Advances in Agronomy, Volume 72

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A gronomy

DVANCES IN

VOLUME

72

Advisory Board Martin Alexander

Ronald Phillips

Cornell University

University of Minnesota

Kenneth J. Frey

Larry P. Wilding

Iowa State University

Texas A&M University

Prepared in cooperation with the American Society of Agronomy Monographs Committee John Bartels Jerry M. Bigham Jerry L. Hatfield David M. Kral

Diane E. Stott, Chair Linda S. Lee David Miller Matthew J. Morra John E. Rechcigl Donald C. Reicosky

Wayne F. Robarge Dennis E. Rolston Richard Shibles Jeffrey Volenec

Agronomy

DVANCES IN

VOLUME

72

Edited by

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

San Diego San Francisco New York Boston

London

Sydney

Tokyo

This book is printed on acid-free paper.

∞

C 2001 by ACADEMIC PRESS 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-2001 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. 0065-2113/01 $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.

Academic Press A Harcourt Science and Technology Company 525 B Street, Suite 1900, San Diego, California 92101-4495, USA http://www.academicpress.com

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

vii ix

IMPACT OF SOIL EROSION ON CROP YIELDS IN NORTH AMERICA Christoffel den Biggelaar, Rattan Lal, Keith Wiebe, and Vince Breneman I. II. III. IV. V. VI. VII. VIII.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Sources and Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Crop Production Loss Due to Erosion in North America . . . . . . . . . . . . . Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implications for Research and Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 8 14 30 32 35 41 42

BIOREMEDIATION OF PETROLEUM HYDROCARBONS IN SOIL Joseph P. Salanitro I. II. III. IV.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Crude Oils and Fuel Hydrocarbons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biodegradation of PHCs in Unsaturated Soils . . . . . . . . . . . . . . . . . . . . . . . . . . Summary and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

54 57 67 90 93

GENETICS OF FLOWERING TIME IN CHICKPEA AND ITS BEARING ON PRODUCTIVITY IN SEMIARID ENVIRONMENTS Jagdish Kumar and Shahal Abbo I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Evolution of the Crop and Genetic Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . III. The Flowering Genes of Chickpea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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108 110 115

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CONTENTS IV. Constraints to Productivity in Semiarid Environments . . . . . . . . . . . . . . . . V. Conclusions and Future Outlook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

124 132 134

ENVIRONMENT-SENSITIVE GENIC MALE STERILITY (EGMS) IN CROPS S. S. Virmani and M. Ilyas-Ahmed I. II. III. IV. V. VI. VII. VIII. IX.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Occurrence among Crop Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Identification and Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Breeding of EGMS Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Use of the EGMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

139 141 148 154 159 170 178 183 185 186

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

197

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

SHAHAL ABBO (107), Faculty of Agricultural, Food and Environmental Quality Sciences, The Hebrew University of Jerusalem, Rehovot 76100, Israel CHRISTOFFEL DEN BIGGELAAR (1), School of Natural Resources, Ohio State University, Columbus, Ohio 43210 VINCE BRENEMAN (1), USDA Economic Research Service, Washington, DC 20036 M. ILYAS-AHMED (139), International Rice Research Institute, 1271 Makati City, Philippines JAGDISH KUMAR (107), Genetic Resources and Enhancement Program, International Crops Research Institute for Semi-Arid Tropics, Patancheru, AP 502 324, India RATTAN LAL (1), School of Natural Resources, Ohio State University, Columbus, Ohio 43210 JOSEPH P. SALANITRO (53), Equilon Enterprises, LLC, Westhollow Technology Center, Houston, Texas 77251-1380 S. S. VIRMANI (139), International Rice Research Institute, 1271 Makati City, Philippines KEITH WIEBE (1), USDA Economic Research Service, Washington, DC 20036

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Preface Volume 72 contains four outstanding chapters dealing with advances in plant and environmental soil sciences. Chapter 1 deals with a timely and signiﬁcant topic in North America, and indeed in the world: the impact of soil erosion on crop yields. This comprehensive review provides background information, data sources and analyses, and implications for research and policy. Chapter 2 is an excellent treatise on cutting-edge developments in bioremediation of petroleum-contaminated soils. Factors affecting successful remediation and future research needs are presented. Chapter 3 presents the genetics of ﬂowering time in chickpea and its effect on productivity in semiarid environments. The authors discuss evolution of crop and genetic variation, ﬂowering genes of chickpea, constraints on productivity, and future prospects. Chapter 4 covers environment-sensitive genic male sterility (EGMS) in crops. Occurrence among crop plants, identiﬁcation and classiﬁcation, genetics, breeding, and use of the EGMS are discussed. Many thanks to the authors for their ﬁrst-rate contributions. DONALD L. SPARKS

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IMPACT OF SOIL EROSION ON CROP YIELDS IN NORTH AMERICA Christoffel den Biggelaar,1 Rattan Lal,1 Keith Wiebe,2 and Vince Breneman2 1

School of Natural Resources, Ohio State University, Columbus, Ohio 43210

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USDA Economic Research Service, Washington, DC 20036

I. Introduction II. Background A. Effects of Soil Degradation B. Factors Affecting Crop Yields C. Effects of Soil Erosion on Crop Yields III. Data Sources and Analyses A. Data Sources B. Data Analysis and Interpretation C. Location of Erosion–Productivity Studies IV. Results A. Extent of Erosion in North America B. Impact of Erosion on Crop Yields V. Crop Production Loss Due to Erosion in North America VI. Assumptions A. Range of Experimental Methods, Management Practices, and Time Periods B. Differential Relationships between Degradation and Yields C. Effects of Technological Advances VII. Implications for Research and Policy A. Decline in Crop Yields B. Productivity and Economic Impact C. Factors Confounding the Effect of Erosion on Productivity VIII. Conclusion References

I. INTRODUCTION Several reviews and research summaries have been published in the past on the relation between soil erosion and productivity (Stallings, 1957; National 1 Advances in Agronomy, Volume 72 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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Agricultural Lands Study, 1981; NSE-SPRPC, 1981; Langdale and Shrader, 1982; Crosson and Stout, 1983; Anderson and Gregorich, 1984; Burnett et al., 1985; Larson et al., 1985; Mannering et al., 1985; Nowak et al., 1985; Renard and Follett, 1985; Maetzold and Alt, 1986; Heimlich, 1989; Lal, 1987, 1988, 1998; Pierce, 1991; Cann et al., 1992). The topic has also been the subject of a number of conferences and workshops (Rijsberman and Wolman, 1984; ASAE, 1985; Larson et al., 1990). In several of these reviews, the authors provided global or national estimates of the long-term decrease in productivity as a result of accelerated erosion, with little attention to spatial variations resulting from soil and climatic differences. In this chapter, we use spatially referenced data to link study sites with soil orders and erosion rates to estimate the productivity losses in each soil order. This information is then used to estimate the production and economic losses due to soil erosion for four crops that have been the subject of most erosion–productivity studies in North America: maize (Zea mays L.), wheat (Triticum aestivum L.), soybeans [Glycine max (L.) Merr.], and cotton (Gossypium spp. L.). A number of studies have investigated productivity effects of erosion on other crops, but they are too few in number, too old, or too narrowly focused on locally signiﬁcant crops to make useful estimates. These are discussed under “Hay and Fodder Crops” and “Miscellaneous Crops”. Soil loss is used as an indicator of erosion. In accord with most of the studies reviewed, topsoil depth (TSD) is used as the independent variable.

II. BACKGROUND Human activities both inﬂuence the structure, fertility, and composition of soils and are inﬂuenced by the properties and availability of soils (Davis and Browne, 1996). The relationship between humans and soils is characteristic of the ways through which humans interact with the environment, responding to potentials, recognizing limits, and adapting the environment to suit human needs (Harris and Warkentin, 1974). Human activities have, in turn, changed soil fertility or eliminated the need for soils for crop production altogether. However, although some high-value specialty crops are today grown in soil-less artiﬁcial media in greenhouses [notably tomatoes (Lycopersicon esculentum Mill.), cucumbers (Cucumis sativus L.), lettuce (Lactuca sativa L.) and ﬂowers], soil-based agriculture continues to be the most important form of food and ﬁber production. Soil provides nutrients, water, and support to plants as well as is host to innumerable macro- and microorganisms both beneﬁcial and harmful to crops. Soil conservation and management are important so that soils can continue to provide these services into the future. In many parts of the world, however, soil conservation and management leave much to be desired, resulting in the degradation

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3

of the soil resource leading to a decrease in its productive potential. Johnson and Lewis (1995) found that there is general agreement in the literature concerning two critical aspects of soil degradation. First, soil degradation involves a substantial decrease in the biological productivity of a soil system and, second, this decrease is the result of processes resulting from human activities rather than natural events. Based on these criteria, we deﬁne soil degradation as the substantial decrease in a soil’s biological productivity or usefulness due to human interference, assuming other factors such as technology, management, and weather remain constant (Boj¨o, 1996). Productivity is a measure of the rate of accumulation of energy. Productivity can be deﬁned and measured in many ways, such as output per unit of land, output per unit of labor, or output per unit of other input(s) used. In the context of soil productivity, it is the productive potential of the soil system that allows the accumulation of energy in the form of vegetation (Stocking, 1984). Production is the total accumulation of energy, irrespective of how quickly, over what area, or with what assistance it accumulates. Yield, or output per unit area over a given time period, is a measure of production, which can be used as an indicator (albeit an imperfect one) of productivity. Yields are an expression of historical production, whereas productivity is a measure of potential (future) productivity (Tengberg and Stocking, 1997). Production (total biomass) can remain constant or even increase as the soil becomes degraded (Dregne, 1995). Stocking (1994) observed that crop yields may increase even though degradation may reduce long-term soil productivity, causing a loss to future economic returns to production. Johnson and Lewis (1995) therefore added usefulness as a crucial attribute of soil degradation. For example, as a result of degradation, species composition changes resulting in poorer quality biomass may make it less useful to people, although total biomass production may not be affected. A similar observation was made by Young et al. (1985).

A. EFFECTS OF SOIL DEGRADATION Soils are a ﬁnite resource created and degraded through both natural and humaninduced processes. Soils are formed in a slow, continuous, and gradual process involving the breakdown of minerals during biological, physical, and chemical processes (National Agricultural Lands Study, 1981). Scientists estimate that 2.5 cm (1 in.) of new topsoil is formed every 100 to 1000 years (Pimentel et al., 1976), which is equivalent to a rate of 0.4–4.0 Mg ha−1 yr−1. The rate varies widely, inﬂuenced by land use, climate, vegetation, soil disturbances, and the nature of the land (Brady and Weil, 1999). Human activities can either aggravate or mitigate soil degradation. Mostly, though, human activities accelerate the natural degradative processes, so that the rate of soil formation is greatly outweighed by soil loss as a result of degradation. While there is widespread evidence that soil losses resulting from erosion far exceed the natural rate of soil formation, the impact of such losses

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on crop yields or production has not been well established in physical or economic terms, although there have been many attempts to do so (van Baren and Oldeman, 1998). The effects of degradation on soil resources can be grouped into two categories: those that are reversible (e.g., nutrient levels, pH, organic matter, and biological activity) and those that are irreversible given present technological and economic resources (e.g., rooting depth, water holding capacity, structure, and texture). The reversibility or irreversibility of a speciﬁc type of soil degradation depends not only on available technology, but also in most cases on economic costs and returns. For example, irrigation can mitigate a decline in water-holding capacity, but may not be economically viable in all circumstances. Results from ﬁeld studies and simulation models indicate that there is a large variation in the way soil degradation affects its quality (Maetzold and Alt, 1986). Some soils experience consistent productivity reductions with degradation, while others suffer no loss until some critical point in one (or more) yield-determining factor(s) is reached, at which time signiﬁcant yield losses occur with further degradation (Hoag, 1998). The effects of degradation may also vary from year to year, so that long-term degradative effects are not easily apparent. For example, eroded soils with reduced plant-available water-holding capacities and/or inﬁltration rates often show greater yield losses in drought years compared with uneroded soils (Shaffer, 1985; Swan et al., 1987). During years with normal or above-average rainfall, however, yields on eroded and uneroded soils may be identical. Irrigation can reduce yield differences even in drought years, but involves an economic cost. While yield differentials can be masked by the use of irrigation, other degradative processes may continue unabated (e.g., loss of organic matter and soil structure) or new ones may be introduced (e.g., alkalinization and salinization). Although there are many forms of soil degradation (e.g., physical, chemical, and biological), in this chapter we focus solely on erosion, which is a form of physical degradation. Erosion is chosen as it is widespread, frequently studied, and the most visible form of soil degradation. Globally, Oldeman et al. (1990) estimated that 85% of soil degradation is due to erosion. Erosion is a natural process that has occurred for as long as the earth has been in existence (Larson et al., 1983a). Some of the most productive soils in the world (e.g., loess and alluvial soils) are a result of erosional processes. Thus, not all erosion can be classiﬁed as degradation. As discussed above, soil degradation results from the acceleration, by humans, of naturally occurring processes (Davis and Browne, 1996). Although a popular notion, fueled by some authors (Brown, 1994; Brown and Wolf, 1984a, 1984b; Ehrlich and Ehrlich, 1991), states that erosion is more severe than ever before and poses severe threats to the long-run productivity of agriculture in the United States (Cleveland, 1995) and Canada (Fairbairn, 1984; Prairie Farm Rehabilitation Agency, 1983; Sparrow, 1984), Larson et al. (1983a) argued that there is insufﬁcient evidence available to support or refute that notion.

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Research by agronomists, agricultural engineers, soil scientists, and agroecologists have identiﬁed the following effects of soil erosion (Follett and Stewart, 1985; Lal and Stewart, 1990; Pimentel, 1993; Cleveland, 1995; Loch and Silburn, 1997): (i) reduction in soil depth and potential rooting depth; (ii) reduction in soil organic matter content; (iii) reduction in nutrient availability; (iv) nonuniform removal of topsoil within a ﬁeld; (v) exposure of, and/or mixing of topsoil with, subsoil of poorer physical, biological, and chemical properties; (vi) changes in soil physical properties (such as changes in bulk density, water inﬁltration, waterholding capacity, texture, or structure); or (vii) some combination of the above factors. These changes in the physical, chemical, and biological qualities of soil are often the primary reason for monitoring soil erosion, as they affect soil productivity. Productivity can reﬂect soil erosion if yields decline with progressive erosion or if input use increases to compensate for declines in soil quality due to erosion (ERS, 1997). However, soils of poor physical quality (as measured by erosion and changes in texture and organic matter) can sometimes produce very high yields without large increases in input use (Vesterby and Krupa, 1993). Because of the emphasis on a soil’s capacity to produce plants or biomass (see Section II), productivity is usually expressed in terms of crop yield or output per unit area over a given time period (NSE-SPRPC, 1981). Yield data are the way that farmers, policy makers, and the public typically consider agricultural production data, and they are also a basic measure of productivity in agricultural experiments (Tomlin and Umphrey, 1996). Crop yields are, therefore, used as the measure of productivity in this review.

B. FACTORS AFFECTING CROP YIELDS While soil provides the basis for agricultural production, it is by no means the only determinant of crop yields. For example, Butell and Naive (1978) identiﬁed weather, fertilizer use, technology, and planted area as the major factors affecting corn yields. In the period of 1954–1977, corn yields in the United States increased from 2.5 to 5.6 Mg ha−1. According to these authors, the higher rate of fertilizer use since the mid-1950s accounted for over half the increase in corn yields, while technology (better management and cultural practices facilitated by improved varieties of corn and advances in pesticides, mechanization, and irrigation) accounted for the rest of the increase. In general, crop yields are a function of interacting factors including soil characteristics (S), management practices used (M), pest and disease incidence (PD), and climatic conditions before and during the respective growing season (C). Crop yield, therefore, can be represented by the following function: Yield = f (S, C, M, PD).

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There is a feedback mechanism between crop yields and soils (Lindert, 1999) which may be positive (e.g., the addition of organic matter from stubble and plant residues and, for N-ﬁxing crops, the addition of nitrogen) or negative (e.g., the removal of nutrients through harvests and loss of soil structure associated with root crops, particularly during harvest operations in wet conditions). Similarly, Young et al. (1985, quoted by Cleveland, 1995) observed that yield-enhancing technological change may not actually offset erosion damage, but may in fact intensify productivity damage from erosion. Each of the factors in the above production function consists of a number of subfactors that may inﬂuence crop production and yield levels (Fig. 1). The question of long-term effects of erosion on crop productivity is, therefore, a complex one due to the many interactive factors that affect plant growth and yield (Frye, 1987). Evaluation of the relationship between soil erosion and productivity is complicated also by the effects of an ever-increasing level of technology on crop production. To determine the effect of a single soil factor (erosion) on yield (as the indicator of soil productivity) therefore requires that all other factors determining yield be kept constant or controlled as much as possible.

C. EFFECTS OF SOIL EROSION ON CROP YIELDS Soil erosion affects crop production and yields in multiple ways. Physical loss of soil through erosion, leading to a decline in TSD, is the most visible form of degradation; its effect is both long-term and cumulative. Critics of the concept of soil loss note that nearly 75% of the “eroded” (detached and transported) soil is eventually deposited on another site and thus is not truly “lost,” as it moves from one part of the landscape to another (Larson et al., 1983a; National Agricultural Lands Study, 1981; Ofﬁce of Technology Assessment, 1982). It is the nutrientrich organic and clay particles that tend to be the soil particles dislodged and carried away by erosion. This loss of soil organic matter (SOM), nutrients, and water-holding capacity causes signiﬁcant qualitative changes in soils (National Agricultural Lands Study, 1981) (see Section II,A). Therefore, it may not be the decrease in depth of topsoil or solum or to a root-restrictive layer per se that impacts yields, but rather the changes the loss of soil brings about in other soil factors, such as nutrient levels, pH, water-holding capacity, texture, inﬁltration rates, and SOM over time, possibly rendering agriculture unproﬁtable or even impossible. For example, changes in soil texture and tilth due to erosion may necessitate heavier machinery to work the soil or more passes to prepare a suitable seed bed, increasing the risk of compaction and the formation of plow and trafﬁc pans (Frye, 1987). This can cause delays in planting in spring as the soils remain too cold and wet to enable seedbed preparation and planting. Other effects of erosion include losses of crop stand and loss of arable land area to gully formation and land slides and

Figure 1 Factors affecting crop yield and productivity. (Adapted from S. J. Perrens and N. A. Trustrum, 1984. Assessment and Evaluation of Soil Conservation Policy–Report Workshop on Policies for Soil and Water Conservation, 25–27 January 1983, East West Center, Honolulu, Hawaii).

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crop burial by sediment deposition (Lal, 1987). However, even if soils become less productive for one crop, they may remain highly productive for others better able to exploit adverse or resource-limiting conditions. Although erosion is the most frequently studied type of degradation affecting crop yields, the precise relationship between erosion and productivity remains unclear and difﬁcult to quantify (Littleboy et al., 1996; Stocking and Sanders, 1993). Erosion and productivity are also not independent; both are inﬂuenced by other factors (Ponzi, 1993). Moreover, the loss in productivity set in motion by accelerated erosion may be a self-sustaining process: Loss of production on eroded soil may further degrade its productivity (through loss of crop cover, poor stands, and reduced amount of residues returned to the soil) which, in turn, may accelerate erosion (Ponzi, 1993). In a majority of studies that aimed at quantifying the relationship between soil erosion and productivity, yield declines were related to a loss in TSD. Hoag (1998), however, concluded that “TSD is not generally an adequate measure of productivity. The most proﬁtable management of a soil will depend on the quality and distribution of soil layers in the overall rooting zone. Soil substitution and mixing, as well as depth, can affect productivity. Because soil layers are not uniform, productivity may even increase or be unaffected by erosion.” In addition to the on-site effects of erosion on soil quality, the export of soil, nutrients, and pesticides may have adverse off-site effects through siltation of streams and reservoirs and damage to water quality (Loch and Silburn, 1997). This chapter does not address the off-site effects and costs of erosion.

III. DATA SOURCES AND ANALYSES A. DATA SOURCES This chapter is limited to studies based on ﬁeld research on soil erosion– productivity in the United States and Canada that reported quantitative yield results (e.g., bushels per acre, tons per acre, or megagrams or kilograms per hectare). Studies which reported results only as a percentage decline in yield were excluded. Also excluded were studies based on simulation models or regression analysis, unless they included data from ﬁeld studies that were used to develop or test the models. Based on concerns articulated by Boardman (1998) about the “misinterpretation and uncritical use of original ﬁeld data“ in studies using secondary data, this analysis is based on original studies. Information on the area of soil orders in the United States was obtained from NRCS’s Soil Survey Staff (1999), while that for Canada was inferred from data of the global soil regions map by NRCS’s World Soil Resources Staff (1997). Information on the extent of erosion and the amount of cultivated cropland by

IMPACT OF SOIL EROSION ON CROP YIELDS

9

soil order in the United States was obtained from the 1997 National Resources Inventory (NRCS, 1999). Crop yield data were obtained from the 1987, 1992, and 1997 Censuses of Agriculture (United States Bureau of the Census 1987, 1993; NASS, 1999). Data on cultivated cropland and crop yields for Canada were obtained from the 1996 census of agriculture (Statistics Canada, 1997) and Internet sites of provincial departments of agriculture. Data on the extent of soil erosion in Canada were obtained from Dumanski et al. (1994) and Agriculture and Agri-Food Canada (1998).

B. DATA ANALYSIS AND INTERPRETATION An Access database was developed to enter the information from the studies identiﬁed in the literature. As several studies comprised and reported on experimental results from more than one soil series, a separate record was created for each at the soil subgroup level. Soil series information was translated into the soil subgroup of the U.S. Soil Taxonomy using the USDA–NRCS Soil Survey Division’s Ofﬁcial Soil Series Descriptions on the Internet. Canadian soils are classiﬁed using a different taxonomy; soil series were reclassiﬁed according to the U.S. Soil Taxonomy at the soil order level (e.g., Chernozems as Mollisols, Luvisols as Alﬁsols, and Podzols as Spodosols). Latitude and longitude information for the location of the experiments, if not provided in the articles, was obtained from the USGS (2000) Geographic Names database for locations in the United States, and from the Natural Resources Canada (1995) Geographic Names of Canada database for that country. The yields reported in the literature were used to calculate mean decreases in yield per centimeter or metric ton of soil loss due to erosion. For ease of calculation and comparison of the various studies, we assumed linear yield declines even though in most cases observed yield declines were not linear. For studies using topsoil removal/addition and TSD as experimental methods, actual TSD values were used. To calculate yield impact per centimeter of soil loss in studies using soil phases as the experimental method, we assumed a difference of 7.5 cm between severely and moderately and between moderately and slightly eroded phases and a difference of 10 cm between slightly eroded and depositional phases (Soil Survey Staff, 1999). Standard conversion factors employed for the U.S. Census of Agriculture (NASS, 1999) were used for weights, measures, and yields of various commodities. Yield declines have generally been calculated using uneroded or slightly eroded phases as a reference, which may not be representative of farmers’ conditions that consist of a range of soil depths. We therefore used the mean yields across all experimental plots as the “standard” reference yield. It would be more correct to use the mean yield for the various crops obtained under farmer management for the areas where the experiments were implemented, but such information is not

10

C. DEN BIGGELAAR ET AL.

available in the desired format (i.e., disaggregated by county, year, and soil order or subgroup). Technological advances have enabled an increase in crop yields over time in spite of accelerated erosion. The impact of these advances is masked by averaging yields and yield impacts of soil loss over all experimental plots, soil series/subgroups, climate regions, experimental duration, and management practices. To account for the effect of technological advances on crop yield and yield loss due to erosion, we compared studies on maize carried out prior to and after 1960 by soil order and experimental method. For other crops, this comparison was not possible as there were too few studies in either one or the other time period. A decal comparison would be more desirable to assess the effect of technological advances, but there are too few soil erosion–productivity studies, even in North America, to conduct such an analysis. To estimate the annual amount of production loss due to erosion, we used four types of data: (1) the area of maize, wheat, soybeans, and cotton by soil order; (2) the mean annual erosion rates for various soil orders; (3) average crop yields obtained by farmers on those soils; and (4) average yield impacts calculated from the review of soil erosion–soil productivity studies. Crop areas by soil order were calculated from the 1997 NRI for the United States and from the Canadian Census of Agriculture for Canada. Average erosion rates by soil order were obtained from an overlay of the soil order map and the 1992 mean annual erosion rates on crop and CRP land for the United States and from Dumanski et al. (1994) and Agriculture and Agri-Food Canada (1998) for Canada. Crop yield data are averages of yields reported in the 1987, 1992, and 1997 Censuses of Agriculture for the United States and averages of the last 5 years of available data for Canada. The mean yield declines used for the calculations are average mean yield declines for the respective crops and soil orders across experimental methods. For ease of calculation, we assumed that erosion causes a uniform soil removal across the ﬁeld. For the estimation of economic impact, we multiplied the annual production loss estimates by the 2000 crop prices from the USDA Agricultural Baseline Projections to 2008 (USDA, 1999). The same prices were used for the calculations of economic impact of soil erosion in the United States and Canada assuming that farm prices are similar in both countries. Total annual production losses were divided by the crop area in each soil order to determine productivity losses on a per-hectare basis. The resulting ﬁgure was then multiplied by the same prices as used to calculate the overall economic impact to obtain the economic impact per hectare.

C. LOCATION OF EROSION–PRODUCTIVITY STUDIES From a review of the literature, 90 ﬁeld-based studies on soil erosion and productivity were identiﬁed. The database resulted in a total of 197 separate records,

11

IMPACT OF SOIL EROSION ON CROP YIELDS

covering 75 soil subgroups—59 in the United States and 16 in Canada. The studies are not evenly dispersed over the territory of these countries, however. Rather, soil erosion–productivity studies are concentrated in a few areas: the corn belt in the Midwest and the Palouse area in the Paciﬁc Northwest in the United States and Alberta in Canada. Figure 2 shows the locations of the various experiments in relation to soil orders. Looking at the study locations on the map of average annual erosion rates shown in Fig. 3 shows that most studies were not done in areas with the highest erosion rates. For example, only one study was located in eastern Colorado, although most of the cropland in that part of the state erodes at a rate of 18 Mg ha−1 yr−1 or more (based on 1992 NRI data). The studies in Texas were also not done in the areas experiencing the most severe erosion in the state. On the other hand, many of the studies in Illinois, Indiana, New York, North Dakota, and Ohio were located on land eroding at a rate of <4 Mg ha−1 yr−1. Mollisols and Alﬁsols are the most frequently studied soils in both the United States and Canada (Fig. 2 and Table I), reﬂecting the importance of these soils for the production of maize, soybeans, wheat and barley. In the United States, a diversity of crops grown are on each soil type. Maize, however, was the most common crop in the experiments on Alﬁsols, Mollisols, and Ultisols. Wheat studies were primarily conducted on Mollisols, whereas the impact on soybean yield has been tested on many soils but mostly on Alﬁsols. In the Canadian studies, maize was the dominant crop in experiments on Alﬁsols, whereas wheat was the dominant test crop on Mollisols. The use of erosion phases was the most commonly used method to determine the effect of erosion on yield. This method was used in 41% of the cases, followed Table I Soil Orders Represented in the Database (Number and Percentage of Records) United States

Canada

Soil order

No. (%) of records

No. of soil subgroups represented

No. (%) of records

No. of soil subgroups represented

Alﬁsols Mollisols Ultisols Aridisols Entisols

54 (35%) 69 (45%) 22 (14%) 1 (0.7%) 2 (1.3%)

17 24 7 1 2

17 (41%) 24 (57%) n.d.a n.d. n.d.

6 9 n.d. n.d. n.d.

Inceptisols Oxisols Spodosols Total

4 (2.6%) 1 (0.7%) 1 (0.7%) 155 (100%)

4 1 1

n.d. n.d. 1 (2%) 42 (100%)

n.d. n.d. 1

a

n.d. = no data.

12 Figure 2 Soil order map of North America showing the location of the soil erosion-productivity experiments included in this chapter.

13 Figure 3 Average annual water and wind erosion on cropland and CRP land in 1992 in relation to the study sites (source: 1992 NRI).

14

C. DEN BIGGELAAR ET AL.

by topsoil removal and addition (27%), TSD (22%), and depth to fragipan (7%). Soil surveys were used in one study, whereas management practices were used in six studies.

IV. RESULTS A. EXTENT OF EROSION IN NORTH AMERICA Concerns about the severity of soil erosion in the United States date to the 1930s, when Bennett (1931) warned that millions of hectares of agricultural land had been “devastated” by erosion (Cleveland, 1995). The rate of erosion on U.S. cropland has declined in recent years due to conservation tillage and other measures encouraged by the 1985 farm bill and the removal from production of the most highly erodible cropland under the Conservation Reserve Program (CRP) (Magleby et al., 1995). According to 1997 NRI data, the mean annual erosion rate on nonfederal cultivated cropland in the United States has declined from 17.9 Mg ha−1 in 1982 to 16.8 Mg ha−1 in 1987, 13.9 Mg ha−1 in 1992, and 12.5 Mg ha−1 in 1997 (NRCS, 1999). These trends correspond to a decline in soil erosion in the United States from 2.8 billion Mg yr−1 to 1.72 billion Mg yr−1 between 1982 and 1997. In 1992, seven states exceeded their 1982 erosion rates (i.e., Arizona, Connecticut, Minnesota, New Mexico, Utah, Washington, and Wyoming); in 1997, only Arizona and Connecticut remained above 1982 erosion rates (Fig. 4). Figure 4 also shows the erosion rate of cultivated cropland for Canada by province in 1997. Erosion rates are relatively high in Ontario and New Brunswick (10–15 Mg ha−1), but rates in the prairie provinces are comparable to those in adjacent U.S. states. Political units are not a particularly useful way to show erosion rates, as they may contain numerous soils differentially affected by erosion and vary in the amount of land in cultivation. The map in Fig. 3 shows the average annual soil erosion on cropland and CRP land in 1992. Comparing the maps in Figs. 3 and 4 reveals that the high erosion rates, for example, in Arizona, Utah, and Nevada are due largely to relatively small cultivated areas eroding at very high rates. To estimate the loss of production due to erosion, we need to know the amount of cropland and cropland erosion rates for predominant soil orders. The data in Table II show average erosion rates by soil order. Erosion rates are highest on cropland on Aridisols (i.e., >25 Mg ha−1). On soils that are most important to crop production in the United States (i.e., Alﬁsols, Mollisols, and Ultisols), average erosion rates are lower (11.5, 11.5, and 9.8 Mg ha−1 yr−1, respectively) and are at or below the tolerable rate (T) established by NRCS. Assuming a bulk density of 1.5 Mg m−3, the erosion rates on these soil orders translate into a soil loss of 0.65 to 0.76 mm yr−1 (Table II). Although T values vary by soil series and can be as

IMPACT OF SOIL EROSION ON CROP YIELDS

15

Figure 4 Mean annual wind and water erosion rates on nonfederal, cultivated cropland by state or province, 1982 to 1997 (source: 1997 NRI; Agriculture and Agri-Food Canada, 1998).

16

C. DEN BIGGELAAR ET AL.

Figure 4

Continued

IMPACT OF SOIL EROSION ON CROP YIELDS

17

Table II Total Area of Soil Orders in the United States and Mean Annual Water and Wind Erosion on Cultivated Cropland and CRP on Those Soil Ordersa Mean erosion rate Area (Mha)

Mg ha−1 yr−1

Alﬁsols

126.9

11.5

0.77

Andisols

15.6

5.0

0.33

76.0 112.2 15.0 88.9 196.7 31.8

25.7 10.7 3.3 9.6 11.5 4.8

1.71 0.71 0.22 0.64 0.77 0.32

84.2 18.2

9.8 10.7

0.65 0.71

Soil order

Aridisols Entisols Histosols Inceptisols Mollisols Spodosols Ultisols Vertisols a

mm ha−1 yr−1

Source: Soil Survey Staff (1999); 1992 NRI.

low as 2.2 Mg ha−1 (1 ton acre−1), for most soils they are 11.2 Mg ha−1. Examples of some representative soils are given in Table III. The percentage of land eroding at or below T value has increased over time. Based on the 1997 NRI data, average annual sheet and rill erosion in the United States was at or below T on 81% of cultivated cropland, up from 73% eroding at or below T in 1982. For wind erosion, 85% of cultivated cropland eroded at or below T values in 1997 (up from 79% in 1982). No precise ﬁgures for erosion rates are available for Canada. According to a report by Dumanski et al. (1994), water erosion exceeded 10 Mg ha−1 on 13% Table III Tolerable Erosion Loss of Selected Soil Seriesa Soil subgroup

Location

T (Mg ha−1 yr−1)

Fayette Keene Austin

Typic Hapludalf Aquic Hapludalf Udorthentic Haplustoll

Wisconsin Ohio Texas

11.2 9.0 4.5

Marshall Shelby Cecil

Typic Hapludoll Typic Argiudoll Typic Kanhapludult

Iowa Missouri Georgia

11.2 11.2 6.7

Soil series

a

Source: Terry (1997).

18

C. DEN BIGGELAAR ET AL.

of Canada’s cultivated land in 1986, whereas wind erosion exceeded 10 Mg ha−1 on 15% of cultivated land. These ﬁgures are comparable to the extent of erosion reported for the United States in the NRI (81 and 85% eroding at or below T for water and wind erosion, respectively). A report by Agriculture and Agri-Food Canada (1998) estimated average erosion rates of 4.8 Mg ha−1 yr−1 for Mollisols and 7.2 Mg ha−1 yr−1 for Alﬁsols, which are much lower than the rates of erosion on these soils in the United States. Tomlin and Umphrey (1996) concluded that soil erosion is less severe in Canada than in most other countries because of its temperate climate and the effects of snow cover, a shorter growing season, and a shorter agricultural history. These authors did not see a clear nationwide trend for reduced soil productivity, although yield improvements due to chemical and energy inputs and improved varieties and agronomic practices may be masking the effect of soil degradation.

B. IMPACT OF EROSION ON CROP YIELDS 1. Maize The majority of the studies we reviewed tested the effect of erosion on maize yields. In the United States, erosion–productivity studies on maize were carried out in 18 states, although most of the studies were done in Illinois (27% of the database records), South Dakota (14%), Missouri (11%), and Indiana (9%). Few studies have been conducted in Iowa and Ohio (6% of the records each). In Canada, all erosion–productivity studies on maize were located in southern Ontario. The mean maize yield across experimental plots in the studies reviewed ranged from 3.3 to 7.8 Mg ha−1 (Table IV). The mean yield decline per centimeter of soil loss ranged from 0.04 Mg ha−1 on Mollisols to 0.153 Mg ha−1 on Ultisols. Mean declines in maize yields are similar on all Mollisols regardless of the experimental method used: 0.054 Mg ha−1 cm−1 soil loss using depth to fragipan, 0.043 Mg ha−1 cm−1 using erosion phases, 0.04 Mg ha−1 cm−1 using TSD, and 0.047 Mg ha−1 cm−1 in topsoil removal and addition experiments. For Alﬁsols, the range in yield decline was larger than for Mollisols, ranging from 0.092 Mg ha−1 cm−1 on TSD experiments to 0.153 Mg ha−1 cm−1 on experiments using topsoil removal and addition. The mean yield decline in depth to fragipan studies was similar to the mean yield decline in TSD studies, although the range of observed yield declines was larger in the depth-to-fragipan studies. Erosion phase studies reported mean yield declines similar to those in TSD and topsoil removal studies. The range of mean yield declines reported in the studies was also larger on Alﬁsols than on Mollisols. Overall, studies using erosion phases as their experimental methods produced the widest range of mean yield declines. Yield declines

Table IV Impact of Erosion on Maize Yields Experimental set-up

Soil order

No. of records

Mean duration of experiments (years)

Mean yield of experiment (Mg ha−1)

Depth to fragipan

Alﬁsols Mollisols

7 6

3 4

7.3 7.3

0.093 (0.039–0.273) 0.054 (0.016–0.093)

Erosion phases

Alﬁsols

42

3

6.5

0.126 (−0.012–1.030)

Mollisols Ultisols

21 8

5 3

7.0 4.7

0.043 (−0.080–0.189) 0.143 (−0.029–0.336)

18, 21, 47, 53, 54, 60, 61, 62, 63, 65, 74, 78, 85 53, 54, 60, 61, 62, 63, 74, 82 66, 77, 86, 87

Topsoil depth

Alﬁsols Mollisols Ultisols

6 7 4

3 3 4

7.8 5.2 3.3

0.092 (0.010–0.202) 0.040 (−0.009–0.099) 0.107 (0.049–0.167)

4, 23, 52, 65, 67 2, 51, 52, 64 1, 33, 45, 65

Topsoil removal and addition

Alﬁsols Mollisols Oxisols

9 10 2

5 5 1

5.9 6.1 4.0

0.153 (−0.008–0.740) 0.047 (0.009–0.094) 0.118 (0.097–0.139)

11, 50, 70, 72 2, 9, 24, 27, 40, 49, 55, 63 80

a b

Negative numbers in parentheses indicate a yield increase. See references; numbers correspond to superscripts.

Mean yield decline (range) (Mg ha−1 cm−1 soil loss)a

Sourcesb 20, 52, 70, 90 24, 41, 52

20

C. DEN BIGGELAAR ET AL.

across all studies ranged from −0.012 to 1.03 Mg ha−1 cm−1 for Alﬁsols and −0.08 to 0.189 Mg ha−1 cm−1 for Mollisols (a negative decline indicates that yields were higher on the eroded plots than on the control plots without erosion). Erosion phases and TSD were the only two experimental methods used to determine the impact of erosion on maize yields on Ultisols. The mean yield decline was 0.143 Mg ha−1 cm−1 soil loss for erosion phase studies compared with 0.107 Mg ha−1 cm−1 for TSD studies. In relative terms, maize yield decline was greatest on Ultisols and Oxisols (3% cm−1 soil loss compared to 0.7% cm−1 for Mollisols). Relative mean yield declines on Ultisols were slightly larger in the TSD studies (3.2%) than in the erosion phase studies (3%), but showed little variation across the experimental methods used on Mollisols (0.6–0.8%). For Alﬁsols, the relative mean yield declines ranged from 1.3% cm−1 in the TSD studies to 2.6% cm−1 on the topsoil removal studies. The average yield decline on Alﬁsols across experimental methods was 1.8% cm−1 of soil loss. Because the studies reviewed in this chapter cover a 50-year time span and are aggregated at the level of soil order, average yields from the experiments in Table IV are lower than those presently achieved on farmers’ ﬁelds. High-yielding varieties, increased use of fertilizers and pesticides, and improved management practices have led to enormous increases in yields since 1950. These advances are masked, however, by averaging yields over all experimental plots across soil series/subgroups, climatic regions, and time. To examine the possible effects of technological advances, results of studies conducted on maize prior to 1960 were compared to those conducted after 1960. The data in Table V show that mean maize yield of the experiments nearly doubled in studies conducted after 1960, irrespective of soil orders and experimental designs, compared to those prior to 1960. More importantly, the mean decline in yield per centimeter of soil loss declined on the Alﬁsol and Mollisol studies, but not on the Ultisol studies. In the TSD experiments, the mean yield decline decreased 32% (from 0.118 to 0.08 Mg ha−1 cm−1) in Alﬁsols and 108% (from 0.059 to −0.005 Mg ha−1 cm−1) in Mollisols. In relative terms, mean yield decline decreased from 2.7 to 0.9% in Alﬁsols and from 1.6 to −0.06% in Mollisols. Although yields of maize grown on Ultisols increased 260% (from 1.79 to 4.73 Mg ha−1 yr−1), the mean yield decline per centimeter of soil loss on these soils increased by 171% (from 0.058 to 0.157 Mg ha−1 cm−1). The yield decline cm−1 of soil loss remained the same in relative terms (3.2 vs 3.3%), though. Last, in the topsoil removal study on a Mollisol, maize yields nearly doubled but yield losses due to soil loss declined in both absolute (from 0.059 to 0.046 Mg ha−1 cm−1) and relative terms (from 1.8 to 0.7%). This demonstrates that changes in technology affect not only yields but also yield response to soil loss; for maize, technological advances may reverse some of the effects of soil loss.

Table V A Comparison of Mean Yields and Yield Impacts Resulting from Erosion in Studies Published before and after 1960 Before 1960

After 1960 (inclusive)

Soil order

No. of records

Mean duration of experiments (years)

Mean yield of experiment (Mg ha−1)

Mean yield decline [Mg ha−1 cm−1 (%)]

No. of records

Mean duration of experiments (years)

Mean yield of experiment (Mg ha−1)

Mean yield decline [Mg ha−1 cm−1 (%)]

Topsoil depth

Alﬁsols Mollisols Ultisols

2 5 2

3 2 5

4.4 3.8 1.8

0.118 (2.7%) 0.059 (1.6%) 0.058 (3.2%)

4 2 2

3 8 3

8.7 8.8 4.7

0.080 (0.9%) (−0.005) (−0.06%) 0.157 (3.3%)

Topsoil removal and addition

Mollisols

1

2

3.3

0.059 (1.8%)

9

5

6.4

0.046 (0.7%)

Experimental set-up

22

C. DEN BIGGELAAR ET AL.

2. Wheat Studies which estimated the impact of erosion on wheat yields were primarily conducted in the Palouse region of eastern Washington, eastern Oregon, and Idaho and the Canadian prairie provinces (e.g., Alberta, Saskatchewan, and Manitoba). Most studies were conducted on Mollisols, using topsoil removal and addition as the experimental method. A study on the inﬂuence of management practices on soil erosion and wheat yields by Monreal et al. (1995) is the longest running experiment included in this chapter. This experiment on an Alﬁsol in Breton, Alberta comprised 60 years of data, whereas the experiment in Swift Current, Saskatchewan was conducted for 23 years (Table VI). The mean yield in these studies ranged from 1.6 to 8.0 Mg ha−1, while the mean decline in yield due to soil loss by erosion ranged from 0.005 Mg ha−1 cm−1 [a wind erosion study by Larney et al. (1998) in Lethbridge, Alberta] to 0.143 Mg ha−1 cm−1 on an Aridisol in Idaho (Table VI). Mean yield decline in TSD studies on Mollisols was 19 kg ha−1 cm−1 higher than in studies using topsoil addition and removal (0.063 vs 0.044 Mg ha−1 cm−1, respectively). The range of yield declines reported in different studies was similar for the TSD studies (0.003–0.225 Mg ha−1 cm−1) and the topsoil removal and addition studies (−0.033–0.179 Mg ha−1 cm−1), even though the yield declines are lower in the topsoil removal studies. The relative decline in mean wheat yield as a result of soil loss was highest in the study of Monreal et al. (1995), which involved different management practices as the experimental method. Mean yield declined 6.4% cm−1 of soil loss on Alﬁsols and 6.7% cm−1 on Mollisols. In the topsoil removal studies on Alﬁsols by Izaurralde et al. (1998) and Larney et al. (1995), the yield decline was 5.4% cm−1 soil loss, which is particularly high, but was obtained from two studies of 1-year duration. Topsoil removal studies on Mollisols resulted in a mean yield decline of 2.3%, obtained from 15 studies with a mean duration of 4 years. For a few studies on wheat, separate measurements were made to determine the impact of erosion on straw yield. A 10-year study by Rasmussen et al. (1991) in Washington showed that wheat straw yield slightly increased (2 kg ha−1 cm−1 or 0.03%) with decreasing TSD (Table VII), whereas studies by Verity and Anderson (1990), Tanaka (1995), and Larney et al. (1995) showed that straw yields declined an average of 110 kg ha−1 cm−1 (range 0.053–0.187 Mg ha−1 cm−1) or 2.7% cm−1 of soil removed.

3. Soybeans The effects of soil loss on yields of soybeans were studied most frequently on Alﬁsols and Ultisols using erosion phases and topsoil removal and addition as the

Table VI Impact of Erosion on Wheat Yields

Soil order

No. of records

Mean duration of experiments (years)

Mean yield of experiment (Mg ha−1)

Management practices

Alﬁsols Mollisols

1 1

60 23

1.8 1.6

0.116 0.107

Topsoil depth

Alﬁsols Aridisols Mollisols

2 1 14

3 2 3

8.0 6.7 4.2

0.028 (0.015–0.041) 0.143 0.063 (0.003–0.225)

4, 42 10 7, 22, 37, 57, 58, 75, 81

Topsoil removal and addition

Alﬁsols

2

1

2.1

0.114 (0.108–0.119)

31, 38

42

4

1.9

0.044 (−0.033–0.179)

Experimental set-up

Mollisols

a

Negative numbers in parentheses indicate a yield increase. See references; numbers correspond to superscripts.

b

Mean yield decline (range) (Mg ha−1 cm−1 soil loss)a

Sourcesb 48 29

13, 14, 15, 25, 30, 31, 34, 35, 36, 38, 43, 44, 68, 69, 73

24

C. DEN BIGGELAAR ET AL. Table VII Impact of Erosion on Wheat Straw Yields

Experimental set-up

Soil order

Topsoil depth Mollisols Topsoil removal Mollisols and addition a

Mean duration Mean yield No. of of experiments of experiment records (years) (Mg ha−1) 1 5

10 3

6.5 4.1

Mean yield decline (range) (Mg ha−1 cm−1 soil loss)a (−0.002) 0.110 (0.053–0.187)

Sourcesb 58 35, 69, 73

Negative numbers in parentheses indicate a yield increase. See references; numbers correspond to superscripts.

b

experimental methods (Table VIII). Mean yields ranged from 0.9 Mg ha−1 on an Entisol on a lower coastal plain site in Alabama (McDaniel and Hajek, 1985) to 2.8 Mg ha−1 on Mollisols in Indiana (Schertz et al., 1985, 1989) and in the Palouse Hills in Washington (Wetter, 1977). In absolute terms, yield of soybeans appear to be little affected by erosion. Yield decline was less than 40 kg ha−1 cm−1 of soil loss on all soils except Ultisols, in which mean decline was about 75 kg ha−1 cm−1 (Table VIII). The ranges of mean yield declines reported in the various studies was also narrow, although the number of records considered is too small to draw any deﬁnitive conclusions. In relative terms, yield declines in these studies ranged from 0.5 to 4.4% cm−1 of soil loss. The largest yield losses occurred on Ultisols (4.1 and 4.3% cm−1 on erosion phases and TSD studies, respectively) and in TSD studies on an Entisol (4.4% cm−1) and an Inceptisol (3.4% cm−1). The relative mean yield decline in Alﬁsols was smallest in studies using topsoil removal (0.5% cm−1 soil loss) and largest on TSD experiments (1.8% cm−1 soil loss). Relative mean yield declines were similar on Mollisols in erosion phase and topsoil removal studies (1.3 and 1.5% cm−1, respectively). The relative decline in yield in Mollisols in Alabama was 3.1% cm−1 (Hairston et al., 1989), more than double the decline observed in Mollisols elsewhere. The relative decline computed for this study is similar to that for Entisol and Inceptisol. However, there was only one study on each of these three soils covering a 2-year period. 4. Cotton The highest relative yield decline of 12% in Table IX was in seed cotton grown on Ultisol in a topsoil removal experiment. This study by Latham (1940), however, is among the earliest erosion–soil productivity studies. The relative decline may

Table VIII Impact of Erosion on Yields of Soybeans

Experimental set-up

Soil order

Depth to fragipan

Alﬁsols

Erosion phases

Alﬁsols Mollisols Ultisols

Topsoil depth

Alﬁsols Entisols Inceptisols Mollisols Ultisols

Topsoil removal and addition

Alﬁsols Mollisols

a

No. of records

Mean duration of experiments (years)

Mean yield of experiment (Mg ha−1)

Mean yield decline (range) (Mg ha−1 cm−1 soil loss)a

3

3

2.1

0.014 (0.013–0.016)

10 3 10

5 6 3

2.5 2.8 1.8

0.026 (−0.001–0.070) 0.036 (0.033–0.040) 0.074 (0.033–0.113)

1 1 1 1 3

2 2 2 2 2

1.9 0.9 1.1 1.2 1.8

0.035 0.040 0.037 0.036 0.077 (0.072–0.082)

45 45 45 26 45, 76

7 2

4 4

2.5 2.7

0.013 (0.001–0.041) 0.040 (0.040–0.040)

56, 71, 72, 79 27

Negative numbers in parentheses indicate a yield increase. See references; numbers correspond to superscripts.

b

Sourcesb 59, 90 16, 18, 60, 61, 74, 89 60, 61, 75 8, 86, 87

Table IX Impact of Erosion on Cotton Yields Experimental set-up Erosion phases

Soil order

No. of records

Mean duration of experiments (years)

Mean yield of experiment (Mg ha−1)

Mean yield decline (range) (Mg ha−1 cm−1 soil loss)a

Sourcesb

Cotton

2

3

2.3

0.067 (0.047–0.087)

87

Topsoil depth

Cotton

4

4

1.2

0.040 (0.028–0.054)

1, 45, 65

Topsoil removal and addition

Cotton-seed

1

4

1.0

0.119

a b

Ultisols

Crop

Negative numbers in parentheses indicate a yield increase. See references; numbers correspond to superscripts.

