V O LU M E
N I N E T Y
ADVANCES
IN
S I X
AGRONOMY
ADVANCES IN AGRONOMY Advisory Board
PAUL M. BERTSCH
RONALD L. PHILLIPS
University of Georgia
University of Minnesota
KATE M. SCOW
LARRY P. WILDING
University of California, Davis
Texas A&M University
Emeritus Advisory Board Members
JOHN S. BOYER
KENNETH J. FREY
University of Delaware
Iowa State University
EUGENE J. KAMPRATH
MARTIN ALEXANDER
North Carolina State University
Cornell University
Prepared in cooperation with the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Book and Multimedia Publishing Committee DAVID D. BALTENSPERGER, CHAIR LISA K. AL-AMOODI
MICHEL D. RANSOM
KENNETH A. BARBARICK
CRAIG A. ROBERTS
HARI B. KRISHNAN
APRIL L. ULERY
SALLY D. LOGSDON
V O LU M E
N I N E T Y
ADVANCES
S I X
IN
AGRONOMY EDITED BY
DONALD L. SPARKS Department of Plant and Soil Sciences University of Delaware Newark, Delaware
AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier
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CONTENTS
Contributors Preface
1. Microbial Ecology of Methanogens and Methanotrophs
ix xiii
1
R. Conrad 1. Introduction 2. Microbial Ecology of Methanogens 3. Microbial Ecology of Methanotrophs 4. Mitigation of Methane Emission from Rice Fields 5. Conclusions and Outlook References
2. Strategies of Plants to Adapt to Mineral Stresses in Problem Soils
2 8 31 42 43 45
65
S. Hiradate, J. F. Ma, and H. Matsumoto 1. Introduction 2. Fe-Deficiency Stress 3. Al-Toxicity Stress 4. P-Deficiency Stress 5. Future Prospects References
3. Water Flow in the Roots of Crop Species: The Influence of Root Structure, Aquaporin Activity, and Waterlogging
66 69 86 104 112 112
133
H. Bramley, D. W. Turner, S. D. Tyerman, and N. C. Turner 1. Introduction 2. Water Movement Through the Plant 3. Root Characteristics and Water Flow 4. Changes in Lpr 5. Plant Aquaporins (AQPs) 6. The Role of AQPs in Root Water Transport 7. Waterlogging 8. Conclusion Acknowledgments References
134 135 140 146 147 167 171 180 181 182 v
vi
Contents
4. Phytoremediation of Sodic and Saline-Sodic Soils
197
M. Qadir, J. D. Oster, S. Schubert, A. D. Noble, and K. L. Sahrawat 1. Introduction 2. Description of Sodic and Saline-Sodic Soils 3. Degradation Processes in Sodic and Saline-Sodic Soils 4. Phytoremediation of Sodic and Saline-Sodic Soils 5. Perspectives Acknowledgments References
199 201 203 206 236 239 239
5. Ecology of Denitrifying Prokaryotes in Agricultural Soil
249
L. Philippot, S. Hallin, and M. Schloter 1. Introduction 2. Agronomical and Environmental Importance of Denitrification 3. Who are the Denitrifiers? 4. Assessing Denitrifiers Density, Diversity, and Activity 5. Natural Factors Causing Variations in Denitrification 6. Denitrification in the Rhizosphere of Crops 7. Impact of Fertilization on Denitrification 8. Effect of Environmental Pollution on Denitrifiers 9. Conclusions and Outlook References
250 253 255 258 262 266 273 279 285 287
6. Linking Soil Organisms Within Food Webs to Ecosystem Functioning and Environmental Change 307 J. R. Powell 1. 2. 3. 4. 5.
Introduction Overview of the Soil Food Web Impacts on Soil Food Web Dynamics Associated with Human Activities Alternative Approaches: Seeing the Forest for the Trees Missing and Ambiguous Components of Current Soil Food Web Knowledge 6. Summary and Conclusions Acknowledgments References
308 309 313 322 335 340 341 341
Contents
7. Comparative Typology in Six European Low-Intensity Systems of Grassland Management
vii
351
R. Caballero, J. A˚. Riseth, N. Labba, E. Tyran, W. Musial, E. Molik, A. Boltshauser, P. Hofstetter, A. Gueydon, N. Roeder, H. Hoffmann, M. B. Moreira, I. S. Coelho, O. Brito, and A´. Gil 1. Introduction 2. Presentation of Study Areas 3. Material and Methods 4. Results 5. Discussion References Index
353 355 361 370 408 414 421
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CONTRIBUTORS
Numbers in parentheses indicate the pages on which the authors’ contributions begin.
Andrea Boltshauser ( 351) UNESCO Biosphere Reserve Entlebuch, CH-Schupfheim, Entlebuch, Switzerland H. Bramley* (133) Wine and Horticulture, Faculty of Agriculture, Food and Wine, The University of Adelaide (Waite Campus), Plant Research Centre, PMB 1, Glen Osmond, South Australia 5064, Australia Olga Brito ( 351) Instituto Superior de Agronomia, Technical University of Lisbon, Baixo Alentejo, Portugal Rafael Caballero ( 351) Centro de Ciencias Medioambientales, CSIC, Madrid, Castile-La Mancha, Spain Inoceˆncio Seita Coelho ( 351) Instituto Nacional de Investigac¸a¨oo Agra´ria e Pescas, Ministe´rio da Agricultura, Desenvolvimento Rural e Pescas, Lisbon, Baixo Alentejo, Portugal Ralf Conrad (1) Max Planck Institute for Terrestrial Microbiology, 35043 Marburg, Germany A´ngel Gil ( 351) Centro de Ciencias Medioambientales, CSIC, Madrid, Castile-La Mancha, Spain Anne Gueydon ( 351) Lehrstuhl fu¨r Wirtschaftslehre des Landbaues, Technische Universita¨t Mu¨nchen, Bavaria, Germany Sara Hallin (249) Department of Microbiology, Swedish University of Agricultural Sciences, Uppsala, Sweden Syuntaro Hiradate (65) National Institute for Agro-Environmental Sciences (NIAES), Tsukuba, Ibaraki 305-8604, Japan
*
Present address: Department of Renewable Resources, 444 Earth Sciences Building, University of Alberta, Edmonton, Alberta T6G 2E3, Canada
ix
x
Contributors
Helmut Hoffmann ( 351) Lehrstuhl fu¨r Wirtschaftslehre des Landbaues, Technische Universita¨t Mu¨nchen, Bavaria, Germany Pius Hofstetter ( 351) Schupfheim Agricultural Education and Extension Center, CH-Schupfheim, Entlebuch, Switzerland Niklas Labba ( 351) Sa´mi Institute, Kautokeino, Norway, Northern Sapmi, Scandinavia Jian Feng Ma (65) Research Institute for Bioresources, Okayama University, Kurashiki 710-0046, Japan Hideaki Matsumoto (65) Research Institute for Bioresources, Okayama University, Kurashiki 710-0046, Japan Edyta Molik ( 351) Department of Sheep and Goat Breeding, Agricultural University of Krakow, Tatra Mountains, Poland Manuel Belo Moreira ( 351) Instituto Superior de Agronomia, Technical University of Lisbon, Baixo Alentejo, Portugal Wieslaw Musial ( 351) Department of Agricultural Economics and Organization, Agricultural University of Krakow, Tatra Mountains, Poland A. D. Noble (197) International Water Management Institute (IWMI), South East Asia Office, 10670 Penang, Malaysia J. D. Oster (197) Department of Environmental Sciences, University of California, Riverside, California 92521 Laurent Philippot (249) INRA, University of Burgundy, Soil and Environmental Microbiology, Dijon, France Jeff R. Powell ( 307) Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada N1G 2W1
Contributors
xi
M. Qadir (197) International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5466 Aleppo, Syria International Water Management Institute (IWMI), P.O. Box 2075, Colombo, Sri Lanka Jan A˚ge Riseth ( 351) Sa´mi Institute, Kautokeino and NORUT Ltd., Troms , Norway, Northern Sapmi, Scandinavia Norbert Roeder ( 351) TUM Business Scholl, Environmental Economics & Agricultural Policy Group, Technische Universita¨t Mu¨nchen, Bavaria, Germany K. L. Sahrawat (197) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, India Michael Schloter (249) GSF-National Research Center for Environment and Health, Institute for Soil Ecology, Oberschleissheim, Germany S. Schubert (197) Institute of Plant Nutrition, Justus Liebig University, 35392 Giessen, Germany D. W. Turner (133) School of Plant Biology, Faculty of Natural and Agricultural Sciences, The University of Western Australia, Crawley, Western Australia 6009, Australia N. C. Turner (133) Centre for Legumes in Mediterranean Agriculture, The University of Western Australia, Crawley, Western Australia 6009, Australia S. D. Tyerman (133) Wine and Horticulture, Faculty of Agriculture, Food and Wine, The University of Adelaide (Waite Campus), Plant Research Centre, PMB 1, Glen Osmond, South Australia 5064, Australia Ewa Tyran ( 351) Department of Agribusiness, Agricultural University of Krakow, Tatra Mountains, Poland
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PREFACE
Volume 96 contains seven cutting-edge reviews on topics of interest to crop and soil scientists. Chapter 1 is a timely review on the microbial ecology of methanogens and methanotrophs in rice fields, including discussions on the global methane budget and processes controlling methane emissions, the role of methanogens and methanotrophs in carbon cycling and methane emission, the microbial ecology of methanogens and methanotrophs, and ways to reduce methane emissions from rice fields. Chapter 2 is a comprehensive review on strategies that plants use to adapt to mineral stresses in soils plagued by Fe-deficiency, Al-toxicity, and P-deficiency. Detailed discussions are included on the chemical aspects of these elements in soils, mechanisms of toxicity and tolerance, and genetic approaches for enhancing plant stress adaptation. Chapter 3 discusses the influence of root structure, aquaporin activity, and waterlogging on water flow into crop roots. Chapter 4 is an interesting review on phytoremediation of sodic and saline-sodic soils, including a historical perspective, mechanisms and processes affecting phytoremediation, efficiency aspects of phytoremediation, and plant species that can be utilized. Chapter 5 deals with the ecology of denitrifying prokaryotes in agricultural soil. Topics that are covered include who are the nitrifiers, assessing denitrification density, diversity, and activity, factors affecting variations in denitrification, denitrification in the rhizosphere of crops, and ways that fertilization and environmental pollution affect denitrification. Chapter 6 is a review on linking soil organisms within food webs to ecosystem functioning and environmental change. A descriptive review of trophic interactions in soil and examples of research on soil biotic responses to biodiversity loss, climate change, and genetically modified crops are discussed. Chapter 7 covers comparative topology in six European low-intensity systems of grassland management. I am grateful to the authors for their excellent reviews. DONALD L. SPARKS University of Delaware
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C H A P T E R
O N E
Microbial Ecology of Methanogens and Methanotrophs Ralf Conrad* Contents 1. Introduction 1.1. Global methane budget and processes controlling methane emission from rice fields 1.2. Role of methanogens and methanotrophs in carbon cycling and methane emission 2. Microbial Ecology of Methanogens 2.1. Physiology and phylogeny of methanogens 2.2. Diversity, habitats, and ecological niches 2.3. Microbiological explanations for macroscopic processes, that is production and emission of methane 3. Microbial Ecology of Methanotrophs 3.1. Physiology and phylogeny of methanotrophs 3.2. Diversity, habitats, and ecological niches of aerobic methanotrophs 4. Mitigation of Methane Emission from Rice Fields 5. Conclusions and Outlook References
2 2 3 8 8 10 16 31 31 34 42 43 45
Rice agriculture feeds about a third of the world’s population. However, rice fields are also an important source in the global budget of the greenhouse gas methane. The emission of methane from flooded rice fields is the result of the activity of methanogenic archaea that produce the methane and of methanotrophic bacteria that oxidize part of it, so that the ecology of these two physiological groups of microorganisms is key for the understanding of methane cycling in rice fields and for possible mitigation of emission from this important agro-ecosystem. In this chapter I will describe the ecology of methanogens and methanotrophs and will give examples where production and emission of methane on the field scale can be understood on the basis of processes on the microscale. *Max Planck Institute for Terrestrial Microbiology, 35043 Marburg, Germany Advances in Agronomy, Volume 96 ISSN 0065-2113, DOI: 10.1016/S0065-2113(07)96005-8
#
2007 Elsevier Inc. All rights reserved.
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Ralf Conrad
1. Introduction 1.1. Global methane budget and processes controlling methane emission from rice fields Methane is next to CO2, the second most abundant carbon compound in the atmosphere. The mixing ratio of CH4 in the atmosphere is presently about 1770 ppbv giving a global atmospheric burden of about 5000 Tg. The total budget of CH4 is around 600 Tg a1, resulting in an atmospheric lifetime of about 8 years. Immediately after the ice age, the atmospheric mixing ratio of CH4 was much lower, about 600 ppbv. After 1800 AD, however, CH4 (like CO2 or N2O) started to increase dramatically and since then increased by about 0.5–1% per year. It is just since the last few years that the CH4 mixing ratio seems to have stabilized at a relatively high level, which is about three times that after the ice age. Methane absorbs in the infrared spectrum of light, causing a greenhouse effect in addition to that by water vapor and CO2 (Lacis et al., 1981). Methane accounts for about 44% of the total anthropogenic radiative forcing due to changes in the concentrations of greenhouse gases and aerosols between 1850 and 2000, being about 0.7 W m2 (Hansen et al., 2000). On a molecular basis and a time frame of 100 years, the global warming potential of CH4 is about 20 times stronger than that of CO2. For pertinent literature and data see the home page of National Oceanic and Atmospheric Administration [NOAA (http://www.cmdl.noaa.gov/)] and the following references (Bousquet et al., 2006; Chen and Prinn, 2005; Cicerone and Oremland, 1988; Lelieveld et al., 1998; Reeburgh, 2003). The global CH4 budget is dominated by biogenic sources, natural wetlands (23%), and rice fields (21%) accounting for almost half of the total budget (Chen and Prinn, 2005). In these environments methane is exclusively produced by methanogenic microorganisms (Cicerone and Oremland, 1988; Conrad, 1989). Additional CH4 sources for which methanogenic microorganisms are exclusively responsible are the intestines of ruminants and termites (20%), landfills, and other waste treatment systems (10%), so that about 75% of the total atmospheric CH4 originates from the activity of methanogens (Chen and Prinn, 2005). Hence, methanogens, for example those in rice fields, contribute significantly to the global budget of the greenhouse gas methane. The emission of CH4 from biogenic sources would even be larger, if methanotrophic microorganisms would not attenuate the flux into the atmosphere by oxidizing part of the produced CH4 (Reeburgh, 2003). Roughly estimated, about 1% of the primary productivity eventually results in CH4 production, of which about half is emitted into the atmosphere, while the remainder is oxidized by methanotrophs (Reeburgh, 2003). From marine sediments, in particular, CH4 emission would be substantially larger if
Microbial Ecology of Methanogens and Methanotrophs
3
anaerobic methane-oxidizing microorganisms would not consume more than 75% of the CH4, which is either produced from organic matter or is degassing from methane hydrate deposits (Reeburgh, 2003). It is probably because of the efficient attenuation by anaerobic methanotrophs that marine sediments are only a minor source in the atmospheric CH4 budget. In freshwater wetlands and rice fields too, a substantial part of methane production is consumed by methanotrophs (Reeburgh, 2003). There, however, aerobic rather than anaerobic methanotrophs, which live at the interface between anoxic and oxic zones, are the important CH4 consumers. Aerobic methanotrophs are not only active in consuming the freshly produced CH4, but can also utilize the CH4 present in the atmosphere. The CH4 is taken up from the atmosphere by aerated upland soils (Dunfield, 2007). In fact, methanotrophs in upland soils account for about 5% of the total sink of atmospheric CH4, the remaining 95% being due to photochemical destruction of CH4 and flux into the stratosphere (Reeburgh, 2003).
1.2. Role of methanogens and methanotrophs in carbon cycling and methane emission In all the environments that act as biogenic sources for atmospheric CH4, methane is produced by the same principle process, that is CH4 is end product of the degradation of organic matter under anaerobic conditions. The methanogenic degradation of organic matter is accomplished by a complex microbial community (Conrad, 1989; Conrad and Frenzel, 2002). When for example degrading polysaccharides, members of the microbial community start hydrolyzing polysaccharides to sugars, which are subsequently fermented in a primary fermentation to various alcohols and fatty acids and to acetate, CO2, and H2 (Fig. 1). Only acetate or H2 plus CO2 are suitable substrates for methanogenic microbes, which convert these substrates to CH4 plus CO2 and CH4 plus H2O, respectively (Ferry, 1993). The other products of the primary fermentation, that is the alcohols and fatty acids, cannot be consumed directly by methanogenic microbes, but have to be converted to acetate, CO2, and H2 in a secondary fermentation, which is carried out by so-called syntrophic microorganisms. They are called syntrophs, since they can accomplish the degradation only in syntrophy with methanogens that immediately consume the formed H2, which must not accumulate to partial pressures higher than a few pascal. Otherwise, the secondary fermentation would become thermodynamically endergonic and cannot proceed. Finally, the methanogenic community often consists of a further physiological group of fermenting bacteria, the so-called homoacetogenic bacteria (Drake, 1994). These bacteria ferment sugars directly to acetate as sole product. Some of the homoactogens, the so-called chemolithoautotrophic acetogens, are able to convert H2 plus CO2 to acetate. The entire pathway of organic matter
4
Ralf Conrad
Polysaccharides
Fermenters
Monomers, for example Hexose
with NO3−
Fermenters
CO2
Homoacetogens
Fatty acids, alcohols
Synthrophs
H2O
with Fe(III), SO42−
Hydrogen
Acetate
with Fe(III), SO42−
CO2
Homoacetogens
Methanogens
Methanogens Methane < 33%
> 67%
Figure 1 Pathway of anaerobic degradation of organic matter (polysaccharides) to methane. Intermediates are shown in boxes, microorganisms in ovals, the thick arrows indicate diversion of the substrate flow to reduction of nitrate, sulfate, or ferric iron.
degradation is schematically shown in Fig. 1. The path of electron and carbon flow from organic matter to CO2 and CH4 eventually produces acetate and H2 at a stoichiometry in which at least two-third of CH4 production is produced from acetate and less than one-third from H2/CO2 (Fig. 1). In rice field soils, the pathway of CH4 production usually operates closely to the theoretically expected ratio (Section 2.2.2). The exact contribution of acetate versus H2 depends on the role of homoacetogenesis, which bypasses formation of H2 in favor of acetate (Conrad, 1999). Rice fields are structured ecosystems and contain various habitats in which methanogens and methanotrophs can occur (Fig. 2). Most conspicuous are the following habitats: (1) The bulk soil, which is generally anoxic and reduced and occupies the largest space of the ecosystem; this habitat is limited by supply of degradable organic matter and its degradation products; it is a suitable habitat for anaerobic methanogens, but not for aerobic methanotrophs. (2) Organic plant debris, such as rice straw or dead roots; this habitat is also anoxic and reduced, but is not limited in substrate; this is also a suitable habitat for methanogens. (3) Rice roots; this habitat is partially oxic, since O2 can locally be released from roots, and furthermore is rich in organic substrate by root exudation and decay; it is a habitat in which anaerobic methanogens and aerobic methanotrophs can live. (4) The
5
Microbial Ecology of Methanogens and Methanotrophs
CH4 Surface soil
90% of the CH4 is emitted via the plants
(oxic; 3 mm)
Plant debris (anoxic; high organic matter)
O2
Bulk soil (anoxic)
Rhizosphere (partially oxic; high organic matter)
Figure 2 Cross section through a rice microcosm illustrating the major habitats of methanogenic and methanotrophic microorganisms and the exchange of CH4 and O2 through the gas vascular system of the rice plants. The photograph of the microcosm was provided by Dirk Rosencrantz.
shallow oxic surface layer of the flooded soil; it is a habitat suitable for aerobic methanotrophs but not for anaerobic methanogens. In rice fields, there are three major sources of organic matter that are eventually converted to CH4 and contribute significantly to CH4 emission (Watanabe et al., 1999). During the early season, it is mainly rice straw that is degraded to CH4 and contributes up to 80% to CH4 emission (Fig. 3). During this period rice plants are still small. Later in the season, however, plant photosynthesis is becoming the more important source for CH4 production. Pulse labeling of the plants with 13CO2 showed that up to 30% of the assimilated 13C is released as 13CH4 within 2 weeks after assimilation (Watanabe et al., 1999). This rather rapid release is probably initiated by root exudation of 13C-labeled photosynthates. Release of 13CH after more than 2 weeks is probably derived from sloughed-off 4 root cells or decaying roots. In total, photosynthetically derived carbon may account for more than 60% of total CH4 emission. Finally, about 20% of total CH4 emission is due to the degradation of soil organic carbon, that is all the organic carbon in soil that is not straw or recently produced plant carbon. The cycling of carbon in rice ecosystems has been reviewed (Kimura et al., 2004).
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Ralf Conrad
CH4 emission rate (mg C pot−1 h−1)
1600
June
July
August
Septembre
October
1200 800 400
Distribution of CH4-C according to origin (%)
0
0
20 40 60 80 100 Days after transplanting
100 Rice plant C1 80 60 40
Released within 2 weeks after photoassimilation (root exudates)
Rice plant C2 Rice straw C
20 Soil organic C 0
120
Released later after photoassimilation (root decay)
20 40 60 80 100 Days after transplanting
Figure 3 Emission of CH4 from rice field microcosms and the major sources of carbon contributing to the emitted CH4. The scheme has been adapted from Watanabe et al. (1999).
The methanogenic pathway of organic matter degradation (Fig. 1) mostly operates in an anoxic and reduced environment. This means that the system is not only devoid of oxygen but also of other inorganic oxidants (electron acceptors) such as nitrate, sulfate, Mn(IV), and Fe(III). In rice fields, these potential electron acceptors, Fe(III) in particular, are depleted by reduction some time after flooding, and significant CH4 production usually does not start before this is achieved (Ponnamperuma, 1981). During the methanogenic phase, reduction of Fe(III), sulfate, and so forth usually is no longer significant in the soil. However, it may take place at the anoxic–oxic interface at the soil surface and in the partially oxic rhizosphere, where reduced Fe(II) and sulfide can be oxidized with O2 to Fe(III) and sulfate, respectively. The production of CH4 and the cycling of oxidants in the rice ecosystem are schematically shown in Fig. 4. The habitats where reduced Fe and S can be oxidized are also the habitats of aerobic methanotrophic bacteria, which require O2 for oxidation of CH4 to CO2. Hence, aerobic methanotrophic bacteria can potentially live only in a few microsites within the rice field (Fig. 2), that is the shallow oxic soil surface layer and the shallow oxic layer at the rice root surface (Frenzel, 2000; Groot et al., 2003). Rice plants, like other aquatic plants, possess a gas vascular system (aerenchyma), which allows the diffusion of oxygen to the roots for respiration
7
Microbial Ecology of Methanogens and Methanotrophs
O2
CH4
N2
N2O
NO
Water
CH4
O2
H2O
Oxic layer (1−3 mm) +
NH4 Anoxic soil
N2
N2O
NO Fe2+ H2S CH4
Straw
NO3− Fe3+ SO2− 4 CO2
Organic substrates
Figure 4 Reduction of CO2, sulfate, ferric iron, and nitrate in the anoxic rice field soil and reoxidation of CH4, sulfide, ferrous iron, and ammonium in the oxic layers at the soil water interface and the surface of rice roots. The scheme has been modified from Conrad (1996).
(Grosse et al., 1996; Jackson and Armstrong, 1999). Some of the O2 leaks from the roots and creates a very shallow and inhomogeneous oxic zone. This zone is adjacent to anoxic soil in which CH4 concentrations can reach saturation (i.e., 1.3 mM at 25 C) due to the permanent production of CH4. Vice versa, the gas vascular system of rice plants also allows the diffusion of CH4 into the atmosphere. In fact, this is the most important path for CH4 flux from the ecosystem into the atmosphere, provided plants have been grown (Fig. 2). Otherwise, CH4 would accumulate in the soil until gas bubbles are formed and then released by ebullition (Kusmin et al., 2006; Schu¨tz et al., 1991). The biogeochemistry and microbiology of anaerobic processes including methanogenesis and methanotrophy have been reviewed in detail, but with focus on anoxic environments in general rather than rice fields in particular (Megonigal et al., 2004). The general chemistry and biogeochemistry of submerged rice field soils has been described in a comprehensive monograph
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Ralf Conrad
(Kirk, 2004). A review describing the CH4 emission rates from rice fields, important biogeochemical processes, field management, and possible mitigation options is also available (Aulakh et al., 2001b). The microbiology of flooded soils has also been reviewed in detail (Conrad and Frenzel, 2002; Kimura, 2000). The present review will focus on methanogens and methanotrophs in rice field ecosystems, and describe our present knowledge of how these two groups of microorganisms are involved in the cycling of CH4 on a microscopic scale and how these processes affect CH4 emission on the field scale.
2. Microbial Ecology of Methanogens 2.1. Physiology and phylogeny of methanogens The methanogenic microorganisms all belong to the phylum Euryarchaeota within the domain Archaea (Boone et al., 1993; Whitman et al., 2006). Within the Euryarchaeota, the methanogens are found in several orders and families (Fig. 5). All of them are characterized by the fact that they gain their energy by producing CH4 from simple substrates such as H2, CO, formate, and a few alcohols (isopropanol, ethanol). These substrates are oxidized to allow reduction of CO2 to CH4. Alternatively, CH4 can also be produced by the reduction of the methyl groups in acetate, methanol, trimethylamine, and dimethylsulfide, part of which are oxidized to CO2 to generate the electrons necessary for reduction of the methyl group to CH4. Some methanogens are able to use H2 as second substrate to reduce the methyl, for example in methanol. All reactions are thermodynamically exergonic at standard Methanopyrus kandleri AV19
Methanococcales Methanopyrus kandleri AV19 Methanobacteriales
Methanococcales
Methanosarcinaceae Methanobacteriales Methanosarcinaceae
Methanosaetaceae
Methanomicrobiales
Methanosaetaceae
0.10
Methanomicrobiales Rice cluster I
Rice cluster I 0.10
McrA
16S rDNA
Figure 5 Comparison of the tree topologies constructed for subunit A of the methyl coenzyme M reductase (McrA) and for the 16S rRNA gene (16S rDNA) illustrating the phylogeny of methanogenic archaea. The scheme has been adapted from Conrad et al. (2006).
Microbial Ecology of Methanogens and Methanotrophs
9
conditions, that is they may operate in nature, if substrate concentrations are sufficiently high. In rice field soils, there are two major physiological groups (guilds) of methanogens active, the acetotrophic and the hydrogenotrophic methanogens. Methanol-utilizing methanogens are also present, but methanol does not contribute significantly to total CH4 production (Conrad and Claus, 2005). The acetotrophic methanogens convert acetic acid to CH4 and CO2:
CH3 COOH ! CH4 þ CO2 ; DG ¼ 35:6 kJ mol1 Members of only two genera of methanogens are able to catabolize acetate, that is Methanosarcina and Methanosaeta, which belong to the families of Methanosarcinaceae and Methanosaetaceae, respectively (Fig. 5). Acetate is catabolized by cleavage, with the carboxyl group being oxidized to CO2 and the methyl group being reduced to CH4. The biochemical sequence of reactions is rather complex and can be found in biochemical reviews (Shima et al., 2002; Thauer, 1998). For the prupose of this review only the following aspects are noteworthy (1) The CH4-producing reaction is catalyzed by the methyl-CoM reductase, which converts methyl-CoM (methyl-coenzyme M) and HS-HTP (N-7-mercaptoheptanoyl-O-phospho-L-threonine) to CH4 and a heterodisulfide consisting of HS-HTP and CoM-SH. This reaction is universal to all methanogens, independently of the primary substrate. This means, CH4 in general is generated by the activity of methyl-CoM reductase. (2) The subsequent reduction of the heterodisulfide to CoM-SH and HS-HTP is coupled to the generation of a proton motive force. This reaction is the most important one for energy conservation and is universal for all methanogens. (3) In the first step, acetate has to be converted to acetylcoenzyme A (acetyl-CoA), which requires the expenditure of energy. Formation of acetyl-CoA occurs by two different reactions (Ferry, 1992). In Methanosarcina spp., acetate is first phosphorylated with ATP by an acetate kinase producing acetyl-P and ADP. Subsequently, the acetyl-P is converted by a phosphotransacetylase with CoA-SH to acetyl-CoA and phosphate. In summary, conversion of acetate to acetyl-CoA requires one energy-rich phosphate bond of ATP in Methanosarcina spp. In Methanosaeta spp., on the other hand, acetate is activated using an acetyl-CoA synthetase, which converts acetate, CoA-SH, and ATP to acetyl-CoA, AMP, and pyrophosphate. In summary, this reaction requires two energy-rich phosphate bonds of ATP. This means that Methanosaeta spp. use more energy for acetate activation than Methanosarcina spp. The hydrogenotrophic methanogens convert CO2 with H2 to CH4:
4H2 þ CO2 ! CH4 þ 2H2 O; DG ¼ 131 kJ mol1
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This type of catabolism is found among most methanogenic taxa, including the genus Methanosarcina (Fig. 5). The biochemical sequence can be found in biochemical reviews (Shima et al., 2002; Thauer, 1998). Briefly, H2 is oxidized to protons and the electrons generated are used to reduce CO2 stepwise via the oxidation states of formate (formyl-MFR, formyl-H4MPT, methenylH4MPT), formaldehyde (methylene-H4MPT), and methanol (methylH4MPT, methyl-CoM) to finally CH4. The individual C1-compounds are bound to the coenzymes MFR (methanofuran), H4MPT (tetrahydromethanopterin), and HS-CoM (coenzyme M). The CH4-generating step is catalyzed by the methyl-CoM reductase, and energy is conserved (by generation of DmHþ) by the reduction of the heterodisulfide, generated during this reaction. A membrane potential (DmNaþ) based on sodium gradient is generated by the methyl transferase reaction from methyl-H4MPT to methyl-CoM (Gottschalk and Thauer, 2001). However, this membrane potential is consumed during the initial activation of CO2 to formyl-MFR and thus does not contribute to net energy gain. The biochemistry of methanogens has consequences for biogeochemical research. One example is the fact that methyl-CoM reductase is the key enzyme present in all methanogens and only in them. This makes the gene of this enzyme a suitable target for specifically detecting methanogens in the environment. The mcrA gene, coding for a subunit of the methyl-CoM reductase, was found to exhibit a congruent phylogeny to that found with the 16S rRNA gene (Fig. 5). Hence, sequence information of mcrA genes retrieved from the environment also gives useful phylogenetic information (Lueders et al., 2001). Another example is the different activation of acetate to acetyl-CoA in Methanosarcina and Methanosaeta spp., which has consequences for the ecological niches of these acetotrophic methanogens (Section 2.2.1). It apparently also affects the stable carbon isotopic signature of the produced CH4 (Penning et al., 2006a). Energetics also seems to affect the extent of isotope fractionation during reduction of CO2 to CH4 in hydrogenotrophic methanogenesis. At a low-energy yield, the reaction sequence from CO2 to CH4 is more reversible than at a high-energy yield, thus resulting in a larger fractionation factor (Penning et al., 2005; Valentine et al., 2004).
2.2. Diversity, habitats, and ecological niches 2.2.1. Acetoclastic methanogens Members of both the genus Methanosarcina (Asakawa et al., 1995; Fetzer et al., 1993; Joulian et al., 1998; Rajagopal et al., 1988) and the genus Methanosaeta (Mizukami et al., 2006) have been isolated from rice field ecosystems. Reports on the detection of genes (16S rRNA or mcrA) of Methanosarcina and Methanosaeta in rice fields are numerous (Chin et al., 1999b; Grosskopf et al., 1998a; Lueders and Friedrich, 2000; Wu et al., 2006). A geographic
Microbial Ecology of Methanogens and Methanotrophs
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survey of several rice fields from Italy, the Philippines, and China indicates that these two acetotrophic genera are present in all soils tested (Ramakrishnan et al., 2001). They were also found in Japanese rice field soil (Watanabe et al., 2006). Hence, it is likely that they are cosmopolitan in all rice field ecosystems. This conclusion is not trivial, since Methanosarcina spp. are often missing in methanogenic lake sediments, which are usually populated by Methanosaeta spp. as sole acetotrophic methanogens (Schwarz et al., 2007). The abundance of methanogens has been determined in rice field habitats by using cultivation techniques and molecular methods. Cultivation techniques, generally most probable number counting using acetate as methanogenic substrate, often gave numbers of about up to 104 acetateutilizing methanogens per gram dry soil ( Joulian et al., 1998; Schu¨tz et al., 1989b). Similar numbers of about 105 acetotrophic methanogens per gram dry soil were found in rooted (upper 3 cm) and unrooted (below 3 cm depth) soil layers (Frenzel et al., 1999). Higher numbers (105–106 acetotrophic methanogens per gram dry soil) were found in a Japanese rice field soil in Kyushu, in particular when treated with rice straw (Asakawa et al., 1998). Molecular techniques usually give higher numbers than cultivation methods. Indeed, quantitative PCR and analysis of terminal restriction fragment length polymorphism targeting archaeal 16S rRNA genes indicated that acetoclastic methanogens are present in numbers of more than 106 per gram dry soil in flooded rice fields (Kru¨ger et al., 2005). Theoretical considerations based on maintenance energy requirement indicate that numbers of about 108 per gram dry soil may be reached, if the soil is amended with rice straw (Conrad and Klose, 2006). Both Methanosarcina and Methanosaeta spp. are able to convert acetate to CH4. However, Methanosaeta spp. invest more energy to activate the acetate to acetyl-CoA (Section 2.1). Therefore, they are able to grow at very low concentrations (<100 mM) of acetate, while Methanosarcina spp. require higher acetate concentrations ( Jetten et al., 1992). On the other hand, Methanosarcina spp. can grow much faster than Methanosaeta spp. when acetate concentrations are sufficiently high ( Jetten et al., 1992). In addition, Methanosarcina spp. can also use H2/CO2, methanol, or trimethylamine as energy substrates and thus are much more versatile than Methanosaeta spp., which only use acetate. These physiological characteristics are reflected in the ecological niches of the acetotrophic methanogens. Thus it was found that the relative dominance of Methanosaeta versus Methanosarcina spp. in anoxic rice field soil reflects the availability of acetate with Methanosaeta spp. becoming more abundant whenever acetate concentrations become lower than 50 mM (Fey and Conrad, 2000; Kru¨ger et al., 2005). In contrast to bulk soil, Methanosaeta spp. seem to play hardly a role on rice roots (Chidthaisong et al., 2002; Chin et al., 2004; Hashimoto-Yasuda et al., 2005; Ikenaga et al., 2004) and degrading rice straw (Sugano et al., 2005b; Weber et al., 2001a),
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where acetate can reach millimolar concentrations. These habitats within the rice ecosystem are dominated by Methanosarcina spp., probably since the availability of acetate is relatively high and therefore Methanosaeta spp. are outcompeted by Methanosarcina spp. (Chin et al., 2004). Hence, low versus high availability of acetate seems to differentiate the ecological niches of the two different acetotrophic methanogenic genera. Niche differentiation may also be caused by temperature, as populations of Methanosaeta spp. in Italian rice field soil were found to tolerate low temperatures (15 C) at nonlimiting acetate concentrations better than Methanosarcina spp. (Chin et al., 1999b; Chin et al., 1999c; Wu et al., 2001, 2002). However, the effects of temperature might be different on other populations of Methanosaeta and Methanosarcina spp. when testing rice field ecosystems other than in Italy. A further interesting feature is the relative sensitivity of Methanosarcina spp. against phosphate on rice roots from Italian rice fields. While Methanosarcina spp. from culture collections easily tolerate phosphate concentrations >50 mM (Smith and Mah, 1980), the Methanosarcina populations on rice roots are inhibited by phosphate >10 mM (Conrad et al., 2000). Although these high phosphate concentrations are irrelevant for in situ conditions and do not influence methanogenesis in situ (Conrad and Klose, 2005), the phosphate sensitivity of Methanosarcina root populations is a conspicuous characteristic (Lu et al., 2005) differentiating this population from Methanosarcina populations in other systems. 2.2.2. Hydrogenotrophic methanogens Members of the family Methanosarcinaceae, including Methanosarcina spp., which are commonly found in rice field ecosystems (Section 2.2.1), are also able to utilize H2/CO2 as energy substrate for CH4 production. However, hydrogenotrophic methanogens are also found among other methanogenic taxa that occur in rice field ecosystems. Members of the order Methanobacteriales, for example Methanobacterium and Methanobrevibacter spp., using H2/CO2 have frequently been isolated from rice field soil (Adachi, 1999; Asakawa et al., 1993; Conrad et al., 1989; Joulian et al., 1998, 2000; Min et al., 1997; Rajagopal et al., 1988). Members of the order Methanomicrobiales, for example Methanospirillum spp. (Tonouchi, 2002) or Methanoculleus spp. (Dianou et al., 2001; Joulian et al., 1998) using H2/CO2, have occasionally been isolated from rice field soil. An important group of hydrogenotrophic methanogens in rice fields is the so-called Rice Cluster I (RC-I), which was first described as a novel cluster of archaeal 16S rRNA gene sequences on rice roots (Grosskopf et al., 1998b). In the meantime, a methanogenic enrichment culture from rice field soil (Erkel et al., 2005) was used to obtain the complete genome sequence of one member of the RC-I (Erkel et al., 2006). Members of RC-I probably form a family on its own or even an order within the Euryarchaota. Just recently, a Japanese group obtained the first isolate of
Microbial Ecology of Methanogens and Methanotrophs
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RC-I (Sanae Sakai et al., personal communication), so that a proper taxonomic description of members of RC-I will soon be possible. Molecular characterization (16S rRNA and mcrA genes) of methanogenic populations showed that potentially hydrogenotrophic Methanosarcinaceae, Methanobacteriales,Methanomicrobiales, and RC-I are widely distributed among Chinese, Philippine, Japanese, and Italian rice fields (Grosskopf et al., 1998a; Ramakrishnan et al., 2001; Watanabe et al., 2006; Wu et al., 2006). Numbers of hydrogenotrophic methanogens are on the same order (around 106 per gram dry soil) as reported for acetotrophic methanogens (Asakawa et al., 1998; Frenzel et al., 1999; Joulian et al., 1998; Kru¨ger et al., 2005). The energetic conditions of methanogens strongly depend on substrate availability. Since H2 partial pressures in rice field soil are generally low (<10 Pa), but acetate concentrations can be high (millimolar range) when soil is supplemented with straw, energetic conditions in the soil may be superior for acetotrophic than for hydrogenotrophic methanogens, thus theoretically allowing maintenance of relatively higher numbers of acetotrophic than hydrogenotrophic methanogens (Conrad and Klose, 2006). However, this is not evident from the presently available data, which rather show similar numbers of potentially hydrogenotrophic and acetotrophic methanogens. In rice field soil, the contribution of hydrogenotrophic methanogenesis to total CH4 production is close to the theoretically expected ratio of a third or less (Bilek et al., 1999; Conrad and Klose, 2000; Rothfuss and Conrad, 1993; Yao and Conrad, 2000b). The same is the case for methanogenically degrading rice straw (Glissmann and Conrad, 2000). Occasionally, however, contributions of hydrogenotrophic methanogenesis larger than 33% were observed in Italian rice fields (Kru¨ger et al., 2001, 2002). The reasons for these relatively large contributions are presently unclear but must be due to imbalance in the degradation path of organic matter to CH4. Possible explanations are temporary accumulation of acetate, consumption of acetate by other processes than methanogenesis, or H2 production processes in addition to carbohydrate fermentation. The methanogenic community on the roots of rice was found to be dominated by hydrogenotrophic methanogenesis, while the simultaneously produced acetate is released into the soil (Conrad and Klose, 1999; Lehmann-Richter et al., 1999; Penning et al., 2006b). This dominance is also reflected in the methanogenic populations found on rice roots, which mostly belong to the hydrogenotrophic groups of Methanomicrobiales, Methanobacteriales, and RC-I (Chin et al., 2004; Grosskopf et al., 1998b; Hashimoto-Yasuda et al., 2005; Ikenaga et al., 2004), but Methanosarcinaceae, which can potentially utilize acetate, were also found (Chin et al., 2004). The question arises why the rice root community consists of so many different groups of hydrogenotrophic methanogens, although they all catalyze the same reaction. Although the reasons are not completely clear, one
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important factor seems to be the availability of H2. When roots were incubated under a H2 atmosphere, populations of Methanosarcinaceae and Methanobacteriales incorporated 13CO2 into their DNA, but when roots were incubated under N2, so that only low amounts of H2 were produced by fermenting bacteria, 13CO2 was mainly incorporated into the DNA of RC-I methanogens (Lu et al., 2005). Hence, the ecological niches of members of the RC-I methanogens seem to include utilization of low H2 concentrations. Further ecological niches for members of the RC-I methanogens possibly are a moderately thermophilic lifestyle (Section 2.3.5), the tolerance of oxic conditions (Section 2.3.6), and adaptation to the acidic conditions found in peat (Conrad et al., 2006). The ecological niches of the other hydrogenotrophic methanogens present on the rice roots are less clear. The experiments by Lu et al. (2005) indicate that Methanosarcinaceae and Methanobacteriales may become active when H2 concentrations are relatively high. However, it is unclear when this would happen under in situ conditions. This study of Lu et al. (2005) also indicates that Methanobacteriales in contrast to Methanosarcinaceae tolerate high phosphate concentrations. Although this is a niche differentiation, it is unlikely that it has relevance for in situ conditions (Conrad and Klose, 2005). 2.2.3. Microorganisms supplying methanogenic substrates The microorganisms supplying the methanogenic substrates H2 and acetate are the fermenting (primary and secondary fermentation) microorganisms and the homoacetogenic microorganisms depicted in Fig. 1. Most of the fermenters are members of the domain Bacteria, but some members of the Eukarya (protozoa, fungi) may also contribute. However, not all of the bacteria and eukarya found in rice field ecosystems are involved in the production of methanogenic substrates, since methanogenic degradation processes in the soil system are not operating for the entire year, but only during the period when the soil is flooded and then, only during the methanogenic phase after Fe(III) has been reduced. Hence, microorganisms respiring organic matter with O2, nitrate, sulfate, and ferric iron also contribute, and may form functionally and taxonomically diverse communities by themselves. The other complexity arises from the diversity of energy substrates, mostly organic matter, but also reduced compounds like H2, CH4, NH4þ, H2S, Fe(II), and so on (Fig. 4). Most of the degradable organic matters are eventually derived from the plants, that is consisting predominantly of carbohydrates (cellulose, hemicellulose), aliphatic (fatty acids, amino acids), and aromatic (lignin, amino acids) compounds. Our knowledge about the diversity of microorganisms in rice field soil is based on molecular studies characterizing the patterns of microbial phospholipid fatty acids (PLFA) or 16S rRNA genes. After early studies (Bai et al., 2000; Bossio and Scow, 1998; Reichardt et al., 1997), the
Microbial Ecology of Methanogens and Methanotrophs
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diversity of microbes in the different habitats of the rice field ecosystem has mainly been studied by the group of Makoto Kimura at Nagoya University. Their PLFA data have been summarized (Kimura and Asakawa, 2006a) showing that the microbial community structures are more or less different between the various habitats, that is floodwater, percolating water, rice soils under flooded and drained conditions, rice straw placed in flooded and drained rice soil, rice straw in the composting process, and rice straw compost placed in a flooded rice field. Their molecular analyses of bacterial 16S rRNA gene diversity give a similar picture (Cahyani et al., 2003; Ikenaga et al., 2003; Murase et al., 2005; Shibagaki-Shimizu et al., 2006; Sugano et al., 2005a; Tanahashi et al., 2005). Determination of the vertical distribution and temporal development of the bacterial populations in rice field soil by analysis of 16S rRNA genes demonstrates that the bacterial community is not uniform and constant, but exhibits quite some dynamics, and is also different between the oxic and anoxic parts of the system (Lu¨demann et al., 2000; Noll et al., 2005). However, all these studies are mostly descriptive and do not allow a conclusive interpretation of which functions the various microorganisms have in the ecosystem. A few studies have applied pulse labeling of the plants with 13CO2 followed by analysis of the rhizosphere bacterial populations that incorporated 13C into their PLFA or nucleic acids (Lu et al., 2004a, 2006, 2007). However, although the detected bacteria can be functionally linked to plant photosynthesis and their phylogenetic position can be determined, it is unclear which reactions they are exactly catalyzing. The functionally relevant populations of fermenting bacteria involved in the methanogenic degradation of carbohydrates have so far been determined only in rice field soil from Italy. The following approach was used. It was shown that propionate accumulates as an important fermentation product in the soil when methanogenesis is inhibited (Chin and Conrad, 1995; Glissmann and Conrad, 2000). To identify the major groups of bacteria producing the propionate, soil was diluted so that only bacteria with an abundance of 108–109 per gram soil were left. These soil dilutions were used to isolate fermenting bacteria growing on carbohydrates (cellulose, hemicellulose, pectin, or sugar mixture) and test their major fermentation product, which indeed was propionate (Chin et al., 1999a). At the same time, these soil dilutions were used to analyze the bacterial 16S rRNA genes (Hengstmann et al., 1999). Thus retrieved environmental 16S rRNA gene sequences and those of the isolated bacteria were similar and mainly belonged to the Verrucomicrobia, the Clostridium Cluster XIVa, and the Cytophaga-Flavobacterium-Bacteroides (CFB). Hence, these bacterial groups were most likely the relevant propionate producers. Less abundant bacteria (<107 per gram soil) isolated from less diluted soil, on the other hand, belonged to other phylogenetic groups and fermented carbohydrates to butyrate or ethanol instead of propionate (Chin et al., 1998).
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The next step, that is the further degradation of propionate, proceeds in Italian rice field soil through the succinate pathway, which is characteristic for some of the known syntrophic fermenting bacteria that convert propionate to acetate, CO2, and H2 (Krylova et al., 1997). The relevant propionateconsuming bacteria have recently been identified in Italian rice field soil by feeding 13C-labeled propionate to methanogenic soil and determining the 16S rRNA gene sequences of the bacteria that assimilated 13C into ribosomal RNA. The genera Syntrophobacter, Pelotomaculum, and Smithella were identified (Lueders et al., 2004). Syntrophic bacteria affiliated with the genus Pelotomaculum seem to be widely distributed in various methanogenic environments (Imachi et al., 2006). Despite this progress for Italian rice field soil, similar experiments are lacking for other rice field ecosystem found in the world. It is quite possible that the important microorganisms involved in production of methanogenic substrates are different.
2.3. Microbiological explanations for macroscopic processes, that is production and emission of methane Methane emission patterns can be quite different at different sites, seasons, management schemes, and so forth (Wassmann et al., 2000b). The most important variables that control CH4 emission include soil type, rice variety, temperature, soil redox potential (Eh), water management, and fertilization with organic carbon and nitrogen (Aulakh et al., 2001b; Kimura et al., 2004; Minami, 1994; Neue and Roger, 2000; Sass and Fisher, 1997; Yan et al., 2005). These variables affect production, transport, and oxidation of CH4 in the field. This knowledge, and field and laboratory data have been used for development and testing of empirical, semiempirical and process-oriented models to simulate CH4 emission from rice fields (Cao et al., 1995; Huang et al., 1998; Li et al., 2004; Matthews et al., 2000). However, the results of these models are not yet satisfactory. One problem is that production, transport, and oxidation of CH4 are basic processes that are by themselves quite complex and consist of a hierarchy of subprocesses, of which the ultimate ones all operate on the microscopic scale and mostly involve microorganisms. In order to find out which are the important parameters and variables for simulation of CH4 emission, the microscopic process level has to be understood. In the following I will review the microscopic knowledge relevant for macroscopic observations focusing on methane production and methanogenic communities. 2.3.1. Sequential reduction and initiation of methanogenesis When rice soils are flooded, production of CH4 starts after a lag phase, then proceeds with a maximum rate and eventually slows down. These events are observed in all rice field soils, but duration and magnitude differ among the various soils (Neue et al., 1994; Patrick and Reddy, 1978; Ponnamperuma, 1981;
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Wassmann et al., 1998; Yao et al., 1999). The first phase (reduction phase) after flooding is characterized by reduction of inorganic electron acceptors, such as nitrate, sulfate, and ferric iron. During this time CH4 production is suppressed, but subsequently develops fast, yields a maximum CH4 production rate (methanogenic phase), and then gradually slows down (steady state phase). Thermodynamic theory predicts that organic matter is preferentially oxidized by coupling to the reduction of nitrate, sulfate, or ferric iron rather than to CH4 production, thus giving the thermodynamic background for the observation of a reduction phase (Ponnamperuma, 1978; Zehnder and Stumm, 1988). The sequential reduction of mostly Fe(III) and sulfate before onset of methanogenesis is often monitored by measurement of the soil redox potential (Eh) using platinum electrodes. The phase of methanogenesis is usually characterized by the Eh becoming lower than 100 mV (Wang et al., 1993). However, closer inspection of the reduction phase shows that CH4 is already produced very shortly after flooding, when the Eh is still high (Roy et al., 1997). Hence, how are the processes regulated on the level of microorganisms. Figure 6 summarizes the most important events during the reduction, methanogenic and steady state phases after flooding. Immediately after flooding, during phase I, saccharolysis of polysaccharides and fermentation starts (Glissmann and Conrad, 2002). The fermenting bacteria produce H2, acetate, and other fermentation products from carbohydrates, for example glucose (Chidthaisong et al., 1999). Thermodynamic analysis of the conditions in various rice field soils showed that hydrogenotrophic methanogenesis is usually feasible briefly after flooding (phase II) due to the relatively high partial pressures of H2 produced by fermentation (Yao and Conrad, 1999) (Fig. 7). Indeed, hydrogenotrophic methanogens seem to be active immediately after onset of organic matter fermentation (Roy et al., 1997). This observation is at the first glance surprising, since methanogens have generally been believed to require reduced conditions (Eh < 100 mV). However, this is obviously not generally true. Many methanogens, those isolated from soil in particular, are neither very sensitive to high redox potentials nor to exposure to O2 (Fetzer and Conrad, 1993; Fetzer et al., 1993). Genomic data show that many of them contain the genes of various O2-detoxifying enzymes (Brioukhanov et al., 2000; Shima et al., 1999, 2001). In RC-I methanogens for instance, the genes coding for superoxide dismutase, superoxide reductase, catalase, desulfoferredoxin, rubrerythrin, peroxyredoxin, and H2 oxidase are present (Erkel et al., 2006). Therefore, it is not surprising that methanogens survive drainage and winter fallow of rice field soils, as they maintain virtually the same numbers per gram soil throughout the different times of the year (Asakawa and Hayano, 1995; Kru¨ger et al., 2002; Mayer and Conrad, 1990; Schu¨tz et al., 1989b). Although we do not know by which mechanism, they apparently survive dry conditions and rapidly regain activity on flooding.
II
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Figure 6 Phases of decomposition of organic matter to methane in anoxic rice field soil. The data of the graph are from Glissmann and Conrad (2002).
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Microbial Ecology of Methanogens and Methanotrophs
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Figure 7 Temporal change of the partial pressures of CH4 and H2 and the Gibbs free energy (DG) of hydrogenotrophic methanogenesis in anoxic incubations of three different rice field soils.The ratio of available organic matter to electron acceptor (mainly sulfate and ferric iron) in the soil decreases in the order Buggallon > Changchun > Urdaneta.The data of the graph are fromYao and Conrad (1999).
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They apparently regain activity faster than the sulfate and iron reducers competing for H2. We also do not know exactly which methanogenic taxa are involved in the CH4 production during this early phase. Molecular analysis of archaeal 16S rRNA in Italian rice field soil demonstrated the presence of Methanosarcinaceae, Methanobacteriaceae, and RC-I methanogens, all potential hydrogenotrophic methanogens, throughout the incubation (Lueders and Friedrich, 2000, 2002). Since RC-I is the most abundant group and in some experiments its abundance is decreasing with time (Conrad and Klose, 2006), RC-I methanogens are the most likely candidates for CH4 production immediately after flooding. Interestingly, sulfate and iron reduction, which would be thermodynamically even more feasible than methanogenesis, do not start as early as methanogenesis. The reasons are unknown, but these bacteria apparently are not yet active during phase II, while methanogens (at least some) are already active. It has been shown that sulfate reducers and iron reducers do not compete with fermenting bacteria for carbohydrates, but compete with methanogens for H2 and acetate (Chidthaisong and Conrad, 2000). Only nitrate reducers compete with fermenting bacteria for carbohydrates, but nitrate usually is very low in rice field soil and is depleted within hours after flooding (Achtnich et al., 1995; Chidthaisong and Conrad, 2000). On becoming active during phase III, sulfate and iron reducers deplete H2 to such low concentrations that hydrogenotrophic methanogenesis is thermodynamically no longer feasible (Roy et al., 1997; Yao and Conrad, 1999). This effect is especially pronounced in soils, where the content of organic matter, which allows for H2 production, is relatively small compared to the content of reducible iron, which allows for H2 consumption (Fig. 7). Acetotrophic sulfate reducers, mostly members of the genus Desulfotomaculum, often occur only as spores in the soil (Wind and Conrad, 1995).The amounts of available iron and sulfate are usually not sufficient to allow for complete depletion of acetate by sulfate and iron reducers, unless the soil is amended with additional sulfate or iron, respectively. Despite the availability of acetate, rates of CH4 production are nevertheless low during phase III, probably since the hydrogenotrophic methanogens are the only active ones, while the acetotrophic methanogens are not yet active during this phase. Indeed, application of molecular methods has shown that acetotrophic Methanosarinaceae increase their numbers and synthesize ribosomes for protein production resulting in increased CH4 production in phase IV (Lueders and Friedrich, 2000, 2002). The relative increase of Methanosarcina spp. is reasonable because acetate concentrations are rather high. In fact, increase of numbers of Methanosarcina spp. is even more pronounced when rice straw is added to the soil, which results in increased fermentative acetate production (Conrad and Klose, 2006). As soon as available sulfate and ferric iron are depleted in phase IV, H2 is no
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longer consumed by sulfate and iron reducers and H2 partial pressures rise again, so that hydrogenotrophic methanogenesis is again thermodynamically feasible and resumes (Yao and Conrad, 1999). The soil conditions then allow methanogenesis from both H2/CO2 and acetate, and methanogenesis becomes the sole terminal process in degradation of organic matter. In this methanogenic phase IV the rate of CH4 production reaches a maximum. At this time, soil redox potentials (Eh) monitored with a platinum electrode have usually decreased to a low Eh of less than 100 mV. The depletion of acetate proceeds until steady state of production and consumption of acetate is attained in phase V. The same is true for H2 turnover for which steady state is usually reached even earlier. Soil Eh is also constantly low. The steady state phase (phase V) is in addition characterized by the production of CH4 and CO2 at equal rates (Yao and Conrad, 2000b), as expected theoretically from the stoichiometry of degradation of polysaccharides, for example C6H12O6 ! 3CO2 þ 3CH4. In this phase methanogenesis is limited by the production of its substrates H2 and acetate. The production of H2 and acetate, on the other hand, is limited by the fermentation process, which in turn is limited by the hydrolysis of polysaccharides. Hence, in the steady state phase, CH4 production is basically limited by the initial step of organic matter degradation (Fey and Conrad, 2003; Glissmann and Conrad, 2002), similarly as in other environments (Billen, 1982; Degens and Mopper, 1975). In summary, the reduction phases (phases I to III) in flooded soils are the most dynamic phases with respect to microbial processes. The most important events are summarized in Fig. 6. These events are paralleled by a change in the relative contribution of hydrogenotrophic versus acetotrophic methanogenic pathways to total CH4 production, which starts with mostly hydrogenotrophic methanogenesis in phase II, followed by mostly acetotrophic methanogenesis in phase III and IV and finally both hydrogenotrophic and acetotrophic methanogenesis at a ratio of about 20–30% to 70–80% in phase V (Conrad et al., 2002; Fey et al., 2004). The extent of CH4 production is most sensitive to the relative availability of degradable organic matter versus reducible inorganic compounds, or electron donors versus electron acceptors. Hence, it is not surprising that the amount of CH4 produced is proportional to the ratio of electron donors versus electron acceptors available in a particular soil (Yao et al., 1999). These variables are more important than the soil redox potential (Eh) measured with a platinum electrode, since CH4 production often operates at Eh > 100 mV (Gaunt et al., 1997). Since the ratio of electron donors to electron acceptors also affects the amount of acetate that accumulates during the reduction phase (phases I–III), it also affects the maximum rate of CH4 production in the subsequent methanogenic phase, that is phase IV (Yao et al., 1999). In rice field soils, ferric iron is the quantitatively most important inorganic electron acceptor. Therefore, the degradable content of organic matter and reducible
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iron are the most important soil characteristics that eventually control CH4 production ( Yao et al., 1999). Methanosarcina spp. and RC-I methanogens seem to be the most important methanogens during the reduction phase and the subsequent methanogenic phase. 2.3.2. Effect of short-term drainage Short-term drainage of flooded rice fields (e.g., midseason drainage) results in a strong decrease in CH4 emission and reduces the total amount of CH4 released from a rice field over the season (Lu et al., 2000; Sass et al., 1992; Yagi et al., 1996; Yan et al., 2005). Short-term drainage is a possible mitigation option for greenhouse gas emission (Frolking et al., 2004). The immediate decrease of CH4 emission on drainage is plausible, since O2 can better penetrate into the soil, when it is not flooded, and thus suppress CH4 production. However, since the suppression of CH4 production usually persists for long time after the soil has been flooded again (Yagi et al., 1996), inhibition of methanogenesis by O2 is not a sufficient explanation for the long-term suppression of CH4 emission. The explanation actually is that short-term drainage reverts the chemical status of the soil to the time at the beginning of flooding. The sulfate and iron in particular, which have been reduced after flooding, are apparently oxidized again during the aeration caused by short-term drainage (Ratering and Conrad, 1998; Sigren et al., 1997). The thus regenerated sulfate and ferric iron allow the operation of sulfate and iron reducers, respectively. These bacteria again compete successfully with methanogens for H2 and acetate as long as sulfate and ferric iron are available and thus suppress CH4 production. Experiments have shown that after brief aeration of methanogenic soil, H2 and acetate concentrations indeed decrease to such low levels that methanogenesis is no longer feasible and stay at such low levels until sulfate and ferric iron are again depleted (Ratering and Conrad, 1998; Sigren et al., 1997). Although the mechanism of short-term drainage on the microbial process level seems to be clear, it is largely unknown which microorganisms are involved in the process. The only clue comes from a field study in Italy, where an accidental short-term drainage at the beginning of the season resulted in unusually low rates of production and emission of CH4 (Kru¨ger et al., 2001). At the same time, concentrations of ferric iron and acetate were unusually high and those of acetate unusually low, an effect expected from short-term drainage. Analysis of the methanogenic populations by targeting archaeal 16S rRNA genes showed that in the season with the relatively low acetate concentrations the ratio of Methanosaeta spp. versus Methanosarcina spp. was much higher than in the season with normal (relatively high) acetate concentrations (Kru¨ger et al., 2005). This observation is reasonable, since the ecological niches of Methanosaeta versus Methanosarcina are characterized by relatively low versus high acetate concentrations (Section 2.2.1). Nevertheless, it is unclear whether this kind of dynamic change in the populations generally
Microbial Ecology of Methanogens and Methanotrophs
23
occurs after short-term drainage. Methanosaeta spp. have a notoriously low growth rate so that they probably can respond only slowly to environmental cues. It is probably a matter of the actual circumstances in a particular soil that define concentrations of ferric iron and acetate and thus affect methanogenic populations. Besides concentration of ferric iron, its mineral composition is an important factor affecting microbial processes. As drainage causes oxidation of ferrous iron, the freshly produced ferric iron may be easily accessible to microbes than the ferric iron that has aged over the winter fallow (Kappler and Straub, 2005). Addition of weakly crystalline ferrihydrite to rice field soil results in a more pronounced competition for available H2 and acetate and suppression of CH4 production than addition of more crystalline lepidocrocite, goethite, and hematite (Qu et al., 2004). The observation is reasonable, since the relatively larger surface area of ferrihydrite crystals allows better accessibility to microorganisms (Roden and Zachara, 1996). 2.3.3. Effect of organic amendment Addition of organic carbon provides electron donors to the microbial community in the rice field soil and thus enhances CH4 production. This effect is generally seen under field conditions, when straw, compost, or manure is added (Denier van der Gon and Neue, 1995; Sass et al., 1991a; Schu¨tz et al., 1989a; Yagi and Minami, 1990; Yagi et al., 1997). Various studies also have shown that addition of rice straw enhances CH4 emission much more than addition of compost or manure, coinciding with the wider range of C/N ratios in fresh straw compared to composted organic matter or manure (Agnihotri et al., 1999; Chareonsilp et al., 2000; Corton et al., 2000; Shin et al., 1996). Straw incorporated in the previous season does not enhance CH4 emission as much as when incorporated in the same season (Yan et al., 2005). Hence, CH4 emission is apparently less stimulated if rice straw has partially been decomposed. The fate of organic matter and the cycling of carbon in rice field ecosystems has been reviewed (Kimura et al., 2004). Here, I will focus on the microbial communities involved in degradation of rice straw and enhancement of CH4 production. The microbial colonization of straw exposed to anoxic rice field soil and its methanogenic decomposition has been studied in some detail. Rice straw is mainly composed of cellulose and hemicellulose with some minor portion (5– 15%) of lignin (Tsutsuki and Ponnamperuma, 1987; Watanabe et al., 1993). Microscopic investigations showed that bacteria colonize rice straw rapidly, with the easily accessible and degradable parts being colonized first (Kimura and Tun, 1999; Tun and Kimura, 2000). It is mainly hydrolytic and fermenting bacteria that colonize the straw thus explaining the rapid accumulation of acetate and various other fatty acids on addition of straw to anoxic rice soil (Glissmann and Conrad, 2000). Aromatic compounds also accumulate (Glissmann et al., 2005; Tsutsuki and Ponnamperuma, 1987). However, the accumulation of the fermentation products is only transient as they are further
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degraded yielding CH4 and CO2 as final degradation products. The bacterial communities colonizing rice straw have been characterized by targeting the 16S rRNA genes (Weber et al., 2001b) or analyzing microbial PLFA patterns (Kimura and Asakawa, 2006b; Nakamura et al., 2003). These studies found that Clostridium spp. and Gram-positive bacteria, respectively, are the major colonizing bacteria in flooded rice field soil, which is a consistent result, and was observed for the rice ecosystems in both Italy and Japan. However, analysis of 16S rRNA gene fragments retrieved from rice straw in Japanese soil showed that Alphaproteobacteria, members of the CFB group and Spirochaetes, that is all Gram-negative bacteria, were the main colonizers both under flooded and drained conditions (Sugano et al., 2005a). The reason for this discrepancy to PLFA studies and results in Italian soil is unclear but may be due to the usage of different primers and PCR conditions. Interestingly, the study by Sugano et al. (2005a) found that the bacterial colonization was different on blade versus sheath straw and also exhibited a succession with exposure time. These two features are consistent with the microscopic investigations (Kimura and Tun, 1999; Tun and Kimura, 2000). Straw placed into drained rice fields, on the other hand, seems to be colonized mainly by Gram negative bacteria and fungi, which probably live aerobically in contrast to those found in flooded soil (Kimura and Asakawa, 2006b). Besides bacteria, the straw is also colonized by methanogenic archaea. In Italian rice soil, they mainly consist of acetotrophic Methanosarcinaceae, hydrogenotrophic Methanobacteriales, and RC-I methanogens (Conrad and Klose, 2006; Weber et al., 2001a) in Japanese rice soil they mainly consist of acetotrophic Methanosarcinaceae, hydrogenotrophic Methanomicrobiales, and also RC-I methanogens (Sugano et al., 2005b). However, it is unclear whether the methanogens detected on the straw are really active. This doubt comes from process studies, which showed that the microbial community on rice straw mainly supports hydrolysis and fermentation reactions, while the further conversion of fermentation products to CH4 occurs in the soil rather than on the straw (Glissmann et al., 2001). The microbial colonization pattern of straw apparently deserves more research. The degradation of compost or manure in rice field soil has not yet been studied on a process level. However, the microbial communities have been analyzed both by targeting PLFA and 16S rRNA genes. The microbial communities were studied during the composting process of rice straw (Cahyani et al., 2002, 2003, 2004a,b) and after the compost was placed into flooded rice fields and there further decomposed (Tanahashi et al., 2004, 2005). Methanogens are involved in both processes. During the composting process, Methanosarcinaceae, Methanomicrobiales, and RC-I methanogens were prevalent (Cahyani et al., 2004b), but thermophilic Methanothermobacter spp., which were found in other composting plant material (Derikx et al., 1989), were not identified. The bacterial community gradually changed after putting the compost into the rice field soil. The most active bacterial groups belonged to clostridia, proteobacteria,
Microbial Ecology of Methanogens and Methanotrophs
25
spirochetes, and myxobacteria (Tanahashi et al., 2005). Similar data on methanogenic archaea are not yet available. So far, the microbial analysis of rice straw compost does not help explaining why addition of compost stimulates CH4 emission to less extent than addition of uncomposted rice straw. 2.3.4. Effect of fertilization with Fe, S, and N Addition of ferric iron can result in substantial suppression of CH4 emission under field conditions and was recommended as an option for mitigation of CH4 emission (Furukawa and Inubushi, 2002; Ja¨ckel et al., 2005). This effect is based on the outcompetition of methanogens by iron-reducing bacteria, which utilize the common substrates H2 and acetate more effectively (Section 2.3.2). The suppression is especially pronounced if lower crystalline forms of iron (ferrihydrite) are applied ( Ja¨ckel et al., 2005), whereas CH4 suppression by higher crystalline forms of ferric iron (furnace slag) is dependent on the natural iron content of the soil (Furukawa and Inubushi, 2004). Since the reduction of Fe(III) to Fe(II) can accept only one electron, ferric iron would reduce the electron flow to CH4 production only if added in large amounts. However, suppression of CH4 production by added ferric iron is much larger than expected from the stoichiometric electron balance between iron reduction versus methanogenesis. Under field conditions, iron is probably frequently recycled into the oxidized state within the rhizosphere where O2 is leaking from roots into the soil and thus supports iron oxidation (Begg et al., 1994) (Fig. 4). It is also possible that Fe(III) has a direct inhibitory effect on methanogens. Experiments in defined microbial culture have shown that amorphous ferrihydrite can indeed inhibit methanogens directly, in particular hydrogenotrophic ones (Van Bodegom et al., 2004). Some of the methanogens apparently can utilize Fe(III) as electron acceptor and reduce Fe(III) to Fe(II) instead of CO2 to CH4 (Bond and Lovley, 2002; Van Bodegom et al., 2004). However, little is known on the detailed biogeochemistry of the microbial processes involved in this complex process of iron cycling and methane suppression in rice field ecosystems (Ratering and Schnell, 2000, 2001). Also only few results are available from experimental microbial model systems and freshwater sediments (Roden, 2003; Roden and Wetzel, 2003; Sobolev and Roden, 2002; Weber et al., 2006). The microbial populations involved in iron reduction are also largely unknown. Besides methanogens rice roots also contain (see above) potential iron-reducing bacteria such as Geobacter spp. and Anaeromyxobacter spp. (Scheid et al., 2004; Treude et al., 2003). However, iron oxidizers have not yet been identified on rice roots, but they occur on roots of Typha latifolia, another aquatic plant (Neubauer et al., 2002). Addition of sulfate to rice field soil (usually as ammonium sulfate or phosphogypsum) has a similar effect on CH4 emission as the addition of
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ferric iron (Corton et al., 2000; Denier van der Gon and Neue, 1994; Lindau et al., 1993, 1994). Sulfate allows sulfate reducers to outcompete methanogens for their common substrates H2 and acetate (Sections 2.3.1 and 2.3.2). The inhibitory effect of sulfate is limited, however, if sulfate is not regenerated by oxidation of sulfide in the rhizosphere. Similarly as for iron cycling, sulfur cycling is probably taking place in the rhizosphere (Fig. 4), since sulfate concentrations increase toward the root surface (Wind and Conrad, 1997). Both sulfur-oxidizing and sulfate-reducing bacteria have been detected on rice roots in rather high diversity (Graff and Stubner, 2003; Scheid and Stubner, 2001) and it has been shown that sulfate reducers can suppress methanogenic activity in root incubations (Scheid et al., 2004). However, details of the sulfur cycling and the microorganisms involved are not known. For suppression of CH4 emission, sulfate may be supplied as gypsum or phosphogypsum. These compounds are not very soluble. Nevertheless, the solubility constant of gypsum is Ks ¼ 4.2 105 M (Stumm and Morgan, 1981), so that the equilibrium sulfate concentration is in the millimolar range. Because of the long-term supply of sufficiently high sulfate concentrations, addition of gypsum or phosphogypsum has a much stronger effect than addition of ammonium sulfate (Corton et al., 2000; Lindau et al., 1998). Suppression of CH4 emission may also happen by the deposition of atmospheric sulfur. Thus, it was found that deposition of sulfate by acid rain inhibited the CH4 emission from peat bogs (Gauci et al., 2002, 2004a). This may well be a global phenomenon and affect CH4 emission from rice fields as well (Gauci et al., 2004b). In analogy to ferric iron and sulfate, one would expect that addition of nitrate also suppresses CH4 emission. Indeed nitrate always results in strong suppression of CH4 production when added to methanogenic soil (Achtnich et al., 1995; Klu¨ber and Conrad, 1998a) or methanogenic rice roots (Scheid et al., 2003). Suppression by nitrate is caused by competition and toxic effects. Competition occurs on two levels. First, availability of nitrate allows the consumption of glucose by nitrate reducers instead of fermenting bacteria so that the methanogenic substrates H2 and acetate are no longer produced (Chidthaisong and Conrad, 2000). Second, the methanogenic substrate H2 is more efficiently utilized by nitrate-reducing bacteria than by methanogenic archaea. Thus, addition of nitrate, or other reducible nitrogen compounds (nitrite, NO, N2O) results in a decrease in the H2 partial pressure below the thermodynamic threshold of hydrogenotrophic methanogenesis, which is then no longer possible (Achtnich et al., 1995; Klu¨ber and Conrad, 1998a). Addition of nitrate also results in oxidation of reduced sulfur and iron, so that sulfate and ferric iron are regenerated. They can then serve as electron acceptors and thus allow sulfate and iron reducers to successfully compete with methanogens for H2 (Klu¨ber and Conrad, 1998a). However,
Microbial Ecology of Methanogens and Methanotrophs
27
a decrease of acetate concentrations was not observed on addition of nitrate, although acetotrophic methanogenesis was nevertheless inhibited (Klu¨ber and Conrad, 1998a). Therefore, the suppressive effect on acetotrophic methanogenesis is believed to be mainly due to the production of nitrite, NO, and N2O as intermediates of denitrification, which can be toxic for various microorganisms, including methanogens (Klu¨ber and Conrad, 1998b; Roy and Conrad, 1999). Suppression of CH4 production on rice roots by nitrate indeed resulted not only in inhibition of CH4 production but also in a decrease of the population of acetotrophic Methanosarcinaceae (Scheid et al., 2003). Despite the clearly suppressive effect of nitrate addition on CH4 production in anoxic soil, suppression of CH4 emission by nitrate fertilization has never been observed under field conditions. One reason for the lacking suppression is probably due to the efficient uptake of nitrate by the rice plants, which scavenge nitrogen for assimilation (Fig. 4). A further reason is the fact that nitrate is reduced to gaseous nitrogen rather than ammonium, so that nitrate nitrogen is permanently lost from the ecosystem rather than recycled by oxidation in the rhizosphere. Insofar, nitrogen cycling is different from sulfur and iron cycling, where gaseous loss is small (sulfur lost as H2S or methylated S) or absent (in case of Fe). On the other hand, fertilization of rice fields with ammonium-based fertilizers (e.g., urea) might have some suppressive effect on CH4 emission. Although controversial reports exist, a small suppressive effect by urea has occasionally been observed (Cai et al., 1997; Dan et al., 2001; Schu¨tz et al., 1989a; Wassmann et al., 2000a; Xu et al., 2004). Suppression of CH4 emission by urea may be due to stimulation of CH4 oxidation (Section 3.2.5) or suppression of CH4 production. This suppression possibly functions via production of nitrate. Rice roots are colonized by ammonia oxidizers (Nitrosospira spp. and Nitrosomonas spp.) (Briones et al., 2002, 2003), which are tightly coupled in their activity to denitrification (Arth and Frenzel, 2000; Arth et al., 1998; Nicolaisen et al., 2004; Reddy and Patrick, 1986; Reddy et al., 1989). Hence, denitrification in the rhizosphere is fed by the supply of ammonia, while the activity of denitrifiers in turn inhibits CH4 production by the mechanisms described above. However, it is questionable whether these processes have relevance for CH4 production under field conditions. Since plants also use ammonium as nutrient, they compete with ammonia oxidizers (Verhagen et al., 1995) and thus limit the production of nitrate and dentirification (Arth and Frenzel, 2000; Kakuda et al., 1999). Addition of nitrification and urease inhibitors to rice fields usually results in suppression of CH4 emission, indicating that coupled nitrification– denitrification in the rhizosphere ultimately benefits rather than impedes the microbial community producing CH4 (Adhya et al., 2000; Lindau et al., 1993; Malla et al., 2005; Xu et al., 2002). The benefit of ammonium probably
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operates via stimulation of plant growth and increased supply of organic substrates to the methanogenic food chain (Section 2.2.6). 2.3.5. Effect of temperature Methane emission rates correlate with increasing temperature according to the Arrhenius equation. This can be observed over the season and on a diel basis (Sass et al., 1991b; Schu¨tz et al., 1990; Wang et al., 1999). The temperature effect on CH4 emission is complex, since temperature affects virtually any biogeochemical process, including CH4 production and CH4 oxidation. However, the soil CH4 production is affected not only in total but in any individual reaction involved. Thus, CH4 production by methanogens is affected, and also the processes upstream of methanogenesis are affected, that is hydrolysis and fermentation of organic matter. As soon as steady state conditions are reached and CH4 production is limited by hydrolysis of polysaccharides and other polymers, temperature sensitivity of hydrolysis controls CH4 production (Fey and Conrad, 2003). However, steady state is reached rather late after flooding of soil, and under field conditions is arguably never reached. Therefore, all the individual reaction steps in the flow path of carbon from organic polymers to CH4 (Fig. 1) may be differentially affected by temperature, if they have a different sensitivity (Q10, activation energy). This may result in the transient accumulation of intermediates if temperature changes. In fact this was observed in laboratory incubations of rice soil, when temperature was shifted from 30 to 15 C (Chin and Conrad, 1995). However, the situation is even more complex, since temperature not only affects the reactions catalyzed by the existing microbial populations but also the microbial populations themselves. Thus, temperature shifts result in pronounced changes in the composition of the methanogenic archaeal community (Chin et al., 1999b; Fey and Conrad, 2000). It is likely that the communities of hydrolytic and fermenting bacteria are also changed, but this has not yet been studied. Eventually, however, temperature also affects the relative contribution of acetotrophic versus hydrogenotrophic methanogenesis to total CH4 production (Chin and Conrad, 1995; Fey and Conrad, 2000) and the 13C-stable isotopic signature of the produced CH4 (Fey et al., 2004). It is presently unclear, how temperature sensitivity of all these individual reactions finally translates into the overall CH4 rate observed under field conditions. An interesting observation is the existence of moderately thermophilic methanogens in rice field soil. Normally, rates of CH4 production in rice field soil reach a maximum at about 35–40 C. However, incubation at 40–50 C eventually leads to proliferation of thermophilic methanogens, so that after some time, CH4 production rates are as high at 50 C as at 35 C (Fey et al., 2001; Yang and Chang, 1998; Yao and Conrad, 2000a). At these elevated temperatures, CH4 production in Italian rice soil was
Microbial Ecology of Methanogens and Methanotrophs
29
found to be mainly due to hydrogenotrophic methanogenesis and the methanogenic archaeal community consists almost exclusively of RC-I methanogens (Fey et al., 2001). Recently it was shown that thermophilic RC-I methanogens are widely distributed in geographically different rice fields, albeit not ubiquitously. In addition it was found that members of other methanogenic taxa are also stimulated by high temperatures, indicating that thermophily is a widespread phenomenon in rice field soil (Wu et al., 2006). The reason for the existence of thermophiles in rice fields that usually do not reach temperatures higher than 30 C is unknown. Possibly, these thermophiles just form a microbial seed bank that is never expressed under field conditions, but this is not known. Also, the origin of these thermophiles is not known. One possibility is that they are introduced by addition of compost to the soil, since thermophilic methanogens are frequently detected in composting materials, RC-I methanogens in particular (Cahyani et al., 2004b; Thummes et al., 2007). 2.3.6. Effect of plants Rice plants greatly affect CH4 emission (Aulakh et al., 2001b). One effect is on transport of CH4 from the soil into the atmosphere. By forming an aerenchyma system the plants provide a passage for gases between soil and atmosphere. Most of the CH4 emission from rice fields occurs via the rice plants. The rate of CH4 transport depends on the CH4 gradient and the transport capacity of the plants (Aulakh et al., 2002; Hosono and Nouchi, 1997). This capacity is a function of plant morphology and thus depends on the type of rice cultivar. The transport of CH4 through rice plants has been reviewed (Aulakh et al., 2001b). However, the plants can ventilate CH4 from the soil only after it has been produced in the soil and the rhizosphere. It was found that plants themselves can produce CH4, possibly by photochemical decomposition of pectin and release of the methyl groups as CH4 (Keppler et al., 2006). Although this process produces only tiny amounts of CH4, detected only by highly sensitive analytical systems, the total amounts can nevertheless be significant because of the large leaf biomass (Kirschbaum et al., 2006; Parsons et al., 2006). For rice fields, this process is probably of only minor importance, but has not been investigated explicitly. Another effect of plants is root exudation that supports the methanogenic food chain in the rhizosphere and eventually leads to enhanced CH4 emission (Aulakh et al., 2001b; Conrad, 2004). More than 50% of total CH4 emission can be due to CH4 production from plant photosynthates (Watanabe et al., 1999) (Fig. 3). Production of photosynthates and loss through root exudation is a feature that affects CH4 production and is characteristic for a particular rice cultivar (Aulakh et al., 2001a). It was found that optimization of grain yields reduces CH4 emission probably by
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reducing the loss of photosynthates through the roots and decay of plant biomass (Denier van der Gon et al., 2002). The processes involved in CH4 production from photosynthates were elucidated by pulse labeling of rice plants, that is exposure of the plant leaves to a pulse of 13C- or 14C-labeled CO2. These studies showed that pulselabeled plants release labeled organic compounds into the rhizosphere (Dannenberg and Conrad, 1999; Lu et al., 2002b, 2004b). Both dissolved organic compounds and soil organic matter become labeled, accounting on the average for 0.2% and 1–5% of the photosynthetically assimilated C, respectively. Only 3–6% of the assimilated C is released as CH4 into the atmosphere within 16–17 days (Dannenberg and Conrad, 1999), but nevertheless accounts for >30% of the CH4 that is emitted in total (Watanabe et al., 1999). These data indicate that small changes in the carbon flow of photosynthates might produce large differences in the production of CH4 from photosynthates. Pulse labeling of the plants also results in the labeling of microorganisms in the rhizosphere demonstrating a tight link between plant roots and soil microorganisms (Lu et al., 2002a, 2004a, 2006). Interestingly, the community composition of the labeled microorganisms changes with distance to the roots, indicating that Proteobacteria and Gram-positive bacteria are more prevalent closely and distantly to the root, respectively (Lu et al., 2007). Repeated pulse labeling also allowed identification of the methanogens that incorporated labeled carbon in the rhizosphere. The RC-I methanogens were the only methanogens that assimilated 13C, when plants were pulse labeled with 13CO2 (Lu and Conrad, 2005). RC-I methanogens seem to be hydrogenotrophic methanogens (Section 2.2.2). The most likely scenario is that the plant roots provide the RC-I methanogens with an energy-rich substrate, most likely a substrate that is rapidly converted to H2, which thus allows these methanogens to produce CH4 and biomass from plant-derived 13C. This result is consistent with the observation that the methanogenic microbial community on rice roots produces CH4 mainly hydrogenotrophically (Section 2.2.2). It is also consistent with genomic data from RC-I methanogens (Erkel et al., 2006). These data show that RC-I methanogens have a complete set of O2-detoxifying enzymes (Section 2.3.1), which is unique among methanogens that generally have no or only a few of these enzymes. Hence, it seems that RC-I methanogens are well adapted to the partially oxic conditions in the rhizopshere. Because of the strong incorporation of labeled carbon, it is likely that RC-I methanogens are responsible for much of the CH4 production in the rhizosphere. However, it cannot be excluded that other methanogens that are present in the rhizosphere, for example Methanosarcina spp., also contribute to CH4 production although they do not specifically assimilate the labeled carbon released from the roots.
