. (2000), Bindi, Fibbi et al. (2001) Bindi, Fibbi, and Miglietta (2001) Giuntoli (2000)

None Miglietta et al. (1997, 1998), Vaccari et al. (2000)

None Bindi et al. (1998, 1999)

Experiment identiﬁcation names. Dates for the start and end of the experiments. c Calendar year dates or criteria for the start and stop of the growing seasons. d Start and stop dates of the FACE treatment(s). (For most experiments, these dates are the same as the growing season dates.). e Mean daily solar radiation obtained during growing season(s), expressed in MJ m−2 day−1. f Maximum and minimum air temperatures during growing season(s) in ◦ C. g Diameter of useable plot area in m, or for rectangular plots, length and width. h Number of replicate CO2 and other treatment plots. i Number of treatment CO2 levels. A “2” means experiments with FACE and Control treatments or with FACE and ambient treatments. “Control” means plots with air ﬂow near identical to that of the FACE plots but without added CO2. “Ambient” means plots with no forced air ﬂow and no added CO2. “2 + ambient” means experiments with FACE, control, and ambient treatments. For experiments with multiple levels of FACE concentrations, a list of the several CO2 levels is given. j A “yes” means a blower system in the FACE apparatus prediluted CO2 with air, or a “no” means pure CO2 was released. k “Set point” or “increment” indicates whether a constant target CO2 set point was used or whether a target increment in concentration above normal air CO2 levels was used. l The set point CO2 concentration(s) or the increment(s) in CO2 concentration preceded by a “+”. Units of μmol mol−1. m Portion of day CO2 enrichment was done such as “24 h” or “daylight” or other amount. n Constraints that were put on the enrichment, such high-wind cutoff or low-temperature cutoff. o Name(s) of any other additional factorial treatments in the experiment such as low water or low nitrogen. p “Split” or “main” indicates whether the CO2 main plots were split or whether additional full-size main plots of the other factor(s) were added. q Levels of the additional treatment factors with units. r Reference(s) that best describe the experimental conditions. b

299

Table II Percentage Increases in Several Plant Response Parameters to Elevated CO2 of Various Agricultural Crops Grown in Monoculture Relative to Their Responses at Ambient CO2a Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

%

+SE

Low water

Ample N %

+SE

Low N %

+SE

Ample N %

+SE

References

Net photosynthesis

300

C3 grasses MCWht93 Wheat upper leaf MCWht93 Wheat ﬂag leaf MCWht93 Wheat 8th leaf MCWht93 Wheat 7th leaf (Daily integral of net CO2 uptake) MCWht96 Wheat upper leaf MCWht97 Wheat upper leaf (Seasonal carbon assimilated) MCWht93,96 Wheat ears MCWht97 Wheat canopy Swiss94 Ryegrass 7-day cut Swiss94 Ryegrass uncut C4 grasses MCSor98,99

Sorghum upper leaf

C3 woody perennials MCCot89 Cotton upper leaf MCCot90 Cotton canopy

31.5 25.6 68.6 ∞ 32.8 21.6

Garcia et al. (1998) Osborne et al. (1998) Osborne et al. (1998) Osborne et al. (1998) 5.0 7.3

25.9 19.7

10.0 9.2

Wall, Adam et al. (2001) Wall, Adam et al. (2001)

58.0 19.2 32.5 43.5

19.0 24.1

32.0 8.7 45.2 45.8

8.5

13.5

23.3

21.2

Wall, Brooks et al. (2001)

28.2 32.1

13.0 38.9

17.8

23.3

Hileman et al. (1994) Hileman et al. (1994)

58.0

Wechsung et al. (2000) Brooks et al. (2001) Rogers et al. (1998) Rogers et al. (1998)

20.8 22.4

Water relations: stomatal conductance MCWht93 MCWht96–97

C4 grasses MCSor98–99

301

Wheat Wheat C3 grass means SE

−32.7 −36.0 −34.4 1.7

Sorghum

−37.3

13.8

−32.4

22.6

Wall, Brooks et al. (2001)

−18.2 −12.3 −14.7 3.7 −19.6 −12 −18 −15 −8 −32 −23.0 −6 4 −21.3 −16.0

8.9 3.2

−22.2

14.3

Hileman et al. (1994) Raschi et al. (1996)

C3 woody perennials MCCot90 Cotton CLAIRE95 Grape C3 woody means SE Literature Literature Wheat Literature Rice Literature Sorghum Literature Cotton Literature Potato Literature Literature Woody SE Literature Wild C3 grass Literature Wild C4 grass

Garcia et al. (1998) Wall, Adam et al. (2001)

−44.0

Kimball and Idso (1983) Cure (1985) Cure (1985) Cure (1985) Cure (1985) Cure (1985) Morison (1985) Curtis and Wang (1998)

−14.5

−12.8

Wand et al. (1999) Wand et al. (1999)

Water relations: canopy temperature (◦ C, not %) C3 grasses MCWht93 MCWht96

Wheat Wheat

0.6 0.6

0.1

1.1

0.1

Kimball et al. (1995) Kimball et al. (1999) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

C3 woody perennials MCCot89 Cotton

%

+SE

Low water

Ample N %

0.8

+SE

Low N %

+SE

Ample N %

+SE

0.1

References

Kimball et al. (1992)

Water relations: evapotranspiration or water use

302

C3 grasses MCWht93 MCWht94 MCWht96 MCWht97 MCWht93–97 MCWht96–97

Wheat; water bal. Wheat; water bal. Wheat; water bal. Wheat; water bal. Wheat; energy bal. Wheat; energy bal.

−3.6 −3.3 −3.5 −3.9 −6.7

C4 grasses MCSor98 MCSor99

Sorghum; water bal. Sorghum; water bal.

−11.1 −8.7

C3 woody perennials MCCot90 Cotton; water bal. MCCot91 Cotton; water bal. MCCot91 Cotton; stem ﬂow

−0.7 −1.3

4.5 −2.2

Hunsaker et al. (1996) Hunsaker et al. (1996) Hunsaker et al. (2000) Hunsaker et al. (2000) Kimball et al. (1999) Kimball et al. (1999)

−19.5 3.0 2.6

−1.1 −1.9 0.0

0.0 −6.5

6.2 4.0

−1.6 −1.6

Conley et al. (2001) Conley et al. (2001) Hunsaker et al. (1994) Hunsaker et al. (1994) Dugas et al. (1994)

Water relations: leaf water potential [Negative % increase values indicate FACE plants had higher (i.e., less negative and less stressful) water potentials] C4 grasses MCSor98–99

Sorghum

C3 woody perennials CLAIRE95 Grape

−2.8

5.5

−2.9

1.2

−8.8

4.2

Wall, Brooks et al. (2001) Raschi et al. (1996)

Peak leaf area index C3 grasses MCWht96 MCWht97 Swiss96-97 Swiss96-97

Wheat Wheat Ryegrass, vege. Ryegrass, repro.

16.4 16.5

13.0 24.0 11.1 8.1

6.7 −6.2 −5.6 12.2

1.0

3.5

0.4

Rice

10.8

6.3

2.0

C3 grass means SE

11.0 3.8

10.8 2.9

1.4 2.9

(Vegetative and reproductive stages in pots) RiceFACE98 Rice RiceFACE99

303

C4 grasses MCSor98 MCSor99

−0.7 −9.8 −5.4 4.7

16.6 14.2

C3 broadleaf forb with tuber storage POTATO95 Potato; 460 ppmv POTATO95 Potato; 560 ppmv POTATO95 Potato; 660 ppmv Chip98 Potato Chip99 Potato Potato means SE

−12.9 5.8 −1.6 −9.8 −11.0 −6.2 3.5

37.7 22.6 11.2 19.0 4.7

C3 woody perennials MCCot89 Cotton MCCot91 Cotton Cotton means SE

3.8 −15.6 −6.4 10.2

23.5 14.2

Sorghum Sorghum C4 grass means SE

Brooks et al. (2001) Brooks et al. (2001) Daepp et al. (2001) Daepp et al. (2001) Kobayashi et al. (unpublished) Kobayashi et al. (unpublished)

−0.3 3.1 1.4 1.7

22.5 14.8

Ottman et al. (2001) Ottman et al. (2001)

Miglietta et al. (1998) Miglietta et al. (1998) Miglietta et al. (1998) Bindi et al. (1998) Bindi et al. (1999)

20.8

22.6

Mauney et al. (1992) Mauney et al. (1994)

continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

%

+SE

Low water

Ample N %

+SE

Low N %

+SE

Ample N %

+SE

References

12.3 16.6

9.1 20.6

Pinter et al. (2002) Pinter et al. (2002)

Biomass accumulation: shoots

304

C3 grasses MCWht93 MCWht94

Wheat Wheat

9.3 7.7

12.2 14.4

[Note: blower effect may have reduced response in 1993 and 1994 (Pinter et al., 2000)] MCWht96 Wheat 4.8 5.8 8.1 MCWht97 Wheat 11.7 15.8 2.8 RiceFACE98 Rice 16.9 7.6 RiceFACE99 Rice 13.9 10.8 8.1 Swiss93 Ryegrass 5.8 3.0 3.7 Swiss94 Ryegrass 8.0 11.0 −8.6 Swiss95 Ryegrass 10.9 9.7 1.6 Swiss96 Ryegrass 18.6 3.0 −6.2 Swiss97 Ryegrass 10.5 5.6 −0.3 Swiss98 Ryegrass 20.1 4.9 6.9 Swiss96–97 Ryegrass, vegetative 20.2 5.1 16.6 5.7 −2.2 Swiss96–97 Ryegrass, reproduc. 25.4 9.6 20.2 9.7 24.2 (Vegetative and reproductive stages in pots) 19.0 C3 grass means SE 2.5

11.5 1.4

3.1 2.6

13.8 15.4

Pinter et al. (2002) Pinter et al. (2002) Kim et al. (unpublished) Kim et al. (2001) Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (1997) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2001) Daepp et al. (2001)

6.1 20.2 20.5 4.6 10.5 8.5 11.3 17.5 14.4 2.2

C4 grasses MCSor98 MCSor99

Sorghum Sorghum C4 grass means SE

6.7 −1.0 2.8 3.9

3.1 4.5

−12.2 −28.6 −20.8 8.6

6.5 5.0

Clover Clover Clover Clover Clover Clover Lucerne; effective Lucerne; effective Lucerne; ineffect. Lucerne; ineffect.

12.0 16.7 7.6 38.3 21.3 28.6 36.4 38.8 2.4 17.4

4.2 10.4 7.9 12.9 9.5

(Inoculated with effective or ineffective nodulators) Legume means SE Legume means SE

24.4 4.5 9.6 7.8

C3 broadleaf forb with tuber storage Chip98 Potato Chip99 Potato Potato means SE

305

C3 legumes Swiss93 Swiss94 Swiss95 Swiss96 Swiss97 Swiss98 Swiss94 Swiss95 Swiss94 Swiss95

15.4 29.0 11.4 24.4

13.3 18.0 15.6 2.4

13.9 16.8

Ottman et al. (2001) Ottman et al. (2001)

Bindi et al. (1998) Bindi et al. (1999)

17.9 19.9 7.9 36.0 19.8 26.3 42.8 37.2 −19.0 −19.8 25.5 4.2 −19.4 0.4

4.8 10.0 7.5 11.9 9.9 16.0 29.2 7.3 14.8

Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (unpub) Hebeisen et al. (unpub) Hebeisen et al. (unpub) L¨uscher et al. (2000) L¨uscher et al. (2000) L¨uscher et al. (2000) L¨uscher et al. (2000) (Clover + lucerne with effective) (Only lucerne with ineffective) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

%

306

C3 woody perennials MCCot89 Cotton MCCot90 Cotton MCCot91 Cotton CLAIRE94 Grape; 700 ppmv CLAIRE95 Grape; 700 ppmv CLIVARA96 Grape; 550 ppmv

+SE

Ample N %

+SE

32.3 34.2 36.8 27.8 32.4 33.3

11.8

Low N %

+SE

Ample N %

17.8 35.4 18.3 24.0 26.9

CLIVARA96

Grape; 700 ppmv

20.6

15.0

CLIVARA97

Grape; 550 ppmv

42.1

19.8

CLIVARA97

Grape; 700 ppmv

25.1

9.4

C3 woody means SE Means all C3 except lucerne ineffect SE all C3 except lucerne ineffect Means all C3 except legumes SE all C3 except legumes Literature C3 (all) means SE

31.5 2.2 17.3 2.9 15.1 3.4 21 2

19.0 2.5 19.0 2.5

Low water

12.0 3.4 3.1 2.6

+SE

References

Mauney et al. (1992) Mauney et al. (1994) Mauney et al. (1994) Bindi et al. (1995a) Bindi, et al. (1995a) Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001)

26.3 9.1 20.2 5.0 20.2 5.0 Kimball (1983, 1986)

307

Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature

C3 herbs C4 herbs N ﬁxing C3 woody C3 herb, crop C3 herb, wild C3 wild fast grow C3 intermediate C3 wild slow gro Dry wt and CER Dry wt and CER Woody coniferous

16 15 5 46 −8 19 21 11 25 21 29 18 27 19 12 20 32 24

Literature

Woody deciduous

40

Literature

Woody total biom. SE Woody SE Wild C3 grasses wild C4 grasses Rice

17 3 42.7 10.1 19.4 4.6 14

Literature Literature Literature Literature

Wheat Rice Sorghum Cotton Potato

5 15 72

16 15 17 10

15

19

39 29

8 2

Cure (1985) Cure (1985) Cure (1985) Cure (1985) Cure (1985) Kimball (1993) Poorter (1993) Poorter (1993) Poorter (1993) Poorter (1993) Poorter (1993) Poorter (1993) Poorter (1993) Poorter (1993) Poorter (1993) Idso and Idso (1994) Idso and Idso (1994) Ceulemans and Mousseau (1994) Ceulemans and Mousseau (1994) Curtis and Wang (1998) Norby et al. (1999)

16.9 2.6

Wand et al. (1999) Wand et al. (1999) Nakagawa and Horie (2000) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

%

+SE

Low water

Ample N %

+SE

Low N %

+SE

Ample N %

+SE

References

Biomass accumulation: roots

308

C3 grasses MCWht93 RiceFACE98 RiceFACE99 Swiss93 Swiss93 Swiss94 Swiss94 Swiss95 Swiss95 Swiss96

Wheat; at dough Rice Rice Ryegrass; 7 cuts Ryegrass; 4 cuts Ryegrass; 8 cuts Ryegrass; 4 cuts Ryegrass; 8 cuts Ryegrass; 4 cuts Ryegrass

Swiss96–97 Swiss96–97

13.5 10.3

Ryegrass, vegetative 30.6 Ryegrass, 40.0 reproductive (Vegetative and reproductive stages in pots) Swiss93 Ryegrass (Root ingrowth bags) 23.0 C3 grass means SE 7.2 C3 legumes Swiss93 Swiss93

Clover; 7 cuts Clover; 4 cuts

5.9 13.6

27.9 19.5 18.4 29.3 78.8 161.3 39.2 86.5 50.8 85.7

51.3

45.6 79.2 119 50.4 94.3 87.9 83.4

22.0 32.4 38.7 94.3 72.0 27.0 9.1 65.9

47.3 41.0 48.3 36.6 38.4 33.2 147

31.8 17.9

6.1 11.2

33.1 20.8

6.1 10.2

Wechsung et al. (1999) Kim et al. (unpublished) Kim et al. (2001) Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (1997) Van Kessel, Horwath et al. (2000) Daepp et al. (2001) Daepp et al. (2001)

91.2

@.05

32.8

@.05

Jongen et al. (1995)

47.4 10.2 39.6 31.0

22.7

38.6 7.4 64.3 49.4

19.3 2.6

22.7

57.9 34.3

Hebeisen et al. (1997) Hebeisen et al. (1997)

Swiss94 Swiss94 Swiss95 Swiss95 Swiss96

Clover; 8 cuts Clover; 4 cuts Clover; 8 cuts Clover; 4 cuts Clover

21.7 25.4 5.6 22.2 33.7

Legume means SE

25.2 4.3

309

C3 woody perennials MCCot89 Cotton; taproot MCCot90 Cotton; taproot MCCot91 Cotton; taproot Cotton; taproot means SE MCCot89 Cotton; ﬁne roots MCCot90 Cotton; ﬁne roots MCCot91 Cotton; ﬁne roots Cotton; ﬁne roots means: SE Literature Woody SE Literature Wild C3 grasses Literature Wild C4 grasses

156.9 37.8 60.4 78.4 36.8 100.0 29.3 34.4 51.5

51.2 33.4 35.1 63.0 446

59.4 17.6 77.8 −7.1 2.4

75.4 46.5 78.1 35.4 301

Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (1997) Hebeisen et al. (1997) Van Kessel, Horwath et al. (2000)

21.4 11.6 63.8 10.1 16.6

Rogers et al. (1992) Prior et al. (1994) Prior et al. (1994)

31.3

Rogers et al. (1992) Prior et al. (1994) Prior et al. (1994)

22.7 23 3 31.3 8.3

5 6 14.7

Curtis and Wang (1998) Wand et al. (1999) Wand et al. (1999)

Biomass accumulation: agricultural yield C3 grasses with grain yield MCWht93 Wheat MCWht94 Wheat

8.0 12.0

7.4 8.6

21.0 25.0

7.2 18.6

Pinter et al. (1997, 2002) Pinter et al. (1997, 2002) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

%

+SE

Low water

Ample N %

+SE

Low N %

310

[Note: blower effect may have reduced response in 1993 and 1994 (Pinter et al., 2000)] MCWht96 Wheat 15.0 8.8 12.0 MCWht97 Wheat 17.0 12.7 5.0 RiceFACE98 Rice 13.8 10.3 7.4 RiceFACE99 Rice 8.2 9.8 3.0 11.0 12.0 6.8 C3 grass means SE 2.8 1.4 1.9 C4 grasses with grain yield MCSor98 Sorghum MCSor99 Sorghum Means SE

0.9 −10.7 −5.1 6.0

4.9 6.9

C3 boadleaf forb with tuber yield POTATO95 Potato; 460 ppmv POTATO95 Potato; 560 ppmv POTATO95 Potato; 660 ppmv Chip98 Potato Chip99 Potato Potato means SE

10.8 21.0 27.0 33.8 47.6 27.5 6.3

21.9 10.8 8.1 5.8 7.8

C3 woody perennial with boll (seed + lint) yield MCCot89 Cotton bolls MCCot90 Cotton bolls

22.0 50.9

+SE

Ample N %

+SE

13.1 16.5

References

Pinter et al. (1997, 2002) Pinter et al. (1997, 2002) Kobayashi et al. (2001) Kim et al. (2001) 23.0 2.0 17.2 34.0 25.3 8.7

20.8 42.6

Ottman et al. (2001) Ottman et al. (2001)

Miglietta et al. (1998) Miglietta et al. (1998) Miglietta et al. (1998) Bindi et al. (1998) Bindi et al. (1999)

43.1

Mauney et al. (1992) Mauney et al. (1994)

MCCot91

MCCot89 MCCot90 MCCot91

Cotton bolls Seed cotton means SE Cotton lint Cotton lint Cotton lint Cotton lint means SE

C3 woody perennial with berry yield CLAIRE94 Grape; 700 ppmv CLAIRE95 Grape; 700 ppmv CLIVARA96 Grape; 550 ppmv

42.6 38.0 9.1 20.7 73.4 81.1 55.9 21.4

42.0 42.5 0.6 11.4 45.8 39.3

11.9 21.0 42.7

13.1 26.2 41.7

311

CLIVARA96

Grape; 700 ppmv

24.5

15.6

CLIVARA97

Grape; 550 ppmv

43.0

24.5

CLIVARA97

Grape; 700 ppmv

28.7

11.5

Literature Literature Literature Literature Literature Literature Literature Literature Literature

Woody means SE All mature ag crops C3 grain crops C4 crops Wheat Rice Cotton Potato Many ag. crops Rice

28.1 5.1 15 23 26 19 8 113 28 19 14

51.7 51.5 51.6 0.1

Mauney et al. (1994)

27.3 20.4

Pinter et al. (1996) Pinter et al. (1996) Pinter et al. (1996)

Bindi et al. (1995a) Bindi et al. (1995a) Bindi, Fibbi and Miglietta (2001) Bindi, Fibbi and Miglietta (2001) Bindi, Fibbi and Miglietta (2001) Bindi, Fibbi and Miglietta (2001)

5 14 9

22

9

19

Kimball (1983, 1986) Kimball (1983, 1986) Kimball (1983, 1986) Cure (1985) Cure (1985) Cure (1985) Cure (1985) Kimball (1993) Nakagawa and Horie (2000) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

%

+SE

Low water

Ample N %

Low N

+SE

%

Ample N

+SE

%

+SE

References

Radiation use efﬁciency

312

C3 woody perennial MCCot89 Cotton MCCot90 Cotton MCCot91 Cotton Cotton means SE

34.7 23.4 26.9 28.3 4.8

Pinter et al. (1994) Pinter et al. (1994) Pinter et al. (1994)

Speciﬁc leaf area C3 grass leaves Swiss93 Swiss94 Swiss95 Swiss96 Swiss97 Swiss98 RiceFACE98 RiceFACE99

Ryegrass Ryegrass Ryegrass Ryegrass Ryegrass Ryegrass Rice Rice

C4 grasses MCSor98 MCSor99

Sorghum Sorghum

−1.3 2.5

−14.3 −6.7 −1.0 0.1 −1.5 0.2 −0.8 −1.3

3.2 2.7 3.6 4.3 4.3 3.6

−1.9 −2.5

6.0 10.0

−15.9 −11.1 −9.8 −12.1 −9.8 −10.7

4.4 3.6 4.4 5.2 6.1 4.2

Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Kim et al. (unpublished) Kim et al. (unpublished) 3.5 3.5

6.1 11.8

Ottman et al. (2001) Ottman et al. (2001)

C3 forb with tuber storage POTATO95 Potato; 460 ppm CO2 POTATO95 Potato; 560 ppm CO2 POTATO95 Potato; 660 ppm CO2 Chip98 Potato Chip99 Potato

−51.1 −19.3 −12.4 −17.1 −7.6

8.5 4.4 4.3 3.3 2.7

Miglietta et al. (1998) Miglietta et al. (1998) Miglietta et al. (1998) Bindi et al. (1998) Bindi et al. (1999)

C3 woody perennial CLAIRE94 CLAIRE95 Literature Literature

−5.7 −5.0 −13.4 −3.2

1.1 1.6

Bindi et al. (1995a) Bindi et al. (1995a) Wand et al. (1999) Wand et al. (1999)

Grape Grape Wild C3 grasses Wild C4 grasses

Chemical composition changes: nitrogen concentration

313

C3 grass leaves MCWht93 MCWht94 MCWht96 MCWht97 RiceFACE98 RiceFACE99 Swiss93 Swiss94 Swiss95 Swiss96 Swiss97

Wheat Wheat Wheat Wheat Rice Rice Ryegrass Ryegrass Ryegrass Ryegrass Ryegrass

−13.2 − 6.3

−2.6 −8.5 −2.9 2.9 −4.4 −7.3 −15.6 −13.7 −12.6 −15.5 −18.9

−5.5 −12.0

6.2 3.9 10.7 8.1

−24.6 −18.7

10.6 20.2

2.2 2.5 2.4 2.5 2.7

−8.9 −12.9 −16.9 −13.1 −14.4 −13.5

3.0 4.2 4.3 4.3 5.1

5.9 3.8

Sinclair et al. (2000) Sinclair et al. (2000) Sinclair et al. (2000) Sinclair et al. (2000) Miura et al. (unpublished) Miura et al. (unpublished) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID Swiss98

Swiss96–98 314

C3 woody perennial MCCot90 MCCot89 MCCot90 MCCot89 MCCot90 MCCot89 MCCot90 MCCot89 MCCot90 MCCot89 MCCot90 MCCot89 MCCot90

Crop; condition Ryegrass C3 grass means SE Ryegrass litter

%

+SE

−9.8 3.5

Cotton leaves Cotton leaf Cotton leaf Cotton stem Cotton stem Cotton root Cotton root Cotton bur Cotton bur Cotton seed Cotton seed Cotton plant total Cotton plant total

C3 grass whole shoots Swiss93–97 Ryegrass Swiss96–97 Ryegrass vegetative

0.0

4.1

Ample N

Low water Low N

Ample N

%

+SE

%

+SE

−11.0 −9.4 1.9 −5.4

+2.9

−16.4 −15.6 1.5

4.1

ns

Sowerby et al. (2000)

−32.1 1.3 −15.4 2.3 −21.8 −11.6 −20.4 −12.8 −33.8 −7.6 −7.2 −10.0 −12.9

0.2 ns @0.1 ns @0.1 @0.1 @0.1 ns @0.1 @0.1 ns @0.1 @0.1

Huluka et al. (1994) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998) Prior et al. (1998)

−14.6 −5.1

3.7 4.9

−9.1

%

+SE

References Daepp et al. (2000)

−8.8 3.3

5.7

Hartwig et al. (2000) Daepp et al. (2001)

Swiss96–97

Ryegrass reproduct.

(Vegetative and reproductive stages in pots) C3 grass shoot means SE

0.0 0.0 0.0

C3 legume whole shoots Swiss93 Clover shoot Swiss94 Clover shoot Swiss95 Clover shoot Swiss93–97 Clover plant Legume shoot mean SE C3 grass grain MCWht93–94 MCWht96–97 315 Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature Literature

Wheat grain Wheat grain Wheat grain means SE Woody Nonwoody N supply for all Organ—leaf Organ—leaf litter Organ—shoot Organ—stem organ—coarse root Organ—ﬁne root Organ—whole plant C3 C4

−13

8.8

−7.9

10.0

−9.3 2.9

−19.8

17.3

Daepp et al. (2001)

7.7 8.9 16.8

Zanetti et al. (1996) Zanetti et al. (1996) Zanetti et al. (1996) Hartwig et al. (2000)

−14.6 5.5

−4.1 −9.7 −5.4 −5.5 −6.2 1.2

8.1 8.6 15.5 2.5

−5.8 0.0 −2.9 2.9 −15 −12 −13 −16 −7 −13 −9 −15 −7 −17 −16 −7

1.2 2.1

−1.9 −9.1 −3.5 −4.9 2.2

−8.5 −8.5

−15

−4.0 3.1

1.2

Kimball et al. (2001) Kimball et al. (2001)

−4.0 Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) Cotrufo et al. (1998) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID Literature Literature

316

Literature Literature Literature

Crop; condition

%

+SE

Ample N %

+SE

−7 −9 1 −10.9 −2.9 −7 4

N2ﬁxing Woody SE Wild C3 grasses Wild C4 grasses Woody mean SE

Low water Low N %

+SE

Ample N %

+SE

References Cotrufo et al. (1998) Curtis and Wang (1998)

−13.0

Wand et al. (1999) Wand et al. (1999) Norby et al. (1999)

Chemical composition changes: nitrogen yield C3 grasses RiceFACE98 RiceFACE99 Swiss93 Swiss94 Swiss95 Swiss96 Swiss97 Swiss98 MCWht93–94 MCWht96–97

Rice plant N content Rice plant N content Ryegrass N yield Ryegrass N yield Ryegrass N yield Ryegrass N yield Ryegrass N yield Ryegrass N yield Wheat grain Wheat grain (Protein N yield) C3 grass means SE

13.0 6.4

9.7 3.3

2.7 2.5 −10.4 −7.1 −4.3 −0.6 −10.5 6.3 3.5 16.8 −0.4 2.6

5.2 5.4 4.5 3.0 3.5 5.5 5.3 8.8

−0.9 −10.0 −23.6 −12.3 −19.3 −13.1 −11.6

18.6 16.0 14.9 8.2 12.0 13.3

−0.9

15.0

17.6

−11.8 2.8

17.6 0.0

8.2

Kobayashi et al. (2001) Kim et al. (2001) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Daepp et al. (2000) Kimball et al. (2001) Kimball et al. (2001)

C3 legumes Swiss95 Swiss95

Lucerne; effective Lucerne; ineffective

30.1 6.3

25.1 19.9

24.0 17.1

@0.2 ns

32.5 −26.9

25.7 11.9

L¨uscher et al. (2000) L¨uscher et al. (2000)

(Inoculated with effective or ineffective nodulators) C3 woody perennial MCCot89 Total cotton plant MCCot90 Total cotton plant

Prior et al. (1998) Prior et al. (1998)

Chemical composition changes: carbohydrates and other carbon-based compounds C3 grass leaves

317

Total nonstructural carbohydrates MCWht93 Wheat ﬂag leaf

15.1

10.0

Water-soluble carbohydrates 8 days after cutting Swiss94 Ryegrass leaf

13.0

10.2

37.4

10.6

Fischer et al. (1997)

37.8

12.8

31.0

10.6

Fischer et al. (1997)

Swiss94 Ryegrass pseudo-stem Water-soluble carbohydrates 7 days after cutting or uncut Swiss94 Ryegrass leaf; 7-day cut Swiss94 Ryegrass leaf; uncut C3 grass means SE C3 broadleaf forb with tuber storage Starch concentration Chip98 Potato tuber Chip99 Potato tuber Potato means: SE C3 woody perennials Starch concentration MCCot90 Cotton leaf

Nie et al. (1995)

24.6

ns

40.6

ns

Rogers et al. (1998)

18.8 21.6 4.4

ns

70.8 44.2 8.7

@.05

Rogers et al. (1998)

0.4 8.7 4.5 4.3

183.3

2.9 1.5

Bindi et al. (1998) Bindi et al. (1999)

277.8

Hendrix et al. (1994) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID MCCot91

Crop; condition Cotton leaf Cotton means SE

%

+SE

Ample N %

+SE

75.0 122.7 60.6

318

3.5

2.9

CLIVARA96

Grape; 700 ppmv

0.9

1.3

CLIVARA97

Grape; 550 ppmv

3.1

4.4

CLIVARA97

Grape; 700 ppmv

0.9

1.9

Grape means SE

2.1 0.7 21.1 0.0 37 9 17.6

Low N %

+SE

Ample N %

+SE

100.0 174.9 102.9

Berry sugar concentration CLIVARA96 Grape; 550 ppmv

Phenolic (isoorientin) concentration MCWht93 Wheat; green leaf MCWht93 Wheat; senescent leaf Literature Woody SE Literature Wild C3 grasses

Low water

5.1 25.0

References Hendrix et al. (1994)

Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001)

0.0 0.0

13.5 25.0

Pe˜nuelas et al. (1999) Pe˜nuelas et al. (1999) Curtis and Wang (1998) Wand et al. (1999)

Phenology (FACE minus control difference in days, not %) MCWht96–97

Wheat

−0.4

0.3

(Average difference in time to eight growth stages from tillering to maturity) RiceFACE98 Rice anthesis −2 RiceFACE99 Rice anthesis −2 −3 MCSor98 Sorghum anthesis −0.3 1.3 MCSor99 Sorghum anthesis −2.7 1.8 Chip98 Potato anthesis −3 9 Chip99 Potato anthesis −1 7 CLAIRE94 Grape; 700 ppmv 0 CLAIRE95 Grape; 700 ppmv 0 CLIVARA96 Grape; 550 ppmv 0

319

CLIVARA96

Grape; 700 ppmv

0

CLIVARA97

Grape; 550 ppmv

0

CLIVARA97

Grape; 700 ppmv (anthesis) Sorghum maturity Sorghum maturity Potato maturity Potato maturity Grape; 700 ppmv Grape; 700 ppmv Grape; 550 ppmv

0

MCSor98 MCSor99 Chip98 Chip99 CLAIRE94 CLAIRE95 CLIVARA96

−6 0 −1 −1 0 0 0

−0.4

0.2

Pinter et al. (2000)

−2 0.5 0.2

−7 9 8

2.4 1.9

Kobayashi et al. (2001) Kobayashi et al. (2001) Ottman et al. (2001) Ottman et al. (2001) Bindi et al. (1998) Bindi et al. (1998) Bindi et al. (1995a) Bindi et al. (1995a) Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001b) Bindi, Fibbi, and Miglietta (2001b) Ottman et al. (2001) Ottman et al. (2001) Bindi et al. (1998) Bindi et al. (1998) Bindi et al. (1995a) Bindi et al. (1995a) Bindi, Fibbi, and Miglietta (2001) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

%

+SE

Ample N %

320

CLIVARA96

Grape; 700 ppmv

0

CLIVARA97

Grape; 550 ppmv

0

CLIVARA97

Grape; 700 ppmv (maturity)

0

+SE

Low water Low N %

+SE

Ample N %

+SE

References Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001) Bindi, Fibbi, and Miglietta (2001)

Soil changes: soil microbiology Biodegradation of wheat stems by white rot fungi after 6-week incubation MCWht93 Wheat 3.9 12.1 Arbuscular mycorrhizal fungi hyphal length (m g−1) in soil from FACE Sorghum experiment MCSor98 Sorghum 109 47 Water-stable soil aggregates MCSor98 Sorghum

Akin et al. (1995) 267

104

Rillig et al. (2001)

32.0

13.8

27.5

4.2

Rillig et al. (2001)

Soil N mineralization in laboratory incubations MCCot91 Cotton; 0–30 days MCCot91 Cotton; 30–60 days