39

IMPACT OF SOIL EROSION ON CROP YIELDS

27

be smaller if modern varieties and production technologies were used. The TSD studies in Ultisols with cotton show a mean yield decline of 0.04 Mg ha−1 cm−1, or 3.3% cm−1, while in erosion phase studies mean yield decline was 2.7% cm−1 (0.067 Mg ha−1 cm−1). Absolute yield decline in these studies (Adams, 1949; Stallings, 1957; McDaniel and Hajek, 1985; and Langdale et al., 1987) ranged from 0.028 to 0.087 Mg ha−1 cm−1 and may provide a more realistic estimation of the impact of erosion on cotton yields. 5. Hay and Fodder Crops There are six published reports on erosion’s impact on the productivity of hay and fodder crops, ﬁve of which used TSD as the experimental method and one of which used the topsoil removal and addition method (Table X). Data on fodder grasses in the topsoil removal experiment on Mollisols by Greb and Smika (1985) showed little to no impact of erosion; yield of crested wheatgrass (Agropyron cristatum L. Gaertn.) declined 0.013 Mg ha−1 cm−1 of soil loss (1.7%), whereas that of sudan grass [Sorghum sudanense (Piper) Stapf.] actually increased 14 kg ha−1 cm−1 of soil loss. The yield of russian wildrye [Psathyrostachys juncea (Fisch.) Nevski] decreased at an average rate of 2 kg ha−1 cm−1 of soil loss (0.2% cm−1). In the TSD experiments, there was a decline in the average yield with progressive reduction of TSD. Mean yield decline was 1.5% cm−1 for both vetch (Vicia sativa L.) grown on Ultisols (0.247 Mg ha−1 cm−1) and alfalfa (Medicago spp. L.) grown on Mollisols (0.025 Mg ha−1 cm−1). The percentage decline in alfalfa yield grown on Aridisols was about half of that for alfalfa grown on Mollisols (0.8% cm−1), although the measured yield decrease was larger in absolute terms (0.048 Mg ha−1 cm−1). Hay yields on Alﬁsols declined 1.1% cm−1 (0.089 Mg ha−1 cm−1) and were the same in both studies. The relative impact of erosion on the yield of hay and fodder crops is small, with little variation reported across the limited number of studies on these crops. 6. Miscellaneous Crops A variety of other crops were studied by researchers on a limited scale. Most of these studies were conducted on one soil series only, and studies were of a 1- to 5-year duration. Six studies investigated the impact of erosion on oat (Avena sativa L.) yields, and two studies each involved potatoes (Solanum tuberosum L.), beans (Phaseolus vulgaris L.), and barley (Hordeum vulgare L.). Three studies involved grain sorghum [Sorghum bicolor (L.) Moench] (Table XI). Other crops reported on in the literature were silage maize, sugar beets (Beta vulgaris L.), and grapes (Vitis vinifera L.). Relative mean yield declines were smallest for the root crops: 0.4% for sugar beets on an Aridisol in Idaho (Carter et al., 1985) and 0.6 and 1.0% for potatoes produced on Aridisols and Spodosols, respectively

Table X Impact of Erosion on Hay and Fodder Yields Experimental set-up Topsoil depth 28

Soil order

Crop

No. of records

Mean duration of experiments (years)

Mean yield of experiment (Mg ha−1)

Mean yield decline (range) (Mg ha−1 cm−1 soil loss)a

Sourcesb

Alﬁsols Aridisols Mollisols

Hay Alfalfa Crested wheatgrass Alfalfa Vetch

2 1 1 1 1

Crested wheatgrass Russian wildrye Sudan grass

2 3 3

3 2 4 4 5 1 3 2

7.5 5.9 2.3 1.7 15.6 0.7 1.0 2.5

0.089 (0.089–0.089) 0.048 0.021 0.025 0.247 0.013 (0.010–0.016) 0.002 (−0.003–0.009) (−0.014) (−0.029–0.003)

4, 65 10 57 57 1 25 25 25

Ultisols Topsoil removal and addition

a b

Mollisols

Negative numbers in parentheses indicate a yield increase. See references; numbers correspond to superscripts.

Table XI Impact of Erosion on Miscellaneous Crop Yields

29

Experimental set-up

Soil order

Crop

No. of records

Mean duration of experiments (years)

Mean yield of experiment (Mg ha−1)

Management practices Erosion phases

Spodosols Alﬁsols

Potatoes Silage maize Beans Oats Sorghum

1 1 1 1 4

1

33.5

Topsoil depth

Alﬁsols

Barley Oats

1 1

1 1 1 3 4 2

20.8 0.6 1.1 5.2 1.6

0.518 0.030 0.063 −0.062 (−0.118–0.016) 0.058 0.020

32 32 19 88 65 4

Aridisols

Beans Barley Potatoes Sugar beets Grapes Oats

1 1 1 1 1 2

2 2 2 2 2 5

2.3 5.4 52.7 64.5 7.8 2.5

0.040 0.057 0.340 0.289 0.32 0.061 (0.043–0.080)

10 10 10 10 3 1, 65

12 1

3 2

3.7 2.1

0.052 (0.006–0.108) 0.017

17, 83 49

Inceptisol Ultisols Topsoil removal and addition a b

Mollisols

Sorghum Oats

Negative numbers in parentheses indicate a yield increase. See references; numbers correspond to superscripts.

Mean yield decline (range) (Mg ha−1 cm−1 soil loss)a 0.340

Sourcesb 12

30

C. DEN BIGGELAAR ET AL.

(Carter et al., 1985; DeHaan et al., 1999). The yield declines of the four crops tested by Carter et al. (1985) were smaller, both in absolute and relative terms, than the yield declines of crops grown on other soils. The absolute yield decrease in barley on the Aridisol and the Alﬁsol (Table XI) was about the same (0.057 and 0.058 Mg ha−1 cm−1, respectively), but in relative terms the decline was much smaller (1.1%) on the Aridisol than on the Alﬁsol (3.7%). The difference in yield, however, cannot be solely attributed to a difference in soils; the study on Aridisols was conducted in the 1980s, whereas that on the erosional impact on barley yield on the Alﬁsol dates from 1957. Advances in crop management and the availability of high-yielding barley varieties led to higher yields, decreasing the relative decline in yields resulting from erosion. The results of studies on sorghum are mixed; topsoil removal and addition studies on Mollisols by Eck (1968, 1987) and Eck et al. (1965) in Texas show a 1.4% decline in yields, whereas a study using erosion phases on Ultisols by Langdale et al. (1987) in Georgia showed that yields increased 1.2% cm−1 soil loss. Erosion’s effects on oats were studied by six researchers on three soils using three experimental methods. Mean yields of all experiments ranged from 1.1 to 2.5 Mg ha−1 [no yield data were reported by Barre (1939)], and yield decline ranged from 0.017 to 0.063 Mg ha−1 cm−1 (Table XI). The relative yield decline was smallest in a topsoil removal study on Mollisols (0.8%) by Murray et al. (1939) and highest on an erosion phases study on an Alﬁsol (5.8%) by Fenton et al. (1971).

V. CROP PRODUCTION LOSS DUE TO EROSION IN NORTH AMERICA Based on the average decline in yield of different crops for each centimeter of soil loss presented in the previous section, we calculated the potential loss of production due to erosion. Susceptibility to erosion differs among soils (Table II). The loss of 1 cm of soil may occur faster on Aridisols (eroding at an average rate of 25.7 Mg ha−1 yr−1) than on Alﬁsols and Mollisols (eroding at an average rate of 11.5 Mg ha−1 yr−1). For the United States, we calculated the annual loss of production using the average erosion rates by soil orders from Table II. For Canada, calculations are based on average annual erosion rates of 4.8 Mg ha−1 for Mollisols and 7.2 Mg ha−1 for Alﬁsols (Agriculture and Agri-Food Canada, 1998). The results of the calculations are shown in Table XII for the United States and Table XIII for Canada. In the United States, the maximum potential crop production lost annually through accelerated erosion is 229 × 103 Mg of maize, 54 × 103 Mg of wheat,

31

IMPACT OF SOIL EROSION ON CROP YIELDS Table XII

Potential Annual Loss in the Production of Maize, Wheat, Soybean, and Cotton in the United States as a Result of Erosion

Soil order

Area in cropa (Mha)

Mean yielda (Mg ha−1)

Mean yield impactb [Mg ha−1 cm−1 (%)]

Annual loss of productionc [103 Mg (%)]

Maize

Alﬁsols Mollisols Ultisols

10.8 17.7 2.8

7.5 7.9 5.8

0.133 (1.8%) 0.048 (0.8%) 0.131 (3.1%)

111.1 (0.14%) 86.1 (0.06%) 32.0 (0.2%)

Soybeans

Alﬁsols Mollisols Ultisols Alﬁsols Mollisols

7.7 10.9 2.9 3.4 20.5

2.4 2.7 1.8 3.1 2.3

0.021 (0.9%) 0.037 (1.5%) 0.075 (4.2%) 0.028 (0.4%) 0.054 (1.4%)

12.9 (0.07%) 33.5 (0.12%) 13.8 (0.27%) 3.2 (0.03%) 50.4 (0.11%)

0.8

1.1

0.049 (3.1%)

1.9 (0.2%)

Crop

Wheat Cottond

Ultisols

a

Mean of 1987, 1992, and 1997 U.S. agricultural census data. Decrease in yield is calculated as the percentage of mean experimental yield across experimental methods (Tables III, V, and VI). c Based on average annual erosion rates of 11.46, 11.54, and 9.78 Mg ha−1 yr−1 on Alﬁsols, Mollisols, and Ultisols, respectively (1992 NRI data) and assuming a bulk density of 1.5 Mg m3. d Cotton production is reported in bales in the census; conversion to megagrams is based on mean bale weight of 217.72 kg. b

Table XIII Potential Annual Loss in the Production of Maize, Wheat, and Soybean in Canada as a Result of Erosion

Crop Soil order Wheat Alﬁsols Mollisols Maize Alﬁsols a

Area in cropa Mean yieldb Mean yield impactc Annual loss of productiond (Mha) (Mg ha−1) [Mg ha−1 cm−1 (%)] [103 Mg (%)] 0.36 9.90 1.05

2.5 2.2 7.0

0.114 (5.7%) 0.047 (2.7%) 0.073 (1.5%)

2.5 (0.27%) 19.0 (0.09%) 5.3 (0.07%)

Source: 1996 Census of Agriculture, Statistics Canada. Sources: Ontario Ministry of Agriculture, Food and Rural Affairs; Alberta Department of Agriculture, Food and Rural Development; Saskatchewan Department of Agriculture and Food; Manitoba Department of Agriculture and Food; and Quebec Ministry of Agriculture, Fisheries and Food. c Decrease in yield is calculated as the percentage of mean experimental yield across experimental methods (Tables III, V, and VI). d Based on average annual erosion rates of 7.2 and 4.8 Mg ha−1 yr−1 on Alﬁsols and Mollisols, respectively (Agriculture and Agri-Food Canada, 1998) and assuming a bulk density of 1.5 Mg m−3. b

32

C. DEN BIGGELAAR ET AL.

61 × 103 Mg of soybeans, and 1.9 × 103 Mg of cotton, respectively (Table XII). The relative decline in production ranges from 0.03 to 0.3% yr−1, or 0.9 to 11.6 kg ha−1 yr−1. Production loss is low for soybeans and wheat on Alﬁsols (0.07% yr−1 and 0.03% yr−1, respectively) and maize on Mollisols (0.06% yr−1). Production losses are intermediate for maize on Alﬁsols (0.14% yr−1) and soybeans and wheat on Mollisols (0.12% yr−1 and 0.11% yr−1, respectively). Crops grown on Ultisols show the highest decline in production as a result of erosion; relative production loss of maize, soybeans, and cotton on these soils is 0.2% yr−1, 0.27% yr−1, and 0.2% yr−1, respectively. At 2000 prices (USDA, 1999), these losses represent a total value of U.S.$37.9 million for the selected crops and soil orders. Scaling up to account for the additional acreage of these crops that is grown on soils other than Alﬁsols, Mollisols, and Ultisols, an estimate annual loss of U.S.$55.6 million is generated for the United States as a whole. This ﬁgure, based on rates of erosion distinguished spatially by crop and soil order, is about 25% lower than the U.S.$82.9 million estimate that results from applying the 1992 average erosion rate across all soils and crops. The latter ﬁgure would be even higher if the 1982 average erosion rate was used. For Canada, the maximum potential production loss is 5.3 × 103 and 21.5 × 103 Mg for maize and wheat, respectively (Table XIII). Using the same prices as for the United States, this translates into a loss of U.S.$3.2 million per year for the selected crops and soil orders. Relative production loss is less than 1/10th of 1% for maize on Alﬁsols (0.07% yr−1) and wheat on Mollisols (0.09% yr−1). Annual production loss of wheat grown on Alﬁsols in Canada, however, is more than four times higher than that on Mollisols (0.27% yr−1). This difference may be due to the different types of wheat grown on these soils: winter wheat on Alﬁsols and spring wheat on Mollisols. In both countries, these aggregate losses to producers conceal spatial differences that are potentially signiﬁcant. Further GIS analysis would enable identiﬁcation of these differences across soil suborders, crops, and erosion rates. In addition to these productivity losses, the off-site societal costs of erosion are also potentially signiﬁcant (Crosson 1986, 1997; Ribaudo, 1989).

VI. ASSUMPTIONS Most studies relating crop productivity to soil erosion reviewed in this chapter were based on two assumptions: (1) All soil properties of the experimental site were similar when ﬁrst cultivated and (2) the productivity of the site was uniform until erosion occurred (Daniels et al., 1987). These assumptions relate any reduction in yield to differences in TSD and thus to erosion severity. Williams and Tanaka

IMPACT OF SOIL EROSION ON CROP YIELDS

33

(1996), however, showed that, at least in the Northern Great Plains, differences in TSD on the landscape are not entirely due to soil erosion. Soils within a landscape or even within one ﬁeld can have varying TSD and solum thickness, sometimes even within small distances. The soils in some positions on the landscape may not have developed as thick of an A-horizon or as mature a soil proﬁle due to different rates of soil formation caused by differences in topography and climate. The assumptions of uniform soil properties and equal productivity, therefore, are usually not valid (Daniels et al., 1987). Research by Lamb et al. (1995, 1997) in Minnesota; Halvorson (1999) at the Kellogg Biological Station near Battle Creek, Michigan; and Karlen et al. (1990) in South Carolina showed great variability in yields over space and time. These researchers found that yields were not spatially consistent over time, i.e., areas with good and poor yields were not similar among years. Crop yield differences on eroded and slightly eroded soils, therefore, may not be the result of TSD differences due to erosion alone. Nevertheless, erosion does have adverse effects on yields, but the precise mechanisms are not yet well understood.

A. RANGE OF EXPERIMENTAL METHODS, MANAGEMENT PRACTICES, AND TIME PERIODS The principal limitation of estimating productivity loss from the available literature is that data are obtained from a range of experimental methods and management practices over varying time periods. While the impact on crop yields and production losses appear to be similar for the different methods used, they are not directly comparable. Topsoil removal and addition experiments provide a measure of the potential impact of future erosion, whereas the other methods reﬂect the effects of past erosion. Each of these methods has inherent strengths, weaknesses, and biases that can result in the measured soil productivity response attributed to erosion being potentially confounded with other variables (Olson et al., 1994). For a description of the various methods employed in soil erosion–productivity studies, as well as their strengths and weaknesses, we direct the readers to NSESPRPC (1981), Rijsberman and Wolman (1984), Follett and Stewart (1985), Lal (1987, 1997, 1998), Larson et al. (1990), and Olson et al. (1994). Despite these differences, for this analysis it was necessary to assume that the results of the studies were comparable. The studies considered in this chapter were carried out over a range of time periods, ranging from 1 to 60 years. Ninety percent of the studies included in this chapter had a duration of 5 years or less; of these, 29% had a duration of 1 year and 28% had a duration of 2 years. Only 4% of the studies collected data over a period of more than 10 years [studies by Alberts and Spomer (1987) and Monreal et al.

34

C. DEN BIGGELAAR ET AL.

(1995)]. According to Rijsberman and Wolman (1984), a period sufﬁciently long to show the effect of erosion on yields would generally be more than 10 years. Even with data over such a time period available, these authors argue that it would not be easy to distinguish trends in yields as a result of erosion from trends as a result of, for example, changes in technology, pest and disease incidence, and climate. Regarding climate, there may be considerable year-to-year variation in yields due to weather. In dry years, this may result in signiﬁcant decreases in yield on eroded plots, whereas in years with abundant rainfall, yields of eroded plots may be the same (and in some cases be higher) than the yield on the uneroded control plots. Shaffer et al. (1994), therefore, concluded that even 3 to 6 years of data may not be adequate to describe long-term climate impacts on crop response to erosion.

B. DIFFERENTIAL RELATIONSHIPS BETWEEN DEGRADATION AND YIELDS For ease of analysis and comparison of the data, we assumed that the relationship between soil degradation and soil productivity was linear for all cases. In reality, linear relationships are seldom found (Hopkins et al., personal communication). On Alﬁsols and Ultisols, Latham (1940) found that yield declines from the ﬁrst few centimeters of soil loss are usually greater than yield declines from subsequent soil loss. In contrast, research on Mollisols by Odell (1950), Tanaka and Aase (1989), and Verity and Anderson (1990) and on Vertisols by Hairston et al. (1989) and Miller et al. (1985) showed that yield declined gradually until a minimum, critical TSD was reached, after which a more rapid decrease in yield was observed. Massee (1990) and Larney et al. (1995) found that wheat yields related exponentially to depth of topsoil on Mollisols in Idaho and quadratically on six Mollisols in Alberta respectively. Productivity decline, therefore, is usually not linear with respect to soil loss, but the precise nature of the relationship is speciﬁc to the soil type and the environment (Stocking and Peake, 1985). Yield–degradation relationships may also not remain the same over the duration of the experiment because of ﬂuctuations in climate or other, noncontrolled variables. Swan et al. (1987) found that the relationship of corn yield to soil depth to residuum on Rozetta and Palsgrove soil series in Wisconsin was dependent on the year selected for measurement and on the associated climatic conditions that occurred during the growing season. Swan et al. go on to say that because the yearly depth–yield relationships are so strongly dependent on climatic conditions, accurate determination of the long-term effect of depth on yield must be based on a statistically sound representation of climatic conditions. With a limited number of years of observation, averaging the yearly depth–yield relationships may, therefore, not provide an accurate estimate of yield because of the variable yields observed between years.

IMPACT OF SOIL EROSION ON CROP YIELDS

35

C. EFFECTS OF TECHNOLOGICAL ADVANCES During the roughly 60 years covered by the studies included in this chapter (from 1939 to 1999), agricultural production practices have changed dramatically, increasing yields in spite of nationwide erosion rates that were well above tolerance levels for much of this period. For example, maize yields in Pottawattamie County, Iowa, increased at a rate of 0.037 Mg ha−1 yr−1 between 1929 and 1953 and 0.145 Mg ha−1 yr−1 during the 1957-to-1970 period (Spomer and Piest, 1982). Overall, crop output increased at an annual rate of 2% between 1948 and 1994 (Ahearn et al., 1998). The increase in yield is highly correlated with the increased use of fertilizers and agrichemicals; U.S. fertilizer use increased at an annual rate of 1.72% in the 1948-to-1994 period, while pesticides use grew at a compound annual rate of 4.73% during this period (Ahearn et al., 1998). The growth in fertilizer use was especially high in the 1970s: 4.73% per annum. The increasing use of chemical fertilizers and pesticides together with mechanization and the use of high-yielding hybrid varieties enabled drastic increases in yields and in production. However, the increase in crop yields made possible by science and technological advances may be masking the effects of erosion on long-term soil productivity (Lindstrom et al., 1986; Spomer and Piest, 1992; Ponzi, 1993; Dregne, 1995). Krauss and Allmaras (1982) and Kaiser (1967) concluded that wheat yields in the Palouse region of the northwestern United States would have been 22% higher in 1976 if the area had not experienced 90 years of excessive erosion. Actual yields on eroded sites had increased as a result of technological advances, despite the erosion.

VII. IMPLICATIONS FOR RESEARCH AND POLICY The amount of erosion occurring on U.S. cropland decreased between 1982 and 1997 as a result of the retirement of the most highly erodible land and the increasing use of conservation tillage. In 1997, the majority of cultivated cropland in the United States and Canada1 eroded at or below tolerable rates, translating into a soil loss of ≤0.8 mm yr −1.2 In general, the studies reviewed showed a decrease in productivity with accelerated soil erosion. Our results, therefore, concur with ﬁndings of earlier reviews of the relationship between soil erosion and productivity. The decline in yield, however, is not the same for different soils and crops. 1

Erosion risk classes in Canada are different from those in the United States. Very low, or tolerable, rate is <6 Mg ha−1 yr−1, while a low rate equals 6–11 Mg ha−1 yr−1 (Wall et al., 1997). 2 Assuming bulk densities of 1.5 g cm3 and annual erosion at a rate of T (11.2 Mg ha−1), 1 cm of soil would be lost in 13.3 years.

36

C. DEN BIGGELAAR ET AL.

A. DECLINE IN CROP YIELDS In general, for the various crops tested in the experiments, Mollisols recorded a smaller decline in yield due to erosion than Alﬁsols and Ultisols (in that order). In the United States, the average annual yield decline due to accelerated erosion for maize was 0.15% on Alﬁsols, 0.06% on Mollisols, and 0.2% on Ultisols. In Canada, maize yields declined 0.07% yr−1 on Alﬁsols. Overall, wheat yields declined by 0.03 and 0.11% yr−1 on Alﬁsols and Mollisols in the United States and 0.27 and 0.09% yr−1 on Alﬁsols and Mollisols in Canada as a result of erosion. In several studies, maize and wheat yields on Mollisols actually increased on moderately and severely eroded plots compared to slightly or noneroded plots. The relative wheat yield declines per centimeter of soil loss are higher in Canada than in the United States for similar soils. The reasons for this are not clear, but we surmise that some of the difference may be a result of aggregating soils at the soil order level. This aggregation may mask differences between soils that could become apparent at a lower level of aggregation (e.g., soil subgroup or soil series). Interactions with other factors (notably climate, management, and varieties) may also cause some of the observed differences. Average soybean yield declined 0.07, 0.12, and 0.27% yr−1 on Alﬁsols, Mollisols, and Ultisols, respectively, whereas average cotton yields on Ultisols declined 0.2% yr−1. The number of studies on other soil orders (Inceptisols, Entisols, and Aridisols) were too few in number to make any deﬁnite conclusions about the impact of erosion; the studies that have been done on those soils generally show a decrease in productivity with accelerated erosion. Similarly, the number of studies using crops other than maize, wheat, soybeans, or cotton is too small to draw conclusions as to how erosion affects their yields. With the steady increase in crop yields over time due to technological advances, the relative reduction of yields per centimeter of soil loss has declined. A comparison of pre- and post-1960 studies on maize in the present chapter revealed that the absolute yield loss per centimeter of soil erosion declined as well on both Alﬁsols and Mollisols, but not on Ultisols. On Ultisols, both yield and erosion-induced loss of yield of maize increased so that the relative yield decline per centimeter of soil loss remains the same. A comparison of yields across time periods for other crops was not possible. Nevertheless, this ﬁnding has important implications for using the results of soil erosion–productivity studies to make predictions about the impact of erosion. First, this comparison shows that, at least for maize, technology can reverse some of the productivity decline due to erosion as revealed in the narrowing of the yield gap between slightly and severely eroded soils. Second, if the impact of erosion on yield (a measure of historical production used as a proxy to determine the potential for future production) changes due to technological advances as revealed in smaller absolute and relative declines in yield per centimeter of soil loss, then continued use of the results of “old” studies may exaggerate

IMPACT OF SOIL EROSION ON CROP YIELDS

37

estimates of the impact of erosion on productivity. Continuing research to monitor the effects of erosion on soil productivity is, therefore, necessary.

B. PRODUCTIVITY AND ECONOMIC IMPACT The amount of production decline resulting from erosion in North America equals 234.5 × 103 Mg yr−1 of maize, 60.2 × 103 Mg yr−1 of soybeans, 75.0 × 103 Mg yr−1 of wheat, and 1.9 × 103 Mg yr−1 of cotton. These ﬁgures represent the maximum potential annual losses due to accelerated erosion based on the assumption that soil is lost to a uniform depth over the entire ﬁeld surface. The total economic value of erosion-induced loss of soil productivity on Alﬁsols, Mollisols, and Ultisols in North America (using 2000 prices) amounts to U.S.$41.2 million per year, U.S.$37.9 million for the United States and the remainder in Canada. To the extent that commodity prices increase as a result of erosion-induced declines in production, the value of production losses would be higher than we estimated using 2000 prices. As these crops are also grown on other soil orders, the economic impact for North America as a whole is larger. We estimate that the economic impact for Canada is fairly accurate, as little wheat and maize are produced on soil orders other than Alﬁsols and Mollisols. However, the estimated cost of yield losses due to erosion in Canada of U.S.$3.2 million is smaller than previous estimates by Sparrow (1984) and Dumanski et al. (1986, 1994). For the United States as a whole, based on the ratio of the area of maize, soybeans, wheat, and cotton on Alﬁsols, Mollisols, and Ultisols to the total area for these crops, the total annual economic impact of erosion on these crops is estimated at U.S.$55.6 million. Finer spatial resolution has improved this ﬁgure from the estimate of U.S.$82.9 million that results from application across all soils of the 1992 U.S. average erosion rate. This ﬁgure is smaller than earlier estimates by Alt et al. (1989), Crosson (1986), and Pimentel et al. (1995), but similar to estimates by Larson et al. (1983) and Crosson (1997). The costs in lost yields, however, are only a part of the total costs incurred by farmers, as they do not include the costs incurred to reduce or offset the yield effects of erosion (Crosson, 1997). On a per-hectare basis, reﬂecting the unit of farmer decision making about erosion-mitigating practices, the annual impact of erosion on crop yield is small, ranging from <1 kg ha−1 for wheat on Alﬁsols in the United States to 11.6 kg ha−1 for maize on Ultisols. The annual economic losses per hectare experienced by farmers in North America are correspondingly small: U.S.$0.39–0.95 ha−1 yr−1 for corn, U.S.$0.12–0.88 ha−1 yr−1 for wheat, U.S.$0.28–0.80 ha−1 yr−1 for soybeans, and U.S.$2.53 ha−1 yr−1 for cotton. Because of the multiple factors affecting crop yields, it may be difﬁcult for a producer to distinguish the annual yield loss due to erosion (e.g., for maize 5 to 12 kg ha−1 and for wheat 1–7 kg ha−1) from yield

38

C. DEN BIGGELAAR ET AL.

variations due to climate, pests and diseases, varieties used, and management practices. In view of (still) increasing yields as a result of crop varietal improvements, chemical and energy inputs, and improved agronomic practices, it may, therefore, be difﬁcult to convince farmers of the seriousness of productivity loss. However, the decrease in yield is cumulative and may cause a noticeable impact if erosion continues unabated over a long period of time. The small annual declines in productivity and economic value provide relatively weak incentives for farmers to adopt erosion-mitigating practices. This underscores the importance of policy measures to encourage the adoption of practices necessary to reduce the off-site effects of erosion. As Crosson (1986) showed, these off-farm effects of erosion are several multiples, if not an order of magnitude, of the on-farm costs. A second implication is that, while estimated aggregate economic losses are relatively small, these aggregates conceal potentially signiﬁcant geographic variations. Locally, losses may be very high; further GIS analysis will be necessary to reveal such spatial differences. Soil erosion and yield loss can be reduced or halted by improvements in technology and management practices, but unless these are demonstrated to be economic they are unlikely to be adopted even though desirable from the standpoint of society. It may also be unrealistic to expect an agriculture without any erosion. Even if this was feasible, it would not necessarily be economical. A certain, at times perhaps very high, level of erosion may be accepted by farmers until it becomes cost-effective to mitigate its adverse effects as yield decreases resulting from the loss of soil start reducing farm proﬁts. This does not mean that farmers are unconcerned about maintaining their resources; on the contrary, the preservation of soil and water resources is considered a primary farm goal by a majority of Midwest producers, both conventional and sustainable (den Biggelaar and Suvedi, 1998; Geisler and den Biggelaar, 1998). Decisions about cropping and management practices by producers will depend foremost on their ability to survive economically (IISD 1999). Farmers do not explicitly choose a level of soil loss (i.e., the amount of soil loss is not the decision variable). Rather, they make crop, rotation, input, and management decisions that result in a particular soil loss (Furtan and Hosseini, 1999). The decision processes employed by farmers about mitigating soil degradation are, however, still not well understood and require additional research.

C. FACTORS CONFOUNDING THE EFFECT OF EROSION ON PRODUCTIVITY Within soil orders, large variations in yields and in the relationship between yields and soil erosion were observed. Yield variations occurred both over space and over time. In several studies, researchers found that yield levels between eroded and uneroded phases were not signiﬁcantly different in years of normal or above-normal rainfall, or when irrigation was used, indicating that water and

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39

water-holding capacity of the soil may become limiting as degradation progresses. Weather and climatic factors, therefore, may play a more important role in determining crop yields than soils. Simmonds (1979), Johnston et al. (1998), Lamb et al. (1997), and Haji and Hunt (1999) also found that the effect of year (climate) on crop yield was greater than that of the location. In addition to varying amounts of erosion, Daniels et al. (1985, 1987), Hoag (1998), Larson et al. (1990), and Wright et al. (1990) observed that the position of experimental plots within a landscape needs to be considered to explain observed yield differences between plots representing different erosion phases or soil depths. In several recent studies, large, spatially inconsistent annual variations in yields were observed within one ﬁeld (Lamb, 1995, 1997; Timlin, 1998) or within one experimental plot (Halvorson, 1999). That is, high and low yields were not consistently found in speciﬁc locations within a ﬁeld or plot. As soil properties change only slowly over time, and as climatic and management differences can be assumed to be minimal within one contiguous ﬁeld or experimental plot, perhaps the practice of attributing yield loss solely to a decrease in TSD, an assumption used in most erosion-productivity studies, needs to be revisited. Plant growth and yield are generally related to the most limiting individual factor, and limiting factors may change with accelerated erosion over time and as a result of management. A number of studies reviewed were multifactorial in design, with erosion levels in the main plots and varying amounts of fertilizers in subplots. The subplots were included to determine the amount of fertilizers necessary to restore the productivity of eroded phases to the same levels of those of uneroded soil. Nutrients applied by themselves, however, may not be enough to maintain productivity if other factors becoming limited are not taken into consideration. In several (but not all) studies, the investigators concluded that no amount of fertilizers would be able to restore productivity levels lost as a result of erosion. Nevertheless, other factors may have become limiting when nutrient levels were boosted in these studies, although this was neither acknowledged nor investigated further. Further studies are required to determine which factors become limited when nutrient levels (or other factors) are increased on erosional phases, and their effect on crop yields. Such studies require systemic, ecological methods to determine the effects of erosion and management on soil properties and to determine how changes in soil properties affect crop yields. According to Rijsberman and Wolman (1984), crop yield is only a good estimator of the differences in productivity between two soils, or between eroded phases of one soil, when (a) the same crop is used and (b) this crop is the “optimal” crop for both soils (or soil phases). The assumption that one crop is optimal for all erosional phases of a soil is unproven. It may well be that the optimal crops for slightly, moderately, and severely phases are not the same; from a management perspective, it is, however, difﬁcult, if not impossible, to produce different crops on the various phases using modern production practices and technologies. The micromanagement of resources by growing speciﬁc crops and crop varieties to

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particular soils and soil phases is not uncommon in small-scale agriculture in developing countries, as observed by den Biggelaar (1994) in Rwanda. Precision farming, however, is a move to increase micromanagement of the soil resource, using varying management practices within one single crop rather than using different crops. Second, even if we use the same crop on a particular soil, and if this crop is optimal for this soil and its various phases, it is not necessarily true that the same variety of this crop is optimal for both eroded and uneroded phases of this soil. No studies were found that tested the differential response of crop varieties to accelerated erosion, at least not deliberately and systematically. The identiﬁcation and/or development of varieties speciﬁcally adapted to perform well on eroded soils and soil phases may offer an alternative to trying to restore soil properties to their original uneroded equivalent (if we even can know the productivity of uneroded soil) with external inputs and variable management practices. Although a decrease in soil depth (in whatever form) was used as the predictor for productivity losses in the studies reviewed here, and some researchers found highly signiﬁcant correlations between depth and yields, there was a great variation in the observed relationships due to years, soils, climate, and management. As shown in Fig. 1 and discussed in Section II,B, there are many factors affecting crop yields, soil depth being only one of them. Given the variability of research results, soil depth by itself does not appear to be a sufﬁcient determinant of decreasing productivity. Erosion does not only lead to a decrease in (top-)soil depth, but causes a host of other changes in the soil that may provide better answers for why yields decline with accelerated erosion over time. Additional research is, therefore, necessary to (1) determine if productivity losses are permanent (Cann et al., 1992) and (2) investigate the spatial relationships between soil variability and yields within a landscape and the variability and stability of yield across a ﬁeld/plot such as the compensatory effects of depositional sites (Fahnestock et al., 1995). This will require studies of localized TSD–yield response relationships to typify speciﬁc soils, climates, and landscapes (Cann et al., 1992), especially in areas with high erosion rates where economically important crops are produced. Bruce et al. (1987) believe that soil orders are too inclusive and can lead to contradictory data interpretations. They suggest using soil family as the classiﬁcation level, as it is at this level that one can identify soil features useful in describing the potential effect of soil erosion on productivity. Yield monitors installed on crop harvesters, GPS, and GIS can be used for this purpose, but it will also require intensive soil surveys to link observed yields with soil physical, chemical, and biological properties as well as climate and management data. Geographic information systems can be used to manage the amount of data necessary for this analysis, which would result in more precise estimations of the productivity impact of erosion at a greater resolution than was done in this and previous reviews. Continuing the list, additional research is needed to (3) develop methods to detect long-term productivity loss in the face of yield increases due to technological advances, (4) determine how different types

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of soil degradation are interrelated (Cann et al., 1992), and (5) investigate farmers’ decision-making processes when estimating actual impacts as opposed to potential impacts determined by soil erosion–productivity research.

VIII. CONCLUSION Several features of the estimated aggregate annual economic losses in productivity due to soil erosion are important. First, the low rate of erosion-induced productivity loss in North America reﬂects the impact of improved technology, especially of the increased use of fertilizers and other amendments and improved crop varieties. Second, losses estimated using soil-speciﬁc erosion rates are smaller than those estimated using national-average erosion rates. Our estimate of U.S.$55.6 million in aggregate annual erosion-induced losses for the selected crops is about 25% lower than the U.S.$82.9 million estimate that results from applying the 1992 average erosion rate across all soils and crops (the latter ﬁgure would be over U.S.$100 million at the 1982 average erosion rate). These losses are smaller than those estimated by others, including Sparrow (1984), Crosson (1986), Alt et al. (1989) Dumanski et al. (1994), and Pimentel et al. (1995). Each of those studies were based on data collected in the 1970s and 1980s, when erosion rates were signiﬁcantly higher than they are today. Agricultural prices have also been following a downward trend in recent years, reducing the economic impact of yield losses. Our results are, however, comparable to estimates by Larson et al. (1983b) and Crosson (1997). Third, current estimated losses are small relative to the total value of agricultural production of these crops. As such, they are likely to be masked over the short term by interannual variation in yields and net returns that arise from weather, pests, and market conditions. As a result, the effects of erosion can be expected, on average, to provide farmers with relatively weak incentives to adopt erosion-mitigating conservation practices in the short run. Fourth, estimated losses in productivity are small relative to the off-site costs of erosion estimated by others, including Crosson (1986) and Ribaudo (1989), in terms of water quality and other impacts. Each of these features underscores the success of past policy measures in providing farmers with incentives to adopt soil conservation measures, and the importance of continued policy efforts to promote such adoption in the future, in order to realize conservation goals that are important to society as a whole and to sustain productivity levels over the long term. They also underscore the importance of further attention to spatial variation in the impacts of soil erosion both on-site and off-site. Speciﬁcally, the aggregate magnitude of estimated losses is less signiﬁcant than the variation in impacts that are revealed by crop and soil order.

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Furthermore, such variation indicates that the distribution of existing studies of erosion’s impact on productivity directs insufﬁcient attention to several geographic areas where the combination of crop production, erosion rates, and yield impacts generate relatively large impacts. Future analysis of variation in impacts at ﬁner spatial resolution will permit improved identiﬁcation of problem areas that are concealed at coarser scales as well as improved targeting of policy incentives. Additional research is also needed on farmers’ responses to erosion and other forms of soil degradation. The estimates provided in this chapter (and in the individual studies on which our analysis is based) are based on the assumption that farmers’ practices are held constant. Over time, of course, practices do change in response to changing circumstances. More precise estimation of actual yield losses or cost increases that will be realized as a result of erosion (as opposed to the potential losses estimated here) will depend on improved understanding of how farmers’ optimal choices will vary in the face of changing physical, market, and policy environments.

ACKNOWLEDGMENTS The views expressed here are those of the authors, and may not be attributed to Appalachian State University, The Ohio State University, or the Economic Research Service. Helpful comments from Jeff Hopkins and Meredith Soule are gratefully acknowledged.

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BIOREMEDIATION OF PETROLEUM HYDROCARBONS IN SOIL Joseph P. Salanitro Equilon Enterprises, LLC1 Westhollow Technology Center Houston, Texas 77251-1380

I. Introduction II. Crude Oils and Fuel Hydrocarbons A. Composition B. Occurrence of Petroleum Hydrocarbons (PHCs) in the Soil Environment C. Summary of PHC Biodegradation in Microbial Cultures and Enrichments III. Biodegradation of PHCs in Unsaturated Soils A. Laboratory and Field Evidence B. Overall Assessment of Soil Bioremediation Experiments IV. Summary and Conclusions A. Current Soil PHC Bioremediation Science and Practice: Prospects and Problems B. Future Research Needs References

A review of the literature on the biodegradability of representative hydrocarbons in petroleum (PHCs) indicates that microbes (bacteria and fungi) isolated from soil, sediments, and biosolids can readily metabolize compounds of chain lengths up to C30–C44 including n-alkanes, branched alkanes with few alkyl groups, and 1- to 3-ring alkylated or nonalkylated aromatics. In general, highly branched alkanes, cycloalkanes, 4- to 6-ring condensed aromatics, and alkylated thiophenes and dibenzothiophenes are partially metabolized or are completely recalcitrant. The extent of microbial degradation of crude oils, oily wastes, and fuels in soils depends on the distribution of PHC structures, concentration, presence of a nonaqueous phase liquid (NAPL), degree of evaporative loss (weathering), and sequestration (nonbioavailability). Declines in bulk PHC in laboratory and field experiments with crude oil or refined oil products in soils are the result of volatilization and biodegradation. Studies on vapor losses of PHC from oily soils indicate that previously reported rates of decline attributed primarily to biodegradation have been overestimated. Bioremediated soils, however, are characterized by low leaching (aqueous) potential, reduced toxicity (soil species bioassays), and the persistence 1

A Shell/Texaco Alliance Company. 53 Advances in Agronomy, Volume 72 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

54

JOSEPH P. SALANITRO of a readily extractable, nonbioavailable residual phase PHC fraction. Additional research is needed to determine the (1) extent and actual biodegradation rates of PHC classes in crude oils and fuels using reference radiolabeled hydrocarbons, (2) biodegradability of PHCs in NAPL phases of soils, and (3) chronic toxicity of sequestered PHC to microbes and higher soil species. This information will be useful for assessing the long-term bioavailability and impact of oily wastes on soil C 2001 Academic Press. ecosystem diversity and function.

I. INTRODUCTION Petroleum hydrocarbons (PHCs) in crude oils are formed through the burial of microorganisms and animal mass as kerogen in deep sediments over hundreds of millions of years. In the process, this kerogenic organic matter, under high pressures and temperatures (≥50–200◦ C) and surface/mineral catalysis, is converted to most of the oil and gas extracted from oil-producing sedimentary basins around the world (Hunt, 1984; Mango, 1991). For over 60 years it has been well established that petroleum hydrocarbon-degrading microbes are widely distributed in marine and fresh waters, sediments, and throughout the soil environment. Indeed, the subject of PHC biodegradation by microorganisms has been reviewed in books and chapters (Zobell, 1946; McKenna and Kallio, 1965; van der Linden and Thijsse, 1965; Davis, 1967; Watkinson, 1978; Atlas, 1984; Gibson, 1984; and Ratledge, 1994). The reviews by Atlas (1981), Bossert and Bartha (1984), and Leahy and Colwell (1990) regarding the degradation of PHC in soil are also noteworthy. The number of citations on PHC biodegradation in laboratory cultures and environmental media (marine and fresh waters, sediments, soil, and biosolids) over the past 50 years is enormous and it is not possible to adequately review them here. Therefore, the purpose of this chapter is to summarize the current knowledge of PHC biodegradation and its practical application in the bioremediation of crude oils and reﬁned oil products (fuels) in unsaturated surface soils. The bioremediation of PHCs in ground water plumes is not discussed in this review except to indicate where anaerobic biodegradation of aromatic hydrocarbons has been reported in some aquifer soil enrichments. Although aerobic and anaerobic microbes are extremely versatile in their ability to degrade many chemical classes, there are limits to the extent of metabolism of many PHC. This is especially true for many compounds in petroleum oils and fuels which are (a) rather insoluble in water; (b) not bioavailable in soils, i.e., sequestered (Alexander, 1995, 1999); (c) cometabolized in the presence of a primary substrate (i.e., incompletely degrades and provides no energy for growth;

BIOREMEDIATION OF PHCs IN SOIL

55

Horvath and Alexander, 1970; Alexander, 1981); and (d) not readily attacked by enzymes. In addition, there are a large number of isomers in complex crude oils and reﬁned products whose isolation, recovery, and identiﬁcation are difﬁcult or analytical techniques have not been developed. As an example, the number of possible isomeric structures that can be associated with a naturally occurring PHCs of carbon numbers 6, 10, 20, and 30 is 5,75, >366,000, and >4 billion, respectively. Most crude oils and oil products (except perhaps gasoline) would contain too many compounds to adequately assess their complete breakdown in soil. It is possible, however, to collect environmental (biodegradation, fate and transport, and toxicity and exposure) data on speciﬁc representative classes of PHCs (e.g., those represented in Table I which could persist and affect human health and ecology. This is the approach of the Total Petroleum Hydrocarbon Criteria Working Group (Edwards et al., 1997; Weisman, 1998a, 1998b) and the search for environmentally acceptable residues of chemicals in soil (Linz and Nakles, 1997). Many studies in these work groups have shown that, based on current knowledge of biodegradability, recalcitrance, degree of mobility, bioavailability, and toxicity of many PHCs in soil, chemicals may or may not pose signiﬁcant environmental or human harm even though the parent compounds may still be extractable from soil. There have been distinct advantages in the isolation of single-culture species degrading PHCs for understanding biodegradation chemistry, enzyme regulation of pathways, accumulation of metabolites, and relative abundance of cultivable degraders and for estimating biodegradability and biotreatability potential. With the advent of the rRNA gene (Amman et al., 1995) and biodegradative enzyme gene (Chandler and Brockman, 1996) sequence analysis from whole-soil DNA extractions, it is clear that culture enrichments and isolations of speciﬁc PHC degraders may represent a small fraction of the population responsible for bioremediation in soil. These genetic analyses, however, cannot determine the capacity or extent of degradation in mixed microbial communities since it is not possible to fully explain how, when, and to what extent gene activity and their enzyme products are expressed when exposed to pollutants in environmental media. We are left, therefore, with a methodology of evaluating the performance of soil bioremediation and its impact on the ecosystem by estimating declines in initial hydrocarbon concentrations, bioavailability (sequestration), and toxicity to sensitive surrogate species. For purposes of the present discussion, soil bioremediation will represent the complete or partial aerobic or anaerobic metabolism of analyzable PHCs to CO2 and/or intermediates by indigenous soil populations in the presence or ab3− sence of added nutrients (e.g., NH+ 4 and PO4 ), terminal electron acceptors (O2 for 3+ 2− aerobic and SO4 and Fe or none for anaerobic conditions) or nonindigenous microbes (bioaugmentation).

Table I Composition and Properties of Major Hydrocarbon Classes in Crude Oils and Oil Productsa Hydrocarbon class (wt. %) b

Substance (carbon no. range)

Density

Crude oili (C2–C60+) Gasoline j (C5–C11) Kerosenek (C6–C16) Diesel no. 2 (C8–C21) Jet fuelsl (C5–C18) (nos. 4,5,7,8) Heating oil (C8–C24) (no. 2) Bunker C (C9–C60+) (Marine diesel No. 6) Motor/lubricating oil (C10–C24) Physical properties (20–25◦ C) Water solubility, mg/l Vapor pressure (atm) Log Kow Biodegradabilitym

0.7–.9 .73 .80 .83 .75–.82 .9 .95 .9–.95

Alkanes

c

Isoalkanes

d

Cycloalkanese

Monoaromaticsf

Polyaromaticsg

Heterocyclich

20 22 30 21 16–28 20 2 24

4 28 5–15 2 2–22 27 11 20

4 3 43 39–59 3–39 20–39 15 35

4 20–28 3 6 3–26 19–26 2 4

3 6 2 20–22 1– 15 15 34 10

0–6 —n — 0.5 — — 36 ≤0.1

10−7–60 10−7–8.5 2.3–10.7 +

0.1–50 10−3–3.5 2.8–7.3 ±

.1–50 10−3–10−1 3.0–7.1 —

1–1750 10−4–5.76 2.1–5.8 +

10−4–30 10−10–10−4 3.0–7.0 ±

0.1–67,000 10−2–10−4 1.0–3.8 ±

a Composition is based on identiﬁed hydrocarbon compounds. Data are from Potter and Simmons (1998), Gustafson et al. (1997), Verschueren (1996), Leo et al. (1971), Nagy and Colombo (1967). b Density, in grams per milliliter. c Includes alkenes (usually <1%). d Branched alkanes. e Naphthene fraction; includes some cycloalkane aromatics. In diesel oil, cycloalkane aromatics may be 20%. f Benzene and alkylbenzenes. g Contain two or more condensed aromatic rings (e.g., naphthalene and phenanthrene). h Present in the asphaltene fraction: polar hydrocarbons with N (pyridines, carbazoles, and indoles), S (mercaptans, thiols, and thiophenes), and O (phenols, cresols, C1–C16 aliphatic monocarboxylic acids, and cycloalkane carboxylic acids) atoms; may be as high as 20% in some heavy crude oils; pristane, phytane, squalane, and triterpenoid (hopanes) biomarkers may also be present. i Composition for a medium gravity (API 30–35) crude oil; average elemental composition (%): C(85.3), H(9.6), S(0.6), N + O(0–3.6). Typical % distributions of saturated/aromatic/heterocyclic fractions in heavy, medium, and light oils of API 14, 30, and 55 are 20/29/44, 56/24/15, and 87/6/0.7, respectively (see Salanitro et al., 1997). j Current reformulated gasolines. k Includes data from Speight (1991). l Carbon ranges for jet fuels vary: JP-4(C5–C14), JP-5(C8–C17), JP-7(C10–C17), JP-8(C7–C18). m Notations refer to relative microbial degradation of congeners in a class by microbial cultures derived from biosolids and soils: +, many ; ± some; and −, few compounds. n None or no data.