Microbial Ecology of Methanogens and Methanotrophs
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3. Microbial Ecology of Methanotrophs 3.1. Physiology and phylogeny of methanotrophs Aerobic methanotrophic bacteria belong to the Proteobacteria. The following genera have been described and are conventionally separated into two groups (Bowman, 2006; Hanson and Hanson, 1996): Type I (belonging to the Gammaproteobacteria, family Methylococcaceae) with the genera Methylococcus, Methylocaldum, Methylomicrobium, Methylosphaera, Methylomonas, Methylobacter, Methylosarcina, Methylothermus, and Methylohalobius; and Type II (belonging to the Alphaproteobacteria, family Methylocystaceae) with the genera Methylocystis, Methylosinus, Methylocella, and Methylocapsa. Type I and Type II methanotrophs not only differ in phylogenetic affiliation but also in several biochemical characteristics, such as the pathway of carbon assimilation (ribulose monophosphate pathway in Type I and serine pathway in Type II) or the dominant phospholipid fatty acids (unsaturated PLFAs with 16 and 14 carbon atoms in Type I and with 18 carbon atoms in Type II). All aerobic methanotrophs activate CH4 with a methane monooxygenase (MMO), which requires molecular O2 and reducing equivalents (reduced cytochrome c or NADH) according to the following equation, and results in the production of methanol (Dalton, 2005; Lieberman and Rosenzweig, 2004; Murrell et al., 2000):
CH4 þ O2 þ 2½H ! CH3 OH þ H2 O The oxygen atoms are recovered in the methanol and the water. The (MMO) occurs as a particulate, membrane-bound form (pMMO) and a soluble, cytoplasmic form (sMMO). With the exception of Methylocella spp., which have only an sMMO (Dedysh et al., 2000), the pMMO is universal to all aerobic methanotrophs. The sMMO is only expressed, when copper concentrations are low (about <1 mM). The gene ( pmoA) coding for the alpha subunit of the pMMO has been used as phylogenetic marker analogously as the 16S rRNA gene (Fig. 8). In contrast to the ribosomal RNA gene, which is universal, the pmoA gene has the advantage of being specific for aerobic methanotrophs (with exception of Methylocella spp.). However, the pmoA gene shares homology with the amoA gene coding for the ammonium monooxygenase (AMO) (Holmes et al., 1995). The AMO is the key enzyme of aerobic ammonium-oxidizing nitrifiers and converts ammonia to hydroxylamine in a reaction anologously to the activiation of CH4:
NH3 þ O2 þ 2½H ! NH2 OH þ H2 O
Methylocaldum tepidum Methylocaldum gracile Methylocaldum szegediense 94% Methylococcus capsulatus 92% Methylococcus thermophilus 10% 95% Methylosarcina fibrata Methylosarcina quis quiliarum 70% Methylobacter bovis Methylobacter vinelandii Methylobacter psychrophilus Methylobacter marinus Gamma Methylobacter luteus proteobacteria Methylomicrobium album Methylomonas rubra Methylomonas scandinavica 99% Methylomonas methanica 99% Methylosphaer ahansonii “Methylothermus” Stamm HB 98% Nitrosococcus oceani 99% Nitrosomonas europaea 99% Nitrosospira multiformis 97% Archaea Methylocystis echinoides Methylocystis parvus Methylosinus sporium 91% Methylosinus trichosporium Isolate K1-14 Isolate K1-16 81% Alpha Methylocapsa acidiphila 98% 92% Methylocella palustris proteobacteria Methylocella silvestris 87% Methylocella tundrae Beijerinckia indica Methylobacterium extorquens 92% Methylobacterium organophilum 72% Hyphomicrobium aestuarii 100% Hyphomicrobium facilis Methyloarcula terricola
16S rRNA
99%
I
“Methylothermus” Stamm HB Methylococcus capsulatus Methylocaldum szegediense Methylocaldum tepidum Methylocaldum gracile
PmoA AmoA
98%
75%
10%
Methylomicrobium pelagicum
II
III
IV
89%
Methylobacter sp. BB5.1 Methylomonas LW13 Methylomonas sp. LW19 Methylomonas sp. LW21 99% Methylomonas methanica Nitrosococcus oceani
99%
E5FB-F E5FB-b WRe3w-e WB5FH-A
USCg USC
Methylocystis echinoides Methylocystis parvus Methylosinus sporium Methylosinus trichosporium M84-P3 98%
99%
73%
PmoA2 (Methylocystis sp. SC2)
V VI VII VIII
72%
Maine10 99% USCa USC MForest Methylocapsa acidiphila Nitrospira multiformis 99% Nitrosomonas europaea GF42 GF86 LOPA 12.4
94%
96%
99%
Isolate K1-14
RA21
93%
RA21 gp2 100% unc MR1
99%
Figure 8 Comparison of the tree topologies constructed for the 16S rRNA genes and the pmoA/amoA gene products of methanotrophic bacteria including environmental sequences.The figure was provided by Stephen Kolb (PhD thesis, University of Marburg, 2003).
Microbial Ecology of Methanogens and Methanotrophs
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Assays for pmoA usually also detect amoA. This is not necessarily a disadvantage, since the AMO of ammonium-oxidizing nitrifiers (in soil mostly affiliated with the Betaproteobacteria) has also the capacity to oxidize CH4 to methanol, albeit at a low cell-specific rate (Bedard and Knowles, 1989). The search for methanotrophs in the environment by molecularly targeting the pmoA gene resulted in the discovery of pmoA sequence clusters for which cultivated representatives do not yet exist (Holmes et al., 1999; Knief et al., 2003; Kolb et al., 2005) (Fig. 8). These novel pmoA sequences have so far only been detected in aerated upland soils, but not in flooded rice field soils. These sequence clusters, which are dubbed USCa, USCg, Cluster I, and so on, are believed to be responsible for the uptake of CH4 from the atmosphere (Dunfield, 2007). Consumption of low atmospheric CH4 concentrations, equivalent to nanomolar concentrations in the soil aqueous phase, requires a higher affinity than consumption of the millimolar CH4 concentrations emerging in the anoxic soil of rice fields. The sequencing of pmoA recently resulted in the discovery that Crenothrix polyspora, which has been known as an uncultured filamentous bacterium in water treatment plants, is actually a methanotroph of the Gammaproteobacteria (Stoecker et al., 2006). After formation of methanol by the MMO, the further dissimilation pathway is shared in methanotrophic and methylotrophic bacteria. Methylotrophs, which oxidize various C1-compounds to CO2, cover a much broader range of taxa than the methanotrophs (Lidstrom, 1992). They may be characterized by targeting the gene (mxaF ) coding for the methanol dehydrogenase. This gene has also occasionally been assayed for characterizing the populations of the methanotrophs in rice field soil (Dubey et al., 2003; Henckel et al., 1999), but it is not specific to this group. Anaerobic methanotrophs also exist, but none of them has yet been isolated. They mostly occur in marine sediments within syntrophic microbial consortia, and oxidize CH4 to CO2 by using sulfate as electron acceptor (Reeburgh, 2003). Consortia oxidizing CH4 anaerobically with nitrate have been discovered in an anaerobic sewage digestor (Raghoebarsing et al., 2006). The anaerobic methanotrophs belong to the domain Archaea. They are characterized by the sequences of their 16S rRNA and mcrA genes, which form the so-called ANME clusters clustering within or next to the methanogenic order of Methansarcinales (Boetius et al., 2000; Hinrichs et al., 1999; Orphan et al., 2001, 2002; Schleper et al., 2005). The mechanism of CH4 activation is probably a reversal of the methylCoM reductase (Kru¨ger et al., 2003). These ANME clusters are frequently found in marine environments, but have not yet been detected in a rice field soil. Process studies indicate that anaerobic CH4 oxidation, possibly coupled to reduction of ferric iron, may occur in the deeper strata of a rice field (Miura et al., 1992; Murase and Kimura, 1994a, 1994b). However, these
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early experiments have not been followed up later on. In the following I will focus on aerobic CH4 oxidation.
3.2. Diversity, habitats, and ecological niches of aerobic methanotrophs 3.2.1. Niche differentiation In general, little is known about niche differentiation among the different groups of methanotrophs, perhaps with exception of the thermophilic (Methylothermus) and halophilic (Methylohalobius) genera, which only occur in such extreme environments. Several hypotheses have been raised for ecological differences among Type I and Type II methanotrophs. For example, it has been hypothesized that Type I methanotrophs prefer relatively low CH4 and high O2 concentrations, while Type II methanotrophs conversely prefer relatively high CH4 and low O2 concetrations (Amaral and Knowles, 1995). Test of this hypothesis using Italian rice field soil showed that Type I in contrast to Type II methanotrophs indeed prefer relatively low CH4 concentrations, but show no preference for high versus low O2 concentrations (Henckel et al., 2000). Furthermore, it was proposed that nitrogen availability would affect the methanotrophic populations, as Type II methanotrophs are N2 fixers while Type I are not (Hanson and Hanson, 1996). This hypothesis was confirmed by competition experiments using defined methanotrophic strains (Graham et al., 1993), and is consistent with the observation that ammonium fertilization seems to stimulate Type I more than Type II methanotrophs in the rice rhizosphere (Bodelier et al., 2000b). However, N2-fixing genes also occur among Type I methanotrophs (Auman et al., 2001) and thus there is no biochemical basis for the general validity of this hypothesis. In summary, we have not yet a theoretical understanding how the different methanotrophic genera differ ecologically. Until recently, it was believed that methanotrophs are obligate methylotrophic bacteria, that is cannot use carbon compounds with a carbon– carbon bond. However, this is obviously not true, since it has been shown that Methylocella spp. are able to use acetate as sole source for energy and carbon and actually prefer this compound over CH4 (Dedysh et al., 2005). Therefore, mixotrophic and heterotrophic growth have to be considered as possible ecological niches for methanotrophs in addition to methylotrophic growth. Hence, likely effectors that may form different ecological niches are concentrations of acetate, CH4, O2; availability of nitrogen and copper, pH and temperature. These factors do influence the capacity of CH4 oxidation in rice field soil (and other soils) (Bender and Conrad, 1995), but it is unknown how they operate on the microbiological scale. In summary, we may expect quite some diversity with respect to ecological
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niches, which is not quite anticipated from the relative similarity in the physiology of the many different methanotrophic taxa. In the following I will review the diversity and physiology of methanotrophs in the major habitats of rice field soil (Fig. 2) and under different management. 3.2.2. Bulk rice field soil Since the first report on aerobic methanotrophs in rice field soil (DeBont et al., 1978), they have been detected in all rice field soils tested. Most probable number counts are usually on the order of 104–107 bacteria per gram soil (Dubey and Singh, 2001; Eller et al., 2005; Gilbert and Frenzel, 1995; Joulian et al., 1997; Watanabe et al., 1995). Although the titers of methanotrophs are about an order of magnitude higher in the rhizosphere (Section 3.2.3), the bulk soil (Fig. 2) is the largest reservoir of the methanotrophic biomass in the rice field ecosystem (Eller and Frenzel, 2001; Eller et al., 2005). However, since methanotrophs require O2 for the oxidation of CH4, they must be in an inactive state when the bulk soil is flooded. They most probably survive the anoxic conditions as a seed bank until the field is drained and O2 becomes available again. This conclusion is consistent with the observation that most probable number counts are about one order of magnitude higher in nonirrigated versus irrigated rice fields (Dubey and Singh, 2001). Methanotrophs are able to survive periods of CH4 or O2 deficiency (Knief and Dunfield, 2005; Roslev and King, 1994; Schnell and King, 1995). Survival ability contributes to niche differentiation of soil methanotrophs. However, it is not quite clear by which taxa and mechanisms the survival is achieved. The composition of the methanotrophic community in rice field soil has been determined by molecular techniques targeting 16S rRNA and pmoA genes (Eller and Frenzel, 2001; Eller et al., 2005; Henckel et al., 1999, 2001; Hoffmann et al., 2002) or determining PLFA profiles (Bai et al., 2000; Macalady et al., 2002). Interestingly, the pmoA clusters (e.g., USCa) that are frequently found in upland soils (e.g., forests) have so far not been detected in the rice field ecosystem. Instead, the well-described genera of both Type I and Type II methanotrophs are detected, including Methylobacter, Methylomicrobium, Methyolococcus, Methylomonas, Methylocaldum, Methylosinus, and Methylocystis. Members of these genera are found in rice field soils from China, the Philippines, and Italy (Hoffmann et al., 2002). However, it is unknown what the ecological niches of these different methanotrophs are. Although the niche preferences of methanotrophs are still unclear, circumstantial evidence based on 16S rRNA analyses indicates that the community of Type II methanotrophs in Italian rice field soil may be rather stable throughout the season, while that of Type I methanotrophs changes more dynamically (Eller and Frenzel, 2001). Analysis of PLFA patterns in California rice fields, on the other hand, indicates that Type II methanotrophs correlate more with
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growth of rice plants than Type I methanotrophs (Macalady et al., 2002). In summary, there is a large diversity of methanotrophs in rice field soil, but little is known about the ecology of the different genera. 3.2.3. Soil surface In contrast to the anoxic bulk soil, the soil surface layers provide a suitable habitat for activity and proliferation of aerobic methanotrophs. This habitat is a shallow (<3 mm deep) layer, where O2 and CH4 gradients overlap (Gilbert and Frenzel, 1998). Nevertheless, CH4 oxidation in this shallow layer effectively scavenges >80% of the diffusive CH4 flux from the soil into the overlying water (Conrad and Rothfuss, 1991). The surface layers of rice field soils are similar in structure to the experimental agar gradient system studied by Amaral and Knowles (1995), who have found that the zonation is Type II methanotrophs on top of Type I methanotrophs according to their preferences for CH4 and O2 concentrations (Section 3.2.1). Experiments on cores of Italian rice field soil showed that the CH4 oxidation in the surface layer is inhibited by ammonium fertilization. Another study showed that Type II methanotrophs in Italian rice field soil are inhibited by ammonium (Mohanty et al., 2006). Hence, it is possible that Type II might be the prevalent methanotrophs in the surface soil layer. Molecular analyses have recorded the occurrence of both Type I and Type II methanotrophs in the soil surface layer (Henckel et al., 2001), but have not yet analyzed which of them account for the observed CH4 oxidation activity. amoA sequences in Japanese surface soil show the presence of Nitrosomonas spp. and Nitrospira spp. of the AMO Cluster I (Bowatte et al., 2006), but their contribution to CH4 oxidation is doubtful (Section 3.2.5). Drainage of the rice soil results in extension of the zone of CH4 oxidation, which then progresses from the surface into deeper layers (Henckel et al., 2001). This progression is accompanied with a change in the methanotrophic community at these depths, with Type I methanotrophs being the most dynamically changing group (Henckel et al., 2001). Eventually, drainage yields an aerated soil, which can harbor a relatively larger number of methanotrophic bacteria than the submerged soil does (Dubey and Singh, 2001). 3.2.4. Rice roots The rice roots with their partially oxic zones also provide suitable habitats for aerobic methanotrophic bacteria (DeBont et al., 1978). Indeed numbers of methanotrophs are usually higher in the rhizosphere than in the bulk soil (Fig. 2), and the surface of the roots is also colonized (Dubey and Singh, 2001; Eller et al., 2005; Gilbert and Frenzel, 1995). Methanotrophs can even invade the root cortex (Gilbert et al., 1998). Although the total methanotrophic biomass on the roots is much smaller than that in the soil, it consists of methanotrophs, which are not dormant (Section 3.2.2) but immediately
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active (Eller and Frenzel, 2001) and attenuate the CH4 flux through the plants into the atmosphere (Bosse and Frenzel, 1997). Similar as at the soil surface, the methanotrophs on the roots operate at the lower end of a CH4 concentration gradient, which extends from the soil toward the root surface (Gilbert and Frenzel, 1998) (Fig. 9). Oxygen penetrates only a short distance (<1 mm) beyond the root surface into the soil (Revsbech et al., 1999). Theoretical considerations suggest that O2 may be limiting for CH4 oxidation (Van Bodegom et al., 2001b). On the other hand, manipulation of the O2 content in the atmosphere has indicated that CH4 oxidation on the roots is limited by CH4 rather than O2 (Denier van der Gon and Neue, 1996). Concentrations of O2 in the rhizosphere are highly variable over a wide concentration range (Gilbert and Frenzel, 1998). Therefore, both concepts are possibly true depending on location, plant variety, and physiological status. With respect to O2 availability, it is important to which extent other processes compete with methanotrophs for O2, such as respiration by heterotrophic microorganisms or O2 consumption by nitrification, sulfide oxidation, or iron oxidation (Van Bodegom et al., 2001a, b) (Fig. 4). However, details on the interaction between the different aerobic microorganisms on rice roots are not known.
A
B 0.7 0.6 30 cm
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0.3 30 cm
0.2
10 cm 20 cm
0.1
10 cm 0 cm
0.0 0
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12 0 4 Distance from the root MAT (mm)
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Figure 9 Concentrations of CH4 in the porewater of (A) a three-week old, and (B) a six-week old rice microcosm. The different symbols indicate different soil depth. The figure has been adapted from Gilbert and Frenzel (1998).
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The extent to which methanotrophs on rice roots attenuate the flux of CH4 into the atmosphere is also unclear. Since CH4 from the rice fields is predominantly emitted by transport through the rice plants and only very little through the surface soil layers (Schu¨tz et al., 1989b), CH4 oxidation in the rhizosphere is an important process controlling the flux of CH4 into the atmosphere. Therefore, rhizospheric CH4 oxidation is considered in process-based flux models (Arah and Kirk, 2000; Van Bodegom et al., 2001c). Over the season, the contribution of plant-mediated transport and CH4 oxidation in the rhizosphere seem to develop in parallel (Schu¨tz et al., 1989b). Depending on the technique used, estimates of rhizospheric CH4 oxidation range between 0% and 94%, by which the CH4 flux is attenuated (reviewed by Groot et al., 2003). It is not quite clear which factors control the attenuation process, but local O2 concentrations are most likely among them (Van Bodegom et al., 2001b). Other important factors include local CH4 concentrations (Gilbert and Frenzel, 1998) and availability of nitrogen (Section 3.2.5). The composition of the methanotrophic community on the rice roots is probably a further important factor, which may vary with cultivar, soil, season, and management. The methanotrophic community on rice roots is highly diverse and consists of both Type I and Type II methanotrophs (Eller and Frenzel, 2001; Horz et al., 2001). Type I methanotrophs seem to be stimulated by ammonium fertilizer (Bodelier et al., 2000b). However, more details on the dynamics of methanotrophic populations in the root environment are not available. 3.2.5. Effect of nitrogen fertilization Treatment of rice fields with nitrogen fertilizers was found to either increase or decrease the flux of CH4 (Bronson et al., 1997; Minami, 1995; Schu¨tz et al., 1989a). One reason could be that ammonium interacts with methanotrophs and CH4 oxidation. This subject has been reviewed (Bodelier and Laanbroek, 2004). For example, ammonium can inhibit CH4 oxidation. Such an inhibition has frequently been observed in nonflooded upland soils for which the sink strength for atmospheric CH4 decreases on fertilization (King and Schnell, 1994; Mosier et al., 1991; Steudler et al., 1989). Inhibition of CH4 consumption by urea was also observed in Indian rice fields under rainfed (dryland) conditions (Singh et al., 1999). The mechanism of inhibition is probably based on the MMO of methanotrophs, which can also react with ammonia, so that less of the physiological substrate CH4 is oxidized (Bedard and Knowles, 1989). In rice field soil, an inhibitory effect of ammonium has frequently been observed when measuring the CH4 oxidation potential at elevated CH4 concentrations (Bender and Conrad, 1995; Cai and Mosier, 2000; Dubey, 2003). Increasing ammonium concentrations intensify inhibition, which is partially reversed by increasing CH4 concentrations (Cai and Mosier, 2000). These observations are in agreement with a competitive inhibition of the MMO by ammonia.
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Inhibition of CH4 oxidation by ammonium has also been observed in the surface soil of flooded rice fields (Conrad and Rothfuss, 1991). However, it is unclear whether this process also plays a role in the rhizosphere, where plants compete for available ammonium and thus keep its concentrations low (Verhagen et al., 1995). In rice fields, the opposite effect, that is stimulation of CH4 oxidation by ammonium, has often been observed (Dan et al., 2001; Kru¨ger and Frenzel, 2003; Singh et al., 1998b; Xu et al., 2004). Like any other organisms, methanotrophic bacteria require nitrogen as a nutrient for biomass formation. Nitrogen is usually limiting in planted rice fields. Lack of sufficient nitrogen may result in inactivation or dormancy of methanotrophs, which is overcome by addition of fertilizer (Bodelier et al., 2000a, 2000b). It is interesting that ammonium-based fertilizers seem to especially stimulate the Type I methanotrophs present in the rhizosphere of rice (Bodelier et al., 2000b). In bulk soil (both rice and forest soil), nitrogen fertilizer also seems to stimulate Type I methanotrophs, while Type II methanotrophs are inhibited (Mohanty et al., 2006). These results indicate that nitrogen fertilization has a differential effect on CH4 oxidation, which is dependent on the resident methanotrophic populations and how they react on nitrogen addition. This means that both inhibition and stimulation are theoretically possible, but depend on the availability (competition by plant uptake) and the community composition of the methanotrophs. The conclusion that the community composition of methanotrophs is important for the behavior of the soil with respect to CH4 oxidation is also consistent with the following observation of Chan and Parkin (2001). These authors found that the relatively low CH4 oxidation rates of soils oxidizing CH4 at ambient atmospheric concentrations were negatively correlated with the nitrogen content of the soil, thus indicating an adverse effect of the nitrogen status on methanotrophic activity (Fig. 10). Periodically flooded soils, on the other hand, which oxidized CH4 at elevated CH4 concentrations, exhibited relatively high oxidation rates, which were positively correlated to the nitrogen status of the soil (Fig. 10). Unfortunately, nothing is known about the methanotrophic bacterial communities in these soils. However, if we assume that the different availability of CH4, O2, and nitrogen in a particular soil translates into a different composition of the methanotrophic community, it is reasonable to assume that these different methanotrophs react differently on changes in the availability of their substrates and nutrients, that is, on fertilization. Treatment of soils with either ammonium or CH4 can result in stimulation or inhibition of growth and activity of methanotrophs and nitrifiers (Bender and Conrad, 1994). In analogy to the unphysiological reaction of methanotrophic MMO with ammonia instead of CH4, the nitrifier AMO can unphysiologically react with CH4 instead of ammonia (Bedard and Knowles, 1989; Bender and Conrad, 1994). Hence, nitrifiers may actively oxidize CH4 to methanol
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A
Effect of soil inorganic N on methane oxidation 100 Ambient CH4
Dp Methane oxidation (pmol CH4 g−1 h−1)
80
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60 40 McF Wht
20
Kn Kb
Df Kc Ki
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100 0
Kn Ki Wht
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Elevated CH4
−100 0
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10
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Inorganic N (mg N kg−1)
Figure 10 Methane oxidation rates as function of inorganic nitrogen concentration in different soils (labeled with letters). Methane oxidation was assayed (A) under ambient headspace CH4 concentrations and (B) elevated headspace CH4 concentrations. The figure has been adapted from Chan and Parkin (2001).
if they are numerous enough to compensate for the relatively low cellspecific CH4 oxidation activity. It has indeed been observed that nitrifiers can become important for uptake of atmospheric CH4, when agricultural upland fields are fertilized with nitrogen so that the methanotrophs are inhibited (Castro et al., 1994). In rice field soil, on the other hand, nitrifiers seem not to be actively involved in CH4 oxidation, which is exclusively catalyzed by methanotrophs (Bodelier and Frenzel, 1999). Quite in contrast, the methanotrophs compared to nitrifers seem to be strongly involved in ammonium oxidation (Bodelier and Frenzel, 1999). The negligible role of nitrifiers in CH4 oxidation is also probably a matter of the CH4 concentrations, which are high in rice fields, but low in upland soils,
Microbial Ecology of Methanogens and Methanotrophs
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which oxidize the CH4 in the ambient air. Hence rice fields require a much larger capacity for CH4 oxidation, which probably cannot be met by the unphysiological reaction of the nitrifier AMO. 3.2.6. Oxidation of atmospheric methane It should be noted that concentrations of atmospheric CH4 are extremely low, corresponding to about 2.4 nM in the aqueous phase. This concentration is about six orders of magnitude lower than the maximum CH4 concentration (about 1.3 mM) in flooded rice field soil. Aerated upland soils are a significant sink for atmospheric CH4 (Dunfield, 2007). Indian rice fields managed by dryland agriculture (rainfed conditions) also act mostly as a sink for atmospheric CH4 (Singh et al., 1998a, 1999). This is not so clear for irrigated rice agriculture. Flooded rice fields are drained at the end of the season and then are similar to an upland soil. Indeed, irrigated rice fields in the Indian Ganges plain were a source for atmospheric CH4, but turned into a sink during the subsequent wheat crop and fallow period (Singh et al., 1996). On the other hand, soil sampled from drained Japanese rice paddies in January decreased the ambient CH4 concentration within 1 day only by a small amount of about 0.1 ppmv (calculated from the data), which is barely significant (Thurlow et al., 1995). Chinese paddy soil also hardly oxidized CH4 at ambient concentrations (1.8 ppmv), but could oxidize CH4 at concentrations >10 ppmv (Yan and Cai, 1997). Italian rice soil apparently has the potential for oxidation of ambient CH4 concentrations, but the CH4 oxidation activity became inactive on drainage faster than the CH4 production activity so that the drained soil still acted as a small source rather than a sink for atmospheric CH4 (Ja¨ckel et al., 2001). A similar behavior has been observed in Chinese rice field soils, where CH4 oxidation potentials were high when the soil was kept wet during the intercropping period, but decreased when the soil was kept dry (Xu et al., 2003). In summary, there seem to be two contrasting situations among rice fields. Rice fields that are frequently drained such as in rainfed and dryland rice agriculture can act as sink for atmospheric CH4 if aerated. This situation seems to be encountered in India, where this type of rice management is widespread. The methanotrophs in these soils apparently are active enough to oxidize atmospheric CH4. Irrigated rice fields, on the other hand, apparently do not act as a sink for atmospheric CH4. Although these soils apparently contain methanotrophs that are able to oxidize atmospheric CH4, they lose their activity rapidly when the soil dries up. Interestingly, Philippine rice soil managed under rainfed conditions also did not act as a net sink for atmospheric CH4 even during the dry season planted with upland crops (Abao et al., 2000). Hence, it may not only be the management but a regional difference that affects the soil behavior. The most likely explanation is that the methanotrophic communities in Indian soils are different from those in other rice-growing countries, but this is unclear,
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since the only geographic overview did not include soils from India (Hoffmann et al., 2002). Unfortunately, there are also few CH4 flux data under field conditions that illustrate the situation after drainage and harvest of irrigated rice fields. However, these few show only an increased CH4 emission on drainage, being due to the release of CH4 bubbles entrapped in the flooded soil, but do not show any net uptake of CH4 from the atmosphere (Denier van der Gon et al., 1996; Wassmann et al., 1994).
4. Mitigation of Methane Emission from Rice Fields Rice fields are flooded to grow rice with the highest possible yield, in order to meet the increasing demand for food. Therefore, any technique used for mitigation of CH4 emission must not compromise food production. The knowledge of the microbial processes involved in CH4 production and emission helps to devise the optimal mitigation strategies. Many studies discuss this problem and offer mitigation options (Majumdar, 2003; Mosier et al., 1998; Wassmann et al., 2000a; Yagi et al., 1997). The following management techniques are usually listed: water management, nutrient management, and crop management. Water management is probably the most efficient mitigation option. Mid-season drainage or frequent intermittent drainage generally results in a drastic reduction of CH4 production and emission. The microbiological background explaining the efficiency of the drainage strategy has been discussed in this review (Section 2.2.2). The most important argument against frequent drainage is that this might increase production and emission of N2O (Bronson et al., 1997; Cai et al., 1997), which has a tenfold higher global warming potential than CH4. However, N2O is usually only emitted for short periods and management can be adjusted such that N2O emission does not compromise the mitigation of CH4 emission in terms of global warming potential (Nishimura et al., 2004; Towprayoon et al., 2005; Yang et al., 2003; Yue et al., 2005; Zheng et al., 2000). Proper management of nitrogen fertilization is in particular important. The most important nutrient management is the amendment of soil with organic matter, which results in a drastic increase of CH4 production and emission. The microbiological basis of this management technique has been discussed (Section 2.2.3). Mitigation of CH4 emission can be achieved when as little organic matter is added to the soil as possible. When organic matter has to be added at all, composted organic matter is preferable over uncomposted material, such as straw. Another nutrient management is addition of oxidants, such as ferric iron or sulfate to the soil, which suppress CH4 production and reduce emission
Microbial Ecology of Methanogens and Methanotrophs
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to quite some extent (Section 2.2.4). However, these mitigation strategies have to be carefully checked against possible adverse effects on the crop yield. Addition of nitrogen fertilizer may result in reduced CH4 emission rates, as CH4 oxidation in the rhizosphere is enhanced (Section 3.2.5). The mitigation effect seems to be relatively short-lived, as the plants rapidly scavenge the nitrogen, but field experiments are scarce and equivocal (Dan et al., 2001; Kru¨ger and Frenzel, 2003; Singh et al., 1998b; Xu et al., 2004). Nitrogen fertilization has the potential to increase the production and emission of N2O. However, very little N2O is normally produced when the rice field is kept flooded (Bronson et al., 1997; Cai et al., 1997). Crop management has also some promise as possible mitigation option for CH4 emission. However, this option must be handled carefully as it affects the crop directly. Fortunately, it seems that increasing the grain yield may go in parallel with reducing the CH4 emission (Denier van der Gon et al., 2002). The beneficial effect is probably due to decreased production of root exudates that drive methanogenesis (Section 2.2.6). However, the plant variety also affects the extent of gas ventilation between soil and atmosphere and thus affects the availability of O2 in the rhizosphere and thus the oxidation of CH4 by methanotrophs (Section 3.2.4). Virtually no data exist on the effect of plant variety on methanogenic and methanotrophic microbial communities in the rhizosphere and on the roots. This knowledge might help to optimize the development of rice varieties with maximum grain yield and minimum support for CH4 emission.
5. Conclusions and Outlook The study of microbial communities has for a long time been limited by the availability of suitable methods. Hence, our knowledge of biogeochemical processes and fluxes is much more mature than our knowledge of the microbial communities that catalyze these processes. This is also true for rice field ecosystems. Nevertheless, substantial progress has been made by applying molecular techniques to the microbiota in rice fields. With respect to the populations of methanogens and methanotrophs these molecular techniques have mostly targeted 16S rRNA genes as phylogenetic marker genes and mcrA and pmoA genes as functional marker genes, respectively. The combination of molecular analysis of the microbial community and functional analysis of biogeochemical processes basically allows the assignment of function to microbial populations. In practice this can be a very difficult task if microbial communities and/or biogeochemical processes are complex. In this respect, the methanogenic and methanotrophic microbial communities in rice field soils provide a rather well-defined model system.
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Both methanogens and methanotrophs catalyze chemical reactions that can be described by a stoichiometrically exact equation. The biochemistry of these processes is rather well understood. The microbes depend on these reactions as sole source for energy. They consist of monophyletic groups, and the phylogenetic trees of the 16S rRNA and functional genes (those coding for the key enzyme) are congruent. Studying these chemically welldefined processes and well-defined guilds of microorganisms clearly helps assigning structure and function within these microbial communities. Flooded rice fields likewise are rather well-defined ecosystems and are relatively easily accessible to experimentation. Wetland soils in contrast to upland soils exhibit a macroscopic redox zonation, which describes the potential occurrence of chemical reactions. Admittedly this zonation can be rather complex around the rice roots, but it is still less complex than in soil crumbs of a forest or meadow ecosystem. Whereas such a redox zonation is also found in lake sediments or sediments of other wetland ecosystems, rice fields have the additional advantage of being managed ecosystems. This makes it possible to collect samples without worrying about sediment history. It is also possible to collect dry soil samples when the fields are drained, transport these samples to a laboratory or greenhouse and restart the rice ecosystem from scratch by flooding and planting. Such excellent experimental accessibility is not given for a natural wetland. Therefore, it has been possible to describe processes and microorganisms involved in the production and oxidation of CH4 relatively well, as reviewed in this article. Hence, it has been possible to describe macroscopic events, such as the temporal change of CH4 production after flooding of the soil or the effect of fertilization, also by analyzing methanogenic or methanotrophic microbial communities. This provides some interpretation of the macroscopic events by processes on the microscopic level. Such interpretation is necessary to gain confidence in how production and oxidation of CH4 is controlled by environmental factors, to generate appropriate process-based models and make regional and global predictions. This task is certainly not yet finished and many more data are required, in particular to better understand the processes occurring in the rhizosphere. Nevertheless, it becomes apparent that most of such data will provide a microbial interpretation of the biogeochemistry. On the other hand, these data do not necessarily provide a profound understanding of the ecology of the methanogens and methanotrophs. In other words, microbial analysis serves the understanding of biochemistry in a descriptive way, but does not help so much to understand why the microbial community is as it is. This demands a better understanding of the intrinsic ecology of the microorganisms, in particular learning more details about the ecological niches that the different microbial species occupy. Although this review has also addressed the question after the various ecological niches of the methanogens and methanotrophs, there are only very few answers. For example, we are now beginning
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to understand niche differentiation (e.g., acetate concentration) between Methanosarcina spp. on the one hand and Methanosaeta spp. on the other. Nevertheless, it is highly unsatisfying that we have still no idea why we have such a large diversity of methanogens and methanotrophs in the rice field soil, although they in principle all serve the same function for the ecosystem. I am advocating the study of the rice ecosystem as a suitable model system for gaining more profound knowledge on the ecology of microorganisms in general. This may be of additional value to that describing the microbiology of rice fields as a dominant source for food and an important ecosystem for the global change of atmospheric greenhouse gases.
REFERENCES Abao, J., Bronson, K. F., Wassmann, R., and Singh, U. (2000). Simultaneous records of methane and nitrous oxide emissions in rice-based cropping systems under rainfed conditions. Nutr. Cycl. Agroecosyst. 58, 131–139. Achtnich, C., Bak, F., and Conrad, R. (1995). Competition for electron donors among nitrate reducers, ferric iron reducers, sulfate reducers, and methanogens in anoxic paddy soil. Biol. Fertil. Soils 19, 65–72. Adachi, K. (1999). Isolation of hydrogenotrophic methanogenic archaea from a subtropical paddy field. FEMS Microbiol. Ecol. 30, 77–85. Adhya, T. K., Bharati, K., Mohanty, S. R., Ramakrishnan, B., Rao, V. R., Sethunathan, N., and Wassmann, R. (2000). Methane emission from rice fields at Cuttack, India. Nutr. Cycl. Agroecosyst. 58, 95–105. Agnihotri, S., Kulshreshtha, K., and Singh, S. N. (1999). Mitigation strategy to contain methane emission from rice-fields. Environ. Monit. Assess. 58, 95–104. Amaral, J. A., and Knowles, R. (1995). Growth of methanotrophs in methane and oxygen counter gradients. FEMS Microbiol. Lett. 126, 215–220. Arah, J. R. M., and Kirk, G. J. D. (2000). Modeling rice plant-mediated methane emission. Nutr. Cycl. Agroecosyst. 58, 221–230. Arth, I., and Frenzel, P. (2000). Nitrification and denitrification in the rhizosphere of rice: The detection of processes by a new multi-channel electrode. Biol. Fertil. Soils 31, 427–435. Arth, I., Frenzel, P., and Conrad, R. (1998). Denitrification coupled to nitrification in the rhizosphere of rice. Soil Biol. Biochem. 30, 509–515. Asakawa, S., and Hayano, K. (1995). Populations of methanogenic bacteria in paddy field soil under double cropping conditions (rice-wheat). Biol. Fertil. Soils 20, 113–117. Asakawa, S., Morii, H., Akagawa-Matsushita, M., Koga, Y., and Hayano, K. (1993). Characterization of Methanobrevibacter arboriphilicus SA isolated from a paddy field soil and DNA-DNA hybridization among M. arboriphilicus strains. Int. J. Syst. Bacteriol. 43, 683–686. Asakawa, S., Akagawa-Matsushita, M., Morii, H., Koga, Y., and Hayano, K. (1995). Characterization of Methanosarcina mazeii TMA isolated from a paddy field soil. Curr. Microbiol. 31, 34–38. Asakawa, S., Akagawa-Matsushita, M., Koga, Y., and Hayano, K. (1998). Communities of methanogenic bacteria in paddy field soils with long-term application of organic matter. Soil Biol. Biochem. 30, 299–303.
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C H A P T E R
T W O
Strategies of Plants to Adapt to Mineral Stresses in Problem Soils Syuntaro Hiradate,* Jian Feng Ma,† and Hideaki Matsumoto† Contents 66 69 70 73 85 86
1. Introduction 2. Fe-Deficiency Stress 2.1. Chemistry of Fe in soils 2.2. Mechanism of Fe acquisition in plants 2.3. Genetic improvement of Fe-acquisition ability in plants 3. Al-Toxicity Stress 3.1. Chemistry of Al and plant-originated Al-detoxifying agents in soils 3.2. Mechanism of Al toxicity 3.3. Mechanism of Al-toxicity tolerance 4. P-Deficiency Stress 4.1. Chemistry of P and plant-originated P-dissolving agents in soils 4.2. Mechanism of P acquisition in plants 4.3. Genetic improvement in plants to tolerate P deficiency 5. Future Prospects References
87 95 99 104 105 107 111 112 112
Mineral imbalance in high-input agricultural ecosystems has become an acute concern in many developed countries. Relapse into low-input agricultural ecosystems, however, will cause mineral stresses to crops, resulting in reduced food productions. Under such natural soil conditions, some endemic plants can tolerate the mineral stresses because they have evolved to adapt to the stresses. Numerous studies have been conducted to clarify the chemistry of the mineral elements of interest in rhizosphere and to utilize the tolerant mechanisms in plants. In this chapter, the authors reviewed the research progress on molecular scale mechanisms of Fe-deficiency, Al-toxicity, and P-deficiency stresses in soils and their tolerances by plants. In low Fe-availability conditions, two Fe-acquisition mechanisms of plants have been clarified: enhanced Fe dissolution in rhizosphere * {
National Institute for Agro-Environmental Sciences (NIAES), Tsukuba, Ibaraki 305-8604, Japan Research Institute for Bioresources, Okayama University, Kurashiki 710-0046, Japan
Advances in Agronomy, Volume 96 ISSN 0065-2113, DOI: 10.1016/S0065-2113(07)96004-6
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2007 Elsevier Inc. All rights reserved.
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by secreted proton/reductants/chelators followed by reduction of Fe3þ to Fe2þ and specific uptake of Fe2þ (Strategy I) and Fe dissolution by secreted hexadentate Fe3þ-transporting molecule (phytosiderophore) by forming Fe3þ-phytosiderophore complex followed by specific uptake of the complex (Strategy II). Two tolerant mechanisms against high Al-toxicity of soils have been reported: exclusion of Al from cytoplasm (exclusion mechanism) and detoxification of Al in plants (internal detoxification mechanism). Phosphorous acquisition mechanisms of plants from low P-availability soils would be (1) alteration of root architecture, (2) secretion of organic acids, (3) secretion of phosphatase, and (4) enhanced expression of P transporter on roots. Some of the molecular mechanisms for the expression of the tolerances and their application to the genetic improvement are also reviewed.
1. Introduction Plants are considered to have evolved to adapt to diverse environmental conditions. As a result, plants are distributed all over the world, even in unfavorable conditions such as alpine belts, deserts, and the polar regions. The purpose of the expansion of their habitats seems to be to find more suitable and advantageous places to reproduce their offspring, rather than to maximize their biomass production. Human beings have utilized some of these plants as foods by cultivating them in agricultural fields. After the industrial evolution, with the increase in the demand for food production, the agricultural fields have been altered to maximize yields through the application of chemical fertilizers, manures, soil amendments, pesticides, and so on. This effort has been successful in sustaining the increasing population, but it also has caused alteration of soil characteristics, such as soil pH, soil nutritional conditions, and soil water conditions. Nowadays, serious mineral imbalance in agricultural fields has become very common all over the world. In Japan, for example, for the year 1997, on an average basis, 218 kg N ha1 was input as fertilizers, manures, and natural sources, and 108 kg N ha1, output as crop products, crop by-products, and denitrification, resulting in 109 kg N ha1 of environmental emission (Mishima, 2001). For phosphate, 158 kg P2O5 ha1 was input as fertilizers, manures, and natural sources, 26 kg P2O5 ha1, output as crop products and by-products, and 1 kg P2O5 ha1 was environmental emission, and the remaining 132 kg P2O5 ha1 was accumulated in soils (Mishima et al., 2003). In addition, a large amount of lime has been commonly applied to the agricultural fields to ameliorate soil acidity. Such a high-input-dependent agriculture is very common in developed countries. It should be noticed that such mineral imbalance will cause not only environmental contamination of these minerals themselves, but also heavy metal contamination (e.g., Cd, Zn, Cu) derived from impurities of
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chemical fertilizers and manures (Mishima et al., 2004, 2005). To overcome the problem of mineral imbalance in soils, ideally, the input should be minimized, and the mineral cycles in an agricultural ecosystem should be completed in a small area. Such a low-input agriculture could restore the original and natural soil conditions, but they are not always suitable for crop productions. With a high average annual precipitation of ca. 1800 mm, the natural soil conditions in Japan are potentially acidic and poor in plant nutrition. Nitrate, especially, is very easily depleted from the surface horizon because it has a very weak affinity on soils. Phosphate bioavailability in Japanese natural soils is also very low because Japanese soils have been more or less influenced by volcanic depositions, which provide P-fixing components (Dahlgren et al., 2004). Therefore, if chemical fertilizers, manures, and soil amendments cannot be applied, the Japanese agricultural soils will be acidified, and the availabilities of plant nutrients in soils, such as N and P, will be decreased, resulting in problem soils. Such potentially low-pH soils, with low bioavailability of N and P, prevail in a region receiving heavy rain all over the world. On the contrary, in a region receiving less rain, alkali metals, such as Naþ, Kþ, and Ca2þ, are transferred from subsoils to the 2 uppermost horizon and precipitated with HCO 3 and/or CO3 , resulting in problem soils, such as saline, sodic, and calcareous soils with a high pH value. Even in such problematic soil conditions, however, some endemic plants can grow and reproduce without any applications of materials because they have adapted to the stressful conditions during their long evolving history. The adaptation mechanisms could provide useful tools to carry out the low-input agriculture. This has fostered recent researches aimed at clarifying the mechanisms governing plant adaptation to mineral stresses and to utilize such abilities to boost plant productions in the low-input conditions. The mechanisms in plants to adapt to stress involve tolerating the stressful conditions or avoiding them. Some of the mechanisms are constitutive and are active before exposure to the stress. In other cases, plants that are exposed to stress alter their physiology as a response (inducible), thereby acclimating themselves to the stressful conditions (Buchanan et al., 2000). The tolerating and inducible mechanism would be advantageous for plant production in agriculture. Here, we will review this type of plant adaptation mechanisms toward mineral stresses. Organic ligands, which can form complexes with metal cations, often play an important role in this kind of mechanism (for their chemical structures, see Fig. 1). These organic ligands either could be secreted from plants to soils or could remain in plant tissues. Many of them have carboxylic (COOH) group or catechol (1,2-dihydroxybenzene) structure, which are responsible for the formation of complexes with soil components and/or metal cations through coordination bond. Their complexation reactions are not simple because these reactions are affected by
OH
COOH
HCOOH H3C
Formic acid
Lactic acid
H3C
COOH
OH
HOOC
OH Malic acid
OH COOH
HOOC
Citric acid
COOH
HOOC
HOOC
HOOC
HO
COOH
OH
HO
O
COOH
OH
Maltol
OH
OH
2-(3′,4′-Dihydroxyphenyl)ethylamine (Dopamine)
CH3
OH
COOH
Succinic acid
NH2
OH O
Tartaric acid
Butyric acid
OH
COOH
Glutamic acid
OH COOH
OH
3-(3′,4′-Dihydroxyphenyl)alanine (DOPA)
COOH
Malonic acid
H3C
NH2
NH2
OH HOOC
OH
COOH
COOH
Propionic acid
HOOC
CH3
Alanine
HOOC H3C
Catechol
NH2
HOOC COOH Oxalic acid
OH
Salicylic acid
COOH
HOOC
Acetic acid
OH
COOH
OH OH
Hydroquinone
Gallic acid
Piscidic acid
OH
HOOC OH H3C
COOH
COOH
O
a-Crotonic acid
HOOC
COOH
trans-Fumaric acid
O
HO
HO OCH3
trans-Ferulic acid
OH Chlorogenic acid
HO
O
OH
OH OH OH
OH
Catechin
Figure 1 Chemical structures of plant-originated important organic ligands in adapting to mineral stresses. Also see Fig. 5.
Strategies of Plants to Adapt to Mineral Stresses
69
solution pH, concentration and kind of the organic ligands and coexisting ions, amount and kind of soil components, and so on. The reaction and behavior characteristics of the organic ligands in soils and plants, which would explain their roles in the adaptation mechanisms, are also reviewed.
2. Fe-Deficiency Stress Iron is 1 of the 14 essential mineral elements (N, P, S, K, Mg, Ca, Fe, Mn, Zn, Cu, B, Mo, Cl, and Ni) for higher plants. In plants, Fe controls many lifesupporting reactions such as chlorophyll synthesis and oxidation-reduction reactions. The mean content of Fe in the Earth’s crust is 41 g kg1, and Fe contents in soils range from 2 to 550 g kg1 (Sparks, 2003). The amount of Fe required for sufficient plant growth is at the 0.1 g kg1 level (Taiz and Zeiger, 1998), but Fe deficiency in plants frequently occurs, especially in alkaline soils (soil pH value > 7.0). The alkaline soils occur where limestone is the parent material of the soils and are formed when the secondary bicarbonate 2 (HCO 3 ) and/or carbonate (CO3 ) minerals are accumulated on the surface of the soils. The latter condition is very common in soil areas where the annual precipitation is less than the annual evapotranspiration, such as in semiarid and arid zones and in greenhouse conditions. In these conditions, the pH of the soil solution is kept alkaline (>7) by the presence of the bicarbonate and/or carbonate minerals. Solubility of Fe3þ is decreased 1/1000-fold per unit increase of the solution pH by the formation of Fe3þ hydroxides (precipitates). Therefore, in alkaline soils, the concentration of Fe3þ in soil solutions tends to become lower than the critical concentration required for the Fe3þ acquisition of plants, and therefore plants face difficulty in Fe acquisition (Lindsay and Schwab, 1982). In the alkaline soil solutions, the concentrations of the other metal ions are also decreased by the formation of hydroxides (precipitates), but since Fe3þ has the lowest solubility among them (solubilities of hydroxides: Ca2þ > Mg2þ > Cd2þ > Fe2þ > Zn2þ > Cu2þ > Al3þ > Fe3þ; Stumm and Morgan, 1996), the occurrence of Fe deficiency would therefore be more frequent than those of other metal element deficiencies in alkaline soils. From the data indicating that 55% of the world’s land receive <20 inches (ca. 500 mm) of annual precipitation, Wallace and Lunt (1960) estimated that 25–30% of the world’s land is calcareous in the surface horizon and potentially troublesome for Fe-deficiency-susceptible plants. The Fe-deficient and potentially Fe-deficient soils could appear in many parts of Arenosols, Calcisols, Chernozems, Gypsisols, Kastanozems, Regosols, Solonchaks, Solonetz, and Vertisols [for distributions and characteristics of these soils, see Bridges et al. (1998) and Deckers et al. (1998)].
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2.1. Chemistry of Fe in soils 2.1.1. Iron-containing components in soils In soils, Fe is involved in primary and secondary minerals, including humus-Fe complexes. The important primary minerals, in which Fe is a major constituent, are biotite (K(FeII,Mg)3Si3AlO10(OH)2), pyroxene [augite, (Ca,Mg,FeII,Al,Ti)2(Si,Al)2O6], amphibole [hornblende, Ca2(Mg,Fe,Al)5 (Si,Al)8O22(OH)2], olivine ((Mg,FeII)2SiO4), ilmenite (FeTiO3), magnetite (Fe3O4), titanomagnetite (Fe3xTixO4), and pyrite (FeS2). They are weathered into various secondary Fe minerals, such as goethite (a-FeOOH), hematite (a-Fe2O3), lepidocrocite (g-FeOOH), ferrihydrite (Fe2O32FeOOH2.6H2O), and maghemite (g-Fe2O3), as an effect of soil environmental factors such as time, temperature, pressure of O2 and CO2, water content, redox potential (Eh), pH, activity of microorganisms, and coexisting molecules (e.g., Al, Si, phosphate, and organic molecules). Because these factors are highly variable, and because they openly and permanently communicate with neighboring compartments of the ecosystem (atmosphere, biosphere, hydrosphere, lithosphere), complete equilibrium is usually not attained (Cornell and Schwertmann, 1996). A ratio of [Fe in secondary minerals]/[Fe in primary plus secondary minerals], which has been used to evaluate the degree of weathering, has been reported to be ca. 0.2–0.3 for the formerly glaciated soils of the Northern Hemisphere (ca. 1000–2000 years old; Cornell and Schwertmann, 1996), indicating that the weathering rate of primary minerals into secondary minerals is too slow for plants to utilize the primary minerals as an Fe source. Solubility of Fe for humus-Fe complexes, which are localized at surface horizon, is strongly influenced by soil pH and coexisting secondary Fe minerals. Therefore, secondary Fe minerals play an important role in controlling the concentration of Fe in soil solution. 2.1.2. Chemistry of secondary Fe minerals in soils Goethite and hematite, which have the lowest Fe3þ solubilities (Fig. 2), are common in calcareous and/or alkaline soils. Lepidocrocite is frequently found in anaerobic, clayey, and noncalcareous soils (e.g., paddy soils) of cooler and temperate regions, and it has a moderate Fe3þ solubility. Maghemite is found in aerobic soils of the tropics and subtropics (Cornell and Schwertmann, 1996), and it also has a moderate Fe3þ solubility. Ferrihydrite, which is a poorly crystalline Fe mineral with the highest Fe3þ solubility, is composed of porous and fine particles. Ferrihydrite is ubiquitous in less-weathered soils (e.g., volcanic ash soils), but it is also found in calcareous and/or alkaline soils although its amount is very small. The solubility diagram calculating the total concentration of soluble inorganic Fe, 4þ which is the sum of Fe3þ, Fe(OH)2þ, FeðOHÞþ 2 , Fe2 ðOHÞ2 , and FeðOHÞ4 , indicates that there is a region of minimum solubility around
71
Strategies of Plants to Adapt to Mineral Stresses
Low
Solubility product p(Fe3+)+3p(OH−) 45 44
Solubility of Fe3+
43 42 41 40 39
Goethite (α-FeOOH) Hematite (α-Fe2O3) Lepidocrocite (γ -FeOOH)
Maghemite (γ -Fe2O3) Ferrihydrite (Fe2O3•2FeOOH•2.6H2O)
High
38 37 36
Figure 2 Solubility products of major secondary Fe minerals in soils. Data are cited from Schwertmann andTaylor (1989).
pH 7–8 for all secondary Fe minerals, and that the total concentration of soluble inorganic Fe is controlled by the solid phase as in the following order; ferrihydrite>lepidocrocite>hematite>goethite (Cornell and Schwertmann, 1996). In general, several kinds of secondary Fe minerals are present in a soil simultaneously. This means that these secondary Fe minerals do not reach a thermodynamic equilibrium, thereby the solubility product p(Fe3þ)þ3p(OH) values for soils would be controlled by an Fe-containing component having the highest solubility in the soils. Norvell and Lindsay (1982) reported a solubility product value range of 39.1–39.7 for three Mollisols developed under udic or aridic moisture regime with a soil pH range of 6.8–7.2 and a DTPA-extractable Fe range of 5.0–6.5 mg kg1 (>4.5 mg kg1 of this value will be adequate for crop growth in terms of Fe nutrition; Lindsay and Norvell, 1978), indicating that relatively crystallized ferrihydrite controls the concentration of Fe3þ in these soils (Fig. 2). The surface of secondary Fe minerals has active surface hydroxyls (frequently termed type A hydroxyls and Lewis acid sites; Sposito, 1989), which can cause adsorption through ion exchange reactions (Fig. 3A) and ligand exchange reactions (Fig. 3B). The active surface hydroxyls are amphoteric; they can develop positive and/or negative charge depending on solution pH value and can adsorb anions (e.g., NO 3 , Cl , ClO4 ) and cations (e.g., Naþ, Kþ, Mg2þ, Ca2þ) by ion exchange reactions through electrostatic force (Coulomb force). In ligand exchange reactions, specific ions, such as phosphates, F, and carboxylic acids, displace the active surface
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Active surface hydroxyls
A Ion exchange reaction Metal (hydr)oxides O O M OH2+0.5 Cl− O O +0.5 M OH2 O O
OH−
H+
O O M OH−0.5 O O M OH2+0.5 O O
O O M OH−0.5 Na+ O O −0.5 M OH O O
OH−
H+
pH at point of zero net charge
pH < point of zero net charge
pH > point of zero net charge
B Ligand exchange reaction O O M OH−0.5 O O M OH2+0.5 O O
O −O
C R
+ OH −O
P O
O O M O O O M O O O
O C R−0.5 OH P O
OH− + −0.5
H2O
OH
OH Coordination bond
Figure 3 Schematic representation of active surface hydroxyls on metal (hydr)oxides (A) adsorbing indifferent ions such as Cl and Naþ by ion exchange reactions through positive or negative charge developed depending on solution pH and (B) adsorbing specifically adsorbed ions such as carboxylic acids and phosphate by ligand exchange reactions.‘‘M’’denotes trivalent octahedral metal cations such as Al and Fe.
hydroxyls associated with the secondary Fe minerals and form strong coordination bonds between the specific ions and the Fe atom on the secondary Fe minerals, resulting in the release of H2O or OH into soil solution. This reaction also occurs on the surfaces of other soil components having the active surface hydroxyls, such as Al (hydr)oxides, edges of layer aluminosilicates, hydroxyaluminosilicate (HAS) ions complexed with 2:1 layer silicates, allophane, imogolite, and Al- and Fe-humus complexes. Adsorption caused by the ligand exchange reaction is often irreversible and, in general, stronger than that by the ion exchange reaction. The adsorption mechanism depends on the combination of an adsorbed molecule and an adsorbing soil component, as summarized by Parfitt (1978). The densities of the active surface hydroxyls, which are determined by acid/base titration, are in similar range for most of the secondary Fe minerals (e.g., 1.68, 1.67, and 1.97 sites nm2 for goethite, lepidocrocite, and ferrihydrite, respectively; Cornell and Schwertmann, 1996). Therefore, the adsorption capacities of the secondary Fe minerals largely depend on their specific surface areas. The specific surface areas of soil minerals depend on their crystallinity, and in general, ferrihydrite shows the largest value (100–400 m2 g1) over goethite (8–200 m2 g1), hematite (2–90 m2 g1), lepidocrocite (15–260 m2 g1), and maghemite (8–130 m2 g1) (Cornell and Schwertmann, 1996).
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2.2. Mechanism of Fe acquisition in plants As described above, Fe solubility is extremely low in alkaline and calcareous soils, and therefore crops cultivated on these soils often suffer from Fe deficiency inducing chlorosis, resulting in low production and poor quality. However, some plant species have developed efficient strategies for acquiring and taking up Fe from low-solubility sources. Two distinct strategies have been identified (Fig. 4; Curie and Briat, 2003; Ma and Nomoto, 1996; Ro¨mheld, 1987; Ro¨mheld and Marschner, 1986; Schmidt, 2003). Strategy I is found in dicotyledonous and nongraminaceous monocotyledonous species, whereas Strategy II is found only in graminaceous species. They differ in the way to solubilize and transport Fe as detailed below. 2.2.1. Strategy I Strategy I plants release proton (Hþ) and reductants/chelators, such as electron (e), organic acids, and phenolics, into the rhizosphere (Brown, 1978; Chaney et al., 1972; Ro¨mheld and Marschner, 1983), thereby increasing Fe solubility (Fig. 4A). The secretion of Hþ increases Fe solubility by promoting the following reaction:
FeðOHÞ3 þ 3Hþ ! Fe3þ þ 3H2 O (37.0 to 39.4 of logK for ferrihydrite; Schwertmann and Taylor, 1989) This type of reaction is termed Hþ-promoted dissolution (Stumm, 1992). Secretion of e is also effective because the following dissolution reaction is enhanced:
FeðOHÞ3 þ 3Hþ þ e ! Fe2þ þ 3H2 O (15.8 of logK; Sparks, 2003) The dissolved Fe2þ has a high solubility even in alkaline solutions, as indicated by a low-solubility product p(Fe2þ)þ2p(OH) of 15.1 for Fe (OH)2 for the following reaction (Dean, 1978):
FeðOHÞ2 ! Fe2þ þ 2OH Bertrand and Hinsinger (2000) reported that Strategy I plants (pea and rape) transformed crystalline goethite into amorphous Fe (acid oxalateextractable Fe) and acquired the Fe, but Strategy II plant (maize) could not acquire Fe from goethite. It seems that reductants play an important role in the transformation of goethite into more labile form.
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A
Strategy I Rhizosphere
Cytoplasm
Cell membrane ATP H+
H+ ADP
Fe(III) (hydr)oxides
Phenolics
Phenolics
Fe3+- chelate
NADH+ + H+ FRO2 NAD+
Fe2+ IRT1
B
Fe2+
Strategy II Rhizosphere
Cytoplasm
Cell membrane
Met cycle SAM MAs
X
MAs R1
Fe(III) (hydr)oxides
COOH COOH
COOH
NH
OH
HN
Fe3+-MAs
R2
O O R1
O N R2
O Fe O N
YS1
Fe3+-MAs
O O
Figure 4 Schematic representation of two strategies for Fe acquisition. Strategy I is found in dicots and nongramineous monocots, while Strategy II is found only in gramineous plants.
Common low-molecular-weight organic acids can dissolve Fe by forming complexes (Table 2). Jones et al. (1996a) indicated that, under optimal condition (pH < 7), citrate and malate secreted from Strategy I plants could contribute to the Fe acquisition through the formation of plant-available Fe3þ-organic complexes in the rhizosphere. In high-pH soil conditions (pH7.0), the Strategy I plants must rely on other sources of Fe because citrate-mediated Fe dissolution is slow and Fe-citrate complexes are unstable
Strategies of Plants to Adapt to Mineral Stresses
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in such high-pH conditions, or the root acidification of the rhizosphere could allow the formation of stable Fe3þ-organic complexes. It should be noted that these low-molecular-weight organic acids are susceptible to microbial degradation and immobilization in soils ( Jones, 1998; Strom et al., 2001). The release of Hþ is mediated via a plasma membrane (PM) P-type ATPase. It is likely that one or more members of the Hþ-ATPase family are activated under Fe deficiency (Palmgren, 2001). The increase in PM HþATPase activity has been associated with an increased steady-state level of a 100-kDa polypeptide in cucumber roots (Dell’Orto et al., 2000). Solubilized Fe3þ is then reduced to Fe2þ by a reductase in PM of root epidermal cells (Moog and Bru¨ggermann, 1994). The activity of root Fe3þchelate reductase is greatly enhanced under Fe deficiency (Brown, 1978). Genes encoding Fe3þ-chelate reductase have been cloned from Arabidopsis thaliana (FRO2) (Robinson et al., 1999). FRO2 is expressed in roots, and its mRNA levels are increased by Fe deficiency. Expression of FRO2 is regulated at both the transcriptional and posttranscriptional levels (Connolly et al., 2003). Robinson et al. (1999) predicted that FRO2 encodes an integral membrane protein of 725 amino acids, with conserved FAD- and NADPHbinding sites. A topology study showed that A. thaliana FRO2 encoding protein (AtFRO2) contains eight transmembrane helices, four of which build up a highly conserved core of the protein (Schagerlof et al., 2006). This core is present in the entire flavocytochrome b family, and a large watersoluble domain of FRO2, which contains NADPH, FAD, and oxidoreductase sequence motifs, is localized on the inside of the membrane. Database searching showed that there are eight FRO-like genes in A. thaliana, AtFRO1 to AtFRO8 (Connolly et al., 2003). Among them, AtFRO3, AtFRO4, AtFRO5, AtFRO7, and AtFRO8 also showed ferric-chelate reductase activity although their activity was lower than that of AtFRO2 (Wu et al., 2005). These genes are expressed in different tissues; AtFRO3 is mainly expressed in the roots as AtFRO2, whereas AtFRO5 and AtFRO6 are in the shoots and flowers, AtFRO7 in the cotyledons and trichomes, and AtFRO8 in leaf veins. It seems that AtFRO2 and AtFRO3 are two ferric-chelate reductases mainly acting in Fe acquisition in the roots, while AtFRO5, FRO6, FRO7, and FRO8 are responsible for Fe homeostasis in different tissues of shoots of A. thaliana (Wu et al., 2005). AtFRO1 and AtFRO4 are weakly expressed and have not been characterized. Homologue genes of AtFRO2 have also been isolated from pea (FRO1) and tomato (FRO1) (Li et al., 2004; Waters et al., 2002). FRO1 has an overall similarity of 74% and an identity of 55% with FRO2 (Waters et al., 2002). Reduced Fe2þ is subsequently taken up through a specific transporter for 2þ Fe in the PM (Fox et al., 1996). The gene encoding the Fe2þ transporter, iron-regulated transporter 1 (IRT1), a member of the Zn- and Fe-regulated transporter (ZIP) in A. thaliana, has been cloned by functional complementation in Saccharomyces cerevisiae mutants defective in Fe acquisition (Eide et al., 1996; Vert et al., 2002). IRT1 is essential for Fe uptake in A. thaliana
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(Vert et al., 2003). It is expressed only in root epidermal cells and induced under Fe deficiency. A gene of another ZIP family member, IRT2, shows perfect coregulation with IRT1 and FRO2 genes in epidermal cells. However, it was found that the function of IRT2 is distinct from that of IRT1 because (1) IRT2 is located on intracellular membranes, (2) unlike IRT1, knocking out IRT2 does not result in Fe-deficiency symptoms, and (3) overexpressing IRT2 in a irt1 knockout mutant does not restore the mutant to a wild-type plant (Vert et al., 2003, 2004). Other homologues of IRT1 have been characterized in A. thaliana, tomato (Lycopersicon esculentum), soybean (Glycine max), and rice (Oryza sativa) (Bughio et al., 2002; Eckhardt et al., 2001; Grotz et al., 1998; Vert et al., 2001). In tomato, the expression of LeIRT1 is upregulated in both the roots and the shoots in response to Fe deficiency, but LeIRT2 does not (Schikora et al., 2006). The LeIRT1 is polarized in membranes of distal tangential walls of epidermal cells. Expression of IRT1 is also controlled at the transcriptional and posttranscriptional levels coordinately with FRO2 (Connolly et al., 2002). In addition, three transporter proteins unrelated to IRT1 and IRT2, designated AtNRAMP1, 3, and 4 (natural resistance-associated macrophage protein), have also been implicated in Fe2þ uptake by the root epidermal cells. AtNRAMP1, 3, and 4 are capable of Fe uptake when expressed in yeast, and their expression in plant roots is induced by Fe deficiency (Curie et al., 2000; Thomine et al., 2000). However, expression of AtNRAMP3:: GFP and AtNRAMP4::GFP protein fusions showed that AtNRAMP3 and AtNRAMP4 proteins are targeted to the vacuolar membrane (Lanquar et al., 2004; Thomine et al., 2003), suggesting that these two proteins may be involved in the remobilization of vacuolar metal pools. A transcriptional regulator gene controlling Fe acquisition, FER, has been isolated from tomato (Ling et al., 2002). The FER encodes a protein containing a conserved basic helix-loop-helix DNA-binding domain, and a mutant line defective in FER function called T3238fer shows a loss of the entire Fe-deficiency responses of Strategy I. FER is expressed in the tomato roots. Interestingly, subsequent studies showed that FER is required only for the transcriptional regulation of LeFRO1, LeIRT1, and LeNRAMP1 in tomato roots, but not of LeIRT2, LeNRAMP3, or CHLN (a gene encoding a nicotianamine synthase) (Ling et al., 2004). An FER homologue (FIT1) isolated from the A. thaliana genome is able to completely complement the T3238fer mutant of tomato, suggesting that FER is a universal regulator controlling the high-affinity Fe-uptake system in Strategy I plants (Yuan et al., 2005). In fact, an A. thaliana homologue of the tomato FER gene was required for induction of Fe-mobilization genes in A. thaliana (Jakoby et al., 2004). Furthermore, Brumbarova and Bauer (2005) showed that FER can affect transcription in the nucleus and its action is controlled by Fe supply at the transcriptional level, posttranscriptional or protein stability levels, and protein action level.
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2.2.2. Strategy II In contrast to the Strategy I plants, Fe acquisition by the Strategy II plants is characterized by secretion of hexadentate Fe3þ-chelating substances (phytosiderophores) and by their specific uptake system (Ma and Nomoto, 1996; Takagi, 1993). After the first finding of phytosiderophore secretion from Fe-deficient plants (Takagi, 1960, 1976), a chemical structure of the first phytosiderophore, mugineic acid (MA, Fig. 5), where ‘‘mugi’’ and ‘‘ne’’ mean barley (Hordeum vulgare) and root in Japanese, respectively, was elucidated by Takemoto et al. (1978). Since then, nine MA-related analogues, including MA (MAs), have been isolated and identified from various graminaceous species and cultivars, including two novel compounds (3-hydroxy-20 -deoxymugineic acid and 20 -hydroxyavenic acid A) identified from perennial grasses (Lolium perenne and Poa pratensis) (Fig. 5; Ma and Nomoto, 1996; Ma et al., 1999, Ueno et al., 2007). All MAs possess six functional groups (an azetidine nitrogen, a secondary amine nitrogen, three carboxylic oxygens, and a 300 -terminal alcoholic oxygen) in each molecule to coordinate with Fe3þ and form 1:1 complexes (Mino et al., 1983; Sugiura et al., 1981). The secreted MAs dissolve soil Fe by forming the complexes (MAsFe) (Iwashita et al., 1983; Nomoto et al., 1981), which will be taken up specifically by the plant roots. The ability of MAs secretion by graminaceous plants well explains the Fe-acquisition ability of the plants (Hinsinger, 1998; Kawai et al., 1988; Ro¨mheld and Marschner, 1990). A similar Fe-acquisition mechanism has been reported in many microorganisms, in which the Fe-solubilizing agents have been termed siderophores. MAs have a similar function with that of siderophores but are produced by higher plants; hence, they are termed phytosiderophores. Phytosiderophores (MW: 294–336 Da) are smaller than most of siderophores (MW: 500–1000 Da). The process of Fe acquisition by the Strategy II plants can be divided into four main steps: (1) biosynthesis of phytosiderophores inside of the roots, (2) secretion of phytosiderophores to the rhizosphere, (3) phytosiderophorepromoting solubilization of low-solubility Fe by chelation in soils, and (4) uptake of phytosiderophore-Fe3þ complexes by the roots (Fig. 4B). 2.2.2.1. Biosynthesis of phytosiderophores in graminaceous plants The biosynthesis of phytosiderophores in the roots is induced under Fe deficiency. Phytosiderophores are biosynthesized during the daytime and accumulated within the root cells until secretion during the next morning (Ma and Nomoto, 1996). Biosynthetic studies have shown that all phytosiderophores are synthesized from three molecules of L-methionine (Ma and Nomoto, 1992, 1993, 1994; Mori and Nishizawa, 1987; Shojima et al., 1990), which is supplied vigorously by a methionine cycle (Ma et al., 1995). All phytosiderophores share the same biosynthetic pathway from L-methionine to 20 -deoxymugineic acid (DMA, Fig. 5), whereas hydroxylation at different positions of DMA gives rise to different derivatives of phytosiderophores in various graminaceous species.
HO 4
499 49 1 COOH COOH COOH 19 199 29 299 3’ NH 399 OH 2 N
3
OH
29-Hydroxyavenic acid A
Methionine cycle S-Adenosyl-methionine (×3)
49 499 1 COOH COOH COOH 19 199 29 299 39 NH 2 N HO 4 399 OH 3 1 COOH 19 2
Avenic acid A 3
N
49 499 COOH COOH 199 29 299 39 NH 399 OH
HO
3
4
3-Hydroxy-2′-deoxymugineic acid
2′-Deoxymugineic acid (DMA)
1 49 499 COOH COOH COOH 19 2 199 29 299 N 3 39 NH 399 OH OH
4
Mugineic acid (MA)
1 49 499 COOH COOH COOH 19 2 199 29 299 HO N 39 NH 399 OH 3 4
OH
3-Hydroxymugineic acid (HMA)
Figure 5
HO 3
N
49 499 COOH COOH 199 29 299 39 NH 399 OH
4
4 1 49 499 COOH COOH COOH 19 2 199 29 299 N 39 NH 399 OH
1 COOH 19 2
3-epi-Hydroxy-29-deoxymugineic acid 1 49 499 COOH COOH COOH 19 2 199 29 299 HO N 39 NH 399 OH 3 OH
4
3-epi-Hydroxymugineic acid (epi-HMA)
1 COOH 19 2 N
49 499 COOH COOH 199 29 299 39 NH 399 OH OH
Distichonic acid
Chemical structures of mugineic acid (MA) and its derivatives (MAs) with their biosynthetic pathways.
Strategies of Plants to Adapt to Mineral Stresses
79
Most genes encoding the enzymes in the biosynthetic pathway from L-methionine to various phytosiderophores in barley have been cloned, including SAMS, NAS, NAAT, IDS2, IDS3, and DMAS (Higuchi et al., 1999; Nakanishi et al., 2000; Negishi et al., 2002; Takahashi et al., 1999; Takizawa et al.,1996). Interestingly, the enzymes involved in hydroxylation differ with the position of C of phytosiderophores and their precursors. IDS3 is located on the short arm of the chromosome 4H and responsible for hydroxylation at the C-20 position of DMA resulting in MA (Ma et al., 1999; Nakanishi et al., 2000), whereas IDS2 is located on the short arm of the chromosome 7H and responsible for the hydroxylation at the C-3 position of DMA resulting in 3-epi-HMA in barley. Two cis-acting elements, IDE1 and IDE2, of the barley IDS2 gene promoter for Fe-deficiency-inducible and root-specific expression were identified (Kobayashi et al., 2003). 2.2.2.2. Secretion of phytosiderophores to the rhizosphere Biosynthesized phytosiderophores must be secreted to the rhizosphere for acquisition of low-solubility Fe from soils. The secretion of phytosiderophores shows a distinct diurnal rhythm (Takagi et al., 1984). Generally, secretion of phytosiderophores starts about 3 h after sunrise and continues for 3 h. The timing of the secretion is controlled by temperature around the root environment (Ma et al., 2003). When the temperature is high, the secretion occurs early, while secretion is delayed when the temperature becomes low. Such distinct diurnal rhythm of phytosiderophore secretion would be advantageous for Fe acquisition because more concentrated phytosiderophores could be served to the rhizosphere and MAs-degrading microorganisms could not follow the trait of the root tip elongation, thereby MAs could persist longer. The site of secretion is localized in the apical root zones (Marschner et al., 1987). The secretion of phytosiderophores is accompanied by a symport efflux of equimolar Kþ. The secretion is inhibited by KCN and dicyclohexylcarbodiimide (DCCD) (Takagi, 1990), suggesting that the secretion system is energy-dependent. From observations of the ultrastructure of barley root cells, particular vesicles are likely to be involved in the secretion of phytosiderophores (Nishizawa and Mori, 1987). A cDNA microarray analysis showed that polar vesicle transport is involved in the diurnal secretion of phytosiderophores (Negishi et al., 2002). However, the genes responsible for the secretion of phytosiderophores have not been identified. A maize (Zea mays) mutant (ys3) defective in the secretion of phytosiderophores was isolated and characterized (Ro¨mheld et al., 2004). This mutant will provide a powerful tool for isolating genes related to phytosiderophore secretion. 2.2.2.3. Phytosiderophore-promoting solubilization of low-solubility Fe in soils Following secretion, MAs solubilize Fe in rhizosphere by chelation. Mugineic acid can dissolve Fe from ferrihydrite, but the amount of Fe dissolved by MA is extremely reduced when MA reacts with goethite,
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hematite, lepidocrocite, magnetite, and maghemite (Fig. 6, Hiradate and Inoue, 1998a; Inoue et al., 1993), indicating that ferrihydrite is an important source of Fe for the Strategy II plants. The mechanism of MA-promoted Fe dissolution includes the adsorption of MA on ferrihydrite by a ligand exchange reaction and desorption of the MA holding an Fe3þ atom, which is an MA-Fe3þ (1:1) complex (MAFe) (Fig. 7). The formed MAFe is also readsorbed on ferrihydrite, although the affinity of MAFe to ferrihydrite is lower than that of MA. This is because a free MA has six vacant functional groups to bind with an Fe atom, whereas in MAFe, these functional groups are already occupied by an Fe3þ atom. This also explains why the adsorption of MA on ferrihydrite is faster than that of the MAFe (Hiradate, 2007). When MAFe is adsorbed on a more stable secondary Fe mineral such as goethite, hematite, and lepidocrocite, only MA alone is desorbed from their surfaces because of the high stability of these crystalline Fe minerals (Fig. 7, Inoue et al., 1993). The adsorption affinities on Fe minerals are in the following order: phosphateMA>sulfateMAFe>nitratechloride. Therefore, adsorption of MA on Fe minerals is competitively inhibited by phosphate, and as a result, subsequent dissolution of Fe (desorption of MAFe) is also inhibited (Hiradate et al., 1998b). The affinity of sulfate on Fe minerals is lower than that of MA and comparable to that of MAFe, therefore, adsorption of MA is
100
A Ferrihydrite
C Hematite
B Goethite
D Lepidocrocite
75 50
Concentration (mM)
25 0 100 75 50 25 0 3
4
5
6
7
8
9
10 11 3 4 Equilibrium pH
5
6
7
8
9
10 11
Figure 6 Interactions of mugineic acid (MA) with four representative secondary Fe minerals as a function of equilibrium pH. Each secondary Fe mineral was added to 100-mM MA solution, and concentrations of MA (○) and Fe () in the filtrate were determined after 4 h of reaction time (Inoue et al.,1993).
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Strategies of Plants to Adapt to Mineral Stresses
Fe minerals
Soil solution
O O Fe O
O OH
O
OH2
OH
MAFe(Mugineic acidFeIII complex)
OH2
O Fe OH
OH
O
O
Adsorption of MA (ligand exchange)
OH2
OH2
MA(Mugineic acid)
O
O Fe
Soil solution
O
O Fe OH
OH O
O Fe
O
Fe minerals
O
Desorption of MA
O
O
Dissolution of Fe by MA (desorption of MAFe)
Adsorption of MAFe
O OH
O Fe O
O MA
O Fe O
H2O
O
O Fe O O Fe minerals
OH Soil solution
Figure 7 Schematic representation of the mechanism of adsorption/desorption reactions between mugineic acid (MA) and secondary Fe minerals (ferrihydrite).
not severely inhibited by sulfate, but readsorption of MAFe on Fe minerals is significantly inhibited by sulfate, resulting in the increase of MAFe dissolution (Hiradate et al., 1998b). Nitrate and chloride ions are also common in soils, but since they do not participate in the ligand exchange reaction, their influences on the interactions of MA/MAFe with soil minerals are minimal (Hiradate et al., 1998b). Other chelate compounds, such as ethylenediaminetetraacetic acid (EDTA), diethylenetriaminepentaacetic acid (DTPA), and deferriferrioxamine B (a siderophore), can also dissolve Fe3þ from soils because they can form complexes with Fe3þ with much higher affinity than MAsFe (Table 1). The amounts of Fe dissolved by these chelate compounds from calcareous soils, however, are significantly reduced by the presence of other metal cations such as Ca2þ, Mn2þ, Cu2þ, and Zn2þ. The effects of these metal cations on the Fe dissolution promoted by MAs, on the other hand, are minimal (Takagi et al., 1988). The Strategy II plants have some advantages in adapting to calcareous soils over the Strategy I plants because they can solubilize low-solubility Fe even in a very high-pH condition in the presence of excessive bicarbonate, Ca2þ, and Mg2þ (Ro¨mheld, 1991; Ro¨mheld and Marschner, 1986). The three major phytosiderophores, epi-HMA, MA, and DMA, have similar chelating abilities (Table 1). It has been reported that hydroxylation of phytosiderophore slightly increased affinity for Fe3þ (von Wiren et al., 2000). However, Fe3þ-solubilizing ability from Fe minerals is decreased with increasing the hydroxyl substitutions in the following
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Table 1 Formation constants for metal complexes with organic ligands including phytosiderophores Metal cations Cu2þ Fe3þ Ni2þ Zn2þ Fe2þ Mn2þ Ca2þ a b c d e f g h
epi-HMAa
MAa
DMAa
EDTAb
DTPAc
17.9e
18.1e 18.1f 14.9e 12.7e 10.1e 8.3e 3.8e
18.7e
18.8g 25.1g 18.6g 16.5g 14.3g 14.0g 10.6g
21.5g 28.6g 20.3g 18.8g 16.6g 15.6g 10.7g
e
14.4 12.4e 10.0e 8.0e 4.3e
e
14.8 12.8e 10.5e 8.3e 3.3e
DFOBd 30.6h
For nomenclatures and chemical structures, see Fig. 5. Ethylenediaminetetraacetic acid. Diethylenetriaminepentaacetic acid. Deferriferrioxamine B. Murakami et al. (1989). Sugiura et al. (1981). The Japan Society for Analytical Chemistry (1994). Neilands (1981).
order: DMA > MA > epi-HMA, probably because hydroxylation of MAs would result in more adsorption on Fe minerals (Takagi, 1993). Interestingly, when MA reacts with ferrihydrite, the maximum Fe dissolution occurs at equilibrium pH 7–8 (Fig. 6A), which corresponds to the most problematic soil pH range for Fe acquisition by plants. The maximum Fe dissolution at equilibrium pH 7–8 also occurs when the concentration of MA and the amount of ferrihydrite are changed (Inoue et al., 1993). This is a consequence of the adsorption characteristics of MA/MAFe on ferrihydrite and the stability of MAFe, depending on solution pH. Because the surface of ferrihydrite has high affinity to OH, adsorption of MA/MAFe decreases with increasing solution pH by competitive adsorption between MA/MAFe and OH (Fig. 6A). Thus, the reduction in Fe dissolution with decreasing equilibrium pH lower than 7–8 is caused by the adsorption of MA/MAFe on ferrihydrite. At equilibrium pH 7–8 or higher, MAFe is not stable and begins to dissociate becoming free MA and Fe (precipitate of Fe hydroxide). This dissociation feature can be also explained by the formation constant for MAFe (Hiradate and Inoue, 1998b). The MA-promoted Fe dissolution from ferrihydrite occurs quickly, and it reaches maximum after 4 h of reaction time with high efficiency (e.g., at equilibrium pH 7.5, ca. 50% of added MA forms MAFe, Fig. 6A). Such a quick dissolution reaction of Fe by MA might be advantageous for Fe-absorbing plants because MAs, being secreted to the rhizosphere, are easily decomposed by microorganisms (Watanabe and Wada, 1989).
