Advances in Agronomy, Volume 73

Agronomy DVANCES I N VOLUME 73 Advisory Board Martin Alexander Ronald Phillips Cornell University University of...

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Agronomy

DVANCES I N

VOLUME

73

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, Chairman 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

73

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

INTERACTIONS AMONG ROOT-INHABITING FUNGI AND THEIR IMPLICATIONS FOR BIOLOGICAL CONTROL OF ROOT PATHOGENS David M. Sylvia and Dan O. Chellemi I. II. III. IV. V.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Functional Diversity in the Root Zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interactions among Root-Inhabiting Fungi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Opportunities for Pest Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research Priorities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 3 13 17 21 24

DWARFING GENES IN PLANT IMPROVEMENT S. C. K. Milach and L. C. Federizzi I. II. III. IV. V. VI. VII.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Biochemical Basis of the Dwarf Phenotype . . . . . . . . . . . . . . . . . . . . . . . . Dwarfing Genes and Their Use for Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . Breeding Challenges and Varieties Developed . . . . . . . . . . . . . . . . . . . . . . . . . . Pleiotropic Effects of Dwarfing Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular Mapping of Dwarfing Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36 38 43 45 48 51 55 56

A REVIEW OF THE EFFECT OF N FERTILIZER TYPE ON GASEOUS EMISSIONS Roland Harrison and J. Webb I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. The Processes Controlling Emissions of Nitrogen Gases from Fertilizers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Measurements of Ammonia Emission Following Nitrogen Fertilizer Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

67 69 78

vi

CONTENTS

IV. Ammonia Emission Factors for Nitrogen Fertilizers . . . . . . . . . . . . . . . . . . . V. Measurements of Nitrous Oxide Emissions Following Nitrogen Fertilizer Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Nitrous Oxide Emission Factors for Nitrogen Fertilizers. . . . . . . . . . . . . . VII. Nitric Oxide Emissions from Nitrogen Fertilizers . . . . . . . . . . . . . . . . . . . . . VIII. Summary and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

88 90 97 99 99 103

RHIZOBIA IN THE FIELD N. Amarger I. II. III. IV. V. VI.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diversity in Rhizobia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rhizobium Systematics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Natural Populations of Rhizobia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction of Rhizobia into Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

110 112 123 129 143 147 148

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

169

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

N. AMARGER (109), Laboratoire de Microbiologie des Sols, Institut National de la Recherche Agronomique, 21065 Dijon, France DAN O. CHELLEMI (1), USDA, ARS, Horticultural Research Laboratory, Ft. Pierce, Florida 34945 L. C. FEDERIZZI (35), Universidade Federal do Rio Grande do Sul, Faculdade de Agronomia, Departamento de Plantas de Lavoura, Porto Alegre, Brazil ROLAND HARRISON (65), ADAS Consulting Ltd., ADAS Boxworth, Boxworth, Cambridge CB3 8NN, United Kingdom S. C. K. MILACH (35), Universidade Federal do Rio Grande do Sul, Faculdade de Agronomia, Departamento de Plantas de Lavoura, Porto Alegre, Brazil DAVID M. SYLVIA (1), Soil and Water Science Department, University of Florida, Gainesville, Florida 32611 J. WEBB (65), ADAS Consulting Ltd., ADAS Wolverhampton, Wolverhampton WV6 8TQ, United Kingdom

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Preface Volume 73 contains four excellent chapters on contemporary and important topics in the agronomic sciences. Chapter 1 is a thoughtful review of interactions among root-inhabiting fungi and their implications for biological control of root pathogens. The fungi are deﬁned, their distribution and abundance are discussed, and their role in agroecosystems is presented. Chapter 2 discusses advances in the role of dwarﬁng genes in plant improvement. Emphasis is placed on breeding and genetics aspects. Chapter 3 covers a topic that is of great environmental interest— the effect of nitrogen fertilizers on gaseous emissions. Processes controlling and measurements of emissions of nitrogen gases are fully discussed. Chapter 4 is a comprehensive review of Rhizobia, including diversity, systematics, natural populations, and ﬁeld introduction of Rhizobia. I thank the authors for their ﬁrst-rate reviews. DONALD L. SPARKS

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INTERACTIONS AMONG ROOT-INHABITING FUNGI AND THEIR IMPLICATIONS FOR BIOLOGICAL CONTROL OF ROOT PATHOGENS David M. Sylvia1 and Dan O. Chellemi2 1

Soil and Water Science Department University of Florida Gainesville, Florida 32611 2

UDSA, ARS Horticultural Research Laboratory Ft. Pierce, Florida 34945

I. Introduction II. Functional Diversity in the Root Zone A. Classification Schemes for Functional Groups B. Clinical Pathogens C. Subclinical Pathogens D. Arbuscular Mycorrhizal Fungi E. Additional Nonpathogenic Fungi III. Interactions among Root-Inhabiting Fungi A. Interactions among Pathogens B. Interactions of AM Fungi with Pathogenic and Nonpathogenic Fungi C. Interactions between Pathogenic and Nonpathogenic Fungi D. Application of Island Biogeography Theory to Root–Fungal Interactions IV. Opportunities for Pest Control A. Current and Future Control Strategies B. Role of Biological Control C. Obstacles to Implementing Biological Control V. Research Priorities References

Soil fungi impact plant health because they grow in, on, and around roots, infecting healthy tissues and colonizing senescent materials. We review the literature concerning these fungi and discuss the various interactions that occur among the root-inhabiting fungi and their diversity at the community level. Root-inhabiting fungi are classified as clinical and subclinical pathogens, mycorrhizal fungi, and additional nonpathogenic fungi. We define each group, present data on abundance and distribution, and describe their roles in agroecosystems.We also discuss the 1 Advances in Agronomy, Volume 73 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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SYLVIA AND CHELLEMI application of island biogeography theory to the understanding of fungal species diversity in the root zone. Our goal is to contribute to a better understanding of the complex ecology of root-inhabiting fungi so researchers can formulate reasonable and testable hypotheses concerning the roles these fungi play in maintaining the delicate balance between plant health and disease. We describe the implications of fungal interactions for biological control strategies of root pathogens using three diverse approaches: single tactic, integrated pest management, and proactive pest management. We conclude that it is the very complex nature of the rhizosphere that makes it imperative that we invest resources into fundamental research of C 2001 Academic Press. rhizosphere ecology.

I. INTRODUCTION Fungi contribute signiﬁcant biomass to soils where they have important functions in nutrient cycling (Harley, 1971) and microaggregate formation (Tisdall et al., 1997). Soil fungi also encounter plant roots; they grow in, on, and around roots and infect healthy tissues and colonize senescent materials (Parke, 1991). In his classic tomes, Garrett (1960, 1970) characterized the edaphic fungal ﬂora as either soil or root inhabiting. He further characterized the root-inhabiting fungi as either unspecialized or specialized parasites. The unspecialized parasites, such as species of Pythium and Rhizoctonia, generally grow on juvenile root tissue. In contrast, the specialized parasites may grow on more mature tissues and result in vascular wilts as well as root rots. The parasitic nature of these associations does not imply that all root-inhabiting fungi are pathogens. In fact, many fungi growing with roots are beneﬁcial, as exempliﬁed by mycorrhizal symbionts (Smith and Read, 1997) and nonpathogenic parasites associated with roots (Deacon, 1987). Here we use “parasite” to describe an organism that infects roots in order to obtain food for energy and growth, while the term “pathogen” is used of an organism that speciﬁcally incites plant disease— “the injurious alteration of one or more ordered processes of energy utilization in a living system” (Bateman, 1978). Our objectives for this chapter are to review the extant literature concerning fungi growing on and in roots and to discuss the various interactions that occur among these fungi. We begin by describing functional classiﬁcations of fungi that occur with roots and then discuss the ecological roles of each major group (clinical and subclinical pathogens, mycorrhizal fungi, and additional nonpathogenic fungi). Next we discuss interactions that occur among these root-inhabiting fungi and their diversity at the community level. Our goal is to contribute to a better understanding

ROOT-INHABITING FUNGI

3

of the complex ecology of root-inhabiting fungi so that researchers will be in a better position to formulate reasonable and testable hypotheses concerning the roles these fungi play in maintaining the delicate balance between plant health and disease. Thus, we conclude this chapter by describing the implications of these fungal interactions for biological control strategies of root pathogens and propose further research priorities to achieve this end.

II. FUNCTIONAL DIVERSITY IN THE ROOT ZONE A. CLASSIFICATION SCHEMES FOR FUNCTIONAL GROUPS Winogradsky (1924) attempted to classify soil microorganisms on the basis of their growth habit and modes of nutrition. Those that grow rapidly when nutrients are readily available were described as zymogenous and those that grow slowly on recalcitrant material as autochthonous. This is similar to the r-K life strategies proposed by MacArthur and Wilson (1967) for animal systems. Pugh (1980), following the reasoning of Grime (1979), expanded on and applied these concepts to fungi, separating them into four broad life strategies: 1. ruderals, which have high sporulation and fast growth rates on simple, exogenous substrates; 2. competitors, which maintain growth over a longer time period by maximizing capture of available resources; 3. stress-tolerants, which have low sporulation and slow growth rates as nutrients are depleted resulting in a stable population; and 4. survivors-escapes, which occupy unique habitats such as the phylloplane or rhizosphere of roots in waterlogged soils. The rhizosphere has been deﬁned as the soil adjacent to roots with altered physical, chemical, and biological characteristics compared to the bulk soil (Bowen and Rovira, 1999). The input of inorganic and organic nutrients from actively growing roots stimulates microbial growth, resulting in rapid increases in populations of bacteria, fungi, and protozoa (i.e., the ruderals). Theoretically, establishment of competitors should follow the ruderals and, as nutrients are depleted, stress-tolerant organisms should predominate. Some have divided the rhizosphere into the ectorhizosphere (zone outside the root), rhizoplane (the root surface), and endorhizosphere (zone inside the root) (Balandreau and Knowles, 1978). Though semantically incorrect (Kloepper et al., 1992), an understanding of the physical, chemical, and biological properties of these adjacent, but dissimilar, locations should help one understand the growth habitat and life strategies of root-associated fungi. Unlike most bacteria, the

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SYLVIA AND CHELLEMI

majority of fungi are ﬁlamentous organisms and their expanding vegetative structures may easily span, and inﬂuence, life processes across these zones. Biodiversity is an important issue and is gaining scientiﬁc, as well as political, attention. Biodiversity may be viewed as comprising taxonomic, genetic, and functional components (Solbrig, 1991). Most research has focused on taxonomic diversity and, with the advent of the new molecular tools, increasing emphasis is being placed on genetic diversity. However, there are few studies that focus on the manner by which genetic or taxonomic diversity affects ecosystem function (Zak et al., 1994). The challenge for soil ecologists is to understand the impact of these fungi on root function and plant health. A classiﬁcation scheme for root-inhabiting fungi may include clinical and subclinical pathogens, mycorrhizal fungi, and additional nonpathogenic fungi (suggesting our lack of knowledge of many fungi that occur in the root). In the remainder of this section we summarize the natural histories and agroecosystem functions of these groups.

B. CLINICAL PATHOGENS 1. Definition Clinical pathogens can be deﬁned as root-inhabiting fungi that cause visual symptoms of disease. Typically these include mortality or elimination of the reproductive potential of the host plant, where reproductive potential is inclusive of both sexual (seed) and asexual (vegetative) propagation. Thus, clinical pathogens have the potential to dramatically impact the survivorship of plant populations. While this deﬁnition addresses the functional role of the fungus in the ecosystem, it does not differentiate the parasitic nature or host speciﬁcity of the fungus. This functional group contains fungi which require living tissue of a speciﬁc plant host to grow and reproduce (obligate parasites), as well as fungi which can survive for extended periods of time in the soil on organic matter (facultative parasites). 2. Abundance and Distribution Clinical pathogens are found throughout the ecological range of terrestrial plants, and epidemics of plant disease occur in a wide array of ecosystems ranging from the subarctic to the equatorial tropics. Their population dynamics within crop production systems have been studied extensively. Typically, populations of clinical pathogens are present at low levels, bordering on the lower detectable range, until presence of the host coupled with favorable environmental conditions create an explosion of the pathogen population resulting in an epidemic of plant

ROOT-INHABITING FUNGI

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disease (Flowers and Hendrix, 1972; Kannwischer and Mitchell, 1981; Mitchell, 1978; Smith and Snyder, 1971). Under most conditions, they probably constitute a small proportion of the community of root-inhabiting fungi and contribute little to the total fungal biomass in the soil. Considerably less information exists on the abundance of clinical pathogens in natural ecosystems (Alexander, 1992; Burdon, 1987). 3. Role in Agroecosystems While comprising a small percentage of the total fungal biomass in soils, clinical pathogens perform a major functional role in the ecosystem because they are primary regulators of plant density and diversity. This is most evident in agroecosystems where large-scale monoculture is practiced (Burdon and Chilvers, 1982). Epidemics of plant diseases in natural systems have also been observed (Dinoor and Eshed, 1984; Newhook and Podger, 1972; Schmidt, 1978; Weste and Ashton, 1994). In a study conducted over 4 years in permanent plots, root infection by Rhizoctonia solani or Pythium irrgulare signiﬁcantly reduced plant populations of the annual legume Kummerowia stipulacea (Mihail et al., 1998). The reductions were more severe at high plant densities. Computer simulation of epidemics caused by Phytophthora spp. and Fusarium oxysporum have indicated that initial increase of the pathogen population requires that the host density be above a threshold level (Thrall et al., 1997).

C. SUBCLINICAL PATHOGENS 1. Definition Subclinical pathogens invade root tissue and cause localized cell death and disruption of vascular functions. However, visual symptoms are often difﬁcult to discern as subclinical pathogens do not cause mortality or eliminate the plants ability to reproduce. Fungi placed in this classiﬁcation have been referred to as “minor pathogens” by Salt (1979). However, unlike Salt’s deﬁnition, which limited this group to fungi that only parasitize root-tips or cortical cells, assignment to the status of subclinical pathogen does not place any restriction on the type or location of host tissue colonized within the root. Included in this group are fungal species belonging to a diverse grouping of genera, including Pythium, Mucor, Fusarium, and Cylindrocarpon. Subclinical pathogens can negatively impact plant health in many ways. Through localized necrosis they disrupt vascular function in the root and alter morphology and limit nutrient uptake or availability in the plant host (Larkin et al., 1995), which results in a reduction in plant vigor and decline in plant health. Infection by subclinical pathogens may predispose plants to injury

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by other plant pests or environmental stress. Their effects on plant health may be synergistic when they parasitize root tissue in conjunction with other soil microbes, such as plant pathogenic nematodes or bacteria. Their effects on the host make the plant more vulnerable to drought, ﬂooding, or other unfavorable environmental conditions. Finally, subclinical pathogens can serve as vectors for plant viruses (Campbell and Fry, 1966; Gerik and Duffus, 1986). The fact that some fungal species can exist as both clinical and subclinical pathogens muddies the distinction between these groups. For example, at ambient temperatures of 28◦ C or less, Pythium aphanidermatum and Pythium myriotylum function as subclinical pathogens on pepper and tomato (Chellemi et al., unpublished data), parasitizing root cells and causing signiﬁcant reductions in growth, but not limiting the plants ability to survive and reproduce. However, at ambient temperatures near 34◦ C these fungi cause extensive plant mortality in the same host. 2. Abundance and Distribution Subclinical root-inhabiting fungi are distributed throughout the range of terrestrial plants. Their diversity and abundance remains relatively unknown due in part to the fact that their status as subclinical pathogens remains largely undetermined. Demonstrable reductions in plant growth or yield in fulﬁllment of Koch’s postulates are required to conﬁrm their status as plant pathogens. These procedures are time consuming and labor intensive and, therefore, determination of pathogenic status has been typically reserved for those fungi suspected of inducing plant mortality. Thus, investigations to determine the status of subclinical pathogens are usually undertaken for alternative reasons (i.e., suspicion of vectoring a plant virus or interaction with other clinical pathogens). In crop production systems, the abundance of subclinical pathogens has been investigated in replant diseases of perennial crops (Mazzola, 1999) and citrus declines of unknown etiology (Graham et al., 1983; Nemec et al., 1980). 3. Role in Agroecosystems Subclinical pathogens also function as regulators of plant density, though to a lesser extent than the clinical pathogens. They do so by affecting the relative ﬁtness of plant populations. This is accomplished by reducing the competitive ability of plants through reductions in vigor or reproduction. There is evidence for this role in natural plant systems (Augspurger, 1983; van der Putten et al., 1993). In a study by Holah and Alexander (1999), root-inhabiting fungi unique to soils associated with Chamaeerista fasciculata (an annual legume) were detrimental to Andropogon geradii (a native tallgrass and one of the dominant perennials in the ecosystem). Subclinical pathogens can also initiate processes leading to the

ROOT-INHABITING FUNGI

7

breakdown of plant tissue and recycling of carbon in the soil, as they are present in root tissue at the time of plant senescence (Waid, 1974).

D. ARBUSCULAR MYCORRHIZAL FUNGI 1. Definition Mycorrhizae are symbiotic associations of speciﬁc fungi with the ﬁne roots of plants. Several mycorrhizal types have been described, and one or more of these plant–fungus associations are found in nearly every biome on Earth (Smith and Read, 1997). The arbuscular mycorrhizal (AM) type is the most widespread mycorrhiza found on plant roots in agroecosystems. The diagnostic feature of arbuscular mycorrhiza is the highly branched arbuscules that develop within root cortical cells. The fungus initially grows between cortical cells but soon penetrates the host cell wall and grows within the cell lumen. Neither the fungal cell wall nor the host cell membrane are breached (Bonfante and Perotto, 1995). As the fungus grows, the host cell membrane invaginates and envelops the fungus, creating a new compartment where material of high molecular complexity is deposited. This apoplastic space prevents direct contact between the plant and fungus cytoplasms and allows for efﬁcient transfer of nutrients between the symbionts. The arbuscules are relatively short lived and are often difﬁcult to observe in ﬁeld-collected samples. Other structures produced by AM fungi include vesicles, auxiliary cells, extramatrical hyphae, and spores. Vesicles are thin-walled, lipid-ﬁlled structures that usually form in intercellular spaces. Their primary function is thought to be for storage; however, vesicles can also serve as reproductive propagules for the fungus. The term vesicular–arbuscular mycorrhiza or VAM was originally applied to this group, but because a major suborder lacks the ability to form vesicles in roots, AM is now the preferred acronym. Auxiliary cells are formed in the soil and can be coiled or knobby. The function of these structures is not known. Spores can be formed either in the root or more commonly in the soil. Spores produced by AM fungi are asexual, formed by the differentiation of vegetative hyphae. For some fungi (e.g., Glomus intraradices), vesicles in the root undergo secondary thickening, a septum (cross wall) is laid down across the hyphal attachment, and a spore is formed, but more often spores develop from hyphal swellings in the soil. The AM fungi may produce an extensive network of extramatrical hyphae (Sylvia, 1990) and can signiﬁcantly increase phosphorus-inﬂow rates of the plants they colonize (Jakobsen et al., 1992). The AM fungi are currently classiﬁed in the order Glomales (Morton, 1988). The order is further divided into suborders based on the presence of (i) vesicles in the root and formation of chlamydospores borne from subtending hyphae for the suborder Glomineae or (ii) absence of vesicles in the root and formation of

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auxiliary cells and azygospores in the soil in the suborder Gigasporineae. The order Glomales is further divided into families and genera according to the method of spore formation. The spores of AM fungi are very distinctive and range in diameter from 10 to >1000 ␮m. The spores can vary in color from hyaline to black and in surface texture from smooth to highly ornamented. More than 150 species of AM fungi have been described; however, taxonomy at the species level is currently going through extensive revision. The reader may visit the INVAM webpage (http://invam.caf.wvu.edu/) to obtain current information on AM taxonomy. 2. Abundance and Distribution Most crop plants are colonized by AM fungi and, in fact, it is much easier to list the predominately nonmycorrhizal plant families—the Caryophyllaceae, Chenopodiaceae, Cruciferae, Juncaceae, Polygonaceae, and Proteaceae—than the mycorrhizal ones. Surveys of ﬁeld-grown crops reveal wide ranges in the extent of colonization of roots by AM fungi (Table I). Many edaphic factors, such as soil type (Frey and Ellis, 1997), soil fertility (Bolgiano et al., 1983), and pH (Clark, 1997), affect the extent of colonization but the conspicuous fact is that the majority of agronomic crops grown under a wide range of conditions consistently have a signiﬁcant portion of their root systems colonized by AM fungi. It is clear that the critical question for the agronomist is not whether their crops are colonized by AM fungi but rather what impact these fungi have on crop and soil productivity. We have incomplete knowledge of the species of AM fungi associated with agronomic crops because numerous difﬁculties are encountered when attempting to characterize diversity of these fungi in the ﬁeld; spores are difﬁcult to identify, some species do not sporulate, and there is little relationship between functional and morphological diversity (Douds and Millner, 1999). The few surveys that quantify AM fungal spore densities or species richness (Table I) suggest that there are often less than 10 spores g−1 soil and between 5 to 10 species of AM fungi present in a given agronomic soil. What these numbers mean relative to soil productivity is unclear because spores may represent only a small proportion of the total mycorrhizal propagules in the soil [colonized roots and hyphae may also initiate new mycorrhizae (Friese and Allen, 1991)]. Furthermore, AM species, and even isolates, may differ dramatically in their effect on plant growth (Boerner, 1990; Boucher et al., 1999), and with current knowledge it is impossible to predict which propagules will have the greatest impact on crop response. Even though AM symbioses are among the best known examples of compatibility between plants and microbes we have little understanding of the factors that contribute to the speciﬁcity of these compatible interactions. The AM symbioses are often considered nonspeciﬁc (Gianinazzi-Pearson, 1984; Sanders, 1993). Nonetheless, there is mounting evidence that “host preference” is an important characteristic of AM symbioses (Dhillion, 1992; Giovannetti and Hepper, 1985). By this

9

ROOT-INHABITING FUNGI Table I Examples of AM Fungal Associations of Agronomic Crops

Crop Aeschynomene americana Allium cepa Apium graveolens Capsicum annuum Cucumis melo Eleusine coracana

Location

Max. root Max. spore Species colonization (%) densitya richness

Florida

30b

Israel Israel Australia Israel Israel India

Minnesota Pennsylvania Gossypium hirsutum Texas Helianthus annuus India Glycine max

Reference

3

6

Medina et al. (1988)

ca. 50b ca. 50b 58b ca. 50b

nac na na na

na na na na

Krikun et al. (1990) Krikun et al. (1990) Olsen et al. (1999) Krikun et al. (1990)

>50b 30b, d

na 6

na na

25b 9,b 67d 70b 23b, d

67e <1,b 1d na 5

14 na na na

Krikun et al. (1990) Harinikumar and Bagyaraj (1989) Johnson et al. (1991) Douds et al. (1993) Zak et al. (1998) Harinikumar and Bagyaraj (1989)

Hordeum vulgare

Demark

50b

2

na

Jakobsen and Nielsen (1983)

Lycopersicon esculentum

Florida

52d

4

4

Unpublished data

Oryza sativa

Japan

55b

3

na

Solaiman and Hirata (1997)

Pisum sativum

Denmark

80b

2

na

Jakobsen and Nielsen (1983)

Solanum tuberosum

England

53b

Triticum aestivum

Pennsylvania Pennsylvania Kansas

15d 35,b 45d 27b

Denmark

50b

Australia

20,b 70d

na

na

Minnesota S. Dakota

34b 10,b 43d

71e na

14 na

Pennsylvania Quebec India

84d 71b, d 29b, d

na 21 4

na na na

Zea mays

a

3

na

Hayman et al. (1975)

8 <1,b 4d 3

na na 6

2

na

Boswell et al. (1998) Douds et al. (1993) Hetrick and Bloom (1983) Jakobsen and Nielsen (1983) Ryan et al. (1994)

Data variously presented as spores per gram or per milliliter. Conventional system. c na = data not available. d Organic/sustainable system. e High value due to an abundance of Glomus aggregatum. b

Johnson et al. (1991) Vivekanandan and Fixen (1991) Boswell et al. (1998) Kabir et al. (1998) Harinikumar and Bagyaraj (1989)

10

SYLVIA AND CHELLEMI

we mean that different species, strains, or isolates of AM fungi colonize plant roots to different degrees and have variable effects on plant growth and development. Here it is important to distinguish among speciﬁcity (innate ability to colonize), infectiveness (amount of colonization), and effectiveness (plant response to colonization). The AM fungi differ widely in the levels of colonization they produce in a root system and in their impact on nutrient uptake and plant growth. Host preference may be under the genetic control of the host, the fungus, or, most likely, a complex interactive effect of both symbiotic partners with soil edaphic factors. Much of the extant literature emphasizes the role of the plant in the interaction. Johnson et al. (1991) found that cropping history (maize vs soybean) and soil type altered the communities of AM fungi in soil, Bever et al. (1996) demonstrated host-dependent sporulation among common lawn plants, and Zhao et al. (1997) reported differential development of AM fungi with two legume species. Host genotype variation in root colonization and plant response also has been demon◦ and Rydberg, strated for citrus (Graham and Eissenstat, 1994), pea (Martensson 1995), wheat (Hetrick et al., 1996), barley (Baon et al., 1993), and tomato (Barker et al., 1998). Less is know about the effect of the fungal genotype on root colonization and plant response. Inoculum density can be a confounding factor when one attempts to differentiate fungal affects (Clapperton and Reid, 1992; Daft and Nicolson, 1969). However, when inoculum densities are not limiting or have been equalized, important ecotypic variation among AM fungi has been reported (Boyetchko and Tewari, 1995; Douds et al., 1998; Graham et al., 1996; Hepper et al., 1988; Monzon and Azcon, 1996; Stahl et al., 1990; Sylvia et al., 1993b). These studies support the hypothesis that the fungal ecotype will have an important impact on root colonization, sporulation, and host plant response 3. Role in Agroecosystems We are becoming increasingly aware of the important multifunctional roles of AM fungi in ecosystems (Newsham et al., 1995b). Besides improving uptake of poorly mobile nutrients (George et al., 1992), AM symbioses may impact drought tolerance (Schellenbaum et al., 1998) and pathogen interactions (Azc´on-Aguilar and Barea, 1997) and contribute to soil quality by channeling carbon to the soil and thereby improve soil aggregation (Jastrow et al., 1998). Furthermore, there is mounting evidence that AM fungi are important determinants of plant community structure and plant succession (Allen et al., 1995; Gange et al., 1993). van der Heijden et al. (1998) concluded that below-ground diversity of AM fungi is a major factor contributing to the maintenance of plant biodiversity and ecosystem function. The function of AM fungi in highly managed agroecosystems is less certain (Hayman, 1987). Under nutrient (Bagyaraj and Sreeramulu, 1982; Beyene et al.,

ROOT-INHABITING FUNGI

11

1996; Osonubi et al., 1995) or moisture stress (Sylvia et al., 1993a), they can signiﬁcantly increase crops yields. However, in highly managed, high-input systems, it is possible to demonstrate growth reductions due to AM colonization (Graham and Eissenstat, 1998; McGonigle and Miller, 1996). Furthermore, Hetrick et al. (1993) found that modern breeding practices have reduced mycorrhizal dependency of wheat. Indeed, in those cases where colonization occurs in the absence of a demonstratable growth enhancement of the plant, the net cost of the symbiosis may exceed the net beneﬁt (Johnson et al., 1997). Nonetheless, when one takes a more holistic view of the plant–soil continuum, the “cost” of the symbiotic association may turn out to be an important beneﬁt (Schreiner and Bethlenfalvay, 1995). For example, Wright and Upadgyaya (1997) have described a high-molecular-weight glycoprotein (termed glomalin) that is produced in abundance by AM fungi. This material accumulates in soil and is positively correlated with aggregate stability (Fig. 1) (Wright and Upadhyaya, 1998). Conventional agronomic practices may adversely affect the diversity and abundance of AM fungi in agroecosystems (Johnson and Pﬂeger, 1992; Thompson, 1994). Large applications of phosphatic fertilizer generally reduce AM root colonization (Harinikumar and Bagyaraj, 1989; Olsen et al., 1996; Vivekanandan and

Figure 1 The relationship between stability of 1- to 2-mm-size aggregates and immunoreactive easily extractable glomalin (IREEG) from soils in four regions of the United States with ≤80% aggregate stability. From Wright and Upadhyaya (1998); used with permission.

12

SYLVIA AND CHELLEMI

Fixen, 1991); however, addition of phosphorus fertilizer to very low phosphorus soils may increase root colonization (Bolan et al., 1984). Tillage has been shown to delay AM colonization of maize roots and result in reduced early season phosphorus uptake (McGonigle et al., 1999; Vivekanandan and Fixen, 1991) as well as reduced hyphal and spore densities near the soil surface (Kabir et al., 1998). Crop rotations that include nonhost plants (Harinikumar and Bagyaraj, 1988) or long-term fallow (Thompson, 1987) may also reduce AM fungal populations. Pesticides, especially fumigants, substituted aromatic hydrocarbons, and benzimidazoles (Johnson and Pﬂeger, 1992), may adversely affect the activity of AM fungi in soil. As an overall generalization one may conclude that conventional management practices reduce AM fungal populations while sustainable, organic, low-input systems tend to increase their activity (Table I) (Douds et al., 1993; Kabir et al., 1998; Ryan et al., 1994).

E. ADDITIONAL NONPATHOGENIC FUNGI 1. Definition This group comprises a diverse collection of fungi capable of invading and occupying inter- and intracellular spaces within the root tissue without disrupting the cellular functions of root organelles. As a group, these fungi survive and function as saprophytes within the soil microbial community and include members from many common genera, such as Fusarium, Gliocladium, Microdochium, Penicillium, Phialophora, Trichoderma, and various poorly deﬁned dark-septate endophytes (Jumpponen and Trappe, 1998; Skipp and Christensen, 1989). They differ from mycorrhizal fungi in that they form no specialized organelles or structures within the root. Also, they are generally thought to have no species-speciﬁc relationships with the plants; however, some host speciﬁcity has been suggested (Skipp and Christensen, 1989). 2. Abundance and Distribution Fungi included in this group are ubiquitous and occur in large numbers wherever terrestrial plants are found. Many saprophytic fungal species are present in the rhizosphere shortly after introduction of the plant host into the soil (English and Mitchell, 1988). Furthermore, epidermal and outer cortical cells of roots are ephemeral and begin to senesce within a few weeks in the zone of cortical lysis (Foster, 1986). Deacon et al. (1987) describe this process as early root cortex death (RCD). These tissues initially appear healthy, but nuclear staining reveals that nuclei have disappeared from the cells. A wide range of nonpathogenic fungi readily colonize these senescing root tissues (Bowen and Rovira, 1976; Huisman, 1988).

ROOT-INHABITING FUNGI

13

Information on the abundance of nonpathogenic root-inhabiting fungi in natural systems is incomplete, partially because they are not normally investigated for their contribution to ecosystem health. In crop production systems, documentation of their abundance usually occurs during investigations related to a plant pathological disorder. For example, the composition of presumably nonpathogenic root-inhabiting fungi in association with replant diseases of perennial fruit trees was studied by Mazzola (1999). Difﬁculties in the identiﬁcation of fungal species also limit quantitative studies on their abundance and distribution as few molecular markers have been identiﬁed for nonpathogenic soil fungi. 3. Role in Agroecosystems The role of nonpathogenic root-inhabiting fungi in ecosystems is complex. As saprophytes present in large numbers they occupy a vital link in the trophic food web in soils. They participate in carbon recycling through the decomposition of plant tissue (Waid, 1974). Their relationships to plant health have been documented in several systems. They may be involved as elicitors of induced systemic resistance in the plant host (Benhamou et al., 1997; Larkin and Fravel, 1999). They can also protect plants from infections by pathogens through direct competition for infection sites on the root or interference with saprophytic colonization of soil organic matter (Couteaudier, 1992; Martin and Hancock, 1986; Schneider, 1984).

III. INTERACTIONS AMONG ROOT-INHABITING FUNGI A. INTERACTIONS AMONG PATHOGENS Co-infection of root systems by more than one pathogenic fungus is most likely the rule rather than the exception. Synergism between co-infecting pathogenic fungi may or may not occur. For example, P. myriotylum interacted synergistically with Fusarium solani to cause damping-off of peanut seedlings, but no synergistic effect was observed when P. myriotylum was combined with R. solani (Garcia and Mitchell, 1975). In the same study, P. myriotylum could not be reisolated from roots co-infected with R. solani. Thus, although multiple infections may take place in the root system among pathogens, the resultant level of disease will vary depending upon the speciﬁc species interactions. Elucidation of the interactions among pathogens may be further complicated by soil edaphic factors and environmental conditions.

14

SYLVIA AND CHELLEMI

B. INTERACTIONS OF AM FUNGI WITH PATHOGENIC AND NONPATHOGENIC FUNGI Mycorrhizal fungi colonize feeder roots and thereby interact with root pathogens that parasitize this same tissue. A large body of literature exists on the interactions among pathogens and AM fungi, and several reviews have been written on the subject (Dehne, 1982; Linderman, 1994; Paulitz and Linderman, 1991; Schenck, 1981). In a natural ecosystem, a major role of mycorrhizal fungi may be protection of the root system from endemic pathogens (Newsham et al., 1995a). In cropping systems the role of AM fungi in disease protection is less clear; however, much of the literature suggests that AM fungi reduce soilborne disease or at least ameliorate the effects of disease. Mechanisms put forward to explain disease protection include (Azc´on-Aguilar and Barea, 1997): r r r r r r

improved nutrient status of the host plant competition for host photosynthates competition for infection sites anatomical and morphological changes in the root system microbial changes in the mycorrhizosphere activation of plant defense mechanisms

Often inoculation with AM fungi prior to challenging with a pathogen is necessary to achieve disease reduction. This is because many root pathogens germinate and grow more rapidly than AM fungi and, if co-inoculated, will attack the root before the mycorrhizae become established. An intriguing ﬁnding is that AM fungi may actually stimulate spore germination of some pathogens (St-Arnaud et al., 1995). A protective effect may result if germination of pathogen spores close to the mycorrhizal mycelium, but far from the roots, results in reduced inoculum potential of the pathogen. Here we present several recent examples of AM fungal interactions with pathogens that have been published since the previously cited reviews. Trotta et al. (1996) reported that precolonization of tomato with the AM fungus, Glomus mosseae decreased both weight reduction and root necrosis caused by Phytophthora nicotianae. They concluded that the AM fungus activates a diseasesuppression mechanism to reduce root damage because improved phosphorus nutrition could not account for increased disease resistance. Cordier et al. (1996) found a similar response, reporting that the number of P. nicotianae hyphae growing in the root cortex of tomato was reduced in mycorrhizal root systems and that the pathogen hyphae never invaded arbuscule-containing cells. Both localized and systemic resistance mechanisms have been demonstrated in this system, including induction of plant wall defense responses (Cordier et al., 1998) and unique chitinase isoforms (Pozo et al., 1998; Pozo et al., 1997). In contrast, Kjøller and Rosendahl

ROOT-INHABITING FUNGI

15

(1997) found no evidence of a localized defense response when G. intraradices protected roots of pea from Aphanomyces euteiches, but rather they reported a general effect on root physiology. Additional biological control interactions with AM fungi that have been reported include protection of onion from Sclerotium cepivorum (Torres-Barragan et al., 1996), alfalfa from Verticillium albro-atrum and Fusarium oxysporum, Java citronella from P. aphanidermatum (Ratti et al., 1998), and tomato from F. oxysporum (Datnoff et al., 1995). An important criticism of most of these recent studies is that they do not provide an adequate P-fertilized control. Future studies should compare mycorrhizal and nonmycorrhizal plants of similar P status and size in order to separate possible direct effects of mycorrhizal formation from nutritional responses (Graham, 1988). In addition to fungal pathogens, mycorrhizae exert a strong inﬂuence on bacteria, actinomycetes, other fungi, mycoparasites, and invertebrates that occur in the mycorrhizosphere (Andrade et al., 1998; Linderman, 1992; Paulitz and Linderman, 1991). Ames et al. (1987) reported that the type of microorganisms found with AM fungi was inﬂuenced by the type of inoculum used, suggesting the existence of speciﬁc microbial associations with AM fungi. The interactions among AM fungi and closely associated nonpathogenic fungi, such as species of Aspergillus, Gliocladium, Paecilomyces, Trichoderma, and Wardomyces, are complex and can vary from antagonistic to neutral to synergistic (Dhillion, 1994; Fracchia et al., 1998; Garcia-Romera et al., 1998; Mcallister et al., 1996). Rousseau et al. (1996) demonstrated that a strain of a Trichoderma sp. selected for its superior biological control potential actively parasitized G. intraradices, at least in their in vitro system, but additional research is needed to evaluate these complex interactions in the ﬁeld.

C. INTERACTIONS BETWEEN PATHOGENIC AND NONPATHOGENIC FUNGI The root and soil surrounding it have high biological activity, including microorganisms (predominately bacteria and fungi) that promote plant growth and yield or are deleterious to plant growth (Bowen and Rovira, 1999; Sturz et al., 1997). The reader should note that in this review we restrict our comments to the fungal component. Recently, Sneh (1998) reviewed the interactions among plant pathogens and closely related nonpathogenic (avirulent) and low virulent (hypovirulent) fungi. Competition for infection sites or for nutrients has been demonstrated for several root-associated fungi, including species of Rhizoctonia, Fusarium, and Pythium, while mycoparasitism was shown for others, such as Trichoderma and some Pythium spp. An interesting case involves Fusarium oxysporum-suppressive soils where disease remains low despite the presence of high levels of the pathogen (Sneh, 1998).

16

SYLVIA AND CHELLEMI

Competition for nutrients by nonpathogenic strains of F. oxysporum can regulate the activity of pathogenic strains (Alabouvette, 1990; Couteaudier, 1992; Mandeel and Baker, 1999). Nonpathogenic stains of F. oxysporum can penetrate the epidermis and colonize the root cortex (Olivain and Alabouvette, 1999) and can suppress disease through parasitic competition for infection sites on the root (Schneider, 1984). Finally, parasitism of root systems by nonpathogenic strains can elicit an induced systemic resistance response in the host (Larkin and Fravel, 1999). Nonpathogenic isolates of F. oxysporum from suppressive soil were identiﬁed as the major group of antagonists responsible for disease suppressiveness (Larkin et al., 1996). In this case the rhizosphere of resistant varieties were less favorable for the pathogenic F. oxysporum isolates, but more favorable for nonpathogenic isolates. Steinberg (1999) concluded that F. oxysporum is fundamentally a rhizosphere species and is strongly stimulated in the root vicinity, probably due to organic nitrogen present in root exudates. Interestingly, the population structure of F. oxysporum associated with different plants can vary (Edel et al., 1999), suggesting that plants have a selective effect on the fungi colonizing their roots. The mechanisms by which these nonpathogenic strains control disease are not fully understood, but the evidence to date suggest a combination of effects, including competition for nutrients and infection sites, induced local and systematic resistance, and mycoparasitism (Sneh, 1998).

D. APPLICATION OF ISLAND BIOGEOGRAPHY THEORY TO ROOT–FUNGAL INTERACTIONS Understanding the interactions within the community of root-inhabiting fungi and between communities of other soil microﬂora is paramount to predicting the outcomes of biological control activities. However, the microcosm comprised of plant root systems and their rhizosphere is complex, dynamic, and therefore very difﬁcult to understand. One way to describe and elucidate this microcosm is to use the dynamic theory of island biogeography. This theory, as proposed by MacArthur and Wilson (1967), states that for a given time a habitat island contains a number of species present (S), as well as an immigration rate (I) of new species onto the island, and an extinction rate (E) of species presently residing on the island. Application of the theory to study the population dynamics of fungal communities has been discussed (Wildman, 1992) and used to study soil fungi colonizing cellophane squares of various dimensions placed into the soil (Wildman, 1987). Consideration of roots as habitat islands is based upon the premise that they are insulated to some extent by the soil medium from exogenous populations located some distance away. Thus, the primary populations of root inhabiting fungi arise from reproduction of species present in the rhizosphere. As summarized

ROOT-INHABITING FUNGI

17

by Simberloff (1986), the theory states that immigration and extinction rates are not constants, but rather monotonic functions of the numbers of species present. Immigration rate is highest when the island is empty and declines to zero when all species in the regional species pool already inhabit the island. The extinction rate is zero when no species are present on the island and rises to a maximum value when all species are present. The two monotonic curves must cross and this intersection constitutes an equilibrium number of species (S), about which the island’s number of species should vary. The equilibrium is dynamic because it is accompanied by a turnover of species at rate X, as species are locally extinguished on the island and are replaced by immigrants from the pool. Thus, it is the number of species, rather that their identity, that is expected to remain constant. While Simberloff (1986) pointed out that island biogeography should be considered a hypothesis that is difﬁcult to test, it may facilitate better understanding of the ecology of root-inhabiting fungi as they pertain to biological control. Plant roots placed into soil that has been disinfested through practices such as fumigation with methyl bromide or steam sterilization represent a resource island with a low number of species present and are rapidly colonized by indigenous fungi. This phenomenon has been observed in crop production systems (Marois and Mitchell, 1981; Welvaert, 1974). As initial colonization rates will be high, systems employing root-inhabiting fungi as biological control agents should consider the competence of the fungi to compete with the aggressive indigenous fungi for colonization sites on the root surface. Most applications of biological control agents are made under the assumption that colonization rates are similar in all pathosystems. Until this assumption has been veriﬁed on a large scale, colonization rates by microorganisms speciﬁc to individual sites should be considered in the determination of the competitive ﬁtness of biological control candidates. The economics of such an approach is not feasible under current systems used for product development and sales of root-inhabiting fungi intended for biological control of root pathogens.