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II. CRUDE OILS AND FUEL HYDROCARBONS A. COMPOSITION The predominant hydrocarbon classes present in petroleum oils and reﬁned products are alkanes, alkenes, isoalkanes (branched alkanes), cycloalkanes, monoaromatics, polyaromatics, and heterocyclic compounds. Modern reﬁneries with highly computerized processes (distillation, thermal cracking, hydrogenation, reformers, isomerization, and alkylation units) produce a variety of fuels (e.g., gasoline, diesel, jet fuel, heating oil, marine diesel, and lubricating oil) and chemical feedstocks (e.g., ethylene, propylene, and aromatics) (Speight, 1991). The composition for gasoline may vary slightly in alkane, isoalkane, and monoaromatic distribution depending upon the crude source (e.g., “heavy” vs “light” API gravity petroleum oil) and reﬁnery process unit design. The carbon range and fuel properties are established by EPA guidelines and ASTM speciﬁcations. The distribution of hydrocarbons in crude oils and reﬁned products is given in approximate values in Table I. The hydrocarbon range for most fuel oils (except Bunker C) is from C5 to C24 and about 50–100% of these compounds can be accounted for by carbon number based on extraction separation and gas chromatographic methodologies (ASTM, 1992; Fan et al., 1994) and boiling-point distribution curves by carbon number. Obviously, these techniques do not account for recovery and identiﬁcation of all isomers. Also, the recovery of PHCs, in crude oils and Bunker C fuel oil can vary considerably because of upper limits of separation and quantitation of hydrocarbons >C30 on gas chromatography columns. Crude oils vary in density and API gravity index (e.g., 10–15 as heavy, 26–35 as medium, and 40+ as light crude oil) depending on the maturity of the oil-bearing reservoir from which the petroleum was produced (Nagy and Colombo, 1967; Speight, 1991). Heavy oils have much higher contents of high-molecular-weight compounds >C30 in the asphaltenic (heterocyclic), cycloalkane, and isoprenoid fractions. Heavy API oils, from some producing ﬁelds in North and South America, Mexico, and the Middle East, may also contain as much as 4–6% sulfur-containing compounds (Speight, 1991) and 1–3% oxygen-containing compounds as polyphenols and cycloalkane carboxylic acids (Aksenov et al., 1983). Medium and light API gravity oils contain smaller fractions of these high-molecular-weight compounds and higher percentages of alkane, branched alkane, and lower molecular weight monoaromatic compounds. Densities of various crude oils and fuels are similar and vary from 0.7 to 0.95 g/ml. Current compositions of reformulated gasolines, based on the 1990 Clean Air Act Amendment requirements (U.S. EPA, 1993) range from C5 to C11 with most of the compounds distributed in the alkane, isoalkane, and monoaromatic classes. The fuel oils, kerosene, diesel no. 2, and jet fuels are similar in carbon number (C5–C21) but vary in percentage of isoalkanes, cycloalkanes, and mono- and polyaromatics. Heating oil, fuel oil, and lubricating

58

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oil are similar (C8–C24) in density and PHC composition except that heating oil has a higher monoaromatic content. Marine Bunker C fuel for ships is produced from crude oil distillation bottoms (“resid” hydrocarbons) and is similar in carbon range and density to the original petroleum, but is characteristically lower in alkanes and monoaromatics and higher in isomers of isoalkanes, cycloalkanes, polyaromatics, and heterocyclics. The gross physical properties of the hydrocarbon classes given in Table I such as water solubility, vapor pressure, and octanol/water partition coefﬁcient (Kow) indicate that in general (a) alkanes, isoalkanes, cycloalkanes, and polyaromatic compounds have lower water solubilities (max. 150 mg/l) while monoaromatics and heterocyclics have higher solubilities (max. 67,000 mg/l); (b) alkanes, isoalkanes, and cycloalkanes (mainly short-chain isomers) and monoaromatics of C4– C15 have high vapor pressures (≥0.1 atm; benzene = 0.1 atm) and readily evaporate at room temperature—isomers with lower vapor pressures (<10−4 atm) are less volatile; and (c) all PHC classes exhibit a wide range of low (log 5) Kow; heterocyclic compounds have the lowest Kow and highest aqueous solubilities. These properties of PHCs indicate that depending on the carbon range, a signiﬁcant fraction of oils and fuels are volatile and of low water solubility. In untreated and bioremediated oily soils, therefore, a portion of the crude oils and fuels would evaporate and sorb to the organic matter and clay mineral matrix. The most water-soluble compounds from the monoaromatic and heterocyclic classes would also be more leachable from soils and could be transported to ground water (rainfall events) or surface water bodies (runoff). Many PHCs in all classes have been shown to be degraded and, therefore, the fraction leached to subsoils would be less. The bioconcentration factor (BCF), which relates to the concentration of chemical in biological tissue and aqueous phases (Ctissue/Cwater), would be high for many compounds of low water solubility (alkanes, isoalkanes, cycloalkanes, and polyaromatics). The BCF of a chemical can be approximated from its water solubility [log BCF = 2.791–0.564(log S, mg/l)] (Lyman et al., 1990) and estimates of bioaccumulation potential into plants and animals can be made. The EPA has a protocol for the relative ranking of fate and effects testing in different environmental media such as water, air, soil, sludges, and sediments (U.S. EPA, 1994). The effects on ecosystems of PHC-containing soils may be subject to the same criteria.

B. OCCURRENCE OF PETROLEUM HYDROCARBONS (PHCS) IN THE SOIL ENVIRONMENT PHCs enter surface and subsurface soils from accidental spills of crude oil and fuels from the large pipeline networks buried below the surface. These subsoil conduits carry large volumes of crude petroleum off-shore and on-shore, from oil exploration and production (E and P) ﬁeld operations to reﬁneries and storage tanks, and oil products (e.g., gasoline, diesel, and jet fuels) from reﬁneries to bulk

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storage terminals. The amount of crude oil spilled on land due to pipeline failures is estimated to be 40,000 barrels per year (ca. 1,680,000 gallons) or 70% of all oil discharged to soil and water bodies (Salanitro, 2000). In such spills the more watersoluble PHCs (mainly monoaromatics, BTEX) can leach to subsoils and then to shallow aquifers where contaminant plumes are formed. However, most of the spilled PHCs remain as a free nonaqueous phase liquid (NAPL) or residual phase displacing air and water spaces of the soil matrix. Oil-contaminated soils are also present at E and P facilities from oil and gas production spills, used drilling muds, and waste oily sludges (tank bottoms) discharged on land. These oily soils are land treated (in a few cases composted) to enhance soil bioremediation and reduce residual PHC levels. Gasoline retail service stations represent another source of PHC in soil when fuel (gasoline and diesel) is spilled from buried tanks, fuel lines, and pump dispensers to unsaturated and saturated soils. Similarly, marine diesel and jet fuel spills from pipelines and tankage at airports and military bases represent discharges of PHC to subsoils and ground water. In summary, most PHCcontaminated soils and ground water are the result of spills from crude oil and fuel production, transportation, tank storage, and pipeline operations.

C. SUMMARY OF PHC BIODEGRADATION IN MICROBIAL CULTURES AND ENRICHMENTS Much of our knowledge since the 1940s and 1950s of the microbial degradation of PHCs in crude oils and fuels is based on the complete or partial metabolism (to CO2, cells, and metabolites) in laboratory experiments of usually speciﬁc compounds or classes of compounds by pure or mixed cultures of bacteria and fungi isolated from soils and biosolids. Bushnell and Haas’s (1941) studies on the biodegradability of PHCs in gasoline, kerosene, and diesel oils and Claude ZoBell’s survey (1946) on the assimilation of crude oil by bacteria were some of the ﬁrst laboratory demonstrations of apparent microbial utilization of petroleum compounds. Table II is a summary of microbial metabolic processes occurring in isolates and culture enrichments. Straight-chain alkanes, C2–C38, are degraded by various species of Pseudomonas, Acinetobacter, Rhodococcus, Nocardia, and other actinomycetes and fungi (e.g., Penicillium and Candida). Initial oxidative metabolism of alkanes (e.g., terminal methyl group) requires molecular O2 and NADH or NADPH cofactors in the presence of an inducible mono or mixed-function oxygenase enzyme system. The oxidation sequence is from alkane→alcohol→aldehyde→acid→␤oxidation (two-carbon units) cleavage to smaller carbon chains. Fatty acids derived from even and odd alkanes are also known to be readily incorporated into the cellular phospholipid structure of the cell membrane (Makula and Finnerty, 1968). Although the degradation of long-chain alkanes has not been studied recently the oxygen consumption experiments of Haines and Alexander (1974) showed that microorganisms present in dilute silty loam suspensions could biodegrade n-alkanes

Table II Degradation of Petroleum Hydrocarbons by Single or Mixed Microbial Culturesa Hydrocarbon class

Representative compound(s) C4–C38

Alkenes

C2–C16

Isoalkanes

Mono and dimethyl branched

60

Alkanes

Essential metabolic featuresb

Key references

Inducible alkane hydroxylase (mono- or mixed-function oxygenase; requires O2 and NAD(P)H; primarily terminal methyl group oxidation; subterminal (“omega”) or diterminal oxidation occurs in some species; metabolic sequence: alkane → alkanol → alkanal → alkanoic acid → fatty acid phospholipids and/or ␤-oxidation to acetate; activity widely distributed in soils and biosolids Few studies of microbial cell yields on alkanes (C7, C8, and C18, 0.44–1 g cells/g alkane); generation times 1–30 h Anaerobic degradation of C12–C20 alkanes by sulfate-reducing bacteria; sulfate used as electron acceptor 4.8–5.2 mg SO−2 4 /mg alkane (C16H34 + 12.25 + − SO−2 4 + 8.5H (16HCO3 + 12.25 + H2S + H2O); corresponding 1-alkanols, 1-alkenes, and alkanoic acids also utilized; growth yield on C16, 0.07 g cells/g C3–C16, 1-alkenes undergo terminal and/or subterminal oxidation by a mono-oxygenase to form 1-alkanols or 2-alkanols followed by terminal acid or ∝-hydroxy acid formation and ␤-oxidation of chains Randomly distributed unsaturated bonds are metabolized to internal alcohols and ketones. C2 and C4, 1-alkenes are readily transformed to epoxides (methanotrophs and propanotrophs)

Van der Linden and Thijsse (1965) Wodzinski and Johnson (1968) Singer and Finnerty (1984) Britton (1984) Aeckersberg et al. (1991) Morgan and Watkinson (1994) Rueter et al. (1994) So and Young (1999)

Terminal and diterminal oxidation to alcohols and acids and diacids with complete or partial mineralization or poor growth ␤-Methyl branched alkanes may accumulate 3-methyl branched alcohols and acids

Hou et al. (1983) Britton (1984) Subramanian (1986) Hartmans et al. (1989) Morgan and Watkinson (1994) Thijsse and van der Linden (1961) McKenna and Kallio (1971) Pirnik et al. (1974) Pirnik (1975)

Cycloalkanes

Multiple methyl branched (acylic iso-prenoids)

Tertiary (e.g., t-butyl) and quaternary (e.g., 2,2-dimethyl alkane) carbons are difﬁcult to degrade; may accumulate alcohols and acids Pristane, phytane, and squalane undergo cleavage via terminal and intraterminal oxidation to acids which may be incorporated into cellular lipids or further metabolized by ␤-oxidation to short-chain acids

Pirnik (1977) Catelani et al. (1977) Schaeffer et al. (1979) Singer and Finnerty (1984) Britton (1984) Nakajima et al. (1985)

n-Alkyl cycloalkanes

Partial oxidation of side chains by ␤-oxidation to shorter alkyl groups Attack of the cycloalkane ring is usually oxidative; some strains use cyclohexane as sole carbon and energy source and adipic acid is a metabolite Tetralin (tetrahydronaphthene) partially oxidized (cometabolized) on the cyclic (hydroxylation) or aromatic (cis-dihydrodiol and meta cleavage as in naphthalene pathway) ring Tetracycloalkanes (C27–C29) partially oxidized Aerobic Benzene transformation via ortho-cleavage (1,2-dioxygenase to catechol and ␤-keto adipate or catechol 2,3-dioxygenase via cis,cis-muconate pathway Toluene degraded by ﬁve potential pathways via methyl hydroxylation (to benzyl alcohol and benzoate), hydroxylation to o-cresol (and 3-methyl catechol), p-cresol (and p-hydroxybenzoate), and m-cresol (and 3-methyl catechol) TOL plasmid degradation pathway common in diverse species (T,mX,pX, 1,2,4-trimethyl benzene degraded via methyl group hydroxylation → benzoate → benzoate diol → hydroxymuconic semialdehyde Degradation and growth on benzene and alkylbenzenes is ubiquitous in soils and biosolids; growth typically 0.5–1.5 mg cells/mg BTEX requires 3 mg O2/mg BTEX for complete degradation Competition for substrate utilization in BTEX mixtures not observed in mixed cultures when concentrations are low (e.g., <10 mg/l) 1,2,4-Trimethylbenzene degraded by the TOL plasmid to 3,4-dimethylcatechol or naphthalene dioxygenase to 3,4,-dimethylbenzoate

Trudgill (1984) Perry (1984) Chosson et al. (1991) Morgan and Watkinson (1994) Smith (1994)

Naphtheno-aromatic (tetralin) Steranes

Monoaromatic 61

Benzene and alkylbenzenes (BTEX)

Gibson and Subramanian (1984) Singer and Finnerty (1984) Ridgway et al. (1990) Mikesell et al. (1993)

continues

Table II—Continued Hydrocarbon class

Representative compound(s)

Essential metabolic featuresb Anaerobic BTE mXpX metabolized under nitrate- (NO3 or N2O) reducing conditions (50–95% converted to CO2) to benzoates via methyl hydroxylase; cell yield 0.1–0.7 g/g BTmX and requires 3 mg NO− 3 /mg BTmX T and B (+Fe chelators) degraded under iron-reducing (Fe3+) conditions (Geobacter); requires 22 mg Fe3+/mg toluene

62

B,T oX, mX degraded under sulfate-reducing conditions; 50–90% converted to CO2; cell yield 0.1 mg cells/mg T and requires 4–5 mg SO−2 4 /mg toluene for complete oxidation BToX are degraded in methanogenic cultures via benzoate as intermediate; cell yield 0.1 g/g BtoX Aromatic ring reduction via benzoyl-CoA reductase is central to ring cleavage

Polyaromatic (PNAs)

Naphthalene, Phenanthrene, Anthracene, Pyrene

Aerobic Monoxygenase (–OH) and dioxygenase (diol) attack with complete degradation (50–90% to CO2); oxidation usually by sequential ring metabolism Naphthalene stimulates other PAH oxidation; nonspeciﬁc PNA oxygenase activated by other compounds

Key references Lovley and Lonergan (1990) Evans et al. (1991) Schocher et al. (1991) Altenschmidt and Fuchs (1991) Koch et al. (1993) Mikesell et al. (1993) Rabus et al. (1993) Fuchs et al. (1994) Edwards and Grbi´c-Gali´c (1994) Lovley et al. (1994) Smith (1994) Biegert and Fuchs (1995) Rabus and Widdel (1996) H¨aner et al. (1997) Burland and Edwards (1999) Gibson and Subramanian (1984) Heitkamp and Cerniglia (1988) Walter et al. (1991) Smith (1994)

Heterocyclic

63 a

Phenols, cresols, thiophenes, dibenzothiophenes naphthenoic acids, pyridines

Anaerobic PAHs degraded under nitrate- and sulfate-reducing conditions; 2–3X slower 2− than aerobic cultures; 4–5 mg NO− 3 or SO4 /mg PAH required; 90% conversion to CO2 Ring carboxylation a central reaction in 2- and 3-ring PAHs

Zhang and Young (1997) McNally et al. (1998) Rockne and Strand (1998) McNally et al. (1999)

Aerobic Phenol and cresol metabolism common Thiophenes are completely or partially (cometabolized) degraded to CO2, sulfones or desulfurized Unsubstituted but not substituted cycloalkane carboxylic acids (naphthenic acids) are degraded Partial or complete oxidation of nitroheterocylic compounds (pyridine, quinoline) Anaerobic Phenols and cresols (o-, m-, p-) readily degraded under nitrate-, sulfate-, and iron-reducing and methanogenic conditions; benzoates as key intermediates

Ensley (1984) Monticello and Finnerty (1985) Fedorak (1990) Gallagher et al. (1993) Herman et al. (1994) Kaiser et al. (1996) Suﬂita et al. (1989) Fedorak (1990) Hopper et al. (1991) Londry and Fedorak (1992) Smith (1994) M¨uller et al. (1999)

Data from enrichment mixed cultures derived from soils and biosolids and several pure cultures of gram-negative (e.g., Pseudomonas, Acinetobacter, Arthrobacter) and gram-positive (actinomycetes, e.g., Nocardia, Mycobacterium, Rhodococcus), yeasts (e.g., Candida), and nitrate-, sulfate-, and iron-reducing and methanogenic consortia or single cultures. b Mostly aerobic unless indicated.

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C7–C44 in 10–20 days. Additional studies have shown that alkanes of chain length C13–C38 in medium and heavy API crude oils are biodegraded with signiﬁcant variability (25–95%) by a soil isolate Acinetobacter sp. T4 and a mixed-culture SM8 (Sugiura et al., 1997; Lai and Khanna, 1996). A psychrotrophic strain of Pseudomonas isolated from Arctic soils was able to degrade C5–C12 alkanes and naphthalene (70–98%) at 5–25◦ C (Whyte et al., 1997). In 1944, Novelli and ZoBell ﬁrst reported the apparent utilization and growth of Desulfovibrio on C14, C20, and C22 long-chain alkanes under sulfate-reducing (SR) conditions. Conclusive evidence for the anaerobic degradation of C6–C20 n-alkanes by SR bacterial enrichments and pure cultures was obtained by Aeckersberg et al. (1991) and more recently by Rueter et al. (1994) and So and Young (1999). The corresponding alcohols and acids were also utilized with observed cell yields of 0.07 g cells/g alkane. Caldwell et al. (1998) also reported on the apparent complete biodegradation in 200 days of n-alkanes (C15–C34) in a weathered Alaskan North Slope crude oil by a marine sediment (20◦ C) under SR conditions; in this same study the authors showed that a [14C] octacosane (C28 n-alkane) was entirely mineralized (100%) to 14CO2. The biodegradation of gaseous alkanes, methane, ethane, and propane has also been extensively reviewed (Vestal, 1984; Hanson and Hanson, 1996). Primary alkenes (1-alkenes) C3–C16 are transformed via terminal or subterminal oxidation by monooxygenases to corresponding 1- or 2-alkanols followed by terminal acid or ␣-hydroxy acid formation and 2C-cleavage of the chain. Internal alcohols and ketones are also formed by microbial enzymes and cultures from alkenes containing randomly distributed double bonds along the chain. Epoxides can be produced as intermediates from C2–C4 1-alkenes by methanotrophs and propanotrophs. Isoalkanes (branched alkanes) containing one or more CH3 groups along a chain are usually completely metabolized to CO2, producing mono- or diterminal carboxylic acids. In general, isoalkanes with methyl groups widely separated along long-chain alkanes will degrade more readily than those which are more vicinally spaced (e.g., 3,6-dimethyloctane; 2,6-dimethyloctane; and 2,6-dimethyldecane; Pirnik, 1975; Schaeffer et al., 1979). Also, microbes degrading 3-methyl branched alkanes may accumulate 3-methylacyl CoA, which is slowly metabolized by three possible pathways (Lau et al., 1980). Methyl groups as tertiary carbons (e.g., tertiary butyl groups) are slowly biodegraded while those in quaternary structure such as 2,2-dimethylheptane are incompletely degraded to the dead-end metabolite 2,2-dimethylpropionate (pivalate) (Catelani et al., 1977). The multiple methyl branched acyclic isoprenoids pristane (2,6,10,14-tetramethylpentadecane), phytane (2,6,10,14-tetramethylhexadecane), and squalane (2,6,10,15, 19, 23-hexamethyl tetracosane) have been shown to be diterminally oxidized and slowly cleaved by isolated aerobic actinomycetes and enrichments to mono- or dicarboxylic acids and then incorporated into cell lipids

BIOREMEDIATION OF PHCs IN SOIL

65

or further degraded, in part, to CO2 (McKenna and Kallio, 1971; Pirnik et al., 1974; Pirnik, 1975; Nakajima et al., 1985). Although pristane and phytane have been used as slowly degrading reference biomarkers for crude oil bioremediation experiments (Kennicutt, 1988), there has been recent evidence that they are apparently biotransformed in long-term (3–12 months) incubations of oily soils and sediments (Fedorak and Westlake, 1981; Venosa et al., 1996; Le Dr´eau et al., 1997). The only report of anaerobic degradation of isoalkanes was that of Bregnard et al. (1997) in which nitrate-reducing enrichment cultures, derived from diesel-contaminated aquifer soil, metabolized pristane (90% to CO2) in 3.5 months. There have been few studies on the biodegradability of the cycloalkane group of hydrocarbons. Cyclohexane, the most studied naphthene, has been shown to be metabolized by cooxidation mechanisms via a P450-mediated cyclohexane hydroxylase (Trudgill, 1984; Perry, 1984; Warburton et al., 1990; Morgan and Watkinson, 1994) by pure or mixed cultures; Trower et al. (1985) isolated a Xanthobacter sp. which can utilize cyclohexane as sole carbon and energy source for growth via adipic acid as intermediate. Most n-alkylcycloalkanes, naphthenoaromatics (e.g., tetralin or tetrahydronaphthalene), and steranes (tetracycloalkanes; Chosson et al., 1991), however, are only partially oxidized by soil, sediment, or mixed microbial cultures, producing hydroxylated derivatives or ring-cleaved metabolites. The literature on the most readily degradable petroleum hydrocarbons, the monoaromatics benzene (B), toluene (T), ethylbenzene (E), and xylenes (oX, mX, and pX) or BTEX compounds is extensive. Complete BTEX degradation by aerobic pathways is ubiquitous in soils and wastewater biosolids or enrichments and single cultures derived from them (Gibson and Subramanian, 1984; Singer and Finnerty, 1984; Ridgway et al., 1990). B is metabolized aerobically via a dioxygenase to catechol. T can be degraded by ﬁve possible routes (Mikesell et al., 1993). A naphthalene dioxygenase system in a Pseudomonas sp. has been shown to oxidize T to benzyl alcohol and E to an acetophenone derivative (Lee and Gibson, 1996). The TOL degradative plasmid is also ubiquitous in soil microbial species and is responsible for the complete degradation of T, mX, pX, and 1,2,4trimethylbenzene via methyl-group hydroxylation and formation of benzoate and benzoate diol intermediates. Although most of the biochemical mechanisms and pathways for the degradation of BTEX compounds under anaerobic conditions are not known, there is consensus that BTE, mX, and pX are metabolized to benzoates via a methyl hydroxylase reaction (Smith, 1994). It is also now apparent that a benzoyl CoA structure is central to the aromatic ring reduction (benzoyl CoA reductase) and cleavage in the anaerobic degradation of monoaromatic compounds (BTEX, trimethylbenzene) (Koch et al., 1993; Schink, 1997). Furthermore, this enzymatic benzoate ring reduction appears to occur in syntrophic association (Syntrophus aciditrophicus; Jackson et al., 1999) with H2-utilizing microbes (sulfate-reducers and methanogens). Nitrate-reducing

66

JOSEPH P. SALANITRO

enrichment cultures and single isolates of denitriﬁers (e.g., Pseudomonas, Thauera, and Azoarcus) have been shown to metabolize BTE pXmX with nitrate or N2O as electron acceptor to CO2 with benzyl alcohol and benzoate as intermediates (Evans et al., 1991; Altenschmidt and Fuchs, 1991; Schocher et al., 1991; Seyfried et al., 1994; Biegert and Fuchs, 1995; Chee-Sanford et al., 1996; Kukor and Olsen, 1996; Rabus and Widdel, 1996; Burland and Edwards, 1999). O-xylene, however, has not been shown to be degraded under denitrifying conditions but is rather transformed to “dead-end” metabolites, 2-methylbenzyl derivatives of succinate and fumarate (Evans et al., 1992). The 1,3,5- and 1,2,4-isomers but not 1,2,3-isomers of trimethylbenzene are degraded by diesel-fuel contaminated aquifer soil enrichments with N2O as electron acceptor (H¨aner et al., 1997). Under anaerobic conditions, Lovley and co-workers (Lovley and Lonergan, 1990; Lovley et al., 1994; Lovley et al., 1996; Scott et al., 1998) demonstrated that aquifer sediment enrichments and pure cultures of the iron-reducing microbe Geobacter metallireducens can oxidize T and B with or without chelators such as humic acids in which humic material shuttles electrons between iron-reducing species and Fe3+ oxides in soil. Radiolabeled B and T have been shown to be degraded to 14CO2 by soil enrichments and pure cultures (e.g., Desulfobacula toluolica) under sulfate-reducing conditions with stoichiometries of 4–5 mg SO2− 4 reduced/mg toluene oxidized (Edwards and Grbi´c-Gali´c, 1992; Edwards et al., 1992; Rabus et al., 1993; Beller et al., 1996; Phelps et al., 1996); the initial oxidation products of B and T, catechol and m-cresol, respectively, have also been shown to be completely degraded to CO2 and cells by species of Desulfobacterium (Szewzyk and Pfennig, 1987; M¨uller et al., 1999). oX and mX are mineralized as well by pure cultures of Desulfobacterium and Desulfococcus isolated from oil– water separators at E and P facilities (Harms et al., 1991). Some of the ﬁrst reports of the transformation and oxidation of BT and oX by methanogenic cultures or aquifer sediment methanogenic enrichments were those of Vogel and Grbi´c-Gali´c (1986), Grbi´c-Gali´c and Vogel (1987), and Edwards and Grbi´c-Gali´c (1994). These earlier studies with 14C-ring or methyl-labeled monoaromatics, incubated in the presence of H2 18 O, suggested that phenol and cresols are formed initially from B, T, and oX and then completely degraded to stoichiometric amounts of CO2 and CH4 (≥50% each) and cells. A recent paper by Kazumi et al. (1997) on the biodegradation of [14C] benzene (50 mg C/l) in anaerobic Michigan aquifer sediment microcosms (22C) have conﬁrmed that 14CH4 and 14 CO2 were formed in approximate stoichiometric amounts after 300–400 days. Deﬁnitive studies on the complete breakdown of 14C-labeled 2-, 3-, and 4-ring PNA (polynuclear aromatic) compounds such as naphthalene, phenanthrene, anthracene, and pyrene by microbial soil cultures and mixed enrichments have shown that 50–90% of the carbon is metabolized to 14CO2 by sequential ring-oxidation steps (Gibson and Subramanian, 1984; Heitkamp and Cerniglia, 1988; Walter et al., 1991; Smith, 1994; McNally et al., 1998). Ring carboxylation appears to be essential in the anaerobic (nitrate- and sulfate-reducing) metabolism of 2- and

BIOREMEDIATION OF PHCs IN SOIL

67

3-ring PNA although rates are signiﬁcantly slower (2–3 orders of magnitude less) than aerobic metabolism (Zhang and Young, 1997; McNally et al., 1998 and 1999; Rockne and Strand, 1998). The complete aerobic breakdown of oxygenated compounds in crude oils (phenols and cresols) by enrichments and microbes, isolated from biosludges and soils, is well known. Thiophenes, substituted thiophenes, dibenzothiophenes, and naphthothiophenes are either completely (to CO2) or partially oxidized by cultures of Pseudomonas and Rhodococcus to sulfones or desulfurized to SO2− 4 (Ensley, 1984; Monticello and Finnerty, 1985; Fedorak, 1990, Gallagher et al., 1993; Kropp et al., 1994, 1997). Among the cycloalkane carboxylic acids (naphthenic acids) it appears that only compounds without alkyl side chains can be metabolized by aerobic bacteria (Herman et al., 1993, 1994). The anaerobic degradation of phenol and cresols has been shown to occur in a variety of mixed cultures and sediments under nitrateand sulfate-reducing and methanogenic conditions. Again, it appears that benzoic acid is a key intermediate in the metabolism of these compounds (Suﬂita et al., 1989; Londry and Fedorak, 1992). Finally, the aerobic and anaerobic degradation of the common N-heterocyclic compounds (pyridine, quinoline, and carbazole), which may be present (0.06–0.6%; Nagy and Colombo, 1967) in some petroleum oils, has been reviewed by Kaiser et al. (1996).

III. BIODEGRADATION OF PHCs IN UNSATURATED SOILS A. LABORATORY AND FIELD EVIDENCE The apparent reductions of PHCs in soil systems, due in part to biodegradation by indigenous soil microorganisms, have been documented since the 1970s in laboratory test systems (e.g., ﬂask, column, pan, “bucket,” or respirometer) and ﬁeld pilot-scale (e.g., lysimeter or small plots) and ﬁeld full-scale (acres) soil biotreatment studies. Laboratory degradation assays of spilled crude oil, oily waste, or fuel products have attempted to assess the overall bioremediation potential for predicting the effectiveness in large ﬁeld applications of land treatment. Oily wastes and sludges accumulated at reﬁneries, however, were land treated at these facilities prior to the 1990 land-ban treatment of certain manufacturing plant wastes by the EPA (U.S. EPA, 1987) under the Resource Conservation and Recovery Act. Although there are numerous studies on the laboratory and ﬁeld biodegradability assessment of hydrocarbons in soils containing manufactured gas plant, creosote, coal tar and wood-preserving wastes, the following discussion focuses primarily on crude oil, oily waste, and fuel oil bioremediation. Table III is a compilation of laboratory and ﬁeld data from the literature on the − 3− mass reductions in PHC when nutrient additions (e.g., NH+ 4 , NO3 , and PO4 or

Table III Petroleum Hydrocarbon Reductions in Treated Soils Containing Crude Oils, Oily Wastes, and Refined Oil Products Hydrocarbon type (study no.)a Crude oils Medium (API 39) (weathered) Heavy (API 21)

68

Initial PHC concn. (mg/kg)c

Amendments (C/N/P)d

Reduction rate (mg/kg/day)e

Max. % reductionf

Treatment duration (days)g

Sand

50,000

100:5:1

43h,150i

20h, 80i

350

Sand

50,000

100:5:1

12h 92i

9h 56i

350

Huesemann and Moore, (1993b) and Huesemann (1995)

10–60i

28

McMillen et al. (1995)

Salanitro et al. (1997)

Experiment typeb

Lab

Soil type

Medium API 15–45; (weathered)

Lab (respirometer)

Loam

Light (API 55) Medium (API 30) Heavy (API 14)

Lab

Light (API 55) Medium (API 30) Heavy (API 14)

h

Reference

5,000

100:5:1

18–107

Silty loam (low organic)j

4,200 26,600 14,000

100:1:0.2 100:1:0.2 100:1:0.2

35 198 78

76 67 50

330 330 330

Lab

Silty loam (high organic)j

9,600 25,700 11,900

100:1:0.2 100:1:0.2 100:1:0.2

141 291 20–79

88 68 10–40

270 270 270

Arabian Crude (weathered)

Lab

Silty loam (marsh)

17,800

None 100:12:3

65–70k 90–165k

20–35 40–84

90 90

Neralla and Weaver (1997)

East Texas Heavy crude

Lab

Silty loam Silty loam + hay or grass

100,000 100,000

100:0.8 100:0.8

473 625

35 40–55

80 80

Chang and Weaver (1998)

Oily wastes

Lab

Clay/sand mix

25,000

None 100:0.3:0.03 100:1.5:0.1 100:6.5:0.5

50 53 63 50

26 28 33 26

130 130 130 130

Dibble and Bartha (1979)

Oily wastes

Lab

Silty loam Silty loam

50,000 50,000

100:1.3 100:11

196 248

63 80

160 160

Brown et al. (1983)

Oily wastes

Lab

Silty loam Sandy loam Clayey loam

4,375 4,375 4,375

100:0.1 100:0.1 100:0.1

15 11 11

63 45 47

180 180 180

Brown and Donnelly (1983)

Oily wastes

Lab

Silty loam

510–595

72–83

42

Graves and Leavitt (1991)

Oily wastes

Lab

Silty clay

20,000

100:2.6 (+sawdust)

120–150

75–95

19

136

75

110

Brown et al. (1998)

Arabian heavy crude

Field

Sandy loam Silty loam Clayey loam

14,900 37,200 17,200

100:<1:<1 100:<1:<1 100:<1:<1

26 87 20

51 66 37

285 285 285

Raymond et al. (1976)

Gulf Coast Medium Crude

Field

Sandy loam Silty loam Clayey loam

20,600 31,300 25,800

100:<1:<1 100:<1:<1 100:<1:<1

57 49 36

79 45 43

285 285 285

Raymond et al. (1976)

Crude oil (NS)m

Field

50,000– 100,000

100:1:0.7

18–25

65–70

3285

Huddleston et al. (1984)

Oily waste

Field

Clayey loam

10,000

100:1:0.2

23

50

450

Huddleston and Cresswell (1976)

Oily waste

Field

NSm

50,000

100:1:0.2

46

58

750

Myers and Huddleston (1979)

Oily waste

Field

Sandy (low organic) Sandy (high organic)

22,000 34,000

100:1–4:0.2–5 100:1–4:0.2–5

8–21 13–29

35–89n 37–82n

960 960

Sandvik et al. (1986)

Clayey loam

69

Sand

30,000

3,000

100:0.1:1.3 ± surfactant (0.5%) 100:0.3:1.5

continues

Table III—Continued Hydrocarbon type (study no.)a

Experiment typeb

Soil type

Amendments (C/N/P)d

Reduction rate (mg/kg/day)e

Max. % reductionf

Treatment duration (days)g

100:2–10:2–10

28–69

65

Loehr et al. (1992)

100:10:1

22

55–60o

420– 600 315

70–85q

330

DeJonge et al. (1997)

20–25p

126

Song et al. (1990)

50–65p

270

Chaˆıneau et al.(1995)

Oily waste

Field

Silty loam

Oily waste (weathered)

Field-pilot (compost)

Silty clay

20,000– 50,000 9,500

Oily waste (weathered)

Field-pilot (lysimeter)

Silty loam

13,200

100:6.7:5

14

Lab

Loam

100,000

100:0.8:0.2

600–2100p

Fuel oils Diesel

70

Initial PHC concn. (mg/kg)c

Diesel (drill cuttings) Diesel

Lab

Silty Clay

Lab(10C)

Alpine

Diesel

Lab (soil slurry)

Diesel

q

Reference

Hayes et al. (1995)

1,600

100:50:75

4–9

50,000

100:10:2

100–925p

11–31p

20

Margesin and Schinner (1997)

Sandy loam

3,200

None or 100:1–10:0.1–1

76

50

37

DeCort et al. (1997)

Lab (column inﬁltration)

Sandy

3,000

High N + P

165

43

27

L¨oser et al. (1998)

Diesel

Lab (10C)

Alpine

5,000

100:10:2

85

56(20–40)r

33

Margesin and Schinner (1999)

Diesel

Lab

Clayey

2,500

None

10p

10p

119

Taylor and Viraraghan (1999)

None

p

38

p

119

83

75

95

Stefanoff and Garcia (1995)

42

74

405

Whitﬁll and Boyd (1987)

10,000 Diesel (no. 2)

Field (compost)

Silty sand-clayey sand

Diesel

Field

NSl

4,000s 23,000

Manure and wood Chips None

25

Diesel

Field (lysimeter)

Sandy loam

55,000– 65,000

Diesel Diesel

Field Field (compost)

Sandy loam Sandy

10,000 1,300–3,000

None 100:0.5:0.1 or tilled only 100:1:0.5 100:3:0.3 (+wood chips) 100:5:0.2

214 952

55 85

140 140

Wang and Bartha (1990)

90 128–342

95 87–92

500 77

Shen and Bartha (1994) Quinn (1995)

71

Diesel

Field

Silty clay

3,000

4

93

720

Chaˆıneau et al. (1996)

Diesel

Field

Silty sand

2,800

100:20:15

36

36

147

Berg et al. (1998)

Jet fuel Jet fuel

Lab Field (lysimeter)

Clay loam Sandy loam

50,000 75,000

100:2:0.3 None 100:1:0.2

714o 1190 1428

10o 93 97

84 140 140

Song et al. (1990) Wang and Bartha (1990)

Jet fuel (no. 5)

Field (5◦ C)

Sandy

16–58t

None 100:125–500:12–32

.03 0.27

5–7 50–60

90 90

Braddock et al. (1999)

Heating oil

Lab

Loam

140,000

None 100:2:0.3

714u 1111

32u 71

126 126

Song et al. (1990)

Heating oil

Field

Sandy loam Silty loam Clay loam

13,800 21,800 14,000

100:<1:<1 100:<1:<1 100:<1:<1

33 68 74

94 80 83

285 285 285

Raymond et al. (1976)

Heating oil

Sandy loam

47,000

Bunker C

Field (lysimeter) Lab

Sand

50,000

Lab (slurry)

Bunker C

Field

Sea sand Illite clay Sandy loam Silty loam Clay loam

2,833 3,833 32,400 35,100 25,600

228 350 9u 18 3u 19 56 40 49

68 89 6u 10 35u 50 50 32 53

140 140 336 336 100 100 285 285 285

Wang and Bartha (1990) Song et al. (1990)

Bunker C

None 100:0.5:0.1 None 100:2:0.3 None None 100:<1:<1 100:<1:<1 100:<1:<1

Apitz and MeyersSchulte (1996) Raymond et al. (1976)

continues

Table III—Continued Hydrocarbon type (study no.)a Bunker C Motor oil

Experiment typeb Field Lab

Soil type l

NS Sandy clay Loam Clay loam

Initial PHC concn. (mg/kg)c m

6,500 50,000 50,000

Amendments (C/N/P)d 100:2:0.2 100:2:0.4 100:2:0.4

Reduction rate (mg/kg/day)e 132 231 294

Max. % reductionf v

85 37w 43w

Treatment duration(days)g 77 128 80

Reference Ciofﬁ et al. (1991) Rhykerd et al. (1995)

a Crude oils (where speciﬁed) varied in API gravity (see footnote to Table I) or whether they were “weathered” naturally or heated to remove VOCs. In some studies reﬁnery oily sludges from API separators, slop oil tanks, or tank bottoms were used in soil experiments. b Laboratory experiments were performed in buckets, pans, columns, jars, or ﬂasks; ﬁeld data was collected from small plots or full-scale land treatment units or pilot-scale lysimeters; incubations were at 17–25◦ C unless otherwise speciﬁed. c Petroleum hydrocarbon (PHC) concentrations varied depending upon the extraction solvent (e.g., freon and CH2Cl2) and method (e.g., total petroleum hydrocarbons, oil, and grease; TPH by GC as given in Standard Methods 5520A-F and ASTM D2887-89). d −3 Amendments were usually as NH+ 4 or NO3-N and PO4 - or P2O5-P in ordinary agricultural fertilizer; other additions included surfactants, mulch or bacterial preparations; given as C:N:P or C:N ratios. e Reduction in PHC rate was calculated based on the maximum observed loss (volatilized + biodegraded) that approximated a ﬁrst or pseudo-ﬁrst order decay and continued to an asymptotic or slower decay. f Calculated or maximum reduction (initial-asymptotic ÷ initial concn × 100%). g Time required for maximum observed PHC reduction. h PHC decrease measured by O2 consumption in a respirometer. i Reduction based on TPH analysis. j Low organic, 0.3% OC; high organic, 4.65% OC. k Higher rates for experiments conduced at 30 C. l Rates of degradation were higher for the higher API gravity oils and n-alkane/aromatics ratios >1. m NS, not speciﬁed. n Highest reduction rates achieved with the higher N doses. o TPH reduction was not affected by added nutrient (N, P) bulking agent, surfactant, or activated sludge. p No nutrients added; values given are subtracted from poisoned controls (1–2%, HgCl2 or azide) for all treatments; volatilization losses 15–60% depending upon time interval and extent of soil mixing in poisoned controls. q Alkanes and branched alkanes were analyzed. r Reduction with 50–1000 mg/kg sodium dodecyl sulfate surfactant. s Treatment of the initial diesel in soil (12,000 mg/kg) in a soil slurry bioreactor reduced the concentration to 4000 mg/kg. t Based on the footnotes under Table III the sum of C14–C20 alkanes and 2- and 3-ring PAH only. u Reduction in PHC in the poisoned control (+Hg Cl2) and no nutrients treatment. v PHC are C10–C30 only. w Salt (NaCl, 0.4–2%) addition decreased oil reduction by 10%.

BIOREMEDIATION OF PHCs IN SOIL

73

surfactants), pH adjustment, moisture, and aeration or tilling are applied to soils. In laboratory studies of petroleum oil biodegradation, reductions in extractable hydrocarbons (e.g., as total petroleum hydrocarbons, TPH, or oil and grease) have varied depending upon the crude oil type. Freshly applied or weathered PHC in medium or light crude oils (4,200–50,000 mg/kg) appear to be more extensively reduced (55–90%) than those containing heavy API oils (10–50%) in either sandy or silty loam soils (Huesemann and Moore, 1993a, 1993b; Huesemann, 1995; Salanitro et al. 1997; Chang and Weaver, 1998). PHC biodegradation also appears to be enhanced with N and P nutrient addition (Neralla and Weaver, 1997) compared to soil experiments when no such amendments are used. The apparent maximum degradation rates calculated for laboratory studies with crude oil vary widely (20–625 mg/kg/day) and are in part inﬂuenced by the loss of volatile PHC (e.g., those with carbon numbers ≤ C12) from the soil matrix over time. Many documented laboratory experiments with crude oil or fuel have not made adequate mass balance assessments to account for volatilization losses, although in some cases (e.g., fuel hydrocarbon biodegradation; Song et al., 1990) poisoned soils were used as controls. For most of the data in Table III (unless noted) PHC reductions include biodegradation and volatilization. The interpretations of studies on the effect of nutrients (Oil Carbon: Nitrogen: Phosphorus ratios) on stimulating microbial growth and degradation of oily wastes are not entirely clear since authors used different soils, nutrient levels, oil concentrations, and time of incubation. Laboratory experiments by Brown et al. (1983) and Dibble and Bartha (1979) on oily wastes (reﬁnery oily sludges from tanks and separators) suggest that C:N:P ratios that vary 100:0.3–11:0.03–0.5 do not signiﬁcantly affect PHC degradation (25,000–50,000 mg/kg initial concentration) in silty loam or clay/sandy soil mixtures. Likewise, in a study by Brown and Donnelly (1983), oily waste (4,375 mg/kg) degraded similarly (45–63% decrease) when added to a silty loam and a sandy or clayey soil; in this test, the C:N level was 100:0.1. In many oily waste studies, wide variations in applied hydrocarbon (3,000–50,000 mg/kg), rates of reduction (10–600 mg/kg/day), and maximum removal (25–95%) were observed within incubation intervals of 20–180 days for sandy, silty, and clayey loams. Also it is not readily apparent that addition of other nutrients and bulking agents (e.g., Fe, S, surfactants, sawdust, and wood chips) actually enhances PHC degradation in soil (Graves and Leavitt, 1991; Brown et al., 1998) or interferes with extraction of the PHC. There have been few well-documented ﬁeld studies of oily soil bioremediation. In most cases PHC-contaminated soil from pipeline releases or oily sludge waste from reﬁneries are incorporated into surface soil layers (e.g., 6–12 in. plow depth), amended with fertilizer nutrient (e.g., 100 lbs. oil C: 1 lb. N: ≥0.1 lb. P), tilled and maintained moist (50–80% soil water-holding capacity). Field studies on the bioremediation of sandy, silty, or clayey loam soils containing heavy or medium

74

JOSEPH P. SALANITRO

crude oils (17,000–37,000 mg/kg) indicate that maximum PHC reductions of 35–79% can be obtained in 9 months to 9 years at rates which are in the range of 20–87 mg/kg/day (Raymond et al., 1976; Huddleston et al., 1984). Experiments on the remediation of oily wastes have also shown similar reduction rates for PHC (35–89%) in landfarms operated for <1 to 2.5 years. One study suggests that increases in nitrogen fertilizer (100:4) enhances total PHC removal (Sandvik et al., 1986) while another indicates that the removal rate may increase with higher N addition but the overall mass decrease does not change (Loehr et al., 1992). An extensive pilot test of oil waste degradation in compost soil piles containing different bulking materials (peat mass, straw, and tree waste), inorganic fertilizer (100 C:10 N:1 P), waste activated biosolids, or surfactant (100 mg/kg alcohol ethoxylate) showed that none of the amendments improved PHC reductions in a silty clay containing 9,500 mg/kg of a weathered oily waste compared to no treatment or tilled soil pile controls; PHC declined in all treatments by 55–60% after 315 days. (Hayes et al., 1995). Several reﬁned products (including diesel, jet fuel, heating oil, and marine diesel) have been tested in various soils in laboratory and ﬁeld-scale bioremediation experiments (Table III). Reductions of 40–65% of diesel hydrocarbons have been observed in laboratory incubations of sandy and silty loams and clayey soils in the presence or absence of high nutrient amendment (Chaˆıneau et al., 1995; De Cort et al., 1997; L¨oser et al. 1998; Margesin and Schinner, 1999). In some experiments, very high PHC removal rates of 400-800 mg/kg/day were observed. It is worth noting, however, that when diesel-amended soils were respiration-inhibited with 2% HgCl2 to account for abiotic (e.g., volatilization) removal mechanisms during the experiment, about 10–40% of the diesel-PHCs were presumably biodegraded (Song et al., 1990, Margesin and Schinner, 1997; Taylor and Viraraghan, 1999). These data suggest that 20–25% of C8–C21 hydrocarbons in diesel can readily volatilize during a “biodegradation” experiment. Also, the high removal rates (100–2,100 mg/kg/day) noted in some tests probably overestimated the extent of soil biodegradation because of PHC evaporation. In the same study by Song et al. (1990), they showed that nearly all the hydrocarbons (45,000 mg/kg) in a gasoline (C5–C11 range) were evaporated from the poisoned control soil and only a small amount (2–3%) may have been biodegraded by soil microbes. The addition of a sodium dodecyl sulfate surfactant (50–100 mg/kg) to a diesel-contaminated soil reduced the overall biodegradation of hydrocarbons by 20–30% presumably because of surfactant toxicity or the stimulation of surfactant degradation at the expense of PHC metabolism (Margesin and Schinner, 1999); also, the extent of diesel degradation (5,000 and 50,000 mg/kg) in an Alpine soil at 10◦ C appeared to be similar to other soils incubated at 20–25◦ C. Data on the reduction of diesel (1,300–65,000 mg/kg) in ﬁeld-lysimeter, compost piles, or land treatment have indicated that high removals (55–95%) can be achieved in sandy, silty, or clayey soils in 3–24 months (Whitﬁll and Boyd, 1987; Stefanoff and Garcia, 1995; Wang

BIOREMEDIATION OF PHCs IN SOIL

75

and Bartha, 1990; Shen and Bartha, 1994; Quinn, 1995; Chaˆıneau et al., 1996). Removal rates in compost piles were similar with or without fertilizer nutrients, although in one ﬁeld study (Berg et al., 1998) addition of a high nutrient level (100:20:15) apparently reduced the overall PHCs (36% removed) compared with those land-treatment plots with lower or no nutrients (Whitﬁll and Boyd, 1987; Shen and Bartha, 1994). The few laboratory and ﬁeld experiments conducted with jet fuels have also suggested that a signiﬁcant fraction of the PHCs may be removed through evaporation. Song et al. (1990) showed that biodegradation of jet fuel in a clay loam may account for only 10% of the PHC reduction when hydrocarbon loss is compared with a poisoned (HgCl2) control. A ﬁeld lysimeter experiment in a sandy loam indicated that 93–97% of the jet fuel components decreased after 140 days with or without N and P amendments (Wang and Bartha, 1990). In a land treatment experiment at 5C in an Alaskan sandy soil removal rates of normal and branched alkanes and low molecular weight PNA in jet fuel were apparently much higher (50–60%) in plots with very high nutrients (C:N:P of 100:125–500:12–32) compared with an unamended control (5–7% decrease) after a 90-day test (Braddock et al., 1999). The bioremediation of heating oil in a soil laboratory experiment by Song et al. (1990) showed that as much as 32% of the extractable PHCs were removed by apparent evaporation in which very high rates of reduction (714–1,111 mg/kg/day) were observed; in this study about 40% of the PHCs may have been biodegraded or metabolized. The carbon number distribution in heating oil is similar to diesel and jet fuels and it is not unexpected that a large fraction of the PHC reduction in an experiment may be attributable to evaporative losses. Remediation of heating oil by land treatment or in ﬁeld lysimeter tests with different soils have shown that the initial PHCs (14,000–47,000 mg/kg) decreased by 70–95% with or without nutrient amendments (Raymond et al., 1976; Wang and Bartha, 1990). Laboratory studies by Song et al.(1990) with one of the heaviest fuel oils, Bunker C (C9–C60+), in a sandy soil have shown that only a small fraction (4%) of the PHC may have been degraded in 336 days. Diesel fuel-enriched bacterial cultures inoculated into sands and clay slurries showed that 35% of the marine fuel hydrocarbons volatilized with 25% apparently degrading during a 100-day test (Apitz and Myers-Schulte et al., 1996). Some of the lowest rates of degradation were observed with marine diesel (5–20 mg/kg/day). Land biotreatment of Bunker C-contaminated soil (25,000–35,000 mg/kg PHC) by Raymond et al. (1976) indicated that 30–50% of the PHCs declined in different soils. In contrast, Ciofﬁ et al. (1991) were able to demonstrate that as much as 85% of the C10–C30 PHCs in a soil containing marine diesel were degraded in a full-scale land-treatment study. The only data reported for the biodegradability of motor oil in soil (sand and clay loams) was that of Rhykerd et al. (1995) in which PHC removals were 35–45% of the initial loading (50,000 mg/kg) and at high rates (230–295 mg/kg/day).