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83
Mugineic acid can interact with other soil components having active surface hydroxyls. For example, allophane and imogolite, which are poorly crystalline aluminosilicates, have active surface hydroxyls coordinated to Al on their surfaces, and so they can adsorb MA. The affinity of MA to Si, however, is very low, so hydroxyls coordinated to Si do not exchange with MA. Therefore, the amount of MA adsorption on layer aluminosilicates, such as smectite, is very small. Mugineic acid can also dissolve Al from allophane and imogolite by forming MA-Al complex, but it cannot dissolve Al from crystalline Al minerals such as gibbsite (g-Al(OH)3) and smectite. Among Al hydroxides and aluminosilicate minerals, allophane and imogolite are the most active in interacting with MA, but the amounts of MA adsorption and MA-metal complex formation are smaller than in the case of ferrihydrite. This is because MA has higher affinity to FeIII than Al (Hiradate and Inoue, 1999). It is well known that soil organic matter can adsorb hydrophobic organic compounds by hydrophobic interactions. However, because MAs are extremely hydrophilic, an interaction with soil organic matter is less likely to happen. MAs cannot be retained on a C18 reversed-phase HPLC column without ion pair reagents (Hiradate and Inoue, 1996). Hence, in a study conducted by Hiradate (1994), little MA was adsorbed on Hþ-type of humic acids prepared from volcanic ash soil (A-type), calcareous soil (Rp-type), and peat soil (P-type). When the exchangeable cations on the humic acids were replaced with Fe3þ or Al3þ, MA could interact with the Al- and Fe-humate complexes through coordination bonds with these metals, as well as with Fe and Al minerals. To estimate the amount of available soil Fe for the Strategy II plants, quantification of ferrihydrite in soils, for example acid-oxalate extraction (Schwertmann, 1964), is effective. In calcareous and/or alkaline soils, where plant Fe deficiency is common, the amount of ferrihydrite is extremely small, and it correlates with the amount of Fe dissolved by MA (Hiradate and Inoue, 2000). To predict Fe deficiency of plants, DTPA soil test has been used in calcareous and/or alkaline soils. In this soil test, soil Fe is extracted with a 5-mM DTPA solution in the presence of 10-mM CaCl2 buffered at pH 7.3, and Fe is dissolved by forming DTPA-Fe3þ complex in solution. Although the formation constant of DTPA-Fe3þ complex (logK ¼ 28.6, Table 1) is extremely greater than that of MAFe complex (logK ¼ 18.1), the fundamental mechanism of the Fe dissolution by DTPA soil test could be similar to that of Strategy II. The amount of DTPA soil test-extractable Fe is closely correlated with that of MA-extractable Fe (Hiradate and Inoue, 2000). It should be noted that DTPA soil test is effective in predicting Fe deficiency for the Strategy II plants but not so effective for the Strategy I plants (Loeppert and Inskeep, 1996). 2.2.2.4. Uptake of phytosiderophore-Fe3þ complexes by the roots Physiological studies have shown that, in contrast to the uptake mechanism in the Strategy I plants, the phytosiderophore-Fe3þ complex is taken up across
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Syuntaro Hiradate et al.
the PM of root cortex cells as an undissociated molecule in the Strategy II plants (Ro¨mheld and Marschner, 1986). This uptake process was suggested to be energy dependent because the uptake was inhibited by metabolic inhibitors (Takagi et al., 1984). Rate of Fe uptake as a form of MA-Fe3þ complex was 100–1000 times faster than the rates as a form of synthetic chelate-Fe3þ complex, such as EDTA-Fe3þ and HEDTA-Fe3þ complexes, in barley plants (Ma et al., 1993; Ro¨mheld and Marschner, 1986). The Fe-uptake rate from phytosiderophore-complexed Fe3þ is much higher than corresponding complexes with Cu2þ, Zn2þ, Co2þ, and Co3þ in Fe-deficient barley roots (Ma et al., 1993). These findings suggest that there is a strict recognition of the stereostructure of the MA-Fe3þ complex for uptake (for review, see Ma and Nomoto, 1996). Ma et al. (1993) showed that a complex formed between an Fe3þ and a ligand containing a chelating unit, a 30 (S),300 (S)-N[30 -carboxy(300 -carboxy-300 -hydroxypropylamino)propyl]glycine moiety (20 -dehydroxydistichonic acid moiety; Fig. 5) in phytosiderophores, is transported by a specific transport system in barley roots. A gene necessary for the uptake of phytosiderophore-Fe3þ complexes (ZmYS1) was identified in maize (Curie et al., 2001). This gene is expressed both in the roots and the shoots and upregulated by Fe deficiency. Further characterization showed that YS1 is an Hþ-Fe3þ-phytosiderophore cotransporter (Schaaf et al., 2004). The transporter has a broad specificity, and it can transport various phytosiderophore-bound metals including Zn2þ, Cu2þ, and Ni2þ and nicotianamine-bound Ni2þ, Fe2þ, and Fe3þ (Roberts et al., 2004; Schaaf et al., 2004). However, a gene encoding a transporter specific to phytosiderophore-Fe complexes (HvYS1) was identified in barley (Murata et al., 2006; Fig. 8). HvYS1 was predicted to encode a polypeptide of 678 amino acids and to have 72.7% identity with ZmYS1. In contrast to ZmYS1, the HvYS1 gene is mainly expressed in the roots and its expression is also enhanced under Fe deficiency. HvYS1 is localized in the PM of the epidermal root cells. Furthermore, HvYS1 functionally complemented yeast strains defective in Fe-uptake grown on a medium containing MA-Fe3þ complex, but not nicotianamine-Fe3þ complex. The expression of HvYS1 in Xenopus oocytes showed the strict specificity for both metals and ligands: HvYS1 transports only Fe3þ chelated with phytosiderophores. Differences in the specificity of YS1 among different plant species remain to be examined in the future. Rice seems to have both Strategy I and II systems for Fe transport (Ishimaru et al., 2006). There are 18 YS1-like genes in rice genome and one of them (OsYS1) shows transport activity for DMA-Fe3þ complex. On the other hand, OsIRT1 and OsIRT2 were also cloned from rice (Ishimaru et al., 2006). They are expressed predominantly in roots and their expression is upregulated by Fe deficiency.
85
Strategies of Plants to Adapt to Mineral Stresses
A
NH2
TM 1
TM 2
TM 3
TM 4
TM 5
TM 6
TM 7
TM 8
TM 9
TM 10
TM 11 COOH
C B
Fe(III)-MA
Fe(II)-NA
VEC HvYS1 ZmYS1 D
Current (%)
120 100 80 60 40 20
C
l
3
A (II
Fe
Fe
)-N
A
A II) o( C
n(
II)
-M
A
-M
A
M I)-
i(I N
M
A
)-M (II
Zn
II) u(
C
Fe
(II
I)-
-M
M
A
0
Figure 8 A specific transporter (HvYS1) of MAs-Fe(III) complex in barley. (A) Predicated transmembrane domains of HvYS1. (B) Localization of HvYS1 in the root. (C) Yeast complementation test showing ligand specificity. (D) Xenopus occyte assay showing metal specificity of HvYS1.
2.3. Genetic improvement of Fe-acquisition ability in plants Attempts have been made to develop Fe-deficiency-tolerant plants with genetic manipulation. Overexpression of some Fe-deficiency response genes has resulted in tolerance to Fe deficiency. For example, introduction of a reconstructed yeast ferric reductase gene, refre1, into tobacco (Nicotiana tabacum) resulted in enhanced tolerance to low Fe-availability conditions (Oki et al., 1999). This modified gene can work at a high pH, resulting in high reductase activity even at a high-pH condition. A study showed that heterologous expression of the A. thaliana chelateFe3þ reductase gene, FRO2, significantly enhances Fe3þ reduction in roots and leaves in transgenic soybean (Vasconcelos et al., 2006). The enhanced Fe3þ reductase activity led to reduced chlorosis, increased chlorophyll concentration, and a lessening in biomass loss in the transgenic events between Fe treatments as compared to control plants grown under hydroponics that mimicked Fe-sufficient and Fe-deficient soil environments. However, the
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Syuntaro Hiradate et al.
data indicated that constitutive FRO2 expression under non-Fe stress conditions may lead to a decrease in plant productivity as reflected by reduced biomass accumulation in the transgenic events. To produce rice with high tolerance to Fe deficiency, a barley genomic DNA fragment containing two naat genes, which encode nicotianamine aminotransferase involved in the biosynthesis of phytosiderophores, was introduced into rice (Takahashi et al., 2001). The two transgenes were expressed in response to low Fe-nutritional status in both the shoots and roots of the transformants. The transgenic rice showed a higher nicotianamine aminotransferase activity and secreted larger amounts of phytosiderophores than nontransformants under Fe-deficient conditions. As a result, the transgenic rice showed an enhanced tolerance to low Fe availability and resulted in 4.1 times greater grain yields than that of the nontransformant rice when grown on an alkaline soil.
3. Al-Toxicity Stress Aluminum is the third most abundant element in the Earth’s crust (mean content: 82 g kg1), therefore in soils, the level of Al content is high which ranges between 10 and 300 g kg1 (Sparks, 2003). Among the abundant elements in the Earth’s crust and soils, Al is exceptionally toxic to most plants and mammals, and it is the solubilized Al that exerts toxic effects. Plants, therefore, are at risk of Al toxicity when grown in acidic soils because Al solubility increases with decreasing soil pH values in acidic pH range. When a soil shows a pH(H2O) value <5.0 or pH(KCl) value <4.5, there would be a risk of Al toxicity for many crops, and when it shows a pH(H2O) value <4.0, plants would be most likely at risk of both Al and Hþ toxicity (Saigusa et al., 1980). The Al toxicity can be more precisely predicted by measuring soil exchangeable Al with 1-M KCl: Al toxicity occurs when exchangeable Al > 2 cmolc kg1 and >1 cmolc kg1 for common agricultural crops and Al-toxicity-sensitive crops, respectively (Dahlgren et al., 2004). Soil exchangeable acidity Y1 is also a good indicator for predicting Al toxicity (Saigusa et al., 1980). Natural acidic soils occur where the soils are composed of acidic materials poor in basic cations or potentially acidic materials (e.g., pyrite FeS2) and are formed when the soil exchangeable basic cations have been depleted because of leaching, resulting in the exchange of surface bases with Hþ followed by the dissolution of toxic Al3þ (Fig. 9). The latter condition is very common in soil areas where the annual precipitation is greater than the annual evapotranspiration. Sumner and Noble (2003) estimated that the total area of topsoils and subsoils affected by acidity amounts to approximately 30% and 75% of the total ice-free land area of the world, respectively. Aluminum toxicity would appear in major parts of Acrisols, Albeluvisols, Alisols,
87
Strategies of Plants to Adapt to Mineral Stresses
A
Exchangeable cations
− -− − −
Exchangeable cations
Leaching by rainfall
− Soil minerals
B
−
Ca2+
−
Soil minerals
K+
H+ − −
Mg2+
− H+
H+ H+ H+ H+
Exchangeable cations
C Dissolution of soil minerals
−
H+
− −
Al3+ −
Soil minerals−
H+
Ca2+, Mg2+ , K+
Figure 9 Schematic representation of (A ! B) depletion of exchangeable basic cations by rainfall and (B ! C) dissolution and formation of Al3þ by concentrated Hþ. Soil minerals assume 2:1 layer silicates such as montmorillonite and vermiculite.
Ferralsols, Podzols, and Unbrisols, and in part of Andosols, Cambisols, Fluvisols, Gleysols, Histosols, and so on. Anthropogenic acidic soils have been reported to occur under the influences of dense acidic depositions (Alewell, 2003) and chemical fertilizers (Bolan and Hedley, 2003; Tang and Rengel, 2003).
3.1. Chemistry of Al and plant-originated Al-detoxifying agents in soils 3.1.1. Phytotoxic Al in soils In acidic soils, the phytotoxic Al will be present in water-soluble form, and most of it will be retained in soils in exchangeable form with 1-M KCl. This assumption is supported by the fact that 1-M KCl-extractable Al can predict the Al toxicity of soils. The exchangeable Al will be in quasi-equilibrium with and readily served from Al-humate complexes (e.g., in surface horizons of volcanic soils, Dahlgren et al., 2004). Primary minerals also contain Al, but their conversions into exchangeable Al or secondary Al minerals are very slow. In a solution, Al could be solubilized as monomer (one molecule includes one Al atom) or polymer (one molecule includes two or more Al atoms), which coordinates only with inorganic ligands or includes organic ligands. Although many soluble Al species are easily formed in laboratory conditions, chemical forms of soluble Al in soil solutions are strictly limited by soil conditions such as soil pH, coexisting molecules, and soil components. In acidic soils, the most probable form of phytotoxic Al would be Al3þ, an inorganic monomeric Al ion which is composed of one octahedral Al coordinated with six aquo ions denoted frequently by AlðH2 OÞ3þ 6 . This Al species can be converted into a hydrolyzed species, as shown in the following reaction: 2þ AlðH2 OÞ3þ þ H2 O 6 þ OH ! AlðH2 OÞ5 ðOHÞ
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Syuntaro Hiradate et al.
The equilibrium constant p*K1 for this reaction has been reported to vary between 4.3 and 5.86 depending on experimental methods and researchers (Nordstrom and May, 1996). The wide variation may be partly due to the difficulty in determining the experimental ‘‘equilibrium.’’ Nordstrom and May (1996) reported that the most reasonable value for the p*K1 value is 5.000.04. Because Al phytotoxicity is observed in a soil with pH(H2O)< 2þ 5.0 or pH(KCl)<4.5, AlðH2 OÞ3þ 6 would dominate over Al(H2O)5(OH) and other more hydrolyzed monomeric Al species in such acidic conditions. However, in a laboratory condition that constructs a pure AlCl3NaOH system, a polymer Al species, AlO4 Al12 ðOHÞ24 ðH2 OÞ7þ 12 molecule (Al13) which consists of one tetrahedral Al in the center surrounded by 12 octahedral Al, can be formed as a major Al species at (OH)/(Al3þ) molar ratio between 1.0 and 2.25 even at a solution pH<5.0 (Bottero et al., 1980). Parker et al. (1989) reported that the plant growth-inhibitory activity of reactive polymer Al species (composed primarily of Al13) is extremely stronger than that of monomer Al species; the Al concentrations required to cause 50% root inhibition to Stafford soybean, Tyler wheat, and Seneca wheat by the reactive polymer Al species (as Al) were ca. 5, 1.5, and 1.5 mM, respectively, and those by monomer Al species were ca. 20, 10, and >40 mM, respectively. Although Al13 could be formed easily in laboratory conditions (Parker and Bertsch, 1992), its occurrence in soils, which has been once reported in organic horizons of forested Spodosols (Hunter and Ross, 1991), is exceptional (Bertsch and Parker, 1996) because the presence of Al13 in soil solution is suppressed by sulfate (Boisvert and Jolicoeur, 1999; Kerven et al., 1995), phosphate (Parker et al., 1989), silicate (Boisvert and Jolicoeur, 1999; Hiradate et al., 1998; Larsen et al., 1995), clay minerals (Taniguchi et al., 1999, 2001), humic substances (Hiradate and Yamaguchi, 2003; Yamaguchi et al., 2004), and low-molecular-weight organic ligands such as acetate, oxalate, lactate, tartarate, citrate, salicylate, catechol, chlorogenic acid, L-DOPA, maltol, and so on (Forde and Hynes, 2002; Krishnamurti et al., 1999, 2004; Masion et al., 1994; Thomas et al., 1993; Yamaguchi et al., 2003). Saigusa et al. (1995) showed that soil-applied monomer Al was more toxic to rice than the soil-applied polymer Al, probably because polymer Al would be more selectively and strongly adsorbed on the soil (a nonallophanic Andosol) than monomer Al. Yamaguchi et al. (2004) showed that Al13 was transformed into a mixture of monomeric Al [presumably AlðH2 OÞ3þ 6 ] and precipitates (Al(OH)3) after 210 days of incubation period, indicating that Al13 is basically metastable þ and it releases AlðH2 OÞ3þ 6 and H to form more stable precipitates [possibly Al(OH)3]. Therefore, for Al13 to be phytotoxic in soils, Al13 should be continuously synthesized in situ, which seems unrealistic. Presence of precipitated Al13 in Al oxyhydroxide flocs that formed in streams polluted by acid drainage of high Al concentration up to 16 mM was reported by Furrer et al. (2002). Other polymer Al, such as dimeric Al2 ðOHÞ2 ðH2 OÞ4þ 8 , has
Strategies of Plants to Adapt to Mineral Stresses
89
been reported in a laboratory condition (Akitt, 1989; Bertsch et al., 1986; Bottero et al., 1980), but its occurrence and importance in soil environments are still obscure. To elucidate the chemical forms of phytotoxic Al in soils, we need to analyze Al in soil solution and exchangeable Al, although the Al concentration in soil solution is generally too low to elucidate the chemical structure. Exchangeable Al in soils is concentrated on the negatively charged surfaces of soil components and is closely related to phytotoxic Al. Hiradate et al. (1998) extracted the exchangeable Al from 7 Japanese soil profiles including 39 horizons (soil pH in H2O: 3.6–5.5) with 1-M KCl and investigated their chemical forms by 27Al NMR. No Al13 was detected, and 92% and 96% of total Al in the extracts were attributed to inorganic monomeric Al (presumably AlðH2 OÞ3þ 6 ) and inorganic plus organic monomeric Al, respectively. Toma et al. (1999) extracted exchangeable Al by equilibrating nonallophanic Andosols with cation exchange resin and analyzed the Al-adsorbing cation exchange resin using a liquid state 27Al NMR. The analysis confirmed the predominant presence of inorganic monomeric Al and the absence of detectable Al13 for both gypsum-treated and -untreated soil samples. Therefore, it is likely that the inorganic monomeric Al can be the major phytotoxic Al in many soils, although the presence of Al13 in soil solutions at a low concentration could not be totally ruled out, especially in a microenvironment, such as the vicinity of fertilizer granules, as pointed out by Bertsch and Parker (1996). 3.1.2. Behavior of plant-originated Al-detoxifying agents in soils Plants can detoxify Al internally or externally by complexing Al with ligands, such as malate, oxalate, citrate, and catechins, which could result in the inactivation of coordination sites on Al3þ and the subsequent inhibition of Al to bind with plant metabolites and constituents. The ability of ligands to complex with Al can be evaluated through their formation constants (Table 2). When comparing the complexing ability among ligands, simple reactions ignoring the Hþ/OH balance, such as [Al þ L ! AlL] type, are frequently quoted, which are useful in understanding the general complexing trends. For example, complexes with Al will be preferentially formed in the following order: oxalic acid > malic acid > acetic acid > formic acid. The affinities of these carboxylic acids to Al3þ are generally lower than the affinities to Fe3þ and comparable to Cu2þ (for these formation constants, see Vance et al., 1996). Just like carboxylic acids, catechol compounds can also form complexes with Al3þ. It is the catechol structure (1,2-dihydroxybenzene structure) in DOPA and dopamine, which seems responsible for their high-affinity complexation reactions with Al3þ, rather than the common structure of amino acids (R-CH (NH2)COOH moiety), because cumulative formation constants of catechol were very close to those of DOPA and dopamine (Table 2). This estimation
90
Table 2 Cumulative formation constants (log K) with Al and Fe and dissociation constants (pK) for selected organic and inorganic ligands (L) Ligands
Reaction
Formic acid (pK1 3.8)
Al þL !AlL Al3þþ2L!AlL2þ Al3þþL!AlL2þ Al3þþ2L!AlL2þ Fe3þþL!FeL2þ Al3þþLn!AlL3n Al3þþ2Ln!AlL232n Al3þþH2L!AlLþþ2Hþ Al3þþLn!AlL3n Al3þþ2Ln!AlL232n Al3þþ3Ln!AlL333n Al3þþ3H2L!AlL33þ6Hþ Fe3þþLn!FeL3n Fe3þþ2Ln!FeL232n Fe3þþ3Ln!FeL333n Al3þþLn!AlL3n Al3þþ2Ln!AlL232n Al3þþH2L!AlLþ4Hþ Fe3þþLn!FeL3n Al3þþLn!AlL3n Al3þþ2Ln!AlL232n Al3þþL3!AlLþHþ Al3þþH3L!AlHLþþ2Hþ Fe3þþL2!FeLþ Fe3þþL3!FeL Al3þþLn!AlL3n Al3þþ2Ln!AlL232n
Acetic acid (pK1 4.8) Malic acid (pK1 3.5, pK2 5.1) Oxalic acid (pK1 1.3, pK2 4.3)
D,L-Tartaric
acid (pK1 3.0, pK2 4.4)
Citric acid (pK1 3.1, pK2 4.8, pK3 6.4)
Salicylic acid (pK1 3.0, pK2 12.4)
3þ
logK 2þ
a 1.36 (I ¼ 1.0 M (ClO 4 ), 25 C) 2.02 (I ¼ 1.0 M (ClO4 ), 25 C)a 1.51 (I ¼ 1.0 M (ClO4), 25 C)a 3.76 (I ¼ 0.3 M)b 3.2 (I ¼ 1.0 M, 20 C)c 5.34 (I ¼ 0.2 M (NO3), 20 C)a 9.32 (I ¼ 0.2 M (NO3), 20 C)a 3.43 (I ¼ 1 M (ClO4), 27–30 C)a 6.1 (I ¼ 1.0 M (ClO4), 25 C)a 11.1 (I ¼ 1.0 M (ClO4), 25 C)a 15.1 (I ¼ 1.0 M (ClO4), 25 C)a 1.26 (I ¼ 0.6 M (Cl), 25 C)a 9.4 (I ¼ 0 M, 25 C)c 16.2 (I ¼ 0 M, 25 C)c 20.2 (I ¼ 0 M, 25 C)c 5.32 (I ¼ 0.1 M (ClO4), 25 C)a 7.65 (I ¼ 0.1 M (NO3), 25 C)a 7.8 (I ¼ 0.5 M (NO3), 25 C)a 7.49 (I ¼ 0 M, 25 C)c 7.98 (I ¼ 0.1 M (NO3), 25 C)a 12.90 (I ¼ 0.1 M (Cl), 25 C)a 18.0 (I ¼ 0.12 M (Cl), 25 C)a 2.20 (I ¼ 0.1 M (NO3), 25 C)a 12.5 (I ¼ 0 M, 25 C)c 25.0 (I ¼ 0 M, 25 C)c 14.5 (I ¼ 0.1 M (ClO4), 25 C)a 23.2 (I ¼ 0.1 M (NO3), 20 C)a
Catechol (pK1 9.4, pK2 13.0)
DOPA Dopamine Alanine (pK1 2.3, pK2 9.9) Glutamic acid (pK1 2.1, pK2 4.3, pK3 9.8)
Hydrogen fluoride (HF, pK1 3.2)
Al3þþ 3Ln !AlL333n Al3þþ HL !AlLþþHþ Fe3þþ Ln !FeL3n Fe3þþ 2Ln !FeL232n Fe3þþ 3Ln !FeL333n Al3þþ Ln !AlL3n Al3þþ 2Ln !AlL232n Al3þþ 3Ln !AlL333n Al3þþ H2L !AlLþþ2Hþ Al3þþ 2H2L!AlL2þ4Hþ Al3þþ 3H2L!AlL33þ6Hþ Al3þþ 3H2L!AlHL32þ5Hþ Al3þþ Ln !AlL3n Al3þþ 2Ln!AlL232n Al3þþ 3Ln!AlL333n Al3þþ Ln !AlL3n Al3þþ 2Ln!AlL232n Al3þþ 3Ln!AlL333n –
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Al3þþ Ln !AlL3n Al3þþ 2Ln!AlL232n Al3þþ 3Ln!AlL333n Al3þþ HL!AlHL2þ Al3þþ F !AlF2þ Al3þþ 2F !AlF2þ Al3þþ 3F !AlF3 Al3þþ 4F !AlF4 Al3þþ 5F !AlF52 Al3þþ 6F !AlF63 Fe3þþ F !FeF2þ Fe3þþ 2F!FeF2þ Fe3þþ3F!FeF3
29.8 (I ¼ 0.1 M (NO3), 20 C)a 4.66 (I ¼ 0 M, 27 C)a 16.48 (I ¼ 0.1 M, 20 C)c 28.12 (I ¼ 0.1 M, 20 C)c 36.80 (I ¼ 0.1 M, 20 C)c 16.1 (I ¼ 0.1 M (NO3), 25 C)a 29.1 (I ¼ 0.1 M (NO3), 25 C)a 37.8 (I ¼ 0.1 M (NO3), 25 C)a 6.337 (I ¼ 0.6 M (Cl), 25 C)a 15.44 (I ¼ 0.6 M (Cl), 25 C)a 28.62 (I ¼ 0.6 M (Cl), 25 C)a 23.45 (I ¼ 0.6 M (Cl), 25 C)a 16.03 (I ¼ 0.2 M (Cl), 25 C)a 29.24 (I ¼ 0.2 M (Cl), 25 C)a 38.36 (I ¼ 0.2 M (Cl), 25 C)a 15.63 (I ¼ 0.2 M (Cl), 25 C)a 28.61 (I ¼ 0.2 M (Cl), 25 C)a 37.56 (I ¼ 0.2 M (Cl), 25 C)a negligible (I ¼ 0.6 M (Cl), 25 C)a 15.12 (I ¼ 0.1 M (ClO4), 25 C)a 29.40 (I ¼ 0.1 M (ClO4), 25 C)a 38.60 (I ¼ 0.1 M (ClO4), 25 C)a 2.30 (I ¼ 0.5 M (ClO4), 25 C)a 7.0 (I ¼ 0 M, 25 C)a 12.7 (I ¼ 0 M, 25 C)a 16.8 (I ¼ 0 M, 25 C)a 19.4 (I ¼ 0 M, 25 C)a 20.6 (I ¼ 0 M, 25 C)a 20.6 (I ¼ 0 M, 25 C)a 5.28c 9.30c 12.06c (continued)
Table 2 (continued) Ligands
Reaction
logK
Sulfuric acid (H2SO4, pK1 3, pK2 2.0)
Al3þþSO42!AlSO4þ Al3þþ2SO42!Al(SO4)2 Fe3þþSO42!FeSO4þ Fe3þþ2SO42!Fe(SO4)2 Al3þþH2PO4!AlH2PO42þ Al3þþHPO42!AlHPO4þ
3.5 (I ¼ 0 M, 25 C)a 5.0 (I ¼ 0 M, 25 C)a 2.03c 2.98c 3.1a 7.4a
ortho-Phosphoric acid (H3PO4, pK1 2.2, pK2 7.2, pK3 12.4) a b c
Nordstrom and May (1996). Vance et al. (1996). Dean (1978). The pK values are cited from Dean (1978). For their chemical structures, see Fig. 1.
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is also supported by the fact that a simple amino acid alanine (CH3CH(NH2) COOH) has an extremely low formation constant. The formation of Al-ligand complexes, however, is influenced by solution pH because chemical forms of Al, ligands, and Al-ligand complexes do change with pH. As discussed earlier, it would be reasonable to assume that Al3þ is the phytotoxic Al in many acidic soils. The protonation/deprotonation of ligands can be estimated from the solution pH and pK values (Table 2); for example, at solution pH 4.0, predominant oxalate/oxalic acid and citrate/citric acid species would be [oxalate] and [citrate], respectively. At present, some formation constants that have taken into account the protonation/deprotonation reactions are available (Nordstrom and May, 1996; Table 2), although the kind of reaction to be assumed in soils remains unclear. Furthermore, the formation of unexpected ternary complexes, such as Al-phosphate-citrate and Al-phosphate-oxalate complexes (Sajdak et al., 2004; Sanz-Medel et al., 2002), could also be possible in a soil– solution systems. Although computer programs can predict chemical species of Al in soil solution, prediction supported by some experimental evidences is more preferable. Determination of Al:ligand molar ratio and analysis with 27Al NMR can be helpful to elucidate the chemical forms of Al-ligand complexes. For Al-carboxylic acid complexes, in general, the formation of one coordination bond between carboxylic group and Al causes a 3- to 4-ppm shift downfield in 27Al NMR spectrum (chemical shift value increases), for example, chemical shifts for Al-oxalate, Al-(oxalate)2, and Al-(oxalate)3 complexes are 6.4, 11.4, and 16.0 ppm, respectively [Kerven et al. (1995), also see Hiradate (2004), for review]. The concentrations of organic ligands in soil solutions are normally low and variable. Vance et al. (1996) summarized the approximate concentration ranges of the organic ligands in soil solutions: 1000–4000 mM for simple carboxylic acids, 80–600 mM for amino acids, and 50–300 mM for phenolic acids. These values are reduced when the ligands undergo rapid destruction owing to microbial metabolism, but are higher in the vicinity where active plant roots secrete the organic ligands. Among these organic ligands, the simple carboxylic acids are the most common ligands, which can form complexes with Al3þ in many soil environments. These carboxylic acids include formic, acetic, propionic, butyric, a-crotonic, lactic, malic, oxalic, succinic, fumaric, tartaric, and citric acids. Strobel (2001) thoroughly reviewed the concentrations of these carboxylic acids in soil solutions in relation to vegetation type, soil type, and soil depth: monocarboxylic acids are usually in the range of 0–1000 mM, di- and tricarboxylic acids are in the range of 0–50 mM. Some plants have extraordinarily great carboxylic acid contents: for example, 50–80 g kg1 (260–420 mmol kg1) of citric acid in citrus fruits (O’Neil et al., 2001), 20 g kg1 of total carboxylic acids in the fruits of Ribes sinanense (Kariyone and Kitamura, 1975), 10 g kg1 (110 mmol kg1) of oxalic acid in the stem of Begonia evansiana (Kariyone and Kitamura, 1975), 5–15 g kg1
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(25–76 mmol kg1) of L-DOPA in the shoot of fresh Mucuna pruriens (velvetbean) (Fujii et al., 1991). Jones (1998) summarized the concentration ranges of malate and citrate in fresh plant roots: 3.2–22 and 1.1–8.1 mmol kg1 for Brassica napus, 2.5–9.7 and 1.0–8.5 mmol kg1 for Sisymbrium officinale, 0.9–18 and 1.3–4.8 mmol kg1 for Sorghum bicolor, 2.7–8.4 and 1.7–1.9 mmol kg1 for H. vulgare, 0.15–33 and 0.015–20 mmol kg1 for Z. mays, and 18–68 and 0.80–5.1 mmol kg1 for Phaseolus vulgaris, depending on their plantnutritional status. Concentrations of these carboxylic acids in soil solutions could be very high in the vicinity of corresponding plants, and they could affect the chemical form of soluble Al. The concentrations of carboxylic acids in soil solutions could be decreased by biological (e.g., microbial degradation and uptake) and physicochemical (e.g., adsorption, transformation, and volatilization) processes. The adsorption of carboxylic acids on soils is controlled by at least one of three possible mechanisms: (1) anion exchange reactions, (2) ligand exchange reactions, and (3) hydrophobic interactions, depending on the chemical characteristics of the carboxylic acids, the soil properties, and the adsorption conditions. The anion exchange reaction, which is caused by an electrostatic interaction between negatively charged carboxylic acids and positively charged soil components, is less important in many soil environments because layer silicate clays and soil organic matter are generally either uncharged or negatively charged. When soils contain the active surface hydroxyls and they are acidic enough to develop positive charge (Fig. 3A), and when the soil pH is high enough to develop negative charge on carboxylic acids, the ion exchange reaction could exert a significant effect. The ligand exchange reaction, in which the carboxylic group of the ligand displaces the active surface hydroxyl associated with metal (hydr)oxides and forms a strong coordination bond between the ligand and the soil solid phase (Fig. 3B), plays a predominant role in carboxylic acid adsorption in soils; especially for a carboxylic acid with high affinity to Al3þ and Fe3þ, it also has a high affinity to metal (hydr)oxides in soils because these two reactions [complexation reactions of organic ligands with metal ions and with metal (hydr)oxides] are basically the same. Adsorption through the hydrophobic interactions is also possible between hydrophobic carboxylic acid molecules and soil organic matter if the hydrophobicity of the carboxylic acids is high enough. In the case of 2,4-dichlorophenoxyacetic acid (a synthetic herbicide), a monocarboxylic acid with a pK value of 2.81 and an octanol-water distribution coefficient KOW of 0.027 in an alkaline condition and 29.23 in an acidic condition, a ligand exchange reaction regulates its adsorption on a humus-rich Andosol (Hiradate et al., 2007). Therefore, for less hydrophobic carboxylic acids, such as listed in most of Table 2, the ligand exchange reaction would play an important role in their adsorption reaction in many soil environments. Some soil minerals [e.g., Mn(IV) and Fe(III) oxides] can transform organic ligands (e.g., hydroquinone, catechol, gallic acid)
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catalytically into a humic substance-like colored macromolecule (Shindo, 1992; Shindo and Huang, 1982, 1984). Catalytic transformation of L-DOPA by soils was also observed (Furubayashi et al., 2005; Hiradate et al., 2005), and it has been clarified that a catechol structure of L-DOPA is responsible for the transformation reaction (Furubayashi et al., 2007).
3.2. Mechanism of Al toxicity 3.2.1. Al-toxicity symptoms Root growth inhibition caused by Al, which is accompanied by an increase in the diameter and a decrease in cell elongation (Matsumoto 2000; Sasaki et al., 1997), is an early symptom of Al phytotoxicity in acid soils. The root apex is the primary target site for Al. In the root apex of maize, the distal part of the transition zone, where cells undergo a preparatory phase for rapid elongation, is primarily attacked by Al (Sivaguru and Horst, 1998). Root apices of Al-toxicity-sensitive genotype of wheat (Triticum aestivum) were stained with hematoxylin after a short exposure to Al, while Al-toxicitytolerant seedlings were less stained (Delhaize et al., 1993a; Sasaki et al., 1997), indicating that inhibition of root growth is related to the Al content in the root apex. The propidium iodine staining of wheat root tip exposed to Al suggests that the inhibition of cell elongation is partially caused by cell death (Sasaki et al., 1997). Attention has been paid to characterize Al toxicity and its tolerance [for review, see Kochian (1995), Kochian et al. (2004), Matsumoto et al. (2001, 2005), Taylor (1991)]. 3.2.1.1. Cell division Cell division in root meristems is inhibited by Al (Clarkson, 1965; Morimura et al., 1978). Doncheva et al. (2005) reported that 5 min of Al exposure was enough to inhibit cell division in the proximal meristem of Al-toxicity-sensitive maize. Silva et al. (2000) found that Al was accumulated in the nuclei of Al-toxicity-sensitive soybean when they were exposed to 1.45-mM Al3þ for 30 min. Cells at elongation zone are transported from the meristematic zone after cell division. Therefore, inhibition of cell elongation at the elongation zone is not fatal for plant growth as long as the cells are divided at the meristematic zone followed by their transport to the elongation zone under Al-toxicity stress [for review, see Matsumoto (1991, 2000, 2002a)]. Morphological research on log-phase cells of tobacco suggested that, after 24-h treatment of Al, no phragmoplast and spindle microtubules were observed in metaphase cells (Sivaguru et al., 1999). 3.2.1.2. Plasma membrane Plasma membrane (PM) is the potential target for Al [for review, see Ahn and Matsumoto (2006), Haug and Caldwell (1985)]. The Al3þ has a high binding affinity to PM and shows a 560-fold higher affinity for phosphatidylcholine surface than Ca2þ does (Akeson et al., 1989). Cells become more leaky and rigid due to Al binding to PM
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(Ishikawa and Wagatsuma, 1998). Exposure of Al3þ to the roots of Al-toxicity-sensitive cultivar of barley resulted in Al binding to PM phospholipids and forming a positively charged layer, and influxes of the cations þ (Ca2þ, NHþ 4 , and K ) and anions (NO3 and phosphate) were repressed and enhanced, respectively (Nichol et al., 1993). The important change on PM mediated by Al is the alteration of membrane potential (Vm) of the PM as well as the changes on surface potential (zeta potential). The Al-induced depolarization has been occasionally observed but contradictory finding has also been reported (Lindberg and Strid, 1997; Papernik and Kochian, 1997). Wherrett et al. (2005) reported that the potential differences on root PM were significantly decreased (depolarized) in Al-toxicity-tolerant wheat (ET8) within minutes of exposure to 50-mM AlCl3, but not in Al-toxicity-sensitive cultivar (ES8). The zeta potential of the cell membrane is known to regulate the accessibility of Al3þ inside of cells (Kinraide et al., 1992). Ahn et al. (2001, 2002, 2004) conducted intensive researches regarding changes in zeta potential and PM Hþ-ATPase activity under Al-toxicity stress, using squash (Cucurbita pepo L. cv Tetsukabuto) and two wheat varieties [for review, see Ahn and Matsumoto (2006)]. They demonstrated the inherent relationship between Hþ-ATPase activity and the zeta potential of PM. The segmental analysis showed that the zeta potential was more negative at root tips than other region. A significant increase in zeta potential (depolarization) was induced concomitant with the decrease in PM Hþ-ATPase activity in squash under Al-toxicity stress. One reason for the decrease in the Hþ-ATPase activity might be the inhibition of Hþ-efflux through PM due to depolarized zeta potential. Another reason was the decrease of Hþ-ATPase protein determined by immunoblotting with antibody prepared against maize (Z. Mays) Hþ-ATPase. Similar relationship between zeta potential and PM Hþ-ATPase was investigated with Al-toxicity-tolerant (ET8) and -sensitive (ES8) wheat root tips. The zeta potential changed to positive and Hþ-ATPase activity decreased in ES8 but the opposite was the case in ET8. 3.2.1.3. Cell wall Aluminum is predominantly localized in cell walls [Vazquez et al. (1999); for review, see Horst (1995)]. The Al binding to cell walls can be advantageous for plants because Al is trapped and entry of toxic Al into cytosol could be inhibited. The root cell walls from an Alresistant wheat cultivar (Atlas 66) adsorbed Al but almost all of the Al was desorbed by 2.5-mM CaCl2 at pH 4.5, indicating that most of Al was electrically bound to the cell wall. Digestion of pectin with pectinase significantly decreased the amount of Al adsorbed, indicating that pectin played an important role in adsorbing Al (Zheng et al., 2004). For the expression of Al toxicity and Al tolerance, binding of Al to the cell wall pectin matrix would be important (Schmohl and Horst, 2000) and their methylation reaction could regulate the affinity for Al (Schmohl et al., 2000). In buckwheat
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(Fagopyrum esculentum), formation of Al-P complexes in the cell wall, such as low-solubility Al4(PO4)3, may be helpful by retarding uptake of Al into cytosol (Zheng et al., 2005). In maize, Al treatment greatly enhanced Si accumulation in the cell wall fraction, resulting in reducing mobility of apoplastic Al (Wang et al., 2004). In Al-toxicity-sensitive wheat (Scout 66), exposure of Al increased the molecular weight of hemicellulose and amount of wall-bound ferulic and diferulic acids, indicating that Al modifies metabolism of cell wall and makes the cell wall thick and rigid, resulting in the growth inhibition (Tabuchi and Matsumoto, 2001). In Al-toxicity-tolerant wheat (Atlas 66), Al-treatment decreased osmotic potential of root cell, which is caused by increased concentration of soluble sugars (major osmotic solutes) in the cell which did not occur in the Al-toxicity-sensitive cultivar (Scout 66), indicating that the Al-toxicity-tolerant cultivar osmotically adapts to water uptake (Tabuchi et al., 2004). 3.2.1.4. Calcium Earlier works mostly with the protoplast and intact root showed that Al inhibited Ca uptake and translocation (Huang et al., 1992; Rengel and Elliott, 1992). Transport of Ca2þ regulated by isolated PM vesicles was clearly inhibited by Al (Huang et al., 1996) but their inhibitory effects were not different between tolerant and sensitive wheat cultivars (Sasaki et al., 1994). Other works suggest that Al-induced inhibition of Ca2þ translocation alone cannot be a critical factor in triggering the Al-toxicity syndrome in plants (Rengel and Zhang, 2003). Function of cell wall and PM can be influenced by replacement of binding Ca with Al, which will disrupt the homeostasis of free Ca2þ in the cytosol resulting in the induction of callose (b-1,3-glucane) synthesis or dissociation of cell structural protein like tubulin (Kinraide et al., 1994). Schofield et al. (1998), however, claimed that the amount of bound Al on root tips was not enough to replace Ca2þ. Some of works on Al/Ca interaction have directed to understand signal transduction affected by Al. In Al-toxicity-sensitive wheat, Al inhibits a key signal transduction enzyme, phospholipase C (PLC) ( Jones and Kochian, 1995, 1997). Lin et al. (2005) found a new plant Ca2þ channel protein from Al-toxicity-sensitive A. thaliana, AtTPC1 (two-pore channel 1), that works in a high Al condition and responds to reactive oxygen species (ROS) which might be induced by Al. Rengel (1992) and Rengel and Zhang (2003) claimed that disruption of cytoplasmic Ca2þ homeostasis plays a decisive role in the earlier stages of Al toxicity. 3.2.1.5. Hormone Treatment of Al to the root cap of Al-toxicity-sensitive maize strongly promoted acropetal transport of auxin, reducing polarity (basipetal transport divided by acropetal transport) from 6.3 to 2.1. Treatment of the root cap with Ca2þ enhanced basipetal movement of auxin, increasing polarity from 6.3 to 7.6, suggesting that Al and Ca have opposite effects (Hasenstein and Evans, 1988). Kollmeier et al. (2000) found that
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auxin transport was inhibited by Al treatment on distal transition zone of Al-toxicity-sensitive maize. 3.2.1.6. Callose Callose formation is a very sensitive phenomenon under Al-toxicity stress (Wissemeier et al., 1992), although its physiological function has not been well understood. Callose formation, however, did not correlate with Al-induced inhibition of root growth of Al-toxicity-sensitive mutants of A. thaliana (Larsen et al., 1996). Turner et al. (1994) reported that callose was localized in the cell wall around plasmodesmata (PD) but it did not appear in the collar. It has been considered that PD offers free passage for transport of solutes < ca. 1000 Da and it controls plant growth and development through regulation of permeability of various solutes (gating). Sivaguru et al. (2000) microinjected a dye into peripheral root cells of an Al-toxicity-sensitive wheat (Scout 66) either before or after Al treatments and found that Al-induced root growth inhibition was closely associated with the Al-induced blocking of cell-to-cell dye coupling. The Al-induced callose deposition at PD may be responsible for the blockage of symplastic transport through PD. In an Al-toxicity-sensitive maize cultivar, the callose formation under Al-toxicity stress also inhibited apoplastic transport of high-molecular-weight solutes such as dextran-TexasRed conjugates (MW: 3000, 10,000, and 40,000 Da). Apoplastic bypass flow of the highmolecular-weight solutes was inhibited by the modification of polarity of the cell wall caused by Al-induced callose formation (Sivaguru et al., 2006). 3.2.1.7. Oxidative stress Oxidative stress could be closely related to the Al toxicity. Cakmak and Horst (1991) first reported that lipid peroxidation in the root tip of soybean was enhanced only after a longer duration of Al treatment (i.e., long-term effect of Al). They also found an increase of superoxide dismutase and peroxidase and a decrease of catalase. Similar results were obtained with tobacco cells exposed to AlCl3 with Fe2þ- or Fe3þ-EDTA, in which both lipid peroxidation and loss of viability were simultaneously enhanced by AlCl3 (Devi et al., 2003; Ikegawa et al., 2000; Ono et al., 1995; Yamamoto et al., 1997). At an early stage of Al toxicity, however, the lipid peroxidation did not cause inhibition of pea root elongation (Yamamoto et al., 2001). Genetic researches clarified enhanced expression of several genes encoding antioxidant enzymes such as glutathione S-transferase, peroxidase, and superoxide dismutase (Basu et al., 2001; Ezaki et al., 1998, 2000; Richards et al., 1998; Watt, 2003), suggesting that Al enhances oxidative stress in plants. Nitric oxide (NO) reduced the Al toxicity by repressing oxidative stress (Wang et al., 2005). It has been shown that the production of various ROS causes cell death under Al-toxicity stress ( Jones et al., 2006; Pan et al., 2001) and that presumably O 2 is produced in mitochondria of Al-treated root cells (Yamamoto et al., 2002). Cell death of root tip of barley caused by Al can be divided into
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two categories; one is programmed cell death (PCD) possibly via ROSactivated signal transduction pathway and other is necrosis (Pan et al., 2001). Zheng et al. (2007) found that yeast cell-transformed antiapoptotic members, Bel-2 and Ced-9, alleviated PCD caused by Al.
3.3. Mechanism of Al-toxicity tolerance There are two types of Al-toxicity tolerance mechanisms: exclusion mechanism and internal detoxification mechanism. The former excludes Al from the root apex and the latter allows the plants to tolerate Al accumulation in their root and shoot symplasm [for review, see Kochian (1995) and Taylor (1991)]. 3.3.1. Exclusion mechanism The exclusion mechanism involves secretion of Al-chelating organic ligands (Miyasaka et al., 1991), binding of Al with cell wall and mucilage (Horst et al., 1982; Li et al., 2000a), and sequestration of Al in plant cells (Ma et al., 2001a). 3.3.1.1. Exudation of organic acids and other molecules Organic acids are natural chelators that could remove the phytotoxic Al (Al3þ) from negatively charged cell components of root apices [for review, see Ma (2000), Matsumoto (2002b), and Ryan et al. (2001)]. The first chelating compound investigated intensively is citrate exuded from snapbean (P. vulgaris) under Al-toxicity stress (Miyasaka et al., 1991). The root of the Al-toxicity resistant snapbean variety excreted 70-times more citrate in the presence of Al than in the absence, and it excreted 10-times more citrate than Al-toxicity-sensitive variety. So far, several organic acids have been found to be exuded from different plant species and cultivars under Altoxicity stress. The major organic acids are: malate from wheat (Delhaize et al., 1993a,b); citrate from snapbean (Miyasaka et al., 1991), soybean (Yang et al., 2000), and Cassia tora (Ma et al., 1997a); both citrate and malate from maize (Kollmeier et al., 2001) and rye (Secole cereale) (Li et al., 2000b); and oxalate from buckwheat (Ma et al., 1997b) and taro (Colocasia esculenta) (Ma and Miyasaka, 1998). The efficiency of Al-toxicity tolerance is dependent on the chelating ability of the secreted organic ligands, which can be estimated by their formation constants (Table 2). The time lag for efflux of the organic ligands depends on crop species (Ma, 2000), and it may be attributable to the differences in their synthetic pathways: de novo synthesis of the organic ligands by inducing new channel or activation of present channel (Yang et al., 2006). In common bean (P. vulgaris) seedlings, the anion channel of tap roots was greater in number and higher in activity than that of basal roots, thus allowing more citrate secretion and Al-toxicity resistance in this root type (Shen et al., 2004a).
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Many researches have suggested that the capability of organic acid exudation correlates with Al-toxicity tolerance. In maize (Pineˆros et al., 2005) and signalgrass (Wenzl et al., 2001), however, correlation between the amounts of exuded organic acid and Al-toxicity tolerance was very weak. Phenolics, especially flavonoid-type, have been found to be powerful molecules in the Al exclusion mechanism (Kidd et al., 2001; Oleksyn et al., 1996). It might be possible that catechol structure is responsible for their complexing ability with Al3þ, as discussed in Section 3.1.2. The meristem and root cap region, where Al toxicity dominantly appears, are coated with exuded mucilage, which might protect root from Al injury. Removal of mucilage prior to Al treatment facilitated accumulation of Al in root apices and enhanced Al rhizotoxicity in cowpea (Horst et al., 1982), suggesting that binding of Al to mucilage is one of the Al-toxicity-tolerant mechanisms. Because mucilage is produced from border cells and is actively detached from the root cap, the border cells may participate in the expression of Al-toxicity-tolerant mechanism (Miyasaka and Hames, 2001; Zhu et al., 2003). To clarify the role of exuded organic ligands on detoxification of Al in the rhizosphere, thorough studies need to be conducted on the adsorption ( Jones, 1998; Jones and Brassington, 1998) and degradation ( Jones et al., 1996b) of the organic ligands in soils. 3.3.1.2. Channel Many researches suggest that the exudation of organic acids is inhibited by blockers of anion transporter. Malate exudation from root tips of wheat occurred within 5 min after exposure to Al (Osawa and Matsumoto, 2001), and the content of malate in the roots was not different between Al-toxicity-tolerant and -sensitive wheat varieties, indicating that transporter of malate on PM controls the exudation of malate. Ryan et al. (1997) reported that 20- to 50-mM AlCl3 depolarized PM of wheat protoplasts and activated inward electrical current for >60 min. This channel is more selective for anions than cations. Zhang et al. (2001) found an Al-activated malate-permeable channel in wheat root. This channel was selective to malate than Cl. The electrical current carried by Al-induced anion-efflux across the PM was greater in density and remained active for longer in Al-toxicity-tolerant (ET8) protoplast than in sensitive (ES8) protoplast. Pineˆros and Kochian (2001) also found an Al-dependent anion channel in excised membrane patches of Al-toxicity-tolerant maize. Kollmeier et al. (2001) also found an Al-activated citrate-permeable anion channel in root apex of maize. All these findings suggest the existence of Al-activated anion channel on PM, which plays an important role for the exudation of organic acids under Al-toxicity stress. 3.3.1.3. Regulation of exudation of organic acids from root apex Several factors might be involved in the regulation of the Al-induced exudation of organic acids. They are: (1) concentration of the organic acids in root
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apex supplied from shoots (Yang et al., 2001), (2) changes in PM potential, (3) protein phosphorylation and plant hormone (Osawa and Matsumoto, 2001; Shen et al., 2004b), and (4) charge balance caused by flux of Hþ, Kþ, and so on. (Ohno et al., 2003; Osawa and Matsumoto, 2002; Ryan et al., 1995). Release of organic acids in response to P deficiency has been reported in rape (B. napus) (Hoffland et al., 1989), white lupin (Lupinus albus) (Gardner et al., 1983), purple lupin (Lupinus pilosus) (Ligaba et al., 2004), and soybean (Nian et al., 2003), with some exceptions (Dong et al., 2004; Yang et al., 2000). The exudation of organic acids is induced by other cations alone or enhanced together with Al (Kataoka et al., 2002; Nian et al., 2002). Magnesium ameliorates Al toxicity by increasing exudation of organic acids (Silva et al., 2001). Efflux of citrate from Al-treated soybean roots was regulated by an upregulation of transcription and translation of PM Hþ-ATPase (Shen et al., 2005). 3.3.1.4. ALMT1 (Al-activated malate transporter) gene Sasaki et al. (2004) isolated a novel gene, ALMT1, which encodes an Al-activated malate transporter, by employing subtractive hybridization of cDNA from Al-toxicitytolerant (ET8) and -sensitive (ES8) wheat varieties. Constitutive expression of ALMT1 at the root apex was much stronger in ET8 than in ES8. Its cDNA full-length was 1517 bp, encoding 459 amino acids, and constructing a protein with a molecular weight of 49.7 kDa. Base sequences of the ALMT1 cDNA differed slightly between ET8 (ALMT1-1) and ES8 (ALMT1–2) at six nucleotides that encode two amino acids. In a Xenopus laeve oocyte, in which ALMT1–1 cRNA was introduced, inward current was specifically induced by Al and malate. The malate-transporting ability of ALMT1–1 was almost same as that of ALMT1–2, suggesting that the rate of ALMT1 expression determines the Al-toxicity tolerance. The first 1000-bp downstream of ALMT1 was conserved, which did not correlate with Al-toxicity resistance but the first 1000-bp upstream of the ALMT1 coding region was more variable. This was confirmed by the analysis of promoter region of ALMT1 in different wheat varieties (Sasaki et al., 2006). ALMT1 is located on PM (Yamaguchi et al., 2005) and consists of six exons interpreted by five introns (Raman et al., 2005). The loss of ALMT1 coincided with the loss of both Altoxicity tolerance and Al-activated malate-efflux, and the Al-toxicity tolerance was correlated significantly with the relative level of ALMT1 expression (Raman et al., 2005). In A. thaliana, a possible molecular determinant for Al-toxicity tolerance involving a homology of the wheat ALMT1 was found and named AtALMT1 (At1g08430) (Hoekenga et al., 2006). AtALMT1 is critical for Al-toxicity tolerance of A. thaliana and encodes Al-activated root malate-efflux transporter. In rape, two ALMT1 homologues, BnALMT1 and BnALMT2, were found to increase Al-toxicity tolerance (Ligaba et al., 2006).
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3.3.2. Internal Al-detoxification mechanism In some plants, the Al-toxicity tolerance mechanism cannot be explained solely by the exclusion mechanism. They detoxify Al internally by chelation. Ma et al. (1997c) found that Al in hydrangea (Hydrangea macrophylla) leaves existed as Al-citrate complex(es) with molar ratio of ca. 1:1 as detoxified form(s). Buckwheat absorbed a large amount of Al and accumulated as an Al-oxalate (1:3) complex, which is a detoxified form (Ma et al., 1997b, 1998). Similarly, phosphate and phenolic compounds have been reported to bind and detoxify Al3þ in vivo (Ofei-Manu et al., 2001; Zheng et al., 2005). In A. thaliana, accumulated Al was redistributed away from sensitive tissues in order to protect growing root from the toxic effects of Al (Larsen et al., 2005). In tea plants (Camellia sinensis), most of Al is bound to catechins, while some portion is bound to phenolic and organic acids (Nagata et al., 1992). It has been reported that Al causes oxidative stress because several genes expressed under Al-toxicity stress are associated with the oxidative stress (Ezaki et al., 1998, 2000, 2004, 2005; Richards et al., 1998; Snowden and Gardner, 1993). Basu et al. (2001) obtained an Al-toxicity resistant line of rape, which overexpressed Al-induced mitochondrial Mn superoxide dismutase. In C. tora, Al toxicity was reduced by NO 3 through preventing oxidative stress (Wang and Yang, 2005). Aluminum-toxicity-tolerant tobacco cells contained antioxidants, ascorbate and glutathione, at a much higher level than in Al-toxicity-sensitive wild cells (Devi et al., 2003). Sequestration of toxic Al from Al-toxicity-sensitive tissue (e.g., root apex) to less-sensitive tissue (e.g., shoot and vacuole) would be a potent internal Al-detoxification mechanism. Transport and sequestration of Al into vacuole associated with organic acids have been reported in buckwheat (Ma et al., 2001a), as well as progressive vacuolation in Al-treated barley root tips (Ikeda and Tadano, 1993). 3.3.3. Gene transformation The final goal of the research on Al-toxicity tolerance is to understand the tolerant mechanism and to develop the plants that can grow under Al-toxicity stress in acid soils. So far, many Al-inducible genes have been detected but their functions are not always understood. Only a few transgenic crops, which were introduced with genes of organic acid secretion, improved their growth under Al-toxicity stress. Overexpression of citrate synthase (CS) isolated from Pseudomonas aeruginosa in tobacco or papaya (Carica papaya) plants enhanced citrate efflux and improved Al-toxicity tolerance (de la Fuente et al., 1997). However, Delhaize et al. (2001) could not obtain reproducible data. In transgenic crops, Al-toxicity resistance or P-acquisition ability was enhanced by overexpression of enzymes involved in the organic acid biosynthesis (Anoop et al., 2003; Koyama et al., 1999; Tesfaye et al., 2001). Transgenic barley whole plant and suspension
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ALMT1 expression in barley Grown in Al-toxic solution
ALMT1
WT
Figure 10 A geneALMT1confers Al-toxicity tolerance to barley grown in hydroponic culture. Transgenic (ALMT1) and wild (WT) barley plants were cultured for over 10 days on nutrient solution containing 3-mM Al. Scanning electron micrograph showed the effects of Al on morphology of root apex from ALMT1 and WT. Courtesy by Drs. P. Ryan and M. Delhaize, CSIRO, Canberra.
cell of tobacco introduced with wheat ALMT1 gene exuded malate and grew well without Al injury on hydroponic culture containing Al (Fig. 10). The enhanced Al-toxicity-tolerance of the transgenic barley was also confirmed in a pot experiment using acid soils (soil pH: 4.5). These findings suggest that ALMT1, which encodes malate transporter and is triggered by Al, is capable of conferring Al-toxicity-tolerance to plant cells (Delhaize et al., 2004; Sasaki et al., 2004). 3.3.4. Loci of Al-toxicity tolerance gene in major crops Understanding the inheritance of Al-toxicity tolerance is essential for breeding Al-toxicity-resistant crops in acid soils. Kerridge and Kronstad (1968) reported that one or more major genes may be responsible for Al-toxicity tolerance in wheat (Atlas 66). In Chinese spring wheat, Luo and Dvorˇa´k (1996) found a single dominant gene, Alt2, located in the proximal region of the long arm of chromosome 4D. Milla and Gustafson (2001) provided an extensive genetic linkage map of the chromosome arm 4DL regarding Al-toxicity tolerance gene, AltBH in BH 1146. Raman et al. (2005) reported that ALMT1 was mapped on chromosome 4DL and
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cosegregated with Al-toxicity tolerance when ALMT1 was polymorphic between parental lines. Barley is extremely sensitive to Al toxicity and it was segregated into various lines differing in Al-toxicity tolerance, indicating a single locus (Minella and Sorrells, 1992). Tang et al. (2000) reported that the Al-toxicity tolerance gene of barley, Alp, was localized to a long arm of chromosome 4H. Quantitative trait locus (QTL) analysis clarified that a gene related to Al-toxicity resistance on barley chromosome 4H is identical to that related to Al-activated secretion of citrate (Ma et al., 2004). Segregation analysis of maize suggested that Al-toxicity tolerance was controlled by a single semidominant nuclear gene, named Alm1 (Moon et al., 1997). In rye, Al-toxicity tolerance factors are located on chromosome arms 3RL, 4RL, and 6RS. Screening of an F6 rye recombinant inbred line derived from a cross between an Al-toxicity-tolerant rye and a sensitive rye showed that a single gene named Alt3 was located on the long arm of rye chromosome 4R (Miftahudin et al., 2002). In rice (O. sativa), exudation of citrate is not significantly different between Al-toxicity-tolerant and -sensitive varieties, and three putative QTLs controlling Al-toxicity tolerance were detected on chromosomes 1, 2, and 6. Ma et al. (2002) clarified that chromosomes 1 and 2 reduced Al-toxicity tolerance but chromosome 6 increased the tolerance of an Al-toxicity-tolerant indica variety, Kasalath.
4. P-Deficiency Stress Mean content of P in the Earth’s crust is 1 g kg1, and P contents in soils range between 0.035 and 5.3 g kg1 (Sparks, 2003). The plant P content required for sufficient growth is at the 2 g kg1 level (Taiz and Zeiger, 1998). In plants, P exists as sugar phosphates, nucleic acids, nucleotides, coenzymes, phospholipids, inositol phosphates (phytic acid), and so on, and it plays an indispensable role in energy storage and structural integrity. After N, P is usually the most limiting mineral nutrient for crop production because of extremely low solubility of P compounds in soils. When P fertilizers are applied to soils, the absorption percentage of the applied P by plants is generally lower than 20% and most of P applied is fixed by soils as low-solubility Al- and Fe-P compounds in acid soils and Ca- and Mg-P compounds in alkaline soils. Chemical form of P in soils determines its bioavailability. In many developed countries, soil-applied P frequently contaminates soils and natural water systems, but P deficiency still occurs especially in acidic soils dominated by 1:1 clay minerals, particularly those with clayey or loamy surfaces, and with substantial Fe and Al (hydr)oxide contents such as Ferralsols, Acrisols, Nitisols, and Andosols (Smithson and Sanchez, 1998).
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4.1. Chemistry of P and plant-originated P-dissolving agents in soils Soil P exists in organic and inorganic P forms. In many soils, as summarized by Stevenson (1994), 15–80% of total P occurs in organic form, and the organic P is composed of inositol phosphates (2–50% of total organic P), phospholipids (1–5%), nucleic acids (0.2–2.5%), sugar phosphates (trace), metabolic phosphates (trace), and unknown components (>50%). The organic P content in soils follows rather closely that for organic C (Stevenson, 1994). In acidic soils, these organic P are likely to exist as adsorbed form on Al and Fe minerals as well as adsorbed inorganic P, and they will be in a resistant form to be mineralized by phosphatase (Otani and Ae, 1999). Although water-soluble soil organic P may be absorbed by plants directly, organic P is made available to plants largely after its mineralization reaction into inorganic P, in which the reaction will be partly catalyzed by phosphatase in the vicinity of plant roots (Dalal, 1977). In general, agricultural field receiving inorganic P fertilizer has higher proportion of inorganic P to total P than in natural soils. Transformation of bioavailable inorganic P (Bray II P) into organic P has been reported in surface horizons of Japanese paddy soils (Akahane et al., 2006). Soil inorganic P exists as a component of soil minerals (or precipitates) or as an adsorbed species on the surfaces of clay minerals (Harris, 2002; Lindsay et al., 1989; Sims and Pierzynski, 2005). The P-containing soil minerals include apatites [Ca10X(PO4)6, where X can be OH, F, Cl, or CO3], aluminum phosphates [e.g., berlinite (AlPO4), variscite (AlPO42H2O), and amorphous aluminum phosphates], and iron phosphates [e.g., strengite (FePO42H2O), vivianite (Fe3(PO4)28H2O), and amorphous iron phosphates]. Their P-releasing abilities can be evaluated by solubility products. The adsorption of P in soils is primarily caused by a ligand exchange reaction (Fig. 3B) between an orthophosphate anion and a metal (hydr)oxide having active surface hydroxyls, such as goethite, ferrihydrite, kaolinite, and gibbsite, forming innersphere complex of monodentate, bidentate, and binuclear (Fig. 11). The monodentate form is considerably more labile than other
A Metal (hydr)oxides
B Metal (hydr)oxides
O O M OH−0.5 OH O O M O P O O O OH Monodentate
O O −0.5
O O
M O M O M
Bidentate
C
OH−0.5 O O
P
O OH
OH−0.5
−1
Metal (hydr)oxides O O M O O P O O M O OH O O Binuclear
Figure 11 Schematic representationof (A)monodentate, (B) bidentate, and (C)binuclear. ‘‘M’’denotestrivalent octahedral metal cations such as Al and Fe.
−1
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adsorbed species. At a soil pH range between 4 and 8, adsorption and precipitation reactions of P with Al and Fe minerals are accelerated with decreasing soil pH, and those reactions with Ca and Mg minerals are promoted with increasing soil pH, resulting in maximum P solubility and bioavailability occurred at a soil pH range between 6 and 7. In acidic soils, hydroxyaluminum (HyA) and HAS ions are formed and they are interlayered into 2:1 aluminosilicate minerals, resulting in complexes such as HyA-smectite, HyA-vermiculite, HAS-smectite, and HAS-vermiculite. These complexes have extremely large capacities to fix P (Saha and Inoue, 1997; Saha et al., 1998). In surface soils, soil organic matter could contribute to the P fixation by forming ternary complexes of soil organic matters, metal cations, and phosphate. The soil organic matter could extend the P-sorption sites on metal cations by inhibiting the polymerization and crystallization of the metal cations (Gerke, 1993; Hiradate and Uchida, 2004), and it could decrease the pH dependency of the P-sorption reaction (Hiradate and Uchida, 2004). Kuo (1996) summarized P-availability soil test according to the chemical characteristics of the extracting agent: (1) water or unbuffered salt solutions (e.g., 0.01-M CaCl2), (2) diluted weak acids (e.g., lactate and acetate) with or without a complexing agent (F or EDTA), (3) diluted strong acids (e.g., HCl and H2SO4) with or without a complexing agent [e.g., 0.05-M HClþ0.0125-M H2SO4 (Mehlich-1-P), 0.001-M H2SO4þ3.0 g liter1 (NH4)2SO4 at pH 3.0 (Truog-P), 0.025-M HClþ0.03-M NH4F (Bray I and II P)], (4) buffered alkaline solutions (e.g., NaHCO3) with or without a complexing agent [e.g., 0.5-M NaHCO3 at pH 8.5 (Olsen-P), 1-M NH4HCO3þ0.005-M DTPA at pH 7.6], (5) anion exchange resin or ion oxide-impregnated filter paper strips, and (6) isotopic exchange with 32P. A combination of these extractants, 0.2-M acetic acidþ0.25-M NH4NO3þ0.015-M NH4Fþ0.013-M HNO3þ0.001-M EDTA (Mehlich-3), has also been used for multielement extraction. For the soil tests to adequately reflect P bioavailability, the P tests should respond to soil characteristics in a similar manner as plants, so soil type and plant species should be taken into account for the selection of the soil P tests. Bioavailability of soil P depends on its chemical form in soils, and it is controlled by dissolution/precipitation of soil P minerals, sorption/desorption of sorbed P, and mineralization/immobilization of organic P. To elucidate chemical forms of soil P, extraction with 0.25-M NaOH in 0.05-M EDTA solution followed by analysis with liquid state 31P NMR has been established (Turner et al., 2003, 2006). In this method, inorganic phosphates (e.g., orthophosphate, pyrophosphate, polyphosphates), orthophosphate monoesters (e.g., adenosine monophosphates, choline phosphate, glucose phosphates, inositol phosphates), orthophosphate diesters (e.g., adenosine cyclic phosphates, phosphatidyl choline, phosphatidyl serine), phosphonates (e.g., phosphonates, phosphonic acids), and organic polyphosphates (e.g., adenosine diphosphate,
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adenosine triphosphate) can be quantitatively determined. By applying this method, presence of scyllo-inositol phosphates, together with orthophosphate (dominant), myo-inositol phosphate, pyrophosphate, and DNA, was identified in three lowland permanent pasture soils from United Kingdom (Turner and Richardson, 2004). Hansen et al. (2004) applied the same procedure to manure-amended alkaline soils and confirmed the dominant presence of inorganic orthophosphate (78–83%), together with inositol phosphates (5–16%) and other orthophosphate monoesters (2–11%). This method has the following shortcomings: (1) inorganic monomeric P present as adsorbed or precipitated forms with various metal cations might be determined as just orthophosphate and (2) some components (e.g., RNA, phosphatidyl choline) are unstable and are degraded rapidly into orthophosphate monoester under such a strong alkaline condition. To overcome the former problem, it will be effective to apply the selective dissolution technique combined with solid state 31P NMR analysis to specify the chemical form of adsorbed and precipitated P species (Lookman et al., 1997). Lookman et al. (1996) estimated the size of Ca-P phase in fertilized sandy soils by such a technique and found that it roughly corresponded to the size of the labile P pool. McDowell et al. (2002, 2003) separated a solid state 31P NMR spectrum of soils into seven peaks: four Al-P (berlinite, variscite, wavellite, and Al-P) and three Ca-P (monetite, hydroxyapatite plus octacalcium phosphate plus amorphous calcium phosphate, and dicalcium phosphate dihydrate), by applying deconvolution technique. For the latter problem, water extraction combined with liquid state 31P NMR analysis would be applicable.
4.2. Mechanism of P acquisition in plants Some plant species have developed strategies to utilize low-solubility P compounds in soils. These strategies include alteration of the geometry or architecture of the root system, secretion of the low-molecular-weight organic ligands, secretion of phosphatase, and increased expression of inorganic P transporters (Fig. 12). 4.2.1. Alteration of root architecture Some plant species respond to P deficiency by altering their root architectures. Phosphorus deficiency results in increased length and density of root hairs and in increased lateral root formation and elongation ( Jungk, 2001; Linkohr et al., 2002). For example, studies with A. thaliana showed that root hair density was fivefold greater in low-P than in high-P media (Ma et al., 2001b). The average length of root hairs on P-deficient A. thaliana was threefold greater than that on P-sufficient plants. Some plants form proteoid roots, which are clusters of short lateral roots that arise from pericycle, in response to P deficiency ( Johnson et al., 1996). Proteoid roots are formed in most members of Proteaceae and in several other plant species adapted
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Rhizosphere
Cell membrane
Cytoplasm
ATP
Ca-P Fe-P Al-P
H+
H+ ADP
OA
OA
Pi
Pht1
Organic P Acid phosphatase
Figure 12 Schematic representation of the mechanisms for P acquisition in plants.
to habitats of extremely low soil fertility, including members of Betulaceae, Casuarinaceae, Cucurbitaceae, Cyperaceae, Eleagnaceae, Leguminosae, Moraceae, Myricaceae, and Restionaceae (for review, see Vance et al., 2003). These alterations in root morphology and development result in increase in the total surface area of the root for P uptake. Plant hormones, including auxins, ethylene, and cytokinins, have been suggested to be involved in P-deficiency-induced alteration of root architecture. Auxin is involved in lateral root development (Casimiro et al., 2001, 2003), root hair elongation (Bates and Lynch, 2000), and modulating root hair density (Ma et al., 2001b). In white lupin and A. thaliana, when auxin transport is inhibited by 2,3,5-triiodobenzoic acid (TIBA) and N-(1-naphthyl)phthalamic acid (NPA), the formation of proteoid or lateral roots was inhibited under P deficiency (Gilbert et al., 2000; Lopez-Bucio et al., 2002). However, it is not known how the concentration of endogenous auxins responds to P deficiency. Ethylene biosynthesis is stimulated by auxin and may cofunction with auxin in controlling root elongation. Ethylene production is enhanced in P-deficient plant roots (Borch et al., 1999). The increase in the ethylene production may be responsible for the increased root hair density and length (Tanimoto et al., 1995). An increase in cytokinin levels is normally associated with stimulating shoot growth and inhibiting root growth (Martin et al., 2000). Cytokinin levels decrease in the roots of P-deficient plants (Kuiper et al., 1988). This decrease may be responsible for the P-deficiency-induced root growth promotion (Martin et al., 2000), although the exact mechanisms remain unknown. It should be noted that P uptake by crops [e.g., buckwheat, castor (Ricinum communis), peanut (Arachis hypogaea), pigeon pea (Cajanus cajan), sorghum (S. bicolor), and soybean] is strongly correlated with root length in
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soils where P availability is high, but not in soils with low-P availability or where soil volume is limited (Otani and Ae, 1996). 4.2.2. Secretion of organic acids Some plant species secrete organic acids such as citrate and malate in response to P deficiency. Organic acids can release P from low-solubility Al-, Fe-, and Ca-P sources (Neumann and Ro¨mheld, 1999; Fig. 12). A typical example is white lupin that secretes a large amount of citrate and malate mostly from the proteoid roots under P deficiency (Dinkelaker et al., 1989; Gardner et al., 1983). The amount of carbon exuded by the roots as these two compounds can range from 10% to >25% of the total plant dry weight. Secretion of these organic acids from the proteoid roots is accompanied by increased in vitro activity of malate dehydrogenase (MDH) and phosphoenolpyruvate carboxylase (PEPC) ( Johnson et al., 1996) and reduced activity of aconitase (Neumann et al., 1999), and is accompanied by downregulated ATP-citrate lyase (Langlade et al., 2002), which are involved in TCA cycle. Genes encoding PEPC and MDH have been cloned from white lupin (UhdeStone et al., 2003). The expression of these genes is upregulated in the proteoid roots under P deficiency. Both PEPC and MDH are expressed in the cortex of emerging and mature proteoid rootlets. In addition, white lupin also has developed a complex strategy to reduce microbial degradation of the organic acids by secretion of phenolic compounds, mainly isoflavonoids (Weisskopf et al., 2006). In addition to white lupin, P-deficiencyinduced secretion of organic acids is also reported in alfalfa (Medicago sativa; citric, malic, and succinic acids) (Lipton et al., 1987), rape (citric and malic acids) (Hoffland et al., 1989, 1992), rice (citric acid) (Kirk et al., 1999), purple lupin (citric acid) (Ligaba et al., 2004), and other plant species. In pigeon pea, malonic, oxalic, citric, malic, and piscidic acids secreted from roots help in the release of low-solubility P in soils (Ae et al., 1990; Ishikawa et al., 2002; Otani et al., 1996). The secretion of citrate is supposed to be mediated by an anion transporter. Two citrate-permeable channels in the PM of the cluster roots have been characterized by a patch clamp technique in white lupin (Zhang et al., 2004). The main channel, an inwardly rectifying anion conductance (IRAC), showed higher selectivity for citrate than for Cl (Pcit/PCl¼26.3). However, the gene encoding this channel has not been isolated yet. Gardner et al. (1983) postulated that secreted citrate reacts in soils to form Fe3þ-OH-P-citrate polymers, which diffuse to the root surface where they are degraded by the action of reducing agents/Hþ in the presence of Strategy I for Fe uptake (Section 2.2.1). On the other hand, Otani et al. (1996) showed that some organic acids can liberate P from Al-P and Fe-P compounds at pH 5.6: oxalate liberates P from both P compounds equally, citrate has similar reaction characteristics with oxalate but lower effectiveness in liberating P, malonate effectively liberates Al-P than Fe-P, and
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piscidate effectively liberates Fe-P than Al-P. The P-liberating ability of malate is close to that of piscidate (Otani et al., 1999). It is likely that some organic acids can replace surface-complexed P on Al- and Fe minerals by ligand exchange reactions. It seems, however, that the contribution of the organic ligands in P acquisition by plants should be more clearly indicated by showing the concentration of the ligands in the vicinity of root surfaces, the influences of microorganism activity, and the amount of liberated and absorbed P mediated by the secreted organic ligands. Secretion of organic acids is accompanied with acidification of rhizosphere, which may lead to increased solubility of low-solubility Ca-P compounds in soils (Fig. 12). This mechanism would be more important in alkaline and calcareous soils than in acidic soils. The export of Hþ is mediated by a PM Hþ-ATPase in white lupin (Yan et al., 2002). A study showed that the release of Hþ is not strictly related to citrate release, and that other cations such as Kþ and Naþ can also serve as counterions for citrate release, whereas malate release shows a strong Hþ-release dependency (Zhu et al., 2005). 4.2.3. Secretion of phosphatase A part of soil organic P, which is derived from residues of plants and soil organisms, can be utilized by some plant species such as white lupin by secreting acid phosphatases (S-APases) (Miller et al., 2001; Wasaki et al., 2003). S-APases hydrolyze organic P compounds in the rhizosphere and supply inorganic P to the plants (Fig. 12). The transcription and activity of S-APases are enhanced by P deficiency (Li et al., 2002; Wasaki et al., 2003). Genes encoding S-APases have been cloned from white lupin (LaSAP2) and A. thaliana (AtSAPase) (Haran et al., 2000; Wasaki et al., 2003). The S-APase gene expression is induced by a decreased internal-P concentration, and is especially high in cluster roots formed under P-deficient conditions in white lupin (Wasaki et al., 2003). 4.2.4. Enhanced expression of P transporters Phosphorus is taken up by plants mainly in the forms of H2 PO 4 and HPO2 4 (inorganic orthophosphate, Pi). The concentration of soluble Pi in soil solution is very low, ranging from 0.1 to 10 mM (Hinsinger, 2001), therefore Pi is principally supplied to plant roots by diffusion rather than mass flow. On the other hand, the concentration of Pi in the root cells is much higher, being 2–20 mM (Schachtman et al., 1998). Therefore, Pi must be transported actively against concentration gradients from the external solution to the root cells. The uptake of Pi across the PM has been demonstrated to be mediated by transporters. There are two types of Pi transporters: one is a high-affinity transporter with a Michaelis constant (Km) of 3–10 mM, and the other is a low-affinity transporter with a Km of 50–300 mM (Furihata et al., 1992).
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Genes encoding high-affinity Pi transporters (Pht1) have been identified from several plant species, including A. thaliana, barley, catharanthus (Catharanthus roseus), white lupin, alfalfa, and tomato (for review, see Smith et al., 2003). In A. thaliana genome, there are nine homologues of Pht1 (Mudge et al., 2002), and in barley, eight different members of this family have been isolated (Smith et al., 1997). Members of the Pht1 family of Pitransporter proteins are 58 kDa in size and contain 520–550 amino acids. They contain 12 transmembrane domains that occur as two groups (6þ6) connected by a hydrophobic domain of 60 amino acids (Smith et al., 2003). The members of the Pht1 family are expressed in roots and their expressions are regulated at both transcriptional and posttranslational levels. Cellular localization studies show that Pi transporters are localized on the PM of root epidermal and root hair cells (Chiou et al., 2001). When plants are stressed from P deficiency, the expression of Pi transporter is greatly upregulated (Liu et al., 2001). This increased expression subsequently results in the enhanced uptake of Pi by the roots.
4.3. Genetic improvement in plants to tolerate P deficiency Attempts have been made to improve P-acquisition ability by manipulating genes involved in P-deficiency responses. Overexpression of the A. thaliana high-affinity Pi-transporter gene (Pht1) in tobacco resulted in threefold greater P uptake than control and 50% greater growth under low-P conditions (Mitsukawa et al., 1997). Overexpression of genes involved in organic acid synthesis has been reported to be useful in enhancing P acquisition. For example, overexpression of CS from bacteria in tobacco resulted in increased secretion of citrate into the rhizosphere and enhanced P acquisition in tobacco (Lopez-Bucio et al., 2000), although this was not repeated by another group (Delhaize et al., 2001). Overexpression of MDH in alfalfa also resulted in increased P accumulation compared with either transformed lines with vector only or untransformed controls (Tesfaye et al., 2001). When a gene encoding mitochondrial CS from Daucus carota (DcCS) was introduced into A. thaliana, the activity of CS in the transgenic plants was about threefold greater than that found in the control plants (Koyama et al., 2000). Both the growth and P accumulation were greater in transgenic plants with high CS activity than those in control plants when they were grown on an acidic soil where the P availability was low due to formation of low-solubility Al-P compounds. A novel transcription factor (OsPTF1) with a basic helix-loop-helix domain for the tolerance to Pi deficiency in rice has been identified (Yi et al., 2005). Overexpression of OsPTF1 enhanced tolerance to Pi deficiency in transgenic rice. Tillering ability, root and shoot biomass, and P content of transgenic rice plants were about each 30% higher than those of the wild-type plants in Pi-deficient conditions in hydroponic experiments. In soil pot and field experiments, more than 20% increase in tiller number,
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panicle weight, and P content was observed in transgenic plants compared to wild-type plants at low-Pi conditions. In Pi-deficient conditions, transgenic rice plants showed significantly higher total root length and root surface area, which resulted in a higher instantaneous Pi-uptake rate over their wild-type counterparts.
5. Future Prospects Some of other mineral-stress-tolerant mechanisms have been clarified. For example, some plants adapt to saline soils osmotically by increasing intracellular concentrations of low-charged low-molecular-weight solutes (osmolytes) such as proline (2-pyrrolidinecarboxylic acid), dimethylsulfoniopropionate [(CH3)2SþCH2CH2COO], glycine betain [(CH3)3NþCH2COO], b-alanine betaine [(CH3)3NþCH2CH2COO], proline betaine (N,Ndimethylproline), choline-O-sulfate [(CH3)3NþCH2CH2OSO 3 , pinitol (3-O-methyl-D-chiro-inositol), and mannitol. Some of these osmolytes will help in alleviating water-deficient stresses caused by drought and freezing (Buchanan et al., 2000). The mechanisms of plants in tolerating oxygendeficient stress, oxidative stress, and heat stress have also been studied, and, at least, a part of them have been clarified. It seems that plants have great flexibility to adapt to various environmental stresses: some plants can grow on anthropogenic polluted soils by heavy metals and toxic organic chemicals, and sometimes even tolerate to synthetic herbicides. Brooks (1998) summarized plants that can hyperaccumulate heavy metals such as Cd (by 1 Brassicaceae species), Co (26 Lamiaceae and Scrophulariaceae), Cu (24 Cyperaceae, Lamiaceae, Poaceae, and Scrophulariaceae), Mn (11 Apocynaceae, Cunoniaceae, and Proteaceae), Ni (290 Brassicaceae, Cunoniaceae, Euphorbiaceae, Flacourtiaceae, and Violaceae), Se (19 Fabaceae), Tl (1 Brassicaceae), and Zn (16 Brassicaceae and Violaceae), although not all of their tolerant and uptake mechanisms have been clarified. Further studies should be conducted on stress tolerance of plants because they might be useful not only for increasing food production in low-input agriculture but also for phytoremediations, rhizofiltrations, mineral explorations, phytomining, covering soil surfaces to preserve from erosion, and so on. They will also contribute in understanding endemic plant vegetation and the conservation of biodiversity.