−16.7 39.1

57.6 62.7

−14.2 107

50.9 211

Wood et al. (1994) Wood et al. (1994)

Soil N mineralization in laboratory incubations MCWht93–94 Wheat; depth, 0–5 cm MCWht93–94 Wheat: 5–10 cm MCWht93–94 Wheat: 10–20 cm

−36.8 12.3 −14.0

ns ns ns

Prior et al. (1997) Prior et al. (1997) Prior et al. (1997)

Net soil N mineralization Literature Gramminoid Literature Herbaceous (+Cotton) Literature Woody

12 8 5

6 7 40

Zak et al. (2000) Zak et al. (2000) Zak et al. (2000)

Nematodes in soil under cotton MCCot91 Cotton; June MCCot91 Cotton; August

2.8 −0.1

8.2 11.4

Runion et al. (1994) Runion et al. (1994)

Rhizoctonia infection on soybean petiole sections after incubation in soil from FACE cotton experiment MCCot91 Cotton; June −1.4 4.2 MCCot91 Cotton; August 15.8 14.0

Runion et al. (1994) Runion et al. (1994)

Mycorrhizae (vesicular–arbuscular) colonization of cotton roots MCCot91 Cotton; June 14.4 MCCot91 Cotton; August 3.0

Runion et al. (1994) Runion et al. (1994)

10.2 7.5

321

Total microbial activity of soil from FACE cotton experiment determined from dehydrogenase assay MCCot91 Cotton; June 13.9 8.2 MCCot91 Cotton; August 19.5 10.1 Soil microbial respiration in laboratory incubations of soil from FACE cotton experiment MCCot91 Cotton; 0–30 days 18.5 28.5 MCCot91 Cotton: 30–60 days 45.0 42.9

Runion et al. (1994) Runion et al. (1994) 20.9 42.4

22.7 33.7

Wood et al. (1994) Wood et al. (1994)

Soil microbial respiration in laboratory incubations of soil from FACE wheat CO2 × water experiment MCWht93–94 Wheat; depth, 0–5 cm 20.7 ns MCWht93–94 Wheat: 5–10 cm −23.2 ns MCWht93–94 Wheat: 10–20 cm −39.8 <.03

Prior et al. (1997) Prior et al. (1997) Prior et al. (1997)

Soil microbial respiration in laboratory incubations of soil amended with plant litter Swiss95 Ryegrass −19.8 @.05

Ball (1997)

Soil microbial respiration from many species Literature Gramminoid Literature Herbaceous (+cotton) Literature Woody

Zak et al. (2000) Zak et al. (2000) Zak et al. (2000)

20 24 13

8 6 6

continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

%

+SE

Ample N %

Numbers of Rhizobium leguminosarum in rhizosphere soil Swiss93–94 Clover, May 1994 63.3 Swiss93–94 Clover; Nov. 1994 138.5 Swiss93–94 Ryegrass, May 1994 11.3 Swiss93–94 Ryegrass, Nov. 1994 7.2

+SE

Low water Low N %

Ample N

+SE

%

+SE

References

322

@0.1 @0.1 ns ns

Schortemeyer et al. (1996) Schortemeyer et al. (1996) Schortemeyer et al. (1996) Schortemeyer et al. (1996)

−12.2 −17.2

ns ns

Schortemeyer et al. (1996) Schortemeyer et al. (1996)

Microbial biomass carbon in bulk soil: Swiss93–94 Clover, May 1994 Swiss93–94 Clover; Nov. 1994 Swiss93–94 Ryegrass, May 1994 Swiss93–94 Ryegrass, Nov. 1994

18.3 13.5 10.9 5.7

ns ns ns ns

Microbial biomass N in bulk soil Swiss96–98 Ryegrass

26.6

20.9

Numbers of autotrophic NH4+ oxidizers in rhizosphere Swiss93–94 Clover, May 1994 Swiss93–94 Ryegrass, May 1994

15.9 10.7 5.4 13.1

ns ns ns ns

Schortemeyer et al. (1996) Schortemeyer et al. (1996) Schortemeyer et al. (1996) Schortemeyer et al. (1996) Sowerby et al. (2000)

Increase in clover nodule occupancy share of Rhizobium strains isolated from FACE when inoculated with strains from control 19.8 Montealegre et al. (2000) Swiss93–96 F1–12 vs C1–3 44.0 Montealegre et al. (2000) Swiss93–96 F3–24 vs C1–3 88.0 Montealegre et al. (2000) Swiss93–96 F2–4 vs C1–3 −15.3 Montealegre et al. (2000) Swiss93–96 F1–12 vs C3–29 81.4 Montealegre et al. (2000) Swiss93–96 F3–24 vs C3–29 Swiss93–96 F2–4 vs C3–29 55.2 Montealegre et al. (2000)

Microbial biomass C for many species Literature Gramminoid Literature Herbaceous Literature Woody

10 17 11

11 5 7

Zak et al. (2000) Zak et al. (2000) Zak et al. (2000)

Soil changes: soil respiration (roots + microbes) MCWht96 MCWht97 Swiss95 Swiss96–98 MCSor98 MCSor99

Wheat Wheat Ryegrass after cutting Ryegrass soil no roots Sorghum Sorghum

71.4 39.4 −8.1 32.2 42.2 0.4

37.8 40.5 4.0 <.01 64.5 32.5

Pendall et al. (2001) Pendall et al. (2001) Ineson et al. (1998) Sowerby et al. (2000) Cheng et al. (unpublished) Cheng et al. (unpublished)

323

Soil respiration at midseason of cotton crop and at 1 and 2 months after stopping FACE in the fall MCCot90 1990 mid by Huluka 15.9 14.0 MCCot91 1991 mid (corrected) 22.6 6.1 MCCot91 1 month after FACE 8.3 7.5 MCCot91 2 month after FACE 0.7 16.5

Nakayama et al. (1994) Nakayama et al. (1994) Nakayama et al. (1994) Nakayama et al. (1994)

Soil respiration of many species Literature Gramminoid Literature Herbaceous Literature Woody

Zak et al. (2000) Zak et al. (2000) Zak et al. (2000)

28 26 27

8 7 3

Soil changes: trace gas emission/consumption Soil N2O production Swiss95 Ryegrass after cutting MCWht97 Wheat, DOY 29 MCWht97 Wheat, DOY 73 MCWht97 Wheat, DOY 90 MCWht97 Wheat, DOY 104

20.8 −57.9 −5.7 −5.8 48.3

12.9 1150 638 637 1501

79.3 −8.9 −29.1 −4.2

1612 376 216 451

Ineson et al. (1998) Weber (1997) Weber (1997) Weber (1997) Weber (1997) continues

Table II—continued Percentage increases due to elevated CO2 Ample water Very high N Experiment ID

Crop; condition

+SE

%

Soil CH4 consumption Swiss95 Ryegrass after cutting

Ample N % −52.8

Low water Low N

+SE

%

+SE

Ample N %

+SE

2.1

References

Ineson et al. (1998)

Soil changes: Soil carbon sequestration 324

Soil organic carbon concentration after three seasons MCCot89–91 Cotton; depth, 0–5 cm MCCot89–91 Cotton; 5–10 cm MCCot89–91 Cotton; 10–20 cm MCCot89–91 Cotton; 0–30 cm

14.3 4.6 9.8 14.3

18.2 7.9 8.4 42.3

Soil organic carbon content after two seasons MCWht93–94 Wheat; depth, 0–15 cm MCWht93–94 Wheat; 15–30 cm

6.0 21.4

10.6 26.4

Leavitt et al. (1996) Leavitt et al. (1996)

Soil organic carbon content after two seasons MCWht93–94 Wheat; 0–5 cm MCWht93–94 Wheat: 5–10 cm MCWht93–94 Wheat: 10–20 cm

14.5 13.4 10.9

<0.1 <.03 <.01

Prior et al. (1997) Prior et al. (1997) Prior et al. (1997)

Total soil organic carbon after 4 years Swiss95–98 Ryegrass

12.4

13.8

9.8

14.7

6.4

15.1

−5.9

12.1

Swiss95–98

Clover

−6.6 1.5 3.8

11.3 3.0 5.9

Wood et al. (1994) Wood et al. (1994) Wood et al. (1994) Leavitt et al. (1994)

Van Kessel, Horwath et al. (2000) Van Kessel, Horwath et al. (2000)

Total C in soil after 6 years Swiss93–98 Ryegrass Swiss93–98

1.3

30.4

−0.6

26.6

Clover

11.2

36.5

4.3

30.1

Means SE

10.7 1.5

1.7 3.4

Fraction of soil C that is new from November 1995 to May 1997 MCWht96–97 Wheat; depth, 0.0 0–15 cm

Leavitt et al. (2001)

New carbon in soil after 4 years Swiss95–98 Ryegrass

5.9

14.0

−6.7

16.6

Swiss95–98

4.6

21.3

−15.7

14.1

Clover

Van Kessel, Nitschelm et al. (2000) Van Kessel, Nitschelm et al. (2000)

325

Van Kessel, Horwath et al. (2000) Van Kessel, Horwath et al. (2000)

The elevated CO2 was provided by FACE mostly at 550 or 190–200 μmol mol−1 above ambient (ca. 360 μmol mol−1). Data from the Swiss (conducted at 600 μmol mol−1) and Italian experiments at other concentrations were scaled linearly to be comparable to 550 μmol mol−1. Likewise, comparison values from prior Literature reviews of CO2-enriched-chamber data were scaled. The “+SE” values in columns were calculated whenever possible (In several cases the authors did not supply standard errors.). The standard errors of the ratios of FACE/(control or ambient) were calculated using the equation r = (|DN | + |N D|)D −2 , where indicates the standard error, D is the ambient or control mean value in the denominator, N is the FACE mean value in the numerator, and the vertical bars denote absolute values. The ratios and standards errors were then transformed to percentage changes and scaled to 550 μmol mol−1. In some cases the authors supplied probability levels of a difference being signiﬁcant, and these are indicated as “@p” in the table. For those cases where there were several values of a response parameter coming from several experiments on similar types of vegetation, the values from each experiment were treated as an individual observation, and means across the experiments were calculated using log–antilog transformations (Kimball, 1983). Standard errors of the means were also calculated using the transformations. However, the + and − differences were nearly the same, so only the larger of the two values (+) is presented. The crops include wheat (Triticum aestivum L.), rice (Oryza sativa L.), perennial ryegrass (Lolium perenne), Sorghum (Sorghum bicolor (L.) M¨oench), potato (Solanum tuberosum L.), white clover (Trifolium repens), lucerne or alfalfa ( Medicago sativa L.), cotton (Gossypium hirsutum L.), and grape (Vitis vinifera L.). a

326

KIMBALL et al.

Potato is a C3 forb with a large tuber storage organ, while clover and lucerne are C3 legumes. Grape and cotton are both C3 woody perennials (although cotton is generally cultivated as an annual for insect control). The various FACE experiments have not used the same target CO2 concentration for their treatments, nor have prior reviews of the CO2-response literature used a particular concentration for their analyses. Such lack of standardization makes it difﬁcult to make comparisons across FACE sites and with other CO2-enrichmentchamber-type experiments. Therefore, we have linearly adjusted all of the relative responses to correspond to 550 or about 190 μmol mol−1 above ambient. Such an adjustment is justiﬁed because the ﬁrst approximation growth responses by plants to elevated CO2 are generally linear between 300 and 900 μmol mol−1 (e.g., Idso and Idso, 1994). Whenever there were enough observations of a particular parameter, we computed averages and standard errors using log–antilog transformations (Kimball, 1983). Each experiment was considered to be a single observation. Ideally, a meta-analysis, such as that performed by Curtis and Wang (1998) and Wand et al. (1999), would be desirable; but we felt the sparseness of the data did not justify the effort at this time.

III. RESULTS AND DISCUSSION OF CROP RESPONSES TO ELEVATED CO2 A. PHOTOSYNTHESIS It is well known that elevated CO2 stimulates photosynthesis. The main uncertainties regard the degrees of stimulation for each species and environmental condition. Also important is whether or not the plants acclimate to the higher CO2 by altering the biochemical makeup of the their photosynthetic apparatus so that the photosynthetic rate at high CO2 decreases to become closer to that at today’s ambient CO2. The biochemical and molecular bases for such photosynthetic acclimation have been reviewed recently by Moore et al. (1999). We have tabulated the percentage increases in net photosynthesis due to free-air CO2 enrichment (FACE) that have been reported by several investigators for agricultural crops (Table II). For upper leaves the CO2-induced responses ranged from about 25 to 45% under ample water and high nitrogen for wheat, ryegrass, and cotton, which are C3 photosynthetic crops. At low N, the values were somewhat lower for wheat, but, surprisingly, they were higher for ryegrass. For sorghum, the stimulation was much less, only about 9%, which is as expected for a C4 crop. However, under water stress the sorghum had a larger response of about 23%, which was probably more due to the effect of elevated CO2 on plant water relations than a direct stimulation of photosynthesis (Wall, Brooks et al., 2001). Wheat ears responded somewhat more than the upper leaves (Wechsung et al., 2000).

CO2 RESPONSES OF AGRICULTURAL CROPS

327

When moving from leaf to leaf within a crop canopy and when moving to the canopy scale, dramatic changes have been observed in the response to CO2. Osborne et al. (1998) measured the daily integral of net CO2 uptake from upper wheat ﬂag leaves, the next below (8th), and 2nd next below (7th) (Table II). They found that the FACE stimulation ranged from 26% at the top to inﬁnite at the 7th leaf level, the latter value arising because elevated CO2 maintained the 7th leaves as being net consumers of CO2, whereas shading and senescence in corresponding control leaves caused them to become net emitters or respirers of CO2. On the other hand, Brooks et al. (2001) observed that wheat canopies with ample nitrogen and water were stimulated about 19% by FACE [compared to about 26% for the upper leaves (Wall, Adam et al., 2001)], while those subjected to low nitrogen exhibited only a 9% increase in net photosynthesis. The differing allocation of nitrogen (and thus chlorophyll) within the canopy, the changing of green plant area index, and the altered leaf tip angles (more erectophile at low N) apparently all acted as compensation mechanisms to decrease the wheat canopy stimulation compared to the upper leaf stimulation by FACE. In contrast, Hileman et al. (1994) found similar upper leaf and canopy stimulation for cotton (Table II). Only a few measurements of photosynthesis have been reported under low water or water-stress treatments (Table II), and no clear pattern emerges. Under low N, on the other hand, substantially lower stimulations of photosynthesis have been observed with wheat but not with ryegrass.

B. WATER RELATIONS 1. Stomatal Conductance Elevated CO2 causes partial stomatal closure, which reduces the conductance for the exchange of gases between the internal tissues of plant leaves and the atmosphere. Under conditions of ample water and nitrogen, FACE reduced the conductance of C4 sorghum most, by 37% (Table II; Wall, Brooks et al., 2001). The reduction in wheat was nearly as much at 34%, while the reduction in the woody perennials was less, about 15%. Reductions in conductance due to FACE have also been reported for water-stress treatments (Table II), but these data are harder to interpret because severe drought would dominate any CO2 effect, so the timing of the observations during a drought cycle following rain or irrigation is very important. Wall, Adam et al. (2001) report a slightly larger reduction in conductance for wheat under low (44%) compared to high (32%) nitrogen. A greater reduction under low N is to be expected because the low N causes reductions in rubisco activity and concentration, which forces reductions in stomatal conductance in order to maintain a constant Ci : Ca ratio (ratio of internal leaf CO2 concentration to that of outside air), at least according to the ecosys model by Grant et al. (2001).

328

KIMBALL et al.

Several reviews of the effects of elevated CO2 on stomatal conductance from chamber-based studies have reported similar results. Kimball and Idso (1983) extracted 46 observations of reductions in transpiration due to increasing CO2 from 330 to 660 μmol mol−1, and they calculated an average reduction of 34%, or of about 20% when scaled to the 190 μmol mol−1 increase in CO2 being used herein (Table II). Similarly, Morison (1985) reviewed about 80 papers with observations of the effects of elevated CO2 on stomatal conductance and concluded that at doubling from 330 to 660 μmol mol−1 reduced conductance by 40%, which scales to about 23% (Table II). Although the data for individual crops was sparse, Cure (1985) and Cure and Acock (1986) reported conductance decreases of 12, 18, 15, 8, and 32%, respectively, for wheat, rice, sorghum, cotton, and potato for conditions of ample water and nutrients (again scaled to 550 μmol mol−1). Wand et al. (1999) performed a meta-analysis on observations reported for wild C3 and C4 grass species. When there were no stresses, elevated CO2 (scaled to a 190 μmol mol−1 increase) reduced stomatal conductance by 21.3 and 16.0% for C3 and C4 species, respectively. Water and nutrient stresses did not signiﬁcantly change the response of C4 grasses. In their meta-analysis focusing on woody species, Curtis and Wang (1998) found a mean reduction of only 11% (scale to 6%), which was not quite signiﬁcant at the 95% probability level. For both the FACE and the prior chamber data, it appears that elevated CO2 reduced the conductances of C3 and C4 herbaceous species similarly. Also, for both types of data, the conductances of the woody species were affected much less than those of herbaceous species. However, FACE appears to have reduced stomatal conductance more than one and a half times the average reductions derived from chamber experiments. Of course, this may be due merely to the sparseness of the number of FACE experiments, or it may not have been appropriate to scale the data linearly. Nevertheless, the chambers used in the prior studies could have produced a “bonsai syndrome” [name from Allen (1994)] on stomatal conductance similar to the reductions reported in CO2 stimulation of photosynthesis (Arp, 1991) and growth (Thomas and Strain, 1991) for plants grown in small pots. In any event, there appears to be a dichotomy between the CO2-induced reductions in conductance coming from the FACE and the prior chamber-based data. 2. Canopy Temperature Devoid of walls, the FACE approach is especially advantageous for determining the effects of elevated CO2 on crop canopy microclimate. When the elevated CO2 reduces stomatal conductance, transpirational cooling of the plant leaves is also reduced causing leaf temperatures to rise. In cotton exposed to FACE at 550 μmol mol−1, canopy temperatures increased by an average 0.8◦ C (Table II). Similarly, wheat temperatures were increased 0.6◦ C under conditions of ample

CO2 RESPONSES OF AGRICULTURAL CROPS

329

water and nutrients (Table II). When nitrogen was limited, however, wheat canopy temperatures increased even more, by 1.1◦ C, consistent with the greater reduction in stomatal conductance discussed previously. Such increases in canopy temperature are comparable to those predicted for global warming, and they are very likely to occur regardless of whether there is any climate warming or not. These temperature increases imply that the climate ranges over which crops can be grown could shift in the future, even in the absence of any change in global air temperature. 3. Evapotranspiration or Water Use The FACE approach is especially advantageous for assessing the impacts of elevated CO2 on microclimatic processes, such as evapotranspiration (ET), because there are no walls to alter wind ﬂow or to shade the plant canopies. Because elevated CO2 causes a decrease in stomatal conductance, transpiration per unit of leaf area is decreased while canopy temperature is increased. The increase in temperature raises the water vapor pressure inside the leaves, which tends to increase leaf transpiration, thereby negating some of the reduction due to the decrease in stomatal conductance (e.g., Kimball et al., 1999). Thus, the resultant effect of elevated CO2 on ET is a combination of individual effects of the CO2 on decreasing stomatal conductance, increasing leaf area, and increasing canopy temperature. In experiments on cotton in Arizona, there was very little effect (<2% reduction) on ET, as determined by soil water balance or stem ﬂow gauges (Table II). Thus, the counteracting effects of CO2 on conductance, leaf area, and canopy temperature must have compensated each other. On the other hand, modest reductions in ET were observed in wheat (Table II). At ample water and nitrogen, for example, soil water balance determinations indicate ET reductions of about 3.6% (Table II) (Hunsaker et al., 1996 , 2000), while those from energy balance suggest a slightly larger reduction of about 7% (Table II) (Kimball et al., 1999). When nitrogen was limited, the energy balance approach indicated water savings of nearly 20% for wheat (Table II) (Kimball et al., 1999), while the estimates from soil water balance were much smaller (<2%; Table II) (Hunsaker et al., 2000). However, simulations with the ecosys model by Grant et al. (2001) predicted a reduction in ET of 16% at low nitrogen, caused by reductions in rubisco activity and concentration, which forced greater reductions in stomatal conductance in order to maintain a constant Ci : Ca ratio (ratio of internal leaf CO2 concentration to that of outside air). Thus, the energy balance result of a 20% reduction in ET of wheat at low N seems reasonable. For the case of sorghum at ample water and nitrogen, somewhat larger reductions in ET of about 10% have been observed by Conley et al. (2001). When seasonal water supply is severely growth limiting, one would expect plants to utilize all the water available to them, so that effects of elevated CO2 on seasonal ET would be minimal, unless perhaps CO2-enriched plants with more

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robust root systems might extract more water from the soil and actually use more water. The latter phenomenon might have happened for wheat in 1993 (Table II) (Hunsaker et al., 1996), but generally the observed effects of FACE on the ET of cotton, wheat, and sorghum under limited water have been inconsistent and small (Table II). Samarakoon and Gifford (1995) conducted an interspeciﬁc comparison of the effects of elevated CO2 on cotton, wheat, and maize using glasshouses that illustrated the importance of relative changes in leaf area and stomatal conductance in determining the relative effects of CO2 on ET. Their CO2-enriched cotton had a large increase in leaf area and a small change in conductance, so water use per pot actually increased. In the FACE experiments, cotton also had a large growth response and a smaller conductance, which in this case must have exactly compensated because there was no signiﬁcant change in ET (Table II). Maize, a C4 plant, had little photosynthetic or leaf area response in the Samarakoon and Gifford experiment, so the reduction in conductance resulted in signiﬁcant water conservation. Likewise, in the FACE experiments on sorghum, which also is a C4 plant, there was no growth response at ample water, so the decrease in stomatal conductance due to the higher CO2 resulted in a 10% reduction in ET (Table II). Wheat was intermediate between the other two species in both the Samarakoon and Gifford (1995) and the FACE experiments (Table II). Allen (1991, 1999) and Kimball et al. (1994, 1999) have reviewed the few early attempts using chamber techniques to measure the effects of elevated CO2 on ET. Kimball et al. (1999) concluded that, with one exception, the effects of elevated CO2 on the ET on plants in chambers is generally small, which is consistent with results from the FACE studies (Table II). The observed reductions in ET due to elevated CO2 in the FACE experiments have been modest. However, coupled with substantial increases in growth (Table II; at least of C3 plants, as will soon be discussed), there are likely to be substantial increases in water-use efﬁciency due to the elevated CO2 in the future. 4. Water Potential Plant water potential is an important measure of the water relations of plants. However, it varies hour by hour through the course of a day, and it decreases (becomes more negative) day by day following a rain or irrigation event that wets the soil. Also, small subtle differences that are difﬁcult to detect can cumulatively affect the way plants grow, which feeds back on their subsequent water potential. For example, suppose one plot of young plants is maintained in a well-watered condition while another is allowed to dry and become stressed for water. Then the dry plot is irrigated and allowed to dry again. During the ﬁrst cycle, substantial differences in water potential are likely to develop, whereas during the second cycle, the differences will be much smaller—because the second plot now has smaller plants which use water at a slower rate. Therefore, it is difﬁcult to determine

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meaningful quantitative average changes in plant water potential due to treatments such as elevated CO2, and relatively few observations have been reported from FACE experiments (Table II). For cotton, a crop with a relatively large biomass response to elevated CO2, Bhattacharya et al. (1994) found little consistent effect on plant water potential by FACE except near the end of the season. Wheat has a somewhat smaller biomass response than cotton, and for it, Wall et al. (1994) report that for most of the daylight period of each day, wheat leaves exposed to FACE at 550 μmol mol−1 had slightly, but statistically signiﬁcant, less negative water potentials compared to ambientgrown plants. Sorghum, a C4 plant with little growth response to FACE under ample water and nutrients, had a higher (less negative) average water potential by 2.8% (Table II). Under water stress, water potential improved relatively even more due to the elevated CO2 by −8.8% (Table II). On the other hand, grape, a woody C3 plant like cotton, exhibited a slight improvement in water potential when well watered (Table II).

C. PEAK LEAF AREA INDEX Rather large differences in peak leaf area index (LAI) have been reported among the various FACE experiments in the responses of various species to elevated CO2 (Table II). Among the C3 grasses—wheat, ryegrass, and rice—the mean increase was about 11% at ample water and ample or very high N. However, at low nitrogen, the leaf area response to elevated CO2 was much smaller, an increase of only 1.4%. For the case of C4 sorghum, in 1 year there was essentially no response in leaf area index to elevated CO2 at both ample and low water (Table II) (Ottman et al., 2001). In the second year, however, there tended to be a decrease at ample water and an increase at low water, but variability was high and the differences were not statistically signiﬁcant. In four out of ﬁve experiments with potato, FACE caused decreases in peak leaf area index, with the mean decrease amounting to about 6% (Table II). Such a decrease is surprising, and it appears to be related to the effects of elevated CO2 on the onset of senescence (Bindi et al., 1999). The leaf areas under elevated and ambient CO2 concentrations increased similarly and were close to the same for more than half the season. However, then LAI peaked under elevated CO2 and started to decline, while that at ambient CO2 continued to rise for another couple of weeks. In the case of cotton there was no signiﬁcant effect in 1989; however, in 1991 there appeared to be a decrease in leaf area index of about 16% at ample water and an increase of about 21% with low water, with both results at the edge of statistical signiﬁcance (Table II, Mauney et al., 1994). A leaf area increase of 21% is consistent with changes in biomass, but a decrease of 16% has no obvious explanation.

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D. BIOMASS ACCUMULATION 1. Shoots By far the greatest amount of reporting from the FACE experiments has been the response of aboveground biomass to the elevated CO2 (Table II). Focusing ﬁrst on the C3 grasses at ample water and nitrogen, there are four seasons of wheat, two of rice, and eight of ryegrass [although two of the latter reported by Daepp et al. (2001) came from subplot experiments in pots]. Under these mostly nonstress conditions, the mean biomass response to elevated CO2 is an 11.5% increase. There are only four observations at very high N, but the growth response of the C3 grasses was large, about 19% (Table II). On the other hand, at low N there are 11 cases, and the mean response to CO2 was only a 3% increase. Daepp et al. (2001) also reported an interesting phenomenon in that during a reproductive stage, ryegrass in pots at low N had a 23% increase due to FACE, whereas observations over several vegetative stages showed zero response to the elevated CO2. There are only two observations for C3 grasses under water stress (Table II) (Pinter et al., 2002), and in both of these cases the response to elevated CO2 (about a 14% increase) was larger than the corresponding response (about 8%) at ample water. However, in these particular FACE experiments, the blowers may have reduced the CO2 response, especially at ample water (Pinter et al., 2000). For the case of the C4 grass, sorghum, there was a small mean response of only about 3% to elevated CO2 under well-watered conditions, which is consistent with the smaller photosynthetic response of this crop (Table II). In contrast, at low water the sorghum grew about 16% more under FACE compared to the controls, which was probably due to the high-CO2-induced partial stomatal closure, which enabled the FACE-grown plants to maintain photosynthesis and grow longer into each drought cycle. Elevated CO2 caused a mean decrease of about 21% in aboveground biomass of potato (Table II), which can probably be explained by the apparent acceleration of senescence in elevated CO2 (Bindi et al., 1999). The earlier senescence shortens the time for biomass accumulation and hastens the translocation of material from the aboveground parts to the tubers below. Such an elevated-CO2-caused decrease is not conﬁned to FACE experiments; however, because in the review of Cure (1985) and Cure and Acock (1986), they too reported a decrease (8%) for the aboveground biomass of potato (scaled to 550 μmol mol−1). Although there were large year-to-year variations ranging from about 2 to 40%, the C3 legumes had large responses to elevated CO2 (mean 24% increase, scaled to 550 μmol mol−1, ignoring lucerne with ineffective Rhizobium; Table II). Moreover, because of their N2-ﬁxing ability, they maintained this large positive response under low as well as high nitrogen levels. However, having the right species of Rhizobium bacteria is important because in the ancillary experiment of L¨uscher et al. (2000),

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when lucerne was inoculated with an ineffective nodulating strain, there was a much smaller response (+10%) to elevated CO2 at high N, while under low N, shoot biomass was reduced 19%. Large increases in shoot biomass due to elevated CO2 have been observed also for the woody perennials (cotton and grape), with a mean increase of about 32% (scaled to 550 μmol mol−1, Table II) at ample water and nutrients. Under water stress, the response was similar one year (35%) but smaller another (18%) (Mauney et al., 1994). Most of the prior chamber studies of the effects of elevated CO2 on plants have reported the response of shoot biomass, and thousands of observations exist in the literature. There have been at least 11 reviews of these data (Table II). Kimball (1986) assembled and analyzed 94 observations of the effects of elevated CO2 on biomass and obtained an average increase of 21% ( ± 2% SE; scaled to 550 μmol mol−1), which is within the error bar range of the mean of all the C3 plants from the FACE experiments (17 ± 3%, Table II). Likewise, Cure (1985) and Cure and Acock (1986) conducted a literature survey and tabulated the results of a doubling of CO2 on the response of 10 major agricultural crops. Scaling to 550 μmol mol−1, they report biomass stimulations of 16, 15, 5, 46, and −8%, respectively, for wheat, rice, sorghum, cotton, and potato for conditions of ample water and nutrients. Thus, these early chamber results are quite consistent with those from the FACE experiments. Concentrating on the literature reports published between about 1983 and 1993, Idso and Idso (1994) assembled observations of the responses of many species of plants, mixing dry weight increment and carbon-exchange rate (i.e., instantaneous net photosynthesis rate) responses in their tables. Scaling the data in their 300 μmol mol−1 tables to a change in CO2 of 190 μmol mol−1 (or to about 550 μmol mol−1), their mean percentage enhancement was 20% under conditions of ample water and nutrients, again consistent with the FACE data and with the other reviews of chamber data. Nakagawa and Horie (2000) recently reviewed the responses of rice to elevated CO2 and temperature, including several years of their own work using temperaturegradient tunnels. They concluded that under ﬁeld conditions a doubling of CO2 increases rice biomass production by about 25% on average, or about 14% when scaled to 550 μmol mol−1, which is slightly larger than the mean response for rice observed in the FACE experiments (Table II) at ample N (about 9%). They also report that the stimulation increases about 2% (for a doubling) per degree C rise in air temperature; and considering that the FACE experiments were conducted at a relatively cool site in northern Japan, a smaller than average observed response would therefore be expected. The smaller response of biomass production in the FACE experiments also could be due to differences among N fertilization regimes. The biomass response tends to increase as N fertilization is increased (Table II), and the “ample” N at the FACE site was in fact 30% less than that in the temperature

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gradient tunnels in Kyoto, Japan (Kim et al., 1996), and 70% less than that in the sun-lit chambers in Gainesville, Florida (Baker et al., 1990). Poorter (1993) computed the biomass response to elevated CO2 of about 125 observations in the literature, and he grouped the results into several functional group categories. Scaled to 550 μmol mol−1, he found the stimulation for both C3 herbaceous and woody plants to be about 22% and that for C4 herbs about 11%, which are a little higher than the 17 and 3%, respectively, found in the FACE experiments (Table II). Species categorized as C3 N-ﬁxers were stimulated by about 25%, the same as the legumes observed the FACE experiments (Table II; ignoring lucerne with ineffective Rhizobium). He also separated the species between crop and wild, ﬁnding that crop species were stimulated by 29% while wild species were stimulated only by 17%. He speculated that the higher crop response might be because crop species tend to be fast growing. When he further divided the wild species into fast, intermediate, and slow growing, he obtained CO2 stimulations of 27, 19, and 12%, respectively, suggesting that fast-growing wild species are nearly as responsive as crop species to elevated CO2. Recently, Wand et al. (1999) performed a meta-analysis on observations reported for wild C3 and C4 grass species. When there were no stresses, elevated CO2 (scaled to a 190 μmol mol−1 increase) increased aboveground biomass by about 19 and 5% for C3 and C4 species, respectively. The mean FACE increases for crop C3 and C4 grass species were 12 and 3%, respectively, which indicates that wild C3 grass species may be more responsive to elevated CO2 than cultivated C3 grass species. Ceulemans and Mousseau (1994) tabulated nearly 100 observations of the effects of elevated CO2 on the accumulation of biomass in woody plants. Scaled to 550 μmol mol−1, the mean biomass stimulations were about 24 and 40% for coniferous and deciduous species, respectively. Recently, Norby et al. (1999) tabulated 15 (taking those not at low- or very-high-N treatment) relative responses to elevated CO2 of woody species in open-top chambers. Scaling to 550 μmol mol−1, the average CO2-induced growth response was about 43%. In their meta-analysis of woody plant response, Curtis and Wang (1998) found that total biomass was increased 17% by elevated CO2 under nonstress conditions. The FACE mean for woody perennial crops (cotton and grape) was 32% (Table II), which is somewhat lower than the 40% from Ceulemans and Mousseau or the 43% calculated by Norby, but larger than the 17% reported by Curtis and Wang. Nevertheless, both the chamber and FACE data suggest a substantial response of woody plant species shoot biomass to elevated CO2 under mostly nonstress conditions. Under conditions of water stress, there have been many reports of stimulation of shoot biomass production by elevated CO2 greater than or equal to wellwatered conditions. For example, in their review of published 1983–1993 research, Idso and Idso (1994) reported stimulations of 20 and 39% for well-watered and water-limited conditions, respectively. On the other hand, Kimball (1993) plotted the percentage increase in biomass under water-stressed conditions against the