IV. OPPORTUNITIES FOR PEST CONTROL A. CURRENT AND FUTURE CONTROL STRATEGIES The strategy of pest management selected by the producer will greatly inﬂuence the opportunities for biological control of root pathogens. Familiarity with current and future control strategies as they pertain to soilborne pests is necessary to better understand the application potential of biological control. Three divergent approaches have been used to develop strategies for the control of soilborne pests.

18

SYLVIA AND CHELLEMI

1. Single-Tactic Approach The single-tactic approach relies upon the routine application of a broad spectrum biocide to eradicate all potential pests. Key to this approach is that applications are made on a regular basis using materials with a range of efﬁcacy broad enough to remove the threat of all potential pests. This approach has gained popularity over the years among large-scale producers of high-cash-value crops. In many instances the approach has been incorporated into the design of the production system (Geraldson et al., 1965; Maynard and Hochmuth, 1995). The single-tactic approach remains popular for several reasons. It eliminates the need to obtain and manage information regarding pest biology and their population parameters in the ﬁeld, thus simplifying the decision-making process. Determination of cropping sequences and application of materials is based upon marketing constraints or a calendar date. Because the risks associated with damage from potential pests is eliminated, growing seasons can be extended and the need for fallow periods or cultivation of a rotation crop is greatly reduced. Since the mid 1900s several soil fumigants or fumigant combinations, including methyl bromide, chloropicrin, and methylisothiocyanate-generating products, have been used to control soilborne plant pathogens. Methyl bromide has been the principal fumigant since the 1970s due to its relatively low cost, ease of handling, performance under a wide range of soil and environmental conditions, low phytotoxicity, and broad range of activity. In many areas of the world where intensive agriculture is practiced, the use of methyl bromide has become indispensable (Vanachter, 1975). Use of a single-tactic approach does have several important disadvantages. Applications based upon calendar dates or other nonbiological criteria can result in the over application of pesticides and increase both production costs and the potential for environmental disruption. Reliance upon a single chemical to control all soilborne pests can leave the producer vulnerable to changes in pesticide availability or regulatory policies. Indeed, methyl bromide has been implicated as a major ozonedepleting compound, and the United States is required by international treaty to phase out production and sale by the year 2005. 2. Integrated Pest Management Integrated Pest Management (IPM) can be deﬁned as the use of multiple tactics to maintain damage from pests below an economic threshold while conserving beneﬁcial organisms. Traditionally, IPM has been used as a strategy to manage foliar insect pests. Far less frequently has it been employed to manage soilborne pests. Use of multiple tactics ensures that growers do not become dependent upon a single chemical. Because treatments are applied “as needed,” the threat of environmental damage is greatly reduced.

ROOT-INHABITING FUNGI

19

Several fundamental obstacles exist which hinder the widespread adaptation of IPM for control of root pathogens. Determination of economic thresholds remains problematic. Sampling methods for soil-inhabiting microbes are technically challenging, expensive, and labor intensive. Residual populations of the pathogens are often below the level of detection for most methods. This, coupled with the explosive growth potential of many root pathogens, can lead to the use of presence/ absence designations rather that quantitative counts. Traditional deployment of an IPM strategy relies heavily upon intervention after sampling thresholds have been reached. The soil medium makes uniform delivery of a tactic to mature root systems difﬁcult to achieve. There are a limited number of chemical and biologically treatments which act systemically within the root system and even fewer with therapeutic affects.

3. Proactive Pest Management Proactive pest management is a strategy which seeks to minimize intervention through the avoidance of pest outbreaks. When incorporated into the design of the crop production system this strategy can be very effective and can broaden the availability of pest-management tactics not routinely considered by conventional growers. The most common example is the integration of soil-less media into greenhouse production systems. Through the use of a sterile medium growers are able to avoid problems associated with soil infested with root pathogens. Crop rotation can become an effective tactic when incorporated into the design of the production system. A 3-year rotation with bahiagrass (Paspalum notatum) pasture has been shown to signiﬁcantly reduce major soilborne pests of tomato, including diseases caused by Sclerotium rolfsii and F. oxysporum f.sp. lycopersici as well as damage from root-knot nematodes (Meloidogyne spp.). This rotation is impractical for tomato producers in Florida because they cannot afford to lose revenue from ﬁelds which have been modiﬁed to utilize their raised bed–plastic mulch production system. An alternative, low-input production system was designed to utilize minimum tillage practices in existing bahiagrass pasture as a means to avoid damage from the aforementioned pests (Chellemi et al., 1999). Designed to be compatible with existing bahiagrass pastures, the alternative production system makes available the 2.5 million acres of improved bahiagrass pasture in Florida for tomato growers. To be effective and practical, a proactive strategy should be considered when designing the production system. This limits its application to existing production systems. While a proactive pest-management strategy may be desirable in theory, avoidance of all potential pests may not be practical. Thus, some ﬂexibility in the strategy that permits intervention when needed is a more realistic approach.

20

SYLVIA AND CHELLEMI

B. ROLE OF BIOLOGICAL CONTROL Biological control of root pathogens can provide substantial beneﬁts to growers utilizing all three pest-management strategies. However, its roles and potential contributions will vary from strategy to strategy. While the single-tactic approach has sustained a high level of productivity and contributed directly to the success of many high-input production systems, it does not necessarily provide complete control of all potential root pathogens. For example, fumigation with methyl bromide or chloropicrin did not provide seasonlong control of bacterial wilt and Fusarium crown rot of tomato, Fusarium wilt of tomato and cucumber, and Phytophthora root rot of azalea and rhododendron (De Ceuster and Pauwels, 1995; Enﬁnger et al., 1979; Hoitink, 1980; Sonoda, 1976). In most cases, plant disease epidemics were attributed to the reinfestation of fumigated soil by a pathogen. While fumigation can eradicate or signiﬁcantly reduce inoculum in the soil, it also dramatically impacts populations of beneﬁcial microorganisms leading to a biological vacuum. Fumigated soil is rapidly recolonized by a diverse group of fungi, including documented biocontrol agents, although the composition of species and rate of colonization is largely driven by edaphic and environmental conditions (Bollen, 1974; Marois and Mitchell, 1981). Despite this information, augmentation of fumigated soil with biocontrol agents has not been widely practiced. Reinoculation of fumigated soils, using a mixture of several isolates of Trichoderma harzianum, has been used in commercial production systems in Belgium (De Ceuster and Hoitink, 1999). Biological control of root pathogens is ideally suited within the context of an IPM approach. The narrow spectrum of biological activity, lack of deleterious effects on the environment, and compatibility with other pest-management tactics associated with biological control all ﬁt within the goals of IPM. In some cases a synergistic effect has been observed when biological control agents are combined with other pest-management tactics. The following examples using soil solarization demonstrated this point: sublethal heating and Talaromyces flavus acted additively to suppress Verticillium wilt of eggplant (Tjamos and Fravel, 1995); integration of soil solarization for 6 weeks, the AM fungus Glomus fasciculatum, and seed treatment with carosulfan were highly effective in reducing damage from root knot nematode and Fusarium wilt of chickpea (Krishna Rao and Krishnappa, 1996); and combining soil solarization with the fungal antagonist Gliocladium virens improved the control of southern blight of pepper and tomato caused by the root pathogen S. rolfsii (Ristaino et al., 1991). Biological control can provide a supporting role in proactive pest-management strategies. It can provide short-term control of root pathogens when outbreaks occur. They offer growers a way of reducing the immediate impact of a root disease while at the same time minimizing the disruption of the biologically suppressive

ROOT-INHABITING FUNGI

21

or exclusive system which the grower has established. Biological control also can be incorporated into the design of the system. For example, designing a production system to encourage and sustain a community of disease-suppressive microorganisms would be in effect designing the system to provide continuous biological control.

C. OBSTACLES TO IMPLEMENTING BIOLOGICAL CONTROL Many obstacles remain which impede the widespread adoption of biological control as a component of root disease control programs. These include a lack of commercially available products, inconsistency of results from season to season or region to region, cost, failure by the grower to correctly identify the disease complex, and delivery systems which are compatible with standard grower operations. Some of these obstacles will be overcome through continual progress in product development and technology transfer. However, the most difﬁcult challenges may lie in addressing the ecological issues underlying some of the obstacles. For example, ﬁnancial support by private companies for product development and technology transfer is dependent upon the assumption that the biological control agent will have broad market appeal, a requirement to reap a return on their investment. However, the fundamental question remains as to whether a root-inhabiting fungus applied outside of the ecological range from which it evolved should be expected to function in the same manner as in its native habitat. In practical terms, is it realistic to expect a fungus isolated from wheat in the midwestern United States to colonize and compete with other rhizosphere organisms on a tomato plant in the southeastern United States?

V. RESEARCH PRIORITIES The biology of the rhizosphere is extremely complex due to the interplay of soil, plant, and microbial factors. Bowen and Rovira (1999) state that a major impetus for rhizosphere research has been the development of biological controls for root diseases. Unfortunately, this short-term goal has overshadowed research aimed at better understanding the basic principles of rhizosphere ecology. It is the very complex nature of the rhizosphere that makes it imperative that we invest resources into fundamental research of rhizosphere ecology before we can expect to predictably manage that environment. Here we suggest research priorities for advancing our understanding of the diversity and behavior of root-inhabiting fungi in the root zone.

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We are still astonishingly ignorant of the fungi that colonize the roots of crop plants. While comprehensive guides are available to describe fungi growing in vitro (von Arx, 1981), there is little information to aid one in identifying the fungi as they occur in and on the root. There has been some attempt at morphological characterization of fungi in roots (Abbott and Robson, 1979; Rillig et al., 1998) and, perhaps, an atlas of fungi growing in root systems would provide a very practical tool for researchers to better characterize fungi in their system. An obvious limitation of this approach is that morphology varies with host and soil type. The popularization of molecular tools offers new opportunities for more precise characterization of fungal diversity in the root zone (Kohn, 1992; Kowalchuk, 1999). In this regard, AM fungi present a special case because they contain hundreds or thousands of nuclei (Giovannetti and Gianinazzi-Pearson, 1994), each with multiple copies of ribosomal genes. Glomales-speciﬁc primers for the small subunit ribosomal gene (Simon et al., 1993) and the internal transcribed spacer region (Hijri et al., 1999; Sanders et al., 1996) have been developed, but variations have proven either too narrow or too broad, respectively, to allow species or isolate characterization. More recently, researchers have found that variation in the large subunit ribosomal gene may be suitable for separating closely related AM fungi within roots (Kjøller and Rosendahl, in press). Further reﬁnement of these approaches should lead to rapid advances in our understanding of fungal diversity in roots, not only for the most obvious pathogens and symbionts, but also for the wide array of nonpathogenic fungi for which we presently have little knowledge. Additionally, the speciﬁcity of these molecular approaches should allow us to move experiments from the laboratory and greenhouse to the ﬁeld, which is where we need to evaluate these complex interactions in a real-world context. As stated above, the challenge for the soil ecologist is not only to characterize the fungi that interact with plant roots, but to understand their impact on root function and plant health. Some progress has been made in this arena. We know that pathogenic and symbiotic root fungi differentially affect the morphological patterns of roots (Fusconi et al., 1999). Furthermore, root tissues can react very differently to the presence of pathogens, symbionts, and saprophytes, with responses ranging from rapid necrosis to nonrecognition (Asiegbu et al., 1999). We need to gain increased understanding of the molecular basis of these reactions by utilizing tools of molecular cytology (Hardham, 1998), molecular genetics (Lange et al., 1999), and immunology (Slezack et al., 1999). The role of the host plant on root colonization also needs further clariﬁcation, especially at the level of molecular interactions (Smith and Goodman, 1999). One example of an important host affect is the variable production of border cells (originally called “sloughed root cap” cells) by various plant species. It is known that border cells can attract and stimulate growth of some microorganisms and repel and inhibit the growth of others (Hawes, 1998). Furthermore, Niemira et al. (1996) recently hypothesized a connection between a host’s propensity to form

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mycorrhizae and its capacity to produce border cells. Future understanding of the basis for this activity may provide new ways to manage microbial populations in the root zone. Understanding the consequences of disturbance events on rhizosphere ecology will be essential to the development of effective tactics to manipulate populations of disease-suppressive organisms in the root zone. Pest control activities and cultural practices can have lasting effects on the dynamics and diversity of fungal communities in and on the root, and the development of effective tactics must begin with the avoidance of activities which are detrimental to the establishment of those organisms. Commonly used insecticides, such as choropyrifos or paraquat, may persist in soil for up to 1 year after application (Buyuksonmez et al., 1999; EXTONET, 1999), even though their application was not intended for the soil. While the effects of nontarget pesticides on mycorrhizal (see previous section) and fungal root pathogens (Levesque and Rahe, 1992) have been investigated, pesticide effects on subclinical or nonpathogenic root-inhabiting fungi and the interactions among functional groups have received little attention. The phenomena of disease-suppressive soils offer an opportunity to study naturally derived communities of disease-suppressive organisms. Some well-known examples include soils suppressive to disease caused by the root pathogens F. oxysporum, Gaeumannomyces graminis f.sp. tritici, Phytophthora cinnamomi, and R. solani (Hornby, 1983; Schneider, 1982). Traditionally, studies have focused on the correlation of speciﬁc factors with the attainment of disease suppression in soil. While strong associations between biotic or abiotic factors and disease suppression have been identiﬁed, successful induction of disease suppressiveness in commercial production systems has been limited, suggesting a more complicated interaction among factors. Deciphering the mechanisms by which suppressive soils are obtained will provide valuable insight into the development of effective tactics to manage the populations of disease-suppressive organisms. More attention should be given to novel approaches for studying disease-suppressive soils. For example, van Bruggen and Semenov (1999) proposed measuring the amplitude of ﬂuctuations in microbial populations and resilience to a disturbance or stress as an approach to the search for indicators of soil health. While many studies concentrate on the attainment of disease-suppressive soils, perhaps studies designed to inactivate the suppressive nature of soils will provide additional insights into the mechanisms involved. Efﬁcacy studies of biological control agents intended for use against root pathogens should be accompanied by basic studies on ecological parameters pertaining to their competitive ﬁtness at the tested sites. Important considerations include colonization rates of root systems, persistence in the rhizosphere, and reproduction and mortality subsequent to the conclusion of the cropping cycle. Inoculant releases (augmentation) are favored as a biological control strategy by developers and marketers of biological control agents as a means of recuperating

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costs incurred during the formulation process. Because these costs are ultimately passed on to the grower, they favor application of biological control agents to high-value crops such as fresh market vegetables and glasshouse crops, where a large budget for production costs is acceptable (Pickett and Bugg, 1998; Trumble and Morse, 1993). Further research on the persistence and competitiveness of biological control agents in the soil will ensure that microbes that contribute to the development of sustainable disease suppression, but that do not achieve levels of disease suppression high enough for commercial release, would receive consideration in low-input production systems.

ACKNOWLEDGMENTS We thank James H. Graham, David J. Mitchell, and Abid Alagely for their reviews of the chapter. Published as Florida Agricultural Experiment Station Journal Series no. R-07408.

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DWARFING GENES IN PLANT IMPROVEMENT S. C. K. Milach and L. C. Federizzi Universidade Federal do Rio Grande do Sul Faculdade de Agronomia, Departamento de Plantas de Lavoura Porto Alegre, Brazil

I. Introduction A. Historical Perspective II. The Biochemical Basis of the Dwarf Phenotype A. GA-Sensitive Mutants B. GA-Insensitive Mutants C. Phytochrome Mutants III. Dwarfing Genes and Their Use for Breeding IV. Breeding Challenges and Varieties Developed A. Breeding Challenges B. Varieties Developed V. Pleiotropic Effects of Dwarfing Genes A. Morphological and Physiological Traits B. Yield and Yield Components VI. Molecular Mapping of Dwarfing Genes A. Association to Quantitative Trait Loci for Plant Height and Lodging Resistance B. Comparative Mapping to Identify Orthologous Dwarfing Genes C. Mapping as a Basis for Cloning VII. Concluding Remarks References

This chapter attempts to summarize the main findings about the dwarfing genes in different plant species with emphasis on their breeding and genetics aspects. Most of the examples presented are on the use of dwarfing genes in cereal breeding because of their importance for agriculture. The biochemical basis of the dwarf phenotype is discussed and examples of GA-sensitive, GA-insensitive, and phytochrome mutants presented. Although several dwarfing genes have been identified and studied in different species, only a few have had wide application for plant improvement. The most universally accepted dwarfing genes have been the Rht1 and Rht2 of wheat and the sd1 of rice. The positive pleiotropic effect of these genes on other plant traits, especially on yield and yield components, is one of the reasons for the success of these genes. Breeding challenges to transfer these and other dwarfing genes to different genetic backgrounds are discussed and their 35 Advances in Agronomy, Volume 73 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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MILACH AND FEDERIZZI impact in variety development presented. Several dwarfing genes have been associated to molecular markers and their orthologous relationships identified through mapping. Cloning of some of the GA-insensitive genes has been recently achieved and opened the possibility of transferring these genes to a range of other crop C 2001 Academic Press. species.

I. INTRODUCTION Advances in grain yield due to the introduction and efﬁcient manipulation of major genes are common in the literature. For most crop plants, modiﬁcations in a few loci with large phenotypic effects may represent drastic changes in adaptation and ﬁnal ﬁtness to the environment. In species of agronomic importance, reduction of plant height has resulted in large increases in yield, due to lodging resistance, improved harvest index, and more efﬁcient utilization of the environment. For the past 4 decades of the 20th century, agriculture has beneﬁted from the use of semidwarf phenotypes in many different crops. The use of these genes was one of the main driven forces of the so called “Green Revolution” that resulted from the adoption of new semidwarf varieties and cultivation methods. The major impact was in cereal crops, ﬁrst in wheat and then in rice. The semidwarf wheat varieties had improved harvest index from the increasing of grain yield at the expense of straw biomass and were more lodging resistant, thus supporting higher fertilizing rates. Because of their importance, a signiﬁcant amount of research efforts was carried out to understand the biochemical basis, genetics, physiology, and pleiotropic effects of the dwarf phenotype in different plant species. Extensive research was done in cereal crops, particularly with wheat and rice, and one of the few reviews on this topic with emphasis on all these aspects is that of Gale and Yousseﬁan (1985) in wheat. Besides that, because of the involvement of the dwarﬁng genes with the gibberellin biosynthesis, fundamental research has been conducted with dwarf mutants involved in the GA biosynthetic and signal transduction pathway. Both of these aspects have been reviewed recently by Hedden and Kamiya (1997), Ross et al. (1997), and Swain and Olszewski (1996). Although these represent important contributions, a review integrating different aspects of the dwarf phenotype with emphasis in breeding is now appropriate. This chapter attempts to summarize the main ﬁndings about the dwarﬁng genes in different plant species with emphasis on their breeding and genetics aspects. Because a signiﬁcant amount of work has been done in cereal crops, most of the examples reported here is on them.

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A. HISTORICAL PERSPECTIVE Drastic modiﬁcations in plant height due to genetic factors have been reported since early 1900s, especially in small grain cereals. However, most of these modiﬁcations were described as aberrations or curiosities that were genetically heritable, but had no practical applications in agriculture (Waldron, 1924). In Japan, nevertheless, since 1873 farmers were using wheat varieties 50 to 60 cm short. In 1917, the dwarf wheat genotype ’Daruma’ was crossed to ‘Fultz,’ a land race imported from the United States to Japan. Then, in 1925, Japanese breeders crossed ‘Daruma’—‘Fultz’ with ‘Turkey,’ a Russian wheat land race. From this combination was selected ‘Norin10,’ which was released in Japan in 1935 (Hanson et al., 1982). It was only after the second world war, in 1946, that S. C. Salmon, an agricultural research scientist working in Japan, observed that farmers were growing a number of stiff, short-stemmed wheat varieties. He introduced 16 varieties of this plant type to the United States, and they were available to breeders in 1947–1948. Voguel from Washington State intensively used one of them, ‘Norin10,’ in crosses. The crosses between ‘Norin10’ and the American varieties presented several problems, which required many years of intense selection until the ﬁrst variety ‘Gaines’ was released in 1962 (Morrison and Voguel, 1962). That intense selection, especially in progenies of the cross ‘Norin10’ × ‘Brevor14,’ was of primary importance because it set the basis for a transformation in the wheat plant that represented a new plant pattern with high number of tillers, high lodging and shattering resistance, medium size spikes, medium plant height (semidwarf), medium culm diameter, and medium to small length and width of leaf (Reitz, 1968). Voguel sent lines of this cross in 1953 to Norman Borlaug, who was at CIMMYT in Mexico. Borlaug used them intensively to develop new types of wheat that became responsible for the “Green Revolution.” Since 1911 the Italian wheat breeders were developing stiff and short stature varieties, when N. Strampelli used the Japanese variety ‘Akakomugi’ in crosses with Italian tall varieties. Several semidwarf Italian wheat varieties were obtained with the Rht8 and Rht9 genes from ‘Akakomugi’ and these genes have been extensively used in Europe (Gale and Yousseﬁan, 1985). Rice varieties on that time were tall and lodged early and did not respond to the application of nitrogen. In order to increase yield potential, the harvest index had to be improved and lodging resistance and responsiveness to N increased. This was accomplished by the reduction of plant height through the incorporation of a recessive gene for short stature from a Chinese variety called ‘Dee-geo-woo-gen’ (DGWG). The ﬁrst variety developed was ‘IR 8’ in 1966 that also inherited from the other parents important traits as sturdy stems, heavy tillering, and dark green and erect leaves. Due to the advantage of this new type of rice plant, breeders throughout the world initiated crosses in order to develop short varieties. Short

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stature varieties are used now in most of the area cultivated with rice (Khush, 1993). The new plant type was so revolutionary that all major breeding programs in the world immediately started to develop short-stature varieties and more than a thousand varieties of different species are today of short stature. Several major genes affecting plant height have been reported for wheat, oats, barley, millet, sorghum, peaches, and so on. From all the dwarf mutants identiﬁed, the most important ones for breeding purposes have been those from ‘Norin10’ in wheat and ‘IR8’ in rice because of their positive effects over other agronomic important traits. The intense selection pressure for fertility and agronomic type in the ﬁrst crosses was of fundamental importance for the success of these genes. The complete sequencing and cloning of these genes will impose new possibilities of using them in improving this trait in other plant species.

II. THE BIOCHEMICAL BASIS OF THE DWARF PHENOTYPE Dwarﬁng genes have been instrumental in dissecting the gibberellins (GAs) biochemical pathway in some species and in elucidating the plant elongation process. In maize (Zea mays L.), rice (Oryza sativa L.), and pea (Pisum sativum L.), there are dwarf mutants defective for different steps in the GA pathway and that respond to the exogenous application of gibberellic acid (Phinney, 1984). In wheat (Gale and Yousseﬁan, 1985), rye (Börner, 1991), maize (Harberd and Freeling, 1989), and rice (Mitsunaga et al., 1994), dwarf mutants have been identiﬁed that are insensitive to the application of gibberellic acid and are dwarfed due to causes not directly related to GA biosynthesis. The early 13-hydroxylation GA biosynthetic pathway leading to active GA1 occurs in maize, rice, and pea (Phinney, 1984). Intermediates of this pathway have been identiﬁed in oat with gas-chromatographic spectrometry, indicating that the pathway also occurs in this species (Kaufman et al., 1976). In wheat, gibberellic acid response assays have been used as a breeding tool to identify the presence of insensitive dwarﬁng genes at the seedling stage (Yamada, 1990; Federizzi et al., 1992). The genetics of the Rht1 and Rht2 recessive dwarﬁng wheat genes were not clearly resolved until the GA-insensitive response assay was used in the genetic analysis, probably due to the moderate effects of these alleles in the wheat hexaploid background (Gale and Yousseﬁan, 1985). Because the dwarf phenotype may result from mutations related to the gibberellin biosynthetic or signal transduction pathway, the dwarﬁng genes have

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39

been classiﬁed as GA-sensitive or -insensitive, respectively. There have also been reported cases of dwarf transgenic plants resulting from the modiﬁed expression of phytochrome genes (Boylan and Quail, 1989, 1991). The occurrence of these mutants and their application to breeding differs from one species to another and is discussed below.

A. GA-SENSITIVE MUTANTS Shoot extension growth is one of the most studied physiological roles of GAs (Swain and Olszewski, 1996). Early experiments using exogenous applications of GA have identiﬁed several dwarf mutants that were defective in the production of at least one enzyme of the GA biosynthesis (Reid, 1990, 1993). These mutants respond to the application of exogenous GA with stem elongation and are called GA-sensitive mutants. A list of the better characterized GA-deﬁcient or -sensitive mutants indicating which is the defective enzyme involved is given by Hedden and Kamiya (1997). In maize, the an, d1, d2, d3, and d5 dwarﬁng genes have been characterized as recessive mutations that correspond to defective enzymes for the GA biosynthetic pathway (Fujioka et al., 1988; Hedden and Kamiya, 1997). These together with the D8 gene are called the “andromonoecious dwarfs” by Coe et al. (1988). Another class of mutants in maize, which includes the brachytic (br) mutations, is that of semidwarf or compact plants that show relatively little other morphological alterations and are conditioned by br1, br2, br3, bv1, cr, ct1, ct2, mi, na1, rd1, rd2, and td (Coe et al., 1988). We are not aware that the response of all these mutants to GA has been characterized. Although there are reports that br2, ctc1, and rd may have yield advantage, especially in high-density plantings (Nelson and Ohlrogge, 1957, 1961), the use of these mutants in corn breeding has been limited (Sprague and Dudley, 1988). Of the 20 mutants described in wheat, 16 are sensitive to GA, representing 80% of the total (Gale and Yousseﬁan, 1985; Konzak, 1987). Rht4, Rht6, Rht7, Rht8, Rht9, Rht11, and Rht17 are recessive genes; Rht12 is a strong dominant gene and all the others are partial or semidominant genes. Rht8 and Rht9 have been used in the European wheat varieties; Rht11 and Rht14 have had some use for durum or bread wheat varieties; and Rht15, Rht16, Rht18, Rht19, and Rht20 have only shown good potential but have not been extensively used (Gale and Yousseﬁan, 1985; Konzak, 1987). A list of the rye dwarﬁng genes has been described by B¨orner et al. (1996) and comprises 11 mutants. Of these, Ddw1, Ddw2 (dominant genes), and d2 (recessive gene) are responsive to GA. According to the authors, the best known short straw mutant in rye, which has been included in many Eastern European rye breeding programs, is the “EM1” mutant that carries the Ddw1 gene.

40

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There are eight dwarﬁng genes ofﬁcially described and classiﬁed in oat (Avena sativa L.), but only Dw6, Dw7, and Dw8 are still readily available (Milach et al., 1998). It is likely that the ﬁrst ﬁve described dwarﬁng genes have lacked utility due to the extreme dwarfness or meiotic irregularities of the lines that carry them (Marshall and Murphy, 1981). Genetic studies have shown that Dw6 (Brown et al., 1980) and Dw8 (Milach et al., 1998) are dominant genes and Dw7 is a partially dominant (Marshall and Murphy, 1981). An allele of Dw7 was identiﬁed in the short-stature cultivar ‘Curt’ (Federizzi and Qualset, 1989). All the three dwarﬁng loci available in oat are responsive to GA and likely involved in the GA metabolism (Milach, 1995). These genes have distinct effects on plant height components and likely different mechanisms of action (Milach, 1995). A double dwarf effect has been observed in crosses between lines with independent dwarﬁng genes (Milach et al., 1998) and likely resulted from the combination of these different mechanisms to reduce plant height. A feature of GA-sensitive mutants is that most of them are recessive and deﬁcient for GA due to a block in the biosynthetic pathway. Recessive genes involve the loss of wild-type function (Herskowitz, 1987). Interestingly, there are dominant or semidominant genes in different species that are responsive to applied GA. This is the case of Rht5, Rht12, Rht13, Rht14, Rht15, Rht16, Rht18, Rht19, and Rht20 of wheat; Dw6, Dw7, and Dw8 of oat; and Ddw1 and Ddw2 of rye, among others. Herskowitz (1987) suggested that dominant negative mutations can also result in loss of function and would include mutant polypeptides that when overexpressed disrupt the activity of the wild-type gene or polypeptides with inhibitory effect. It is possible that these genes are dominant mutations that regulate the GA metabolism. While GA-sensitive mutants have played a fundamental role in understanding the GA biosynthetic pathway, they have not had the same impact in terms of breeding applications. The likely reason for that is because their phenotype is usually more extreme than what is acceptable for plant height of a variety. In cereals, varieties that are too short in height may have problems during harvest and become even shorter in stress conditions. This is our experience in growing dwarf oats in Southern Brazil where genes that reduce plant height to 50 to 70 cm cannot be used because they usually end up producing genotypes that range from 35 to 50 cm across different environments.

B. GA-INSENSITIVE MUTANTS Another class of GA mutants is of those that do not respond to the exogenous application of GA. A fewer number of mutants have been identiﬁed that belong to this class compared to the higher number for the GA-sensitive mutants. On the other hand, some of the GA-insensitive mutants have been the widest used in plant breeding, as is the case of the Rht1 and Rht2 genes from ‘Norin10’ in wheat and sd1 from ‘Dee-geo-woo-gen’ in rice. These three genes have been the most

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41

extensively used in developing the wheat and rice semidwarf varieties throughout the world. Other GA-insensitive mutants that have been extensively study but have not had the same use for breeding are those that carry the d8 and d9 genes in maize (Harberd and Freeling, 1989; Winkler and Freeling, 1994) and the gai gene in Arabidopsis (Koornneeff et al., 1985). In barley, GA-insensitive dominant mutants have been recently identiﬁed and studied (Falk, 1994; Ivandic et al., 1999, B¨orner et al., 1999a). The Dwf 2 dominant mutant allele has a strong effect on the phenotype and plants that carry it may be too short for breeding purposes. Recently, B¨orner and associates (1996) have proposed a new nomenclature for the GA-insensitive Rht wheat alleles which takes into account the chromosome location with the homoeologous set number and assigns a lowercase letter to the allele present. According to this nomenclature the Rht1 = Rht-B1b, the Rht2 = Rht-D1b, the Rht3 = Rht-B1c, and the Rht10 = Rht-D1c. In wheat, there are two GA-insensitive loci that can carry different alleles, one on chromosome 4B and the other on 4D (B¨orner et al., 1996). The alleles identiﬁed for chromosome 4B are Rht-B1a (rht, tall allele), Rht-B1 (Rht1), Rht-B1c (Rht3), Rht-B1d (Rht1S), Rht-B1e (RhtKrasnodari 1), and Rht-B1f (RhtT. aetiopicum). The alleles identiﬁed for chromosome 4D are Rht-D1a (rht, tall allele), Rht-D1b (Rht2), Rht-D1c (Rht10), and Rht-D1d (RhtAi-bian 1a). Because the names Rht1, Rht2, and Rht3 are easier to be recognized by breeders, we use that nomenclature in this Chapter. Despite their importance, the role of these genes in decreasing plant height has not been fully understood. It is well known that the wheat mutants do not respond to the exogenous application of GA because they accumulate several times more GA than their tall counterparts (Radley, 1970), which indicates that they have a block in the utilization of GA (King, 1991). Swain and Olszewski (1996) suggested that since GAs are required for normal internode elongation, all mutants affected in stem growth by mechanisms other than altered GA metabolism can be considered as potential GA signal-transduction mutants. They deﬁne GA signal transduction as “the series of biochemical events leading from the perception of the active GA molecule to the ﬁnal response.” In a recent study, Peng et al. (1999a) reported that Rht1, Rht2, and D8 are orthologs of the Arabidopsis gibberellininsensitive (GAI ) gene and encode proteins that resemble nuclear transcription factors and contain an SH2-like domain, which indicates that they may participate in gibberellin signaling.

C. PHYTOCHROME MUTANTS There are studies in pea (Weller et al., 1994) and cucumber (Lopes-Juez et al., 1995) which indicate that type-B phytochromes modify the response to GA. Other

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evidences of the involvement of GAs and phytochromes come from studies with transgenic plants. Tomato transgenic plants transformed with the oat phytochrome A (phyA) gene are dwarf and dark green and accumulate anthocyanin (Boylan and Quail, 1989). Transgenic tobacco plants with the rice phyA are dwarf and dark green (Nagatani et al, 1991) and Arabidopsis plants with the oat phyA gene have short-hypocotyl elongation (Boylan and Quail, 1991). Arabidopsis transgenic plants have been produced that overexpress either the Arabidopsis or the rice phytochrome B ( phyB) genes; they also have a dwarf phenotype (Wagner et al., 1991). It appears that the overexpression of either phyA or phyB genes can cause dwarfness in different species, suggesting that these genes may play an important role in plant elongation. In oat, we have observed in growth chamber and greenhouse environments that seedlings of the ‘NC2469-3’ dwarf line with the Dw7 gene are dark green and accumulate anthocyanin, similarly to what happens to some of the transgenic phytochrome mutants. Because of the semidominant nature of the Dw7 gene, it is possible that this mutation results from the overexpression of a gene that can inﬂuence plant height, for instance one of the phytochrome genes. To investigate this hypothesis, we mapped the oat phyA and the rice phyB structural genes on the oat hexaploid RFLP map (O’Donoughue et al., 1995) and compared their map locations to that of the Dw7 gene. In a previous study (Milach et al., 1997), the Dw7 gene was mapped on linkage group 22 of the oat RFLP hexaploid map. We mapped the oat phyA gene on linkage group 28 and the rice phyB gene on the linkage group 30 of the hexaploid oat RFLP map (unpublished data). Because Dw7 is independent from the mapped phytochrome loci, as are also Dw6 and Dw8, it cannot be a mutation of these genes. In oat, three phytochromes have been identiﬁed: two in green oat leaves, which differ from the phytochrome that is most abundant in etiolated oat tissue (Wang et al., 1991). It is possible, however, that there is homeology between Dw7 and phytochrome genes. Because the hexaploid oat genetic map has more linkage groups than chromosomes and a chromosome homeologous series in oat is not completely established, it is difﬁcult to determine these relationships at the moment. It is also possible that Dw7 is a mutation in a phytochrome structural gene not investigated in this study or in a gene regulating phytochrome production. A barley mutant, BMR-1, has been recently identiﬁed which has a dwarf phenotype and contains higher levels of phytochromes A and B in the dark (Hanumappa et al., 1999). Further study is needed to determine if Dw7 is a photomorphogenic mutant. On the other hand, it is interesting to note that the wheat Rht1 and Rht2 and the maize D8 genes have been mapped closely to a PhyA gene (Peng et al., 1999a). Swain and Olszewski (1996) pointed out that very little has been done to investigate the interaction between phytochromes and GAs using a genetic approach and that mutants involved in both GA and phytochrome signal transduction pathways will help to elucidate these questions. The potential of phytochrome mutants for breeding short-stature plant varieties remains to be investigated.

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43

III. DWARFING GENES AND THEIR USE FOR BREEDING Although several dwarﬁng genes have been identiﬁed and studied in different species, only a few have had wide application for plant improvement (Table I). The most universally accepted dwarﬁng genes have been the Rht1 and Rht2 of wheat, probably because they have been spread throughout the world in the wheat germplasm developed by CIMMYT and are present in around 90% of the world acreage of semidwarf wheats (Dalrymple, 1986). A similar situation occurred with the sd1 locus of rice that has been used by IRRI in developing semidwarf germplasm that was sent to several countries of the world. There have been at least 60 dwarﬁng genes identiﬁed in rice and a list of them can be found in Futshura and Kikuchi (1997). Even though a large number of semidwarf high-yielding rice varieties have been developed, semidwarﬁng genes of practical importance have been limited only to the sd-1 locus (Futshura and Kikuchi, 1997). Different alleles of this locus have been used by the American and Asian countries (Rutger, 1983). The reasons for the success of the Rht1, Rht2, and sd1 genes are probably due to the great efforts that were made to transfer them to good genetic backgrounds early when they were ﬁrst identiﬁed and because they allowed the production of varieties with semidwarf desirable plant height, combining height reduction of about 20% with signiﬁcant increases in spikelet fertility and yield (Gale and Yousseﬁan, 1985). Their positive pleiotropic effects on different traits made these genes very attractive for breeding purposes. Other Rht genes have been most used in Europe, such as Rht8 and Rht9 (Gale and Yousefﬁan, 1985). The same is the case of the Ddw1 gene of rye (B¨orner et al., 1996). Because the dwarf phenotype in oat is often accompanied by decreases in yield and changes in internode and panicle length, the use of dwarﬁng genes has been limited in oat breeding programs. Dw6 has been used for cultivar development in Australia (Anderson and McLean, 1989) and in the United States (Marshall et al., 1987; Meyers et al., 1985; and D. D. Stuthman, University of Minnesota, unpublished observations), and Dw7a has been used to develop the short-stature cultivar ‘Curt.’ Although the Dw6 gene causes a failure of the panicle to fully emerge from the leaf sheath, panicle exertion genes have been used to help correcting this problem for cultivar development (Farnham et al., 1990a). However, transferring both Dw6 and panicle exertion genes to new oat lines is a difﬁcult task, and seed size often is reduced in the derived lines. The Dw8 gene causes an extreme phenotype in a series of different genetic backgrounds, which is not desirable for oat breeding (Milach et al., 1998). There is, therefore, an urgent need to identify and use new sources of dwarﬁsm in oat, which would produce a phenotype similar to that of the Rht1 and Rht2 genes in wheat.

Table I Most Important Dwarfing Genes Used in Cereal Improvement, Their Inheritance, Chromosome Location, and Marker Association Response to GAa

Inheritance

Chromosome location

‘OT207’

S

dominant

18

Xumn 145B (3.3 cM)

Brown et al. (1980) Milach et al. (1997)

Dw7

‘NC2469-3’

S

semidominant

19

Xcdo 1437B (4.3 cM)

Marshall and Murphy (1981) Milach et al. (1997)

denso

‘Triumph’

S

recessive

3HL

WG110 (12.8 cM) OPH7-H800 (31.7 cM)

Barua et al. (1993) and Laurie et al. (1993)

GPert

‘Golden Promise’

S

recessive

5H

‘Dee-geowoon-gen’

I

recessive

1

Species

Gene

Avena sativa L.

Dw6

Hordeum vulgare L.

Oryza sativa L.

sd-1

Source

Marker Linkage

—

Key references

Thomas et al. (1984)

Xrg220 (0.3 cM)

Cho et al. (1994)

Secale cereale L.

Ddw1

EM-1

S

dominant

5RL

Xwg199 and Hp1 (6.1 cM) ␤-amy-R1 (12.7 cM)

Korzun et al. (1996) Borner et al. (1999)

Triticum aestivum L.

Rht1

‘Norin10’

I

part.dominant

4BS

Rht2

‘Norin10’

I

part.dominant

4DS

Rht8 Rht9 Rht12

‘Mara’, Sava’ ‘Mara’ Karcag522

S S S

recessive recessive dominant

2DS 7BS 5AL

Rht14

‘Castelporziano’

S

semidominant

?

Xprs144 (11.0 cM) Xmwg634 (30 cM) Xprs921 (0.8 cM) Xmwg634 (1.5 cM) — — ␤-amy-A1 (2.5 cM) Xprs1201 (15 cM) —

Konzak (1987) B¨orner et al. (1997) Konzak (1987) B¨orner et al. (1997) Konzak (1987) Konzak (1987) Konzak (1987) Korzun et al. (1997) Konzak (1987)

a

S, GA-sensitive; I, GA-insensitive.