REFERENCES Ae, N., Arihira, J., Okada, K., Yoshihara, T., and Johansen, C. (1990). Phosphorus uptake by pigeon pea and its role in cropping systems of the Indian subcontinent. Science 248, 477–480. Ahn, S. J., and Matsumoto, H. (2006). The role of the plasma membrane in the response of plant roots to aluminum toxicity. Plant Signal. Behav. 1, 37–45.
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T H R E E
Water Flow in the Roots of Crop Species: The Influence of Root Structure, Aquaporin Activity, and Waterlogging H. Bramley,1,* D. W. Turner,† S. D. Tyerman,* and N. C. Turner‡ Contents 134 135 135 137 138 140 140 141 146 147 148 150 152 167 167 169 170 171 171 172 174
1. Introduction 2. Water Movement Through the Plant 2.1. Driving forces 2.2. Hydraulic conductance 2.3. Hydraulic conductivity of roots (Lpr) 3. Root Characteristics and Water Flow 3.1. Factors that influence root growth and water uptake 3.2. Root anatomy 4. Changes in Lpr 5. Plant Aquaporins (AQPS) 5.1. AQP structure 5.2. AQP selectivity 5.3. Control of water permeability 6. The Role of AQPs in Root Water Transport 6.1. Inhibition studies 6.2. Expression and transformation studies 6.3. The contribution of AQPs to radial water flow 7. Waterlogging 7.1. Effect on O2 in the rhizosphere 7.2. Effect on root growth 7.3. Effect on water use
* {
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1
Wine and Horticulture, Faculty of Agriculture, Food and Wine, The University of Adelaide (Waite Campus), Plant Research Centre, PMB 1, Glen Osmond, South Australia 5064, Australia School of Plant Biology, Faculty of Natural and Agricultural Sciences, The University of Western Australia, Crawley, Western Australia 6009, Australia Centre for Legumes in Mediterranean Agriculture, The University of Western Australia, Crawley, Western Australia 6009, Australia Present address: Department of Renewable Resources, 444 Earth Sciences Building, University of Alberta, Edmonton, Alberta T6G 2E3, Canada
Advances in Agronomy, Volume 96 ISSN 0065-2113, DOI: 10.1016/S0065-2113(07)96002-2
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2007 Elsevier Inc. All rights reserved.
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7.4. Anoxia and AQP activity 8. Conclusion Acknowledgments References
175 180 181 182
The hydraulic properties of plant roots depend on the morphology and anatomy of the root system, the length of the absorbing region and the influence of aquaporins (AQPs). These features change during development and in response to environmental stimuli, and alter the hydraulic conductivity of the root system (Lpr). AQPs are proteins that form water selective channels to facilitate water flow across membranes. A large proportion of AQP isoforms are predominantly expressed in roots and their localization indicates a putative role in the transport of water across the root. AQP activity can finely regulate the rate of water flow across the root by changes in abundance and opening/closing the water channels. Since water will flow by the pathway of least resistance, AQPs will only influence radial water flow if the hydraulic conductivity of the apoplast is relatively less than that of the cell-to-cell pathway. There is growing evidence that AQPs influence water flow through the roots of some, but not all, species. Waterlogging is a significant environmental constraint to crop growth, but its influence on Lpr is poorly understood. Depending on the tolerance of the species, waterlogging through oxygen deficiency reduces root growth and tends to reduce Lpr. Oxygen deficiency can directly or indirectly close AQPs or alter their abundance. Changes in AQP activity may be the key component which ultimately influences water transport through waterlogged roots.
1. Introduction Aquaporins (AQPs) are proteins that form channels to facilitate the transport of water across biological membranes. By altering their abundance and/or opening and closing the channel AQPs can control the rate of water flow into and out of cells and intracellular compartments. Since water is a fundamental requirement for most life processes, there has been a prodigious amount of research into AQP-facilitated water transport since the first AQP was discovered in the early 1990s. AQPs have now been found to exist in the membranes of almost all organisms, with the largest number of AQP genes expressed in plants. The sedentary lifestyle of plants presents a fundamental challenge in the uptake and transport of water, to meet the demands of transpiration and growing tissues. AQPs have the potential to mediate not only the rate of flow across membranes, but also through tissues and organs, and to provide regulation that may minimize adverse effects during abiotic perturbations. It is now widely accepted that water flow through the roots of plants is
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regulated by AQP activity ( Javot and Maurel, 2002), but the majority of the current research tends to overlook the rudimentary features that make plant roots successful as absorbing organs. The hydraulic properties of roots are dependent not only on AQP activity, but also on the anatomy and morphology of the root system, as well as the length of the absorbing regions. These features vary between species, depend on the physical characteristics of the soil, and can change in response to changes in the environment. The nature of these changes in root structure can alter the hydraulic conductivity of the pathways for water flow through roots, either to conserve water during adverse conditions or function in some other way, for example, to increase the transport of gases during waterlogging. AQP activity also varies between species and can be regulated by development, time of day, and in response to abiotic perturbation. Water flow through roots is therefore a multifaceted process where a number of variables control the mechanisms. There has been very little research into the effects of waterlogging and/or O2 deficiency on water transport through roots, despite waterlogging being a significant constraint to crop growth. The influence of waterlogging on water flow through roots appears to be an enigma, as unlike drought and salinity there is an abundance of freely available water and yet root water transport tends to be reduced. Despite the effects of waterlogging on root water transport being known for more than a century, there has been no adequate explanation for this phenomenon. A significant discovery linked the closure of AQPs with cytoplasmic acidosis, which occurs when cells respire anaerobically during anoxic conditions (Tournaire-Roux et al., 2003). This mechanism of reducing membrane permeability may be the mechanism that reduces the rate of water flow through roots, when roots are submerged during waterlogging and the O2 concentration in the rhizosphere declines. This chapter begins with an introduction into the processes driving water flow and the definitions used to describe water fluxes through roots. The review then details some of the physical characteristics that influence water transport through roots and discusses how AQPs may be involved in regulating water flow through the radial pathway. Finally, the effects of waterlogging on root growth and water flow through roots are reviewed. The response of AQPs to O2 deficiency is reviewed and related to their potential control of water flow through waterlogged roots.
2. Water Movement Through the Plant 2.1. Driving forces Water movement in a plant is driven by gradients in water potential (Boyer, 1985; Passioura, 1982). Water potential (C) is a measure of the free energy associated with water and is expressed in units of pressure, usually
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megapascals (MPa). The net water potential of a plant is composed of two principal components, hydrostatic (P) and osmotic (p) pressure:
C¼Pp
ð1Þ
P is the result of internal or external pressures, such as the tension generated by transpiration. In cells, P includes turgor, which is an internally generated positive pressure, due to the rigidity of plant cell walls (Tyree and Jarvis, 1982). p depends on the concentration of solutes in solution. A gravitational component is sometimes included in Eq. (1), but only for tall trees (Boyer, 1985; Passioura, 1982). Water passively moves down a gradient in total C, from high to low energy, until reaching equilibrium. Hence, gradients in C can drive water transport through the soil-plant-atmosphere continuum (SPAC), and by manipulating its C through transpiration or the accumulation of salts, a plant can control the process, within limits. Water movement through the SPAC occurs in two phases, in the liquid phase by bulk flow through the soil and plant, and in the gas phase through the stomatal region (Baker, 1989). The traditional theory describing the ascent of water through the SPAC is the cohesion-tension (CT) theory, although the theory is still vigorously challenged and debated (Steudle, 2001; Zimmermann et al., 1993, 2004). Transpiration during the day generates hydrostatic pressure gradients to draw water into the roots and through the xylem of the plant. This hydrostatic pressure gradient is created by the surface tension that develops at the air–water interface in leaves and is transmitted as a negative pressure throughout the water column, where it lowers C of the roots below the soil C (Tyree, 1997). The negative pressure is equivalent to a tension or pulling force, drawing water upward (Tyree, 1997). The tension within the xylem increases as the soil water decreases and/or as the transpiration rate increases (Tyree and Sperry, 1988). By controlling the size of the stomatal apertures, the plant can regulate transpiration, and therefore P, to avoid an excessive amount of cavitation. However, when stomata are closed, photosynthesis is also inhibited, creating a compromise between CO2 uptake and water loss. The accumulation of solutes across semipermeable membranes also establishes a gradient in C that induces water uptake (Oertli, 1991). An osmotic gradient generates root pressure, causing xylem sap to exude from a cut shoot or detopped root system (reviewed by Zholkevich, 1991). The endodermis probably forms a semipermeable barrier, preventing the net efflux of solutes from the stele. Solutes secreted into the xylem lower C and induce water uptake across the root. An upward flow of solution results as more water is drawn into the xylem. Osmotic gradients are only important in driving water transport when transpiration is low (Kramer, 1983). When transpiration increases, the
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increasing mass flow of water dilutes the solutes in the xylem sap until the osmotic component is negligible and the uptake of water is predominantly controlled by the hydrostatic pressure within the xylem (Section 2.3). Transpiration can produce a much steeper gradient in C, from soil to roots, than an osmotic mechanism, as reviewed by Kramer (1983). Even so, water uptake may be limited when a plant experiences water deficit through drying soil or saline conditions because the plant cannot lower its C sufficiently to create a gradient between the soil and roots.
2.2. Hydraulic conductance The flux of water (rate of water flow) through a plant depends not only on the size of the driving forces, but also on the conductance (reciprocal of resistance) of the pathways, through which water flows (Boyer, 1985):
Q ¼ LðDP sDpÞ
ð2Þ
where Q is the water flux or volume flow rate (m3 s1), L the hydraulic conductance (m3 s1 MPa1), s the reflection coefficient, and DP and Dp the hydrostatic and osmotic pressure differences (MPa), respectively. s is a unitless parameter, relating the interaction between water and solute crossing a membrane. The value of s depends on the particular solute, with a value of 1 for a perfect osmometer and 0 when the membrane does not reflect the solute relative to water. If s ¼ 1, then Eq. (2) reduces to (Boyer, 1985):
Q ¼ LðDCÞ ¼
ðDCÞ R
ð3Þ
where R is the apparent hydraulic resistance (MPa s m3) and hence, Eq. (3) is a simple analogy to Ohm’s law (van den Honert, 1948). A common error in plant water relations is the interchangeable use of hydraulic conductance and conductivity (Lp). Equation (4) shows the relationship between the two parameters. L is a measure of the ability of an entity to conduct water, independent of the entity’s dimensions, whereas Lp is a property of an entity with specified dimensions, usually surface area (A).
L ¼ LpA
ð4Þ
The SPAC has been described as a system of hydraulic resistors arranged in series (van den Honert, 1948). Plants can vary the resistance (and conductance) of the pathways to maintain the water balance of the shoot (Steudle, 2000). When water exists in the vapor phase, the greatest resistance is the stomatal aperture. However, in the liquid phase, the root system constitutes
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a highly significant and important resistance to overall flow of water in the plant (Steudle, 2000).
2.3. Hydraulic conductivity of roots (Lpr) The surface area limits water uptake by the root (Steudle, 2001; Tyree, 2003). Therefore, water transport parameters are usually normalized per unit surface area and revision of Eq. (2) incorporates this:
Jv ¼ Lpr ðDP sr DpÞ
ð5Þ
where Jv is the volume flow, per unit area (m3 m2 s1 or m s1), Lpr the hydraulic conductivity of the root (m s1 MPa1), and sr the apparent root reflection coefficient (Passioura, 1988). The relative influences of DP and Dp depend on the rate of salt secretion into the xylem and the rate of water flow (Weatherley, 1982). When DP is zero, water flow is driven by the osmotic gradient, but the xylem sap becomes diluted when water flow increases through transpiration, and Dp becomes negligible (Kramer, 1983). The majority of studies have used detopped root systems to measure Jv, where the exudation of sap from the cut stump is collected under root pressure, or water flow is induced either by applying suction to the cut stump or externally pressurizing the root system (Fiscus, 1975; House and Findlay, 1966; Nobel et al., 1990). In the root pressure exudation technique, the driving force is osmotic and Dp is determined by measuring the osmotic pressure of the exudate and the medium bathing the roots. In the external pressure technique, the root system is sealed in a pressure chamber with the cut stem protruding through the lid of the chamber. The root system is pressurized and Jv determined at different pressure increments. Plotting Jv against DP typically produces a curvilinear relationship (Passioura, 1984, 1988), due to the osmotic component dominating flow at low flow rates (Dalton et al., 1975; Fiscus, 1975). At high flow rates, the solutes are so diluted that Jv(DP) is inherently linear and the slope gives Lpr [Eq. (5)]. If the linear part of the Jv(DP) relationship is extrapolated to the x-axis, the intercept should equal the osmotic pressure of the external medium (po), but invariably it exceeds po (Passioura, 1984). This anomaly has not been satisfactorily explained (Passioura, 1984), but the offset depends on the species and can vary diurnally or with abiotic perturbations (Boursiac et al., 2005; Emery and Salon, 2002; Munns and Passioura, 1984; Murphy, 2003; Passioura and Munns, 1984; Rieger and Litvin, 1999). The root and cell pressure probes have been developed to measure water flow through individual roots and across cell membranes (Steudle, 1993; Tomos and Leigh, 1999). In summary, the root pressure probe (RPP) measures root pressure and water flow is induced either by applying
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hydrostatic pressure (with the aid of the probe) to the root, or by changing the osmotic pressure of the bathing medium. The rate of water flow across the root, and subsequently, Lpr is calculated from the rate of transient relaxations in root pressure (Steudle, 1993). The cell pressure probe (CPP) measures turgor pressure via the tip of a microcapillary introduced into an intact cell. Analogous to the RPP, water flow is induced by applying hydrostatic pressure, with the aid of the probe (Steudle, 1993). If the dimensions of the cell are known, the CPP can be used to determine the volumetric elastic modulus (e) and hydraulic conductivity (Lpc) of the cell. Osmotic flows can also be induced, but due to the uncertainty of the effects of unstirred layers for cells located within tissue, the values of Lpc may be erroneous. The CPP and RPP are appealing because they provide real-time measurements that are useful for estimating the location of the principal resistances to water transport through roots. Measurements of cell turgor have revealed gradients in C across a radial profile in the roots of some species (Pritchard et al., 1989; Zimmermann et al., 1992). Combining results from CPP and RPP measurements on roots of the same species can potentially identify the main radial pathway for water transport across the root (Section 3.2.2). For example, Lpc of epidermal and cortical cells was much greater than Lpr of Hordeum distichon and Phaseolus coccineus roots, indicating that water flows via the cell-to-cell pathway (Steudle and Brinckmann, 1989; Steudle and Jeschke, 1983). In comparison, analogous measurements on maize (Zea mays) roots revealed a predominantly apoplastic flow (Steudle et al., 1987). Comparing the measured values of Lpr and Lpc for each cortical cell layer indicated that radial water flow through wheat (Triticum aestivum) roots occurs by a combination of the parallel pathways, but radial water flow in the roots of narrow-leafed lupin (Lupinus angustifolius) and yellow lupin (L. luteus) appears to be predominantly apoplastic (Bramley, 2006). 2.3.1. Transport models The root has been modeled as a system containing either two or three compartments, a series of membranes or as a perfect osmometer, to explain the processes of water flow across plant roots (Dalton et al., 1975; Fiscus, 1975; Pickard, 2003; Tyree et al., 1994). However, there has been considerable debate about whether Lpr of roots was dependent on Jv (reviewed by (Kramer, 1983; Passioura, 1982). Before the models of Fiscus (1975) and Dalton et al. (1975), the nonlinear relationship between flow and driving force was interpreted as Lpr increasing with flow rate, so water flux was considered to be independent of DC across the root (reviewed by Weatherley, 1982). The landmark papers of Passioura and Munns (Munns and Passioura, 1984; Passioura and Munns, 1984) demonstrated a linear relationship between driving force and flow. The slope of the relationship
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varied with time and treatment, indicating that the apparent hydraulic conductance was truly variable. Steudle (1994) extended the two compartment models and developed the Composite Transport model. The model incorporates the composite nature of water flow across different tissues, the parallel radial pathways, the hydrostatic and osmotic driving forces, variable Lpr, and also accounts for sr < 1 (Steudle and Peterson, 1998). The model explains the variability in Lpr in relation to the nature and intensity of the driving forces (Steudle and Peterson, 1998). Osmotic forces will primarily drive water flow through the cell-to-cell pathway, as s1 for cell membranes. Since there are no membranes in the apoplastic pathway, the apoplastic s0. Thus, osmotic gradients will drive very little water transport in the apoplast. When the driving force is primarily hydrostatic (e.g., generated by transpiration), water can flow via a combination of apoplastic and cell-to-cell pathways.
3. Root Characteristics and Water Flow 3.1. Factors that influence root growth and water uptake External factors can influence water uptake by the roots, such as interactions between the roots and the soil, and the distribution of roots within the soil profile (Passioura, 1988). Close contact with the soil is imperative for roots to maintain hydraulic continuity at the soil–root interface and minimize the interfacial resistance to flow. Poor hydraulic contact necessitates a large drop in water potential across the interface to induce water uptake (Passioura, 1988). Roots growing through large soil pores may have poor hydraulic contact with the soil, but there is also evidence that some roots shrink when plant C is low, so the root–soil contact may be reduced (Huck et al., 1970; Palta et al., 1987). The growth of root hairs or the exudation of compounds that adhere to soil particles may assist roots in maintaining an intimate connection with the soil. The distribution of roots in the soil profile depends on soil texture and structure, and the type of inherited root system (Kramer, 1983). Roots tend to grow through preexisting soil pores, toward regions with nutrients and water, and avoid unfavorable regions, so the distribution is generally not uniform. Roots of many crop species can penetrate to depths of several meters in well-aerated, deep, soft soils (Kramer, 1983). In addition, tolerance to the range of abiotic limitations encountered by roots in a growing season may determine root growth behavior. There are two main types of root system. Monocots develop a fibrous root system with initial roots (3–5 axes for wheat) emerging from the seed (seminal roots) and subsequent roots emerging from the basal nodes of the stem (nodal roots, also called adventitious roots) (Greacen et al., 1976). In comparison, the radicle of eudicots (the first root to emerge from the
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seed) can develop into the dominant root of a taproot system that can become extensively branched. Although monocot and eudicot root systems can extend to similar depths in the field, the total root length density (root length/soil volume) of monocots is often much greater. For example, the root length density of wheat is two- to tenfold larger than narrow-leafed lupin in Western Australian soils (Dracup et al., 1993; Gallardo et al., 1996; Gregory and Eastham, 1996; Hamblin and Tennant, 1987). Despite these differences in root length, the roots of eudicotyledon species tend to have a higher specific rate of water uptake than cereals (Bremner et al., 1986; Hamblin and Tennant, 1987; Mason et al., 1983). Moreover, these greater rates of water uptake appear to be due to a greater hydraulic conductivity. For example, Lpr of lupin root systems is at least twofold greater than Lpr of wheat root systems (Bramley, 2006; Gallardo et al., 1996).
3.2. Root anatomy Kramer (1983) stated that the ‘‘effectiveness of roots as absorbing organs’’ depends on anatomical and morphological features. The dynamics of root water permeability in relation to these features were succinctly summarized by Moreshet and Huck (1991). The root apex typically has high axial and radial resistances to water flow compared with the remainder of the root with its developed xylem (Steudle, 2001). Water and nutrient absorption generally commences >10 mm behind the tip, which coincides with the root hair zone. The length of the absorbing region depends on the species, but may change with transpirational demand and during adverse conditions (reviewed by Kramer, 1983). Lpr of wheat roots decreases with distance from the root tip, indicating that water absorption occurs preferentially in the apical region (Bramley, 2006; Jones et al., 1988). In addition, water absorption by several or all of the individual roots may contribute to Lpr of the whole root system (Bramley, 2006). Cereal roots apparently have maximum water absorption within a region <100 mm from the root tip (Greacen et al., 1976; Sanderson, 1983). There is also evidence that individual roots are able to vary their hydraulic conductivity. Vysotskaya et al. (2004a,b) excised four of the seminal roots of durum wheat (Triticum durum), and this increased Lpr of the remaining root, so that the water supply to the shoot was maintained. In comparison, lupin roots absorb water more evenly along the length of the taproot as Lpr is constant with length (Bramley, 2006). Using a novel CAT-scanning and microelectrode technique, Hamza and Aylmore (1992a) observed uniform water absorption along the length of roots of 17-day-old narrow-leafed lupin plants, in comparison with radish (Raphanus sativus) plants that extracted more water closer to the soil surface. Lupins had lower potential difference between leaves and root surface (i.e., smaller DC, which equates to a smaller driving force) and higher water flow rates than radish, indicating that lupins had lower plant resistances (Hamza and Aylmore, 1992b).
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This disparity in root water uptake and Lpr were initially thought to be due to differences in axial and radial anatomy (Gallardo et al., 1996; Hamblin and Tennant, 1987; Hamza and Aylmore, 1992a), but this was proposed before AQPs were discovered. Nevertheless, differences in anatomy can influence the hydraulic conductivity of the pathways for water flow. The root has been described as a porous pipe or leaky cable, where the ratio of the radial (across the pipe wall) and axial (through the length of the pipe) resistances determines the resistance of the whole root and the distribution of water uptake (Landsberg and Fowkes, 1978; Zwieniecki et al., 2003). In the radial pathway, water taken up by the root has to traverse living tissue before reaching the lumen of the xylem vessels. In the axial plane, water flow occurs through the xylem vessels and tracheids. 3.2.1. Axial pathway Mature xylem vessels are dead cells that form a continuum of tube-like structures, separated by thin perforated walls, so that longitudinal flow through the vessels is analogous to flow through a conduit or pipe (Zwieniecki et al., 2003). The driving force for longitudinal flow through the vessels will be hydrostatic, unless membranes (e.g., living cells) interrupt the pathway. If the perforated walls provide negligible resistance, the radius of the vessels (to the fourth power) determines the rate of water flow through the xylem continuum, according to the Poiseuille-Hagen equation (Steudle and Peterson, 1998). The development of the vasculature is different between monocots and eudicots. For example, the stele of wheat contains one central pitted metaxylem vessel, which increases in diameter away from the root tip (40to 100-mm diameter), and seven or eight xylem strands containing one small metaxylem vessel (10-mm diameter). Consequently axial conductance does not appear to change much along the length of the root (Bramley, 2006). Within the stele of lupins, the vasculature develops in a diarch pattern, with bundles of metaxylem vessels up to 100 mm in diameter (Hamblin and Tennant, 1987). The abundance and diameter of vessels increase with distance from the root tip, which result in axial conductance also increasing in a similar pattern (Bramley, 2006). The axial conductance may be associated with Lpr, probably reflecting the capacity of the axial pathway in relation to all water absorption distal to any point. The axial conductance may be several orders of magnitude greater than the total hydraulic conductance of a root and hence the radial pathway creates the greatest constraint on water flow through the root (reviewed by Steudle and Peterson, 1998). 3.2.2. Radial pathway Water taken up by the root has to cross a series of concentric cell layers: the epidermis, cortex, endodermis, and stele, before reaching the lumen of the xylem vessels (Fig. 1).
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X
A
B
en
ep
C ep
C en
Figure 1 Transverse sections of (A) wheat and (B) narrow-leafed lupin roots, 20^40 mm from the root tip, showing the radial cell layers that water must cross before reaching the lumen of the xylem vessels. For wheat and lupins, water must cross the epidermal (ep), cortical (C), endodermal (en), and stelar cell layers. Scale bars represent 50 mm.
The epidermis consists of a single layer of elongated, tightly packed cells. Epidermal cell walls are generally thin, but may contain deposits of suberin or cutin (Kramer, 1983; Moreshet and Huck, 1991). Epidermal walls of the majority of 181 species surveyed appeared autoflourescent when viewed under ultraviolet light, indicating the presence of suberin or lignin (Perumalla et al., 1990). Root hairs arise from epidermal cells, and their abundance and longevity depend on the plant species and environment. A structurally different subepidermal layer may exist, called the hypodermis (Perumalla et al., 1990). In some species, the hypodermal walls contain a radial strip of suberin (Casparian bands) or suberized secondary walls and the layer is called an exodermis (Enstone and Peterson, 1997, 1998; Perumalla and Peterson, 1986; Perumalla et al., 1990). Suberin is composed of hydrophobic, fatty compounds, believed to act like a waterproofing agent (Nawrath, 2003; Zeier and Schreiber, 1998; Zeier et al., 1999). The presence of a suberized exodermis can change with root maturity and environmental conditions. For example, in maize roots, aeroponic culture (mist culture) induces the development of an exodermis (Hose et al., 2000; Zimmermann et al., 2000). In comparison, the roots of lupin species do not form a hypodermis or exodermis, at least up to 300 mm from the tip when grown in hydroponic or sand culture (Bramley, 2006; Hartung et al., 2002; Perumalla et al., 1990) and cannot be stimulated by aeroponics (Hartung et al., 2002). A suberized exodermis does not form in members of the Tritaceae family (Greacen et al., 1976; Perumalla et al., 1990) possibly because the cortex eventually deteriorates. The cortex consists of cell layers, the number of which varies depending on species, root development, and environment. The cortex of cereal roots generally consists of 6–8 cell layers, but this shrivels with maturity (Greacen et al., 1976; Hamblin and Tennant, 1987). The cortex also shrinks when the water potential of the root decreases, which implies it is not buffered against
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large changes in C in the xylem. Hence, the greatest resistance to radial water flow may occur in the epidermis (Passioura, 1988). Cortical cell walls are thin and usually contain no suberin. The cubic or hexagonal arrangement of cells determines the shape of the intercellular spaces between cells, which influences the air-filled root porosity and the size of the apoplast. The endodermis is a single cell layer that forms a sheath around the stele (Moreshet and Huck, 1991). The cell walls of the endodermis may contain suberin and thicken to form a similar structure to the exodermis (Perumalla and Peterson, 1986; Zeier and Schreiber, 1998; Zeier et al., 1999). During root growth, the endodermis develops in three stages: (1) appearance of Casparian strip; (2) continuous layer of suberin (suberin lamellae) covering the protoplast, between plasmalemma and cell wall; and (3) inner tangential walls thicken, and a layer of cellulose is deposited over the suberin lamellae (Ma and Peterson, 2003). The location of the different endodermal stages along the root axis depends on species, but appears to mature earlier in cereals compared with eudicots (Bramley, 2006; Greacen et al., 1976; Hartung et al., 2002; Perumalla et al., 1990; Sanderson, 1983). Water deficit may also promote earlier development of the endodermis (Enstone et al., 2003). The endodermis may also contain unsuberized passage cells, opposite xylem poles (Esau, 1977). The pericycle, which is the layer from which lateral roots arise, bounds the stele. The stele contains small, tightly packed parenchyma cells and the vascular tissue (xylem and phloem). Primary xylem forms a core in the root that develops in an exarch pattern, that is, centripetally (Esau, 1977). Protoxylem elongates and matures (deposition of secondary walls) to become a functioning conduit, before eventually differentiating into metaxylem. 3.2.2.1. Parallel pathways for radial water flow There are three parallel pathways for radial water flow: symplastic, transmembrane, and apoplastic (Fig. 2). The symplastic pathway is from cell to cell, through the cytoplasmic continuum, via plasmodesmata (Fig. 2). Water must cross at least two membranes, once to enter and once to exit the symplast. The transmembrane pathway involves water flow from cell to cell (vacuole to vacuole), and the most direct pathway is by crossing cell walls, plasma membrane, cytoplasm, and tonoplast (Fig. 2). Water crossing each cell layer in series, via the transmembrane pathway, would hence cross four membranes per cell (Fig. 2). The apoplastic pathway is around protoplasts, via the cell walls and intercellular spaces (Fig. 2). Analogous to flow through the xylem, hydrostatic pressure gradients drive water flow in the apoplastic pathway because it contains no membranes. However, the apoplastic path has small crosssectional surface area of the radial pathway and may be interrupted at the
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Epidermis
Exodermis
Cortex
Endodermis
Stele
V
X
Cb Plasma membrane
Cb
Tonoplast
Plasmodesmata
Cell wall
Apoplastic path Symplastic path Transmembrane path
Cell-to-cell path
Figure 2 Pathways of radial water flow, from the root surface to the lumina of xylem vessels (X).The apoplastic pathway (cell walls and intercellular spaces) may be blocked at the endodermis and exodermis (if present) by the Casparian strip (Cb).The symplastic path is within the cytoplasm, through plasmodesmata. Flow in the transmembrane path crosses the plasma membrane and vacuole (V).
endodermis and exodermis (if present) by suberin, forcing water to cross at least the plasma membrane to reach the apoplast of the stele. Suberization of the endodermis and exodermis is thought to block the apoplast and prevent the transport of water and ions (Enstone et al., 2003), but Steudle et al. (1993) demonstrated that the endodermis of young maize roots did not influence Lpr. Water flow across the root can occur by any combination of apoplastic and cell-to-cell pathways, or one pathway may dominate. The pathways may differ between species, change in different regions of the root, and alter with different environmental conditions. Identifying the contribution of the different pathways is important for understanding the nature of the resistances to root water transport. Some researchers believe the cell-to-cell pathway is the main pathway for water flow, and that the epidermis poses the greatest resistance (reviewed by Passioura, 1988). Transport through the cellto-cell pathway, crossing membranes or via plasmodesmata, provides the opportunity for regulatory control and hence the potential to influence the size of the resistance, without necessitating anatomical changes. Moreover,
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the discovery of AQPs is changing the traditional view that water transport across membranes has a high invariant resistance to water flow (Tyerman et al., 1999). The apoplast is often considered to be the dominant pathway, despite the fact that it only constitutes 5% of total root volume and an even smaller fraction of the cross-sectional surface area. If the apoplast were the main pathway, the plant would have little control of water flow through the root, unless the endodermis forms a tight barrier and therefore, plays a central regulatory role. If the radial conductance is indeed the rate-limiting factor in water transport through roots, then theoretically, thicker roots should have a smaller Lpr than thin roots because the path length for flow is longer. However, anatomy and AQP activity (Section 6.3) influence the resistivity of the radial pathways to flow, which can counteract the significance of the path length. Rieger and Litvin (1999) found that root diameter was negatively correlated with Lpr in five species with contrasting root anatomy. In addition, Rieger and Litvin (1999) found that drought stimulated suberization and other anatomical changes that reduced Lpr. The development of an exodermis in maize roots decreases the radial hydraulic conductivity (Hose et al., 2000; Zimmermann et al., 2000). Conversely, Barrowclough et al. (2000) discovered that in developing onion (Allium cepa) roots, the greatest values of radial hydraulic conductivity were correlated with the presence of an exodermis. Huang and Eissenstat (2000) also found that structural differences in the radial pathway were the primary factor determining Lpr of roots of citrus rootstocks.
4. Changes in Lpr Lpr for individual roots and whole root systems typically ranges between 10–8 and 10–7 m s1 MPa1, for herbaceous species, reflecting differences in root structure and experimental techniques (Table 6 of Steudle, 1989; Table 2 of Rieger and Litvin, 1999). For any given species, Lpr can alter with development, environment, and other regulatory controls. During the day, water flux through the plant varies with time, usually following the fluctuation in transpiration. A number of studies have found Lpr to vary diurnally in excised roots of wheat, maize, Lotus japonicus, castor oil (Ricinus communis), white lupin (L. albus), and sunflower (Helianthus annuus) (Carvajal et al., 1996; Clarkson et al., 2000; Else et al., 2001; Everard and Drew, 1987, 1989; Henzler et al., 1999; Passioura and Munns, 1984). Exogenous addition of phytohormones to the root-bathing medium tends to influence root water transport, although the responses to abscisic acid (ABA) treatment are contradictory. The hydraulic conductivity of maize, sunflower, and common bean (Phaseolus vulgaris) roots increased in
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response to exogenous ABA (Aroca et al., 2006; Freundl et al., 2000; Hose et al., 2000; Quintero et al., 1999). ABA also increased Lpc of maize root cortical cells, but the effect was only transient with Lpc returning to control levels after 2 h (Hose et al., 2000). Conversely, ABA did not influence water flux from detopped Populus tremuloides roots, but reduced Lpr of P. coccineus roots (Fiscus, 1981; Wan and Zwiazek, 1999). Abiotic factors also affect hydraulic conductivity. Gradual soil drying or addition of external osmoticants (e.g., NaCl, polyethylene glycol) decreased root water transport by 27–100% in long- and short-term experiments (Aroca et al., 2006; Azaizeh and Steudle, 1991; Carvajal et al., 1999, 2000; Joly, 1989; Liu et al., 2006; Lu and Neumann, 1999; Martre et al., 2001; Munns and Passioura, 1984; Nobel et al., 1990; Rieger and Litvin, 1999). Other factors that can reduce Lpr include O2 deficiency (Section 7.3), nutrient deficiencies, chilling, and high concentrations of aluminium or other toxicants (Aroca et al., 2005; Carvajal et al., 1996; Clarkson et al., 2000; Gunse´ et al., 1997; Kamaluddin and Zwiazek, 2002a, 2003; Lee et al., 2004). However, the results are not consistent, and for example, starving the roots of potassium more than doubled Lpr of rice (Oryza sativa) roots, which was correlated with an increase in expression of AQP genes (Liu et al., 2006). The response of Jv and Lpr is frequently rapid, occurring within minutes and when the treatment is removed the recovery in Lpr is often just as rapid (Carvajal et al., 1996; Gaspar et al., 2001; Kamaluddin and Zwiazek, 2002a, 2003). The magnitude of the effect on Lpr appears to depend on the nature of the driving force with salinity and chilling inhibiting osmotic Lpr more than hydrostatic Lpr (Azaizeh and Steudle, 1991; Lee et al., 2004; Martinez-Ballesta et al., 2000). Physical changes in root structure and anatomy occur in some species (Section 3) and these may decrease the conductance to bulk water flow, for example deposition of suberin in hypodermal cell walls (Steudle, 2000). However, anatomical changes are slow and dependent on growth, and may act as a survival strategy to reduce Lpr in the long term, when environmental changes in the field are slow. There is no evidence where anatomical changes provide the means to regulate Lpr diurnally, nor do they account for the rapid and reversible changes in Lpr measured under laboratory conditions. If a significant proportion of radial water flow occurs through the cell-to-cell pathway by crossing cell membranes, Lpr may be controlled by AQP activity.
5. Plant Aquaporins (AQPS) Plant AQPs are ubiquitous and expressed at such high levels that they can constitute up to 15% of total membrane protein (Johansson et al., 1996; Rivers et al., 1997). Plant AQPs are also highly diverse. The Arabidopsis
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thaliana genome contains 35 sequences that are AQP homologues and at least 31 AQPs have been identified in maize and 33 in rice (Chaumont et al., 2001; Quigley et al., 2001; Sakurai et al., 2005). The high number of AQPs alludes to their importance in plant membrane transport and possibly plant hydraulics (The Arabidopsis Genome Initiative, 2000). Based on the sequence alignment of their amino acids, the plant AQP family is classified into four subfamilies: plasma membrane intrinsic proteins (PIPs), tonoplast intrinsic proteins (TIPs), nodulin-like intrinsic proteins (NIPs), and small basic intrinsic proteins (SIPs). The PIPs are further subdivided into two groups, PIP1 and PIP2. The PIP and TIP nomenclature implies that these proteins are localized to the respective plasma and vacuolar membranes, but they may also be localized to other membrane compartments (Barkla et al., 1999; Frangne et al., 2001; Kirch et al., 2000; Liu et al., 2003). The diversity of plant AQPs implies different functional roles, although the nature of these roles is still generally unclear. Phylogenetic analysis shows close similarities between monocots and eudicots and the genomes of A. thaliana, rice and maize contain similar numbers of AQP homologues within each subfamily/group (Table 1). Rice has two less PIP members than A. thaliana and maize, and maize has fewer NIPs compared with the other two species. Although there is a high degree of homology between AQPs within each subfamily, each species also has unique AQP members (Chaumont et al., 2001; Johanson et al., 2001; Quigley et al., 2001; Sakurai et al., 2005).
5.1. AQP structure In AQP proteins there are six transmembrane helices connected by five loops of varying lengths and the amino (NH2) and carboxy (COOH) termini are located on the cytoplasmic side of the membrane (Fig. 3). Amino acid motifs in loops C and E are highly conserved in a wide range of PIPs, but are not present in AQPs localized in the tonoplast, and so these motifs are thought to be necessary for signal recognition and targeting to the respective membrane (Barone et al., 1997). The two longest loops, B and E, contain the signature AQP motifs asparagine-proline-alanine (NPA) that fold into the membrane as a component of the single aqueous pore (Fig. 3). The novel folding of the polypeptide creates a pore shape that is analogous to an hourglass, with a narrow constriction in the center of the membrane and widens at the membrane surfaces ( Jung et al., 1994; Murata et al., 2000). The obverse symmetry of the pore provides AQPs with bi-directionality without rectification (Meinild et al., 1998; Murata et al., 2000). Loop D in PIPs is four to seven amino acid residues longer than other AQPs, which folds under the protein on the cytosolic side of the membrane and forms a cap occluding the cytosolic side of the pore (To¨rnroth-Horsefield et al., 2006, Section 5.3.3.1.1).
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Table 1 The number of genes identified within each AQP group in the genome sequences of arabidopsis, rice, and maize AQP subfamily/ group
Arabidopsis thaliana
Orysa sativa
Zea mays
References
PIP1
5
3
6
PIP2
8
8
7
TIP
10
10
11
NIP
9
10
3
SIP
3
2
4
Chaumont et al., 2001; Johanson et al., 2001; Quigley et al., 2001; Sakurai et al., 2005 Chaumont et al., 2001; Johanson et al., 2001; Quigley et al., 2001; Sakurai et al., 2005 Chaumont et al., 2001; Johanson et al., 2001; Quigley et al., 2001; Sakurai et al., 2005 Chaumont et al., 2001; Johanson et al., 2001; Quigley et al., 2001; Sakurai et al., 2005 Chaumont et al., 2001; Johanson et al., 2001; Quigley et al., 2001; Sakurai et al., 2005
A cysteine residue on the third transmembrane domain (Fig. 3) has been found to impart mercury sensitivity to several plant AQPs and this residue is highly conserved in PIPs and TIPs (Daniels et al., 1996; Quigley et al., 2001). Barone et al. (1997, 1998) also demonstrated that Hg 2þ induces a conformational change to the plant plasma membrane AQPs instead of direct occlusion of the water channel. Several PIPs and at least one TIP appear to be mercury insensitive, despite the presence of several cysteine residues on the protein (Biela et al., 1999; Daniels et al., 1994; Krajinski et al., 2000). Despite the caveats of using this compound (Section 5.3.3.1.3), the application of mercury is one of the main tools used to estimate the activity of AQPs by determining its effect on reducing membrane permeability (Maurel, 1997). Other features that are highly conserved in plant AQPs include a pHsensitive histidine residue on Loop D (Fig. 3), present in all PIPs (Tournaire-Roux et al., 2003). Serine residues, which are putative phosphorylation sites, are located in the first cytosolic loop of most PIPs and
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A
B
C
D
E
Apoplast or vacuole C
P 1
2
N
A
3
4
5
N
A P
6
Cytosol H
S
S
H2N
HOOC
Figure 3 Overall topology of a plant PIP2 showing the highly conserved and important residues (circled). AQPs contain six membrane-spanning domains connected by loops A to E. Loop D of PIPs contains four to seven more amino acid residues than other AQPs and this loop folds under the protein, on the cytosolic side, to block the pore.The NPA motifs form the aqueous pore. Serine (S) and histidine (H) residues are involved in phosphorylation and protonation of the protein, respectively. The cysteine (C) residue on the third transmembrane domain confers mercury sensitivity in some AQPs.
TIPs, and the C-terminal of all PIP2s and NIPs (Fig. 3) (Guenther et al., 2003; Johansson et al., 1998; Quigley et al., 2001). In the membrane, AQPs form tetrameric structures with each monomer acting as an independent water channel (Murata et al., 2000; Walz et al., 1997). There is increasing evidence that at least some plant AQPs form heterotetramers through interaction with different PIP isoforms and this interaction activates water channel function (Fetter et al., 2004; Temmei et al., 2005). However, for functional importance to be implied, studies need to demonstrate that the relevant isoforms are coexpressed in the same tissue and cells.
5.2. AQP selectivity AQP activity is predominantly assayed by heterologous expression of individual isoforms in Xenopus laevis oocytes. The oocytes have an intrinsically low permeability to water and are invariably unperturbed when exposed to a hypotonic bathing medium. After expression of exogenous AQPs the oocytes swell rapidly and burst within a few minutes. The swelling rate is used to calculate the osmotic water permeability of the membrane (Pf). Although this method does not reveal the true function of AQPs in native membranes, the procedure does demonstrate that many AQP homologues increase the rate of water flow across membranes. The activity of individual
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AQPs varies considerably, with some isoforms having very low water permeability, while others are also permeable to small nonelectrolytes, such as CO2, glycerol (aquaglyceroporins), boric acid, and urea (Biela et al., 1999; Dordas et al., 2000; Liu et al., 2003; Uehlein et al., 2003). PIP1s appear to have little or no water permeability, although when expressed individually they may not be activated in oocytes (Chaumont et al., 2000; Fetter et al., 2004; Li et al., 2000; Suga and Maeshima, 2004). Conversely, PIP2s have high water permeability and can increase Pf by at least tenfold (Chaumont et al., 2000; Suga and Maeshima, 2004). NOD26 from soybean (Glycine max) and its ortholog LIMP2 from L. japonicus have moderate water permeability and only increase oocyte swelling rates by threefold, but are also permeable to glycerol (Guenther and Roberts, 2000; Guenther et al., 2003). NtAQP1 from tobacco (Nicotiana tabacum), a PIP1 ortholog, is moderately permeable to water, glycerol, and CO2 (Biela et al., 1999; Siefritz et al., 2001; Uehlein et al., 2003). TIPs appear to have constitutively high permeability to water and several A. thaliana TIPs also transport urea (Daniels et al., 1996; Liu et al., 2003). No plant AQPs that have been functionally characterized are permeable to ions, unlike the mammalian AQP6 that is permeable to anions, particularly nitrate, when exposed to HgCl2 or when the cytosol is acidified (Hazama et al., 2002; Ikeda et al., 2002; Yasui et al., 1999). HgCl2 can cause turgor pressure of plant cells to decrease due to a leakage of ions (Zhang and Tyerman, 1999), albeit the loss of ions is probably through ion channels. The shape of the pore and the size of the permeating molecule confer the selectivity of the individual AQP for water or other small neutral solutes (Murata et al., 2000). However, a simple steric model does not account for the diversity of solutes that can permeate some AQPs and not others. As Meinild et al. (1998) demonstrated, there is no connection between the permeation of mammalian AQPs and the solute size. Selectivity of the protein is more likely determined by the properties of the amino acid residues lining the pore. Identification of the important residues for selectivity is progressing for prokaryotic and mammalian AQPs (Sui et al., 2001; Tajkhorshid et al., 2002), but no specific signature motifs have been identified in solute-transporting plant AQPs thus far (reviewed by Santoni et al., 2000; Tyerman et al., 2002). There is also some conjecture whether the fourfold axis in the center of the AQP tetramer could function as a channel, and possibly as an ion channel, which is activated when the state of the water channels is altered (Chrispeels et al., 1999; Tyerman et al., 2002). Considerable advances have been made in determining the permeability of different AQPs to various compounds, but knowledge of the specifics of permeability alone does not infer a physiological role. With new technology and molecular techniques, research is progressing in understanding of the role of AQPs in situ.
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5.3. Control of water permeability The existence of AQPs is profoundly significant for living cells, because they endow the organism with the potential to control water flow (Tyerman et al., 2002). AQPs can control flow across the membrane (and tissue/organs) in two ways: (1) by changing their abundance in the membrane and/or (2) by changing the rate of flow through the water channel. Changing the rate of flow through the AQP may be achieved by the opening and closing of the channel (gating), or by changing the gradient of the driving force (Tyerman et al., 2002). 5.3.1. AQP abundance and patterns of expression in roots The expression pattern of AQPs tends to be greater in tissues and cells with high water permeability. In plants, the patterns of expression are complex, varying between organs and tissues and are dependent on the species. Very few AQP isoforms appear to be cell specific, but rather, are expressed at varying levels in a range of cell types under developmental control or in response to environmental stimuli. When focusing on roots, the literature reveals several principal features. First, a high proportion of AQP isoforms is preferentially expressed in roots (Table 2). PIP2;1 and/or PIP2;2 homologues are abundantly expressed in the roots of most species studied (Table 2). The tissue-specific expression of these isoforms also reveals a putative role in the radial transport of water across the root with the high abundance of PIP2 homologues particularly in the endodermis (Hachez et al., 2006; Javot et al., 2003; Suga et al., 2003). If suberization of the endodermis blocks the apoplastic pathway, then water would have to cross membranes in this tissue to reach the apoplast of the stele (Section 3.2.2.1). Geometrically, the endodermis also creates a high resistance to water flow, because of the decreasing surface area toward the center of the root. A high expression of PIP2s with high water permeability (Section 5.2) in the endodermis could increase the radial hydraulic conductance and confer an efficient mechanism of regulating Lpr. Additional support for PIP2s facilitating radial water transport across membranes where the apoplast is blocked comes from aeroponically grown maize roots. Roots grown under these conditions develop a suberized exodermis and endodermis, in comparison with roots grown hydroponically (Hose et al., 2000; Zimmermann et al., 2000). Labeling of ZmPIP2;1 and ZmPIP2;5 by immunocytochemical localization was strongest in the epidermis, exodermis, and cortex of primary root tissue, correlating with the regions that were highly suberized (Hachez et al., 2006). Moreover, localization of ZmPIP2;5 in the epidermis exhibited polarity, with a stronger signal on the plasma membrane facing the external medium. The development of an exodermis in aeroponically grown maize roots tends to reduce Lpr compared with the less suberized hydroponic roots,
Table 2
AQP transcript (shown in italics) and protein isoforms highly and/or preferentially expressed in roots
Species
Isoform
Cell/tissue-specific localization
References
Arabidopsis thaliana
PIP2;2
Cortex, endodermis (highly expressed), outer layers of stele. Expression levels varied among the different studies, reflecting different growth conditions (soil versus hydroponic) and growth stage. Primarily expressed in roots, but in low amounts.
Javot et al., 2003
a
PIP1;1, PIP1;2, PIP1;5 protein, a PIP2;1, aPIP2;2, aPIP2;7, TIP1;1, TIP1;2, TIP2;1
SIP2;1 Craterostigma plantagineum Hordeum vulgare
Juglans regia Lotus japonicus
Lycopersicon esculentum Mesembryanthemum crystallinum
CpPIPa (PIP1), CpPIPb (PIP1) HvPIP2;1 HvPIP2;1 protein JrPIP2;2 LIMP2 (NIP protein) LIMP1 (TIP protein) PIP1-like protein LeAQP2 MIP-A (PIP protein)
MIP-B (PIP protein) MIP-C (PIP protein) MIP-F (TIP protein)
All cells near the tip, but epidermis and vascular bundles in maturing region. Symbiosome membrane. Roots and symbiosome membrane.
Epidermis and endodermis of young roots, vasculature of mature roots. Pericycle and cortex, not apex. Endodermis. All cells of elongation zone. All root cells.
Alexandersson et al., 2005; Boursiac et al., 2005; Daniels et al., 1996; Jang et al., 2004; Santoni et al., 2003 Johanson and Gustavsson, 2002 Mariaux et al., 1998 Katsuhara et al., 2002 Katsuhara et al., 2003a Sakr et al., 2003 Guenther and Roberts, 2000 Henzler et al., 1999 Werner et al., 2001 Kirch et al., 2000; Yamada and Bohnert, 2000
(continued)
Table 2
(continued)
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Species
Isoform
Nicotiana glauca Nicotiana tabacum
NgMIP1 (TIP), NgMIP5 (PIP) a NtAQP1 (PIP1b)
Olea europaea Orysa sativa
OePIP1;1, OePIP2;1 rwc1 (PIP1) OsPIP2;3, OsPIP2;4, and OsPIP2;5 Lsi1 (NIP)
Pisum sativum Populus tremula x tremuloides Raphanus sativus
PsPIP1;1, PsPIP2;1, PsTIP1;1 PttPIP1;1, PttPIP2;2, PttPIP2;3, PttPIP2;5 a g-VM23 (TIP) a PAQ1s (PIP1s) and aPAQ2s (PIP2s) RsPIP1 and RsPIP2 proteins RsTIP
Spinacia oleracea
RsPIP2;1 pm28a (PIP2;1) a SoPIP1;2
Cell/tissue-specific localization Transcript highly abundant in roots. Root tip and meristem. Associated with xylem. Protein detected in exodermis, endodermis, cortex, near vascular bundles, xylem parenchyma, and apical region. Highest in roots. Predominantly expressed in roots.
References Smart et al., 2001 Biela et al., 1999; Otto and Kaldenhoff, 2000; Siefritz et al., 2001
Secchi et al., 2007 Li et al., 2000 Sakurai et al., 2005
Silicon transporter primarily expressed in roots, localized at the plasma membrane of the distal side of the exo- and endodermis. Highest in roots. Preferentially expressed in roots.
Ma et al., 2006
Growing roots and taproot. Highest in young roots, but also high in mature taproots. Vasculature of taproots, but endodermis of seedling. All tissue of taproots, epidermis of seedling roots.
Suga et al., 2001
Highest in roots.
Schuurmans et al., 2003 Marjanovic et al., 2005
Suga et al., 2003
Suga et al., 2002 Fraysse et al., 2005 Johansson et al., 1996
Vitis hybrid Richter 110
PIP1;1 and TIP1
Zea mays
PIP2;1 and PIP2;2 ZmPIP1;2, ZmPIP2;4 a
ZmPIP1;5
ZmTIP1
ZmPIP1a, ZmPIP2a ZmPIP1;2 ZmPIP2;5 a ZmPIP1;1, a ZmPIP2;1,aZmPIP2;5, a ZmPIP1;5
ZmPIP1;5 ZmPIP2;4, ZmTIP2;1–2;3 ZmTIP2;3 155
a
Indicates expression of both transcript and protein.
Baiges et al., 2001
Highest in root tip and lateral roots. Highest in roots. Highest in mature compared to elongating tissue. Preferentially expressed in roots. Stele and cortex. Meristems of primary and lateral roots. Epidermis and endodermis, also xylem parenchyma of elongation zone. In mature roots highest in xylem parenchyma. ZmPIP2a—root specific. Xylem parenchyma. Cortex and xylem parenchyma. Developmentally regulated in aeroponically grown roots, greatest expression in mature primary root tissue. ZmPIP2;1 and ZmPIP2;5 labeling in mature zones greatest in epidermis, exodermis, and cortex. Also in opericlinal plasma membrane of epidermis exposed to external medium. Primarily expressed in roots.
Chaumont et al., 2001
Root specific.
Lopez et al., 2004
Hukin et al., 2002 Gaspar et al., 2003 Barrieu et al., 1998; Chaumont et al., 1998
Chaumont et al., 2000 Fetter et al., 2004 Hachez et al., 2006
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but does not appear to influence Lpc (Hose et al., 2000; Zimmermann et al., 2000). Therefore, it would have been interesting if Hachez et al. (2006) had examined whether the expression of these PIP2s only occurred in roots with an exodermis. Certain PIP1 homologues are also highly expressed in roots, but there is less correlation with tissue-specific expression (Table 2). PIP1;1 homologue has highest expression associated with the apical meristem in Vitis R-110 roots, but is associated with vascular tissue in mature radish roots and a variety of cell types in tobacco roots (Baiges et al., 2001; Otto and Kaldenhoff, 2000; Suga et al., 2003). These differences may reflect different functional roles for PIP1s that vary with developmental stage, but because most PIP1s have low water permeability when expressed in Xenopus oocytes (Section 5.2), they may serve some regulatory role through interaction with other PIPs (Fetter et al., 2004), or as osmotic or turgor sensors (Hill et al., 2004). Some TIP homologues are specifically expressed in roots and no other organs (e.g., ZmTIP2;3, Lopez et al., 2004). Conversely, other TIPs are ubiquitous, being expressed in all root cells (e.g., MIP-F, Kirch et al., 2000), suggesting their importance in ‘‘housekeeping’’ processes such as the osmoregulation of the cytosol. Some studies have shown that the tonoplast has a much higher Pf than the plasma membrane, which would allow the vacuole to protect against deleterious cytoplasmic volume changes (reviewed by Chrispeels et al., 2001; Maurel, 1997; Tyerman et al., 1999, 2002). This role in osmotic equilibration is further supported by the observation that several TIPs are predominantly expressed in tissue, such as the epidermis, that potentially experience the greatest fluctuations in apoplastic water potential (e.g., ZmTIP1, Barrieu et al., 1998, Table 2). However, according to Hill et al. (2004) the greater AQP activity in the tonoplast serves some function other than buffering against rapid changes in cytoplasmic volume because pressure changes induced through transpiration or osmotic perturbations in the soil are not instantaneously transmitted through the apoplast. Several TIP1 homologues are also primarily expressed in root tips of grapevines (V. vinifera), radish, and maize (Table 2), implying a putative role in turgor maintenance that is required to drive the cell expansion process (Chaumont et al., 1998; Dolan and Davies, 2004). 5.3.2. Changes in AQP abundance The stages of protein synthesis provide mechanisms for controlling the amount of functional AQPs present in a membrane. The majority of studies investigating changes in AQP abundance have focused on expression levels of AQP mRNA transcripts, but this does not always correlate with abundance of the respective proteins. AQP gene expression or protein abundance in roots changes both temporally and in response to environmental stimuli, and the expression patterns are multifaceted, varying between
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genes and treatment (Table 3). Some AQPs are upregulated by abiotic factors, whereas others are either downregulated or not affected. One of the most comprehensive studies undertaken was conducted by Jang et al. (2004), who analyzed the transcript levels of all PIPs expressed in roots and aerial parts of A. thaliana, in response to drought, salinity, cold, or ABA treatments. No consistent expression patterns existed in roots. Drought and low temperature downregulated a large number of PIPs in roots, including most PIP2 transcripts, whereas salinity had a stimulatory effect. In comparison, Boursiac et al. (2005) found that salinity generally depressed AQP gene expression, although the effect depended on the particular transcript and the time of exposure to the salinity treatment. An increase in AQPs may facilitate water uptake under harsh conditions, without necessitating large osmotic or hydraulic gradients between soil, xylem, and shoot. Alternatively, if water uptake is low, the presence of AQPs may increase the backflow of water into the dry soil and cause tissues to dehydrate. Therefore, it is critical where AQPs or their activities are localized. In comparison with the downregulation of a large number of AQP transcripts in response to chilling in A. thaliana and rice roots (Table 3), PIP1 and PIP2 transcripts, as well as PIP2 protein abundance appear to increase with cold acclimation of wheat crowns, which is associated with improved freezing tolerance (Herman et al., 2006). An increase in PIP2 abundance possibly enhances water export to the apoplast to reduce intracellular injury from the development of ice crystals, although no physiological measurements were undertaken. The phytohormones ABA and gibberelic acid (GA) appear to be involved in facilitating transcription of PIPs and TIPs from different plant species (Table 3). ABA is involved not only in signaling and stomatal closure during drought, but ABA also potentially enhances Lpr (Section 4). Most PIP genes appear to be diurnally regulated (Table 3). PIP transcript expression gradually increases, peaking 2–8 h into the photoperiod (depending on the plant species) and then declines to a basal level at the start of or during the dark period (Gaspar et al., 2003; Henzler et al., 1999; Lopez et al., 2003; Sakurai et al., 2005). However, peak transcript and protein expression occur at midnight in barley (Hordeum vulgare) roots (Katsuhara et al., 2003a). Lopez et al. (2003) observed that the diurnal pattern continues during continuous darkness suggesting circadian regulation, although the amplitude of expression diminishes with time. The changes in AQP expression during the day/night cycle could contribute to diurnally fluctuating Lpr (Section 4). Henzler et al. (1999) found that the diurnal fluctuation of AQP transcripts of L. japonicus roots coincided with fluctuations in Lpr. The oscillation in transcript abundance and Lpr was slightly offset, with transcript expression increasing in anticipation of the light period, before changes in Lpr. Paradoxically, the permeability of root cell membranes did not vary diurnally. However, regulation
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Table 3 AQP transcript expression (shown in italics) and/or protein abundance shown to vary with time, development, or environmental stimuli Stimulus
Plant species
Upregulated
Downregulated
References
Drought
Arabidopsis thaliana
PIP1;3, PIP1;4, PIP2;1, PIP2;5
Jang et al., 2004
Craterostigma plantagineum Glycine max
CpPIPa2, CpPIPa6, CpPIPa7, CpPIPc
PIP1;1, PIP1;5, PIP2;2, PIP2;3 PIP2;4, PIP2;6, PIP2;7
Helianthus annuus
SunTIP7, SunTIP20 (transient)
Latuca sativa Mesembryanthemum crystallinum Nicotiana glauca Oryza sativa Oryza sativa Phaseolus vulgaris Salinity
Raphanus sativus Arabidopsis thaliana Arabidopsis thaliana
McTIP1;2 protein (mannitol or sorbitol induced)
OsPIP1;1, OsPIP1;2 (24 h PEG treatment) PvPIP1;1, PvPIP1;2, PvPIP2;1 PIP1;1, PIP1;2, PIP1;3, PIP1;4, PIP2s
Mariaux et al., 1998
GmPIP1, GmPIP2 (effect greater for mycorrhizal plants) SunTIP18
Porcel et al., 2006
LsPIP1, LsPIP2
Porcel et al., 2006 Vera-Estrella et al., 2004
NgMIP2, NgMIP3 (TIPs), NgMIP4 (PIP) RWC1 (PIP1) (mannitol) OsPIP1;3, all OsPIP2s (24 h PEG treatment) PIP2 proteins
Smart et al., 2001
RsPIP2;1 protein PIP1;5,
Suga et al., 2002 Jang et al., 2004
PIP1;1 (24 h 60–100 mM NaCl)
Martinez-Ballesta et al., 2003
Sarda et al., 1999
Li et al., 2000 Liu et al., 2006 Aroca et al., 2006
Nutrient status
Hordeum vulgare Mesembryanthemum crystallinum Oryza sativa Raphanus sativus Arabidopsis thaliana Lotus japonicus
MIPC (PIP2 protein)
RWC1 (PIP1) RsPIP2;1 protein AtTIP1;2, AtTIP2;1 and AtTIP4;1 (Nstarvation induced)
Oryza sativa Oryza sativa
Arabidopsis thaliana
OsPIP1;1–1;3, OsPIP2;2, OsPIP2;3, OsPIP2;5, OsPIP2;7 (24h Kþ starvation) ZmPIP1;5 (NO3 induced after N starvation) PIP2;5, PIP2;6
Oryza sativa
OsPIP1;3
Triticum aestivum
PIP2b, PIP2 protein (cold acclimation)
Zea mays
Cold
HvPIP2;1, HvPIP2;1 McTIP1;2, (MIPF protein)
PIP1-like (NO3 starvation) Lsi1(continuous silicon supply)
Katsuhara et al., 2002 Kirch et al., 2000; VeraEstrella et al., 2004 Li et al., 2000 Suga et al., 2002 Liu et al., 2003 Clarkson et al., 2000 Ma et al., 2006 Liu et al., 2006
Gaspar et al., 2003 PIP1;1, PIP1;2, PIP1;5, PIP2;2, PIP2;3, PIP2;4; PIP2;7 RWC1 (PIP1), OsPIP1;1, OsPIP1;2, OsPIP2;1–2;6, OsTIP1;1, OsTIP2;2
Jang et al., 2004 Li et al., 2000; Sakurai et al., 2006 Herman et al., 2006
159
(continued)
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Table 3
(continued)
Stimulus
Plant species
Upregulated
Pathogen
Zea mays Lycopersicon esculentum Nicotiana tabacum
PIP1 proteins LeAqp2 (infection by parasite Cuscuta) TobRB7 (root-knot nematode) NOD26 protein (N2 fixing nodule) MtAQP1 (g-TIP) (mycorrhizal) PttPIP1;1, PttPIP2;3, PttPIP2;5 (mycorrhizal) PIP1;1, PIP1;2, PIP1;3, PIP1;4, PIP2;2, PIP2;3, PIP2;4 LeAqp2
Symbiosis
Glycine max Medicago truncatula Populus tremula x tremuloides
Phytohormones Arabidopsis thaliana (ABA or GA)
Developmental
Lycopersicon esculentum (IAA) Mesembryanthemum crystallinum (ABA) Nicotiana tabacum (ABA or GA) Phaseolus vulgaris (ABA) Arabidopsis thaliana
Downregulated
References
Aroca et al. 2005 Werner et al., 2001 Opperman et al., 1994 Guenther et al., 2003 Krajinski et al., 2000 Marjanovic´ et al., 2005 PIP1;5, PIP2;6
Jang et al., 2004; Kaldenhoff et al., 1996 Werner et al., 2001
McTIP1;2
Vera-Estrella et al., 2004
NtAQP1
Siefritz et al., 2001
PIP1 proteins
Aroca et al., 2006
AtTIP1;2, AtTIP2;1, AtTIP4;1
Liu et al., 2003
Diurnal control
Mesembryanthemum crystallinum Nicotiana tabacum
MIP-A to MIP-F proteins NtAQP1
Raphanus sativus
RsPIP1, RsPIP2, PAQ2 (RsPIP2 protein) HvPIP2;1, HvPIP2;1 protein PIP1a OsPIP1;2, OsPIP1;3, OsPIP2;3, OsPIP2;4, OsPIP2;5, OsTIP1;2, OsTIP2;1 PAQ1 and PAQ2 proteins ZmPIP1;1, ZmPIP1;5, ZmPIP2;1, ZmPIP2;5, ZmPIP1 protein, ZmPIP2 protein PIP1b (white or blue light activated)
Hordeum vulgare Lotus japonicus Oryza sativa
Raphanus sativus Zea mays
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Light
Arabidopsis thaliana
Dark
Arabidopsis thaliana
Circadian
Zea mays Zea mays
TIP1;1, TIP1;2, TIP2;2, PIP1;2, PIP2;1, PIP2;3 ZmPIP1 protein ZmPIPs
Kirch et al., 2000 Otto and Kaldenhoff 2000; Siefritz et al., 2001 Suga et al., 2001 Katsuhara et al., 2003a Henzler et al., 1999 Sakurai et al., 2006
Suga et al., 2001 Gaspar et al., 2003; Lopez et al., 2003
PIP1;2, PIP2;1, PIP2;3, TIP1;1, TIP2;1, TIP2;2
Kaldenhoff et al., 1996; Sato-Nara et al., 2004
ZmPIP2 protein
Lopez et al., 2003 Lopez et al., 2003
Sato-Nara et al., 2004
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in Lpr could be controlled at the endodermis or stele, and the cell pressure probe measurements were only conducted on cortical cells. Lopez et al. (2003) observed a positive correlation between the amount of ZmPIP2 proteins (but not PIP1 proteins) and the rate of water flow through de-topped maize seedlings. Under an osmotic gradient, stimulated water flux from the root system, but continuous darkness stopped sap flow and caused the relative amount of PIP2 proteins to decline. Other regulatory mechanisms also appeared to be involved, as the abundance of PIP2 proteins began to increase before the start of the light period and before the start of sap-flow. Katsuhara et al. (2003a) also correlated AQP protein abundance with Lpr of barley roots. Contrary to ZmPIP2 proteins, HvPIP2;1 protein accumulated during late evening and peaked at night, following the same diurnal oscillation as Lpr. HvPIP2;1 was detected in most cells 2 mm from the tip, but was localized to the epidermis, outer cortical layer and surrounding the vascular cylinder, 50 mm from tip. Katsuhara et al. (2003a) speculated that posttranslational modifications of HvPIP2;1 could also be modulating Lpr because the amplitude of the oscillation in Lpr was greater than that of HvPIP2;1 protein abundance. The studies of Katsuhara et al. (2003a) and Lopez et al. (2003) are also important because they demonstrate that the turnover rate of AQPs is rapid. For example, the abundance of ZmPIP1 proteins in maize roots can increase more than 20-fold within 4 h (Lopez et al., 2003). Gaspar et al. (2003) also found that ZmPIP1;5 transcript varied diurnally, with high expression in the stele and cortex during the day and lower expression during the night. However, analogous to HvPIP2;1, the expression of ZmPIP1;5 during the night was limited to the epidermis. The cell walls of the epidermis, or (if present) the exodermis, of some plant species are suberized so expression of AQPs in the epidermis may facilitate water uptake in the symplast when the apoplast is blocked. However, this does not explain why the localized expression of ZmPIP1;5 changes diurnally. If epidermal expression during the evening is a common feature of certain PIP isoforms, it raises the question about possible involvement in the redistribution of soil water by plant roots. The roots of several tree species ‘‘redistribute’’ soil water during the night by absorbing water from the wetter parts of the soil profile and releasing it to the dryer regions (Burgess et al., 1998). The water is then taken up during the day, when transpiration resumes. The expression of AQPs in the epidermis of roots, during the night, could mediate this hydraulic redistribution by either enhancing the backflow of water into the dry soil and/or enhancing the uptake of water from wet soil. 5.3.3. Posttranslational regulation When extrapolating information on transcription levels, care is needed as protein abundance or activity is not necessarily altered (reviewed by Chrispeels et al., 2001; Tyerman et al., 2002). Translation of the mRNA
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transcript into protein may be delayed or inhibited until a signal is received, or the RNA may require molecular changes after transcription. The amount of protein translated often does not correlate with the amount of transcript expressed (Alexandersson et al., 2005; Aroca et al., 2005; Kirch et al., 2000; Lopez et al., 2003; Suga et al., 2001). Once the message in mRNA is translated, a polypeptide chain forms, but the protein may still not function as a water channel. The majority of AQPs have putative glycosylation and phosphorylation sites where attachment of carbohydrate and phosphate groups, respectively, may be required to enable correct folding of the protein and/or insertion into the membrane. Little is known about endocytosis processes in plants, but hormones and environmental conditions can regulate the internalization and recycling of plasma membrane proteins, including some AQPs (Murphy et al., 2005). Santoni et al. (2000), Chrispeels et al. (2001), and Tyerman et al. (2002) have reviewed trafficking of AQPs to and from the membrane, as a regulatory mechanism of activity. Since then, Vera-Estrella et al. (2004) demonstrated in Mesembryanthemum crystallinum that McTIP1;2 becomes glycosylated, in response to osmotic perturbation, and is trafficked to other compartments, where it then becomes de-glycosylated. Activation of the cAMP-signaling pathway and phosphorylation of the protein by protein kinase are also involved in redistribution of this AQP. An innovative proteomic study by Santoni et al. (2003) demonstrated that individual PIP isoforms can be present in different forms and at least one of these forms was due to posttranslational modification by phosphorylation. Methylation is also a potential posttranslational regulatory mechanism of AQP function (Santoni et al., 2006). Several residues of the N-terminal tail of PIPs can be methylated, which may influence protein stability and subcellular localization. However, expression of PIP2;1 with altered methylation sites in A. thaliana suspension cells did not significantly change their osmotic water permeability (Santoni et al., 2006). 5.3.3.1. AQP gating Once the AQP is located in the membrane, opening and closing of the channel can then control water flow through the pore. Phosphorylation, cytoplasmic pH and heavy metals directly control AQP gating, either through conformational changes in the shape of the pore or direct blockage of the pore. Ca2þ also potentially acts by direct or indirect obstruction of the pore (Alleva et al., 2006; Gerbeau et al., 2002). Other mechanisms that affect water permeability, speculated to be via interaction with the AQP pore, include mechanical stimuli and osmotic pressure (Wan et al., 2004; Ye et al., 2005).
5.3.3.1.1. Phosphorylation By site-directed mutagenesis, Johansson et al. (1996, 1998) demonstrated that the phosphorylation/dephosphorylation of two serine residues regulates PM28A (now called SoPIP2;1) of spinach
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(Spinacia oleracea) leaf, which increases/decreases Pf, respectively. In vivo, phosphorylation of Ser-274 depends on the water potential of the leaf apoplast and based on their observations, Johansson et al. (1998) developed a model for the regulation of flow through PM28A. During ambient conditions, the leaf apoplast has a high water potential and cell turgor is high. Submicromolar Ca2þ activates a membrane-associated protein kinase that phosphorylates PM28A, on Ser-274, and the water channel opens. When the leaf experiences water deficiency, cell turgor begins to fall, the protein kinase inactivates (possibly by closure of a stretch-activated Ca2þ channel) and dephosphorylation of PM28A closes the pore. It is likely that water-potential-induced phosphorylation regulates water channel activity in roots as well as leaves, as PM28A is predominantly expressed in roots and Ser-274 is conserved in all PIP2s. Using X-ray crystallography To¨rnroth-Horsefield et al. (2006) made a remarkable breakthrough by identifying the phosphorylation-induced structural mechanism that gates SoPIP2;1 in spinach. Plant PIPs typically have a longer Loop D (four to seven amino acid residues) that folds under the protein and occludes the pore. Phosphorylation of Ser-115 and Ser-274 causes the ‘‘cap’’ of Loop D to be displaced so that the pore is open. Through molecular modeling, To¨rnroth-Horsefield et al. (2006) also demonstrated how Ca2þ and protonation of His 193 may regulate the gating of PIPs (Section 5.3.3.1.2). The increasing evidence implies that phosphorylation of AQPs is an important regulator of water channel activity. The water channel of radish RsPIP2;2 and soybean NOD26 is activated by phosphorylation (Guenther et al., 2003; Suga and Maeshima, 2004). Phosphorylation of RsPIP2;2 could be a fundamental factor influencing Lpr, particularly during changes in external osmotic pressure, since this AQP is highly expressed in roots (Table 2). Phosphorylation of NOD26 is dependent on the developmental state of the nodule, with maximum activity in mature nodules (Guenther et al., 2003). Contrary to their effects on PM28A, drought (and salinity) enhances the phosphorylation of NOD26 in planta, although a concurrent increase in water permeability was not assessed in planta, phosphorylation induced by okadaic acid increased Pf in Xenopus oocytes (Guenther et al., 2003). However, decreasing the external osmotic potential reduced Pf of membranes vesicles isolated from the peribacteriod membranes (PBM) of soybean root nodules, in which NOD26 is highly abundant (Vandeleur et al., 2005). The response of NOD26 in PBM vesicles is consistent with Johansson’s model ( Johansson et al., 1998), but conflicts with the findings of Guenther et al. (2003) and highlights the importance of measurements on native membranes. Temmei et al. (2005) demonstrated that phosphorylation of PIP1 proteins was involved in the cooperative regulation of water channel activity with PIP2 proteins. While phosphorylation did not affect the formation of
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heterotetramers, phosphorylation of Ser-131 of MpPIP1;1 (in Mimosa pudica) activated the water channel activity of MpPIP2;1. Presently, no studies have investigated phosphorylation-regulated Lpr in response to abiotic perturbations in root systems. However, Kamaluddin and Zwiazek (2002b) applied exogenous ethylene to hypoxic or aerated P. tremuloides seedlings, which increased Lpr. They interpreted this response as an increase in water channel activity by phosphorylation, as ethylene induces protein phosphorylation with involvement of Ca2þ-dependent protein kinases. In plants that are waterlogged, hypoxia strongly stimulates the biosynthesis of ethylene, but at the same time tends to reduce Lpr (Drew, 1997; and Section 7.3), which conflicts with Kamaluddin and Zwiazek’s ideas. Experiments inhibiting ethylene biosynthesis or action, or phosphorylation during hypoxia, or stimulating phosphorylation in the absence of ethylene during hypoxia, may resolve this issue. 5.3.3.1.2. CytoplasmicpH Studies on the vacuoles and plasma membranes of Beta vulgaris, and the plasma membranes of A. thaliana, have shown that cytoplasmic pH affects the gating of AQPs and that acidification of the cytosol reduces the membrane permeability to water (Alleva et al., 2006; Amodeo et al., 2002; Gerbeau et al., 2002; Sutka et al., 2005). This is important as respiratory inhibitors, anoxia, and some other abiotic perturbations induce acidification of the cytosol (Dat et al., 2004; Felle, 1987, 2005). The connection between anoxia-induced cytoplasmic acidification and AQP gating by internal pH is particularly relevant in understanding the waterlogging-induced reductions in Lpr (Section 7.3). A pioneering study by Tournaire-Roux et al. (2003) identified a histidine residue conserved in all PIPs, which is a major site for cytosolic pH sensing. Tournaire-Roux et al. (2003) also found that a group of charged amino acid residues on Loop D of the protein manipulate AQP gating by controlling either the conformation and/or the conductance of the pore, in response to cytosolic protons. When a cell experiences anoxia, the cytoplasm acidifies due to an accumulation of by-products from anaerobic respiration. The histidine of PIPs becomes protonated, which closes the water channel and so reduces the permeability of the membrane to water (Tournaire-Roux et al., 2003). It should also be noted that during O2 deficiency root cells leak ions, which is assumed to be due to depolarization of the plasma membrane that stimulates the opening of ion channels (e.g., wheat; Greenway et al., 1992; Zhang and Tyerman, 1997). Given the acid-induced gating of the mammalian AQP6 (Hazama et al., 2002), anoxia could stimulate similar ion permeability in analogous AQPs in plants. 5.3.3.1.3. Heavy Metals Mercuric chloride has been widely used to demonstrate the function of AQPs in isolated native membranes and in planta. Hg2þ binds to cysteine residues on the protein and acts either by direct obstruction of the pore or by conformational change (Section 5.1, Barone
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et al., 1997, 1998). Blocking water channels reduces the membrane permeability to water and this has been demonstrated in Xenopus oocyte swelling assays, membrane vesicles, isolated protoplasts, and intact cells (Daniels et al., 1996; Kaldenhoff et al., 1998; Niemietz and Tyerman, 1997; Zhang and Tyerman, 1999). AQP inhibitors are valuable because they provide an estimate of AQP activity in real time, without destruction of the tissue and can be used to examine AQP activity in relation to changing environmental conditions. Mercury is nonspecific (cannot target individual isoforms or may affect other channel proteins), may be phytotoxic and may reduce cellular metabolism and other biochemical processes (reviewed by Santoni et al., 2000; Tyerman et al., 1999), but despite all these caveats a reduction in cell water permeability indicates a decrease in AQP activity. Niemietz and Tyerman (2002) demonstrated that gold and, in particular, silver ions inhibit AQPs more effectively than mercury. Silver ions reduced the water permeability of membrane vesicles from different organisms, to almost the predicted inherent water permeability of the lipid bilayer. The effect of silver was not reversible and the mechanism of inhibition is unknown, but Niemietz and Tyerman (2002) reasoned that Agþ and Au3þ react with sulfhydryl groups of cysteine residues, analogous to mercury. Silver is considered less toxic than mercury and provides a promising alternative for AQP inhibition studies (Niemietz and Tyerman, 2002). Confirming Niemietz and Tyerman’s observations, gold and silver inhibited the activity of AQP1 in erythrocytes and epithelial cells from mice, but to a similar extent as mercury (Yang et al., 2006). Despite Tazawa and Iwasaki (1996) demonstrating that Zn2þ reduced Lpc of Chara corallina cells, Zn2þ and other transition elements did not significantly reduce the water permeability of membrane vesicles (Niemietz and Tyerman, 2002). However, Tazawa and Iwasaki (1996) used a high concentration of Zn2þ (5 mM) and the speed of the inhibition on Lp depended on the pH of the external medium, suggesting that AQP activity may have been indirectly affected. 5.3.3.1.4. OtherAQP gating mechanisms Meinild et al. (1998) found that AQP pore dimensions are not fixed and that lowering the temperature caused the pore to narrow, which increased the reflection coefficient for various solutes. Wan et al. (2004) speculated that mechanical stimulus closes AQPs, either by creating tension that collapses the pore or causes a conformation change in the pore shape. However, Ca2þentering through stretch-activated ion channels may also indirectly close AQPs during pressure pulses. Large positive or negative pressure pulses (>0.1 MPa) induced by the cell pressure probe reduced the water permeability of cortical cells in maize roots (Wan et al., 2004). The reduction was transient with 0.1–0.2 MPa pressure pulses, but pulses >0.2 MPa were only reversible by the addition of high concentrations of ABA. Wan et al. (2004) verified that the
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reduction in water permeability was by a reduction in AQP activity, as those cells whose water permeability had been reduced by large pressure pulses were unaffected by mercury. This finding by Wan et al. (2004) is important as the cell pressure probe is widely used for measuring the water permeability of cells in intact tissue. Future measurements with the cell pressure probe should be restrained to applying small pressure changes (pressure relaxations) to prevent confounding effects on cell water permeability. Osmotic pressure has also been suggested to gate AQPs through dehydration of the pore (Ye et al., 2004, 2005). The observation that the permeability of the membrane to water decreases with increasing external solute concentration has been reviewed by Tyerman et al. (2002) and Vandeleur et al. (2005). They speculate that high osmotic pressure could not only restrict the pore through dehydration of the protein but also could create tension in the central vestibule of the tetramer causing conformation changes of the monomers. The inhibition of AQP activity by high external solute concentration has relevance to water flow through root systems, as salinity tends to reduce Lpr (Section 4). Salinity appears to stimulate the expression of AQPs in A. thaliana roots ( Jang et al., 2004), but this will probably be ineffectual at influencing Lpr if high solute concentrations close these AQPs.