76

JOSEPH P. SALANITRO

B. OVERALL ASSESSMENT OF SOIL BIOREMEDIATION EXPERIMENTS 1. Evaporation and Weathering Most of the laboratory and ﬁeld bioremediation data just discussed have assessed biodegradation potential of PHC in crude oils, oily wastes, and reﬁned fuels without accounting for hydrocarbon mass removal due to physical weathering and evaporation during the experimental period. PHC removals, as expressed for much of the data in Table III, therefore, are a combination of volatile organic carbon (VOC) loss and apparent biodegradation by soil microbes. In the few reports (e.g. Song et al., 1990; Chaˆıneau et al., 1995; Margesin and Schinner, 1997) in which abiotic losses from soil have been measured, the data for fuel oils (diesel, jet fuel, and heating oil) indicate that 15–60% of the initial concentration (2,000–50,000 mg/kg) was removed solely by evaporation during the experiment; with gasoline-contaminated soils nearly all (95–98%) of the PHC losses were attributed to volatilization. In an experiment with diesel volatilization losses of PHC in biosolids-amended soils poisoned (2% HgCl2) to inhibit microbial activity, it was observed that n-alkanes C10, C11, C12, C13–15, and C16–22 evaporated in 18 days to the extent of 100, 45, 25, 5–10, and 0–5%, respectively (Stronguilo et al., 1994). Experiments by Berg et al. (1998) on the release of C12–C24 n-alkane diesel PHCs from a silty sand and using XAD-2 resin to sorb hydrocarbons showed that 25–70% and 3–15% of the C12– C14 and C16–C24, respectively, were desorbed from the soil. Chaˆıneau et al. (1995) showed that 16% of the total mass of diesel PHCs (2,600 mg/kg) present in drill cuttings apparently evaporated in 90–120 days, leaving the remaining saturated and aromatic compounds subject to biodegradation; in this experiment, however, only C11-C13 compounds were analyzed. There are few studies on the mass ﬂuxes of speciﬁc PHC from large-scale land treatment units for oily soils or fuel-impacted wastes. One study by Loehr and Webster (1996) showed that a small fraction of noncarcinogenic (e.g., naphthalene and acenaphthalene) and carcinogenic PNA (i.e., 4- and 5-ring compounds) volatilized (≤0.1–.15%) from a creosote-contaminated land-treatment unit during a 2-month period. However, in a review of soil bioremediation of PNA, Wilson and Jones (1993) indicated that abiotic losses (20–35% volatilized) of even 4- and 5-ring compounds [e.g., ﬂuoranthene, pyrene, chrysene, and benzo (b) ﬂuorene] can occur from in situ test systems and slurry bioreactors. In contrast, laboratory simulations of petroleum spills on water have shown that 30–80% of the PHCs in medium and light API gravity (e.g., 31–44 API) crude oils and 15% of a heavy oil (API 12) could evaporate in 4 days at 20◦ C (Reijnhart and Rose, 1982; Mackay and McAuliffe, 1988). In soils and sediments, however, some of the PHCs from oils and fuels will be sequestered through sorption to organic carbon and physical

BIOREMEDIATION OF PHCs IN SOIL

77

entrapment into the mineral and clay matrix and air and water pore spaces (Mihelcic et al., 1993; Scow and Johnson, 1997; Mader et al., 1997). PHCs in the carbon range ≤C15 which have high vapor pressures (e.g., >10−3 to 10−4 atm) and exceed the soil binding capacity would be present as a residual NAPL (nonaqueous phase liquid) phase. These compounds would be expected to be volatilized from soils. This may be the condition with many bioremediation experiments with high concentrations (e.g., 1,000–100,000 mg/kg) of crude oils and fuels. Water wet soils will reduce PHC volatilization rates but only at low levels near the maximum soil sorption potential. However, wetting will have a minor effect on evaporation from soils with PHC present in a free phase liquid state (Thomas, 1990). Determining evaporative release rates of PHC from well-controlled laboratory treatability studies (e.g., with microbial-inhibited respiration) is essential for approximating ﬁeld VOC emissions (weathering). Volatilization rates of PHCs ≤C15which are greater than the microbial degradation rates will represent signiﬁcant physical losses that could be erroneously interpreted as biodegradation. Physical chemical methods for estimating chemical vapor emissions from soil surfaces have been described in detail for pure chemicals by Thomas (1990). In addition VOC emissions from land treatment units will depend upon soil type, oil/fuel mass loadings, soil moisture, wind speed, tilling frequency, and other seasonal variations (temperature and precipitation). Such mass losses are critical for assessing actual bioremediation potential and performance of oil and fuel impacted soils. 2. Nutrient, Microbial Growth, and Inoculation Effects Data presented in Table III on the reduction in PHCs have shown that there is a wide variation in the use of C:N:P formulations for oily soil amendments to enhance biodegradation. In some oily soil (1,000–100,000 mg/kg PHC) studies the addition of nutrients (C:N:P of 100:0.1–50:0.1–75) or no nutrients had no signiﬁcant effect on the rate or extent of PHC degradation (Dibble and Bartha, 1979; Wang and Bartha, 1990; Sandvik et al., 1986; DeCort et al., 1997), while in other reported experiments with oily wastes and fuels, hydrocarbon decline was signiﬁcantly enhanced with nutrients. (Brown et al., 1983; Song et al., 1990; Braddock et al., 1999). It is not clear why the nutrient effects on biodegradation for PHC-containing soils are unequivocal. Some reasons why nutrients may not stimulate hydrocarbon degradation could be that some fraction of PHC (1) degrades to CO2 with little cell growth, (2) is sequestered and not readily available for metabolism and growth, and (3) is partially oxidized only by soil microbial enzymes to PHC intermediates. It is also possible that only a very small fraction of PHC in oily soils supports the growth of bacteria. The molecular formula for a bacterial cell has been given as C5O2N1.4H0.8P0.3K0.2S 0.1(Neidhardt et al., 1990).

78

JOSEPH P. SALANITRO

Based on a C:N:P of C5N1.4P0.3 or 100◦ C:28N:6P, it would appear that most laboratory and ﬁeld PHC soil bioremediation experiments may have been N and P limiting for microbial growth if a majority of the hydrocarbons were utilized for growth and energy production. Most engineering applications traditionally have employed organic substrate carbon and N and P nutrient ratios of 100:20:1 (Bailey and Ollis, 1986) for microbial fermentation or 100:5:1 (Beneﬁeld and Randall, 1980) for growth of mixed cultures in wastewater treatment systems. One of the problems with the addition of high NH4 −N levels to soil is the rapid nitriﬁcation − of NH+ 4 → NO3 by indigenous soil nitriﬁers (Schmidt and Belser, 1994) thereby reducing available NH4-N for PHC degradation. Recent studies by Venosa et al. (1996), however, have shown that NO3-N can be used by hydrocarbon degraders in a ﬁeld bioremediation study of an oil slick applied to sandy beach surface plots. More studies are needed with pure and mixed microbial cultures to conﬁrm that NO− 3 is a good N nutrient for growth on PHCs since most bacteria use primarily NH4-N as nitrogen source for growth on a variety of organic compounds (Hutner, 1972). What is not clear from some oily soil bioremediation studies is that microbial counts (hydrocarbon degraders and/or heterotrophs) in soil do not always correlate with the decline (utilization) of PHCs. For example, experiments by Raymond et al. (1976) and Chaˆıneau et al. (1995, 1996) showed that hydrocarbon degraders, evaluated by MPN methods in media containing crude oil or hexadecane, increased by 1–3 log units during the soil bioremediation test with crude oils, diesel, or heating oil. In contrast, results of oily soil bioremediation by Huesemann and Moore (1993a, 1993b) and Salanitro et al. (1997) have shown that, within the accuracy of microbial count assays, heterotrophs and hydrocarbon degraders do not change during a test. Again such microbial enumeration assays may not reﬂect a signiﬁcant portion of the soil population growing on PHC because of (a) poor recovery from soil and growth in standard media with speciﬁc hydrocarbon substrates; (b) the fact that cell yields in soil are much less than in culture (e.g., <1 mg cells/mg PHC), with most of the microbial metabolism diverted to CO2 formation, and (c) a signiﬁcant PHC fraction is sequestered in soil and not readily attacked by microbial enzyme systems. In one study by Wang and Bartha (1990), however, heterotroph populations in soil containing 10 mg diesel or hexadecane/g soil showed increases (108 to 1010/g) in numbers that approximately reﬂected the theoretical cell yield (0.5 mg cells/mg hexadecane or diesel). Chemical surfactants have been used as nutrient additives to oily soils for enhancing solubilization of sorbed NAPL (nonaqueous phase liquid) and PHCs and stimulating biodegradation and growth of soil microbes (Ellis et al., 1985). The results of experiments with oily wastes in a silty loam (30,000 mg/kg TPH) and in a compost biopile (9,500 mg/kg TPH), cited previously (Table III), showed that a nonionic alcohol ethoxylate at low (10 mg/kg; Hayes et al., 1995) or high (5,000 mg/kg; Graves and Leavitt, 1991) concentrations had little effect on the rate or

BIOREMEDIATION OF PHCs IN SOIL

79

extent of PHC degradation. Indeed numerous reports have shown that many surfactants added to soils and slurries at high doses (tens to thousands of milligrams per kilogram) at or above the CMC (critical micelle concentration) for promoting PHC solubililization actually inhibit or reduce biodegradation due to surfactant toxicity on microbial cells and/or rapid utilization of surfactant in preference to hydrocarbons (Mihelcic et al., 1993). An additional consideration with the use of biodegradable surfactants at high concentrations is increased BOD loading and O2, N, and P nutrient consumption in soils. Recent experiments by Macur and Innskeep (1999) on the biodegradation of [14C]phenanthrene (100 mg/kg) have shown that while high concentrations of a readily utilizable nonionic alcohol ethoxylate, required to promote solubilization (ca. 13,000 mg/kg), were inhibitory to mineralization, levels of 200-2000 mg/kg stimulated hydrocarbon metabolism. Various glycolipid biosurfactants of microbial origin which have CMCs less than that of chemical surfactants (e.g., 200–400 mg/l) have been proposed for use in enhancing soil PHC bioremediation (Finnerty, 1994). Experiments by Noordman et al. (1998) on the desorption of phenanthrene (<100 mg/kg) in soil with a rhamnolipid biosurfactant demonstrated that 90% of the PNA could be desorbed at a lipid concentration of 500 mg/kg. Advantages of some biosurfactants is that they may biodegrade slower and the CMC activity may be retained for longer periods in soils. Currently, there is no consensus on the practical application of commercial surfactants or biosurfactants to enhance the bioremediation of oil- or fuel-impacted soils. Additional studies are needed to assess the cost-effectiveness of surfactants and their potential residual ecotoxicity to higher soil-dwelling organisms (invertebrates and plants). The inoculation of soils containing crude oil or fuel with commercial bacterial cultures for enhancing PHC degradation has been evaluated in a few studies. These studies by Huesemann and Moore (1993b) in soil and by Dott et al. (1989) and Thouand et al. (1999) in liquid culture enrichments indicate that there is little or no stimulation of PHC degradation over the indigenous degraders present. Experiments by Hayes et al. (1995) with biopile remediation showed that addition of reﬁnery biotreater solids to weathered oily soils did not enhance PHC degradation over control soils with no amendments. In a study to evaluate ﬁve commercial inoculants for bioremediating (simulated) oil slicks of weathered Arabian oil (17,800 mg/kg) in marsh sediment microcosms, Neralla and Weaver (1997) showed that bacterial preparations did not signiﬁcantly enhance PHC declines at − 3− 30◦ C compared to controls only with fertilizer (NH+ 4 , NO3 , and PO4 ). Similarly, there have been few ﬁeld demonstrations which have adequately evaluated microbial cultures for degrading PHCs in soil. One study (Venosa et al., 1992), involving spray application of two commercial cultures to weathered oily beach material, was inconclusive because of analytical variability and difﬁculty in estimating biodegradation from highly weathered crude oil components. There are many reasons why bacterial inoculations into soil may fail to promote PHC biodegradation and many

80

JOSEPH P. SALANITRO

of these have been suggested by Alexander (1999) for a variety of chemical structures including (a) decline in nutrient availability, (b) inability to compete with indigenous degraders, (c) inability to grow on or oxidize the organic compounds, (d) decreasing availability of compounds sequestered in soil, and (e) protozoan predation and elimination of added microbes. 3. Persistence in Bioremediated Soils There are many examples cited (see Table III) which, in the course of bioremediation with crude oils or fuels, the decline in total recoverable hydrocarbons occurs rapidly (i.e., pseudo- or ﬁrst-order decay) and then reduces (“levels off”) to a much slower rate (see, e.g., Loehr et al., 1992; Song et al., 1990; Chaˆıneau et al., 1995; Salanitro et al., 1997). This common “hockey stick” degradation curve is observed regardless of initial PHC concentration or soil type, whether the PHC mixture is freshly applied or “weathered” (“aged”) or the nutrient dose is applied or amended and regardess of the tilling (mixing) frequency, the laboratory test system (solid phase or soil slurry) or ﬁeld land treatment, or the method used for extraction and analysis of residual PHC. In many experiments a signiﬁcant fraction of the PHC are retained in the soil and are not further biodegraded or metabolized. This material appears to be entrapped, unavailable, and/or may be recalcitrant. Alexander and coworkers have evaluated much of the literature and studied this phenomenon of reduced bioavailability (and slow desorption) and sequestration of compounds in soils with pesticides and PNA incubated over long periods (e.g., “aged” several months) using microbial biodegradability, uptake into biomass, and acute toxicity to soil-dwelling species (earthworms, insects, and plants) as endpoint effects (Alexander, 1995; White and Alexander, 1996; Robertson and Alexander, 1998; Chung and Alexander, 1998; Tang and Alexander, 1998). Their experiments show that although compounds are extractable from soils, a signiﬁcant fraction of the applied compounds are apparently held within the soil particles, pore spaces, organic matter, and mineral matrix. The degree of this sequestration in one study, in which 16 different soils (sandy, silty, and clayey loams) were spiked with phenanthrene, showed that the decrease in biodegradability and extractability was 35 and 20%, respectively, as the PNA aged in the soil for 200 days (Chung and Alexander, 1998). Many soil bioremediation experiments have similarly demonstrated that a large fraction of the applied or weathered crude oil or fuel is retained and sequestered and not further biodegraded. Huesemann (1995) showed that nutrient-amended oily soils (heavy and medium crude oils, 10,000–30,000 mg/kg) and diesel-containing (44,000 mg/kg) soil retained 30–80% of the polars, 15–30% of the saturates, and 30–75% of the aromatics even after soils were bioremediated for several months. In other microcosm experiments with diesel drill cuttings in silty clay soil (2,600 mg PHC/kg), 15% of the PHC evaporated in an inhibited control and 45%

BIOREMEDIATION OF PHCs IN SOIL

81

degraded in 3–4 months. In this experiment although most of the diesel normal and branched alkanes were degraded, the residual undegraded fraction of saturates and aromatics (30% of each original fraction) consisted of higher molecular weight cycloaliphatic and PNA compounds while the resin (polar) fraction was entirely biorefractory. In another example of crude oil PHCs remaining sequestered after soil bioremediation, Salanitro et al. (1997) showed that of the initial compounds in the range of C11–C22 and C23–C44, 10–30% and 40–60%, respectively, were retained and not degraded after 9–11 months incubation. It is important to note that this degree of PHC sequestration occurred with heavy, medium, and light oils and regardless of whether the soils were of low or high soil organic carbon content. Extensive analytical characterization of land-treated soil containing diesel and heating oil (10,000 mg PHC/kg) by Angehrn et al. (1998), showed that 90% of the residual soil-associated PHC consisted of C16–C25 cyclic and branched aliphatics, alkylated PNA (3 and 4 rings), and polar compounds (alcohol, ketone, and acid derivatives of C14–C36 PHCs). In another study by the same workers (Angehrn et al., 1999), sequestered PHC residues were extracted and reapplied to bioremediated soil plots to assess whether those PHC were, in fact, biodegradable but nonbioavailable to soil microbial enzymes. Their results indicate that the freshly reapplied PHC residues decreased by an additional 20–25%, suggesting perhaps that only part of the sequestered residues are biodegradable. The remaining PHCs in the treated soil had high log Kow (>4) and boiling points (>280◦ C) and low vapor pressures (<10−6 atm), indicating the very low potential to inﬁltrate subsoils and volatilize from the land-treatment unit (Angehrn et al., 1998). In another study on the bioavailability of hydrocarbons in a reﬁnery oily soil lysimeter experiment, DeJonge et al. (1997) observed that 70–85% of the C16–C20 n-alkanes were apparently degraded (5–6 months) when the initial PHC loading was <4000 mg/kg. However, this same nalkane fraction was slowly metabolized and most likely sequestered as a NAPL soil phase when the initial PHC levels were 4000–12000 mg/kg. For some artiﬁcially weathered crude oils in soil there appears to be some correlation between API density and composition of n-alkanes and aromatics and biodegradability (McMillen et al., 1995; Salanitro et al., 1997). Higher gravity API oils (medium and light; API ≥ 30) with n-alkane/aromatic ratios ≥0.6 appear to be degraded more extensively (30–60%) than lower density oils (API 14–30) and n-alkane/aromatic ratios <0.6 (10–30%). To summarize ﬁndings on the residual PHCs in soils from bioremediation experiments, it appears that (a) as much as 10–60% of the original PHC may be sequestered in soils over a wide variation of PHC concentrations (1,000 to ≥30,000 mg/kg); (b) a fraction of these PHCs (C11–C44) could further degrade under certain conditions (e.g., slurry treatment or surfactant amendment), but the remaining PHCs may be biorefractory or slowly or partially metabolized (e.g., the high-molecular-weight branched alkanes, cycloparafﬁns, PNAs, and polars); and (c) the signiﬁcance of the nonbioavailable PHC fraction depends largely on

82

JOSEPH P. SALANITRO

characteristics of the oil or fuel, PHC concentration, presence of a NAPL phase (e.g., concentrations ≥4,000 mg PHC/kg), and soil type. The physical/chemical properties of the NAPL phase and its role in soil sequestration and bioavailability, however, have not been adequately studied. Indeed, Huesemann (1997) has presented two models of sequestration and recalcitrance in which PHC are retained in soil and not bioavailable (rapid sorption and slow desorption mechanisms dominate over biodegradation rates of degradable compounds) and a PHC fraction which contains numerous structures that are inherently poorly metabolized. Such “sterically hindered” compounds poorly biodegrade even under optimum nutrient amendments, mixing, and aeration because soil microbes fail to grow on them or do not possess competent metabolic enzymes. 4. Toxicity in Soils From the preceeding discussion it is clear that a signiﬁcant portion of residual PHCs remaining in bioremediated or weathered oily soils will not biodegrade further because of sequestration and inherent recalcitrance. However, these oil fractions remain unchanged, are not bioavailable, and may not pose signiﬁcant ecotoxicological risk or migrate through soil layers to potential human receptors (e.g., drinking-water wells). The more common soil ecotoxicological bioassays which measure lethality and reproduction in earthworms (U.S. EPA, 1988; Gibbs et al., 1996) and germination, root elongation, and growth in plants (OECD, 1984; ASTM, 1994) have been used as a measure of residual toxicity effects for bioremediation effectiveness and approximating residual ecorisk in site assessments for PCBs (Meier et al., 1997), PNAs (Baud-Grasset et al., 1993; Hund and Traunspurger, 1994; Sayles et al., 1999), pesticides (Van Gestel, 1992), and PHCs (Salanitro et al., 1997; Saterbak et al., 1999). More common bioassays for mutagenicity using the Ames (Salmonella) mutant strains (Wang and Bartha, 1990; Marvin et al., 1995; Sayles et al., 1999), Allium chromosome aberration (Potter et al., 1999; Sayles et al., 1999), and solid phase Microtox (Hund and Traunspurger, 1994; Salanitro et al., 1997) have also been used to assess residual toxicity of PHCs in soils. Table IV is a summary of pertinent available data for the no observable effect (NEC) soil PHCs concentrations in these bioassays for weathered and biotreated soils containing crude oils, fuels, and PNAs. The NEC may represent a threshold level at or below which poses no adverse effects to the survival (acutely toxic), reproduction, and growth of sensitive test species. Although NEC may not be entirely protective of all populations of species in the soil ecosystem it can be used as a benchmark indicator derived from chronic effects experiments (van Straalen et al., 1994). For weathered or bioremediated crude oils in soil, there is a wide variation in PHC concentration which affects earthworm (Eisenia) survival. Eisenia can apparently tolerate high residual PHC levels from medium and heavy (API 30 and

BIOREMEDIATION OF PHCs IN SOIL

83

14, respectively) crude oils (6,500 and 8,100 mg/kg) but is sensitive (no effect level of 500 mg/kg) to light oils (API 55) (Salanitro et al., 1997; Dorn et al., 1998). However, in a study by Saterbak et al. (1999) the no effect concentration for weathered residual PHC for several oily soils (crude oil contaminated) was highly variable (115–11,700 mg/kg) for Eisenia survival by PHC (TPH) analysis. However, Eisenia survival correlated with TPH-GC analysis (C6–C25 range) but not by compositional amounts of percentage of saturates, ring saturates, aromatics, and polars. In the experiments reported, no effect concentrations for Eisenia survival and reproduction (i.e., cocoons or juveniles/adult) in oily soils were similar and <1,000 mg/kg based on TPH-GC. In a study by Randolph et al.(1998) with a Kuwaiti crude oil, a 1,000 mg PHC/kg soil concentration had no effect on the survival of a benthic amphipod. The effects of invertebrate population reduction caused by fuel oil in soil is limited to one study by Raymond et al. (1976), who examined resident nematodes in soil after land treatment of a diesel (No. 2) oily waste. PHC concentrations of 1,000 mg/kg did not affect different trophic levels of nematode species. Studies on the threshold concentrations of 2- to 6-ring PNAs in creosote waste and their effects on Eisenia and Lumbricus survival have been somewhat variable. Potter et al. (1999) and Sayles et al. (1999) showed that 2- to 4-ring and 5- to 6-ring PNAs had no effect on both earthworm species at soil concentrations as high as 685–740 mg/kg and 425–450 mg/kg, respectively. In contrast, Hund and Traunspurger (1994) and Erstﬁeld and Snow-Ashbrook (1999) observed that soil concentrations of 2- to 6-ring PNAs of 5–80 mg/kg or 2,000 mg/kg had no effect on Eisenia survival or body weight. It is possible, however, that soil PNA concentrations causing effects in invertebrates may be compromised because of the presence of other PHCs. In a study with a freshwater benthic oligochaete, Lumbriculus, pyrene had no effect on survival at levels <132 mg/kg (Kukkonen and Landrum, 1994). Studies on the effects of oily soil residual contamination on plant seed germination indicate that the results are variable depending upon the species and API oil type (see Table IV). For example, Salanitro et al. (1997) showed that both germination (total seeds germinated) and growth (plant yield) in corn, wheat, and oat were not affected by residual PHCs from light oils (NEC of ≤500 mg/kg) while plants tolerated much higher residual PHC levels (NEC of 6,500 and ≤8,100 mg/kg) from bioremediated soils containing medium and heavy crude oils. In a subsequent study by Saterbak et al. 1999, the NEC of PHCs in weathered oily soils varied widely between mustard and corn germination and growth (150–11,700 mg/kg); the mustard plant was the most sensitive species with an NEC of 150–180 mg/kg PHC. It should be emphasized that much of the data prior to 1995 on the effects of crude oil hydrocarbon (analyzed as a total mixture) on germination and growth among plant test species (corn, wheat, oat, rye, soybean, and barley) in nontreated oily soils indicated high variability for NEC (<500 to 12,200 mg PHC/kg) compared to similar noncontaminated control soils. In bioremediated soils containing diesel

Table IV No Effect Concentrations (mg/kg) for Petroleum Hydrocarbons in Soil Bioassay Systemsa Hydrocarbon type (study no.)b Crude oils Oil (NS)g

Test soil bioremediatedc

Invertebrate survivald

Plant speciese Germination

—

Growth

Soybean (4700)

Other testsf

84

−

—

Oil (NS)

−

—

Wheat (12200)

—

Nitriﬁcation (1,680)h

—

Light (API 40)

−

—

Oat/rye (10,000)

—

—

Reference

Carr (1919) Murphy (1929) Schwendinger (1968)

Oil (NS)

−

—

Corn (11,000)

Corn (<10,000)

—

Udo and Fayemi (1975)

Heavy(API 23)

−

—

—

Wheat (<500)

—

DeJong (1980)

Heavy (Canadian)

−

—

—

Barley/wheat (<5,000)i

—

Xu and Johnson (1995)

Oily sludge

+

—

—

—

Bossert and Bartha (1985)

Heavy (API 14) Medium (API 30) Light (API 55)

+ + +

Eisenia (6,500)j Eisenia (8,100) Eisenia (500)

Corn/wheat/oat (6,500)j Corn/wheat/oat (≤8100) Corn/wheat/oat (≤500)

Corn/wheat/oat (6,500)j Corn/wheat/oat (≤8,100) Corn/wheat/oat (≤500)

Oil (NS-several weathered oily soils)

−

Eisenia (115–11,700) (100–3,600)k

Corn (1,500–11,700) Mustard (150–7,600)

Corn (<1,800) Mustard (180)

−

—

Kuwait (med.–light) (weathered) Kuwait (med.–light) Fuel oils Diesel no. 2

Soybean (≤840)

—

Microtox (6,500)j Microtox(≤8,100) Microtox (≤500) —

Salanitro et al. (1997)

Saterbak et al. (1999) Kuhn et al. (1998)

Tomato (1,200)

−

Amphipodl (1,000)

—

—

—

Randolph et al. (1998)

+

Nematode (1,400)

—

—

—

Raymond et al. (1976)

Diesel no. 2

−

—

Turnip/alfalfa/(≤13,800) Ryegrass

—

85

Diesel

+

—

Soybean/rye (<5,000)

Diesel no. 2

+

—

Corn (1,000)

Diesel

+

—

Diesel (drill cuttings)

+

—

Diesel

−

—

Maize/clover (≤10,000)m Barley/lettuce (<5,000)

Speciﬁc compounds PNA (3–4 rings)

+n

—

Lettuce/millet/ (600–1,800) Oat

—

PNA (2–6 rings)

+

Eisenia (2,000)

Oat (2,000)

—

Pyrene

−o

Lumbriculusp (<132)

—

—

PNA (5–6 rings) PNA (2–6 rings)

−q −

— Eisenia (≥5–80)

— —

— —

PNA (2–6 rings)

+s

Eisenia/Lumbricus (685; 2–4 rings) (450; 5–6 rings)

—

PNA (2-6 rings)

+u

Eisenia/Lumbricus (738; 2–4 rings) 423; 5–6 rings)

—

— Corn (1,000) — Corn/wheat/pea (1,250)

Lettuce/oat (738; 2–4 rings) (423; 5–6 rings)

—

Duell and Katz (1987)

—

Wang and Bartha (1990)

—

Shen and Bartha (1990)

Ames (5,000)

Wang and Bartha (1994)

—

Chaˆıneau et al. (1996)

—

Chaˆıneau et al. (1997)

—

Baud-Grasset et al. (1993)

Microtox (2,000) —

Hund and Traunspurger (1994) Kukkonen and Landrum (1994)

Ames (≤110)r —

Marvin et al. (1995) Erstﬁeld and SnowAshbrook, (1999)

Lettuce/oat (685; 2–4 rings) (450; 5–6 rings)

Alliumt (<685; 2–4 rings) (<450; 5–6 rings)

Potter et al. (1999)

Lettuce/oat (738; 2–4 rings) (423; 5–6 rings)

Alluimt (738; 2–4 rings) Ames (≤423; 5–6 rings)

Sayles et al. (1999)

continues

Table IV—Continued Hydrocarbon type (study no.)b n-Alkanes (C7–C10) Toluene, o-xylene Naphthalene Styrene Acenaphthene

Plant speciese

Test soil bioremediatedc

Invertebrate survivald

Germination

Growth

Other testsf

−

—

—

Lettuce (>1,000) (>1,000) (>1,000) (>320) (>25)

—

Reference Hulzebos et al. (1993)

86

a Concentrations (calculated or estimated as mg TPH/kg dry wt soil) of crude oil, fuel oil, or compound tested that result in no observed effect (e.g., survival, germination, and growth) in the soil bioassay compared to the control soil without hydrocarbon. b See footnote i under Table I. c Laboratory or ﬁeld land treating experiment; +, yes; −, no. d A 14-day animal survival (Eisenia fetida, compost worm, Lumbricus terrestris, earthworm) or nematode population assay in remediated soil. e Plant species germination and growth tests for 2–4 weeks (lab) or 1–3 months (ﬁeld); germination as number of seeds germinated/total planted; plant growth measured as whole plant material dry wt produced unless otherwise speciﬁed. f Nitriﬁcation (amount of NO− 3 formed), Microtox solid phase (% light decreased), or mutagenicity assay using Ames tester strains (number of revertants per milligrams of soil extract). g NS, not speciﬁed for type of crude oil in soil. h After 3 months in ﬁeld soils. i Root elongation. j All values subtracted from background soil TPH. k TPH-GC concentration for C6–C25 compounds. l Rhepoxynius, a benthic oligochaete. m Hydrocarbons >C15 were less toxic than
BIOREMEDIATION OF PHCs IN SOIL

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fuel the NEC for residual PHCs have also been variable (1,000 to ≤13,800 mg/kg) for several plant species. PNA with 2–6 rings were relatively nontoxic to plants for germination and growth (NEC, 425–2,000 mg/kg) even for some of the more sensitive (i.e., small seed) species such as lettuce, oat, and millet (Table IV). In a study by Hulzebos et al. (1993) individual n-alkanes (C7–C10), toluene, o-xylene, naphthalene, and styrene were of low toxicity (NEC) at levels of >320–1,000 mg/kg for lettuce growth; the 3-ring PNA acenaphthene (C12H10) was the most toxic with an NEC of <25 mg/kg. There are very few studies on the effects of PHC in other bioassays (e.g., Microtox, mutagenicity) or how they correlate with invertebrate and plant tests. In some oily soil studies, Microtox, the photoluminescent bacterial assay, appears to correlate with the NEC obtained with invertebrate and plant tests (Salanitro et al., 1997; Hund and Traunspurger, 1994). Mutagenicity bioassays on extracts of PNAcontaminated soils using Ames tester bacterial mutant strains show the same low toxicity (i.e., number of increased mutations over controls) as those observed for the chromosome aberration assay in Allium (NEC for 2–4 rings, 685–740 mg/kg and for 5–6 rings, ≤425–450 mg/kg) (Table IV). Marvin et al. (1995), however, observed that coal tar sediments containing residual concentrations of <110 mg/kg of 5- to 6-ring PNA were mutagenic in the Ames assay. In summary, it is difﬁcult to derive meaningful and acceptable endpoints for regulating soil clean-up criteria given the paucity of available surrogate species toxicity data for PHC in soils. Currently, the 50 states vary in their clean-up requirements for PHCs (total petroleum hydrocarbons) in soils containing crude oils, fuel oils, and oily wastes from 100 to 10,000 mg/kg (Simmons et al., 1999). Based on the data in Table IV, PHC concentrations that may be acceptable in terms of being protective of the soil ecosystem appear to be in the range of 500–1,000 mg/kg, based on NECs in earthworms and plants. Regulatory clean-up criteria among the states for PNA in soils also varies from 3 to 5,000 mg/kg for noncarcinogenic compounds (2–4 rings) to <1 to 50 mg/kg for carcinogenic (5- and 6-ring) compounds. However with the few available studies it appears that NECs of 400–700 mg/kg for PNAs in soil may be protective for low-risk (little or no human impact) oily soils based on existing earthworm, plant, Ames mutagenicity, and Alluim chromosome aberration bioassays. 5. Uncertainties in PHC Analysis Several analytical methods have been employed to assess the decline in total PHC during soil bioremediation experiments. PHCs from crude oil and fuels are extracted with solvents (e.g., methylene chloride, carbon tetrachloride, freon, or hexane) and analyzed for total extractable mass (e.g., total petroleum hydrocarbons using Method 418.1 with silica gel treatment to remove nonpetroleum polar material) by infrared spectrometry (IR) or gravimetry (G) after drying

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(Fan et al., 1994; Standard Methods, 1995; Weisman, 1998b). Within the detection limits of 5 and 50 mg/kg soil, respectively, for the IR and G methods, loss of volatiles (
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analysis of other sediments from the same spill, Douglas et al. (1996) observed that PHC depletions (after 16 months) were 50–100% and 20–100% for n-alkanes (C10–C33) and total PNAs respectively, based on their hopane ratios. The reporting of biodegradation rates using ratios of “unstable” or more biodegradable compounds relative to “stable” or recalcitrant biomarkers is not valid. The depletion of one species relative to another is subject to varying degrees of evaporative weathering, sequestration, and recovery inconsistencies from a soil matrix. Again, it is not possible to separate abiotic loss from authentic biodegradation/biotransformation unless these processes are determined separately. Changes in stable isotope ratios, e.g., 13C/12C (referred to as gas chromatography isotope ratio mass spectrometry, GCIRMS) have been used by some researchers to differentiate sources of oil and fuel spills to the environment (Mansuy et al., 1997) since speciﬁc hydrocarbon GCIRMS signatures appear to be stable. Other workers have used the changes in 13C/12C ratios of speciﬁc n-alkanes as indicators of biodegradation (Aggarwal and Hinchee,1991; Jackson et al., 1996). However, Mansuy et al. (1997) showed that the 13C/12C ratios of C9–C30 n-alkanes in several oils differed by ␦13C of 7 per mil (i.e., ␦13C −26 to −33) but that weathering (artiﬁcial evaporative loss) and enhanced biodegradation did not change the ␦13C for several compounds within the PHC source; all were within 1 per mil for saturates and aromatics. In the experiments described by Jackson et al. (1996), the mineralization of light Louisiana crude oil in marsh sediments from a laboratory assay and ﬁeld plots showed that ␦ 13C/12C-CO2 produced was not signiﬁcantly different for the unfertilized (−26 per mil) or fertilized (−28 per mil) test system; the n-alkane (C15–C44): hopane ratio, however, decreased by 80% over the unfertilized (lab) test. It is important to note also in the studies of Jackson et al. (1996) that the evaporative loss of n-alkanes and alkylated naphthalenes and phenanthrenes was signiﬁcant (35 and 65%, respectively) in azide-inhibited controls, again demonstrating that PHC/hopane declines in bioremediation experiments are not entirely due to biodegradation processes. In summary, the techniques of monitoring “light” and “heavy” isotope (13C/12C) abundance of speciﬁc compounds by GCIRMS in oily soils is still problematic because of evaporative losses (differences in volatilization of isomers), potential redistribution of isotopes within the NAPL, reliability of isotope recovery from soil, and the varying equilibria between stable CO2-C isotopes in the soil mineral and vapor phases. Finally, it should be mentioned that, as part of the analytical uncertainties associated with PHC bioremediation, an appropriate soil sampling plan for lab and ﬁeld experiments is essential for understanding signiﬁcant differences due to evaporative and biodegradative losses. It has been estimated that as much as 90% of the variability in oily soil contaminant levels may be due to inadequate sampling methodology (The Hazardous Waste Consultant, 1992). Nonuniform mixing of oil and fuel in different soils types can cause wide variations (high standard deviations) in recoverable analyte concentrations. This is especially applicable to large ﬁeld

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and pilot-scale remediation tests involving oil and fuel contaminants distributed in soil depths of 6–12 in. (land treatment) or compost piles (4- to 6-ft. windrows). Statistical sampling guidelines for PHC in soil have been proposed in the SW-S46 manual (U.S. EPA, 1986) and summarized with examples by Huesemann (1994) and The Hazardous Waste Consultant (1992). Soil sampling using grid layouts are superior to random plot sampling in large ﬁeld tests since it improves site coverage and “hot spot” locations and minimizes areal biases. Although composite soil sampling from grids is cheaper and requires fewer samples for analysis, it may not adequately account for spatial variability and “hot spot” identiﬁcation. Also it may be difﬁcult to homogenize composite mixtures from wastes and clayey soils with high concentrations of PHCs. In practice, a combination of composite and discrete sampling strategies may be a suitable compromise for assessing land treatment of PHCs at large ﬁeld sites.

IV. SUMMARY AND CONCLUSIONS A. CURRENT SOIL PHC BIOREMEDIATION SCIENCE AND PRACTICE: PROSPECTS AND PROBLEMS The bioremediation of PHC in the soil environment has traditionally been practiced as a cost-effective land treatment method for reducing petroleum hydrocarbons from crude oil and fuel product spills through the stimulation of indigenous microbes. Indeed, there is historical evidence for the ubiquitous presence of hydrocarbon degraders in most soils and the ready isolation of species which can obtain energy for growth from the metabolism of C1–C44 compounds. In general, n-alkanes, isoalkanes (with few methyl and quaternary groups), and alkylated and nonalkylated 1-, 2-, and 3-ring aromatic compounds are readily mineralized aerobically, although there is evidence for the anaerobic degradation of some of the latter compounds under nitrate-, iron-, and sulfate-reducing conditions. In general, highly branched alkanes [pristane, phytane, cycloalkanes (naphthenes), naphthenoaromatics, 4- to 6-ring PNAs, steranes (multicyclic alkanes such as hopane), alkylated thiophenes and dibenzothiophenes, and high-molecular-weight resins (polar N-S-O compounds)] are slowly metabolized or completely recalcitrant. Soil PHC bioremediation experiments are characterized by the generalized “hockey stick” decline in bulk extracted hydrocarbons as shown in Fig. 1. In general, studies have shown that for crude oils and oily wastes (3,000–10,000 mg/kg PHC) apparent ﬁrst-order declines vary from 10–250 mg/kg soil/day (laboratory) to 10–90 mg/kg/day (ﬁeld) with maximum reductions varying from 10 to 90%. Very similar total PHC removal has been reported for reﬁned products (diesel and jet fuels) with decay rates of 10–1,000 mg/kg/day. In the few laboratory

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Typical rate of decline in PHC *Evaporation/weathering *Microbial degradation (CO2 and metabolites) PHC concn. % or mg/kg Soil Residual Phase PHC (NAPL) Low volatility (vapor pressure < E-4) High sorption (log Kow > 5) Low toxicity (low leaching potential) Sequestered (non-bioavailable) Recalcitrant hydrocarbons Time (weeks or months)

Figure 1 Typical decrease of PHCs in laboratory and ﬁeld experiments in soils containing freshly applied or weathered crude oils, oily wastes, and fuels.

studies where evaporative weathering losses have been determined, removal of PHCs from soil also accounted for 10–90% of the decline depending upon oil and fuel and concentration, soil type, and duration of the test. It would appear that while biodegradation may account for some of the decrease in bulk PHCs in oily soil experiments a signiﬁcant fraction (at least compounds ≤C15–C18) of the immobile NAPL phase undergoes evaporative weathering and ﬁxation/sequestration (Fig. 1). Many past studies in the literature which neglected such abiotic losses therefore may have overestimated the actual extent of PHC biodegradation/mineralization. Experimental data from Fussell et al. (1981) and calculations made by Brost and DeVaull (2000) have shown that the soil concentration of residual phase NAPL for gasoline, diesel, and heavy fuel oil (e.g., no. 6 fuel) can vary from 3,000–10,000 to 8,000–23,000 to 17,000–50,000 mg/kg, respectively. Sandy soils retained lower concentrations (3,000–17,000 mg/kg) than silty loams. However, estimations of the maximum saturated soil concentrations of PHC distributed in soil air and water pore spaces indicated that these values are orders of magnitude lower than NAPL phase levels and vary from 150–400 mg/kg for gasoline to only 5–20 mg/kg for most reﬁned products and probably crude oils. What is not known from oily soil bioremediation experiments is whether these lower soil pore saturated concentrations represent the predominant available and biodegradable fraction of PHCs. Biodegradability studies of soluble, insoluble, and sorbed phase compounds in cultures and soils suggest that microbial growth (and degradation rates) on biodegradable PHCs is controlled by water-soluble or micellar phase concentrations

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(Thomas et al., 1986; Mihelcic et al., 1993; Scow and Johnson, 1997) and the production of biosurfactants by degrading species to facilitate higher dissolution rates and transport into cells (Zhang and Miller, 1992; Finnerty, 1994). Therefore, the large NAPL fraction (≥500–1000 mg/kg) present in bioremediated oily soils contains nonbioavailable but degradable PHC as well as recalcitrant structures in a sequestered state. What is also unclear is whether adequate O2 diffusion occurs into microsites and bioﬁlms when soil pore spaces are occluded with NAPL. Calculations of diffusive transport of O2 through shallow layers by Huesemann and Truex (1996) showed that O2 concentrations should be sufﬁciently air equilibrated in unsaturated soils with 50% of the moisture-holding capacity and low levels of PHCs (≪500 mg/kg). Li et al. (1993) have shown that models of mass ﬂux of O2 into pore water spaces appeared to control PHC biodegradation rates; in laboratory tests with a fuel oil, they showed that pore water hydrocarbon (50 mg/l) degraded at a rate of 0.16 mg/l/day or 0.40 mg/kg soil/day on a dry weight of soil basis and fully aerated soil (pore water oxygen = 8.0 mg/l). Such rates are 2–3 orders of magnitude less than those estimates given in Table III, indicating again that previously reported bioremoval rates are too high. Furthermore, the lack of stimulation of microbial metabolism in many nutrient-amended soil experiments may also be explained by slow nutrient diffusion to pore water spaces when signiﬁcant NAPL phase blocks these microsites. In addition, toxicity and inhibition of soil-borne microbes by high concentrations of PHCs present in residual NAPL has been largely ignored by bioremediation science. A review by Sikkema et al. (1995) has revealed that many PHCs (tens to hundreds of milligrams per kilogram) represented by cyclic alkanes (e.g., cyclohexane and tetralin), n-alkanes, PNA(1–4 rings), isoprenoid structures, and mineral oils affect respiration (O2 uptake), membrane-lipid integrity and function, energy transduction; disrupt electron transport; increase Na+, K+ and proton leakage from cells; decrease ATP synthesis; and denature membrane-bound enzymes in bacteria and yeast cultures. The persistence and sequestration of PHC in biotreated soil as a residual NAPL, however, has been associated with relatively low or no toxicity to simple surrogate soil-dwelling species such as earthworms (survival and reproduction) and plants (germination and early seedling growth) (see Table IV). No effect concentrations of residual PHCs in these bioassays vary widely (from hundreds to thousands of milligrams per kilogram) depending on the weathering/biotreatment duration, the oil and fuel type, and initial concentrations. The acceptance of remediation endpoints for PHC-contaminated soils is currently under scientiﬁc study and regulatory consideration. It seems prudent in such environmental exposure assessments, however, to consider the low mobility (e.g., leaching potential), high soil sorption and sequestration, relatively low bioavailability and ecotoxicity, and low biodegradability (bioresistance) of bioremediated oily soils as low risk for many sites depending on direct exposure to sensitive receptors and whether it signiﬁcantly compromises the structure, function, and diversity of the soil ecosystem.

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B. FUTURE RESEARCH NEEDS Progress made since the 1960s and 1970s in our understanding of the biodegradability and biotransformation potential of PHCs by isolated cultures and in soils has demonstrated a remarkable metabolic diversity of soil-borne microorganisms. Biostimulation of microbial activity has been widely used in the bioremediation industry for crude oil and fuel spills to land and sediments. However, in many studies, microbial degradation has been assumed to dominate the soil remedial process while abiotic losses (evaporation/weathering), sequestration of NAPL phase PHCs, the presence of a large petroleum fraction of nonbioavailable and/or nonbiodegradable material (especially from medium and heavy gravity crude oils and heavy reﬁned oil products), and microbial toxicity and inhibition of PHC degradation has been largely ignored. Data on the extent and rates of mineralization of PHC in the saturated soil pore spaces and NAPL phases of oily soils would put perspective on the actual microbial degradation contribution to the observed extractable petroleum hydrocarbon declines relative to evaporation, weathering, and bioavailability. In this respect, speciﬁc 14C- or 13C-labeled noncarcinogenic reference PHCs (Massachusetts DEP, 1994) from the major classes (saturates/aromatics/polars), e.g., hexane/cyclohexane (C6), n-nonane (C9), n-eicosane (C20), and pyrene (C16) for the carbon ranges C5–C8, C9–C18, C19–C32, and PNAs (C9–C32), respectively, could be used to assess overall bioremediation and microbial toxicity in oil- and fuelimpacted soils. Soils amended with respiration inhibitors (e.g., azide or HgCl2) or sterilized by ␥ -irradiation may also be used to derive abiotic loss rates relative to biodegradation removal. Such laboratory experiments can estimate PHC mass ﬂuxes which have application to ﬁeld bioremediation evaluations. Finally, there is a need to develop additional soil toxicity data (acute and chronic) on speciﬁc PHC fractions (saturates, aromatics, and polars) and for representative compounds within those fractions for residual NAPL phase material remaining in remediated oily soils. These data will be helpful in the overall evaluation of ecorisk and environmental acceptance and long-term impacts to ecosystems and sensitive receptors.