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In the environment of the South Cone of South America, the Dw6 or Dw7 genes produce oat plants that are approximately 77 cm in normal environmental conditions, but that can be of 50 cm or less and have reduced biomass and yield under stress conditions. The use of one or two recessive genes from the Brazilian oat varieties ‘UFRGS 7’ and ‘UFRGS 15’ give an adequate plant height for preventing lodging in excellent conditions (near 110 cm) and a plant height near 90 cm that will be enough for producing acceptable grain yield in stress environments (Federizzi et al., 1996). These recessive genes have been intensively used for developing Brazilian oat varieties and are GA-sensitive (unpublished data). A short-statured barley mutant stock containing the sdw gene was obtained from Norway in 1957 and transferred to American germplasm (Rasmusson et al., 1973). This gene has been widely used in breeding programs in the United States and Canada (Rasmusson, 1991). The denso and Gpert (from ‘Gold Promisse’ cultivar) dwarﬁng genes are present in the European barley germplasm and have been used for variety development (Ivandic et al., 1999). Quinby and Karper (1954) found four recessive dwarﬁng genes, dw1, dw2, dw3, and dw4, present in different combinations in sorghum varieties grown in the United States. Pleiotropic effects of these genes (except for dw4) on yield and yield components have been found (Casady, 1965; Graham and Lessman, 1966; Blum et al., 1997), probably hindering their potential for breeding. In foxtail millet (Setaria italica Beauv.), three new sources of dwarf germplasm have been identiﬁed recently and characterized as GA-insensitive (Dineshkumar et al., 1992). All three sources have a major recessive gene, but it is not mentioned if these alleles are from different genes or belong to a common locus. The authors suggested that the high-yielding nature and superior morphological frame of these dwarfs make them ideal for breeding purposes.

IV. BREEDING CHALLENGES AND VARIETIES DEVELOPED A. BREEDING CHALLENGES Although major dwarﬁng genes are easy to transfer by crosses, breeding semidwarf varieties using these genes has not always been so straightforward. One of the reasons for this is that not in all crops there are genes available that reduce plant height and have positive pleiotropic effects on grain yield, as it is the case for some of the wheat dwarﬁng genes. In sorghum (Windscheffel et al., 1973; Campbell et al., 1975), pearl millet (Bidinger and Raju, 1990; Rai and Rao, 1991), and oat (Meyers et al., 1985) dwarﬁng genes have generally negative effects on grain yield.

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One way of avoiding this problem was ﬁrst proposed by Law et al. (1978) with the “tall-dwarf” model for bread wheats carrying dwarﬁng genes that did not have positive effects on yield. In this model, the authors proposed to ﬁx the dwarﬁng genes in populations at an early stage, followed by positive selection for height in subsequent generations. Testing this hypothesis in pearl millet, Bidinger and Raju (1993) were able to produce short-stature hybrids with increased grain yield. Another situation is that semidwarf varieties may fall below their potential in terms of farm yields because of poor seedling establishment and low early vigor that has been associated to the presence of the GA-insensitive genes (Allan, 1980; Niklas and Paolillo, 1990). In the presence of these genes, cell elongation in juvenile leaf and stem tissue is reduced (Hoogendoorn et al., 1990), resulting in a shorter coleoptile and a plant with decreased initial vigor (Richards, 1992). Attempting to minimize this problem, Rebetzke et al. (1999) studied the relationship of plant height and coleoptile length in order to identify ways to breed shorter Australian wheat varieties with longer coleoptiles. They found that for GAsensitive wheats, height and coleoptile length appeared to be largely under independent genetic control, which indicates that GA-sensitive Rht genes could be used to select for short height and longer coleoptile wheats with improved establishment and seedling vigor. A similar problem identiﬁed in rice has also been overcome with the development of semidwarf germplasm that can produce long coleoptile (Dilday et al., 1990). Wheat in Brazil is grown in two major macro environments; in soils with aluminum (above parallel 24◦ S) and in soils without aluminum (below parallel 24◦ S). Since the early 1970s, wheat lines from CIMMYT have been introduced by different Brazilian breeding programs. It is interesting to observe that the varieties with dwarﬁng genes have been extensively used only in soils without aluminum. In the past 3 decade, few varieties carrying Rht genes were released in southern Brazil. While the major gene for aluminum tolerance present in the Brazilian wheat germplasm is also located on chromosome 4D (Lagos et al., 1991; Riede and Anderson, 1996), there is genetic evidence that these two traits are independent. So, the restricted use of the dwarf phenotype is probably due to the variation on plant height and total biomass produced in soils with high levels of aluminum. This can be illustrated by observing the plant height obtained for near isogenic lines from the Brazilian variety ‘Maringá’ carrying Rht1, Rht2, or Rht1 + Rht2 genes grown in two different environments. In a nonstress environment, without aluminum, the average plant height is 115 cm for ‘Maring´a,’ 93 cm for an isogenic line with Rht1, and 65 cm for an isogenic line with Rht1 + Rht2 (Fantini et al., 1994). In contrast, in soils with aluminum, 85 cm is observed for ‘Maring´a,’ 74 cm for isogenic lines with Rht1 or Rht2, and 54 cm for isogenic lines with Rht1 + Rht2 (Zanata and Oerlecke, 1991). Rosa and Camargo (1991) demonstrated that tall wheat varieties are more efﬁcient than short varieties in the uptake of phosphorus in soils with aluminum and low P availability.

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In oats, in order to have lines with short plant height that are very competitive in the yield regional trials, we have developed a screening strategy for segregating populations with genes for short stature, selecting among F3 and F4 lines for high vigor and apparent biomass at the six- to seven-leaf stages (Pacheco et al., 1997). High frequency of tall off-types has been found in some semidwarf varieties, as in the UK variety ‘Brigand,’ which carries the Rht2 gene. According to King and collaborators (1996), this frequency was sufﬁciently high to cause the variety to fail United Kingdom statutory uniformity tests. The authors prevented the appearance of tall off-types in lines carrying a translocation involving the 4D chromosome, where it is located the dwarﬁng gene.

B. VARIETIES DEVELOPED The ﬁrst semidwarf rice cultivar released in California was Calrose 76, in 1976. In 1981, the seven cultivars with the Calrose 76 semidwarf gene were grown on an estimated 54% of the 245,000 ha of rice in California (Rutger, 1983). Other 35% of the area was with the semidwarf M9, which carries an allele of sd1 derived from IR8. In California, as well as in most of other parts of the world, none of the semidwarf sources nonallelic to sd1 (and to the DGWG source) has become important in breeding (Rutger, 1983; Futshura and Kikuchi, 1997). A study conducted in 1985 by Worland (1986) indicated that 25% of the European winter wheat varieties carried GA-insensitive dwarﬁng genes and that their use would be limited to the areas not subjected to temperature stress at a critical growth stage. By then, only 4 of 18 French varieties carried insensitive dwarﬁng genes. In another study conducted 8 years later, the same author pointed out that there had been a swing toward the acceptance of these genes in French varieties because they found 10 of the 17 varieties carrying insensitive dwarﬁng genes (Worland et al., 1994). A total of 49 GA-insensitive varieties were identiﬁed among the 127 entries tested. Although the authors have not mentioned this, it is possible that some of the remaining 78 GA-sensitive varieties would carry either Rht8 or Rht9 dwarﬁng genes. They also found that all but 1 UK wheat variety carried insensitive genes. On the other hand, only 11 of 49 Germany varieties tested in their study were GA-insensitive. These data show that the use of dwarﬁng genes in wheat varieties does change from one country to the other. The authors explained that these differences are associated to the fact that in places where the varieties are subjected to temperature stress during the critical growth stage of ﬂag leaf to ear emergence, as seems to be the case in Germany during the summer, there is not much advantage of growing GA-insensitive wheat varieties. They argue that one way of increasing the use of these genes in German varieties would be to combine photoperiod-insensitive with dwarﬁng genes to escape the summer temperature stress.

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Pecetti and Annicchiarico (1998), studying the agronomic value and plant type of four groups of Italian durum wheats, identiﬁed a signiﬁcant decrease in plant height from 132 to 83 cm on average in varieties from different eras. Although they did not indicate which dwarﬁng genes were present in varieties of groups three and four, in group 3 they included ‘Castelporziano,’ which according to Konzak (1987) carries the Rht14 GA-sensitive dwarﬁng gene. Their group 4 includes varieties developed from crosses between Italian and CIMMYT materials and it is possible that some of these have GA-insensitive genes transferred from the CIMMYT bread wheat program. In Brazil, of 328 improved wheat varieties released from 1922 to 1992, 32 were direct introductions from CIMMYT (Lagos, 1983; Sousa, 1994) carrying genes for short stature grown by farmers in soils without aluminum. In contrast, for soils with high levels of aluminum, only 4 varieties may have dwarﬁng genes. Many crosses were made between Brazilian varieties and different European sources of Rht8 and Rht9 but no varieties were released with these genes. Among the semidwarf barley varieties released in North America are ‘Kombar,’ ‘Kombyne,’ ‘Samson,’ ‘Duke,’ and ‘Micah’ (Rasmusson, 1991). Several other released cultivars, tracing to composite populations, are also suspected to have the sdw dwarﬁng gene.

V. PLEIOTROPIC EFFECTS OF DWARFING GENES Dwarﬁng genes can have positive and negative pleiotropic effects in other plant traits, probably because of their involvement with the GA biosynthetic or signal transduction pathways, which is a growth regulator involved in many of the plant physiological processes. Gale and Yousseﬁan (1985) pointed out that studies that provide reliable information about the pleiotropic effects of Rht genes are those that involve either comparisons of groups of random lines derived by selﬁng single hybrids or comparisons of isogenic lines in which the different alleles have been isolated in a speciﬁed genetic background by backcrossing. Care in using the appropriate genetic materials and in minimizing lodging of the tall genotypes is fundamental in avoiding confounding effects that may cause misinterpretation of results. The pleiotropic effects of the GA-insensitive genes Rht1, Rht2, and Rht3 have been the most extensively studied and are reviewed elsewhere (Gale and Yousseﬁan, 1985; Gent and Kiyomoto, 1998). A summary of the main studies carried out up to 1985 on the pleiotropic effects of Rht alleles on plant height, yield, yield components, and grain protein is presented by Gale and Yousseﬁan

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(1985). A more recent review by Gent and Kiyomoto (1998) discuss the physiological and agronomic consequences of Rht genes in wheat. Because this topic has been well reviewed elsewhere, here we attempt to summarize some of the most important pleiotropic effects on dwarﬁng genes that have consequences for plant improvement.

A. MORPHOLOGICAL AND PHYSIOLOGICAL TRAITS Most of the dwarﬁng genes appear to be active throughout the plant cycle. So, the ﬁrst effects of these genes can be seen at the ﬁrst stages of plant development. This is the case with the Rht3 gene, which causes a marked reduction in the rate of ␣-amylase synthesis during germination (Flintham and Gale, 1982; Gale et al., 1987). The positive consequence of this is on reducing preharvest sprouting damage to bread-making quality in wheat. Unfortunately, because it causes extreme dwarﬁsm, this gene has had limited value in improving wheat for reduced preharvest sprouting. An interesting feature of Rht3 is that it also inhibits ␣-amylase synthesis during the latter part of grain ripening (Mrva and Mares, 1996), which shows that this gene has constitutive expression in the plant. A less pronounced effect on reducing the expression of late maturity ␣-amylase is found for Rht1 and Rht2. After germination, the effects of Rht genes in wheat and the sd1 dwarﬁng gene of rice can be seen on decreasing the length of the coleoptile, which may reduce stand establishment (Allan, 1980; Dilday et al., 1990; Niklas and Paolillo, 1990). Subsequently there is a decrease in leaf and stem size (Lenton et al., 1987). Nilson et al. (1957) were the ﬁrst to demonstrate that reduced height of some semidwarf cultivars of wheat was attributable to smaller cells. Alan et al. (1962), investigating the causes for reduced coleoptile length in two selections with GA-insensitive genes from ‘Norin10,’ concluded that it was related to fewer parenchyma cells, while coleoptiles in a Rht3 genotype were shorter because of smaller cells. The authors suggested that these two dwarﬁng genes operate in a qualitatively different manner. In barley, dwarf mutants are usually associated with fewer cells, but reduction in cell size has also been noted (Blonstein and Gale, 1984). The dwarf phenotype in oat has usually been associated with decreases in yield and internode and panicle length (Brown et al., 1980; Marshall and Murphy, 1981; Meyers et al., 1985). These drawbacks have probably caused limited use of oat dwarﬁng genes in breeding programs. The effect of the Dw6 gene on the plant height components of internode number and length and panicle length was studied by comparing OT207 with its tall mother-line OT184 (Brown et al., 1980). The authors described a signiﬁcant difference between semidwarf and tall lines in the

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length of the three uppermost internodes, particularly the peduncle. This has been associated with the failure of the panicle to fully emerge from the leaf sheath, an undesirable trait in oat breeding. Panicle exertion genes correct this problem in lines carrying the Dw6 gene (Farnham et al., 1990a). In a more recent study comparing oat dwarf lines carrying the Dw6, Dw7, or the Dw8 with they tall counterparts, it was observed that the three oat dwarﬁng loci reduced plant height in different ways (Milach, 1995). The Dw6 gene reduced primarily the length of the three uppermost internodes and did not affect internode number. The Dw7 gene shortened all internodes but particularly the ﬁrst and the last, and reduced internode number in the dwarf line. The Dw8 gene signiﬁcantly shortened all internodes of the Japanese lines but did not affect internode number. In oat, the lower internodes are the ﬁrst to differentiate and elongate. Internodes elongate basipetally from the intercalary meristem, a typical pattern of elongation in grasses (Kaufman et al., 1965). Although the Dw6 gene is inﬂuencing the elongation of the ﬁrst internodes to some extent, the major effect of this gene appeared to be later in development. This hypothesis is supported by the ﬁndings of Farnham et al. (1990b), who were able to reverse to a great extent the effect of the Dw6 gene on peduncle elongation by applying GA3 to the dwarf plants at the boot stage. According to Kaufman and Brook (1992), oat internodes elongate in a sequential fashion: As the previous internode ceases elongating, the next enters its burst of growth. Because of this growth pattern, it is possible that the effect of Dw6 is speciﬁc to the elongation of the upper internodes.

B. YIELD AND YIELD COMPONENTS Many reports indicate that the Rht1 and Rht2 wheat genes have a positive effect on yield (Gale and Yousseﬁan, 1985; Fischer and Quail, 1990; B¨orner et al., 1993). One of the main reasons for this yield advantage appears to be a pleiotropic positive effect of these genes on spikelet fertility (Gale and Yousseﬁan, 1985), due to increased kernel number (Fischer and Stockman, 1986). This occurs despite the fact that dwarf plants have reduced lamina elongation from smaller epidermal cells (Keyes et al., 1989). There are reports that indicate that the reduction in leaf cell sizes concentrates the leaf photosynthetic machinery, thereby increasing photosynthetic capacity per unit leaf area or leaf weight (LeCain et al., 1989; Morgan et al., 1990). An increase on radiation use efﬁciency was observed during the postanthesis in Rht lines and, according to Miralles and Slafer (1997), this appears to be closely and positively associated with the number of grains per unit biomass at anthesis. It has been observed that the increase in grain number per spike is accompanied by a decrease in kernel weight (Brandle and Knott, 1986; Nizam Uddin and Marshall, 1989), although this is not sufﬁcient to decrease the yield advantage of the dwarf Rht lines.

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VI. MOLECULAR MAPPING OF DWARFING GENES Classic genetics and cytogenetics were extensively used in the 1970s to place dwarﬁng genes on chromosomes and linkage groups. Because these genes have a major effect on the phenotype and are highly heritable, placing them on chromosomes and linkage groups had a double advantage toward understanding their genetic relationships as well as using them as morphological markers to map other traits. So, Rht1 and Rht3 genes (Gale and Marshall, 1976) were placed on former chromosome 4A, and Rht2 was placed on chromosome 4D (Gale et al., 1975). Years later, due to a change on the wheat genome nomenclature, 4A became chromosome 4B. Crosses between Rht1 and Rht3 varieties revealed that these were two alleles of the same locus, conﬁrming their assignment to the same chromosome. In rice, several dwarﬁng genes have been placed to chromosomes since the 1960s (Hsiehh and Yen, 1966; Iwata and Omura, 1973). Barley GPert dwarﬁng gene was placed on chromosome 5H through a combination of morphological markers and translocation stocks (Thomas et al., 1984). Using trisomic analysis, Sturm and Engel (1980) located rye dwarﬁng gene Ddw1 on chromosome 2, which corresponds to chromosome 5R. The advent of molecular markers on the 1980s made possible the construction of more detailed linkage groups and genetic maps, making easier the association of important genes to genetic markers. Thus, a number of important dwarﬁng genes from different species were associated to molecular markers (Table I) and all this information today allow us to study the genetic relationships of different mutants within and across the species. The sd-1 semidwarﬁng gene was mapped on rice chromosome 1 (Cho et al., 1994). Huang et al. (1996) present a list of 13 other rice dwarﬁng genes, their linkage to markers, and chromosome position. Except for chromosomes 7, 8 , 9, and 10, 1 or more dwarﬁng genes have been found in all other rice chromosomes. The GA-insensitive Rht-B1 and Rht-D1 wheat dwarﬁng loci were mapped in three F2 populations segregating for Rh-B1c (Rht3), Rht-D1b (Rht2), or Rht-D1c (Rht10) (B¨orner et al., 1997). Rh-B1c was found linked distally to the RFLP markers Xprs144 (11.9 cM) and Xprs584 (17.8 cM) and proximal to Xmwg634 (30 cM) on chromosome 4BS in the centromere region. Rht-D1c was closely linked to the distally located markers Xprs921 (0.8 cM) and Xmwg634 (1.5 cM). Sourdille and associates (1998), mapping the same loci independently, found similar results. Korzun et al. (1997) mapped Rht12 on chromosome 5AL with a genetic distance of 15 cM to marker Xprs1201. The ﬁrst dwarﬁng gene mapped in barley was denso, which was located on the long arm of chromosome 3HL independently by two research groups (Barua et al., 1993; Laurie et al., 1993). The Dwf 2 gibberellic acid insensitivity dwarfing gene was mapped recently (Ivandic et al., 1999) on the short arm of barley

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chromosome 4H proximal to microsatellite XhvOle (5.7 cM) and distal to RFLP marker Xmwg2299 (18.3 cM). This gene was found to be at a homoeologous position to the multiallelic Rht-B1 and Rht-D1 loci in wheat, suggesting that there is syntheny between these GA-insensitive dwarﬁng genes within the Triticeae. Other two genes, gai (GA-insensitive) and gal (GA-less sensitive) were mapped on the centromere region and on the long arm, respectively, of the barley chromosome 2H (B¨orner et al., 1999a). Three dwarﬁng genes have been associated to molecular markers in rye. These are ctc2 linked to Xembp (5 cM) and Xprs1194 (13 cM) and located on the chromosome 5RL (Plaschke et al., 1993); ctc1 mapped close to the centromere between markers Xprs163 (1 cM) on the short arm of chromosome 7R and Xa-Amy-2 on the long arm of 7R (3 cM) (Plaschke et al., 1995); and Ddw1, which is distal to Hp/Xwg199 (5.6 cM) and proximal to the isozyme marker ␤-amy-A1 (11.5 cM) on chromosome 5RL (Korzun et al., 1996). Using bulked segregant analysis (BSA) and F2 RFLP linkage data, three dominant oat dwarﬁng loci were mapped to different regions of the oat genome. Dw6, in oat line OT207, was 3.3 ± 1.3 cM from the Xumn145B locus, which has not been placed on the hexaploid oat map. Dw7, in line NC2469-3, was 4.3 ± 2.3 cM from Xcdo1437B and 33 ± 4.1 cM from Xcdo708B. This placed Dw7 in linkage group 22. Dw8, in the Japanese lines AV17/3/10 and AV18/2/4, mapped 4.9 ± 2.2 cM from Xcdo1319A in an AV17/3/10 × Kanota F2 population and 6.6 ± 2.6 cM from it in an AV18/2/4 × Kanota population. This placed Dw8 in linkage group 3. Aneuploid analysis of markers linked to the dwarﬁng genes located Dw6 on the smallest oat chromosome (chromosome 18) and Dw7 on the longest satellited chromosome (chromosome 19) (Milach et al., 1997).

A. ASSOCIATION TO QUANTITATIVE TRAIT LOCI FOR PLANT HEIGHT AND LODGING RESISTANCE The location of major genes and quantitative trait loci (QTLs) on linkage maps using DNA technology provides insights about the association between qualitative and quantitative gene loci. Robertson (1985) suggested that alleles with qualitative effects may represent the extreme of a spectrum of possible alleles at a locus. Paterson et al. (1995) noted the existence of both qualitative and quantitative loci at corresponding map positions among several of the cereals for traits that have been involved in the domestication of these species for grain production. The identiﬁcation and location of qualitative and quantitative trait loci on DNA linkage maps is important for better understanding the basis of quantitative trait inheritance and searching for genomic regions of interest in crop breeding. There are several examples of QTLs for height that have been mapped on close proximity to major dwarﬁng genes. Of the three QTLs identiﬁed for height in

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a double haploid population from a cross between two spring barley varieties (Blenheim × Kym), that with largest effect was mapped on the region containing the denso dwarﬁng gene (Bezant et al., 1996). The other two were in regions previously identiﬁed as carrying the Sh and Sh2 vernalization response genes. A total of 23 QTLs were located in all 12 rice chromosomes on RFLP maps of a total of ﬁve mapping populations, and 8 of these QTLs were shared by at least two populations (Huang et al., 1996). The position of the 23 mapped QTLs were then compared to that of 13 major dwarﬁng genes previously linked to RFLP markers. The authors found that the 13 dwarﬁng genes were in close proximity to the QTLs, providing evidence to support Robertson’s (1985) hypothesis. Similar results were obtained for sorghum, where four QTLs detected for plant height corresponded to map positions of dw1, dw2, dw3, and dw4 dwarﬁng genes (Pereira and Lee, 1995). The evidence of a major genes–QTL association became stronger comparing the sorghum and maize data for plant height because (a) three QTLs mapped in sorghum were correspondent to QTLs in maize for plant height, (b) at the QTL positions on chromosome 1 of maize is the br1 dwarﬁng gene and at A of sorghum is the dw3 dwarﬁng gene, (c) the maize chromosome 9 d3–QTL region (Beavis et al., 1991) corresponded to the sorghum chromosome H dw2– QTL region, and (d) the maize chromosome 6 Py1–QTL region (Veldboom et al., 1994) corresponded to the sorghum chromosome E dw4–QTL region (Pereira and Lee, 1995). Thus, across three regions and two different species QTLs and major dwarﬁng genes mapped coincidentally to the same approximate positions. A QTL for plant height was mapped in rye close to the Ddw1 dwarﬁng gene (B¨orner et al., 1999b). In oat, the location of Dw7 on linkage group 22 of the hexaploid oat RFLP map coincides with a major QTL for plant height detected in the Kanota × Ogle mapping population (Siripoonwiwat et al., 1996). QTLs for lodging resistance and plant height were identiﬁed in a segregating wheat × spelt population (Keller et al., 1999). The authors indicate that a QTL for plant height located on close proximity to markers linked to the Rht12 locus is strong evidence for an allelic relationship between QTL and dwarﬁng locus. Thus the locations of major dwarﬁng loci on DNA linkage maps appear to be a starting point for the identiﬁcation of genomic regions which control plant height in different species. Another interesting association reported has been that found between dwarﬁng and rust resistance genes. Knott (1989) found that the dominant dwarﬁng gene present in the wheat cultivar Webster is closely linked to the Sr30 stem rust resistance gene located on chromosome 5D. Resistance to Septoria tritici Blotch in winter wheat is associated with the presence of the dwarﬁng gene Rht2 (Baltazar et al., 1990). In oat, marker Xumn145B, linked to the Dw6 dwarﬁng gene, appears to be on an important region of the oat genome that contains several important and interesting genes, such as Pc 91 (Rooney et al., 1994a), which is a complex

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locus of various rust resistance genes (B.-C. Wu, personal communication). The implication of these results for oat breeding is that the dwarﬁng gene might be used as a morphological marker to select lines for rust resistance. However, a 3-point genetic analysis still needs to be carried out to determine the genetic order and the distance among Xumn145B, Dw6, and Pc 91. Also, as for Dw6, Dw7 on the hexaploid oat map is located near a disease resistance gene: Pg13, a stem rust resistance gene located in linkage group 22 by O’Donoughue et al. (1996).

B. COMPARATIVE MAPPING TO IDENTIFY ORTHOLOGOUS DWARFING GENES Molecular linkage maps are available for many agronomically important species. In the case of cereal crops, a detailed comparison of maps using common markers shows that there is a extensive collinearity among species within Triticeae. As several dwarﬁng genes have been placed on the cereal maps, it is possible to study their homeology. Comparative mapping for the known GA-insensitive dwarﬁng genes of wheat, rye, and barley has been done recently by B¨orner and associates (1998). They concluded that GA-insensitive dwarﬁng genes are unrelated across species and belong to four different homeologous groups (group 4 of wheat, groups 5 and 7 of rye, and group 2 of barley). On the other hand, they found that the GA-sensitive genes Rh12 of wheat and Ddw1 of rye are members of a homeologous series within Triticeae. In a subsequent study, however, the Dwf2 barley GA-insensitive gene was found to be homeologous to Rht-B1c and Rht-D1c of wheat (Ivandic et al., 1999). Saghai Maroof et al. (1996) noted the potential orthology of the rice sd-1 and the barley sdw-b genes, based on their common comparative map positions, and Van Deynze et al. (1995a) noted putative orthologous loci for dwarﬁng located on homeologous chromosomes Triticeae 2L, rice 4, and maize 2 or 10. There is currently no evidence for homology among the oat chromosome regions carrying the three dominant dwarﬁng genes nor of these regions with dwarfing genes in wheat and rye (Van Deynze et al., 1995a; B¨orner et al., 1996), but homeology is difﬁcult to determine in hexaploid oat, as the species is characterized by numerous chromosomal rearrangements including translocations, duplications, and inversions (Rooney et al., 1994b; O’Donoughue et al., 1995; Van Deynze et al., 1995b; Leggett and Markhand, 1995).

C. MAPPING AS A BASIS FOR CLONING All the genetic and biochemical information available for various dwarﬁng genes make them very good candidates for cloning and sequencing.

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One of the ﬁrst dwarﬁng genes to be cloned and characterized was gai of Arabidopsis thaliana (Peng et al., 1997, 1999b). Sequencing this gene has revealed that the gai allele encodes a mutant protein lacking 17 amino acids from near the terminus that is thought to confer the altered gibberellin responses characteristic of the gai mutant (dwarf). The wild-type allele, GAI, encodes a protein containing features that are characteristic of transcription factors. Based on this information, Peng and associates (1999a) searched the database and found a rice expressedsequence tag (EST; D39460) that encoded a potential polypeptide containing a sequence nearly identical to the 17 amino acids lacking in the gai mutant allele. Using this EST they were able to isolate a wheat complementary DNA C15-1 that ended up being a product of one of the Rht homoalleles. Mappings of C15-1 place it on wheat chromosome 4B on the region that is homeologous to wheat chromosome 4D (where Rht-D1b is located), rice chromosome 3, and maize chromosome 1 (where D8 is located). This is strong evidence that C15-1 is in fact associated with the GA-insensitive phenotype in all these species, a nice example of how mapping can be used to support the cloning and characterization of these genes. C15-1 was then used to isolate genomic DNA clones containing the putative Rht-B1a and Rht-D1a (the RhtB1 and RhtD1 wild-type alleles) and maize d8 (the D8 wild-type allele) genes. Comparative sequencing of all these genes revealed an 58% similarity between both Rht-D1a and d8 and GAI and a very similar carboxyterminal region in all of them (Peng et al., 1999a). An SH2-like domain was identiﬁed within the C-terminal region, which acts as a transcriptional regulator. The authors concluded that these dwarﬁng GA-insensitive genes encode proteins that resemble nuclear transcription factors and contain an SH2-like domain, indicating that phosphotyrosine may participate in gibberellin signaling. Using the mutant GAI allele (dwarf) they were able to produce transgenic rice plants with reduced response to gibberellin and dwarf phenotype. While this has been a great contribution to our understanding of the GA-insensitive mutants, it remains to be shown if transferring these genes will increase yield in a range of other crop species.

VII. CONCLUDING REMARKS In this Chapter we attempted to summarize the main aspects of biochemistry, genetics, physiology, agronomy, plant breeding, and molecular biology that have contributed to our understanding of the most important dwarﬁng genes available for plant improvement. The research conducted with different mutants of these genes has had a great contribution toward both the basic and applied areas and it is a model of how to integrate basic and practical knowledge in agriculture. Genetics has allowed us to understand the inheritance and biochemistry of the defective enzymes in some of the dwarf mutants. The consequence was the

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elucidation of the GA biosynthetic pathway for different species. Physiology investigated the many important positive and negative effects of the dwarﬁng genes for plant height and other traits. The breeding experience from different crops showed us that only a few dwarﬁng genes have had a real impact for plant improvement, which has not occurred in all plant species. Molecular biology allowed us to clone and sequence some of these genes and to better understand the possible action of GA-insensitive genes in the GA signaling transduction pathway. All this knowledge will be fundamental to our next step in using the dwarﬁng genes for plant improvement. This will provide the possibility of transferring them through the use of transgenic technology to species where valuable mutants for breeding have not yet being identiﬁed. Although there is a great expectation that these genes will have positive effects for plant height and other traits in a range of plant species, these will have to be investigated through the interaction of all the research areas that have been so fundamental to our current knowledge of the plant dwarf phenotype.

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A REVIEW OF THE EFFECT OF N FERTILIZER TYPE ON GASEOUS EMISSIONS Roland Harrison1 and J. Webb2 1

ADAS Consulting Ltd. ADAS Boxworth Boxworth, Cambridge CB3 8NN, United Kingdom 2

ADAS Consulting Ltd. ADAS Wolverhampton Wolverhampton WV6 8TQ, United Kingdom

I. Introduction A. The Need for Abatement of Emissions of Nitrogenous Gases from Agriculture B. Previous Reviews of the Effect of N Fertilizer Type on Ammonia Emissions II. The Processes Controlling Emissions of Nitrogen Gases from Fertilizers A. Ammonia Volatilization B. Nitrous Oxide and Nitric Oxide Emissions III. Measurements of Ammonia Emission Following Nitrogen Fertilizer Application A. Field Measurements of Ammonia Emission B. Agronomic Evaluations: Effects on Fertilizer Efficiency C. Laboratory Measurements D. Emissions from Fertilizer Solutions E. Urease Inhibitors IV. Ammonia Emission Factors for Nitrogen Fertilizers V. Measurements of Nitrous Oxide Emissions Following Nitrogen Fertilizer Applications A. Comparative Studies of Emissions B. The Effect of Nitrification Inhibitors on Nitrous Oxide Emissions VI. Nitrous Oxide Emission Factors for Nitrogen Fertilizers VII. Nitric Oxide Emissions from Nitrogen Fertilizers VIII. Summary and Conclusions A. Ammonia B. Nitrous Oxide C. Nitric Oxide D. Overall References

65 Advances in Agronomy, Volume 73 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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Between 10 and 20% of the N in fertilizers applied as urea is lost to the atmosphere as ammonia (NH3). In contrast only small (<4% of N applied) emissions of NH3 have been measured following the application of ammonium nitrate (AN) fertilizer. In consequence the replacement of urea fertilizer with AN has been proposed as a cost-effective measure to reduce NH3 emissions in Europe. However, because of the greater susceptibility of nitrate- (NO− 3 ) based fertilizers to denitrification, the replacement of urea by AN may lead to increased emissions of nitrous oxide (N2O). There was a need therefore to critically review the evidence for substantially greater emissions of NH3 from urea than from other N fertilizers and also to appraise the effect of fertilizer - N type on emissions of N2O. Ammonia emissions from N fertilizers are consistent with their known effects on soil chemistry. Those that increase soil solution pH, for example, by increasing HCO− 3 concentration or by reducing the concentration of Ca2+ , have the greatest potential for NH3 emission. In consequence the greatest emissions of NH3 are from urea applied to any soil and from ammonium sulfate (AS) applied to soils of pHs >7.0. Losses of NH3 from AN were confirmed to be consistently less than from urea. Emissions of NH3 from solutions composed of urea and AN were found to be intermediate between the two fertilizers. Thus applying urea in solution will not reduce NH3 emissions. However, NH3 emissions from urea may be reduced by the use of urease inhibitors. Nitrous oxide emissions are crucially dependent on the interaction between timing of N fertilizer application and weather. Conditions in spring are more likely to be wet so that emissions are greater from NO− 3 -based fertilizers than from AS. In the summer conditions may be dry or wet; under dry conditions emissions are usually smaller than under wet conditions. For urea the effect of pH appears to be important. Generally greater emissions can take place from urea, except where temperature (controlling the rate of urea hydrolysis) and rainfall (controlling the dispersion of alkalinity) limit this. Thus, the substitution of AN for urea for spring applications is likely to increase emissions of N2O. For summer applications, the substitution of AN for urea is likely to decrease N2O emissions providing conditions are relatively dry; when conditions are wet large emissions may occur from both AN and urea. At this stage it is difficult to say with any certainty whether a strategy based on urea or AN would result in the smaller N2O emissions. Nitric oxide (NO) may also be released from soils following N fertilizer application. While soil emissions of NO are small in comparison with other sources of NOx, it is worth considering the effect of fertilizer type on this gas as well. Insufficient data is available to predict the effect of urea substitution on NO emissions, but since these are mainly a consequence of nitrification then replacing urea with AN should C 2001 Academic Press. also reduce NO emissions.

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I. INTRODUCTION A. THE NEED FOR ABATEMENT OF EMISSIONS OF NITROGENOUS GASES FROM AGRICULTURE Agriculture is the source of a number of gases that pollute the environment. Ammonia (NH3) causes acidiﬁcation and eutrophication when deposited to soils and waters (Roelofs et al., 1992), with indications (e.g., Hettelingh et al., 1992) that even if maximum feasible reductions in sulfur dioxide (SO2) and oxides of nitrogen (NOx) are achieved, deposition of ammonium species (NHx) would exceed critical loads for acidity in some parts of Europe. In consequence reductions in NH3 emissions are to be included in the forthcoming “multipollutant multieffects” protocol agreed on by the United Nations Economic Commission for Europe (UNECE) Convention on Long-Range Transboundary Air Pollution (Bull and Sutton, 1998). Agriculture is responsible for about 80–90% of NH3 emissions (Anonymous, 1994), of which about 10–20% is from N fertilizers (Anonymous 1994). While NH3 emissions from N fertilizers such as ammonium nitrate (AN) are considered to be small (∼1–3%), those from urea are estimated to be much greater at ∼10–20% of total N application (Anonymous, 1994) and has been estimated to contribute 50% of NH3 emissions from fertilizers in western Europe (Anonymous, 1994). Reduction of NH3 emissions from fertilizers could therefore be achieved by using alternative N fertilizers with smaller N emissions. Cost–beneﬁt analysis of means to reduce NH3 emissions by European agriculture has identiﬁed the replacement of urea by AN as one of the most cost-effective measures (Cowell and ApSimon, 1998). However, the replacement of urea by AN has implications for the emission of other N gases, since losses of nitrous oxide (N2O) have been considered greater from AN than from urea. Nitrous oxide contributes to global warming (Bouwman, 1990) and to depletion of ozone in the stratosphere (Crutzen, 1981). Mosier et al. (1998) did not consider there to be sufﬁcient evidence to discriminate between N fertilizer types as sources of N2O. Following application to soils N fertilizers may also lead to emissions of nitric oxide (NO) which contributes to acid deposition (Logan, 1983) and to ozone formation in the troposphere (Crutzen, 1979). Estimates of NO emissions from soil are very uncertain. St¨ohl et al. (1996) estimated soil emissions to be ∼7% of total European NOx emissions; Simpson et al. (1999) presented a range of estimates using different methodologies of between 2 and 20% of European NOx emissions. In all cases soil NO emissions are considered to be largely of agricultural origin. It was therefore considered necessary to critically review once again the evidence for substantially greater emissions of NH3 from urea than from other fertilizers and also of the effect of replacing urea with AN on emissions of N2O and NO. This chapter focuses on N fertilizers and cropping systems relevant to the UNECE

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area, viz. Europe and North America. Losses of NH3 following application of N fertilizer to ﬂooded rice ﬁelds were considered to be potentially greater than from other cropping systems by Fenn and Hossner (1985), and large losses of N from denitriﬁcation have also been measured. However, rice cultivation is not extensive in the UNECE area and hence N losses from it are not dealt with in this chapter. Ammonium bicarbonate fertilizer has also been omitted from the review. This fertilizer is the main source of mineral N in China and gaseous N losses following its application may be considerable (e.g., Cai et al., 1998; Hou et al., 1998). However, this product is little used in the UNECE area.

B. PREVIOUS REVIEWS OF THE EFFECT OF N FERTILIZER TYPE ON AMMONIA EMISSIONS The effect of N fertilizer type on emissions of NH3 has been discussed by several authors (e.g., Anonymous, 1994; Fenn and Hossner, 1985; Fox et al., 1996; Sutton et al., 1994). In a review of NH3 volatilization from ammonium (NH+ 4 ) or NH+ -forming N fertilizers, Fenn and Hossner (1985) focused on possible future 4 trends for reduction in NH3 losses from surface-applied N. These authors indicated that the inﬂuence of fertilizer type on NH3 loss was essentially controlled by physicochemical volatilization reactions and the N fertilizers which are most susceptible to NH3 loss are those which produce ammonium carbonate (NH4)2CO3) when added to the soil system, although some loss occurs from AN (NH4NO3) in calcareous soils. Both ammonium sulfate ((NH4)2SO4) and diammonium phosphate ((NH4)2HPO4) react with calcium carbonate (CaCO3) to produce an increase in soil solution pH and NH3 loss. They concluded that inorganic fertilizers are not susceptible to gaseous NH3 loss in acid soils, but that large losses can occur from urea in all soils. Byrnes (1990) stated that there had been no in-depth global estimates of NH3 volatilization based on types of N fertilizers or their uses, although a reasonable database exists to allow this. However, a recent collation of work in western Europe has been carried out (Anonymous, 1994). ApSimon (1993), in summarizing the information from a workshop on atmospheric emissions of NH3 and their control, stated that nearly all the measurements on fertilizer loss apply to conditions in northwestern Europe and that there was virtually no data for the wide variety of other climatic and soil conditions and associated agricultural practices in Europe. As emissions following fertilizer application vary greatly with soil type and pH as well as the type of fertilizer used, the ECETOC inventory (Anonymous, 1994) ascribed emission factors for different fertilizers in different countries according to predominant soil categories. Fenn and Hossner (1985) also stated that the magnitude of measured NH3 losses depended to some extent on the method of measurement (i.e., laboratory,

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greenhouse, and ﬁeld), with greatest values obtained under optimum conditions in laboratory experiments. However similar values could be obtained in the ﬁeld under favourable circumstances, although this was less likely for urea because of the requirement that urease activity be optimal. Thus laboratory studies are likely to overestimate urea losses, both absolutely and in relation to other fertilizers. Other workers, comparing three ﬁeld measurement techniques of NH3 volatilization from urea granules broadcast to pasture (Black et al., 1985a), found that despite differences in the measured patterns of emission, the total cumulative losses of NH3 determined by an enclosure method and the micrometeorological mass balance method (i.e., conﬁned versus unconﬁned methods) were similar. They hypothesized that under conditions where the loss is complete within a short time (i.e., less than 6 days), the total loss depended mainly on the NH+ 4 concentration developed at the soil surface and the pool of bicarbonate ions (HCO− 3 ) inducing the pH rise. The loss may be expected to continue until the pH drops sufﬁciently to minimize the proportion of NH3 relative to NH+ 4 . Factors such as temperature and windspeed, which may be expected to vary between the two methods, will alter the rate of movement of NH3 into the atmosphere, but the total loss will not change. Ammonia volatilization reactions are elaborated below.