6. The Role of AQPs in Root Water Transport 6.1. Inhibition studies The inhibition of water flow by mercuric chloride has illustrated that AQPs are an important component in regulating water transport across the roots of some species (Barrowclough et al., 2000; Maggio and Joly, 1995; Martinez-Ballesta et al., 2000; Martre et al., 2001; Quintero et al., 1999; Wan and Zwiazek, 1999). The use of mercuric chloride has revealed that AQPs can account for up to 90% of total root water flow either through occlusion of the water channel or indirect inhibition by lowered metabolism (Martre et al., 2001 and references therein). The employment of mercury, in combination with an abiotic perturbation, has confirmed the inhibition of AQP activity. For example, salinity reduced Lpr (based on flux per gram root weight) of Cucumis melo and Capsicum annuum roots by 80% and 100%, respectively (Carvajal et al., 1999, 2000). Mercuric chloride reduced Lpr of control plants, to a similar extent as the effect of salinity, but barely influenced Lpr of salinity-treated roots (Carvajal et al., 1999, 2000). Similarly, nutrient deficiency reduced Lpr of wheat roots by 70– 80%, which was not further inhibited by mercury, but mercury reduced Lpr of nutrient-sufficient roots by 63% (Carvajal et al., 1996). Measurements of the radial hydraulic conductivity after removal of tissue layers and application of mercury demonstrated the varying activity of AQPs in different regions of roots of cacti and desert succulents (Martre et al., 2001;
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North et al., 2004). Lpr decreased with soil drying in the distal and mid-root regions of Opuntia acanthocarpa, with no additional reduction by mercury (Martre et al., 2001). In wet soil, mercury reduced Lpr of the distal region, but not the mid-root region. However, mercury may not have fully permeated the mid-root region. After removing the cortex and periderm, mercury reduced the radial hydraulic conductivity of the stele, which accounted for almost 75% of Lpr (Martre et al., 2001). In an analogous study on Agave deserti roots, AQPs were found to be active external to the stele, being important in the epidermis/exodermis of the basal region, but more important in the endodermis of the distal region (North et al., 2004). Wan and Zwiazek (1999) and Siemens and Zwiazek (2003) used an apoplastic permeant dye to demonstrate that mercury inhibits the water flux from P. tremuloides roots, but the relative contribution of the apoplastic pathway increased. The relative contribution of the apoplastic pathway also increased when AQP activity was inhibited by mercury treatment in A. thaliana roots (Martinez-Ballesta et al., 2003). The contribution of the different pathways to Lpr could also be estimated using the cell pressure probe to measure the effects of mercury on the water permeability of root cells and comparing them with measurements of root hydraulic conductivity. However, such comparisons in the literature are rare. Barrowclough et al. (2000) combined cell pressure probe and root potometer measurements, to identify the dominance of apoplastic transport in the young region of onion roots, but AQPs facilitated water flow in the mature root regions containing a suberized exodermis. Although Zhang and Tyerman (1999) did not measure Lpr, the inhibition of Lpc by mercury was similar to that observed by Carvajal et al. (1996), in whole roots, suggesting a significant contribution of the cell-to-cell pathway to Lpr of wheat roots. The combination of CPP, RPP, and mercury treatment demonstrated the contribution of the radial pathways to flow in wheat and lupin roots (Bramley, 2006). Mercury treatment reduced Lpc to 15–20% of untreated cells, indicating a significant influence of AQPs in membrane water permeability. This inhibition at the cellular level was associated with a similar inhibition in Lpr (40–50% of control roots) of wheat roots, indicating that a significant proportion of radial water flow occurs through the cell-to-cell pathway and AQPs may be involved in regulating that flow. However, despite the presence of mercury sensitive AQPs in lupin roots, mercury did not inhibit Lpr (Bramley, 2006) suggesting that radial water flow may be predominantly apoplastic. Moreover, the study by Bramley (2006) implies that AQPs may have other, as yet undiscovered, functional roles in roots. AQP activity in roots may be an important component in the refilling of embolized vessels in shoots of grapevine. The inhibition of root water flow by mercuric chloride impaired the recovery of shoot hydraulic conductance after a water deficit (Lovisolo and Schubert, 2006). Despite using a high concentration of mercury, Lovisolo and Schubert (2006) were able to
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reverse the inhibition of root hydraulic conductance and consequently the shoots rehydrated. The mechanism of repair after drought is unknown, but embolized vessels in grapevines refill in spring under root pressure (Scholander et al., 1955; Sperry et al., 1987). Inhibition of AQP activity in roots may therefore have some regulatory effect on root pressure, although due to the nonspecificity of mercury nutrient transport would also have decreased, which would affect root pressure.
6.2. Expression and transformation studies Molecular transformation techniques creating AQP-deficient or overexpressing mutants have corroborated the mercury inhibitory experiments in assessing the function of AQPs in plants. Knocking out NtAQP1 from tobacco reduced Lpr by 58% compared with control plants, without altering plant morphology (Siefritz et al., 2002). Conversely, downregulation of PIPs in antisense A. thaliana plants increased the root mass, but had no effect on root water uptake (Kaldenhoff et al., 1998; Martre et al., 2002). Kaldenhoff et al. (1998) speculated that antisense plants compensated for the lack of PIPs by increasing the absorption area of the root system. In support of this, root systems of transgenic tobacco plants overexpressing PIP1b were able to support a larger shoot system (Aharon et al., 2003). Knocking out NtAQP1 from tobacco or PIP1s from A. thaliana reduced the tolerance of antisense plants to a water deficit (Kaldenhoff et al., 1998; Siefritz et al., 2002), which is surprising given that PIP1 genes seem to be downregulated under adverse conditions (Table 3). However, transgenic tobacco and rice plants over expressing PIP1s and HvPIP2;1, respectively, were also less tolerant of drought or salinity (Aharon et al., 2003; Katsuhara et al., 2003b). There appears to be a fine balance between the abundance of AQPs, root morphology, and water transport, so that some plants compromise performance under favorable conditions to minimize deleterious effects in the event of adverse conditions. Javot et al. (2003) knocked out PIP2;2 that is predominantly expressed in A. thaliana roots. Lpc of cortical cells was almost one-third less for mutants than wild-type plants, but this reduction did not translate into an equivalent reduction in Lpr. The Lpr of detopped roots was only reduced by 14% under an osmotic driving force (natural sap exudation) compared with zero reduction under a hydrostatic driving force (pressurized root system). Javot et al. (2003) interpreted their observations according to the composite transport model, that is under a hydrostatic driving force, water flow was predominantly via the apoplast (Section 2.3.1). However, expression of PIP2;2 is predominantly localized in the endodermis (Table 2), implying an important function of this AQP in this tissue. Manipulating AQP activity in the endodermis would be a geometrically efficient method of controlling Lpr, given the decreasing surface area for water flow toward the root
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axis. If the endodermis is suberized, then the apoplastic pathway may be blocked and water would have to cross the plasma membrane at least twice (i.e., through AQPs) to reach the apoplast of the stele. Therefore, even under a hydrostatic driving force there should be some transcellular flow. It would have been useful, if Javot et al. (2003) had examined the anatomy of the roots, not only to confirm the above speculations, but more importantly, to ensure that A. thaliana mutants did not compensate for an absence of PIP2;2 by some other mechanism. The analysis of AQP expression in conjunction with measurements of the water permeability of cells or protoplasts has been used to identify the contribution of AQPs to membrane permeability. Hukin et al. (2002) observed a higher Lpc of cells in mature compared with elongating tissue of maize roots, which was correlated with a greater expression of PIP genes. They suggested that AQPs are required in mature tissue to mediate radial water flow because there was also less symplastic connection between cells via plasmodesmata in mature tissue. Suga et al. (2003) measured Pf of cells with different AQP protein content. The osmotic water permeability of cortical or endodermal protoplasts, isolated from radish roots, was the same despite the endodermis having much greater expression levels of PIP1, PIP2, and a TIP. High Pf of protoplasts isolated from young rape (Brassica napus) and flax (Linum usitatissimum) roots were considered to be related to high amounts of PIP1 and PIP2 proteins, but this was not true for two wheat genotypes that had low Pf, despite the presence of PIPs (Morillon and Lassalles, 2001). Interestingly, wheat species with low Pf values were more tolerant of severe water deficit and were still able to germinate, suggesting stronger regulation of AQP activity may mediate the effects of water deficit, at least in germinating seeds. It should also be noted that the Pf values for wheat protoplasts and plasma membranes vesicles are much lower than in situ values obtained with the CPP (Bramley, 2006; Niemietz and Tyerman, 1997; Zhang and Tyerman, 1999), indicating that the isolation procedures may affect AQP activity. Gerbeau et al. (2002) demonstrated that plasma membrane vesicles isolated in the presence of chelating agents had higher Pf than those isolated in the presence of divalent cations.
6.3. The contribution of AQPs to radial water flow Given the ubiquitous nature, diversity, and high expression levels of many AQPs, it is tempting to speculate that they have an important role in water flow through the plant. However, AQPs will only have an influence on Lpr if a significant proportion of water flow occurs by the cell-to-cell pathway (Steudle, 1997). In some species, water appears to flow predominantly via the apoplast, for example maize and lupins, despite an abundance of AQPs expressed in the roots (Bramley, 2006; Chaumont et al., 2001; Steudle et al., 1987).
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Therefore, the function of AQPs in these plants is an enigma, although the contribution of the radial pathways to flow may vary under different conditions, as Gibbs et al. (1998b) thought that the contribution of the apoplast in maize had been overestimated. There is the potential for finer control of Lpr, mediated by AQP activity, when water flows via the cell-to-cell pathway. The apoplast only constitutes a small fraction of the root cross-sectional surface area, so the cell-to-cell pathway could potentially transport larger volumes of water. Additionally, AQPs create parallel pathways for water flow across a membrane and therefore their respective conductances are additive, so their abundance can have a dramatic effect on Lpc. Increasing Lpc requires a lower gradient to drive the same amount of flow. Under conditions of high transpiration, increasing the abundance of AQPs could considerably increase Lpr and hence avert large tensions in the xylem. Unless negative pressure (tension), generated by transpiration and transmitted throughout the apoplast, causes a conformational change in the protein and closes the pore, water flow through the cellto-cell pathway would be advantageous. Measurements demonstrating that abiotic perturbation, respiratory inhibitors, and mercury reduce hydrostatically induced water flux, point toward a significant proportion of root water flow occurring via the cell-to-cell pathway, in many species (Section 4).
7. Waterlogging Waterlogging occurs when the infiltration of water from rainfall or flooding is greater than the rate of subsurface drainage and evapotranspiration, leading to saturation of the soil. The frequency and duration of waterlogging, and the depth of soil saturation, depend not only on the soil properties, but also on location in the landscape and climate. For example, during winter in Western Australia, when rainfall exceeds pan evaporation, the soil profiles susceptible to waterlogging often saturate close to the soil surface, for days to several weeks at a time (Cox and McFarlane, 1995; Tennant et al., 1992; Zhang et al., 2004). Waterlogging is a significant environmental constraint for crop growth and can ultimately reduce yields by as much as 80% (Dracup et al., 1992; McFarlane and Williamson, 2002).
7.1. Effect on O2 in the rhizosphere Oxygen diffuses 10,000 times slower in water than in air (Grable, 1966). Consequently, when the soil is saturated the O2 concentration in the rhizosphere declines due to respiring roots and microorganisms (Armstrong et al., 1991; Cannell and Jackson, 1981). Greater respiration rates caused by high temperatures lead to a greater consumption of O2 and a more rapid decline in O2 concentration in waterlogged soils over time (Belford, 1981
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and Drew, 1992). The lack of soil aeration leads to the formation of many biochemically reduced compounds that can accumulate to concentrations that are phytotoxic, but a low O2 supply is the primary factor detrimental to plant growth (Drew, 1992). Even in winter, when the average daily temperatures are low, the rhizosphere can become hypoxic (low O2) within a few days of waterlogging. Barrett-Lennard et al. (1987) used specially designed plots to control waterlogging in the field, at a nonplanted site in Western Australia. Waterlogging gradually reduced the O2 concentration in the soil profile to 4% O2 (0.05 mol m3) within 4–8 days of waterlogging, depending on the depth in the profile. The decline in O2 would be more rapid in cropped fields, depending on root density and root depth (Drew, 1992).
7.2. Effect on root growth The lack of O2 causes respiration to become anaerobic, which affects metabolism and leads to a cascade of biochemical and physiological changes that may ultimately cause cell injury or death (Drew, 1992). Aerobic respiration switches over to glycolysis, once the O2 supply drops below the critical oxygen pressure (COP). The COP varies depending on the species but at 25 C is generally less than 10% O2 (Drew, 1997). Glycolysis only produces one-eighteenth of the energy of aerobic respiration and therefore growth ultimately becomes inhibited (reviewed by Drew, 1992). In general, roots are more adversely affected than shoots (Davies et al., 2000a; Trought and Drew, 1980). Because of their high rates of O2 consumption the root apical meristems are the most sensitive parts of the root to O2 deficiency (Drew, 1992). In maize roots the COP for the root tip is equal to, or slightly greater than the concentration of O2 in equilibrium with air (Saglio et al., 1984). The COP of wheat roots is 0.16 mol m3 O2 in the apical region (0–2 mm from the tip) compared with a COP of 0.05 mol m3 O2 for mature tissue (10–12 mm from the tip) (Thomson et al., 1989). Peak water uptake in cereal roots occurs behind the root tip (Section 3.2), so growth should be inhibited before water uptake is affected during O2-deficient conditions. The effects of waterlogging and/or O2 deficiency depends on the species and the duration of submergence, with the complete cessation of root growth or even root death in the more sensitive species (Cannell and Jackson, 1981; Grable, 1966). Waterlogging or hypoxia reduces the growth of wheat roots and the extent of the inhibition depends on the genotype (Huang and Johnson, 1995; Huang et al., 1994; Thomson et al., 1992). Seminal roots of wheat appear to be more adversely affected by waterlogging than nodal roots, which often increase in abundance and continue to elongate (Huang et al., 1994; Thomson et al., 1992; Trought and Drew, 1980; Wiengweera et al., 1997).
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Based on their growth response, yellow lupin was more tolerant to waterlogging than other lupin species (Broue´ et al., 1976; Davies et al., 2000a,b). Narrow-leafed lupin is very sensitive to excess water and mild O2 deficiency. Watering to 130% field capacity only reduced the air-filled porosity from 28% to 21%, but depressed root growth (White and Robson, 1989). The air-filled porosity is the proportion of the bulk soil volume filled with air and the critical value considered to affect plant growth is <10% (Grable, 1966). By reciprocal grafts of roots and shoots of narrowleafed lupin with yellow lupin, Davies et al. (2000c) identified that it was the root system of lupins that conferred the genotypic tolerance to waterlogging. Wheat is considered to be sensitive to waterlogging, but anecdotal evidence in the field indicated that wheat was more tolerant to waterlogging than lupin, which was confirmed in a glasshouse study (Bramley, 2006; Dracup et al., 1992). Lupins have thick taproots (1- to 2-mm diameter), which would require a higher rate of O2 supply from the rhizosphere for normal functioning, compared with thin wheat roots (Dracup et al., 1992). Wheat and lupin roots also differ in their waterlogging tolerance due to anatomical changes that occur during waterlogging. The tolerances of different wheat genotypes to hypoxia were associated with changes in root system anatomy that increased the internal aeration of roots (Boru et al., 2003; Erdmann and Wiedenroth, 1986; Huang et al., 1994; Thomson et al., 1992). An increase in internal aeration or root porosity, in response to hypoxia, is a common feature of many wetland species including rice and some dryland species including the cereal crops Hordeum marinum, maize and wheat, and the legume species soybean, cowpea (Vigna unguiculata) and Lotus species (Colmer, 2003; Evans, 2003; Thomas et al., 2005 and references therein). Hypoxia stimulates the development of gas spaces, called lysigenous aerenchyma, through ethylene-induced cell death (reviewed by Evans, 2003). In roots, aerenchyma usually forms in the cortex and these air spaces are connected longitudinally, creating a pathway for the diffusion of gases, from the aerial parts of the plant to the roots (Colmer, 2003; Evans, 2003). The effectiveness of internal aeration depends on root porosity (i.e., the amount of aerenchyma), respiration, and radial O2 loss (ROL) to the soil (Armstrong et al., 1991). Many wetland species, such as rice, develop a barrier in the epidermis or outer hypodermal layers that reduces the loss of O2 from the root to the soil (Colmer, 2003). The nature of the barrier is unresolved but may involve tightly packed cells and wall modifications such as thickening and lignification (Armstrong et al., 2000). Waterlogging and/or low O2 supply stimulates the development of aerenchyma in wheat roots, predominantly in nodal roots, although the seminal roots of some genotypes also increase their root porosity (Erdmann et al., 1986; Huang and Johnson, 1995; Huang et al., 1994; Thomson et al., 1992;
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Trought and Drew, 1980; Wiengweera et al., 1997). Wheat roots do not appear to develop a tight barrier to ROL (Colmer, 2003), so even with aerenchyma the internal aeration may be inefficient. Without a barrier to ROL, O2 can diffuse across the cortex in the basal region of the root and be lost to the soil environment, so that the root tips have insufficient O2 to maintain viable meristems. Root tips have tightly packed cells with high metabolic rates and few air spaces. It is therefore not surprising that the root tips are the most sensitive part of the root when the O2 supply becomes limited. Modeling the diffusion of O2 within graminaceous roots Armstrong et al. (1991) demonstrated that even with the development of aerenchyma, root growth would only extend into anaerobic media to a depth of 30 cm. In addition, they predicted that the stele would become anoxic before the cortex, which was later experimentally confirmed in maize roots (Darwent et al., 2003; Gibbs et al., 1998a). The majority of research has focused on the effects of waterlogging during the waterlogging event. Far less is known about recovery, when the wet soils have drained. Dracup et al. (1992) suggested that wheat roots might resume growth more rapidly than lupins, based on observations in different field studies. Yellow lupin roots are better at recovering after waterlogging than narrow-leafed lupin, but only when waterlogged at an early stage of growth (Davies et al., 2000a). In a glasshouse study, Bramley (2006) found that wheat roots survived a waterlogging event better than lupins and recovery in root growth was more rapid, with a significant amount of growth resuming from the apical region. In comparison, narrow-leafed lupin roots died with no subsequent recovery. Although the majority of the root system of yellow lupin roots died, root growth resumed from the basal region, and this was associated with the weak development of aerenchyma (Bramley, 2006).
7.3. Effect on water use Synonymous with the majority of abiotic perturbations, waterlogging tends to alter nutrient and plant water use. Lowered metabolism and subsequently less available energy to drive nutrient transport across plasma membranes may reduce nutrient absorption by roots (Cannell and Jackson, 1981; Drew and Stolzy, 1991). Water uptake can also decrease (Kramer, 1983 and references therein). Reductions in leaf gas exchange indicate the adverse effect of waterlogging on plant water use (Davies et al., 2000b; Huang et al., 1994). Despite closure of the stomata, wilting and a decrease in leaf C imply that the water transport from the root is insufficient to maintain the water status of the shoot (Davies et al., 2000b). Waterlogging and/or O2 deficiency tends to reduce Lpr. Kramer (1949, 1983) reviewed the earlier literature and in the more recent literature the extent of the inhibition on Lpr appears to be species and treatment
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dependent (Table 4). Apart from root death, Lpr may be influenced by waterlogging in the longer term (days to weeks) through changes in root structure and anatomy (Section 3.2). For example, Erdmann et al. (1986) found that low O2 increased cell wall lignification and Huang et al. (1994) found that the xylem vessels of nodal roots decreased in diameter, although no measurements of root water transport were undertaken. Aerenchyma and barriers to ROL could also influence radial water flow, but this would depend on the length of the absorbing region. The formation of aerenchyma and barriers to ROL do not appear to affect Lpr of rice and H. marinum roots (Garthwaite et al., 2006; Miyamoto et al., 2001; Ranathunge et al., 2003), but these measurements were undertaken in O2-sufficient conditions and water uptake in cereals occurs preferentially in the apical region (Section 3) where these anatomical features do not occur. A metabolically controlled mechanism could also influence Lpr under O2 deficiency, providing that a significant proportion of water flow across roots occurs via the cell-to-cell pathway, since respiration rates decline when the O2 supply is low. In further support of this, respiratory inhibitors have been shown to reduce water flow through roots (Kamaluddin and Zwiazek, 2001; Tyerman et al., 1992).
7.4. Anoxia and AQP activity To date, no studies have directly investigated AQP transcript or protein abundance (altered production or breakdown of in situ proteins) in plant roots in response to anoxia or hypoxia. However, several microarray analyses monitored the expression of hundreds of A. thaliana genes, when the O2 concentration surrounding the roots or seedlings was reduced (Klok et al., 2002; Loreti et al., 2005; Liu et al., 2005). Cross-referencing those lists of genes with GenBank (http://www.ncbi.nlm.nih.gov/Genbank/ index.html; Benson et al., 2000) reveals that O2 deficiency modulates the expression of some AQP genes (Table 5). Klok et al. (2002) exposed cultured A. thaliana roots to hypoxia (5% O2 and 95% N2), which highly upregulated TIP1;2 in the first 0.5 h of treatment and TIP4;1 during 2–20 h of hypoxia. Submergence of the roots will dilute the apoplast leading to uptake of water by cells and so TIPs may be induced as a defence against adverse cytoplasmic volume changes (Section 5.3.1). Several studies identified NIP genes that were rapidly and strongly upregulated by anoxia or hypoxia (Table 5). Since these NIPs are aquaglyceroporins, they may be induced to transport glycerol that accumulates during hypoxia (Weig et al., 2004). Additionally, NIPs could also be upregulated to facilitate the transport of gases.
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Table 4 Summary of the effects of waterlogging or O2 deficiency on water transport in roots
Plant Species
Waterlogging treatment
Agave deserti Anoxia (3 h) Arabidopsis thaliana Anoxia (30 min) Helianthus annuus Anoxia (16 h – 6 d) Lupinus species Hypoxia (30 min) Lupinus species Hypoxia (cortical cells in (30 min) expanded region) Lycopersicon Flooding esculentum (8 h) Lycopersicon Flooding esculentum (24 h) Populus tremuloides Hypoxia (1–10 d) Ricinus communis Flooding (2–24 h) Triticum aestivum Hypoxia (root cells near tip) (30 min) Triticum aestivum Hypoxia (cortical cells in (30 min) expanded region) Triticum aestivum Hypoxia (30 min) Zea mays Anoxia (12 h) Zea mays Anoxia (>20 h) Zea mays Hypoxia (2 h) Zea mays Hypoxia (30 min) Zea mays (root cells) Hypoxia (30 min)
Reduction in Lpr (or Lpc)(%)
References
10
Nobel et al., 1990 Tournaire-Roux et al., 2003 Everard and Drew, 1989 Bramley, 2006
70
Bramley, 2006
50 (transient)
Bradford and Hsiao, 1982 Jackson et al., 1996
0 42 46 (transient)
0 26–75 >50 85
Kamaluddin and Zwiazek, 2002a Else et al., 2001
46
Zhang and Tyerman, 1991 Bramley, 2006
20
Bramley, 2006
28
27 (transient)
Birner and Steudle, 1993 Everard and Drew, 1987 Gibbs et al., 1998b
18
Tyerman et al., 1992
60
Tyerman et al., 1992
<70
Downregulation of AQP genes occurs later than upregulation, but overall, hypoxia appears to repress water channel activity, especially after 12 to 24 h of treatment (Liu et al., 2005; Table 5). Several of the PIPs downregulated are predominantly expressed in roots (Table 2). Therefore,
Table 5 AQP gene expression in roots and whole seedlings modulated by low O2 concentrations Expression
Locusa
Transcript
Details
References
Upregulated
At3g26520 At2g25810 At2g34390
TIP1;2 TIP4;1 NIP2;1
Highly upregulated within 0.5 h of hypoxia treatment Very highly upregulated 2–20 h of hypoxia treatment Rapidly (within 1 h) and highly upregulated in response to anoxia or hypoxia
Klok et al., 2002 Klok et al., 2002 Liu et al., 2005; Loreti et al., 2005 Liu et al., 2005
At2g29870
AJ 781013
NIP NIP
Pseudo-gene rapidly (within 1 h) and strongly upregulated by hypoxia Highly upregulated in the central cylinder of roots Highly upregulated in flooding tolerant rice roots
At4g01470 At1g31885 Downregulated At3g61430
TIP1;3 NIP3;1 PIP1;1
At2g45960
PIP1;2
Transiently upregulated during 2–6 h of hypoxia Transiently upregulated during 6–12 h hypoxia Rapid (2 h hypoxia) and sustained downregulation, but expression not downregulated when exogenous sucrose applied Same as PIP1;1
At4g17340
TIP2;2
Same as PIP1;1
Weig et al., 2004 Agarwal and Grover, 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005; Loreti et al., 2005 Liu et al., 2005; Loreti et al., 2005 Liu et al., 2005; Loreti et al., 2005 (continued)
Table 5 (continued) Expression
a
Locusa
Transcript
Details
References
At5g47450 At2g39010
TIP2;3 PIP2;6
Same as PIP1;1, but effects of sucrose not known Downregulated with addition of exogenous sucrose
At3g56950
SIP2;1
Same as PIP2;6
At4g19030 At2g45960 At1g01620 At3g53420 At2g37170 At5g60660 At4g35100 At3g26520 At3g16240 At4g23400 At3g54820 At2g16850 At2g36830
NIP1;1 PIP1;2 PIP1;3 PIP2;1 PIP2;2 PIP2;4 PIP2;7 TIP1;2 TIP2;1 PIP1;5 PIP2;5 PIP2;8 TIP1;1
Sustained downregulation after 12 h hypoxia Same as NIP1;1 Same as NIP1;1 Same as NIP1;1 Same as NIP1;1 Same as NIP1;1 Same as NIP1;1 Same as NIP1;1 Same as NIP1;1 Not downregulated until 24 h of hypoxia Same as PIP1;5 Same as PIP1;5 Same as PIP1;5
Liu et al., 2005 Loreti et al., 2005 Loreti et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005 Liu et al., 2005
The loci, identified by microarray analyses of genes significantly altered by low O2, were given in the supplemental tables attached to the references listed above and were used to search the genetic database GenBank (Benson et al. 2000) for the description of the respective AQP transcripts.
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such a dramatic reduction in AQP gene transcripts could negatively impact on root water transport and potentially explain the increased root resistance to water flow during waterlogging. Downregulation of TIPs and PIPs in the apical meristem could also be a component that reduces root growth during waterlogging. Interestingly, Loreti et al. (2005) reported that the addition of exogenous sucrose altered which particular genes were downregulated by anoxia. Under anoxic conditions PIP1;1, PIP1;2 and TIP2;2 were downregulated, but not when sucrose was added to the bathing medium. conversely, PIP2;6 and SIP2;1 were not downregulated by anoxia unless sucrose was present. Both of these latter genes are expressed in low amounts in roots and the proteins have unknown functions ( Jang et al., 2004; Quigley et al., 2001). The addition of sugars to the bathing medium often increases the tolerance of roots to anoxia, but by unknown mechanisms. Exogenous sucrose could indirectly mitigate the reduction in Lpr induced by O2 deficiency. Under field conditions, waterlogging is often transient, but no studies have analyzed the expression of AQPs in roots after O2 deficiency followed by re-aeration. A problem with these studies could be separating the effects of tissue death from adaptation under O2 deficiency. Few studies have examined AQP expression during a recovery period after other forms of abiotic perturbations. However, Martre et al. (2002) found that PIPs are important in the recovery of A. thaliana after a water deficit. A key component in the recovery of a species after waterlogging could be the installation of new AQPs in root membranes, particularly if O2 deficiency leads to the breakdown of AQPs or root death. Moreover, given that the turnover rate of some AQPs can occur within a few hours, the resumption of water uptake and growth could also be rapid. After a short-term (0.5 h) mild hypoxia treatment followed by 1 h re-aeration, Lpr of wheat roots was 1.5 times greater than initial values of Lpr, which was associated with an increase in AQP activity (Bramley, 2006). Moreover, in a long-term glasshouse experiment waterlogged wheat roots rapidly resumed growth, when the pots were drained and the growth rate was greater than before the plants were waterlogged (Bramley, 2006). An increase in AQP activity could not only facilitate the rapid transport of water to the shoots, but may also mediate changes in growth rate during the recovery period. Rapid responses in Lpr to O2 deficiency may also be mediated by the gating of AQPs. As detailed in Section 5.3.3.1.2, protonation of PIPs during cytosolic acidification closes the AQP pore and reduces the membrane permeability to water (Tournaire-Roux et al., 2003). Anoxia causes the cytoplasm to become acidified (Menegus et al., 1991), which therefore may close AQPs. The significance of this reaction on Lpr depends on the contribution of AQPs to radial water flow, and also on the degree of cytoplasmic acidification, which increases over time during anoxic conditions. However, some species such as rice are more tolerant of anoxia and
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regulate their cytoplasmic pH to avoid acidosis (Menegus et al., 1991), but it is not known what effect this has on Lpc and hence on Lpr. Furthermore, in nature plants experience a gradual decline in O2 rather than a sudden exposure to anaerobic conditions (i.e., hypoxia prior to anoxia), which triggers acclimatic responses such as avoidance of proton accumulation and expression of proteins required for anaerobic respiration. A pretreatment with hypoxic conditions increases the tolerance of many species to subsequent anoxia, and one of the processes related to this enhanced tolerance is better regulation of cytoplasmic pH (Germain et al., 1997; KatoNoguchi, 2000; Waters et al., 1991; Xia and Saglio, 1992). When exposed to hypoxic or anoxic conditions, the internal pH is generally not as low as that used to demonstrate the inhibitory effects of pH on Lpc or Pf (Alleva et al., 2006; Menegus et al., 1991; Tournaire-Roux et al., 2003; Xia and Roberts, 1994, 1996). However, the permeability of wheat and lupin root cell membranes can decrease significantly, when exposed to only 0.5 h of 3–4% O2 (Bramley, 2006; Zhang and Tyerman, 1991), which is considered a relatively mild hypoxic treatment. It is therefore, not clear whether a reduction in internal pH is the initial factor that reduces Lpc through a closure of AQPs. The effect may also be related to other mechanisms of AQP gating such as changes in apoplastic water potential and osmoregulation that arises from the effect of O2 deficiency on ion leakage from a depolarization of the membrane potential (Zhang and Tyerman, 1991, 1997). Calcium fluxes also change in response to O2 deficiency, which can affect AQP gating (Dat et al., 2004). Moreover, it has never been tested whether O2 itself affects AQP gating as could be demonstrated with stopflow spectrophotometry. Overall, AQP activity does appear to decrease in response to O2 deficiency. This could have significant consequences on the rate of water flow through roots, provided that a large proportion of water flow occurs through the cell-to-cell pathway (Section 3.2.2.1). In addition, given that some AQPs such as PIP1s are permeable to other small neutral molecules, a reduction in AQP activity may influence other cellular processes under O2 deficiency. For example, CO2 and ethanol build up under anaerobic conditions (Gibbs and Greenway, 2003), which may be because these molecules are permeable to some AQPs (Steudle and Henzler, 1995; Uehlein et al., 2003).
8. Conclusion Water flow through roots can be controlled by a variety of mechanisms. The structure of the root and the anatomy of the radial pathways for flow dominate the process because they determine the physical hydraulic conductance of the root system to water flow. Changes to this hydraulic conductance will be slow and growth dependent, as the root lays down new
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tissue, extends and/or becomes suberized. AQPs can influence the rate of water flow through the radial pathway of roots, but only if the hydraulic conductance of the apoplast is less than that of the cell-to-cell pathway. AQPs may increase the hydraulic conductivity of the cell-to-cell pathway by increasing their abundance and/or opening their pores. The distinct advantage of a significant proportion of radial water flow through AQPs is that it provides the ability for the plant to rapidly and reversibly regulate flow in response to a fluctuating environment. AQP transcripts and proteins have been found to vary significantly and rapidly in response to abiotic perturbations. Moreover, AQP gating provides an even finer control. Despite the abundance of AQP homologs in roots, indicating that they play a part in regulating root water flow, there are also many other important functional roles that they may perform. The majority of research to date has focussed on the molecular aspects of AQP activity, but more detailed physiology is required to understand AQP functions. Of those species where radial water flow occurs through the cell-to-cell pathway, AQPs may influence water transport through roots when plants are waterlogged, since AQP genes tend to be downregulated under O2-deficient conditions. However, long-term exposure to O2 deficiency causes root systems to die, particularly in less tolerant species, which hence would reduce the absorbing surface area for water uptake. Root systems that do not die avoid anoxia by forming aerenchyma and barriers to ROL. These anatomical features may obstruct water flow through the radial pathway and measurements of their effect on Lpr have not been undertaken during O2 deficiency. AQPs in the apical region are likely to be downregulated and/or close as the cytoplasm becomes acidified, since this region is the most sensitive to O2 deficiency. Conversely, AQP activity in O2-sufficient cells (e.g., adjacent arenchyma) could increase and hence compensate for the reduced hydraulic conductance caused by anatomical changes. Although little has been investigated, AQPs are likely to be beneficial during recovery when the root system is no longer submerged and the O2 levels in the rhizosphere return to normal. The speed in the resumption of growth and transport of water and nutrients are key features that influence the tolerance of a species to waterlogging and all these attributes can be influenced by AQP activity. For us to understand the physiological mechanisms that enhance a species’ tolerance to waterlogging, it is important that more research focuses on AQP-mediated processes that are affected by O2 deficiency.
ACKNOWLEDGMENTS A scholarship for HB and funding for research were supported by the Grains Research Development Corporation (GRDC) of Australia.
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C H A P T E R
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Phytoremediation of Sodic and Saline-Sodic Soils M. Qadir,*,† J. D. Oster,‡ S. Schubert,§ A. D. Noble,} and K. L. Sahrawatk Contents 199 201 203 206 208 212 223 233 236 239 239
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Introduction Description of Sodic and Saline-Sodic Soils Degradation Processes in Sodic and Saline-Sodic Soils Phytoremediation of Sodic and Saline-Sodic Soils 4.1. Historical perspective 4.2. Mechanisms and processes driving phytoremediation 4.3. Comparative efficiency of phytoremediation 4.4. Plant species for phytoremediation 5. Perspectives Acknowledgments References
Sodicity-induced soil degradation is a major environmental constraint with severe negative impacts on agricultural productivity and sustainability in arid and semiarid regions. As an important category of salt-affected soils, sodic soils are characterized by excess levels of sodium ions (Naþ) in the soil solution phase as well as on the cation exchange complex, exhibiting unique structural problems as a result of certain physical processes (slaking, swelling, and dispersion of clay) and specific conditions (surface crusting and hardsetting). Saline-sodic soils, another category of salt-affected soils, are generally grouped with sodic soils because of several common properties and management approaches. Sodic and saline-sodic soils occur within the boundaries of at least 75 countries, and their extent has increased steadily in several major irrigation schemes throughout the world. The use of these soils for crop production is on the increase as they are a valuable resource that cannot be * { { } } k
International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5466 Aleppo, Syria International Water Management Institute (IWMI), P.O. Box 2075, Colombo, Sri Lanka Department of Environmental Sciences, University of California, Riverside, California 92521 Institute of Plant Nutrition, Justus Liebig University, 35392 Giessen, Germany International Water Management Institute (IWMI), South East Asia Office, 10670 Penang, Malaysia International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, India
Advances in Agronomy, Volume 96 ISSN 0065-2113, DOI: 10.1016/S0065-2113(07)96006-X
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2007 Elsevier Inc. All rights reserved.
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neglected, especially in areas where significant investments have already been made in irrigation infrastructure. It is imperative to find ways to improve sodic and saline-sodic soils to ensure that they are able to support highly productive land-use systems to meet the challenges of global food security. Nearly a century-old record reveals amelioration of sodic soils through the provision of a readily available source of calcium (Ca2þ) to replace excess Naþ on the cation exchange complex; the displaced Naþ subject to leaching from the root zone through the application of excess irrigation water in the presence of a drainage system. Many sodic soils do contain inherent or precipitated sources of Ca2þ, that is calcite (CaCO3), at varying depths within the soil profile. However, due to its negligible solubility, natural dissolution of calcite does not provide sufficient quantities of Ca2þ to affect soil amelioration with routine management practices. Consequently, amelioration of these soils has been predominantly achieved through the application of chemical amendments. However, amendment costs have increased prohibitively over the past two decades due to competing demands from industry and reductions in government subsidies for their agricultural use in several developing countries. In parallel, scientific research and farmers’ feedback have demonstrated that sodic soils can be brought back to a highly productive state through a plantassisted approach generically termed ‘‘phytoremediation.’’ Typical plant-based strategies for contaminated soils, such as those containing elevated levels of metals and metalloids, work through the cultivation of specific plant species capable of hyperaccumulating target ionic species in their shoots, thereby removing them from the soil. In contrast, phytoremediation of sodic soils is achieved by the ability of plant roots to increase the dissolution rate of calcite, thereby resulting in enhanced levels of Ca2þ in soil solution to effectively replace Naþ from the cation exchange complex. Phytoremediation has shown to be advantageous in several aspects: (1) no financial outlay to purchase chemical amendments, (2) accrued financial or other benefits from crops grown during amelioration, (3) promotion of soil-aggregate stability and creation of macropores that improve soil hydraulic properties and root proliferation, (4) greater plant-nutrient availability in soil after phytoremediation, (5) more uniform and greater zone of amelioration in terms of soil depth, and (6) environmental considerations in terms of carbon sequestration in the postamelioration soil. Phytoremediation is particularly effective when used on moderately salinesodic and sodic soils. It is a viable solution for resource-poor farmers through community-based management, which would help in strengthening the linkages among researchers, farm advisors, and farmers. These linkages will continue to be fostered as the use of sodic soils becomes more prevalent. The success of phytoremediation of sodic soils requires a greater understanding of the processes fostering phytoremediation, the potential of plant species to withstand ambient salinity and sodicity levels in soil and water, and also of the uses and markets for the agricultural products produced. Strategic research on such aspects would further elucidate the role of phytoremediation in the restoration of sodic soils for sustainable agriculture and conservation of environmental quality.
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1. Introduction Soil degradation resulting from salinity and/or sodicity is a major environmental constraint with severe negative impacts on agricultural productivity and sustainability, particularly in arid and semiarid regions of the world (Pitman and Lau¨chli, 2002; Qadir et al., 2006; Suarez, 2001; Tanji, 1990). Salt-affected soils are characterized by excess levels of soluble salts (salinity) and/or Naþ in the solution phase as well as on cation exchange complex (sodicity). These salts and Naþ originate either by the weathering of parent minerals (causing primary salinity/sodicity) or from anthropogenic activities, involving the inappropriate management of land and water resources (contributing to secondary salinity/sodicity). Salt-affected soils occur within the boundaries of at least 75 countries (Szabolcs, 1994). These soils also occupy more than 20% of the global irrigated area (Ghassemi et al., 1995); in some countries, they occur on more than half of the irrigated land (Cheraghi, 2004). Over the last few decades, salt-prone soil degradation has increased steadily in several major irrigation schemes throughout the world. Examples include Indo-Gangetic Basin in India (Gupta and Abrol, 2000), Indus Basin in Pakistan (Aslam and Prathapar, 2006), Yellow River Basin in China (Chengrui and Dregne, 2001), Euphrates Basin in Syria and Iraq (Sarraf, 2004), Murray-Darling Basin in Australia (Herczeg et al., 2001; Rengasamy, 2006), and San Joaquin Valley in the United States (Oster and Wichelns, 2003). Salt- and irrigationinduced soil degradation is prevalent in the Aral Sea Basin of Central Asia with the consequent environmental changes in that region being considered as the largest ones caused by humanity (Cai et al., 2003). Several other examples of salt-prone soil degradation exist elsewhere in the world (Ghassemi et al., 1995; Szabolcs, 1994). Salt-prone soil degradation has triggered imbalances between the functions (goods and services) supplied by the natural resources (land and water) and the demands of societies that exploit these functions. Since such degradation occurs both ‘‘on-site’’ and ‘‘off-site,’’ it affects the livelihoods within and outside the farming communities (Abdel-Dayem, 2005). In comparison with the biophysical aspects, the social and economic dimensions of saltinduced soil degradation have received little attention. It is generally recognized that a large proportion of these soils occur on land inhabited by smallholder farmers, who rely on this resource to satisfy their food and feed needs (Qadir et al., 2006). Although it is easy to link salinity and sodicity to poverty, limited information is available that puts a monetary value on their social and economic impacts (Ali et al., 2001). The information available addresses mainly crop-yield losses on salt-affected soils, revealing estimates of annual global income losses in excess of US$12 billion (Ghassemi et al., 1995).
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As an important category of salt-affected soils, sodic soils exhibit unique structural problems as a result of certain physical processes (slaking, swelling, and dispersion of clay) and specific conditions (surface crusting and hardsetting) (Qadir and Schubert, 2002; Shainberg and Letey, 1984; Sumner, 1993). These problems can affect water and air movement, plant-available water-holding capacity, root penetration, seedling emergence, runoff and erosion, as well as tillage and sowing operations (Oster and Jayawardane, 1998). In addition, changes in the proportions of soil solution and exchangeable ions lead to osmotic and ion-specific effects together with imbalances in plant nutrition, which may range from deficiencies of several nutrients to high levels of Naþ (Grattan and Grieve, 1999; Mengel and Kirkby, 2001; Naidu and Rengasamy, 1993). Such physical and chemical changes have a bearing on the activity of plant roots as well as on soil microbes, and ultimately on crop growth and yield. Saline-sodic soils, another category of salt-affected soils, are generally grouped with sodic soils because (1) they share several characteristics and (2) the management approaches required for either soil type are similar. Sodic and saline-sodic soils account for more than 50% of the world’s salt-affected area (Beltra´n and Manzur, 2005; Tanji, 1990). Despite considerable research undertaken in elucidating the cause and effects of salinity and sodicity on the chemical and physical properties of sodic and saline-sodic soils, there has been a paucity of examples that have successfully translated this understanding into effective amelioration and sustainable management (Oster et al., 1999). The use of sodic and salinesodic soils for crop production is expected to increase in the near future, which could aggravate salinity and sodicity problems through mismanagement. Despite the implications associated with the amelioration and management of sodic and saline-sodic soils, the fact remains that these soils are a valuable resource that cannot be neglected, especially in areas where significant investments have already been made in irrigation infrastructure (Qadir et al., 2006). Consequently, if the challenges of global food security are to be met, it is imperative to find ways to improve these soils to ensure that they are able to support highly productive land-use systems. Over the past 100 years, several different approaches—involving chemical amendments, tillage operations, crop-assisted interventions, water-related approaches, and electrical currents—have been used to ameliorate sodic and saline-sodic soils. Of these, chemical amendments have been used most extensively (Oster et al., 1999). A number of tillage options, such as deep plowing and subsoiling, have also been used to break up the shallow, dense, sodic clay pans and/or natric horizons that occur within 0.4 m of the soil’s surface (Abdelgawad et al., 2004; Rasmussen et al., 1972). However, in recent decades, the crop-based approach, phytoremediation, has shown promise as an effective low-cost amelioration intervention (Ghaly, 2002; Ilyas et al., 1993; Robbins, 1986a), as it is much cheaper than chemical amelioration, the
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costs of which are prohibitively high for resource-poor farmers in many developing countries (Qadir and Oster, 2004). This chapter focuses on the phytoremediation of sodic and saline-sodic soils. After providing information on the characterization of sodic and saline-sodic soils and the degradation processes that occur resulting in their formation, we address the process of phytoremediation of these soils along with different aspects such as historical perspective, driving forces contributing to the process, comparison of phytoremediation with other amelioration approaches, selection of phytoremediation crops, and the role of cropping in securing environmental integrity under sodic and salinesodic conditions. Finally, we offer our views on the prospects for improved management of sodic and saline-sodic soils as an opportunity to shift from subsistence farming to progressive and income-generating ventures.
2. Description of Sodic and Saline-Sodic Soils Sodic and saline-sodic soils are generally described in terms of the relative amounts of Naþ in the soil solution or on the cation exchange complex, given the accompanying levels of salinity. Therefore, soil sodicity represents the combined effects of (1) salinity as measured by electrical conductivity of the soil, and (2) soluble Naþ concentration relative to soluble divalent cation concentration in soil solution, that is sodium adsorption ratio (SAR), or as exchangeable sodium fraction (ESF) expressed as a percentage, that is exchangeable sodium percentage (ESP). SAR is calculated by using Eq. (1):
SAR ¼
CNa ½ðCCa þ CMg Þ=21=2
ð1Þ
where C represents concentrations in soil solution in terms of mmolc liter1 (mmolc liter1 ¼ meq liter1) of the cations identified as subscripts. ESP is calculated from Eq. (2) by incorporating the values of exchangeable Naþ (ENa) and cation exchange capacity (CEC), both expressed as mmolc kg1 or cmolc kg1 (cmolc kg1 ¼ meq 100 g1) of the soil
ESP ¼
100ðENa Þ CEC
ð2Þ
ESP may also be calculated by replacing CEC in Eq. (2) with the sum of exchangeable cations such as calcium (ECa), magnesium (EMg), potassium (EK), exchangeable sodium (ENa), and aluminum (EAl), with all the cations expressed as mmolc kg1 or cmolc kg1 of the soil (Sumner et al., 1998)
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ESP ¼
100ðENa Þ ðECa þ EMg þ EK þ ENa þ EA l Þ
ð3Þ
The sum of exchangeable cations, as given in Eq. (3), may be replaced by the term effective cation exchange capacity (ECEC). The incorporation of EAl in Eq. (3) is for acid sodic soils (pH < 6), which may contain some Al3þ on the cation exchange complex. However, most sodic soils are alkaline in reaction with pH more than 7. Various approximate relationships have been derived between ESP and SAR of soils belonging to different areas of the world. Based on the ESP and SAR values of soil samples from the Western states, the following relationship [Eq. (4)] was developed by the US Salinity Laboratory Staff (1954):
ESP ¼
f100½a þ bðSARÞg f1 þ ½a þ bðSARÞg
ð4Þ
where a ¼ 0.0126 and b ¼ 0.01475. The relationships between ESP and SAR have also been developed for soils from other regions and countries (Franklin and Schmehl, 1973; Ghafoor et al., 1988; Paliwal and Gandhi, 1976; Table 1). It is largely accepted that ESP and SAR values remain close to each other within the range 0–40, which is most common in agricultural soils. Therefore, SAR has been widely used as an approximation of ESP within this range. An ESP of 15 (SAR 13) is generally taken to be the threshold below which soils are classified as nonsodic, and above which soils are dispersive and suffer serious physical problems when water is applied. However, considerable data exist for infiltration rates and hydraulic conductivities that show that soil behavior typical of sodic soils may occur at ESP values of less than 15 if accompanying levels of salinity (ECe) are lower than 4 dS m1 (Sumner et al., 1998). Therefore, the principal factor determining the extent of the adverse effects of Naþ on soil properties is the ambient electrolyte concentration in the soil solution, with low concentrations exacerbating the deleterious effects of exchangeable Naþ. Other nomenclature—alkali, black alkali, solonetz, and slick-spot—has also been used for sodic and saline-sodic soils in different parts of the world. For instance, alkali soils are characterized by high sodicity (ESP > 15) and pH (pH > 8.3), and contain soluble carbonate (CO2 3 ) and bicarbonate (HCO3 ) ions of Naþ. The concentrations of Naþ are greater than the accompanying levels of chloride (Cl) and sulfate (SO2 4 ), that is CNa : ðCCl þ CSO4 Þ ratio greater than 1. Alternatively, the ratio ð2CCO2 þ CHCO3 Þ : ðCCl þ 2CSO4 Þ is 3 more than 1 in soil solution phase, when expressed as mol m3 (Chhabra, 2005). These soils contain Naþ and CO2 3 þ HCO3 as the dominant ions and tend to have low salinities and high pH values, which cause an increase in
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Table 1 Approximate relationships derived between ESP and SAR of soils from different regions of the globe
a b
Equations
Samplesa
ESP at SAR 20b
ESP ¼ [100 (0.0126 þ 0.01475 SAR)]/[1 þ (0.0126 þ 0.01475 SAR)] ESP ¼ [100 (0.0063 þ 0.0124 SAR)]/[1 þ (0.0063 þ 0.0124 SAR)] ESP ¼ [100 (0.1149 þ 0.0109 SAR)]/[1 þ (0.1149 þ 0.0109 SAR)] ESP ¼ [100 (0.0867 þ 0.02018 SAR)]/[1 þ (0.0867 þ 0.02018 SAR)] ESP ¼ [100 (0.0268 þ 0.02588 SAR)]/[1 þ (0.0268 þ 0.02588 SAR)]
59
22
US Salinity Laboratory (1954)
15
20
Franklin and Schmehl (1973)
150
25
Paliwal and Gandhi (1976)
180
24
Ghafoor et al. (1988)
144
33
Ghafoor et al. (1988)
References
Number of soil samples used to develop the relationship between ESP and SAR. Equivalent values of ESP calculated at SAR levels of 20.
swelling and dispersion of clay (Gupta et al., 1984). On the other hand, the pH of sodic soils can be greater or less than 7 and such soils can be either saline or nonsaline.
3. Degradation Processes in Sodic and Saline-Sodic Soils Sodicity influences the soil at the level of the clay fraction (Quirk, 2001), which is categorized with a particle size of <2-mm diameter. It is an important component of the soil matrix because of its charge properties and larger surface area per unit mass than other major fractions such as silt and sand. In an aqueous suspension, the charge on clay particles is neutralized by hydrated ions of opposite charge. In the case of sodic and saline-sodic soils, clay surfaces usually carry a net negative charge, which is neutralized by a diffuse cloud of ions in
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which the concentrations of cations increase and that of anions decrease as the surface is approached. This phenomenon is commonly referred to as a diffuse double layer. This electrical layer consists of the surface charge and compensating counterions that form a surrounding ion swarm. The thickness of the diffuse double layer depends on the nature of exchangeable cations and electrolyte concentration of soil solution (Van Olphen, 1977; Verwey and Overbeek, 1948). The counterions are subject to two opposing tendencies: (1) the cations are attracted electrostatically to the negatively charged clay surface; and (2) the cations tend to diffuse away from the surface of clay particles where their concentration is higher, into the bulk solution where their concentration is low. Such opposing tendencies result in an exponentially decreasing exchangeable cation concentration with distance from the negatively charged clay surface. Since divalent cations are retained by the clay surface with a force greater than the monovalent cations, the thickness of the diffuse double layer is more compressed when divalent cations dominate the system. In a similar way, increasing the electrolyte concentration in the bulk solution has a compressing effect on the double layer since high concentrations reduce the tendency of exchangeable cations to diffuse away from clay surface ( Van Olphen, 1977). When two clay colloids approach each other, their diffuse double layers overlap and the electrical repulsion forces are activated between the two positively charged exchangeable ion atmospheres. Such electric repulsion force is also known as ‘‘swelling pressure.’’ The greater the compression of the exchangeable cations toward the clay surface the smaller the repulsion forces between the clay colloids, that is the smaller the swelling pressure, resulting in a lower propensity for clay swelling. Clay swelling is a process that reduces the radius of soil pores and plays a crucial role in reducing hydraulic conductivity of the soil (Quirk and Schofield, 1955; Rengasamy et al., 1984; Russo and Bresler, 1977; Xiao et al., 1992), thereby influencing the movement of water through the soil profile. The process decreases with increasing (1) electrolyte concentration of the bulk solution, and (2) valence of the exchangeable cations, as in the case of polyvalent cations. For example, montmorillonite clay dominated by Naþ swells freely in low electrolyte solutions as the single platelets tend to persist in such dilute salt solutions. However, when divalent cations such as Ca2þ dominate the montmorillonite surfaces, individual clay platelets develop aggregates, which are known as tactoids (Blackmore and Miller, 1961). Synonymous terms for tactoids are quasicrystals (Quirk and Aylmore, 1971) or clay domains (Sumner, 1993). Tactoids consist of 4–9 clay platelets in parallel array with interplatelet distance of 0.9 nm (Shainberg and Letey, 1984; Sposito, 1984). The Ca2þ-dominated clay fraction behaves like a system having a much smaller surface area. Thus, swelling of Ca2þ-montmorillonite remains much smaller than that of Naþ-montmorillonite because only the external surfaces of the quasicrystals contribute to swelling.
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Soil degradation under sodic conditions occurs through a series of mechanisms (Fig. 1). Initially, the dry soil aggregates are strong with high attractive forces between clay particles, but application of water results in wetness of soil aggregates and hydration reactions lead to repulsive forces between clay particles, which reduce the attraction between them resulting in weak wet aggregates (Rengasamy and Sumner, 1998). Generally, initial hydration of sodic clays leads to slaking and swelling. Slaking refers to the breakdown of macroaggregates into microaggregates on wetting (Abu-Sharar et al., 1987; Cass and Sumner, 1982). This process results in a reduction in number and size of large pores at the soil surface, thereby limiting infiltration of rainfall or irrigation water (Nelson and Oades, 1998). Dispersion is a process that leads to the release of individual clay platelets from soil aggregates. When individual clay particles are detached from soil aggregates, dispersion begins and creates an unstable structure. In case of extensive hydration of sodic and saline-sodic soils, the release and spontaneous dispersion of clay particles from the aggregates occurs. Flocculation of such clay particles may be brought about by the addition of electrolytes, particularly Ca2þ, which results in osmotic effects, causing dehydration of the clay–water system, and reduces the distance of separation between particles (Rengasamy and Sumner, 1998). Soil aggregates at the surface have a greater degree of vulnerability to the degradation processes because of the stresses generated by rapid water uptake, release of entrapped air, mechanical impact and stirring action caused by the flowing water applied through irrigation or precipitation (Oster and Jayawardane, 1998). In addition, the surface soil is more unstable
Continuous hydration (sodic days) Stage 3 Dispersion Flocculation
kPa 0
Stage 4
Swelling slaking
io at
Mechanical separation
Hy dr
Attractive MPa
Osmotic dehydration
+ Chemical bonding
Stage 2
kPa n
Net pressure
Repulsive
MPa
Aggregate Stage 1 (Dry)
0
1
2 3 4 5 Particle separation (nm)
6
7
Figure 1 Schematic presentation of processes and intensity of attractive and repulsive forces involved when a dry aggregate of a sodic soil is wetted [adapted from Rengasamy and Sumner (1998)].
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than the underlying soil because of a low electrolyte concentration (Shainberg et al., 1992) and high ENa (Shainberg and Letey, 1984) and EMg levels (Keren, 1991). Therefore, aggregates at the soil surface are destroyed first through the processes of slaking and dispersion. As a consequence a rearrangement of soil particles occurs on the surface on drying, resulting in a densely packed thin soil layer with high shear strength, which is referred to as a structural crust or seal (McIntyre, 1958; Moore and Singer, 1990). Crust formation in soils is attributed to two processes: (1) physical disintegration of soil aggregates and their compaction, and (2) dispersion and movement of clay particles into a region of 0.1- to 0.5-mm depth, where they lodge and clog the conducting pores (Agassi et al., 1981; McIntyre, 1958). Although both processes occur simultaneously, physical disintegration of soil aggregates enhances dispersion and movement of clay particles. In addition, physical disintegration of soil aggregates is controlled mainly by the type of cations and their concentrations in the soil and applied water (Agassi et al., 1981; Kazman et al., 1983). Crusting is a major mechanism affecting the steady-state infiltration rate in soils of arid and semiarid regions where organic matter is usually low and soil structure is unstable. With effects similar to sealing, hardsetting is another mechanism leading to soil degradation under sodic conditions. The major difference between hardsetting and sealing is that sealing effects remain within 0.1- to 0.5-mm depth of the soil, while hardsetting leads to complete aggregate breakdown and clay movement usually within the entire plowing zone. On drying, hardsetting exhibits massive, compact, and hard conditions in the upper soil layer (Mullins et al., 1990), which is not disturbed or indented by the pressure of a forefinger. Occurrence of hardsetting in soils reduces infiltration rate and increases runoff and erosion and impairs water movement into and through the soil and decreases seedling emergence with subsequent impacts on crop growth and yield.
4. Phytoremediation of Sodic and Saline-Sodic Soils Sodic and saline-sodic soils are ameliorated by the provision of a readily available source of Ca2þ to replace excess Naþ on the cation exchange complex. The displaced Naþ is leached from the root zone through the application of excess irrigation water. This requires adequate amounts of water and unimpeded flow through the soil profile as well as the provision of natural or artificial drainage systems (Gupta and Abrol, 1990; Oster et al., 1999; Rhoades and Loveday, 1990), which plays an important role in the management of drainage water in a sustainable manner. Considering the fact that sodic soil amelioration is accomplished by providing a source of Ca2þ, most sodic and saline-sodic soils do contain a
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source of Ca2þ, that is calcite (CaCO3), at varying depths within the soil profile. Calcite may be a constituent of the parent material or formed in situ through precipitation as coatings on soil particles and in pores that may result in cementation of particles. However, due to its negligible solubility (0.14 mmol liter1), natural dissolution of calcite does not provide sufficient quantities of Ca2þ to affect soil amelioration at partial pressures of carbon dioxide (PCO2 ) that are typically present in the atmosphere. A further common Ca2þ-containing mineral in sodic and saline-sodic soils is dolomite. The solubility of dolomite is several-fold less than calcite. The more soluble CaCO3 minerals such as vaterite, aragonite, or CaCO3 hydrates are not commonly found in soils or observed to form pedogenically (Suarez and Rhoades, 1982). Consequently, amelioration of these soils has been predominantly achieved through the application of chemical amendments (Gupta and Abrol, 1990; Oster et al., 1999; Rhoades and Loveday, 1990). In this respect, amendments such as gypsum (CaSO4 2H2O) supply soluble sources of Ca2þ to the soil solution, which then replace excess Naþ on the exchange complex. Other amendments such as sulfuric acid (H2SO4) assist in increasing the dissolution rate of calcite to release adequate amounts of Ca2þ in soil solution (Table 2). A century-old practice in sodic soil management reveals extensive use of chemical amendments, particularly gypsum, in different parts of the world. There have been some constraints with chemical amelioration of sodic soils in several developing countries because of (1) low quality of amendments containing a large fraction of impurities; (2) restricted availability of amendments when actually needed by the farmers for amelioration; and/or (3) increased costs due to competing demands for amendments in the Table 2 Chemical composition and equivalent amount of a chemical amendment that can substitute One metric ton (t) of gypsum in ameliorating sodic soilsa
a
Amendment
Chemical composition
Amount equivalent to 1 Mg of gypsum
Gypsum Calcium chloride Calcium carbonate Sulfuric acid Ferrous sulfate Ferric sulfate Aluminum sulfate
CaSO4 2H2O CaCl2 2H2O CaCO3 H2SO4 FeSO4 7H2O Fe2(SO4)3 9H2O Al2(SO4)3 18H2O
1.00 0.85 0.58 0.57 1.61 1.09 1.29
The amount of any amendment to be applied for sodic soils amelioration is based on the amount equivalent to that of gypsum, which is called GR and determines the amount of calcium (Ca2þ) needed to replace sodium (Naþ) from the soil.
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industrial sector and substantial reductions in or termination of government subsidies for agricultural use of the amendments. With the last factor having overriding importance, chemical amelioration has become prohibitively expensive for subsistence farmers since the early 1980s. In parallel, scientific research and farmers’ feedback have demonstrated that sodic and salinesodic soils can be ameliorated through a plant-assisted approach, phytoremediation (Kumar and Abrol, 1984; Mishra et al., 2002; Qadir et al., 2002; Robbins, 1986a). Typical plant-based amelioration strategies for contaminated soils, such as those containing elevated levels of metals and metalloids, work through the cultivation of specific plant species capable of hyperaccumulating target ionic species in their shoots, thereby removing them from the soil (Baker et al., 1994; McGrath et al., 2002; Salt et al., 1998). In contrast, phytoremediation of sodic and saline-sodic soils is achieved by the ability of plant roots to increase the dissolution rate of calcite, thereby resulting in enhanced levels of Ca2þ in soil solution to effectively replace Naþ on the cation exchange complex. The salinity levels in soil solution during phytoremediation maintain adequate soil structure and aggregate stability that facilitate water movement through the soil profile and enhance the amelioration process (Oster et al., 1999). Synonymous terminology for phytoremediation includes vegetative bioremediation, phytomelioration, and biological reclamation.
4.1. Historical perspective In the 1920s and 1930s, Kelley and associates (Kelley, 1937; Kelley and Brown, 1934) conducted a seminal series of field experiments in California, which are among the earliest research studies on phytoremediation of sodic soils (Qadir and Oster, 2002). The soil used in these studies was a fine sandy loam solonetz (sodic) located on the Kearney Ranch near Fresno. It had the following chemical properties in the upper 0.3 m layer: pH ¼ 9.2–9.7; CEC ¼ 43–44 mmolc kg1; ESP ¼ 57–70. Based on the reported cation composition, the salinity levels (EC1:5) were 6.1–7.2 dS m1. The soil was approximately uniform in texture to a depth of 0.6–0.9 m, below which there was a compact layer that was 0.05- to 0.15-m thick and rich in calcite. In the first phase of the field studies, Kelley and Brown (1934) applied a total of 37 Mg ha1 of gypsum in two splits, 22 Mg in 1920 and 15 Mg in 1921. Each year after gypsum application the plots were flooded and kept submerged for 3 weeks by repeated applications of well water (EC ¼ 0.3 dS m1, SAR ¼ 0.7). The same amount of water was applied to the phytoremediation treatment, which consisted of cropping and irrigation without gypsum application. Barley (Hordeum vulgare L.) was the first crop used as phytoremediation treatment, which was grown for 2 years. It was followed by a 1-year green manuring each by Indian sweet clover (Melilotus indicus L.) and white sweet clover (M. albus Medik.), and 5 years
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under continuous alfalfa (Medicago sativa L.). After the final alfalfa crop, the plots were kept fallow for 1 year and then cropped with cotton (Gossypium hirsutum L.), the first postamelioration crop. Cotton yields were 1.82 Mg ha1 for the gypsum treatment and 2.10 Mg ha1 for the phytoremediation treatment. ESP of the upper 0.3 m soil depth decreased from 70 to 5 in the gypsum-treated soil and from 65 to 6 in the plots subjected to phytoremediation (Table 3). In the next phase of the field studies in California, Kelley (1937) initiated a phytoremediation experiment in 1930 with Bermuda grass [Cynodon dactylon (L.) Pers.] as the first phytoremediation crop used in the sequence. The grass was grown for 2 years followed by cultivation of barley for 1 year, alfalfa for 4 years, and oats (Avena sativa L.) for 1 year. In all, there was 8 years of cropping. In the postamelioration soil, the ESP of the upper 0.3 m soil depth decreased from 57 to 1 (Table 3), with a reduction in average profile (0–1.2 m) ESP from 73 to 6. The overall decrease in ESP under the phytoremediation treatment was even greater than that obtained with the gypsum treatment of the earlier experiment, possibly owing to the introduction of Bermuda grass at the beginning of the cropping sequence. In addition, there was more uniform and greater zone of amelioration in terms of soil depth. The phytoremediation approach used by Kelley and coworkers in California (Kelley, 1937; Kelley and Brown, 1934) was based on the same principles and involved some of the techniques used in the irrigated-meadow experiment at Bekescsaba, Hungary (deSigmond, 1924). In the Hungarian experiment, a mixture of several different grasses and legumes was grown successfully on a heavy (low-permeability) black-alkali (sodic) soil, which Table 3 Effect of chemical (gypsum) and phytoremediation (cropping) treatments on exchangeable sodium percentage (ESP) of the Fresno soil (based on the data reported by Kelley and Brown (1934) and Kelley (1937) 1920^1930
a b c
1930^1937
Gypsuma þ Croppingb
Croppingc
Croppingc
Soil depth (m)
Initial
Final
Initial
Final
Initial
Final
0.0–0.3 0.3–0.6 0.6–0.9 0.9–1.2 Profile mean
70 67 54 35 49
5 8 9 19 10
ESP (%) 65 70 46 28 52
6 21 26 53 27
57 97 90 46 73
1 4 13 4 6
Gypsum application at 37 Mg ha1 in two splits: 22 Mg in 1920 and 15 Mg in 1921. Cultivation of barley for 2 years, green manuring by clovers for 2 years, and alfalfa grown for 5 years. Cultivation of Bermuda grass for 2 years, barley for 1 year, alfalfa for 4 years, and oats for 1 year.
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resulted in gradual amelioration of the soil. Similar crop-based approaches leading to the management of sodic soils were successfully used at Fallon, Nevada (Knight, 1935) and at Vale, Oregon (Wursten and Powers, 1934). The last major effort concerning amelioration of salt-prone soils in California was on new lands along the west side of the San Joaquin Valley brought under irrigation during the 1950s and 1960s. The soils were calcareous, with a wide variation in terms of salinity, sodicity, gypsum, and boron (B) levels. Cropping during amelioration was a common practice in the area with new lands. Chemical amendments were used selectively, with reclamation being possible without them on many soils (Kelley, 1951; Overstreet et al., 1955). Gypsum, sulfur, and sulfuric acid were used when an increased rate of amelioration was desired. Barley, a winter crop, was usually the first crop grown on new lands as a part of the amelioration process. In addition to annual rainfall, the amount of water used for irrigation and leaching of salts was supplemented by border irrigation. After one or more barley crops, cotton was often added to the rotation. Cotton fields were ripped before planting, amended with gypsum if desired, listed to create furrows, and preirrigated. Large amounts of water (0.25–0.35 m) were infiltrated during the preirrigation phase, resulting in considerable leaching and subsequent amelioration of the soil. Various cropping systems have been used in the twentieth century by farmers elsewhere in the world for the management of salt-prone soils. The farming history of the Indian Subcontinent reveals the cultivation of certain salt-resistant grasses and trees as an important step in the management of salt-affected soils. The prominent grass and forage species used for soil amelioration were: Bermuda grass locally known as dub grass; Kallar or Karnal grass [Leptochloa fusca (L.) Kunth] commonly known as narri; fodder cane (Saccharum spontaneum L.) locally identified as kans grass; and sesbania [Sesbania bispinosa ( Jacq.) W. Wight] (Singh, 1998). The results of field experiments in the early part of the twentieth century supported by soil analyses found the use of sesbania as an important intervention for fodder, green manuring, and improvement of salt-affected soils (Dhawan et al., 1958; Uppal, 1955). Most farmers in the Indian Subcontinent typically began amelioration during high rainfall (0.6–0.9 m) months of July to September (Gupta and Abrol, 1990; Oster et al., 1999). Farmers’ financial sources had a pivotal role in using different amelioration options, which included: (1) applying gypsum at rates of about 10–15 Mg ha1, sometimes based on personal experience without testing the soil for actual requirement of gypsum; (2) leaching with excessive irrigations for about 15–20 days, prior to transplanting rice seedlings; (3) installing tubewells for groundwater pumping in high water table areas, sometimes with a government subsidy, and utilizing the pumped water for irrigation and amelioration purposes; (4) cultivating certain salt-resistant crops without the application of chemical
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amendments; (5) prolonged leaching and accompanying application of farm manure; and (6) green manuring, usually with sesbania species, prior to rice cultivation. Sesbania green manuring is widely practiced on salt-affected soils in terms of an increase in nutrient availability status and a decrease in salinity and sodicity levels (Gupta and Abrol, 1990; Qadir et al., 2001). The pioneer work of Kelley and coworkers (Kelley, 1937; Kelley and Brown, 1934) and others (deSigmond, 1924; Knight, 1935; Wursten and Powers, 1934) clearly demonstrated the successful amelioration of calcareous sodic soils accomplished by the selection of appropriate plant species and their cropping sequence. However, these studies were long term as it took nearly 10 years to demonstrate the amelioration effects of the crop-assisted approach. In another experiment (Kelley, 1951), a different combination of cropping was used with similar levels of soil amelioration observed after 7 years. While looking at decreases in soil sodicity levels in the phytoremediation treatment, it is argued that sufficient amelioration had already been achieved even earlier than the 7-year period. This might well have been demonstrated had the soil sampling and respective analyses made on a yearly basis after the initiation of these studies. However, the general perception at that point in time reflected the crop-based approach as an intervention that may take several years to ameliorate calcareous sodic and saline-sodic soils. Understanding of the driving forces leading to the enhancement of the phytoremediation process in terms of temporal and technical efficiencies was rudimentary at that time. Chemical amendments such as gypsum were available and the amendment costs were affordable by the farmers mainly because of the provision of government subsidies in many countries. Although phytoremediation involved even lower levels of initial investment, its pace of soil amelioration as perceived at that time did not attract many scientists, farm advisors, agricultural extension workers, and farmers of salt-affected soils to a great extent. This scenario did not change much until the early 1980s when the cost of the commonly used chemical amendment, gypsum, increased in several parts of the world because of its increased usage by industry and reduction in government subsidies to farmers for its purchase. This provided an incentive for additional research on alternative and efficient methods of low-cost amelioration of sodic soils. Promising results obtained by Robbins (1986a,b) on the amelioration of a calcareous sodic soil as a result of cropping and irrigation without gypsum application stimulated research into phytoremediation. Field-scale studies in the Indian Subcontinent around the same time (Ahmad et al., 1990; Kumar and Abrol, 1984; Singh and Singh, 1989) demonstrated that amelioration through phytoremediation was achievable in much less time than initially anticipated. Such findings were based on the use of appropriate plant species and irrigation and soil management practices that assisted in higher rates of soil amelioration (Qadir and Oster, 2002).
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4.2. Mechanisms and processes driving phytoremediation Phytoremediation of calcareous sodic and saline-sodic soils (PhytoSodic) assists in enhancing the dissolution rate of calcite through processes at the soil–root interface resulting in increased levels of Ca2þ in soil solution. It is a function of the following factors:
PhytoSodic ¼ RPCO2 þ RHþ þ RPhy þ SNaþ
ð5Þ
where RPCO2 refers to increased partial pressure of CO2 within the root zone; RHþ is enhanced proton (Hþ) released in the root zone in case of certain crops that include legumes; RPhy addresses physical effects of roots in improving soil aggregation and hydraulic properties of the root zone; and SNaþ represents Naþ content of shoot, which is removed through harvest of the aerial plant portion. The collective effects of these factors ultimately lead to soil amelioration, provided drainage is present and adequate leaching occurs (Fig. 2). 4.2.1. Partial pressure of CO2 in the root zone Dissolution and precipitation kinetics of calcite are determined by the chemistry of the system. A typical reaction for the dissolution of calcite may be expressed as a function of CO2 in the root zone:
CaCO3 þ CO2 þ H2 O , Ca2þ þ 2HCO 3
ð6Þ
The reaction presented in Eq. (6) summarizes three processes (Dreybrodt, 1992), which occur concurrently: (1) conversion of CO2 in an aqueous matrix, such as soil solution, into H2CO3 and its reaction with CaCO3 as given in Eq. (7); (2) dissociation of H2CO3 into Hþ and HCO 3 and the reaction of Hþ with CaCO3 as presented in Eq. (8); and (3) dissolution of CaCO3 resulting in Ca2þ and CO2 3 as shown in Eq. (9):
CaCO3 þ H2 CO3 , Ca2þ þ 2HCO 3 CaCO3 þ Hþ , Ca2þ þ HCO 3 CaCO3 þ H2 O , Ca2þ þ CO2 3 þ H2 O
ð7Þ ð8Þ ð9Þ
The dissolution of CaCO3 through the above reactions results in the 2 release of Ca2þ, HCO 3 , and CO3 to the soil solution. Due to the low solubility of calcite in water, the reaction presented in Eq. (9) yields a minor amount of Ca2þ through mineral hydrolysis process. The reactions represented in Eqs. (7) and (8) occur concurrently with a major contribution to the enhanced dissolution of calcite.
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Salt and Na+ removal by shoot harvest
CO2
Root chemical effects that help dissolve calcite (CaCO3) in soil
Root physical effects that help improve soil structure
Respiration
CO2 + H2O
Proton (H+) release s by plant roots −
HCO3 + H+
H2CO3
Decomposition
Organic matter
Irrigation water
H2O
Calcite Na+ Ca2+ +
Na+
Soil colloid
Ca2+ 2Na+ +
Soil colloid
Leaching
Figure 2 Schematic illustration of driving forces for phytoremediation of calcareous sodic and saline-sodic soils: increased partial pressure of CO2 within the root zone; enhanced proton (Hþ) release in the root zone in case of certain crops; physical effects of roots in improving soil aggregation and hydraulic properties of the root zone; and salt and sodium (Naþ) content of shoot, which is removed through harvest of aerial plant portion.
In aerobic soils, PCO2 may increase to a maximum level of 1 kPa, which is equivalent to 1% of the soil air by volume (Nelson and Oades, 1998) and much higher under anaerobic conditions of flooded soils (Narteh and Sahrawat, 1999; Ponnamperuma, 1972) where saturated conditions inhibit the escape of CO2 to the atmosphere. Such retention of CO2 increases PCO2 in the soil. Similarly, PCO2 in the root zone is enhanced by root respiration under cropped conditions (Robbins, 1986a). In noncalcareous soils, an increase in CO2 results in the production of Hþ and a corresponding reduction in soil pH. However, pH usually does not decrease to a great extent in calcareous soils (Nelson and Oades, 1998), since changes in pH are buffered by the enhanced dissolution of calcite (Van den Berg and
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Loch, 2000). Therefore, increased levels of PCO2 in calcareous sodic and saline-sodic soils result in enhanced dissolution of calcite thereby providing adequate levels of Ca2þ for soil amelioration. Root respiration is not the only mechanism influencing PCO2 in the root zone. It is also affected by the following mechanisms that can act individually or collectively: (1) production of CO2 from oxidation of plant root exudates as soil organisms assist in producing CO2 when they oxidize polysaccharides, proteins, and peptides; and (2) production of organic acids by soil organisms, which help in dissolving calcite. Regardless of the source of CO2 production in soils, whether it be from respiring roots, decomposing organic matter and root exudates, or organic acid dissolution of calcite, the end result is the same: Ca2þ becomes available to replace exchangeable Naþ at a much higher rate than can be achieved by dissolution of calcite at the level of PCO2 in the atmosphere. Some studies on phytoremediation of calcareous sodic soils have quantified the levels of PCO2 produced under different crops. Robbins (1986a) measured PCO2 in the root zone of several crops during the amelioration of a calcareous sodic soil (pHs ¼ 8.6, ECe ¼ 2.4 dS m1, ESP ¼ 33) packed in lysimeters. The crops evaluated for the quantification of PCO2 levels in the root zone were barley, alfalfa, cotton, tall wheat grass [Agropyron elongatum (Host) Beauv.], and a sorghum-sudan grass hybrid called sordan [Sorghum drummondii (Steud.) Millsp. & Chase]. There was also a set of noncropped treatments consisting of a control, the application of fresh manure at 5 kg m2 soil (50 Mg ha1), and the addition of gypsum at 5 kg m2 soil (50 Mg ha1). The soil atmosphere samples collected from the root zone at different time intervals indicated that among the cropped treatments, cotton had the lowest PCO2 values (<3.6 kPa). Sordan produced the highest levels of PCO2 reaching 14 kPa (Table 4). Sodium removal efficiencies as measured in leachates collected from the cropped treatments were found directly proportional to the corresponding levels of PCO2 in the soil (Robbins, 1986b). Analysis of the postamelioration soil samples revealed the amelioration effect of the cropped treatments throughout the root zone. This was particularly applicable in case of sordan. In the noncropped gypsum-treated lysimeters, the greatest amelioration occurred within the top 0.2 m of the soil, the layer in which the amendment was incorporated to ameliorate the soil. The hydraulic conductivity of the gypsum-treated soil declined to near zero after the passing of one pore volume of drainage water. Contrasting this, the hydraulic conductivity in the lysimeters cropped with sordan was maintained at an adequate level throughout the study period. In another phytoremediation study conducted in lysimeters, Qadir et al. (1996a) leached columns of a calcareous saline-sodic soil (pHs ¼ 9.1, ECe ¼ 9.8 dS m1, SAR ¼ 103) cropped with Kallar grass. Leaching cycles were undertaken during early, peak, and slow growth periods of the grass. Similar leaching schedules were practiced in three noncropped treatments,
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Table 4 Mean values for net Naþ removal ( standard error) in various cropped and noncropped treatments as a function of partial pressure of CO2 (PCO2 ) in a lysimeter experiment (modified from Robbins, 1986a)
a b c d e
Treatment
PCO2 (kPa)a
Naþ removal (mol)b
Controlc Gypsumd Manuree Cotton (Gossypium hirsutum L.) Alfalfa (Medicago sativa L.) Sordan [Sorghum drummondii (Steud.) Millsp & Chase]
0.9–4.3 0.9–2.4 3.1–6.0 3.0–3.6 4.8–7.2 5.8–14.1
1.0 0.1 3.3 0.3 1.6 0.2 1.4 0.1 2.6 0.2 4.0 0.3
The PCO2 values fluctuated during the experimental period. The highest values in the cropped treatments were obtained during vigorous vegetative growth. Initially there were 7.5 mol of Naþ (soluble and exchangeable) in each soil column. Without crop or chemical amendment application. Gypsum applied at 5 kg m2 soil and incorporated in 0–0.2 m. Fresh manure applied at 5 kg m2 soil and incorporated in 0–0.2 m.
which consisted of a control (without gypsum) and two receiving gypsum at 50% and 100% gypsum requirement (GR) to remediate the soil. The rate of Naþ removal in the grassed treatment during its early growth stages (3.3 mmol day1) was less than that with control (4.7 mmol day1). However, the rate of Naþ leaching increased substantially to a maximum of 16.2 mmol day1 during peak growth of the grass, which was comparable to that from the soil columns treated with gypsum at 100% GR (19.3 mmol day1). The rate of Naþ removal in the grassed lysimeters again decreased (4.6 mmol day1) when leaching was undertaken during a subsequent period of slow growth. This suggests that the critical time for leaching of Naþ from the soil during phytoremediation should be undertaken during periods of vigorous plant growth so as to take advantage of increased calcite solubility associated with an increase in the PCO2 in the root zone. Since soils remain at or near saturation during leaching events, CO2 diffusion from the soil surface is greatly reduced. Hence, leaching when PCO2 is at its highest levels would result in the entrapment of the maximum amount of CO2 leading to a substantial increase in the rate of calcite dissolution. Bauder and Brock (1992) evaluated alfalfa, barley, and sordan—alone and in combination with surface-applied chemical amendments—to mitigate the impacts of long-term irrigation of fine loamy, calcareous soils from waters of various combinations of low and high salinity and sodicity levels. They essentially concluded that C3 (grass-type) crops, which produce relatively significant amounts of soil atmosphere CO2 facilitated the leaching of Naþ as a consequence of minor acidification of the soil solution.