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GENETICS OF FLOWERING TIME IN CHICKPEA AND ITS BEARING ON PRODUCTIVITY IN SEMIARID ENVIRONMENTS Jagdish Kumar,1 and Shahal Abbo2 1

Genetic Resources and Enhancement Program International Crops Research Institute for the Semi-Arid Tropics Patancheru, AP 502 324, India 2

Faculty of Agricultural, Food and Environmental Quality Sciences The Hebrew University of Jerusalem Rehovot 76100, Israel

I. Introduction II. Evolution of the Crop and Genetic Variation A. The Origin of the Crop B. Natural History of the Crop under Domestication III. The Flowering Genes of Chickpea A. General B. Genetic Control of Flowering Time C. Association of Flowering Genes with Agronomic Traits D. Photothermal Modeling of Flowering Time E. Earliness-Mediated Drought-Escape as a Means to Increased Productivity IV. Constraints on Productivity in Semiarid Environments A. Traditional Systems B. Modern Systems V. Conclusions and Future Outlook References

Chickpea (Cicer arietinum L.), a grain legume of Near-East origin has a unique natural history. The crop cycle in most of its traditional growing areas is completely different from the autumn germination, spring flowering, and summer maturation of its wild progenitor, Cicer reticulatum Ladiz., in eastern Turkey. Millennia of summer cropping in the Near-East and later dissemination into the lower latitude growing areas of eastern Africa and the Indian subcontinent, as a postrainy season crop, had profound effects on allelic variation in major adaptive loci of chickpea. In this chapter we discuss the consequences of the traditional cropping practices on the flowering time genes of chickpea. The recently identified genes for flowering 107 Advances in Agronomy, Volume 72 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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KUMAR AND ABBO time are described with special reference to their effect on chickpea adaptation, seed weight, seed yield, and stability under semiarid Near-East and Indian subcontinental growing environments. It is suggested that the genetic research on flowering time of this species and its wild relatives needs much attention, as only two genes affecting this trait are identified so far. Genes allowing a reduced crop cycle will provide pathways for new cropping systems and increased population density. Reduced crop duration may also help chickpea escape damage by the major biotic and abiotic stresses that mostly affect the crop at flowering and podding stages. It is concluded that the relatively simple inheritance of flowering time opens up new possibilities for breeding high yielding and stable chickpea cultivars for the C 2001 Academic Press. semiarid and arid regions globally.

I. INTRODUCTION Chickpea (Cicer arietinum L.), with total annual production of 9.1 million tons from an area of about 11.1 million ha, ranks third among the world’s food legumes or pulse crops (FAO, 1999). The Indian subcontinent (India, Pakistan, Myanmar, Bangladesh, and Nepal) accounts for about 80% of the global production while the rest is produced in eastern Africa, Mediterranean and Near-East countries, Australia, southern Europe, and North and South America. Chickpea provides highquality protein and starch to the predominantly vegetarian population in India and large population sectors in other South Asian and Near-East countries and is considered a health food in developed nations. Chickpea does not contain any speciﬁc major antinutritional factors such as ODAP in grasspea (Lathyrus sativus L.), vicin in faba bean (Vicia faba), and trypsin inhibitors in soybean (Glycin max), although it has oligosaccharides which cause ﬂatulence (Williams and Singh, 1987). At present the demand for this popular pulse in the developing countries is higher than their current production. The major reason for this trend is the expansion of cereal cropping, with progressively smaller and marginal areas being devoted to legume crops like chickpea and lentil. During the past 4 decades, the productivity of chickpea has not kept pace with the dramatic increases in the cereal production, thus it has lost and is still losing traditional areas to wheat, which produces higher and more stable yields under high input irrigated environments (Kelley and Parthasarthy, 1994). The relegation of chickpea to marginal lands, with lower productivity, further aggravates the situation, since low productivity is also accompanied by yield instability. Therefore, international trade is on the increase. For instance, a lucrative chickpea industry developed recently in Australia (FAO, 1999; Siddique and Sykes, 1997), mostly for export to India. Area under chickpea in Australia rose from practically nil to ca. 200,000 ha with total production of

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nearly 180,000 tons in 1998 (FAO, 1999). We consider these ﬁgures as a trend unlikely to reverse in the foreseeable future and stress the urgency in achieving a major leap in chickpea production in the Indian subcontinent, eastern Africa and the Mediterranean region, where the bulk of the produce is consumed. The semiarid tropics include parts of 49 countries in South Asia, northern Australia, sub-Saharan Africa, parts of southern and eastern Africa and some countries of Latin America. One-sixth of the world population, the poorest on Earth, inhabits these regions. Half of them live on less than U.S.$1 per day and “. . . work hard to sustain a living through daily and seasonal struggle to protect poorly endowed natural resources, conserve scarce water, improve soil fertility, and diversify crop choices” (Barghouti, 1999). Chickpea is one of the vital crops that can produce sustainable seed and stover yield in these harsh environments to provide quality-protein food to the inhabitants. Chickpea is also important in the cropping systems outside the semiarid tropics, e.g., in Asia, northern Africa, southern Europe, North and South America, and southern Australia. Thus it contributes to sustainability of agriculture in all these regions. A major rationale for including chickpea in the cropping systems of the semiarid environments is its demonstrated potential to contribute to enhancement of the natural resource base used for the production of the other crops that are staple foods of the poor communities who rely on marginal rainfed lands. The crop’s natural drought resistance makes it eminently suitable for such lands. Its beneﬁts to traditional cropping systems in the Indian subcontinent are well documented (Ryan, 1997). Although chickpea can ﬁx up to 140 kg N ha−1 in a growing season, reported values usually range from 20 to 60 kg N ha−1. Inclusion of more legumes like chickpea in cropping systems should enhance N ﬁxation in the system and can reduce the need for fertilizer, saving inputs and preventing environmental degradation. The additional beneﬁts include disruption of disease cycles affecting nonlegumes and higher water-use efﬁciency by disruption of cereal–cereal rotations. We believe that lack of genetic knowledge is responsible for the slow progress in chickpea breeding. Even after a quarter-century of international effort the addition to the chickpea gene map is minimal. Only a few linkages are worked out at the end of the century (Muehlbauer and Kumar, 1999) and its molecular map is still sketchy and based on an interspeciﬁc cross (Winter et al., 1999). In contrast the pea (Pisum sativum L.) gene map, particularly with its ﬂowering genes, is perhaps among the best genetically characterized systems (Marx, 1985; Weller et al., 1997). A comprehensive classic gene map of Pisum was developed in the late 1940s (see Marx, 1985) and detailed DNA marker maps are available (Ellis et al., 1992). Flowering is a major adaptive trait material to survival and cultivation (Marx, 1985). Genetic analysis of ﬂowering time and its bearing on agronomic performance is fundamental to crop improvement. The need to manipulate ﬂowering time stems from the fact that chickpea growing season is generally too long for

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obtaining a meager mean seed yield of about 0.8 t ha−1 (Kumar et al., 1996). This could be produced in a much shorter period. Therefore, it is dangerous to let such an attractive crop remain in the ﬁeld for a longer period than is necessary. It is estimated that major biotic and abiotic stresses reduce at least 50% realizable potential yield of this crop in the major production regions of the world (Ryan, 1997). Much of these losses occur at ﬂowering and podding time during February/March in the subtropical Indian subcontinent, where the bulk of the crop is grown. If chickpea can be harvested early, much of these losses could be avoided (Kumar et al., 1996). In this chapter, we describe the natural history with special emphasis on its direct bearing on the phenology of the central chickpea stocks and the newly reported ﬂowering genes. In addition, the potential role of these genes for future improvement of chickpea in the semiarid environments is discussed.

II. EVOLUTION OF THE CROP AND GENETIC VARIATION A. THE ORIGIN OF THE CROP Chickpea is a self-pollinating diploid species having basic chromosome number 8. The genus Cicer holds more than 40 species (van der Maesen, 1987), nine of which (including the cultigen) are annuals. Two among the eight wild annual Cicer species, native to eastern Turkey, are closely related to the cultigen. The ﬁrst, Cicer echinospermum P. H. Davis (echinate seed coat), grows in steppe plant formations on soils of basaltic origin. The second closely related species is Cicer reticulatum Ladiz. (reticulate seed coat), which is found in oak shrub formations on hilly limestone bedrock (Ladizinsky, 1975). Based on meiotic chromosomes pairing data, C. reticulatum was suggested as the immediate wild progenitor of domesticated chickpea (Ladizinsky and Adler, 1976a, 1976b). This early identiﬁcation is also supported by seed storage protein proﬁles (Ladizinsky and Adler, 1975) and by more recent morphological comparisons (De Leonardis et al., 1996) as well as by DNA marker analyses (Patil et al., 1995). C. reticulatum was ﬁrst collected and described in 1974 (Ladizinsky, 1975). Ever since, only 10 populations have been located in southeast Turkey (Ladizinsky, 1995). However, the ICARDA catalog of wild annual Cicer species (Robertson et al., 1995) lists 51 C. reticulatum accessions. Upon close examination of the catalog entries, one realizes that ICARDA currently maintains 10 original collections (Robertson et al., 1995) while the remaining are selections from the original material. Unfortunately, the number of C. reticulatum accessions utilized in genetic analyses is also small; that is, not all the 10 accessions have been utilized (e.g., Gaur and Slinkard, 1990a, 1990b; Singh and Occampo, 1997). We believe that the meagre number of C. reticulatum accessions deposited in gene banks

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reﬂects a low interest in this species at the time of its discovery, and in recent years is an unfortunate consequence of the uncertain political situation in Turkish Kurdistan. The earliest remains of chickpea seeds were unearthed from archaeological digs within or near the known distribution range of C. reticulatum (Zohary and Hopf, 1993). The earliest excavated chickpea remains were dated to the Pre-Pottery Neolithic B period of a number of Near-East sites (Zohary and Hopf, 1993). Unlike cereals’ archaeobotanic remains, in most cases it is impossible to distinguish between wild and cultivated pulses. Due to the very limited distribution of the wild progenitor, the common view is that chickpea was domesticated somewhere in the west arch of the Fertile Crescent alongside the rest of the founder crops of the Near-East Neolithic agriculture (Zohary and Hopf, 1993; Lev-Yadun et al., 2000). It is interesting to note that the area delimited by the actual range of C. reticulatum is the only region in the Fertile Crescent where all the wild progenitors of the founder crops of the Near-East Neolithic agriculture grow together. This includes the wild species of diploid and tetraploid wheat, barley, lentil, pea, bitter vetch, and ﬂax as well as wild rye (Lev-Yadun et al., 2000). The earliest occurrence of chickpea in India dates back to 2000 BC at Atranjikhera in Uttar Pradesh, although it may have been introduced independently to the southern parts of the country by sea (Chowdhury et al., 1971; van der Maesen, 1987). A few morphological characters and geographic distribution are commonly used for classiﬁcation of chickpea into two main cultivar groups. The desi type, grown mainly in the Indian subcontinent and East Africa, is characterized by pink ﬂowers and small (100- to 200-mg), usually angular, and yellow-brown-(or other) colored seeds. The kabuli type, native to the Mediterranean and Near-East region, possess white ﬂowers and large (200- to 680-mg) smooth or wrinkled light-colored seeds. Vavilov (1950) suggested two primary centers of diversity, Southwest Asia and the Mediterranean center, and designated Ethiopia as a secondary center. He observed that large-seeded varieties were cultivated in the Mediterranean basin and progressively small-seeded varieties abounded eastward. It is believed that kabuli chickpea was introduced into India through Kabul, Afghanistan (therefore named kabuli) in the mid-to late 17th century. The spread of chickpea to tropical Africa, North and South America, and Australia has occurred in more recent times.

B. NATURAL HISTORY OF THE CROP UNDER DOMESTICATION Five major cool-season food legumes, garden pea, lentil, faba bean, grass pea, and chickpea, originated in a fairly well-deﬁned area of the eastern Mediterranean basin. They have developed two distinct patterns of distribution subsequent to their domestication (Smartt, 1990). The garden pea and faba bean show northward spread and can be cultivated throughout Europe. Lentil, grass pea, and chickpea show limited adaptation to northern Europe. This may be related to the duration of

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growing season required. Satisfactory maturation of their pods may not occur in cool, moist conditions with declining autumnal daylength. They have spread east and west, covering the latitudes of the place of their origin, and moved southward, probably due primarily to their drought tolerance. 1. The Mediterranean and the Near-East Gene Pool The Greek botanist Theophrastus (1977, in translation) and the Roman historian Pliny (1971, in translation) have described chickpea as a summer crop (sown in March/April and harvested in June/July). Such a crop begins and completes its life cycle under increasing photoperiod and rising temperatures and depends mainly on stored soil moisture (Khanna-Chopra and Sinha, 1987; Kostrinski, 1974). It is unclear whether the chickpea crop cycle in the initial stages of domestication was similar. In any case, the crop cycle described in the ancient reports is entirely different from that of the wild ancestor. In the wild, C. reticulatum germinates after the autumn rains and develops vegetatively during the rainy winter under shortening photoperiod and cool temperatures. Flowering and reproduction occur in the late spring when mean temperatures are high and the days are long. Springsown, wild C. reticulatum plants in Rehovot, Israel, yield less than 1/5th of total biomass and seed produced by the winter sown crop (S. Abbo, HUJ, Rehovot, Israel, unpublished observations). What could have been the reason for the readiness to compromise to such an extent on seed yield? The common view is that the incipient farmers were fully aware of the devastating effects of the blight disease caused by the fungus Didymella rabiei (Kovacevski) v. Arx [anamorph: Ascochyta rabiei (Pass.) Labr]. In the Near East, the climatic conditions favoring spread of the disease occur from early February until early April. Since an autumn-sown crop would have a fully closed canopy by this time, an ascochyta epidemic is likely to destroy the crop completely. Indeed, ascochyta blight is the major biotic constraint for chickpea production in the Mediterranean basin to this very day (Singh and Reddy, 1996; Vir et al., 1975). In other parts of the world where chickpea has been introduced, ascochyta blight epidemic can occur. The disease destroyed much of the chickpea crop in Australia, during 1998. In South Australia, which also has a Mediterranean climate, the area planted to this crop in 1999 was reduced to 8,000 ha from over 80,000 in 1997 (Jan Bert-Brouwer, Victoria Dryland Agricultural Institute, Horsham, Australia, personal communication). Since the disease is not a serious problem in spring-sown chickpea, it is considered as the prime reason for the ancient practice of chickpea spring sowing. This change in the plant cycle following domestication is unique to chickpea (and to some extent to lentil). The founder crops of the Near-East agriculture, einkorn and emmer wheat, barley, pea, bitter vetch, and ﬂax, all retained their original plant cycle (as winter crops) in the ancient and traditional Near-East farming (Zohary and Hopf, 1993; Elazari-Volcani, 1930). This is because both

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chickpea and lentil are poor competitors with the aggressive winter weeds and, probably more importantly, both crops are susceptible to closely related species that cause ascochyta blight (Khare, 1981; Vir et al., 1975). Whatever the primary reason was for the ﬁrst attempts of spring sowing, and the adoption of this cropping system, we argue that its success was a major junction in the natural history of the crop. This is because shifting from the natural wild plant cycle to spring sowing was accompanied with selection in the direction of increased daylength sensitivity. Timing of ﬂowering independent of the daylength usually means that the plant would enter reproduction upon accumulation of a certain biomass value (often expressed as number of internodes) typical to the genotype (Sachs, 1999). Indeed, Roberts et al. (1985) have demonstrated this phenomenon in chickpea using daylength-sensitive types and a daylength-insensitive (ICC 5810) chickpea cultivar. Interestingly, in Roberts et al.’s (1985) experiment, in only two of the nine tested cultivars did ﬂowering commence below the 15th internode. It should be stressed that in the Middle East, spring-sown chickpea often completes its life cycle with about 15–19 internodes or less. In our experience, winter- and spring-sown (in Rehovot, Israel) C. reticulatum rarely ﬂowers at the 15th internode and values of 19–22 are more common (S. Abbo, HUJ, Rehovot, Israel, unpublished observations). Following spring sowing, a delay of ﬂowering until a relatively large number of nodes have developed might imply that the plant would enter reproduction when soil moisture is nearly depleted and only a meager seed yield (if any) might be expected. On the other hand, following spring sowing, increased daylength sensitivity might turn into a major adaptive advantage. This is because it might allow the plant to enter reproduction early enough in the season regardless of its developmental stage (node number). In such a way, seed set and pod development will take place before the onset of the summer drought and the grain yield (although modest) will be secured. The long-term consequence of selection under millennia of spring sowing was a (nearly complete) ﬁxation of the relatively high daylength sensitivity in the Mediterranean kabuli germplasm. This is evident from data of cultivar screens (Roberts et al., 1985) and from the phenology of recently developed modern ascochyta tolerant germplasm (Singh and Reddy, 1996). In an effort to produce blight resistant cultivars for winter sowing in Mediterranean environments, an extensive crossing and selection scheme was developed in ICARDA (Singh and Reddy, 1996). In the selection procedure, the F3 and the F6 /F7 generations were grown in an off-season nursery in Terbol (Beka Valley, Lebanon) “under normal day-length conditions . . . to eliminate the late maturing types.” Looking at the products of this selection scheme, it appears that in most cases, following autumn sowing in Syria, mean number of days to 50% ﬂowering never occurred before 130 days from germination (Singh and Reddy, 1996). Assuming germination on the 1st day of December this means that ﬂowering of the ICARDA material starts from mid-April onward. In another report from ICARDA (Singh et al., 1997), it is

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mentioned that winter sowing took place between November 20th and December 5th and that the mean value of days to 50% ﬂowering was 136 days (Singh et al., 1997). Allowing 5 to 14 days for germination, this means that the crop commenced reproduction between late April and mid-May. Based on the above considerations, selection and/or seed increase in such off-season nurseries might imply that types of reduced daylength sensitivity would be relatively less productive if ﬂowering is delayed until a critical number of nodes is accumulated. Consequently, such daylength-insensitive types might have been selected against as either less productive or relatively late to ﬂower. A relatively late start of the reproductive phase (April/May) in the Mediterranean might also impose selection in the direction of high temperature requirement of the reproductive process. This might have included high temperature requirement for proper pollen tube germination and growth, for the meiotic process, and for proper ﬂoral meristem development. Indeed, sensitivity of ﬂoral development to chilling was recently reported for modern Israeli material, bred and selected using relatively late sowing practice (Or et al., 1999). Problems with proper pod set were also encountered in Australia, where chickpea is sown quite early in the cool season (Lawlor et al., 1998). Accordingly, temperatures below 20◦ C were reported to have adverse effect on pollen germination and pollen tube growth (Savithri, 1980; Srinivasan et al., 1999). 2. The Indian Subcontinent and the East African Gene Pool Despite the Near-East origin of the crop, currently about 80% of its global production takes place on the Indian subcontinent. This remarkable adaptive success in an environment so very different from its native origin area must have depended upon the presence of allelic variation in major adaptation loci. As a rule, successful introduction of a new crop species into a new growing area (e.g., a Near-East species into India or Africa) is dependent on the presence of such allelic variation in the introduced plant material and adequate agrotechniques to ensure crop establishment and correct timing of ﬂowering. In the absence of such allelic variation in the introduced plant material the newly introduced species will most likely fail to reproduce and consequently might be abandoned after a few cropping attempts. In India, chickpea is mostly sown in October/November and in Ethiopia from August/September onward to January (van der Maesen, 1972). In both regions, the growing season is characterized with shortening photoperiod. Based on the Near-East origin of the ﬁrst chickpea introductions to India and Ethiopia, one must assume that the ﬁrst attempts of chickpea cropping encountered problems in terms of poor adaptation, namely incorrect timing of ﬂowering. Furthermore, it is difﬁcult to see how repeated sowing of nonadapted material took place until reduced daylength-insensitive types gradually occurred in the seed stocks. This is for two reasons: First, farmers are unlikely to spare seed for more than one sowing

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season and, second, there is no incentive for repeated sowing of an ill-adapted crop. We, therefore, suggest that the spread of chickpea into its Indian and East African growing areas and its most successful establishment as a staple protein crop therein must have required adequate allelic variation in ﬂowering-time genes to be present in the founder seed stocks. The seasonal daylength variation in the low-latitude chickpea growing areas of India and Africa suggests that insensitive alleles at photoperiod response loci had a central role in the successful spread of chickpea into these regions. Such a variation might have included alleles at both major and minor photoperiod and perhaps temperature response loci as well. These offtypes of reduced required daylength gave rise to the Indian and African chickpea gene pools. Recent screening results of a collection of Ethiopian land races and its performance compared to a set of Mediterranean chickpea stocks by Or et al. (1999) provide supportive evidence to the above considerations. Flowering time of the Ethiopian material in Rehovot (Israel, 32◦ N), ranged between 2 to 6 weeks earlier compared with local Mediterranean material (Or et al., 1999). The inherent early ﬂowering habit of the Ethiopian material as well as intravariation of its ﬂowering time suggest the presence of either an allelic series at a major ﬂowering locus and/or respective variation in minor (modiﬁer) ﬂowering time loci. Or et al. (1999) attributed the inherent earliness of the Ethiopian material to the repeated selection under two contrasting seasonal daylength proﬁles following the sowing seasons in Ethiopia, one starting from August/September and the second (in the highlands) starting from April (van der Maesen, 1972).

III. THE FLOWERING GENES OF CHICKPEA A. GENERAL The literature covering the above topic in other crop plants is immense and we make no attempt to cover it in full, but rather use a number of selected references relevant to chickpea. The number of days taken from sowing to onset of ﬂowering (ﬂowering time) is a major component of crop adaptation, particularly in rain-fed environments (Subbarao et al., 1995). The timing of ﬂowering is dependent upon the genotype, the seasonal temperature proﬁle, photoperiod, and vernalization responses of the plant. In indeterminate species, early ﬂowering may enable the plants to prolong the reproductive phase, especially when the ﬂowering duration is delimited by terminal drought that terminates seed set. Probably due to their central role in determining crop plant adaptation, the ﬂowering genes of many crop plants and their role in environmental adaptation were studied thoroughly (e.g., reviews by Quinby, 1973; Worland, 1996). In most cases, major as well as minor gene effects involved in determining ﬂowering time were reported. The involvement of

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KUMAR AND ABBO Table I Ranges and Mean Number of Days to 50% Flowering for 25 Chickpea Genotypes at Three Contrasting Locations in India Attribute

Hisar (29◦ N)

Gwalior (26◦ N)

Patancheru (18◦ N)

Range Mean SE ±

80–102 95.6 6.4

71–78 75.5 3.9

40–61 51.3 1.3

several genetic systems responding to daylength and/or temperature, their possible interaction, and the genotype × environment interaction cause in many hybrid progeny analyses a typical continuous frequency distribution of ﬂowering time. Therefore, the isolation of any major ﬂowering gene effect is best done using deﬁned genetic stocks (e.g., Weller et al., 1997), which is not always possible in conventional breeding material. In chickpea, however, information on the genetic control of ﬂowering time is only beginning to accumulate. This is despite the fact that early ﬂowering mediated by photoperiod insensitivity was suggested as a means to increase chickpea adaptability nearly 3 decades ago (Sandhu and Hodges, 1971). Regrettably, no genetic studies followed until recent years (Kumar and van Rheenen, 2000; Or et al., 1999). The ﬂowering time of chickpea genotypes varies with latitude and temperature variations. ICRISAT conducted trials of breeding lines at three locations: Patancheru (18◦ N), Gwalior (26◦ N), and Hisar (29◦ N). The ranges for 25 genotypes tested in these locations did not overlap (Table I). The mean number of days to 50% ﬂowering were 51, 76, and 96 for the three locations, respectively. Thus the genes controlling ﬂowering time are sensitive to temperature and day length. The existence of wide genetic variation for ﬂowering time was documented by Pundir et al. (1988), who evaluated the world chickpea germplasm maintained at ICRISAT and listed 43 accessions that ﬂowered in less than 39 days at Patancheru (18◦ N). Most of these lines originated in tropical India (Maharashtra and Karnataka), a few in Ethiopia, and 2 in Mexico and 5 have their origin in Iran (>30◦ N). This might indicate that mutations for early ﬂowering genes also survived in subtropical environments. They probably out-yielded the traditional long-duration varieties under severe drought conditions. Lack of knowledge on the genetic control of ﬂowering time did not prevent Kumar et al. (1985) from developing extra-early chickpea ICCV 2 as a transgressive segregant from a cross of ﬁve chickpea lines. However, further manipulation of these genes is difﬁcult without understanding individual effects of other genes governing this trait, interaction among them, and their responses to variations in temperature and daylength.

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B. GENETIC CONTROL OF FLOWERING TIME A major recessive gene “efl-1,” for “early ﬂowering,” was identiﬁed in a cross between the extra-early variety ICCV 2 and the medium-duration variety JG 62 (Kumar and van Rheenen, 2000). This gene is responsible for about 3 weeks’ difference in ﬂowering time between the two parents at ICRISAT, Patancheru. A super early chickpea segregant, ICCV 96029, was selected from the F6 generation from a cross of two extra-early varieties, ICCV 2 and ICCV 93929. ICCV 96029 ﬂowers about a week earlier than either of the parents (Kumar and Rao, 1996). The allele efl-1 is common between the two parents. Thus other complementary genes with smaller effects exist between these two extra-early parents. Complementary gene action for ﬂowering time was also evident in crosses between chickpea genotypes ICC 4958 (India) and Guamuchil (Mexico), two of the ﬁve parents of cv. ICCV 2 (Kumar et al., 1985). Thus at least two different loci control ﬂowering time in ICCV 2. This observation was further corroborated by a diallel analysis among three extra-early lines, ICCV 2, ICCV 93929, and Harigantars (ICC 5810), that produced three different types of F1s, indicating that more than two complementing genes operate ﬂowering time in chickpea (Jagdish Kumar, ICRISAT, Patancheru, India, unpublished results). In these studies one of the three F1s (ICCV 2 × ICCV 93929) ﬂowered earlier than the mid-parent, the second at the same time as the mid-parent, and the third ﬂowered later than the mid-parent. The super-early genotype ICCV 96029 and control Pant G 114 were evaluated for their ﬂowering time at Patancheru and at Hisar. The number of days taken to ﬁrst ﬂowering by ICCV 96029 were 24 and 43 at Patancheru and Hisar (Table II). Table II Performance of Superearly Chickpea ICCV 96029 and Long-Duration Controls at ICRISAT, Patancheru and CCS Haryana Agricultural University, Hisar, 1997/1998 and 1998/1999 Patancheru(18◦ N) (two environments, mean)

Hisar (29◦ N) (three environments, mean)

Character

ICCV 96029

C 235

Pant G 114 (projected)

ICCV 96029

Pant G 114

Days to ﬁrst ﬂower Days to ﬁrst pod Plant height (cm) Days to maturity Seed yield plant-1 (g) Biomass plant-1 (g)

24 29 40 79 14 —a

61 69 46 109 21 —

58 65 46 119 — —

43 75 54 128 17 43

83 107 45 155 16 48

a

Data not recorded. Source: Kumar et al. (2001a).

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This difference for the long-duration control Pant G 114 was 25 days at the two locations (Kumar et al., 2001a). First podding for ICCV 96029 was at 75 days after sowing and for Pant G 114 it was at 107 days at Hisar. The two produced similar seed yield under experimental conditions. It was observed that the extra-early-duration cultivar ICCV 2 grew at a rapid pace and produced the ﬁrst ﬂower at 16th node at Patancheru (K. Anupama and Jagdish Kumar, ICRISAT, Patancheru, India, unpublished data). The slow-growing medium-duration cultivar JG 62 produced its ﬁrst ﬂower at the 23rd node. Under good management JG 62 out-yields ICCV 2. However, under severe drought conditions the latter out-yields the former. As moisture is often a major limiting factor in farmers’ ﬁelds, early maturity is desirable. Or et al. (1999) studied chickpea ﬂowering time in a cross between an extraearly line ICC 5810 and a late-ﬂowering Israeli cultivar (Hadas) at Rehovot (32◦ N), Israel. The ﬂowering gap between these two genotypes was subject to considerable year-to-year variation. Similarly, the ﬂowering range displayed by the progeny from the segregating generations changed across seasons (Or et al., 1999). The above cross was designed to analyze the ﬂowering syndrome of the Mediterranean chickpea stocks, hence the choice of the modern relatively late-ﬂowering cv. Hadas. The early parent ICC 5810 (originated in Maharashtra, India) was chosen based on the screening of Roberts et al. (1985), who characterized it as a nearly dayneutral type. The 3:1 segregation of late:early individuals among the F2 progeny was interpreted as an evidence to a major gene action affecting ﬂowering time through determination of photoperiod response (PPD). In this cross, the late condition (photoperiod responsive allele) was dominant. Plants carrying the recessive allele were more prone to environmental effects (mainly temperature), while the ﬂowering time values of individuals with the late allele were more stable (this may be the result of favorable temperatures during the later part of crop growth). At present, it is unclear whether the efl-1 gene described by Kumar and van Rheenen (2000) and the PPD gene reported by Or et al. (1999) differ from one other. However, there are indications that the major recessive allele for earliness in ICC 5810 is located at the same locus as the efl-1 gene in ICCV 2 (Jagdish Kumar, ICRISAT, Patancheru, India, unpublished data). Several major gene loci were reported to affect ﬂowering time in sorghum and differences within the welldeﬁned maturity groups were attributed to speciﬁc gene combinations rather than to allelic series operating in the Ma loci of sorghum (Quinby, 1973). In contrast, both in pea and Arabidopsis, allelic series were reported for some of the ﬂowering loci (Weller et al., 1997; Koornneef et al., 1998). Test crosses are required to assess the situation in chickpea. While it is clear that at least three loci affect ﬂowering time, at present there is no evidence for the existence of allelic series for these. Furthermore, in the absence of DNA markers linked to the chickpea ﬂowering genes we are also unable to relate either gene to its homologous counterparts among the well-deﬁned pea ﬂowering genes (Weller et al., 1997). Despite clear evidence to

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a certain degree of linkage group similarity between pea and chickpea (Kazan et al., 1993), the chickpea basic chromosome number of 8 [different from the basic number (7) of pea] makes such comparisons quite difﬁcult without clonedgene sequences from both species. Major as well as minor gene actions affecting ﬂowering time were recently reported in lentil (Sarker et al., 1999). These authors have suggested that the lentil gene is equivalent to the SN gene of pea (therein), but provided no experimental evidence or theoretical consideration to favor this suggestion over the alternative options that the identiﬁed gene was, perhaps, an equivalent of the pea PPD or the DNE loci. The ﬂowering genes inﬂuence maturity type and crop yield through their effects on the onset of reproduction, duration of reproductive phase, number of branches, and number of ﬂowers per node (Murfet and Reid, 1985). In pea it is known that photoperiod-sensitive types have a marked tendency to produce basal branches. Thus knowledge of gene action and epistatic effects and genotype × environment (g × e) interaction enable selection of genotypes suited to particular regions. In pea it is known that photoperiod-sensitive types have a marked tendency to produce basal branches.

C. ASSOCIATION OF FLOWERING GENES WITH AGRONOMIC TRAITS Abbo and co-workers have used the cross Hadas × ICC 5810 (and the reciprocal) to detect possible associations between the major ﬂowering gene PPD and a number of agronomic traits. The two parents involved in the crosses differ in many traits, with cv. Hadas presenting partial resistance to ascochyta blight and large grain weight (450 mg) and ICC 5810 extremely susceptible to ascochyta and having a small grain size (150 mg). Both parents also differ in their developmental response to temperature in terms of internode length, branching and growth habit, and ﬂoral development (Or et al., 1999; S. Abbo, HUJ, Rehovot, Israel, unpublished data). As a result, in comparisons conducted under Israeli environments, ICC 5810 exhibits its early ﬂowering habit in an ill-adapted agronomic background. The phenotypic correlation estimate between ﬂowering time and mean grain weight calculated from the F2 data of the Hadas × ICC 5810 was 0.29 (P<0.0001). Based on the data from the reciprocal population the respective r value was lower and not signiﬁcantly different from zero. Phenotypic correlation estimates from the F3 progeny were 0.26 and 0.23 for the Hadas × ICC 5810 and the ICC 5810 × Hadas, respectively. The differences between the reciprocal populations and the year-to-year variation were attributed to g × e interaction affecting the time to ﬂowering trait (Or et al., 1999). Genotypic correlations between time to ﬂowering and mean grain weight based on the variances and covariances between

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and within F3 families were 0.64 and 0.51 for the Hadas × ICC 5810 and the reciprocal cross, respectively (both with P<0.0001). These data imply that ﬂowering time loci as well as grain weight loci are scattered throughout the chickpea genome, and in some cases these loci are linked, as expressed by the r values calculated between the two traits (Hovav, 1999). The large kabuli-seed phenotype also occurs in an extra-early ﬂowering background, e.g., ICC 7344 and ICCV 92311 (Pundir et al., 1988; J. Kumar, ICRISAT, Patancheru, India, unpublished data). This fact is in accord with the assumption that the PPD locus is linked to grain weight gene(s) rather than affects the grain weight trait directly (pleiotropy). Under such a situation the daylength response locus as well as the grain weight loci may harbor either allelic variant at any of the loci affecting each trait, thereby allowing desired combinations to suit grower as well as consumer preferences. The days to ﬁrst ﬂower and data obtained from the F3 families of the Hadas × ICC 5810 (and reciprocal cross) were correlated with the response to the pathogen of ascochyta blight (Didymella rabiei) in an infested ﬁeld nursery of F3’s single-seed descendants (F4 generation of the above crosses). The genetic correlation between resistance to D. rabiei and days to ﬁrst ﬂower was signiﬁcantly negative [r > −0.4; P(F)< 0.05]. In the studied cross combinations, the tolerant parent was the late-ﬂowering one. The negative correlation means that some of the ﬂowering loci are linked to quantitative loci governing resistance to ascochyta blight. Or et al. (1999) suggested that in a Mediterranean environment, early ﬂowering might allow a longer reproductive period expressed as a relatively large number of pods along the main branches of the plant. Due to its indeterminate growth habit, such a trait might be an important yield component for chickpea. Their comparisons showed that in certain genetic backgrounds early ﬂowering types do set more pods along their main branches compared to late-ﬂowering ones. When measured under ﬁeld conditions among the progeny of the Hadas × ICC 5810 crosses this trait was subject to large environmental inﬂuence. The strong environmental effect on this trait was expressed in the absence of any correlation between time to ﬂowering and number of pods along the main branches (Hovav, 1999). Although ﬂowering is a prerequisite for pod set, the latter phenotype is also dependent on the sensitivity of the reproductive process to temperatures (Savithri et al., 1980) and on the pod-set rate during the season. The pod-set feature might be related to the seed weight, with relatively large seed dictating a slower pod set compared with types possessing small seed. This is because large seeds might pose a heavier sink load compared with smaller seeds. Such a relationship was reported for lentil and served to suggest smaller seeded microsperma types as better adapted to drought-prone environments (Erskine, 1996). Our observations with late ﬂowering kabuli types support such a relationship; e.g., despite being later to ﬂower, cv. Bulgarit consistently produces more pods along its main branches compared with cv. Hadas (Or et al., 1999; S. Abbo, HUJ, Rehovot, Israel, unpublished data).

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Possible effects on ﬁeld productivity of the PPD allele were tested using bulks of F4 seed material from the Hadas × ICC 5810 (and reciprocal) (Shai, 2000). The ﬂowering, grain weight and color, and ascochyta response data of the ﬁeld-tested F3 families produced three comparisons between relatively early and relatively late F3 families from the above crosses. The ﬁrst comparison included large-seeded lines (ca. 250 mg and above) (late vs early to ﬂower), regardless of seed color or ascochyta blight response. The second comparison included only beige-seeded lines (late vs early to ﬂower) regardless of other traits, and the third comparison was made in a relatively ascochyta blight-resistanct background (late vs early to ﬂower) regardless of seed size or color. Total biomass production and grain yields were compared following autumn sowing under current agronomic practice in Israel. In this way, a comparison of the possible PPD effect was held under three independent genetic backgrounds. In all three tested backgrounds, both the grain yields and total biomass production of the relatively late-ﬂowering bulks were superior compared with those obtained from the early-ﬂowering bulks. The superiority of the agronomic alleles donated by the late-ﬂowering modern cultivar Hadas over those of the early-ﬂowering parent in the tested environment is evident from their own performance (S. Abbo, HUJ, Rehovot, Israel, unpublished). The results of the above comparisons are nonetheless important. First, such PPD chickpea material was never tested in agronomic stand under Mediterranean conditions. Second, the ﬁeld results support the genetic analyses performed on individual plant basis. Third, despite the clear evidence for the PPD gene action, ﬂowering time is heavily affected by polygenes, similar to grain yield. Under such circumstances, it becomes clear that numerous combinations between promoting and demoting alleles at any linked ﬂowering and yield loci (major and/or minor) may exist. Therefore, bearing in mind the poor adaptation of the ICC 5810 parent to the Israeli conditions such results of bulk comparisons are not surprising. The presumed loose associations between the ﬂowering loci (PPD included) and agronomic performance affecting loci suggest that selection to produce desired combinations in any direction should be possible. These conclusions support the hypothesis proposed by Wallace and Yan (1998) that the majority of the genes of the plant control the ﬂowering time.

D. PHOTOTHERMAL MODELING OF FLOWERING TIME Ever since the early 1980s (Roberts et al., 1980, 1985) attempts have been made to characterize chickpea varietal responses to environmental factors as expressed in the time to ﬁrst ﬂower or as the developmental rate to ﬂowering. This approach resulted in the conclusion that in chickpea daylength and temperature have an additive effect on the time to ﬁrst ﬂower, assuming no interaction between these two environmental factors (Ellis et al., 1994). Although the above model is well

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supported by experimental evidence for a number of crop species, an alternative model was proposed, in which the photoperiod × temperature interaction is an integral part of the model (Yan and Wallace, 1996). Such experimental approaches are most useful to predict the ﬂowering time of the tested genotypes in a range of environments and to classify them according to the relative importance of the factors affecting their ﬂowering time, i.e., temperature, photoperiod, or both. However, these models fail to fully describe the underlying genetic mechanisms governing the action of the loci responding to the environmental cues. Ideally, such experimental approaches should be applied to segregating progeny of the kind analyzed by Kumar and van Rheenen (2000) or Or et al. (1999). Indeed, Alcalde et al. (1999) have recently quantiﬁed the effects of the Lf, Sn, E, and Hr genes on time to ﬂowering in pea. This was done using a set of standard pea lines homozygous for different allelic situations in the respective ﬂowering genes. In a similar manner, comparisons of late vs early ﬂowering progeny from the crosses studied by Kumar and van Rheenen (2000) and Or et al. (1999) could assess the effect of the efl-1 and the PPD genes of chickpea. Such an analysis might assist in determining the relative importance of the temperature response in the different genetic backgrounds and help to allocate genes for earliness per se operating in either daylength-sensitive or daylength-neutral backgrounds of chickpea. We anticipate that such a combined approach may result in better understanding of major adaptive loci later to contribute to improved chickpea crop productivity.

E. EARLINESS-MEDIATED DROUGHT-ESCAPE AS A MEANS TO INCREASED PRODUCTIVITY Drought is the major constraint to increased productivity, as nearly 90% of the world’s chickpea is grown rainfed (Kumar et al., 1996). It is estimated that if moisture stress is alleviated, up to a 50% increase in chickpea production could be achieved, with a present value of ca. U.S.$ 900 million (Ryan, 1997). One way to escape end-of-season drought is to develop varieties with early growth vigor, early ﬂowering, and early maturity (Calcagno and Gallo, 1993; Johansen et al., 1997). In drought-prone environments such as those in the tropics normally a strong positive association exists between water transpired by the crop and biomass formation (Sinclair et al., 1984). Therefore, rapid early growth of the crop is desirable. This will also ensure early attainment of full crop canopy and prevent soil-surface evaporation. Johansen et al. (1997) measured the relationship between early growth vigor and shoot mass and seed yield at harvest in 123 chickpea genotypes grown on a vertisol at ICRISAT, Patancheru (Fig. 1). There was a linear positive relationship between early crop growth and seed yield. They suggested that the most feasible way to increase productivity is to shorten the crop duration.

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Figure 1 Relationship between early crop growth rate (CGR) and seed yield at harvest in 123 chickpea genotypes grown on a vertisol, ICRISAT Center, postrainy season 1988–1989 (from Johansen et al., 1997).

Often end-of-season drought is associated with increasing temperature (Calcagno and Gallo, 1993; Singh, 1997). Sedgley et al. (1990) suggested that early pod set should be a prime strategy for avoiding drought stress in environments prone to end-of-season moisture stress. Thus development of early maturing varieties may help drought-escape and result in increased productivity and extending this crop to even more drought-prone areas (Kumar et al., 1996). Earliness is considered important in cowpea, pea, and other grain legume crops (Hall and Patel, 1985; Sharma and Khan, 1997). Genes allowing a reduced crop cycle will provide pathways for new cropping systems (Ortiz et al., 1999). Early maturing varieties will also allow increased population per unit area and consequently help maximize yield in drought-prone environments. Penalties associated with earliness include short time available to accumulate biomass and development of a shallower root system. The ﬁrst can limit the grain yield potential and the latter will render plants vulnerable to adverse effects of intermittent drought (Johansen et al., 1997). However, relatively higher temperatures faced by the late-maturing crop will also reduce seed yield. Summerﬁeld et al. (1981) observed that the reproductive growth of chickpea suffered considerably in hot environments (35/18◦ C, day/night). This was reﬂected in yield reduction of almost 33% when compared with that in a milder controlled environment (30/10◦ C, day/night). Thus in farmers’ situations a compromise is necessary between the reduced yield potential of short-duration cultivars and the losses caused by endof-season drought. In order to test the above rationale, a series of experiments with standard late-ﬂowering Israeli cultivars (cvs. Hadas and Bulgarit), early-ﬂowering material

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(Ethiopian land races and ICC 11299), and very early ﬂowering materials from ICRISAT (ICC 7344 and ICCV 95333) were conducted between 1995 and 1999 in several Israeli sites (Bonﬁl et al., in preparation). As expected, in a semiarid site, the early-ﬂowering types produced ca. 1.3 t ha−1 less biomass yield compared with the late-ﬂowering high-yielding Israeli cultivars. However, the grain yields of the early-ﬂowering types (e.g., ICC 8625) were equivalent to those of the modern Israeli varieties (3.3 t ha−1). In other words, the early-ﬂowering types were more efﬁcient in terms of their harvest indices. In the same site, in two successive seasons, the yields of the very early line ICC 7344 were inferior in biomass production and in its grain yields. In a dry site (with average precipitation of 240 mm), yields in the range of 1.4 to 1.9 t h−1 were achieved with ICC 7344, ICC 11299, and ICCV 95333, with the Israeli cultivar Bulgarit yielding 0.7 t ha−1. These results demonstrate clearly the potential of restricting vegetative growth, and early ﬂowering in semiarid environments, and the potential of very early onset of podding under extreme water shortage of less than 250 mm (Bonﬁl et al., in preparation). Early ﬂowering and podding restrict vegetative growth in indeterminate plants like chickpea (Saxena et al., 1997). In subtropical environments winter rains may induce excessive vegetative growth leading to dense canopy and high humidity. Such conditions are conducive for the development of foliar diseases. Thus restricted vegetative growth can help avoid seed yield losses in these environments. Therefore development of early ﬂowering and podding cultivars should be a major objective for chickpea improvement. In our view this could be a major step toward stabilizing and increasing mean seed yields in subtropic environments (see Section III,B).

IV. CONSTRAINTS ON PRODUCTIVITY IN SEMIARID ENVIRONMENTS Chickpea is predominantly grown under rainfed conditions in a postrainy season, on marginal lands, often without monetary inputs. The crop is, therefore, vulnerable to various abiotic and biotic constraints occurring under these situations. Drought at various stages of the crop cycle is a major yield reducer. Plant stands may be sparse because of poor emergence (Saxena et al., 1997). Although the chickpea plant can produce extra vegetative growth (in a favorable moisture regime) to cover available space, poor plant stands and stunted growth are often a major cause of low seed yields in semiarid environments. Adverse soil conditions such as salinity and toxicity may also cause poor plant stands and stunted growth. The crop-growing season is often restricted by receding soil moisture. Winter rains in the Indian subcontinent may help alleviate drought stress and increase productivity. However, occasionally excess moisture is conducive to the spread of foliar diseases leading to seed yield losses in the subtropics. Fusarium wilt, ascochyta blight, root

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rots, botrytis gray mold, chickpea stunt, helicoverpa pod borer, and leaf miner are important diseases and pests that limit the crop productivity. All these constraints may not occur together in a particular region or year. Drought, suboptimal plant stands, stunted growth, and root diseases are relatively more important in short-season tropical environments. In subtropical environments early drought often may affects plant stands and late drought may affect seed ﬁlling. Alternatively, excess winter rains can encourage overgrowth and foliar diseases. In the following sections we analyze constraints to productivity in the major chickpea production systems.

A. TRADITIONAL SYSTEMS 1. The Mediterranean Basin In the traditional cropping systems of the Mediterranean basin chickpea is a summer crop sown in March/April and harvested by pulling in June (ElazariVolcani, 1930). The crop is sown as soon as the temperatures are favorable for emergence. The growing season is short, often 80–90 days, limited by increasing temperatures and drought. Ground cover is never complete and leaf area index is low. The erratic nature of the winter precipitation in the Middle East and the frequent hot spells typical of the Mediterranean spring usually result in relatively low and unstable yields ranging between 100 and 600 kg ha−1 (Elazari-Volcani, 1930; Kostrinski, 1974). Potential seed yield does not exceed 1.5 t ha−1. Besides limited water, low nutrients, salinity, high temperatures, root diseases, chickpea stunt, leaf miner, and weeds may cause seed yield losses. In past years, spring cropping was the only effective means to avoid ascochyta blight epidemics. The spring crop is also nearly weed free, since presowing cultivation destroys most of the winter weeds. The extremely short season (at best, late March to June) allowed a relatively short period of vegetative and reproductive growth, which in turn, relies completely on residual soil moisture. Under such practice, ﬂowering and podding occurs pretty close to the ground thereby preventing mechanical harvesting. 2. The Indian Subcontinent and the East African Region Chickpea is grown on conserved soil moisture as a sole or a mixed crop following rainy season fallow or after a short rainy-season crop. Thus it often is subject to end-of-season drought which coincides with ﬂowering and podding. The effects of such drought are progressively enhanced by increasing temperatures, particularly in lower latitudes (<25◦ N) (Johansen et al., 1997). Although there may be overlaps, for discussion we broadly classify chickpea-growing environments on the Indian subcontinent into two categories as follows.