II. THE PROCESSES CONTROLLING EMISSIONS OF NITROGEN GASES FROM FERTILIZERS A. AMMONIA VOLATILIZATION 1. Volatilization Reactions There have been many investigations of the volatilization process (Black et al., 1985a; Fenn, 1988; Fenn and Kissel, 1973, 1975; Fenn et al., 1981b; Larsen and Gunary, 1962; Lightner et al., 1990; Meyer and Jarvis, 1989; Roelcke et al., 1996; Sherlock and Goh, 1985; Sommer and Ersbøll, 1996; Terman, 1979; Touchton and Hargrove, 1982), which is now well understood. Volatilization is essentially a physicochemical process which results from the equilibrium (described by Henry’s law) between gaseous phase NH3 and NH3 in solution [Eq. (1)]; NH3 in solution is in turn maintained by the NH+ 4 –NH3 equilibrium [Eq. (2)] as follows: NH3 (aq) ⇔ NH3 (g) NH+ 4 (aq)

(1) +

⇔ NH3 (aq) + H (aq).

(2)

High pH (i.e., low [H+ (aq)]) favors the right-hand side of Eq. (2), resulting in a greater concentration of NH3 in solution and also, therefore, in the gaseous phase.

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Thus, where the soil is buffered at pH values less than ∼7(in water), the dominant form of ammoniacal-N (NHx) will be NH+ 4 and the potential for volatilization will be small. In contrast, where the soil is buffered at higher pH values, the dominant form of NHx will be NH3 and the potential for volatilization will be high, although other chemical equilibria may serve to increase or decrease this. The ambient soil pH results in the establishment of a bicarbonate–carbonate equilibrium with dissolved carbon dioxide (CO2) as follows: CO2 (aq,g) ⇔ H2 CO3 (aq) ⇔ HCO− 3 (aq) + + H+ (aq) ⇔ CO2− (aq) + 2H (aq). 3

(3)

In acid soils, this equilibrium lies to the left so that the concentration of free carbonate (CO2− 3 ) ions is negligible. However, in alkaline (calcareous) soils, the CaCO3 solubility equilibrium also becomes important: Ca2+ (aq) + CO2− 3 (aq) ⇔ CaCO3 (s)

(4)

It is apparent that the addition of soluble Ca2+ will move this equilibrium [Eq. (4)] to the right, reducing the concentration of CO2− 3 in solution, thus generating additional H+ ions (i.e., reducing the pH) via equilibrium [Eq. (3)]. Further, the addition of any other ion which forms sparingly soluble salts with Ca2+ (e.g., sulfate) will act in the opposite manner by reducing [Ca2+ ] and hence increasing [CO2− 3 ] [Eq. (4)]. This will move the equilibrium [Eq. (3)] to the left and reduce [H+ ] (i.e., increase pH). Similar considerations apply to systems buffered by, say, magnesium carbonate (MgCO3) or barium carbonate (BaCO3). Equations relating NH3 loss potential to equilibrium constants and the concentrations of Ca2+ and NH+ 4 were given by Larsen and Gunary (1962). They described laboratory experiments carried out to investigate NH3 volatilization from NH+ 4 salts applied to a calcareous soil and an acid soil amended with Mg-,Ca-, or BaCO3. The ranking of NH3 volatilization potential from the calcareous soil was as follows: ammonium sulfate (AS) > monoammonium phosphate (MAP) ∼ = diammonium phosphate (DAP) ∼ = AN > magnesium ammonium phosphate. On the basis of solubility criteria it was expected that, for calcareous soils, NH3 loss should have followed the order DAP > AS > AN. It was postulated that the formation of insoluble calcium ammonium phosphates reduced the potential for NH3 volatilization by reducing the activity of NH+ 4 in solution. The results for the acid soil were as follows: MgCO3 amended {AS > AN ≫ DAP}, CaCO3 amended {AS ≫ AN = DAP}, and BaCO3 amended {AN ≫ AS = DAP}. Small losses were obtained from DAP in MgCO3-treated soil because of the low solubility of the magnesium ammonium phosphate product (also observed when this material was added to the calcareous soil, above). This means that the expected order would be AS > AN {Ca : = DAP} {Mg : > DAP}. It was suggested that the small losses from AS from the BaCO3-treated soil were due to precipitation of BaSO4 on the surfaces of

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BaCO3 particles, thus reducing the activity of the latter. No reason was given for the greater N loss from AN compared to DAP in the BaCO3-treated soil. Fenn and Kissel (1973) measured average NH3 losses after 100 h from NH+ 4 salts applied to Houston Black clay (a calcareous montmorillonitic soil, pH 7.6) and Wilson clay loam (a neutral to acid montmorillonitic soil). The emissions (NH3-N as a percentage of NH+ 4 -N applied) from the calcareous soil were as follows: ammonium ﬂuoride (NH4F), 68%; ammonium chloride (NH4Cl), 18%; ammonium iodide (NH4I), 16%; AN, 18%; AS, 54%; and DAP, 51%. Thus, larger emissions were measured where the Ksp (i.e., the solubility product constant) of the salt formed from Ca2+ and the anion associated with the NH+ 4 compound was low (i.e., sparingly soluble) and less where the equivalent salt was very soluble. Volatilization of NH3 from AS added to Wilson clay loam soils saturated with Mg2+ or Ba2+ (10% by weight of the respective carbonates added) was similar— 21 and 33% respectively. The initial pH of the Mg2+ system was 8.5 compared to 7.4 for the Ba2+ system, which should have favored NH3 volatilization from the former. However, the solubility of MgSO4 is high while that of BaSO4 is low, which would favor larger emissions from the Ba2+ system. In fact, the Ba2+ -saturated soil produced rapid initial NH3 volatilization rates, with the pH rising to 8.9 before decreasing to 7.4. The pH for the Mg2+ -saturated soil decreased to 8.2 before returning to the initial value of 8.5. Although not conclusive, these results support the hypothesis that the formation of sparingly soluble salts between the cation in carbonate-buffered systems and the anion from added NH+ 4 compounds results in increased NH3 volatilization. Fenn et al. (1981b) examined whether the addition of soluble Ca (or Mg) salts with urea could result in precipitation of CaCO3 and thus reduce the pH which would otherwise result [see Eqs. (3) and (4)]. Measurements were made from three soils: calcareous Harkey silty clay loam (15% CaCO3, pH 7.7), Darco ﬁne sand (pH 5.8), and Beaumont clay (pH 4.8). Measurements of NH3 loss were made following additions as solutions. Results showed signiﬁcant reductions from urea plus Ca(NO3)2, CaCl2, and MgCl2 compared to urea alone (76 and 59% N loss to 10–15% N loss for the Harkey and Darco soils, respectively; and 46 to <3% for the Beaumont soil). No reductions were achieved with CaSO4 due (it was postulated) to its low solubility and hence insigniﬁcant effect on removing carbonate from solution. Addition of CaCl2 with AS or AN also reduced NH3 loss from the Harkey soil—76 to 31% and 33 to 18%, respectively. Other results (using the calcareous Harkey soil and the acidic Darco soil) showed that increasing the Ca2+ :N ratio further reduced NH3 volatilization (Fenn et al., 1981a). These + results support the view that NH3 volatilization from NH+ 4 or NH4 fertilizers can be explained in terms of the chemical reactions occurring in the soil. However, further work on this subject (Fenn, 1988) designed to investigate whether the degree of Ca2+ saturation affected the Ca2+: urea ratio required to control NH3 volatilization produced somewhat contradictory results. In this latter

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study, solutions of urea alone or in combination with CaCl2 (at ratios of 0.25 and 0.50 moles Ca2+ per mole urea) were added to three acid soils and one calcareous soil. Pretreatments were imposed on the acid soils (nil, Ca2+ saturation, addition of 1% w/w CaCO3, and addition of 5% w/w CaCO3) and the calcareous soil (nil, Ca2+ saturation, 25% Na+ saturation, and 50% Na+ saturation). The results showed that NH3 loss from urea alone was not affected by soil pretreatment. This suggests that urea hydrolysis occurs very close to the site of application (i.e., affects only a small volume of soil) and so the bulk soil pH has little inﬂuence on the volatilization process. However, differences in pretreatment resulted in small but apparently signiﬁcant differences in NH3 loss for additions of urea in combination with CaCl2. Fenn (1988) claimed that these differences reﬂect the initial soil pH, but this was not the case, as the lowest pH values were measured for nil soil pretreatment and NH3 emissions from these treatments were never the least. Even after excluding these treatments, no consistent pattern emerges between initial soil pH and NH3 volatilized. Fenn (1988) quotes earlier work (Fenn et al., 1981a) which showed that Ca2+ greatly depressed the rate of urea hydrolysis and states that the Ca–urea compound must reduce the rate of diffusion into the surrounding soil and (hence) increase the effect of (bulk) soil pH on NH3 losses. If the effect of Ca2+ is indeed to extend the existence (“residence time”) of urea, this should serve to increase the rate of diffusion into the bulk soil which would indeed increase the effect of soil pH on the volatilization reaction. Nevertheless, although this was not stated (Fenn, 1988), it is apparent from the ﬁgures presented that the effect of increased Ca:urea ratio in the applied solution was to reduce NH3 volatilization, conﬁrming the results of the previous study (Fenn et al., 1981b). Fenn and Kissel (1975) describe results from laboratory experiments which investigate the effect of CaCO3 on NH3 volatilization from NH+ 4 -containing fertilizers. For AS, NH3 losses increased rapidly with CaCO3 content up to 6.1%, less rapidly up to 9.7%, and not at all above 9.7%. At low (0.5%) CaCO3 content, NH3 losses decreased with increasing application rate (110 to 550 kg/ha N). There was no effect of N application rate with CaCO3 contents of 1.3 and 2.9%. However, at 6.1% soil CaCO3 and above, NH3 loss increased with N application rate. These patterns were repeated at three temperatures (12, 22, and 32◦ C), although for conditions under which less than maximum NH3 volatilization occurred, emissions increased with temperature. At 12◦ C, losses were 20–30% at 2.9% CaCO3 and 30–40% at 14.7% CaCO3 (at 110 to 550 kg/ha N). Differences between N application rates tended to be less marked at higher temperatures. These results appear to illustrate both the effect of NH+ 4 addition in reducing soil pH (i.e., NH3 loss decreases with increasing N application rate, at low CaCO3) and the effect of CaSO4 precipitation in promoting CaCO3 dissolution and thus increasing soil pH (i.e., NH3 loss increases with increasing N application rate at high CaCO3). The pH values 120 h after application tended to be smaller for greater N applications,

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and this effect was less marked at greater soil CaCO3 content. Thus, these results are consistent with the effect of N application in reducing NH3 loss at low soil CaCO3 content, but do not reﬂect the effect of rate of N application at high soil CaCO3 content. Maximum NH3 losses were associated with pH values of 7.6 or greater. Fewer results were reported for AN. In contrast to AS, NH3 volatilization from AN was consistently less from the greater application rate (up to 6.1% CaCO3, the greatest content for which results were reported). Thus, in the absence of other reactions, the addition of NH+ 4 serves to reduce pH. Emissions from AN were less than those from AS. Other work also conﬁrmed the importance of rate of application of NH+ 4 fertilizers (Fenn and Kissel, 1974) on the extent of NH3 volatilization. For AS and DAP, both compounds which form sparingly soluble salts with Ca2+ , NH3 loss as a percentage of N applied was 19–23, 26–45, 38–46 and ∼49% for application rates (kg/ha N) of 33–66, 100, 275, and 550, respectively. The ranges represent the variation associated with temperatures 12–32◦ C. In contrast, NH3 volatilization did not vary with application rate of AN and was 14–26% for the temperature range 12–32◦ C. Although the CaCO3 content of the soil (Houston Black clay) was not stated, it appeared that this soil was well buffered such that addition of increasing concentrations of NH+ 4 (as AN) did not result in a reduction in pH and, hence, NH3 volatilization as occurred in the later study of Fenn and Kissel, (1975). In contrast, for AS and DAP, increasing application rate increased NH3 volatilization because pH was increased via Eqs. (3) and (4), as described above. Fenn and Kissel (1974) suggested in their article that increasing temperature had less effect on total NH3 losses from AS and DAP compared to AN. However, the variation in the data do not allow such a conclusion to be made; it seems more likely that the effect of temperature is comparable for both compounds which form sparingly soluble Ca2+ salts and those which do not. Overall, therefore, it appears that the relative NH3 emissions from different NH+ 4 (containing or forming) compounds applied to soils under laboratory conditions can be predicted on the basis of the chemistry involved. Reactions controlling pH (particularly at, and in the immediate vicinity of, the site of application) are most important. Urea hydrolysis, in particular, greatly increases pH around the fertilizer granule leading to a large NH3 potential. Ammonium salts with anions which form sparingly soluble Ca2+ salts also tend to increase pH, while those with anions which form soluble Ca2+ salts do not. Addition of Ca2+ reduces pH, while for AN NH3 losses will not increase linearly with increasing N fertilizer addition because of pH reduction. However, in many cases reactions involving phase changes are involved (i.e., precipitation of sparingly soluble salts) and these need to be considered with some care, as the formation of precipitates is not necessarily an instantaneous process. Nonequilibrium conditions may therefore persist and affect volatilization reactions.

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2. The Role of pH The key role of pH has been conﬁrmed in a number of studies, although no consistent method to measure the effective pH at the site of volatilization has been developed. For example, pH for the fertilizer solution (Al-Kanani et al., 1990), soil pH after 120 h (Fenn and Kissel, 1975), and soil pH after 24 h (Sommer and Ersbøll, 1996; Whitehead and Raistrick, 1990, 1993) have all been used to explain variations in NH3 volatilization. Work by Al-Kanani et al. (1990), investigating the effect of the urea:AN ratio on NH3 volatilization from solutions applied to soil and carried out under laboratory conditions, showed that increasing AN at constant urea induced a logarithmic reduction in NH3 volatilization (postulated + to be due to NH+ 4 exchange with H on soil colloids, thus reducing pH). The pH of the fertilizer solution was related to N loss, but this effect was less pronounced for soil with a large clay content. The pH of the soil suspension after (10 days) incubation was not related to NH3 volatilized. Whitehead and Raistrick (1990) reported measurements of NH3 volatilization under (laboratory) controlled conditions over 8 days for surface-applied N fertilizer and from cattle urine at 20◦ C applied to 22 soils (1993), with assessments also made of soil pH. The results indicate the importance of soil–fertilizer reactions on pH in controlling NH3 volatilization. In general, AN and MAP had either no or a slight acidifying effect on soils with pH < 7; consequently, NH3 loss was small (<4%). For soils at pH > 7, NH3 loss increased from AN (up to 10%) and also from MAP where the soil contained signiﬁcant CaCO3 (35%) due (it was suggested) to the precipitation of octacalcium phosphate (OCP). Emissions from AS increased with pH (from <2 to 48%). Emissions from urea also increased with pH although not so markedly (from 24 to 43%, excluding one very acid soil). Thus, comparing urea with AS, emissions were greater from urea for soils with pH <7 and from AS for soils with pH >7. The results from DAP were rather anomalous: For soils with pH <7, NH3 loss was intermediate between AS and urea; for the calcareous soil (pH >7) NH3 loss was 52% greater than both urea and AS but for the noncalcareous soil with pH >7, NH3 loss was 8% less than both urea and AS. It was suggested that this difference could be ascribed to the differing reaction products of DAP in soils: in those with low CaCO3 it gives calcium ammonium phosphate; in those with high CaCO3 it gives OCP. This did not seem to be the case for MAP. There was a strong exponential relationship between NH3 volatilization (as percentage of NH+ 4 - or urea-N applied) and the pH of the soil–fertilizer mixture after 24 h [Eq. (5)] which accounted for 85% of the observed variance: V = −4.61 + 0.0324 × 2.465pH .

(5)

The pH of a soil/urine mixture after 24 h was also quite closely correlated with NH3 loss, whereas two measures of titratable acidity were not (Whitehead and

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Raistrick, 1993). Ammonia loss from urine was inversely related to the soil cation exchange capacity (CEC), and greater losses were measured from grassland soils relative to arable soils for a given CEC (although “across the fence” comparisons did not reveal strong differences between grassland and arable soils, except that volatilization on the ﬁrst day was greater for the former). It was postulated that the greater contact between NH+ 4 -N and soil from urine (as opposed to urea) application meant that the NH+ 4 retention properties of the soil had a greater effect (compared to pH) on NH3 volatilization. In another recent study (Sommer and Ersbøll, 1996), NH3 volatilization from three fertilizers [urea, DAP, and calcium ammonium nitrate (CAN)] was investigated. A ventilated chamber system was used with a ventilation rate which had been shown in a separate study to be nonlimiting with respect to NH3 volatilization. Two soils (sandy and sandy loam texture), either limed or not limed for 25 years, were used. Addition of CAN always reduced pH [see Eq. (2) above], whereas urea and DAP generally increased pH. This increase was greater for urea than for DAP, except for the soil with the lowest initial pH (4.5) which was ascribed to the low activity of urease under these conditions. For the soil with the highest initial pH (7.9), DAP and urea addition decreased the pH (possibly through the “salt effect”) although this rose subsequently following the urea treatment. Soil pH showed an exponential rise to ammonium from the value measured at 24 h following urea addition, but there was no change after 24 h following the DAP and CAN treatments. 3. Models of Ammonia Volatilization It is apparent that the main factors controlling NH3 volatilization are the rate of hydrolysis for urea and the changes in soil pH following application for all fertilizers, although other factors such as windspeed and CEC also have an inﬂuence. As already stated above, NH3 emissions tend to increase with soil pH, albeit there is a strong interaction between the fertilizer and the soil solution which may (e.g., for urea) override the effects of initial soil pH through hydrolysis and precipitation reactions. Important in this regard is the effect of CEC; large soil CEC (more speciﬁcally, high NH+ 4 retention) tends to reduce NH3 volatilization potential by + reducing the concentration of NH+ 4 in the soil solution by adsorption of NH4 on the exchange sites. In the ﬁeld, however, these factors inﬂuence the maximum potential emission, which may be reduced if signiﬁcant rainfall occurs during the main volatilization period, essentially in the 10 days after fertilizer application. There have been a number of attempts to model NH3 volatilization, both mechanistic (Fleisher et al., 1987; Rachhpal-Singh and Nye, 1986; Roelcke et al., 1996; Sherlock and Goh, 1985) and empirical (Stevens et al., 1989), which have been more or less successful. The equations developed by Sherlock and Goh (1985) included the following terms and constants: total NHx concentration, Henry’s Law

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constant, volumetric moisture content, pH at the soil surface (site of volatilization), temperature, the distribution ratio for the partition of NH+ 4 between exchange sites and the soil solution, and the exchange coefﬁcient for transport of gaseous NH3 from the soil to the bulk atmosphere. In a later experiment (Sherlock et al., 1995), these relationships were incorporated into a linear relationship of the form: F = k × u z × ␳0,

(6)

where, F is the NH3 ﬂux, k is a dimensionless (site-speciﬁc) exchange coefﬁcient, u z is the mean wind speed at reference height z, and ␳ 0 is the NH3 concentration in equilibrium with the liquid phase and is calculated from the following:

[NH3 ]soln

␳ 0 = [NH3 ]soln × 10(1.6937−1477.7/T ) (0.0918+2729.92/T −pH) = [NH+ 4 + NH3 ]soln / 1 + 10 [NH+ 4 + NH3 ]soln = [NHx ]total /Mv ,

(7) (8) (9)

where Mv is the volumetric water content and corrects the amount of NH4-N extracted in KCl in a speciﬁed volume or soil to give a soil solution concentration. In fact, in this form, k also incorporates a term for a partition of NH+ 4 between exchange sites and the soil solution, but since this parameter also describes other site-speciﬁc factors such as surface aerodynamic roughness, it is not necessary to include it explicitly. Thus, NH3 emission was calculated from measurements of NHx-N in the 0- to 1-mm or 0- to 3-mm soil layers, pH and temperature of the soil surface, and windspeed at 1.2 m above the soil surface. These results were compared with those obtained for ∼2-hourly periods over up to 8 days from the micrometeorological mass balance method (Black et al., 1985a) at two sites. The correlation coefﬁcients for experiments involving granulated urea fertilizer and synthetic urine were 0.870 and 0.879, respectively, indicating that a large proportion of the variation in NH3 emission could be explained on the basis of variation in windspeed, NHx-N concentration, pH, and temperature. A mechanistic model of the reaction of urea in the soil has also been developed by Rachhpal-Singh and Nye (1986). The sensitivity of various parameters was determined (as was the effect of placement or mixing urea in the surface), and it was shown that the rate of diffusion of HCO− 3 to the soil surface was an important rate-limiting process for NH3 volatilization. Roelcke et al. (1996) report the results of a comparison between NH3 volatilization measured in the laboratory from vented chambers and these predicted from the Rachhpal-Singh and Nye (1986) mechanistic model and modiﬁcations based thereon. The soil studied had a pH value of 7.7 and a CaCO3 content of 10%. Because the model (Rachhpal-Singh and Nye, 1986) was based on a neutral, noncalcareous soil, simulations were carried out using (a) an initial pH of 7.7 but with remaining parameters as in the original model, (b) bulk density and the parameters relating to gaseous and liquid

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diffusion altered to reﬂect the more porous nature of the soil used in this study (e.g., bulk density = 0.95 cf. 1.5 kg/dm3), (c) as (b) but with the pH buffering capacity increased threefold, and (d) as (b) but with CaCO3 precipitation and dissolution included in the model equations. Overall, the best ﬁt was given by (c). In the early stages, simulations (b) and (d) overestimated NH3 volatilization. Despite the more porous nature of the soil in this study, the parameters relating to the standard model appeared adequate in describing NH3 loss in the early stages. In fact simulation (c) also gave a good ﬁt during this period. It is probable that the good ﬁt for simulation (a) resulted from the possibly fortuitous canceling of high pH and low gaseous diffusion terms. Indeed, after about 6 days, simulation (a) began to signiﬁcantly underestimate NH3 volatilization, as the effect of the initial high pH became less important and the lower gaseous diffusion term dominated. It seems likely, therefore, that the initial overestimation of NH3 volatilization by simulations (b) and (d) was the result of overestimation of pH during this period. The fact that simulation (d) overestimated NH3 loss, whereas simulation (c) was reasonably accurate, indicates the importance of pH; these workers suggest that the description of pH buffering capacity in Rachhpal-Singh and Nye’s model (1986) may be incomplete, since increasing this parameter threefold gave a good ﬁt to measured NH3 volatilization, whereas including the (known) reactions involving CaCO3 did not. In fact, the pH of this soil was such that it was close to the upper limit of buffering for the CaCO3–H2O–CO2 system. In addition, there was some evidence that at this pH, precipitation of CaCO3 was a slow, rather than instantaneous, process. Ultimately, even simulation (c) was not a good description of the volatilization process over the full period of the experiment, since after 10 days NH3 loss continued at a rate greater than predicted. Another quantitative model of NH3 volatilization from the uppermost 1 cm of calcareous soils was presented by Fleisher et al. (1987). Factors included were as follows: monovalent–divalent exchange, diffusion impedance in the gaseous phase, and nitriﬁcation. It was assumed that in practice the addition of NHx fertilizers does not change soil pH (because of the high buffering capacity of such soils), that most NH3 losses occur while soil water content in not far from ﬁeld capacity, and that the rate of diffusion is the rate-limiting step for volatilization. The model results predict that volatilization declines with decreasing pH, increasing CEC, and increasing nitriﬁcation rate. The model also predicted a small increase in the proportion of N volatilized with increasing N application rate. In contrast to these mechanistic models, Stevens et al. (1989) used results from ventilated laboratory enclosures and stepwise multiple linear regression to relate total NH3 loss (Amax ) and time to maximum volatilization rate (Tmax ) and to soil properties for 36 soils. Values of Amax ranged from 1.6 to 26.1% (mean 16.8%) and were best explained by variation in titrateable acidity. Values of Tmax ranged from 0 to 10.6 days and were best explained by nonbuffered urease activity and

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HARRISON AND WEBB

loss-on-ignition. These authors also outlined other work where the relationship between the timing and amount of rainfall (or irrigation) and N volatilization had been studied. They noted that, in the absence of rainfall, the most important characteristic of the NH3 volatilization process will be Amax ; however, under ﬁeld conditions, the dynamics of NH3 volatilization will depend on Tmax and when rainfall occurs.

B. NITROUS OXIDE AND NITRIC OXIDE EMISSIONS In soil, N2O and NO are predominantly produced biologically, through nitriﬁcation and denitriﬁcation. A number of possible pathways exist for the production of NO and N2O by chemoautotrophic nitrifying bacteria. Denitrifying bacteria both produce and consume NO and N2O. In addition, a number of other soil microorganisms are capable of producing NO and N2O (Robertson and Tiedje, 1987), and abiological reactions involving NO and N2O also occur in soil (Nelson, 1982). The processes controlling the emission of NO and N2O from soil are illustrated by the “hole in the pipe” model (Davidson, 1991; Firestone and Davidson, 1989); namely factors affecting the rates of nitriﬁcation and denitriﬁcation, factors affecting the relative proportions of the end products, and factors affecting gaseous diffusion through the soil to the atmosphere. Early work (ca. pre-1980) suggested that denitriﬁcation was the major source of soil-derived N2O, but work by Bremner and Blackmer (1978, 1979, 1981) showed that nitriﬁcation was also a signiﬁcant source. In contrast, in agricultural soils where pH is likely to be maintained above 5, nitriﬁcation is thought to be the major source of soil-derived NO (Skiba et al., 1997) since denitriﬁers both produce and consume NO (Anderson and Levine, 1986). The magnitude of N2O and NO emissions from soil is depen− dent on soil-available NH+ 4 and NO3 concentrations as well as on factors such as soil temperature and soil moisture content (soil aeration). Thus N-fertilized soils are signiﬁcant contributors to total N2O and NO emissions.

III. MEASUREMENTS OF AMMONIA EMISSION FOLLOWING NITROGEN FERTILIZER APPLICATION A. FIELD MEASUREMENTS OF AMMONIA EMISSION Although there have many measurements of NH3 volatilization from N fertilizers, there have been relatively few simultaneous comparisons of losses from different N fertilizers in the ﬁeld (Bussink and Oenema, 1997; Fox et al., 1996; Gasser and Penny, 1967; Gezgin and Bayrakli, 1995; He et al., 1995; Keller and

EFFECT OF N FERTILIZER TYPE ON GASEOUS EMISSIONS

79

Mengel, 1986; Lightner et al., 1990; Ryden et al., 1987; Sommer and Jensen, 1994; Stevens and Laughlin, 1988, 1989; Touchton and Hargrove, 1982; van der Weerden and Jarvis, 1997; Velthof et al., 1990). Hargrove (1988) stated that N sources inﬂuence NH3 losses through cation and anion effects and indicated measurements from N sources under ﬁeld conditions (Hargrove and Kissel, 1979; Hargrove et al., 1977; Keller and Mengel, 1986; Urban et al., 1987). These studies showed that the NH3 loss potential is greatest with urea, intermediate with urea-ammonium nitrate (UAN) solution, and least with NH+ 4 salts on noncalcareous soils, but greatest with AS and much less with urea or AN on calcareous soils. In general, most fertilizer comparisons in the ﬁeld (Table I) have been carried out using either wind tunnels (Ryden et al., 1987; Sommer and Jensen, 1994; van der Weerden and Jarvis, 1997; Velthof et al., 1990) or chambers (Gezgin and Bayrakli, 1995; Keller and Mengel, 1986; Lightner et al., 1990), although a number of agronomic trials involving different fertilizers expected to differ in their NH3 volatilization potential have also been undertaken (Gasser and Penny, 1967; Hargrove et al., 1977; He et al., 1995; Stevens and Laughlin, 1988, 1989; Touchton and Hargrove, 1982; Watson et al., 1998). 1. Micrometeorological Methods While there have been a number of studies of NH3 emissions from individual N fertilizers by micrometeorological techniques, the only comparative studies of NH3 volatilization measured in the ﬁeld using micrometeorological methods have been those of McInnes et al. (1986a,b) and Fox et al. (1996) and their results are reported in the section on fertilizer solutions below. 2. Wind Tunnel Measurements Ryden et al. (1987) made measurements using wind tunnels following fertilizer N application to grass swards (no details of soils given). Results for NH3 volatilized as a proportion of N applied were 5.7, 16.7 and 20.7% from urea at 70 kg N/ha 35.6, 19.9% from urea at 100 kg N/ha, and <2.5 and <1.6% from AN at 70 and 100 kg N/ha, respectively. Velthof et al. (1990) reported the results of measurements of NH3 volatilization from urea and CAN applied to permanent grassland on a heavy clay soil, measured using wind tunnels. Their results showed (a) emissions of 19 and 32% from urea applied at 80 and 120 kg N/ha, respectively, during a dry period in late March/early April; (b) an emission of 7% from urea applied at 90 kg N/ha during a wet period in late June/early August; and (c) generally small or negative emissions [ascribed to deposition of NH3 emitted from adjacent (1.5 km) slurry application] from CAN. However, on one occasion (the 4th week of August) an emission of 11% was recorded for CAN, for which no explanation was given. Over all times and N rates, emissions were calculated as 22.6 ± 15.6%

Table I Fertilizer Comparisons Carried out in the Field

Reference Gasser and Penny (1967) Hargrove et al. (1977)

Touchton and Hargrove (1982) Keller and Mengel (1986)

80 Ryden et al. (1987)

Fertilizersa

Emission factors

UN; UP; UP:U (1:2); AN AN AS AS (aq) U; U-AN (aq); AN U Cogranulated U-UP U-AN (aq) AN U AN

Stevens and Laughlin (1988)

AS; CN; U

Stevens and Laughlin (1989)

U; AN

Lightner et al. (1990)

U U (aq) U-KCl (aq) Cogranulated U-Up U-CaCl2 (aq) AN

Notes

Measurement technique Results of agronomic evaluation Indirect estimates

Results over 2 years applied to calcareous clay

0.30, 0.11 For sand loam and silt loam 0.15, 0.06 soils (pH <7), respectively 0.09, 0.05 0.04, 0.02 For differing weather <0.03 conditions and application rates

Results for spring and summer applications in 2 years
Results of agronomic evaluation Chambers

Wind tunnel

Results for utilization by 15N Results for utilization by 15N Chambers

Velthof et al. (1990)

U CAN

He et al. (1995)

U DAP AS CAN AS AN U U; AS; AC

Fox et al. (1996)

U

Bussink and Oenema (1997)

(aq) (sprayed) (aq) (dribbled) U, (C)AN

Sommer and Jensen (1994)

Gezgin and Bayrakli (1995)

81 van der Weerden and Jarvis (1997)

U AN CN

Watson et al. (1998)

U CAN

Low

0.05 0.02 0.14 0.04 0.11

For differing weather conditions and application rates For sandy soil (pH 6.1) on 5 occasions

For calcareous clay loam soil

For applications to no-till corn in 3 years Synthesis of eld data from The Netherlands, southern England, and Northern Ireland For differing weather conditions and application rates on two soil types (pH 7)

Wind tunnel

Wind tunnel

Chambers

Studied effect of lime-N agronomic Micromet

Agronomic

Wind tunnel

Results of agronomic evaluation

a Abbreviations: AC, ammonium chloride; AS, ammonium sulphate; (C) AN, (calcium) ammonium nitrate; CN, calcium nitrate; DAP, diammonium phosphate; U, urea; UN, urea nitrate; UP, urea phosphate.

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and −1.8 ± 6.0% for urea and CAN, respectively; the wide range for urea being ascribed to differences in N rate, rainfall, and temperature. Sommer and Jensen (1994) stated that there are few simultaneous studies of NH3 volatilization from AS, DAP, An and urea applied in the ﬁeld, despite these fertilizers being among the most used in Europe. Wind tunnel measurements of NH3 volatilization from fertilizers (urea, DAP, AS, and CAN) applied to a sandy soil (winter wheat and grass) were made on ﬁve occasions between March and June. Total losses were calculated from NH3 rate loss measurements over 15–20 days by ﬁtting a sigmoidal (urea) or exponential (DAP, AS, and CAN) function. Total losses were estimated to be 25% (18–30%) for urea provided there was sufﬁcient moisture for hydrolysis (otherwise losses were negligible), 14% (1–17%) for DAP, <5% (not signiﬁcantly greater than zero) for AS, and <2% (not signiﬁcantly greater than zero) for CAN. Van der Weerden and Jarvis (1997) noted that quoted emission factors for urea and AN range from 5 to 16.5% and 0 to 10%, respectively. The objectives of their study were (a) to measure NH3 volatilization from three N fertilizers applied to grassland on two contrasting soil types, (b) to derive new emission factors based on this and previous published work, and (c) to estimate NH3 emissions from fertilizer applied to agricultural land in the UK. Measurements were made using wind tunnels following application of urea, AN, and calcium nitrate (CN) on three dates (March: 120 kg N/ha; June: 90 kg N/ha; August: 70 kg N/ha) on two soils (freely drained sandy clay loam, pH 6.0; poorly drained clay loam, pH 5.6). The results showed that the greatest NH3 volatilization was from urea. Measured losses in August were inﬂuenced by rainfall, with 17 mm 1 day after fertilizer application on the poorly drained site and 14 mm 3 days after fertilizer application on the welldrained site. Losses for March were greater from the poorly drained site compared to the well-drained site, this being ascribed to the effect of more rapid hydrolysis outweighing that of lower temperatures; soil moisture contents were 108 and 29%, respectively. For the June application, emissions were greater from the well-drained site compared to the poorly drained site. The authors did not comment on this observation, although they did state that they considered the effect of soil type to be small in this study as the initial soil pH values were similar. They also noted that, whereas N loss increased from 12 to 38% as N application increased from 70 to 120 kg N/ha for the poorly drained site, there was no obvious relationship for the freely drained site. Average emissions for both AN and CN on both soil types were <1%, and the results indicated that there was little seasonal or soil effect. 3. Chamber Measurements Lightner et al. (1990) carried out ﬁeld measurements of NH3 volatilization using ventilated chambers in spring (May) and summer (August) over 2 years (1982 and 1983). The fertilizers studied were urea, urea solution (Uaq), cogranulated UUP,

EFFECT OF N FERTILIZER TYPE ON GASEOUS EMISSIONS

83

urea + KCl in solution (UKCl), urea + CaCl2 in solution (UCaCl2), and AN. Greatest losses were from urea, Uaq and UKCl, ranging from 27 to 41% of applied N in the spring and from 12 to 27% in the summer. No signiﬁcant volatilization losses were observed with AN. Ammonia losses from UUP (an acidic material) were signiﬁcantly less than those from urea in the spring and at least 40% less in the summer. Relative to Uaq, mixing with CaCl2 reduced volatilization signiﬁcantly in both 1983 studies. Smallest losses occurred in summer 1983 when conditions were particularly dry. The work of Gezgin and Bayrakli (1995) concentrated on the effect of additives on NH3 volatilization, although useful comparisons for the unamended fertilizers were included. The results from ﬁeld (enclosure method) measurements of NH3 volatilization from fertilizers (AS, AN, and urea) applied to a calcareous clay loam soil were AS (14.0% of N added), AN (4.4%), and urea (10.6%).

B. AGRONOMIC EVALUATIONS: EFFECTS ON FERTILIZER EFFICIENCY Hargrove et al. (1977) reported some early estimates of NH3 loss on a calcareous (Houston Black clay) soil based on agronomic evaluations. Two methods were used: indirectly using comparison of N response curves of NH+ 4 -containing fertilizers with CN and directly using enclosure methods. The results over 2 years (indirect method) showed that N losses (of total N applied) were 3–10% for AN, 36–45% for AS, and 25–55% for AS applied as a solution. Note that all the fertilizers were applied after the soil had ﬁrst been wetted to ﬁeld capacity using irrigation equipment. The results for AS varied somewhat between the 2 years. Direct measurements were carried out from the AS treatment only and showed losses of 43–59% and 27–39% in late summer and spring, respectively, due (it was postulated) to differences in temperature. Application rates were 33–280 kg N/ha, but there was no evidence of an effect of rate at either time, in contrast to other reported laboratory results. Gasser and Penny (1967) investigated urea nitrate (UN), urea phosphate (UP), and an ∼1:2 urea phosphate: urea mixture (UPU) as N fertilizers to ﬁnd out whether the presence of the anion decreased the damage urea causes to germinating seeds and seedlings and increased the efﬁciency of urea by preventing loss of NH3. Touchton and Hargrove (1982) reported the results of ﬁeld studies which were conducted for 3 years on a Cecil sandy loam (pH 6.1, CEC = 3.5 meq./100 g) to compare the efﬁciency of urea, UAN solution, and AN as affected by method of application in a direct drilled maize production system. In the UK, Stevens and Laughlin (1988, 1989) compared AS, CN, and urea and AN and urea, respectively, using 15N-labeled fertilizers. In the ﬁrst study, AS, CN, and urea were applied to ryegrass at three different times in the spring (February,

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March, and April) on a lowland and an upland site (in 1983 and 1984, respectively). The efﬁciency of fertilizer N use was measured in the following ways: at the ﬁrst cut by dry matter (DM) yield, percentage recovery by difference, percentage utilization of 15N, and for all cuts by percentage utilization of 15N. For the lowland site, only the percentage utilization for all cuts data showed a signiﬁcant effect of fertilizer form. Averages for the three dates of application (note that there was a signiﬁcant effect of timing but no interaction with fertilizer form) were AS (48.5%) > urea (46.3%) > CN (43.1%). It was suggested that more N from AS may have been immobilized and subsequently mineralized during the growing season. Alternatively, more N from CN may have been leached or denitriﬁed (see Section V below). For the upland site, all measures showed signiﬁcant effects of timing and fertilizer form with a signiﬁcant interaction between them. Using the percentage utilization for all cuts data, the results were as follows: February: AS (41.9%) > urea (28.9%) > CN (9.6%); March: CN (61.2%) > AS (56.8%) > urea (50.8%); and April: AS (92.9%) > CN (67.4%) > urea (57.8%). Thus, AS always performed better than urea, but the effectiveness of CN in relation to the other fertilizers depended on time of application, being markedly worse for the earliest application. In the second study, using conﬁned microplots, measurements were made of the recovery of N in the above-ground herbage from labeled AN and labeled urea and in soil (0–15 cm) at three sites and two rates of application. In this experiment, fertilizers were injected as solutions 5 cm below the soil surface. Recovery of urea was always greater and often statistically signiﬁcantly greater than that of AN. Differential labeling also indicated that recovery of the nitrate moiety was less than that of the ammonium moiety. Bussink and Oenema (1997) used data from ﬁeld trials in The Netherlands, southern England, and Northern Ireland on grassland with urea and CAN to develop a relationship between the relative yields from these fertilizers and environmental conditions. They noted that although there are few studies where gaseous and leaching losses were measured simultaneously after urea and CAN application, these losses were reﬂected in dry matter production and N uptake. Their analysis, combining all trials and grass cuts, generated the following relationship: AURY = 89.48(±0.781) + 2.188(±0.148) × R3 − 1.091(±0.070) × T 3 (10) 2 Radj

= 89.9%,

(11)

where AURY is the apparent urea relative yield (i.e., with respect to CAN), R3 is the total rainfall (in millimeters) within 3 days after fertilizer application, and T 3 is the average temperature (◦ C) within 3 days after fertilizer application. This relationship suggests that high temperature and low rainfall either promote NH3 volatilization from urea or reduce N losses through denitriﬁcation and nitrate leaching from CAN or both.