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percentage increase under well-watered conditions for 33 experiments reported in the literature; however, the response under water stress was not signiﬁcantly different from that of well-watered conditions. The mean response from the FACE experiments with water stress was a 20% increase for the C3 crops, which was not signiﬁcantly greater than the 17% observed with ample water, but the number of experiments was small (four) and the variability was high. Thus, the FACE data obtained under water stress are not inconsistent with the prior chamber data. Many experiments have found smaller shoot biomass responses to elevated CO2 when nutrients are limiting compared to the responses with ample nutrient levels. Kimball (1993) plotted the percentage increase in biomass for 67 observations obtained from the literature, and the average response under nutrient stress was only 74% of that observed with ample nutrients. Idso and Idso (1994) reported stimulations of 29 and 32% with limiting and ample supplies of nutrients, respectively. In their meta-analysis of woody plant response, Curtis and Wang (1998) found that under conditions of nutrient stress, the CO2-induced growth response was only half as great as that for ample nutrients. Likewise, in their meta-analysis of wild grass species, Wand et al. (1999) found that low nutrients tended to reduce the response of C3 species, but there was much scatter. In the FACE experiments with low nitrogen, elevated CO2 stimulated the growth of C3 plants by an average 12% when legumes were included or 3% when legumes were excluded compared to 17% under ample nutrients (Table II). Kimball et al. (1997) have made the most direct comparisons of the shoot biomass response to elevated CO2 between FACE- and open-top-chamber-grown plants. They grew cotton in open-top chambers (OTCs) for ﬁve seasons and later with FACE for three seasons. Although not done simultaneously, the experiments were conducted in the same climate using similar cultural techniques by teams with several of the same scientists. They found a mean increase in shoot biomass of about 35% in the FACE experiments (Table II) (Mauney et al., 1992, 1994) and of about 39% in the OTCs (scaled to 550 μmol mol−1), which were not quite signiﬁcantly different. They also pointed out that although the relative responses to CO2 were similar, the absolute growth rates were typically 30% higher inside the OTCs compared to outside under well-watered conditions. They also reported on another experiment with wheat in 1992–1993, when OTCs were operated simultaneously in the same ﬁeld as a FACE experiment. In this single case, both the relative response of the aboveground biomass to CO2 and the absolute growth rates were nearly identical. 2. Roots Determination of root biomass is extremely labor intensive, and the necessary soil sampling is very destructive. Consequently, there are far fewer observations on the effects of elevated CO2 on root biomass accumulation compared to shoot

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biomass. However, the observations that have been made suggest a substantially greater stimulation by elevated CO2 of belowground than aboveground biomass. Root growth of C3 grasses in the FACE experiments was stimulated by about 47% on the average by the elevated CO2 under conditions of ample water and nutrients, which is substantially more than the average 12% for the shoots (Table II). Root biomass was also increased by about 39% under low N compared to only about 3% for the shoots. There was only one report under low water, and for that case, wheat root growth was stimulated by about 23%, compared to 14% for the shoots in the same experiment. Thus, there were substantial increases in the root:shoot ratio of the C3 grasses due to FACE under all conditions. The root growth of C3 legumes (clover) was stimulated about 25% by elevated CO2 under ample N, which was nearly the same as that of the shoots (24%) (Table II). When N was low, the root increase averaged 21%, which is a little less than the 26% increase for the shoots. Thus, CO2 stimulated shoots and roots about the same under both limited and ample N, so there was little effect on the root:shoot ratio of the C3 legumes. For the case of woody perennial cotton, both tap and ﬁne root biomass were stimulated by 78 and 52% on the average, respectively, although variability was high (Table II). This was more than the average 32% increase for woody perennial shoots, so the root:shoot ratio also increased. Just as there have been relatively few observations of the effects of elevated CO2 on root growth, so there have been few reviews of these data. Rogers et al. (1994) compiled a list of 183 literature observations of various effects of elevated CO2 on roots, but it lacked any quantitative analysis. Wand et al. (1999) performed a meta-analysis on observations reported for wild C3 grass species from experiments mostly conducted using chambers. When there were no stresses, elevated CO2 (scaled to a 190 μmol mol−1 increase) increased belowground biomass by 31%, which is signiﬁcantly lower than the average 47% increase observed in the FACE experiments. Low nutrients tended to reduce the response of C3 grass roots to elevated CO2 in their analysis, but there was much scatter. Similarly, in the FACE experiments the response under low N (39%) was lower than that at ample N (47%). In their meta-analysis of woody plant response, Curtis and Wang (1998) found that root biomass was increased 23% (scaled to 550 μmol mol−1) by elevated CO2 under nonstress conditions. This increase is much smaller than the 50–80% found in the FACE experiments, but only one woody species was examined in the FACE experiments—cotton, which may be more responsive than most species. Stulen and den Hertog (1993) reviewed observations of dry matter partitioning in response to elevated CO2 from experiments performed using chambers. Under conditions of ample nutrients and water, there were only 3 observations of decreases in the root:shoot ratio, whereas there were 12 observations of no change and 7 of increases. In experiments where nutrients were limited, there were no observations of a decrease, 3 of no change, and 6 of increases. When water was limited, there

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were two decreases, 15 of no change, and 7 of increases. These results show wide variability, but there is a tendency for the root:shoot ratios to increase at elevated CO2, more so under water stress, and considerably more so under nutrient stress. Rogers et al. (1996) also reviewed the inﬂuence of elevated CO2 on the root:shoot ratios of agricultural crops. Variability was high with about 60% of the observations showing an increase, 3% no change, and 37% a decrease. Grouping by type of crop, they calculated percentage changes in the root:shoot ratio of −6% for ﬁber crops, +16% for fruit, +6% for C3 grains, +4% for C4 grains, −6% for leaf, +8% for legume seed, −3% for oil seed, +7% for forage (nonlegume + legume), and +23% for root and tuber crops. Although the data are sparse and the variability is higher than desired, and although legumes may be an exception, it appears that the FACE results are not consistent with the prior chamber data; rather, it appears that elevated CO2 stimulates root growth relatively more than shoot growth under FACE conditions compared to chamber conditions. 3. Agricultural Yield Because of the economic importance, there have been numerous observations over the past century of the effects of elevated CO2 on the yield of agricultural crops, especially greenhouse crops (e.g., Kimball, 1986). However, with impending global change and the need to secure food supplies for the future, several FACE experiments have concentrated on open-ﬁeld agricultural crops, and they are the main focus of this paper, with yield serving as an important economic parameter. In the shoot biomass section, the responses for the forage crops of ryegrass and clover were already presented, for which biomass is in fact the agricultural yield (Table II). Reiterating, the average biomass increases due to FACE for C3 grasses were about 12% at ample N and ample water, 3% under low N, and 14% under low water. Generally for wheat and rice, the yield is the grain, which was increased by an average of 12% at ample N and water (Table II). Under very high N, rice yields increased an average 11%; while at low N, wheat and rice yields were stimulated an average 7% by elevated CO2. The latter ﬁgure is considerably higher than the average 3% increase for shoot biomass. When water was limiting, wheat yields increased 23%, which is signiﬁcantly greater than the biomass stimulation (14%), and the harvest index was increased by elevated CO2 under the dry condition. For the case of the C4 grass, sorghum, there was a small average negative response (−5%) under ample water, but variability was high (Table II). In contrast, at low water, the sorghum yielded about 25% more grain under FACE compared to the controls, which was probably due to the high-CO2-induced partial stomatal closure, which enabled the FACE-grown plants to maintain photosynthesis and grow longer into each drought cycle. Tuber yields from potato, a C3 forb, increased by a substantial 28% due to the FACE treatments (Table II), which stands in marked contrast to the average 21%

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decrease in biomass of the shoots. The latter appears related to effects of elevated CO2 on the onset of senescence (Bindi et al., 1999), as discussed previously. Nevertheless, such a marked difference in response between shoots and tubers represents a major reallocation of resources for this plant under high CO2 to improve harvest index. The boll (seed + lint) yields of woody perennial cotton were increased about 40% by FACE under both ample and low-water conditions (Table II; Mauney et al., 1994). Further examination of the lint ﬁber portion of the yield indicated it was increased even more, by about 54% (Table II) (Pinter et al., 1996). Likewise, the berry yields from woody perennial grape were stimulated about 28%. All of these yield stimulations are slightly higher than the mean biomass stimulations and therefore represent slight improvements in harvest index. Kimball (1986) assembled and analyzed 53 observations of the effects of elevated CO2 on agricultural yields and obtained an average increase of 15% (scaled to 550 μmol mol−1). Included in his tabulation were classes for C3 grains and C4 crops, which increased 23 and 26%, respectively. Similarly, Cure (1985) and Cure and Acock (1986) conducted a literature survey and tabulated the results of a doubling of CO2 on the response of 10 major agricultural crops. Scaling to 550 μmol mol−1, they reported yield increases of 19, 8, 113, and 28% for wheat, rice, cotton, and potato, respectively, for conditions of ample water and nutrients. Nakagawa and Horie (2000) recently reviewed the responses of rice to elevated CO2 and temperature, including several years of their own work with temperature-gradient tunnels. They concluded that under ﬁeld conditions a doubling of CO2 increases rice yields by about 25% on average, or about 14% when scaled to 550 μmol mol−1. For the case of wheat at ample water and nutrients, the average FACE value of about 15% is at the lower edge of the chamber values from the literature. Similarly for rice, the FACE values (10%) are slightly lower than the recent and fairly extensive review value (14%) of Nakagawa and Horie (2000) derived from chambers and temperature-gradient tunnels. As already discussed with regard to shoot biomass, cooler temperatures and/or lower “ample” N applications in the FACE rice plots are reasons to expect somewhat smaller responses there. The average FACE potato yield increase of about 28% agrees exactly with the chamber mean value from Cure. In the case of cotton, Cure’s early review included only a single experiment which suggested that cotton’s response to CO2 is huge, so it is not surprising that the FACE value of about 38% is lower, but it is still larger than that of most other species. In a series of open-top chamber experiments Kimball and Mauney (1993) found that elevated CO2 at 650 μmol mol−1 increased cotton yields by about 60%, which scales to 39% at 550 μmol mol−1 and is in close agreement with the FACE results. The percentage increases in agricultural yield under water stressed conditions were plotted by Kimball (1993) against the percentage increases under wellwatered conditions for 25 experiments reported in the literature, but the responses

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under water stress were not signiﬁcantly different from those of well-watered conditions. Such is also the case for the FACE cotton results. However, the FACEgrown wheat and especially sorghum had larger yield increases due to elevated CO2 under water-stressed compared to amply watered conditions. Kimball (1993) similarly plotted the percentage increases for 19 observations obtained under nutrient stress, and the average response was only 49% of that observed with ample nutrients. This low-nutrient result is consistent with the FACE C3 grain yield data, which show a mean 7% stimulation due to elevated CO2 under low N compared to a 12% increase at ample N. Thus, overall the yield responses to elevated CO2 obtained from chamber-based experiments agree closely with those from the FACE experiments.

E. RADIATION-USE EFFICIENCY Radiation-use efﬁciency (RUE), usually deﬁned as the micromoles of dry matter produced per mole of photosynthetically active photons absorbed by green canopy components, is an important parameter used by many of the simpler plant growth models to simulate photosynthesis, i.e., the conversion of light energy and CO2 to biomass. Important though it may be, it appears that few results have been published from the FACE experiments (Table II), although it is likely that necessary measurements for its determination have been made at most of them. We urge more reporting of this important parameter. The only FACE values reported are those of Pinter et al. (1994), who found that elevated CO2 increased RUE of cotton by about 35% in 1989 and about 25% in 1990 and 1991 (Table II). Shoot biomass was increased about 35% by FACE in all three experiments (Table II); hence, it is somewhat puzzling that the increase in RUE was smaller than this for the latter two experiments.

F. SPECIFIC LEAF AREA Speciﬁc leaf area (SLA; leaf area/leaf mass) is an important parameter to plant growth modelers because it determines how much new leaf area to deploy for each unit of biomass produced. Under elevated CO2, any storage of the extra carbohydrate in the leaves or any reallocation of biomass to thicker leaves would tend to increase leaf mass more than leaf area, thereby decreasing SLA. During the ﬁrst 2 years of the Swiss FACE Project, SLA of ryegrass was reduced by 14 and 7% due to the elevated CO2 at ample N, but from 1995 though 1998 SLA was unaffected (Table II). In contrast, at low N, it was reduced by about 11% most years. Considering that the shoot biomass was relatively unchanged by elevated CO2, as discussed previously, a decrease of 11% in SLA implies there

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were signiﬁcant decreases in the leaf area of the ryegrass due to elevated CO2 under low N. The SLA of rice did not change due to elevated CO2 at ample water and N (Table II). For sorghum, there was a tendency for it to decrease at ample water and to increase at low water, but variability was high. The potato’s SLA showed a fairly consistent reduction of about 17% due to the elevated CO2, while that of grape was reduced by about 5%. In one review of mostly chamber-based CO2-enrichment literature that included SLA, Wand et al. (1999) performed a meta-analysis on observations reported for wild C3 and C4 grass species. When there were no stresses, elevated CO2 (scaled to a 190 μmol mol−1 increase) decreased SLA by averages of 13 and 3% for C3 and C4 grasses, respectively. Considering how the ryegrass response ranged from a 14% reduction to zero and how rice was unaffected, it seems likely that the SLA of C3 grasses under FACE was changed somewhat less than that of chamber plants, but the variability is too high to have much conﬁdence in such a conclusion. For the C4 plants, any differences were too small to be detected within the considerable variability.

G. CHEMICAL COMPOSITION CHANGES 1. Nitrogen Concentration It is likely that the increasing atmospheric CO2 concentration will change not only the quantity of plant material produced but also the composition of that material. One important parameter is the nitrogen concentration, which in the case of leaves is a measure of the amount of protein in the photosynthetic apparatus and therefore of the capacity of the plants to photosynthesize. For any organ, however, the nitrogen or protein concentration is an important measure of the nutritional value of the plant material to other trophic levels. Focusing ﬁrst on the leaves of the C3 grass plants, the mean response was a decrease of 9% in N concentration due to the elevated CO2 under conditions of ample water and nitrogen, with the wheat and rice leaves affected somewhat less than the ryegrass (Table II). Under low water, the response was similar to that under ample water and N, although there were only two observations. Not surprisingly, when soil nitrogen was low, there was a greater mean decrease in leaf N of 16% due to elevated CO2 with a reversal so that wheat was affected more than ryegrass. Among seasons and authors, there is a range of responses to elevated CO2 of the N concentration reported for the leaves of the C3 perennial woody plant, cotton (Table II). No change and a 15% decrease were determined by Prior et al. (1998) from 1989 and 1990 samples, respectively, while Huluka et al. (1994) reported a reduction of 32% for 1990. Therefore, the response of cotton leaves to elevated

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CO2 is probably a large decrease, but it is difﬁcult to say just how large. Prior et al. (1998) also determined the N concentration of other organs of cotton. The overall N concentration of the whole plant was reduced by about 11%, while that of the seeds was reduced somewhat less, by about 7%. Some observations of the N concentration of the whole shoot of ryegrass also have been reported (Table II), and the reductions due to elevated CO2 appear similar to those for the leaves alone, as discussed previously. One interesting observation from a pot study within a FACE experiment was reported by Daepp et al. (2001), who found that under low-soil N the N concentration of the shoots during a single reproduction stage was reduced by about 20%, as compared to reductions of about 9% during vegetative stages. The N concentration of the shoots of the C3 legume, clover, was reduced about 5% by elevated CO2 under conditions of both ample- and limited-soil N, which is about half the reduction of the C3 grasses (Table II). The N concentration of wheat grain was measured by Kimball et al. (2001), who found that the FACE treatment caused a mean reduction of about 3% under amplesoil N and water, 4% under low-water, and 9% under low-soil N (Table II). The low-soil N by itself caused serious reductions in both nutritional and baking quality, and the further reduction of 9% due to elevated CO2 made a bad situation worse. These reductions in N concentration of the wheat grain are considerably smaller, however, than those observed for the leaves as discussed previously (Table II). Wand et al. (1999) performed a meta-analysis on observations reported in the literature for wild C3 and C4 grass species. When there were no stresses, elevated CO2 (scaled to a 190 μmol mol−1 increase) reduced leaf nitrogen concentration by 11 and 3% for C3 and C4 species, respectively. When nutrients were low, the reduction was similar. Such a reduction for the C3 grasses agrees very closely with the mean 9% reduction observed in the FACE experiments (Table II) at ample N and water. However, unlike the chamber experiments, when soil N was low, the N concentration of the leaves and shoots of C3 grasses grown in FACE experiments was reduced considerably more, by about 16%. Cotrufo et al. (1998) did an extensive examination of nearly 400 literature observations of the effects of elevated CO2 on the nitrogen concentrations of plant tissues. The FACE work of Jongen et al. (1995) was included, but otherwise the data came from chamber studies. About half the studies had been done at 700 μmol mol−1, but a range of enriched CO2 concentrations from 450 to 1500 μmol mol−1 had been used. Surprisingly, there was little discernible effect of changing CO2 concentration over this range, so the values attributed to Cotrufo et al. (1998) in Table II were not linearly scaled like those of the other authors. Also surprisingly, they found little effect of the N regime of the rooting media. Overall, the N concentration of C3 plants was reduced by 16% due to elevated CO2 compared to 7% for C4 plants and also to 7% for N2-ﬁxing plants. The concentrations in leaves and shoots as a whole were reduced more than those of ﬁne roots (about −16%

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compared to −7%). The concentration in green leaves was reduced more than that of leaf litter (−16% compared to −7%). Comparing to the FACE results (Table II), the overall reduction for the FACE C3 plants of about 9% was less than the 16% reported for the chamber plants by Cotrufo et al. In the case of the FACE experiments, the reduction at low-soil N was about 16%, whereas Cotrufo et al. found little effect of media N. For legumes, Cotrufo et al. found a smaller reduction of about 7%, which is consistent with the FACE result of about a 5% reduction. In their meta-analysis of woody plant responses in the literature, Curtis and Wang (1998) found that leaf nitrogen concentrations were reduced 9% by elevated CO2 (scaled to 550 μmol mol−1). Similarly, Norby et al. (1999) compared about 60 observations of N concentration from leaves of trees grown in CO2-enriched opentop chambers to those of trees grown at ambient CO2, and they found an average 7% reduction (scaled to 550 μmol mol−1). There was a wide range of responses from about 0 to −32% in the FACE cotton experiments, so the FACE and chamber results are not inconsistent although it seems probable that the changes in leaf N from FACE may be larger. Whatever the differences in green foliage, however, plants catabolize many compounds during senescence, and Sowerby et al. (2000) did not detect any signiﬁcant differences in nitrogen concentration of ryegrass litter in the Swiss FACE experiment (Table II). On the other hand, Ball (1997) found that elevated CO2 increased the C:N ratios from 0 to 88% for C3 species, and, as expected, there was almost no effect on C4 species. However, he reported an increase of 77% in the C:N ratio for the C3 ryegrass from the Swiss FACE experiment, which is within the range for the prior chamber-based values but inconsistent with the observation of Sowerby et al. (2000) that CO2 concentration did not affect N concentration (Table II) unless, of course, there was in fact an increase of 77% in C concentration. However, such a large C increase seems unlikely. Ball also tabulated the lignin:N ratios of the litter, ﬁnding CO2 responses similar to those of the C:N ratios. 2. Nitrogen Yield Several of the FACE projects have reported the yield of nitrogen removed from the plots (Table II). The mean change in N yield of nonlegume plants at ample water and N was essentially zero, and the increased growth and reduced N concentration, as presented previously, negated each other overall. Looking more closely at the data, the ryegrass had mostly reductions in N yield, while wheat grain was positive. When soil N was limiting, however, elevated CO2 caused a substantial overall reduction in N yield, especially with the ryegrass, which is somewhat puzzling as to why elevated CO2 would cause actual reductions in N yield compared to ambient-grown plants. For the case of a legume, lucerne, L¨uscher et al. (2000) found 30% increases in N yield due to elevated CO2 at both ample and low N when the plants were inoculated with effective symbiotic nodulating Rhizobium

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bacteria. In contrast, when the plants were inoculated with ineffective nodulating Rhizobium, the plants behaved much like nonlegumes with a small increase in N yield (+6%) due to elevated CO2 at ample N and a substantial reduction (27%) at low N. For the case of the C3 woody perennial, cotton, Prior et al. (1998) report substantial increases of about 20% in nitrogen yield of the entire plant. Thus, there appears to be a fairly wide range in the effects of elevated CO2 on N yield depending on species and on soil N status. 3. Carbohydrates and Other Carbon-Based Compounds When plants are grown at elevated atmospheric CO2 concentrations, a common observation is that, with the increased production of photosynthate, there is a corresponding increase in the concentration of carbohydrates in the plant tissues. At ample water and N in the FACE experiments, there was a mean increase of about 22% in the water-soluble or total nonstructural carbohydrate levels of the foliage of C3 grasses due to the elevated CO2 (Table II). This mean increase includes a 38% increase observed by Fischer et al. (1997) for the pseudo-stems of ryegrass, which was higher than the 13% increase they found for the leaves. Rogers et al. (1998) observed that the stimulation by CO2 at ample N tended to be greater in ryegrass leaves 7 days after the tops of the plants had been cut (harvested) than it was in plants that had not been cut, indicating that the cutting affected the translocation of photosynthate within the plants. Variability was high, however. When soil N was limited, the trend reversed, and the leaf carbohydrate levels of ryegrass were increased by 71% for the uncut compared to 41% for plants cut 7 days earlier, suggesting that the low-soil N was reducing the ability of the plant to grow and to process the extra photosynthate being produced at high CO2. The changes of carbohydrates in the C3 grass foliage were dwarfed by the changes in the leaf starch concentration of the woody perennial cotton, which went up tremendously (by about 123%) due to elevated CO2 (Table II) (Hendrix et al., 1994). Besides the foliage, the carbohydrate concentration of other organs can be affected by elevated CO2 as well, but usually to a lesser extent. Bindi et al. (1998, 1999) found the starch concentration of potato tubers was stimulated one year by about 9%, but in another year it was unaffected (Table II). Elevated CO2 also appears to have raised the sugar concentration of grape berries slightly, about 2% on average. Wand et al. (1999) performed a meta-analysis on observations from the literature reported for wild C3 grass species. When there were no stresses, elevated CO2 (scaled to a 190 μmol mol−1 increase) increased total nonstructural carbohydrates by 18%, which is consistent with the mean of about 22% found for C3 grasses in the FACE experiments. In their meta-analysis of woody plant response, Curtis and Wang (1998) found that leaf starch concentrations of angiosperms were increased by 37% (scaled to 550 μmol mol−1). This value is higher than that for the grasses

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but still far lower than the 123% value found for cotton in the FACE experiments. Thus, except that cotton leaf starch seemed to be stimulated more by the FACE treatments, the results in the prior chamber-based literature are consistent with the changes in carbohydrate concentrations found in the FACE experiments. The concentrations of other carbon-based compounds also can be affected when plants are grown at higher levels of atmospheric CO2. For example, Pe˜nuelas et al. (1999) found that the concentration of phenolics in green FACE-grown wheat leaves increased by about 21% compared to ambient-CO2-grown leaves. Such an increase in phenolics would be expected to increase the wheat’s resistance to attack by insects and later to slow down the rates of microbial decomposition of the plant litter. However, they also found that as the plants senesced, the phenolic compounds apparently were catabolized so that the phenolic concentration in the senescent leaves dropped and was unaffected by the CO2 concentration under which the plants were grown. Thus, microbial decomposition of these senescent wheat leaves would likely be unaffected.

H. PHENOLOGY Growth at elevated concentrations of atmospheric CO2 also may affect the phenology or rate of plant development. Indeed, increased earliness has been one reason touted for enriching commercial greenhouses with CO2 in order to shorten the time to market and reduce heating fuel costs (e.g., Enoch and Kimball, 1986). However, the changes in phenology observed in the FACE experiments have been small or nonexistent. An initial report of an acceleration of the development of FACE-grown wheat by Kimball et al. (1995) proved to be a false alarm when it was discovered that the blowers in the FACE plots were causing the faster development compared to blowerless ambient CO2 plots (Pinter et al., 2000). When proper controls were in use with blowers like those of the FACE plots, wheat development was accelerated by an average of only 0.4 days based on the difference in time to eight growth stages from tillering to maturity (Table II) (Pinter et al., 2000). For rice, growth at elevated CO2 hastened the occurrence of anthesis by about 2 days (Table II). Maturity was believed to be hastened several days to a week also, but the latter was more difﬁcult to determine (Kobayashi, personal communication). Under ample water, sorghum appeared to be hastened in its development by elevated CO2 similarly to rice (Table II) (Ottman et al., 2001). Potato maturity was accelerated by only about a day, even though it appears that senescence caused the leaf area to begin decreasing about 2 weeks earlier in the FACE plots (Bindi et al., 1999), as discussed previously. Grape phenology was unaffected by growth at high CO2. In several of these FACE experiments, secondary treatments such as moisture stress, low-soil N stress, and plant variety affected phenology far more than did the high CO2.

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I. SOIL CHANGES 1. Microbiology Because the quantity and quality of plant material entering the soil are altered by the increasing atmospheric CO2 concentration, the probability exists that microorganisms in the soil will be affected also, and then so too will the rates of many soil processes mediated by the microorganisms. A variety of observations have been made in the FACE experiments on aspects of soil microbiology, some of which have shown changes and some of which have not. Wood et al. (1994) found high variability in the rates of N mineralization in incubated soil samples from the 1991 FACE cotton experiment, and no signiﬁcant effects of elevated CO2 were detectable. In the same experiment, Runion et al. (1994) found no difference between the population numbers of nematodes in the soil under FACE or ambient CO2 treatments. However, there were signiﬁcantly more (15%) Rhizoctonia in August samples, but not in those from June. Conversely, they detected a 15% increase in mycorrhizal colonization of the cotton roots in June, but not in August. However, total microbial activity was increased about 16% both in June and in August, as determined from a dehydrogenase assay. In soil samples from the same experiment, Wood et al. (1994) found that soil microbial respiration, another measure of microbial activity, was stimulated by about 45% during the second month of incubation, whereas the stimulation had been smaller during the ﬁrst month (19%). Prior et al. (1997) measured N mineralization in incubated soil samples taken after 2 years of the Arizona FACE wheat experiments and were unable to detect any effect of CO2 amid wide variability (Table II). They also measured microbial respiration on the samples and similarly did not detect any changes in the layers at 0–5 or 5–10 cm, but there was a 40% decrease in the layer at 10–20 cm. Akin et al. (1995) incubated wheat stems (straw) from the 1993 FACE wheat experiment, but they found no signiﬁcant change in biodegradation by white rot fungi (Table II). A much larger response was observed by Rillig et al. (2001), who measured the lengths of arbuscular mycorrhizal fungi hyphae in soil samples following the 1998 FACE sorghum experiment (Table II). They found 109 and 267% increases due to elevated CO2 under ample and low-water supplies, respectively. Moreover, there were 30% increases in the amount of water-stable soil aggregates, as well as increases in easily extractable glomalin, a “glue” that helps stabilize aggregates. Such a change in soil structure would be expected to increase inﬁltration, decrease erosion, and alter other hydrological soil properties. However, because the sorghum growth only increased by 7 and 13% under ample and low water (Ottman et al., 2001), respectively, as discussed previously, it is surprising that such dramatic changes could occur in the soil. Treseder and Allen (2000) have reviewed the literature on effects of elevated CO2 on mycorrhizal fungi, and they report

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a huge variability in response exits from no effect to 300% increases. Thus, the observations of Rillig et al. (2001) fall within the range in the literature. Schortemeyer et al. (1996) measured several microbiological parameters during the second year of the Swiss FACE Project (Table II). They were unable to detect any effect of the FACE treatment on microbial biomass carbon or on numbers of autotrophic NH4+ oxidizers in the rhizosphere under either ryegrass or clover. They also measured the numbers of Rhizobium leguminosarum in rhizosphere soil and found no FACE effects under ryegrass, which would be expected for a nonlegume. Under clover, however, there was roughly a doubling of these symbiotic bacteria in response to the elevated CO2. Four years later Marilley et al. (1999) measured the population numbers of heterotrophic bacteria in the soil under both ryegrass and clover. Their data provide evidence for a CO2-induced increase in bacterial numbers in the soil that adhered to the roots at sampling (rhizospheric soil) but not in the bulk soil nor on the washed roots (rhizoplane–endorhizosphere). In contrast, Sowerby et al. (2000) found a 27% increase in microbial biomass N in the bulk soil from which ryegrass plants had been removed a few days previously (Table II). Ball (1997) summarized the results of experiments in which soil respiratory activity (a measure of decomposition rate) was measured on incubated soil samples amended with senescent plant litter from ﬁve species of plants grown at elevated CO2. For two C4 species, the CO2 had no effect, whereas for three C3 species there were 20–30% reductions in soil respiratory activity. One of the species was ryegrass from the Swiss FACE experiment, and the reduction for it was 25% (Table II), consistent with the samples from the other two C3 species which were chamber grown. Ball attributed the reductions in respiratory activity of the C3 plants grown in elevated CO2 to higher C:N and lignin:N ratios in the litter from these plants. Recently Torbert et al. (2000) did a review of elevated CO2 effects on residue decomposition processes in cotton, wheat, sorghum, and soybean. Their analysis included some of the FACE work listed in Table II, as well as results from open-top chambers. They concluded that the rate of residue decomposition may be limited by N and that the release of N from decomposing plant material may be slower in high-CO2-grown plants. However, the most interesting data are probably those of Montealegre et al. (2000), which show that from 1993 through 1996 in the Swiss FACE experiment there was a qualitative change in the strains of Rhizobium that occupied the nodules of their white clover (Table II). In 1996 they isolated several strains of the bacteria and found shifts in the “ﬁngerprints” of the microbial DNA. They conducted an ancillary experiment whereby strains from FACE-grown plants were inoculated together with strains from the ambient-grown plants on clover seeds which were then grown in growth chambers at 350 or 600 μmol mol−1 CO2. In ﬁve out of six cases the strains from the FACE plots had a higher nodule occupancy share in the plants growing at high CO2 compared to their share at normal CO2 (Table II).

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These higher occupancy rates indicate that these high-CO2 selected strains had gained a competitive advantage when inoculated on the plants growing at highCO2. Therefore, natural selection occurred in the soil under the FACE plots of the Swiss experiment to favor strains better adapted to the high-CO2 regime. Zak et al. (2000) recently assembled reports from the literature of the effects of elevated CO2 on several measures of soil microbial activity (Table II). They included a few observations from FACE experiments, but most were chamber based. Net soil N mineralization rates for gramminoid, herbaceous, and woody species were increased by averages of 12, 8, and 5%, respectively, although the latter was not statistically signiﬁcant. The observations of Wood et al. (1994) and Prior et al. (1997) (Table II) were highly variable and therefore not inconsistent with the overall means of Zak et al. Zak et al. similarly compiled data on microbial respiration, ﬁnding increases of 20, 24, and 13% for gramminoid, herbaceous, and wood species, respectively. The observations of Wood et al. (1994) and Prior et al. (1997) (Table II) are mostly consistent with these ﬁgures, except that Prior et al. found a signiﬁcant decrease for wheat at depths of 10–20 cm. With regard to total microbial biomass C, Zak et al. found increases of 10, 17, and 11% for gramminoid, herbaceous, and woody species, respectively. Considering how variable the results from FACE experiments have been (Table II), they are not inconsistent with the compilations of Zak et al. 2. Soil Respiration A few measurements have been made of soil respiration (i.e., soil surface CO2 ﬂux from the respiration of roots and microorganisms) in the FACE experiments, and most have shown a stimulation from elevated CO2 attributed to greater root growth and to greater activity of microorganisms due to more root exudates. Pendall et al. (2001) measured substantial increases in soil respiration of about 40 and 70% in 1996 and 1997, respectively, due the elevated CO2 in the FACE wheat experiments (Table II). They also measured the isotopic composition of the evolved CO2 and discovered that it was “old,” which suggested that the high-CO2grown wheat was “priming” the soil microorganisms to more rapidly decompose ancient (i.e., normally slow cycling) soil organic matter. For the case of cotton, Nakayama et al. (1994) found increases in soil respiration due to the FACE treatment of 16 and 23%, respectively in 1990 and 1991 (Table II). Moreover, they observed that an increase in soil respiration of 8% continued 1 month after the FACE treatment was stopped in the fall, but after 2 months the increase had disappeared. With C4 sorghum at ample water, Cheng et al. (unpublished) found considerable variability, and they did not detect a signiﬁcant effect of FACE on soil respiration, which is consistent with the lack of any growth response of the sorghum under the well-watered condition.

