Advances in Agronomy, Volume 73

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

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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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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

5

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

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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

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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 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 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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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 ( DAP (23–28%) > CAN ( 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 ( 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 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, 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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Radiatively Active Trace Gases” (R. S. Oremland, Ed.), pp. 278–289. Chapman and Hall, New York. Bronson, K. F., Mosier, A. R., and Bishnoi, S. R. (1992). Nitrous oxide emissions in irrigated corn as affected by nitrification inhibitors. Soil Sci. Soc. J. 56, 161–165. Buijsman, E., Maas, H. F. M., and Asman, W. A. H. (1987). Anthropogenic NH3 emissions in Europe. Atmos. Environ. 21, 1009–1022. Bull, K. R., and Sutton, M. A. (1998). Critical loads and the relevance of ammonia to an effects-based protocol. Atmos. Environ. 12, 565–572. Bussink, D. W., and Oenema, O. (1997). Rainfall and temperature effects on nitrogen losses from fertilizer types on grassland in the Netherlands and the UK. In “Gaseous Nitrogen Emissions from Grasslands” (S. C. Jarvis and B. F. Pain, Eds.), pp. 55–61. CAB International, Wallingford, Oxfordshire, UK. Byrnes, B. H. (1990). Environmental effects of nitrogen fertilizer use—An overview. Fertil. Res. 26, 209–216. Cai, G. X., Fan, X. H., Yang, Z., and Zhu, Z. L. (1998). Gaseous loss of nitrogen from fertilizers applied to wheat on a calcareous soil in North China Plain. Pedosphere 8, 45–52. Clayton, H., McTaggart, I. P., Parker, J., Swan, L., and Smith, K. A. (1997). Nitrous oxide emissions from fertilised grassland: A 2-year study of the effects of fertiliser form and environmental conditions. Biol. Fertil. Soils 25, 252–260. Cochran, V. L., Elliot, L. F., and Papendik, R. I. (1981). Nitrous oxide emissions from a fallow field fertilized with anhydrous ammonia. Soil Soc. Sci. Am. J. 45, 307–310. Conrad, R., Seiler, W., and Bunse, G. (1983). Factors influencing the loss of fertilizer nitrogen into the atmosphere as N2O. Geophys. Res. 88, 6709–6718. Cowell, D., and ApSimon, H. M. (1998). Cost-effective strategies for the abatement of ammonia emissions from European Agriculture. Atmos. Environ. 32, 573–580. Crutzen, P. J. (1979). The role of NO and NO2 in the chemistry of the troposphere and the stratosphere. Ann. Rev. Earth Planet Sci. 7, 333–372. Crutzen, P. J. (1981). Atmospheric chemical processes of the oxides of nitrogen, including nitrous oxide. In “Denitrification, Nitrification and Nitrous Oxide” (J. Delwiche, Ed.), pp. 17–44. Wiley, New York. Davidson, E. A. (1991). Fluxes of nitrous oxide and nitric oxide from terrestrial ecosystems. In “Microbial Production and Consumption of Greenhouse Gases: Methane, Nitrogen Oxides and Halomethanes” (J. E. Rogers and W. B. Whitman, Eds.), pp. 219–235. American Society of Microbiology, Washington, DC. Duxbury, J. M., and McConnaughey, P. K. (1986). Effect of fertilizer source and nitrous oxide emissions in a maize-field. Soil Sci. Soc. Am. 50, 644–648. Eggleston, H. S. (1992). An improved UK ammonia emission inventory. In “Ammonia Emissions in Europe: Emission Coefficients and Abatement Costs: Proceedings of a Workshop, 4–6 February, 1991” (G. Klaassen, Ed.), pp. 95–107. IIASA, Laxenburg. Eicher, M. J. (1990). Nitrous oxide emissions from fertilized soils: Summary of available data. Environ. Qual. 19, 272–280. Fenn, L. B. (1988). Effects of initial soil calcium content on ammonia losses from surface-applied urea and calcium-urea. Fertil. Res. 16, 207–216. Fenn, L. B., and Hossner, L. R. (1985). Ammonia voltilization from ammonium or ammonium-forming nitrogen fertilizers. Adv. Soil Sci. 1, 121–169. Fenn, L. B., and Kissel, D. E. (1973). Ammonia volatilization from surface applications of ammonium compounds on calcareous soils. I. General theory. Soil Sci. Soc. Am. Proc. 37, 855–859. Fenn, L. B., and Kissel, D. E. (1974). Ammonia volatilization from surface applications of ammonium compounds on calcareous soils. II. Effects of temperature and rate of ammonium nitrogen application. Soil Sci. Soc. Am. Proc. 38, 606–610.

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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 (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 (