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85

C. LABORATORY MEASUREMENTS There have also been a number of laboratory comparisons of NH3 volatilization from different fertilizers (e.g., Meyer and Jarvis, 1989; Meyer et al., 1961; Prasad, 1976; Shahandeh et al., 1992; Sommer and Ersbøll, 1996; Whitehead and Raistrick, 1990). Early laboratory experiments (Meyer et al., 1961) with AN; urea; and a solution made up as 44.3% AN, 35.4% urea, and 20.3% water (SOLN) were carried out on three soils ranging in pH from 5.6 to 7.8. The results of NH3 volatilization measurements showed that N loss declined with decreasing pH for all three N sources. Losses were relatively small from the acid soil and were generally greatest from the soil with the highest pH; however, for SOLN applied to straw residues covering the soil surface, emissions from the acid soil were almost as large as those from the alkaline soils. Some laboratory studies have focused on the effect of soil or fertilizer amendments on NH3 volatilization. Thus, Prasad (1976) carried out laboratory incubation experiments to study NH3 volatilization from urea, sulfur-coated urea (SCU), and AS applied to a calcareous soil at two temperatures (22 and 32◦ C) and three moisture contents (25, 50 and 80% of soil water-holding capacity). Higher temperature increased the NH3 losses from all sources. Losses usually followed the order urea > AS > SCU; there were three forms of SCU representing fast (F), medium (M), and slow (S) dissolution rates, and the order of losses for these was SCU(F) > SCU(M) > SCU(S). At both temperatures, increasing soil moisture content led to a reduction in NH3 loss regardless of the N source. It was suggested that this was because greater moisture content would provide a larger surface area for NH3 absorption and also increase the rate of nitriﬁcation. On the other hand, the rate of urea hydrolysis was only slightly retarded at lower moisture contents, even at the lower temperature. At 22◦ C, NH3 losses from urea and AS at low, medium, and high moisture contents were 11.1 and 10.2%, 7.7 and 8.8%, and 4.2 and 3.5% of N applied. It was noted that the time course of the losses for these fertilizers differed, with AS showing moderate losses for the whole 21-day experiment, whereas urea showed high losses for days 3–14 and low or negligible losses for days 0–3 and 14–21. Increased temperature resulted in losses more than double for SCU, about double for urea, and less than double for AS. Shahandeh et al. (1992) compared NH3 volatilization from AS, Uaq, and a sludge produced as a by-product of NutraSweet manufacture (NS). Laboratory incubations were carried out for 16 days at 25◦ C; a static (i.e., nonvented) system was used to collect volatilized NH3, which would tend to underestimate the loss compared to that which occurs in the ﬁeld. Their results showed average losses of 0.9 and 0.4% from AS solution and 14.2 and 17.9% from Uaq for Tifton [pH 6.8, CEC = 3.62 cmol(+)/kg] and Dothan [pH 5.5, CEC = 0.54 cmol(+)/kg] soils, respectively. The difference between the fertilizers was as expected, and that between the soils for Uaq was ascribed to the effect of the smaller buffering capacity

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HARRISON AND WEBB

in the Dothan soil. These authors also discuss the effect of surface residues on NH3 volatilization from Uaq. They state that although other workers have suggested that emissions are less from soils than from residues (presumably because of high urease activity and low buffering capacity), in this study NH3 volatilization for the Dothan soil was reduced by a straw covering (16.9 cf. 18.9%). They suggest that this may have been due to increased rates of nitriﬁcation in the presence of straw. Other workers have used laboratory studies to investigate the interactions between fertilizer application and urine from grazing animals by comparing NH3 emissions from urea and AN alone and in the presence of urine from intact cores and disturbed soils (Meyer and Jarvis, 1989). The results for intact cores show that emissions from urea (6.2 and 17.8% for applications of 2 × 30 and 2 × 60 kg N/ha, respectively) are much greater than from AN (<0.1%). However, the addition of urine resulted in increased emissions from the fertilizers. Emissions were 72.0 and 21.8% for urea and 47.1 and 16.0% for AN from application rates of 2 × 30 and 2 × 60 kg N/ha, respectively. It was suggested that this was the result of localized increases in pH associated with the hydrolysis of urea (in urine). There was no consistent effect of disturbance, except that the period of emission was shorter, presumably due to greater adsorption. These authors suggest that although other workers consider that changes in fertilizer practice for grazed swards (compared with cut grass, which receives no urine) are not necessary (since only a small proportion of the soil surface receives urine each day), there is a need to assess the longevity of the effects reported in ﬁeld conditions. Ammonia volatilization from ﬁve nitrogen compounds used as fertilizers following surface application to soils reported by Whitehead and Raistrick (1990) has already been discussed above. However, these workers considered that their results (obtained in the laboratory) were probably twice those which could be expected in the ﬁeld for spring N fertilizer application. This assessment was made on the basis of a comparison of the results for urea on one of the soils (pH 7.1, CaCO3 1.8%) in this study with those from a ﬁeld study using wind tunnels (Ryden, 1984). A similar study with urea, DAP, and CAN (Sommer and Ersbøll, 1996) and two soils (sandy and sandy loam texture), either limed or not limed for 25 years, indicated that cumulative emission followed a sigmoidal pattern with urea, a negative exponential pattern with DAP, and a linear pattern with CAN, except for the unlimed soil with the (lowest) pH value of 4.5, where very small emissions were obtained for urea. Overall losses were urea (23–28%) > DAP (23–28%) > CAN (<5%), excluding the results from urea for the most acidic soil.

D. EMISSIONS FROM FERTILIZER SOLUTIONS McInnes et al. (1986a,b) reported 4–17% loss from 120 kg N/ha applied to bare soil as urea solution and 7–17% from 200 kg N/ha applied to wheat straw and stubble as UAN solution. Similarly, Keller and Mengel (1986) reported the results

EFFECT OF N FERTILIZER TYPE ON GASEOUS EMISSIONS

87

from ﬁeld experiments carried out on two soil types to measure NH3 volatilization using ventilated chambers from urea, cogranulated urea-urea phosphate (UUP), UAN, and AN. Fertilizers were applied at 168 kg N/ha to a maize residue. The soils were Lyles sand loam ( pH 5.6, CEC = 6.7 cmol(+)/kg) and Bedford silt loam ( pH 5.5, CEC = 12.3 cmol(+)/kg). For the Lyles sand loam, NH3 volatilized was 30, 15, 9, and 4% of the applied N for urea, UUP, UAN, and AN, respectively. Although the overall losses were less from the Bedford silt loam (11, 6, 5, and 2%), relative losses from the different N fertilizers were similar. These results show that emissions from UAN solutions are intermediate between those from urea and AN granules, but it is difﬁcult to make ﬁrm conclusions on the effect of application in solution per se. However, Lightner et al. (1990) measured NH3 volatilization from urea, Uaq and AN. No signiﬁcant volatilization losses were observed with AN; greatest losses were from urea and Uaq. Measurements were made in spring (May) and summer (August) over 2 years (1982 and 1983). There was no signiﬁcant difference between urea and Uaq in May 1983 (∼31% volatilized) and August 1982 (∼34% volatilized). In May 1982 the losses were 41% for urea and 29% for Uaq. This was ascribed to two rainfall events of 5.0 and 3.3 mm which occurred during the 2nd and 4th day after application which (it was postulated) were sufﬁcient to enhance urea granule dissolution, urea hydrolysis, and evaporative water ﬂux from the soil, but inadequate to move the urea into the soil and terminate volatilization. Smallest losses occurred in August 1983 when conditions were particularly dry, and emissions were 10 and 14% from urea and Uaq, respectively. In this case, it appeared that the additional moisture associated with fertilizer solution treatment was important in promoting additional volatilization. Fox et al. (1996) report the results of ﬁeld (ZINST method) measurement of NH3 volatilization from urea, sprayed UAN, and dribbled UAN on direct-drilled maize at an application rate of 134 kg N/ha. Results from measurements in three (annual) experiments indicated that N losses were as follows: urea, 30% of N applied; and UAN, 15%. In all 3 years, the 6 days following N application were relatively rain free. In 1 year, however, 10 mm rain fell 6 days after application, and this appeared to reduce emissions from urea but not from UAN. The results were as follows: urea, 39–47% and 33%; sprayed UAN, 19–24%; and dribbled UAN, 16–19%. Approximately 90% of the total emission took place within the ﬁrst 10 days in the drier years, although emissions appeared to continue beyond day 16 (for urea) for the year when signiﬁcant rainfall fell 6 days after applications.

E. UREASE INHIBITORS Watson et al. (1998) investigated the effect of the urease inhibitor nBTPT by comparing dry-matter production and N uptake by grass fertilized with urea (with and without inhibitor) and CAN. Results from 15 fertilizer application periods in

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Northern Ireland during the period 1994–1996 were reported (and results from six equivalent fertilizer application periods in the Republic of Ireland were also noted). On 4 of the 15 occasions in Northern Ireland (and 2 of 6 occasions in the Republic of Ireland), CAN signiﬁcantly outperformed urea. Fertilizer applications were carried out during the summer when conditions for NH3 volatilization are optimal in Ireland. Addition of the inhibitor signiﬁcantly improved the performance of urea so that dry-matter production and N uptake were almost the same as those with CAN. Laboratory soil enclosure studies showed both that NH3 volatilization was less and the peak of volatilization occurred later (4.9 cf. 1.6 days after urea application) with the inhibitor. There was no evidence for a decline in the efﬁcacy of the inhibitor with repeated application; indeed the inhibitor appeared to be more persistent than had been indicated in earlier pot studies, due (it was thought) to lower temperatures in the ﬁeld. There was still an effect of the inhibitor 10–12 weeks after the third application, but this had disappeared by the following growing season.

IV. AMMONIA EMISSION FACTORS FOR NITROGEN FERTILIZERS Emission factors for different N fertilizers have been published by a number of authors (see, e.g., Anonymous, 1994). Although NH3 emission from urea has been extensively studied, information for other fertilizer types is more sparse. In general, it is considered that emissions from other fertilizers are less than those from urea, with the possible exception of AS and DAP on calcareous or otherwise alkaline soils. In a recent report Sutton et al. (1994) examined the different emission factors for fertilizers used in UK inventories (Table II). These authors noted (with surprise) the fact that despite the large range in emission of measurements from urea, the range for emission factors is rather narrow. Nevertheless, they concurred and stated that a reasonable best estimate for emission from urea (after accounting for some recapture by vegetation) was 10%. The suggested range for AN is (proportionally) somewhat larger. This also is surprising. However, Sutton et al. (1994) point out that the values quoted by Buijsman et al. (1987), and consequently Klaassen (1992) and Eggleston (1992), were based partly on the work of Fenn and Kissel (1974), which investigated NH3 volatilization from surface applications of NH+ 4 compounds on calcareous soils. If these estimates are excluded, and noting that Jarvis and Pain (1990) excluded emissions from fertilizer other than urea from their calcuations, the range for emission factors from AN is much narrower, and Sutton et al. (1994) considered that 1% was a reasonable best estimate. More recently, van der Weerden and Jarvis (1997) have revised emission factors for fertilizer N applied under UK conditions. These authors considered that the emission factor for urea suggested by Buijsman et al. (1987), Klaassen (1992),

EFFECT OF N FERTILIZER TYPE ON GASEOUS EMISSIONS

89

Table II Comparison of Average NH3 Emission Factors from Application of Nitrogen Fertilizers Loss as % applied N Author Buijsman et al. (1987) Kruse (1986) Jarvis and Pain (1990) Whitehead and Raistrick (1990) Asman (1992) Klaassen (1992) Eggleston (1992) ECETOC (1994) Sutton et al. (1994)

Country

Urea

NH4NO3

Othera

Average

UK GB UK UK Europe UK UK Europe UK

10 — 15, 5b 16.5 15 10 10 15/20 10

10 — 0 2.5 2 10 10 3 1

1 — 0 1.5–4.9d 4d 1–15d 1 8 2.5

5.8 1 0.6c 3.4 3.8 5.4 — 4.4 2.4

After Sutton et al., 1994. Mainly combined NPK-N fertilizer in the UK. b For grassland and arable and respectively. c Calculated using total N fertilizer consumption in the UK of 1460 Gg/year N (Asman, 1992). d Several other subcategories provided. e Based on Buijsman et al. (1987); see Klaassen (1991). a

Eggleston (1992), and Sutton et al. (1994) was too low because it lies near the bottom of the range of losses from ﬁeld experiments. They estimated an emission factor for urea based on their work and other ﬁeld results using only data where applications were <200 kg N/ha. Averaging the information from these studies they calculated an emission factor of 23% for urea applied to grassland. They acknowledged that this represents a value applicable to “average” conditions and note that it will be inﬂuenced substantially by application rate, weather, and soil conditions. This value is somewhat larger than the ﬁgure proposed by Whitehead and Raistrick (1990), who calculated their factor by, ﬁrst, correcting their laboratory results for ﬁeld conditions (×0.5, see above) and, second, by making assumptions about the representative nature of the soils in their study. Hence, they considered that soils represented by the Hucklesbrook (pH 6.1, CaCO3 0.6%) and Frilsham (pH 7.1, CaCO3 1.8%) series were three times as common as represented by the Batcombe (pH 5.5, CaCO3 0%) and Andover (pH 7.4, CaCO3 75.0%) series. For AN, an emission factor of 1.6% was estimated (van der Weerden and Jarvis, 1997) based on this study and other published results (Meyer and Jarvis, 1989; Ryden et al. 1987; Whitehead and Raistrick, 1990). This factor was also suggested for other N fertilizers such as CAN and compounded mixtures (e.g., containing CN, ammonium phosphate, and AS). Whitehead and Raistrick (1990) had proposed emission factors of 1.5, 4.9, and 9.9% for MAP, DAP, and AS, respectively. Emission factors for fertilizers applied to arable soils were estimated as half of those for

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grassland soils. This was based on work presented by Black et al. (1985b) which showed emissions from urea applied to winter wheat were 65 and 4% of those from urea applied to pasture for surface application and incorporation, respectively. In contrast, Whitehead and Raistrick (1990) had suggested that emission factors for arable crops should be reduced by 30% to allow for a proportion being applied by drilling rather than top-dressing.

V. MEASUREMENTS OF NITROUS OXIDE EMISSIONS FOLLOWING NITROGEN FERTILIZER APPLICATIONS A. COMPARATIVE STUDIES OF EMISSIONS Early reported results from “highly replicated” ﬁeld experiments (Breitenbeck et al. 1980) showed that fertilizer-derived emissions of N2O from plots treated with nitriﬁable forms of fertilizer N markedly exceeded those from plots receiving an equivalent application as nitrate. The fertilizer-induced emissions over 96 days were 0.11–0.18% of N applied for AS, 0.12–0.14% for urea, and 0.01–0.04 for CAN. Measurements supported earlier laboratory studies (Bremner and Blackmer, 1978, 1979) which showed that most of the N2O evolved from soils fertilized with AS or urea is generated within 2 weeks and that N2O emissions from such soils after about 3 weeks are not signiﬁcantly greater than N2O emissions from unfertilized soils. Soil analyses showed that most of the N added to the AS and urea plots was nitriﬁed within 3 weeks. These workers also noted that despite rainfall considerably above average and moisture contents frequently near, and at times above, ﬁeld capacity, there was no marked increase in N2O emission from CNtreated plots. In a later study (Breitenbeck and Bremner, 1986), the effect of 180 kg N/ha as anhydrous NH3, aqueous ammonia, and urea on N2O emission from three soils (pH 6.9–7.9, CaCO3 0–9.8%) was determined in the ﬁeld using chambers with measurements at 3- to 7-day intervals over a 140-day period. The anhydrous NH3 was applied by injection at a depth of 20 cm, while the other fertilizers were applied as solutions evenly spayed over the surface; applications occurred on June 3, 1980, and all plots were rototilled to a depth of 20 cm shortly afterward. The results showed that fertilizer-derived N2O emissions were (as a proportion of N applied) as follows: anhydrous NH3, 0.86–2.08% (average = 1.33%); aqueous NH3, 0.04–0.12 (average = 0.07%); and urea, 0.07–0.11% (average = 0.08%). A laboratory study (utilizing one of the soils) showed that the proportion of N applied which was emitted as N2O increased markedly with increased application rate of NH3, rising from 0.18% at 100 ␮g/g soil to 1.15% and 1.19% at 500 and 1000 ␮g/g soil, respectively. It has been shown that the concentration of NH+ 4 -N

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frequently exceeds 500 ␮g/g soil where anhydrous NH3 is applied by injection. Thus, these workers suggest that the large emissions observed with anhydrous NH3 were due, at least in part, to the highly alkaline conditions which occur due to the method of application and which were not present where the fertilizers were broadcast. Thornton et al. (1996) also reported the results of measurements of N2O emission from anhydrous NH3 and urea solution. Fertilizers were banded 15 and 10 cm below the surface, respectively, at a rate of 168 kg N/ha. Measurements were made every 3 h from May 6 to September 12, 1994. The results showed that fertilizer-derived emissions were 7.3 and 3.8% of the applied N for anhydrous NH3 and urea, respectively. N2O emission from urea solution was much greater than had previously been reported. The large N2O emission from urea solution in this study was ascribed to the fact that it was banded below the surface rather than surface applied. Correlations with soil properties in the zone of fertilizer application suggested that the main source of N2O was denitriﬁcation (correlated with nitrate concentration and water-ﬁlled pore space >60%). Two other points were worthy of note. First, whereas most studies extrapolate total emissions from, say, weekly measurements, in this study determinations were based on quasicontinuous data. It was shown that for these data, N2O loss estimates would have varied by a factor of up to 2.6 if these had been based on extrapolation from weekly measurements. Second, while other studies with AN have shown that most of the N2O was lost in the ﬁrst 42 days after application, in this study the period of elevated emissions lasted for about 60 days. Duxbury and McConnaughey (1986) reported the results of ﬁeld measurements (using static chambers) of N2O emission and denitriﬁcation (in situ acetylene block technique). The experiment was carried out over an 85-day period between fertilizer application (July 5) and harvest (September 28, 1981) of maize on a silt loam soil. Measured N2O ﬂuxes were greatest following rainfall, especially during the middle 14 days of August when there was 72 mm of rain. Total emissions during the experiment period were 0.3, 0.3, and 2.5 kg/ha N2O-N for nil fertilizer, CN, and urea, respectively. The fact that there was no difference between nil fertilizer and CN suggested that NO− 3 availability did not limit emission, which may have resulted from nitriﬁcation or denitriﬁcation. The additional emission from urea was ascribed to nitriﬁcation. Keller et al. (1988) reported measured N2O emissions following fertilization of tropical forest soils. Their results showed that NO− 3 addition resulted in a loss of ∼0.5% of the applied N, which was more than ﬁve times greater than − that for NH+ 4 addition. The emission factor for NO3 was considered by these authors to be large in comparison to other reported values, particularly as their data were integrated over only 2 weeks; they quoted values for 28 separate measurements of N2O from N fertilizer. They concluded that their study revealed a large potential source of N2O from denitriﬁcation, with a much smaller response associated with nitriﬁcation. No heavy rainfall events were recorded during the

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experiment period and soil temperature at 5 cm remained in a narrow range between 24 and 26◦ C. Recent studies in Scotland (Clayton et al., 1997) and France (H´enault et al., 1998b) clearly showed that N2O emissions from N fertilizers may vary substantially within and between seasons. Clayton et al. (1997) presented the results of ﬁeld measurements over 2 years of N2O emission from different fertilizers applied at 120 kg N/ha to grassland at three times of the year (April, June, and August). Their results showed that over the 2 years N2O losses followed the order urea ∼ = = AN ∼ CN > AS, with annual fertilizer-derived emissions ranging from 0.2 to 1.4% of the N applied. However, the temporal pattern of losses was different for different fertilizers and between years. Emissions following the April application followed the order CN ∼ = AS. Thus, in both years conditions were more favorable = AN > urea ∼ for denitriﬁcation than for nitriﬁcation. The observation that emissions from CN were similar to those of AN in April (and at other times of the year, see below) might be explained in part by the difference in soil pH between these treatments; higher pH values in the CN treatment (probably associated with the liming effect of calcium and root anion exchange) would tend to decrease the N2O:N2 denitriﬁcation ratio (Granli and Bøckman, 1994). This might more than offset any increase in denitriﬁcation rate due to increased pH. However, the denitriﬁcation product ratio also increases with increasing NO− 3 concentration (Granli and Bøckman, 1994). These workers noted, however, that a large proportion of the total N2O emission from CN and AN in each period was emitted within 1–3 weeks of fertilizer application. They suggest that this might indicate a rapid decline in the soil NO− 3 concentration due to plant uptake (and denitriﬁcation). Emissions following the June and August applications varied depending on the prevailing conditions. When these were dry (June 1992 and August 1993) emissions were small; greatest for urea (0.5 and 0.4%) and least for AS (0.1 and 0.1%). Emissions from AN (0.1 and 0.4%) and CN (0.1 and 0.1%) were more or less intermediate. It appears that for these applications, conditions were not especially favorable for denitriﬁcation, and probably both nitriﬁcation and denitriﬁcation contributed to the observed ﬂuxes, with the effect of increasing temperature and increasing N2O:NO− 3 nitriﬁcation ratio being more important relative to the situation in April. The marked feature of these measurements was the consistently greater emissions from urea and lesser emission from AS. This observation was again ascribed to the differing effects of the fertilizers on soil pH. It is generally thought that the large NH3 concentration associated with the pH rise resulting from the hydrolysis of urea inhibits the oxidation of nitrite to nitrate by Nitrobacter (Cochran et al., 1981), leading to nitrite accumulation (Van Cleemput and Samater, 1996) and hence enhanced N2O production. In contrast, lower pH values (in this case associated with AS application) have been shown to inhibit nitriﬁcation and the production of N2O. When the prevailing conditions were wet (June 1993 and August 1992), the order of emissions was urea > AN ∼ = CN > AS. Under these conditions, denitriﬁcation became more important relative

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to drier conditions at the same time of year. However, recent data comparing AN and urea have suggested that the relative emissions from different fertilizers may be more complicated than originally suggested (K. A. Smith, personal communication). While emissions in spring tend to be greater from AN than from urea, this is not always the case: In fact, from 11 series of measurements, emission was greater from AN on 8 occasions, but N2O emission was greater from urea than AN on the occasion with the greatest emission. The picture is even more complicated for the later fertilizer applications. From 21 series of measurements, emission was greater from urea on 14 occasions, but for the 10 occasions with the greatest N2O emissions, there were 5 each with the greater emission from AN and urea. Velthof and Oenema (1994) measured N2O emissions in the spring following the application of different fertilizer types or urine. Frequent measurements were carried out over a 23-day period using closed chambers and photoacoustic infrared spectroscopy. Fertilizers AS, CAN, CN, and urea were applied at 80 kg N/ha, and urine was applied at 275 kg N/ha. Emissions from the fertilizers were not signiﬁcantly different from each other. Fluxes were generally small (mean 2.5 g N/ha/day), which was ascribed to the cold and dry conditions. In a further series of experiments (Velthof et al., 1997), carried out to investigate the effect of fertilizer type on N2O emission from grassland in The Netherlands, fertilizer-derived emissions measured over 3–4 weeks under cold and dry conditions (6.0◦ C, 13 mm rainfall) were small (<0.1%) and showed no differences between the different fertilizers CAN, CN, AS, and urea. However, in another spring experiment under slightly warmer and signiﬁcantly wetter conditions (8.2◦ C, 42 mm rainfall) emissions were much greater, especially from the nitrate-based fertilizers. Fertilizer-derived emissions were 5.2, 5.2, 0.2, and <0.1% from CAN, CN, AS, and urea, respectively. That these emissions were derived largely from denitriﬁcation was conﬁrmed by the large total denitriﬁcation ﬂuxes and by the fact that the use of a nitriﬁcation inhibitor (DCD) with AS only reduced N2O emissions slightly (to <0.1%). Similar results were obtained from a summer experiment (16.0◦ C, 68 mm rainfall), with emissions of 8.3, 12.0, 1.0, and 0.7% from CAN, CN, AS, and urea, respectively. In this experiment, N2O appeared to be the main product of denitriﬁcation (i.e., N2O ﬂuxes were equal to total denitriﬁcation ﬂuxes). DCD reduced N2O emission from AS to 0.1%. In comparison to other workers (e.g., McTaggart et al., 1994), N2O emissions from urea and AS were similar. It was postulated that in the experiments reported above, conditions were either cold enough to slow down urea hydrolysis and hence limit the pH rise at microsites or wet enough to disperse the alkalinity such that N2O emission from urea and AS were similar. In contrast (Velthof and Oenema, 1994), emission from urine was 0.5% of the N applied. It was postulated that this was due to the high NH+ 4 concentration; this would support a high NH3 concentration which would lead therefore to enhanced N2O production. Indeed, Wang and Rees (1996) found (under laboratory conditions which were favorable for nitriﬁcation) that ammonium concentration alone

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was not sufﬁcient to predict N2O emission; including pH as an additional variable in the regression equation allowed 92% of the variability to be accounted for. The experiment (carried out on a range of 10 Scottish soils at a matric potential of 500 mm) consistently showed that emissions over a 14-day period were greater for urea (average-60.1 mg N2O/kg soil) than for CN (11.6 mg N2O/kg soil; cf. 8.4 mg N2O/kg soil for no fertilizer control). Emissions from a wider range of fertilizers with a single soil over 16 days were greatest for AS (215 mg N2O/kg soil) and least for CN (6.3 mg N2O/kg soil; cf. 4.2 mg N2O/kg soil for no fertilizer control). H´enault et al. (1998a) carried out a comparison of N2O emissions from different fertilizers applied to oilseed rape at Longchamp, Burgundy, France. Measurements were made periodically from November 1996 to July 1997. Four fertilizers were used: AN, AS, urea, and, potassium nitrate (PN). The results showed that fertilizer type affected both soil pH and soil mineral N. Soil pH was lower in fertilized plots (compared to the no fertilizer control) and the decrease was particularly marked for AS. Nitrate concentrations were greatest in the PN treatment in March and in the AS treatment at the beginning of May. Measurement of N2O emission showed that fertilizer type had an effect during two periods: for 1 month following fertilizer application and from mid-June until harvest. For the earlier period, N2O emissions were greatest with AN and AS and least with PN. The urea treatment showed a strongly increasing trend, but with high temporal variability which could not − be accounted for on the basis of soil mineral N (NH+ 4 or NO3 ) or pH. For the later period, N2O emissions were greatest for PN and least for AN and urea. These differences between fertilizer treatments could not be explained in terms of differences in soil mineral N or pH. Overall, fertilizer-derived emissions were 0.53, 0.55, 0.42, and 0.42% of applied N for AN, AS, urea, and PN, respectively. However, these values were small compared to measurements obtained 2 years earlier from a similar soil approximately 2 km from the site used in this study, where emissions had been ∼2.5% of the applied N (H´enault et al., 1998b). In that study, the largest ﬂuxes were obtained from AN and urea > nitrate > NH+ 4 . The smaller losses in the later study were ascribed to a drier spring for that period compared with the equivalent period in the earlier study. The results indicated that the effect of N fertilizer type were very limited with respect to time and that in this study differences were due to differences in nitriﬁcation rates. It is clear that although N fertilizer type certainly affects the extent of N2O emission, appropriate emission factors for individual fertilizer types have not been generally agreed. Leaving to one side the magnitude of emissions from anhydrous NH3 and from injected urea solution, it is apparent that emission factors for broadcast urea, AS, AN, and NO− 3 salts depend on the conditions in the period after fertilizer application. Both nitriﬁcation and denitriﬁcation contribute to the emission, and it is possible to rationalize (but more difﬁcult to predict) results for NH+ 4 and nitrate fertilizer types on the basis of the season of application. The position of urea appears anomalous, however; whereas Clayton et al. (1997) suggested

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Table III Proposed Scheme for Assessing Relative Emissions of N2O from Differing Fertilizers

Moisture

Relative emission for N forms

Dry

Low

Wet

High

Very wet

High

Nitrate ∼ = ammonium Nitrate > ammonium Nitrate ≫ ammonium

Relative emission from urea Urea ≥ ammonium Urea ≫ ammonium Urea ∼ = ammonium

Notes Rate of urea hydrolysis limited Rate of urea hydrolysis increases with temperature High pH associated with hydrolysis dispersed by moisture

that emissions can be relatively large under conditions when emissions from AS are relatively small, Velthof et al. (1997) obtained similar emissions from both fertilizer types. Work by Smith and co-workers for a range of Scottish sites indicated that there was a good relationship between rainfall at and around the time of fertilizer application and N2O emission from AN. Although earlier results had suggested a similar relationship for urea (albeit with lower emissions), greater than expected emissions were measured from urea in the extremely wet 1998 season (K. A. Smith, personal communication). The suggestion that temperature (controlling the rate of urea hydrolysis) and rainfall (controlling the dispersion of alkalinity) are important determinants obviously needs further investigation. As a ﬁrst approximation, we suggest that the relative emissions from N fertilizers mught be considered as outlined in Table III. Thus, emissions are generally greater + from NO− 3 -based fertilizers compared to NH4 fertilizers, and this difference increases with increasing moisture. Under dry conditions, emissions from urea may be slightly greater than from other nitriﬁable fertilizers, but since emissions under these conditions are small this is not of great signiﬁcance. Under wet (and particularly, warm) conditions, emissions from urea are signiﬁcantly greater than those from NH+ 4 -based fertilizers and urea emissions may exceed those from -based fertilizers, particularly at the lower end of this moisture category. NO− 3 However, the work of Smith and co-workers shows that as moisture increases so + does the relative emission from NO− 3 compared to NH4 -based fertilizers, and so at the high end of the “wet” moisture category, there are signiﬁcant emissions from both NO− 3 -based fertilizers and urea; the fertilizer from which gives greater emission is probably extremely sensitive to both moisture and temperature. The results of Velthof et al. (1997) seem to suggest that there is a further “very wet” category where emissions from urea become similar to those of NH+ 4 -based fertilizers; however, it is important to account for the effect of soil type on the “wetness”

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category. The results of Velthof et al. (1997) were obtained from a poorly drained sand soil compared to the heavier textured soils studied by Smith and co-workers.

B. THE EFFECT OF NITRIFICATION INHIBITORS ON NITROUS OXIDE EMISSIONS Studies with nitriﬁcation inhibitors have demonstrated that there is consider+ able scope for reducing N2O emissions from NH+ 4 or NH4 -forming fertilizers. Magalh˜aes et al. (1984) reported results from measurements of N2O emission following banded application of anhydrous NH3 at a depth of 15 cm. The results from this study suggest a much smaller N2O emission factor for anhydrous NH3. In one soil (Mywybilla, pH 6.9) there was no signiﬁcant effect of fertilizer application on N2O emission. For the other two soils (Anchorﬁeld, pH 7.5; Norillee, pH 8.5, calcareous) there was an effect of fertilizer application; however, the largest emission factor was only ∼0.05% (for the Norillee soil). This experiment was carried out under relatively cool and dry conditions, with average soil temperatures at 17 cm of between 12.5 and 17.5◦ C and the soil water content at 0–20 cm—well below ﬁeld capacity. It has been suggested that N2O losses in soil fertilized with anhydrous NH3 result from the reduction of nitrite, which tends to accumulate in the soil surrounding the zone of NH3 injection. In this study, application of a nitriﬁcation inhibitor (nitrapyrin) signiﬁcantly reduced N2O emission from the Norillee soil; this was associated with a large (72%) decrease in nitrite concentration but only a moderate (48%) decrease in the rate of nitriﬁcation of the fertilizer. In contrast, nitrapyrin had no effect on N2O emissions from the Anchorﬁeld soil but slightly reduced nitrite concentration and the rate of nitriﬁcation of the fertilizer. The difference between these two soils was ascribed to a lower bioactivity of the inhibitor in the higher organic matter content Anchorﬁeld soil. Aulakh et al. (1984) carried out a ﬁeld experiment on a wheat stubble ﬁeld to compare N2O emissions from PN and urea. Fertilizers were applied at 50 kg N/ha in autumn and measurement showed that fertilizer-derived emissions continued for about 4 weeks. There was a signiﬁcant increase in N2O emission from the urea treatment over and above the unfertilized control but this did not occur for the PN treatment. Addition of the nitriﬁcation inhibitor N-serve (2chloro-6-(trichloromethyl)-pyridine) reduced emissions from urea to background levels. These results suggest that the mechanism for N2O production in this experiment was nitriﬁcation. Soil moisture was 26–37% saturation during the period of the experiment. Bronson (1993, 1992) and co-workers determined N2O emission from soils in the ﬁeld (using vented chambers) in irrigated corn systems. In one experiment (Bronson et al., 1992), the treatments were urea alone, urea plus nitrapyrin (U+np), urea plus 20 kg/ha encapsulated calcium carbide

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(U + ECC20), and urea plus 40 kg/ha encapsulated calcium carbide (U + ECC40). Fluxes of N2O were positively correlated with soil NO− 3 , indicating that the nitriﬁcation inhibitors indirectly controlled N2O emissions by preventing nitrate from accumulating in the soil. For the ﬁrst crop (1989) total losses were 3.2, 1.1, 1.0, and 1.0 kg/ha N2O-N from urea, U + np, U + ECC20, and U + ECC40, respectively. For the second crop (1990) losses were less (1.7 kg/ha N2O-N for U), probably because there were fewer irrigation events. In a second experiment (Bronson and Mosier, 1993) the results for cumulative emissions (from time of fertilization 2 months after planting to harvest 97 days later) were as follows: 1.65, 0.98, 0.48, 0.43, and 0.11 kg/ha N2O-N for urea alone, with nitrapyrin, with ECC at 20 kg/ha, with ECC at 40 kg/ha, and for the no-fertilizer control, respectively. McTaggart et al. (1994, 1997) demonstrated that there was considerable scope + for reducing N2O emissions by applying nitriﬁcation inhibitors with NH+ 4 or NH4 forming fertilizers. This is especially so for crops such as intensively managed grass where there are several applications of fertilizer N over the season, as the effect of inhibitors applied in April persisted until after a second fertilizer application in June. Their results showed that for fertilizer applied to grass at 120 kg N/ha in April, June, and August (i.e., 360 kg N/ha in total), DCD applied in April and August reduced the annual N2O emission from urea by 58% in 1992–1993 and by 56% in 1993–1994 and reduced the annual N2O emission from AN by 35% in 1993–1994. The results for nitrapyrin applied in April showed a reduction of 40% in the annual N2O emission from urea in 1992–1993. There was no effect of DCD or nitrapyrin on the annual emission from AS in 1992–1993, although there was a reduction for DCD in 1993–1994. However, emissions from AS were less than from the other fertilizers. These authors also reported the results of measurements of N2O emission from fertilizers applied to spring barley. Their results show that total N2O emissions over 56 days were 0.6, 0.4, and 0.3 kg N/ha from two spring applications of 60 kg N/ha as urea, from AN, and from a no fertilizer control, respectively. Because water-ﬁlled pore space was <60% throughout the measurement period, it was suggested that nitriﬁcation was the main mechanism for the emissions. The nitriﬁcation inhibitor DCD reduced emissions from urea by 40% but had no effect on those from AN.

VI. NITROUS OXIDE EMISSION FACTORS FOR NITROGEN FERTILIZERS In contrast to NH3 emissions, there have been fewer studies of the effect of N fertilizer type onN2O emissions. Bolle et al. (1986) stated that “until recently it was generally assumed that about 10–15% might be lost as N2O [but] new data show

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that this ﬁgure is much too high and that the loss as N2O is strongly dependent on the mode of application and type of fertilizer.” These authors noted that the greatest loss rates have been observed for anhydrous NH3 and NH+ 4 fertilizers, which supports the view that nitriﬁcation is the dominant N2O production process, and also note results (Breitenbeck et al., 1980; Conrad et al., 1983; Slemr and Seiler, 1984) which show average N2O loss rates of 0.04% for nitrate, 0.15–0.19% for ammonium and urea, and 5% for anhydrous NH3. Eicher (1990) reviewed direct measurements of fertilizer-derived N2O emissions from 104 ﬁeld experiments published in the literature and carried out between 1979 and 1987. Of the 104 experiments, 49 did not sample emissions from an unfertilized control site so fertilizer-derived emissions could not be distinguished from natural sources of N2O and 8 of the controlled studies had fertilizer application rates >250 kg N/ha and were therefore excluded from subsequent analysis because such rates are much greater than typically used in agricultural systems. The results showed fertilizer type to be an important factor inﬂuencing emissions. The averages were as follows: anhydrous NH3 (2.70%, range 0.86–6.84%) > AN (0.44%, range 0.04–1.71%) > ammonium (0.25%, range 0.02–0.90%) > urea (0.11%, range 0.07–0.18%) > nitrate (0.07%, range 0.001–0.50%). It was noted that several important variables that affect emissions were not summarized or considered in the experiments surveyed; e.g., temperature and the amount or intensity of rainfall, the timing of these events with respect to emission, and the presence of residual plant material. In addition, the studies reviewed suggested that most of the fertilizer-derived N2O is emitted during the growing season (often shortly after fertilization), although a signiﬁcant amount has been found to be released during spring thaw. In most of the experiments, the duration of the sampling period captured the large, often episodic ﬂux that occurs shortly after fertilization. Thornton et al. (1996) brieﬂy reviewed other work dealing with emissions from different fertilizers. They pointed out that although the OECD/OCDE use different factors for different fertilizers (based on the work of Eichner, 1990), Mosier (1993) noted that these were based on very limited data; they cited recent laboratory studies (Mulvaney and Khan, 1994) of N2O and N2 losses which indicated that emissions decreased in the order anhydrous NH3 > urea > DAP > AS > AN > MAP. Median values of N2O loss from urea appear to be in the range 0.1 to 0.6% of applied N (Granli and Bøckman, 1994). Most recently, Smith et al. (1997) noted that no allowance has been made for different fertilizer types in IPCC emission factors for N2O on the basis that soil management and cropping systems and unpredictable rainfall inputs were more important variables. Mosier (1994) and Bouwman (1994) concluded that the literature data relating to N2O emissions from agricultural soils were too limited to calculate the individual emission factors for different fertilizers. Smith et al. (1997) also noted that it had been suggested that the large emission factor suggested for anhydrous NH3 may be unrepresentative because in the data set used there was no plant sink to compete for the

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applied N (fallow soil) and cropresidues were also present possibly enhancing denitriﬁcation.

VII. NITRIC OXIDE EMISSIONS FROM NITROGEN FERTILIZERS There is very little published data concerning nitric oxide (NO) emissions from different N fertilizer sources. Veldkamp and Keller (1997) have summarized and evaluated 23 published studies of NO emission following fertilizer addition. Emission factors were estimated using only studies carried out over at least a full growing season and where measurements had been made on a ﬁeld scale. Only 6 studies met these criteria, all of which were carried out in temperate regions. An emission factor of 0.5% was determined (although this was considered to represent the lower limit), and the data were too limited to separate the effect of fertilizer type (or soil type or crop management). Slemr and Seiler (1984) made measurements of NOx emission at two sites: Finthen, Germany and Utrera, Spain. Their results showed that application of mineral fertilizers increased NO and NO2 emission rates. The largest fertilizer-derived emission rates were obtained from urea (3.3 and 2.2% for NO and NO2, respectively), followed by NH4Cl, AN, and least from NaNO3 (0.04 and 0.07% respectively). Thornton et al. (1996) measured emissions of 0.2 and 0.3 kg/ha NO-N for anhydrous NH3 and urea banded 15 cm and 10 cm below the surface, respectively, at a rate of 168 kg N/ha. Measurements were made every 3 h from May 6 to September 12, 1994.

VIII. SUMMARY AND CONCLUSIONS A. AMMONIA There have been many investigations of NH3 volatilization, which is now well understood. Volatilization is essentially a physicochemical process. Overall, results from ﬁeld comparisons of NH3 loss from different fertilizers follow the pattern expected on the basis of known chemistry. Emissions from urea are the most variable, ranging from 6 to 47% of applied N, and are very dependent on factors such as soil type (via CEC rather than soil pH), weather conditions, and application rates. In contrast, reported emissions from AN (and CAN) were small, never exceeding 4% of applied N. There are fewer studies involving other fertilizers such as AS and DAP, but for these, emissions are greater than that for AN and less than that for urea, although on calcareous or other high pH soils losses from AS

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may be greater than those from urea. Variations in emissions result from differences in soil type and time of application. In general, it is considered that emissions from other fertilizers are less than those from urea, with the possible exception of AS and DAP on calcareous or otherwise alkaline soils. With respect to urea, a greater NH3 loss on calcareous soils does not appear to be justiﬁed. While NH3 losses from AS and AN have been found to increase markedly with increasing pH (e.g., Whitehead and Raistrick 1990), the hydrolysis of urea to (NH4)2CO3 increases pH around the fertilizer granule to ∼9 and so tends to override bulk soil pH. Moreover, reaction with calcium ions reduces the volatilization potential of (NH4)2CO3 produced by urea hydrolysis (Fenn and Hossner 1985), and hence, NH3 losses from urea have not been found to be greater on a calcareous soils (Whitehead and Raistrick, 1990; Gezgin and Bayrakli, 1995). Results show that emissions from urea–AN solutions are intermediate between those from urea and AN granules (Fox et al., 1996; Keller and Mengel, 1986; McInnes et al., 1986a,b). Studies by Lightner et al. (1990) indicate that the effect of applying urea in solution depends on the moisture status of the soil at and immediately after application. Where the soil is dry, emissions will be small but application as a solution may increase NH3 volatilization. Rainfall appears to have a greater inﬂuence in increasing emissions from urea granules than from urea applied in solution. Addition of urease inhibitor has been shown in ﬁeld studies to signiﬁcantly improve the performance of urea (Watson et al., 1998). There have been a number of attempts to derive NH3 emission factors for fertilizers in recent years. Without additional data or speciﬁc cause, it does not seem appropriate to increase this number. We, therefore, suggest that, with the exception of AS, the factors proposed by van der Weerden and Jarvis (1997), who considered that NH3 emissions are greater from fertilizers applied to grassland than arable land, should be accepted. Thus, emission factors are as follows: grassland: urea, 23%, and AN (and compound fertilizers), 1.6%; arable land: urea, 11.5%, and AN (and compound fertilizers), 0.8%. However, there is strong evidence that emissions from AS are strongly dependent on soil pH, and we therefore suggest factors of 2 and 18% for this fertilizer for soils with pHs <7 and >7, respectively. These estimates have been made following the methodology of Whitehead and Raistrick (1990). Applying these emission factors to current N fertilizer use, we estimate that replacing urea with AN would reduce UK NH3 emissions by ∼6,700 tonnes NH3-N. It should be noted that on calcareous soils, or those limed to a pH >7 (as may be the case in arable rotations involving sugarbeet), losses of NH3 from AS may be as great or greater than from urea. Due to very large reductions of emissions of SO2 and subsequent decreases in sulfur deposition since 1998, sulfur deﬁciency has become apparent in some crops in some areas. The use of AS fertilizer is one means of overcoming this deﬁciency. As yet, use of AS is small, but in the interests of minimizing NH3 emissions it may be argued that other means of preventing sulfur deﬁciency need to be encouraged.