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In contrast to the wheat and cotton in Arizona, during the year 1995 of the Swiss FACE Project, Ineson et al. (1998) measured the soil respiration of ryegrass after cutting, and they found an 8% decrease in the FACE plots (Table II). In this case, the lower quality of the plant material (higher C:N and lignin:N ratios) evidently slowed decomposition. In contrast, in 1998, Sowerby et al. (2000) measured a signiﬁcant 32% increase in microbial respiration from the soil surface of the FACE plots a few days after removal of ryegrass plants and roots. Zak et al. (2000) recently assembled 43 observations of the effects of elevated CO2 on soil respiration, including two observations from FACE experiments (Table II). They calculated mean increases of 20, 24, and 13% (scaled to a mean CO2 increase of 190 μmol mol−1 to 550 μmol mol−1) for gramminoid, herbaceous, and woody species, respectively. The observations from the FACE experiments reported here fall easily within the range of observations from the prior, mostly chamber-based experiments.

3. Trace Gas Emission /Consumption A few measurements have been made of the exchange of other radiatively active (greenhouse effect) trace gases from the soil surface in the FACE experiments. Ineson et al. (1998) measured the exchange of N2O and CH4 from the soil surface of ryegrass after cutting in 1995. They found that N2O production was stimulated by about 21% due to the FACE treatment, which they attributed to enhanced availability of root-derived soil C that could act as an energy source for denitriﬁcation. They also determined that CH4 consumption was reduced by 53% (Table II) due to the FACE treatment. Much conﬁrmation work needs to be done; however, if such a decrease were found to be true generally, then an important feedback exists such that the increasing atmospheric CO2 concentration reduces the removal of CH4 from the air by soils. Thus, with reduced removal of CH4 from the air there could be additional global warming beyond that due to the elevated CO2 alone. Weber (1997) measured N2O emissions from the FACE wheat experiment in Arizona in 1997. She encountered enormous variability (Table II) and therefore was unable to detect any treatment effects. Preliminary data from Matthias et al. (2001) suggest that in the FACE sorghum experiment in Arizona, elevated CO2 had no effect on the emissions of N2O under ample water, consistent with little effect of elevated CO2 on shoot biomass accumulation under the well-watered condition (Table II) (Ottman et al., 2001). In contrast, under the low-water treatment, N2O emissions from the FACE plots often exceeded those from the control plots, which is consistent with the greater sorghum growth in the FACE plots and higher soil water contents present due to the CO2-induced partial stomatal closure as previously discussed.

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4. Soil Carbon Sequestration There is much effort among the global change community to devise ways to slow or stop the rise of the atmospheric CO2 concentration. One pertinent question is whether the greater growth of plants at higher CO2 will result in greater storage of carbon in the soil and thereby decrease the rate of rise. We have already discussed how elevated CO2 in the numerous prior chamber experiments and in the FACE experiments has stimulated the growth of plant roots and shoots, which increases carbon inputs to the soil. However, we also have discussed that microbial activity and soil respiration also are often stimulated, although variability is high. Therefore, it is not clear from these observations whether the size of the pool of soil organic carbon will be changed by future elevated CO2 concentrations. Unfortunately, detection of changes in soil organic carbon (SOC) concentrations is very difﬁcult because the typical net annual additions are very small compared to the size of the pool already present. Cotton appears to be one of the most responsive crops to CO2 enrichment, and Wood et al. (1994) found a mean SOC increase of about 14% in the surface 5-cm of soil after 3 years of FACE at ample water, but the increase was not statistically signiﬁcant (Table II). They did detect a signiﬁcant 10% increase in the layer at 10–20 cm, however. Using separate samples from 0 to 30 cm in the same experiment, Leavitt et al. (1994) reported an increase of 14%, but it also was not statistically signiﬁcant. Under low water, no signiﬁcant changes were detected. Leavitt et al. (1996) similarly measured the SOC in soil under wheat after two seasons of FACE, and they found mean increases of 6 and 21% at depths of 0–15 and 15–30 cm, respectively, but again these changes were not statistically signiﬁcant. On the other hand, in the samples taken by Prior et al. (1997) from the same experiments, a signiﬁcant increase of about 13% was determined. In the Swiss project from 1995 to 1998, Van Kessel, Horwath et al. (2000) measured 5 to 12% increases in SOC under ryegrass and clover at ample N, but none of the changes were statistically signiﬁcant. Van Kessel, Nitschelm et al. (2000) compared SOC from 1993 to 1998 and even with the 6-year span, no signiﬁcant changes due to FACE were found at either high or low N. In spite of the lack of statistical signiﬁcance of the individual observations of SOC, it is important to note that in 13 out of 13 cases in Table II there was an increase in SOC due to the FACE treatment at ample N. Treating each of these as an individual observation, the mean increase in SOC due to elevated CO2 was about 11% with a standard error of 2%. For the case of low N, the mean increase was a nonsigniﬁcant 2%. Thus, looking at the combined experience from these several experiments, it appears that signiﬁcant increases in SOC have occurred under the FACE plots, at least when N was nonlimiting. All of the determinations of SOC discussed previously were performed using traditional chemical analytical methods to measure changes in the total organic carbon in the soil. However, if the carbon isotopic composition of the plants is

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different from that of the SOC, then the possibility exists to determine only the amount of new carbon added to the soil separate from the total. One example of such a circumstance is when C4 plants grow into soil that had only grown C3 plants for many years previously or vice versa (e.g., Balesdent and Balabane, 1992). In the case of FACE experiments, it is likely that the CO2 used for enrichment will have a different isotopic ratio than that of normal air. Therefore, FACE-grown plants will likely have their own inherent isotopic tracer. Indeed, such labeling enabled Leavitt et al. (1994) to determine that after 3 years of FACE on cotton, about 10% of the carbon in the soil was new carbon derived from the FACE-grown plants. Unfortunately, however, no similar label existed for the ambient-grown plants, so it was impossible to know whether or not a similar percentage of new SOC was produced by them. To circumvent the lack of label in the ambient- or control-grown plants during the 1996 and 1997 FACE wheat experiments in Arizona, Leavitt et al. (2001) installed subplots of exotic soils with C4-plant-signature SOC. In other subplots, they also pulse-labeled control plants by exposing them to richly labeled CO2 for a few hours. Variability was high for both methods, but the results were consistent between the two methods. It appeared that about 5% of the SOC under the FACE plots was new carbon after 2 years. However, a similar portion was new under the control plots, so the net change in new SOC due to FACE was zero (Table II). Van Kessel and co-workers (2000) similarly installed subplot boxes of C4-plantsignature soil in their FACE experiments with ryegrass and clover from 1995 to 1998. After these 4 years, about 40% of the SOC was new carbon, with ryegrass at high N having the largest percentage of new C (Van Kessel, Horwath et al., 2000). However, the mean change due to elevated CO2 was only a nonsigniﬁcant 5% at high N for both ryegrass and clover. At low N, there were mean decreases of 7 and 16% due to the FACE treatment for ryegrass and clover, respectively, although the former ﬁgure was not statistically signiﬁcant. Thus, these two studies using isotopic analyses in the FACE experiments suggest that the greater plant biomass production due to elevated CO2 may not necessarily lead to larger pools of SOC. Indeed, if N is limited, there may actually be a decrease. Nevertheless, in spite of the huge variability within any of the individual studies and considering all the standard chemical as well as the isotopic determinations, it appears that some increase in the SOC should happen due to the increasing atmospheric CO2 concentration, at least where N is nonlimiting.

IV. COMPENDIUM AND CONCLUSIONS The quantitative relative changes due to elevated CO2 (550 or about 190 μmol mol−1 above ambient) in the several crop growth and other parameters as determined from the FACE experiments are summarized in Table III. Except as noted,

Table III Compendium of Table II Summarizing the Changes in Several Response Parameters to Elevated CO2 of Several Agricultural Crops Categorized with Respect to Functional Groupsa Relative changes due to elevated CO2 (%, unless otherwise noted) conditions Measured parameter Photosynthesis Upper leaves Canopy

Water relations Stomatal conductance

Canopy temperature Evapotranspiration or water use

Water potential

Functional group

Crop species

Ample H2O, ample N

Low N

C3 C4 C3 grass

All C3 Sorghum Wheat

↑ 31 ± 3 ↑ 9 ± 14 ↑ 19

↑ 29 ± 7

C3 C4 Woody C3 grass Woody Woody

Wheat Sorghum Cotton & grape Wheat Cotton Cotton

↓ −34 ↓ −37 ↓ −15 ↑ 0.6 ↑ 0.8 nc −1

↓ 44

C3 grass

Wheat

↓ −4 to −7

C4 grass

Sorghum

↓ −10 ± 2

All C3 & C4

All C3 & C4

All C3 & C4 C4 grass

All C3 & C4 Sorghum

± ± ± ± ± ±

2 14 4 0.1 0.1 1

↑

Low H2O

Most lower at low N, but ryegrass about same ↑ 23 ± 21

9

Values less than those for upper leaves due to compensatory changes in canopy architecture

↑ 1.1 ± 0.1

↓ 20

nc

nc

3 ± 3

Comment

↑ 9 ± 4

Such reductions under FACE are about 1 1/2 times greater than observed in prior chamber experiments. ◦ C change, not % ◦ C change, not % Species with large growth response and small stomatal conductance response Species with medium growth response and medium stomatal conductance response Species with little growth response and large stomatal conductance response When water limiting over seasonal time frame, little change in ET because plants use all water available. % Increase (less negative) % Increase (less negative) continues

Table III—continued Relative changes due to elevated CO2 (%, unless otherwise noted) conditions Measured parameter

Functional group

Crop species

Ample H2O, ample N

Low N

Low H2O

Comment

Peak leaf area index C3 grasses

C4 grass C3 forb Woody Biomass accumulation Shoots

C3 grasses

352 C4grass C3 forb C3 legume Woody Roots

Agricultural yield

C3 grasses

C3 legume Woody C3 grasses C3 grasses C4 grass C3 forb

Wheat Ryegrass Rice Sorghum Potato Cotton Wheat Ryegrass Rice Sorghum Potato Clover Cotton Grape Wheat Ryegrass Rice Clover Cotton Ryegrass Wheat Rice Sorghum Potato

↑

12 ± 3

nc

2 ± 3

nc −5 ± 5 ↓ −6 ± 4 ↑↓ −6 ± 10 ↑

12 ± 1

↑ 3 ↓ −21 ↑ 24 ↑ 32

± ± ± ±

4 9 5 2

↑

3 ± 3

↑

25 ± 4

↑

47 ± 10

↑

39 ± 7

↑ ↑ ↑ ↑

25 64 12 12

± ± ± ±

↑

21 ± 2

↑

7 ± 2

4 32 1 1

nc −5 ± 6 ↑ 28 ± 6

nc

1 ± 2

↑

21 ± 23

↑

14 ± 2

↑

16 ± 2

↑

26 ± 9

↑

23

↑

23 ± 2

↑

25 ± 8

Inconsistent at ample H2O and N

Except for the legume clover, elevated CO2 appears to have stimulated root growth relatively more under FACE conditions compared to chamber conditions.

Forage biomass Grain ( wheat only at low H2O) Grain Tubers

Grape

↑ ↑ ↑ ↑

24 38 56 28

Woody

Cotton

↑

28 ± 5

C3 grasses

Wheat Ryegrass Rice Sorghum Potato Grape

↓ −3 ± 2

C3 legume Woody

Clover Cotton

±5 ± 9 ± 21 ±5

↑

25 ± 4

↑ ↑

43 ± 1 52 ± 1

Forage biomass Bolls (seed + lint) Lint Berries

Radiation-use efﬁciency Speciﬁc leaf area

C4 grass C3 forb Woody Chemical composition changes N concentration C3 grasses 353 N yield

Carbohydrates and other C-based compounds

Woody C3 legume Woody C3 grass Woody C3 grass C3 grasses

C3 legume Woody C3 grasses

Woody C3 forb Woody

Wheat Ryegrass Rice Cotton Clover Cotton Wheat Cotton Ryegrass Wheat Ryegrass Rice Lucerne Cotton Wheat Ryegrass Rice Cotton Potato Grape

↓ −13 ± 1

nc −2 ± 8 ↓ −30 ± 16 ↓ 5 ± 1 ↓ −9 ± 2 ↓ −15 ↓ −6 ↓ 11 ↓ −3 ↓ −7 nc −5 ↑↓ −1 ↑ ↑ ↑

Down slightly in C3 grasses at ample N, and down more in ryegrass at low N nc

↓ −16 ± 2

4 ± 12

↓ −9 ± 3

Leaves

Leaves Whole shoot Whole plant Grain Seeds Senescent litter No consistent change at ample N

± 17 ± 1

↓ −5 ± 2

± 2

↓ −9 ± 3

↓ −4 ± 1

± 3

↓ −12 ± 3

↑

30 ± 25 20 ± 4 22 ± 4

↑ 123 ± 61 ↑ 5 ± 4 ↑ 2 ± 1

↑

33 ± 26

↑

44 ± 9

18 ± 8

Water-soluble carbohydrates in foliage

↑ 175 ± 103

Starch in leaves Starch in tubers Sugar in berries continues

Table III—continued Relative changes due to elevated CO2 (%, unless otherwise noted) conditions Measured parameter

Functional group

Crop species

Ample H2O, ample N

C3 grass C3 grass

Wheat Wheat

↑ nc

C3 grass

Wheat

↓ − 0.4 ± 0.3

C3 grass C4 grass C3 forb Woody

Rice Sorghum Potato Grape

↓ ↓ ↓ nc

C3 grass C3 grass Woody Woody Woody Woody C3 grass

Wheat Wheat Cotton Cotton Cotton Cotton Wheat

C4 grass C3 legume C3 grass

Sorghum Clover Ryegrass

C3 legume C3 grass C3 legume C3 grass

Clover Ryegrass Clover Ryegrass

nc 4 ± 12 ↑↓ − 12 ± 14 ↑↓ 22 ± 60 nc 2 ± 10 ↑↓ 7 ± 9 ↑ 16 ± 10 nc 21 ± 31 ↓ −40 ± 31 ↑ 109 ± 47 nc 16 ± 21 nc 9 ± 21 ↑↓ 27 ± 21 nc −12 nc −17 ↑ 101 nc 9

Low N

21 ± 5 0 ± 25

Low H2O nc nc

0 ± 14 0 ± 25

Comment Phenolics in green leaves Phenolics in senescent leaves

Phenology

354

Soil changes Soil microbiology

−2 −2 −2 ± 8 0 ± 0

↓ −2 nc

0 ± 2

Avg. acceleration to reach 8 growth stages from tillering to maturity (days, not%) To reach anthesis (days, not%) To reach anthesis (days, not%) To reach anthesis (days, not%) To reach anthesis (days, not%) Biodegradation of stems by white rot fungi N mineralization in soil

↑↓ 93 ± 131

nc 13 ± 21 nc 9 ± 21

↑ 267 ± 104

Nematode populations in soil Rhizoctonia in soil Total microbial activity Total microbial activity near surface Total microbial activity at 10–20 cm Mycorrhizal fungi hyphae length Total microbial biomass in a particular year Total microbial biomass in a particular year Total microbial biomass in another year Numbers of autotrophic NH4+ oxidizers Numbers of Rhizobium leguminosarum in Rhizosphere soil

Soil respiration (surface CO2 ﬂux)

Trace gas emission/ consumption

355

Soil C sequestration (from chemical analyses)

C3 grass

Ryegrass

C3 legume

Clover

C3 grass C3 grass

Wheat Ryegrass

C4 grass Woody C3 grass C3 grass C4 grass C3 grass Woody

Sorghum Cotton Ryegrass Wheat Sorghum Ryegrass Cotton

C3 grass

Wheat

↓ −20

↑ 55 ↓ −8 ↑ 32 nc 21 ↑ 20 ↑ 21 nc −5 nc ↓ −53 nc 9 ↑ 10

± 39 ± 4 ± ± ± ±

13

In a particular year In another year (roots removed)

49 10 13 43

± 2 ± 13 ± 8

nc 14 ± 19 ↑

(from isotopic analyses)

Soil respiratory activity (decomposition rate) in incubated soil amended with senescent ryegrass litter Qualitative change in strains of Rhizobium in nodules after four years

↑ Slight nc −3 ± 7 nc 4 ± 6

N2O emissions N2O emissions; highly variable N2O emissions CH4 consumption Total organic C in layer, 0–10 cm, after 3 years Total organic C in layer, 10–20 cm, after 3 years Total organic C after 2 years by one investigator Total organic C after 2 years by another investigator Total organic C after 4 years

C3 grass Ryegrass nc 7 ± 22 nc 5 ± 21 Clover nc 9 ± 26 nc −1 ± 21 C3 legume However, in all reported cases the mean increase in total organic C was positive. Averaging across experiments, the mean increase was 11 ± 2% at ample N and no change (2 ± 3%) at low N. C3 grass Wheat nc 0 New C after 2 years from isotopic analyses (about 5% new C in both FACE and control plots) C3 grass Ryegrass nc 6 ± 14 ↓ −7 ± 17 New C after 4 years from isotopic analyses Clover nc 5 ± 21 ↓ −16 ± 14 (about 40% new C in both FACE and C3 legume ambient plots)

The changes have been linearly scaled to correspond to a 550 μmol mol− 1 CO2 concentration (or 190 μmol mol− 1 increase above ambient). Increases are indicated by ↑, decreases by ↓, no changes by nc, and inconsistent changes by ↑↓. a

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the responses agree with prior chamber-based results. Obviously, the FACE experiments on agricultural crops during this past decade have produced a vast amount of information about the responses of several species to elevated CO2. The selected species were representative of several plant functional types, and the many reported responses have varied according to plant type generally in ways that were expected on the basis of prior chamber-based experiments. Over all the data and parameters considered in this review, there are only two parameters for which the FACE- and chamber-based data appear to be inconsistent. One is that elevated CO2 from FACE appears to reduce stomatal conductance about one and a half times more than that observed in prior chamber experiments. Similarly, elevated CO2 appears to stimulate root growth relatively more than shoot growth under FACE compared to chamber conditions. In a review of ﬁeld research on the effects of elevated CO2 on crop photosynthesis and productivity conducted using open-top chambers, Lawlor and Mitchell (1991) concluded that the ﬁeld experiments generally conﬁrmed the results from prior controlled-environment chamber studies. They stated “CO2 increases photosynthesis, dry matter production, and yield substantially in C3 species, but less in C4. It decreases stomatal conductance and transpiration in C3 and C4 species and greatly improves water-use efﬁciency in all plants.” This review goes the next step beyond Lawlor and Mitchell because we have compared the results from free-air CO2 enrichment (FACE) experiments to those obtained in open-top and other types of chambers, and we have been more quantitative. However, the quoted statement from Lawlor and Mitchell still appears valid (Table III). Some other general statements can be added. Growth stimulations are as large or larger under water-stress compared to well-watered conditions (Table III). Growth stimulations of nonlegumes are reduced at low soil nitrogen, whereas elevated CO2 strongly stimulates the growth of legumes both under ample and low-N conditions. Roots are generally stimulated more than shoots. Woody perennials appear to have larger growth responses to elevated CO2, while at the same time their reductions in stomatal conductance are smaller. The nitrogen concentrations go down while carbohydrate and some other carbon-based compounds go up due to elevated CO2, with leaves and foliage affected more than other organs. Phenology is accelerated slightly in most but not all species. Elevated CO2 affects some soil microbes greatly but not others, yet overall microbial activity appears to be stimulated. Detection of changes in soil organic carbon in any one study is nearly impossible; yet combining results from several sites and years, it appears that elevated CO2 does indeed increase sequestration of soil carbon. Kimball et al. (1997) reviewed the literature that compared the biomass accumulation of plants growing inside open-top chambers (at ambient CO2 with that of plants growing in adjacent open-ﬁeld plots. The responses ranged from decreases of 26% to increases of 104% with the mean being 10% greater growth inside the chambers than outside. Of course, it would be much more difﬁcult to make a meaningful comparison between the growth of plants inside controlled-environment

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chambers and those outside because differences in growing conditions are often so much greater, with experiments sometimes being conducted during seasons when nothing will grow outside. Therefore, in general the absolute growth of plants in chambers is not the same as that outside. Because plants generally grow differently inside chambers, we must be concerned about whether or not the vast body of prior chamber-based research on the responses of plants to elevated CO2 is applicable to open-ﬁeld grown plants. In their comparisons, Kimball et al. (1997) found a mean increase in the growth of cotton of about 35% in FACE experiments (Table II) (Mauney et al., 1992, 1994) and of about 39% in open-top chambers (scaled to 550 μmol mol−1), which were not quite signiﬁcantly different. They also reported on another experiment with wheat in 1992–1993, when OTCs were operated simultaneously in the same ﬁeld as a FACE experiment. In this single case, both the relative response of the above-ground biomass to CO2 and the absolute growth rates were nearly identical. These comparisons suggest that the relative responses from FACE and open-top chamber experiments are very similar. On the other hand, Van Oijen et al. (1999) have recently reported that open-top chambers systematically overestimate the effects of elevated CO2 because there is a positive interaction between elevated CO2 and the warmer temperature found in ordinary open-top chambers. In an experiment with wheat, they installed coolers on half of their open-top chambers, which made the temperature inside closer to that outside, and they claim to have determined more accurate relative responses to CO2 in their cooled chambers. However, this review provides compelling evidence that, for the most part, the chamber-based experiments have produced accurate results; hence, the interactive effects between elevated CO2 and all the other variables that are different between inside and outside open-top chambers (Kimball et al., 1997) must not have dominated over the ﬁrst-order linear main effects of the CO2 itself. Tables II and III list the relative responses to elevated CO2 as reported from FACE experiments for many plant parameters and physiological processes besides biomass accumulation. In the Results and Discussion section, for parameter after parameter, we compared these FACE-determined relative responses to CO2 to those from the prior chamber-based literature. Except for the greater reduction of stomatal conductance and the greater stimulation of root growth under FACE, the bulk of the data suggest that the relative responses to elevated CO2 determined from FACEand chamber-based experiments are consistent with one another. Therefore, we have gained much conﬁdence that conclusions based on both types of data are accurate and robust. Even though we have gained more conﬁdence that the relative responses to elevated CO2 can be determined using chambers, FACE experiments are still needed, however, to determine absolute responses under open-ﬁeld conditions. It is impossible to conduct experiments under every combination of soil, climate, crop, etc.; therefore, in order to predict what will happen at regional or global scales, it is

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important that we develop crop growth models which can simulate the effects of elevated CO2 and other changing environmental variables on crop growth. In order to validate such models, data are needed from open-ﬁeld experiments, especially from FACE experiments, in order to test the CO2 aspects of the models. Data sets from the Arizona Wheat FACE experiments and from the Japanese Rice FACE experiments are currently being used by the GCTE (International Geosphere– Biosphere Programme, Global Change Terrestrial Ecosystems) Focus 3 Wheat and Rice Networks (e.g., GCTE, 1996) for such validation purposes. In their recent review of free-air CO2 enrichment in global change research, McLeod and Long (1999) have persuasively listed many other advantages of the FACE approach, and they also list several important contributions FACE experiments have made to global change research. They state that the measurements of radiation use efﬁciency by Pinter et al. (1994) provide the ﬁrst clear evidence that elevated CO2 increases the efﬁciency of the crop above an apparent ceiling for C3 photosynthesis in the current atmosphere. They note that FACE eliminates the possibility of overestimation of the CO2 effect that can happen in open-top chambers (Van Oijen et al., 1999). They also mention that FACE provides an appropriate scale for ﬁeld trials of crops such as wheat so that appropriately sized buffers can be utilized to minimize oasis effects. With roots from annual crops able to spread 1.5 m, such buffers in the FACE experiments have enabled measurements of many plant and soil processes that are unlikely to have been compromised by roots taping resources outside of the plot or by having to work with disturbed soil. The larger plot size enables production of enough plant material to support the work of several cooperating scientists who need to make complementary measurements on the same plant material. It also enables frequent sampling with time so that robust data sets can be obtained for validation of the plant growth models at all growth stages. The lack of walls with the FACE approach especially enables measurements of energy balance, canopy temperature, and evapotranspiration to be made. Last, although overall costs are high, because of the economy of scale, the FACE approach is least expensive per unit of high-CO2-grown plant material (Kimball, 1992). In conclusion, a multitude of plant response parameters to elevated CO2 have been tabulated from FACE experiments on wheat, ryegrass, rice, sorghum, clover, potato, cotton, and grape (Tables II and III). These responses varied with the functional type of the plant as well as with the levels of soil nitrogen and water supply. When possible, comparisons were made with the results from chamber-based CO2enrichment experiments. For the most part, the FACE- and chamber-based results have been consistent, which gives conﬁdence that conclusions drawn from both types of data are meaningful and accurate. However, the more realistic FACE environment and the larger plot size have enabled extensive robust multidisciplinary data sets to be obtained under conditions more representative of open ﬁelds in the future high-CO2 world.

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V. SUMMARY Over the past decade, free-air CO2-enrichment (FACE) experiments have been conducted on several agricultural crops: wheat (Triticum aestivum L.), perennial ryegrass (Lolium perenne), and rice (Oryza sativa L.) which are C3 grasses; sorghum (Sorghum bicolor (L.) M¨oench), a C4 grass; white clover (Trifolium repens), a C3 legume; potato (Solanum tuberosum L.), a C3 forb with tuber storage; and cotton (Gossypium hirsutum L.) and grape (Vitis vinifera L.) which are C3 woody perennials. Using reports from these experiments, the relative responses of these crops was tabulated with regard to photosynthesis, stomatal conductance, canopy temperature, water use, water potential, leaf area index, shoot and root biomass accumulation, agricultural yield, radiation-use efﬁciency, speciﬁc leaf area, tissue nitrogen concentration, nitrogen yield, carbohydrate concentration, phenology, soil microbiology, soil respiration, trace gas emissions, and soil carbon sequestration. Generally, the magnitude of these responses varied with the functional type of plant and with the soil nitrogen and water status. As expected, the elevated CO2 increased photosynthesis and biomass production and yield substantially in C3 species, but little in C4, and it decreased stomatal conductance and transpiration in both C3 and C4 species and greatly improved water-use efﬁciency in all the crops. Growth stimulations were as large or larger under water-stress compared to well-watered conditions. Growth stimulations of nonlegumes were reduced at low-soil nitrogen, whereas elevated CO2 strongly stimulated the growth of the clover legume both at ample and under low N conditions. Roots were generally stimulated more than shoots. Woody perennials had larger growth responses to elevated CO2, while at the same time their reductions in stomatal conductance were smaller. Tissue nitrogen concentrations went down while carbohydrate and some other carbon-based compounds went up due to elevated CO2, with leaves and foliage affected more than other organs. Phenology was accelerated slightly in most but not all species. Elevated CO2 affected some soil microbes greatly but not others; yet overall activity appears to be stimulated. Detection of statistically signiﬁcant changes in soil organic carbon in any one study was impossible, yet combining results from several sites and years, it appears that elevated CO2 did increase sequestration of soil carbon. Whenever possible, comparisons were made between the FACE results and those from prior chamber-based experiments reviewed in the literature. Over all the data and parameters considered in this review, there are only two parameters for which the FACE- and chamber-based data appear to be inconsistent. One is that elevated CO2 from FACE appears to reduce stomatal conductance about one and a half times more than observed in prior chamber experiments. Similarly, elevated CO2 appears to have stimulated root growth relatively more than shoot growth under

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FACE conditions compared to chamber conditions. Nevertheless, for the most part, the FACE- and chamber-based results have been consistent, which gives conﬁdence that conclusions drawn from both types of data are accurate. However, the more realistic FACE environment and the larger plot size have enabled more extensive robust multidisciplinary data sets to be obtained under conditions representative of open ﬁelds in the future high-CO2 world.

ACKNOWLEDGMENTS The senior author greatly appreciates the Science and Technology Agency (STA) Fellowship (ID 300005), which enabled him to work on this paper at the National Institute of Agro-Environmental Science, Tsukuba, Japan, for 3 months. We also appreciate the cooperation of Drs. Josef Noesberger and Herbert Blum, Institute of Plant Sciences, ETH, Zurich, Switzerland, who furnished preprint copies of several papers with data from the Swiss FACE Project and provided helpful comments.

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THE AGRONOMIC AND ECONOMIC POTENTIAL OF BREAK CROPS FOR LEY/ARABLE ROTATIONS IN TEMPERATE ORGANIC AGRICULTURE M. C. Robson,1 S. M. Fowler,2 N. H. Lampkin,2 C. Leifert,3 ∗ M. Leitch,2 D. Robinson,1 C. A. Watson,4 and A. M. Litterick,4, 1

Department of Plant and Soil Science Aberdeen University Aberdeen, AB24 5UA, United Kingdom 2 Welsh Institute of Rural Studies University of Wales Aberystwyth, SY23 3AL, United Kingdom 3 Tesco Centre for Organic Agriculture University of Newcastle, Newcastle upon Tyne, NE1 7RU, United Kingdom 4 Land Management Department SAC, Craibstone Estate, Bucksburn Aberdeen, AB21 9YA, United Kingdom

I. Introduction II. Crop Rotations as the Central Management Tool in Organic Farming A. Types of Organic Arable Rotations B. Soil Fertility C. Soil Physical Characteristics D. Weed Management E. Pest Management F. Disease Management G. Break Crop Functions III. Break Crops for Nutrient Management A. Beans (Vicia faba) B. Lupins (Lupinus albus) C. Soybean (Glycine max) IV. Break Crops for Improving Soil Structure A. Hemp (Cannabis sativa) B. Oilseed Rape (Brassica napus subsp. oleifera) V. Break Crops for Weed Management A. Potatoes (Solanum tuberosum) VI. Break Crops for Pest and Disease Management A. Carrot (Daucus carota) B. Swede (Brassica napus Var napobrassica) ∗ To

whom correspondence should be addressed. 369 Advances in Agronomy, Volume 77 Copyright 2002, Elsevier Science (USA). All rights reserved. 0065-2113/02 $35.00

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ROBSON et al. C. Sugar Beet (Beta vulgaris) D. Linola (Linum usitatissimum) VII. Conclusions References

Organic farming principles dictate that cereals cannot be grown continuously, and in practice they are rarely grown for more than 50% of the rotation. Choice of break crops to grow in addition to cereals and the fertility building phase are crucial to both the agronomic and economic success of the rotation on organic arable farms. There are four speciﬁc functions that a break crop may perform, namely, addition, conservation, and cycling of nutrients; pest and/or disease control; weed control and improvement in soil physical characteristics. Individual break crops may perform one or several of these functions. A good break crop must also produce satisfactory yields, be of marketable quality, and produce an economic return for the farmer. This review assesses the potential of 10 break crops (bean, lupin, soybean, hemp, oilseed rape, potato, carrot, swede, sugar beet, linola) in terms of their break function, their impact on the subsequent crop in temperate organic agricultural systems, and their economic value in UK agriculture. All species assessed had valuable break crop characteristics. Hemp, lupin, and faba bean had the greatest economic potential, but hemp and lupin currently generate poor economic returns. Linola and soybean are useful break crops, although soybean may have allelopathic effects on subsequent wheat seedlings. Swede, potato, and carrot are the most proﬁtable crops, but are less valuable in the rotation in terms of soil fertility than hemp, bean, or lupin. Sugar beet and oilseed rape are difﬁcult to grow organically and at present have limited organic markets. C 2002 Elsevier Science (USA).

I. INTRODUCTION Within the European Union (EU) the area of land that is farmed organically is growing rapidly. In the 15 EU countries in 1985, certiﬁed organic land and land in conversion accounted for just 100,000 ha, less than 0.1% of the total utilizable agricultural area (UAA). By the end of 1998, this had risen to 2.71 Mha (2.1% of the total UAA), a 27-fold increase in 13 years (Foster and Lampkin, 2000). Around 21% of this area was in arable production, 12% was in horticultural crops, and 52% was used for production of fodder crops (Foster and Lampkin, 2000; Soil Association, 2000a). Provisional estimates for the end of 2000 indicate that the growth in land area under organic management is continuing, with nearly 4 Mha in the EU and 475,000 ha in the UK (Lampkin and Measures, 2001). In the UK, around 70% of this area is rough grazing and permanent pasture, 17% is temporary ley, 10% is in arable production, and 3% is used for horticultural crops (Soil Association, 2000a).

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The widespread adoption of policies to support conversion and maintenance of organic land in Europe has undoubtedly inﬂuenced the expansion of the organically farmed area. In relation to the size of the market for organic products, however, there has been an important shift from supply-driven to demand-led markets during the past 10 years (Lampkin and Padel, 1999). This applies not only to crops for human consumption but also to oil and protein crops for animal feed. The avoidance of animal feeds with GM ingredients and the feed restrictions in the new EU Livestock regulation introduced in August 2000 (EC, 1991, 2092/91) have increased the need to produce more livestock feed on organic farms. Crop rotations are a key component of successful organic arable systems. They are the means by which soil fertility, soil organic matter, nitrogen levels, and soil structure are maintained. Rotations can be optimized to conserve and recycle nutrients and minimize pest, disease, and weed problems (Lampkin, 1990). A major component of the success of organic arable dominated or mixed farming systems may be the efﬁcacy of the break crop. This should be selected to maximize the beneﬁts to the rotation in terms of crop yield and quality, economics, and overall system performance. A break crop may perform some or all of four speciﬁc agronomic functions: nutrient addition, conservation and cycling, improvement in soil physical characteristics, pest or disease control and weed control. In addition to this, a good break crop should produce satisfactory yields, be of marketable quality, and produce an economic return for the farmer; that is, it must have a viable market. In many organic rotations, particularly in stockless systems, the break crop is a point of economic weakness and agronomic diversiﬁcation that is initially expensive and has associated risks. Any improvements in performance over the break period, in both the crop quality and the yield, and in the direct or indirect effects of the break crop, will contribute positively to the viability of organic arable rotations. The aim of this review is to assess the potential of 10 break crops in terms of their break function, their impact on the subsequent crop in temperate organic agricultural systems, and their economic value in UK agriculture.