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In the subtropical Indian subcontinent, which used to be the principal chickpeaproducing region, the crop is sown from October to November and can produce high seed yield in a growing season of 160 to 170 days (Smithson et al., 1985). Where chickpea follows a rainy-season crop, its planting is determined by the harvest of the preceding crop and the turnaround period. Sorghum and maize or short-season legumes may mature in time to allow early planting of chickpea (Rahman et al., 1995). However, crops like rice take a much longer time to mature, rendering large tracts of land to remain fallow in the postrainy season. Chickpea planting is often delayed in this situation. As the soil-moisture proﬁle may not be full such planting results in poor emergence. Late planting also reduces the length of the growing season as rising temperatures enhance maturity. Farmers may intercrop chickpea with other postrainy-season crops such as wheat, barley, mustard, linseed, or even sugarcane. Diseases such as ascochyta blight, botrytis gray mold, chickpea stunt, and fusarium wilt and the pod borer are important biotic constraints. Drought and freezing temperatures can also limit seed yields substantially (Kumar et al., 1996). On the tropical Indian subcontinent the growing season is limited to between less than 90 days and 130 days by increasing temperatures and reduced soil moisture (Saxena et al., 1993). Potential seed yield may range from about 1.5 to 2.0 t ha−1(Saxena et al., 1997). In these situations early planting when the soil moisture proﬁle is fully charged is advantageous. However, the prevailing high temperatures early in the season could adversely affect the ﬁnal seed yield. As chickpea tolerates partial shading, intercropping may be the best solution to guard against drought effects and take advantage of winter rains. Major constraints are drought, salinity and poor nutrition, fusarium wilt and root rots, chickpea stunt, and helicoverpa pod borer. Early maturity may help alleviate the major constraints to productivity. In eastern Africa, chickpea is cultivated in Ethiopia, Tanzania, Malawi, Zambia, Uganda, and Kenya. It is grown between 1400 and 2300 m in the northern and central highlands of Ethiopia, but southward was introduced only recently (Smithson et al., 1985). Sowing may be undertaken at the end of the rainy season, from August to September in Ethiopia and southward from February to April. In bimodal rainfall areas (e.g., Kenya) chickpea is cultivated at the ends of both rainy seasons. Potential seed yields of 1 to 2 t ha−1 are possible. Major constraints include drought, pod borer, fusarium wilt, viruses, and poor management. Thus constraints to chickpea productivity on the tropical Indian subcontinent and the low-altitude East African region are generally similar.

B. MODERN SYSTEMS 1. The Mediterranean Basin Chickpea may be grown under nearly nonlimiting conditions of moisture supply and soil fertility through the application of inputs or natural endowment of

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environment (Saxena et al., 1997). This can be achieved by advancing the sowing date from spring to winter, thereby providing a favorable moisture regime through the growing season. In these environments the growing season can be as long as 6 months and the seed yields may range between 3 and 5 t ha−1 as a result of high biomass production of up to 10 t ha−1. The crop season can also be extended through irrigation to the spring-planted crop. The seed yields can be increased by 25 to 30% of the traditional spring-season crop. Nutrient supply and crop protection measures are undertaken to prevent yield losses. The winter-sown crop is vulnerable to damage by ascochyta blight, high weed and orobanche parasite growth, and leaf miner and sometimes by freezing temperatures. Other biotic constraints may not be of major concern. Therefore, resistance to ascochyta blight and cold is necessary to achieve stable crop production (van Rheenen, 1991). The ﬁrst successful experiments with winter sowing of chickpea were conducted in Israel in 1959 when yields of about 3 t ha−1 were obtained with cv. Bulgarit, a cultivar with high ﬁeld resistance to ascochyta blight (Kostrinski, 1974). These experiments were initiated following repeated observations that chickpea volunteer plants (from a previous crop) develop well following the autumn showers, survive the chilling temperatures of the Israeli winter, commence ﬂowering in late March or early April, and mature in June. Kostrinski (1974) assumed that autumn (or winter) sowing would allow a higher plant stand in the ﬁeld, more efﬁcient utilization of the winter rains, higher biomass production per area unit, and consequently support higher seed yield. Indeed, this was the case (Kostrinski, 1974). The adoption of Kostrinski’s (1974) and Hawtin’s (1975) ideas and the large-scale experimentation with winter sowing of chickpea across ICARDA’s mandate area have promoted winter sowing of chickpea in many Mediterranean countries (Singh et al., 1997). An immediate consequence was the development of large-scale research programs in the two international centers ICRISAT and ICARDA, focusing on ascochyta blight epidemiology, chemical control, and breeding for resistance (Saxena and Singh, 1984; Singh and Reddy, 1996). Eshel (1967) and Keatinge and Cooper (1983) were the ﬁrst to provide a detailed crop development analysis of winter-sown chickpea. Keatinge and Cooper (1983) have demonstrated that winter-sown chickpea (in northern Syria) develops higher green area indices and consequently build-up higher biomass yield per area unit compared with spring-sown crop. This is mainly due to reduced evaporation from the bare soil before full canopy closure occurs and the better water extraction capacity of the root system of the winter sown chickpea. The increased productivity of the winter crop is also evident in impressive seed yield ﬁgures from a range of semiarid environments in Australia (Loss et al., 1998). Despite yield increases following winter sowing, reported from many Mediterranean countries, a number of workers have noticed that still higher yields could be expected should the reproductive phase of the crop be extended. Eshel (1967) has demonstrated a strong positive correlation between the duration of the growth period and chickpea seed yield. Looking at Eshel’s (1967) ﬂowering data

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it appears that higher yields were indeed obtained when ﬂowering duration was longer. A yield increase of about 56% was obtained with supplementary irrigation, which extended the reproductive phase by nearly 10 days (Saxena et al., 1990). Bonﬁl and Pinthus (1995) have conducted a detailed crop growth comparison of chickpea and wheat under a typical semiarid Mediterranean environment. In their experiments, both crops were sown so that ﬂowering would start at the same time. Comparing canopy development; dry-matter accumulation of both crops, prior and after the initiation of ﬂowering; and seed yields the authors showed that the inherent need to support both reproductive and vegetative growth in chickpea is a major constraint on seed yield build-up (Bonﬁl and Pinthus, 1995). Or in other words, due to the indeterminate growth habit of chickpea, the duration of the reproductive phase is a major yield determinant. In most Mediterranean chickpea-growing areas, the duration of the reproductive phase of the crop is delimited between the initiation of ﬂowering and the summer drought that terminates seed set. Therefore, the prospects for extending the reproductive period into the summer season are quite limited and depend mainly on water availability for supplementary irrigation to allow further growth (Auld et al., 1988; Saxena et al., 1990). Mild seasonal temperature proﬁle is also required to allow proper seed set and further pod development. Since the end of the growing season is almost ﬁxed under dryland conditions in the Mediterranean environments, an alternative option for extending the reproductive phase of chickpea could be through early ﬂowering (Or et al., 1999).

2. The Indian Subcontinent and the East African Region a. The Indian Subcontinent The world chickpea outlook is greatly inﬂuenced by that of the Indian subcontinent. Among legumes the potential chickpea seed yields are large. More than 5 t ha−1 seed yields have been harvested from large plots in the subtropical and up to 3 t ha−1 in irrigated plots in tropical regions (Smithson et al., 1985). However, the mean seed yields of around 0.8 t ha−1 show that most farmers do not obtain such high productivity because of the constraints mentioned earlier (see Section IV,A,2). The cereals’ “green revolution” relegated chickpea to less endowed lands (Kelley and Parthasarthy, 1994). As the increase in the genetic potential and stability of productivity has not kept pace with the major competing crops (wheat, mustard, and sunﬂower), farmers do not prefer to grow chickpea in their more productive lands. Chickpea has lost more than 1 million ha in the high input subtropical environments. Indian subcontinent’s share of 87% of the world’s production (1971–1973) is fast declining (78% in 1989–1991). This downward trend is likely to continue unless a major breakthrough in its mean productivity is achieved to enhance its competitiveness through yield and price increases (Kelley and Parthasarthy, 1994). Such stability can be possible for a determinate crop with

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resistances to major stresses. Early maturity can help escape major end-of-season constraints. Chickpea, being an indeterminate crop, puts up excessive vegetative growth under high input conditions in the subtropics. Such a canopy is prone to damage by foliar diseases, pod borer, lodging, and even rotting. Usually overgrown crop does not produce stable high seed yields. Therefore, farmers are reluctant to commit their best land and resources to an unstable crop. They are, however, willing to cultivate chickpea with assured 2 t ha−1 seed yield, the value of which at the current prices is equivalent to about 5 t wheat. Attaining this seed yield is possible with the available cultivars. What is lacking is the stability of production. Ryan (1997) analyzed estimated damages caused by major constraints to chickpea productivity and observed that the cumulative losses attributed to these may actually be more than the current production. Kumar et al. (1996) further analyzed the timing of occurrence of these stresses and observed that much of the adverse effects of these constraints were limited to the ﬂowering and podding stages of the crop. In subtropical environments this coincides with rising temperatures. They concluded that if the crop duration is genetically reduced by about a month, the mean seed yield in these environments could be doubled using an escape mechanism. It is necessary to restrict chickpea vegetative growth at a reasonable canopy level and induce fruiting. A determinate chickpea plant is still elusive (van Rheenen et al., 1994). The currently grown cultivars in the subtropical areas continue to develop vegetatively during the cool winter months and pod only when the temperatures start rising. The earliness gene efl-1 becomes ineffective in freezing temperatures. Although chickpea ﬂowers in cool temperatures, it does not pod at <8◦ C. Srinivasan et al. (1998) observed in controlled environments that pod set in chickpea could occur at night temperatures of 0◦ to +5◦ C as long as the day temperatures were above 20◦ C. Such genotypes may produce sufﬁcient pods during the cool months and thus grow less proliﬁcally under good growing conditions. Early growth vigor, early ﬂowering, and podding through cool temperatures may help the crop mature before severe onset of drought, foliar diseases, and pod borer attacks (van Rheenen et al., 1997). A few such genotypes have already been developed, and these could avoid damage by most of the abiotic and biotic stresses, as they mature in relatively cooler temperatures (Kumar et al., 1996). A newly developed genotype ICCV 96029 combines efl-1 and other genes for earliness, early growth vigor, and chilling tolerance. In experiments conducted at Hisar (29◦ N) over 2 years this genotype matured about 4 weeks earlier than the local control Pant G 114 (Kumar et al., 2001a) (Table II). It produced similar seed yield as the longer duration control Pant G 114. Its agronomic potential is being evaluated further in a few locations. In recent years in this subcontinent chickpea cultivation has moved toward the tropics. Its area increased by nearly 750,000 ha in the tropical region (Kelley and Parthasarthy, 1994). This has partly offset the area loss in the subtropical region mentioned above. Development of short and extra short duration chickpea

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varieties combined with fusarium wilt resistance has dramatically increased their competitiveness (Kumar, 1997). Traditional chickpea varieties took 90–130 days to mature in the tropics and succumbed to fusarium wilt. Hall and Patel (1985) also found that short-duration varieties produced high seed yield in cowpea. ICRISAT in collaboration with the Indian National Agricultural Research System (NARS) released ICCV 2 and ICCC 37 in the state of Andhra Pradesh in 1989 and ICCV 10 in Central Zone in 1992. ICCV 2 was released in Maharashtra in 1992. Chickpea production in Andhra Pradesh registered a sevenfold increase in the past 10 years (Kumar, 1997). The productivity of the <90-day crop increased from less than 0.3 t ha−1 to nearly 0.8 t ha−1. This is now equivalent to the Indian national mean, which is based mostly on a 130- to 170-day crop. Chickpea seed yield in Maharashtra also showed signiﬁcant increases. However, Karnataka, where the new variety adoption is low, has not shown much improvement, as the improvedvariety seed has not been multiplied on a large scale (J. Kumar, ICRISAT, Patancheru, India, unpublished data). One way to increase chickpea competitiveness is to ensure high returns to the farmers. Kabuli chickpea, which covers 10 to 15% of the total chickpea area in the world, commands up to three times the price paid for the more common desi types (Kumar, 2000). The bulk of the international trade involves mainly kabuli types, with the exception of the Australian desi export. The available kabuli cultivars are long duration and require the cooler environments of the subtropics for their cultivation. Development of the extra short duration, fusarium wilt-resistant cultivar ICCV 2 has extended kabuli cultivation to tropical regions (Kumar et al., 2001b). ICCV 2 has been named as a national kabuli check cultivar by the Indian NARS. Based on the present requirement of breeder seed it is the most popular kabuli cultivar in India (Fig. 2). The variety has now been released in Myanmar and Sudan. It has also shown promise in Ethiopia, Tanzania, and Egypt (Kumar et al., 2001b). Its sister line, ICCV 3 has been released in Myanmar and is under consideration for release in Brazil. A major prospect for chickpea area expansion is under fallow replacement in rice-based cropping systems (Kumar et al., 1994). Of the estimated 20 m ha rice fallows, 4 m ha is suitable for chickpea cultivation in the Indian subcontinent. Farmers have little choice of crops capable of producing enough seed yield under receding moisture in the difﬁcult-to-manage paddy soils. The present relatively longer duration varieties do not ﬁt in the available window as rice may be harvested too late, leaving little available moisture in the top layers. However, recent successes in the Barind region in northwestern Bangladesh indicate that rice farmers are willing to compromise on rice yield to accommodate chickpea in their cropping systems (Rahman et al., 1995; Musa et al., 1999). If chickpea seed can be planted soon after the harvest of rice when the topsoil still has sufﬁcient moisture, it can emerge and produce reasonable seed yields of probably higher value than the main season rice crop (Mazid et al., 1998). Here again, short-duration varieties

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Demand (t)

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0 1991

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Figure 2 Breeder seed demand for all of India for kabuli variety Swetha (ICCV 2) and that for all other kabuli varieties of chickpea. Source: Chickpea breeder seed requirements, Indian Institute of Pulses Research, Kanpur, 1998 and 1999 (limited circulation).

of both chickpea and of rice can play a pivotal role in the fallow replacement and in sustaining and increasing the productivity of one of the most fragile agricultural ecosystems of the world (Rahman et al., 1995). Scientists are now breeding shorter duration cultivars of rice. b. The East African Region One way of increasing yield in East African highlands is to advance the sowing date so that the crop will have much better moisture regime for early growth and produce larger biomass. Seed yields of around 3 t ha−1 are possible in the cooler highlands. Short-duration cultivars can help extend fruiting period and increase productivity. However, chickpea is susceptible to foliar diseases, especially when rains create high humidity and wash off the plant-acid exudates. It appears that fast-growing shorter duration cultivars with resistances to fusarium wilt, root rots, and pod borer will enhance stability and productivity of chickpea in this region. Super early chickpea ICCV 96029 appears to produce relatively high seed yield under experimental conditions in Kenya (R. Jones, ICRISAT-Nairobi, Kenya, personal communication). This also indicates drought as a major constraint in that region. The super early lines may extend chickpea cultivation to even drier regions. Extension of chickpea cultivation to wheat-based farming systems of Southern Africa may be possible, as the two crops have a similar range of adaptation. Such a development will help diversify crop choices and may thus enhance sustainability of agriculture in the region. We believe short-duration cultivars will have greater

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scope for success. However, experimental data on chickpea genotypes are required to understand the system.

V. CONCLUSIONS AND FUTURE OUTLOOK Understanding ﬂowering behavior is fundamental to crop adaptation. There is a large gap in our knowledge of ﬂowering genes of chickpea. Only two genes controlling ﬂowering time have been identiﬁed so far (Kumar and van Rheenen, 2000; Or et al., 1999). Much genetic information is available on this aspect for the closely related genus Pisum (Weller et al., 1997). More research should be undertaken to identify new loci controlling ﬂowering behavior in chickpea so that a wider array of adapted cultivars could be developed. Chickpea breeders, geneticists, pathologists, and physiologists are open to criticism. They have tended to follow models on wheat and rice where the crop environments were modiﬁed to complement genetic improvement of these crops to achieve stable and high productivity. Such favorable environments, when provided to chickpea in the subtropics, often induce excessive vegetative growth and result in decreased seed yield. Therefore, the chickpea crop ideotypes need to be modiﬁed (Singh, 1997; Saxena et al., 1997). There is an urgent need to develop near-determinate ideotypes with early ﬂowering and podding through the cooler season for these environments. These ideotypes might produce restricted vegetative growth and mature early. Such cultivars may respond to high-input conditions without producing excessive canopy. Early maturity should help avoid the losses caused by late-season biotic and abiotic constraints that are often faced by this crop. This development could help produce much higher seed yields than are presently realized and ensure that chickpea becomes competitive among predominant cropping systems of the subtropics. There is also an urgency to collect more accessions of wild C. reticulatum to better deﬁne its ecogeographic range and obtain greater insight into the biology and genetics of this important species. This development will be essential for widening the genetic base of the cultivated chickpea. The genetics of chickpea are not well investigated. Knowledge of other traits is also scanty (Kumar, 1997). Established rules for chickpea genetic studies, following the pea model (Marx, 1985; Muehlbauer and Kumar, 1999), would be very useful. Chickpea breeders/geneticists will need to establish some selected standard genotypes to relate newly characterized genetic differences. These genetically deﬁned “Type lines” should form a basis for identiﬁcation and naming new genes. This information is essential to develop integrated genome maps. The fast-developing ﬁelds of recombinant DNA technology and bioinformatics have given a huge boost to genome research. These techniques have the potential to increase the span of coverage, speed, efﬁciency, and precision of genetics and

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breeding research to a great extent. Although the present level of genetic understanding of chickpea lags behind most economically important cereals and legumes, molecular markers can narrow these differences in a matter of years. Winter et al. (2000) published a most advanced 303-marker map for Cicer that covers a distance of 2080 cM. The synteny chickpea shares with ﬁeld pea and lentil should be useful in developing the chickpea genome map more quickly (Kazan et al., 1993; Simon and Muehlbauer, 1997). Chickpea with only a few known linkages is being investigated as never before. Recently, several workers have demonstrated the power of DNA marker techniques to complement breeding efforts in chickpea. Patil et al. (1995), Ahmad (1999), and Udupa et al. (1999) have studied genetic relationships in and among annual Cicer species. Sant et al. (1999) showed the potential of simple sequence repeat markers to predict heterotic performance in Indian chickpea germplasm. Staginnus et al. (1999) studied the molecular structure and chromosomal location of major repetitive chickpea DNA elements. Molecular markers linked to resistance genes and QTL to ascochyta blight were reported by Santra et al. (2000). DNA markers linked to fusarium wilt resistance genes were reported by Mayer et al. (1997), Tullu et al. (1998), and Ratnaparkhe et al. (1998a, 1998b). A successful attempt to develop marker tags to a ﬂowering locus was recently made in an ICCV 2 × JG 62 RIL population (Cho et al., 2001). In contrast to earlier reports on restricted resolution power of the AFLP system in chickpea, 9 polymorphic primer combinations of a total of 64 were found between the Israeli cultivar Hadas and the Indian accession ICC 5810 (I. P. Singh and K. Upadhyaya, Jawaharlal Nehru University, New Delhi, India, in preparation). Such efforts are likely to result in a better genomic understanding in terms of coding and noncoding sequences, a high-resolution genetic map, and, most important, in tags for agronomic traits. It is necessary to reverse the trend of humans’ overdependence on a narrowing range of crop species. This negative aspect of the green revolution that has relegated most of the high-protein crops like chickpea to marginal lands should be corrected. The potential seed yield of the chickpea crop is not really in question, but its mean productivity is far behind competing cereals and well-researched legumes such as pea and soybean. Therefore, it is necessary to substantially increase and stabilize its mean seed yield to ensure that it becomes a competitive crop in highinput environments. The earliness genes can play a major role in increasing and stabilizing chickpea seed yield.

ACKNOWLEDGMENTS The authors thank Drs. G. Ladizinsky, C. Johansen, R. Ortiz, T. Fahima, R. P. S. Pundir, S. K. Srivastava, and D. J. Bonﬁl for their valuable comments on the chapter. S. A. acknowledges support from the Israeli Ministry of Science through the Indian-Israeli Biotechnological initiative, from the Chief Scientist of the Israeli Ministry of Agriculture and from BARD, the United States–Israel Binational Agricultural Research and Development Fund.

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ENVIRONMENT-SENSITIVE GENIC MALE STERILITY (EGMS) IN CROPS S. S. Virmani and M. Ilyas-Ahmed International Rice Research Institute 1271 Makati City, Philippines

I. Introduction II. Occurrence among Crop Plants A. Genic Male Sterility Sensitive to Environmental Factors B. Cytoplasmic-Genic Male Sterility Sensitive to Environmental Factors III. Identification and Classification A. Identification of EGMS B. Classification IV. Genetics A. Inheritance B. Linkage with Molecular Markers V. Characterization A. Procedure for Characterization B. Classification of PTGMS Lines C. Characterization and Classification of TGMS Lines VI. Breeding of EGMS Lines VII. Use of the EGMS A. Two-Line Rice Hybrids in China B. Two-Line Hybrids in Other Crops C. Seed Production of Two-Line Rice Hybrids VIII. Future Outlook IX. Summary and Conclusions References

I. INTRODUCTION Male sterility is a deviant condition in normally bisexual plants (monoecious or hermaphrodite) when no viable pollen is formed or no functional pollen is released. The phenomenon assumes importance in global agriculture because of the development and large-scale commercialization of hybrids in several economically important crops. Commercial hybrids in just four crops—maize, sorghum, rice, and sunﬂower—have provided an additional 90 million tons of food production 139 Advances in Agronomy, Volume 72 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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annually (Duvick, 1999). This additional output enables us to spare approximately 34 million ha for the cultivation of other crops. For commercial cultivation of hybrid varieties, F1 hybrid seed is produced on a commercial scale anew each season by crossing genetically dissimilar genotypes. Male sterility facilitates this laborious, arduous, and herculean task over several thousand hectares worldwide. Male sterility in higher plants has been reviewed comprehensively in recent years (Kaul, 1988; Rao et al., 1990; Horner and Palmer, 1995; Phul et al., 1996). Two well-known male sterility systems (cytoplasmic-genetic male sterility, CMS, and genic male sterility, GMS) have been used in many crops to exploit heterosis. Recently, another type of male sterility that is sensitive to the environment and is induced by the interaction of environmental factors with nuclear genes has also been used to develop hybrids in some crops in China (cf. Yuan, 1997). This is known as environment-sensitive genic male sterility (EGMS). A particular range, duration, or concentration of an environmental factor, at a sensitive stage of plant development, induces male sterility, whereas some other range, duration, or concentration of an environmental factor at the sensitive stage induces fertility in plants. During the sterile phase, EGMS plants can be used as a female parent to produce hybrid seed through outcrossing, whereas, during the fertile phase, these plants are multiplied through self-fertilization without the use of a maintainer line as required in the CMS system. Since only two lines are required for the maintenance and multiplication of male sterile lines and production of hybrid seed, the system using this type of male sterility is known as the two-line system of hybrid breeding as opposed to the three-line system involving CMS (A), maintainer (B), and restorer (R) lines of the widely utilized CMS system. The two-line system of heterosis breeding using EGMS is reported to have certain advantages over the conventional three-line system of heterosis breeding (Yuan, 1998). Although environment-sensitive genic male sterility was reported in crop plants such as wheat (Fisher, 1972; Jan, 1974), maize (Duvick, 1966), barley (Batch and Morgan, 1974; Sharma and Reinbergs, 1976; Ahokas and Hockett, 1977), soybean (Caviness et al., 1970), tomato (Rick and Boynton, 1967; Abdallah and Verkerk, 1968; Sawhney, 1983), pepper (Martin and Crawford, 1951), and cabbage (Rundfeldt, 1960), no attempts have been made yet to explore prospects for its use to develop hybrids. In the 1980s, Chinese scientists (Shi, 1981, 1985) reported an EGMS mutant which was sensitive to photoperiod. This mutant was subsequently utilized by Chinese scientists in development of hybrid rice. The environmental factors of temperature and photoperiod and their interactions were found to be the major factors inﬂuencing the induction of sterility and fertility. Subsequently, papers on EGMS were also published from Japan (Maruyama et al., 1991), the IRRI in the Philippines (Virmani and Voc, 1991; Borkakati and Virmani, 1996), India (Ali et al., 1995 and Siddiq et al., 1998), and Vietnam (Du et al., 1997; Hoan et al., 1998). A few reports were also published (Agarwala et al., 1980; Dell, 1981; Rerkasem and Jamjod, 1997) on the effects of micronutrient deﬁciency on the induction of male sterility in wheat and some other crop plants.

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The extensive work done on environment-sensitive genic male sterility in China has been reported mostly in Chinese research journals, in Chinese, with only a brief and sometimes uninformative abstract in English. Outside China, the available information on this subject is scanty and scattered in various journals, symposia proceedings, research bulletins, and newsletters and has not been collected and reviewed critically in English. This chapter brings together signiﬁcant ﬁndings on EGMS and its use in crops. Since most of the applied work on the subject has been done in rice, this chapter is biased toward rice, but the principles and practices reported are applicable to other crops as well. This chapter also discusses the current status of and future outlook for the use of EGMS to develop and use two-line hybrids.

II. OCCURRENCE AMONG CROP PLANTS Systematic efforts to study the occurrence of environment-sensitive genetic male sterility in crop plants are limited except in China. The unexpected occurrence of male sterility in many breeding populations is considered undesirable, and it often goes unreported. Even if the male sterile mutant is spotted in time and is further propagated by appropriate artiﬁcial pollination, it is not always tested at the right stage (sensitive stage) against the varying levels or doses of environmental factors that are likely to affect sterility or fertility expression. Hence, in view of the above, it is quite likely that only a few of the EGMS mutants occurring naturally in crop plants get reported in the literature. Some reported cases of genic and cytoplasmic male sterility in crop plants that are sensitive to environmental factors are summarized below.

A. GENIC MALE STERILITY SENSITIVE TO ENVIRONMENTAL FACTORS Sensitivity of genic male sterility to environmental factors has been reported in crop plants such as rice, wheat, maize, sorghum, barley, soybean, brassica, tomato, pepper, cabbage, cucurbits, Vicia faba, and sesame. A brief description in chronological order of reports on sensitivity of genetic male sterility to environmental factors follows. In pepper, considerable environmental inﬂuence over the degree of pollen sterility was observed. Some genotypes were male sterile in the ﬁeld (summer) but male fertile in the glasshouse (winter), whereas others showed a reverse trend (Martin and Crawford, 1951, Daskaloff, 1972, 1973). Peterson (1958) also observed partial to complete male sterility due to temperature alterations such that selﬁng as

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well as crossing of the male sterile genotype was possible through manipulation of temperature. Rundfeldt (1960) reported a cabbage mutant that was male sterile when grown in the summer and male fertile when grown in the winter. In maize, Duvick (1966) reported the inﬂuence of environment over stability, mutability, and expression of male sterility. Some genotypes were completely male sterile in one environment (usually hot and dry) and partially fertile in another (cool and humid). A thermosensitive genic male sterile maize mutant was discovered on China’s Hainan Island in 1992 (He et al., 1997). This mutant was named Qiong 6 Qms and is being used to develop two-line maize hybrids in China. It was fertile under autumn and winter sowings on Hainan Island, but was sterile when it was grown in summer. Its fertility/sterility expression was not affected by photoperiod. A temperature-sensitive spontaneous tomato mutant was reported by Rick and Boynton (1967) in variety Sau Marzano. This mutant was male sterile in the summer but it produced normal and viable pollen grains in other seasons. A minimum temperature of 30◦ C in the ﬁeld and 32◦ C in the greenhouse was required to induce sterility in this mutant. The male sterility was conditioned by the single recessive gene vms, which the authors reported to be located on the long arm of chromosome 8. Abdallah and Verkerk (1968) reported opposite responses of two tomato mutants to environmental conditions. One mutant was male sterile in the ﬁeld and male fertile in the glasshouse, whereas the other exhibited the reverse behavior. Temperature appeared to be the major factor controlling sterility or fertility expression. At higher temperatures, partial to complete male sterility was observed. Stevens and Rudich (1978) reported that temperatures such as 38◦ C (day) and 27◦ C (night) caused a considerable reduction in pollen production, anther dehiscence, and pollen germination in tomato. Sawhney (1983) reported that, in the male sterile stamenless-2 mutant of tomato, sterility or fertility expression was controlled mainly by temperature. This mutant produced abnormal stamens and nonviable pollen at 23◦ C (day) and 18◦ C (night). When the mutant plants were grown at a relatively low temperature (18–15◦ C), most ﬂowers produced normal stamens with viable pollen. Mutant plants grown at a still higher temperatures (28–23◦ C) produced carpel-like structures in place of stamens, with no evidence of pollen formation. These three temperature regimes have no effect on the pollen viability of the wild type. Fisher (1972) observed that subjecting wheat plants to 10 h of photoperiod treatment at a stage when stamen primordia were visible on the ﬁrst ﬂoret of the most advanced spikelet of the main shoot led to the transformation of stamens into ovaries. Ovules were found to develop on the anther lobes to make plants sterile. A temperature-sensitive male sterile mutant in wheat was also reported by Jan (1974), who recognized this mutant from the differential behavior of early- and late-formed tillers for fertility. Chinese scientists also reported photothermosensitive genic male

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sterility in wheat in 1992 (cf. Yuan, 1997). Zhou et al. (1997) and Luo et al. (1998) used photosensitive ecotype male sterile (EMS) wheat lines ES-3, ES-4, ES-5, ES-8, and ES-10 for developing two-line wheat hybrids. Batch and Morgan (1974) reported that exposure of barley cultivar Proctor to a photoperiod of 10 h for 2 weeks after 42 and 56 days of sowing drastically reduced ﬂoret fertility and grain set. Sharma and Reinbergs (1976) reported the induction of three male sterile mutants sensitive to temperature in barley. At higher temperatures (>30◦ C), complete male sterility occurred and at lower temperatures (<15◦ C) fertility was induced. A mutant in barley ms9 was reported to exhibit sensitivity to photoperiod and physiography (Ahokas and Hockett, 1977). It was completely male sterile when grown in Finland (61◦ N latitude, light period up to 24 hours) but partially male fertile when grown at Bozeman, Montana (46◦ N latitude, light period up to 15–16 h). Temperature and light intensity affect the expression of male sterility in Vicia faba. Maximum pollen fertility occurs in the range of 17–27◦ C (Berthelem and Le Guen, 1975). By transferring the male sterile mutants at the time of meiosis of pollen mother cells from 8,000 lux (9 h) to 25,000 lux (16 h), pollen fertility increased up to 80% in 66% of the treated plants (Duc, 1980). Among cucurbits, sexuality in watermelon is greatly inﬂuenced by environmental factors. Rudich and Peles (1976) demonstrated under phytotron conditions that temperature has a more pronounced effect than daylength on sex expression in the watermelon cultivars Sugarbaby and Malali. In musk melon, the ratio of female to male ﬂowers is altered appreciably by environmental inﬂuence, especially that of soil moisture and nutrients, photoperiod and light intensity, and day/night temperature ﬂuctuations. Usually, low temperatures, high soil nutrient content, or short-day conditions promote female expression and the reverse conditions cause male expression (Kaul, 1988). EGMS has been studied in much greater detail in rice. Shi (1981, 1985) identiﬁed male sterile mutants in rice sensitive to photoperiod. Subsequently, Zhou et al. (1988), Sun et al. (1989), Maruyama et al. (1991), and Virmani and Voc (1991) developed temperature-sensitive genic male sterile mutants in rice and studied them in detail. In general, longer photoperiod (>14 h) or higher temperatures caused sterility and shorter photoperiod (<13.5 h) and lower temperatures resulted in fertility. There was also a close interaction of temperature and photoperiod in causing male sterility or fertility expression (He et al., 1987). In a few TGMS mutants, such as Duan XinA (Jiang, 1988), IVA (Zhang, 1991), and JP-38S (Ali and Siddiq, 1999), lower temperatures caused sterility and higher temperatures fertility. Such TGMS mutants are called reverse TGMS mutants. In sesame, Brar (1982) reported a mutant that was male sterile in the ﬁeld but male fertile in the glasshouse. In sorghum, a photothermosensitive genic male sterile line, Xiangnusliang S-1, was developed at the Hunan Soil and Fertilizer Research Institute, Changsha,

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China (Tang et al., 1997). A two-line sorghum hybrid with strong heterosis named Xiangliangyou Nouliang-1 was released in Hunan Province of China during 1996. Chinese scientists (Wei et al., 1994, 1997) also identiﬁed a photoperiod-sensitive genic male sterile line (88-428 Y) of soybean that was developed from a local cultivar, “Tumeidou.” This photoperiod-sensitive line is being used to develop two-line hybrids in soybean. Both temperature and photoperiod have been found to inﬂuence pollen and ovular fertility in soybean, but the impact is more in partial male sterile lines than in complete male sterile lines. At 35◦ C day and 27 or 21◦ C night temperatures, no pod set occurs (Caviness and Fagala, 1973) because of complete male sterility in partially male sterile soybean. Maximum pod setting occurs at 29/21◦ C day/night temperatures, indicating high male fertility under this temperature regime. There are contradictory reports of both higher and lower temperatures causing sterility and fertility in soybean (Caviness et al., 1970; Hashimoto and Yamamoto, 1976). In Brassica napus, a thermosensitive genetic mutant P3-3 was reported in China by Xi et al. (1997). Using the mutant, TGMS line Xiangyou 91S was developed and used to develop two-line Brassica hybrids. There are also some reports of induction of genetic male sterility due to deﬁciency of certain micronutrients. Male sterility in wheat was reported to be caused by deﬁciency of copper (Agarwala et al., 1980) and boron (Rerkasem and Jamjod, 1997). Copper deﬁciency is also reported to result in male sterility in maize, barley, oats, and sunﬂower (Dell, 1981). Boron and molybdenum deﬁciencies decrease pollen fertility signiﬁcantly in wheat and some other crops (Agarwala et al., 1979).

B. CYTOPLASMIC-GENIC MALE STERILITY SENSITIVE TO ENVIRONMENTAL FACTORS Environmental factors also inﬂuence cytoplasmic-genetic male sterility. Genes in the cytoplasm and fertility-restoring genes in the nucleus are susceptible to the inﬂuence of the environment. The potent environmental factors are temperature, photoperiod, light intensity, and soil factors such as soil micronutrients and soil pH. Temperature and photoperiod are the major ones. The CMS system, sensitive to environmental factors, has been reported in crop plants and described below in chronological order of the ﬁrst report in a crop. In onion, the expression and manifestation of male sterility are profoundly affected by temperature. Barham and Munger (1950), from their extensive studies, concluded that photoperiod had no effect, but temperature had a signiﬁcant effect, on the induction of fertility or sterility. Below 21◦ C, no viable pollen was produced; between 21 and 25.5◦ C, some germinable pollen developed but no seeds were produced on selﬁng. This indicated that pollen was either physiologically weak or inviable. The reverse type of response to temperature in onion was reported

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by Vander Meer and Van Bennekom (1978), who found some onion populations to be male sterile at 14◦ C, partially fertile at 21–23◦ C, and fully fertile at higher temperatures. Other populations did not respond to these temperatures in a similar way. Since the S cytoplasm from a common source is present in all of them, the differential temperature response is due to their different genetic backgrounds. Genetic differences exist among different genotypes of onion for their reaction to various temperatures. In grain sorghum, a high degree of male fertility was induced in male sterile plants by subjecting them to a high temperature of 40◦ C at the premeiosis stage (Kidd, 1961; Kongtian and Hongyi, 1981; Zhang and Fu, 1982). In glasshouse experiments, Downes and Marshall (1971) demonstrated that night temperatures of 13◦ C or below during meiosis induced male sterility in sorghum. This temperature sensitivity was restricted to the prezygotene stage (Brooking, 1976). The widely used milo cytoplasm (A-1 cytoplasm) in sorghum has been quite stable across locations and seasons. Another cytoplasm from IS 12662, designated A-2 (Schertz, 1977) and investigated intensively by Murty (1986), was found to interact with different genotypes, resulting in complete male sterility in some genotypes during the winter season (short days, <12 h photoperiod, and low maximum temperature, <30◦ C) and varying degrees of fertility during the rainy and summer seasons. These genotypes behaved like a reverse TGMS type, being sterile at low temperatures and fertile at high temperatures. By using this type of male sterility in CS 35341 A2, a successful two-line sweet sorghum hybrid, SSH-1, has been developed (Murty, 1995). In cotton, male sterility expression is highly inﬂuenced by temperature. In alloplasmic Gossypium hirsutum, expression of male sterility is high at 32◦ C and is almost complete around 38◦ C (Meyer and Meyer, 1965). Reciprocal hybrids having G. hirsutum cytoplasm are male fertile at such temperatures. Sarvella (1966) reported that, in lines having Gossypium anomalum cytoplasm, male sterility is negatively correlated with temperature and solar radiation. All these environmental factors are effective during 3 weeks before anthesis, as they primarily impair anther differentiation and development. In pearl millet, environmental effects, mainly temperature and humidity, can increase the frequency of plants shedding pollen (Vittal-Rao, 1969; Reddi and Reddi, 1970; Sexena and Chaudhary, 1977). In sugarbeet, low temperature induces male sterility in plants having normal cytoplasm and xxzz genes (Kinoshita, 1971). Sterility is considerably higher in plants grown with vermiculite to which no fertilizer is added than in those with normal fertilizer. Low temperature during premeiosis causes a reduction in pollen mother cells entering meiosis, imbalanced meiosis, tapetal anomalies, and microspore abortion (Kinoshita, 1971). Cytoplasmic-genetic male sterility in wheat with Aegilops umbellulata or Secale cereale cytoplasm was stable in autumn but unstable in spring when grown

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in the glasshouse (Maan, 1973). Both high temperatures and long photoperiods, occurring especially during early meiosis, induce pollen abortion in wheat. Murai and Tsunewaki (1993), in Japan, reported an alloplasmic line, Norin 26 (Triticum aestivum with Aegilops crassica cytoplasm), showing male sterility under long days (>15.0 h) and male fertility under short days (<14.5 h) with no inﬂuence of temperature. Murai (1998) utilized the photosensitive cytoplasmic male sterile lines for F1 seed production and performance of F1 hybrids in wheat was evaluated. In F1 seed production plots 14 to 33% outcrossing was observed with an F1 seed yield of 19 to 55 g/m2. Heterosis observed in F1 was 40% over the midparental value. Like that of male sterility genes, expression of fertility-restoring genes has also been sensitive in wheat to the inﬂuence of some environmental factors such as location (Lucken and Mann, 1967; Schmidt et al., 1970), photoperiod (Welsch and Klatt, 1971), day temperature (Johnson and Patterson, 1973), and planting date, which ultimately involves the effect of both temperature and photoperiod. Long photoperiods or higher temperatures or both enhance pollen sterility. Thus, with increasing latitude toward the north, photoperiod increases, which induces sterility. In rice, sensitivity to low temperature is maximum at the microspore release stage in cytoplasmic male sterile lines (Satake and Hayase, 1974) when ms and fr genes act. High temperature (35–41◦ C) applied during anthesis induces pollen sterility in rice (Satake and Yoshida, 1977). Fertility restoration in hybrid rice is inﬂuenced by environmental conditions (Virmani and Edwards, 1983) and is genotype speciﬁc. Among the 218 elite breeding lines tested for restoration ability, 15% were effective restorers in Changsha, China, 24% were effective restorers at IRRI (Philippines), but only 6% were effective restorers at both locations. The environmental effect on the CMS system has also been recorded in chillies (Horner and Rogers, 1974) and carrots (Timin and Dobrutskaya, 1981). The effect of temperature on two CMS systems—nap and pol cytoplasms in rape (Brassica napus)—was investigated by Fan and Stefansson (1986). The day/night temperature regimes used were 22/16, 26/20, and 30/24◦ C. Male sterility in both systems was expressed consistently at the lower temperature (22/16◦ C). The plants with nap cytoplasm became partially fertile at the intermediate temperature (26/20◦ C) and fully fertile at the high temperature (30/24◦ C). Plants with pol male sterile cytoplasm were comparatively more stable. They remained sterile at intermediate temperature and became partially fertile at high temperature 30/24◦ C. Table I summarizes environmental inﬂuences on male sterility systems in crop plants. Considering the large number of male sterility genes known in crop plants, the inﬂuence of environment has been reported in only a few of them. Of the cases reported, in about 44%, temperature is the major factor inﬂuencing male sterility gene action and expression, whereas in 12% of the cases it is photoperiod, and, in the remaining 44%, the precise environmental factor is not yet known (Kaul, 1988).

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ENVIRONMENT-SENSITIVE GENIC MALE STERILITY Table I Some Reports on Environment-Sensitive Genic Male Sterility Systems in Crop Plants Crop Genic male sterility Pepper

Reference

Environmental factor

Martin and Crawford (1951) Peterson (1958) Daskaloff (1972)

Thermosensitivity Thermosensitivity Thermosensitivity

Cabbage Maize

Rundfeldt (1960) Duvick (1966) He et al. (1992)

Thermosensitivity Thermosensitivity Thermosensitivity

Tomato

Rick and Boynton (1967) Abdallah and Verkerk (1968) Stevens and Rudich (1978) Sawhney (1983)

Thermosensitivity Thermosensitivity Thermosensitivity Thermosensitivity

Wheat

Fisher (1972) Jan (1974) He et al. (1992) (cf. Yuan, 1997) Tan et al. (1992) (cf. Yuan, 1997) Batch and Morgan (1974) Sharma and Reinbergs (1976) Ahokas and Hocket (1977)

Photoperiod sensitivity Thermosensitivity Photothermosensitivity Photothermosensitivity Photoperiod sensitivity Thermosensitivity Photoperiod sensitivity and physiography Thermosensitivity Light intensity

Barley

Vicia faba

Berthelem and Le Guen (1975) Duc (1980)

Cucurbits Rice

Rudich and Peles (1976) Shi (1981, 1985)

Sesame Sorghum

Zhou et al. (1988); Sun et al. (1989) Maruyama et al. (1991) Virmani and Voc (1991) Brar (1982) Li et al. (1994)

Thermosensitivity Photosensitivity and photothermosensitivity Thermosensitivity Thermosensitivity Thermosensitivity Thermosensitivity Photothermosensitivity

Soybean Brassica napus

Wei et al. (1994) Xi et al. (1997)

Photoperiod sensitivity Thermosensitivity

Wheat

Agarwala et al. (1979, 1980)

Deﬁciency of micronutrients, copper, boron, and molybdenum Deﬁciency of micronutrient boron Deﬁciency of micronutrient copper

Maize, barley, oats, and sunﬂower

Rerkasem and Jamjod (1997) Dell (1981)

(continued)

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Reference

Cytoplasmic-genic male sterility Onion Barham and Munger (1950) Sorghum Kidd (1961) Downes and Marshall (1971) Kongtian and Hongyi (1981) Zhang and Fu (1982) Murty (1986) Cotton Pearl millet

Sugarbeet Wheat (Aegilops crassa cytoplasm; Aegilops umbellulata or Secale cereale cytoplasm)

Meyer and Meyer (1965) Sarvella (1966) Vittal-Rao (1969) Reddi and Reddi (1970) Saxena and Chaudhary (1977) Kinoshita (1971) Mann (1973)

Environmental factor

Thermosensitivity Thermosensitivity

Thermosensitivity Photoperiod sensitivity Thermosensitivity and humidity

Thermosensitivity Edaphic factors Photoperiod sensitivity

Murai and Tsunewaki (1993)

Thermophotosensitivity

Chillies Brassica napus

Satake and Hayase (1974) Virmani and Edwards (1983) Horner and Rogers (1974) Fan and Stefansson (1986)

Thermosensitivity Physiography Thermosensitivity Thermosensitivity

Carrots

Timin and Dobrutskaya (1981)

Environmental factor not speciﬁed

Rice

From the results reported so far, it is evident that the phenomenon of EGMS occurs in almost all the major food crops. Efforts made so far to use this phenomenon in hybrid breeding, however, are scanty except in rice crop. If systematic, intensive, and sustained efforts are undertaken, a successful two-line system of heterosis breeding can probably be developed in many other crops as well.

III. IDENTIFICATION AND CLASSIFICATION A. IDENTIFICATION OF EGMS The environment-sensitive genic male sterility or fertility character is manifested when these genotypes are exposed, at the sensitive stage, to the varying duration,

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degree, intensity, or presence or absence of the environmental factor that induces sterility or fertility expression. EGMS genotypes originate as a result of spontaneous or induced mutations. In crop plants, there is a high mutation frequency of the Ms gene to the ms gene, as indicated by the wide occurrence of spontaneously occurring monogenic recessive male sterile mutants (Kaul, 1988). This mutation rate is much higher than that observed in self-pollinated crops since many mutants in such crops are lost due to self-sterility. Unlike in cross-pollinated crops, where there is seed set on such mutants because of outcrossing, in self-pollinated crops such mutants remain seedless or low in seed set because of a poor outcrossing rate; hence, they are generally lost. Male sterile mutants in self-pollinated crops are detected when ﬂowering and fruiting of male fertile plants are completed. The controlled propagation of male sterile mutants is often difﬁcult due to their detection at this late stage, unless new ﬂowers are produced, which can be used for artiﬁcial cross-pollination. A detailed procedure for identifying thermosensitive genic male sterile (TGMS) lines under ﬁeld conditions, from existing germplasm and mutagenized populations in rice, has been given by Virmani et al. (1997a). These plant populations, under high-temperature conditions (>30–32◦ C) before and during ﬂowering, are closely examined at maturity for differential seed setting among various panicles of a rice plant or for complete sterility. This sterility can be identiﬁed easily in the ﬁeld by partially ﬁlled hanging panicles and erect panicles with sterile spikelets in the same plant. Those showing a complete lack of seed formation (in self-pollinated crops), or partial seed set in cross-pollinated crops during the sterility-inducing phase, and partial seed set in both self- and cross-pollinated crops under the fertility-inducing phase are suspected to be EGMS mutants. Pollen sterility is monitored in the sterile spikelets to conﬁrm that sterility is more than 99%. Suspected TGMS plants are evaluated critically for fertility or sterility transformations in phytotrons or growth chambers or under appropriate ﬁeld conditions by periodical sowings. It is sometimes easier to identify suspected EGMS mutants in the ﬁeld in a multitillered crop or in a crop in which ﬂowering is extended over a period of time if the sensitive stage of the crop approximately coincides with the transitional period of the environmental factor such as temperature or photoperiod. In such cases, there will be a clear change from seed set on earlier opened ﬂowers or earlier formed tillers to no seed set or partial seed set because of outcrossing on later opened ﬂowers or later formed tillers if there is a transition from the fertility phase to sterility phase and vice versa (Fisher, 1972; Shi, 1981; Virmani et al., 1997a). The nature of EGMS can be conﬁrmed under controlled growth chambers by setting up the required temperatures, photoperiods, and so on. If such facilities are not available, the suspected EGMS mutants can be sown at 1-week or 2-week intervals to expose their sensitive stage to various temperatures and photoperiods, some of which would cause sterility or fertility expression.

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The suspected EGMS mutants can also be checked for characteristics (such as linked phenotypic alteration, anther phenotype, pollen form, stainability and viability, enzymatic activity, and molecular markers) that are often useful for identifying or conﬁrming male sterile mutants. Normally, the search for male sterile mutants involves closer scrutiny of anthers and pollen grains. Nevertheless, in a few cases linked phenotypic alterations are also sought; these are much easier to identify in the ﬁeld when a large population of plants is to be examined. For instance, in pearl millet, barley, tomato, pea, and cotton crops, male sterile plants are bushy and late, have luxuriant vegetative growth, and remain green in the ﬁeld when the fertile plants have seeded and dried. In rice, the panicle remains green and upright at the time of crop maturity in male sterile mutants, whereas, in male fertile plants, panicles turn brown and are bent down due to the formation and ﬁlling of grains. Such types of alterations, though providing an easy form of phenotypic identiﬁcation in the large population of plants, are very uncommon. Most of the male sterile mutants do not exhibit any phenotypic alterations, which will make them easier to identify in the ﬁeld (Kaul, 1988). Anther phenotype is the most frequently used, easiest to distinguish, and most reliable marker for the identiﬁcation of male sterile mutants. But this requires much closer and more meticulous scrutiny and involves lots of time and effort. In male sterile mutants, anthers may be absent, aborted, deformed, transformed, shriveled, or shrunken. In a few cases of male sterile mutants, the anthers look perfectly normal phenotypically but are indehiscent. Another frequently used and reliable but laborious method for identifying male sterile mutants is a critical examination of pollen form, stainability, and viability. In the sporogenous type of male sterility, which is more common in crop plants then structural or functional male sterility, pollen when formed is shrunken and deformed and has degenerated cytoplasmic and nuclear contents, which make it unviable or sterile. The commonly used stains for pollen fertility determination are acetocarmine, aceto-orcein, and iodine-potassium iodide. Those pollen grains that are darkly stained, round, and plump are considered as fertile. Those that are unstained, shriveled, and irregular shaped are considered sterile. The staining procedure, though fairly useful, is not always reliable for differentiation of fertile or sterile pollen. For instance, pollen grains of some male sterile mutants of rice, sugar beet, wheat, barley, pea, tomato, and maize are phenotypically normal and get stained, but they are nonfunctional (Kaul, 1988). The action of the male sterility gene in these mutants is delayed until after pollen mitosis has been completed. An evaluation of enzymatic activity in pollen grains also provides a method for detecting male sterile mutants because the enzymatic activity in sterile pollen is much less than in fertile pollen. Peroxidase and dehydrogenase activities have been used commonly to differentiate sterile pollen from fertile pollen. HeslopHarrison and Heslop-Harrison (1970) standardized a pollen viability test based on

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enzymatically induced ﬂuorescence, which is readily observable under a ﬂuorescent microscope. Molecular markers, both proteins and DNA, if identiﬁed and tagged with the male sterility gene, also provide a reliable criterion for identifying male sterile mutants. Pollen viability can be tested by a nondestructive method developed by Dumas et al. (1983) that depends on the use of nuclear magnetic resonance spectroscopy. The test is based on changes in pollen water content as it becomes nonviable. Pollen staining supplemented by tetrazolium testing is a practical method for distinguishing sterile pollen from fertile pollen in many crop plants. Whenever the phenotype and stainability of pollen are normal, an easy and reliable method for identifying male sterility is to bag the young ﬂower buds of suspected male sterile plants and pollinate some of the ﬂowers by their own pollen and some by foreign pollen. Additionally, ﬂowers of normal male fertile plants that have been previously emasculated and bagged should be pollinated by pollen from suspected male sterile plants. Seed set would reliably distinguish sterile pollen from fertile pollen.