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A major criticism of the present estimates is their reliance on simple ﬁxed (%) emission factors, given in relation to amounts of N applied. More work needs to be done in the development of mechanistic process-based models for predicting NH3 emissions from N fertilizers and the foliage of fertilized crops, which take into account the known physicochemical equilibria as well as interactions with biological processes to predict net ﬂuxes. It is well established that NH3 may be exchanged with the soil surface and with leaves via stomata and cuticular absorption/desorption as well as with decomposing leaves, and future work needs to quantify the interactions and exchange cycles between these different components. This study conﬁrms there is potential for reducing NH3 emissions by switching from urea to other N fertilizers. A further possibility is to add urease regulators/inhibitors to urea fertilizer, which are expected to reduce emissions. Costs of these measures would include the differential price of more expensive fertilizers or of inhibitors. Emissions may also be reduced by placing the fertilizer granule into the soil at the same depth as the seed (∼7–8 cm). This will only be applicable for crops sown in the spring (apart from grass reseeds in autumn).

B. NITROUS OXIDE In contrast to NH3 emissions, there have been fewer studies of the effect on N fertilizer type on nitrous oxide (N2O) emissions. Emission factors of 0.04, 0.15– 0.19, and 0.50 for nitrate salts, ammonium salts and urea, and anhydrous NH3, respectively, have been proposed by Bolle et al. (1986). Recent laboratory studies (Mulvaney and Khan, 1994) of N2O and N2 losses indicated that emissions decreased in the following order: anhydrous NH3 > urea > DAP > AS > AN > MAP. Median values of N2O loss from urea appear to be in the range 0.1 to 0.6% of applied N (Granli and Bøckman, 1994). Studies in Scotland (Clayton et al., 1997) and France (H´enault et al., 1998b) clearly showed that N2O emissions from N fertilizers may very substantially within and between seasons. Results from the Scottish study indicated that emissions are likely to be greater from calcium nitrate and AN than from urea in the spring if conditions are cool and wet and vice versa in the summer if warm and wet. Emissions from AS will probably be less than from other fertilizers. However, recent data comparing AN and urea have suggested that the relative emissions from different fertilizers may be more complicated than originally suggested. While emissions in spring tend to be greater from AN than from urea, this is not always the case. Overall, the literature appears to support the view that a signiﬁcant proportion of N2O emissions occur from nitriﬁcation, although this is crucially dependent on the interaction between timing of fertilzer application and weather. Conditions

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in spring are more likely to be wet, and in this situation (and excluding urea for the moment) emissions are greater from NO− 3 -based fertilizers and least from AS. In the summer conditions may be dry or wet: under dry conditions emissions are smaller than under wet conditions. Again excluding urea, greatest emissions in the summer occur from AN. For urea, the effect of pH appears to be important. Generally, greater emissions can occur from urea, except where temperature (controlling the rate of urea hydrolysis) and rainfall (controlling the dispersion of alkalinity) limit this. Thus, the substitution of AN for urea for spring applications is likely to increase N2O emission. For summer applications, the substitution of AN for urea is likely to decrease N2O emissions, providing conditions are relatively dry; when conditions are wet high emissions may occur from both AN and from urea. At this stage it is difﬁcult to say with any certainty whether a strategy based on AN or urea will result in the lowest N2O emissions; further work both on the factors controlling emission from urea (and AN) combined with assessments of weather variablity are required. However, it seems likely that the optimum strategy will be one involving a sophisticated appreciation of the interaction between N fertilizer form and timing of application. Studies with nitriﬁcation inhibitors (e.g., McTaggart et al., 1997) have demonstrated that there is considerable scope for reducing N2O emissions from + ammonium (NH+ 4 ) or NH4 -forming fertilizers. This is especially so for crops such as intensively managed grass where there are several applications of N fertilizer over the season, as the effect of inhibitors applied in April persisted until after a second fertilizer application in June.

C. NITRIC OXIDE On theoretical grounds, since most NO emissions occur during nitriﬁcation, replacing urea with AN should reduce those emissions. The results from Slemr and Seiler (1984) are consistent with this hypothesis, but these conclusions can only be tentative as there is still a paucity of data from ﬁeld experiments.

D. OVERALL We conclude that replacing urea with AN has the potential to signiﬁcantly reduce NH3 emissions without increasing losses of N2O, albeit emissions of this gas are less predictable and more dependent upon season and weather. The effect on emissions of NO is uncertain, but current data do not suggest these will be increased. Published studies do not suggest the use of urea in solution will reduce losses of NH3. Data on the use of urease inhibitors is limited, but those available suggest the use of urease inhibitors may signiﬁcatly reduce NH3 emissions from urea.

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ACKNOWLEDGMENTS This work was funded by the UK Ministry of Agriculture, Fisheries and Food. We thank K. A. Smith for discussion during the preparation of this chapter and E. Lord for helpful comments on the text.

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Thornton, F. C., Bock, B. R., and Tyler, D. D. (1996). Soil emissions of nitric oxide and nitrous oxide from injected anhydrous ammonium and urea. J. Environ. Qual. 25, 1378–1384. Touchton, J. T., and Hargrove, W. L. (1982). Nitrogen sources and methods of application for no-tillage corn production. Agron. J. 74, 823–826. Urban, W. J., Hargrove, W. L., Bock, B. R., and Raunikar, R. A. (1987). Evaluation of urea–urea phosphate as a nitrogen source for no-tillage production. Soil Sci. Soc. Am. J. 51, 242–246. Van Cleemput, O., and Samater, A. H. (1996). Nitrate in Soils: Accumulation and role in the formation of gaseous N compounds. Fertil. Res. 45, 81–89. van der Weerden, T. J., and Jarvis, S. C. (1997). Ammonia emission factors for N fertilizers applied to two contrasting grassland soils. Environ. Poll. 95, 205–211. Veldkamp, E., and Keller, M. (1997). Fertilizer-induced nitric oxide emissions from agricultural soils. Nutr. Cyc. Agroecosys. 48, 69–77. Velthof, G. L., and Oenema, O. (1994). Effect of nitrogen fertilizer type and urine on nitrous oxide ﬂux from grassland in early spring. In “Grassland and Society: 15th General Meeting of the European Grassland Federation” (L. ‘t Mannetje, and J. Frame, Eds.), pp. 458–462. Wageningen, The Netherlands. Velthof, G. L., Oenema, O., Postma, R., and Van Beuischem, M. L. (1997). Effects of type and amount of applied nitrogen fertilizer on nitrous oxide ﬂuxes from intensively managed grassland. Nutr. Cyc. Agroecosys. 46, 257–267. Velthof, G. L., Oenema, O., Postmus, J., and Prins, W. H. (1990). In situ ﬁeld measurements of ammonia volatilization from urea and calcium ammonium nitrate applied to grassland. In “Soil-GrasslandAnimal Relationships: Proceedings of the 13th General Meeting of the European Grassland Federation II” (N. G´aborcik, V. Krajcovic, and M. Zimkv´a, Eds.), pp. 51–55. The Grassland Research Institute, Bansk´a Bystrica, Slovakia. Wang, W. J., and Rees, R. M. (1996). Nitrous oxide production from different forms of fertiliser nitrogen. In “Progress in Nitrogen Cycling Studies: Proceedings of the 8th Nitrogen Workshop held at the University of Ghent, 5–8 September, 1994” (O. van Cleemput, G. Hofman, and A. Vermoesen, Eds.), Vol. 68, pp. 659–662. Kluwer, Dordrecht, The Netherlands. Watson, C. J., Poland, P., and Allen, M. B. D. (1998). The efﬁcacy of repeated applications of the urease inhibitor N -(n-butyl) thiophosphoric triamide for improving the efﬁciency of urea fertilizer utilization on temperate grassland. Grass For. Sci. 53, 137–145. Whitehead, D. C., and Raistrick, N. (1990). Ammonia volatilization from ﬁve nitrogen compounds used as fertilizers following surface application to soils. J. Soil Sci. 41, 387–394. Whitehead, D. C., and Raistrick, N. (1993). The volatilization of ammonia from cattle urine applied to soils as inﬂuenced by soil properties. Plant Soil. 148, 43–51.

RHIZOBIA IN THE FIELD N. Amarger Laboratoire de Microbiologie des Sols Institut National de la Recherche Agronomique 21065 Dijon, France

I. Introduction II. Diversity in Rhizobia A. Cultural, Physiological, and Biochemical Characteristics B. Serological Characteristics C. Chemical Composition D. Symbiotic Characteristics E. Deoxyribonucleic Acid III. Rhizobium Systematics A. From Cross-Inoculation Grouping to Polyphasic Taxonomy B. Phylogeny and Taxonomy C. Identiﬁcation IV. Natural Populations of Rhizobia A. Diversity of Populations B. Population Structure V. Introduction of Rhizobia into Soil A. Inoculation B. Soil Colonization by Inoculant Rhizobia C. Interactions with Indigenous Rhizobial Populations D. Agricultural Implications VI. Concluding Remarks References

Most of the legumes have the ability to establish a dinitrogen-ﬁxing association with bacteria deﬁned as rhizobia. The legume crops will beneﬁt from this symbiosis only when the plant roots encounter, during their development, compatible and efﬁcient rhizobia that can induce the formation of fully effective nodules. The rhizobial populations present in the ﬁeld soils therefore play a key role in legume productivity. In recent years, the development of molecular biology has provided new tools that have allowed molecular characterization of rhizobia. This has lead to the description of a new diversity and to the creation of new taxons in order to handle it. Studies on the distribution of this diversity among rhizobial populations isolated from the nodules of the most widespread legume crops have shown that these populations are composed of a great variety of genotypes which can belong to different species or genera. Although environmental constraints may reduce the diversity, in many instances a great part of the genetic variation present within each species can be maintained within populations 109 Advances in Agronomy, Volume 73 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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N. AMARGER and individual plants. The structures of the rhizobial populations suggest that genetic recombination plays an important role in generating diversity within these populations. Following introduction of new rhizobial genotypes through seed inoculation, highly diversiﬁed populations can develop in relatively short periods of time. The extensive diversity that has been revealed has now to be managed in order to optimize nitrogen C 2001 Academic Press. ﬁxation by legume crops.

I. INTRODUCTION The contribution of legumes to the maintenance of soil fertility has been recognized for centuries. However, it was only in the middle of the 19th century that Boussingault demonstrated that leguminous plants have the ability to obtain their nitrogen from a source other than mineral nitrogen, which thus could only be from the atmosphere. Later in that century, in 1888, Hellriegel and Wilfarth established that the small tubers, or nodules, present on the root systems of legumes are the seat of the assimilation of atmospheric nitrogen. The same year, from root nodules of several legumes, Beijerinck isolated bacteria that he demonstrated were the causative agents of the ﬁxation of atmospheric nitrogen. These bacteria which possess the property of forming nodules on the root systems of legumes are now collectively referred to as rhizobia. The nodules are the expression of a symbiotic association between a rhizobium and a legume: the bacteria reduce dinitrogen to ammonia and supply nitrogenous compounds to the plant, which in return supplies nutrients to the bacteria. The bacteria can multiply in a protected habitat from which they are released in large numbers upon senescence of the nodules, and the plant gains independence of the presence of nitrogenous compounds in the soil environment. Rhizobia are facultative symbionts. In the free-living state, they are common soil inhabitants and are, with very few exceptions, unable to ﬁx dinitrogen. Legumes are very diverse and distributed worldwide, and many, but not all, of the 10 to 15% that have been checked for the presence of nodules are able to nodulate. Among the 16,000 to 19,000 species composing the Leguminosae family (Allen and Allen, 1981), less than 1% mostly Papilionoideae, are of agricultural importance. Nevertheless, these plants, in addition to being economically important, with edible and highly nutritional crops for both human and animal consumption, forage crops, and green manure, also play a key role in sustaining long-term soil fertility in agricultural systems. Global annual inputs of biologically ﬁxed nitrogen in agricultural systems through the activity of rhizobium–legume associations are estimated to be equivalent to

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inputs in the form of nitrogen fertilizer. Some 35 to 44 million tons would come from legumes growing in arable land and another 40 million tons from permanent pasture (Peoples et al., 1995). The symbiotic interactions between rhizobia and legumes are host speciﬁc. It became apparent soon after the isolation of the ﬁrst rhizobia that a given nodule isolate has the ability to produce nodules on certain plants and not on others. The range of hosts a particular strain of rhizobium nodulates can be rather narrow, a few legume species, or extremely broad, hundreds of species. These differences in host range between strains of rhizobia served as the ﬁrst basis of the classiﬁcation into “cross inoculation groups” and later into species. Further steps in the development of the nitrogen ﬁxing symbiosis can also show some degree of host speciﬁcity. As a result, a given bacterial strain can form nitrogen-ﬁxing nodules with certain hosts (the symbiosis is effective) and nonﬁxing nodules with others (the symbiosis is ineffective). Great variations in speciﬁcity of interaction with rhizobia are also observed among legume species. Some legumes form nodules with a restricted range of strains with similar nodulation and physiological characteristics that are usually classiﬁed in the same taxon. Other legumes, said to be promiscuous, nodulate with strains differing in many respects and belonging to different taxa. In this latter case, various levels of effectiveness of the nitrogen-ﬁxing symbiosis may be attained depending on the rhizobia that formed the nodules. Legumes cultivated in temperate regions tend to be less promiscuous than those of tropical regions. The ability of a legume crop to beneﬁt from the ﬁxation of atmospheric nitrogen will be dependent on the presence in the ﬁeld soil of rhizobia able to nodulate the legume and to form an effective nitrogen-ﬁxing symbiosis with the host plant under ﬁeld-growth conditions. When soils are devoid of the speciﬁc rhizobia, inoculation with effective, host-speciﬁc rhizobia usually allows the legume crop to proﬁt from nitrogen ﬁxation. When the crop nodulates but fails to reach its yield potential, the question is whether the rhizobia which nodulated the plants are optimally effective in N2 ﬁxation or not. Although other biotic and abiotic environmental factors may exert constraints on N2 ﬁxation by legume crops, inadequacy of compatibility between the ﬁeld rhizobia and the legume is often considered as being the major cause of suboptimal yields. Whether this is the case is difﬁcult to ascertain, as long as it is not shown that fully effective strains allow expression of the crop potential in the same environment. This proof is most often missing since current inoculation practices usually fail to displace the soil rhizobia from the nodules. Due to the lack of suitable methodologies to characterize and identify rhizobia, our knowledge of ﬁeld populations of rhizobia is rather restricted. As a consequence, the possibilities that we have to better control and exploit them are very limited. However, since 1985, biochemical and genetic approaches have allowed important advances to be made in our understanding of the host-speciﬁc relationships of rhizobia with leguminous plants. In parallel, the development

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of molecular biology has provided new tools to characterize rhizobia, tools that are currently used to study natural rhizobial populations. In this chapter we ﬁrst consider the progress made in molecular characterization of rhizobia and the new diversity this has revealed. We then approach the distribution of this diversity and its variability in ﬁeld rhizobial populations and the impact that the introduction of an alien rhizobium might have on this diversity. Our overall intention is to pool current knowledge on the symbiotic partners a leguminous crop is confronted with during its growth.

II. DIVERSITY IN RHIZOBIA The characteristic that qualiﬁes a bacterium to be named rhizobium, as presently understood, is its capacity to form a deﬁnite nodule on the root or on the stem of a leguminous plant. This designation has no taxonomic signiﬁcance; as is shown below, bacteria belonging to different species and classiﬁed in distantly related genera are capable of nodulating leguminous plants. Rhizobia can show wide variations in numerous characteristics; only those useful for identiﬁcation or as markers in population studies are considered here.

A. CULTURAL, PHYSIOLOGICAL, AND BIOCHEMICAL CHARACTERISTICS Differences in rates of growth allowed early separation of the rhizobia into two basic groups (Fred et al., 1932). Fast growers have generation times of less than 6 h and generally form visible colonies (2–4 mm in diameter) on agar media within 2–5 days, whereas slow growers have generation times exceeding 6 h and give detectable growth after more than 5 days. These differences in rates of growth between strains can be lessened, but not completely eliminated, by modiﬁcation of the carbon or/and nitrogen sources (Allen and Allen, 1950). Recently, extraslowly growing rhizobia were isolated from nodules of soybeans collected in the People’s Republic of China (Xu et al., 1995). Their generation times varied with the strain from 16 to 40 h. Rhizobia isolated from nodules of alfalfa, clover, pea, and bean are typical fast growers; those isolated from US-grown soybean, lupine, and Vigna spp. are typical slow growers. Under standardized conditions most of the slow-growing rhizobia produce alkali while the fast-growing produce acid (Fred et al., 1932; Norris, 1965). Variations in colony morphology among isolates from the nodules of a given legume are relatively common. They have been used as a convenient criterion for differentiation of isolates from nodules of cowpea (Sinclair and Eaglesham, 1984; Eaglesham et al., 1987; Mpepereki et al.,

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1997), common bean (Beynon and Josey, 1980; Amarger et al., 1994), soybean (Desa et al., 1997; Martins et al., 1997), and diverse legumes grown in Kenya (Odee et al., 1997). Rhizobia are chemoorganotrophs that have long been known as being able to utilize a variety of carbon and nitrogen compounds for their growth (reviewed in Allen and Allen, 1950; Graham and Parker, 1964; Stowers, 1985). Although variations are seen within each group, fast growers tend to metabolize a wider variety of carbohydrates than slow growers. The former can use a broad range of hexoses, pentoses, disaccharides, trisaccharides, polyols, and organic acids, whereas the latter seem less able to use disaccharides, trisaccharides, and organic acids for growth (Elkan and Kwik, 1968; Chakrabarti et al., 1981; Parke and Ornston, 1984; Stowers and Eaglesham, 1984). Martinez-Drets et al. (1972) have pointed out that a key enzyme of the pentose pathway, the NADP-linked 6-phosphogluconate dehydrogenase, present in the fast-growing rhizobia, was absent in the slow growers. This characteristic has proven useful to ascertain the classiﬁcation of rhizobia into fast- or slow-growing groups (Kennedy and Greenwood, 1982; Sadowsky et al., 1983; Anand and Dogra, 1991; Batzli et al., 1992). Besides sugars, a wide variety of aromatic compounds can be metabolized by rhizobia through degradative pathways involving inducible or constitutively expressed enzymes (Glenn and Dilworth, 1981; Muthukumar et al., 1982; Chen et al., 1984; Parke and Ornston, 1984; Gajendiran and Mahadevan, 1988; Hartwig et al., 1991; Hopper and Mahadevan, 1997). The spectrum of compounds that can be metabolized seems strain dependent and may reﬂect differential adaptation to legume rhizosphere or soil organic components. Its variation among isolates might have ecological implications. With the exception of rhizobia isolated from stem nodules of Sesbania rostrata (Dreyfus et al., 1988), free-living rhizobia are incapable of utilizing dinitrogen for growth. They can use nitrate, ammonium, or amino acids as a sole source of nitrogen. Variability in the use of these different sources has been demonstrated among isolates of slow- as well as fast-growing rhizobia. Some amino acids, such as glutamate, can be used as an N source by almost every rhizobium and others, such as glycine, by only a few strains (Elkan and Kwik, 1968; Chakrabarti et al., 1981). Differences in substrate utilization are useful not only to differentiate between slow- and fast-growing rhizobia but also to reveal the diversity among each type. When the substrates are various and numerous enough, meaningful groupings can be made using numerical analysis. Recently, miniaturized systems that allow many substrates to be easily tested have been developed primarily for clinical applications. They use color reactions to indicate metabolic activities such as the oxidation of carbon compounds. Two of these systems, API 50 or Biolog, which allow testing of the ability of 147 and 95 compounds, respectively, to serve as sole carbon source, were used with rhizobia isolated from tropical woody legumes (de Lajudie et al., 1994; Dupuy et al., 1994; McInroy et al., 1999) and clover

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(Leung et al., 1994a; Nour et al., 1994a). The results showed that such systems enable grouping of otherwise genetically related rhizobial strains. Therefore, they might be useful for identiﬁcation purposes, provided that many more reference strains are entered into the databases. Physiological traits such as tolerance to abiotic or biotic factors are intrinsic traits of each rhizobium strain. Their variation among strains can be wide enough to allow distinction between nodule isolates. Such variations have been observed in fast- as well as slow-growing rhizobia for tolerance to elevated temperature (Graham and Parker, 1964; Munevar and Wollum, 1981, 1982; Hartel and Alexander, 1984; de Lajudie et al., 1994; Michiels et al., 1994; Surange et al., 1997), to acidity (Bryan, 1923; Graham and Parker, 1964; Norris, 1965; Graham et al., 1982; Hartel and Alexander, 1983; Aarons and Graham, 1991; Graham et al., 1994; Elidrissi et al., 1996; Surange et al., 1997), and to salinity (El Essawi and Abdel-Ghaffer, 1967; Bhardwaj, 1975; Mendez-Castro and Alexander, 1976; Yelton et al., 1983; Saxena and Rewari, 1992; Mpepereki et al., 1997). Large differences in degree of tolerance to antibiotics among fast- and slowgrowing rhizobia have been reported (Graham, 1963; Pattison and Skinner, 1974; Pinto et al., 1974; Pankhurst, 1977; Cole and Elkan, 1979; Hagedorn, 1979). Since the ﬁrst exploitation of these natural strain to strain variations in intrinsic resistance to antibiotics (IAR) for identiﬁcation and differentiation of nodule isolates from common bean (Josey et al., 1979; Beynon and Josey, 1980), IAR has been used extensively in ecological studies to identify inoculant strains and to determine heterogeneity in natural populations (Eaglesham, 1987). The method has proven practical, rapid, and reliable with a discriminating ability dependent on the number of antibiotics and of concentrations used. IAR has been used either as a primary criterion or as a complement to other(s) method(s) to describe diversity in nodule isolates from alfalfa (Jenkins and Bottomley, 1985a; Shishido and Pepper, 1990), pea (Turco and Bezdicek, 1987; Brockman and Bezdicek, 1989), clover (Hagedorn, 1979; Glynn et al., 1985), Phaseolus sp. (Arredondo-Peter and Escamilla, 1993), chickpea (Kingsley and Ben Bohlool, 1983; Garg et al., 1985), soybean (Meyer and Pueppke, 1980; Dowdle and Ben Bohlool, 1986; Mueller et al., 1988; Mpepereki et al., 1997; Ramirez et al., 1997), cowpea (Sinclair and Eaglesham, 1984; Xavier et al., 1998), and various tropical legumes (McLaughlin and Ahmad, 1984; Date and Hurse, 1991; Subramaniam and Babu, 1993). In recent years the results of tolerance tests to different abiotic factors have tended to be analyzed as a whole. They form, with the tests involving trophic capabilities on different carbon and nitrogen sources the core of the phenotypic characters that are used for numerical taxonomic purposes (Batzli et al., 1992; Novikova et al., 1994; Madrzak et al., 1995; van Rossum et al., 1995; Elidrissi et al., 1996; Desa et al., 1997; Gigova et al., 1997; Odee et al., 1997; Strufﬁ et al., 1998; Vasquez Arroyo et al., 1998). Root nodule bacteria were found to differ in their susceptibility to differ-

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ent phages abundant in soils as early as 1932 (Laird, 1932). Such differences in susceptibility to various numbers of phages have served as the basis for phage typing of nodule isolates from alfalfa (Lesley, 1982; Bromﬁeld et al., 1986), clover (Kankila and Lindstr¨om, 1994), Galega spp. (Lindstr¨om et al., 1983), common bean (Dhar et al., 1993), soybean (Hashem et al., 1996; Ali et al., 1998), sulla (Strufﬁ et al., 1998), and different-temperate legumes (Conn et al., 1945; Staniewski, 1970; Lindstr¨om and Kaijalainen, 1991; Novikova and Limeshchenko, 1992; Novikova et al., 1993). Although phage typing can be highly discriminatory, its use is limited because it requires prior isolation and constitution of a battery of phages differing in their ability to lyse the strains under study.

B. SEROLOGICAL CHARACTERISTICS Serological methods, which are based on the antigenic nature of the cell surface and on the speciﬁcity of these antigens, provide rapid means for identifying bacteria. Several variants of the serological method, agglutination, immunodiffusion, direct or indirect immunoﬂuorescence (FA), or enzyme-linked immunosorbent assay (ELISA), have been used in the examination of serological diversity of rhizobial isolates (Schwinghamer and Dudman, 1980; Vincent, 1982; and references therein). On the basis of reactions with a set of antisera against reference strains rhizobial isolates can be assigned to a given serogroup or serotype. The antigenic diversity of the slow-growing nodule isolates from soybean is probably the best known and most of the populations can be described from the existing serogroups (Johnson and Means, 1963; Date and Decker, 1965; Caldwell and Weber, 1970; Ham et al., 1971; Berg and Loynachan, 1985; Ayanaba et al., 1986; Vargas et al., 1993, 1994; Madrzak et al., 1995; Ramirez et al., 1997). Other rhizobia whose serological diversity has been examined are those that nodulate clovers (Hagedorn and Caldwell, 1981; Dughri and Bottomley, 1983; Renwick and Gareth Jones, 1985; Valdivia et al., 1988; Leung et al., 1994b), peas (Mahler and Bezdicek, 1980; Turco and Bezdicek, 1987), chickpeas (Kingsley and Ben Bohlool, 1983), alfalfa (Purchase et al., 1951; Olsen and Rice, 1984; Rajapakse and Macgregor, 1992), lotus (Irisarri et al., 1996), Hedysarum spp. (Kishinevsky et al., 1996), common bean (Robert and Schmidt, 1985), and different tropical legumes (Sinclair and Eaglesham, 1973; Ikram and Broughton, 1980; Ahmad and Hassouna, 1981). The reaction can be performed directly on nodule crushes, a unique advantage enabling many nodules to be screened rapidly. An intrinsic limitation of these methods in studying the diversity of nodule occupants is that they can only provide information on those rhizobia that cross-react with the antisera at one’s disposal, not on those that do not react.

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C. CHEMICAL COMPOSITION Different methods based on the chemical composition of bacteria are of common use for classiﬁcation and identiﬁcation purposes. Several of them have been tested for their ability to differentiate rhizobia. Whole-cell composition of bacteria has been approached by pyrolysis mass spectrometry (PyMS), a procedure in which bacterial cells are pyrolyzed under vacuum and the compounds produced are ionized and analyzed by a mass spectrometer. PyMS provides a simple method for discriminating very closely related strains wherever apparatus is available. It has been used successfully for characterization of nodule isolates from alfalfa (Goodacre et al., 1991) and Lupinus spp. (Barrera et al., 1997) and for identiﬁcation of inoculant strain in nodule isolates from soybeans (Kay et al., 1994). Wholecell extracts of protein separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) show a large number of bands. Comparison of the patterns obtained from different isolates provides information about the relatedness of these isolates. Although visual comparison may be sufﬁcient for rapid comparison, numerical analysis of the patterns allows quantiﬁcation of the resemblance, and grouping of the patterns can then be made. SDS–PAGE of whole proteins has been used as an initial approach often associated with a physiological or serological method to describe the diversity of nodule isolates of clovers (Dughri and Bottomley, 1983; Demezas and Bottomley, 1984; Dughri and Bottomley, 1984; Zahran, 1992), alfalfa (Jenkins and Bottomley, 1985a,b), common bean (Arredondo-Peter and Escamilla, 1993), soybean (Noel and Brill, 1980; Kamicker and Brill, 1986), and tropical plants (Moreira et al., 1993). It is now more often utilized as one of the several methods used to determine relationships between rhizobial isolates (Batzli et al., 1992; de Lajudie et al., 1994; Madrzak et al., 1995; van Rossum et al., 1995; Irisarri et al., 1996; Tan et al., 1997). Enzyme electrophoresis, which separates allelic forms of enzyme molecules by differences in their surface charge, reveals the polymorphism of the gene loci corresponding to the different enzymes analyzed. Electrophoretic mobility variants thus give an indication of the number of alleles at a particular gene locus. In multilocus enzyme electrophoresis (MLEE), mobility variants of several housekeeping enzymes are used to characterize strains (each distinct allelic variant proﬁle is designated an electrophoretic type or ET) and to assess genetic relatedness among isolates or ETs and genetic variations among populations (Selander et al., 1986). MLEE has been employed to explore genetic diversity and genetic structure of various rhizobial populations. Strains from collections or ﬁeld populations have been characterized on the basis of allele proﬁles at a number of polymorphic enzyme loci that varied from 3 to 28 depending on the study. So far, the method has been used to study fast-growing rhizobia isolated from a few leguminous species, clover, pea, common bean, and alfalfa (for references see Section IV), and slow-growing rhizobia from lupines, seradella, and siratro (Bottomley et al.,

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1994; Barrera et al., 1997). MLEE has also been used to study the genetic diversity of nonsymbiotic rhizobia isolated directly from the rhizosphere of common beans (Segovia et al., 1991) and of Lotus corniculatus (Sullivan et al., 1995). The method can be highly discriminatory. It is useful to provide information of genetic variation within a species. Its main advantage is that it relies on the polymorphism of a number of gene loci dispatched all over the chromosome and thus gives an image of the overall genome, with the exception, however, of the symbiotic genome. Lipopolysaccharides (LPS) are important components of the external cell wall of gram-negative bacteria. Variability of the sugar components and in the numbers of the O-speciﬁc side chains of LPS molecules result in different migration patterns when gram-negative bacteria are applied to SDS–PAGE. Such LPS patterns have been used successfully for determining the diversity of some fast-growing (de Maagd et al., 1988; Casella et al., 1992; Zahran, 1992; Lindstr¨om and Zahran, 1993) and slow-growing rhizobia (Alves and Lemos, 1996; Santamaria et al., 1997; Jayaraman and Das, 1998). Since LPS molecules are only present in gram-negative bacteria, LPS proﬁling can be performed directly on individual nodule squashes, which is a signiﬁcant advantage when large numbers of nodule isolates have to be studied (Santamaria et al., 1998). Fatty acids are constituents of phospholipids and of LPS of the outer membrane of gram-negative bacteria. The ability of fatty acid methyl ester (FAME) analysis to discriminate among strains belonging to the known diversity of fast- and slow-growing rhizobia has been determined by several authors (Mac Kenzie and Child, 1979; Jarvis and Tighe, 1994; So et al., 1994; Graham et al., 1995; Jarvis et al., 1996, 1998; Dunﬁeld et al., 1999). Principal component analysis distinguished clusters of strains that corresponded to the known species or to groups already formed by other methods, suggesting that FAME analysis could form the basis of a rapid method of identiﬁcation.

D. SYMBIOTIC CHARACTERISTICS Because of their agricultural importance, the variability in symbiotic properties of rhizobia was the ﬁrst to be described. Wide differences among rhizobia relative to their ability to produce nodules on any one legume species and to their effectiveness or ability to aid plant growth through nitrogen ﬁxation have long been recognized. Rhizobia can thus be characterized by the range of hosts that they nodulate. Some rhizobia appear highly speciﬁc and nodulate only plants belonging to a single genus or to some species within a genus. For instance, rhizobia isolated from Galega spp. induced nodules only on Galega spp. plants (Lindstr¨om, 1989); rhizobia associated with European species of clovers will not nodulate clover species native to mid-Africa (Vincent, 1974). Other rhizobia are symbionts of multiple legume species, e.g., some Brazilian isolates from

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common bean (Martinez-Romero et al., 1991) or some fast-growing isolates from soybean (Krishnan and Pueppke, 1994). The range spectrum of one of these latter strains, Rhizobium fredii USDA 257, and of Rhizobium sp. NGR 234, isolated from Lablab purpureus (Trinick, 1980), has recently been the subject of an extensive investigation (Pueppke and Broughton, 1999). USDA 257 and NGR 234 were able to nodulate 135 and 232 of the 452 legume species tested, respectively. When the N2-ﬁxing effectiveness of the rhizobium–host association is taken into account, a further complex pattern of speciﬁcity (effectiveness speciﬁcity) is usually observed. According to the host genotype, levels of effectiveness of a strain can vary from fully effective to fully ineffective. Effectiveness speciﬁcity is observed within broad as well as narrow host range strains. For instance, the broad host range strain NGR 234 formed effective nodules with only 135 of the 232 legume species it nodulated (Pueppke and Broughton, 1999) and the narrow host range strains isolated from Galega officinalis were effective with this host plant, but ineffective with Galega orientalis and vice versa (Lindstr¨om, 1989). The abilities of a strain to form nodules and to ﬁx nitrogen with a range of leguminous hosts are important practical characteristics and are crucial characteristics for the description of any rhizobium. However they are difﬁcult to establish because they are multiple. For this reason, the range of host plants tested is most often limited to the plant from which the strain was isolated and in the best cases, to the few species known to be nodulated by closely related strains.

E. DEOXYRIBONUCLEIC ACID As they have the advantage of being independent of gene expression, methods based on the analysis of genomic DNA have, since 1970, provided the main basis for deﬁning groups of closely related strains of microorganisms. The methods, which were ﬁrst limited in their applicability since they were laborious and time consuming, have become, with the advent of molecular techniques, more and more diversiﬁed and easy to use. As their development proceeded they have been adapted to rhizobial characterization and utilized to reveal the diversity of nodule isolates. Early genetic evidence (Higashi, 1967) suggested that some symbiotic genes had an extrachromosomal location. Physical evidence of the presence of relatively small plasmids (<100 kb) in fast-growing rhizobia was given some years later by Sutton (1974). The subsequent development of extraction methods allowed the detection of larger plasmids (up to 500 kb) in different fast- and slow-growing rhizobia (Nuti et al., 1977) and of extralarge (>1000 kb) plasmids, also called megaplasmids, in rhizobia isolated from alfalfa (Rosenberg et al., 1982). Since then, large and/or extralarge plasmids have been shown to be common components of the fast-growing rhizobium genome and can constitute up to 50% of the

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cell genome (Prakash and Atherly, 1986; Sobral et al., 1991). Their number varies from 1 to 10. In these fast-growing rhizobia, most of the genes involved in the symbiotic function are located on one, sometimes several, plasmid(s) named symbiotic plasmids or pSyms. The presence of large plasmids in slow-growing rhizobia is more difﬁcult to establish. Although strains carrying plasmids have been described (Nuti et al., 1977; Gross et al., 1979), the presence of plasmids is not a general feature of the slow-growing rhizobia. When they are present, the amount of genetic information that they carry is limited and does not include genes essential in symbiotic function. Plasmid content of fast-growing rhizobia can be easily revealed by agarose gel electrophoresis of cell lysates. The in-well lysis procedure ﬁrst developed by Eckhardt (1978) and subsequently modiﬁed (Hirsch et al., 1980; Rosenberg et al., 1981; Hynes et al., 1986; Wheatcroft et al., 1990) permits detection of plasmids with extremely large molecular size. With this method, overall composition and approximate size of each plasmid present in every strain can be established at the same time for a large number of strains. Variation in plasmid proﬁles of ﬁeld isolates from diverse leguminous crops has been reported. In rhizobia isolated from alfalfa, one or two bands corresponding to the megaplasmids could be observed in all the isolates (Bromﬁeld et al., 1987; Brockman and Bezdicek, 1989; Shishido and Pepper, 1990; Hartmann and Amarger, 1991). Additional plasmid bands ranging in size from approximately 50 to 300 kb were visualized in many of these isolates. Their number in these cases was most often limited to 1 or 2 but could reach 4. Megaplasmids were not observed in isolates from clovers, pea, fababean, and lentil, but the number of large plasmids with size ranging from 50 to 700 kb varied from 2 to 10 and was commonly higher than 4 (Tichy and Lotz, 1981; Glynn et al., 1985; Thurman et al., 1985; Harrison et al., 1987, 1988, 1989; Brockman and Bezdicek, 1989; Hynes and McGregor, 1990; Laguerre et al., 1992; Zahran, 1992; Hirsch et al., 1993; Moenne-Loccoz et al., 1994; van Berkum et al., 1995; Castro et al., 1997b; Handley et al., 1998; Moawad et al., 1998; Wilson et al., 1998). An equivalent variability in the number of large plasmids was observed in isolates from common bean but in addition a megaplasmid could be detected in some isolates (Pepper et al., 1989; Geniaux et al., 1993; Laguerre et al., 1993b; Amarger et al., 1994; Geniaux et al., 1995; Sessitsch et al., 1997a; Aguilar et al., 1998b; Mhamdi et al., 1999). Other rhizobia analyzed by plasmid proﬁling represent isolates from Hedysarum spp. (Mozo et al., 1988; Strufﬁ et al., 1998), Astragalus sinicus (Zou et al., 1997), soybean (Gross et al., 1979), Amorpha fructicosa (Wang et al., 1999b), Onobrychis sativa (Gigova et al., 1997), and diverse tree and shrub legumes (Thomas et al., 1994; Kuykendall et al., 1996; Milnitsky et al., 1997; Santamaria et al., 1997). Isolates with identical plasmid proﬁles were usually found to be very closely related genotypically. Whereas isolates with similar chromosomal background may yield different plasmid proﬁles, different chromosomal genotypes do not share common plasmid proﬁles. Plasmid proﬁling has proven

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a practical and reliable method to rapidly screen and characterize fast-growing rhizobia at the subspecies level. The key tools that have allowed major progress to be made in analysis of genomic DNA are, on one hand, restriction endonucleases that cleave DNA at speciﬁc sites they recognize and, on the other hand, Taq polymerase that allows ampliﬁcation of DNA sequences by polymerase chain reaction (PCR). Restriction endonucleases speciﬁcally cleave DNA into fragments whose number and length depend on the number and position of the recognition sequences. Electrophoresis of the cleaved DNA allows the separation of the fragments according to their size and gives a pattern of bands, which can be stained by ethidium bromide. Modiﬁcations in DNA sequences generate restriction fragment-length polymorphism (RFLPs) which will give new patterns of bands; the similarities between these patterns are thus, a measure of the relatedness of isolates. DNA restriction proﬁles have been used to distinguish among rhizobia that nodulate peas (Hynes and OConnell, 1990; Laguerre et al., 1992), Galegae spp. (Lindstr¨om et al., 1990), alfalfa (Mielenz et al., 1979; Hartmann and Amarger, 1991), and clover (Glynn et al., 1985). Although this type of ﬁngerprinting is rather simple and gives a complete image of total DNA, its utilization is limited because of the complexity of the patterns generated. Simpliﬁed patterns can be obtained by using restriction endonucleases that rarely cut and pulse-ﬁeld gel electrophoresis (PFGE), which enables large fragments to be separated. This method has been used to study the genomic diversity of slow-growing soybean isolates belonging to the same serotype (Sobral et al., 1990; Ramirez et al., 1997) and to ﬁngerprint nodule isolates from a leguminous tree (Haukka and Lindstr¨om, 1994). The method gave a good resolution of isolates primarily at the subspecies level. Another way to obtain simpliﬁed patterns is by transfer of the separated DNA fragments to nitrocellulose or nylon membranes (Southern, 1975) followed by hybridization with a speciﬁcally labeled DNA probe. Such RFLP patterns provide information about deﬁned DNA regions. Depending on the level of conservation of their sequences, DNA probes will allow RFLP analyses of more or less divergent isolates. As ribosomal RNA genes (rDNA) are highly conserved among bacteria, hybridization of total DNA digest with rDNA probes generates RFLP patterns from bacteria belonging to remote species, each species being characterized by one or several rDNA RFLP patterns (ribotype). Such a probe has permitted discrimination of different species of rhizobia among nodule isolates of common bean (Geniaux et al., 1993; Sessitsch et al., 1997a; Mhamdi et al., 1999). More speciﬁc DNA fragments have also been used as hybridization probes to identify restriction polymorphism in the homologous gene regions of nodule isolates. They include chromosomally located regions such as the lac and LPS gene regions of Rhizobium leguminosarum (Young and Wexler, 1988; Cava et al., 1989), symbiotic plasmid regions such as various nitrogen ﬁxation (nif ) or nodulation (nod ) genes (Quinto et al., 1982; Ruvkun et al., 1982; Russel et al., 1985; Watson and Schoﬁeld, 1985; Young and Wexler, 1988; Demezas et al., 1991), plasmid regions such as replication