II. CROP ROTATIONS AS THE CENTRAL MANAGEMENT TOOL IN ORGANIC FARMING Crop rotation is a system of growing different types of crops in a recurrent succession and in an advantageous sequence on the same land (Bullock, 1992). The practice of crop rotation dates back to the Han Dynasty of China over 3000 years ago (MacRae and Meheys, 1985) and the Romans recognized the beneﬁts of alternating leguminous crops with cereals more than 2000 years ago (Karlen et al., 1994). Modern crop rotation was established around 1730 in England, and became known as the Norfolk four-course rotation (Bullock, 1992; Lampkin, 1990; Wibberley, 1989). This development marked the movement toward the reduction

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or elimination of fallow periods and the inclusion of roots and clover into the rotations (Bullock, 1992; Lampkin, 1990). Economic pressures and increasing demand for certain agricultural products hailed the end of the Norfolk four-course rotation at the beginning of the 1900s. In conventional agriculture, chemical alternatives have allowed the intensiﬁcation and specialization that have taken place over the last century. Economic pressures have led to the simpliﬁcation of cropping systems and the substitution of cultural methods of weed, pest and disease control, and fertility management with synthetic chemical alternatives. The reliance on crop rotations in conventional agriculture is now much reduced and continuous monocultures and short rotations are now common in temperate parts of the developed world (Karlen et al., 1994). In organic farming systems, the use of temporally and spatially diverse crop rotations remains fundamental to the success of organic production systems; positive management of biological and ecological systems replaces inputs of synthetic pesticides and soluble NPK mineral fertilizers. While crop selection must, inevitably, be market driven to provide efﬁcient economic production, a well-balanced sequence of crops should be chosen that requires minimum external inputs, nutrients, machinery, and energy to maintain soil fertility and quality, health, and yield (Jordan and Hutcheon, 1996; Vereijken, 1995). Due to economic constraints, a farmer may be unable to maintain high plant and animal biodiversity at any one time. However, this may be compensated to a certain extent by the rotation of principal crops over a deﬁned period (Zettel, 1995). Crop rotations inﬂuence plant production in many ways. Early studies concentrated on their effects on soil fertility and survival of plant pathogens, but crop rotations also inﬂuence soil physical and chemical properties (e.g., structure, nutrient levels), soil erosion, soil microbes, and larger fauna (e.g., nematodes, insects, mites, earthworms). Increased N supply is a major beneﬁt where legumes form part of the rotation, but other factors and mechanisms responsible for increased yield due to crop rotation are not completely understood (Bullock, 1992; Karlen et al., 1994). Factors such as improved utilization of other soil nutrients, decreased weed, insect and disease pressure caused by growing a succession of crops with different root systems, changes in disease and pest resistance patterns, and the ability to compete with weed species also contribute to the rotation effect. Application of chemical fertilizers and pesticides in conventional systems does not always completely compensate for the rotation effects noted in organic and low-input systems (Bullock, 1992; Karlen and Sharpley, 1994).

A. TYPES OF ORGANIC ARABLE ROTATIONS Crop rotations always have to serve multiple objectives, which often conﬂict (Olesen, 1999). In all organic rotations, however, there will be a fertility building phase and a cash crop or income-generating phase. In terms of time, the way

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in which these are partitioned will depend on the climate, soil type, topography, available markets, and the presence/absence of livestock on the farm. Organic rotations should ensure sufﬁcient nutrients for the crops and minimize nutrient losses for both environmental and economic reasons. Depending on the type of rotation, a major proportion of the required nitrogen should be provided through leguminous crops. Organic standards also demand that farmers should aim to minimize pests, diseases, and weeds through rotations and careful choice of break crops (UKROFS, 2001). Soil structure and organic matter levels should be maintained, and a proﬁtable output of organic cash crops and/or livestock should be achieved (Soil Association, 1998, 2000a, 2000b). The principles of rotation design are described in greater detail in Table I. Traditional, mixed organic farms produce a range of cereals, fodder crops, and livestock products and operate what are known as “stocked” rotations. These constitute the majority of organic farms in Europe (Foster and Lampkin, 1999), Table I Key Principles of Crop Rotation Designa Principle Rotate deep and shallow rooting crops Alternate crops with large and small root biomass Rotate N2-ﬁxing and N-demanding crops Alternate weed-susceptible and weed-suppressing crops Grow crops with different pest and disease susceptibilities Grow catch crops, green manures, and undersow crops Alternate autumn and spring sown crops Use appropriate crops, suited to climate and soils Balance forage and cash crops Balance labor requirements and availability Good timing of tillage operations

a

Lampkin (1990).

Reasoning Improve soil structure, aeration, water-holding capacity, and drainage High biomass crops increase the organic matter remaining in the soil for soil microbial and macrofaunal populations Attempt to meet farm’s N demands from within the system Interrupt weed life cycle to reduce populations Break pest and disease life cycles, reduce host plant presence in rotation Maintain soil cover to protect for erosion and leaching Combat weeds and distribute workload

To make rotation economically as well as ecologically viable Prevent over- and understafﬁng Balance positive results of tillage such as weed control, with negative impacts such as disruption of macrofaunal activities and decrease in soil organic matter

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although there are increasing numbers of specialized organic units throughout temperate areas in Europe and North America that are operating without livestock. Such farms implement “stockless” rotations, or in some cases they are deﬁned as “semistockless” rotations if manures are imported from nearby livestock holdings. Due to different land capabilities, farmers may operate more than one type of rotation on the farm. 1. Stocked Rotation In a mixed ley/arable rotation, a short- or medium-term ley would typically be used, occupying 30–50% of the rotation (Anon., 1991; Lampkin, 1990). A grass clover ley with 30% clover could accumulate 120–180 N kg ha−1 y−1 (Kristensen et al., 1995; Scholeﬁeld and Smith, 1996). This will accumulate sufﬁcient nitrogen for the exploitative phase, while additionally providing fodder for livestock through both grazing and silage. The livestock return nutrients, importantly P and K, which may be distributed to soil around the farm and provide an income during the fertility building phase. The ley period also provides opportunities for weed control by mowing, hand weeding, or cultivations prior to sowing (Anon, 1991). Examples of typical mixed ley/arable rotations are shown in Table II. There is a high proportion of graminaceous species present in many organic ley/arable rotations, and this seems to contradict organic farming guidelines on rotation design (Table I). Crops often lack variation in rooting depths, rooting habits, and root biomass production (Litterick, 2001). Cereals in the rotation are susceptible to similar pests and diseases, and if several years of cereals are grown with only a single year break, the similar crop life cycles can increase the predominance of problem weeds, in particular graminaceous species such as blackgrass (Alopecurus myosuroides) and volunteer cereals. Most cereals are susceptible to diseases such as septoria (Septoria tritici and S. nodorum), take-all (Gaeumannomyces graminis), Table II Two Examples of Typical Ley/Arable Rotationsa Example 1

Example 2

2- to 3-year short-term ley (red clover or lucerne on calcareous soils) Wheat (or potatoes), then green manure Potatoes/roots Wheat Rye or oats (undersown)

4- to 5-year grass/clover ley Winter wheat Winter oats or barley 2-year red clover/Italian ryegrass ley Winter or spring wheat Cereal/grain legume mix (or cereal undersown with ley)

a

From Lampkin (1990).

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eyespot (Pseudocercosporella herpotrichoides), rusts (Puccinia spp.) and mildew (Erisyphe spp.), and pests such as aphids (various species). Take-all and eyespot can reduce yield by up to 30% if uncontrolled (Wibberley, 1989). Oat is an exception as it is not susceptible to take-all, and rye has fewer pest and disease problems than other cereals (Agrios, 1997; Lampkin, 1990). Although most organic crops suffer from less serious infestations of soilborne pests and diseases than conventional crops (van Bruggen, 1995), the fact remains that organic crop yields are often signiﬁcantly less than those obtained from their conventional counterparts (Lampkin and Measures, 2001; SAC, 2000). There are many reasons for such yield differences; however, the difference may be partly addressed through further diversiﬁcation of organic rotations. It may be possible to reduce dependence on cereals and increase production of novel break crops, which bring different beneﬁts and characteristics to the system, thereby improving the performance of the individual components and the system as a whole. 2. Stockless Rotation The trend toward specialization in conventional farming has meant that there are large areas of countryside in Europe, e.g., Eastern and South Eastern Denmark, Eastern Germany, and East Anglia in the UK, where livestock have not been farmed for many years (Høgh-Jensen, 1999). The cost of bringing livestock into previously stockless farms during conversion (to organic) is frequently prohibitive (Lampkin, 1990). An increasing number of European farmers are operating stockless organic rotations for this reason (David et al., 1996; Stopes et al., 1996; von Fragstein, 1996). With no livestock to generate an income, medium-term fertility-building leys become less economically viable. The fertility-building stage cannot be omitted and typically, short-term leys (1 year) are used in conjunction with green manures and catch crops. Currently, European regulations allow the fertility-building phase of a rotation, for example, 2-year red clover green manures, to qualify for set-aside payments, which considerably improves the economics of stockless rotations (Lampkin and Measures, 2001). The absence of livestock means that all nutrients must be imported from outside, ﬁxed by leguminous crops (in the case of N), or returned as crop residues. Some stockless rotations do not use imported manures. Only through creative and informed use of the different characteristics of catch crops and green manures, can stockless systems ensure their sustainability. Work to develop viable stockless rotations has been carried out in the UK by CWS Agriculture (Leake, 1999) and by ADAS (Cormack, 1999). Both found that the rotations tested performed well in the short time in which they have been established (10 and 9 years from establishment to publication of results, respectively). The crop sequence used within these rotations is shown in Table III. Crop yields were good; pest, disease, and weed problems were generally manageable; and the

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Norfolk

2-year red clover

1-year red clover cut and left as mulch (2 for conversion) Potatoes Winter wheat Spring beans Spring wheat (undersown) Barley (undersown)

Wheat Oats Beans Wheat Oats

rotations were economically viable. However, both experiments were carried out on deep, fertile clay loam soils. Further work is required to determine whether such stockless rotations would be equally viable elsewhere and over a longer time period. Research in several countries including France, Germany, Sweden, Denmark, and the UK is currently looking at the economic and agronomic potential of different stockless rotations and the implications of these for long- and short-term soil fertility, and pest, weed, and disease incidence (ADAS, 2001). Several workers are currently examining the potential for composts as a source of nutrients for stockless systems, where both on-farm waste and waste from the local community can be composted and used as additional organic fertilizers during the arable phase of the rotation (von Fragstein and Schmidt, 1999). The use of nutrient inputs other than those ﬁxed from legumes and reincorporated in crop residues are particularly important on stockless rotations on lighter soils. Some European farmers are currently operating a semistockless system in that they own no livestock, but import manures from surrounding stocked farms. Farmers tend to stack the manure according to organic regulations, prior to application, or mix it with other materials and compost it (Soil Association, 1998; Rose, pers. comm.). Manure from extensive conventional farms can be used; manure from intensive livestock units or from farms growing genetically modiﬁed crops is forbidden (EC,1991, 2092/91). The importation of up to 170 kg ha−1 N is currently allowed under European organic regulations, although the rules governing such practices are likely to become more restrictive in the future (EC, 1991, 2092/91; Brenman and Haward, pers. comm.). Farmers operating stockless rotations including one or more high value horticultural crops often need to import nutrients to meet crop requirements, particularly on light soil (Lampkin, 1990; W. Rose, pers. comm.). Two examples of semistockless rotations operating in North East Scotland are shown in Table IV. In all ley/arable rotations, there is a clear need for a break crop. In a stocked rotation, the break is needed predominantly for pest, disease, and weed management.

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Table IV Examples of Semistockless Rotations Used in North East Scotland Rotation 1

Rotation 2

2-year red clover Potatoes (+15 t ha−1 compost)a Carrots Wheat Barley

2-year red clover Potatoes (+15 t ha−1 compost) Winter wheat Carrots Barley Leeks (+5 t ha −1 compost)

a

Compost made from cattle manure, straw, and grass clippings.

In the stockless rotations, the break crop is needed to provide a pest, disease, and weed break, and for nutrient addition. Soil physical and structural problems can occur in both stocked and stockless rotations and particularly in certain soil types (Shepherd et al., 2000). Some break crops can be used to help alleviate such problems, either because of the nature of the break crop itself or as a result of cultivation methods used during production.

B. SOIL FERTILITY The aim of nutrient management in organic systems is to optimize the use of on-farm resources and minimize losses (K¨opke, 1995). Organic agriculture often has to deal with a scarcity of readily available nutrients, in contrast to agricultural systems which rely on soluble fertilizers. Maximum use should be made of crops that can contribute toward building soil fertility (Jordan and Hutcheon, 1996). The supply and management of N are more complex in organic than in conventional agriculture. The major challenge for N management in organic systems is to synchronize the availability of N mineralized from manures and crop residues with crop demand. Apart from N imported with manures, composts, and seeds or as atmospheric depositions, organic agriculture relies mainly on symbiotically ﬁxed N2. Cereals and many horticultural crops are very demanding in terms of their N requirements. It is therefore important in organic ley/arable systems to maximize the symbiotic N2 ﬁxation in legumes, maximize the cycling of N (and other nutrients) from the entire soil proﬁle, and minimize N losses through leaching, volatilization, and denitriﬁcation in organic ley/arable systems (K¨opke, 1995). Ensuring an adequate supply of P, K, and trace elements can also be difﬁcult in organic systems (particularly on stockless farms) due to the restrictions on acceptable nutrient sources in the organic standards (UKROFS, 2001). Organic materials such as FYM and some types of compost are valuable sources of P and K. Various

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naturally occurring P, K, and trace-element fertilizers including rock phosphate, basic slag, potassium sulfate, and seaweed extracts are approved for restricted use in organic agriculture; but many are costly and may have to be imported (Simpson and Stopes, 1991). Break crops can contribute to the nutrient status of the soil in three main ways. First through biological N2 ﬁxation; second, through reincorporation and recycling of nutrients and organic matter; and third by reducing nutrient losses. Each crop species has slightly different characteristics, e.g., N demanding or N2 ﬁxing, shallow or deep rooting, amount and quality of crop residue returned. The appropriate choice of crops within the rotation and their sequence are crucial if nutrient cycling within the farm system is to be optimized and losses minimized over the short and long term (Lampkin, 1990; Stockdale et al., 2001; Vereijken, 1995). 1. N Fixation The ability of legume–rhizobium symbioses to ﬁx N2 enables many organic farming systems to be self-sufﬁcient in nitrogen. The amount of N2 ﬁxed in any one year is dependent on the climate, soil type, and the crop species and variety grown (Ledgard and Steele, 1992): average values of 200 kg N ha y−1 have been recorded from grass/clover leys in temperate climates (White, 1987). Many temperate leguminous crops do not return large quantities of N to the soil, since almost all of the N ﬁxed is removed in the grain at harvest, e.g., dry beans and peas (Fisher, 1996). In several cases it has been shown that soils may be depleted in N as a result of legume production (Peoples et al., 1995). Where the inclusion of legume green manures is not possible in a rotation, legumes must be chosen which combine reasonable grain yield with relatively high residue returns. For example, when peas are harvested green (e.g., for freezing), much of the ﬁxed N is left in unharvested plant parts which can be incorporated into the soil or used for animal feed (Sprent and ’t Mannetje, 1996). The appropriate choice of leguminous crops for organic rotations will allow the farmer to maximize the amount of N ﬁxed and optimize the balance between N offtake and return in crop residues. 2. Effects on Nutrient Cycling and Soil Organic Matter An important feature of organic systems is that crop residues are returned to the soil (directly or indirectly) after harvest. Due to their long roots, certain break crops (e.g., lucerne, Medicago spp.; hemp, Cannabis sativa) can access nutrients from soil depths inaccessible to cereal roots. Therefore nutrients from deep soil layers present in the nonharvested break crop parts eventually become available in the upper soil layers following microbial decomposition (Bosca and Karus, 1998; Lampkin, 1990). Soil organic matter (SOM) is important due to its stabilizing effects on soil structure and because it acts as a reservoir for plant nutrients (Brady and Weil,

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1999; Cresser et al., 1995). Some break crops, particularly grass/clover leys and leguminous and nonleguminous green manures, can make a positive contribution to SOM levels (Karlen et al., 1994). For example, work in Illinois showed that a conventional rotation of maize, oats, and clover increased SOM more than continuous maize, regardless of fertility inputs, probably because the rotation used tillage less frequently and produced more root residues (Odell et al., 1984). Similarly, Drinkwater et al. (1998) found that SOM levels increased over 15 years in two maize/soybean organic rotations which used legumes for fertility building. They found that SOM levels decreased over the same period in a conventional maize/soybean rotation which relied on synthetic fertilisers for fertility. In the UK, Clements and Williams (1964, 1967) showed that SOM tends to accumulate under grass clover leys. The nutrients accumulated within the SOM, in particular, available forms of N, then become available to following crops when the ley is ploughed in. 3. Reduction of Nutrient Losses The choice of crops and cropping sequence inﬂuences the movement of soluble N through the soil proﬁle and ultimately into the groundwater (Karlen and Sharpley, 1994). Fertilizer spread on ploughed agricultural land causes the greatest release of nitrate into the environment in European countries (Powlson, 2000). Nitrate leaching is particularly severe where the ground is left with no crop following harvest. Losses of other nutrients including P and K through leaching and soil erosion can also be a problem where soil is left bare for long periods. Therefore the practice prescribed by organic standards of balanced rotations with judicious use of catch crops and green manures provide an opportunity to signiﬁcantly reduce losses of nutrients, in particular, nitrate (Smith et al., 1996; UKROFS, 2000). Good manure management within the crop rotation is also important if nutrient losses are to be minimized (Smith and Shepherd, 2000).

C. SOIL PHYSICAL CHARACTERISTICS Break crops and rotations in general can have a great impact on soil physical characteristics. The most signiﬁcant beneﬁt results from the incorporation of SOM (Sumner, 1982). Soil organic matter content strongly inﬂuences the soil’s structural stability: it aids soil aggregation, leading to a more stable structure with improved aeration and drainage. Microbial decomposition of SOM produces organic materials, such as polysaccharides, that cement soil aggregates, promoting their stability. Aggregates are also cemented by soluble salts, oxides, calcium carbonate, and oxyhydroxides of iron and aluminium (Marshall et al., 1996). As plant roots die, they decompose to provide energy and nutrients for the microbial population (Cresser et al., 1995). Crops with different root biomass and

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architecture will deposit organic matter in varying amounts at different depths in the soil proﬁle. This encourages microbial activity at different rooting depths and means that nutrients will be available for other plants at those depths. The greater the root biomass left in the soil, the greater the supply of substrate and nutrients (Killham, 1994). In soils such as clays with a large fraction of small pores, the addition of SOM can decrease bulk density by creating larger pores, thus assisting root penetration, water ﬂow, and gas exchange. On sandy soils, SOM addition can improve the proﬁle’s ability to retain water by creating differently sized pores and through the colloidal properties of the organic matter (Brady and Weil, 1999; Marshall et al., 1996). The cultivations required for the production of most crops reduce SOM levels (Bullock, 1992; Cox and Atkins, 1979). Given its importance, most organic farmers strive to conserve and enhance the SOM in their soils. Grass clover leys contribute signiﬁcantly to SOM levels in ley/arable rotations (Clements and Williams, 1964, 1967). Certain break crops including some grain legumes and green manures also contribute small but signiﬁcant amounts of organic matter to the soil following cropping. The quantities of organic matter returned to the soil following production of the less common organic crops such as hemp, sugar beet, and oilseed rape have not been previously documented. Some break crops have the potential to alleviate soil physical problems such as compaction and reduced aggregate stability caused by cultivations carried out in unsuitable soil conditions and/or with heavy machinery. These crops can also aid soil structure because their root architecture, biomass, and nutrient requirements are different from those of cereals. Kirkegaard et al. (1993, 1994) suggested that tap-rooted species, for example, oilseed rape, may improve subsoil porosity through “biological drilling.” Angus et al. (1991) support this idea, but Cresswell and Kirkegaard (1995) could ﬁnd no evidence for it. Materechera et al. (1992) suggested that a root’s penetration of dense soils is related to its ability to thicken, thus increasing the radial pressure on the soil. Deep-rooted species, such as lucerne (Medicago sativa), can penetrate up to 3 m over several years, leading to the development of extensive biopores. These would facilitate air and water movement into the subsoil, and as roots follow the path of weakest mechanical resistance, the pores would provide ready-made, low-resistance channels for new roots to occupy (Cresswell and Kirkegaard, 1995). Soil structural characteristics can be substantially affected by root systems (Angus et al.,1991; Kirkegaard et al., 1993, 1994). Roots exude large quantities of polysaccharides, which help to bind soil aggregates to form larger, more stable aggregates. Axial pressure exerted by roots as they grow and move through the soil compresses the area adjacent to the root channel, pressing aggregates together and increasing their stability. It is by this means that the stable crumb structures found under long-term pastures and prairies are formed (Marshall et al., 1996). Where

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the roots take up water, the soil in the vicinity dries and shrinks, compressing aggregates and increasing their stability, and the soil can become cracked in all but the sandiest of soils. These cracks, along with the root channels, create aeration and drainage pathways in the soil (Brady and Weil, 1999; Marshall et al., 1996). Not all break crops beneﬁt the soil’s physical characteristics; soil compaction and structural damage are potential problems wherever heavy machinery is used. Certain crops, in particular roots such as carrot and swede, are often harvested using heavy machinery, late in the year, under wet conditions. It follows that where the soil is less stable when wet, the damage from heavy machinery will be more serious, and soil erosion and the creation of soil pans and compacted layers below plough level are more likely (Bullock, 1992; Cox and Atkins, 1979; Marshall et al., 1996). Agricultural soils, particularly in conventional systems, are often left with no ground cover following harvest, and less stable aggregates are more prone to destruction by raindrop action and erosion. They are carried away by run-off or wind action, or remain in situ, clogging the remaining soil pores in the surface layer, a phenomenon known as capping (Marshall et al., 1996). Organic farming standards encourage the use of cover crops to minimize nutrient losses and soil erosion (EC, 1991; UKROFS, 2001). In compacted soils there is a greater risk of retarded mineralization caused by hypoxia, inhibition of soil fauna, injury to root growth and function, and accumulation of CO2, ethylene, and organic acids (Hansen, 1996; Rendig and Taylor, 1989). Where soil structural problems exist, root crops should perhaps be avoided, or at least harvested early (or perhaps by hand in more intensive systems), allowing the establishment of a green manure to minimize soil erosion, nitrate leaching, and adding SOM (Lampkin, 1990).

D. WEED MANAGEMENT Weeds are often regarded as the main problem for organic farmers (Leake, 1996; Swisher et al., 1994). Weed populations can increase during conversion to organic production (Albrecht and Sommer, 1998), although these can stabilize over time, with appropriate organic husbandry (Davies et al., 1997). Weeds can make cultivation and harvest operations difﬁcult or impossible, and they compete with the crop for resources. They may be parasitic on crop plants and can be poisonous to livestock. Weeds may also act as hosts for pests and diseases during crop growth and act as a bridge for pests and diseases to the following crop (Gwynne and Murray, 1985). However, weeds also have positive roles that are recognized in organic agricultural systems; elimination of all weeds should not, therefore, be the goal of weed control in organic systems (Lampkin, 1990). Weeds can supply ground cover to an otherwise bare soil, reducing the risk of erosion, leaching, and soil crusting. The additional roots in the soil contribute to biological activity and

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soil structure. Weed species increase the plant diversity within the cropping system and provide habitats for a wider range of insects and other invertebrates. Weeds can also act as a reservoir for beneﬁcial mycorrhizal fungi, which naturally occur on most crop species (Atkinson et al., 2001). Plant roots infected with mycorrhizal fungi have been shown to take up more soil nutrients and have greater resistance to root pathogens (AzconAguilar and Barea, 1996). Weed control strategies can be successfully targeted toward key problem species, such as couchgrass (Elymus repens) or dock (Rumex spp.), and key growth periods (Bond et al., 1998; Welsh et al., 1997) in order to minimize the impact of weeds on crop yield and quality. The main weed control strategies used in organic farming often combine cultural or husbandry techniques with direct mechanical and thermal methods (Lampkin, 1990; Stockdale et al., 2001). In conventional farming systems, total weed control is often the aim. However, in organic systems, farmers aim to maintain weeds at a manageable level using cultural means in order to ensure that direct weed control measures are effective in preventing the loss of crop quality or farm proﬁtability. Husbandry practices include adjustment of soil conditions (e.g., by irrigation), various cultivation techniques, use of stale seedbeds, diverse crop rotations, preplant mulches for high value crops, and use of cultivars particularly suited for organic production. Mechanical and thermal intervention includes ridging-up in potatoes, inter-row cultivation in root crops and cereals, postemergence harrowing to control weeds in cereal crops, and heat treatment of weeds (infra-red or direct ﬂaming) prior to crop emergence and in between rows. Weed control through crop rotation is more likely to be successful when the growth habit and characteristics of the crop contrast with those of the previous crop and the predominant problem weeds. Ideally the sequence of crops within a rotation allows for the use of a range of different cultivation methods and soil treatments. This should ensure that each weed association meets with competition, or that its life cycle is disturbed (Klingman, 1961). The design and management of an efﬁcient crop rotation for weed control are complex, because a practice that controls one weed may be ineffective or even encourage another species. Diverse crop rotations are effective in reducing weed seedbank numbers and preventing highly adapted weeds such as blackgrass, wild oats, and volunteer crops from becoming dominant (Karlen et al.,1994; Lampkin, 1990). These rotations should r alternate between autumn and spring germinating crops (and their respective weed compliments). r alternate between annual and perennial crops (e.g., cereals and leys). r alternate between closed, dense crops which shade out weeds (e.g., ﬁeld beans or rye) and open crops such as maize which encourage weeds. r provide a variety of cultivations and cutting/topping operations (in particular, the traditional cleaning crops, leys, and green manures).

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Break crops can aid weed control in one of two ways. First, a crop can function as a weed break if it requires or allows the opportunity for mechanical/thermal or hand weeding as part of the production process. Second, a crop can out-compete the weeds and reduce their numbers and seeding potential. Crops differ in their ability to compete with weeds; for example, van Heemst (1985) ranked 25 crops for their competitive ability based on the mean reduction in crop yield. Wheat was considered the most competitive, followed by peas, potatoes, and soybean; carrots and onions were the least competitive. Some of these differences may be due to allelopathic properties of the crop, i.e., a capacity to release chemical compounds either directly or indirectly through microbial decomposition of residues, which suppress weed germination and growth in their vicinity (Karlen et al., 1994). Allelopathy may have great potential in organic rotations. Liebman and Dyck (1993) stated that the inclusion of allelopathic plants in crop rotations may provide a nonherbicide method of weed control. Few studies focus on the use of allelopathy in organic rotations, but work has been done on the use of allelopathic cover crops in conventional systems, and the results are directly applicable to organic crop rotations. For example, Putnam et al. (1983) found that compared to unplanted control treatments, residues of several autumnsown cereal and grass cover crops signiﬁcantly reduced the growth and dry matter production by several weed species during the following summer. Rye, wheat, and barley had greater apparent allelopathic effects and suppressed weeds much more than oats, grain sorghum (Sorghum bicolor L. Moench), or sorghum-sudan grass [Sorghum arundinaceum (Desv.) Stapf var. sudanense (Stapf) Hitch.]. Shading and cooling of the soil may have contributed to these effects, but several other studies have shown suppressive effects that cannot be attributed to the presence of a mulch (Karlen et al., 1994).The identiﬁcation of the nature and magnitude of allelopathic effects of both traditional and novel break crops may lead to opportunities for improved weed control in organic systems. Biological control is the introduction into the environment of a pathogen or antagonist, which exerts a pressure on the target pest, disease, or weed population. Potential biological control agents exist for some weed species. However, their practical application in the ﬁeld is complicated because soil conditions are often unsuitable to support live biocontrol agents for long enough to allow them to damage the target species (Ghorbani et al., 2000; Markellou et al., 2000; Marshall et al., 1996). European organic regulations do not, at present, permit the use of biological agents to control weeds.

E. PEST MANAGEMENT Lampkin (1990) states that pests are generally not a signiﬁcant problem in organic systems, since healthy plants living in good soil with balanced nutrition

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ROBSON et al.

are better able to resist pest and disease attack. Work carried out by Bezdickek and Powers (1984) and Culliney and Pimentel (1986) supports this view. However, major pest damage is sometimes seen in organic crops, particularly in vegetables such as carrot and brassicas, which are very susceptible to damage from root ﬂies (Lampkin, 1990; McKinlay, 1990; Soil Association, 1999). Pest control strategies in organic farming systems are mainly preventive, rather than curative. The balance and management of cropped and uncropped areas, crop species and variety choice, and the temporal and spatial pattern of the crop rotations used all aim to maintain a diverse population of beneﬁcial organisms including competitors, parasites, and predators of pests. Damaging populations of pests and pathogens are less likely to establish in soils which sustain high levels of beneﬁcial organisms (Wynen and Fritz, 1987). The choice of break crops and the design of rotations can have a major impact on the incidence and severity of certain types of pest problems. Nonmobile pests which have a speciﬁc or narrow host range, such as nematodes, are particularly susceptible to crop rotation. For example, in trials in the Netherlands, continuous (conventional) cereals yielded 20% less than cereals grown in a rotation including sugar beet and bean break crops (Oostenbrink et al., 1956). The yield difference was largely because the break crops were not hosts for the plant parasitic nematodes (Pratylenchus and Tylenchorhynchus spp.) which were affecting the cereal crops. The nematodes declined during the break crop years, and the following cereal showed less pest damage. Beet cyst nematode can also reduce yields, and when an attack occurs, a rotation of up to 6 years avoiding beets and brassicas is recommended (Bray and Thompson, 1985). The potato cyst nematodes Heterodera rostochiensis and Globodera pallida, at densities of 10 cysts per 100 g of soil, can cause yield losses of 1.25 t ha−1. Rotations of 7–8 years are necessary to reduce populations, as the cysts are very persistent in soils and can survive for up to 30 years (Raman and Radcliffe, 1992). Rotations also help to some extent in the case of several insect pests such as cabbage stem weevil (Ceutorhynchus quadridens) and celery ﬂy (Euleia heraclei) which have a limited host range and live in the soil for part of their life cycle (Soil Association, 1998). Work in the United States has shown that rotations of small grains and corn (maize) with nonrelated crops are frequently beneﬁcial in controlling speciﬁc pests, such as the hessian ﬂy (Mayetiola destructor Say) and the wheat strawworm (Harmolita grandis, Riley; Leonard and Martin, 1963). Highly mobile, often nonspeciﬁc pests such as aphids are less affected, or unaffected by rotation design (Wijnands, 1999). Reactive treatments for pest outbreaks are permitted under regulations for speciﬁc situations in organic systems. For example, natural pesticides such as rotenone and quassia are permitted on a restricted basis under UKROFS regulations for intractable pest problems such as ﬂea beetle, leatherjackets, and carrot ﬂy (UKROFS

AGRONOMIC AND ECONOMICAL POTENTIAL

385

2001). Biological control agents, originally developed for use in integrated horticultural systems, are also permitted in some situations; for example, Bacillus thuringiensis may be used to control lepidopterous pests in organic horticultural ﬁeld crops. Cultural pest prevention techniques including the use of break crops within balanced rotations will, however, remain the most important means for pest control in organic systems.

F. DISEASE MANAGEMENT Levels of soilborne pathogens and root disease are generally lower in organic than in conventional systems (Lampkin, 1990; van Bruggen, 1995). Cooke (1993) has also shown that foliar and stem-base cereal disease levels were generally lower in long-term organic ﬁelds than in those which had recently undergone conversion. These reductions in disease are probably due to the regular application of organic matter and to the use of crop mixtures and long and diverse rotations (Cook and Baker, 1982; van Bruggen, 1995). Many farmers and scientists have concluded that soil management is the key to successful control of soilborne disease in organic farming systems. Several factors may contribute to the control of root disease, including increased soil microbial activity, leading to increased competition, parasitism and predation in the rhizosphere (Knudsen et al.,1995; Workneh and van Bruggen, 1994), the presence of beneﬁcial root colonizing bacteria (Keel and Defago, 1997), and the increased colonization of roots by arbuscular mycorrhizal fungi (AzconAguilar and Barea, 1996). Airborne pathogens do not generally cause serious problems in organic systems, but there are a few notable exceptions such as potato late blight (P. infestans) and powdery and downy mildews (various species) in vegetable and fruit crops. As with weed control, disease control in organic farming systems is largely based on preventive measures, and direct controls are rarely necessary (Lampkin, 1990; Stockdale et al., 2001). As with pests, the less mobile, soilborne diseases such as rhizoctonia root rot and stem canker of potatoes (Rhizoctonia solani), stalk rot of grain sorghum and maize (Fusarium moniliforme), and clubroot of brassicas (Plasmodiophora brassicae) can usually be adequately controlled through the use of balanced rotations, appropriate break crops, and good soil husbandry (Agrios, 1997). Detailed knowledge of both crops and their likely pathogens is required in order to design rotations to manage disease. The length of time for which plant pathogens can survive in the absence of a suitable host plant is important. For example, some specialized fungi, such as the wilt pathogens Fusarium oxysporum Fr. and Verticillium alboatrum, produce resting bodies such as microsclerotia or chlamydospores which can persist in the soil for long periods, typically between

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ROBSON et al.