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In a modeling study, Simunek and Suarez (1997) predicted that sodic soil amelioration with calcite was feasible, but the time and quantity of water required for amelioration to increase the PCO2 to 2 kPa to increase dissolution of calcite was approximately two times greater than that required for amelioration with gypsum. In a more recent evaluation, Suarez (2001) simulated a similar time and quantity of water for calcite dissolution at PCO2 of 5 kPa. As determined by Robbins (1986b), crops such as cotton producing root zone PCO2 in this range (3.0–3.6 kPa) may need greater time and quantity of water than required for chemical soil amelioration. However, using crops for phytoremediation that produce PCO2 as high as 14 kPa in the root zone as in the case of sordan, the requirement for water and time would be greatly reduced. Thus, a leaching strategy for calcareous sodic and saline-sodic soils under cropping with high PCO2 in the root zone would result in significant savings in the amount of water required and therefore a decrease in the drainage volume. Although soil atmosphere data from lysimeter experiments may not represent field conditions, such information provides an insight into PCO2 data under controlled conditions. The PCO2 data show considerable CO2 production differences between crop species at different plant growth stages, and the amount of irrigation water required to leach Naþ. If the differences in CO2 production by different crops and crop management are known, it may well be possible to select crops and management practices that would enhance Naþ removal from the cation exchange complex more efficiently than has been achieved before. 4.2.2. Proton release by plant roots The release of Hþ from plant roots is considered as a process contributing to a decrease in pH of the rhizosphere. Several studies have shown that various plant species supplied with ammonium (NHþ 4 ) nutrition acidify their rhizosphere, whereas the species alkalize it when nitrate (NO 3 ) is supplied as a nitrogen (N) source (Marschner and Ro¨mheld, 1983; Schubert and Yan, 1997). In addition, legumes relying on symbiotic N2 fixation have been shown to acidify their rhizosphere (Schubert et al., 1990b). Although considerable Hþ extrusion has been recorded in the rhizosphere of various N2-fixing plant species (Hinsinger, 1998; Marschner and Ro¨mheld, 1983; Nye, 1981; Schubert et al., 1990a), this biological acidification mechanism has been studied mainly in acidic soils rather than its possible role in the remediation of sodic and saline-sodic soils. Protons released by N2-fixing plant species in the root zone of sodic soils assist in calcite dissolution resulting in Ca2þ and HCO 3 . This chemical reaction is the same as in the case of enhanced PCO2 in the root zone as shown in Eq. (8). The release of Hþ by plants at the soil–root interface results in an electrochemical gradient. Cation uptake increases net Hþ release through partial depolarization of the membrane potential, which facilitates active
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217
Hþ pumping (Schubert and Yan, 1997). Due to Hþ release cytosolic pH increases, which triggers organic anion synthesis. The organic anion complement of a crop or the litter component of trees is thus a measure of net Hþ release at the root–soil interface that has been named ash alkalinity ( Jungk, 1968). Ash alkalinity has been routinely measured on several crops, forages, and tree species to assess their acidification potential (Moody and Aitken, 1997; Noble and Randall, 1999; Noble et al., 1996). In a comprehensive evaluation of ash alkalinity of 106 plant species in the semiarid tropics of northern Australia, Noble and Nelson (2000) observed a range of values from 25 to 347 cmolc kg1 for Themeda triandra and Brunoniella acaulis, respectively. While the range of ash alkalinity varies considerably among species, there appears to be little variability within a species. For example, in an assessment of the ash alkalinity of a range of legume species adapted to the wet and semiarid tropics, variation in ash alkalinity between accessions of the same species was relatively low while among species there was a greater degree of variation. It would appear that there is a link between adaptation to a specific agroecotype and ash alkalinity. For example, Calliandra calothyrsus, a species well adapted to highly weathered soils of the wet tropics, had the lowest ash alkalinity (44 cmolc kg1) while Stylosanthes seabrana, a species well adapted to heavy-textured base-rich soils of the semiarid tropics, had an ash alkalinity value of 125 cmolc kg1, three times greater than that of C. calothyrsus. Intuitively one could hypothesize that plant species that have evolved on soils of high base status may have a high ash alkalinity and hence a greater propensity to generate Hþ at the root–soil interface. Therefore, the measurement of ash alkalinity in species that are adapted to sodic soil conditions could be used to select the most appropriate species to enhance the rate of calcite dissolution through Hþ release in the root zone. In several studies undertaken to measure ash alkalinity over a wide range of plant species, a highly significant relationship between the Ca2þ concentration in plant material and ash alkalinity has been observed suggesting a simple and practical surrogate for the measurement of this attribute (Fig. 3). In order to maximize the benefits of net acid addition in sodic and salinesodic soils through growing species with high ash alkalinity, a key component would be the removal of as much aboveground biomass as possible (Yan and Schubert, 2000). In a study quantifying the net acid addition rate (NAAR) associated with Stylosanthes-based legume systems in the semiarid tropics of northern Australia, extensive grass/legume-based pasture systems were found to have a NAAR of 0.2 kmol Hþ ha1 year1 (Noble et al., 1997). Contrasting this, in a Stylosanthes seed production system where the entire aboveground biomass was removed from the field for processing, there was a large increase in NAAR (10.6 kmol Hþ ha1 year1) with an equivalent value of 530 kg CaCO3 ha1 year1. This evidence clearly demonstrated the association of greater rate of acid addition with highly exploitive production systems. This aspect has yet to be fully understood and
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400
Ash alkalinity (cmolc kg− 1)
350 300 250 200 150 100 50 0 0
50
100 150 200 Ca concentration (cmolc kg− 1)
250
300
Figure 3 Relationship between calcium (Ca2þ) concentration in plant material and ash alkalinity. The outliers (closed circles) are from Salsola kali and two collections of Portulaca oleracea. Regression equation for (n ¼ 93): y ¼ 30.52 þ 1.15x; r 2 ¼ 0.958 does not include the three outliers [adapted from Noble and Nelson (2000)].
appreciated in the context of Hþ release by the roots and phytoremediation of sodic, saline-sodic, and alkali soils. Limited information is available on the contribution of Hþ release by the roots of legume plant species to the phytoremediation process of calcareous sodic and alkali soils. In a lysimeter study on a calcareous sodic soil (pHs ¼ 7.4, ECe ¼ 3.1 dS m1, ESP ¼ 27.6), Qadir et al. (2003a) evaluated alfalfa without N supplement (relying on N2 fixation) with that relying on ammonium nitrate (NH4NO3) nutrition. Despite the fact that both the treatments produced statistically similar root and shoot biomass, there was 8% greater removal of Naþ in the leachate collected from the soil columns grown with N2-fixing alfalfa (Table 5). This evidence indicated dissolution of an additional amount of calcite in the N2-fixing treatment, suggesting that the amelioration rate of calcareous sodic soils could be increased by means of crop management conducive for the release of greater amount of CO2 and Hþ in the root zone. In addition, using appropriate N2-fixing crops as a phytoremediation tool has the advantage of enhanced availability of N in the soil for the postamelioration crops. 4.2.3. Physical effects of roots Plant roots are essential for maintaining soil structure, and the presence of roots at the lower depths of the soil profile drives the processes of macropore formation. Plant roots improve soil porosity by creating either biopores or
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Table 5 Shoot and root dry matter production and cumulative Naþ removal ( standard error) in phytoremediation treatments from a calcareous sodic soil in a lysimeter experiment (modified from Qadir et al., 2003a)
Treatment
N2-fixing alfalfa (Medicago sativa L.)a NH4NO3-fed alfalfa (Medicago sativa L.)b
Shoot (g lysimeter1)
Root (g lysimeter1)
Naþ removal (mmol)
31.2 0.9 a
12.0 0.3 a
26.1 0.4 a
30.2 0.3 a
11.8 0.1 a
24.2 0.5 b
a
Inoculated with rhizobium (Rhizobium meliloti, strain No. 6052). Supplied with mineral N (100 mg kg1 soil at sowing þ 50 mg kg1 soil 12 days after sowing). Means followed by the same letter within a column are not statistically different ( p ¼ 0.05). b
structural cracks (Czarnes et al., 2000; Oades, 1993; Pillai and McGarry, 1999; Yunusa and Newton, 2003). In addition, roots stimulate changes in the root zone through removal of entrapped air from larger conducting pores and generation of alternate wetting and drying cycles. Aggregate stability is enhanced because of in situ production of polysaccharides and fungal hyphae in conjunction with differential dewatering at the root–soil interface (Boyle et al., 1989; Tisdall, 1991). In addition, roots of some crops act like a potential tillage tool as they can grow through compacted soil layers and improve the soil below the plow pan (Elkins et al., 1977). Plant roots play an important role in facilitating the process of leaching Naþ, replaced from the cation exchange complex, to the deeper soil layers. This process can be triggered by deep-rooted vegetation that can withstand ambient levels of salinity and sodicity during phytoremediation. This is consistent with the observation that deep-rooted perennial grasses and legumes can improve structure of the plow layer (Tisdall, 1991) with concurrent improvement in hydraulic properties of sodic soils (Akhter et al., 2004; Ilyas et al., 1993). Observations from field studies reveal the beneficial effects of root growth in sodic soils during phytoremediation. Ilyas et al. (1993) tested different phytoremediation treatments—deep-rooted alfalfa, and sesbaniawheat (Triticum aestivum L.)-sesbania rotation—alone and in conjunction with the application of gypsum to ameliorate a low-permeability hard salinesodic soil (pHs ¼ 8.8, ECe ¼ 5.6 dS m1, SAR ¼ 49) in the Indus Plains of Pakistan. Alfalfa grown for 1 year resulted in a twofold increase in saturated hydraulic conductivity (K s ). The original K s values in the upper 0.8 m of soil ranged from 0.8 to 1.5 107 m s1. Alfalfa roots penetrated as deep as 1.2 m in the gypsum-treated plots as compared to 0.8 m in untreated plots. Other phytoremediation treatment, sesbania-wheat-sesbania rotation, caused a similar increase in Ks up to 0.4 m depth (Table 6). Sesbania roots
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M. Qadir et al.
Table 6 Phytoremediation and gypsum effects on field-saturated hydraulic conductivity (Ks) of a saline-sodic soil after 1 year (modified from Ilyas et al., 1993) Hydraulic conductivity (Ks) (107 m s1) Soil depth increment (m) Treatment
Alfalfa Wheat straw added at 7.5 Mg ha1 Sesbania-wheat-sesbania Fallow Alfalfa Wheat straw added at 7.5 Mg ha1 Sesbania-wheat-sesbania Fallow a
0.0^0.2
0.2^0.4
0.4^0.6
0.6^0.8
Without gypsum application 3.8 a 2.0 a 3.4 a 2.4 aba 1.8 b 1.4 b 1.1 a 1.1 a 3.4 a 1.9 b 1.9 a 1.7 a 1.2 b 1.1 b 1.6 a 2.6 a 1 Gypsum applied at 25 Mg ha 6.5 a 3.9 a 4.4 a 4.2 a 3.5 b 2.1 b 1.8 b 2.9 ab 7.9 a 2.0 b 1.8 b 2.1 b 2.9 b 1.2 b 1.2 b 1.5 b
Means followed by the same letter within a column and gypsum treatment are not statistically different ( p ¼ 0.05).
were healthy, thick, and well branched, but grew only to a depth of 0.3 m. Other options attempted were physical manipulation of the same soil, but they did not improve soil permeability to an appreciable extent. These options were subsoiling (by curved chisels to a depth of 0.45 m at 0.5 m intervals) and open-ditch drains (1 m deep). In another field study on a duplex soil in Australia, Cresswell and Kirkegaard (1995) found that the inclusion of crops such as canola (Brassica napus L.) in cereal rotations did not improve porosity of the dense B-horizon. They proposed inclusion of deep-rooted crops such as alfalfa to mixed cropping systems as a potential biological drilling strategy to improve subsoil permeability. Akhter et al. (2004) evaluated the impact of growing Kallar grass over different periods (from 1–5 years) on different soil properties such as available water content, bulk density, porosity, and Ks of a saline-sodic field (pHs ¼ 10.4, ECe ¼ 22.0 dS m1, SAR ¼ 184). The preamelioration Ks value was 0.035 mm day1 (0.4 109 m s1) in the upper 0.2 m of soil. The K s increased substantially within 5 years to a final value of 55.6 mm day1 (6.4 107 m s1); this increase was significantly correlated with increases in porosity and water retention. In addition, the Ks increase was accompanied by a reduction in soil bulk density, which fell from an average value of 1.62 to 1.53 Mg m3 over the same period (Table 7). Soil porosity, on the other hand, increased from 38.9% to 42.8%. These changes were probably due to the fact that Kallar grass has an extensive root system, which can penetrate the soil to a depth of 1 m (Malik et al., 1986).
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Table 7 Effect of various phytoremediation treatments (growing of kallar grass for different time periods) on the available water content, bulk density, porosity, and hydraulic conductivity (Ks) of the upper 0.2 m of a calcareous, saline-sodic soil (pHs ¼ 10.4, ECe ¼ 22.0 dS m1, SAR ¼ 184) with sandy clay loam texture in a field (based on the data reported by Akhter et al., 2004)
Treatment
Control (noncropped) Kallar grass (1 year) Kallar grass (2 years) Kallar grass (3 years) Kallar grass (4 years) Kallar grass (5 years) a b
Available water (kg kg1)
Bulk density (Mg m3)
Porosity (%)
0.155
1.62
38.9
0.04
0.175
1.61
39.1
1.5b
0.184
1.58
40.4
9.0b
0.195
1.55
41.5
18.0b
0.216
1.54
42.3
38.0b
0.214
1.53
42.8
55.6
Ks (mm day1)a
1 mm day1 ¼ 1.16 108 m s1. Estimated values from a graph of soil hydraulic conductivity (Ks) against time.
Although deep tillage has shown to be effective in ameliorating subsoils with low porosity, the benefits in some cases have been short lived (Cresswell and Kirkegaard, 1995). In addition, the high cost of deep tillage has restricted its large-scale adoption. As the roots of some plant species can act as potential tillage tools, biological drilling has shown promise as an alternative to deep tillage for the amelioration of dense subsoils (Elkins et al., 1977). Biological drilling has two stages: (1) creation of macropores in the subsoil by the roots that penetrate the compacted soil layer as they decay, resulting in improved water movement and gaseous diffusion; and (2) benefits for the subsequent crop(s) after improvements in subsoil macroporosity (Cresswell and Kirkegaard, 1995; Elkins, 1985). For example, the roots of some crops such as Bahia grass (Paspalum notatum Flu¨gge) and tall fescue [Festuca arundinacea (L.) Schreb.] have been shown to grow through compacted soil layers with subsequent improvement of the soil below the plow pan. Field experimentation with tall fescue showed an advantage for large diameter roots of the species in penetrating low-permeability soils (Elkins et al., 1977). In addition to quantifying the effects of rooting systems of grasses and forage species during phytoremediation, studies have been conducted to evaluate the role of tree roots on physical properties of sodic soils (Garg, 1998; Mishra and Sharma, 2003; Mishra et al., 2002). Mishra and Sharma (2003)
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evaluated 3-, 6-, and 9-year-old plantations of two leguminous tree species—Prosopis juliflora (Sw.) DC. and Dalbergia sissoo Roxb. ex. DC.—for their effects on the extent of changes in the physical properties of a sodic soil in India. The porosity of the soil increased and bulk density decreased with the age of plantations as compared to the respective control plots where no tree was grown. The control corresponding to the 9-year-old P. juliflora and D. sissoo plantations had 40.4% and 44.5% porosity in the surface soil, which increased to 46.9% and 51.0% after 9 years of their growth, respectively. The mean soil permeability in the upper 0.1 m soil depth increased with the age of the plantation. Nine years after planting, the mean soil permeability increased from 0.24 1010 to 10.95 1010 cm2 in the P. juliflora plantation and from 0.37 1010 to 11.69 1010 cm2 in the D. sissoo plantation. Soil bulk density was maximum in control plots and reduced after afforestation in case of both tree species with nonsignificant treatment differences. The improvement in soil physical properties was attributed to increased levels of organic matter that improved aggregation of soil particles, resulting in the development of suitable soil structure. 4.2.4. Salt and Naþ uptake by shoots Removal of aboveground biomass of plant species, used for phytoremediation of sodic and saline-sodic soils, removes salts and Naþ taken up by the plants and accumulated in their shoots. Highly salt-resistant species such as halophytes may accumulate quite high levels of salts and Naþ in their shoots. For example, Atriplex species grown under rangeland conditions have leaf ash concentrations 130–270 g salts kg1 (Hyder, 1981) and if grown in saltaffected soils, the species can have leaf ash concentrations as high as 390 g salt kg1 (Malcolm et al., 1988). Despite these high levels of salt removal via shoot harvest of the plant species, such salt removal alone does not play a significant role in the amelioration process of salt-affected soils, which contain huge amounts of salts. For example, Barrett-Lennard (2002) predicted that under nonirrigated conditions, halophytic crops with an annual productivity of about 10 t ha1 and 25% shoot salt concentration on dry weight basis (250 g kg1) would require about 20 consecutive years to remove half of the initial content of salts (86 Mg ha1) present in 2 m depth of a salt-affected soil. It must be noted that under nonirrigated conditions, fodder shrubs such as Atriplex species rarely produce more than 2 Mg ha1 annually (Barrett-Lennard et al., 1990). In addition, a major fraction of salts that accumulates in leaves is recycled back to the soil in the form of leaf fall. Therefore, the effects of growth and salt uptake by halophytes on reduction in soil salinity and sodicity are likely to be minimal under nonirrigated conditions. Under irrigated conditions, which are a prerequisite for enhanced calcite dissolution and subsequent removal of Naþ from the root zone during phytoremediation of sodic soils, the contribution of typical salt
223
Phytoremediation of Sodic and Saline-Sodic Soils
Table 8 Shoot dry matter and removal of salt and Naþ in the aboveground harvest of some plant species (modified from Gritsenko and Gritsenko, 1999) Plant speciesa
Japanese millet Amaranth Sunflower Sudan grass Alfalfa a
Shoot dry matter (Mg ha1)
8.2 5.0 9.1 5.0 11.3
Salt removal (kg ha1)
Naþ removal (kg ha1)
224 182 172 72 178
46 3 4 2 26
Japanese millet [Echinochloa esculenta (A. Braun) H. Scholz], amaranth [Amaranthus cruentus L.], sunflower [Helianthus annuus L.], Sudan grass [Sorghum drummondii (Steud.) Millsp. & Chase], alfalfa (Medicago sativa L.).
accumulators through shoot harvest to net removal of salt and Naþ is minimal (Table 8). The reasons for this are that, besides native soil salinity and sodicity, salts and Naþ are also added to sodic soils during irrigation, particularly in cases where the irrigation waters are already saline and/or sodic. For example, Kallar grass is grown on calcareous sodic and saline-sodic soils as a potential phytoremediation crop in several parts of the world. Its aboveground vegetation (forage) contains salt levels in the range of 40–80 g kg1 when grown in soils with salinity levels of about 20 dS m1. Considering annual forage production of the grass of 25 Mg ha1, the volume of irrigation water required to grow the grass is estimated to be 104 m3 ha1 (107 liter ha1). If the irrigation water has a salinity level 1.5 dS m1, which is typical of most waters used for irrigation, the amount of salt added in irrigation water would be equivalent to 9.6 Mg ha1 compared with 1–2 t ha1 of salt removed in forage. The estimates of Gritsenko and Gritsenko (1999) reveal that Naþ uptake by aboveground biomass of several plant species constitutes 2–20% of the total salt uptake (Table 8). In an evaluation, Qadir et al. (2003b) found that Naþ removal by shoot harvest of crops such as alfalfa would contribute to only 1–2% of the total Naþ removed during phytoremediation of sodic soils. Therefore, the principal source of sodicity decrease through phytoremediation of calcareous sodic soils is leaching of salts and Naþ from the root zone to deeper soil depths rather than removal by harvesting the aboveground plant biomass.
4.3. Comparative efficiency of phytoremediation The efficiency of different plant species used in phytoremediation of sodic and saline-sodic soils has been found to be highly variable. In general, the species with greater production of biomass together with the ability to withstand ambient soil salinity and sodicity as well as periodic inundation have been found to be efficient in soil amelioration (Kaur et al., 2002; Qadir et al., 2002).
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Phytoremediation has two major advantages for the farmers: (1) no financial outlay to purchase chemical amendments, and (2) accrued financial or other farm-level benefits from crops grown during amelioration. Several studies involving phytoremediation and other approaches aimed at the improvement in sodic soils have been carried out in various parts of the world (Ahmad et al., 1990; Ghaly, 2002; Kumar and Abrol, 1984; Robbins, 1986b). In addition to the ameliorative effects on soil sodicity, these approaches have been compared for their effects on the nutrient availability status in the postamelioration soil, zone of amelioration in terms of soil depth, and environment conservation in terms of C sequestration (Bhojvaid and Timmer, 1998; Garg, 1998; Kaur et al., 2002). 4.3.1. Soil sodicity amelioration Various evaluations in the field have revealed that chemical amelioration and phytoremediation approaches perform similarly in terms of their ability to decrease the soil sodicity levels. Results of a field experiment (Kumar and Abrol, 1984) conducted on a barren, calcareous, and alkali soil (pH1:2 ¼ 10.6, EC1:2 ¼ 2.7 dS m1, ESP ¼ 94) indicated that the amelioration efficiency of two grasses, Para grass [Brachiaria mutica (Forssk.) Stapf.] and Karnal grass, was comparable with soil application of gypsum at 12.5 Mg ha1 (Table 9). The yield of the first rice crop in the gypsum treatment averaged 3.7 Mg ha1 as compared to 3.8 and 4.1 Mg ha1 from the treatments cropped for 1 year with Para and Karnal grasses, respectively. The corresponding rice yields after 2 years of grass cropping were 5.3 and 6.1 Mg ha1. Hamid et al. (1990) evaluated the amelioration efficiency of Kallar grass during different periods of root decay. They leached a calcareous, silty clay loam, saline-sodic field (pHs ¼ 8.3–9.3, ECe ¼ 16.8–37.5 dS m1, SAR ¼ 32.5–108.9) 3, 6, 9, and 12 days after each harvest during 2 years of grass Table 9 Effect of gypsum and grass-based cropping systems on grain yields of first rice and wheat crops after gypsum application or after completion of Para or Karnal grass cultivation on an alkali soil (modified from Kumar and Abrol, 1984)a,b
a b
Treatment
Rice yield (Mg ha1)
Wheat yield (Mg ha1)
Rice-wheat rotation (without gypsum) Gypsum (12.5 Mg ha1) þ Rice-wheat Para grass grown for 1 year Para grass for grown 2 years Karnal grass for grown 1 year Karnal grass for grown 2 years
0.00 3.70 3.80 5.30 4.10 6.10
0.00 2.60 0.13 2.56 0.26 3.41
Initial soil pH1:2 for the 0–0.15 m depth was 10.6. Initial soil EC1:2 for the 0–0.15 m depth was 2.7 dS m1.
Phytoremediation of Sodic and Saline-Sodic Soils
225
cultivation. Each plot was kept flooded for 3 days during leaching. The amelioration efficiency of Kallar grass was greater in the plots leached 6 days after harvesting, and it was comparable with the gypsum-treated soil. In addition to Kallar grass, Ahmad et al. (1990) tested two plant species, sesbania and sordan, as phytoremediation treatments in a field study. The study compared the performance of these species with each other and with that of a commonly used gypsum application (13 Mg ha1) and a noncropped control in the context of a calcareous, sandy clay loam, saline-sodic field (pHs ¼ 8.2–8.6, ECe ¼ 7.4–9.0 dS m1, SAR ¼ 55.6–73.0). The plant species were grown for two seasons (15 months). The efficiency of each treatment, as indicated by a decrease in SAR in the upper 0.3 m of soil, was as follows: gypsum (postamelioration SAR ¼ 24.7) > sesbania (30.1) Kallar grass (32.5) > sordan (40.0) > control (57.2). Sesbania yielded the largest amount of seasonal forage, providing 40.8 Mg ha1 of fresh biomass. In comparison with sesbania, smaller amounts of forage were yielded by Kallar grass (29.3 Mg ha1) and sordan (24.7 Mg ha1), indicating a direct relationship between forage production and decrease in soil sodicity. In a later field experiment, Qadir et al. (1996a) compared four phytoremediation treatments—Kallar grass, sesbania, millet rice [Echinochloa colona (L.) Link], and finger millet [Eleusine coracana (L.) Gaertn.]—and a noncropped chemical treatment where gypsum was applied at 14.8 Mg ha1. The study was conducted on a calcareous, medium-textured, saline-sodic field (pHs ¼ 8.4–8.8, ECe ¼ 9.6–11.0 dS m1, SAR ¼ 59.4–72.4). The effectiveness of each treatment, in terms of an observed decrease in soil SAR, was as follows: gypsum (postamelioration SAR ¼ 28.2) > sesbania (33.5) > Kallar grass (36.9) > millet rice (42.6) > finger millet (48.1) > control without amendment or crop (53.2). The forage yield of each species was directly proportional to the subsequent reduction observed in soil sodicity (Table 10). Some field trials on phytoremediation techniques have not been successful primarily because a salt-resistant crop was not the first crop in the rotation. Muhammed et al. (1990) compared phytoremediation (rice-wheat rotation), physical þ phytoremediation (subsoiling by curved chisels to a depth of 0.5 0.05 m at a chisel spacing of 1.2–1.5 m þ rotation), chemical þ phytoremediation (gypsum at 100% GR of the upper 0.15 m of soil þ rotation), and chemical þ physical þ phytoremediation (gypsum þ subsoiling þ rotation) approaches to ameliorate two calcareous saline-sodic soils. Irrigation water (EC ¼ 1.8 dS m1, SAR ¼ 9.8) was applied according to the crop water requirement. The first crop in the rotation was rice, which was a complete failure and did not produce any grain on one soil (pHs ¼ 8.6–9.1, ECe ¼ 12.3–15.0 dS m1, ESP ¼ 58.7–74.6), and a grain yield of 0.72 Mg ha1 on the other soil (pHs ¼ 8.8–8.9, ECe ¼ 9.6–15.2 dS m1, ESP ¼ 42.5–45.6). Four years after cropping, the average rice grain yield from both soils was in the order: chemical þ phytoremediation (1.99 Mg ha1) > chemical þ physical þ phytoremediation (1.84 Mg ha1) > physical þ
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M. Qadir et al.
Table 10 Relationship between aboveground biomass production (Forage) by various plant species and relative decrease in soluble salt concentration of a saline-sodic field (pHs ¼ 8.6 0.2, ECe ¼ 10.3 0.7 dS m1, SAR ¼ 66 6) (modified from Qadir et al., 1996b) Plant species Common name
Botanical name
Sesbania
Sesbania bispinosa ( Jacq.) W. Wight Leptochloa fusca (L.) Kunth Echinochloa colona (L.) Link Eleusine coracana (L.) Gaertn. — —
Kallar grass Millet rice Finger millet Gypsum (no crop) Control (no crop)
Forage yield (Mg ha1)
Final soil SAR
32.3
33.5
24.6
36.9
22.6
42.6
5.4
48.1
— —
28.2 53.2
phytoremediation (1.41 Mg ha1) > phytoremediation (1.02 Mg ha1). Chemical þ phytoremediation and chemical þ physical þ phytoremediation treatments had similar values for the wheat grain yield (2.72 Mg ha1) followed by physical þ phytoremediation (1.79 Mg ha1) and phytoremediation (1.46 Mg ha1). Within the upper 0.15 m soil depth, all the treatments decreased salinity (ECe) to levels less than 5 dS m1 and sodicity (ESP) to levels less than 22 on both the soils. Several crop rotations have been evaluated to ameliorate sodic soils. Qadir et al. (1992) tested three irrigated crop rotations—sesbania-barley, rice-wheat, and Kallar grass-alfalfa—to ameliorate a calcareous saline-sodic field (pHs ¼ 8.1–8.2, ECe ¼ 9.2–13.7 dS m1, SAR ¼ 30.6–42.7). All the crop rotations ameliorated the upper 0.15 m of soil after 1 year (SAR < 10) as did amelioration by the noncropped gypsum treatment (SAR < 14). Although initial salinity and sodicity levels of this field were closer to that used by Muhammed et al. (1990), there were three differences: (1) the soil was relatively coarser in texture, (2) the plots were irrigated with canal water (EC ¼ 0.3 dS m1, SAR ¼ 0.5), and (3) the irrigation water was applied in excess of crop water requirements to leach Naþ to lower depths. It is pertinent to note that growing of rice in submerged soils has been recognized as a component of technology for the amelioration of moderately sodic and saline-sodic soils and for keeping these soils productive during the remediation phase. In fact, combining phytoremediation (with or without gypsum addition) with lowland rice crop has been found to
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Phytoremediation of Sodic and Saline-Sodic Soils
decrease Naþ on the cation exchange complex along with facilitating the process of leaching the salts from the root zone. Under submerged condition of lowland rice, accumulated CO2 has extended residence time in the soil atmosphere to react and neutralize alkalinity (Gupta and Abrol, 1990; Sahrawat, 1998; Van Asten, 2003). In an evaluation of 17 experiments, carried out in different parts of the world, a comparable effect of chemical and phytoremediation approaches has been found in most cases (Fig. 4). The chemical treatment (application of gypsum in all experiments) resulted in a 60% decrease over initial sodicity levels (ESP or SAR) whereas a 48% decrease was calculated for the phytoremediation treatments. In some experiments, however, the phytoremediation approach was either unsuccessful or much less efficient than the Phytoremediation
Chemical approach
18 17
Experiments
16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
20
40
60
80
100
Percentage decrease in ESP or SAR over initial level
Figure 4 Summary of 17 experiments where chemical and phytoremediation treatments have been compared for their effects on a decrease in soil sodicity (SAR or ESP). The bars for respective treatments indicate percentage decrease over the respective levels of original soil SAR or ESP values. References to the experiment numbers are: 1 (Robbins,1986a), 2 and 3 (Kausar and Muhammed,1972), 4 (Qadir et al.,1996b), 5 and 6 (Rao and Burns, 1991), 7 (Ahmad et al., 2006), 8 (Singh and Singh, 1989), 9 (Ahmad et al., 1990), 10 (Ilyas et al., 1997), 11 (Kelley and Brown, 1934), 12 (Batra et al., 1997), 13 and 14 (Muhammed et al.,1990),15 (Qadir et al., 2002),16 (Ghaly, 2002),17 (Helalia et al.,1992), and 18 (mean values of the 17 experiments). The experiments 1^7 were conducted in lysimeters, others under field conditions.
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M. Qadir et al.
chemical treatment for the following four reasons: (1) a crop resistant to ambient soil salinity and sodicity levels was not the first in the crop rotation, (2) a phytoremediation crop was grown over a period that was not its most suitable growing season, (3) duration of time was not sufficient to exploit the potential impact of the phytoremediation crop, and/or (4) irrigation was not applied in excess of crop water requirement, which restricted the downward movement of Naþ from the root zone. In general, phytoremediation worked well on moderately sodic and saline-sodic soils, provided: (1) irrigation was done in excess of crop water requirement to facilitate adequate leaching, and (2) the excess irrigation was applied when the crop growth and hence PCO2 were at their peak. On such soils, the performance of phytoremediation was comparable with soil application of gypsum. On highly sodic and saline-sodic soils, use of chemical amendment outperformed phytoremediation treatments. 4.3.2. Zone of soil amelioration The depth of sodic soil impacted by different amelioration approaches, that is anticipated zone of amelioration, is an important parameter to determine relative efficiency of these approaches. Phytoremediation and chemical approaches have been evaluated in terms of their effects on the depth of soil amelioration. In most comparative studies, amelioration in chemical treatments, gypsum in almost all cases, occurred primarily in the zone where the amendment was incorporated (Ilyas et al., 1993; Qadir et al., 1996a; Robbins, 1986b). Gypsum was mixed into the soil surface, and in most cases, it was agricultural grade and applied according to the GR of the upper 0.15 m of the soil. Only as amelioration approached completion in the region where gypsum was present, amelioration in the deeper depths began. This was a direct implication of the degree of Ca2þ saturation of the cation exchange sites relative to Naþ(Oster and Frenkel, 1980; Suarez, 2001). In the case of phytoremediation of sodic and saline-sodic soils, amelioration occurs throughout the root zone. This has been commonly observed in these soils when grown with a range of crops. However, different crops caused variable degree and depth of soil amelioration, which was influenced by the morphology and volume of root and the depth of root penetration (Ahmad et al., 1990; Akhter et al., 2003; Ilyas et al., 1993; Robbins, 1986b). Deep-rooted crops and those with tap root system have shown advantages in terms of greater depth of soil amelioration. For example, alfalfa roots can penetrate as deep as 1.2 m in the soil (Ilyas et al., 1993). 4.3.3. Nutrient dynamics during soil amelioration In addition to the beneficial effects on reducing salinity and sodicity levels in sodic and saline-sodic soils, phytoremediation provides additional benefits over other amelioration approaches, which do not provide such benefits, or at best to a lesser extent than phytoremediation. Improved nutrient
Phytoremediation of Sodic and Saline-Sodic Soils
229
availability of postamelioration soil is desirable for the growth of subsequent crops because nutritional problems occur in sodic soils, which range from deficiencies of several nutrients to the presence of phytotoxic levels of Naþ and Cl (Naidu and Rengasamy, 1993). Some studies have been conducted on nutrient behavior in sodic and saline-sodic soils during amelioration by phytoremediation and chemical approaches. Qadir et al. (1997) determined the availability of some macroand micronutrients during amelioration of a calcareous saline-sodic soil (pHs ¼ 8.2–8.6, ECe ¼ 7.4–9.0 dS m1, SAR ¼ 55.6–73.0). The phytoremediation treatments included the cropping of sesbania, sordan, or Kallar grass for 15 months. There was an increase in phosphorus (P), zinc (Zn), and copper (Cu) availability in the phytoremediation plots probably resulting from the production of root exudates and likely dissolution of some nutrient-coated calcite. Conversely, the noncropped gypsum treatment caused a decrease in the availability status of these nutrients. Besides leaching losses, adsorption of nutrients on some newly formed CaCO3, a secondary consequence of gypsum dissolution, contributed to this decrease. Soil N content was decreased in all the treatments except for the N2-fixing sesbania treatment where N content was increased from 0.49 to 0.53 g kg1. There was no treatment effect on soil potassium (K) availability since illite, a K-bearing mineral, was dominant in the clay fraction. Ghai et al. (1988) reported that sesbania, grown for 45 days and used as green manure, enriched sodic soils by making up to 122 kg N ha1 available to the rice crop which followed it. Studies using the 15N isotope dilution technique have also provided evidence of N conservation by other phytoremediation crops such as Kallar grass (Malik et al., 1986). When amelioration is undertaken on sodic soils using chemical amendments, some N loss may occur via NO 3 leaching (Qadir et al., 1997). Soil microbial biomass is an agent of transformation for added and native organic matter and acts as a labile reservoir for several plant-available nutrients. The activity of microbial biomass is commonly used to characterize the microbiological status of a soil and to determine the effects of agricultural practices on soil microorganisms. Dehydrogenase activity (DHA) in soils is related to microbial populations, respiration activity, and soil organic matter, and provides an index of the overall microbial activity ( Włodarczyk et al., 2002). This parameter has been studied in experiments dealing with sodic soil amelioration through chemical and biological means. Batra et al. (1997) determined DHA and microbial biomass carbon (MBC) after using various combinations of chemical and phytoremediation treatments, which consisted of Karnal grass grown for 1 or 2 years (harvested biomass removed or left to decompose on the soil surface), gypsum application (at 14 Mg ha1) þ Karnal grass, gypsum þ sorghum, gypsum þ rice, and gypsum þ sesbania. The soil on which these treatments were applied was alkali (pH1:2 ¼ 10.6, EC1:2 ¼ 2.1 dS m1, ESP ¼ 95, DHA ¼ 4.5 mg triphenylformazan g1,
230
M. Qadir et al.
MBC ¼ 56.7 mg kg1). The levels of DHA in postamelioration soil were greater (118.7 mg triphenylformazan g1) in the phytoremediation treatments than gypsum þ crop treatments (96.1 mg triphenylformazan g1). The MBC values were greater in gypsum þ crop treatments (206.3 mg kg1 soil) than in the cropped treatments (161.7 mg kg1 soil). The overall average MBC (184 mg kg1 soil) for all the treatments was more than three times the initial level of 56.7 mg kg1 soil. In an earlier study, Rao and Ghai (1985) reported that permanent vegetation such as grasses caused significant increases in urease and dehydrogenase activities in alkali soils. Rao and Pathak (1996) reported an increase in urease and dehydrogenase activities after green manuring an alkali soil with sesbania. In a 20-year study involving several tree plantations on an alkali soil (pH ¼ 10.2–10.5), Singh and Gill (1990) found a considerable decrease in pH and increase in organic matter (organic C) content, and available levels of P and K of surface 0.15 m soil. The tree species included P. juliflora (Sw.) DC., Acacia nilotica (L.) Willd. ex Delile, Eucalyptus tereticornis Sm., Albizia lebbeck (L.) Benth., and Terminalia arjuna (Roxb. ex DC.) Wight & Arn. (Table 11). 4.3.4. Environment conservation Sodic and saline-sodic soils have lost a large fraction of their original carbon (C) pool (Lal, 2001). The magnitude of the loss may range between 10 and 30 Mg C ha1, depending on the antecedent pool and the severity of degradation. The soil C pool is not only important for the soil to perform its productivity and environmental functions, but also plays an important role in the global C cycle (Lal, 2004). In addition to the amelioration effect, Table 11 Ameliorative effect of 20-year-old tree plantations on pH, organic carbon (OC), and available P and K of the upper 0.15 m of an alkali soil in India (modified from Singh and Gill, 1990) Available P (kg ha1)
Available K (kg ha1)
Tree species
pH1:2
Organic C (g kg1)
Acacia nilotica (L.) Willd. ex Delile Eucalyptus tereticornis Sm. Prosopis juliflora (Sw.) DC. Terminalia arjuna (Roxb. ex DC.) Albizia lebbeck (L.) Benth. Prestudy soil status
8.4
8.5
59
499
8.5 7.5 7.9
6.6 9.3 8.6
33 111 68
359 702 410
7.9 10.2
6.2 2.2
43 28
387 278
Phytoremediation of Sodic and Saline-Sodic Soils
231
cultivation of appropriate crops, shrubs, and trees on sodic and saline-sodic soils has the potential to mitigate the accelerated greenhouse effect by increasing soil C through biomass production (Bhojvaid and Timmer, 1998; Garg, 1998; Kaur et al., 2002). Garg (1998) monitored changes in an alkali soil under four tree species, which included acacia [A. nilotica (L.) Willd. ex Delile], shisham [D. sissoo Roxb. ex DC.], mesquite [P. juliflora (Sw.) DC.], and arjuna [T. arjuna Bedd.]. Shisham and mesquite were more efficient in terms of biomass production and decreasing Naþ levels in the soil. Similarly, there was greater microbial activity in upper 0.6 m soil under these species due to the accumulation of humus from the decomposition of leaf litter and root decay, which increased soil organic C. The rate of increase was low for the first 2–4 years, exponential between 4 and 6 years, and plateau at a low rate for 6–8 years. Bhojvaid and Timmer (1998) reported that establishment of mesquite on a sodic field increased organic C of the top 1.2 m soil from 11.8 to 13.3 Mg C ha1 in 5 years, 34.2 Mg C ha1 in 7 years, and 54.3 Mg C ha1 in 30 years. The average annual rate of increase in soil organic C was 1.4 Mg ha1 over the 30-year period. Other estimates from field studies on alkali soils suggest that various land-use systems consisting of a number of grasses and trees can sequester organic C in the range of 0.2–0.8 Mg C ha1 year1 (Table 12). Soils in arid and semiarid areas generally contain the largest pools of inorganic C, which consist of two components: (1) primary inorganic carbonates or lithogenic inorganic carbonates, and (2) secondary inorganic carbonates also known as pedogenic inorganic carbonates (Lal, 2002). Secondary carbonates are formed through the dissolution of primary carbonates and from the reprecipitation of weathering products. The reaction of CO2 with H2O and Ca2þ and Mg 2þ in the upper soil horizon, followed by the leaching of the products into the subsoil and their subsequent reprecipitation results in the formation of secondary carbonates and in the sequestration of CO2 (Sahrawat, 2003). Therefore, the leaching of HCO 3 through the soil profile, especially by irrigation management, could be a significant pathway leading to sequestration of soil inorganic C. Moreover, inorganic form of C is converted to organic form by plants through photosynthesis, and in soils through the reaction of CO2 3 with decomposing organic matter (added via phytoremediation). In soil, inorganic C gets dissolved through the actions of acidic root exudates and H2CO3 formed by the reaction with CO2 resulting from root respiration in aqueous medium. Thus, the transfer of C from inorganic to organic form provides a better environment for C sequestration, soil conservation, and environmental quality (Bhattacharyya et al., 2004; Sahrawat et al., 2005). The rate at which C is sequestered through this pathway may range between 0.25 and 1.0 Mg C ha1 year1 ( Wilding, 1999). When phytoremediation is used to ameliorate sodic soils and HCO 3 is leached as a by-product of the overall reaction, the amelioration process could sequester
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M. Qadir et al.
Table 12 Potential of two land-use systems (grass only and tree-grass) for carbon (C) sequestration in a calcareous alkali soil (pH ¼ 10.0–10.2; EC ¼ 2.0–6.4 dS m1) (calculated from the data reported by Kaur et al., 2002) Organic C in soil (g kg1) at different depthsb
a
b c
Treatmenta
0^0.075 m
0.075^0.15 m
Mean
C sequestration (Mg ha1 year1)c
Desmostachya Sporobolus Acacia þ Desmostachya Dalbergia þ Desmostachya Prosopis þ Desmostachya Acacia þ Desmostachya Dalbergia þ Desmostachya Prosopis þ Desmostachya
2.9 2.4 3.6 4.6
1.6 1.3 1.8 2.4
2.3 1.8 2.7 3.5
0.33 0.17 0.47 0.73
4.7 2.6 3.2
2.5 1.4 1.7
3.6 2.0 2.5
0.77 0.23 0.40
3.6
1.9
2.8
0.50
Desmostachya [Desmostachya bipinnata (L.) Stapf.], Sporobolus (Sporobolus marginatus Hochst. ex A. Rich), Acacia [Acacia nilotica (L.) Willd. ex Delile], Dalbergia (Dalbergia sissoo Roxb. ex DC.), Prosopis [Prosopis juliflora (Sw.) DC.]. After 6 years of plantation. Assuming initial C content in the soil as 1.3 g kg1 (average of the C content, which ranged from 1.0 to 1.6 g kg1) and mass of 0.15 m depth of 1 ha as 2 106 kg, the rate of organic C sequestration in the soil under each treatment was calculated as: Organic C sequestration (Mg ha1 year1) ¼ [(mean C content original C content in soil) 2]/6.
soil inorganic C (Lal, 2001; Sahrawat, 2003). Thus, phytoremediation could lead to both organic and inorganic C sequestration simultaneously. The plant material added to sodic or saline-sodic soils as a part of the phytoremediation process leads to organic C sequestration, and the rate of which depends on several soil and environmental factors. Among the soil factors, texture and mineralogy are more important. Among the environmental factors, moisture regime and temperature control decomposition of organic matter added and the residence time of C in the soil. In addition, the amount, and more importantly, the quality of organic matter added via plant shoots and roots have an overwhelming effect on soil organic C turnover and storage in the soil profile. Also, the plant species used for phytoremediation have a wide range in their decomposition and turnover rates, and C storage in the soil (Kiem and Koegel-Knabner, 2003; Oades, 1988; Sahrawat, 2004; Sariyildiz and Anderson, 2003; Six et al., 2002; Torn et al., 1997). As discussed earlier in this section, fresh organic matter added to the soil influences C sequestration via soil inorganic C (Sahrawat, 2003; Sahrawat et al., 2005). However, no documented evidence exists that quantifies the effect of different sodic soil amelioration methods on inorganic C sequestration. With growing interest in C sequestration, the degraded soils in the arid
Phytoremediation of Sodic and Saline-Sodic Soils
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and semiarid regions are expected to play a crucial role in stabilizing the atmospheric concentration of CO2 by employing means that are in line with the sustainable agricultural practices (Ce´sar Izzaurralde et al., 2001).
4.4. Plant species for phytoremediation An appropriate selection of plant species capable of producing adequate biomass is vital during phytoremediation. Such selection is generally based on the ability of the species to withstand elevated levels of soil salinity (Maas and Hoffman, 1977) and sodicity (Gupta and Abrol, 1990) while also providing a saleable product or one that can be used on-farm (Qadir and Oster, 2002). The salt resistance of a crop is not an exact value because it depends on several soil, crop, and climatic factors. It reflects the capability of a crop to endure the effects of excess root zone salinity. Considerable variation exists among crops to resist ambient levels of salinity (Table 13) and sodicity (Table 14). Such inter- and intracrop diversity can be exploited to identify local crops that are better adaptable to saline-sodic soil conditions (Maas and Grattan, 1999; Shannon, 1997). Maas and Hoffman (1977) proposed a linear response function model to characterize crops regarding their salt resistances. Two parameters obtained from this model are: (1) the threshold soil salinity (the maximum allowable soil salinity for a crop without yield reduction), and (2) the slope (the percentage yield decrease per unit increase in salinity beyond the threshold salinity level). The data, presented in terms of ECe at 25 C, serve only as a guideline to relative capabilities of the crops to withstand salinity. The threshold salinity levels and slope values obtained from Maas–Hoffman equation can be used to calculate relative yield (Yr) for any given soil salinity exceeding the threshold level by using Eq. (10):
Yr ¼ 100 bðECe ECth Þ
ð10Þ
where ECth is threshold saturated paste extractable salinity level expressed in dS m1, b is slope expressed in percentage per dS m1, and ECe is average electrical conductivity of the saturated soil paste extract of the root zone expressed as dS m1. The two-piece linear response function (Maas and Hoffman, 1977) is also reasonably accurate when salinity is expressed in terms of osmotic potential of the soil solution at field capacity. In cases where the osmotic potential of the soil solution is known, the crop yield response can be determined as a function of the osmotic stress that the plants experience (Maas and Grattan, 1999). Crops used as phytoremediation tool for saline-sodic soils may also experience oxygen deficiency. This can be expected for three reasons: (1) the need to overirrigate in providing the needed leaching to control salinity levels in the
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Table 13 Yield potentials of some grain, forage, vegetable, and fiber crops as a function of average root zone salinitya Average root zone salinity (dS m1) at specified yield potentials
Crop Common name
Botanical name
50%
80%
100%
Triticale (grain) Kallar grassb Durum wheat Tall wheat grass
Triticosecale Leptochloa fusca (L.) Kunth Triticum durum Desf. Agropyron elongatum (Host) Beauv. Hordeum vulgare L. Gossypium hirsutum L. Secale cereale L. Beta vulgaris L. Cynodon dactylon (L.) Pers. Sorghum sudanese (Piper) Stapf. Sesbania bispinosa ( Jacq.) W. Wight Triticum aestivum L. Portulaca oleracea L. Sorghum bicolor (L.) Moench Medicago sativa L. Spinacia oleracea L. Brassica oleracea L. (Botrytis Group) Oryza sativa L. Solanum tuberosum L. Zea mays L.
26 22 19 19
14 14 11 12
6 9 6 8
18 17 16 16 15 14 13
12 12 13 10 10 8 9
8 8 11 7 7 3 6
13 11 10 9 9 8
9 8 8 5 5 5
6 6 7 2 2 3
7 7 6
5 4 3
3 2 2
Barley Cotton Rye Sugar beet Bermuda grass Sudan grass Sesbania Wheat Purslane Sorghum Alfalfa Spinach Broccoli Rice Potato Maize a
b
Based on the salt tolerance data of respective crops and percentage decrease in yield per unit increase in root zone salinity in terms of dS m1 (Calculated from the data reported by Maas and Grattan, 1999). These data serve only as a guideline to relative resistances among crops. Absolute resistances vary and depend on climate, soil conditions, and cultural practices. Yield potential calculated from Malik et al. (1986).
soil, (2) the likelihood that problem soils—excessively saline and sodic with low infiltration rates and hydraulic conductivities—will be selected in the first place, and (3) inundation (surface ponding) due to a prolonged rainy season. Root zone salinity and sodicity in conjunction with oxygen deficiency affect active transport and exclusion processes in root cell membranes compared with saline nonwaterlogged conditions (Drew, 1983). The genotypes showing greater resistance against the combined effects of salinity, sodicity, and hypoxia would be a better choice for the phytoremediation approach.
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Table 14 Ranges of ESP in soils indicating about 50% of the potential yields of different crops (based on the data reported by Gupta and Abrol, 1990) Crop ESP range
Common name
Botanical name
10–15
Safflower Mash Pea Lentil Pigeon pea Urd-bean Bengal gram Soybean Groundnut Cowpea Onion Pearl millet Linseed Garlic Guar Indian mustard Wheat Sunflower Guinea grass Barley Sesbania Rice Para grass Bermuda grass Kallar/Karnal grass Rhodes grass
Carthamus tinctorius L. Vigna mungo (L.) Hepper Pisum sativum L. Lens culinaris Medik. Cajanus cajan (L.) Millsp. Phaseolus mungo L. Cicer arietinum L. Glycine max (L.) Merr. Apios americana Medik. Vigna unguiculata (L.) Walp. Allium cepa L. Pennisetum glaucum (L.) R. Br. Linum usitatissimum L. Allium sativum L. Cyamopsis tetragonoloba (L.) Taub. Brassica juncea (L.) Czern. Triticum aestivum L. Helianthus annuus L. Panicum maximum Jacq. Hordeum vulgare L. Sesbania bispinosa ( Jacq.) W. Wight Oryza sativa L. Brachiaria mutica (Forssk.) Stapf. Cynodon dactylon (L.) Pers Leptochloa fusca (L.) Kunth Chloris gayana Kunth
16–20 20–25
25–30
30–50
50–60 60–70 70þ
Several crops, shrubs, trees, and grasses have been used during phytoremediation of sodic and saline-sodic soils. Some successful examples are Kallar grass (Kumar and Abrol, 1984; Malik et al., 1986), sesbania (Ahmad et al., 1990; Qadir et al., 2002), alfalfa (Ilyas et al., 1993), Bermuda grass (Kelley, 1937; Oster et al., 1999), or sordan (Robbins, 1986a). Several other plant species have produced adequate biomass on salt-affected soils. These include shrub species from the genera Atriplex and Maireana (Barrett-Lennard, 2002; Malcolm, 1993), Kochia scoparia L. (Garduno, 1993), Salicornia bigelovii Torr. (Glenn et al., 1996), E. crusgalli (L.) P. Beauv. (Aslam et al., 1987), Portulaca oleracea L. (Grieve and Suarez, 1997), and Glycyrrhiza glabra L. (Kushiev et al., 2005), among others. However, it is imperative to compare them with other
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species already tested for sodic soil amelioration. In addition, efforts are needed to assess other crops such as high-value medicinal and aromatic species that could have the potential for adequate growth on sodic and saline-sodic soils. A number of tree plantations have been grown on sodic and saline-sodic soils. These include: T. arjuna (Roxb. ex DC.) Wight & Arn. (Jain and Singh, 1998), P. juliflora (Sw.) DC. (Bhojvaid and Timmer, 1998), D. sissoo Roxb. ex DC., A. nilotica (L.) Willd. ex Delile (Kaur et al., 2002), Parkinsonia aculeata L. and P. cineraria (L.) Druce (Qureshi and Barrett-Lennard, 1998), Sesbania sesban (L.) Merr. and Tamarix dioica Roxb. ex Roth. (Singh, 1989), and Leucaena leucocephala (Lam.) de Wit (Qureshi et al., 1993), among others. In Australia, Farrington and Salama (1996) recommended revegetation by trees to be the best long-term option for controlling dryland salinity. Qureshi and Barrett-Lennard (1998) have provided useful information regarding sources of seeds, nursery raising techniques, and land preparation and planting procedures for 18 different tree and shrub species having potential for growth on salt-affected soils. Any change in a cropping pattern or farm operation is driven by the cost of inputs involved and the subsequent economic benefits. Several studies have compared the economics of sodic soil amelioration. Singh and Singh (1989) found a net loss (cost:benefit 1.00:0.75) during phytoremediation although the growth of Karnal grass was adequate, which helped reduce soil sodicity. They attributed this economic loss to the small market demand of the grass in the presence of other good-quality forages in that locality. On the other hand, the phytoremediation strategy has been found economically beneficial when there was a market demand or local utilization of the crops at the farm level (Chaudhry and Abaidullah, 1988; Sandhu and Qureshi, 1986). Qureshi et al. (1993) found agroforestry systems comprising several tree species to be economically viable because of a need for firewood in local markets and effectiveness in amelioration of calcareous saline-sodic soils. On the other hand, the market for firewood is not sufficient to make agroforestry economically viable in California (Oster et al., 1999). Preliminary assessments in Australia suggest that there are 26 salt-resistant plant species capable of producing 13 products (or services) of value to agriculture (Barrett-Lennard, 2002). From an economic perspective much depends on local needs. In an immediate sense, phytoremediation can only be economically beneficial if the selected crops, grasses, or trees have a market demand or local utilization at the farm level. In the long run, one must also consider the value of the improved soils.
5. Perspectives Recent trends and future projections suggest that the need to produce more food, feed, energy, and fiber for the world’s expanding population and changing lifestyles and preferences, will lead to an increase in the use of
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salt-prone land and water resources (Qadir et al., 2007). This is particularly relevant to less-developed, arid and semiarid countries in which the problems of salinity- and sodicity-induced soil degradation are common. Such widespread occurrence of sodic and saline-sodic soils reveals the need for concerted efforts to rehabilitate these soils in order to enhance their productivity. A comparable performance of phytoremediation with that of chemical amelioration highlights the effective role of cropping in the amelioration of calcareous sodic and saline-sodic soils. Phytoremediation has shown to be advantageous in several aspects: (1) no financial outlay to purchase chemical amendments, (2) accrued financial or other benefits from crops grown during amelioration, (3) promotion of soil-aggregate stability and creation of macropores that improve soil hydraulic properties and root proliferation, (4) greater plant nutrient availability in soil after phytoremediation, (5) more uniform and greater zone of amelioration in terms of soil depth, and (6) environment consideration in terms of C sequestration in the postamelioration soil. Phytoremediation is effective when used on moderately saline-sodic and sodic soils. However, it does have disadvantages in that it reduces sodicity more slowly than chemical approaches and requires calcite to be present in the soil (although this is commonly found in most sodic soils). In addition, the feasibility of phytoremediation is limited when soil is highly sodic, as this is likely to result in the phytoremediation crop’s growth being variable and patchy. Under these conditions, the use of chemical amendments such as gypsum is inevitable. The process of Naþ removal from calcareous sodic and saline-sodic soils during phytoremediation has been found to be dominated by PCO2 within the root zone. Large differences in root zone PCO2 values of the crops used in phytoremediation have been observed. The PCO2 and hence soil amelioration efficiency have been found to be directly proportional to crop biomass, root activity, and rate of crop growth. In addition, excess irrigation during peak growth stages would significantly increase the retention of CO2 through entrapment thereby enhancing the rate of calcite dissolution during phytoremediation. The identification of PCO2 as the single largest driving force for sodic soil amelioration suggests the need to identify crops and crop management practices that enhance CO2 production within the root zone to ameliorate sodic soils more efficiently, especially in areas where chemical amendments are not available or are too expensive. Furthermore, it is evident from studies quantifying processes contributing to accelerated soil acidification under cropping systems that they could be effectively used to enhance Hþ generation under sodic conditions. In this respect, plant species with high ash alkalinity, large aboveground biomass production, and the promotion of highly exploitive production systems that rely on N2-fixing species and encompass net biomass export would maximize Hþ addition rates. Such a system could be typically of a cut-and-carry forage crop production that
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includes a N2-fixing legume component. The removal of Naþ by crop harvest has a minor contribution to amelioration of sodic and saline-sodic soils. Degradation of soil resources and desertification of drylands has led to the depletion of soil organic C, decline in biomass production, contamination of water resources, and emission of greenhouse gases such as CO2 at an accelerated rate. Such trends will intensify in the foreseeable future if due attention is not given to reverse the resource degradation. Amelioration of sodic and saline-sodic soils vis-a`-vis C sequestration hold promise to reverse the resource degradation processes. However, to achieve such objectives socially acceptable and economically attractive policies are needed for the implementation of technically sound practices on a long-term basis that should also involve provision for monitoring the actual amount of C sequestration. Soil management under different levels of salinity and sodicity will continue to be a challenge for researchers, farm advisors, and farmers. Crop-based sodic soil management built on the accumulated wisdom of stakeholders will not only enhance farmers’ participation but will also assist them in the adoption of pertinent measures, as these need to be adopted at the community level. Such participatory approaches will ensure that the views and ideas of the local population are taken into account, and may create a sense of ownership among the members of the farming community. Community-based sodic soil management would help to strengthen linkages among researchers, farm advisors, and farmers. These linkages will continue to be fostered as the use of sodic soils becomes more prevalent. The successful amelioration of these soils through phytoremediation will require a greater understanding of the potential of phytoremediation species to withstand ambient salinity and sodicity levels in soil and water, and also of the uses and markets for the agricultural products produced. Considering the challenges associated with sodic soil management and environmental conservation, we believe that the time has come to consider such soils a useful resource of economic value rather than an environmental burden. The use of sodic soils should therefore be considered to be an opportunity to shift from subsistence farming to progressive and incomegenerating farming. Clearly, phytoremediation is an effective low-cost intervention for the amelioration of these soils that is a viable solution for resource-poor farmers. This approach has the potential for large-scale adoption under government- or community-based programs aimed at the amelioration and improved productivity of degraded common property resources. We believe that the information provided herein will stimulate strategic research for further elucidation of the role of phytoremediation in the restoration of sodic and sodic-saline soils for sustainable agriculture and conservation of environmental quality.
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ACKNOWLEDGMENTS We dedicate this contribution to Dr. Walter Pearson Kelley (1878–1965), University of California, Berkley for his pioneer research conducted in California that demonstrated phytoremediation to be an effective amelioration strategy for saline-sodic and sodic soils. This publication is a part of the joint initiative of the International Center for Agricultural Research in the Dry Areas (ICARDA) and the International Water Management Institute (IWMI) for the assessment and management of marginal-quality water resources and salt-affected soils.
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C H A P T E R
F I V E
Ecology of Denitrifying Prokaryotes in Agricultural Soil Laurent Philippot,* Sara Hallin,† and Michael Schloter‡ Contents 1. Introduction 2. Agronomical and Environmental Importance of Denitrification 2.1. Consequences of denitrification for agriculture 2.2. Impact of denitrification on the environment and human health 3. Who are the Denitrifiers? 3.1. Denitrifiers and nitrate reducers 3.2. Denitrifying populations 4. Assessing Denitrifiers Density, Diversity, and Activity 4.1. Measuring denitrification and N2O emissions 4.2. Resolving diversity of denitrifiers 4.3. Quantification of denitrifiers 5. Natural Factors Causing Variations in Denitrification 5.1. Temperature and water 5.2. Freeze–thaw cycles 5.3. Dry–wet cycles 6. Denitrification in the Rhizosphere of Crops 6.1. Crops as a factor influencing denitrifiers 6.2. Impact of crop species, crop cultivars, and transgenic plants 7. Impact of Fertilization on Denitrification 7.1. Fertilization affects denitrification 8. Effect of Environmental Pollution on Denitrifiers 8.1. Pollution affects denitrification 8.2. Pesticides 8.3. Heavy metals
* { {
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INRA, University of Burgundy, Soil and Environmental Microbiology, Dijon, France Department of Microbiology, Swedish University of Agricultural Sciences, Uppsala, Sweden GSF-National Research Center for Environment and Health, Institute for Soil Ecology, Oberscheissheim, Germany
Advances in Agronomy, Volume 96 ISSN 0065-2113, DOI: 10.1016/S0065-2113(07)96003-4
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2007 Elsevier Inc. All rights reserved.
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9. Conclusions and Outlook References
Denitrification is a microbial respiratory process during which soluble nitrogen oxides are used as an alternative electron acceptor when oxygen is limiting. It results in considerable loss of nitrogen, which is the most limiting nutrient for crop production in agriculture. Denitrification is also of environmental concern, since it is the main biological process responsible for emissions of nitrous oxide, one of the six greenhouse gases considered by the Kyoto protocol. In addition to natural variations, agroecosystems are characterized by the use of numerous practices, such as fertilization and pesticide application, which can influence denitrification rates. This has been widely documented in the literature, illustrating the complexity of the underlying mechanisms regulating this process. In the last decade, however, application of molecular biology approaches has given the opportunity to look behind denitrification rates and to describe genes, transcripts, and enzymes responsible for the process. In order to reduce denitrification in arable soil, it is important to understand how different factors influence denitrification and how the denitrifier community structure is related to in situ activity. This chapter focuses on the impact of natural events as well as agricultural practices on denitrifying microorganisms.
1. Introduction In nature, nitrogen is present in different oxidation forms ranging from reduced compounds, for example, –3 in ammonia, to fully oxidized, for example, þ5 in nitrate (NO 3 ). The conversion between these different forms of nitrogen is mainly mediated by microorganisms (Fig. 1). The major pool of nitrogen is found Nitrogen fixation DNRA
NH+ 4 NO2
Nitrification
NO3
NH3
NO2
NH2OH
N2 NO
N2O
N2
Denitrification NH2OH
Anammox NH+ 4
N2H2 N2
Figure 1 Microbial processes contributing to the biological nitrogen cycle.
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in the atmosphere as dinitrogen gas. It can be converted into ammonia by symbiotic as well as free-living prokaryotes (Bacteria and Archeae) called diazotrophs, which can break the triple covalent bond of dinitrogen gas. This process is named biological nitrogen fixation. Ammonia itself can be oxidized into NO 3 during a two-step process called nitrification. The NO3 produced may be reduced either to dinitrogen gas via denitrification or by dissimilatory NO 3 reduction to ammonium (DNRA). These steps form the major parts of the inorganic nitrogen cycle in soils. Other reactions, like the anaerobic ammonia oxidation (Anammox), where nitrite (NO 2 ) is reduced to dinitrogen gas using ammonia as an inorganic electron donor (Mulder et al., 1995), have been shown to occur in several environments. Nevertheless, it has not been proven yet, that Anammox plays a major role in soil ecosystems (Jetten, 2001). Ammonia and NO 3 can be used by most living cells to produce organic forms of nitrogen, like proteins, amino acids, and so on, which are essential for life. During decay of biomass (plants, animals, fungi, bacteria), these organic nitrogen forms are degraded and transferred into ammonia again. Therefore, ammonia is the link between organic and inorganic nitrogen cycle. Together these processes form the global nitrogen cycle and microorganisms are essential for maintaining the balance between reduced and oxidized forms of nitrogen. In many soil ecosystems, nitrogen is often the limiting nutrient for plant growth and it is continuously lost by denitrification, soil erosion, leaching, and ammonia volatilization. Nitrogen losses through ammonia volatilization and denitrification are significant factors to consider when developing nitrogen management strategies in agricultural cropping systems. In particular, denitrification leads to nitrogen loss from soil, and results in the release of nitrous oxide (N2O), which is among the six greenhouse gases considered by the Kyoto protocol on climate change in 1997. Thus, increasing our knowledge of microbial communities involved in the nitrogen cycle is important, not only for increasing plant available nitrogen, but also for reducing the negative impact of agriculture on the environment. Denitrification can be defined as a microbial respiratory process during which soluble nitrogen oxides are used as alternative electron acceptor when oxygen is not available for aerobic respiration. It consists in the sequential reduction of NO 3 into dinitrogen in four steps concomitant with energy conservation (Fig. 2). This reduction of NO 3 by bacteria was discovered in the second-half of the nineteenth century by Gayon and Dupetit (1886). Substantial progress has been made during the last 20 years concerning the biochemistry and genetic of denitrification, which has been summarized in a number of comprehensive reviews (Berks et al., 1995; Philippot, 2002a; Zumft, 1997). Briefly, two types of molybdoen zymes catalyzing the first step of the pathway, the reduction of NO 3 to NO2 have been described: a membrane bound (Nar) and a periplasmic (Nap) NO 3 reductases. Both types of enzymes can be present in the same strain
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N2
- Nitrous oxide reductase reductase
(nosZ )
- Nitric oxide reductase
(norB)
- Quinol nitric oxide reductase
(qnorB)
- Cd1 nitrite reductase
(nirS)
- Cu nitrite reductase
(nirK )
N2O
NO
NO2− - Membrane bound nitrate reductase (narG) - Periplasmic nitrate reductase
(napA)
NO3−
Figure 2 The denitrification cascade with the different reductases and name of the genes encoding the corresponding catalytic subunits (in parentheses).
(Carter et al., 1995; Roussel-Delif et al., 2005). The reduction of soluble NO 2 into gaseous nitric oxide (NO), the key step in the denitrification cascade, can be catalyzed by evolutionary unrelated enzymes that are different in terms of structure and of prosthetic metals—a copper (NirK) and a cyto chrome cd1 (NirS) NO 2 reductase. In contrast to the NO3 reductases, bacteria carry either the copper or the cd1 NO2 reductase but the two enzymes are functionally equivalent (Glockner et al., 1993). Reduction of NO into nitrous oxide is also catalyzed by two types of enzymes: one NO reductase receives the electrons from cytochrome c or pseudoazurin (cNor) and the other from a quinol pool (qNor). The last step of the denitrification cascade, reduction of N2O into dinitrogen gas, is performed by the multicopper homodimeric N2O reductase (NosZ), which is located in the periplasm in Gram-negative bacteria.
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The general requirements for biological denitrification are: (1) the presence of bacteria possessing the metabolic capacity; (2) suitable electron donors such as organic carbon compounds; (3) anaerobic conditions or restricted O2 availability; and (4) presence of N-oxides (NO 3 , NO2 , NO, or N2O) as terminal electron acceptors. The process of denitrification is therefore generally promoted under anaerobic conditions, high levels of soil NO 3 , and a readily available source of carbon. In this chapter, we will highlight the agronomical and environmental importance of denitrification and give a brief overview of the methods used to assess denitrifier activity, diversity, and density. The activity and diversity of denitrifiers is discussed in relation to natural factors, plant effects in crop production, fertilization regimes, or use of pesticides.
2. Agronomical and Environmental Importance of Denitrification 2.1. Consequences of denitrification for agriculture Denitrification leads to considerable nitrogen losses in agriculture. The losses tend to increase with fertilization, and between 0% and 25% of the applied nitrogen can end up as nitrogen gas or N2O, thus limiting crop production (Aulakh et al., 1992; De Klein and Van Logtestijn, 1994; Mogge et al., 1999). Studies have shown that up to 340 kg N ha1 can be lost through denitrification during 1 year under extreme conditions, although values in the range 0–200 kg N ha1 year1 are more normal (Hofstra and Bouwman, 2005). The values obtained depend highly on the methods used to determine denitrification rates (Section 4.1). Models have estimated the total annual denitrification for the global agricultural area (excluding leguminous crops) to be 22–87 Tg nitrogen (Drecht et al., 2003; Hofstra and Bouwman, 2005). Intensively cultivated soils have higher denitrification activity compared with native noncultivated soils. Nevertheless, denitrification events in the field occur irregularly in time and space because of weather conditions, heterogeneity of soil conditions, and management practices. The highest rates are often measured in spring and fall, which indicates that soil water status is a strong controlling factor. Hence, flood-irrigated cropping systems are especially prone to denitrification and recovery of fertilized nitrogen is often poor (Aulakh et al., 2001; Mahmood et al., 2000, 2005). To minimize the nitrogen losses, the feasible option is to focus on agricultural practices. After compiling 336 datasets on denitrification measurements, Hofstra and Bouwman (2005) demonstrated that crop-type, fertilizer-type, and nitrogen application rate were the most significant management-related factors influencing denitrification in agricultural soils. These factors not only affect the
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nitrogen availability and the form of available nitrogen in soil, but also affect the type and amount of carbon available for denitrification.
2.2. Impact of denitrification on the environment and human health Denitrification together with nitrification are considered as the primary biological sources of N2O, which exhibits a global warming potential 300 times higher than that of carbon dioxide as defined by the Intergovernmental Panel on Climate Change (IPCC) and contributes up to 6% of the anthropogenic greenhouse effect (Cicerone, 1989). N2O also participates in depletion of the stratospheric ozone layer through stratospheric NO production (Tabazadeh et al., 2000; Waibel et al., 1999). N2O emission by denitrification is the net result of the balance between production and reduction of N2O by denitrifying bacteria. Soil ecosystems are the dominant sources of atmospheric N2O (Conrad, 1996), contributing to 70% (10 Tg year1) of the total annual global emission with about 6.3 Tg year1 from agricultural soils, animal production, and other agricultural activities (Mosier et al., 1998). From the preindustrial period to our days, the atmospheric concentration of N2O increased from 0.275 to 0.314 ppm with an actual increase rate of 0.3% per year. This has been attributed to the increased use of nitrogen fertilizers (Skiba and Smith, 2000). Only between 1960 and 1995, there was a sevenfold increase in fertilization (Tilman et al., 2002). The 1996 IPCC guidelines used a fixed N2O emission rate of 1.25% for all nitrogen applied as fertilizer (Houghton et al., 1996). However, studies suggested N2O emissions from agricultural soils might be twice as high as IPCC estimates (Giles, 2005). Denitrification is also of interest for nitrogen removal in agricultural drainage and runoff water, groundwater, wastewater, and drinking water, the latter being of a special concern for human health. The removal of nitrogen in the form of ammonia and NO 3 is effected through the biological oxidation of nitrogen from ammonia (nitrification) to NO 3, followed by denitrification. Nitrogen gas is then released to the atmosphere and thus removed from the water. High NO 3 concentrations in drinking water are toxic, especially to infants under 6 months. However, NO 3 itself does not normally cause health problems unless it is reduced to NO 2 by bacteria that live in the digestive tract. As NO 2 enters the blood stream, it reacts with hemoglobin to form methemoglobin, and oxygen transportation is blocked. This causes asphyxiation, a disease commonly called ‘‘blue baby syndrome’’ or methemoglobinemia. Nitrate in groundwater originates primarily from fertilizers, septic systems, and manure storage or application. Thus, fertilizer nitrogen that is not taken up by plants, volatilized, denitrified, or carried away by surface run-off leaches to the groundwater in the form of NO 3 . The World Heath Organization has stipulated a safe upper limit of 45 mg NO3 liter1 in drinking water for human consumption.
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3. Who are the Denitrifiers? 3.1. Denitrifiers and nitrate reducers Many soil prokaryotes can denitrify and exhibit a variety of reduction pathways for nitrogenous oxides. Both cultivation-dependent and -independent methods showed that the proportion of denitrifiers represent up to 5% of the total soil microbial community (Henry et al., 2004, 2006; Tiedje, 1988), thus outranking other functional groups involved in the N-cycle such as diazotrophs or nitrifiers. Some microorganisms produce only nitrogen gas as end denitrification product, while others give a mixture of N2O and nitrogen gas, and some only N2O (Stouthamer, 1988). In addition, a few microorganisms cannot reduce NO 3 and use NO2 as the first electron acceptor in the denitrification cascade. By contrast, some NO 3 -reducing bacteria reduce the produced NO into ammonium and not into NO. The 2 dissimilatory NO reduction into ammonium should be distinguished 3 from denitrification, even though it may produce nitrogenous gases as byproducts. Therefore, many NO 3 -respiring ammonium-producing isolates have been misidentified as denitrifiers. Accordingly, different criteria have been proposed to identify ‘‘true’’ denitrifiers and to distinguish them from the NO 3 -respiring, ammonium-producing bacteria (Mahne and Tiedje, 1995): (1) N2O and/or nitrogen gas must be the major end product of NO 3 or NO 2 reduction; and (2) this reduction must be coupled to an increased in growth yield increase that is greater than when NO 3 or NO2 simply served as an electron sink. Using these criteria, it is also possible to distinguish bacteria possessing only the NO reductase as a protection against exogenous or endogenous nitrosative stress (Philippot, 2005).
3.2. Denitrifying populations More than 60 genera of denitrifying microorganisms have been identified including archeae and fungi (Table 1). Consequently, the distribution of the denitrification trait among microorganisms cannot be predicted simply by the taxonomical affiliation. In addition, while distantly related microorganisms can denitrify, closely related strains can exhibit different respiratory pathways. For example, analysis of the ability to use NO 3 as alternative electron acceptor among a collection of fluorescent pseudomonads showed that strains were either denitrifiers, NO 3 reducers, or not capable to respire NO 3 (Clays-Josserand et al., 1995). Among the phygenetically diverse group of denitrifiers, it is interesting that several bacteria are also involved in other steps of the nitrogen cycle, such as nitrification or nitrogen fixation. Thus, ammonia-oxidizing strains belonging to either the Nitrosospira or Nitrosomonas genus have been shown to be capable to denitrify (Shaw et al., 2006). It is also worth to note that the newly discovered group of
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Table 1 List of archaeal, bacterial, and fungal genera for which at least one denitrifying strain has been characterized
Genus Archaea Haloarcula Halobacterium Pyrobaculum Bacteria Firmicutes Bacillus Paenibacillus Actinomycetes Corynebacterium Streptomyces Bacteroides Flavobacterium
Example of species
Source
marismortui denitrificans aerophilum
(Yoshimatsu et al., 2000) (Tomlinson et al., 1986) (Vo¨lkl et al., 1993)
azotoformans, stearotermophilus terrae
(Ho et al., 1993; Pichinoty et al., 1976b) (Horn et al., 2005)
nephridii thioluteus, sp.
(Har et al., 1965) (Che`neby et al., 2000; Shoun et al., 1998)
sp., denitrificans
(Horn et al., 2005; Pichinoty et al., 1976a) ( Jones et al., 1992)
Flexibacter canadiensis Aquifaceae Hydrogenobacter thermophilus Proteobacteria Alphaproteobacteria Agrobacterium sp. Azospirillum lipoferum Bradyrhizobium sp., japonicum Brucella melitensis Hyphomicrobium sp. Mesorhizobium loti Ochrobactrum anthropi Paracoccus pantotrophus Pseudovibrio denitrificans Rhizobium sp. Rhodobacter sphaeroides Rhodopseudomonas salustris Sinorhizobium meliloti Betaproteobacteria Acidovorax sp. Alcaligenes Achromobacter Aquaspirillum Azoarcus
faecalis sp. magnetotacticum tolulyticus, anaerobius
(Suzuki et al., 2006) (Che`neby et al., 2000) (Neyra et al., 1977) (Monza et al., 2006; van Berkum and Keyser, 1985) (Baek et al., 2004) (Sperl and Hoare, 1971) (Monza et al., 2006) (Kim et al., 2006) (Robertson and Kuenen, 1983) (Shieh et al., 2004) (Arrese-Igor et al., 1992) (Sabaty et al., 1994) (Kim et al., 1999) (Daniel et al., 1982) (Heylen et al., 2006; Schloe et al., 2000) (Vanniel et al., 1992) (Youatt, 1957) (Bazylinski and Blakemore, 1983) (Fries et al., 1994; Springer et al., 1998) (continued)
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Table 1
(continued)
Genus
Example of species
Source
Azonexus Azospira Azovibrio Burkholderia Chromobacterium Comamonas
caeni sp. sp. sp. sp. sp., denitrificans
Cupriavidus Dechloromonas Denitratisoma Kingella
necator denitrificans oestradiolicum denitrificans, sp.
Microvirgula Neisseria Nitrosomonas
aerodenitrificans sp. europaea, eutropha
(Quan et al., 2006) (Heylen et al., 2006) (Heylen et al., 2006) (Che`neby et al., 2000) (Grant and Payne, 1981) (Gumaelius et al., 2001; Patureau et al., 1994) (Pfitzner and Schegel, 1973) (Horn et al., 2005) (Fahrbach et al., 2006) (Grant and Payne, 1981; Snell and Lepage, 1976) (Patureau et al., 1998) (Grant and Payne, 1981) (Poth and Focht, 1985; Zart and Bock, 1998) (Springs et al., 2004) (Stamper et al., 2002) (Magnusson et al., 1998) (Tarlera and Denner, 2003) (Schloten et al., 1999; Song et al., 1998) (Hole et al., 1996)
Ottowia Ralstonia Rubrivivax Sterolibacterium Thauera
thiooxydans basilensis sp. denitrificans aromatica, mechernichensis Thibacillus denitrificans Gammaproteobacteria Halomonas desiderata, campisalis Luteimonas Pseudomonas
mephitis fluorescens, sp.
Pseudoxanthomonas taiwanensis Shewanella putrefaciens, denitrificans Stenotrophomonas nitritireducens Thioalkalivibrio denitrificans Zobellella denitrificans, taiwanensis Epsilonproteobacteria Nitratifractor salsuginis Nitratiruptor tergarcus Thiomicrospira denitrificans Eukaryota Fungi Fusarium oxysporum
(Berendes et al., 1996; Mormile et al., 1999) (Finkmann et al., 2000) (Gamble et al., 1977; Philippot et al., 2001) (Chen et al., 2002) (Brettar and Hofle, 1993) (Finkmann et al., 2000) (Sorokin et al., 2001) (Lin and Shieh, 2006)
(Nakagawa et al., 2005) (Nakagawa et al., 2005) (Brettar et al., 2006)
(Tanimoto et al., 1992)
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ammonia oxidizers within the chrenoarcheota, possess the nirK gene encoding the denitrification NO 2 reductase (Treush et al., 2005), which suggests that they can perform at least one step of the denitrification pathway. Similarly, many nitrogen-fixing rhizobia can denitrify (Daniel et al., 1980, 1982; O’Hara and Daniel, 1985; van Berkum and Keyser, 1985). Even though the diversity of denitrifiers is very high, it is likely that several yet unknown microorganisms in nature contribute to the overall denitrification. As an example, Risgaard-Petersen et al. (2006) demonstrated that a benthic foraminifer Globobulimina pseudospinescens accumulates intracellular NO 3 stores, which can be respired to dinitrogen gas.
4. Assessing Denitrifiers Density, Diversity, and Activity 4.1. Measuring denitrification and N2O emissions Since denitrification is responsible for the loss of available NO 3 for plants, many methods have been developed to estimate denitrification rates in soils. The most basic approach calculates denitrification losses from the nitrogen balance budget. However, other processes such as leaching can lead to NO 3 losses, which result in an overestimation of denitrification. An alternative approach is based on the determination of the amount of N2O and/or dinitrogen gas emitted by denitrification using various methods described in the following sections. 4.1.1. Acetylene inhibition method In this approach, acetylene (C2H2) is used to inhibit N2O reduction so that total denitrification losses (N2 þ N2O) can be measured as N2O. The blockage of N2O reduction in soil is obtained in an atmosphere containing 0.1–10% (v/v) C2H2. This method developed independently by Balderston et al. (1976) and Yoshinari et al. (1977) has been a revolutionary key step in estimating denitrification rates and has paved the way for hundreds of studies measuring denitrification rates in situ (Stevens and Laughlin, 1998; Tiedje et al., 1989). The C2H2 inhibition method has been applied to soil slurries and cores (Ryden et al., 1987), as well as in field measurements using closed chambers (Ryden and Dawson, 1982). For the latter, chambers are placed on the soil surface and C2H2 is injected, which results in the accumulation of N2O in the headspace of the chamber. The production of N2O is estimated by analyzing gas samples from the headspace with a gas chromatograph, preferably equipped with an electroncapture detector. The method has some limitations related to the diffusion of C2H2 in soil, C2H2 degradation by bacteria, and inhibition of other processes, for example, nitrification (Keeney, 1986; Rolston, 1986).
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A widely used ex situ assay based on C2H2 inhibition has been developed to measure the N2O production rate from the pool of active or activateddenitrification enzymes in a sample at the time of sample collection (Smith and Tiedje, 1979b; Tiedje, 1982). This assay, termed the denitrifying enzyme assay, is performed by incubating soil slurries under nonlimiting denitrifying condition (i.e., no oxygen, saturating NO 3 concentration, and addition of a surplus of electron donors). To avoid de novo enzyme synthesis, samples are either incubated during a short period of time or in presence of chloramphenicol, which blocks protein synthesis. The rate of N2O production, which is positively correlated to the amount of denitrification enzymes in the samples, is then measured. As an alternative, the assay can be used without addition of chloramphenicol and the denitrification rate can be estimated by nonlinear regression (Pell et al., 1996). These assays can be used to compare the effect of agronomical treatments on denitrification. However, it does not provide information on field rates. 4.1.2. The isotope N-labeled methods Denitrification activity can be determined using stable nitrogen isotopes in both laboratory incubations and in field measurements. With this approach, one or several 15N-labeled nitrogen compounds, such as NO 3 , ammonium, fertilizers, or plant litter, are added to the soil. The subsequent production dinitrogen and N2O by denitrification is measured by quantifying the increase of 15N-labeled gases by mass spectrometry. As with the C2H2 inhibition method, closed chambers are used to estimate denitrification activity in the field (Nason and Myrold, 1991). This method is limited by the high cost of 15N and the need to add nitrogen in the soil. Methods based on the use of 13N have also been described (Smith et al., 1978; Tiedje et al., 1979), but these cannot be applied in the field (Tiedje et al., 1989).
4.2. Resolving diversity of denitrifiers Over several decades, diversity of denitrifiers in soil was studied by isolating bacterial strains. Basically, dilutions of soil suspension were spread on various agar medium supplemented with NO 3 . After incubation under anaerobic conditions, isolated colonies were characterized using phenotypic or metabolic tests, and later on by using molecular approaches (Che`neby et al., 2000, 2004; Garcia, 1977; Pichinoty et al., 1976a,b). The most complete survey was reported by Gamble et al. (1977). From 19 soils, 3 freshwater lake sediments, and oxidized poultry manure, around 1500 bacteria were isolated and characterized. The dominant denitrifier populations in most samples were related to Pseudomonas fluorescens. However, these isolation-based techniques are limited by the fact that only a fraction of the bacterial community is cultivable. Research on microbial diversity was completely revolutionized 20 years ago by the application of molecular methods to
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explore microorganisms in the environment without including a cultivation step. These culture-independent molecular approaches have then been used to assess the composition of denitrifier communities in soils. The most frequently used approaches today to target denitrifiers in soil start with extraction of nucleic acids (DNA or RNA) from the soil (Fig. 3). The extracted nucleic acids are then purified and amplified by PCR using primers targeting the denitrifier community. Since the ability to denitrify is sporadically distributed both within and between different genera, and cannot be associated with any specific taxonomic group, a 16S rRNAbased approach is not possible to target denitrifiers. However, in the late 1990s, the genes nirS and nirK encoding the key enzymes of the denitrification pathway were first used as molecular markers to describe the diversity of the denitrifier community (Braker et al., 1998; Hallin and Lindgren, 1999). Since then, this approach has been extended to all the denitrification genes (Braker and Tiedje, 2003; Flanagan et al., 1999; Philippot et al., 2002; Scala and Kerkhof, 1999). Amplification of extracted nucleic acids using primers targeting the denitrification genes is actually the most common way to analyze denitrifier communities (Bothe et al., 2000; Hallin et al., 2007; Philippot and Hallin, 2005, 2006). The sequence polymorphism of the obtained mixed pool of PCR amplicons should reflect the composition of the denitrifier community in the studied environment. The mixture of PCR amplicons is analyzed by separating them based on their nucleotide sequence polymorphism using either clone libraries combined with sequencing or by fingerprinting techniques (Bothe et al., 2000; Hallin et al., 2007; Philippot and Hallin, 2006). The most commonly used fingerprinting techniques to study denitrifier communities are terminal restriction fragment length polymorphism (T-RFLP), restriction fragment length polymorphism (RFLP), and denaturing gradient gel electrophoresis (DGGE). These cultivation-independent approaches have limitations related to the nucleic acids extraction, the choice of PCR primers, and the PCR itself (Martin-Laurent et al., 2001; Philippot and Hallin, 2005).