B. CLASSIFICATION Because environment-sensitive male sterility is a relatively new phenomenon for study, no systematic efforts have been made for its classiﬁcation. At present, EGMS is classiﬁed in rice on the basis of the environmental factor causing sterility or fertility expression: photoperiod-sensitive genic male sterility (PGMS), thermosensitive genic male sterility (TGMS), and photothermosensitive genic male sterility (PTGMS). In wheat and barley a fourth type, micronutrient deﬁciencyinduced male sterility, has also been designated. 1. Photoperiod-Sensitive Genic Male Sterility This type of EGMS is sensitive to photoperiod. Professor Shi Ming Song discovered a male sterile plant in late japonica variety Nongken 58 in Hubei Province of China in 1973. Subsequent investigation revealed the inﬂuence of daylength (photoperiod) in sterility or fertility expression of this male sterile mutant. A photoperiod or daylength of more than 14 h at the sensitive stage of the crop induced male sterility, whereas a photoperiod of less than 13 h and 45 min resulted in fertility (Shi, 1981, 1985; Shi and Deng, 1986; Lu and Wang, 1988). The male sterile mutant was originally designated as Hubei-photoperiod-sensitive genic male sterile rice (HPGMSR). Subsequently, the mutant was designated as Nongken 58S (NK58S). Extensive work has been done on this PGMS mutant at Wuhan in Hubei Province and at other centers in China. The PGMS trait from Nongken 58S has

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been transferred to several elite indica and japonica lines in various provinces of China by the backcross method of breeding (Virmani, 1994). Sano (1983) identiﬁed a sterility gene (S3) in F1 hybrids of Oryza glaberrima and Oryza sativa that was associated with photoperiod sensitivity. Another PGMS mutant, X-88 in rice, was reported from progeny of a cross of an Egyptian variety and Japanese variety at the National Institute of Genetics at Mishima, Japan (Satoh et al., 1992). This mutant was sterile under normal ricegrowing conditions of Japan, where ﬂowering starts in July/August. When grown under short-day conditions (less than 13.5 h of daylength), the mutant was fertile. The PGMS gene from X-88 is being transferred to Hokuriku-156 and other elite cultivars in Japan (Uehara et al., 1997). The PGMS system can be used in temperate regions where marked differences in daylength exist during rice growing seasons (Virmani, 1996). 2. Thermosensitive Genic Male Sterility The TGMS system wherein transformation of sterility to fertility and vice versa is controlled entirely by the temperature at the sensitive stage of the crop was discovered in China (Zhou et al., 1988; Sun et al., 1989). Subsequently, it was reported from Japan (Maruyama et al., 1991), the International Rice Research Institute (IRRI, The Philippines; Virmani and Voc, 1991), India (Ali, 1993; Ali et al., 1995; Satyanarayana et al., 1995; Pandey et al., 1998), and Vietnam (Du et al., 1997). In a majority of the mutants, such as Annong S-1 (from China), Norin PL12 (from Japan), IR32364 TGMS (from IRRI) and mutants reported from India, Vietnam, and elsewhere, higher temperatures induced male sterility, whereas lower temperatures resulted in male fertility. In a few cases, however, sterility is induced at lower temperatures and fertility is observed at higher temperatures. This type of response is referred to as a reverse TGMS type. The few examples of the reverse TGMS type reported are mutant Diaxin 1A and IVA and a mutant in indica rice variety 26 Zhaizao from China (Jiang, 1988; Zhang et al., 1991; Shen et al., 1994) and JP-38S from India (Ali and Siddiq, 1999). There is a difference in the method of recording and reporting critical temperatures at the sensitive stage for sterility or fertility expression of TGMS lines. Studies from China reported daily mean temperatures (mean of daily maximum and daily minimum) for inducing sterility or fertility, whereas Maruyama et al. (1991) and Viraktamath and Virmani (2000a, 2000b) reported maximum temperatures to be effective for inducing sterility or fertility at the critical stage of the crop. TGMS genes from the originally reported mutants are being transferred to elite agronomic backgrounds in China as well as at IRRI for their effective use in developing two-line hybrids. The TGMS system can be used in tropical and subtropical countries, where large temperature differences occur across locations and seasons

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(Virmani, 1996) and in the smaller tropical countries close to equator where low temperature areas are available in the hills (Virmani, 1994). 3. Photothermosensitive Genic Male Sterility In this type of environment-sensitive genic male sterility, the sterility/fertility expression is due to the interaction between temperature and photoperiod. Detailed investigations undertaken on fertility/sterility expression of PGMS mutant Nongken 58S and several of its indica derivatives indicated that the expression of male sterility or fertility was caused not only by daylength but also by the interaction between photoperiod and temperature (He et al., 1987). The major role of the interaction between photoperiod and temperature in sterility or fertility expression was conﬁrmed in subsequent studies (Xue and Zhao, 1990; Zhang et al., 1990; Lu et al., 1994; Deng and Yuan, 1998). It is now believed that few EGMS lines are sensitive to photoperiod alone for their sterility/fertility expression. In most cases, sensitivity is for both photoperiod and temperature. The strong interaction of these two environmental factors induces the appropriate sterility or fertility expression in these EGMS lines, which are designated as photothermosensitive genic male sterile (PTGMS) lines. Most of the indica rice lines developed by using the Nongken 58S gene in China can be classiﬁed as PTGMS lines (Zhang et al., 1993; Cheng et al., 1996; Deng and Yuan, 1998). 4. Micronutrient Deficiency-Induced Male Sterility Deﬁciencies of copper (Agarwala et al., 1980), boron (Rerkasem and Jamjod, 1997), and some other micronutrients are reported to induce male sterility in wheat and other crop plants. High phenotypic variation has been reported in sensitivity to the deﬁciency of these micronutrients. Very sensitive types are completely male sterile under micronutrient-deﬁcient conditions. It has been suggested that these sensitive genotypes can be used under deﬁcient conditions as females and tolerant genotypes as males for producing F1 hybrid seed. The sensitive types can be multiplied or maintained by growing them under micronutrient-sufﬁcient conditions. According to Graves and Sutcliffe (1974) and Adams et al. (1975), copper deﬁciency delays ﬂoral initiation and affects ﬂowering physiology. In wheat, copper deﬁciency caused pollen sterility, which resulted in decreased yields (Graham, 1975, 1976). Similar effects were observed in barley, oats, maize, and sunﬂower (Dell, 1981). Boron or molybdenum deﬁciencies also decreased pollen fertility (Agarwala et al., 1979). The partial male sterility induced by insufﬁcient copper nutrition in wheat resulted in delays in ear, anther, or pollen grain development (Agarwala et al., 1979). Under severe deﬁciency, the ﬂowers did not open and the anthers did not contain

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pollen or were indehiscent; the pollen grains then formed were small and had low germinability (Agarwala et al., 1980). Alloway et al. (1986) and Jewell et al. (1988) reported that pollen sterility induced by copper deﬁciency in barley and wheat was related to tapetum dysfunctioning characterized by cell hypertrophy. Azouaou and Souvre (1993) found that copper deﬁciency induced nearly complete sterility of the pollen formed and inhibited grain production. Temporary copper deﬁciencies signiﬁcantly reduced the viability rate and number of proline-rich pollen grains without affecting pollen grain production. Because of severe copper deﬁciency, the RNA content of cytoplasm of tapetum cells dropped by 34–48%. Rerkasem and Jamjod (1997) suggested the boron method of hybridization in wheat, in which boron deﬁciency was used as a selective medium or fertility and male sterile female parents and fertile male parents were provided by genotypic variation in the response to low boron content. Maintenance of female parents can be done simply in soils with sufﬁcient boron content, unlike genetic male sterility, which can be maintained only in heterozygous populations. Thus, this can be a possible alternative to hybridization methods currently in use in wheat, including cytoplasmic male sterility and chemical hybridizing agents (Lucken and Johnson, 1988). Boron deﬁciency-induced male sterility has also been reported in rice (Garg et al., 1979) and barley (Simojoki, 1972; Rerkasem and Jamjod, 1989). So there is a possibility that the method suggested above for hybridization may be applicable to these and other small grains too. Rerkasem and Jamjod (1997) reported that “male sterility induced by boron deﬁciency was more manageable than thermo-photosensitive male sterility reported in rice. But this is yet to be tried on a large ﬁeld scale.” The chances for successful development of the method, however, could be strengthened by a better understanding of the physiological, genetic, and molecular bases for genotypic variation in responses to low boron. Furthermore, the basic understanding may in turn lead to better insights into the nature of reproductive development, and therefore opportunities for more effective control, in wheat and other small grains. Although occurrence of micronutrient deﬁciency-induced male sterility has been reported in a few crop plants, it has not been used successfully so far for hybrid breeding purposes.

IV. GENETICS A. INHERITANCE The inheritance of photoperiod-sensitive genic male sterile spontaneous mutant Nongken 58S in japonica rice, the identiﬁcation of which paved the way for

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extensive studies on the phenomenon of EGMS in China, was reported to be monogenic recessive (Shi, 1985; Feng et al., 1985; Shi and Deng, 1986). Further studies also indicated that this trait was controlled by two pairs of recessive nuclear genes without any effect of cytoplasm (Zhu and Yu 1987). Several other studies (Lu and Wang, 1988; Mei et al., 1990; Sheng, 1992; Yuan et al., 1993; Yang, 1997; Deng et al., 1997) on inheritance of photoperiod-sensitive male sterility in Nongken 58S and its several derivatives conﬁrmed its digenic recessive inheritance. These genes are designated as ms1Phms1Ph and ms2Phms2Ph (Zhang et al., 1994b). The original PGMS Nongken 58S is a spontaneous mutant and an initial study (Shi, 1985) indicated that fertility segregation in crosses between Nongken 58S and its wild progenitor Nongken 58 is conditioned by a single Mendelian locus. The involvement of a second locus in the expression of the PGMS trait in mutant Nongken 58S (Yang, 1987; Zhu and Yu, 1987; Jiang, 1988; Lu and Wang, 1988; Mei et al., 1990; Sheng, 1992) implied that cultivar Nongken 58 was already homozygous for a recessive allele at a second locus before it mutated to becomes a PGMS line, Nongken 58S. Zhang et al. (1990) reported a linkage between the PGMS gene and a locus for dwarﬁsm located on chromosome 5. Thus, it seems likely that the “second locus” may not be the same in different lines and crosses. This is not surprising since pollen fertility is the end result of many complex processes controlled by numerous loci, and mutation in any one of the loci involved in these processes or pathways may have an effect on pollen fertility. It is almost certain, however, that only one of the two loci, most likely the one that segregates between Nongken 58S and its wild progenitor Nongken 58, triggers the photoperiod response for inducing male sterility; apparently the other is merely a locus for male sterility, like many of those identiﬁed previously in rice (Kinoshita, 1991). Which of the two loci governs the response to photoperiod induction and what is the molecular basis of interaction between the two loci are yet to be determined (Zhang et al., 1994b). Wang et al. (1991) studied the inheritance of PGMS in several crosses. The F1 was usually fertile. Extensive segregation occurred in the F2. They concluded that a single gene controlled photoperiod sensitivity and a set of genes regulated the metabolism of uninucleate microspores, the effects of which were cumulative. Oard et al. (1991), from their studies on PGMS mutants in cultivar M-201 in the United States, concluded that the PGMS trait was controlled by two nuclear genes with epistatic effects. Oard and Hu (1995), after studying the inheritance of the PGMS trait in induced mutants in the United States, concluded that the trait was controlled by one to three recessive nuclear genes, depending on the cross involved. Xue and Deng (1991), however, reported that the PGMS trait was quantitatively inherited. Huang and Zhang (1991) reported a single dominant gene for the PGMS character in CIS 25-10S, a photoperiod-sensitive genic male sterile mutant, totally free from the inﬂuence of temperature. Association with minor genes, however, has been reported to inﬂuence its effect.

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The inheritance of the TGMS trait is reported to be mainly monogenic recessive in TGMS lines 5460S (Sun et al., 1989; Yang et al., 1992), R 59TS (Yang et al., 1992), H 89-1 (later designated as Norin PL-12; Maruyama et al., 1991; Borkakati and Virmani, 1996), and IR32364 TGMS (Borkakati and Virmani, 1996; Subudhi et al., 1996) and in several TGMS lines developed in India (Ali, 1993; Reddy et al., 1998). However, this trait was reported to be controlled by two recessive genes in TGMS mutants Annong-S-1 (Li et al., 1994) and UPRI 95-140 (Li and Pandey, 1998). The TGMS genes in 5460S and Norin PL-12 were designated as tms1 and tms2, respectively (Kinoshita, 1991). Borkakati and Virmani (1996) found that the TGMS genes in Norin PL-12 and IR32364 TGMS were nonallelic and tentatively designated the TGMS gene in IR32364 TGMS as tms3(t). For lack of accessibility to Chinese TGMS mutant 5460S, the allelic test could not be carried out with tms1. A study of the allelic relationship of the mutants discovered in India (Ali, 1993) indicated that the TGMS gene in the mutants ID-24 and JP-1 was allelic to tms2 and the gene in mutants JP 8-8-1S and IC10 was allelic to tms3(t). These studies also indicated that TGMS genes in mutants SA-2 and F-61 were nonallelic to tms2 and tms3. The allelism test with tms1 could not be undertaken for the reason given above.

B. LINKAGE WITH MOLECULAR MARKERS Mutants with EGMS genes may not have all the other desirable agronomic attributes required in a cultivar. Often there is a need to transfer EGMS genes to desirable agronomic backgrounds. Linkage with morphological or molecular markers facilitates the transfer of these genes and thus increases the efﬁciency of the breeding process. In most cases, EGMS genes are not linked to any easily identiﬁable morphological characters. Until ﬂowering, EGMS mutants appear like their normal counterparts or wild types. Second, this character of sensitivity to environmental factors is expressed only under certain speciﬁc ranges or conditions of the environmental factors. Under such situations, molecular markers are very handy and useful. Zhang et al. (1994b), using the bulked extremes and recessive class, mapped the genes for PGMS in rice. Two chromosome regions, each containing a PGMS locus, were identiﬁed on a published restriction fragment length polymorphism (RFLP) linkage map. One locus was designated pms1, on chromosome 7, and another was designated as pms2, on chromosome 3. The effect of pms1 was reported to be two to three times larger than that of pms2. The locus pms1 is located between the markers RG477 and RG511. It is located at 3.4 recombination units from RG477 and 13.0 recombination units from RG511. The locus pms2 is located between the markers RG191 and RG348. It is about 7.0 cM from RG191 and 10.6 cM from RG348 (Fig. 1).

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Rice chromosome 7 cM

RG146B

5.4 RG650 7.4 RG678 8.5 1.6 5.6 3.5

WG719, RG30 CDO533 RG477 pms1

Rice chromosome 3 15.0 cM

RG348 RG511, RZ272

10.6 pms2 18.5

7.0 1.4 3.2 pms2

RG191 (RG266) RG450 (RG117) RG335

RG128 pms1

Figure 1 Location of PGMS genes on RFLP linkage map in rice (Zhang et al., 1994b).

Wang et al. (1995) used bulked segregant analysis of the F2 population of the cross 5460S × Hong Wan 52 to identify the random ampliﬁed polymorphic DNA (RAPD) marker linked to the TGMS gene tms1. They found that one single copy fragment (size = 1.2 kb) ampliﬁed by primer OPB-19, and subsequently named TGMS 1.2, cosegregated with the TGMS gene. tms1 and was mapped on chromosome 8 with a genetic distance of about 6.7 cM from the RAPD primer (Fig. 2a). TGMS 1.2 has been sequenced and changed to a sequenced tagged site (STS) marker, which is available for public use. Yamaguchi et al. (1997) reported the linkage of molecular markers with the TGMS gene tms2. They estimated the locus of tms2 using RFLP markers in an F2 population derived from a cross between Norin PL-12 and aus variety Dular. They concluded that tms2 was located between the markers R643A and R1440, with a distance of 0.2 cM from R143A on chromosome 7 (Fig. 2b). Subudhi et al. (1997) employed bulk segregant analysis in conjunction with the RAPD technique in the IR32364 TGMS X IR68 cross to identify the molecular markers linked to TGMS gene tms3(t). An F2 population from a cross between TGMS mutant line IR32364 TGMS and IR68 was used to map this TGMS gene. Fertile and sterile bulks were constructed following the classiﬁcation of F2 plants into true-breeding sterile, fertile, and segregating fertile plants based on F3 family

158

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Chromosome-7 cM

29.7

R1788(D24362) 1.0

3.2

IR32364TGMS/IR68

RG333 RZ562 R643A(23948) tms2

12.6 12.7

TGMS 1.2 tms1 RG978

1.7

7.2

2.4

RG1 R1440(D24156)

RZ66 RG136

9.3 RZ649 (a) tms1 (Wang et al 1995)

cM 10.6 5.7 16.7

9.0 7.6

Chromosome-6

7.7 10.0

0.4 R646(D23951) (b) tms2 (Yamaguchi et al 1997)

10.6

RZ516 RZ398 RZ2 OPF18-2600 OPAC3640 tms3(t) OPAA7-550 OPB19-750

(c) tms3(t) (Subudhi et al 1997)

Figure 2 Location of TGMS genes on linkage map in rice.

studies. From the survey of 389 arbitrary primers in bulked segregant analysis, four RAPD markers were identiﬁed in which three, OPF 182600, OPB19750, and OPAA7550, were linked to tms3(t) in the repulsion phase and one, OPAC3640, was linked to tms3(t) in the coupling phase. The tms3(t) gene was ﬂanked by OPF 182600 and OPAC3640 on one side and by OPAA7550 and OPB19750 on the other side (Fig. 2c). Subsequently, using a mapping population available at IRRI, OPAC3640 was mapped to the short arm of chromosome 6. No RFLP markers from this region, however, showed linkage to tms3(t) owing to a lack of polymorphism between the parents. Lang et al. (1997, 1999) developed a polymerase chain reaction (PCR)-based marker for the tms3(t) gene. The sequence information from RAPD markers was used to design several pairs of primers for PCR ampliﬁcations. One of the RAPD marker, OPF 182600, could be converted into two codominant STS markers tightly linked to tms3(t) gene. The primers F 18F/F 18RM and F18 FM/F 18 RM were linked to tms3(t) at a distance of 2.7 cM. The efﬁcacy of marker assisted selection for this trait was calculated as 84.6%. Polymorphism survey of 12 prospective elite lines to be introgressed with TGMS gene indicated that these PCR markers for tms3(t) can now be used in selecting TGMS plants at seedling stage in segregating populations in environments independent of controlled temperature regime. Since tms1, tms2, and tms3(t) are reported to be located on different chromosomes, it is implied that these are three different genes and tms3(t) can now be designated as tms3.

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Table II Linked Molecular Markers and Chromosomal Location for EGMS Genes in Rice

EGMS gene

Linked molecular markers

Chromosomal location

Reference

PGMS gene pms1 PGMS gene pms2

RG 477 and RG 511 RG 191 and RG 348

7 3

Zhang et al. (1994b) Zhang et al. (1994b)

TGMS gene tms1

RAPD marker 1.2 TGMS RFLP markers R 643A and R 1440 RAPD markers OPF182600, OPAC3640, OPAA7550, and OPB19750

8 7 6

Wang et al. (1995) Yamaguchi et al. (1997) Subudhi et al. (1997)

TGMS gene tms2 TGMS gene tms3

Table II summarizes the linkage of molecular markers with the EGMS genes reported in rice and their location on the chromosomes. It is noteworthy that pms1 and tms2 are located on the same chromosomes (7). This may explain the frequent occurrence of PTGMS lines rather than PGMS or TGMS lines.

V. CHARACTERIZATION Characterization of EGMS lines with respect to the range of environmental factor(s) causing sterility and fertility expression is a prerequisite for their effective and economic use to develop and commercialize hybrids. Characterization basically involves determining the “sensitive stage” during the development and growth of EGMS lines, the critical sterility point (CSP), critical fertility point (CFP), and critical daylength (CDL) or critical light length (CLL). In Chinese literature, vastly different terms and their acronyms are used, which sometimes makes things difﬁcult to comprehend. We therefore clarify and illustrate these terms (Fig. 3) so that they can be understood easily. We have also attempted to standardize the acronyms of these terms in this chapter. The sensitive stage of an EGMS line in a crop plant is the stage in its development and growth when it is inﬂuenced by environmental factor(s) that cause sterility or fertility expression. Generally speaking, the sensitive stage is around the initiation, formation, and growth of the ﬂower or panicle primordia. In some crop plants studied, this stage occurs 1–3 weeks before the onset of ﬂowering. The sensitive stage differs markedly from crop to crop and within the same crop; it also differs among various EGMS lines. Hence, the sensitive stage must be determined

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Figure 3 A model for the photothermosensitive reaction in PTGMS lines (Zhang et al., 1993). Abbreviations: CSP, critical sterility point; CFP, critical fertility point; and TRPS, temperature range for photoperiod sensitivity.

precisely in EGMS genotypes. For Nongken 58S, the sensitive stage during panicle development is from the secondary rachis branch and spikelet primordia differentiation stage to the pollen mother cell formation stage (Yuan et al., 1993; Zhang et al., 1993). A photoperiod of more than 14 h during the entire period of the sensitive stage resulted in full sterility (Zhang et al., 1993). A long photoperiod, if occurring for only a part of the sensitive stage, resulted in partial fertility. Among the different phases of the sensitive stage, the differentiation of stamen and pistil primordia phase is the most sensitive. At 26–27◦ C, 1 short day occurring during this phase of the sensitive stage resulted in 12% seed set. During the pollen mother cell formation stage, 1 short day resulted in 6% seed set, whereas only 2% seed set resulted when 1 short day occurred during the secondary rachis branch and spikelet primordia differentiation phase (Zhang et al., 1993). Thus, the sensitivity of different phases during the sensitive stage differs markedly. The critical sterility point (CSP) is the temperature during the sensitive stage of an EGMS line at and above which complete or maximum sterility is induced. Similarly, the critical fertility point (CFP) is the temperature during the sensitive stage of an EGMS line at and below which complete or maximum fertility is induced. Temperature range for photoperiod sensitivity (TRPS) in photothermosensitive genic male sterile (PTGMS) lines is the range of temperature from

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CFP to CSP. Within this range only sensitivity to the photoperiod is functional. Critical daylength (CDL) or critical light length (CLL) is the minimum period of time for the length of the day or light period (in hours) required by PGMS or PTGMS lines during their sensitive stage, which results in complete induction of sterility. Any day or light period shorter than this results in partial fertility. Zhang and Yuan (1989) demonstrated that the period of twilight, in addition to the period of daylength, was effective for inducing sterility/fertility; therefore, it is considered more appropriate to use CLL rather than CDL. Figure 3 illustrates these concepts. To characterize PGMS lines, the role of temperature must also be taken into account since almost all the indica lines and some japonica lines developed by transferring the gene from Nongken 58S have shown a strong interaction with temperature (He et al., 1987; Xue and Zhao, 1990; Zhang et al., 1990; Liu et al., 1994; Deng and Yuan, 1998). Most of the PGMS lines developed in China are now classiﬁed as PTGMS lines. All the PTGMS lines are sterile at and above CSP and are fertile at and below CFP. Photoperiod is effective only in the range of temperature from CFP to CSP, which is referred to as the temperature range for photoperiod sensitivity. Within TRPS, light duration equal to or longer than CLL results in induction of sterility, and any light duration shorter than CLL results in fertility. Thus, the effects of photoperiod and temperature on the induction of sterility or fertility expression in PGMS lines are interdependent and inseparable (Zhang et al., 1992). The critical light length of different PTGMS lines also depends on their genetic background. Though the same gene from Nongken 58S was transferred when developing two PTGMS lines (7AC-13S and Shuang 8-2S), their CLLs were 13 and 14.25 h, respectively (Zhang et al., 1993). The critical light intensity for inducing sterility in PTGMS lines is 50 lux and above (Zhang et al., 1987). Critical light intensity is affected by temperature. At a high temperature of 29◦ C, it is 40–50 lux, whereas at a lower temperature of 26◦ C it is around 100 lux (Zhang et al., 1993). During the twilight hours before sunrise and after sunset, light intensity is generally in the range of 300–600 lux. Therefore, before sunrise and after sunset, the light intensity is sometimes above critical light intensity, which justiﬁes consideration of critical light length rather than critical daylength (Zhang and Yuan, 1989). Within the temperature range of photoperiod sensitivity, CLL decreases as the temperature increases and vice versa (Zhang et al., 1993, 1994a). For instance, at 26◦ C, CLL of Nongken 58S is 14 to 14.33 h, whereas at 28 and 30◦ C it is 13.67 to 14 h and 13.67 h, respectively. At the same short day of 13 h, the fertility of Nongken 58S is 61.8% at 24◦ C, 54.6% at 26◦ C, 42.8% at 28◦ C, and 21.5% at 30◦ C (Zhang et al., 1993). Zhang et al. (1994a) quantitatively regressed seed set percentage on light and temperature within the temperature range of photoperiod sensitivity: Y = 465.4 − 23.8X 1 − 4.2X 2 ,

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where Y = seed set percentage, X 1 = light length (including twilight period above 50 lux), and X 2 = mean temperature during the sensitive period. EGMS lines are characterized by using two procedures: (1) controlled conditions in a growth chamber or phytotron and (2) natural ﬁeld conditions by undertaking periodical sowings or plantings at 1-week or 2-week intervals throughout the growing season. Virmani et al. (1997a) give a detailed procedure for characterizing TGMS lines in a phytotron or growth chamber and under ﬁeld conditions. For any EGMS line, inﬂuenced by temperature and photoperiod, the critical temperature points for sterility and fertility expression, and temperature range for photoperiod sensitivity and CLL, are to be determined. To characterize EGMS lines affected by deﬁciency of micronutrient or water stress, for example, the lines at their sensitive stage are exposed to different levels of environmental factor(s) causing fertility and sterility transformation. The precise levels of environmental factor(s) resulting in complete sterility and in maximum fertility are to be determined. Because temperature and photoperiod are the major environmental factors reported in the literature for inducing sterility or fertility expression in crop plants, EGMS lines are usually characterized for these two factors.

A. PROCEDURE FOR CHARACTERIZATION Prospective PGMS/PTGMS lines are exposed to various combinations of different photoperiods and temperatures and prospective TGMS lines are exposed to different day and night temperatures at their sensitive stage in growth chambers or phytotrons. After the genotypes receive adequate exposure well before the onset of the sensitive stage until well after its completion (3 to 4 weeks is a safer period in rice), pollen and spikelet fertility or sterility are critically monitored in the exposed genotypes after their panicles emerge 2–3 weeks later and mature about 4 weeks after emergence. Pollen and spikelet fertility data obtained from plants treated with different combinations of temperature and daylength in different growth chambers help to determine the critical sterility or fertility points, temperature range for photoperiod sensitivity, and critical light length. In China, newly developed PGMS and TGMS lines are characterized for their critical sterility or fertility points and for critical daylength in phytotrons. The photoperiods usually tested are 11.5, 12.5, 13.5, and 14.5 h, and mean daily temperatures set up in phytotrons are 29, 28, 24, and 23◦ C. When the materials being tested reach the initial stage of stamen and pistil primordia differentiation, they are transferred into the phytotron and retained there until 3 days after the late stage of pollen mother cell meiotic division. Pollen and spikelet fertility or sterility are determined at ﬂowering and harvest, respectively. For ﬁeld characterization of EGMS lines in China, locations at Hangzhou and Guangzhou are used because of the marked differences in their latitude and light

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length. Sterile lines for which fertility alteration is conﬁrmed in the phytotron are grown in the ﬁeld at different predetermined locations in China to conﬁrm sterility or fertility expression and to ﬁnd out their ecological adaptability. Sequential sowings are usually done at Wuhan (30◦ N30′ ), Guiyang (26◦ N35′ ), and Sanya (17◦ N30′ ). After 2 years of testing, the range for geographical adaptation for newly developed PGMS/TGMS lines is deﬁned (Lu et al., 1998). Deng and Yuan (1998) characterized more than 90 PTGMS lines of rice. They set up several temperature and photoperiod ranges under artiﬁcially controlled conditions to determine the sensitive stage, critical sterility/fertility points, and critical light length of these lines. They concluded that 1. The sensitive stage for temperature and photoperiod varied from line to line. Among the lines tested 73% of the entries (including Annong S-1) had their sensitive stage from the pollen mother cell formation stage to meiosis, which morphologically corresponds to 10–14 days before heading. The sensitive stage in 19% of the entries (including Pei’ai 64) was from pistil and stamen formation to pollen mother cell formation (i.e., 12–17 days before heading). The sensitive stage in 8% of the entries was only 3–8 days before heading. Such variation for the sensitive stage among different EGMS lines clearly suggests the need to analyze all the newly developed EGMS lines individually to identify their sensitive stage. 2. All the lines tested were photothermosensitive. None was sensitive to temperature alone or photoperiod alone. Even japonica EGMS line 7001S, which was considered as solely PGMS, was sensitive to temperature. 3. Critical sterility/fertility points and critical light length differed among different lines. Hence, there is a need to determine these individually for all the newly developed lines. 4. During the process of seed multiplication of PTGMS lines, the critical sterility point drifts upward in the absence of speciﬁc selection. To overcome this problem, Yuan (1994) suggested producing core (nucleus) seed of the PTGMS lines each season. EGMS lines are characterized using the ﬁeld screening method by monitoring their pollen and spikelet fertility or sterility obtained in various sowings. The occurrence of fertility or sterility is then correlated with the prevalent temperature and photoperiod at the sensitive stage of the EGMS line. For this method to be successful, meticulous record keeping of the speciﬁc environmental factor(s) and precise determination of the sensitive stage of the line through a reliable morphological marker (e.g., exact leaf number) are essential since this method of screening in the ﬁeld does not require sophisticated facilities such as growth chambers and phytotrons. It is much more economical. With the help of long-term weather data on photoperiod and temperature, the periods in the growing season for obtaining sterility and fertility can be identiﬁed so that EGMS multiplication can be planned

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during the period when fertility is obtained and hybrid seed production can be planned during the period when sterility is observed. This method has also been used in different provinces of China to identify the period for undertaking hybrid seed production and multiplication of PTGMS lines (Liu et al., 1997; Deng et al., 1997; Wang et al., 1997). In general, photoperiod is much more stable than temperature from year to year. Sudden unexpected ﬂuctuations in temperature often occur. Hence, it is desirable to have PTGMS lines with a low critical fertility point and in which sterility is not reverted by a short interruption of low temperature so that minor ﬂuctuations in temperature do not result in self-fertility, which would cause impurity in hybrid seed production. Although all the current PTGMS lines in China come from the same source (Nongken 58S) and follow the same photothermosensitive model of fertility or sterility expression, their critical sterility and fertility points and critical light length differ from one another because of their different genetic backgrounds (Zhang et al., 1993; Lu et al., 1994). In general, critical sterility point is the key factor causing ﬂuctuations in sterility under long-day conditions. If the CSP of a line is not low enough, using this line in hybrid seed production is risky because a temperature lower than CSP can cause the sterile line to become partially fertile irrespective of light length, and the purity of the hybrid seed cannot be guaranteed. China has seen failures in commercial seed production earlier because the inﬂuence of temperature on pollen fertility in the then-PGMS (now PTGMS) lines was not sufﬁciently appreciated (Zhang et al., 1993). Conversely, critical fertility point is the factor that may make multiplication of PTGMS lines uneconomical under short-day conditions, if the CFP of a line is not high enough. Occasional exposure to high temperatures results in sterility, which causes low and uneconomical seed yield in PTGMS multiplication plots. China has many excellent indica PTGMS lines with stable sterility, but very low CFP. These cannot be used to develop hybrids in Hunan and Hubei provinces on account of their very low and uneconomical seed yield during multiplication. Critical light length and intensity of interaction between photoperiod and temperature are the main factors in controlling the latitudinal adaptation of a PTGMS line. A line with a longer CLL could be suited to higher latitudes and one with a shorter CLL to lower latitudes since during rice-growing seasons light length in lower latitudes is shorter than in higher latitudes. A line with a strong interaction between photoperiod and temperature could be more widely adapted because high temperature can counteract the insufﬁciency of long photoperiod at low latitude, or alternatively, a longer photoperiod can counteract the insufﬁciency of high temperature in higher latitudes.

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B. CLASSIFICATION OF PTGMS LINES Based on characterization parameters such as critical sterility or fertility points and critical light length, PTGMS lines in China have been classiﬁed into the following four categories (Zhang et al., 1993). 1. Low CFP with High CSP (or Nongken 58S Type) Lines belonging to this group have a relatively low critical fertility point, high critical sterility point, and wide temperature range for photoperiod sensitivity. PTGMS lines are stable in sterility under long-day conditions and can be multiplied easily under short-day conditions regardless of changes in temperature. In the central and southern rice-growing areas of China, their fertility alteration is mainly photoperiod sensitive and they can be multiplied easily, but it is slightly risky to use them in hybrid seed production because a slight reduction in temperature induces some fertility. This group includes PTGMS lines 31111S, 5088S, N 5047S, 1541S, 7001S, and others. Lines 5088S and 7011S, which have a comparatively lower critical temperature for fertility induction and show more stable sterility under changing environmental conditions, can be used safely in hybrid seed production. 2. Low CFP with Low CSP (or Pei’ai 64 Type) This group has PTGMS lines that have low CFP and low CSP with narrow TRPS, are stable in sterility under long-day conditions at all temperatures, and are stable under high temperatures regardless of daylength. Seed multiplication is difﬁcult under short-day conditions if high temperatures occur during the sensitive stage. Seed multiplication can be carried out only in a suitable season or at higher altitudes in tropical rice-growing countries. This group includes Pei’ai 64S, HNS-2S, M 901S, 8906S, and W91607S. 3. High CFP with High CSP (or 8902S Type) This group of PTGMS lines has a high critical fertility point and relatively high critical sterility point. These lines can be multiplied easily with high seed yield under short-day conditions. Hybrid seed production is risky as the sterility is unstable when the temperature decreases under long-day conditions. This group includes lines such as 8902S, 8912S, 5047S, Shuangguang S, and 9044S.

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4. High CFP with Low CSP (or W 6154S Type) This group shows almost thermosensitive reaction under natural conditions. It has stable sterility at higher temperatures irrespective of photoperiod. Sterility is not stable under long-day conditions if lower temperatures are encountered. This group may be used for hybrid seed production in the tropics, but PTGMS seed multiplication can be taken up only in certain locations such as higher altitudes in tropical rice-growing areas. This group includes lines such as W 6154S, W 6184S, W 8013S, and 8801S and can be used as thermosensitive sterile germplasm in the tropics. An ideal type of PTGMS line, with low critical fertility point, a high critical sterility point, and a wide temperature range to allow for interaction with photoperiod, is not yet available (Yuan, 1992; Zhang et al., 1993). Such lines will be safe for both PTGMS seed multiplication and hybrid rice seed production.

C. CHARACTERIZATION AND CLASSIFICATION OF TGMS LINES The temperature-responsive stage of the TGMS lines is determined by physically examining the developing panicles and correlating the observations with easily measurable morphological indicators such as ﬂag leaf (nth leaf) length (Wang and Mei, 1990). By relating ﬂag leaf length periodically to the size of the developing panicle, four stages have been identiﬁed (Ali et al., 1995). Based on the stage of panicle development on the main tiller, the corresponding stages in the secondary and tertiary tillers can also be judged. Ali et al. (1995) followed two methods, the physical-cum-morphological index method and tracking method, to determine the sensitive stage of TGMS lines and to characterize them. In the physical-cum-morphological index method, the different stages of panicle development from the day of initiation were determined by periodically split-opening the main tillers of test lines. The corresponding developmental stages of the secondary and tertiary tillers were also determined similarly. For instance, when the main tiller was at stage V, or at the pollen mother cell formation stage (panicle length = 2.5 cm), the secondary tillers were younger by 6–8 days at stage IV or at the stamen–pistil primordia stage (panicle length = 2 mm). Length of the ﬂag leaf was also used as a morphological index to determine the stage of panicle development in the main, secondary, and tertiary tillers. When the ﬂag leaf is 4–5 cm long, or 20% of the length of the preceding leaf, the panicle developing inside the main tiller is at panicle development stage V. Thus, by following this methodology, sensitive stages were determined and were subjected to various temperature ranges. In the tracking method, the date of panicle emergence and pollen fertility status of each of the test lines raised in an adequate

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number in a net house were recorded daily. The date when the ﬁrst spikelet protruded was taken as the day of panicle emergence. Five spikelets were examined on the same day for pollen fertility. The observations were continued until the pollen was found to be totally sterile in the newly emerging panicles and 3–4 such days were chosen as tracking dates. On the basis of earlier reports suggesting that the most sensitive phase of panicle development, i.e., the stamen–pistil primordia stage, lies between 15 and 24 days before heading, the days of the sensitive stage were determined with the help of a weather chart showing maximum–minimum temperatures. Days were counted backward from the tracking date between 15 and 24 days and the dates when the maximum temperature in this period was above 30◦ C and were noted. The temperature record for the 15- to 24-day preheading period of each test line was examined for three tracking dates, and the dates on which the maximum temperature was above 30◦ C were regarded as critical temperature days coinciding with the sensitive stamen–pistil primordia stage of the line. Ali et al. (1995) have mentioned the following advantages of the tracking technique they developed for identifying the temperature-sensitive stage of TGMS sources. r It is an easier technique. r It enables more precise determination of the sensitive stage without injury to the plant (as in the physical split-opening technique) and leaves no scope for arbitrariness (which is possible in the ﬂag leaf index method). r Abrupt ﬂuctuations in temperature that affect vegetative growth and development are not a limitation, as the sensitive stage is determined by counting backward from the sterility induction day. r It enables simultaneous determination of temperatures for critical sterility and fertility points. This technique, in combination with the ﬂag leaf index, becomes even more efﬁcient for precisely determining the sensitive stage. By setting up different sets of day and night temperatures in growth chambers and screening the test entries, Ali et al. (1995) determined CSP and CFP for six TGMS lines using the indices of the temperature-sensitive stage of panicle development. Chinese researchers (Sun et al., 1993; Zhang et al., 1994a; Lu et al., 1994; and many others) have used and reported mean temperature for determining sterility or fertility points for characterization, whereas Maruyama et al. (1991) and Viraktamath and Virmani (2000a, 2000b) suggested that maximum temperature inﬂuences sterility or fertility expression in TGMS lines. Viraktamath and Virmani (2000a, 2000b) have stated, “Since the same mean value can be obtained from different maximum and minimum temperature values, the use of mean temperature for indicating sterility/fertility expression would be misleading.”

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As indicated earlier, temperatures ﬂuctuate much more than photoperiod. Hence, an ideal or desirable TGMS line should have the ability to maintain complete sterility if sudden interruptions of low temperatures occur for a few hours to days during their high-temperature sterility phase. TGMS lines differ markedly in this characteristic. For instance, Viraktamath and Virmani (2000a, 2000b) have found that, for TGMS line IR68945-4, fertility was induced even with 2 h of a lower temperature of 27◦ C during its sterility phase, whereas TGMS line ID 24 remained completely sterile even after 10 h of interruption with a low temperature of 27◦ C during its sterility phase. The behavior of another TGMS line, Norin PL-12, was intermediate in this respect. It remained sterile with 2 h of interruption with 27◦ C, but showed some fertility induction after 4–10 h of interruption. On the basis of their critical sterility or fertility points, Ali et al. (1995) classiﬁed the six TGMS lines into three categories: high CSP (>32◦ C) with low CFP (20–24◦ C), high CSP (>32◦ C) with high CFP (24–30◦ C), and low CSP (30–32◦ C) with low CFP (20–24◦ C). The fourth possible group of low CSP (30–32◦ C) with high CFP (24–30◦ C) was not found. Essentially, the grouping is based on relative differences between CSP and CFP as designated by Zhang et al. (1994a) for PTGMS. Unlike in the PTGMS system, the phenomenon of sterility or fertility expression in the TGMS system is least affected by photoperiod, making it convenient and suitable for tropical conditions. It is the critical sterility/fertility points that largely determine the stability and commercial usefulness of TGMS lines. If the CFP of a TGMS source is not low enough, even a slight drop in temperature during the hybrid seed production period (sterility phase) would result in fertility and selﬁng, where 100% male sterility and only hybrid seed set are desired. On the other hand, if the CSP is not high enough, multiplication of TGMS lines would become a problem, especially under short-day and high-temperature conditions. Thus, an ideal CSP–CFP system is highly environment speciﬁc. In China, where moderate temperature is combined with distinct short and long photoperiods, the ideal TGMS lines for commercial exploitation should have high CSP and low CFP (Yuan, 1992; Zhang et al., 1994a). For tropical conditions, on the other hand, low CSP and low CFP types appear to be desirable (Ali et al., 1995). The speciﬁc characteristics of the six TGMS lines grouped into three types on the basis of CSP and CFP and brieﬂy as follows: 1. Type 1: high CSP, low CFP. Chinese researchers recognized this group as ideal for China, as it would be safe for both multiplication and hybrid seed production. However, such a type has yet to be identiﬁed (Yuan, 1992, Zhang et al., 1994a). Although it is not clear where to draw the line for high CSP and low CFP, based on the available temperature regime in a given region, a range can be deﬁned. Based on the response of PTGMS and TGMS rice lines in China, India, the Philippines, and some other Asian countries, high CSP can be around 32◦ C

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and low CFP can be around 23◦ C. However, this needs to be discussed after wider experience of using EGMS lines in these countries. Two TGMS lines, SM-3 and SM-5, were classiﬁed under this category. 2. Type 2: high CSP, high CFP. Most of the EGMS lines identiﬁed by Chinese researchers belong to this group, although they may not be much suitable for Chinese conditions as such. Seed multiplication in these lines is easier, but hybrid seed production is very risky since slight lowering of temperatures occasionally may result in selfed seed. One TGMS line JP-2 was classiﬁed under this category. 3. Type 3: low CSP, low CFP. The stable sterility phase over a large region prompted Chinese workers to use TGMS lines of this group (Pei’ai 64) commercially in the initial years. Difﬁculties experienced in the maintenance of such TGMS lines, however, ultimately limited their use (Zhang et al., 1994a). In tropical countries, where the sterility phase must be stable for a longer period, this group of TGMS lines may prove to be very useful in undertaking large-scale hybrid seed production. Multiplication of TGMS lines can be taken up in hilly areas where much lower temperatures pravail. Among the six TGMS lines characterized by Ali et al. (1995), three lines JP8-1A-12, SA-2 and F-61 belonged to this group. 4. Type 4: low CSP, high CFP. Among the six TGMS lines characterized, none belonged to this group. TGMS lines in this group have CSP <32◦ C and CFP >24◦ C. It is rare to ﬁnd such TGMS lines. So far, such TGMS lines have not been reported from China or elsewhere. Most of the EGMS lines developed in China have been thoroughly characterized (Yuan, 1992; Sun et al., 1993; Zhang et al., 1994a; Liu et al., 1997; Wan et al., 1997; Xiao et al., 1997; Wang et al., 1997; Deng et al., 1997). Those developed at IRRI have been characterized by Borkakati and Virmani (1997), Viraktamath and Virmani (2000a, 2000b), and Lopez and Virmani (2000). TGMS lines identiﬁed and developed in India have been characterized by Ali (1993), Ali et al. (1995), Rangasamy and Jayamani (1997), and Reddy et al. (1998) and those identiﬁed and developed in Vietnam have been characterized by Du et al. (1997). In the absence of the daylength differences required to make use of the PGMS system, the day-neutral TGMS system is most appropriate for two-line hybrid breeding in many tropical countries of Asia. The successful use of the TGMS system, however, depends on choosing highly region-speciﬁc sources. To use male sterility induced by micronutrient deﬁciency, moisture stress, and soil factors, the range of these factors resulting in complete sterility and maximum fertility needs to be determined precisely. Detailed characterization of EGMS lines in crops other than rice is yet to be done; only after detailed characterization this system can be deployed for commercial hybrid breeding in those crops.

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VI. BREEDING OF EGMS LINES The EGMS mutants identiﬁed or induced seldom possess all the desirable characteristics, such as good plant type, higher yield potential, higher outcrossing, good combining ability, resistance to major pests and diseases, and desirable grain quality, required for their commercialization. Hence, there is a need for speciﬁc breeding of EGMS lines by transferring EGMS genes from the mutants to elite agronomic backgrounds before using them in crop improvement programs. Procedures for breeding EGMS lines are similar to conventional breeding procedures, with one major difference. Since the major attribute to be selected in EGMS breeding is sterility, the F2 and subsequent segregating generations are to be grown under appropriate environmental conditions (i.e., high temperature and long photoperiod) to identify segregants carrying male sterility genes. To advance the generations, the selected segregants carrying male sterility genes are grown either by using remnant seed or by growing ratooned plants in environmental conditions conducive to inducing fertility. Other major aspects of the breeding procedures remain essentially the same. In a two-line hybrid breeding program sources of the EGMS trait can be derived from naturally occurring spontaneous or induced EGMS mutants. The PGMS rice mutant Nongken 58S and TGMS mutant Annog S-1 were identiﬁed as spontaneous mutants, whereas PGMS mutant M 201 and TGMS mutants Norin PL-12, 5460S, and IR32364 TGMS were induced by using mutagens such as gamma rays or ethyl methane sulfonate. Some TGMS mutants, both spontaneous and induced, have also been identiﬁed by Ali (1993), Ali et al. (1995), Satyanarayana et al. (1995), and Pandey et al. (1998) in India. Chinese plant breeders have set standards for EGMS lines to be used in two-line hybrid rice breeding. Besides possessing high yield potential, resistance to major pests and disease, and acceptable grain quality, these lines should also have good outcrossing and combining ability. Their fertility or sterility expression must also meet the following standards (Lu et al., 1998) before they can qualify for use in commercial hybrid seed production: r Among more than 1000 identical plants evaluated, the proportion of sterile and fertile plants should be 100% in the respective sterility and fertility phases. r Pollen and spikelet sterility must be ≥99.5% during the sterility phase and seed set must be ≥30% during the fertility phase. r The critical sterility-inducing temperature for TGMS lines should be in the range of 23.5◦ –28.0◦ C for mean temperature. r The critical light length in PTGMS lines for fertility induction should be less than 13 h.