3 and 7 years (Agrios, 1997). Rotations must allow sufﬁcient time between susceptible crops for pathogen numbers to fall below economic damage thresholds. Rotations can be adapted to allow crops to be grown in particular short periods, when lower disease pressure is likely. This “disease escape” technique is often used in intensive horticultural systems, where later sowing dates and earlier harvest can be accommodated more easily than in extensive cereal-based rotations. Not all soilborne pathogens are controlled adequately through crop rotation. It is often difﬁcult to control root-inhabiting pathogens that survive saprophytically in SOM and exist for long periods in the absence of a host plant. These pathogens include Pythium spp., some Fusarium spp., some Phytophthora spp., and Sclerotium rolfsii (Sumner, 1982). Highly mobile, airborne pathogens are generally not controlled through crop rotation. The choice of species and varieties within a rotation is, however, important. For example, the introduction of and the increase in the use of disease-resistant cereal varieties such as the wheat variety Capelle, which is not susceptible to eyespot, have reduced yield losses considerably in organic and conventional farming systems. Similarly, the use of potato varieties with some resistance to late blight (Phytophthora infestans) may make the difference between a proﬁtable crop and a ﬁnancial loss, since other effective controls are not available (Varis et al., 1996). There is increasing interest in the use of variety mixtures within individual crops as an aid to foliar disease control. Such mixtures include two or more varieties with different resistance patterns to common diseases. Variety mixtures can signiﬁcantly reduce disease levels in cereals (Wolfe, 1985). Intercropping (i.e., the management of two or more crop types in a single ﬁeld) is also used in organic agriculture to increase plant diversity within rotations and has been shown to provide several beneﬁts including reduction of foliar disease (Theunissen, 1997). Direct disease control methods are rarely used in most organic crops, although sulfur, copper, and some plant extract-based fungicides are applied regularly to control foliar disease in some high value crops such as top fruit, grapes, and vegetable transplants. Biological control of fungal diseases is permissible in organic systems, but few products are available because of difﬁculties in registration and the lack of cost-effective mass production (Fokkema, 1996). There are increasing numbers of new products available in the UK and Europe which their manufacturers claim stimulate plants’ own defenses to disease. Research is required to determine whether these are effective and appropriate for organic systems.

G. BREAK CROP FUNCTIONS Most break crops serve multiple functions when grown in organic ley/arable rotations, although the extent to which their main attributes manifest themselves depends on the year, climate, soil type, cultivar or variety, and the farming system

387

AGRONOMIC AND ECONOMICAL POTENTIAL Table V The Value of Break Crops in Organic Ley/Arable Rotations Agronomic beneﬁta

Crop

Soil structure

Soil nutrient status

Weed break

Pest/disease break

Beans Lupins Soybean Hemp Rape Potato Linola Carrot Swede Sugar beet Winter oats

+ + + ++ −/+ −/+ −/+ − − −/+ −/+

++ ++ ++ + −/+ − −/+ − − − −

−/+ ++ −/+ + −/+ ++ ++ + + −/+ −

+ + + + + + + + + + −

a b

Financial returnb + − − − − ++ − ++ ++ −

−, detrimental effect; +, small positive beneﬁt; ++, large positive beneﬁt. Compared with winter oats as standard.

in question (Altieri, 1987). The performance of a range of crops including beans, lupins, soybean, hemp, oilseed rape, potato, linola, carrot, swede, and sugar beet in ley/arable systems trials throughout the UK is being studied in a 4-year Ministry of Agriculture, Fisheries and Food (MAFF) funded project which began in 1998. In selecting a break crop, the practical and direct ﬁnancial implications must be considered along with the agronomic effects. The new crop may require specialist machinery and/or labor inputs that are not available on-farm. Marketing must be considered at an early stage. The organic market is currently very ﬂuid, therefore the market should be established before each cash crop is grown. To assess the realistic return to the farm through inclusion of the 10 crops in the rotation, net margins were prepared for each crop. As well as usual gross margin costs and any applicable subsidies, the calculation included ﬁeld operations, ground preparations, weeding, and harvesting based on contractor charges. Even using net margin ﬁgures, the direct ﬁnancial return does not provide a complete economic picture, because the agronomic effects of the crops may be reﬂected in improved yields of subsequent crops in the rotation. The price achieved for organic vegetables for human consumption in 1999 showed the effect of strong demand and high prices (Soil Association, 2000). Forage crops and oilseeds, ﬁber, and crops requiring processing were less proﬁtable and had lower net margins than winter oats. Where organic processing capacity was not present in the UK, no organic premium was assumed. As organic infrastructure

388

ROBSON et al. Table VI Agronomy of Organic Break Crops—Climate, Sowing, and Harvesting

Agronomic characteristic

Faba bean (spring)

Sugar beet

Carrot

Hemp

Climate

A cold spell is Optimal required to temperature induce ﬂowering. 16–21◦ C. Large diurnal Cool nights ﬂuctuations increase sucrose concentrations.a accelerate growth.b

Wind

Susceptible to wind Susceptible to wind damage j damage j Late March to early Late May to early Sow after last frost April. In March, Junel (16) central– use bolt-resistant northern j varieties. Europe—3rd week in Arpil to 3rd week in Maym Pelleted seed sown Using uniformly Any adjustable using a sized seeds and a seed drillm conventional spreader shoe for drill j precision drilling; also a Hestair drill with a K27 or K42 cell wheels j 25–30 mm 5–20 mmv 30–40 mmm

Sowing date

Late February to mid-Marchk

Sowing method

Conventional or combine cereal drillr

Sowing depth

100–125 mm. At least 75 mm on very heavy soilsr

Row width

45 cm if Mechanically weededr

Up to 50 cm; Narrower rows give higher yields j

Plant density/ seed rate

40–80 plants m-2 large–small seeds∗

75,000–10,000 plants ha−1j

Harvest date

Harvested green in Mid-November. early September, Harvesting to or dried in ﬁeld early December j and harvested into November.r

Ridges are 55–75 cm apart, beds are typically 1.5 m with 3 or 4 rows of carrotsl Optimum plants density depends on the market requirements; small roots at 100–200 m−2 large roots at 40–70 m−2l October until risk from frost. Carrots are often harvested before they are fully mature.b

Linola

Two geographical Suitable for growth races of hemp, in a wide range Russian and of climate.d Mediterranean. The former tolerates cold, the latter, a mild, humid climate.c

12 cm (20 cm for paper)m

Sow as soon as weather conditions permit in March. Average sowing date in UK is 3rd Apriln Combination power harrow and drill is effective on lighter land. On heavier land, a precision seed drillt 15–25 mm. Only if land is prone to rapid moisture loss should this be cautiously exceeded.t 12 cmn

60–70 kg ha-1 or 400 plants m−2n about 3.5 million plants per hectare. For paper, 45–60 kg ha−1 or 2.2–3 million plants per hectarem Late August to Late August to early Septemberc early September†

389

AGRONOMIC AND ECONOMICAL POTENTIAL

Lupin

Oilseed rape

Potato

Swede

Soybean

New autumn sown cultivars more suited to UK climate. Spring sown crops restricted to central and southern UKe

Temperate crop. Different cultivars Cool, moist climate Winter sown crops suitable for growth without too much must allow in a wide range of sunshineh adequate growth climatesg before winter f

Different cultivars suitable for growth in a wide range of climatesi

Autumn sown lupins planted early to mid-September. Spring lupins sown late March to early Aprile

Winter rape sown Earlies sown in Between early April mid-August February/March. and early Juneq (Scotland) to 1st Main crops and September (SE seed potatoes England). Spring planted in Aprilp rape sown April to early Mayo Direct drilled or By hand or Direct drilledq ploughed and mechanically with drilledo automatic planters. Increased risk of damaging chitted tubers with a machineu

Northern areas, sow in early Junei

30–40 mmq

12.5–20 mmw

150 mmx

20–30 mmq

25–38 mmi

15–20 cmy

12 cmo

70 cmx

45–50 cmz

25–100 cm Narrower rows give higher yields but management is more difﬁculti 40–60 plants m−2i

Corn drill. Scariﬁed seed can also be used to encourage germinationq

50–75 plants m−2 Optimum for winter 8–10 plants m−2x Seed rate of rape 80–100 plants m−2 For spring 185–250 kg ha−1 depending on seed rape, 110–130 ∗∗ size¶ plants m−2

15–20 plants m−2 where seeds are direct drilled and no thinning is plannedp

August–September‡

October–November. However, if planted early, can be harvested end of July p,∗ ∗ ∗

Winter rape harvested early August. Spring rape early Septemberq

August to September; however, blight may result in earlier lifting p

Bean, beet, or grain drilli

Septemberi

continues

390

ROBSON et al. Table VI—Continued

Agronomic characteristic

Faba bean (spring)

Harvest method Swathed and combined, cut using pea harvester, or harvested by handr

Sugar beet Topped and ﬂaited then roots liftedj

Carrot A vibrating undercutter to loosen the soil, then carrots picked or lifted mechanicallyl

Hemp Hemp can be topped, cut, windrowed, chopped, press baled, or ensiledm

Linola Combine†

a

Hellmers and Warrington (1982). Rubatzky and Yomaguchi (1997) and Wiseman et al. (1993). c Bosca and Karus (1998) and van der Werf et al. (1996). d Green (1992). e Wiseman et al. (1993), Milford and Shield (1997), and Shield and Scott (1996). f Weiss (1983) and Archer (1981). g Burton (1989). h Wiseman et al. (1993), and Sanders (1996). i Tanner and Hume (1978). j Bray and Thompson (1985). k Anon. (2001) and Hebblethwaite et al. (1983a). l Lampkin (1990). m Bosca and Karus (1998). n Ramans (1993a). o Pouzet (1995) and Ward et al. (1985). p Lockhart and Wiseman (1978). q Wiseman et al. (1993). b

develops, the ability to command an organic price premium will change rapidly. For example, the Company British Sugar plc (public limited company) is now offering contracts to organic growers for sugar beet production following successful crop trials in 2000. The predicted net margin of £198 ha−1 (without premium calculated in 1999) can be amended to £441 ha−1 as a result of premium prices and the use of appropriate, high yielding cultivars. The 10 crops listed have been scored in terms of their ﬁnancial returns, their value as a pest, disease, and/or weed break, and their effects on soil structure and nutrient status (Table V). The 10 break crops are discussed in the following section under the heading which relates to their most important agronomic beneﬁt for organic rotations. Due to the lack of available data relating speciﬁcally to organic systems, information has also been incorporated from work done in integrated and conventional cropping systems. For the sake of brevity, only those aspects of the agronomy relating to break crop functions are discussed in the text. Detailed agronomic requirements and information about individual crops are listed in Tables VI

391

AGRONOMIC AND ECONOMICAL POTENTIAL

Lupin Combine

§

Oilseed rape

Potato

Swathed, left to dry, then combinedq

Tops ﬂailed 2–3 weeks before potatoes undercut and lifted. The two-stage harvest allows the skins to harden before lifting.x

Swede Topped and lifted, possibly with a forage harvester p

Soybean Combines with attached ﬂating cutter barsi

r

Hebblethwaite et al. (1983a). Rubatzky and Yamaguchi (1997), Lampkin (1990), and Sanders (1998). t Turner (1993). u Lockhart and Wiseman (1978) and Blake (1990). v Rubatzky and Yamaguchi (1997). w Ward et al. (1985) and Bearman (1981). x Burton et al. (1992). y Wiseman et al. (1993) and Milford and Shield (1997). z Wiseman et al. (1993) and Michaud (1997). ∗ Anon. (2001). † Ramans (1993b). ‡ Lampkin (1990), Sheild and Scott (1996), and Shield et al. (1999). § Shield et al. (1999). ¶ Wiseman et al. (1993) and Milford and Shield (1997). ∗∗ Ogilvy (1995). ∗∗∗ Michaud (1997). s

to IX. In selecting a break crop, the farmer must optimize the agronomic beneﬁts, management implications, and ﬁnancial returns for the particular farm situation.

III. BREAK CROPS FOR NUTRIENT MANAGEMENT A. BEANS (Vicia faba) Faba beans are grown mainly as an on-farm protein supplement for livestock (Langer and Hill, 1991). However, they can also be used for human consumption, poultry feed, and silage (DeBoer, 1995; Patriquin et al., 1975; Walton, 1993). This species is a cool tolerant annual and is the major grain legume in northern Europe (Langer and Hill, 1991). It is becoming increasingly important in European organic agriculture as the demand for organically produced stockfeed develops (Lampkin,

392

ROBSON et al. Table VII Agronomy of Organic Break Crops—Fertilizer Requirements

Agronomic characteristic

Faba bean (spring)

Sugar beet

Carrot b

Fertiliser No N necessary, Low. Excessive N Low requirements—N can reduce reduces sucrose nodulation.a concentrations.b Phosphorus Noneb Low j Medium to highb Potassium Potash may be Low j Medium to highb b required

Other

FYM equivalent

Manure may help establishment and meet K requirement.b

Mg and B are likely to be deﬁcient in sandy soils and those with high pH.m None

Hemp Medium to high

Linola c

Mediumc Mediumc

Low

d

Lowd Mediumd

Magnesium may The role of trace need to be added elements in on low index hemp has never h soils been clariﬁedn

Manure additions to supply phosphate and potash may be necessary.b

Manure additions (3–15 t ha−1) may be necessary if hemp does not follow a fertility building phase.n

Best grown on a low input basis.q P requirement mean soil reserves need to be signiﬁcant.b

a

McEwen et al. (1989) and Roughley et al. (1983). Lampkin (1990). c Bosca and Karus (1998). d Roughley et al. (1983), Lampkin (1990), Ramans (1993), and Norman (1993). e Archer (1985). f Lampkin (1990) and Holmes (1980). g Lockhart and Wiseman (1978). h MAFF (1993–1994). i Tanner and Hume (1978). j Lampkin (1990) and Bray and Thompson (1985). b

1990). Winter and spring genotypes are available, with winter types being more common in Europe due to their generally superior yields and yield stability (Fox and Milford, 1996). Conventional global production is around 4 Mt, with China being the largest producer, and Germany the largest European producer (DeBoer, 1995). There are currently no ﬁgures for the land area in Europe used to produce organic beans. In the United States, 2110 ha of organic beans was produced in 2000 (Economic Research Service, USDA, 2001).

393

AGRONOMIC AND ECONOMICAL POTENTIAL

Lupin No N required

Lowe Lowe

Oilseed rape e

High

f

Potato Medium to high

Swede g

Soybean

h

Medium

None recommendedi

None recommendedi None recommendedi

Low to mediumk Medium. Very low amount removed at harvestl Sulfur and magnesiumo

Mediumg Highg

Mediumh Mediumh

Potato crops are used as an opportunity for FYM addition,b

Swedes are especially sensitive to Boron deﬁciencyp

Should follow a fertility building crop. In addition, manure will be needed to meet the high nutrient requirements.b

An opportunity for FYM addition also beneﬁts from soil improvements. In the UK different manures composts and seaweed extracts are used. Most used rates 6–25 t ha−1, and up to 75 t ha−1.r

15–25 t ha−1 if crop is not following a fertility building crops

k

Lampkin (1990), Holmes (1980), and Perkin (1981). Holmes (1980) and Orson (1995). m Bray and Thompson (1985). n Bosca and Karus (1998). o Orson (1985). p Archer (1985) and Rhodes (1972). q Turner (1993). r Lampkin (1990), Lockhart and Wiseman (1978), and Ginger (1987). s Lampkin (1990) and Simpson and Stopes (1991). l

Faba beans are relatively tolerant of low soil N status, and if no inorganic N is available, they rely on N-ﬁxing nodule-forming bacteria of the species Rhizobium leguminosarum to provide N for their growth and development (Roughley et al., 1983). Rhizobia associated with faba beans ﬁx more N in 2 months than those on any other crop provided that the temperature is below 15◦ C (DeBoer, 1995). Fixation rates are about 170 to > 330 kg ha−1 N under careful management (DeBoer, 1995; Dyke and Prew, 1983; Mytton, 1997; Patriquin et al., 1975). Rhizobia will ﬁx about 87% of the plants’ N requirements in unfertilized soils, or about 42% in

394

ROBSON et al. Table VIII Agronomy of Organic Break Crops—Soil Requirements

Agronomic characteristic Soil pH, optimum

Faba bean (spring) 6.5–7.0a

Soil type

Wide range of suitable soils. Drought prone and water-logged soils unsuitablek

Soil temperature

Germinate and grow well in cool soilu 251–300 mm during growing seasonx

Water requirements

Sugar beet

Carrot

Hemp

Linola

6.5–7.0b

5.5–7.0c

>5.0d

6.0–7.0e

Lighter, wellDeep, friable, fertile, Rich loam with high structured soils well–drained levels of organic l are optimal sandy loams or matter; well Uncompacted root peats. structuredo zone, freely Uncompacted root drainingm zone and stone freen

Above 5–8◦ Cv 250–330 mm during growing seasonm

Prepare, if possible, in autumn. On heavier soils, a coarsely broken and level furrow slice is preferable to allow for frost action. On lighter soil, a furrow pressm Loose, medium, Level, surface with coarse with no some coarse obstructions in top aggregates to ∗∗∗ 90 cm prevent cappingm

450–600 mm during the growing seasonc

Land preparation Loosen compacted soil prior to planting, avoid surface compactionk

Carrots can be planted on raised beds or ridges†

Seedbed

Fine, ﬁrm and level∗ ∗

a

At least 670 mm per annum, with at least 250–300 in vegetative periody Ploughed in autumn to 20–23 cm, then harrow and roll in springo

Droght-resistant cropz

Firm and moisto

Fine, even, ﬁrm, and moistz

Anon. (2001) and Lockhart and Wiseman (1978). Bay and Thompson (1985). c Rubatzky and Yamaguchi (1997). d Bosca and Karus (1998). e Lockhart and Wiseman (1978) and Milford and Shield (1997). f Wiseman et al. (1993), and Milford and Shield (1997). g Pouzet (1995), Weiss (1983), and Ward et al. (1985). h Burton (1989). i Lockhart and Wiseman (1978) and Anon. (1998). j Tanner and Hume (1978). k Hebblethwaite et al. (1983). l Bray and Thompson (1985) and Allison et al. (1996). m Bray and Thompson (1985). n Rubatzky and Yamaguchi (1997) and Wiseman et al. (1993). o Bosca and karus (1998). p Ramans (1993a) q Lockhart and Wiseman (1978), Wiseman et al. (1993), and Shield (1999). b

Wide range of soils, not very lightp

Compacted land loosened. Heavy land cultivated in autumn to allow maximum weathering. Light land ploughed and pressed in spring‡

395

AGRONOMIC AND ECONOMICAL POTENTIAL

Lupin <7.0. Inoculation is necessary above pH 6.5 f Deep well-drained loams. Can be grown on shallow, sandy soils. Poor germination in crusted soilsq

Oilseed rape

Potato

Swede

>5.0h

>6.0i

6.0–6.8 j

Well drainedr

Loose, friable well-drained, and aerateds

Well drainedt

Well drained. Production is least stable in sandy soil j

Above 5◦ Cw

Compacted land broken up to allow free drainage†

Firm, with a good structurel

r

700 mm for maximum seed development, 450–500 mm in growing seasonw Compacted land broken up

Fine, ﬁrm, well structured, and moist

10◦ C minimum j 450 mm through the growing seasonh

200–300 mm through growing season∗

Deep cultivated to 30 cm. Raised beds allows for easier management. Beds formed to create a stale seedbed and, if necessary, destoned. Beds remade just prior to planting¶

In very wet areas swedes may be grown in ridges. Planting in beds makes for easier management∗ ∗

After ploughing, minimum tillage possible j

Deep and ﬁne, except on light soils

Fine, ﬁrm, and moistu

Quite ﬁne and ﬁrm j

Wiseman et al. (1993), and Pouzet (1995). Burton (1989) and Harris (1992). t Sanders (1996), Anon. (1998), and Michaud (1997). u Lockhart and Wiseman (1978). v Ramans (1993a) and Turner (1993). w Weiss (1983). x Anon. (2001). y Bosca and Karus (1998) and van der Werf et al. (1996). ∗ Sanders (1996). † Wiseman et al. (1993) and Sanders (1998). ‡ Raman (1993) and Turner (1993). § Ward et al. (1985). ¶ Lockhart and Wiseman (1978) and Whitney and McRae (1992). ∗∗ Lockhart and Wiseman (1978). ∗∗∗ Anon. (2001) and Hebblethwaite et al. (1983). 1 Wiseman et al. (1993). 2 Wisemand et al. (1993), Pouzet (1995), Weiss (1983), Ward et al. (1985). s

Soybean

5.5–8.0g

396

ROBSON et al. Table IX Agronomy of Organic Break Crops—Pests, Diseases, and Weeds

Agronomic characteristic

Faba bean (spring)

Sugar beet

Carrot

Hemp

Linola

Pests and disease Chocolate spot problems and greater on acid soils.a Long their control rotations recommended for the general control of many bean pests and diseases.b

Long rotations for Plant end of control of beet May-mid-June cyst and other to avoid the ﬁrst nematodes and hatch of carrot wireworms. root ﬂy. Careful Repeated harvesting cultivation for slug reduces rots in control. Removal storec of debris and host plant weeds for aphid and downy mildew controlk

Hemp is relatively pest and disease free. It is intolerant of many biocides.d

Linola is more prone to disease problems, such as Altenaria,e in wet climates

Weed problems and their control

Sugar beet is a poor Carrots are a poor competitor with competitor with weeds. Crops need weeds. Crops to be kept weed need to be kept free by mechanical weed free by means.k mechanical means.k

Hemp is a natural suppressor of weeds.l

Linola is a poor competitor with weedsm

Beans are a poor competitor with weeds. Crops need to be kept weed free by mechanical means.k

a

Hebblethwaite et al. (1983). Anon. (2001). c Rubatzky and Yamaguchi (1997). d Bosca and karus (1998). e Ramans (1993b). f Carter and Faut (1984). g Weiss (1983), Ward et al. (1985), NIAB (1993), and Ekbom (1995). b

fertilized soils (Richards and Soper, 1979). Inoculant containing the bacterium can be added to the seed or the soil, although many soils naturally contain sufﬁcient numbers of the required bacteria. For example, soils at Rothamsted had populations of 106 g−1 irrespective of inoculant addition or previous bean crop (Roughley et al., 1983). Nitrogen ﬁxation by grain legumes can contribute to the overall N balance of the organic rotation in stocked and stockless systems (Cormack, 1996; Prew and Dyke, 1979; Rochester et al., 2000). On stockless farms, where the grain is sold off the farm, a signiﬁcant proportion of the available N is removed from the system. Beans can leave a net negative N balance (e.g., Jensen, 1989, 1995; Patriquin, 1986), but other studies have reported a N residue of between 45 and 100 kg ha−1

AGRONOMIC AND ECONOMICAL POTENTIAL

Lupin Susceptible to damage from birds and rabbits, also pea and bean weevil. Netting and ﬂeecing are options for control.f

Oilseed rape

Potato

Swede

397

Soybean

Clean seed and long Main problem for potato Clubroot and mildew Many of the diseases rotations to control is blight. Use high are the primary can be controlled Sclerotinia and quality seed. Destroy disease. Good by using clean Phoma. Soil pH ground keepers, drainage, removal of seed, long over 6.5 reduces infected debris, and infected debris, and rotations, and clubroot. Stem ﬂea potato dumps to limit long rotations can where possible, beetle and pollen the spread of disease. be effective. resistant varieties. beetles are serious Potatoes host wide Maintenance of pH There are few pests. Pyrethroid range of other above 6.5 can also pests of soybean sprays are the only diseases and pests help avoid clubroot. that are of alternative to such as aphids, slugs, Flea beetles and root economic chemical control.g and potato cyst ﬂy controlled by importance with nematode. Removal ﬂeecing. Delayed the exception of of host plants, clean planting avoids the nematode pests j ﬁelds, and long ﬁrst generations of rotations used for root ﬂy i control.h Swedes are a poor competitor with weeds. Crops need to be kept weed free by mechanical meansk

h

Raman and Radcliffe (1992). Lockhart and Wiseman (1978), Sanders (1996), and Rose (1998). j Tanner and Hume (1978). k Lampkin (1990). l Bosca and Karus (1998). m Turner (1993). i

(Stopes et al., 1996). For example, Prew and Dyke (1979) found that bean residues could contribute 45–50 kg ha−1 of N to the soil in one season. Sprent and ’t Mannetje (1996) found that soils following bean crops at 35 sites in a European study had positive N balances after seed removal. N mineralized from faba bean residues, as with all crop residues, is susceptible to leaching during the winter, depending on the C : N ratio of the residue and whether aboveground debris was incorporated into the soil with the root material (Justus and K¨opke, 1995; Mitchell and Webb, 1996). There has been substantial documentation of the positive effect of bean crops on a subsequent cereal. Prew and Dyke (1979) found that wheat and barley crops needed 45–50 kg ha−1of additional N to achieve the same yield after an oat break crop than after a bean break crop. This translated to cereal yield advantages of

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1.1 t ha−1when no fertilizer was applied to the wheat or barley, and 0.6, 0.3, and 0.3 t ha−1 when 50, 100, and 150 kg ha−1 fertilizer N were added, respectively. The Rothamsted classical experiments at Broadbalk and Hoos Barley conducted between 1969 and 1978 illustrated further the positive impact of beans on the subsequent cereal. Without N fertilizers, wheat yielded 47% more grain after beans than after wheat, and barley yielded 52% more after beans than after barley (Rothamsted Experimental Station, 1970). These results may not be caused entirely by N2 ﬁxation, because beans also provide an efﬁcient break from take-all (Prew and Dyke, 1979). The cause of the yield increases on the subsequent cereal may therefore be multiple. This is often referred to as the “rotation effect” (McEwen et al., 1989). Beans ﬁt well into a cereal rotation as they can be drilled, harvested, dried, and stored with only minor modiﬁcations to existing equipment (Dyke and Prew, 1983), an important factor in minimizing ﬁxed costs. The faba bean’s capacity to grow in N-poor soils and produce high yields with only an annual application of FYM increases the attractiveness of faba bean as a break crop in organic agriculture. A problem with the crop is the wide variation in yields that can be obtained from season to season: average yields of organic crops in Europe are around 3.5 t ha−1 (Lampkin and Measures, 2001), but yields of 50 to 200% of that ﬁgure are not uncommon in conventional farming systems (Halder and Taylor, pers. comm). On heavier soils, there may be difﬁculty in planting the seed deep enough, as the seeds need a covering of at least 7–10 cm (Dyke and Prew, 1983; Wilson, 1997). The seeds can be broadcast and ploughed in to achieve sufﬁcient sowing depth (Wilson, 1997). If the beans are to be harvested dry, this may cause a conﬂict of timing between harvesting and sowing a winter cereal (Table VI). In the UK, faba beans can be harvested green in early September, but can also be left on the ﬁeld until early November. In the latter case, a winter cereal would not be a suitable following crop. In Canada and the United States, faba beans are harvested between late August and early October (Aldhouse and Patriquin, 1985; DeBoer, 1995). Harvesting the crop green has the possible advantage that it removes less N from the soil (Sprent and ’t Mannetje, 1996). Organic faba beans have the highest net margin of the legumes assessed in this review (Table X). The net margin for faba beans (£747 ha−1) is higher than that for organic winter oats (£621 ha−1) (Lampkin and Measures, 2001). Partly due to difﬁculties in sourcing GM-free soya beans, there is a good market for beans for organic livestock feeds in the UK. In-conversion beans can also attract a considerable price premium (Lampkin and Measures, 2001).

B. LUPINS (Lupinus albus) Several species of lupins are grown in temperate agriculture. White lupins (Lupinus albus) are the most common and provide the greatest beneﬁts

Table X Summary of Economic Evaluation (£ha−1 Unless Otherwise Indicated) of Break Crops in Organic Agriculturea

Crop Carrot Carrot (low mechanisation) Swedes Potatoes Potatoes (low mechanisation) Faba bean Winter oats Sugar beetb Linola Lupin Oilseed rape Hemp Soybean a b

Yield (t ha−1)

Price (£t−1)

38.3 33.8

250 300

28.0 28.0 28.0 3.5 4.3 44.0 1.4 2.4 2.4 5.5 2.3

Output

Variable inputs

Gross margin

Contractor cost

Casual labor

Net margin

0 0

9,563 10,238

805 2,858

8,758 7,379

1,947 1,403

460 1,932

6,351 4,045

225 250 300

0 0 0

6,335 7,035 8,470

268 1,803 3,151

6,068 5,233 5,319

1,668 1,858 1,593

1,081 253 851

3,319 3,122 2,875

200 180 45 250 175 195 60 250

340 236 0 456 340 254 500 254

1,043 1,110 1,980 806 756 717 832 824

149 125 182 115 90 79 225 195

1,043 985 1,799 691 666 638 607 629

295 353 1,128 317 308 353 356 390

0 12 230 0 0 0 0 23

748 621 441 374 358 285 250 216

Fowler and Lampkin (1999, unpublished). Amended 2001 (Lampkin and Measures, 2001).

Subsidies

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(Gladstones, 1998). Lupins have the highest protein content of any grain legume (35–40%) and as such provide a valuable food source for humans and animals (Haq, 1993; Milford and Shield, 1997). There has been a recent resurgence of interest in the potential of lupins as a human food and for soil improvement in marginal tropical and subtropical areas of Brazil, Peru, and the Mediterranean (Haq, 1993). Lupin ﬂour has been used in bread, biscuits, cakes, pasta, baby foods, powdered milk, snack foods, and fast foods. The inclusion of white lupins in rotation with cereal crops is an established feature of conventional and sustainable agricultural systems in several temperate areas of the world including southern Australia (Hamblin et al., 1993) and Europe (Sprent and ’t Mannetje, 1996). Until recently, they were rarely grown in temperate regions, because the available varieties had an indeterminate growth habit and were unsuitable for cultivation. In rotations, they are grown as either a grain crop or a green manure, although no ﬁgures are available on the UK, European, or world acreage of organic lupins. Here, their potential as an organic grain crop is examined. New, ﬂorally determinate genotypes of lupins have recently made their arable cultivation in the UK a realistic proposition (Shield and Scott, 1996). These are autumn sown and have a greater and more reliable yield potential than spring sown genotypes (typically 3–4.5 t grain ha−1). The crops are consistently ready for harvest from late August to early September in the south of the UK (Shield and Scott, 1996). This early harvest allows a subsequent autumn sown crop to be grown, unlike the later harvested spring lupins, preventing an over-winter fallow between crops. Lupins are combinable, therefore no specialized equipment is required for their production. Once dried, the thick seed coat makes lupins relatively resistant to losses caused by storage pests and diseases (Haq, 1993). When well nodulated, the rhizobia associated with lupin (Rhizobium lupini) can ﬁx between 150 and 350 kg N ha−1 y−1, up to 79% of the crop’s N requirement (Hardy, 1982; Milford and Shield, 1997; Smith et al., 1992). Hamblin et al. (1993) reported that more N is ﬁxed by a lupin crop than is removed in the harvested grain, giving a positive N balance. Where soil pH is below 6 or where lupins have not been grown before, inoculation of the soil or seed may be necessary with the speciﬁc bacterium. The amount of residual N remaining following harvest depends on the lupin species and genotype, but it increases with earlier planting and higher sowing rates and varies between 7 and 113 kg N ha−1(Hamblin et al., 1993). Lupins also possess specialized acid-secreting cluster roots which make them particularly efﬁcient at obtaining phosphorus from the soil (Milford and Shield, 1997; Neumann et al., 2000). The beneﬁt of lupin in conventional rotations has been demonstrated in Australia and South Africa where it is grown as a grain break crop, in the southeastern United States, where it is grown in rotation with maize, and in northern Europe, where it is grown as a green manure and grain legume (Hamblin et al., 1993; Haq, 1993; Reeves 1984). In Australia, Reeves (1984) found that cereal grain yields increased

AGRONOMIC AND ECONOMICAL POTENTIAL

401

by 30–100% after lupins in a rotation, whereas Hamblin et al. (1993) found that lupins in the rotation consistently increased wheat yields by around 45%. In the UK, wheat yielded 3.1–6.7 t ha−1 after lupins (unfertilized) compared to a mean of 2.0 t ha−1 after wheat (McEwen et al., 1989). Lupins have a relatively high P and K demand, and this could be a limiting factor in some soils under organic management, although yields of 2.9–4.8 t ha−1 have been recorded in organic systems (Milford and Shied, 1997). Conventional producers apply high doses of phosphates, 300–500 kg ha−1, and up to 120 kg ha−1 of potash prior to lupin production (Haq, 1993). Organic growers would have to pay careful attention to P and K budgets on the farm in order to ensure that their availability was sufﬁcient to allow economic lupin yields, since applications of P and K fertilizers are restricted under organic regulations (e.g., EC, 1991, Regulation 2092/91). The gross margin for organic lupins is low compared to that of faba bean, despite its great potential for use as both a human and animal food (Table X). The demand for UK organic animal feed is currently met by on-farm feed production and by imports of grains such as soybean, mainly from outside the European Union. However, the UK and European organic livestock industry is in a period of rapid expansion, which is not being matched by increased production of organic animal feeds (Soil Association, 2000a). There is, therefore, a strong, developing market for high quality animal feeds, and some of this demand could be met by grain legumes such as lupin. Lupin seed contains 35–40% protein and 11–13% oil, making it a good replacement for soybean meal in the diets of most farm animals (Milford and Shield, 1997; Rossetto, 1990; Shield et al., 1999). Lupin seed is already used in the production of high quality livestock rations and in pet food biscuits (Haq, 1993). Africa, Europe, and the United States all currently import lupins from Australia (Haq, 1993). If the potential of lupins was more widely recognized by farmers and feed manufacturers, the crop could provide a ﬁnancial opportunity for UK and European farmers. With a 50% price premium, the net margin for lupins is £358 ha−1 (Table X), 57% of the net margin anticipated from winter oats.