4.3. Quantification of denitrifiers Denitrifiers were first quantified by plating serial dilutions of soil suspension and counting true denitrifying isolates based on their ability to reduce NO 3 into gaseous nitrogen production. However, the most common way to count denitrifiers using a cultivation technique is to apply the most probable number (MPN) method (Volz, 1977). Serial dilutions of soil suspension are inoculated into anaerobic replicates medium tubes amended with NO 3 and C2H2. Dilution tubes are then scored positive when N2O is detected, and results are then converted into cell numbers copy using the McCrady table. These methods refer only to microorganisms that can be cultivated and therefore underestimate the actual number of denitrifiers in the sample.
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To circumvent this problem, molecular methods have also been developed to quantify this functional community (Cho and Tiedje, 2002; Gruntzig et al., 2001; Mergel et al., 2001; Michotey et al., 2000; TaroncherOldenburg et al., 2003; Tiquia et al., 2004; Ward et al., 1993). Two reviews of these quantitative methods have been published (Philippot, 2006; Sharma et al., 2007). Today, quantitative PCR is the main method used in soil environments (Henry et al., 2004, 2006; Kandeler et al., 2006; LopezGutierrez et al., 2004; Qiu et al., 2004) (Fig. 3) with the same bias as for the cultivation-independent approach for resolving community structure outlined earlier. 1.000 E+1 1.000
Density analysis
1.000 E-1 1.000 E-2 1.000 E-3 1.000 E-4 1.000 E-5 0
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Competitive-PCR
Quantitative-PCR
Nucleic acids extraction
Structure analysis
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Fingerprint analysis
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T-RFLP
RFLP
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RFLP
Figure 3 Methods used to assess diversity and density of denitrifiers with a PCR-based approach.
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5. Natural Factors Causing Variations in Denitrification 5.1. Temperature and water Both the overall denitrification rates and the proportions of N2O and dinitrogen gas produced by denitrifying microbes can vary depending on numerous environmental factors, such as pH, carbon, NO 3 , and NO2 availability, soil moisture, pore structure, aeration, temperature, freezing– thawing, and drying–wetting events. Several of these are natural factors influenced by climatic conditions that cannot be managed. In addition, they are not constant, but show large variation over the vegetation period as well as between field sites. The estimated nitrogen losses are therefore highly variable in time and space. Emissions of N2O and dinitrogen show no consistent seasonal pattern. In some studies, the largest N2O emissions were recorded during spring (Kaiser and Heinemeyer, 1996; Parsons et al., 1991; Ryden, 1985), in others during spring and autumn (Ambus and Christensen, 1995; De Klein and Van Logtestijn, 1994), or in summer (Bremner et al., 1980; Cates and Keeney, 1987). The difference in the results could not be related to environmental factors and management practices. A better understanding of factors contributing to variability of denitrification activity would be helpful to improve estimations and modeling of nitrogen fluxes by denitrification. Soil temperature and soil water content are known factors that affect gaseous nitrogen losses and the N2O/N2 ratio. Under constant laboratory conditions, this ratio increased exponentially with increasing soil temperature (Maag and Vinther, 1996). However, the ratio was strongly influenced by soil type, although these data could not be confirmed by field measurements. Whereas Bailey (1976) and McKeeney et al. (1979) found a positive correlation between soil temperature and denitrification activity, others observed no relationship with temperature (Focht, 1974; Lensi and Chalamet, 1979). The reason might be the lower water content caused by increased plant transpiration rates at higher temperatures, which leads to a water deficiency. Under laboratory conditions, similar to the effects of increasing temperature, the overall denitrifying activity and N2/N2O ratio increased with increasing soil water content (Colbourne and Dowdell, 1984; Vinter, 1984). This was also confirmed in a pasture after harvest (Rudaz et al., 1997). Linked to soil water content is oxygen availability. Hochstein et al. (1984) showed that soil oxygen concentrations below 5% resulted in denitrification being the main microbial respiratory process when NO 3 was available. In addition, at 10% oxygen concentration and moisture content between 40% and 60%, denitrification was the main source of emitted N2O.
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Water content depends on the pore structure of the soil, which in turn is affected by soil type, organic matter content, and land use. Bakken et al. (1987) demonstrated that the pore space structure appears to be the major factor explaining the difference in mean denitrification rates by comparing pasture and cropped soil. In the field, Bijay-Singh et al. (1989) found higher actual denitrification in cropped soil than in pasture, despite similar NO 3 contents. They explained their results as the consequence of better drainage in the pasture soil, due to the higher porosity of this soil. Complementary measurements after the application of various amounts of water showed denitrification activity in pasture soil was higher than denitrification in cropped soil only at water suctions greater than 5.5 kPa (Bijay-Singh et al., 1989). In contrast, potential denitrification has often been reported to be higher in pasture than in cropped soil (Bijay-Singh et al., 1989; Lensi et al., 1995; Sotomayor and Rice, 1996).
5.2. Freeze–thaw cycles 5.2.1. Freeze–thaw effects on nitrous oxide emissions Christensen and Tiedje (1990) were the first to report peak N2O emissions from arable soils in spring during thaw periods. Emissions of carbon dioxide and N2O and uptake of methane throughout the snow-covered period even at temperatures near 0 C were later reported (Sommerfeld et al., 1993). In order to decide whether N2O production can be attributed also to nonmicrobial processes in soil, emissions from a g-ray sterilized and a nonsterilized soil were compared in a laboratory experiment, where the freezing and thawing cycles were simulated. The results clearly indicated that microbial processes were responsible for N2O production in thawing and even frozen soils (Ro¨ver et al., 1998). Therefore, efforts have been done to investigate the effects of freezing and thawing cycles on microbial denitrification, and to understand the mechanisms behind. Sehy et al. (2003) first demonstrated the importance of denitrification for nitrogen losses during winter in arable soil. They separated the 12 months of investigation into the growing season (March to November) and the winter period (December to February). Independent of the amount of applied fertilizer, about 70% of the annual N2O amounts was emitted during the winter period. The temporal changes of the N2O emission rates were correlated to changes in soil temperature. Similarly, Do¨rsch et al. (2004) found persistently high N2O emissions in arable soil with peak emissions during midwinter thawing, diurnal freezing–thawing, and spring thaw. Low and stable temperatures below the insulating snow or ice cover, in contrast, decreased N2O emissions. Several other field studies in the temperate regions also reported high N2O emissions from agricultural soils during freeze–thaw periods reaching 20–70% of the annual budget (Flessa et al., 1995; Nyborg et al., 1997; van Bochove et al., 1996, 2000; Wagner-Riddle et al., 1997).
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Nevertheless, a few studies have also reported that moderate freeze–thaw fluctuations had little impact on nitrogen dynamics and N2O emissions in soils (Grogan et al., 2004; Neilsen et al., 2001). There is considerable debate on which factors could be critical controllers of winter N2O emissions from arable soils. However, most authors state that emissions during winter are related to the release of nutrients. Christensen and Christensen (1991) could show that soluble carbon, applied as plant extract, was necessary to induce N2O production during freezing and thawing events. Therefore, plant residues from catch crops and green manure may play an important role in the regulation of N2O emissions in winter, since frost enhances the release of organic compounds from plant residues. Additionally, freeze–thaw events may result in transient pulses of carbon and nitrogen due to disruption of soil aggregates (Christensen and Christensen, 1991; Mu¨ller et al., 2002) and lysis of microorganisms (Schimel and Clein, 1996; Skogland et al., 1988). Mu¨ller et al. (2002) showed that the increased ammonium and NO 3 concentrations during freezing were associated to peak N2O emissions during the following thawing period. Enhanced oxygen consumption during degradation of plant residues combined with a high water content of the thawing soil increases the anaerobic volume, thus enhancing denitrification. The freeze–thaw-induced emission of N2O could thus be a straightforward result of enhanced denitrification. N2O may also be produced by microorganisms in unfrozen water films on the soil matrix during freezing. Several authors showed that an ice layer covering the unfrozen water film could be a diffusion barrier, which reduces oxygen supply to the microorganisms and partly prevents the release of N2O to the air (Burton and Beauchamp, 1994; Goodroad and Keeney, 1984; Teepe et al., 2001). Nitrification could also be of significance for N2O emissions during winter. It has been demonstrated that freeze–thaw cycles enhances nitrogen mineralization, which results in the release of substrate for ammoniaoxidizing bacteria (Deluca et al., 1992). Lowered oxygen availability during freeze–thaw-induced respiration could also induce higher N2O emissions from nitrifiers, since the N2O/(NO 3 þ NO2 ) ratio of nitrification increases sharply in response to oxygen limitation (Davidson, 1991; Dundee and Hopkins, 2001; Goreau, 1980). However, it has been demonstrated that only a few percent of the measured N2O originate from nitrification. Denitrification was the main N2O source at various oxygen concentrations investigated in freeze–thaw-affected soil (Ludwig et al., 2004; M rkved et al., 2006). 5.2.2. Freeze–thaw effects on denitrifier communities Although microbial denitrification is believed to be the major source of N2O during freeze–thaw events, few have analyzed the denitrifier communities involved. Actually, little is known about the significance of the
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denitrifier community composition for N2O emissions in general, since most of the work conducted has focused on gas and soil analysis. Freeze– thawing effects on total bacterial community structure are contradictory. Eriksson et al. (2001) observed a change in ribosomal internal spacer analysis patterns during freeze–thaw events, whereas Koponen et al. (2006) concluded that neither microbial biomass nor community structure was affected in boreal soils. It has been postulated that the relative activity of N2O reductase can be lowered at near-freezing temperatures (Holtan-Hartwig et al., 2002b; Melin and No¨mmik, 1983), possibly resulting in high N2O/(N2 þ N2O) ratios in soil during thawing. A high N2O/(N2 þ N2O) ratio could also be a ‘‘postfreezing trauma’’; the N2O reductase appears to be more vulnerable to perturbations than the other denitrification enzymes, and if this holds for frost damages, it would result in a higher proportion of produced N2O to total denitrification after freezing (Do¨rsch and Bakken, 2004; HoltanHartwig et al., 2002; Melin and No¨mmik, 1983). Nevertheless, how specific enzymes involved in denitrification are influenced by freezing and thawing is still not answered. Sharma et al. (2006) investigated the mRNA levels of genes encoding the periplasmic NO 3 reductase gene (napA) and cytochrome cd1 NO2 reductase (nirS) in the upper horizon of a grassland soil during thawing in a laboratory experiment. By using a MPN-based reverse transcriptase PCR approach they could show that high transcript levels occurred for both genes 2 days after thawing had begun, followed by a decrease. The peak of N2O production coincided with the peak for napA and nirS transcripts, and it timely shifted after 2 days. In the same study, the napA and nirS genotype diversity was analyzed. Interestingly, DNA-based profiles showed no change in banding patterns, whereas those derived from cDNA showed a clear succession of the genotypes, with the most diverse community structure at the time point of the highest gene expression.
5.3. Dry–wet cycles Similar to freeze–thaw cycles in soil, dry–wet cycles can enhance N2O emissions. Prieme´ and Christensen (2001) compared the effects of drying– wetting and freezing–thawing cycles on the emission of N2O, carbon dioxide, and methane from intact soil cores from farmed organic soils. During the first week, following wetting or thawing, up to a 1000-fold increase in N2O emission rates were recorded from the cores. The total N2O emission ranged between 3 and 140 mg N–N2O m2, and between 13 and 340 mg N–N2O m2 due to the first wetting and thawing event, respectively. Nevertheless, the emission rates declined after two successive freeze– thaw events. Many other studies have also documented differences in the rate of denitrification following wetting (Ambus and Lowrance, 1991;
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Gilliam et al., 1978; Groffman and Tiedje, 1989; Rice and Tiedje, 1982; Robertson and Tiedje, 1985, 1988; Sexstone et al., 1986). Some studies have also noted denitrification differences between the wet-up and dry-down phases of soil moisture following rainfall events (Gilliam et al., 1978). Bergsma et al. (2002) showed that a short wet-treatment significantly decreased the relative amount of N2O emitted from cropped soil compared with a long wet-treatment, while no effect of moisture history was seen in a successional agrosystem. The authors hypothesized that these differences in N2O production were due to selection of denitrifiers with enhanced capacity for enzyme maintenance at lower levels of NO 3 , such as found in the successional soil. Others later confirmed differences in denitrifier community composition in the successional and cropped soil at this site (Stres et al., 2004). Denitrification enzymes were also more sensitive to oxygen in the cropped soil and N2O activity was higher in the successional soil (Cavigelli and Robertson, 2000). Soil moisture history seems to be important for denitrification. If denitrification enzymes are induced differentially in response to wetting, then both the overall rate of denitrification as well as the relative amount of N2O will differ substantially among ecosystems.
6. Denitrification in the Rhizosphere of Crops 6.1. Crops as a factor influencing denitrifiers The rhizosphere is the volume of soil influenced by plant roots (Hiltner, 1904). The growth and activity of the root system induce significant modifications in the physicochemical and biological properties of the soil surrounding the roots, which correspond to the so-called rhizosphere effect. It is well known that the major factors regulating denitrification: carbon, oxygen, and NO 3 can be modified in the rhizosphere of plants. Thus, carbon compounds, which can be used as electron donor by denitrifiers, are released by plants roots in the surrounding soil through rhizodeposition. The effect of plants on oxygen and NO 3 concentration is more complex. Oxygen concentration can be lowered in the rhizosphere by respiration of the roots and microorganisms. On the other hand, consumption of water by plant roots increases soil gas exchange and oxygen concentration. Some plants, such as rice, also transport oxygen from the air down to the soil in water-saturated soil. Finally, when roots grow and penetrate the soil, they can modify soil compaction, which affects oxygen diffusion. Nitrate is used by both plants and microorganisms and the competition for NO 3 is therefore high in the rhizosphere during the growing season. However, plants can also potentially provide NO 3 for denitrification when organic matter present in root exudates is mineralized. Moreover, during plant senescence and litter decomposition in fall and winter, nitrogen becomes bioavailable and can be denitrified. Overall, factors
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regulating denitrification in the rhizosphere are strongly interwoven and the stimulating effect of root-derived carbon is only observed under nonlimiting concentrations of NO 3 and oxygen. It is therefore not possible to state that plant roots always stimulate denitrification. 6.1.1. Effect of crops on the denitrification activity Comparison of denitrification rates between planted and nonplanted soil in the field or in incubation experiment has been the most common approach to investigate the influence of crops on this process. Early reports showed enhanced denitrification rates in the rhizosphere compared with bulk soil (Smith and Tiedje, 1979a; Stefanson, 1972; Woldendorp, 1962). The key role of plant on denitrification has later been confirmed in several studies, although the mechanisms responsible for the higher denitrification rates are still not clear. Among the agricultural plants studied, barley (Hordum vulgare) has received the greatest attention so far. Klemedtsson et al. (1987) observed that denitrification rates in pots planted with barley increased with time along with increased root biomass. Stimulation of the denitrification rates in planted pots was 2–22 times compared with the unplanted pots. Similar results were reported by H jberg et al. (1996) who observed an average NO 3 reduction and denitrification rates in the rhizosphere of barley 1.8 times higher than in the bulk soil, with the most pronounced increase of 7 times. By using monoclonal antibodies against the copper nitrite reductase, Metz et al. (2003) clearly showed the presence of active enzymes in the rhizosphere of wheat. Vinter et al. (1984) demonstrated that this increase of denitrification in the barley rhizosphere was positively correlated with soil NO 3 concentration. Their results showed that for fertilizer applied to barley at 30 kg N ha–1, the denitrification rate increased 2.5 times while a fivefold increase was observed in field plots receiving 150 kg N ha–1. These results were consistent with those of Mahmood et al. (1997), who carried out a field experiment to examine the –1 effect of maize plants on denitrification. At low soil NO 3 levels (1–4 mg N g dry soil), the presence of maize plants resulted in a nearly 50% increase in –1 dry soil) the denitrification, whereas at higher NO 3 levels (7–19 mg N g observed increase due to plants was 2.5 times. The combined effect of plant roots and NO 3 concentration on denitrification was first pointed out by Smith and Tiedje (1979a). They found that denitrification was lower in planted than in unplanted soil when NO 3 concentration was low (0.002 g 1 dry soil), while at higher NO concentration (0.1 g NO –N NO –N kg 3 3 3 kg1 dry soil) the presence of plants increased denitrification. Qian et al. (1997) also reported higher denitrification rates in the unplanted soil compared with planted soil at late maize growth stages when the amount of NO 3 was limiting in the planted soil. These neutral or negative effects of plant roots on denitrification were attributed to NO 3 depletion around the roots.
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It has also been reported that the rhizosphere effect on denitrification was associated with air-filled porosity (Wollersheim et al., 1987). At a low moisture tension, Bakken (1988) observed a tenfold increase in the denitrification rate in the planted soil compared with the unplanted soil. At medium or high moisture tension, the plants had no or even a negative effect on denitrification. Similarly, Prade and Trolldenier (1988) reported that the rhizosphere effect on denitrification was confined to air-filled porosity lower than 10–12% (v/v). Thus, the lack of stimulation on denitrification in the rhizosphere at nonlimiting NO 3 concentrations reported by Haider et al. (1985) was attributed to a high air-filled porosity in both planted and unplanted pots. Carbon, the third factor regulating denitrification, is probably responsible for the stimulating effect of plants on denitrification activity. Several investigators have demonstrated the influence of different organic substrates on denitrification. Denitrification was correlated with soluble organic matter (Bijay-Singh et al., 1988; Burford and Bremner, 1975; Cantazaro and Beauchamp, 1985; McCarty and Bremner, 1993) and easily mineralizable carbon (Bijay-Singh et al., 1988). The release of organic compounds by living roots can directly affect denitrification rates by providing an additional source of electron donor, but also indirectly by increasing microbial activity, which lowers the oxygen concentration. This amount of carbon released by roots into the soil can be up to 20% of photosynthetically fixed carbon during the vegetation period (Hu¨tsch et al., 2002; Nguyen, 2003). The nature of the root-derived carbon is highly variable (mucilage, exudates, root cap cells, and so on). The mucilage is composed of highmolecular-weight polysaccharides, mainly arabinose, galactose, fucose, glucose, and xylose, and up to 6% is proteins. In contrast, exudates are low-molecular-weight compounds released passively from roots such as sugars, amino acids, and organic acids. As expected, daily addition of 70 mg C g–1 dry soil of maize mucilage to an agricultural soil increased denitrification 2.8 times compared with water addition (Mounier et al., 2004). Similarly, daily addition at a rate of 150 mg C g–1 dry soil of different mixtures of amino acids, organic acids, and sugars mimicking maize root exudates greatly stimulated denitrification rates (Henry et al., unpublished data). In addition, several investigations have shown that denitrification rates were also positively related to the distribution of fresh plant residues in the soil profile (Aulakh et al., 1984, 1991; Cantazaro and Beauchamp, 1985; Christensen and Christensen, 1991; Parkin, 1987). 6.1.2. Effect of crop on the denitrifier community In contrast to denitrification activity, there have been fewer studies of the effect of plant on the denitrifier community. Vinther et al. (1982) reported some early estimates of the diversity and the density of denitrifiers in agricultural soils under continuous barley cultivation. Counts of denitrifiers
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performed using the most-probable-number method with NO 3 agar broth as growth medium revealed densities ranging between 103 and 106 bacteria g1 of dry soil, which represented less than 1% of total bacteria. In contrast, NO 3 reducers counts for less than 10% of total viable count. Identification of denitrifying isolates based on selected physiological and morphological properties showed that numerically predominant denitrifiers belonged to Pseudomonas spp., Alcaligenes sp., and Bacillus sp. The effect of plant roots on the taxonomic diversity of denitrifiers has further been investigated by isolating denitrifiers from unplanted or maize planted soil in a 3-month incubation experiment (Che`neby et al., 2004). Density of denitrifiers was 2.4 106 and 1.6 107 cells g1 of dry soil in the unplanted and planted soil, respectively. A total of 3240 NO 3 -reducing isolates were obtained and 188 of these isolates were identified as denitrifiers based on their ability to reduce at least 70% of the NO 3 to N2O or N2. Comparison of the distribution of the denitrifying isolates between planted and unplanted soil showed a difference in the composition of the denitrifier community with an enrichment of phylogenetically Agrobacterium-related denitrifiers in the planted soil. In addition, these predominant Agrobacterium-related isolates from the rhizosphere soil were not able to reduce N2O while dominant isolates from the unplanted soil emit N2 as end denitrification product. Direct molecular approaches have recently been applied to investigate the effect of maize on NO 3 reducers community performing the first step of the denitrification pathway. The narG gene encoding the membrane-bound NO 3 reductase was used as molecular marker to analyze the composition of the NO 3 reducers community from planted and unplanted pots after 3 months of repeated maize culture. A shift in the community composition between unplanted and planted soils was reported without significant modification of the diversity indices (Philippot et al., 2002b). Clone library analysis revealed that most of the dominant sequences in the planted soil were related to narG from the Actinomycetes suggesting a specific selection ` neby of NO 3 -reducing actinobacteria by the maize roots. In contrast, Che et al. (2003) detected a reduction of the reciprocal Simpson’s diversity index in the maize planted soil compared with the unplanted soil, but without any major modification of the composition of the NO 3 -reducing community in another soil type. The results from these two studies suggest that the rhizosphere effect on the structure of the denitrifier community is strongly dependent on the soil type. Several studies aiming at sorting out the relative importance of plant and soil confirmed that these two factors might act simultaneously in determining the composition of the indigenous soil microbial community (Clays-Josserand et al., 1999; Costa et al., 2006; Marschner et al., 2004; Wieland et al., 2001). In two studies, effort has been devoted to disentangle the mechanism of the rhizosphere effect by investigating the influence of the two major rhizodeposits, mucilage and exudates, on the genetic structure of denitrifiers
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(Henry et al., unpublished data; Mounier et al., 2004). Analysis of the structure of the denitrifier community by direct molecular approaches revealed only minor changes after mucilage amendment (Mounier et al., 2004). Similarly, the addition of sugar, amino acids, and organic acids mimicking maize exudates resulted in minor changes in the structure and the density of the denitrifier community (Henry et al., unpublished data). Even though root-derived carbon can stimulate denitrification activity, it does not seem to be an important driver of the denitrifier community structure in soil. However, the community structure of the active members of the denitrifying community might be influenced by root exudates, but this has not yet been clarified. 6.1.3. Denitrification provides a selective advantage in the rhizosphere Since most of denitrifiers are chemoheterotrophs, the increase of denitrifier density together with total microbial density observed in the rhizosphere was mainly attributed to the higher availability of organic substrates in the root vicinity. However, it has been suggested that the ability to grow by respiring nitrogenous compounds when oxygen is limited could be a selective advantage for denitrifiers in the rhizosphere. Thus, using DNA probes for the gene encoding the NO 2 and N2O reductase, von Berg and Bothe (1992) found that the denitrifier to other heterotrophic organism ratio was increased near the roots. Such influence of plants on the distribution of denitrifying abilities has also been reported by Clays-Josserand et al. (1995), who observed that the proportion of denitrifying pseudomonas isolates gradually increased in the root vicinity of tomato. To demonstrate that this selection of denitrifiers in the rhizosphere was due to ability to respire nitrogenous and not to other traits, the competitive abilities of denitrifying strains in the rhizosphere have been compared with those of their isogenic nondenitrifying mutants. Mutants unable to synthesize either the membrane-bound NO 3 reductase, the cd1 NO2 reductase, or the copper nitrite reductase were outcompeted by the denitrifying wildtype strains in the rhizosphere of maize demonstrating that denitrification itself could provide an advantage for root colonization (Ghiglione et al., 2000; Philippot et al., 1995).
6.2. Impact of crop species, crop cultivars, and transgenic plants Because both shoot and root properties, for example, different litter types and roots architecture, and the amount and composition of root exudates are varying among plant species and cultivars (Hu¨tsch et al., 2002), it has been hypothesized that effect of plants on microorganisms differ depending on plant species or cultivars. Therefore, in the last decade many studies were
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performed to prove this hypothesis. Most were based on 16S rRNA approaches, which make it impossible to relate any changes in the microbial community structure to functions. Only few attempts were made to use functional genes to measure possible impacts of crop species or cultivars on microorganisms involved in nitrogen cycling. 6.2.1. Rhizosphere effect on denitrification depends on crop species Effects of crop species or cultivars have mainly been investigated on denitrification activity rather than on the diversity of denitrifiers. Crush (1998) reported a tendency for higher potential denitrification rates in association with bigger root mass in a lysimeters study with various forage plants. Differences in the denitrification rates between small grains (barley, wheat, and oats) and grasses were also reported by Bakken (1988). Since legume plants associated with nitrogen-fixing bacteria can be used as substitute for mineral fertilizers, several authors studied whether their cultivation affect the nitrogen cycle processes. Using the C2H2 inhibition technique on intact soil cores sampled during 2 years in a field, Svensson et al. (1991) reported significant differences between plant species with higher denitrification rates with lucerne (Medicago sativa L.) than with barley (Hordeum disticum) and grass ley (Festuca pretensis Huds.). Larger denitrification rates under legumes than other plants were also reported by other studies (Kilian and Werner, 1996; Scaglia et al., 1985). The higher positive effect of legume on denitrification rates was observed not only with living plants but also during their decomposition process. Aulakh et al. (1991) and McKenney et al. (1993) showed higher denitrification rates in soil amended with legume residues than in soil amended with grass, corn, or wheat residues. However, lower denitirification rates were observed with clover than with small grains or grasses (Bakken, 1988). It has been hypothesized that the higher denitrification rates caused by legumes could be due to their symbioses with denitrifying Rhizobiacaea. Thus, several studies reported that denitrification was very common in rhizobia (Asakawa, 1993; Daniel et al., 1980, 1982; O’Hara and Daniel, 1985; Tiedje, 1988; van Berkum and Keyser, 1985; Zablotowicz et al., 1978) and that many strains can denitrify both as nodule bacteroids and in the free-living state (Arrese-Igor et al., 1992; Garcia-Plazaola et al., 1995). Accordingly, Kilian and Werner (1996) showed that mean denitrification was increased fourfold in plots of the nitrogen-fixing bean Vicia alba compared with nonnodulated V. alba mutant. On the other hand, GarciaPlazaola et al. (1993) suggested that even with optimal conditions for denitrification and the highest rhizobial populations found in agricultural soils, the contribution of Rhizobiacaea to the total denitrification was virtually neglectable as compared with other soil microorganisms. The fact that different legume plants were analyzed may explain these contrasting results. Since the symbiosis between rhizobia and legume plants is highly specific,
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different rhizobial strains, which can exhibit contrasted denitrification abilities, are selected according to the legume species. This hypothesis is supported by the work of Sharma et al. (2005) who studied the diversity of transcripts of the NO 2 reductase in the rhizosphere of three different legumes: Vicia faba, Lupinus albus, and Pisum sativum. A significant plantdependent effect on the transcripts was observed, suggesting that the active denitrifiers were different in the rhizosphere of three legumes. The denitrifier community structure, based on the DNA analysis of nirK and nirS genes, was not as variable between the different plant rhizospheres, indicating a stable denitrifier community. Similar results were also found by Deiglmayr et al. (2004). When investigating the effect of Lolium perenne and Trifolium repens on the NO 3 reducer community, based on DNA analysis of narG, no plant species effect was observed. In contrast, with a similar approach, Patra et al. (2006) observed an effect of the plant species on both the structure and the activity of the denitrifier community among Arrhenatherum elatius, Dactylis glomerata, and Holcus lanatus in grasslands. 6.2.2. Impact of transgenic crops Transgenic crops offer agronomic advantages, such as improved yield, improved product quality, herbicide tolerance, or insect resistance, over their corresponding nontransgenic wild-type cultivar. These modifications are mostly obtained by adding a gene in the genome of the parental wildtype crop via genetic manipulation. Plant genetic engineering can be beneficial when it improves agronomic features, but ethical concerns and the impact of genetically modified crops on human health and on the environment is under debate. Therefore, quantitative risk assessments have been undertaken to determine the safety of transgenic plants. Such studies were performed on not only insects, earthworms, nematodes, and so on, but also on microorganisms, which dominate soil-borne communities. Like plant developmental stage or genotype can influence microbial diversity and activity in the rhizosphere (Rengel et al., 1998), introduction of a transgene might modify the plant effect on microorganisms, due to altered root rhizodeposition (Kowalchuk et al., 2003). For example, Bacillus thuringiensis toxins (Bt) produced by transgenic plants are released in the soil by root exudates (Saxena et al., 1999), which possibly affects the soil microorganisms. Indirect effects of transgenic crops on soil microbes could arise from repeated application of herbicide during cultivation of herbicide-resistant plants (Sessitsch et al., 2004). Most of the studies investigating effects of transgenic crops on soil microorganisms have focused on total bacteria (Baumgarte and Tebbe, 2005; Heuer et al., 2002; Lukow et al., 2000; Milling et al., 2004; Schmalenberger and Tebbe, 2002). However, Philippot et al. (2006) compared the effect of glyphosate-tolerant maize, treated with either glyphosate or atrazine, and two cultivars of pyrale corn pest-resistant maize, treated
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with atrazine, on the NO 3 -reducing community in a field experimented during 8 years. The nitrate reductase activity was higher in the rhizospheric soil than in the bulk soil, but no difference between the three cultivars was observed. A rhizosphere effect was also observed on the NO 3 -reducer community structure together with a strong influence of the sampling date, but the type of cultivar did not matter. Accordingly, analysis of the NO 3 -reducing community structure in the rhizosphere of five different cultivars of transgenic maize and the corresponding parental wild-type cultivars in a greenhouse experiment did not reveal any transgene effect (Sarr et al., unpublished data).
7. Impact of Fertilization on Denitrification 7.1. Fertilization affects denitrification Research on denitrification in agricultural soil has mainly focused on effects of fertilizers. Not surprisingly, nitrogen fertilizers promote denitrification activity in agricultural soil and substantial amounts of fertilizer added nitrogen is lost through denitrification (De Klein and Van Logtestijn, 1994; Kaiser et al., 1998; Mulvaney et al., 1997; Ryden, 1983). Fertilization can also affect the N2O to N2 ratio from denitrification, and N2O emissions are most likely increasing due to an increased input of fertilization (Skiba and Smith, 2000). It has often been suggested that denitrification is limited under field conditions by NO 3 availability (Bronson et al., 1992; Mahmood et al., 2005), which in turn is influenced by the fertilizer type and application rate together with timing and application method. For example, losses by denitrification are often highest shortly after fertilization application and these losses can account for 50–75% of the annual loss in a field (Ellis et al., 1998; Mogge et al., 1999). The combination of high nitrogen application rates and poor soil drainage give rise to higher denitrification activity than lower application rates and good drainage (Hofstra and Bouwman, 2005). De Klein and Van Logtestijn (1994) showed that high nitrogen losses were associated to soil water content rather than as an effect of application rates in mineral fertilized grasslands. Fertilization sometimes causes secondary effects that affect denitrification. Such secondary effects can be changes in pH. Changes in pH can both directly and indirectly affect denitrification activity, and in general, denitrification is higher at neutral rather than acidic conditions (Bremner and Shaw, 1958; No¨mmik, 1956; Sˇimek and Cooper, 2002). Organic fertilizers can also cause secondary effects on denitrification by the various organic and inorganic compounds that are found in the fertilizers. For example, the high heavy metal content occasionally found in sewage sludge can decrease denitrification.
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7.1.1. Inorganic and organic fertilizer effects on denitrification activity The various ammonium-based fertilizers affect denitrification differently, due to the effect the fertilizer has on soil pH. Some of these fertilizers hydrolyze in soil, which gives an acidic reaction, while others are alkaline forming. Not only denitrification, but also nitrification is higher at neutral or alkaline compared with acidic conditions (Prosser and Embley, 2002) and, therefore denitrification is additionally supported by the supply of NO 3 from the nitrifiers under these conditions. It is also known that alkaline forming fertilizers affect the dissolution of organic matter (Norman et al., 1987; Sen and Chalk, 1994), thus increasing the amount of solubilized carbon and nitrogen that can be used for denitrification. Other microbial processes also benefit from the released nutrients, which results in reduced oxygen concentrations that promote denitrification. Accordingly, Mulvaney et al. (1997) reported higher emissions of N2O and dinitrogen gas after application of alkaline-hydrolyzing fertilizers than after application of acidic fertilizers, with the following order: anhydrous NH3 > urea >> (NH4)2HPO4 > (NH4)2SO4 NH4NO3 NH4H2PO4. In this laboratory study, all the fertilizers tested promoted denitrification, but from a 20-yearold field experiment, Simek et al. (Sˇimek and Hopkins, 1999; Sˇimek and Kalcik, 1998) reported that large amounts of a mix of different fertilizers could decrease denitrification, in some cases even below the rates observed in unfertilized soils, when no lime was applied. Results from a long-term field trial showed that potential denitrification rates were much lower in plots fertilized with ammonium sulfate, which had acidified the soil to pH 3.97, compared with calcium nitrate fertilized plots having pH 6.26 (Enwall et al., 2005) (Fig. 4). Similarly, application of potassium nitrate increased the rates of denitrification more than an ammonium sulfate-based fertilizer in a flooded subtropical soil (Aulakh et al., 2000). Organic fertilizers often promote denitrification more than mineral nitrogen fertilizers and this has been reported in numerous studies (Dambreville et al., 2006; Ellis et al., 1998; Enwall et al., 2005; Magnusson et al., 1998; Rochette et al., 2000; Wolsing and Prieme´, 2004). Organic fertilizers include the various types of farm manure commonly used, but also green manures, crop residues, sewage sludge, composted wastes, and other wastes. The stimulation of denitrification by organic fertilizers is probably due to the additional supply of readily available organic carbon (Christensen, 1985). However, since organic fertilizers release nitrogen slowly, the supply of nitrogen is initially low. This explains why some studies reported low denitrification rates in organically fertilized soil compared with soils with mineral fertilization the first years after application in new field experiments (Estavillo et al., 1994, 1996; Schwarz et al., 1994). Similarly to mineral fertilizer, the type of organic fertilizers influences the denitrification rates. Different fertilizers by default contain different
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Figure 4 Long-term fertilization experimental field site established in 1956 at Ultuna campus, Uppsala, Sweden.
nitrogen and carbon concentrations, as well as different amounts of inorganic and organic pollutants. They also differ in acidification capacity. All these factors affect denitrification. For example, nitrogen losses by denitrification from a site fertilized with farmyard manure were twice those from a site fertilized with cattle slurry, even though the nitrogen addition was three times higher in the latter (Mogge et al., 1999). This could be explained by the difference in C/N ratio, but an effect of pH and different crop rotation history cannot be ruled out. It has also been reported that digested pig slurry and composted pig slurry reduced the denitrification losses by 30% compared to untreated pig slurry (Vallejo et al., 2006). Others showed that pretreatment affects both the nutrient status of the fertilizers and the amount of and type of organic pollutants present, which affected nitrogen cycling in soil (Leve´n et al., 2006; Nyberg et al., 2006). Long-term fertilization with cattle manure was shown to increase potential denitrification rates compared with fertilization with sewage sludge, even though equal amounts based on carbon content had been added and both the soil nitrogen and carbon content was comparable between the treatments (Enwall et al., 2005). It was argued that the lower pH itself caused by the sewage sludge was not a sufficient explanation for the lower denitrification activity, and elevated heavy metal concentrations were found in the sewage treated plots (Bergkvist et al., 2003; Witter and Dahlin, 1995). In two other field experiments, 12 and 16 years of sewage sludge application had positive effects on soil potential denitrification, even though copper increased in the soil and pH dropped slightly during this period ( Johansson et al., 1999). Amendment with different crop residues has also been shown to affect
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nitrogen losses by denitrification differently (Velthof et al., 2002). Various Brassicacaea species caused higher losses than residues from grasses, probably due to the lower C/N ratios and higher amounts of mineralizable nitrogen in the former crop residues. Despite possible increased denitrification activity, declines in soil organic matter have renewed the interest in using organic fertilizers. It is also the only option in organic farming systems. Using organic fertilizers is also a means of recycling nitrogen already available in the biosphere, instead of increasing the rate of nitrogen fixation in fertilizer production. Thus, organic fertilizers can aid in slowing down Earth’s accelerating nitrogen cycle. However, the amount used, application time, and way to apply the organic fertilizer can lead to inefficient use of nitrogen and carbon substrates, which promotes nitrogen loss through both NO 3 leaching and denitrification. 7.1.2. Fertilization effects on nitrous oxide emissions Besides promoting denitrification activity, fertilization also positively affects the N2O emissions from agricultural soil. Higher N2O emissions in response to fertilization could simply be due to higher denitrification rates or an increase of the N2O/N2 ratio. By reviewing data for N2O emissions from agricultural soils, Eichner (1990) found rates of emission ranging from 0.2 to 42 kg N2O–N ha1 year1. Calculated as the percentage of the nitrogen fertilizer applied, nitrogen losses varied from 0.1% to 5% for N2O (Akiyama et al., 2004; Eichner, 1990; Germon et al., 2003; Granlı´ and Bockman, 1994; Mosier et al., 1998; Sherlock et al., 2002; Whalen et al., 2000) and 0% to 25% for dinitrogen gas (Barraclough et al., 1992; Ryden, 1983; Svensson et al., 1991). The application of 220 kg nitrogen as a mineral fertilizer to soil induced higher N2O losses throughout the crop season compared with an unfertilized soil (Sehy et al., 2003). In addition, Mulvaney et al. (1997) demonstrated an increase in the mole fraction of N2O emissions in mineral fertilized treatments compared to an unfertilized control. During the first week of incubation, the N2O/N2 ratio was larger for ammonium sulfate, ammonium nitrate, or mono-ammonium phosphate than for anhydrous ammonia, di-ammonium phosphate, or urea treated soil. Application of different manures also stimulates N2O emissions and a strong effect of poultry manure compared with swine or cattle manure was reported by Dong et al. (2005). Accordingly, Akiyama et al. (2004) showed that emissions sewage sludge or poultry manure-fertilized soil was higher than those from farmyard manure or composted plant residues. It has also been demonstrated that N2O emissions increase with the amount of manure applied (Akiyama et al., 2004; Chang et al., 1998). The relative effect of mineral or organic fertilization on N2O emissions is still in controversy. Ellis et al. (1998) inferred that cattle slurry application stimulated both the total nitrogen losses and the N2O production compared
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with mineral fertilizer additions, while no difference were observed by Meng et al. (2005). On the other hand, Lampe et al. (2006) inferred that more N2O was emitted after mineral than cattle slurry fertilization. Different N2O/N2 ratios between organic and mineral fertilized were reported by Dittert et al. (2005) who observed that application of either calcium nitrate or slurry resulted in a ratios of around 1:1 and 1:14, respectively. In a long-term field experiment, Dambreville et al. (2006a) also measured lower N2O/N2 ratio from experimental plots fertilized with pig slurry than from plots fertilized with mineral fertilizers. When comparing organic with conventional farming practice receiving mineral fertilizers, Flessa et al. (2002) showed that the former led to lower N2O emissions per hectare, but yield-related emissions were the same. An interaction between organic and mineral fertilizers was reported by Ellis et al. (1998) who showed that N2O losses were greater following mineral fertilizer application to soils that had previously (<5 months) been fertilized with cattle slurry. The effect of combined organic and mineral fertilization on increased emissions was confirmed by Dittert et al. (2005). Application of fresh cattle slurry together with calcium nitrate increased N2O emission six times during the first 4 days after application compared with single application of one of the fertilizers. The easily decomposable slurry carbon probably induced N2O emissions from the calcium nitrate fertilizer, as indicated from 15N-labeling experiments. Similar effects were reported by Arcara et al. (1999) when investigating additive effects of pig slurry and urea. After comparing N2O emissions from two different soils under different mineral nitrogen fertilization and slurry application, van Groenigen et al. (2004) concluded N2O emissions varied with soil type, fertilizer type, and fertilizer application rates. The importance of the soil type was confirmed in other studies (De Klein and Van Logtestijn, 1994; Terry and Tate III, 1980; Velthof et al., 2002; Wever et al., 2002). Even though denitrification research in agriculture has been dealing with the gaseous nitrogen losses for decades, there is still no clear-cut answer to how the organic fertilizers affect the ratio of N2O to total denitrification. Nonetheless, it can be agreed on that organic fertilizers increase denitrification. The activity of the denitrifying community is also a crucial factor in regulating N2O emissions since denitrification is both a source and a sink for N2O. Research is only at the beginning of resolving how the denitrifying community is affected by fertilization and the interplay between the environmental factors, the denitrifier community structure, and nitrogen emissions caused by denitrification. 7.1.3. Fertilization can modify denitrifier communities How fertilization affects the sporadic events of denitrification and N2O emissions are fairly well studied, but how the dynamics of denitrifier population relate to fertilization practice and its importance for nitrogen
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emissions are not. Short-term effects of mineral nitrogen fertilizers on the structure of the denitrifier community have only been investigated in a couple of studies. Avrahami et al. (2002) explored the effect of different ammonium concentrations (6.5, 58, or 398 mg NH4þ–N g1 of dry soil) on the nirK genotypes using T-RFLP. After a 4-week incubation experiment, significant differences in the nirK community structure were observed at the two highest ammonia concentrations. In contrast, others reported that the narG community structure was unaffected by different NO 3 concentrations after 2 weeks of incubation, although the NO reduction rate 3 increased with the highest amendment (300 mg NO –N) (Deiglmayr 3 et al., 2006). Effect of organic fertilization on the denitrifier community composition was investigated by a few authors in field experiments of various ages. A 3.5-year amendment with different fermented organic fertilizer or common organic manuring practices revealed slight changes in the nirS singlestranded conformation polymorphism patterns between the treatments (Schauss, 2006). In addition, the changes coincided with a change in the nirS gene copy numbers, but no differences were observed in potential denitrification rates. In a 6-year-old field trial, analysis of the denitrifier community structure in fields treated either with mineral fertilizer (60 and 120 kg N ha1 year1) or cattle manure (75 and 150 kg N ha1 year1) showed that the main differences in nirK T-RFLP patterns were due to seasonal variation (Wolsing and Prieme´, 2004). However, small differences that might be explained by the type of fertilizer were also observed, whereas the amount of fertilizer did not have any effect. Similarly, comparison of fertilization with either ammonium nitrate (162 kg N ha1 year1) or composted pig manure (213 kg N ha1 year1) during 7 years showed significant, but small differences in structure of the narG and nosZ communities, although the potential activity differed (Dambreville et al., 2006b). The most complete survey on the impact of fertilization regime on denitrifiers was performed by Enwall et al. (2005). Effects of calcium nitrate, ammonium sulfate, cattle manure, and sewage sludge were analyzed in an experimental field established in 1956 on narG and nosZ communities (Fig. 5). Fingerprint analyses showed differences in the denitrifier community structure in plots treated with ammonium sulfate and sewage sludge, which were the treatments with the lowest pH. No differences were observed between the unfertilized plots and those treated with calcium nitrate or manure. As expected, potential denitrification rates were higher in plots treated with organic fertilizer than in those treated with mineral fertilization. Altogether, these results suggest that long-term fertilization can affect activity and composition of the denitrifier community differentially.
279 Sewage sludge
Cattle manure
(NH4)2SO4
Ca(NO3)2
No fertlization
No fertlization No crops
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Figure 5 Denaturing gradient-gel electrophoresis banding pattern of PCR amplified partial nosZ genes derived from soil treated with six different fertilization regimes in triplicates at the Ultuna long-term experimental field site established in 1956 (redrawn from Enwall et al., 2005).
8. Effect of Environmental Pollution on Denitrifiers 8.1. Pollution affects denitrification For agricultural soil, there is concern about responsible use and maintenance of microbial functions and diversity for sustainable ecosystem management and crop production. Several studies have shown that denitrification is inhibited by organic pollutants, for example, polyaromatic hydrocarbons (PAHs) (Richards and Knowles, 1995; Roy and Greer, 2000; Sicilano et al., 2000) and pesticides (Bollag and Kurek, 1980; Pell et al., 1998), in addition to heavy metals (Bardgett et al., 1994; Bollag and Barabasz, 1979; HoltanHartwig et al., 2002; McKenney and Vriesacker, 1985). It is also known that the enzymes involved in the denitrification chain are differently affected by various stress factors, with N2O reductase being the most sensitive (Bonin et al., 1989; Firestone et al., 1980; Holtan–Hartwig et al., 2002; Sicilano et al., 2000). Inhibition of this enzyme results in increased production of N2O, and this has been shown to be the case in heavy metal contaminated soil (Va´squez-Murrieta et al., 2006). PAHs are not considered a big problem in agroecosystems, although they can reach agricultural soil accidentally by
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deposition. The same goes for heavy metals. However, copper is still used as a fungicide, mainly in organic farming systems and vineyards. In addition, some organic fertilizers from different waste residues may be contaminated by heavy metals. Pesticides, on the other hand, are frequently used and are often crucial for reaching sufficient crop yields in conventional farming systems. Similarly to fertilizers, the use of pesticides is expected to increase globally during the next 50 years by nearly three times reaching 107 metric tons year1 (Tilman et al., 2001). Therefore, more analyses of the response of denitrifiers after the application of pesticides to agricultural soil are highly warranted.
8.2. Pesticides The influence of pesticides on different nitrogen transformation processes has mainly been studied for nitrification (Gadkari, 1988; Sattar and Morshed, 1989; Stratton, 1990). In most cases, nitrification rates were significantly reduced. This could be explained by the nearly monophyletic nature of the ammonia-oxidizing bacteria, associated to the first step of nitrification. Even if the chrenarchaeal nitrifiers are taken into account, the taxonomic diversity, as we know it today, is rather limited. In contrast, the reaction patterns of denitrifiers in response to pesticide application are not as clear, which may be related to the vast number of taxonomically, distantly related genera of denitrifiers in soils. 8.2.1. Inhibition or stimulation of denitrification activity Early studies by Bollag and coworkers (Bollag and Kurek, 1980; Bollag and Nash, 1974) reported an accumulation of NO 2 and N2O in soils incubated under anaerobic conditions when derivatives of the insecticide chlordimeform ([N-4-chloro-o-tolyl]-N 0 ,N 0 -dimethylformamidine) were added. Interestingly, this inhibition was not caused by the insecticide itself but by the metabolites formed during degradation (N-formyl-4-chloro-o-toluidine and 4-chloro-o-toluidine). The same researchers also showed that aniline intermediates of other pesticides have stronger inhibitory effects on denitrification in soil than their parent compound. The most comprehensive study on the effect of pesticides on denitrification was conducted by Pell et al. (1998). The acute toxic effect of 39 herbicides, 10 fungicides, and 5 insecticides was tested on potential denitrification activity in one Swedish agricultural soil. They demonstrated that 23% of the pesticides tested at 100 mg active ingredient g1 dry soil had an effect on potential denitrification (Table 2). For example, potential denitrification was stimulated by the addition of AMPA or fenvalerate, whereas the herbicide ioxynil, the fungicides mancozeb and maneb, and the insecticide zineb showed the most pronounced inhibition of denitrification activity. Other studies also reported an inhibitory effect of maneb (Bollag and Henninger, 1976) and mancozeb
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Table 2 Effect of 54 pesticides and their degradation products on the Potential Denitrification Activity (PDA)
Pesticide
Herbicides Alloxydim AMPA Atrazine Bentazone Bromacil Chlorbromuron Dalapon-Na 2,4-D 2,4-DB 2,4-DP 2,4-Dichlorophenol Diuron Glyphosate free acid Glyphosate isopropylamid Hexazinone Imazapyr Ioxynil Lenacil Linuron MCPA MCPB MCPP Metobromuron Metribuzin Napropamide p-Chlorophenoxy acid Picloram Propazine
Effect on PDA (% of control)
115 5 135 8* 98 5 97 5 101 3 102 2 119 16 112 3* 115 2* 115 9 75 1* 101 5 115 7 97 7 106 2 n.a. 31 8* 97 2 113 1 114 6 107 4 99 6 103 8 86 4* 101 1 101 2 117 3 114 2
Pesticide
Effect on PDA (% of control)
Herbicides Simazine TBA TCA 2,4,5-T Terbuthylazine Tertbutryn Tri-allat Triclopyr Trifluralin
97 13 96 6 114 5 104 3 99 0 93 3 97 7 n.a. 96 2
Fungicides Benomyl Carbendazim Iprodione
110 6 92 3* 113 11
Mancozeb Maneb Thiphanatemethyl Triadimefon Triadimenol Vinclozolin Zineb
6 1* 7 4* 110 8 72 5* 99 5 122 10 33 1*
Insecticides Aldrin Cyromazine Fenvalerate
96 6 97 1 121 6*
Heptachlor Permethrin
94 4 101 6
Figures given are mean values standard deviation (n ¼ 3) in percentage of a control soil without pesticide addition (from Pell et al., 1998). * significantly different from control in Student’s t-test (p< 0.05).
(Kinney et al., 2005) on denitrification. In the latter, the authors observed inhibitory effects of the fungicides mancozeb and chlorothalonil, and the herbicide prosulfuron on denitrification with increasing pesticide concentration, ranging from 0.02 to 10 times that of a standard application rate.
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The absence of significant effects of dalapon, atrazine, and simazine on denitrification in soil observed by Pell et al. (1998) was also reported by Yeomans and Bremner (1987) at concentration ranging from 5 to 100 mg g1 soil. Contradictory results on the effect of the same pesticides were also reported. Yeomans and Bremner (1985a,b) tested the effect of 20 herbicides, 7 insecticides, and 6 fungicides on denitrification in different soils. At a concentration of 10 mg g1 soil, none of the pesticides tested affected denitrification. When applied at a concentration of 50 mg g1 soil, only the fungicide captan inhibited denitrification while mancozeb or maneb, had no effect or enhanced denitrification. The herbicides butylate, EPTC, diuron, simazine, and dalapon had no effect on denitrification and all the others either enhanced or inhibited denitrification, with effects varying according to the soil type. These contradictory results could be at least partially explained by the work of Tu (1994). They showed that the majority of 14 insecticides applied to a sandy soil had a significant effect on denitrification during the first week after application, but most of the effect had disappeared after 2 weeks of incubation. These results suggest that denitrification activity has a high capacity to return to its initial level after a temporary disturbance. To summarize findings in the literature, fungicides have more often been reported to have a negative impact on denitrification activity than herbicides. The impact of pesticides on denitrification activity in soil is likely to be dependent on the soil type, the concentration and nature (pure active ingredient or formulated preparation) of the pesticide applied, the climatic conditions, and in which way it is degraded. Addition of pesticides can reduce bacterial denitrification, probably due to cell death or cell inactivation, but it can also stimulate this process due to (1) the use of the pesticide as an electron donor by the denitrifiers; (2) death of organisms caused by the pesticide, which results in an easily available source of carbon for denitrification; or (3) an unspecific stress response. However, stimulation of denitrification in response to pesticide addition is a symptom that should be considered just as severe as decrease of denitrification. 8.2.2. Pesticide effects on denitrifier community structure Only a few studies investigated the effect of pesticides on the size and the structure of the denitrifier community. Cen et al. (2002) investigated effects of carbofuran, carbendazim, and butachlor during 4 weeks on the population size of denitrifying bacteria and their activity in different Chinese paddy soils. Lower concentrations of the pesticides (1 mg g1 dry weight soil) in general increased the population size and activity, whereas higher concentrations reduced both parameters. Increased numbers of denitrifiers were also reported after addition of 50–300 mg g1 soil of malathion (Gonza´lezLo´pez et al., 1993) and 5–10 kg ha1 of captan or alachlor (Martı´nezToledo et al., 1998; Pozo et al., 1994). Similarly, addition of the herbicide Topogard 50 WP at a concentration of 3 kg ha1 in soil with varying pH
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resulted in a temporary increase of denitrifiers, and the effect of Topogard on bacteria was likely to be dependent on soil pH (Kara et al., 2004). In a study, Philippot et al. (2006) analyzed the community structure and the activity of the NO 3 -reducing bacteria in a maize field treated with atrazine or glyphosate. While temporal shifts in both structure and activity of the denitrifying community were recorded after 8 years of cultivation, no pesticide effect was observed.
8.3. Heavy metals 8.3.1. Negative effects on denitrification activity In contrast to organic pesticides, denitrifiers are highly sensitive toward heavy metal stress. Denitrification has been shown to be more sensitive to heavy metals than aerobic soil respiration (Bardgett et al., 1994). Also, Kandeler et al. (1996) concluded that heavy metals influenced enzymatic processes in nitrogen cycling more negatively than those in carbon cycling. Hence, denitrification tests have been used to assess the presence of bioavailable heavy metals in soil (Speir et al., 2002). Heavy metals such as arsenic, cadmium, chromium, copper, lead, silver, and zinc have all shown a negative effect on denitrification activity in soil and sediment, and the effect is usually immediate (Bardgett et al., 1994; Bollag and Barabasz, 1979; Holtan– Hartwig et al., 2002; Johansson et al., 1998; McKenney and Vriesacker, 1985; Probanza et al., 1996; Sakadevan et al., 1999; Throba¨ck et al., 2007). Interestingly, Holtan-Hartwig et al. (2002a) observed that the N2O reduction was more affected than the N2O production rate after addition of a heavy metal mixture of Cd, Cu, and Zn at different concentrations. After incubating the soil for 2 months, a complete recovery in denitrification activity and N2O production rate was shown, but the N2O reduction capacity was still not fully restored. Also, Bollag and Barabasz (1979) observed an increased accumulation of N2O from soil incubated with Cd, Cu, Zn, and Pb. These findings indicate a more severe inactivation of the N2O reductase by heavy metals than other enzymes in the denitrification cascade. Differences in resistance to heavy metals among soil denitrifier communities are probably large and depend also on soil chemical properties, such as pH, cation exchange capacity, and organic matter content, which determine the bioavailability of metals. Reduced availability of the heavy metals is expected in clay soils those with high organic matter content. 8.3.2. Heavy metal effects on community composition and abundance of denitrifiers The most commonly reported effect of heavy metals on microbial communities is decreased genetic diversity (Kozdro´j and Van Elsas, 2001; Moffett et al., 2003; Muller et al., 2002). Nevertheless, increased bacterial diversity has been observed in soil after 10–60 days of Cu, Cd, and Hg exposure
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(Ranjard et al., 2006), and after long-term applications of sewage sludge with high levels of Cu, Ni, Cd, Zn, and Cr (Sandaa et al., 2001). Giller et al. (1998) explained increased diversity in heavy metal contaminated soils with the intermediate disturbance hypothesis. This hypothesis postulates that stable environments with high numbers of competitive species have increased diversity because metal stress reduces the innate competitive exclusion between bacterial populations and induces enrichment of other populations. The diversity continues to increase until eventually the stress becomes so high that it begins to decrease. How the diversity or community composition of soil denitrifiers is affected by heavy metal pollution is not well studied. Holtan-Hartwig et al. (2002a) showed that soil-extracted denitrifier cells exposed to heavy metals developed a higher tolerance to these after 2 months. As the community metal tolerance progressed, estimated growth rates were lowered. In soil spiked with silver, the kinetically derived specific growth rate of the denitrifying community indicated that part of the community was resistant to silver, although there was a negative impact on soil microbial biomass ( Johansson et al., 1998). In a follow-up study, it was shown that silver increased the diversity of denitrifiers in soil and induced enrichment of a certain clade of nirK denitrifiers (Throba¨ck et al., 2007). However, the number of nirK-type denitrifiers was negatively correlated with increasing concentrations of silver. The specific activity (k0), determined as the potential denitrification activity per nirK copy number, was also shown to decrease with increasing silver concentrations, which indicates that physiological properties of the denitrifiers could be affected by heavy metals (Throba¨ck et al., 2007). A detailed analysis of heavy metals effects on Proteobacteria was done with a system-biology-like approach by Kesseru et al. (2002), using Ochrobactrum anthropi a well-known Gram-negative bacterium as a model. Surprisingly, the cells were able to denitrify even in the presence of high concentrations of different heavy metals. The reason for that might be the good nutrient status in the media, which gave the organisms enough energy to protect themselves against the heavy metals. Bacterial communities can develop heavy metal tolerance (Ba˚a˚th, 1989; Mergeay et al., 2003) and within the a-, b-, and g-subdivison of Proteobacteria, where many denitrifiers belong, it is known that several genera show high tolerance to heavy metals. There are several reasons for increased tolerance, such as substitution of sensitive strains by tolerant ones, spread of resistance genes, and genetic modifications to produce heavy metal resistance. The transfer of genes coding for resistance or tolerance against heavy metals by plasmids among bacteria of different phylogenetic groups in soil has been described in many studies not exclusively related to denitrification (Smalla and Sobecky, 2002). We should be aware of that increased heavy metal resistance often is connected to antibiotic resistance. The emergence of community resistance against heavy metals, or other types
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of pollutants, can be regarded as a process describing deterioration of the ecosystems. We should therefore continue with risk assessment of pollutants in agricultural soils.
9. Conclusions and Outlook Agronomical practices resulting in an increase of carbon or NO 3 availability or a decrease of the oxygen partial pressure stimulate the denitrification activity in soil, which can cause major losses of nitrogen and emissions of N2O. The denitrification activity is rapidly regulated by these factors and therefore, the effect of agricultural practices on denitrification activity can be observed very fast. For example, NO 3 fertilization results in higher nitrogen gas fluxes by denitrification within few days or even hours if the oxygen partial pressure is low in the soil, for example, after a heavy rainfall. The factors regulating denitrification interact, which makes it difficult to interpret the highly variable effects measured in the field. Future research should consider the small-scale heterogeneity, including soil aggregates and other hotspots in soil to deepen our understanding of the regulation of denitrification. Experimental studies have pointed out the necessity of taking into account such microheterogeneities (Sexstone et al., 1985; Sierra and Renault, 1996) and the microscale approach to study denitrification is motivated by the fact that conditions experienced by soil organisms are not reflected by measurements of these conditions made on bulk soil samples (Parkin, 1987). For example, O2 concentrations may decrease from values nearly equal to the atmospheric concentration to zero values within a few millimeters in soil clods (Curie, 1961; Sexstone et al., 1986; Sierra et al., 1995). For readily decomposable organic matter particles, a thin layer of covering water with a thickness of about 160 mm may be sufficient for anaerobiosis to occur (Parkin, 1987). On the other hand, it is important to upscale and generalize the results from 1 g of soil to the field or landscape scale to make them appealing for model developers and decision makers. This requires integration of geostatistical tools and application of remotesensing techniques to identify landscape patterns. Another question, that we have to answer in the future if we want to integrate the data on larger scales is, if we have not overseen important hotspots for denitrification. For example, it is known that denitrifiers in the earthworm gut is involved in the in vivo emission of N2O by earthworms and denitrification also occurs in earthworm casts (Horn et al., 2006). In contrast to the weak resistance to changes in environmental conditions observed for denitrification activity, summarizing the data available on how the total denitrifier community composition responds to various agronomical practices suggests that it exhibits a high capacity to withstand
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perturbations. Hence, major modifications of the community structure were only observed in long-term experiments by which the soil’s physical and chemical parameters were also modified, whereas most of the laboratory experiments lasting only weeks or months resulted in minor or no modifications. Even though carbon and NO 3 strongly affects the activity, these factors apparently do not drive the composition of this functional community in agricultural soil. Long-term amendment of organic fertilizer or addition of root-derived carbon only slightly affected the denitrifier community structure. Similarly, addition of NO 3 at high concentration in a 3-week laboratory experiment or at 80 kg N ha1 year1 during 50 years in the field did not result in significant differences compared with the controls. The understanding of mechanism connecting denitrifier diversity and activity has mainly been based on some nice data sets of the genetic potential using DNA analysis and potential denitrification rates. These results also emphasize the redundancy of functional genes involved in denitrification. However, we do not know if a change in the diversity or composition of the denitrifier community plays a role for denitrification activity or N2O fluxes. Since the denitrifier communities represented by the total gene pool seem to be highly resistant to changes, a better understanding might be gained by focusing on the active denitrifiers under different conditions. More data on the induction of a genetic potential by targeting gene transcripts and the active enzymes in denitrification is needed if actual denitrification rates occurring in the field are to be explained. Due to the great taxonomic and physiological diversity of denitrifiers, processes shaping the denitrifier community structure are probably not different from those shaping other heterotrophic bacterial communities. Thus, bacterial DNA-fingerprinting analysis of 98 soil samples from across North and South America revealed that soil pH was the best predictor of bacterial community composition (Fierer and Jackson, 2006). Similarly, several authors investigating effects of fertilization regimes or increase of CO2 concentration reported that the main differences in the structure of the denitrifier community were linked to soil pH rather than the treatment per se, which again implies that pH is an important driver of the denitrifier community composition. Since the diversity of the denitrifiers is governed by factors that also shape the diversity of other heterotrophic bacteria and the fact that denitrifiers are facultative, the diversity of the denitrifier community can be affected by agricultural practices independently from the denitrification trait of its members. While no effect of such agricultural practices will be observed on denitrification on a short-term basis, it can result in changes in the denitrification activity on a long-term basis. This is true if the populations in the modified denitrifier community exhibit different physiological properties and react in different manners to additional perturbations. It is therefore important to consider the consequences of agronomical practices in a dynamic manner. Investigating consequences
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of agronomical practices under a limited number of environmental conditions is not very helpful and informative to predict the effects of a modified denitrifier community on the functioning of this process. Unfortunately, it is not conceivable to compare the effect of agronomical practices on denitrification activity under all possible environmental conditions or stresses, which can be faced today or in the future. Learning more about the ecology of denitrifiers, integrating structure and function of this community in soil, and developing methods to do that is essential for answering questions concerning nitrogen economizing and environmental impact from modern agriculture. It is also in line with the main challenge in microbial ecology—the understanding of the role of biodiversity for ecosystem functioning. In contrast to plant and animal ecology, which emerged in the nineteenth century and generated most of the general ecological theories, microbial ecology is a relatively young science and ecological theories and concepts are still under construction.
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Linking Soil Organisms Within Food Webs to Ecosystem Functioning and Environmental Change Jeff R. Powell* Contents 308 309
1. Introduction 2. Overview of the Soil Food Web 3. Impacts on Soil Food Web Dynamics Associated with Human Activities 3.1. Biodiversity loss 3.2. Invasive species 3.3. Climate change 3.4. GM crops 4. Alternative Approaches: Seeing the Forest for the Trees 4.1. Nematode faunal analysis 4.2. Modeling food web dynamics 5. Missing and Ambiguous Components of Current Soil Food Web Knowledge 5.1. Resolution 5.2. Integration of the detritivore and herbivore food webs 5.3. Role of technology in resolving soil food webs 6. Summary and Conclusions Acknowledgments References
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Agronomists, ecologists, ecosystem scientists, and various other researchers are recognizing the value of studying responses of soil biota to environmental perturbations because of their functional roles in ecosystem processes and varying sensitivities to environmental perturbations. In this chapter, I provide a descriptive overview of trophic interactions in soil and selected examples of current research on soil biotic responses to human-associated disturbance [biodiversity loss, invasive species, climate change, and genetically modified (GM) crops]. In many cases, researchers generally use population estimates of *Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada N1G 2W1 Advances in Agronomy, Volume 96 ISSN 0065-2113, DOI: 10.1016/S0065-2113(07)96007-1
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functional groups as a surrogate for food web interactions and, from these, infer that responses could cascade through food webs to impact ecosystem functioning. I then review alternative approaches to monitoring soil food web dynamics, approaches that focus on estimating the emergent properties of food webs themselves. Because these emergent properties are directly linked to nutrient cycling and energy flow, they should provide a more robust indication of ecosystem functioning in response to environmental perturbations. The first, nematode faunal analysis, is an empirical approach that utilizes a subset of soil organisms representing multiple functional groups and incorporates information regarding organism life history to estimate emergent properties of the existing soil food web, such as productivity and sensitivity to disturbance. The second is a modeling approach that attempts to predict how localized changes within a food web will influence the overall stability and productivity of the food web. Finally, I address some shortcomings in our current understanding of soil food web structure and resolution, and promising avenues for addressing these shortcomings.
1. Introduction The study of soil biodiversity arose primarily out of research necessitated by the impacts of agricultural pests, with most research focusing on plant–pathogen interactions and the potential for biological control of herbivores (Baker and Cook, 1974). Researchers placed emphasis primarily on promoting plant health, isolating and culturing microorganisms, taxonomy and classification, and characterizing microbial succession (Baker and Cook, 1974; Garrett, 1981; Wall et al., 2005). The study of trophic interactions in soil emerged as a discipline in the 1970s when research revealed the important roles played by trophic interactions in ecosystem processes such as nutrient cycling, litter decomposition, and energy flow (Anderson et al., 1978; Cole et al., 1978; Coleman et al., 1976, 1978a,b; Herzberg et al., 1978). This sparked a number of microcosm and field studies in the 1980s that shed light on soil food web structure, mapped the flow of energy through food webs, and quantified rates of nutrient mineralization and immobilization as influenced by trophic interactions in soil (Andre´n et al., 1990; Hunt et al., 1987; Ingham et al., 1985, 1986a,b; Petersen and Luxton, 1982). Data from a handful of field studies, conducted primarily in the late 1980s and the early 1990s, parameterize current models of soil food web dynamics; the current template for the organization of soil food webs is derived from a Georgia cropping system (Hendrix et al., 1986), a Colorado pasture (Hunt et al., 1987), a Swedish cropping system (Andre´n et al., 1990), a Dutch cropping system (de Ruiter et al., 1993), a Dutch Scots pine forest (Berg, 1987), and, most recently, an Arctic Tundra system (Doles, 2000).
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Recent and current uses of this template generally fall into three interrelated categories: (1) estimating the current and predicted responses of communities and ecosystems to environmental change, (2) the formation and revision of ecological concepts, and (3) manipulating trophic interactions to manage agricultural pests. Agronomists and ecosystem scientists are recognizing the value of studying responses of soil biota because of their functional roles and varying sensitivities to environmental perturbations. Ecologists recognize the utility of studying soil food webs due to the small spatial scales at which the interactions occur and the similarities with, and differences from, aboveground terrestrial, aquatic, and marine food webs. Studies on entomopathogenic nematodes, the parasites and predators of plant-feeding nematodes, predatory mites, and invertebrate seed predators highlight the biological control potential of trophic interactions in soil. Each category shares a common need to quantify and interpret the dynamics that are occurring in soil. However, as will be demonstrated here, the ways that soil food web dynamics are perceived and estimated are evolving. Some focus on changes in the abundance and strengths of interactions between particular taxonomic and functional groups, while others recognize that these changes can affect the emergent properties of the soil food web as a whole. I intend this chapter for readers contemplating the use of soil organisms for monitoring ecosystem responses to, and recovery from, environmental perturbations. This chapter (1) provides a descriptive overview of trophic interactions in soil, while pointing out some gaps in our current understanding of soil food webs; (2) highlights selected examples of current research estimating effects of some human activities on soil–trophic interactions; and (3) suggests alternative methods for estimating and predicting such effects. Several recent publications provide general and in-depth discussions on the roles that trophic interactions in soil, and soil biota in general, play in ecosystem functions (Adl, 2003; Bardgett, 2005a,b; Coleman et al., 2004; Paul, 2007; Wardle, 2002). A number of publications describe methodology for sampling, extraction, and enumeration of soil organisms (Burlage et al., 1998; Carter, 1993).
2. Overview of the Soil Food Web Descriptions of soil food webs are resolved at a functional level, with taxa aggregated into trophic groups (Fig. 1). This is by necessity due to the high taxonomic richness associated with most soil food webs and lack of knowledge regarding the specific feeding behavior of many of these taxa (Hunt et al., 1987). Here, I provide a general description of the current model of soil food web organization, adapted from Coleman et al. (2004), Wardle (2002), and various other sources.
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Shoots
Rootsfeeding nematodes
Collem bolans
Predaceous mites
Mites I Roots
Nematodefeeding mites
Mycorrhizae
Mites II Inorganic N
Labile substrates
Saprophytic fungi
Predaceous nematodes Fungusfeeding nematodes
Bacteria
Omni vorous nematodes
Flagellates Resistant substrates
Amebae Bacteriafeeding nematodes
Figure 1 Connectivity web indicating trophic relationships among various functional groups of soil biota and their substrates. Reprinted from Hunt and Wall (2002), with permission from Blackwell Publishing.
As in terrestrial aboveground food webs, plants are the dominant primary producers in soil. Resources enter the soil food web either via living plant material (roots and other underground structures) or via detritus (litter, dead roots, sloughed root cells, root exudates, and organic matter originally derived from flora and fauna). The distinction between living plant material and detritus is significant; abundance of each of these resources differs both spatially and temporally, which goes on to influence the abundance and activities of the organisms utilizing these resources (Bardgett et al., 2005a; De Deyn and Van der Putten, 2005). Thus, trophic interactions and energy flow in the soil food web tend to cluster into a ‘‘herbivore food web’’ and a ‘‘detritivore food web’’ (Wardle, 2002). Algae and other photosynthetic protists are additional producers occurring in soil, but these represent significant sources of productivity only where plants are absent or sparse (e.g., in the dry valleys of Antarctica; Adams et al., 2006). Symbiotic microorganisms and root-feeding invertebrates represent firstorder consumers of living plant material. Some symbionts engage in mutualistic interactions with the host plant, providing access to some limiting resource or protection from antagonists in exchange for photosynthate. For example,
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mycorrhizal fungi can increase nutrient uptake for most plant species, and rhizobial bacteria fix atmospheric nitrogen for leguminous plant species. Other symbionts extract photosynthate to the detriment of the plant host. These parasitic symbionts include a diverse assemblage of endophytic bacterial, fungal, and nematode species. Several invertebrate species graze on plant roots, including a variety of nematodes and arthropods. Nematodes, as a group, exhibit highly variable feeding strategies, ranging from sedentary endoparasitism to migratory endoparasitism to ectoparasites (Yeates et al., 1993). Second-order consumers in the herbivore food web are spatially and temporally distributed based on their feeding behaviors. Predators are active in the rhizosphere and bulk soil, where they encounter herbivores in search of food (consumed by, e.g., nematode-trapping fungi, predatory nematodes, collembolans, and mites) and the external hyphae of symbiotic fungi (consumed by, e.g., fungal-feeding nematodes and collembolans). A variety of parasites and pathogens are not only active in the rhizosphere and bulk soil (e.g., Bacillus spp., Pasteuria spp.) but also encounter herbivores at the feeding site (e.g., various fungal parasites of nematode eggs). The detritivore food web is active in the rhizosphere and extends into the soil where litter and organic matter are present. Saprotrophic bacteria and fungi represent first-order consumers of detritus. Some invertebrates (e.g., collembolans, enchytraeids) also feed directly on detritus and make resources available to other saprotrophs. For instance, the size of litter and structural barriers within it may prevent bacteria and fungi from accessing the nutrients contained within; these barriers are removed following comminution and digestion of the litter. Invertebrates that engage in this activity are called ‘‘litter transformers.’’ A variety of bacterial predators, including some nematodes and protists, and fungal grazers, including some nematodes and microarthropods, represent second-order consumers in the detritivore food web. Among second-order consumers in both the herbivore and detritivore food webs, morphological characteristics of consumers and their resources indicate general patterns of consumption. Protozoan predators and bacterial-feeding nematodes consume their prey whole, while fungal-feeding mites, collembolans, and nematodes have mouthparts that are specialized for chewing or piercing. The distinction between consumers of bacteria and fungi also turns out to be important as bacterial and fungal feeders are spatially and temporally separated in terms of their activities in the soil: bacterial predators forage primarily in water-filled soil pores and water films adhering to the surfaces of soil particles where bacteria occur, while fungal grazers can occur in water films (nematodes) and in the humid, air-filled soil pores (various microarthropods) through which fungal hyphae pass (Coleman et al., 1983). Thus, energy flow in the detritivore food web is compartmented further into a ‘‘bacterial pathway’’ and a ‘‘fungal pathway.’’ Rates of production and turnover also differ between
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these two pathways (Coleman et al., 1983). Production in the ‘‘fast’’ bacterial pathway rapidly increases in response to labile resource inputs and then falls off as resources are depleted. The ‘‘slow’’ fungal pathway is active over longer timescales, breaking down more recalcitrant substrates that are less variable through time. The fungal pathway is also less constrained by water availability than the bacterial pathway. Such generalizations are useful in practice, even though variation in resource consumption and environmental constraints exist within these broad taxonomic categories. For example, zygomycete ‘‘sugar fungi’’ are early colonizers of plant litter, disappearing early during fungal succession (Garrett, 1981). Predatory nematodes, collembolans, mites, and larger arthropods represent higher-order consumers. These consumers tend to prey on consumers from both the herbivore and detritivore food webs and from the bacterial and fungal pathways, linking these energy flows. Earthworms greatly influence soil structure and trophic interactions via their feeding and migratory activities. However, in general, earthworms are not included as a component of the soil food web even though they feed on most trophic groups within the web, albeit indirectly while digesting litter and soil organic matter. Small soil-dwelling mammals, such as moles and ground squirrels, also feed on larger soil invertebrates but are usually not included in soil food web models. These deletions illustrate that further descriptive research on soil food webs is necessary. The environment in which these interactions occur represents an additional player in the soil food web. Soil is a complex, three-dimensional matrix with hierarchical levels of structure at particulate, micro-, and macroaggregate levels (Rillig and Mummey, 2006). Soil texture modifies bacterial population dynamics at fine spatial scales by mediating interactions with predators (Elliott et al., 1980). Bacteria gain access to particulate organic matter sequestered within microaggregates via narrow-necked pores; bacterial feeders require pores with neck size greater than 3, 20, and 30 mm for flagellates, nematodes, and ciliates, respectively (Brussaard, 1998). In addition, soil texture and structure influence trophic interactions indirectly by affecting water potential (Brady and Weil, 2002). Thus, spatial patterns of soil food web dynamics depend, to a certain extent, on fine-scale patterns of soil structure. Soil biota are important drivers of soil structure. Earthworms and plant root systems have strong effects on soil structure and texture (Brady and Weil, 2002). Direct effects of microorganisms on aggregate formation are believed to be active at different scales: fungal activity influences the formation of macroaggregates while bacteria and archaea are thought to be more important at the microaggregate level (Rillig and Mummey, 2006). Indirect effects may arise due to interactions among microorganisms and other soil biota; for example, microbiota associated with arbuscular mycorrhizal (AM) fungi had differential effects on soil aggregate stability depending on the
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identity of the fungal isolate they were associated with (Rillig et al., 2005). Trophic interactions in soil have received little study with regard to their effects on soil structure. One hypothesis is that alterations in grazing intensity on fungi might influence soil aggregation via physical (effects on mycelial structure) and chemical (altered exudation patterns from grazed hyphae) mechanisms (Rillig and Mummey, 2006).
3. Impacts on Soil Food Web Dynamics Associated with Human Activities In recent years, the monitoring of groups of soil biota occupying different trophic levels has grown as a method of evaluating trophic interactions in soil following environmental impacts and remediation programs (Sochova et al., 2006). In fact, the study of how human activities and environmental change influence soil organisms and their functions drove, to a large extent, the development and evolution of soil ecology (Wall et al., 2005). In one example, Wardle (1995) provided an extensive review and synthesis of the literature pertaining to the effects of tillage and other weed management practices on detritivore food webs in agro-ecosystems; this synthesis is broad in that, in addition to demonstrating the differential sensitivities of soil biota to weed management, it contextualizes soil food web dynamics within the testing of ecological theories. A single soil sample can contain multiple trophic groups, which is appealing given recent suggestions that evaluating restoration projects by monitoring solely the plant community inadequately estimates ecosystem recovery (Gratton and Denno, 2006; Levin et al., 2006). In this section, I briefly summarize four topics of recent, general interest in which soil biota represents major response variables: biodiversity loss, invasive species, climate change, and genetically modified (GM) crops. Most of the studies mentioned in this section quantify effects at more than one trophic level, although the distinction is not always made between direct effects at multiple trophic levels and indirect effects that cascade through trophic interactions. These topics could be subjected to extensive literature reviews if expanded to studies where only a single trophic level is considered, and have been in some cases (Dunfield and Germida, 2004; Wardle, 2002; Wolfe and Klironomos, 2005).
3.1. Biodiversity loss Ecosystem responses to the loss of biodiversity may be due to some intrinsic property of biodiversity (e.g., niche complementarity) or the loss of functionally important species (sampling effect) (Hooper et al., 2005). Researchers have attempted to determine the potential effects of biodiversity loss on soil food
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web dynamics by estimating the responses of soil organisms to different numbers of species in microcosms or field plots, then statistically evaluating the importance of plant species diversity, per se, relative to other factors, such as the traits of the plant species present. When Porazinska et al. (2003) observed differences in organism abundance within trophic groups associated primarily with the identity of grass species rather than number of species present in monoculture, plant species richness had few effects on abundance within trophic groups. In another study, plant species identity, but not plant species richness, affected the abundance of bacterial-feeding and plant-parasitic nematodes, but not fungal-feeding nematodes (De Deyn et al., 2004). Furthermore, plant-parasitic nematodes were generally greater in the presence of Holcus lanatus, Leucanthemum vulgare, and Centaurea jacea and reduced in the presence of Plantago lanceolata, regardless if they were measured in monoculture or in the presence of other plant species, further suggesting the importance of plant species identity in relationships between plant and soil biodiversity (De Deyn et al., 2004). Synthesizing these results, species identity effects were stronger than diversity effects per se. The particular traits that resulted in species identity effects are not clear. Plant development time may have been important since it represented a significant source of variation for all trophic groups except fungal feeders in the study by De Deyn et al. (2004). Resource quality homogeneity may be another important factor. Resource quality (C3 vs C4 photosynthetic pathway) and species origin (native vs exotic) did not represent significant sources of variation in the study by Porazinska et al. (2003). Gastine et al. (2003) observed no effect of plant functional group (grasses, legumes, forbs) diversity on microbial activity, microbial-feeding nematodes, or predatory nematodes. However, Wardle et al. (1999) observed complex responses of various groups of soil biota to removal of plant functional groups (C4 grasses, C3 annual and perennial grasses, legumes, forbs) from field plots, linking these responses to shifts in resource quality. Caution is necessary when interpreting these results since the effects of plant biodiversity on soil food web dynamics are not clearly understood. In one case, increased plant species diversity often negatively affected abundance within soil functional groups relative to their abundance in monoculture. Wardle et al. (2003) observed that plant species identity in monoculture had significant effects on abundance of enchytraeids and plant-parasitic, microbial-feeding, and predatory nematodes; however, when plant species and functional group diversity were manipulated, these trophic groups often did not achieve their abundance in the monocultures. In addition, focusing on abundance within functional groups may underestimate responses when compared with responses within taxonomic groups. Korthals et al. (2001) studied the effect of low diversity (initially four plant species) versus high diversity (initially 15 plant species) on nematode
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communities of abandoned agricultural land and observed positive and negative effects of diversity on several plant-, bacterial-, and fungal-feeding nematode genera responding to the treatment; however, the only trophic group showing an overall response was that of bacterial feeders, the abundance of which was reduced in the high diversity treatment. In the study by De Deyn et al. (2004), plant species identity influenced taxonomic diversity within functional groups but had little effect on overall functional diversity, as indicated by various indices of trophic structure. Currently, biodiversity researchers are using increasingly complex experimental designs in an attempt to approximate the complexity of interactions occurring in reality. Recent studies attempted to identify the linkages, direct and indirect, between aboveground and belowground communities, and to determine the consequences of these linkages for the structure of plant communities and higher trophic levels (De Deyn and Van der Putten, 2005; Hooper et al., 2000; Wardle, 2002; Wardle et al., 2004). These linkages are manifested via general mechanisms, such as detrital inputs and effects on primary productivity, as well as specific mechanisms, such as the role of plant species identity on herbivory and decomposition. For example, aboveground influences on plant growth, such as herbivory, can influence the movement of energy through soil food webs; in an assembled grassland community, increasing defoliation intensity progressively increased shootto-root ratios, and reduced root mass, resulting in increased abundance of bacterial consumers and fungal consumers and reduced herbivore abundance in soil (Mikola et al., 2001). Linkages between aboveground and belowground communities increase the potential for effects in one community as a result of biodiversity loss in the other community, and greater understanding of the strengths of these linkages may allow for more accurate predictions as to how ecosystems will respond to further biodiversity loss.