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genes (Rigottier-Gois et al., 1998), or repeated sequences distributed over the entire genome such as IS sequences (Wheatcroft and Watson, 1987; Bromﬁeld et al., 1995; Mazurier et al., 1996) or RS␣ and RS␤ (Hartmann et al., 1992; Minamisawa et al., 1992). By allowing characterization of different parts of a single genome, RFLP ﬁngerprinting with different probes can provide information on the relationships between these different parts whether they are located on the same replicon or not. For instance, by comparing the repartition of pSym types within the host strain nonsymbiotic genome, it has been shown that transfer of symbiotic genes, located either on plasmids or on the chromosome, has occurred in nature (Schoﬁeld et al., 1987; Young and Wexler, 1988; Laguerre et al., 1993c; Sullivan et al., 1995). The degree of speciﬁcity has not been determined for all the different probes used in RFLP analysis, but some of them, such as those including lac region or nod genes, were found to be speciﬁc enough to position isolates in a given taxon (Harrison et al., 1988; Laguerre et al., 1993a). Although restriction enzymes used in conjunction with DNA probes have proven to be very potent tools to demonstrate sequence divergences and, thus, to reveal diversity in the housekeeping, symbiotic, and plasmid genomes, their use is decreasing with the development of more rapid methods based on PCR. These methods present a wide spectrum of application and several of these methods have been used for detecting genetic diversity among rhizobia. In order to get an image as complete as possible of the genome in its totality, ampliﬁcation of multiple DNA fragments of variable lengths distributed over the entire genome has been obtained by using as primers either short arbitrary oligonucleotides, which produce randomly ampliﬁed polymorphic DNA (RAPD), or naturally occurring repetitive sequences interspersed throughout the genome, which amplify deﬁnite segments included between copies (rep-PCR). Using gel electrophoresis, the ampliﬁed fragments are separated according to their size and yield a complex pattern of bands of variable intensity that indicates the polymorphism of total DNA. The quantiﬁcation of the similarity between the generated ﬁngerprints can be performed by numerical analysis, which enables groupings of the isolates to be made by similarity levels. Several arbitrary primers of 10 to 15 nucleotides in length have been used to differentiate the genomes of a range of rhizobia belonging to different taxa (Harrison et al., 1992; Dooley et al., 1993; Lunge et al., 1994; Richardson et al., 1995; Selenska-Pobell et al., 1995; van Rossum et al., 1995; Laguerre et al., 1996; Nathan et al., 1996; Paffetti et al., 1998; Agius et al., 1997; Harrier et al., 1997; Sikora et al., 1997; Gonzalez Andr´es and Ortiz, 1998; Handley et al., 1998; Hebb et al., 1998; Teaumroong and Boonkerd, 1998; Wilson et al., 1998; Young and Cheng, 1998). Among the diverse repetitive sequences identiﬁed in bacteria, two of them, the repetitive extragenic palindromic (REP) sequence and the enterobacterial repetitive intergenic consensus (ERIC) with, to a lesser extent, the BOX element, are at the basis of the development of repPCR ﬁngerprinting in rhizobia (de Bruijn, 1992; Judd et al., 1993; Nick and

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Lindstr¨om, 1994; Madrzak et al., 1995; Schneider and de Bruijn, 1996; Ibekwe et al., 1997; Laguerre et al., 1997; Niemann et al., 1997; Sessitsch et al., 1997a; Del Papa et al., 1999; Santamaria et al., 1999). Rep-PCR ﬁngerprinting can be performed directly on DNA extracted from crushed nodules, which allows rapid processing of large numbers of nodule samples. The RAPD and rep-PCR ﬁngerprinting methods were comparable in their ability to resolve differences among isolates at the subspecies level and approximately equivalent to protein ﬁngerprinting. To study the polymorphism of deﬁned regions of the genome, speciﬁc primers can be used to amplify these regions and yield enough DNA to perform restriction analysis (PCR–RFLP). This type of analysis, ﬁrst limited by the availability of the DNA sequences necessary to the design of primers, is, with the increasing number of sequences available, becoming applicable to a more and more diversiﬁed number of gene and intergene spacer (IGS) regions. Ribosomal DNA sequences, because of their importance in phylogenetic investigations, were the ﬁrst to be determined. They contain both conserved regions that can be used to deﬁne primers and variable regions that can be used to differentiate strains. PCR–RFLP analysis of the 16S rRNA genes, which enables differentiation of rhizobia at the species level (Laguerre et al., 1994, 1997), is now of common use for rapid assignation of nodule isolates to a given species. PCR–RFLP of a more divergent region, the spacer region between the 16S and the 23S rDNA, can differentiate rhizobia within a species with a resolution level intermediate between 16S-RFLP analysis and rep-PCR ﬁngerprinting (Nour et al., 1994a; Laguerre et al., 1996). Differentiation of rhizobial isolates based on their symbiotic genome can be performed by PCR–RFLP analysis of conserved symbiotic genes such as certain nod and nif genes (Eardly et al., 1992; Laguerre et al., 1996; Haukka et al., 1998). PCR–RFLP analysis of repC sequences of the plasmid replication region has also been used as a tool to reveal the diversity of this region within and among rhizobia (Rigottier-Gois et al., 1998). As for conventional RFLP analysis using DNA probes, comparison of the polymorphism observed in PCR-ampliﬁed chromosomal and plasmid genes can provide information on the relationships between the different replicons that bear these genes. For instance, RFLP analysis of nod and repC sequences, ampliﬁed from pea rhizobia representing different chromosomal genotypes, has given circumstantial evidence that pSym and cryptic plasmid transfer had occurred within the population studied (Rigottier-Gois et al., 1998). PCR-based techniques can also be used, like molecular probes, for the detection of groups of rhizobia. Pairs of primers are then designed for ampliﬁcation of regions of DNA speciﬁc to the rhizobia that have to be detected. Speciﬁc pairs of primers able to differentiate between different rhizobial species that nodulate common bean, symbiotic genomes associated with different plant–host speciﬁcity, or groups of plasmid replication sequences have been designed from 16S–23S IGS (de Oliveira et al., 1999), nif H (Aguilar et al., 1998a), or repC (Turner et al., 1996) sequences, respectively.

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In this ﬁrst part, we have seen that the development of molecular techniques and of automated systems for more traditional techniques has introduced new sources of data for bacterial characterization. When applied to isolates from legume nodules, these methodologies have revealed a vast diversity in any character studied. Yet, the symbionts of only very few nodulated legumes have been explored. For each character considered, the data generated and analyzed using computerized methods of numerical analysis and clustering have provided estimation of interstrain similarity and difference among each set of isolates studied. Groups that were considered homogenous appeared diverse in many respects. Although clustering using different methods did not always agree, new groups of isolates could be made at different levels of similarity. In order to be conveniently handled, this new diversity needs to be structured. To do this, the classiﬁcation is permanently adapted. The current organization of rhizobia into groups or taxa is given in the next part.

III. RHIZOBIUM SYSTEMATICS A. FROM CROSS-INOCULATION GROUPING TO POLYPHASIC TAXONOMY The bacteria that nodulate leguminous plants were classiﬁed for a long time in the unique genus Rhizobium. The observation that rhizobia from a given host would nodulate a limited number of legume species led to the concept of “cross-inoculation groups,” deﬁned by Fred (1932) as “groups of plants within which the root nodule organisms are mutually interchangeable.” These authors gave the status of species to 6 of the 16 groups they recognized. Four species, Rhizobium meliloti, Rhizobium trifolii, R. leguminosarum, and Rhizobium phaseoli, were constituted of fast growers, and two, Rhizobium japonicum and Rhizobium lupini, of slow growers. Deﬁciencies of the cross-inoculation concept for delineating rhizobial species accumulated over the years (Wilson, 1944). Lange (1961), in a study that further demonstrated the weakness of a classiﬁcation scheme based solely on symbiotic features, proposed the application of a taxonomic system based on Adansonian principles. The application was taken on by Graham (1964), who proposed major taxonomic changes within the Rhizobiaceae. Two of these changes were retained by Jordan (1982, 1984) in his modiﬁed classiﬁcation: the creation of the genus Bradyrhizobium for slow-growing rhizobia and of three biovars (bv.), viciae, phaseoli, and trifolii, within R. leguminosarum in place of the former species R. leguminosarum, R. phaseoli, and R. trifolii. The inclusion of Agrobacterium spp. in the genus Rhizobium was not retained. Since then, new species have been created at a pace that has accelerated over the past

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few years. These species were ﬁrst created mainly on the basis of a few phenotypic and genetic characters. Following the development of molecular techniques and the proposal of minimum standards (Graham et al., 1991), more diverse criteria have been progressively included in the description of new species. Today, in order to delineate a new bacterial taxonomic unit, the polyphasic approach is recommended (Vandamme et al., 1996). This approach requires the integration of genotypic, phenotypic, and also phylogenetic information. It should lead to the constitution of hierarchical groups of strains with increasing degrees of similarity between strains. The species represents the basic unit of the classiﬁcation scheme and is deﬁned as a group of strains sharing at least 70% of DNA–DNA relatedness measured under speciﬁed conditions (Stackebrandt and Goebel, 1994). A designated type strain serves as the reference for the species. So far, no general rules have been speciﬁed on the degree of similarity that bacteria should share to belong to the same genus. The grouping of species into genera relies mainly on phylogenetic data based on 16S rDNA sequencing.

B. PHYLOGENY AND TAXONOMY With the agrobacteria and phyllobacteria, the rhizobia form the Rhizobiaceae family within the alpha subclass of the Proteobacteria (Stackebrandt et al., 1988). Reviews on the phylogeny and taxonomy of rhizobia have been recently published (Martinez-Romero and Caballero-Mellado, 1996; Young, 1996; Young and Haukka, 1996; van Berkum and Eardly, 1998). The phylogenetic relationships among bacteria are mainly inferred from analysis of the 16S ribosomal sequences. Figure 1 shows a phylogenetic tree based on 16S ribosomal sequences of rhizobium species and of some related bacteria. The currently recognized species, their principal host legumes, and references to the article in which they were ﬁrst published are listed in Table I. −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−→ Figure 1 Phylogenetic tree, based on aligned sequences of the small subunit ribosomal RNA genes, showing relationships among rhizobia and some related bacteria in the alpha-subdivision of the Proteobacteria. The tree is constructed by the Neighbour-Joining method. Bootstrap probability values (100) greater than 80% are indicated at the branch point. Abbreviations: Ag., Agrobacterium; Al., Allorhizobium; Az., Azorhizobium; B., Bradyrhizobium; Bl., Blastobacter; M., Mesorhizobium; R., Rhizobium; Rhp., Rhodopseudomonas, and S., Sinorhizobium. The genbank accession numbers are, top to bottom, as follows: D30778, X67221, L11661, S46917, D25312, Z35330, U69638, X87273, M69186, M65248, U35000, X70405, X70404, X70403, X70401, D32226, M59060, U86344, X67228, X67223, X67225, Y17047, X67231, X68387, X68390, D12783, L39882, D12786, U71079, U50165, UO7934, X67229, U50164, L38825, U50166, Y14158, D12797, AFO41442, D12794, L26167, D12793, AF025852, U71078, U28939, U28916, U47303, U89831, X67227, U29388, X67224, X67234, X67223, U38469, U89823, U89816, U89819, U89817, U89818, and U86343.

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N. AMARGER Table I Current List of Rhizobium Species Species

Rhizobium R. leguminosarum bv. viciae bv. trifolii bv. phaseoli R. galegae R. tropici R. etli bv. phaseoli bv. mimosae R. gallicum bv. gallicum bv. phaseoli R. giardinii bv. giardinii bv. phaseoli R. hainanense R. huautlense R. mongolense Bradyrhizobium B. japonicum B. elkanii B. liaoningensis Sinorhizobium S. meliloti S. fredii S. teranga bv. acaciae bv. sesbaniae S. saheli bv. sesbaniae bv. acaciae S. medicae Mesorhizobium M. loti M. huakuii M. ciceri M. mediterraneum M. tianshanense M. plurifarium M. amorphae Azorhizobium Az. caulinodans Allorhizobium Al. undicicola

Principal host legumes

Lathyrus, Lens, Pisum, Vicia Trifolium Phaseolus Galega Phaseolus, Leucaena Phaseolus Mimosa affinis Phaseolus, Onobrychis Phaseolus Phaseolus, Leucaena Phaseolus Desmodium spp., Stylosanthes, . . . Sesbania herbacea Medicago ruthenica Glycine max Glycine max Glycine max Medicago, Melilotus, Trigonella Glycine max, . . . Acacia Sesbania Sesbania Acacia Medicago Lotus Astragalus sinicus Cicer arietinum Cicer arietinum Glycine max, Glycyrrhiza, . . . Acacia, Leucaena Amorpha fruticosa Sesbania rostrata Neptunia natans

References Frank (1889) Frank (1889) Jordan (1984) Jordan (1984) Jordan (1984) Lindstr¨om (1989) Martinez-Romero et al. (1991) Segovia et al. (1993) Segovia et al. (1993) Wang et al. (1999a) Amarger et al. (1997) Amarger et al. (1997) Amarger et al. (1997) Amarger et al. (1997) Amarger et al. (1997) Amarger et al. (1997) Chen et al. (1997) Wang et al. (1998) van Berkum et al. (1998) Jordan (1982) Jordan (1982) Kuykendall et al. (1992) Xu et al. (1995) de Lajudie et al. (1994) Dangeard (1926) Scholla and Elkan (1984) de Lajudie et al. (1994) Lortet et al. (1996) Lortet et al. (1996) de Lajudie et al. (1994) Haukka et al. (1998) Haukka et al. (1998) Rome et al. (1996) Jarvis et al. (1997) Jarvis et al. (1982) Chen et al. (1991) Nour et al. (1994) Nour et al. (1995) Chen et al. (1995) de Lajudie et al. (1998b) Wang et al. (1999b) Dreyfus et al. (1988) Dreyfus et al. (1988) de Lajudie et al. (1998a) de Lajudie et al. (1998a)

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In the phylogenetic tree (Fig. 1), rhizobia are found intermingled with nonrhizobia showing that the current division in genera is not entirely supported by the 16S rRNA phylogeny. The genus Azorhizobium forms a separate branch and is distantly related to other rhizobia. Bradyrhizobium clusters with a number of bacteria placed in different genera. This genus includes the slow-growing, alkali-producing rhizobia that are not assigned to any species (referred to as Bradyrhizobium sp. followed by the genus name of the host in parentheses) and the extraslow- and slow-growing symbionts of soybean distributed in three species. Unnamed strains may be more closely related to Bradyrhizobium japonicum strains than strains of B. japonicum are to each other. In all these strains the symbiotic genes are chromosomally located. The current genus Rhizobium is heterogenous. It harbors two monophyletic groups and one species, Rhizobium giardinii, phylogenetically distinct from the lineages that contain other rhizobia. The group composed of six species, R. leguminosarum, Rhizobium tropici, Rhizobium etli, Rhizobium gallicum, Rhizobium hainanense, and Rhizobium mongolense, is considered to represent the true genus Rhizobium. R. tropici has been subdivided into two types, A and B, which are genotypically and phenotypically different. Agrobacterium rhizogenes is part of this cluster. The other monophyletic group is composed of two closely related species, Rhizobium galegae and Rhizobium huautlense. Its phylogenetic position is not determined with certainty but appears to be distant from the other Rhizobium species. The genus Sinorhizobium, initially proposed to accommodate fast-growing soybean isolates (Chen, 1988), has been amended based on the results of a polyphasic approach and contains the former R. meliloti and R. fredii as well as three new species, Sinorhizobium saheli, Sinorhizobium teranga, and Sinorhizobium medicae. It represents a coherent phylogenetic group. In Sinorhizobium and Rhizobium isolates, most of the symbiotic genes are plasmid borne. The genus Mesorhizobium is another monophyletic group, recently created on the basis of distinct genetic as well as phenotypic characters. It harbors the former Rhizobium loti, Rhizobium ciceri, Rhizobium mediterraneum, Rhizobium tianshanense, and Rhizobium huakuii and two new species, Mesorhizobium amorphae and Mesorhizobium plurifarium. Strains from this genus are acid producers but the growth rate of some of them is intermediate between that of Bradyrhizobium and Rhizobium strains. The location of the symbiotic genes is chromosomal in Mesorhizobium loti and plasmidic in M. plurifarium and Mesorhizobium huakuii. Allorhizobium has been created recently based on the results of a polyphasic study. It belongs to the Agrobacterium lineage. As the 16S ribosomal sequences of rhizobia accumulate, sequence variability within species is observed and may be even greater than variability between species (Fig. 1). Sequence heterogeneity is also found between copies of the rRNA genes within a single genome (Haukka et al., 1996; Wang et al., 1999b). 16S rRNA sequencing may thus be limited for species delineation; it is, however, very useful for determining the closest relatives of an isolate and for assigning species to

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genera. In the current classiﬁcation, the division in species still reﬂects, in most cases, differences in symbiotic characteristics. However, some species within the genera Rhizobium and Sinorhizobium are symbiotically heterogeneous. Transfer of a plasmid is supposed to be at the origin of this heterogeneity, since isolates within these species may differ by the symbiotic plasmid they harbor, a plasmid which confers distinct host speciﬁty. Biovars have been created to account for this difference in symbiotic speciﬁcity within species. So far, four species of Rhizobium and two of Sinorhizobium have been subdivided into biovars (Table I). Recently, transfer of symbiotic genes located on the chromosome has also been demonstrated in Mesorhizobium species (Sullivan et al., 1995; Sullivan and Ronson, 1998). Therefore, it might be possible to identify distinct host range determinants within species of Bradyrhizobium or Mesorhizobium. Comparison of phylogeny of 16S rRNA to that of nod or nif genes (Young and Johnston, 1989; Lindstr¨om et al., 1995; Ueda et al., 1995; Laguerre et al., 1996; Haukka et al., 1998; Wernegreen and Riley, 1999) also suggests a nonparallel evolution of symbiotic genes and of the rest of the genome within genera. This reinforces the hypothesis that the symbiotic genes have moved within major rhizobial lineages.

C. IDENTIFICATION As long as the basis of the classiﬁcation was the growth rate and the isolation host of rhizobia, identiﬁcation of nodule isolates was simple. Since it has become apparent that a given legume genotype can be nodulated by rhizobia belonging to different species and/or different biovars, identiﬁcation requires more thorough characterization. FAME analysis and miniaturized phenotypic ﬁngerprinting are largely used in bacterial identiﬁcation. Their potential as identiﬁcation methods for rhizobia has been demonstrated (references in Section II), but these methods need further development before they can be routinely used for rhizobial identiﬁcation. In the absence of rapid tests which would allow straightforward identiﬁcation of isolates, the most direct approach for identifying an isolate is, ﬁrst, to place it in the 16S rRNA phylogenetic framework and then to determine its levels of identity with existing reference strains. RFLP analysis of ampliﬁed DNA sequences provides a simple and reliable alternative to sequencing in the estimation of phylogenetic position for identiﬁcation purposes. Methods that give good levels of resolution at the subspecies level and allow estimation of relatedness to type strains include MLEE, RFLP with hybridization probes, and PCR–RFLP of 16S–23S IGS. Whole-cell protein, RAPD, or rep-PCR analyses can be useful but are often too discriminatory. Nodulation tests are necessary to ascertain the symbiotic position of isolates within the identiﬁed species or within one of its biovars. Phylogeny of

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common nod genes can also be used, as a complement or in place of a nodulation test, to position isolates at the symbiotic level.

IV. NATURAL POPULATIONS OF RHIZOBIA Rhizobia are saprophytic bacteria representing only a small fraction of the soil microﬂora and do not possess properties selective enough to allow their quantitative recovery from soil by direct plating. Successful isolation of rhizobial populations directly from soil has only been performed in a few cases by using different multistep procedures (Jarvis et al., 1989; Soberon-Chavez and Najera, 1989; Segovia et al., 1991; Laguerre et al., 1993a; Bromﬁeld et al., 1995; Louvrier et al., 1996; Sullivan et al., 1996; Hartmann et al., 1998). The quasitotality of the rhizobial populations described so far has thus been recovered from the nodules of leguminous plants that had grown in the ﬁeld, in pots containing soil samples, or in any axenic device in which the plants were inoculated with a soil suspension. They represent the result of the selection exerted by the plant from the soil rhizobia under the tested growth conditions and do not correspond to a random sample of the speciﬁc rhizobia present in the soil. The size of the soil population capable of nodulating a given leguminous plant can be assessed by the indirect plant infection method (Vincent, 1970). This method allows estimation of most probable numbers (MPN) of speciﬁc rhizobia when the numbers in soil are at least 10 g−1. For lower numbers, semiquantitative estimates can be made by growing the host plant on sand mixed with increasing amounts of soil. The size of rhizobial populations in agricultural soils varies widely. In most soils, different populations of rhizobia can coexist at an average density of 102 to 104 g−1. Population density sometimes appears to be correlated with different factors that include soil pH, base saturation, soil texture, organic matter content, mean annual rainfall, or temperature. However, most often the differences in population densities cannot be explained by simple variations in environmental parameters. The cultivation of the plant host generally induces a transient increase in the speciﬁc rhizobium population. The density of the speciﬁc rhizobia can then reach up to 107 g−1 of soil under the crop but decreases more or less rapidly after harvesting.

A. DIVERSITY OF POPULATIONS Many studies have been conducted on the diversity of populations of rhizobia that can be recovered from different legume species. Only those related to the populations of rhizobia that nodulate the most widespread legume crops are considered here.

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1. Rhizobia That Nodulate Clovers So far, all the rhizobia isolated from nodules of plants belonging to the genus Trifolium are classiﬁed in the bv. trifolii of R. leguminosarum. However, R. leguminosarum bv. trifolii may not be the only symbiont of the Trifolium species since, in a collection of strains isolated from different species of Trifolium, Eardly (1993) has detected strains, the 16S rDNA alleles of which were identical to that of R. etli. The host speciﬁcity of bv. trifolii is restricted to plants of the genus Trifolium. Some speciﬁcity in effectiveness has been observed among strains of bv. trifolii and effectiveness host-groups have been delineated (Vincent, 1974). Clover species occur naturally or have been introduced in most parts of the world and their microsymbionts are usually detected in soils of pH in the range of 4.5 to 8, in numbers varying from 102 to 105 g−1. Low numbers of rhizobia (<102 g−1 ) are most often found in soils with pH below 5 (Rice et al., 1977; Hagedorn, 1978; Harrison et al., 1989; Nazih et al., 1993; Slattery and Coventry, 1993) or in soils contaminated with heavy metals (Coventry and Hirth, 1992; Chaudri et al., 1993; Giller et al., 1993). The different methods of characterization used to study clover rhizobial populations have each indicated the existence of heterogeneous groups of isolates within these populations. MLEE and RFLP analyses with chromosomal probes were found equivalent in their ability to differentiate strains of R. leguminosarum bv. trifolii (Demezas et al., 1991, 1995). Although the correlation was not so tight between serotypes and ETs, the great majority of serotypes was restricted to very similar ETs (Leung et al., 1994a). Analysis of plasmid content (Harrison et al., 1989; Laguerre et al., 1992) and rep-PCR or Biolog ﬁngerprinting (Leung et al., 1994a; Strain et al., 1994; Laguerre et al., 1996) were shown to group isolates sharing the same ET or the same chromosomal RFLP. Thus, besides differentiation of isolates, MLEE and RFLP analysis with chromosomal probes should provide a reasonable estimation of the overall genetic relatedness among isolates composing clover nodulating populations. When presented with the same soil population of rhizobia, different species or cultivars of clover can select different samples of the strains that it contains. For instance, a greater number of serotypes were identiﬁed in nodules of perennial species, including Trifolium repens or Trifolium pratense, than in nodules of Trifolium subterraneum and four other annual species grown on the same soil (Valdivia et al., 1988; Leung et al., 1994b). Also, Dughri and Bottomley (1984) found differences in the distribution of unique and common ETs among populations isolated from different cultivars of T. subterraneum. In contrast, preferential nodulation was not observed among four cultivars of T. repens (Harrison et al., 1987) and was weak among four cultivars of T. pratense (Hagen and Hamrick, 1996b). Depending on the clover genotype used to trap the rhizobia, the image obtained from soil populations may therefore appear different

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and the actual diversity in R. leguminosarum bv. trifolii present in soils may be underestimated. Variations in the diversity of populations of clover rhizobia can also be observed among sites. Hagen and Hamrick (1996a) found high levels of genotypic diversity among eight populations of red clover rhizobia collected over 2 years from ﬁve sites in two 1500-km distant regions of the eastern United States. Among the 912 isolates analyzed, they distinguished 272 ETs, 64% of which were represented by unique isolates. Within a same region, a variable fraction of multiple isolates was shared between two or more populations and caused, with the variable frequency of unique ETs, differentiation among populations. Differences among populations increased with geographical distances, suggesting that geographic separation contributed to the differenciation of populations. Most of the total genetic variation was found on individual plants (58%) and among plants within populations (20%). Strain et al. (1994) compared the diversity of populations of clover rhizobia isolated from two sites 1-km distant. These sites differed by the natural abundance of clover species and by the densities of R. leguminosarum bv. trifolii in soil. The levels of genetic diversity (average level of polymorphism of the chromosomal loci for the different enzymes studied) of the populations were nearly identical, indicating that low-density populations are not necessarily of little genetic diversity. Nevertheless, the populations were genotypically differentiated. Among the 70 and 198 isolates analyzed, 28 and 54 distinct ETs were identiﬁed respectively and only 1 was common to both sites. This difference between the two populations was attributed to the different nature of the vegetation present at each site. The overall genotypic diversity detected among populations of white clover rhizobia from soils sampled in 10 different sites in the UK was more limited (Harrison et al., 1989). Only 10 ETs represented more than 98% of the 721 isolates analyzed. Many ETs were common to two or more sites and the populations at different sites differed more in the occurrence and relative frequencies of common ETs than in the presence of unique ETs. Much of the total genetic variation was found within populations. The between-site variations observed might have been inﬂuenced by geographical separation. The strain polymorphism was reduced at sites that were acidic, suggesting a better tolerance of particular genotypes to acid soils. Seven of the genotypes common to at least ﬁve of these sites had also been identiﬁed in another UK population isolated from white clover by Young (1985). Four of these genotypes common to several UK sites were shared with the two Oregon populations described by Strain (Strain et al., 1994), one of them being also found in a population isolated from red clover in France (Mazurier, 1989). In addition, the French population and the UK population described by Young (1985) shared their dominant genotype, a genotype that had also been identiﬁed by Strain et al. (1994) from the two Oregon populations. The conservation of some genotypes among the different populations suggests migration over large distances and implies that these genotypes were particularly competitive in a wide range of

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situations and/or that limited genetic exchanges among genotypes had taken place at each site. Environmental factors other than pH can also modify the diversity of populations of clover rhizobia. Soil contamination by heavy metals has been reported to induce decreases in the diversity and effectiveness of populations of rhizobia isolated from subclover (Castro et al., 1997a) and white clover (Hirsch et al., 1993). In this latter case, a single group of very closely related rhizobia ineffective with white clover had survived in the contaminated soil, causing the decrease in the effectiveness of the population. The levels of genetic diversity found in the different studies, which ranged from 0.346 (Hagen and Hamrick, 1996b) to 0.559 (Demezas et al., 1995), were similar to that calculated by Demezas et al. (1991) from strains of diverse origins (0.58), showing that the chromosomal diversity is high within R. leguminosarum bv. trifolii as a whole. A great part of the genetic variation within the species was maintained within populations and individual plants. The genetic diversity of symbiotic gene regions has been investigated by RFLP analysis, using bv. trifolii-speciﬁc probes, for two Australian (Schoﬁeld et al., 1987; Demezas et al., 1995), one French (Laguerre et al., 1993b), and several Californian (Wernegreen et al., 1997) populations. The plasmid-encoded Sym region was polymorphic in all the populations and different Sym types could be identiﬁed in similar chromosomal backgrounds. Similar Sym types were restricted to identical or related chromosomal backgrounds with the exception of two Sym types, one in the population described by Schoﬁeld et al. and one in the French population, that were each found in two different chromosomal backgrounds. This suggests that, although pSym tranfers occur among isolates, there are limitations in these transfers between major R. leguminosarum bv. trifolii chromosomal types. 2. Rhizobia That Nodulate Plants of the Genera Pisum, Vicia, Lens, or Lathyrus Nodule isolates of plants belonging to the genera Pisum, Vicia, Lens, or Lathyrus are classiﬁed in the bv. viciae of R. leguminosarum. Cross-infection seems the rule within this biovar. One exception is a genotype of pea collected in the Middle East, Pisum sativum cv. Afghanistan (Lie, 1978), the nodulation of which is restricted to strains of R. leguminosarum bv. viciae, harboring a speciﬁc nod gene, nodX, common to the Middle East strains but absent in the European strains. The effectiveness behavior of strains of R. leguminosarum bv. viciae is quite diverse. It is frequent to isolate strains effective with a host plant and ineffective with others, but no subdivision of the bv. could be delineated on the basis of effectiveness speciﬁcity. Rhizobium leguminosarum bv. viciae are found in many soils. Similarly to R. leguminosarum bv. trifolii, they can be absent or present in low numbers in acid

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soils. In two surveys conducted in Denmark (Engvild, 1989) and France (Amarger, 1980) on 44 and 57 soils, respectively, R. leguminosarum bv. viciae were not detected (<10g−1 ) in three Danish and seven French soils, all of pH below 5. Their densities in the soils of higher pHs varied from 103 to 105 g−1. Different host plant species have been shown to display different selectivity toward the chromosomal genotypes of R. leguminosarum bv. viciae present in a soil (Turco and Bezdicek, 1987; Mazurier, 1989; Louvrier, 1995; Handley et al., 1998). When peas and lentils (Mazurier, 1989) or peas and fava beans (Louvrier, 1995) were grown in the same soil, less chromosomal genotypes were detected in the nodules of peas than in the nodules of lentils or fava beans. However, the difference was not signiﬁcant in one site, where the diversity of the population directly isolated from soil (Louvrier, 1995) was low, indicating that genotypespeciﬁc selection is dependent on the soil population diversity. Nevertheless, these results as a whole suggest that peas are more selective and may give a more distorted image of the real soil population of R. leguminosarum bv. viciae than the other species. Actually, the high genetic diversity (0.66) and variability in plasmid content and serology observed by van Berkum et al. (1995) among fava bean strains from diverse origins suggest that fava bean is not a selective host. Highly diverse populations of R. leguminosarum bv. viciae can also be found at low soil temperatures. Two populations of cold adapted rhizobia were isolated from two Lathyrus species, Lathyrus japonicus and Lathyrus pratensis, growing in northern regions of Quebec by Drouin et al. (1996). They had a genetic diversity of 0.45 each and were genotypically very diverse and highly differentiated. Environmental factors can inﬂuence the composition of populations of pea rhizobia. Differences in topographic position within ﬁelds in a hilly region of eastern Washington state caused variations among populations of pea rhizobia (Mahler and Bezdicek, 1978; Brockman and Bezdicek, 1989). Populations of nodule isolates recovered from peas grown on the south slopes and ridge tops differed from those recovered from peas grown on the north slopes and bottom lands in the distribution of serogroups and in the diversity of plasmid proﬁle groups within serogroups. These differences were attributed to the differences in soil microclimate that was warmer and dryer on the south slopes and ridge tops than on the north slopes and bottomlands. Labes et al. (1996) also identiﬁed an effect of the slope position on the composition of populations of pea rhizobia while the northern or southern exposure had no impact. The application of slurry induced changes in these populations, some groups of isolates disappeared, and new groups were detected in the slurry-polluted soil. At the two Oregon sites studied by Strain et al. (1994), the results obtained with populations of R. leguminosarum bv. viciae were similar to those found with bv. trifolii. Despite wide differences in the numbers of soil rhizobia present at each site, the genetic diversity of the populations recovered from plant nodules was similar. However, the composition of these two populations differed, which was attributed

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to the absence of host plants at one site. In a ﬁeld population of pea rhizobia, Young et al. (1987) observed that although the population on each individual plant was highly diverse, ET frequencies were similar from plant to plant, which indicated a remarkably homogenous distribution of the rhizobial genotypes within the soil. A majority of these genotypes had been previously identiﬁed at another ﬁeld 25 km distant (Young, 1985) and the authors suggested that genetic exchange was infrequent relative to the rate of migration between the two populations. Some of these genotypes were also shared with R. leguminosarum bv. viciae populations from France (Laguerre et al., 1992) and Oregon (Strain et al., 1995), indicating that the rates of migration of these genotypes were indeed rather high. The diversity in the plasmid-encoded symbiotic region has been studied in several populations of R. leguminosarum bv. viciae isolated from nodules of peas (Engvild et al., 1990; Young, 1988) or from peas and lentils (Laguerre et al., 1992) and in two populations isolated directly from soils (Louvrier et al., 1996). High levels of polymorphism were observed in each population. The distribution of this polymorphism across chromosomal backgrounds differed greatly among the populations. Whereas this distribution was at random in the two populations directly isolated from soil, more or less strong associations between Sym types and chromosomal backgrounds were detected in the populations isolated from nodules. Each chromosomal type but one was associated to a different Sym type in the population studied by (Engvild et al., 1990). In the other populations, several Sym types were associated to a same chromosomal background. Identical Sym types were found in different chromosomal backgrounds, in greater proportion in the French (Laguerre et al., 1992) than in the UK population (Young, 1988). These ﬁndings suggest that pSym transfer occur in natural populations but that opportunities for such transfers differ greatly between sites. At the sites where populations of R. leguminosarum belonging to the three biovars, trifolii, viciae, phaseoli, had been isolated (Laguerre et al., 1992; Young, 1988) some chromosomal genotypes were shared among the three biovars, giving additional evidence of pSym transfer among populations of R. leguminosarum. 3. Rhizobia That Nodulate Phaseolus spp. The symbionts of Phaseolus spp., to which I collectively refer as Phaseolus rhizobia, have long been recognized as a group of rhizobia that shows considerable genetic and phenotypic diversities (Graham, 1964; Gibbins and Gregory, 1972; Jarvis et al., 1977; Beynon and Josey, 1980; Roberts et al., 1980; Crow et al., 1981; Robert and Schmidt, 1985; Pinero et al., 1988), but it is only in recent years that this group, which formed the species R. leguminosarum bv. phaseoli, has been partitioned into different species. To date, ﬁve Rhizobium species have been recognized as microsymbionts of Phaseolus vulgaris (Table I), but potentially there are others (van Berkum et al., 1996; Herrera-Cervera et al., 1999). The host speciﬁcity

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of strains belonging to the bv. phaseoli of R. leguminosarum, R. etli (formerly type I strains), R. gallicum, and R. giardinii is restricted to plants of the genus Phaseolus. Rhizobium tropici (formerly type II), R. gallicum bv. gallicum, and R. giardinii bv. giardinii have wider host ranges, probably not fully described yet, that include Leucaena spp. in addition to P. vulgaris. Moreover, R. tropici isolates also form nodules on a wide spectrum of other tropical legumes (Hernandez-Lucas et al., 1995) and R. gallicum bv. gallicum nodulates Onobrychis spp. (Amarger et al., 1997), Vigna unguiculata, and Gliricidia spp. (Sessitsch et al., 1997a). Strains of R. giardinii bv. giardinii are totally ineffective with their two known hosts, P. vulgaris and Leucaena spp. P. vulgaris is a highly promiscuous leguminous plant that under laboratory conditions nodulates with many other classiﬁed and unclassiﬁed rhizobia (Graham and Parker, 1964; Eardly et al., 1985; Sadowsky et al., 1988; Bromﬁeld and Barran, 1990; Michiels et al., 1998), which in most cases form ineffective nodules. Field nodulation of common bean is often sparse and nodules may even be absent on roots of ﬁeld-grown beans (Graham, 1981). However, soil populations of Phaseolus rhizobia commonly range in size from 102 to close to 106 g−1 (Amarger, 1980; Robert and Schmidt, 1983; Kucey and Hynes, 1989; Anyango et al., 1995; Aguilar et al., 1998b). Great genetic heterogeneity has been found among populations of rhizobia recovered from nodules of soil- or ﬁeld-grown Phaseolus. Many of the populations analyzed, even those originating from single sites, were composed of several species, in numbers up to 5 (Herrera-Cervera et al., 1999; Mhamdi, 1999). It is noteworthy that the species identiﬁed in single-species populations was either R. etli or R. leguminosarum and that, in populations composed of two or more species, R. etli or R. leguminosarum bv. phaseoli were always present. It is commonly accepted that P. vulgaris is native to the Americas and that domestication of wild beans took place independently in Mesoamerica and in the Southern Andes. R. etli bv. phaseoli, the predominant species found in the rhizobial populations associated with wild beans from the southern Andes (Aguilar et al., 1998b) and from Mexico (Souza et al., 1994), is supposed to have coevolved with P. vulgaris. Populations of rhizobia isolated from Mexico and considered to belong to R. etli appear to be very diverse. The total genetic diversity found in three different studies was similar (0.487, Souza et al., 1994; 0.504, Silva et al., 1999; and 0.531, Segovia et al., 1991). The ﬁve populations collected over 2 years from nodules of wild and cultivated common beans sampled in three locations by Souza et al. (1994) were found to differ in terms of ETs and of allelic frequencies, among sites, and within a site across years. The differences among sites were attributed to differences in soil nutrient characteristics. On average, 50% of the genetic diversity in a sampled population was present within plants. Populations isolated from cultivated P. vulgaris and Phaseolus coccineus, grown in six traditionally managed plots in close proximity, were also very diverse, globally and within each plot (Silva

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et al., 1999). They were genotypically differentiated and, as for the populations described by Souza et al. (1994), these differences in composition were related to soil characteristics. The main proportion of the variability was found within the plant level (70%) rather than at higher local geographical scales. P. coccineus plants were found to nodulate with a wider range of genotypes than P. vulgaris. On the basis of DNA ﬁngerprinting and plasmid proﬁle, Aguilar et al. (1998b) observed diversity of R. etli isolates from wild beans at all levels of sampling, within plants and within and among sites across north western Argentina. Highly diverse populations of R. etli bv. phaseoli have been found not only in the Americas but also in tropical regions of Africa and in Indonesia (Tjahjoleksono, 1993; Anyango et al., 1995). Tjahjoleksono (1993) isolated populations of 100 isolates each from P. vulgaris grown in two ﬁelds 40 km apart in Indonesia and in one ﬁeld in Burundi. All isolates were identifed as R. etli bv. phaseoli with the exception of two from Burundi which were identiﬁed as R. tropici. Using RFLP ﬁngerprinting with chromosomal probes, 42 genotypes were described. The two Indonesian populations were well differentiated from each other and equally diverse; they counted 15 genotypes each and shared only 1 of these genotypes. The Burundian population counted 13 genotypes, none of which was closely related to the Indonesian ones. Anyango et al. (1995) isolated from two Kenyan soils with contrasting pH rhizobia that could be considered as R. etli bv. phaseoli and represented almost all (40/41) isolates from the nearneutral soil and a small fraction (5/35) of the isolates from the acid soil. This species has also been identiﬁed as a constituent of populations recovered from bean grown in soils from Tunisia (Mhamdi et al., 1999), Spain (Herrera-Cervera et al., 1999) Austria (Sessitsch et al., 1997a), and France (unpublished results), representing 5 to 58% of the isolates. Although numbers of isolates studied in these populations were limited, various chromosomal genotypes were detected among the R. etli bv. phaseoli isolates at each site. Collectively, these results show that R. etli is highly diverse not only in the countries where it likely coevolved with Phaseolus spp., but also in countries where Phaseolus spp. have been introduced for less than 5 centuries. This high diversity might have been generated by frequent recombinations between the limited numbers of R. etli bv. phaseoli genotypes introduced with the seeds or by pSym transfer from these introduced genotypes to nonsymbiotic R. etli or R. etli of different plant speciﬁcity previously present in the soils. The distribution of R. leguminosarum bv. phaseoli, among the populations studied so far, is more limited. It was a component of the Tunisian and Spanish populations (Herrera-Cervera et al., 1999; Mhamdi et al., 1999) and has also been identiﬁed in South American populations by Aguilar et al. (1998b). This species was found in the different French populations analyzed in proportion varying from 2 to 100% (Geniaux et al., 1993; Amarger et al., 1994), and was the only species recovered from the UK population studied by Young (1985). The chromosomal diversity among isolates varied with the sites. One chromosomal genotype that represented 98% of the UK population was shared with isolates from two French populations (Geniaux et al., 1993).