C. SOYBEAN (Glycine max) Soybean is the most important oil and protein crop in the world (USB, 2000). Soybean oil is used for cooking, to make margarine, and for a range of industrial products including paints, linoleum, inks, and soap. Once oil has been extracted from the seed, the residual protein cake is used to manufacture foods for animals and humans. This crop provides 52% of the world’s conventional oilseeds (155.1 Mt in 1999) and is also the fourth biggest grain crop produced (6% of global production: USB, 2000). The United States is the leading soybean producer at 46%, with

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around 1.1% being produced collectively in the E-15 countries, of which France and Germany are the most important (USB, 2000). Soybeans are a subtropical crop, but the breeding of varieties tolerant of conditions in northern latitudes now allows their cultivation as far north as Canada and Sweden. Figures are not currently available on the quantities of organic soybeans produced in Europe and the United States, although it is known that soybean was grown on 37,338 acres of organic land in the United States in 1997 (USB, 2000). The crop is increasingly being grown in several European countries including France and Germany (Fowler and Lampkin, 1999). When effectively nodulated, soybeans can derive up to 40% of their N from ﬁxation (Tanner and Hume, 1978; Whigham, 2000). Soybeans are sensitive to soil N status and on heavily fertilized soil may derive only up to 25% of their N through ﬁxation, between 60 and 95 kg N ha−1 y−1 (Burns and Hardy, 1975; Criswell et al., 1976). Bradyrhizobium japonicum is the bacterium which infects soybean, and inoculation of seed is recommended if soybean has not been grown within the previous 5 years (Whigham, 2000). Soybean crops grown for seed leave between 30 and 60 kg N ha−1as residue in the soil (Mays et al., 1998; Tanner and Hume, 1978) and as such are a valuable component of organic rotations. Soybean can also be used in sustainable systems as a green manure. Sumner (1982) found that the highest vegetable yields were obtained when soybean was used as a green manure every 4 years, with a winter cover crop, in comparison with other green manures and cover crops in a rotational experiment in Connecticut. Reports on yield of subsequent crops are mixed. Doll and Link (1957) found increased yields of cereals following soybean. Bundy et al. (1993) and Ding and Hume (1996) also found positive effects on following crops. Crookston (1994) found no effect on yield, and Sarbol and Anderson (1992) and Mays et al. (1998) found yield depressions in subsequent wheat crops, probably due to allelopathic effects of the soybean residues. Soybeans can be sown with a bean, beet, or grain drill, as long as it provides good depth control to within 1 cm (Tanner and Hume, 1978). The crop is combinable and therefore is mechanically compatible with cereals. However, the capacity of the soybean crop to depress the yields of subsequent crops of wheat is a major disincentive to place them in a wheat rotation as a break crop (Mays et al., 1998). There is already a substantial worldwide market for soybean and its products. In 1999, conventional soymeal constituted 64% of the world protein meal consumption, and soy oil provides 28% of the global consumption of marine and vegetable oil (Chomchalow et al., 1993; USB, 2000). Soybean meal is becoming increasingly important in the production of high protein foods and drinks for human consumption (Hymnowitz, 1993). The UK imported 1.8 Mt of conventional soybean seed and meal for the production of livestock feed alone in 1998 (Shield et al., 1999).

AGRONOMIC AND ECONOMICAL POTENTIAL

403

There is consumer concern about the increasing proportion of soybean meal imported from the United States that is genetically modiﬁed (Brenman, pers. comm.). Approximately 50% of the 1999 U.S. soybean harvest was genetically modiﬁed (Food and Drink Federation, 2000). Many European consumers in particular do not feel that the safety of genetically modiﬁed crops has been adequately tested and they therefore prefer to avoid them (Greenpeace and Soil Association, 1999). Organic agriculture prohibits the use of GM feed or seeds (EC, 1991, 2092/91), so there is an expansion in the market for organic soybean products. UK production is competing with imports from continental Europe; the net margin for a typical organic soybean crop produced in the UK is £216 ha−1 (Table X), which is considerably lower than that for winter oats. Agronomic reasons must therefore compensate for lower returns if the crop is to be grown.

IV. BREAK CROPS FOR IMPROVING SOIL STRUCTURE A. HEMP (Cannabis sativa) Hemp is one of the oldest and most versatile crops. Human uses of hemp go back at least 6000 years during which time is has been used for manufacturing ﬁber, paper, rope, and oil (Bosca and Karus, 1998; Van Der Werf et al., 1996). Competition from synthetic materials and natural ﬁbers as well as a decline in the industries that used hemp, such as shipping, led to a decline in the demand for hemp products. In addition, drug prevention acts led to international bans and restrictions on hemp production from the 1930s onward in the United States. Many countries followed suit with the exception of central and eastern Europe. Low tetrahydrocannabinol (THC) varieties of hemp were permitted in some countries under strict guidelines, but many of the restrictions were not modiﬁed or removed until the mid-1990s. Consequently, the production and the research interest in hemp waned and did not revive until the mid-to late 1990s. Since then, there has been a rapid increase in hemp production and interest in optimizing production systems. Trials of hemp varieties and agronomic methods are now running in several European countries including Germany, UK, and Denmark (R. Taylor, pers. comm; Hemp Industries Association, 2001). In Europe in 1997, the total land area used for hemp production was 22,000 ha (Bosca and Karus, 1998), but there are currently no ﬁgures for the land area in Europe used to produce organic hemp. In the United States, 3660 ha of organic hemp was produced in 2000 (Economic Research Service, USDA, 2001). UK studies found hemp to be a valuable break crop, providing a disease break for cereal crops and improving soil structure (Low, 1995). Under ideal conditions,

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hemp provides complete ground cover by about the third week after seedling emergence, thus protecting the soil surface from capping and erosion (Bosca and Karus, 1998; Section II.E). Adequate nutrients and water must be available if the rapid early growth is to be sustained, and the soil must be relatively well structured and uncompacted to allow optimal yield. Hemp has a high leaf turnover rate and is prone to considerable density-induced mortality (Van Der Werf et al., 1996). The leaf litter beneath the very dense canopy acts like a mulch, reducing water loss from the soil by minimizing air turbulence and evaporation (Marshall et al., 1996). Mulches also have a positive effect on soil structure through protection from wind and rain. Retting (controlled decomposition prior to processing) of hemp stalks in the ﬁeld provides large inputs of organic matter for the soil at the end of the season. In addition to this, the stubble and other debris on the soil provide a protective cover against structural damage from rain over winter (Bosca and Karus, 1998; Scheifele, 1994). Hemp has exceptionally long roots which can penetrate to a depth of 2–3 m under good soil conditions. It can therefore extract residual nutrients from deep in the soil (Bosca and Karus, 1998; Low, 1995; Scheifele, 1994). This can increase the nutrient use efﬁciency of the cropping system and can also help soil structural development by extracting water from deep in the soil proﬁle, creating cracks and the formation of soil peds. Hemp improves the yields of subsequent crops including cereals (Anon., 1996). Yield increases of 10–20% in winter wheat crops have been reported when grown after hemp, primarily due to improved soil structure and weed suppression (Bosca and Karus, 1998). Due to the very rapid early growth and biomass accumulation, strong weed suppression is almost guaranteed, and no weed control measures are required in the organic crop (Bosca and Karus, 1998). The high degree of weed suppression beneﬁts both the hemp and the succeeding crop. For example, the weed seed bank was reduced following a hemp crop, and consequently weed competition was reduced in the subsequent crop (Bosca and Karus, 1998). Hemp is a self-tolerant plant, which is susceptible to few serious pests and diseases (Bosca and Karus, 1998; Partland et al., 2000; van Der Werf et al., 1996). The lack of insect predation may be due to the lack of close wild relatives of hemp, which would have the same pests (Bosca and Karus, 1998). There is also anecdotal evidence that even the low concentrations of THC found in hemp has an insect-deterring effect (Bosca and Karus, 1998). Gutberlet and Karus (1995) found that pests simply prefer other crops to hemp. This minimal susceptibility to pests and diseases could promote the use of hemp as a pest and disease break in cereal rotations, perhaps providing the necessary break for take-all. There is also evidence that hemp can restrain some types of nematodes, which could be important in the cultivation of potatoes (Bosca and Karus, 1998) and cereals. The company Hempﬂax (The Netherlands) reports that the soil in hemp ﬁelds dries faster and warms up more rapidly earlier in the year than soils supporting other

AGRONOMIC AND ECONOMICAL POTENTIAL

405

crops (Bosca and Karus, 1998). This may be an advantage for the early sowing of the next crop. However, in a particularly dry season, the subsequent crop may become water stressed causing a depression in yield. For similar examples in other crops, see Hamblin et al. (1993) for lupins, and Kirkegaard et al. (1994) for oilseed rape. Hemp has a high nutrient requirement, and prior to harvest, the hemp extracts more nutrients per hectare than grain crops, removing 2 to 3 times as much N, 3 to 6 times as much P, and 10 to 20 times as much K (Anon., 1999). These requirements are due to fast biomass production. However, up to 70% of the nutrients taken up are returned to the soil through fallen leaves during the season, mechanically stripped leaves and ﬂowers at harvest, and the retting process (Anon., 1999; Bosca and Karus, 1998). It is recommended in an organic rotation that hemp is grown after a grain legume or clover to ensure adequate nutrition (Bosca and Karus, 1998). In an organic arable rotation, a cereal would usually occupy this optimum position in the rotation. However, on a nutrient-rich soil, or with adequate nutrient addition from manures, the hemp crop could be grown as a break between cereal crops (Tables II–V). Since hemp is sown in late April to early May, it can easily be used as an intermediate crop in a rotation. Harvest dates for hemp depend on the variety and climate. Between 50 and 55 degrees north latitude, hemp is harvested between the end of August and the ﬁrst 10 days in September (Bosca and Karus, 1998). Hemp can be sown with a cereal drill, so no specialized machinery is needed. For harvesting, the hemp crop can be mowed or chopped. Chopping makes for simpler management in the ﬁeld and enables existing harvesting machinery to turn and press the chopped hemp plants, ready for transport to a processing plant. The Kemper cutter (John Deere International) is suitable for chopping hemp, and a range of new machinery suitable for hemp production and harvest is becoming available throughout Europe and North America (Hemp Industries Association, 2001; Hemptech, 2001). There is a growing market for both conventional and organic hemp and their many products. High quality ﬁbers are being used for clothing, and both are currently produced in Germany at prices competitive to those of cotton. Fibers are also being used for thermal insulation, although they cannot, at present, compete economically with synthetic ﬁbers (Bosca and Karus, 1998). Hemp is also used in the production of speciality pulp and paper, which could be an important use in the long term (Schlegelmilch, 1995). Hemp ﬁbers are increasingly taking the place of wood ﬁbers in the manufacture of press-molded interior panels in the automotive industry, due to their low weight and high tensile strength (Bosca and Karus, 1998). There is a market in the selling of hemp hurds (the nonﬁber portion of the stalk) which are produced at every stage of mechanical processing. These have various uses, for example, animal bedding especially for horses. There are additional

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markets for hemp seed and oil, which can be used to produce body-care products such as soaps, detergents, oils, dyes, and as a source for the essential nutrient gamma-linolenic acid. There is also a market for hemp seeds as a high quality nutritious food (Bosca and Karus, 1998). The area of organic hemp grown in the UK doubled in 2000. A license is required in order to plant the crop which must be grown under contract. Crop quality in the UK is currently unsatisfactory for the rapidly growing organic clothing market, therefore no organic premium was assumed in this review. Organic hemp has one of the lowest net margins of any of the crops assessed in this review (Table X). Only EU subsidies make organic hemp cultivation a proﬁtable venture under current economic conditions in Europe. The point where ﬁber costs and market prices meet dictates the subsidy level, so that the costs of producing hemp are just covered and no more. However, cost reductions in hemp processing techniques can be expected in the near future due to higher crop yields from new varieties combined with lower harvesting costs due to new machinery (Bosca and Karus, 1998). This, along with the fact that hemp contributes positively to soil structural development and suffers from few pest, disease, and weed problems, means that is has signiﬁcant potential for use within temperate organic rotations.

B. OILSEED RAPE (Brassica napus SUBSP. oleifera) Oilseed rape has been grown since the 16th century in Europe, but it is only since the 1960s that it has become a major world crop (Kimber and McGregor, 1995). In global terms, conventionally grown oilseed rape is the second most important vegetable oilseed after soybean, accounting for 14% of total oilseed production at 40 Mt in 2000 (Weiss, 2000). Fifteen megatons of rapeseed oil was produced in 2000, and this constituted 15% of the global consumption of vegetable and marine oils (USB, 2000). According to Scarisbrick and Ferguson (1995), brassica crops will play an increasing role in supplying the world’s need for human and animal foodstuffs and industrial oils. At present, very little oilseed rape is produced organically in any temperate region. The beneﬁts of planting oilseed rape before a cereal in a rotation are widely reported (e.g., Angus et al., 1991; Cresswell and Kirkegaard, 1995; Gregory, 1998; Kirkegaard et al., 1997; Pouzet, 1995). However, the exact mechanisms of such beneﬁts are still under investigation. The effects include subsoil amelioration, leading to increased nutrient and water uptake and disease suppression in the following cereal (Kirkegaard et al., 1994). The main beneﬁt which oilseed rape brings to a rotation is its effect on soil structure. Actively growing root systems have the potential to ameliorate subsoil under poor physical conditions by “biological drilling” (see Section II.E) and taprooted species such as oilseed rape are generally considered superior to grasses in

AGRONOMIC AND ECONOMICAL POTENTIAL

407

their ability to ameliorate poorly structured soils (Lal et al., 1979). Some workers have reported that oilseed rape roots were more affected by soil impedance than cereal species. For example, Cresswell and Kirkegaard (1995) found no signiﬁcant subsoil structural differences after two seasons of growing oilseed rape, but that biological drilling may occur if the crop was retained for longer. Unfortunately the pest and disease implications for growing oilseed rape for more than 2 years would be too great, particularly in organic systems (Cresswell and Kirkegaard, 1995). Wheat yield increases following oilseed rape of 10–103% have been reported in Australia (Gregory, 1998; Kirkegaard et al., 1994, 1997), along with an increase in protein content of wheat grain (Kirkegaard et al., (1994)). The wheat N requirement was reduced by 30 kg ha−1 after an oilseed rape crop, and regardless of season, there was sufﬁcient residual N after oilseed rape to supply winter wheat until spring (McEwen et al., 1989). Increases in wheat yields after oilseed rape often depend on seasonal conditions. In dry areas, oilseed rape can limit the growth of the subsequent cereal by leaving insufﬁcient available soil water (Kirkegaard et al., 1994). The efﬁcacy of oilseed rape as a break crop in a cereal rotation is reduced where levels of disease in the cereals are low (Kirkegaard et al., 1997). Kirkegaard et al. (1994) reported that the plant population of a winter wheat crop following a brassica break was reduced, and that this was due to the signiﬁcant allelopathic effect that brassica residues have on wheat seedlings. The allelopathic effect depends on the type of residue and the state of decay. Fresh residues resulted in a reduction of emergence and growth of wheat. However, decayed residues, particularly of oilseed rape, signiﬁcantly increased growth (Purvis, 1990). The reduction in wheat seedling density was more than compensated for by the stimulation of seedling growth after brassicas, which on average was increased by 29%. Further work is required to determine the reasons for increased cereal growth following rape, since it cannot be explained by residual N, or reduced disease incidence alone. There is considerable evidence that compounds present in oilseed rape residues have a controlling effect on cereal pathogens. For example, isothiocyanates (ITCs), which are formed in brassica plants during the hydrolysis of glucosinolates, inhibit the growth of some soilborne fungi (Walker et al., 1937). Angus et al. (1994) found that dried, irradiated brassica roots were consistently effective in reducing the growth of G. graminis, but inhibition by young live roots was not consistent. Brassica residues can also inhibit the activity of beneﬁcial and plant pathogenic nematodes in the ﬁeld (Mojtehedi et al., 1991). Rape is susceptible to pests such as slugs (Deroceras reticulatum and other species), cabbage stem ﬂea beetle (Psylliodes chrysocephala), virus vector aphids such as Myzus persicae, and cabbage root ﬂy (Delia radicum) (Ekbom, 1995). It is also susceptible to a number of diseases including sclerotinia stem rot (Sclerotinia sclerotiorum Lib.), stem canker (Leptosphaeria maculans [Desm.] Ces. & de Not.), and alternaria leaf and pod spot (Alternaria spp.) (Rimmer and Buchwaldt, 1995;

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Evans and Gladders, 1981). Weeds, too, cause yield losses and are acknowledged to be the most important limiting factor in rapeseed production in Canada (Orson, 1995). The choice of cultivar and crop husbandry methods limit the impact of pests, diseases, and weeds in rape (Ekbom, 1995; Pouzet, 1995), and this will be particularly important in organic systems, where the choice of prevention and control methods is limited. Oilseed rape is combinable and is therefore mechanically compatible with a cereal rotation. However, organic production of oilseed rape is difﬁcult due to its very high nutrient requirements and its susceptibility to pests and diseases, many of which can prove difﬁcult to control under organic regulations (Tables VI–IX). If oilseed rape were to be included in an organic rotation, it would ideally follow a clover ley or similar crop which leaves high levels of residual nitrogen. Winter rape is a good crop to grow prior to winter cereals because its extensive rooting and early harvest leave the soil in good condition for sowing in the autumn (Lampkin, 1990; Pouzet, 1995). The spring rape crop is also compatible with a cereal rotation and is proposed by some as contributing toward a more environmentally responsible rotation (Fisher et al., 1996). The stubble from the preceding cereal can be left over winter to provide a feeding ground for birds (Fisher et al., 1996). It also acts as a protective measure against soil erosion and nitrate leaching in common with other winter cover crops. Spring oilseed rape has smaller nutrient demands and lower incidences of pests and diseases than winter sown crops (Fisher et al., 1996). Top dressing with slurry or liquid manure would be beneﬁcial in late spring and/or summer during the period of maximum nutrient demand. Long rotations (4–6 years) are necessary between oilseed rape and other crucifers due to susceptibility to similar diseases such as sclerotinia (Pouzet, 1995). There is a large and well-established market for conventionally produced oilseed rape, predominantly as an edible oil (Holmes, 1980). There is also an increasing diversity of cultivars with specialized fatty acid proﬁles for niche markets. In addition, new cultivars offer alternative or new sources of raw materials for edible, industrial, and pharmaceutical use (Pouzet, 1995). There is also a market for rapeseed cake for livestock feed (Scarisbrick et al., 1989). With a 50% premium assumed, the net margin for organic oilseed rape, however, is low, at £285 ha−1 (Table X). Some processors are currently interested in making health products from the crop and may be prepared to pay a higher premium, but this market is not yet established. An organic processing capacity is required in countries interested in producing the crop before farmers will begin to consider it as a serious option for organic systems. (Fowler and Lampkin, 1999). The advent of genetically modiﬁed rape in conventional agriculture may provide an incentive to establish a larger alternative organic market for rape, since GMOs are not permitted as organic animal feed and since an increasing number of consumers are choosing to avoid genetically modiﬁed products (Food and Drink Federation, 2000).

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V. BREAK CROPS FOR WEED MANAGEMENT A. POTATOES (Solanum tuberosum) The potato originated in South America and has become a major dietary staple in almost all temperate countries. Conventional world production is of the same order as the major cereals. Organic potato production has increased dramatically in the past 10 years in Europe. For example, 731 ha of root crops (a large percentage of which was potatoes) was produced in EU countries, the Czech Republic, Norway, and Switzerland in 1993 (Lampkin and Foster, 2000). By 1998, the area of land used for organic root crop production had increased to 5733 ha. In the United States 1970 ha of organic potatoes was produced in 2000 (Economic Research Service, USDA, 2001). At present, many European countries including the UK are net importers of organic potatoes, and there is great potential for further development of the industry in these countries (Soil Association, 2000a). Potatoes provide a valuable weed, pest, and disease break for cereal rotations. They are the only common member of the Solanaceae family grown outdoors in the cooler temperate climates and they therefore provide a useful pest and disease break from crops in other important agricultural crop families including Poaceae, Brassicaceae, Fabaceae, Chenopodiaceae, and Apiaceae. Tobacco also belongs to this family, but it is frost tender and is rarely grown in areas that are subject to late frosts, hail storms, or high winds. Although potatoes are often quoted as being a useful break crop, most of their break functions have not been quantitatively tested. The production of potatoes under most organic systems requires deep, thorough cultivation. This, along with postplanting weed control treatments and the fact that the crop rapidly produces a dense canopy, means that weeds are generally out-competed (Litterick et al., 1999). Weed numbers in following cereal crops are consistently lower after potatoes than after cereals (A. Litterick, unpublished; Rankin, pers. comm.; Halder, pers. comm.; Rose, pers. comm.). The incidence and severity of damage by several cereal pests can be signiﬁcantly reduced through the introduction of potatoes into the rotation. For example, leatherjackets (Tipula oleracea) are most commonly associated with damage to spring barley and grassland (Coll and Blackshaw, 1996); however, large numbers can damage other cereal crops such as winter wheat. One method of cultural control used in the UK is the use of early potatoes lifted in late July or early August (Wiseman et al., 1993). The ﬁeld is cultivated at harvest in August to prevent the eggs being laid, avoiding the subsequent problem of over-wintering eggs emerging as voracious larvae in the spring. Slug damage in cereals is more likely to occur following leys, cereals, or brassica seed crops than following fallows, potatoes, or sugar beet (Glen et al., 1996). Cereals or brassica crops provide good surface cover

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over much of the season under which slugs are more likely to increase in contrast to less cover over the season afforded by carrots or sugar beet. Soil disturbance associated with root crop (including potato) harvest is also likely to reduce slug numbers (Glen et al., 1996). The use of potatoes in cereal rotations also helps to control a number of diseases prevalent in cereal crops. For example, potatoes have the potential to aid the control of take-all, which is prevalent throughout Europe where wheat and barley are grown (Prew and Dyke, 1979). After harvest, the fungus remains alive on the stubble and root residues of the infected crop. Volunteer plants after harvest are attacked by mycelium surviving on the crop debris and can be so badly affected that they die. More often, however, they survive and carry the disease through to the next year. If a nonsusceptible crop such as potatoes is grown, the pathogen has no host, and the fungal populations decline. A single nonhost year can reduce populations of G. graminis to levels at which wheat or barley can be economically viable again (Butterworth, 1989; Werker et al., 1991). An increasing number of organic producers are choosing to specialize in potato production. In these cases, potato is not considered as a break crop, and rotations are based on the need to optimize potato quality and yield. In some cases, specialist potato growers rent land from local organic cereal growers on an annual basis and place the potato crop in the host farmer’s rotation at a point which is mutually suitable (Redpath and Rankin, pers. comm.) The fact that dedicated machinery is required for land preparation, planting, harvest, and packing means that organic potatoes will increasingly be produced by specialist growers who can afford to make the investment based on the high returns which successive potato crops can bring (Haward, pers. comm.). Early potatoes, and sometimes maincrop potatoes, can be harvested sufﬁciently early to allow a winter cereal to be planted afterwards. If potatoes follow a cereal, however, the land is often left without a crop until February–April (Wiseman et al., 1993). In this case, the cereal stubble could be left over winter to protect the soil from erosion and possible nitrate leaching (Fisher et al., 1996). There are three main challenges in organic potato production: provision of adequate nutrients, prevention and control of potato late blight (P. infestans), and weed control (EAGGF, 2000). Potato crops are ideally grown after a fertility building phase in the rotation due to their high demand for nutrients and poor nutrient use efﬁciency (Soil Association, 1998). There may be beneﬁts in applying manure, particularly if soil analysis and past cropping suggest that soil K or N are deﬁcient. To protect against blight, organic farmers are advised to plant resistant varieties early, using clean, high quality seed, and using a permitted copper fungicide if absolutely necessary (EAGGF, 2000; Soil Association, 1998). Approval for the use of copper fungicides in organic farming systems in Europe will be revoked in August 2002, and until effective alternative strategies for blight control are devised, organic potato production may be difﬁcult or impossible in mild wet

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seasons. Although weeds can cause serious yield losses in some seasons, particularly on heavy soils in wet areas, weed control is usually easily achieved in potato crops through the use of mechanical cultivations and thermal techniques (Rankin, Redpath, and Rose, pers. comm). If the required machinery can be obtained at reasonable cost, the high returns from growing the crop and other break crop beneﬁts make potato an attractive break crop in organic rotations. The net margins possible from growing organic potatoes with both high and low mechanisation are high (Table X). These returns in some cases justify the expenditure on machinery used only for root crops. Well-established markets exist in Europe and the UK, as there is high demand for organically grown potatoes for human consumption (Caspell and Creed, 2000; Fowler and Lampkin, 1999). Prices vary considerably depending on quality and marketing channel: direct marketing provides opportunities for producers to gain much higher prices. Location and earliness are important for early crops. With the full impact of EC regulation 2092/91 enforcing the use of organic seed, there are opportunities for the production of seed potatoes which may achieve up to £400 t−1.

VI. BREAK CROPS FOR PEST AND DISEASE MANAGEMENT A. CARROT (Daucus carota) The cultivated carrot was known to both the ancient Greeks and the Romans and had spread to the rest of Europe by the middle ages. It was introduced into America in the early 17th century and is now grown throughout the world. Carrots are a major commercial organic vegetable crop, used fresh and in processing. Although no ﬁgures have been published on the quantity of organic carrots currently being produced in Europe, it is known that the tonnage is steadily increasing year to year (Rose, pers. comm.). Around 3000 t of organic carrots with a revenue of £0.9 m was produced in the UK in the year ending April 2000 (Soil Association, 2000a). Carrot can provide a valuable break from a range of cereal diseases including take-all and eyespot (Pseudocercosporella herpotrichoides) and pests such as cereal nematodes and cereal leaf beetle (Oulema melanopa) (Agrios, 1997; Alford, 1999). Carrot plants are poor competitors with weeds; their early growth is very slow, with emergence taking between 1 and 3 weeks and the ﬁrst true leaves developing between 3 and 4 weeks after planting (Rubatzky and Yamaguchi, 1997). The cultivations that are necessary and possible in a carrot crop can result in weeds being controlled and populations diminished, but in practice, farmers aim to remove weeds only in order that the gross margin for the carrot crop is maximized.

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Although total elimination of weeds is never the aim in organic systems (see Section II.D), it is important to point out that weed populations have frequently been observed to be higher after the carrot crop than before (Lampkin, 1990; Rose, pers. comm.; Halder, pers. comm.). Carrots can be grown at several points in a rotation including after cereals, as they do not have a high N requirement (Lampkin, 1990). They are increasingly sown with precision seeding equipment, and uniformly sized seed is used to promote even germination (Lampkin, 1990; Rubatzky and Yamaguchi, 1997; Sanders, 1996). Since harvesting also requires specialized topping and lifting machinery, carrots are not mechanically compatible with a cereal rotation. Carrot harvest usually begins in late summer and can continue into winter in areas where severe frosts are unlikely (Wiseman et al., 1993). Alternatively, the roots can be covered with straw or straw and polythene and left to over-winter in the ground. They are then sold according to market demands throughout the winter and early spring (Lampkin, 1990; Rose, pers. comm.; Rubatzky and Yamaguchi, 1997). Late harvests may cause problems if a winter cereal is to follow the carrot crop. Earlier harvest dates can be achieved by early sowing, by promoting faster growth through soil warming (through the use of polythene on carrot beds prior to sowing), and by sowing at high densities to produce “baby” carrots rather than full size roots. Organic carrots, grown with high and low mechanization give the highest net margins of all the crops analyzed (Table X). The high net margins reﬂect the demand for organic carrots for human consumption and justify the use of hand weeding and/or specialized equipment for growing carrots. There is a high and increasing demand for organic carrots (Caspell and Creed, 2000; Fowler and Lampkin, 1999) for fresh sales and for processing, although as with all products in rapidly changing markets, marketing channels should be established before the crop is grown.

B. SWEDE (Brassica napus VAR. napobrassica) Swedes are among the most commonly grown and widely adapted root crops (Sanders, 1996) and are an essential part of rotations on many traditional mixed organic farms in Europe (Blake, 1990). Traditionally they have been grown as winter fodder, e.g., in the Norfolk four-course rotation (Wiseman et al., 1993); however, more recently farmers have been growing shopping swedes which have been bred for human consumption (Michaud, 1997). Most countries do not publish production ﬁgures for swedes separately from those for organic vegetables in general, although it is known that around 3000 t of swedes with a market value of £1.13 M was produced in the UK in 2000 (Soil Association, 2000a). Like many noncereal crops, swedes can provide a valuable break from a range of cereal pests and diseases. Along with many root crops, swedes are poor competitors

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with weeds and require weeding on at least one or two occasions throughout the season. If time and money are available for rigorous weed control, then swede can be regarded as a cleaning crop, but weed populations are often higher after swede crops (Lampkin, 1990; W. Rose; F. Halder, pers. comm.). Swedes make a positive contribution to soil organic matter, and leave a residual biomass of between 1.3 and 1.5 t ha−1 of dry matter (Lampkin, 1990). This assumes that the tops are incorporated into the soil at harvest. Swede crops would ideally follow a ley in rotation due to their moderate N requirement. However, they can also follow a cereal with a possible application of manure (Lampkin, 1990). Crops are usually direct drilled using a specialized swede drill. At harvest, they are either eaten off the ﬁeld by sheep or topped and lifted in one pass using specialized root harvesting machinery (Wiseman et al., 1993). In most districts of the UK, swedes are harvested in October–November (Lockhart and Wiseman, 1978). This makes it difﬁcult to establish a winter cereal after swedes. If winter cereals are to be planted after a swede crop, the swedes should be planted early and harvested from late July (Michaud, 1997). Alternatively, the swedes can be lifted early and allowed to ripen in a clamp (Lockhart and Wiseman, 1978). The high net margin of £3319 ha−1 makes organic shopping swedes a proﬁtable break crop (Table X) and may justify the expenditure on machinery not used in cereal production as well as providing grade outs for livestock feed. As with most organic edible horticultural crops, there is a high and increasing demand for organic swedes (Caspell and Creed, 2000; Fowler and Lampkin, 1999).

C. SUGAR BEET (Beta vulgaris) Sugar beet has been grown for sugar production since the mid-18th century in eastern Europe and is now a valuable conventional crop throughout temperate areas of the world. Around 37% of the world’s sugar comes from beet (the remainder being extracted from sugar cane) and around 40 Mt of sugar is produced annually from beet on a global basis (Winner, 1993). At present, sugar beet is rarely grown in organic systems because of the high weeding costs and the absence of markets and organically certiﬁed processing plants (Lampkin, 1990), although a market is now available in the UK (Lampkin and Measures, 2001) and some other countries. Like other noncereal crops, sugar beet can provide a valuable break from a range of cereal diseases and pests. However, it should not be grown in the same rotation as oilseed rape, since it suffers from shared pest and disease problems including beet cyst nematode and alternaria diseases (Agrios, 1997; Cooke, 1993; Duffus and Ruppel, 1993). A signiﬁcant break between subsequent sugar beet crops is also required to avoid build-up of weed beet, the seeds of which can survive in the soil for several years.