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The EGMS trait in an elite line is incorporated by hybridization followed by pedigree selection or anther culture. In China, to increase the selection efﬁciency for breeding commercially usable TGMS and PGMS lines, selections in early generations (F2 and F3) are made under long-day/lower temperature and shortday/higher temperature conditions (Yuan, 1992). Breeding materials are planted in summer at higher altitudes, where the temperature (mean 24◦ C) is low at the sensitive stage. Sterile plants with desirable agronomic traits are selected at ﬂowering. The selected sterile plants are ratooned under short-day/higher temperature conditions at low altitude. The ratooned plants under short-day/higher temperature conditions usually show two types of behavior at heading: one type reverts back to fertility and the other type remains sterile. The former is probably PGMS and the latter TGMS (Yuan, 1992; Lu et al., 1992). These are conﬁrmed subsequently by proper characterization. Mou et al. (1998) reviewed breeding work done in China to develop PGMS and TGMS lines in rice for the past 15 years. They concluded that the photoperiodsensitive genic male sterile Nongken 58S, which was used as a donor for PGMS gene(s), was crossed, backcrossed, and multicrossed with various elite indica rice cultivars. Several PGMS and TGMS lines were developed. After a thorough screening over several years, 12 promising lines were selected. The characteristic response of these lines to various photoperiod and temperature ranges was studied in phytotrons and in various environments under ﬁeld conditions. Most of these lines expressed complete male sterility for a period of about 45 days at Wuhan. The sensitive stage in these lines ranged from the pollen mother cell differentiation stage to the ripe pollen stage. The daily critical mean temperature for fertility alteration was different for different TGMS lines. For instance, it was 26.5◦ C for W6154S, W6184S, W6111S, W6417S, and W8103S. It was 25.5◦ C for W9046S and W9056S and 24◦ C for 91607S. Temperature and photoperiod simultaneously controlled the fertility expression of PGMS. The critical mean daily temperature and photoperiod for fertility expression of PTGMS lines W7415S, W9451S, W9461S, and W9593S were 26◦ C and 13.5 h, 24◦ C and 14 h, 24◦ C and 14 h, and 24◦ C and 13 h, respectively. Over the years, an effective procedure for breeding PGMS and TGMS lines has been established and practiced in China (Lu et al., 1993; Mou et al., 1998). Table III summarizes the essential features of this procedure. A breeding cycle could be completed in 5 years by following this procedure. Many EGMS lines with different characteristics have been developed in china by following this procedure. Yang (1997) bred several japonica PGMS lines through F1 anther culture populations in about 3 years’ time. Good stable male sterility and transformation to fertility were observed in these lines. He concluded that anther culture is a rapid and effective way to develop and improve PGMS lines adapted to different

172

VIRMANI AND ILYAS-AHMED Table III Breeding Procedure for TGMS and PGMS Lines in China (Mou et al., 1998)

Step

Time (year)

1

1st

Environment Any

Method

Anther culture approach

2

1st

Any

PGMS and TGMS donors× target parents (elite cultivar) F1 grown

3

2nd

Long day and lower temperature

F2 generation grown and sterile plants selected

4

2nd

Short day and higher temperature

Selected sterile F2 plants ratooned and grown for identiﬁcation of fertile and sterile plants

H1 planting

5

3rd

Long day and lower temperature

F3 plants grown in phytotron or growth chambers for selection of sterile plants

H2 planting

6

3rd

Short day and higher temperature

H3 planting and selection of sterile plants

7

4th

Short day and lower temperature

8

4th

Long day and higher temperature

Selected sterile plants ratooned and grown for distinguishing sterile and fertile plants Planting and harvesting of F4 generation F5 planting in rows and identifying genetic stability for sterility expression

9

5th

Short day and lower temperature

Multiplication in F6 generation

—

10

5th

Combination of different photoperiod and temperature in phytotron

Formal identiﬁcation of PTGMS and TGMS lines in F7generation in phytotron

—

Anther culture H0 planting in ﬁeld

H4 multiplication H5 formal identiﬁcation of PTGMS and TGMS in phytotron

ecological conditions. Zhang and Xue (1996) also used anther culture successfully to develop EGMS lines. Xue et al. (1999) used protoplast culture to develop japonica PGMS lines. Plants were regenerated from protoplast isolated from embryonic suspension cultures of EGMS line N 5047S. A promising PGMS protoplast clone, ZAU 11S, was developed. Its male sterility was conﬁrmed as the photothermosensitive type.

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The effective and efﬁcient breeding of EGMS lines is much more economical, easier, and feasible if the appropriate locations and speciﬁc periods (months) during the year are identiﬁed when the EGMS lines can be multiplied economically with higher seed set under natural conditions in the ﬁeld. Similarly, determining a period for safe and reliable hybrid seed production, under natural ﬁeld conditions, when the EGMS lines remain completely male sterile is also necessary. Such periods for PTGMS/TGMS multiplication and hybrid seed production have been identiﬁed in China (Mou et al., 1998), at IRRI in the Philippines (Lu et al., 1998; Lopez and Virmani, 2000), in India (Siddiq et al., 1998; Lu et al., 1998; Vijaya Kumar et al., 1998), and in Vietnam (Du et al., 1997; Hoan et al., 1998). At the International Rice Research Institute, in the TGMS breeding program, such speciﬁc periods during the year at Los Ba˜nos, Philippines are used for selecting the segregants for complete sterility as well as for advancing generations to transfer the TGMS trait from temperate japonica TGMS mutant Norin PL-12 to desirable lines with indica and tropical japonica genetic backgrounds using the pedigree selection procedure (Lu et al., 1998; Lopez and Virmani, 2000). Seeding of TGMS lines from the 2nd week through the end of November results in ﬂowering during February, when the maximum temperature is less than 30◦ C and, hence, plants express male fertility. For the expression of complete sterility, TGMS lines are seeded from the end of January to early February, which results in ﬂowering in April/May, when the maximum temperature is above 30◦ C. Virmani et al. (1997a) have given a detailed procedure for breeding TGMS lines. It involves selecting a stable and suitable TGMS donor with well-deﬁned critical sterility or fertility points and crossing it with a well-adapted local cultivar. The F2 generation is grown under high temperatures (sterility-inducing phase) and desirable fertile plants are selected from the segregating population. The F3–F5 generations are also grown under the sterility-inducing phase and 8–10 fertile plants are selected from the progeny rows segregating for sterility. A higher number of fertile plants are selected to ensure the probability of selecting at least one heterozygous fertile plant that would segregate for sterility in the next generation. Again, F5–F6 generations are grown under the sterility-inducing phase and the most desirable male sterile plants are selected and ratooned. Ratooned male sterile plants are transferred to phytotrons or glasshouses with day and night temperatures of 27◦ /21◦ C to induce fertility. Seeds are collected from these plants, which revert to fertility, and the next generation is raised under the sterility-inducing phase. Plants that show complete male sterility are selected and characterized under either controlled conditions or ﬁeld conditions. These are the new TGMS lines, which are further evaluated for agronomic characters, combining ability, outcrossing rate, resistance to major pests and diseases, and so on, before selecting some for use in two-line heterosis breeding. By following this procedure, six new TGMS rice lines adapted to tropical conditions were developed that showed complete pollen and spikelet sterility when

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the maximum temperature was higher than 30◦ C 1 to 2 weeks after panicle initiation. Up to 85.5% spikelet fertility was observed, however, when these lines were exposed to 26–29◦ C at their sensitive stage (Lopez and Virmani, 2000). The TGMS lines developed for the tropical conditions at IRRI have been evaluated in several national agricultural research systems. For instance, ﬁeld evaluation of these TGMS lines at Hyderabad, India, revealed that the lines were fully sterile when they ﬂowered during June and July. When ﬂowering occurred from August to February, these TGMS lines were fertile. From August to October, temperature ﬂuctuates depending on the presence or absence of clouds, rainfall, and so on. Generally, however, the temperature is very stable and low from November to February. Hence, at Hyderabad, India, TGMS lines can be multiplied by making them ﬂower during the late wet season and hybrid seed production can be carried out in the regular dry season (Siddiq et al., 1998). Similarly, speciﬁc periods are being determined for TGMS multiplication and hybrid seed production by extensive ﬁeld evaluation in Vietnam, the Philippines, Indonesia, Egypt, and Bangladesh. During the past 15 years in China, PGMS genes from the naturally occurring mutant Nongken 58S have been transferred to several elite indica and japonica backgrounds and around 90 PTGMS/PGMS lines have been developed (Deng and Yuan, 1998). After rigorous screening of these lines for several desirable traits, 15 promising PTGMS/PGMS lines (Table IV) have been identiﬁed (Lu et al., 1998). Yin (1999) has emphasized that identiﬁcation and characterization of EGMS sources are only an initial step. An identiﬁed source has to go through many stages of rigorous evaluation, and few lines ﬁnally qualify as suitable for use in commercial seed production. In China, after a preliminary screening, 62 EGMS lines were selected for further testing and evaluation. Finally, only eight (Table V) of these lines were found suitable for commercial use and they are now being used extensively in hybrid seed production of the released two-line hybrids. Other promising EGMS lines in the pipeline are: GD-25, X0 7S, 1103 S, XW-2 S, K 1405 S, and SE 21 S (Yin, 1999). At the International Rice Research Institute, the TGMS gene from Norin PL-12 has been transferred to several indica backgrounds and six promising TGMS liens (Table VI) have been developed (Lu et al., 1998; Lopez and Virmani, 2000). Efforts to breed EGMS lines began recently in India and Vietnam. Six promising TGMS lines (Table VII) have been developed and characterized in India (Siddiq et al., 1998), and 15 promising lines were identiﬁed in Vietnam (Table VIII) and evaluated for agronomic traits (Hoan et al., 1998). The major constraints to develop and using TGMS lines in the tropics are as follows: r Limited availability of stable TGMS germplasm. r Insufﬁcient training and experience of researchers in breeding and using TGMS lines.

175

ENVIRONMENT-SENSITIVE GENIC MALE STERILITY Table IV Some Elite Photoperiod-Sensitive and Thermosensitive Lines Developed in China (Lu et al., 1998) EGMS line

Subspecies

N 5088S 7001S

Japonica Japonica

Type of reactiona PGMS PGMS

Origin of geneb

Place where developedc

HPGMR HPGMR

Hubei AAS Anhui AAS

Pei’ai 64S

Indica

P(T)GMS

HPGMR

Hunan HRRC

GD 2S KS 1S 6442 S Shuguang 612S W 91607S W 9451S

Indica Indica Indica Indica Indica Indica

TGMS TGMS TGMS TGMS TGMS PGMS

HPGMR HPGMR HPGMR HPGMR HPGMR HPGMR

Guangdong AAS Guangxi AAS Jiangxi AAS Sichuan Univ. Hubei AAS Hubei AAS

3418S 1103S Anxiang S Xiang 125S 9201 HS-1

Indica Indica Indica Indica Indica Indica

PGMS PGMS TGMS TGMS TGMS PGMS

HPGMR HPGMR Annong S Annong S 560S HPGMR

Anhui AAS Wuhan Univ. Hunan HRRC Hunan HRRC Fujian Univ. Fujian Univ.

a

PGMS, photoperiod-sensitive genic male sterility; TGMS, thermosensitive genic male sterility. HPGMR, Hubei photoperiod genic male sterile rice. c AAS, Academy of Agricultural Sciences; HRRC, Hybrid Rice Research Center. b

Table V EGMS Rice Lines Used in Large-Scale Seed Production of Released Two-Line Hybrids in China (Yin, 1999)a

EGMS line

Type

Pedigree

Approved in

No. of hybrids commercialized

7001S Pei’ai 64S N 5088S Xiang 125S 810S Anxiang S

Japonica, PS Indica, TS Japonica, PS Early indica, TS Early indica, TS Early indica, TS

Nongkong 58S/917 Nongkong 58S/Pei’ai 64 Nongkong 58S/Nongfu 26 Annong S-1/6711/Xiang 2B Annong S-1/Suweon 287 Annong S-1/1356

1989 1991 1992 1994 1995 1994

4 5 1 1 1 1

Shu 612S

Medium indica, P(T)S Early indica, TS

8902S/(Minghui 63/CPSLO 17) Annong S-1/Mili Guangluai

1995

1

1993

1

F131S a

PS, photosensitive; TS, thermosensitive.

Table VI Parentage and Agronomic Traits of Some TGMS Lines Bred at IRRI (Lu et al., 1998) Agronomic traits TGMS line

Parentage

Days to 50% ﬂowering

Spikelet fertility (%) when seeded in

Plant height (cm)

Nov. 1994a

Jan. 1995b

Nov 1995a

IR68945-4-33-4-14

Norin PL-12/IR36

78

112

65.4

0

72.8

IR68949-11-5-31

Norin PL-12/BG90-2

96

108

67.2

0

81.7

IR71018-13-73-2

Norin PL-12/IR46830B

88

98

70.2

0

80.6

IR68294-1-18

Norin PL-12/IR62829B

83

125

80.7

0

67.8

IR68297-1-13-15

Norin PL-12/IR9761-61-1R

88

123

84.8

IR68948-4-14-1-4 Norin PL-12

Norin PL-12/IR47686-17-2 Induced mutant of Reimei

82 68

102 98

—c 38.5

0 0

85.8 77.3

0

91.3

a

Maximum temperature from panicle initiation (PI) to heading = 28.0–28.8◦ C. Maximum temperature from PI to heading = 30.6–31.0◦ C. c No data. b

177

ENVIRONMENT-SENSITIVE GENIC MALE STERILITY Table VII Characterization of Promising TGMS Lines Bred in India (Siddiq et al., 1998)

Critical sterility point (◦ C)

Critical fertility point (◦ C)

Max. seed set under fertile phase (%)

ATG-1 ATG-2 ATG-3 ATG-4 ATG-7 ATG-8

26 26 28 27 25 24

22 20 24 24 20 28

75 15 18 75 23 80

Mutated population at DRRa Mutated population at DRR Segregating population at DRR Germplasm maintained at DRR Germplasm maintained at DRR Germplasm maintained at DRR

IR32364 IR68945

25 32

20 28

18 80

IR68949

32

28

80

IR70118

32

28

80

Mutated population at IRRI TGMS gene transferred from Norin PL-12 at IRRI TGMS gene transferred from Norin PL-12 at IRRI TGMS gene transferred from Norin PL-12 at IRRI

TGMS line

a

Source

DRR, Directorate of Rice Research, Hyderabad, India. Table VIII Agronomic Characters of Promising TGMS Lines Bred in Vietnam (Hoan et al., 1998) Days to 50% heading

Plant height (cm)

Tillers/ hill

Leaves/ plant

Spikelets/ panicle

1000-grain weight (g)

VNTGMS 3 VNTGMS 6 VNTGMS 7

75 80 86

81.5 82.6 85.5

4.2 6.3 6.0

14.0 15.0 15.5

102 106 99

25.0 24.0 25.0

VNTGMS 8 VNTGMS 9 VNTGMS 10 VNTGMS 11 TGMS 8 TGMS 3

82 72 75 70 89 85

90.5 65.8 105.2 95.5 83.2 75.2

5.8 4.8 5.5 5.2 8.4 9.1

15.5 13.5 13.5 15.2 15.2 13.8

112 79 115 114 176 132

25.0 24.0 24.0 25.0 23.4 22.6

TGMS 1 TGMS 7 TGMS 5 CR203 1S 15S

84 75 50 82 76 65

74.9 78.3 61.2 87.3 92.6 71.9

6.8 7.3 6.5 5.2 4.5 3.9

13.4 13.7 12.1 14.0 15.5 13.1

213 134 114 147 309 152

24.1 22.4 23.1 23.0 — —

TGMS line

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VIRMANI AND ILYAS-AHMED

VII. USE OF THE EGMS Environment-sensitive genic male sterility can be used successfully to develop and commercialize two-line hybrids. The concept of the two-line system of hybrid breeding was postulated by Yuan (1987) in rice by using photoperiodsensitive genic male sterility. In this system, selected EGMS lines are multiplied by selﬁng by growing them in environmental conditions that induce fertility. Seed so multiplied is used under environmental conditions that induce complete male sterility along with the line designated as the male parent for hybrid seed production. The two-line system has certain distinct advantages over the three-line system of hybrid breeding: 1. In the two-line system, much more of the genetic variability and diversity present in a species can be used to develop hybrids than in the three-line system, in which only those genotypes having restorer genes can be used as male parents. Similarly, only those with maintainer genes can be converted into female parents. On average, the frequency of maintainers and restorers in rice germplasm is 0–15%. So, at most, only 30% of the genetic diversity present in a species can be used by the three-line system, whereas the two-line system has no such restrictions. Choice of parents in developing heterotic hybrids is broadened (Virmani, 1994). Any genotype with good combining ability can be used as a male parent. Because wider genetic diversity is deployed, the frequency of heterotic hybrids among experimental two-line hybrids is higher (Virmani, 1996; Lu et al., 1998). 2. The seed production system is simple and efﬁcient compared with the threeline system because only two lines are involved in EGMS seed multiplication and in hybrid seed production. EGMS lines are easier to multiply (Virmani, 1994) as compared to CMS lines. The ﬁeld area ratio of EGMS multiplication, hybrid seed production, and commercial cultivation of the two-line system is 1:100:15,000 as compared to the ratio of 1:50:6,000 in three-line system (Tran and Nguyen, 1998). 3. The negative effects on agronomic characters associated with sterilityinducing cytoplasm, including the risk of a sudden outbreak of an epidemic associated with the large-scale use of a unitary source of cytoplasm, are avoided altogether. 4. Higher seed yields of female lines are obtained in the fertile phase of selfmultiplication than in the A x B seed plots of the three-line system, thereby making hybrid seed production more efﬁcient and cost effective. 5. In rice, the two-line system is speciﬁcally suited for developing hybrids in japonica types and in high-quality basmati types. The frequency of restorers in these two groups is very low. In the two-line system of heterosis breeding, any genotype with good combining ability can be used as a male parent.

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6. The magnitude of heterosis in two-line hybrids is 5–10% higher than in three-line hybrids (Yuan, 1997) and this system gives higher frequency of heterotic hybrids compared to the CMS system (Virmani, 1996) since there are less restrictions in choice of parents.

A. TWO-LINE RICE HYBRIDS IN CHINA Intensive efforts in China since 1986 to develop and evaluate two-line hybrids in rice resulted in a very promising, pioneer two-line hybrid, Pie’ai 64S/Teqing (Liao, 1994), which became popular in Yunnan Province in 1994 (Yuan, 1998). During the past 15 years, more than 60 two-line hybrids developed in different provinces in China have been evaluated in regional and national trials. Based on extensive trials in China’s rice-growing regions, 14 hybrids were approved and released for general cultivation (Table IX). The area under two-line hybrids in China has increased steadily from 27,000 ha in 1993 to 270,000 ha in 1997 (Mao and Deng, 1993; Yuan, 1998). According to Yin (1999), the area under two-line rice hybrids in 1998 was 433,000 ha and it is estimated that, in 1999, 1.286 million ha have been planted to two-line rice hybrids. Thus, from 1993 to 1999, two-line rice hybrids were cultivated in a cumulative are of 2.36 million ha. At IRRI and in some South and Southeast Asian tropical countries such as India, Vietnam, and the Philippines, two-line rice hybrids are at the experimental Table IX Two-Line Rice Hybrids Released in China (Yin, 1999) Hybrid

Pedigree

Type

Year of release

Peiliangyou Teqing

Peiai 64S/Teqing

Indica

1994

70 you 04 70 you 9 Huajinza 11 Ejinza 1 Liangyou 681 Peiliangyou Shanqing

7001S/Xiushui 04 7001S/Wanhui 9 7001S/1514 N5088S/R 187 Shu 612S/Shanqing 11 Peiai 64S/Shanqing 11

Japonica Japonica Japonica Japonica Indica Indica

1994 1994 1995 1995 1997 1997

Peiliangyou Yuhong 70 you Shuangjiu Peiliangyou Shuangjiu Xiangliangyou 68 8 Liangyou 100 Tainliangyou 402 Anliangyou 25

Peiai 64S/ 7001S/Shuangjiu Peiai 64S/77 Xiang 125S/D 68 810S/D 100 F131S/R 402 Anxiang S/Zao 25

India Japonica Japonica Indica Indica Indica Indica

1997 1997 1998 1998 1998 1998 1998

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and evaluation stage. It may take another 5 years for their commercialization in these countries. In China, as well as in other countries, emphasis is being given to developing using intersubspeciﬁc hybrids because their magnitude of heterosis is reported to be higher (Yuan, 1994, 1997). The occurrence of sterility in intersubspeciﬁc crosses was a major problem earlier, until Ikehashi and Araki (1986) discovered and used sterility-neutralizing wide compatibility. Use of the CMS system to develop indica/japonica hybrids is constrained by the negligible frequency of restorer lines among japonica germplasm. Therefore, the EGMS system provides a practical solution for developing indica/japonica rice hybrids.

B. TWO-LINE HYBRIDS IN OTHER CROPS Because of the success of the two-line hybrid breeding system in rice, efforts have been made to use it in other crops such as sorghum, brassica, wheat, and maize. In sorghum, EGMS line Xiangnuoliang-S was used by the Hunan Soil and Fertilizer Research Institute in China to cross with the male line Xiang 10721 to develop sorghum hybrid Xiangnuoliang-1. It was planted on 2500 ha in the provinces of Hunan, Sichuan, and Guizhou in 1996 (cf. Yuan, 1997). This twoline sorghum hybrid has yielded 10% more than the three-line sorghum hybrids. It is early maturing and has good-quality grains, wide adaptability, and strong ratooning ability. Another two-line sorghum Hybrid, SSH-1, was developed in India by Murty (1995). The female line was CS 3541 A2, which is sterile during winter but fertile in other seasons. The male parent was SPV 489. This hybrid has all the features of sweet sorghum with a juicy stem, although both of its parents are of a grain type without sweet stems. SSH-1 has moderately good grain yield and its juice content and brix value are comparable with those of the released sweet sorghum variety SSV-84. On-farm trials of this hybrid are being conducted now before its release. In Brassica, seven two-line hybrids were developed by using the EGMS line Xiangyou 91S in China (cf. Yuan 1997). These hybrids were evaluated extensively in 1995 and yielded about 20% more than conventional inbred varieties of Brassica. The good yield potential of these hybrids was also demonstrated in 1996 in largescale trials in China. These Brassica hybrids were developed and tested by the Hunan Crop Research Institute. The Hunan Agricultural University has developed the two-line wheat hybrid E-you 137. This hybrid has shown its superiority at a low seeding rate (45 kg/ha) by yielding 20% more than conventional varieties of wheat (cf. Yuan, 1997). EGMS lines have been developed in soybean (Wei et al., 1997) and experimental hybrids are under evaluation in China.

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The above information indicates that use of the EGMS system will probably extend beyond China for breeding hybrids in rice as well as in other crops.

C. SEED PRODUCTION OF TWO-LINE RICE HYBRIDS Seed production of two-line rice hybrids is not much different from that of three-line hybrids. The important consideration for two-line hybrid seed production is the precise determination of the stable sterility-inducing period for a given sterile line at a certain location. All the plants of the sterile line must ﬂower only during the period conducive to complete male sterility and ﬂowering process must be completed 15 days before the end of this period (Lu et al., 1998). Currently, seed yield obtained in two-line hybrid seed production plots in China is around 3.0 t/ha, which is comparable with seed yields obtained for three-line hybrids. EGMS lines are multiplied at appropriate locations and in seasons where stable fertility-inducing environmental conditions occur for a continuous period of 30 days or more. EGMS lines are planted at such a time that the sensitive stage of the crop falls in the middle of the stable fertility inducing period. If photoperiod and temperature are appropriate, sufﬁciently high seed yields are obtained. For instance, seed multiplication yields of PGMS lines N 5088S and 7001S reached 4.5 t/ha with good management of seed plots (Lu et al., 1998). To multiply TGMS lines, the optimum temperature must be between the critical temperature for fertility expression and the critical temperature for cold injury. Indica TGMS lines generally require a mean temperature regime of 22–23.5◦ C. At IRRI, seed multiplication of TGMS lines is done when the daily temperature ranges between maximums of 27◦ –29◦ C and minimums of 20–22◦ C (Lopez and Virmani, 2000). Lu (1992) reported that TGMS lines are most responsive to temperature at the ﬁeld surface (0 cm). Accordingly, when the TGMS lines in seed production plots reach the secondary rachis branch primordial differentiation stage, cool water from the deep layers of reservoirs is used to irrigate the ﬁeld continuously for 10– 15 days. The water temperature is about 19–20◦ C at the entrance of the ﬁeld and 24◦ C at the exit. This method has successfully improved seed yields in TGMS multiplication plots in China. For example, Zhou and Liu (1993) reported seed yields of 3.75 to 4.0 t/ha for TGMS line Pei’ai 64S in Guangdong Province by the following this method. Maintenance of the purity of EGMS lines is of paramount importance for developing and using two-line hybrids. Certain special procedures need to be adopted to maintain the purity of EGMS lines. When EGMS lines are reproduced for several generations without any selection, plants in a population will segregate for critical temperature point and the proportion of plants that will require a higher critical temperature will increase. As a result, the average critical temperature value in

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Figure 4 Procedure for nucleus and breeder seed production of TGMS lines (Virmani et al., 1997a).

a population will become higher and higher. Ultimately, this makes TGMS lines useless for breeding purposes. To overcomes this problem, Yuan (1994; Deng and Fu, 1998) proposed a system of nucleus and breeder seed production of EGMS lines that involves r selecting about 100 plants with the typical characteristics of the original EGMS lines and planting them separately in pots r transferring the pots at the sensitive stage into a glasshouse with a controlled temperature or phytotron where appropriate temperature and photoperiod are set r monitoring pollen sterility critically at the time of heading, and selecting plants with 100% sterility r ratooning selected plants in suitable short-photoperiod/low-temperature conditions and collecting their selfed seed (nucleus seed)

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r bulking nucleus seed from each selected plant in a row and comparing the agronomic characters and fertility or sterility traits of the selected rows or lines with those of the original line r selecting those lines that are identical/similar to the original ones and harvesting. The harvested seed is also called nucleus seed r multiplying nucleus seed to produced breeder seed and multiplying breeder seed to produce foundation seed of EGMS lines Foundation seed is used directly to produce hybrid seed. By following this procedure, the purity of EGMS lines can be maintained and they can be used effectively to develop two-line hybrids. Virmani et al. (1997b) have also described a procedure based on ﬁeld testing for maintaining the purity of and producing nucleus seed of TGMS lines in the tropics (Fig. 4).

VIII. FUTURE OUTLOOK In conventional inbred breeding programs, sterility, when encountered in breeding nurseries, is considered undesirable and, therefore, male sterile plants occurring in segregating progenies during the changing season and varying locations and altitudes are usually rejected. The experience in rice have given a new impetus to this phenomenon and plant breeders worldwide have recognized its signiﬁcance in hybrid breeding. Concerted efforts are being made to develop and use the EGMS system for developing two-line hybrids. In the coming decade, these would play important role in enhancing the yield of crop plants by exploiting heterosis. Therefore, greater attention should be paid for development and use of EGMS system in crops. The efﬁciency of breeding EGMS lines can be increased by developing genetically diverse EGMS populations using conventional genetic male sterilityfacilitated recurrent selection (Bharaj and Virmani, 1994). Such populations are being developed at the International Rice Research Institute, Philippines, for sharing with national rice programs to help them extract locally adaptable improved thermosensitive genic male sterile lines. Marker-aided selection can be used to expedite the breeding of EGMS lines, since the commonly used EGMS genes in rice from the PGMS mutant Nongken 58S (such as pms1 and pms2) and TGMS mutants 5460S, Norin PL-12, and IR32364 TGMS (such as tms1, tms2, and tms3, respectively) have been linked to molecular markers. Currently, EGMS segregants in the breeding populations are identiﬁed by growing them in the environmental conditions that induce male sterility. The sterile segregants are then ratooned and grown in the environmental conditions that

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induce fertility to advance the generations. This procedure is very cumbersome. With marker-aided selection, there is no need to grow the breeding populations during the “sterility phase” to identify sterile segregants. They can simply be identiﬁed by the presence of linked molecular markers and selected. The selected segregants can be grown in the “fertility phase” to select other agronomic traits and to advance the generations. Thus, by using marked-aided selection, the time required for breeding EGMS lines can be reduced considerably. The linked molecular markers for the PGMS and TGMS genes in rice can also be used to pyramid these genes and develop populations from which diverse EGMS lines having a wide range of critical sterility of fertility points and critical light lengths can be extracted. From such lines, rice breeders working with different locally adaptable EGMS lines could develop two-line rice hybrids locally. To overcome the problem of seed impurity due to selﬁng of TGMS lines in rice, caused by unexpected drops in temperature in the hybrid seed production phase, the phenol reaction gene (ph gene) may be incorporated into TGMS lines. Some rice varieties possess this gene, by which paddy grains, when treated with solutions of phenolic compounds, such as phenol, catechol, hydroquinone, pyrogallol, and tyrosine, become uniformly black. A monogenic recessive gene controls the expression of this trait. If the ph gene is recessive and if the pollen parent has this gene in the homozygous dominant form, then the selfed seed of the TGMS line from the hybrid seed production plot can be identiﬁed easily by the appearance of black grains after staining treatment. The true hybrid F1 seed material appears brownish white after staining. These two classes of grains can then be separated easily by a color-sorting machine (Virmani and Maruyama, 1995). The EGMS system is the only option for developing F1 hybrids in situations where restorer genes are either absent or infrequent among the elite inbred lines and, hence, the CMS system cannot be used effectively. Such a situation is known to exist in wheat and japonica and basmati rice varieties. Serious efforts should be made to develop EGMS lines in these corps to develop commercial hybrids. In the past, environmental inﬂuences on some CMS lines in pearl millet, sorghum, and rice have been considered impediments to their use in developing commercial hybrids. Such lines should be reevaluated critically under different temperature and daylength conditions to ﬁnd out whether signiﬁcant fertility can be induced in them in certain environments. If so, their multiplication would also become easier and more economical. Perhaps the environmentally inﬂuenced CMS system could also turn out to be an asset rather than a liability in hybrid breeding. From the foregoing information, it is abundantly clear that EGMS is an additional genetic tool with a considerable potential for developing and commercializing hybrid varieties. We hope that this system will be used widely during the 21st century to develop heterotic hybrids in crops that would contribute to food security and environmental protection.

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IX. SUMMARY AND CONCLUSIONS EGMS is a novel genetic tool in crop plants that can be used successfully to develop and commercialize two-line hybrids that have distinct advantages over the current three-line hybrids developed via the CMS system. This phenomenon has been reported in several major crop plants during the past 3 decades, but has been used only in rice crop to develop commercial hybrids. Recently, hybrids in some other crops (such as wheat, sorghum, maize, and Brassica) are also being developed in China using this tool. Elsewhere, efforts in this directions have been scanty. In most cases, EGMS has been reported to be affected by temperature, photoperiod, and an interaction of the two. Some reports, however, also mention sensitivity to micronutrient (boron, copper, or molybdenum) deﬁciency, soil factors, and the like. EGMS lines are currently classiﬁed based on their sensitivity to particular environmental factor(s). Both naturally occurring spontaneous and induced mutants for this trait have been widely reported in crops. Initial screening and identiﬁcation are done in the ﬁeld under appropriate environmental conditions, followed by rigorous screening and characterization for critical sterility or fertility-inducing conditions in growth chambers or phytotrons. Work on developing and using this tool to develop hybrids has advanced considerably in rice. Therefore, the available information on various aspects in this crop is reviewed. Photoperiod and photothermosensitive genic male sterility in rice are reported to be controlled by monogenic or digenic recessive genes and the inﬂuence of minor genes as well. Two PGMS genes (pms1 and pms2) have been identiﬁed and located on chromosomes 7 and 3, respectively. Thermosensitive genic male sterility in rice has also been reported to be controlled by a single recessive gene. Three or more nonallelic genes (tms1, tms2, and tms3) have been identiﬁed and these are located on chromosomes 8, 7, and 6, respectively. The EGMS lines need to be characterized with respect to, for example, their critical sterility/fertility points, critical light length, temperature range for photoperiod sensitivity before using them in a breeding program. With EGMS genes from the naturally occurring spontaneous or induced mutants, several agronomically elite, commercially usable EGMS lines in rice and some other crops (such as wheat, maize, sorghum, and Brassica) have been developed in China. Elite TGMS rice lines are also being developed in the Philippines (at IRRI), India, and Vietnam, and elite TGMS sorghum lines are being developed in India. To breed EGMS lines, methods such as hybridization followed by pedigree selection, anther culture, and mutagenesis have been used with screening and selection for the trait under appropriate environmental conditions either in the ﬁeld or in the phytotron or growth chambers. The critical sterility/fertility points for temperature

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and photoperiod vary depending on the genetic background in which the EGMS gene is transferred. For commercial use of EGMS lines, safe periods during the growing seasons for hybrid seed production and EGMS multiplication need to be identiﬁed in various regions and locations in a country. There is a need to reevaluate CMS lines critically under different daylengths and temperature regimes. If certain environmental conditions can induce signiﬁcant fertility consistently, this system can also be useful for developing two-line hybrids. Two-line rice hybrids were reported to be cultivated on 1.28 million ha in China during 1999. Recently, China has also commercialized such hybrids in wheat, maize, and Brassica. Following these developments, considerable area is expected to be covered by EGMS-based two-line hybrids in different crops in China during the next 25 years. At IRRI, Philippines, and in several Asian countries, prospects for developing and using TGMS-based two-line rice hybrids are being explored extensively and these hybrids are expected to commercialized within the next 10 years. Intensive, sustained, and concerted efforts are needed to precisely understand the genetics, physiology, biochemistry, seed technology, and breeding aspects of this novel source of male sterility in several crops worldwide so that effective use of this genetic tool can take place in crop improvement programs to meet the ever-increasing food and feed demands caused by rapidly expanding populations and increased incomes.

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Index A Acetocarmine, pollen fertility determination, 150 Aceto-orcein, pollen fertility determination, 150 Acinetobacter, petroleum hydrocarbon degradation, 59 Alﬁsol, erosion and crop yield, 11, 14, 17, 20, 36–37 Ames assay, soil toxicity, 87 B Barley environment-sensitive genic male sterility, 143 soil erosion effects on yield, 27, 30 Biodegradation, see Petroleum hydrocarbon bioremediation BTEX hydrocarbons, bioremediation pathways, 61–62, 65–66 C Cabbage, environment-sensitive genic male sterility, 142 Candida, petroleum hydrocarbon degradation, 59 CFP, see Critical fertility point Chickpea cultivar groups, 111 East Africa modern cropping systems, 131–132 natural history under domestication, 114–115 traditional cropping systems, 125–126 ﬂowering time drought-escape with early ﬂowering, 122–124 genetic control early-ﬂowering genotypes, 117–119 ﬁeld productivity effects of PPD alleles, 121

late-ﬂowering genotypes, 118 marker tags, 133 variations, 116 grain weight correlation, 119–120 kabuli-seed phenotype in early ﬂowering, 120, 130 latitude and temperature effects, 116 overview of factors, 115–116 photothermal modeling, 121–122 pod-set phenotype effects, 120 gene mapping, 109, 133 growing season, 109–110 Indian subcontinent fallow replacement in rice-based cropping systems, 130–131 growth season, 129–130 modern cropping systems, 128–131 natural history under domestication, 114–115 traditional cropping systems, 125–126 tropical cultivation, 129–130 yield and production trends, 128–129 market and demand, 108–109 Mediterranean basin growth period, 127–128 modern cropping systems, 126–128 natural history under domestication, 112–114 traditional cropping systems, 125 winter sowing, 127 origins, 110–111 production yields, 108 semiarid environment cropping, 109, 124–132 CLL, see Critical light length Corn environment-sensitive genic male sterility, 142, 148–149 soil erosion effects on yields mean yield declines, 18–20 soil order effects, 20, 36 technological impact, 10, 20, 36 trends in yield, 5, 10

197

198

INDEX

Cotton environment-sensitive genic male sterility, 145 soil erosion effects on yield, 24, 27 Critical fertility point (CFP), environment-sensitive genic male sterility, 159, 164–166, 168–169 Critical light length (CLL), environment-sensitive genic male sterility, 159, 161, 164 Critical sterility point (CSP), environment-sensitive genic male sterility, 159–161, 164–166, 168–169 Crop yield factors affecting, 6–7 ﬂowering time effects in chickpea, 119–124, 133 representative function, 5 soil erosion effects assumptions of study, 32–33 barley, 27, 30 confounding factors experimental plot placement, 39 soil type, 39–40 weather, 38–39 corn, 18–20 cotton, 24, 27 differential relationships between degradation and yields, 34 hay and fodder crops, 27 implications for research and policy applications, 41–42 decline in crop yields, 36–37 economic impact, 37–38, 41 management practices over study time period, 33–34 oat, 27, 30 overview, 6, 8 potato, 27 potential losses Canada, 31–32, 35 North America, 30–32, 35 prospects for study, 40–42 sorghum, 27, 30 soybean, 22, 24 sugar beet, 27 technological advances and impact, 10, 20, 35–36 wheat, 22, 36 soil feedback, 6 soil productivity measure, 3, 5, 39–40

Crude oil, see Petroleum hydrocarbon bioremediation CSP, see Critical sterility point Cytoplasmic-genic male sterility, see Environment-sensitive genic male sterility D Desulfobacterium, petroleum hydrocarbon degradation, 66 Desulfovibrio, petroleum hydrocarbon degradation, 64 Diesel, see Petroleum hydrocarbon bioremediation Drought, escape with early chickpea ﬂowering, 122–124 E Earthworm, soil toxicity assays, 82–83 efl-1, early ﬂowering gene in chickpea, 117–118, 129 EGMS, see Environment-sensitive genic male sterility Environment-sensitive genic male sterility (EGMS) characterization critical fertility point, 159, 164–166, 168–169 critical light length, 159, 161, 164 critical sterility point, 159–161, 164–166, 168–169 ﬁeld characterization, 162–164 growth chambers, 162–163 sensitive stage characterization, 159–160 temperature role, 161, 167–168 thermosensitive genic male sterility physical-cum-morphological index, 166 tracking method, 166–167 crop type distribution barley, 143 cabbage, 142 corn, 142, 148–149 cotton, 145 musk melon, 143 onion, 144–145 overview, 140–141 pearl millet, 145 pepper, 141–142 rape, 144, 146, 180 sesame, 143

199

INDEX sorghum, 143–145, 180 soybean, 144 sugarbeet, 145 tomato, 142 watermelon, 143 wheat, 142–143, 145–146, 151, 180 cytoplasmic-genic male sterility sensitive to environmental factors, 144–148 environmental factors, 146–148 genotype corn, 148–149 heredity, 154–156 identiﬁcation ﬁeld assays, 149 growth chambers, 149 phenotypic alterations, 150 pollen fertility determination, 150–161 micronutrient deﬁciency-induced male sterility, 144, 153–154 photoperiod-sensitive genic male sterility, 151–152 photothermosensitive genic male sterility, 153 research prospects, 183–186 status, 140–141, 185 rice breeding overview, 170 photoperiod-sensitive genic male sterility lines, 171–174 photothermosensitive genic male sterility lines, 174 standards for mutant lines, 170 thermosensitive genic male sterility lines, 171–173 two-line hybrid breeding, 170 classiﬁcation of mutants, 151–153 development of new lines, 174–177, 185–186 genes, 185 identiﬁcation of mutants, 149–151 linkage with molecular markers, 156–159, 183–184 mutant types, 143 photoperiod sensitivity heredity, 154–155 photothermosensitive genic male sterility line classiﬁcation, 165–166 temperature effects, 143, 146 thermosensitive genic male sterility classiﬁcation, 168–169

thermosensitivity heredity, 156 two-line hybrids China, 179–180 seed production, 181–183 thermosensitive genic male sterility, 152–153, 166–169 two-line hybrid breeding, 170, 178–183 Erosion, see Soil erosion F Flowering, see Chickpea Fodder crops, soil erosion effects on yield, 27 G Gas chromatography (GC), petroleum hydrocarbon assay, 88–89 Gas chromatography isotope ratio mass spectrometry (GCIRMS), petroleum hydrocarbon assay, 89 Gasoline, see Petroleum hydrocarbon bioremediation GC, see Gas chromatography GCIRMS, see Gas chromatography isotope ratio mass spectrometry Genic male sterility, see Environment-sensitive genic male sterility Geobacter metallireducens, petroleum hydrocarbon degradation, 66 Gravimetry, petroleum hydrocarbon assay, 87–88 H Hay, soil erosion effects on yield, 27 Heating oil, see Petroleum hydrocarbon bioremediation I Infrared spectrometry (IR), petroleum hydrocarbon assay, 87–88 Iodine-potassium iodide, pollen fertility determination, 150 IR, see Infrared spectrometry J Jet fuel, see Petroleum hydrocarbon bioremediation

200

INDEX K

Kerosene, see Petroleum hydrocarbon bioremediation M Maize, see Corn Male sterility, see also Environment-sensitive genic male sterility economic impact, 139 hybrid variety cultivation, 140 types, 140 Mass spectrometry (MS), petroleum hydrocarbon assay, 88–89 Micronutrient deﬁciency-induced male sterility, 144, 153–154 Microtox assay, soil toxicity, 82, 87 Mollisol, erosion and crop yield, 11, 14, 17, 20, 36–37 Motor oil, see Petroleum hydrocarbon bioremediation MS, see Mass spectrometry Musk melon, environment-sensitive genic male sterility, 143 N Nocardia, petroleum hydrocarbon degradation, 59 O Oat, soil erosion effects on yield, 27, 30 Onion, environment-sensitive genic male sterility, 144–145 P Pearl millet, environment-sensitive genic male sterility, 145 Penicillium, petroleum hydrocarbon degradation, 59 Pepper, environment-sensitive genic male sterility, 141–142 Petroleum hydrocarbon (PHC) bioremediation analytical techniques extraction, 87 gas chromatography, 88–89

gas chromatography isotope ratio mass spectrometry, 89 gravimetry, 87–88 infrared spectrometry, 87–88 mass spectrometry, 88–89 sampling, 89–90 culture studies Acinetobacter, 59 Candida, 59 Desulfobacterium, 66 Desulfovibrio, 64 Geobacter metallireducens, 66 isolation, 55 Nocardia, 59 Penicillium, 59 Pseudomonas, 59, 64–65, 67 Rhodococcus, 59, 67 Syntrophus aciditrophicus, 65 Xanthobacter, 65 limiting factors, 54–55 metabolic pathways alkanes, 60, 64 alkenes, 60, 64 cycloalkanes, 61, 65 heterocyclic compounds, 63, 66–67 isoalkanes, 60–61, 64–65 monoaromatic BTEX hydrocarbons, 61–62, 65–66 overview, 90 polyaromatic hydrocarbons, 62–63, 66 occurence in soils and sources, 58–59 oxygen diffusive transport, 92 properties of crude oils and fuel hydrocarbons bioconcentration factors, 58 composition, 55, 57–58 densities, 56–57 hydrocarbon classes, 55–58 octanol/water partition coefﬁcients, 56, 58 solubilities, 56, 58 vapor pressure, 56, 58 prospects for study, 93 rates of degradation, 90–91 research interest, 54 toxicity studies alkanes, 86 Ames assay, 87 bioassays, 82, 92 crude oils, 82–84 diesel, 84–85 earthworm assays, 82–83

201

INDEX Microtox assay, 82, 87 plant germination assays, 82–83, 87 polyaromatic hydrocarbons, 83, 85, 87 regulatory requirements, 87 unsaturated soil studies bacterial inoculation, 79–80 Bunker C, 71–72, 75 crude oils, 68–70, 73–74, 80–81 diesel, 70–71, 74–75 evaporation and weathering, 76–77 gasoline, 74 heaing oil, 71, 75 jet fuel, 71, 75 microbial growth effects, 78 motor oil, 72, 75 nutrient addition, 67, 73, 77–78 persistence of compounds, 80–81, 90–92 surfactant utlization, 78–79 PHC bioremediation, see Petroleum hydrocarbon bioremediation Photoperiod-sensitive genic male sterility, 151–152, 171–174 Photothermosensitive genic male sterility, 153, 165–166 Potato, soil erosion effects on yield, 27 PPD, early ﬂowering gene in chickpea, 118–119, 121 Productivity, soil, 3 Pseudomonas, petroleum hydrocarbon degradation, 59, 64–65, 67 R Rape, environment-sensitive genic male sterility, 144, 146, 180 Rhodococcus, petroleum hydrocarbon degradation, 59, 67 Rice chickpea fallow replacement in rice-based cropping systems, 130–131 environment-sensitive genic male sterility, see also Environment-sensitive genic male sterility breeding overview, 170 photoperiod-sensitive genic male sterility lines, 171–174 photothermosensitive genic male sterility lines, 174 standards for mutant lines, 170

thermosensitive genic male sterility lines, 171–173 two-line hybrid breeding, 170 classiﬁcation of mutants, 151–153 development of new lines, 174–177, 185–186 genes, 185 identiﬁcation of mutants, 149–151 linkage with molecular markers, 156–159, 183–184 mutant types, 143 photoperiod sensitivity heredity, 154–155 photothermosensitive genic male sterility line classiﬁcation, 165–166 temperature effects, 143, 146 thermosensitive genic male sterility classiﬁcation, 168–169 thermosensitivity heredity, 156 two-line hybrids China, 179–180 seed production, 181–183 S Sesame, environment-sensitive genic male sterility, 143 Soil contamination, see Petroleum hydrocarbon bioremediation Soil erosion crop yield effects assumptions of study, 32–33 barley, 27, 30 confounding factors experimental plot placement, 39 soil type, 39–40 weather, 38–39 corn, 18–20 cotton, 24, 27 differential relationships between degradation and yields, 34 hay and fodder crops, 27 implications for research and policy applications, 41–42 decline in crop yields, 36–37 economic impact, 37–38, 41 management practices over study time period, 33–34 oat, 27, 30 overview, 6, 8 potato, 27

202

INDEX

Soil erosion (continued ) potential losses Canada, 31–32, 35 North America, 30–32, 35 prospects for study, 40–42 sorghum, 27, 30 soybean, 22, 24 sugar beet, 27 technological advances and impact, 10, 20, 35–36 wheat, 22, 36 data for productivity impact studies analysis and interpretation of data, 9–10, 14 economic impact data, 10 geographic locations, 10–11 soil orders in database, 11 sources of data, 8–9 effects, 5 formation rate of soil, 3 human activities in degradation, 2–4 rates Canada, 17–18 North America, 11, 14 soil order effects, 14, 17, 36–37 tolerable rates, 14, 17 reversible versus irreversible degradation, 4 Sorghum environment-sensitive genic male sterility, 143–145, 180 soil erosion effects on yield, 27, 30 Soybean environment-sensitive genic male sterility, 144 soil erosion effects on yield, 22, 24 Sugar beet environment-sensitive genic male sterility, 145 soil erosion effects on yield, 27 Surfactant, utilization in petroleum hydrocarbon bioremediation, 78–79 Syntrophus aciditrophicus, petroleum hydrocarbon degradation, 65

T Tetrazolium, pollen fertility determination, 151 Thermosensitive genic male sterility, 152–153, 166–169, 171–173 Tomato, environment-sensitive genic male sterility, 142 Two-line hybrid, genic male sterility advantages over three-line hybrids, 178–179 breeding, 170 rape, 180 rice hybrids China, 179–180 seed production, 181–183 sorghum, 180 wheat, 180 U Ultisol, erosion and crop yield, 11, 14, 17, 20, 36–37 V VOC, see Volatile organic carbon Volatile organic carbon (VOC), loss from unsaturated soils, 76–77 W Watermelon, environment-sensitive genic male sterility, 143 Wheat environment-sensitive genic male sterility, 142–143, 145–146, 151, 180 soil erosion effects on yield, 22, 36 X Xanthobacter, petroleum hydrocarbon degradation, 65 Y Yield, see Crop yield