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The strains of collections ﬁrst described as R. tropici, (Martinez-Romero et al., 1991) were isolated from Brazil and Columbia. Eleven of the 35 ETs described corresponded to Phaseolus isolates, the others being Leucaena isolates. In the American populations of bean rhizobia studied since then and that do not include Brazilian populations, R. tropici has only been detected as a scarce constituent: 1 isolate of 53 from a Mexican population (Vasquez Arroyo et al., 1998) and 1 of 64 isolates sampled in 17 sites in Argentina (Aguilar et al., 1998b). The same is true for African populations isolated from near-neutral soil (Tjahjoleksono, 1993; Anyango et al., 1995), but not for populations from acid soils either from Kenya (Anyango et al., 1995) or from France (Amarger et al., 1994), in which R. tropici was the predominant species. These different results suggest that when R. tropici is present in soils, it is not competitive enough to nodulate beans in the presence of R. etli or R. leguminosarum. However, when the densities of the competitive species diminish due to acid pH, R. tropici, which is more tolerant to acidity (Graham et al., 1994), can express its bean-nodulating capacity. R. gallicum has been identiﬁed as a component, which represented 21 to 42% of the isolates, of the populations studied in Austria (Sessitsch et al., 1997a), Spain (Herrera-Cervera et al., 1999), and Tunisia (Mhamdi et al., 1999). It was also detected in collections of strains isolated from Mexico (Sessitsch et al., 1997b) and from France (Amarger et al., 1997), suggesting a wide distribution. In the Spanish population, MLEE analysis distinguished two clusters of ETs only distantly correlated among the isolates carrying 16S rDNA alleles similar to R. gallicum and that could correspond to different species. The distribution of R. giardinii seems more limited. It formed about one-third of the isolates in two French populations (Geniaux et al., 1993) and 5% in the Spanish and Tunisian populations (Mhamdi, 1999). In addition to four of the recognized species of Phaseolus rhizobia, isolates with 16S rDNA RFLP patterns matching Sinorhizobium fredii have been identiﬁed in the Spanish and in the Tunisian populations. These S. fredii-like strains were not soybean symbionts, as they did not have the capacity to nodulate American or Chinese soybeans, but represent a new type of Phaseolus symbiont that forms effective nodules on common bean. In a recent study, Caballero Mellado et al. (1999) have shown that levels of fertilization commonly used in agricultural ﬁelds in Mexico diminish the genetic diversity among nodule isolates of some but not all P. vulgaris cultivars, almost eliminating strains that diverged from the main R. etli group at a genetic distance of 0.8. Hybridization of digested genomic DNAs with R. etli bv. phaseoli nif H and nodB gene probes has been used in several studies to characterize the symbiotic genome. All isolates identiﬁed as R. etli or R. leguminosarum in the different populations analyzed, a portion of the isolates identiﬁed as R. giardinii in the French populations (Geniaux et al., 1993), or as R. giardinii or R. gallicum in the Spanish population (Herrera-Cervera et al., 1999) appeared to contain three

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copies of nif H, a characteristic of the bv. phaseoli described in the four species. In contrast to the high chromosomal diversity observed among and within these four species, only seven nif H hybridization patterns were detected among the many chromosomal genotypes analyzed. Two of these patterns were represented in the four species and in high proportions in every population analyzed. This suggests that transfer of symbiotic genes has occurred within and between species. This also implies that the symbiotic gene region carried by the pSym is remarkably stable and/or that there is a strong selective advantage for the few gene arrangements that have been conserved in the course of evolution and among diverse genetic backgrounds. The remaining portion of the isolates identiﬁed as R. gallicum by Herrera-Cervera et al. (1999); the R. gallicum present in the populations studied by Sessitsch et al. (1997) and Mhamdi (1999); the few R. tropici identiﬁed by Anyango et al. (1995), Aguilar et al. (1998b), and Tjahjoleksono (1993); and the S. fredii-like isolates found by Herrera-Cervera et al. (1999) and Mhamdi (1999) all contained a single band hybridizing to the nif H probe. The size of the band was the same for the isolates of the same species and corresponded in both R. gallicum and R. tropici to that of the type strain, suggesting conservation of the symbiotic genome within each species and over large distances. It appears from the different studies that the Phaseolus rhizobia are very diverse at the species, intraspecies, and population levels. Paradoxically, the highly specialized symbiotic genome that likely coevolved with the plant is very conserved. It can be expressed in different species backgrounds and succeeded to be ubiquitous in less than 5 centuries. The populations of rhizobia present in the nodules of Phaseolus spp. are highly differentiated between sites. It is difﬁcult from the available data to identify factors that might be involved in the distribution of the different species among sites if we except low soil pHs that enhance the proportion of R. tropici in the populations. 4. Rhizobia That Nodulate Plants of the Genera Medicago, Melilotus, or Trigonella Rhizobia isolated from nodules of plant species belonging to the related genera Medicago, Melilotus, and Trigonella were classiﬁed in a single species, R. meliloti. This species has been transferred to the genus Sinorhizobium (de Lajudie et al., 1994). Nodulation and effectiveness interactions are commonly observed between host plants and strains from this group, some species, like Melilotus sativa and Melilotus alba, being promiscuous, others, like Melilotus laciniata, extremely selective. Nodulation and effectiveness host groups have been deﬁned to take into account these interactions (Brockwell and Hely, 1966). The existence of two major phylogenetic divisions (A and B) in Sinorhizobium meliloti has been demonstrated by Eardly et al. (1990) and conﬁrmed by Gordon et al. (1995).

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Division A represented a cosmopolitan collection of strain from a variety of annual and perennial medics, including alfalfa, and division B was restricted to strains from annual species growing in the Mediterranean region. Isolates of division B are now classiﬁed in the species S. medicae (Rome et al., 1996). Recently, R. mongolense has been created to accommodate isolates from Medicago ruthenica that also nodulate P. vulgaris. Some of these isolates form ineffective nodules with M. sativa. In addition to these three species, strains that belong to an unrecognized taxonomic group of Rhizobium (Eardly et al., 1992) have been recovered from alfalfa grown in moderately acid soils (5.0 < pH < 6.0) in Oregon (Eardly et al., 1985) and in central Argentina and Uruguay (Del Papa et al., 1999). Strains from this group nodulate alfalfa and P. vulgaris but form ineffective nodules on both species. Sinorhizobium meliloti is highly sensitive to acid conditions and its abundance in soils decreased sharply as the pH of the soils decreased below pH 6. It reaches undetectable levels at pH 5 (Rice et al., 1977; Amarger, 1980). For this reason, inoculation of alfalfa is a common practice in soils of pH < 6.5.The different methods used to study ﬁeld or soil populations of S. meliloti have all revealed considerable levels of diversity in these populations. Phage typing has been used in separate studies to evaluate the composition of populations of S. meliloti isolated from different host genotypes growing in ﬁeld or in soils collected from different sites in Canada (Bromﬁeld, 1984; Thurman and Bromﬁeld, 1988). In both studies, the variety and distribution of phage types differed markedly between sites, a difference that was attributed by the authors to differences in cropping histories. Signiﬁcant variations in the frequency of occurrence of particular phage types were observed both among the three host species, M. sativa, Melilotus lupulina, and Melilotus alba, and the two M. sativa cultivars tested by Thurman and Bromﬁeld (1988) and Bromﬁeld et al. (1984) respectively, indicating host genotype variation in nodulation preferences for speciﬁc soil rhizobia. However, this genotype-speciﬁc selectivity was not revealed at each site. Variation in nodulation preferences was also observed among plants in each species. This variation ﬂuctuates with the species and appeared to be related to pollination characteristics, less variation being present for the self than for the cross-pollinated species. Site-dependent variations in the diversity of strains nodulating different varieties of M. sativa were also reported by Paffetti et al. (1998), conﬁrming that genotype-speciﬁc selectivity is related to local characteristics that could be the composition of the soil population and/or soil properties. Direct evidence of host preference for speciﬁc members of soil populations of R. meliloti has been given in two studies that have used IS typing to compare the diversity of populations isolated directly from soil and from nodules of plant hosts. The variety of IS genotypes from the two plant species studied by Bromﬁeld et al. (1995) was either similar, in the case of M. albus, or greater, in the case of M. sativa, than that obtained directly from the soil. Eight of the 9 genotypes identiﬁed in soil were present in different proportions in the two plant species, showing

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that the plants have selected the most abundant soil rhizobial genotypes. However, their relative distribution differed between the three populations, indicating that they have been differently selected by the two plant species and that relative abundance of rhizobial genotypes in nodules is not indicative of that in the soil population. Moreover, the isolation of 7 additional genotypes from alfalfa indicates that this plant has also selected less abundant and likely more competitive genotypes. Marked difference in the diversity of the IS genotypes isolated from soil relative to that sampled from nodules of alfalfa has also been observed in the populations studied by Hartmann et al. (1998). Although the level of diversity detected among soil and nodule isolates were similar, 38 and 37 genotypes respectively, only 11 genotypes were common to both populations. Moreover, the distribution of these common genotypes varied among the two populations. These results conﬁrm that nodule populations are not representative of soil populations. They also show that the population of S. meliloti present in the ﬁeld was highly diverse and that each method of sampling has revealed a different portion of the diversity. Chromosomal and megaplasmid variations have been examined by Bromﬁeld et al. (1998) in samples of two ﬁeld populations of S. meliloti in close proximity using RFLP analysis with gene probes from the chromosome and from each of the two megaplasmids (pnod and pexo). Comparative analysis of the polymorphisms observed at the different chromosomal and megaplasmid loci have revealed that the same chromosomal type commonly coexists with different pexo or pnod types and, conversely, the same megaplasmid types occur with different chromosomal types. This suggests that genetic exchanges of megaplasmids sequences have occurred among isolates in the two populations. However, transfer of the pexo sequences appeared more limited than the pnod ones since the distribution of the megaplasmid loci across chromosomal backgrounds was random for the pnod plasmid and not random for the pexo plasmid. Measurements of symbiotic effectiveness of isolates representing the diversity found in alfalfa populations has revealed high average levels of symbiotic effectiveness (86 to 95%) relative to standard inoculant strains (Bromﬁeld et al., 1987; Shishido and Pepper, 1990; Hartmann and Amarger, 1991; Gandee et al., 1999). 5. Rhizobia That Nodulate Soybean The soybean microsymbiont are presently classiﬁed in three species of Bradyrhizobium, B. japonicum, Bradyrhizobium elkanii, and Bradyrhizobium liaoningense, and in one species of fast-growing rhizobia, S. fredii. These four species are indigenous to Chinese soils. Their relative abundance in these soils is not well documented, but the four species have been detected in different Chinese provinces (Chen et al., 1988; Xu et al., 1995). Slow- and fast-growing species can coexist in a same soil and compete, more or less successfully, depending on soil and soybean cultivar, for the formation of nodules (Dowdle and Ben Bohlool, 1986).

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Since soybean rhizobia are not indigenous to Western countries, their introduction in these countries necessitates the use of rhizobial inoculants. Soybean rhizobia have thus been progressively introduced with the soybean crop, ﬁrst in the United States and later in South America, Europe, and Africa. Since the strains used in the inoculants were bradyrhizobia, which appeared later to belong to the two species B. japonicum and B. elkanii, the populations of soybean rhizobia that have developed in the soils of these countries are composed of one or both species. Since soybean inoculation is routinely practiced, the established populations generally result from several successive introductions. Serological methods have been the most commonly utilized methods in studying the diversity of soybean bradyrhizobial populations. Serogroups are correlated with most groupings based on other phenotypic and genetic characteristics (reviewed by Fuhrmann, 1993) and provide convenient means of characterizing nodule isolates. However, no apparent correlation between symbiotic effectiveness and any of the phenotypic or genotypic characteristics of soybean bradyrhizobia has been found so far. Serological studies of ﬁeld populations of soybean bradyrhizobia have revealed considerable diversity within and among geographical locations in U.S. soybean production areas. Serogroup 123 isolates (later identiﬁed as B. japonicum) were the more prevalent in the upper Midwest (Damirgi et al., 1967; Ham et al., 1971; Keyser et al., 1984; Kamicker and Brill, 1986; Weber et al., 1989) while serogroups 31, 46, 6, 76, and 94 isolates (later identiﬁed as B. elkanii) were common in Southern soils (Caldwell and Hartwig, 1970; Keyser et al., 1984; Fuhrmann, 1989; Weber et al., 1989; Mpepereki and Wollum, 1991; Ramirez et al., 1997). The prevalence of distinct serogroups in these regions could be related to a certain extent to the presence of strains of the corresponding serogroups in early inoculants used in these regions (Weber et al., 1989). In some instances the serogroups present in soybean nodules appeared related to soil properties such as soil nitrogen (Bezdicek, 1972) or pH (Damirgi et al., 1967; Ham et al., 1971). Whereas some studies have demonstrated that soybean genotype can affect serogroup recovery (Caldwell and Vest, 1968; Caldwell and Weber, 1970; Kvien et al., 1981), no cultivar effect could be shown in other locations (Fuhrmann, 1989), indicating that such effects are dependent on local parameters. In Japan, where soybean is generally cultivated without inoculation, sitedependent variations were observed in the distribution of Bradyrhizobium species and of RS␣ and RS␤ genotypes among populations of soybean isolates (Minamisawa et al., 1999), suggesting that bradyrhizobia have diversiﬁed in association with various factors found in individual ﬁelds. Highly diverse populations of soybean rhizobia are also encountered in countries with a more recent history of soybean cultivation. In tropical soils from Brazil, new genotypes adapted to local environments have developed from early inoculant strains of B. elkanii (Neves and Rumjanek, 1997). In Poland, although most of the isolates recovered from different sites belonged to two serotypes, they were found to be highly diverse on

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the basis of antibiotic resistance, protein content, rep-PCR patterns, and nif and nod gene hybridization proﬁles, which suggests that they have diversiﬁed since their release in the late 1980s (Madrzak et al., 1995). Surprisingly, in soils where soybean has been inoculated with a single bradyrhizobial strain, strains differing from the inoculant strain have been identiﬁed in nodule isolates from soybean cultivated several years later (van Rensburg and Strijdom, 1985; C. Revellin, personal communication). Although these strains might have been introduced with seeds, they might as well result from genetic interactions between inoculant strain and soil bradyrhizobia of different plant speciﬁcity or nonsymbiotic. On the whole, these results show that, following the introduction of a limited number of strains in ﬁeld environment, highly diversiﬁed populations of soybean bradyrhizobia can develop in relatively short periods of time.

B. POPULATION STRUCTURE Great diversity is a common feature among rhizobial populations, no matter which legume species they have been isolated from. Levels of genetic diversity, estimated by MLEE analysis, are similar for the different rhizobial species studied, with values near 0.50–0.55, and are retained in most of the individual populations. Lower levels of genetic diversity found in some populations were generally associated with speciﬁc soil conditions such as acid pH (Harrison et al., 1989) or high nitrogen content (Souza et al., 1994; Caballero-Mellado, 1999). Most of the variation observed in a given population is found within individual plants (Young et al., 1987; Souza et al., 1994; Hagen and Hamrick, 1996a, 1996b; Silva et al., 1999). Thus, with the exception of recently introduced legume species, each legume plant has access to a great proportion of the diversity found in its associated rhizobial species. This genetic diversity has been associated with levels of linkage desequilibrium (nonrandom association between the alleles of different enzyme loci) that varies with the population studied. Absence or low levels of linkage desequilibrium have been estimated in populations of the species R. leguminosarum bv. viciae (Gordon et al., 1995), R. leguminosarum bv. trifolii (Hagen and Hamrick, 1996a,b), nonsymbiotic R. etli (Segovia et al., 1991), R. etli bv. phaseoli (Souza et al., 1994), and S. meliloti (Bromﬁeld et al., 1998), suggesting that frequent genetic exchanges have occurred in these populations. Conversely, signiﬁcant levels of linkage desequilibrium have been found in other populations of R. leguminosarum bv. viciae (Young et al., 1987; Strain et al., 1995), R. leguminosarum bv. trifolii (Harrison et al., 1989; Demezas et al., 1991; Strain et al., 1995; Hagen and Hamrick, 1996a,b), R. etli bv. phaseoli (Pinero et al., 1988; Souza et al., 1992; Souza et al., 1994; Silva et al., 1999), and R. meliloti (Eardly et al., 1990; Gordon et al., 1995). They have been interpreted as resulting predominantly from clonal reproduction, implying

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that chromosomal recombination is a rare event in these populations. However, in some of these populations (Eardly et al., 1990; Demezas et al., 1991; Gordon et al., 1995; Strain et al., 1995), cluster analysis has revealed deeply diverging lineages and absence or low levels of linkage desequilibrium within these lineages, suggesting that the source of linkage desequilibrium was more likely reproductive isolation than clonality. This type of population structure corresponds to the structure described by Maynard Smith et al. (1993) as reticulated. Recently Silva et al. (1999) have observed reticulated structure in populations of R. etli bv. phaseoli. The reticulated structure of these populations was associated with an epidemic structure, which corresponds to populations “in which recombination occurs but occasionally a highly successful genotype arises and increases in frequency to produce an epidemic clone” (Maynard Smith et al., 1993). It can also generate linkage desequilibrium in populations. It is therefore possible that genetic recombination occurs more frequently in rhizobial populations than ﬁrst estimated. The differences observed in the composition of populations directly isolated from soils and isolated from nodules have shown that populations isolated from nodules are not representative of soil populations (Bromﬁeld et al., 1995; Hartmann et al., 1998). Same conclusions are drawn when comparing the composition of populations isolated from different genotypes growing on the same soil. Structures of rhizobial populations that have been inferred from the populations studied to date are therefore not the structures of the actual populations of rhizobia present in the soils but rather structures of portions of these populations that have been differently ﬁltered by plants. In populations directly isolated from soil, frequent genetic exchanges are suggested both by the absence of linkage desequilibrium (Segovia et al., 1991) and by the random distribution of Sym types across chromosomal genotypes (Louvrier et al., 1996). It seems therefore likely that the linkage desequilibrium observed in nodulating populations of rhizobia result more from the plant selectivity than from the absence of recombination in the soil populations. This suggests that genetic recombination plays an important role in generating diversity within populations of rhizobia.

V. INTRODUCTION OF RHIZOBIA INTO SOIL Rhizobia are very widespread as a result of the natural distribution and of the cultivation of legumes. Despite this, there are still soils where strains of rhizobia speciﬁc for a legume crop are absent or present in low numbers. Such situations are encountered when a new crop is introduced into a region where symbiotically related legumes are absent, e.g., soybean in western Europe (Obaton and Rollier, 1970; Madrzak et al., 1995), or where the soil or environmental conditions are detrimental for the occurrence or the survival of a rhizobial species, e.g., acid soils

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for R. leguminosarum bv. trifolii (Nazih et al., 1993), S. meliloti (Amarger, 1980), alkaline soils for Bradyrhizobium sp. Lupinus (Amarger, 1980), and high temperatures for chickpea rhizobia (Ruppela et al., 1974). Introduction of rhizobial strains may also be desirable where soils harbor populations of rhizobia composed of a majority of strains symbiotically ineffective with a particular legume (Gibson et al., 1975; Hagedorn, 1978; Bottomley and Jenkins, 1983; Quigley et al., 1997; Moawad et al., 1998) or when better performing strains become available (Bosworth et al., 1994).

A. INOCULATION Legume inoculation is an agricultural practice that has been used for more than a century to introduce rhizobia into the soil at sowing (for review see Brockwell, 1977; Date and Roughley, 1977; Roughley, 1988; Smith, 1992; Somasegaran and Moben, 1994; Brockwell and Bottomley, 1995). Commercial inoculants are produced in many countries. Their quality depends on both the number of rhizobia they contain and their effectiveness in ﬁxing nitrogen with the target host. Besides symbiotic effectiveness, strain selection usually takes into consideration other characters such as genetic stability, ability to survive in inoculant, to persist in soil, and to compete in nodule formation with soil rhizobia. Inoculants are produced in powder, granular, or liquid forms. They can be applied directly on the seed, which is the traditional and most commonly used means of inoculation, on mineral granules (Wadoux, 1991), or into the seedbed (Hynes et al., 1985). Their quality, evaluated by enumeration of viable rhizobia, is highly variable and it appears that a high proportion of those presently on the market in countries where quality controls are not systematically practiced are of poor quality (Catroux et al., 1999). However, high-quality inoculants are produced and are available in powdered or liquid form in North America, Europe, and some other countries. They can be stored one year at room temperature and provide at least 106 viable rhizobia per seed for soybean. Assuming minimal losses during inoculation, high-quality inoculants introduce in soil approximately 2 × 1011 to 4 × 1011 rhizobia ha−1, which represents about 1% of the numbers of rhizobia present in the top 20 cm of soil containing 104 g−1 rhizobia (Catroux and Amarger, 1992).

B. SOIL COLONIZATION BY INOCULANT RHIZOBIA When soils are devoid of rhizobia, the inoculant strain nodulates the host legume and multiplies in the nodules and, upon nodule senescence, high numbers of viable cells are released into the soil. For soybean, numbers of 106 bradyrhizobia per gram

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of soil are commonly observed after cropping (Brockwell et al., 1987; Lagacherie, 1978; Hiltbold et al., 1985). The introduced rhizobia most often become a persistent if not permanent component of the soil microﬂora. Soybean rhizobia present in early inoculants are still prevalent members of the soil rhizobial populations in many parts of the United States (Weber et al., 1989). The survival of the introduced rhizobia depends on how the strain responds to or resists the conditions that prevail in the soil. Multiple abiotic and biotic factors (reviewed recently by Sadowsky and Graham, 1998) can affect the persistence of rhizobia in soils. When soils contain indigenous rhizobia, the inoculant rhizobia have to compete with these indigenous rhizobia for the formation of nodules. Very often they are not successful and cannot be recovered from the plant nodules (Dudman and Brockwell, 1968, Ham, 1980; Carter et al., 1995). Although in these cases inoculation has failed, it does not mean that introduction of the inoculant rhizobia in soils has also failed. Due to methodological problems, quantitative data on the presence of inoculant bacteria in soils containing indigenous rhizobia are scarce. Two separate studies have shown that marked strains of R. leguminosarum bv. viciae, ﬁeld released as inoculants in soils containing indigenous rhizobia, could survive at the level of the indigenous population for several years, whether they have formed nodules or not (Hirsch and Spokes, 1994; Amarger and Delgutte, 1990). In another ﬁeld experiment, a nonsymbiotic strain of R. leguminosarum was also found to survive in numbers similar to those of the indigenous populations, several years after its release (Hirsch, 1996). These ﬁndings provide evidence that inoculant strains can persist in soils containing indigenous rhizobia even if they have not multiplied in nodules. However, such an introduction was found to depend on local conditions, since a same strain, inoculated to a pea crop in three different European countries, behaved differently and 1 year after release was only detectable at one site (Hirsch, 1996).

C. INTERACTIONS WITH INDIGENOUS RHIZOBIAL POPULATIONS Evidence that strains, once introduced into soils, can exchange genetic information with indigenous bacteria has been given recently by Sullivan et al. (1995). A single strain of M. loti was introduced as an inoculant of a Lotus corniculatus crop in a New Zealand soil devoid of M. loti. Seven years later, the mesorhizobia recovered from the other nodules were genotypically diverse. Despite their diversity all strains contained a chromosomally integrated symbiotic region identical to the original inoculant strains. Since then, transfer of a 500-kb symbiosis island from an M. loti strain to at least three genomic species of nonsymbiotic mesorhizobia has been demonstrated in laboratory matings (Sullivan et al., 1996). These ﬁndings give evidence that the inoculant bacteria have generated, in the ﬁeld environment,

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a new diversiﬁed population of Lotus-nodulating mesorhizobia by lateral transfer of their chromosomal symbiotic genes to nonsymbiotic mesorhizobia. Knowing that transfer of symbiotic genes is not limited to rhizobia with plasmidencoded symbiotic genes, we can speculate that gene transfer also occurs between bradyrhizobia. This could explain why, in soils which were devoid of indigenous soybean rhizobia, bradyrhizobia different from the original inoculant strain were recovered from soybean nodules (van Rensburg and Strijdom, 1985; C. Revellin personal communication). We can also suppose that the high diversity presently found in populations of soybean bradyrhizobia, which was introduced less than a century ago, has developed from genetic exchanges between inoculant strains and local bradyrhizobia. The presence of conserved DNA regions around nif genes among different genotypes of soybean bradyrhizobia identiﬁed in Japanese populations would support this hypothesis (Minamisawa et al., 1999). Although there is circumstancial evidence of pSym transfer within and between species of Rhizobium, evidence that such transfers occur in the ﬁeld is still missing. Transfer of a marked conjugative pSym from an R. leguminosarum bv. viciae strain, released as an inoculant, to indigenous R. leguminosarum was not detected in ﬁeld experiments (Amarger and Delgutte, 1990; Hirsch and Spokes, 1994), nor was the acquisition of genetic elements from indigenous bacteria by introduced inoculant strains (Hirsch, 1997). This suggests that plasmid transfers in natural environments are not frequent (<10−5), at least in the absence of selection pressure for the plamid-encoded characters.

D. AGRICULTURAL IMPLICATIONS It has to be realized when performing inoculation that the inoculant strain has the potential to become a permanent member of the soil microﬂora and to exchange genetic information with the soil-resident bacteria, even in case of inoculation failure. Once introduced into the soil, the inoculant rhizobia may form an ineffective symbiosis with a subsequent crop presenting the same nodulation speciﬁcity. They may also exchange genes with soil bacteria and create new rhizobia less effective than the original inoculant strain with the inoculated crop. In both cases they are likely to form a barrier to the introduction of more effective strains. Presently, there are no reliable means of estimating the potentialities of a strain to persist in soil and to exchange genes with other bacteria. It is therefore neither possible to evaluate the risks that are taken when inoculation is performed nor to prevent them. Although there is no clear evidence of the existence of any critical agricultural problem caused by the practice of legume inoculation, which suggests that the risks taken were not too high, the problem should be considered and more knowledge on the behavior of rhizobia in soil has to be acquired.

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VI. CONCLUDING REMARKS Since 1985, molecular and genetics analyses have considerably increased our understanding of legume–rhizobium symbiosis and there is evidence that these processes beneﬁt legume productivity through targeted selection or modiﬁcation of one or both partners. However, more knowledge on the ecology of rhizobia is needed for reasoned exploitation of such organisms. Progress in molecular biology has allowed the development of bacterial characterization methods more reliable than the phenotypic methods used previously. The application of these methods to rhizobia has revealed a huge diversity in any characters studied. Such diversity is difﬁcult to handle and although species are created at an accelerated pace, they accommodate only a part of this diversity. These numerous species, created on the basis of polyphasic taxonomy, are difﬁcult to distinguish and there is an increasing need for simple standardized methods that could be used easily for identifying and classifying the large numbers of rhizobial isolates that are required in ecological studies. Nevertheless, progress in the determination of the composition of rhizobial populations has been made. The diversity of rhizobia recovered from nodules of a single legume crop, and even of a single plant, is great. Many legume species are nodulated by several rhizobial species or genera. This diversity gives the plant multiple opportunities to nodulate with effective rhizobia. Its agricultural drawback is that it is likely to present a barrier to the establishment of inoculant strains in the legume crop ecosystem. However, populations studied up to now represent almost exclusively populations isolated from nodules. Although they are those of interest in agriculture, they represent only a portion of the rhizobial populations present in soils. In order to control introduction of new strains and/or better manage indigenous strains, we need to acquire knowledge on the identity and behavior of rhizobia in soils. Methods for direct isolation from soil, in situ localization, and identiﬁcation need to be developed. Finally, and likely most importantly, we have gained evidence that genetic exchanges occur in ﬁeld conditions and can build rhizobial diversity in just a few years. We have now to determine how frequent these exchanges are and which conditions favor or repress them. For the past century, through introduction of new legume crops and rhizobial inoculation in most parts of the world, humans have actively participated in the spread of the existing rhizobial diversity and the creation of a new one, the challenge now is to manage this diversity.

ACKNOWLEDGMENTS The author is grateful to Louise Nelson for reviewing the Chapter and to Gis`ele Laguerre for constructing the phylogenetic tree.

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Young, J. P. W., and Haukka, K. E. (1996). Diversity and phylogeny of rhizobia. New Phytol. 133, 87–94. Young, J. P. W., and Johnston, A. W. B. (1989). The evolution of speciﬁcity in the legume–rhizobium symbiosis. Trends Ecol. Evol. 4, 341–349. Young, J. P. W., and Wexler, M. (1988). Sym plasmid and chromosomal genotypes are correlated in ﬁeld populations of Rhizobium leguminosarum. J. Gen. Microbiol. 134, 2731–2739. Young, J. P. W., Demetriou, L., and Apte, G. (1987). Rhizobium population genetics: Enzyme polymorphism in Rhizobium leguminosarum from plants and soil in a pea crop. Appl. Environ. Microbiol. 53, 397–402. Zahran, H. H. (1992). Characterization of root-nodule bacteria indigenous in the salt-affected soils of Egypt by lipopolysaccharide, protein and plasmid proﬁles. J. Basic Microbiol. 32, 279–287. Zou, X. H., Li, F. D., and Chen, H. K. (1997). Characteristics of plasmids in Rhizobium huakuii. Curr. Microbiol. 35, 215–220.

Index A AM fungi, see Arbuscular mycorrhizal fungi Ammonia acidiﬁcation of soils, 66–67 emission factors for nitrogen fertilizers, 88–90, 100–101 emission measurement following nitrogen fertilizer application agronomic evaluations, 83–84 ﬁeld measurements chamber measurements, 82–83 micrometeorological methods, 79 overview, 78–79 summary of studies, 80–81 wind tunnel measurements, 79, 82 laboratory measurements, 85–86 solution emissions, 86–87, 100 urease inhibitor studies, 87–88 environmental factors in soil release, 68–69, 99 fertilizer sources, 67–68, 101 volatilization barium soils, 70–71 bicarbonate–carbonate equilibrium and soil pH, 70 calcium addition studies, 71–73 calcium soils, 70–73, 77, 100 fertilizer type effects, 75 ionization equilibrium, 69 magnesium soils, 70–71 modeling, 75–78 pH role, 74–75 rate of fertilizer application effects, 73 Apparent urea relative yield (AURY), calculation, 84 Arbuscular mycorrhizal (AM) fungi abundance and distribution, 8–10 agroecosystem role, 10–12 host preference, 8, 10 interactions at root with pathogenic and nonpathogenic fungi, 14–15

morphology, 7 taxonomy, 7–8 AURY, see Apparent urea relative yield B Barley, dwarﬁng gene mutants breeding application, 45 gibberellin-insensitive mutants, 41 molecular mapping, 51–52 phytochrome mutants, 42 quantitative trait loci and linkage mapping, 52–53 C Clover rhizobia, see Rhizobium leguminosarum bv. trifolii Competitor, soil fungus classiﬁcation, 3 Corn, dwarﬁng gene mutants gibberellin-insensitive mutants, 41 gibberellin-sensitive mutants, 39 D Dwarﬁng genes agronomic importance, 36, 56 breeding application barley, 45 challenges, 45–47 foxtail millet, 45 oat, 43, 45, 47 pearl millet, 45–46 rice, 43, 47 sorghum, 45 wheat, 43, 46–48 cereal improvement genes, 44 cloning and sequencing, 54–55 gibberellin mutants biosynthetic pathway, 38, 55–56 insensitive mutants barley, 41vigor effects, 46 corn, 41

169

170

INDEX

Dwarﬁng genes (continued) rice, 40–41 wheat, 40–41 sensitive mutants corn, 39 dominance of genes, 40 oat, 40 rye, 39 wheat, 39 signal transduction, 38–39 historical perspective of breeding, 37–38 molecular mapping barley, 51–52 oat, 52 rice, 51 wheat, 51 ortholog identiﬁcation by comparative mapping, 54 phytochrome mutants barley, 42 oat, 42 transgenic plants, 42 pleiotropic effects Dw genes in oat, 49–50 morphology and physiology, 49–50 Rht genes in wheat, 48–50 rice, 49 yield, 50 quantitative trait loci and linkage mapping barley, 52–53 oat, 53–54 rice, 53 rye, 53 Dw genes, see Dwarﬁng genes E Emission factors, nitrogen fertilizers ammonia, 88–90, 100–101 nitrous oxide, 97–98, 101 F FAME analysis, see Fatty acid methyl ester analysis Fatty acid methyl ester (FAME) analysis, rhizobia, 117, 128 Fava bean rhizobia, see Rhizobium leguminosarum bv. viciae

Foxtail millet, dwarﬁng gene breeding application, 45 Fungus, see also Arbuscular mycorrhizal fungi; Rhizosphere; specific fungi additional nonpathogenic fungi in classiﬁcation abundance and distribution, 12–13 agroecosystem role, 13 deﬁnition, 12 functional group classiﬁcation in soil, 3–4 pathogens clinical pathogens abundance and distribution, 4–5 agroecosystem role, 5 deﬁnition, 4 interactions at root arbuscular mycorrhizal fungi, 14–15 co-infection, 13 island biogeography theory, 16–17 nonpathogenic fungi, 15–16 subclinical pathogens abundance and distribution, 6 agroecosystem role, 6–7 deﬁnition, 5–6 pest control biological control obstacles to implementation, 21 role, 20–21 strategies integrated pest management, 18–20 proactive pest management, 19 single-tactic approach, 18, 20 symbiosis versus pathogenicity in soil, 2 Fusarium oxysporum disease-suppressive soils, 23 interactions at root between pathogenic and nonpathogenic fungi, 15–16 proactive pest management, 19 G Gibberellin, dwarﬁng gene mutants biosynthetic pathway, 38, 55–56 insensitive mutants barley, 41 corn, 41 rice, 40–41 vigor effects, 46 wheat, 40–41 sensitive mutants

171

INDEX corn, 39 dominance of genes, 40 oat, 40 rye, 39 wheat, 39 signal transduction, 38–39 I Integrated pest management (IPM), advantages and limitations in pest control, 18–20 IPM, see Integrated pest management Island biogeography theory, application to root–fungal interactions, 16–17 L Legume, see Rhizobia Lentil rhizobia, see Rhizobium leguminosarum bv. viciae Lipopolysaccharide (LPS), rhizobia analysis, 117 LPS, see Lipopolysaccharide M Maize, see Corn Mass spectrometry, pyrolysis mass spectrometry of rhizobia, 116 MLEE, see Multilocus enzyme electrophoresis Multilocus enzyme electrophoresis (MLEE), rhizobia characterization, 116, 128, 142 N Nitric oxide emission measurement following nitrogen fertilizer application, 99, 102 microbial production in soil, 78 modeling of soil release, 78 soil emission consequences, 67 Nitrogen fertilizer gaseous emissions, see Ammonia; Nitric oxide; Nitrous oxide Nitrogen ﬁxation, see Rhizobia Nitrous oxide emission factors for nitrogen fertilizers, 97–99, 101 emission measurement following nitrogen fertilizer application chamber studies, 91

comparative studies by fertilizer type, 90–96, 101 seasonal effects, 92–93, 102 tropical forest soils, 91 microbial production in soil, 78 modeling of soil release, 78 nitriﬁcation inhibitor effects on soil emissions DCD, 97 N-serve, 96 nitrapyrin, 96–97 soil type effects, 96, 102 relative emission assessment for fertilizers, 95 soil emission consequences, 67–68 O Oat, dwarﬁng gene mutants breeding application, 43, 45, 47 gibberellin-ssensitive mutants, 40 molecular mapping, 52 phytochrome mutants, 42 pleiotropic effects, 49–50 quantitative trait loci and linkage mapping, 53–54 P Pea rhizobia, see Rhizobium leguminosarum bv. viciae Pearl millet, dwarﬁng gene breeding application, 45–46 Phytochrome, dwarﬁng gene mutants barley, 42 oat, 42 transgenic plants, 42 Plasmid, rhizobia analysis, 118–120 Proactive pest management, advantages and limitations in pest control, 19 R Randomly ampliﬁed polymorphic DNA (RAPD), rhizobia analysis, 121–122, 128 RAPD, see Randomly ampliﬁed polymorphic DNA Restriction fragment length polymorphism (RFLP), rhizobia analysis, 120–122, 128 RFLP, see Restriction fragment length polymorphism

172

INDEX

Rhizobia, see also individual species assessment of soil populations, 129 beneﬁts of symbiosis, 110–111 deﬁnition, 112 discovery, 110 fatty acid methyl ester analysis, 117, 128 gel electrophoresis analysis denaturing gel electrophoresis of proteins, 116 lipopolysaccharide analysis, 117 multilocus enzyme electrophoresis, 116–117, 128, 142 plasmids, 118–120 genetic exchanges, 147 growth rates, 112 host range, 111, 117–118, 147 identiﬁcation, 128–129 introduction into soil agricultural implications, 146 colonization, 144–145 indigenous population interactions, 145–146 inoculation, 144 overview, 143–144 legume association and agricultural importance, 110–111 metabolism and substrate utilization assays, 113–114 phage susceptibility, 115 population structure, 142–143 pyrolysis mass spectrometry for chemical composition analysis, 116 randomly ampliﬁed polymorphic DNA analysis, 121–122, 128 restriction fragment length polymorphism analysis, 120–122, 128 ribosomal RNA gene analysis, 122, 124, 127 serological characterization, 115 soybean rhizobia bradyrhizobia, 141–142 serogroups, 141 species, 140–141 systematics biovars, 128 historical perspective, 123–124 phylogeny, 124–125, 127–128 species list, 125 taxonomy, 124, 126

tolerance to external factors in classiﬁcation, 114 Rhizobium etli, genetic diversity, 133–134, 137–138 Rhizobium gallicum, features, 137–138 Rhizobium giardinii, features, 137–138 Rhizobium leguminosarum bv. phaseoli genetic diversity, 134–135, 137–138 host speciﬁcity, 134–135 species, 134–135 Rhizobium leguminosarum bv. trifolii characterization, 130 chromosomal diversity, 132 clover host speciﬁcity, 130 geographic effects on root populations, 131–132 heavy metal effects on population, 132 pH dependence, 130 Rhizobium leguminosarum bv. viciae colonization, 145 environmental factors in pea colonization, 133 host speciﬁcity, 133 pH dependence, 132–133 Rhizobium meliloti, features, 139 Rhizobium tropici, features, 137–138 Rhizosphere, see also Fungus; Rhizobia divisions, 3 research prospects biological control, 23–24 disease-suppressive soils, 23 identiﬁcation of root fungi, 22 molecular biology, 22 pesticides, 24 root colonization, 23–24 Rht genes, see Dwarﬁng genes Ribosomal RNA (rRNA), gene analysis in rhizobia, 122, 124, 127 Rice, dwarﬁng gene mutants breeding application, 43, 47 gibberellin-insensitive mutants, 40–41 historical perspective of breeding, 37–38 molecular mapping, 51 pleiotropic effects, 49 quantitative trait loci and linkage mapping, 53 rRNA, see Ribosomal RNA Ruderal, soil fungus classiﬁcation, 3 Rye, dwarﬁng gene mutants gibberellin-ssensitive mutants, 39 quantitative trait loci and linkage mapping, 53

173

INDEX S Single-tactic approach, advantages and limitations in pest control, 18, 20 Sinorhizobium meliloti host speciﬁcity, 138–139 pH dependence, 139 symbiotic effectiveness, 140 Sorghum, dwarﬁng gene breeding application, 45 Soybean rhizobia, see rhizobia Stress-tolerant, soil fungus classiﬁcation, 3 Survivors-escapes, soil fungus classiﬁcation, 3

U Urease inhibitor, effects on ammonia volatilization, 87–88 W Wheat, dwarﬁng gene mutants breeding application, 43, 46–48 gibberellin-insensitive mutants, 40–41 gibberellin-sensitive mutants, 39 historical perspective of breeding, 37–38 molecular mapping, 51 pleiotropic effects, 48–50

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