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The beet crop could potentially have a positive effect on weed control through the necessity and opportunity to control weeds (Lampkin, 1990). However, due to the relatively low crop value, weed control is carried out only to prevent crop loss due to competition, and weed populations can in many cases be higher after the sugar beet crop than before (Halder, pers. comm.). Sugar beet is usually sown with a swede drill and requires specialist root harvesting machinery to top and undercut the beets at or prior to lifting (Bray and Thompson, 1985). The crop has a low N requirement, and high residual N concentrations in the soil can reduce the sugar content of the beet. It should, therefore, be grown after a cereal or an N demanding crop (Lampkin, 1990). Where sugar beet is grown before a winter cereal, the normal harvest date of the beet would be after the ideal sowing date for the cereal. It may be necessary, especially on heavier soils, to harvest the beet early. This would result in a yield loss, but it would allow earlier sowing and, therefore, better establishment of the more proﬁtable winter cereal crop (Bray and Thompson, 1985). A beet crop sown after a cereal can also cause problems within a rotation. Cereals are usually harvested in July/August and the beet crop is not sown until the following March/April. This leaves the soil bare for around 8 months during the winter which would make nitrate susceptible to leaching (Allison et al., 1996). The risk of soil erosion is also increased on fallow soils, particularly in winter. Organic farmers often choose to grow a cover crop such as grazing rye or winter vetch over winter between crops which are harvested early and those which are planted in the following spring. Such crops reduce the likelihood and severity of soil erosion and nitrate leaching and can be ploughed in prior to sowing the next crop (Soil Association, 1998). When the crowns and tops of the sugar beet crop are ploughed back into the soil after harvest, signiﬁcant quantities of organic matter and nutrients are added. For example, a sugar beet crop can contribute 0.6–1.0 t ha−1 dry matter to the soil following incorporation of crop residues (Lampkin, 1990). Bray and Thompson, (1985) estimated that 105 kg ha−1 N, 35 kg ha−1 P, and 145 kg ha−1 K were returned to the soil from the crowns and tops of a 50 t ha−1 sugar beet crop. Wheat planted after sugar beet needed less N when the tops had been incorporated than after wheat (Sylvester-Bradley and Shepherd, 1997). However, the N released from the incorporated tops is prone to losses through leaching, denitriﬁcation, or volatilization (Sylvester-Bradley and Shepherd, 1997). There is a small but growing market for the crop in Europe. Until recently in the UK, the need for processing capacity was limiting interest in the crop which can only be grown on contract. Without price premiums, sugar beet has reasonable gross margins, comparable to ﬁeld beans and winter oats (Table X), but following trials in 2000 British Sugar plc are offering contracts in 2001 with a price premium, bringing the gross margin to £1799 ha−1. However, the costs of ﬁeld operations reduces the net margin to below that for winter oats. Demand for organic sugar, particularly for processing, is increasing, and processing capacity is

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likely to increase. No subsidies exist for sugar beet production in UK or European agriculture at present. There is a small but growing market for the crop in Europe.

D. LINOLA (Linum usitatissimum) Linola, a crop recently developed by CSIRO in Australia, is a low linoleic acid linseed (Hocking, 1995). The genetic changes involved in reducing linoleic acid from the original seed have no effects on any other part of the plant, therefore there are no alterations to agronomic performance associated with the quality change. So, although Linola can be regarded as a new crop, the fact that it was derived from ﬂax means that its agronomy and cultivation methods are already well known (Anon., 1995). There are currently no ﬁgures available on the UK, European, or world acreage of linola. Unlike the other agricultural crops in this review, there is relatively little information available on the break crop effects of linola. It is not a cereal, therefore it can be grown as a break crop in a cereal rotation as it is not susceptible to cereal pests and diseases. For example,Kirkegaard et al. (1997) found that wheat following Linola had a decreased incidence of rhizoctonia diseases (R. cerealis) and complete suppression of take-all. Linola is reported to have no effects (Thomas, 1996) or positive effects on wheat yields (Angus et al., 1991; Kirkegaard et al., 1997). The increases may come from reductions in take-all or rhizoctonia diseases in the following wheat crop. Linola has a low nutrient requirement (COG Inc., 1992; Kirkegaard et al., 1997) and is best grown on a low input basis (Turner, 1993) making it suitable for organic systems. This is important in a rotation to contrast with cereals, which are nutrient demanding. There is some evidence from trial work carried out in the UK that the growing Linola crop may have allelopathic effects on weeds, but the work will have to be repeated before conclusions can be reached (Robson and Litterick, unpublished). The production of Linola does not require different machinery or equipment from that used for cereals. The crop ﬁts well into a cereal rotation, and while potentially increasing subsequent cereal yields, it also shows a yield increase when grown after wheat or barley (COG, Inc., 1992). The harvest dates for linola range from 170 to 210 days after sowing, and this may pose problems for their inclusion in a cereal rotation, particularly in northern temperate latitudes, where growing seasons are shorter (Flax Council of Canada, 1999). Linola is better adapted to cooler environments than other polyunsaturated oilseed crops, such as sunﬂower and maize, thus providing an opportunity to produce highly polyunsaturated oil in more northern latitudes, for example, northern Europe (Anon., 1995). The low linolenic acid oil that is produced from Linola is a high quality polyunsaturated oil similar in composition to sunﬂower oil and

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is suitable for widespread edible uses (Green, 1992). There is therefore a strong interest in Linola for health food markets, but processing facilities are required before large-scale production of the crop can be contemplated. The net margin for Linola is £374 ha−1 assuming a 50% price premium (Table X). Although this is not high in comparison to most of the other break crops discussed in this review, the potential beneﬁts from growing the crop within a rotation have created signiﬁcant interest from organic growers (Rose; Haward; Halder, pers. comm.).

VII. CONCLUSIONS All the species discussed here have valuable characteristics if used as break crops in organic arable rotations. Hemp, faba bean, and lupin have the greatest agronomic potential as break crops, but with the exception of bean, they generate poor returns for the farmer. Linola and soybean are also useful break crops, although soybeans may have allelopathic effects on subsequent wheat seedlings. Swede, potato, and carrot are the most proﬁtable crops, but are less valuable in the rotation in terms of soil fertility than hemp, bean or lupin. Sugar beet and oilseed rape are challenging crops to grow organically, and there is currently a limited market for their produce. If successfully grown, they could have some positive contributions to a rotation. The beneﬁts of organic farming for consumers, livestock, and the environment are increasingly being demonstrated. Interest from consumers, environmentalists, farmers, and policy makers is strong, and there is little doubt that the area of land devoted to organic production in temperate areas will continue to increase over the next decade at least. A great deal of work is required if organic rotations involving novel break crops are to be optimized in terms of agronomy, economics, and environmental impact. There is a signiﬁcant amount of valuable agronomic and market information already available on the production of the more common organic crops, including cereals such as oats and barley. However, the potential of a wide range of more novel crops including pulses, oilseeds, vegetables, salads, fruit, ﬁber, and essential oil crops, and the less common cereals such as spelt must be evaluated in order to determine their break crop characteristics and the beneﬁts and challenges which they bring to organic systems. Many of the varieties developed for conventional cropping are unsuitable for organic systems. Breeding work is urgently required to develop crop varieties with characteristics particularly suited for organic systems. Detailed agronomic studies concerning nutrition, crop husbandry, pest, disease, and weed control are then required to optimize production systems for these varieties. The rapid expansion of the amount of land under organic husbandry is bringing a concomitant expansion in the range of crops grown. For many crops with

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established organic markets, and more especially for “new” organic crops, the marketing and processing infrastructures are immature. Investment in the supply chain will be necessary to allow the development of crops requiring processing. Organic products with complex processing and marketing chains may be slower to establish, but will provide entrepreneurial opportunities. Meanwhile, farmers aiming to solve agronomic problems in their rotations using the more nascent organic break crops will need to use all channels of expertise to help them balance the agronomic solutions and ﬁnancial returns against possible practical and marketing difﬁculties.

ACKNOWLEDGMENTS The authors wish to thank the UK Ministry of Agriculture, Fisheries and Food for funding a 4-year field- and desk-based project on the use of break crops in organic systems. We also wish to thank the many farm staff, consultants, and scientists who helped us with this review. In particular, we wish to thank Kate Barnard, Simon Brenman, Donald Clerk, John Fraser, Fred Halder, Rob Haward, Hugh Ironside, Jan Redpath, William Rose, Alan Schoﬁeld, Andrew Skea, Dr. Dick Taylor, Paul Van Midden, Dr. Robin Wood, and David Younie. We also wish to thank Feli Pomares and Frances Haldane for help in preparation of the manuscript. SAC receives ﬁnancial support from the Scottish Executive Rural Affairs Department.

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Index A Active transport passive nutrient uptake comparison, 126–130 passive transport partitioning, 166–167 Aggregation, virus sorption modeling, 53–57 Agricultural yield, crop response to CO2, FACE, 337–339 Agroecosystem, deﬁnition, 5 Air–water interface protein/virus inactivation, 67–70 virus transport modeling, 84–86 AMF, see Arbuscular mycorrhizal fungi Anions, virus transport in porous media, 76 Apoplastic pathways symplastic pathway comparison, 122–125 water and nutrients in roots, 121–122 Aquifer systems, virus removal, 40–42 Arbuscular mycorrhizal fungi, crop plants, 218–219 Arid regions, see Desertiﬁcation Atmospheric dust desertiﬁcation process, 7 effect on radiation balance, 10 AWI, see Air–water interface

soil and foliar fertilization, 230–231 supply and uptake, 214 pH effect, 196–197 soil concentrations, 190–192 soil organic matter, 200 temperature and moisture, 203 Brassica napus subsp. oleifera, soil structure enhancement, 406–408 Brassica napus var. napobrassica, pest and disease management, 412–413 Break crops crop rotations in organic farming, 386–391 nutrient management beans, 391–398 lupins, 398–401 soybean, 401–403 pest and disease management carrot, 411–412 linola, 415–416 sugar beet, 413–415 swede, 412–413 soil structure hemp, 403–406 oilseed rape, 406–408 weed management, potatoes, 409–411 Brownian motion, virus sorption modeling, 54

B C Beans, nutrient management, 391–398 Beta vulgaris, pest and disease management, 413–415 Biomass accumulation, crop response to CO2, FACE agricultural yield, 337–339 roots, 335–337 shoots, 332–335 Boron, crop plants growth, 187 micronutrient supply and acquisition deﬁciency and toxicity, 207 disease and insect resistance, 245 element interactions, 221–222 plant improvement, 236–237

Cannabis sativa, soil structure enhancement, 403–406 Canopy, temperature, crop response to CO2, FACE, 328–329 Capillary forces, water potential, 112–113 Carbohydrates, crop response to CO2, FACE, 343–344 Carbon, crop plant micronutrients, 220 Carbon dioxide crop response, see Crop response, CO2, FACE transpiration coefﬁcient, 118–119 Carbon sequestration, soil, crop response to CO2, FACE, 349–350 Carrot, pest and disease management, 411–412 429

430

INDEX

Carrying capacity, desertiﬁcation, 10–12 Casparian band, apoplastic vs. symplastic pathway, 124 Cations virus sorption, 45, 50 virus transport in porous media, 76 Cavitation, water transport in plants, 114–115 CDE, see Convection–dispersion equation Cell walls, water and nutrients in roots, 122 Cereal bean crop effect, 397–398 potato crop effects, 410 Chlorine, crop plants availability factors soil organic matter, 200 temperature and moisture, 203 micronutrient supply and acquisition deﬁciency and toxicity, 207–208 disease and insect resistance, 245 element interactions, 222 plant improvement, 237–238 pH effect, 197 plant growth, 186–187 soil concentrations, 192 soil and foliar fertilization, 231–233 Climate change, semiarid ecosystems, 29–35 Cobalt, crop plants micronutrient supply and acquisition, element interactions, 227 plant growth, 186 Cohesion theory, water transport in plants, 110 Collision efﬁciency, virus sorption modeling, 56–57 Colloids, virus transport in porous media, 81–82 Composite root conductance, root water uptake, 142 Computer models, root water and nutrient uptake, 107–108 Conductance, stomatal, crop response to CO2, FACE, 327–328 Convection–dispersion equation nutrient transport, 163–164 plant root–soil interfaces, 134 Copper, crop plants availability factors soil organic matter, 200–201 temperature, 203 micronutrient supply and acquisition deﬁciency and toxicity, 208

disease and insect resistance, 245 element interactions, 222–223 plant improvement, 238–239 rhizosphere, 220 supply and uptake, 214 pH effect, 197 soil concentrations, 192 supply and acquisition, soil and foliar fertilization, 233 Crop plants, micronutrients availability organic matter, 200–202 pH, 195–199 temperature, moisture, and light, 202–206 deﬁciency, 187 supply and acquisition boron, 221–222 chlorine, 222 cobalt, 227 copper, 222–223 deﬁciencies and toxicities, 206–211 disease and insect resistance, 244–246 iron, 223–224 manganese, 224–225 microbial associations, 243–244 molybdenum, 225 nickel, 227 oxidation–reduction reactions, 216–218 plant improvement, 236–243 rhizosphere, 218–220 soil improvement, 227–229 supply and uptake, 211–216 zinc, 225–227 supply and acquisition, soil and foliar fertilization deﬁciency correction, 230–235 overview, 229–230 residual effects, 235–236 Crop response, CO2, FACE biomass accumulation agricultural yield, 337–339 roots, 335–337 shoots, 332–335 canopy temperature, 328–329 carbohydrates, 343–344 compendium of relative changes, 350–358 evapotranspiration, 329–330 nitrogen concentration, 340–342 nitrogen yield, 342–343

431

INDEX peak leaf area index, 331 phenology, 344 photosynthesis, 326–327 radiation-use efﬁciency, 339 soil changes microbiology, 345–347 soil carbon sequestration, 349–350 soil respiration, 347–348 trace gas emission/consumption, 348 speciﬁc leaf area, 339–340 stomatal conductance, 327–328 water potential, 330–331 Crop rotations, organic farming break crop functions, 386–391 disease management, 385–386 organic arable rotations overview, 372–374 stocked rotations, 374–375 stockless rotations, 375–377 overview, 371–372 pest management, 383–385 soil fertility N ﬁxation, 378 nutrient cycling and SOM effects, 378–379 nutrient loss reduction, 379 overview, 377–378 soil physical characteristics, 379–381 weed management, 381–383 CT, see Cohesion theory

water resources, 13–16 Diffusion, crop plant micronutrients, 212 Disease, crop plant micronutrients, 244–246 Disease management carrot, 411–412 crop rotations in organic farming, 385–386 linola, 415–416 sugar beet, 413–415 swede, 412–413 Disjoining pressure, water potential, 113–114 Drinking-water resources, protection, 42 Drought desertiﬁcation process, 6–7 Sahel region, 18, 25 Dryness, virus inactivation, 64 E Electrochemical gradients, active vs. passive nutrient uptake, 130 El Ni˜no/Southern Oscillation, desertiﬁcation process, 7 ENSO, see El Ni˜no/Southern Oscillation Equilibrium protein sorption modeling, 60 virus sorption modeling, 50–52 Essential nutrients, deﬁnition, 186 Evapotranspiration crop response to CO2, FACE, 329–330 soil water ﬂow models, 117–118

D F Daucus carota, pest and disease management, 411–412 Denudation, land–surface changes, 8 Desertiﬁcation arid ecosystems overview, 2 basic deﬁnition, 3–4 carrying capacity, 10–12 climate change prospects, 29–35 drought atmospheric dust, 7 land–surface changes, 8–10 ocean–atmosphere dynamics, 7–8 overview, 6–7 monitoring, 20–21 primary production, 10–12 Sahel region, 16–19 social factors, 16 soil degradation, 12–13

FACE, see Free-air carbon dioxide enrichment Film-straining theory, virus transport in porous media, 77 Filtration theory for virus transport, 82–84 virus sorption modeling, 53–57 Flooding, crop plants, micronutrient oxidation and reduction, 217 Foliar fertilization, crop plant soil improvement overview, 229–230 soil and foliar fertilization, 230–235 Free-air carbon dioxide enrichment, crop response to CO2 biomass accumulation agricultural yield, 337–339 roots, 335–337 shoots, 332–335

432

INDEX

Free-air carbon dioxide enrichment (continued) canopy temperature, 328–329 carbohydrates, 343–344 compendium of relative changes, 350–358 evapotranspiration, 329–330 experimental protocols, 295–326 nitrogen concentration, 340–342 nitrogen yield, 342–343 peak leaf area index, 331 phenology, 344 photosynthesis, 326–327 radiation-use efﬁciency, 339 soil changes microbiology, 345–347 soil carbon sequestration, 349–350 soil respiration, 347–348 trace gas emission/consumption, 348 speciﬁc leaf area, 339–340 stomatal conductance, 327–328 water potential, 330–331 G Gas–liquid interface, role in protein/virus inactivation, 67–70 GCMs, see Global climate models GG, see Greenhouse gases Global climate models, semiarid regions global climate overview, 21–22 IPCC working groups, 23 model types, 23–29 region instability, 22–23 Glycine max, nutrient management, 401–403 Grain legumes, nitrogen ﬁxation, 396–397 Gravity, water potential, 112 Greenhouse gases, global climate models, 24–25 H Hemp, soil structure enhancement, 403–406 HEV, see Human enteroviruses Human enteroviruses, indicators, 86–88 I Inorganic fertilizer, soil science in tropical regions, 276–278 Insect resistance, crop plants, 244–246 Intergovernmental Panel on Climate Change, semiarid regions, 23

Intertropical convergence zone, desertiﬁcation process, 7 Ion channels, active vs. passive nutrient uptake, 127–129 Ionic strength solution, virus inactivation, 70 virus transport in porous media, 75–76 IPCC, see Intergovernmental Panel on Climate Change Iron, crop plants availability factors soil organic matter, 201 temperature and moisture, 204 micronutrient supply and acquisition deﬁciency and toxicity, 209–210 disease and insect resistance, 245 element interactions, 223–224 oxidation and reduction, 217 plant improvement, 240–241 rhizosphere, 220 soil and foliar fertilization, 233–234 supply and uptake, 215 pH effect, 197–198 soil concentrations, 192–193 Irrigation, desertiﬁcation, 13–15 ITCZ, see Intertropical convergence zone K Kinetics Michaelis–Menten-type, root nutrient uptake, 130–131 protein sorption modeling, 60–64 virus sorption modeling, 52–53 L LAI, see Leaf area index Land degradation, desertiﬁcation deﬁnition, 4–5 Land–surface changes, desertiﬁcation process, 8–10 Langmuir model, protein sorption, 60–61 Leaf area index, peak, crop response to CO2, FACE, 331 Light, crop plant micronutrients, 202–206 Liming, crop plant soil improvement, 229 Linola, pest and disease management, 415–416 Linum usitatissimum, pest and disease management, 415–416

INDEX Lupins, see Lupinus albus Lupinus albus, nutrient management, 398–401 M Macronutrients, plants, micronutrient comparison, 186 Macroscopic water uptake, root water uptake, 136–138 Manganese, crop plants availability factors soil organic matter, 201–202 temperature and light, 204–205 micronutrient supply and acquisition deﬁciency and toxicity, 210–211 disease and insect resistance, 245–246 element interactions, 224–225 oxidation and reduction, 217 plant improvement, 241–242 rhizosphere, 220 soil and foliar fertilization, 234 supply and uptake, 216 pH effect, 198 soil concentrations, 193–194 Mass ﬂow, crop plant micronutrients, 212 Metals, virus inactivation, 65 Michaelis–Menten-type kinetics, nutrient uptake in root, 130–131 Microbes, crop plant micronutrients, 243–244 Microbiology, crop response to CO2, FACE, 345–347 Micronutrients, crop plants availability factors organic matter, 200–202 pH, 195–199 temperature, moisture, and light, 202–206 bioavailability, soil pH and SOM effects, 187–188 deﬁciency, 187 macronutrient comparison, 186 soil concentrations amounts and distribution, 188–190 boron, 190–192 chlorine, 192 copper, 192 iron, 192–193 manganese, 193–194 molybdenum, 194 serpentine soils, 195 zinc, 194

433

supply and acquisition boron, 221–222 chlorine, 222 copper, 222–223 deﬁciencies and toxicities, 206–211 disease and insect resistance, 244–246 iron, 223–224 manganese, 224–225 microbial associations, 243–244 molybdenum, 225 nickel, 227 oxidation–reduction reactions, 216–218 plant improvement, 236–243 rhizosphere, 218–220 soil improvement, 227–229 supply and uptake, 211–216 zinc, 225–227 supply and acquisition, soil and foliar fertilization deﬁciency correction, 230–235 overview, 229–230 residual effects, 235–236 Mineral ﬂux, crop plant micronutrients, 213 MM-type kinetics, see Michaelis–Menten-type kinetics Models plant root–soil interfaces, 132–134 protein sorption, 60–64 root water–nutrient uptake mechanisms, 152–154 model considerations, 152–154 multidimensional approach, 155–161 virus inactivation, 65–66 virus sorption aggregation and ﬁltering, 53–57 equilibrium, 50–52 kinetics, 52–53 virus transport, 82–86 Moisture crop plant micronutrients, 202–206 virus inactivation, 64 Molybdenum, crop plants availability factors soil organic matter, 202 temperature, 205 micronutrient supply and acquisition deﬁciency and toxicity, 211 disease and insect resistance, 246 element interactions, 225 oxidation and reduction, 218

434

INDEX

Molybdenum, crop plants (continued) plant improvement, 241 soil and foliar fertilization, 234–235 supply and uptake, 216 pH effect, 198–199 soil concentrations, 194 MS-2 behavior at TPB, 69–70 transport in porous media, 78–80 N NDVI, see Normalized difference vegetation index Nickel, crop plants micronutrient supply and acquisition element interactions, 227 soil and foliar fertilization, 235 pH effect, 199 plant growth, 186 Nitrate concentration effect on root growth, 167 nutrient transport, 150–151 Nitrogen, crop response to CO2, FACE concentration, 340–342 yield, 342–343 Nitrogen ﬁxation grain legumes, 396–397 soil fertility, 378 Normalized difference vegetation index, desertiﬁcation monitoring, 20–21 Norwalk virus, indicators, 88 Numerical models, virus transport, 84 Nutrient management, break crops beans, 391–398 lupins, 398–401 soybean, 401–403 Nutrients essential, deﬁnition, 186 hemp requirements, 405 micronutrients, see Micronutrients plant, macronutrients vs. micronutrients, 186 soil, temperate and tropical regions, 284–285 soil fertility, 378–379 Nutrient transport convection–dispersion equation, 163–164 nitrate uptake, 150–151 plant root active vs. passive uptake, 126–130 apoplastic vs. symplastic pathway, 122–125

Michaelis–Menten-type kinetics, 130–131 plant root structure, 120–122 soils, 145–146 NV, see Norwalk virus O Ocean–atmosphere dynamics, desertiﬁcation process, 7–8 Ohm-type root water uptake, basic formulation, 143 Oilseed rape, soil structure enhancement, 406–408 Organic arable rotations overview, 372–374 stocked rotations, 374–375 stockless rotations, 375–377 Organic farming, crop rotations break crop functions, 386–391 disease management, 385–386 organic arable rotations overview, 372–374 stocked rotations, 374–375 stockless rotations, 375–377 overview, 371–372 pest management, 383–385 soil fertility N ﬁxation, 378 nutrient cycling and SOM effects, 378–379 nutrient loss reduction, 379 overview, 377–378 soil physical characteristics, 379–381 weed management, 381–383 Osmosis, water potential, 112 Oxidation–reduction reactions, crop plant micronutrients, 216–218 P Palmer Drought Stress Index, Sahelian region drought prediction, 26–29 Passive transport active nutrient uptake comparison, 126–130 active transport partitioning, 166–167 Pathogens, drinking water contamination, 42 PDSI, see Palmer Drought Stress Index Permeability, plant root, 125 Pest management carrot, 411–412 crop rotations in organic farming, 383–385

435

INDEX linola, 415–416 oilseed rape, 407–408 sugar beet, 413–415 swede, 412–413 PET, see Potential evaporation pH crop plant micronutrients availability, 195–199 bioavailability, 187–188 supply and acquisition, rhizosphere, 219 virus transport in porous media, 75 Phenology, crop response to CO2, FACE, 344 Photosynthesis, crop response to CO2, FACE, 326–327 Plant root nutrient uptake active vs. passive uptake, 126–130 Michaelis–Menten-type kinetics, 130–131 water and nutrient transport apoplastic vs. symplastic pathway, 122–125 plant root structure, 120–122 Plant root–soil interfaces soil water ﬂow modeling, 132–133 solute transport, 134 Plant transpiration, linking with assimilation root uptake process integration, 115–118 transpiration coefﬁcient, 118–119 Plant water transport cavitation, 114–115 driving forces, 108 soil–plant–atmosphere continuum, 109–110 water potential, 110–114 Pollution indicator, human enteroviruses, 87–88 Porous media, virus transport, mechanisms colloid-facilitated transport, 81–82 size exclusion, 80–81 soil properties, 71 soil water content, 76–78 solution chemistry, 75–76 virus type, 78–80 Potatoes, weed management, 409–411 Potential evaporation, Sahelian region drought prediction, 26–29 Primary production, desertiﬁcation, 10–12 Protein inactivation, gas–liquid interface role, 67–70 Protein sorption equilibrium modeling, 60 kinetics modeling, 60–64 mechanisms, 57–60

Proton pump, active vs. passive nutrient uptake, 129 R Radial pathways, water and nutrients in roots, 121 Radiation balance, effect of atmospheric dust, 10 Radiation-use efﬁciency, crop response to CO2, FACE, 339 Rainfall, Sahel region, 18 Random sequential adsorption theory, protein sorption, 62–64 RDF, see Root distribution function Recombinant Norwalk virus indicators, 88 transport in porous media, 78–80 RED, see Reduction factor Reduction factor, macroscopic root water uptake, 136–137 rNV, see Recombinant Norwalk virus Root distribution function, macroscopic root water uptake, 137 Root nutrient uptake basic understanding, 104–105 computer models, 107–108 equations, 166 system, 147–150 Roots crop response to CO2, FACE, 335–337 growth NO3–N concentration effect, 167 simulation, 165 interception, crop plant micronutrients, 212–213 uptake process integration, 115–118 Root water–nutrient uptake mechanisms, 152–154 model considerations, 154–155 multidimensional approach example, 159–161 overview, 155–159 Root water uptake basic understanding, 104–105 biophysical mechanisms, 143–144 computer models, 107–108 ﬂow paths, 141–143 macroscopic water uptake, 136–138 Ohm-type formulation, 143 overview, 135–136

436

INDEX

Root water uptake (continued) physiology studies, 105–106 site distribution, 166 soil salinity, 141 types I and II, 138–140 RSA, see Random sequential adsorption theory RUE, see Radiation-use efﬁciency S Sahel region destruction and resilience, 19 development efforts, 18–19 drought, 18, 25 geographic area, 16–17 human history, 17 human population, 19 mean annual temperature, 17 rainfall, 18 soils, 17 weather conditions, 18 Sea–surface temperatures, desertiﬁcation process, 7–8 Semiarid regions global climate change instability, 22–23 IPCC working groups, 23 models, 23–29 overview, 21–22 overview, 2 Serpentine soils, metal concentrations, 195 Shoots, crop response to CO2, FACE, 332–335 Silicon, plant growth, 186–187 Single-collector efﬁciency, virus sorption modeling, 55 Size exclusion, virus transport in porous media, 80–81 SLA, see Speciﬁc leaf area Social factors, semiarid ecosystems, 16 Soil acidity, temperate and tropical regions, 283–284 Soil degradation, desertiﬁcation, 12–13 Soil fertility, crop rotations in organic farming N ﬁxation, 378 nutrient cycling and SOM effects, 378–379 nutrient loss reduction, 379 overview, 377–378 Soil nutrients, temperate and tropical regions, 284–285 Soil organic matter

crop plants micronutrient bioavailability, 187–188, 200–202 soil improvement, 228–229 soil fertility, 378–379 Soil–plant–atmosphere continuum root water uptake, 135 water potential, 111 water transport in plants, 109–110 Soil respiration, crop response to CO2, FACE, 347–348 Soils crop plant micronutrients amounts and distribution, 188–190 boron, 190–192 chlorine, 192 copper, 192 improvement for supply and acquisition, 227–229 iron, 192–193 manganese, 193–194 molybdenum, 194 pH, 187–188 serpentine soils, 195 zinc, 194 crop response to CO2, FACE microbiology, 345–347 respiration, 347–348 soil carbon sequestration, 349–350 trace gas emission/consumption, 348 crop rotations in organic farming, 379–381 nutrient transport, 145–146 Sahel region, 17 virus removal, 40–42 virus transport in porous media, 71 Soil salinity, root water uptake, 141 Soil science research impact, 285–286 scientists, tropical regions, 279–281 temperate regions funding and scope, 274 overview, 271–272 post-World War II, 272–274 soil acidity, 283–284 soil nutrients, 284–285 tropical regions ﬁrst theories, 275 important themes, 278–279 inorganic fertilizer use, 276–278 journal publications and scientists, 279–281

437

INDEX overview, 274–275 post-World War II, 276 soil acidity, 283–284 soil myths, 281–282 soil nutrients, 284–285 Soil structure, break crops hemp, 403–406 oilseed rape, 406–408 Soil water ﬂow plant roots, 164–165 plant root–soil interfaces, 132–133 virus transport in porous media, 76–78 Soil–water uptake, convection–dispersion equation, 163–164 Soil weathering, crop plant micronutrients, 213 Solanum tuberosum, weed management, 409–411 Solid surface, virus survival, 64–65 Solute transport, plant root–soil interfaces, 134 Solution chemistry, virus transport in porous media, 75–76 SOM, see Soil organic matter Soybean, nutrient management, 401–403 SPAC, see Soil–plant–atmosphere continuum Speciﬁc leaf area, crop response to CO2, FACE, 339–340 SSTs, see Sea–surface temperatures Stomatal conductance, crop response to CO2, FACE, 327–328 Stress response function, root water uptake, 140 Subsurface fate, virus characteristics, 43–45 Sugar beet, pest and disease management, 413–415 Sulfate aerosols, global climate models, 24–25 Swede, pest and disease management, 412–413 Symplastic pathway, apoplastic pathway comparison, 122–125

crop plant micronutrients, 202–206 Sahel region, 17 Toxicity, crop plant micronutrient supply and acquisition, 206–211 TPB, see Triple-phase boundary Trace gas, emission/consumption in crop response to CO2, FACE, 348 Transpiration coefﬁcient, linking with assimilation, 118–119 Transport active, passive transport partitioning, 166–167 active vs. passive nutrient uptake, 126–130 nutrient, see Nutrient transport solute, plant root–soil interfaces, 134 virus, see Virus transport water, see Water transport Transport indicator, human enteroviruses, 87–88 TRC, see Transpiration coefﬁcient Tree density, Sahel region, 19 Triple-phase boundary, virus inactivation, 69 Tropical regions, soil science ﬁrst theories, 275 important themes, 278–279 inorganic fertilizer use, 276–278 journal publications and scientists, 279–281 overview, 274–275 post-World War II, 276 soil acidity, 283–284 soil myths, 281–282 soil nutrients, 284–285

T

V

Temperate regions, soil science funding and scope, 274 overview, 271–272 post-World War II, 272–274 soil acidity, 283–284 soil nutrients, 284–285 Temperature canopy, crop response to CO2, FACE, 328–329

U Ultramaﬁc soils, metal concentrations, 195 Ultraviolet radiation, virus inactivation, 65 UNCOD, see United Nations Conference on Desertiﬁcation United Nations Conference on Desertiﬁcation, 4

Vicia faba, nutrient management, 391–398 Viruses composition, 42–43 groundwater contamination, 40 removal from soil, 40–42 subsurface fate, 43–45 Virus inactivation gas–liquid interface role, 67–70 modeling, 65–66

438

INDEX

Virus sorption human enteroviruses, 86–88 mechanisms, 45, 50 modeling aggregation and ﬁltering, 53–57 equilibrium, 50–52 kinetics, 52–53 Virus survival, affecting factors, 64–65 Virus transport air–water interface modeling, 84–86 characteristics, 43–45 ﬁltration theory, 82–84 human enteroviruses, 86–88 numerical models, 84 porous media characteristics, 43–45 colloid-facilitated transport, 81–82 size exclusion, 80–81 soil properties, 71 soil water content, 76–78 solution chemistry, 75–76 virus type, 78–80 W Water potential crop response to CO2, FACE, 330–331 water transport in plants, 110–114 Water resources, desertiﬁcation, 13–16 Water transport, plants apoplastic vs. symplastic pathway, 122–125 cavitation, 114–115 driving forces, 108 plant root structure, 120–122 soil–plant–atmosphere continuum, 109–110 water potential, 110–114

Water uptake macroscopic, root water uptake, 136–138 Ohm-type root, basic formulation, 143 root, see Root water uptake soil, convection–dispersion equation, 163–164 Water use, crop response to CO2, FACE, 329–330 Weather, Sahel region, 18 Weed management break crops, potatoes, 409–411 crop rotations in organic farming, 381–383 World War II, post-war soil science temperature regions, 272–274 tropical regions, 276 X X174 behavior at TPB, 69–70 transport in porous media, 78–80 Z Zinc, crop plants availability factors soil organic matter, 202 temperature and moisture, 205–206 micronutrient supply and acquisition deﬁciency and toxicity, 211 element interactions, 225–227 oxidation and reduction, 218 plant improvement, 242–243 soil and foliar fertilization, 235 supply and uptake, 216 pH effect, 199 soil concentrations, 194