3.2. Invasive species Increased human migration and economic trade has resulted in accelerated transcontinental and intercontinental movement of biota. Species introductions into exotic environments can impact native species and ecosystems via a number of direct and indirect ecological mechanisms, including competition, facilitation, and trophic cascades (White et al., 2006). Species invasions in aquatic systems can impact food web structure resulting in diet shifts and trophic cascades (Townsend, 1996; Vander Zanden et al., 1999); however, effects on trophic cascades in terrestrial systems are poorly understood (White et al., 2006). Several studies have looked at the impacts of exotic plant invasions on soil biota and the mechanisms by which soil biota may mediate exotic plant invasions (Belnap and Phillips, 2001; Belnap et al., 2005;
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Callaway et al., 2004a,b; Kourtev et al., 2002; Stinson et al., 2006). Few of these studies, however, have measured responses in soil biota at more than one trophic level and effects are often plant and/or environment specific. Belnap and Phillips (2001) observed several variable and inconsistent effects of cheatgrass (Bromus tectorum) invasion on various groups of soil biota; they compared invaded and uninvaded plant communities in Utah and observed inconsistent responses among the two native grassland types. Similarly, Yeates and Williams (2001) detected effects of wandering jew (Tradescantia fluminensis) and gorse (Ulex europaeus), but not wild ginger (Hedychium gardnerianum), on fungal-feeding, plant-feeding, and omnivorous nematodes at various sites in New Zealand; however, the direction of the effect often differed among sites. In one Colorado field experiment, phospholipids associated with Gram-negative bacteria were less abundant in the presence of an exotic grass (Caucasian bluestem, Andropogon bladhii) than native grasses, but nematode trophic structure or mite assemblages were not affected (Porazinska et al., 2003; St. John et al., 2006). Pritekel et al. (2006) observed effects of invasive leafy spurge (Euphorbia esula) on mite assemblages in Colorado grasslands, with prostigmatid (in 1 of 2 years) and cryptostigmatid (in both years) mites being less abundant in leafy spurge-invaded field plots, but not on collembolan abundance; various herbicides had been applied in prior years to invaded plots, but not uninvaded plots, in an effort to control leafy spurge, so effects on mites may not have been entirely or in part due to the invasive plants themselves. Recent research on the role of phytochemistry in plant invasion suggests that many exotic plants exude bioactive compounds into the rhizosphere, including compounds with antiherbivore, antimicrobial, and antifungal activities (Cappuccino and Arnason, 2006). For instance, glucosinolates exuded from the roots of garlic mustard release isothiocyanates that have negative effects on AM fungi (Roberts and Anderson, 2001; Stinson et al., 2006) and possibly other fungi (Smolinska et al., 2003). Negative effects on fungal dynamics might result in cascading effects for trophic interactions involving fungal feeders and the partitioning of energy through the bacterial trophic pathway in stands of garlic mustard; however, more research is required to determine if phytochemicals associated with plant invasions can have such cascading effects on soil food webs. There are several examples of humans introducing exotic soil organisms into new environments, with consequences for ecosystem functioning. For example, exotic earthworms alter nutrient cycling and plant community dynamics (Bohlen et al., 2004), nonnative ectomycorrhizal fungi facilitated the colonization of South Africa by exotic Pinus spp. (Richardson et al., 2000), and agronomists are concerned about the movement of plantparasitic nematodes and other plant pathogens. Whether introductions of exotic soil organisms can affect trophic interactions in the invaded range is an open question. Soil food webs are considered to contain high levels of
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functional redundancy, with many similar consumers feeding on many similar prey (Seta¨la¨ et al., 2005). Such a generalist view of soil trophic interactions suggests that exotic organisms are likely to encounter predators in the invaded range similar to those in their native ranges, buffering the influence of the invader on the rate and direction of energy flowing through a soil food web. However, if specialist relationships with parasites and pathogens dominate population regulation in the native environment, the invader may exert a significant influence on trophic structure and function in the invaded range. For example, mole crickets (Scapteriscus spp., Gryllotalpidae) are particularly damaging pests of pasture and turf in the southeastern United States, inadvertently introduced from South America (Walker and Nickle, 1981). Released from natural enemies in their native range, including an entomopathogenic nematode (Parkman et al., 1993) and a carabid beetle (Weed and Frank, 2005), mole crickets attain high population densities in the absence of control measures (Adjei et al., 2003). Populations follow an annual cycle, with adults emerging, laying eggs, and dying in the spring; synchronized mortality likely results in resource pulses, similar to pulses following periodic emergence and mortality of cicadas stimulating bacterial and fungal biomass and altering abundance of various detritivorous macroarthropods (Yang, 2004, 2006).
3.3. Climate change Climate change is occurring in many forms, including changing temperature and precipitation regimes, prolonged exposure to UV-B radiation, and exposure to increasing concentrations of CO2. Soil biota may be affected by climate change as a direct result of these factors or, indirectly, due to effects on litter quality and quantity, evapotranspiration rates, and nutrient availability (Norby and Luo, 2004). One approach to studying the effects of climate change on soil biota under field conditions is to artificially increase temperature. Ruess et al. (1999) simulated climate change by manipulating temperature and nutrient availability at two sites in northern Sweden. After 6 years of manipulation, fertilization stimulated microbial biomass, and active fungal biomass at one of the sites; bacterial- and fungal-feeding nematodes appeared to increase in abundance under the elevated temperature treatment, although differences were not statistically significant. Sohlenius and Bostrom (1999) increased temperature by transplanting soil cores from northern Sweden to various warmer locations throughout Sweden, and then monitored changes in the nematode communities over the course of 1 year. This approach also takes into account that plant community responses to climate change may indirectly influence soil biota. Total nematode abundance increased in soils transplanted to warmer sites, and various effects on plant-, bacterial-, and fungal-feeding taxa and predatory taxa were observed. Effects were more
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pronounced at sites with less plant cover, suggesting that plant and soil variables may be more important drivers of climate change induced shifts than increased temperature (Sohlenius and Bostrom, 1999). The most common approach to studying the effects of climate change on soil biota is to measure their responses to elevated concentrations of CO2. Several field studies, at a number of locations worldwide, have attempted this. Sonnemann and Wolters (2005) exposed a German grassland to a 20% increase in [CO2] for 3 years. Bacterial biomass, but not fungal biomass, increased in the elevated CO2 treatment relative to the ambient CO2 treatment over the course of the experiment; trophic cascades were not observed, however, since the abundance of bacterial-feeding nematodes was not affected by the CO2 treatment. Root hair-feeding nematodes were stimulated and predaceous nematodes were reduced in the first year of the CO2 treatment, but no treatment effects were observed in subsequent years. In a Michigan forest, Hoeksema et al. (2000) observed shifts in nematode community composition following exposure of young aspen (Populus tremuloides) to twice-ambient [CO2]; however, responses were dependent on soil origin (low N and soil organic matter vs high N and soil organic matter). As a group, plant-feeding nematodes were more abundant under elevated CO2 in the high N, but not the low N soil. Predaceous nematodes, on the other hand, were more abundant under elevated CO2 in only the low N soil. At a Swiss calcareous grassland, Niklaus et al. (2003) observed responses in various components of the soil food web exposed to elevated CO2 for 6 years. They observed no significant effects of elevated CO2 on protozoans, collembolans, microbial-feeding nematodes, plant-feeding nematodes, or mites. Predaceous and omnivorous nematodes were less abundant in the elevated CO2 treatment. Hungate et al. (2000) observed that responses to 4 years of elevated CO2 at sandstone and calcareous grasslands in California were sensitive to seasonal fluctuations, with stimulatory effects on fungi, flagellate protozoans, and plant-feeding nematodes (sandstone only) detected early in the growing season. Other groups, including bacteria, other bacterial-feeding protozoans, and microbial-feeding nematodes were not responsive to the CO2 treatment. In the same experiment, after 6 years of elevated CO2, Rillig et al. (1999a) observed that fungal biomass was greater in the elevated CO2 treatment (sandstone only), as were fungal-feeding microarthropods. No effects on bacterial biomass or the biomass of bacterial-feeding protozoans were detected. Allen et al. (2005) estimated responses of bacteria, fungi, and their consumers to elevated CO2 in a Californian chaparral over a 3-year period. Biomass of bacteria and their consumers did not respond to elevated CO2. Biomass of fungi and their consumers also did not respond to elevated CO2, except for one
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growing season in which mite biomass increased with increasing [CO2]. Fungal biomass was not affected by elevated CO2 over this same sampling period; however, when the authors estimated fungal biomass consumed by mites and added this estimate to the standing fungal biomass estimates, it revealed a positive relationship between fungal biomass and [CO2] up to 550 ppm. Neher et al. (2004) evaluated the abundance and energetics of nematode communities at two locations in the eastern United States. Biomass and respiration of fungal and bacterial feeders decreased in response to elevated CO2. Bacterial feeders decreased in abundance, in contrast to fungal feeders, which increased in abundance, in response to elevated CO2. Responses of predators, omnivores, and root feeders were significant but inconsistent at the two locations. These results suggest that elevated CO2 stimulates various functional groups in soil, although effects were often inconsistent among studies. Effects on soil biota are usually attributed to changes in the abundance and quality of resources. Elevated CO2 can increase net primary productivity, resulting in increased root growth and litter fall (Norby and Luo, 2004). Cotrufo et al. (1998) reviewed the elevated CO2 literature and estimated a 14% reduction of nitrogen concentration of plant tissues associated with elevated CO2; differences varied among plant types with C3 plants demonstrating greater reductions than C4 and nitrogen-fixing plants. As a result, responses to elevated CO2 are linked to the availability of other nutrients that limit productivity and influence resource quality; for example, Klironomos et al. (1996) demonstrated the dependence of elevated CO2 effects on soil fertility. Sagebrush (Artemisia tridentata) plants were grown in a low fertility soil either supplemented with nutrients or unfertilized. Soil food web responses to elevated CO2 were muted in the unfertilized soil and exaggerated in the fertilized soil. CO2 stimulated fungal and bacterial biomass and microbefeeding microarthropods, but only in the fertilized treatment. Only nematode abundance was stimulated by elevated CO2 in the absence of fertilization, and further stimulation was observed following nutrient addition. Further research is necessary to determine the identity and relative importance of the mediating factors resulting in inconsistencies among studies. Ecosystem models that estimate soil food web dynamics and nutrient cycling in response to various components of climate change, such as the approach used by Kuijper et al. (2005), will likely be required to reconcile inconsistencies among studies in the observed effects. The model by Kuijper et al. (2005) predicts that fungal-feeding taxa will be more sensitive to climate change than bacterial-feeding taxa, a trend that is supported by many of the previously mentioned studies, and that omnivory and weak trophic links will limit effects of trophic cascades on higher trophic levels. Further experimental research is necessary to test these and other predictions.
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When discussing functional group responses to elevated CO2, researchers less frequently ascribe these responses to interactions with abiotic factors than to trophic cascades. In one example, elevated CO2 lead to reduced evapotranspiration rates in the study by Niklaus et al. (2003), resulting in increased soil moisture and reduced soil aggregate sizes; reductions in predatory and omnivorous nematodes were speculatively attributed to reduced mobility under the modified soil structure. Rillig et al. (1999b), however, observed the opposite effect of elevated CO2 on soil structure in California calcareous and sandstone grasslands. Larger soil aggregates (1–2 mm) increased in abundance, as did the stability of aggregates down to 0.25 mm in diameter, in the elevated CO2 treatment; abundances of omnivorous and predatory nematodes were not reported (Hungate et al., 2000; Rillig et al., 1999a). Extrapolating published results of experiments applying abrupt changes in [CO2] is problematic, however, as responses may be quite different from those under gradual increases in [CO2]. Klironomos et al. (2005) observed effects of elevated CO2 on structural and functional properties of AM fungal communities; these effects were only observed following an abrupt increase in [CO2] and not when [CO2] was increased gradually, over a period of 6 years. More research is required to determine if responses in soil food web dynamics to artificial climate change represent adequate estimates of responses to actual rates of climate change over the coming decades.
3.4. GM crops GM cropping systems have been the focus of recent attention, largely due to public concern about their safety. There are two general mechanisms in which GM cropping systems can affect organisms in and around fields: (1) effects associated with the modification itself and (2) effects associated with the management of transgenic varieties. Initially, attention focused on the modified traits themselves. Several studies document shifts in endophytic and rhizosphere microbial communities associated with GM crops (pleiotropic effects or other varietal changes), but few look at effects at higher trophic levels. Donegan et al. (1997) observed increased nematode abundance but reduced collembolan abundance associated with litter from transgenic proteinase inhibitor I producing tobacco in field soil. In another field experiment, Donegan et al. (1999) observed shifts in soil microbial communities and enzymatic activities associated with a recombinant nitrogen-fixing bacterium (Sinorhizobium meliloti) and transgenic lignin peroxidase- (but not amylase-)producing alfalfa, but observed no effects on protozoa, nematodes, and microarthropods. Saxena and Stotzky (2001) observed no effect of CryIAb protein, either in corn root exudates or in litter added to field soil, on microbial, protozoan, or nematode abundance. Griffiths et al. (2006) observed that omnivorous nematodes and protists were more
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abundant in the presence of a Bt-corn variety, but other nematode trophic groups, microarthropods, and cabbage root fly were not affected; soil type and insecticide treatment modified the responses and had greater overall effects than did the Bt trait in this greenhouse experiment. Not only do environmental factors and the type of modification affect functional group responses to GM crops, but variation among crop species also exists that could modify these responses. Saxena et al. (2004) observed exudation of insecticidal Cry protein from roots of Bt-corn, rice, and potato but not tobacco, canola, and cotton. There also appears to be a temporal component to when effects are more likely to occur. For example, Griffiths et al. (2006), in a greenhouse study conducted in two soil types, observed that Cry protein concentration increased in soil with corn growth stage. In addition, in the presence of living plants, protein concentration eventually became much more abundant in the soil (10.11–42.86 mg per kilogram soil) than in the roots (0.74–6.85 mg per kilogram tissue) or leaves (9.08–13.53 mg per kilogram tissue) at corn maturity, suggesting that responses of soil biota to litter addition may underestimate responses occurring in the presence of mature plants (Griffiths et al., 2006). An additional fear is that transgenes in GM crops may be incorporated into the genomes of weedy relatives, resulting in a fitness benefit to the recipients of the transgene and the evolution of new and/or stronger agricultural pests (Snow et al., 2003). Given the species-specific effects of plants on soil food webs (see discussion in Section 3.1 and 2), the potential impacts of gene transfer, if they occur, on trophic interactions in soil are likely dependent on the identity of the recipient species. More recently, impacts of GM crops are viewed in the context of the whole cropping system, as opposed to just the modified plant traits, as effects on nontarget organisms may be manifested via management approaches. Arguably, changes in the types and ways that pesticides are used and reduced reliance on tillage for effective weed control are more likely to have stronger effects on soil biota as these practices, directly and indirectly influence their abundances (Roper and Gupta, 1995; Wardle, 1995). As the adoption by growers of GM crops has increased, so has the adoption of the management practices associated with these crops. This is especially true for varieties modified to tolerate sprays of broad-spectrum herbicides. Glyphosate (RoundupÒ ) has quickly replaced the use of other herbicides in glyphosatetolerant cropping systems (Baucom and Mauricio, 2004; Carpenter et al., 2002); for instance, glyphosate is applied to >85% of soybean land area in the United States, the same percentage of land that is sown to glyphosatetolerant soybeans (NASS, 2006a, 2006b). Liphadzi et al. (2005) observed transient increases in soil microbial biomass associated with glyphosatetolerant corn–soy rotations relative to conventional systems, but no effects on nematode communities. In the UK farm scale evaluations (Bohan et al., 2005; Brooks et al., 2003), the authors compared nontarget population
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responses in fields containing GM, herbicide-tolerant varieties managed with their corresponding herbicides (glufosinate-ammonium or glyphosate) to those containing conventional varieties managed using conventional herbicides. Abundance of detritivores and seed-feeding carabid beetles varied among crop species but responded to the GM cropping systems in a similar way as did weed abundance, suggesting that responses were due to management effects on resource availability. However, it is difficult to determine the direct/indirect nature of responses in soil biota without manipulating additional factors that are known to respond to management or simulating responses using ecosystem models.
4. Alternative Approaches: Seeing the Forest for the Trees Ecosystems are inherently complex and contain a mind-boggling array of biotic and abiotic interactions. The studies mentioned in the previous section estimated effects of perturbations on functional groups within a soil food web. Many authors then attempted to scale the results up to gain information about the response of the soil food web as a whole; several interpret effects on consumer abundance as an indirect response to effects on resource abundance and, sometimes, lack of an effect on resource abundance as an indirect response to stimulatory effects on consumer abundance. However, interpretation of functional group abundances becomes difficult when time lags mask the manifestation of consumer responses to increased resource availability (Ettema et al., 1999; Wardle et al., 1999), especially when the sampling design does not take temporal variation into account. Alternatively, responses in any one functional group may be independent of the trophic interactions involving that functional group. Instead, functional groups may respond to the direct effects of the perturbation or indirect effects on abiotic factors. Therefore, it is not always valid to interpret results in the context of food web interactions, or to extrapolate changes in population estimates to ecosystem-level responses. Fortunately, soil ecologists have developed two approaches to collapse large datasets on organism abundance and trophic status into interpretable estimates of the structure and function of the food web itself. The first, nematode faunal analysis, is an empirical approach that incorporates information regarding life history characteristics (e.g., rate of population growth) and trophic status of a subset of soil organisms to estimate emergent properties of the existing soil food web, such as stability and productivity. The second is a modeling approach that attempts to (1) predict how localized changes within a food web (i.e., within a functional group) will influence
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the overall stability and productivity of the food web and (2) determine what properties of food webs make them resistant or resilient to perturbations.
4.1. Nematode faunal analysis 4.1.1. Theory It is typically difficult to quantify the condition of an ecosystem, which is dependent on many factors (e.g., nutrient status, disturbance history). The nematode faunal analysis concept attempts to gain information that surrogates for ecosystem-level factors by estimating components of food web structure from nematode communities. Nematodes are particularly suited as environmental indicators since they contain more trophic complexity than other taxonomic groups of soil organisms (Fig. 1); nematodes represent multiple trophic levels and occupy energy pathways based on all three resource-types (roots, bacteria, fungi). Nematodes are also important as their trophic activities influence nutrient cycling in natural and managed systems (Anderson et al., 1983; Ingham et al., 1985). An analogous system for estimating food web structure does not exist for any other group of soil organisms. Various indices are used to interpret nematode community shifts at a relatively high level of taxonomic resolution (family/genus); the most frequently used are the maturity index (MI), channel index (CI), enrichment index (EI), and structure index (SI). The indices combine information regarding the trophic guild (bacterivore, fungivore, herbivore, carnivore, or omnivore) and life history of the sampled nematodes. Life history is scored along a colonizer-persister scale; colonizer taxa have high population growth rates and are typical of nematode communities following a recent disturbance. Persister taxa are slower growing and typical of nematode communities in environments with low frequency of disturbance. The maturity index (MI; Bongers, 1990)
MI ¼
n X i¼1
n cp kcp x n
ð1Þ
accounts for the relative proportion (nc–p/n) of nematodes in a sample (excluding plant feeders) that fit into categories (c–p) along the colonizer-persister scale, with k representing the weighting for any particular c–p category. A sample with a low MI indicates that the sample is dominated by opportunist taxa; as the MI approaches the maximum (5), the sample becomes increasingly dominated by slower growing, disturbance-sensitive taxa. An analogous index exists for plant-feeding nematodes, the plant-parasite index (Bongers, 1990), and the weighted MI (Yeates, 1994) includes plant-feeding and free-living taxa.
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Nematologists proposed additional indices that incorporate life history characteristics and trophic behavior of nematodes to a greater extent. The channel index (CI; Ferris et al., 2001)
0:8Fu2 CI ¼ 100 3:2Ba1 þ 0:8Fu2
ð2Þ
estimates the relative weighting of the bacterial and fungal pathways of the soil food web by measuring the relative abundances of opportunitistic, freeliving nematodes in these guilds. A CI that approaches 0 indicates dominance by the bacterial energy pathway, while an index approaching 100 indicates dominance by the fungal pathway. The perceived benefit of employing the CI, as opposed to estimating the ratio of bacterial- or fungal-feeding nematodes to all microbivorous nematodes (the nematode channel ratio), is that the CI focuses on the faster-growing, opportunistic bacterial- and fungal-feeding species that respond rapidly to enrichment, while attempting to correcting for differences in the rate at which energy flows through the two pathways. The EI (Ferris et al., 2001), which estimates responses associated with the nutrient status of a system, is calculated
EI ¼ 100
Pn
e ne i¼1 kP Pn n i¼1 ke ne þ i¼1 kb nb
ð3Þ
and the SI (Ferris et al., 2001), which estimates the degree to which trophic interactions within food webs have developed, is calculated
SI ¼ 100
Pn
s ns i¼1 kP Pn n i¼1 ks ns þ i¼1 kb nb
ð4Þ
where n represents abundance and k represents the weightings for feeding guilds associated with enrichment (e), structure (s), and basal (b) components of the food web. Both indices scale on a range from 0 to 100. A high EI indicates greater availability of labile nutrients in the system, which stimulates the more rapidly cycling bacterial pathway. A high SI indicates the greater abundance of carnivorous and omnivorous nematodes, presumably due to a lack of disturbance in the system or greater resilience/resistance of the food web as structured. Estimates from the enrichment and structure indices can be calculated from the same sample and graphed together (Fig. 2); the placement of data points in one of the four quadrats in the bivariate plot space suggests certain functional properties of the ecosystem within which the food web resides (Table 1).
325
Linking Soil Food Webs to Ecosystem Functioning and Environmental Change
cto ry
Enriched
Quadrat B
t in
de
x
Quadrat A
Structured
en ric hm
Fu2 (0.8)
Quadrat D
Quadrat C
En
En
ric hm
en t tr aje
Ba1 (3.2)
Fu2 (0.8) Basal condition
Ba2 (0.8)
Basal
Structure index Ca2 (0.8) Om4 (3.2) Om5 (5.0) Ca4 (3.2) Ca3 (1.8) Ca5 (5.0) Fu3 (1.8) Fu5 (5.0) Fu4 (3.2) Ba3 (1.8) Ba5 (5.0) Ba4 (3.2) Structure trajectory
Figure 2 Functional groups of soil nematodes characterized by trophic group and life history characteristics. Groups belonging to basal, enriched, or structured food webs are included and their weightings for calculation of structure and enrichment indices indicated. Reprinted from Ferris et al. (2001), with permission from Elsevier.
4.1.2. Application Several recent studies have employed this version of the nematode faunal analysis concept. Most of these studies were conducted in agricultural systems, estimating soil food web responses to soil and crop management practices. In a series of papers, Wang et al. (2003, 2004, 2006b) evaluated the main effects of amendments on nematode trophic structure and their interactive effects with other management practices. Compost amendment (269 Mg ha1 year1, derived from sticks, lawn clippings, and wood fragments) for 5 years increased nutrient availability (higher EI: 31.8 vs 23.9 in the absence of compost) and the relative contribution of the bacterial energy pathway (low CI: 18.5 vs 59.4); the SI (38.4–52.2) indicated an intermediate level of trophic organization but was not significantly affected by compost amendment (Wang et al., 2004). Amending soil from compost-incorporated and control plots with sunn hemp (Crotalaria juncea) hay (1 g per 100 g soil) resulted in a greater MI in one of two greenhouse experiments (2.02–2.12 vs 1.97–2.00 in the C. juncea unamended soil) but no effects on the structure, enrichment, or channel indices (Wang et al., 2003). In a field experiment, amendment with C. juncea hay resulted in a greater reduction in the maturity and channel indices, suggesting increased abundance of opportunitistic, bacterial-feeding nematodes, and a greater increase in the EI, indicating more rapid nutrient cycling, than ammonium nitrate application (Wang et al., 2006b).
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Table 1 Soil nutrient status and food web condition inferred from combined calculation of nematode community structure and enrichment Indicesa
General diagnosis
Quadrat A
Quadrat B
Quadrat C
Quadrat D
Disturbance
High
Low to moderate N-enriched Balanced
Undisturbed
Stressed
Moderate Fungal
Depleted Fungal
Moderate to high Structured
High
Enrichment N-enriched Decomposition Bacterial channels C:N ratio Low Food web condition a
Disturbed
Low Maturing
Degraded
Quadrats refer to those presented in Fig. 2. Reprinted from Ferris et al. (2001), with permission from Elsevier.
In another study, Liang et al. (2005) observed reduction in the CI following fertilization with urea, associated with increased NO3 and NH4 levels; however, the slow-release urea formulation resulted in a higher value for the SI, indicating greater trophic diversity. In a comparison of long-term organic, low-input, and conventional management systems, Berkelmans et al. (2003) observed that the organic and low-input systems, relative to the conventional system, were frequently associated with higher enrichment and SI, indicating higher fertility and greater trophic structure, and lower basal and channel indices, reflecting reduced abundance of opportunistic nematodes and rapid nutrient cycling through the bacterial pathway of the soil food web. Ferris et al. (2004) manipulated the trophic structure of nematode communities (and presumably, other microbial feeders) through a combination of fall irrigation and carbon input, following which they observed greater nitrogen mineralization in the subsequent cropping season. The type of amendment used will play a role in determining the overall effect on nutrient availability. Ferris and Matute (2003) observed structural and functional succession of the nematode community in response to substrates of differing C/N ratios. The EI declined over time at a rate regardless of the substrate added. Progression toward fungal domination of energy flow was faster for wheat straw (C/N ¼ 75.9) than for alfalfa (C/N ¼ 10.6), but not faster than for compost (C/N ¼ 10.6), indicating that factors in addition to C/N are also important. There was also a succession from enrichment opportunist bacteriovores to general opportunist bacteriovores, but the rate of succession did not differ among the types of amendments (Ferris and Matute, 2003). Other studies have incorporated the nematode faunal analysis concept into estimates of soil biodiversity in grasslands and pastures, the advantage
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being that functional components of the ecosystem are also measured with potential implications for nutrient cycling and grassland productivity. For example, Zolda (2006) studied the nematode fauna of grazed and ungrazed grasslands in Austria, Stirling and Lodge (2005) estimated the relationships among climatic and plant species factors and nematode communities in Australian pastures, Bell et al. (2005) studied the nematode fauna of New Zealand tussocks, and De Deyn et al. (2004) employed the nematode faunal analysis concept to address the effects of plant diversity on nematode taxonomic and functional diversity. Hoeksema et al. (2000) and Sonnemann and Wolters (2005) used the MI in their evaluations of the effects of elevated CO2 on nematode community structure. Hoeksema observed an increase in the MI associated with elevated CO2 in a low-N soil, indicating greater abundance of slower-growing nematode taxa; however, this result was not observed in the high-N soil, nor in the study by Sonnemann and Wolters (2005). Nematode faunal analyses suggest that nematode communities are quite susceptible to disturbance. For example, Berkelmans et al. (2003) observed that 1 year of a common crop and tillage undid the effects of several years of divergent management practices (organic/low input/conventional). However, some analyses suggest that nematode communities are also resilient to some disturbances. Wang et al. (2006a) observed only short-term effects of solarization or cowpea cover cropping on the SI, disappearing by the end of the experiment (5–6 months); methyl bromide fumigation, however, had persistent effects. Wang et al. (2004) observed little difference in the trophic structure of nematode fauna when comparing untilled plots versus plots undergoing multiple roto-tilling events for 25 years; the tilled plots had been left fallow for 1.5 years prior to sampling, leaving the possibility open that the nematode community recovered quickly once frequent manual disturbance was removed from the system. The time required to recover from disturbance provides additional information regarding ecosystem recovery and should be a focus of future research. Further research should improve the utility and sensitivity of nematode faunal analysis. Debate continues regarding the placement of taxa into c–p groups (Bongers, 1990) and the generalities of genera and family-level resolution of trophic groups (Yeates et al., 1993). Both are based largely on observations of nematode behavior on agar media, which may not be representative of behavior in nature. Tylenchid nematodes, classified as plant-, algal-, and lichen feeders but possibly also fungal feeders (Yeates et al., 1993), can constitute 30% or more of a sample (Ferris and Bongers, 2006). Furthermore, an evaluation of nematode community indices in three different ecosystem types (wetland, forest, and agricultural) indicated that the indices were differentially sensitive to disturbance in the different ecosystems and that variance within community composition at the genus level within families was more sensitive than the community indices to ecosystem type and disturbance (Neher et al., 2005). Fiscus and Neher
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(2002) used multivariate statistical techniques to evaluate the sensitivity of nematode taxa to particular agricultural disturbances, suggesting that individual analyses could be tailored to have greater sensitivity by selecting particular taxa relevant to the disturbance(s) under study.
4.2. Modeling food web dynamics 4.2.1. Theory The modeling approach to studying food webs highlights properties of the system emerging from the individual interactions occurring within. Early models focused on connectivity food webs, in which linkages between two interacting groups indicate where trophic interactions occur but all linkages are assigned equal weight. Models by May (1972, 1973) arrived at the conclusion that complex food webs (i.e., those containing many interacting species) are less likely to be stable than simple webs; increases in species richness (S) must be accompanied by a decrease in either connectance,
L C¼ S2
ð5Þ
where L is the proportion of all possible linkages that are realized, or the average strength of the interactions (per capita effect of one species on another) occurring in the system. May observed, however, that the presence of compartments in food webs, within which species interact readily with each other but very little with species in other compartments, increased the feasibility of constructing large food webs (May, 1972, 1973). Lower richness within individual compartments allowed for more and stronger interactions among species without risking instability. In the 1980s and early 1990s, soil ecologists conducted surveys of soil food webs whereby they represented interactions as quantifiable flows of material cascading through the web. These surveys, and subsequent modeling exercises, are built upon available descriptions of connectivity webs (Fig. 1) by assigning weights to these linkages. Weights represented either the amount of energy present within and moving between pools (energy webs), or the per capita effects of one functional group on another (functional or interaction strength webs). From these models it became clear that, in determining the stability of food webs, the number of interactions within a food web is less important than how those interactions are structured. As a result, these researchers were capable of addressing questions related to the emergent properties of the food web, emphasizing properties associated with trophic diversity and structure (how many trophic levels/linkages are supported at any particular level of productivity?) and ecosystem stability (how resistant/resilient is food web structure to environmental perturbation?).
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Hunt et al. (1987) derived equations for modeling the flux of energy, as carbon and nitrogen, through food webs. For the consumption rate, F, of a consumer, j
Fj ¼
ðdj Bj þ Pj Þ aj pj
ð6Þ
where P and d represent the predatory and nonpredatory death rates, respectively, B represents biomass, and a and p represent the assimilation (ingested and not lost in feces) and production (retained as biomass) efficiencies, respectively, of the consumer. For consumers that feed on more than one prey, the consumption rate of prey, i, is a function of the preference, w, and biomass of i relative to that of all prey, k, so that
Fij ¼
wij Bi Pn Fj k¼1 wkj Bk
ð7Þ
Energy webs are particularly useful for estimating how sensitive the length and reticulation of the food web is to the amount of energy entering and moving within the web. Moore and Hunt (1988) demonstrated that energy channeled through a soil food web largely via compartments (roots, bacteria, and fungi as basal resources in each pathway) with little movement of energy between pathways at intermediate trophic levels. The authors’ analysis of published connectivity webs of trophic relationships showed that the number of energy pathways (resource richness) in a food web correlated positively with the richness of consumers and negatively with connectance. This result supports resource compartmentation, reducing the proportion of species that directly interact, as a mechanism allowing stable species rich food webs to persist (May, 1972, 1973; Moore and Hunt, 1988). Functional webs represent the dynamic effects of trophic interactions, with a change in abundance at one trophic level eliciting a quantifiable change at another. DeAngelis (1992) and Moore et al. (1993) derived the dynamics of producer, consumer, and detritus density. Biomass density, X, of producer i changes over time in relation to growth (at both the individual and population levels combined) and consumption, such that
dXi dt
¼ ri X i
n X
cij Xi Xj
ð8Þ
j¼1
where r represents the specific growth rate of the producer and c represents the consumption coefficient for consumer j. Biomass density of detritus, d,
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Jeff R. Powell
changes over time in relation to the amount of detritus entering the system from allochthonous inputs, autochthonous inputs due to unassimilated and unconsumed prey, and autochthonous inputs due to nonpredatory death of consumers, and the consumption of detritus, such that
dXd dt
¼ Rd þ
n X n n n X X X ðð1 ai Þcji Xj Xi Þ þ d i Xi cdj Xd Xj i¼1 j¼1
i¼1
j¼1
ð9Þ where Rd represents the rate of allochthonous input. Biomass density of consumer, j, changes over time in relation to decline due to nonpredatory death, decline due to being consumed by n consumers, l, and growth associated with consumption, such that n n X X dXj cjl Xi Xl þ aj pj cij Xi Xj ¼ dj Xj dt i¼1 l¼1
ð10Þ
functional webs are particularly useful for estimating how perturbations of the web, such as the removal of one or more trophic groups, will affect the abundance of other trophic groups. de Ruiter et al. (1995) linked the functional and energy web models by assuming that feeding rates (Fij) and biomass (Bi,Bj) in the energy model equal consumption rates (cijXiXj) and biomass density (Xi,Xj) in the functional model, respectively, in order to estimate the consumption coefficient
cij ¼
Fij Bi Bj
ð11Þ
from nutrient flux data and estimate interaction strengths, a, as the per capita effects of consumer j on prey i,
Fij aij ¼ Bj
ð12Þ
and vice versa,
aj pj Fij aji ¼ Bi
ð13Þ
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331
in soil food webs. Strong interactions occur when per capita effects of consumers on prey or vice versa are large. In an analysis of several soil food webs by de Ruiter et al. (1995), complex interactions, both strong and weak, had strong effects on stability. Varying the interaction strengths of most pathways in the root-pathway and at intermediate and higher trophic levels (secondary consumer and up) had strong impacts on food web stability, while varying the strengths of interactions among fungi or bacteria and their consumers had very little impact on stability. Rooney et al. (2006) further linked the models by relating interaction strength to the speed of energy flow, v, represented by the rate that consumer biomass is turned over, such that
Pn vj ¼
i¼1 aij
Bj
ð14Þ
for energy flux into consumer j and
Pn l¼1 alj vj ¼ þ dj Bj
ð15Þ
for energy flux out of consumer j, suggesting that fast energy flux webs are composed of strong interactions and slow energy flux webs contain weak interactions. Rooney et al. (2006) observed similar asymmetrical partitioning of energy to pathways in six marine (pelagic vs benthic) and terrestrial (bacterial vs fungal) food webs, with higher-order consumers deriving energy from both pathways and coupling the pathways. By varying the energy flowing through one pathway relative to a second constant pathway, they observed that stability (associated with both resilience and resistance) was lowest when the two were equal and increased with increasing difference between the variable and constant pathways. Temporal asynchrony in the flux of energy through different pathways means that consumers at higher trophic levels, where the soil food web is much more reticulate, may be less likely to encounter highly variable resource availability (McCann et al., 2005). Moore et al. (2005) modeled the stability of a two-channel food web, containing a single resource base, two primary consumers, two secondary consumers, and a single top predator and using parameters from the Colorado shortgrass steppe food web (Hunt et al., 1987), and varied the proportion of energy partitioned to each pathway; they found that the system demonstrated stability when 20–60% of energy was partitioned to the fast (bacterial) pathway, the optimum being 40%. Simulated patterns of allocation outside of this range result in unstable dynamics in food web structure. Stability is
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Jeff R. Powell
thought to correspond to the nature of resource inputs into the system. Roots respond dynamically to herbivory, so availability is subject to negative feedback dynamics between resource inputs and consumer activity; detritus, however, is donor controlled, so consumer activity has no direct effect on future resource inputs (Moore et al., 1993). In addition, the greater resistance and/or resilience of the bacterial energy pathway also facilitates compartmentalization and overall system stability (Moore and de Ruiter, 1997; Whitford, 1989).
4.2.2. Application An environmental stressor may have an effect on one functional group or individual species within a number of functional groups. However, if the strengths of interactions with that functional group or those species, or if energy flow through the food web is sufficiently structured that the web is stable in the face of environmental stressors, these impacts may be less ecologically significant. For example, a simulated disturbance to an empirically based, two-compartment food web suggested that compartments improve total food web stability by retaining the effects of disturbance to the affected compartment, thus protecting other compartments (Krause et al., 2003). On the other hand, environmental perturbations that alter abundance within one or more components of the food web may affect overall food web structure over a timescale greater than that of the experiment. Thus, modeling responses in soil food webs might be useful to (1) predict how such perturbations may affect ecosystem function or (2) estimate the degree to which one or more functional groups must be affected to show a reduction in ecosystem stability. To utilize this modeling approach, parameter estimates should be appropriate for the system under study. Moore et al. (1996) described the roles of laboratory and microcosm experimentation required to parameterize these models. Researchers estimated predation and death rates, consumption coefficients, and assimilation and production rates of the organisms involved in the food web (Table 2). They based estimates on laboratory experiments (for lifespan and feeding behavior) and field measurements (for tissue digestibility, C:N ratios, and biomass C or N present within each of the trophic groups). It is feasible to use many of these parameter estimates for studies conducted in similar ecosystem types. However, the distribution of biomass and energy flow in soil food webs varies in a number of ecosystem types and assembled communities and, therefore, caution is necessary when employing parameter estimates derived from other studies. For example, meadows typically have higher levels of available nitrogen, higher denitrification rates, contain litter with lower C/N ratios, and retain less mineralized nitrogen than do forests (Griffiths et al., 2005; Ingham et al., 1989).
Table 2
Estimates of parameter values used in food web models Consumption coefficient cij [(g m^2)^1 year^1]
Functional group Herbivores Phytophagous nematodes Microbes Bacteria Fungi Microbivores Mycophagous collembola Mycophagous oribatida Mycophagous prostigmata Mycophagous nematodes Protozoa Bacterivorous nematodes Omnivorous nematodes Predators Predatory nematodes Nematophagous mites Predatory mites a
Horseshoe Bend
Lovinkhoeve
Kjettslinge
ai
pi
di (year )
CPER native
0.25
0.37
1.08
0.010
0.013
0.018
0.166
0.133
0.026
0.026
1.00 1.00
0.40.5 0.40.5
0.501.20 0.501.20
<0.001 <0.001
<0.001 <0.001
<0.001 <0.001
<0.001 <0.001
<0.001 <0.001
<0.001 <0.001
<0.001 <0.001
0.50
0.35
1.84
0.016
0.008
0.009
0.026
0.045
0.002a
0.002a
0.50 0.50
0.40 0.40
1.20 1.84
0.011 0.016
0.005 0.008
0.006 0.009
0.018 0.026
0.033 0.045
0.38
0.37
1.92
0.032
0.010
0.011
0.596
0.733
0.004
0.002
0.95 0.60
0.40 0.37
1.006.00 2.68
0.005 0.006
0.001 0.002
0.001 0.003
0.002 0.022
0.003 0.023
<0.001 0.004
<0.001 0.004
0.60
0.37
4.36
0.008
NA
NA
NA
NA
NA
NA
0.50 0.90 0.30
0.37 0.35 0.35
1.69 1.84 1.84
0.003 0.058 0.060
NA NA 0.327
NA NA 0.294
0.013 0.554 0.485
0.017 0.865 0.545
1.081 0.155b
1.016 0.178b
^1
ct
nt
if
cf
B0
B120
Mycophagous arthropods were treated as a single group. Predatory arthropods were treated as a single group. Data were obtained from a native shortgrass prairie at the Central Plain Experimental Range (CPER) in Colorado, Horseshoe Bend in Georgia (ct, conventional tillage; nt, no tillage), Lovinkhoeve in the Netherlands (if, integrated farming; cf, conventional farming), and Kjettslinge in Sweden (B0: barley low nitrogen; B120: barley high nitrogen). ai, assimilation efficiency; pi, production efficiency; di, nonpredatory death rate; cij, consumption coefficient; NA, group was not present at the site or was included with another functional group in the description. Reprinted from Moore et al. (1993), with permission from AAAS. b
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Stoichiometric differences among ecosystems are often used to explain shifts in the relative contributions of bacterial and fungal energy pathways. In a comparison of soil food webs from a prairie, a meadow, and a pine forest (Ingham et al., 1989), fungal feeders were more dominant in the forest than the meadow, with the opposite being observed for bacterial feeders, suggesting that the relative strengths of each energy pathway changed as the ratio of bacterial-to-fungal biomass changed (10:1 in the meadow, 1:8 in the forest). In a global literature survey representing 65 different sites, Ruess (2003) used the CI derived from nematode communities to test the hypothesis that the relative contribution of the fungal energy pathway increases as the ecosystem type moves from grassland through various stages of forest. Coniferous forest sites had the highest mean CI (see previous section; CI ¼ 50) relative to deciduous forest (CI ¼ 18), grassland (CI ¼ 24), and cropland (CI ¼ 18). However, all ecosystem types displayed a wide range of CI estimates (3–99 for coniferous forest, 12–31 for deciduous forest, 8–66 for grassland, 3–67 for cropland), suggesting that climatic, edaphic, and/or other factors exerted greater control. Management practices also affect energy allocation to fungal versus bacterial energy pathways. Moore and de Ruiter (1991) compared the dynamics of the soil food web from the native shortgrass prairie (Hunt et al., 1987) to those of the Lovinkhoeve winter wheat cropping systems food webs (Andre´n et al., 1990). The authors observed that conventional management practices resulted in the disappearance of much of the temporal separation in activities of the energy pathways, but that the proportion of energy derived from the different energy pathways by polyphagous predators was similar among the native prairie and the winter wheat fields, even though the ratio of bacterial-to-fungal biomass differed to a certain extent (10:1 and 50:1 at each site, respectively). In another example, Bardgett et al. (2001) sampled submontane ecosystems in the United Kingdom that varied in either grazing history (short-term and long-term ungrazed vs grazed) and in grazing intensity, associated with changes in the structure of plant communities. Moderate and intense grazing intensities were associated with relatively low and high ratios of bacterial-to-fungal biomass, respectively. Distribution of biomass within consumer trophic groups was not measured, but grazing history and intensity were associated with changes in nematode abundance, suggesting that trophic interactions may have been affected. Ettema et al. (1999) monitored the belowground impacts of fertilizing a riparian forest and observed that bacterial-, but not fungal-feeding taxa, increased in abundance following fertilization. Fertilization also resulted in stronger correlations between measurements of microbial biomass/activity and predator abundance for both bacterial- and fungal-feeding nematodes, indicating a synchronization of predator–prey dynamics (Ettema et al., 1999); responses of fast growing, opportunistic taxa to enrichment may overwhelm the time lag associated with slower-growing nematode taxa.
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Mulder et al. (2006) analyzed 99 Dutch soil food webs, derived from estimates of bacterial, nematode, microarthropod, earthworm, and enchytraeid abundances, differing in the degree to which humans impacted them. Food web connectance was lowest for the unmanaged ecosystems (dry heathlands and mature grasslands), indicating greater structuring of trophic interactions, even though these were not necessarily the most diverse or complex ecosystem types. Presence of macroherbivores was associated with greater eukaryotic biomass but also reduced soil biodiversity, trophic diversity, and food web complexity. The models presented earlier may also be used to enhance functional group abundance estimates that may be masked by trophic interactions. Allen et al. (2005) used consumption coefficients [cij; Eq. (11)] to estimate bacterial and fungal biomass consumed by nematodes and microarthropods. Total biomass (bacterial or fungal biomass in soil plus bacterial or fungal biomass consumed) allowed the authors to test for effects of elevated CO2 on these functional groups independent of top–down interactions that may mask the effects. In one case, elevated CO2 was associated with greater total fungal biomass but this effect was not detected when the estimate of only the standing crop of fungi was used.
5. Missing and Ambiguous Components of Current Soil Food Web Knowledge A number of factors may affect the robustness of using this modelbased approach to soil food web dynamics for evaluating ecosystem-level effects. For example, food web connectance is dependent on the species richness of the food web. Amalgamation of soil food webs at the level of functional groups underestimates food web richness and, probably, biases connectance estimates (Wardle, 1995). Studies by Martinez and coworkers (Martinez, 1993; Martinez et al., 1999) indicate that food web structure predicted by theory is highly dependent on the resolution at which the interactions within the web are resolved. We also lack a complete understanding of the roles that many soil organisms play in the food web. Here, I discuss some shortcomings of the current soil food web model and draw attention to techniques that should help to address some of these shortcomings.
5.1. Resolution Functional groupings underestimate the diversity of organisms taking part in soil food webs. For example, carnivorous nematodes are represented in one or two trophic groups, as predatory or omnivorous, in most food web models.
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However, researchers observe differences in feeding behavior of predatory nematodes, generally, depending on taxonomic affiliation, that may affect the prey items that they feed on (Khan and Kim, 2007; Yeates et al., 1993). Predators in the order Mononchida eat nematode prey whole or cut nematodes into smaller pieces before ingesting them. Those in the Diplogasterida can be omnivorous, feeding on microbes and microbivores, and possess a smaller buccal cavity than mononchs, limiting their ability to feed on large prey. Predators in the order Dorylaimida are also omnivorous but ingest prey contents after either piercing the prey with an odontostyle or slicing it with a mural tooth. Predators in the Aphelenchida feed in a fashion similar to dorylaims, piercing prey with a stylet and paralyzing it, followed by ingestion of body contents; aphelenchs are generally smaller than dorylaims but are able to enzymatically digest and consume large prey items. Khan and Kim (2007) summarize the results of feeding experiments and measurements of field populations from studies addressing the biological control potential of various predatory nematode species, revealing particular effects at finer levels of taxonomic resolution. Some predatory species (e.g., Mononchus aquaticus) feed on a wide range of plant-feeding nematodes, while others (e.g., Discolaimus arenicolus) have a narrow observed prey preference. Nematode species also differ in terms of the degree with which they suppress prey populations, ranging from no effect to complete elimination. The data summarized by Khan and Kim (2007) are limited to observations where prey items were plantfeeding nematodes, but these observations suggest that finer resolution will reveal greater trophic structure in other parts of the soil food web, as well. Another simplification is that food web diagrams often depict unidirectional flows of energy through consumers. Real webs, however, contain omnivory (feeding at more than one trophic level) and cannibalism (feeding within one’s own functional group), which are difficult to detect unless stable isotope abundance within the different biomass pools is also estimated (Section 5.2). As a result, the extent of consumption in food web models is generally underrepresented. In addition, some organisms are grouped within lower trophic levels but actually consume organisms at higher trophic levels. For example, nematophagous fungi feed on a variety of nematode species but, when quantifying soil food web dynamics, the abundance of nematophagous fungi is represented as fungal biomass, thus overestimating resource abundance and underestimating consumer abundance.
5.2. Integration of the detritivore and herbivore food webs A great deal of effort has gone into describing trophic interactions involving detritivores and root herbivores, mainly nematodes, to determine their roles in nutrient cycling and plant health, respectively. However, interactions involving consumers of root herbivores and symbiotic microorganisms are
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often poorly integrated into food web models. Aboveground, pathogenic fungal biomass was similar to herbivore biomass in a grassland experiment (Mitchell, 2003), suggesting that pathogenic microorganisms, in theory, could form the base of a food web energy pathway through which a considerable proportion of energy flows. Pathogenic bacteria and fungi are likely similarly abundant belowground; however, the extent to which these pathogenic organisms are consumed directly, and thus their direct involvement in soil food webs, is unclear. Many symbiotic organisms are susceptible to predation at some point in their life cycles. Some microbial antagonists (e.g., myxobacteria) kill bacterial and fungal pathogens and assimilate the contents of lysed cells (Dawid, 2000); further predation on these antagonists would constitute a food chain. Protozoan predation regulates population density of nitrogen-fixing rhizobia (Danso and Alexander, 1975). Mycorrhizal fungi have extensive hyphal networks, extending into the soil from plant roots, that may be subject to grazing (Smith and Read, 1997). Ectomycorrhizal fungi are able to access carbon through their association with ectomycorrhizal plant hosts or via decomposition of recalcitrant carbon sources, suggesting that their consumers access carbon derived from both the detritus and living plants. AM fungi, on the other hand, are unable to access carbon other than that derived from the mycorrhizal host and, thus, the path of energy flow is less ambiguous. However, there is currently some debate as to how AM fungi fit into soil food webs. Laboratory experiments (Moore et al., 1985) and studies with field soil using hyphal in-growth cores (Johnson et al., 2005) suggest that mites and collembola represent significant sources of biomass loss for AM fungi, yet choice experiments and vertically structured microcosm experiments (Klironomos and Kendrick, 1996) suggest that AM fungi are less palatable to microarthropods and that the vertical distribution of fungi in litter and soil plays a significant role in determining whether trophic interactions occur among fungi and their consumers. Root-feeding arthropods are attacked by a variety of parasites and predators. Entomopathogenic nematodes of the families Steinernematidae and Heterorhabditidae, as a group, can infect and kill a range of soil insects, herbivorous and otherwise (Poinar, 1979). The nematodes are essentially bacterivores, feeding on bacteria that they carry around with them and inoculate into the host hemocoel, and only occur outside of the host as an infective juvenile stage. As these infective juveniles have patchy distributions (Stuart and Gaugler, 1994), they represent an ephemerally abundant food source containing energy derived largely, but not entirely, from living plants. Tracking the source of that energy, however, is complicated since taxonomic identification of infective juveniles is difficult, abundance is generally determined through indirect and imprecise measures, and, even in cases where relationships involving specific herbivores and nematodes in
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the field are described (Parkman et al., 1993; Strong et al., 1996), unknown alternative hosts may contribute significantly to energy flows. In addition to their involvement within the herbivore food web, root herbivores and symbionts may indirectly influence the detritivore food web via their effects on the productivity of individual plant species (De Deyn et al., 2004; Klironomos, 2002, 2003) and entire plant communities (Burdon, 1987; De Deyn et al., 2003; van der Heijden et al., 1998), thus controlling the amount of detritus entering the soil food web. However, other factors complicate the relationship between plant productivity and activity in the detritivore food web. In a 3-year field experiment, Wardle et al. (1999) did not observe consistent associations between plant biomass and abundance within various functional groups of soil biota; the authors suggested that long-term litter and soil organic matter dynamics, resource quality, and regulation of consumer populations by predators structured soil communities to a greater extent than short-term changes in plant biomass. More detailed descriptions of soil food web dynamics may help to address uncertainties regarding the linkages between the herbivore and decomposer food webs, and the indirect versus direct effects of herbivore activity on consumer activity in the decomposer food web. Some invertebrate herbivores spend their entire life cycle belowground and actively disperse over relatively short distances (e.g., various plantfeeding nematodes), while others have both aboveground and belowground components of their life cycles and can actively disperse over long distances (e.g., various dipterans have larval stages described as ‘‘root maggots’’). This distinction may be of functional consequence since the consumption of invertebrates with aboveground dispersal stages prevents their emergence and dispersal and, thus, retains nutrients within the system. This process is analogous to that recently suggested for aquatic food webs contained within bromeliads (Ngai and Srivastava, 2006). Therefore, consumption of/by invertebrates with aboveground dispersal stages should have a greater per capita effect on local nutrient dynamics than consumption on/by those without aboveground dispersal stages.
5.3. Role of technology in resolving soil food webs Initially, when describing trophic interactions, soil ecologists were limited to conducting feeding trials under artificial, laboratory conditions and examining gut contents of field-collected specimens. The adoption of stable isotopes was a significant technological advance that allowed soil ecologists to describe feeding behavior and energy flow through soil food webs in the field (Hunt et al., 1987). Isotopic signatures (13C, 15N) in consumer biomass vary predictably in response to signatures in resource biomass. The isotopic signature of a material is depleted each time that material passes through a consumer; therefore, an organism’s isotopic signature can be use to infer its
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trophic level and whether it has engaged in omnivory (McNabb et al., 2001). Soil ecologists were thus able to validate the placement of functional groups within trophic levels and estimate the number of trophic levels present within the web, but were not able to detect specific consumer– resource interactions. However, a number of recently developed techniques should facilitate the mapping of these interactions in much greater detail. Analysis of dietary fatty acids may provide further resolution of trophic interactions to a level where the investigator can identify, broadly, the taxonomic group that the sampled organisms fed upon. Fatty acids present in cellular membranes of bacteria and fungi are assimilated into neutral (storage) lipids of their consumers and can be detected across at least three trophic levels (Ruess et al., 2005). Groups of bacteria (e.g., aerobic bacteria, anaerobic bacteria, cyanobacteria) and fungi (AM fungi, other fungi) can be resolved by the relative abundance of phospholipid fatty acids (PLFAs) that make up cellular membranes, and certain fatty acids are used as biomarkers to determine the presence and relative abundance of some groups in soil (Frostega˚rd et al., 1993; Olsson, 1999) or the diets of consumers (Ruess et al., 2005). Ruess et al. (2005) found the technique powerful enough to discriminate the regions from which collembolans were sampled; some species (e.g., Folsomia quadrioculata) had similar fatty acid compositions in the different regions, suggesting similar diets, while others (e.g., Neanurum muscorum) differed in fatty acid composition at the different regions, suggesting a geographic pattern in diet. This technique is limited to detecting trophic interactions in which fatty acids are assimilated into consumer biomass; organisms that are consumed but whose fatty acids are not assimilated are not detected. Recently developed approaches combine the analyses of stable isotopes and dietary fatty acids to gain simultaneous estimates of dietary preferences and food quality. Haubert et al. (2006) provided a variety of bacterial isolates to each of three different collembolan species, observing shifts in the neutral lipid fatty acid (NLFA; i.e., storage lipids) profiles of collembolans depending on the bacteria on which they fed. Three different surrogate variables, in addition to body mass and C/N ratio, were used to infer that one of the bacterial species represented poor food quality for the collembolans; collembolan NLFA:PLFA ratios were reduced and both 13C and 15N were enriched, suggesting metabolic mobilization of lipid reserves. Another approach for studying trophic interactions, stable isotope probing, involves the pulse-labeling of a resource and attempting to detect its presence in potential consumers; by looking for the presence of the isotopic signature within biomarker PLFAs, the consumer of the resource can be identified (Dumont and Murrell, 2005). Johnson et al. (2005) used stable isotope probing to estimate the extent to which grazing by collembola reduced AM fungal growth, indicated by the PLFA 16:1o5. Another novel approach to resolving trophic interactions in soil is the analysis of DNA in the gut contents of predators. Soil contains a number of
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materials (e.g., humic acids) that inhibit the polymerase chain reaction and can, therefore, lead to false negatives in detection of target DNA. Two recent studies describe protocols that allow for detection of prey DNA in the gut contents ( Juen and Traugott, 2006) and whole specimens (Pons, 2006) of predatory soil invertebrates. Juen and Traugott observed DNA degradation over two days in their experiment, suggesting that this method would be useful for determining the identity of prey that had been recently consumed. Clearly, the use of this method would be constrained to consumers that ingest their prey prior to digestion, as do many bacterial- and fungal feeders and organisms at higher trophic levels. Many consumers occupying low levels in the soil food web, including bacteria and fungi, obtain nutrients following the secretion of extracellular enzymes. Advances in stable isotope probing of DNA sequences may allow for high resolution of trophic interactions at the base of the soil food web. Dumont and Murrell (2005) review the use of stable isotope probing in environmental microbiology. Here, microorganisms in environmental samples are exposed to a stable isotope-labeled substrate (e.g., 13C-labeled glucose). RNA is then extracted from the sample, labeled with a fluorescent probe, and hybridized to an oligonucleotide array to identify the RNA sequences extracted from the sample. The array is then viewed using autoradiography to determine which of the extracted RNA sequences were derived from organisms utilizing the labeled substrate (i.e., contain radioactive elements). A recent study adapted the technique to characterize microbial trophic interactions. Lueders et al. (2006) amended field soil with 13C-labeled Escherichia coli, separated labeled RNA from unlabeled RNA using equilibrium density gradient centrifugation, and then characterized the RNA sequence heterogeneity of the two fractions. Sequences belonging to fungi in the Microascaceae and bacteria in the Xanthomonadaceae, Myxococcales, and Bacteroidetes were associated specifically with the 13C-labeled RNA fraction, suggesting that some organisms in these groups assimilated nutrients derived from the amended E. coli.
6. Summary and Conclusions Soil communities are sensitive indicators of environmental disturbance and recovery. Monitoring soil food web dynamics provides information regarding not just organism abundance but also energy flow, nutrient dynamics, and ecosystem stability. Many current studies do not consider these latter, emergent properties of soil food webs; those that do are advanced in that they attempt to estimate the functional consequences of environmental perturbations, and it is these consequences that many stakeholders are interested in. Evaluating these latter properties is difficult since
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they may not be observed at the spatial and temporal scales used in the experiment. Bacteria, fungi, and their consumers, particularly nematodes, are frequently used for these purposes. Advances in our understanding of food web structure and function should help improve the sensitivity and utility of soil communities by further indicating functionally important components of the soil food web and provide greater predictive power as to how environmental disturbances will influence soil food webs and ecosystem functioning.
ACKNOWLEDGMENTS I thank John Klironomos for reviewing this chapter and the Natural Sciences and Engineering Research Council of Canada for providing a postgraduate scholarship.
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C H A P T E R
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Comparative Typology in Six European Low-Intensity Systems of Grassland Management Rafael Caballero,1 Jan A˚ge Riseth,2,3 Niklas Labba,3 Ewa Tyran,4 Wieslaw Musial,5 Edyta Molik,6 Andrea Boltshauser,7 Pius Hofstetter,8 Anne Gueydon,9 Norbert Roeder,10 Helmut Hoffmann,9 Manuel Belo Moreira,11 Inoceˆncio Seita Coelho,12 Olga Brito,11 and A´ngel Gil1 Contents 353 355 355 358 359 359 360 361 361 362 367 368
1. Introduction 2. Presentation of Study Areas 2.1. Northern Sapmi, Fennoscandia 2.2. Tatra mountains, Poland 2.3. UNESCO Biosphere Entlebuch, Switzerland 2.4. Bavaria, Germany 2.5. Baixo Alentejo, Portugal 2.6. Castile-La Mancha, Spain 3. Material and Methods 3.1. Main criteria and indicators 3.2. Management units 3.3. Sampling process
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Centro de Ciencias Medioambientales, CSIC, Madrid, Castile-La Mancha, Spain NORUT Ltd., Troms , Norway, Northern Sapmi, Scandinavia Sa´mi Institute, Kautokeino, Norway, Northern Sapmi, Scandinavia Department of Agribusiness, Agricultural University of Krakow, Tatra Mountains, Poland Department of Agricultural Economics and Organization, Agricultural University of Krakow, Tatra Mountains, Poland Department of Sheep and Goat Breeding, Agricultural University of Krakow, Tatra Mountains, Poland UNESCO Biosphere Reserve Entlebuch, CH-Schupfheim, Entlebuch, Switzerland Schupfheim Agricultural Education and Extension Center, CH-Schupfheim, Entlebuch, Switzerland Lehrstuhl fu¨r Wirtschaftslehre des Landbaues, Technische Universita¨t Mu¨nchen, Bavaria, Germany TUM Business Scholl, Environmental Economics & Agricultural Policy Group, Technische Universita¨t Mu¨nchen, Bavaria, Germany Instituto Superior de Agronomia, Technical University of Lisbon, Baixo Alentejo, Portugal Instituto Nacional de Investigac¸a¨oo Agra´ria e Pescas, Ministe´rio da Agricultura, Desenvolvimento Rural e Pescas, Lisbon, Baixo Alentejo, Portugal
Advances in Agronomy, Volume 96 ISSN 0065-2113, DOI: 10.1016/S0065-2113(07)96001-0
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2007 Elsevier Inc. All rights reserved.
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4. Results 4.1. Land uses 4.2. Size of farm-holding, land prices, and grazing fees 4.3. Institutional economics 4.4. Institutional and legal frameworks 4.5. Forage deficit 4.6. Grazing infrastructure 4.7. Labor 4.8. Productivity estimates 4.9. Economic performance 4.10. Grazing management and trends 4.11. Main limiting factors 4.12. Interface to biodiversity 5. Discussion References
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European biodiversity significantly depends on large-scale livestock systems with low input levels. In most countries forms of grazing are organized in permanent or seasonal cooperations (land-owner/land-user agents) and covers different landscape such as alpine areas, forest, grasslands, mires, and even arable land. Today, the existence of these structures is threatened due to changes in agricultural land use practices and erratic governmental policies. The present chapter investigates six low-input livestock systems of grassland management with varying degrees of arrangements in different European countries and landscapes. These large-scale grazing systems (LSGS) are reindeer husbandry in Northern Sapmi (Fennoscandia), sheep grazing in the Polish Tatra mountains, cattle grazing in the Swiss and German Alps, cattle, sheep, and pig grazing in Baixo Alentejo, Southern Portugal, and sedentary sheep grazing in Central Spain. These systems showed very heterogeneous organizational patterns in their way of exploiting the pastoral resources. At the same time, these LSGS showed at least some of the following weaknesses such as poor economic performance, social fragility, and structural shortcomings for proper grazing management. Lack of proper mobility of herds/flocks or accession to specific grazing grounds can be a cause of environmental hazards. The surveyed LSGS are mostly dependent on public handouts for survival, but successive policy schemes have only showed mixed effects and, in particular study areas, clear inconsistencies in their aim to stop the general declining trend of LSGS. This research assumed that detailed system research may open the way for better-focused policy intervention, but policymakers need to take advantage of this period of support to push ahead for reforms. Recent European Union (EU) guidelines (2007–2013) on Rural Development Policy (RDP) and its operative scale of high nature value (HNV) farmland can easily fit the structure and functions of low-input grazing systems and LSGS.
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1. Introduction Large-scale extensive livestock systems in Europe represent large chunks of European land relative to the size of the business operations. As large-scale systems, they may represent links with nature values at the landscape level, and as extensive systems, they may represent low input and low value of production relative to the size of the business at the farming level. These large-scale grazing systems (LSGS) are mainly located in the most remote and less favored areas (LFAs) with harsh environmental and sometimes difficult social conditions. A small part of the rural population stands to make a living by maintaining traditional grazing practices, which, in turn, shaped the environment. As being located in mostly developed countries, these systems have faced two main threats: intensification and abandonment (the most extreme form of extensification). In the first case, the harsh environments have limited the impact. In the second case, some studies have pointed out the risk of abandonment in the LFAs of the European Union (EU) (Baudry et al., 1996; Caravelli, 2000; Garcia-Ruiz et al., 1996; Muller, 1996; Zervas, 1998), but a pan-European coordinate socioeconomic research on the viability of LSGS is still lacking. Farming systems thought to satisfy ecological sustainability objectives must be economically attractive to farmers, if they are to be voluntarily adopted and continued (Dobbs, 2004). However, only 6 of the 22 studies on extensification of European livestock systems detailed in a review paper (Marriot et al., 2004) collected data on animal performance and only two individual studies showed some indicators on economic performance. None of these studies was previously coordinated at the European level. Maintenance of LSGS may thus be dependent on the fact that marginalization of agriculture, undermining viability of rural communities, does not go so far. In turn, LSGS may manage to fill in gaps created by a declining intensity of land use. In fact, the LFAs of the EU-12 represented some 56% of the EU’s total surface area, and contained much of the high nature value (HNV) farmland (Brouwer et al., 1997). Left to their own or under insensible schemes of policy support, the abandonment threat can be more prejudicial than the intensification threat (Atance et al., 2000; Kristensen et al., 2004; Vicente-Serrano et al., 2004). Both threats, however, may derive similar effects: disappearance of potential economic, environmental, and social values (Angelstan et al., 2003; Donald et al., 2002; Krohmer and Deil, 2003; Loumou and Giourga, 2003; Tucker and Heath, 1994; Waldhardt et al., 2004). It is common ground to highlight the importance of the agronomic and environmental services (pollinators, biological pest control, cultivated plants, and wild relatives, and so on) provided by these relatively undisturbed ‘‘natural ecosystems’’ (Hillel and Rosenzweig, 2005). It is less
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common to change the arrow of causality and posing the question on how these truly LSGS are going to survive and continue to provide their potential assets. European extensive systems of grassland management, notwithstanding their ample variation in environmental and structural components, face an encounter with modern farming or farming intensification. Can they survive with tactical concessions to modernity? Do they share some qualities that helped them to survive? Are they adept at anticipating or adapting to changes? Are their stakeholders shrewd managers of their assets? Are they good advertisers of their cultural, economic, or environmental utility? Are these systems able to integrate new values and functions to their products? These are some questions to which a typology of policy relevance may provide some answers. This research will argue that European policy intervention can be devised at the space scale of LSGS and HNV farmland. Structural and social constraints, as well as potential environmental assets, are linked to specific systems. Sensible policy schemes can only be devised after untangling these constraints. In the following chapter, we sum up the results of a parametric analysis of six European study areas. This study was conducted within the EUfunded research project ‘‘Landscape Development, Biodiversity and Cooperative Livestock System’’ (Caballero and Ferna´ndez-Santos, 2004; LACOPE, 2002). These six study areas, and their respective LSGS, cover a wide range of different ecological, social, and economic conditions and exhibit different adaptations of the grazing system. The investigated largescale extensive grazing systems are representative for some of the most widespread types of this kind of agricultural land use. One study focused on the reindeer grazing system of the boreal-alpine biogeographical region (Northern Sapmi); three LSGS represented different mountainous systems (Tatra Mountains in Poland, Bavarian Alps, and Swiss Alps). The remaining two covered outstanding Mediterranean systems: the open fields of Campo Branco and the surrounding Montado system in Portugal and the cereal–sheep system in Spain. Extensification is the process of reducing fertilizer inputs, management intensity, and stocking rates at the farm level and is central to sustainable rural policies. However, typology research in the LFAs is fragmented and extensification studies should adopt an approach that will allow their results to be applied throughout Europe (Marriot et al., 2004; Strijker, 2005). Successfully decoupling payments from production while maintaining HNV farming systems represent a severe challenge to the Common Agricultural Policy (CAP) of the EU. Studies are needed across a range of HNV areas in all of Europe’s biogeographical zones (Beaufoy et al., 2003). The main objective of this research was to assess whether some typology categories or common features can be drawn from the data of the six study areas or divergences between systems are perceptive for most headings and indicators.
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2. Presentation of Study Areas All study areas have in common that they represent pastoral systems under harsh environments. A significant part of the biodiversity depends on open land and the maintenance of particular pastures. To dismiss pastoralism as a backward pursuit, an embarrassment to notions of modernization, is to put aside a proven response to harsh environments. In most study areas, except Baixo Alentejo (Portugal) and to a lesser extent Entlebuch (Swiss Alps), pastoralists are not the owners of the land. This fact may represent problems of mobility of herds/flocks or access to pastureland. Full pastoral operations are carried out in the management unit (MU). This MU may encompass more than one farming unit of seasonal grazing use. Four of the investigated systems (Sa´mi reindeer management and the three alpine systems in central Europe) are still showing a pronounced seasonal migration pattern. The distance can easily exceed 200 km in Northern Sapmi, while it is rather short in the three alpine systems. The two Iberian systems are sedentary, albeit not devoid of mobility and access problems, especially in the cereal–sheep system of Castile-La Mancha (Spain). Key figures of the six study areas are shown in Table 1. Geographical location of the seven LACOPE study areas is depicted in Map 1. The study area of Connemara (west of Ireland) was not integrated in this report.
2.1. Northern Sapmi, Fennoscandia These LSGS take up a large tract of the northern part of the Scandinavian peninsula (Northern Fennoscandia), encompassing land of Norway, Sweden, and Finland. Reindeer management culture by Sa´mi herders is well entrenched in the area. National differences exist in historical background (Sandberg, 2006), herder production strategies (Riseth, 2000, 2003, 2006), as well as in the national legal and subsidy systems. In Sweden and Norway, reindeer management, with a couple of regional exceptions, is culturally and ethnically connected to Sa´mi people, while in Finland it is open to everyone and mainly a side industry to agriculture. Full seasonal migration, short-distance migration, and stationary patterns of reindeer herding can be envisaged, the first most common in Norway and Sweden and the latter in Finland. In Norway, the husbandry unit is the base for most subsidies. Husbandry unit leaders, by cultural tradition, are usually concession holders. The concession model, dated from 1978, is the legal foundation for awarding subsidies. Within husbandry units, other right holders, apart from the leader, can be herders with subsidy allocation rights. In the western Finnmark area (24,290 km2), to which the average Norwegian data are referred, 26 pasture
Table 1
Key figures of the regional agriculture
Total acreage of LSGS per study area (ha)g Grassland (t/ha dry matter) Wheat grain (t/ha) Orientation of the livestock production on the LSGS a b c d e f g
Northern Sapmia
Tatra Mountainsb
Entlebuchc
Bavariad
Baixo Alentejoe
Castile-La Manchaf
42,000,000
2500 g
7000 g
61,000 g
220,000
6,000,000
1–1.5
2.5 (mountain), 4 (valley) – Sheep (milk and meat-oriented)
1.5 (mountain), 7.5 (valley) – Mainly heifers
7 (foothills)
0.8
2
– Mainly heifers
1 Meat-oriented flocks (sheep, black pig, cattle)
2.2 Sheep (milk and meat-oriented)
– Reindeer (meat-oriented)
S yland et al. (2002). Reindriftsforvaltningen, 2005. Statistical Yearbook (2002). Data only for Tatra National Park. Regional management Biospha¨renreservat Entlebuch (2002). LBA (2002); Agrargebiet: Alpenvorland; meadows cut four to five times. INE (2001, 2002) and de Sequeira (1988) for the productivity figures. Caballero (2001). Total acreage of the grazing systems under collective form.
Biogeographical regions Alpine Anatolian Artic Atlantic Black sea Boreal Continental Mediterranean Pannonian Steppic
N
0
1000
2000
3000
4000
5000 Kilometers
Study areas 1. Northern Sapmi, Fennoscandia 2. Connemara, Ireland 3. Tatra, Poland 4. Bavaria, Germany 5. Entlebuch, Switzerland 6. Castile-La Mancha, Spain 7. Baixo Alentejo, Portugal National borders
Map 1 Biogeographical regions of Europe and location (hot spots) of LACOPE study areas.Source:http://dataservice.eea.eu.int/dataservice/ metadetalls.assp?table=Biogeo01&j=1.
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districts, 241 husbandry units, 1279 reindeer owners, and 84,200 reindeers are in the area (year 2003–2004). The subsidies are bound to the concession holder who has to produce a certain minimum amount of meat to qualify. Reindeer herders with no concession are not qualified for subsidies and with little incentive to get into the reindeer industry. Reindeer families are represented for one husbandry leader. The Swedish and Finish subsidy systems are not based on concession holders. In Sweden, the system is based on reindeer owners. Any owner with a minimum level of meat production counts in the official statistics. In Finland, the subsidy system requires husbandry masters to reach a minimum of 80 animals to qualify. Reindeer owners, with reindeer herding as their main source of living, receive a headage payment of 22 € per animal. In Sweden and Finland, one reindeer husbandry family may encompass several husbandry master or doallu (household). Regional data for Sweden (northern Norbotten la¨ns) average 332 husbandry masters, 1249 reindeer owners, and 56,522 reindeer in the area. Regional data for Finland (Ka¨sivarren paliskunta area) encompassed 128 husbandry masters, 168 reindeer owners, and 10,000 reindeer in the area (Paliskuntain, 2004).
2.2. Tatra mountains, Poland The LSGS in the Polish Tatra mountain (Carpathian region) are strongly linked to milk sheep. These sheep stay from late autumn to mid-spring on the lowland farms and graze clearings in the mountain forests or areas above the timberline (alps) in the summer for roughly 160 days under care of a flock master (baca). Usually, one baca, which is a small sheep farmer, gathers the sheep of other small farmers (gazdas) and takes them together to the alps. One average baca flock (some 300–350 sheep) can be composed of the flocks of some 20 sheep of different owners. Sheep flocks under baca’s care are allocated to several clearings (around 12 clearings per baca of an average size of some 5 ha) in the alpine forests. Some 75% of the clearings are privately owned and 25% is public (Tatra National Park) land. The clearings have several owners who claim property but do not have proper legal documents. Similarly, bacas cannot claim for subsidies in the alps as they do not have proper renting documents. As a whole result, devising and implementing proper policy schemes and incentives for moving sheep to the natural pastures in the alpine and subalpine zone (alps) cannot be properly established without an overhauling of the legal and institutional framework. Currently, farmers can only receive subsidies for land and sheep they own on the lowland farms. Although sharing shepherding for the summer season is traditional, cooperation between different stakeholders (bacas, gazdas, and landowners) is difficult to manage, people are independent and difficult to be engaged for any form of cooperation. Some 20 years ago, sheep were moved to distant
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clearings (up to 100–150 km). These clearings are currently underused due to difficulties in transportation and the low profitability. There is a need of keeping traditional ways of milk processing, but also to fulfill EU sanitary requirements. From mid-October to early May, individual sheep flocks are moved to lowland farms and fed indoor with hay produced in summer, while sheep flocks in the clearings.
2.3. UNESCO Biosphere Entlebuch, Switzerland In the UNESCO Biosphere Entlebuch (canton Lucerne) there are 211 units of alpine pastures (Hofstetter et al., 2006a) and 1015 (AfS, 2007) farm units in the valleys in the year 2005. Some 20% of the farm units of the valleys shared also one alpine unit, moving animals to the latter in the summer for some 110–130 grazing days, depending on the altitude, exposition, and grazing management (Hofstetter et al., 2006b). Boarded (external) animals stay an average of 110 days with small variation. Owned animals stay a mean of 130 days with larger variations, depending on whether and how much the owner provides alpine pastures’ hay to the herd. Most summer pastures are located in the prealpine area (67% between 1200 and 1400 m) and a small part in the high alpine area (up to 2500 m). The dominant grazing lot is heifers from the lowland farms. Most alpine units (72%) have only one stable and 28% more than one when allocated pastures of different altitude. Often, the area of the lowland farm unit in Entlebuch with an average of 14 ha (Hofstetter et al., 2006a) is small compared with the area of the alpine unit (mean of 57 ha). As a consequence, private owners or tenants of alpine units cannot properly stock the alpine units only with their own animals, and thus external animals are added to the mixed herd. Owners of these external animals pay a grazing fee to the owner/tenant of the alpine unit (LBL, 2004a). These grazing fees, together with the subsidies, are the main sources of income of most alpine units over the summer season. In the alpine units of Entlebuch, most external animals are heifers and sheep from the canton of Lucerne (Office for Agriculture and Forest, 2004).
2.4. Bavaria, Germany The Bavarian study area covers the German part of the Alps and the foothills in their vicinity. In the year 2005, 50,000 cattle grazed on rough pastures in this area (Miller, 2006). Heifers are the dominant livestock species on these rough pastures, which are mainly alps, or less frequently grazed moorlands in the foothills. Over 40% of the area used by this grazing system is under some form of cooperative livestock management or cooperative livestock system (CLS). This makes the Bavarian study one of the remaining strongholds of CLS, which were fairly widespread in Germany until the
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nineteenth century. For convenience, all CLS in the Bavarian study areas will be labeled Allmende. Typically, several lowland dairy farmers participate in the use of one Allmende on which they raise their replacement heifers during the vegetation period. Despite the awarded incentives, the intensity of land use declined within the last 20 years. The number of active members, the ones who send animals to the pastures of the Allmende, not only dropped from 27 to 17—or 2% per year—for the average Allmende, but also the number of animals using these pastures declined. The mean size of the upland Allmende units in the sample was some 400 ha. The average unit is more or less evenly divided between forest and grazing land. A shared characteristic of the Bavarian and Swiss systems is that they encompass a low-intensity farming unit of HNV, which is only an appendix to a more intensive form of land use (the lowland farm unit).
2.5. Baixo Alentejo, Portugal Two types of landscape characterize the Baixo Alentejo study area: the open field on the flatland (Campo Branco) and, in the surrounding area with a slightly rougher morphology and/or shallower soils, the Montado system. In the open fields, farmers develop a more or less long rotation according with the soil type, based on rain-fed cereal and extensive grazing in fallow areas. This type of land habitat suited to steppe bird species and determined the classification of the area as a Special Conservation Area (ICN, 2006). The Montado system is an agrosilvopastoral system comprising an open formation of cork (Quercus suber L.) and/or holm (Q. rotundifolia Lamk.) oaks, combined with grazing activities (Coelho, 1997; Moreira and Coelho, 1997; Pinto-Correa, 2000; Pinto-Correa and Mascarenhas, 1999). In the Portuguese case, a proper CLS does not exist, but rather a privatedominant property system with private grazing rights is dominant. Main actors are large landowners, either managers of their farm-holding or renters; medium-size farmers who frequently have to rent additional land in support of a mixed crop and livestock operation; and small farmers and landless pastoralists, the latter keeping their animals under renting agreements with landowners. Nevertheless, livestock production in this study area is predominantly assured by large landowners even if, in many cases, the wage granted to pastoralists includes the right to keep own animals jointly with the landowner flock. A more complicated picture appears when land uses and livestock species are considered. In Baixo Alentejo three main land uses are dominant: the open areas of cereal cultivation, where operate a mix of cereal cropping with cattle and sheep grazing; the Montado, open forest of Quercus spp. (holm and cork oaks), where mixed farming of Alentejano pig, meat cattle and sheep may operate with cork extraction; the shrubby encroachment
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areas under large fallow periods, where hunting appears as a strong competitor to grazing activities as well as EU-supported afforestation programs. Tourism operators may overlay in different land uses.
2.6. Castile-La Mancha, Spain The southern Castilian plain forms most of the basin of the Tajo and Guadiana rivers. The whole region occupies an area of some 7.8 106 ha and is divided into five administrative provinces (Albacete, Ciudad Real, Cuenca, Guadalajara, and Toledo) and 916 municipalities. The central part of the region is properly called the La Mancha plain, where arable land is dominant (some 80% of total agricultural land, TAL). In this Spanish study area, private landownership is dominant with some common grazing land in the mountains surrounding the plain. Mixed arable and sheep operations, where existing, are carried out of the same land units (grazing allotments or polı´gonos de pastos), under private ownership of arable land and public grazing rights, awarded to landless pastoralists (customary use-rights). Sheep farmers (both milk- and meat-oriented) take advantage of agricultural residues in arable land (mainly cereal stubble and fallow land).
3. Material and Methods Field data were gathered in agreement with common headings and indicators, based on previous coordinate effort (Caballero and Ferna´ndezSantos, 2004). Most teams except Northern Sapmi, which used public statistics for Norway, used questionnaires as field data collection tool and livestock farmers as individual recipients. In the case of Northern Sapmi, the reindeer district was used as sample unit and official records as source of information gathered at the level of the MU, in this case the husbandry unit within district. In case of the three alpine systems, the whole MU encompassed two farm units as livestock farmers moved animals from alpine private and commons grazing land over the summer season to farm holdings in the lowland over the rest of the year. In the two Mediterranean systems, the animals stayed over the year in the same MU, being private farm holdings in the Montado of Baixo Alentejo (Portugal) and grazing allotments ( polı´gonos de pastos) composed of several farm holdings under public allocation of grazing rights in Castile-La Mancha (Spain). Questionnaires were drafted based on main criteria as agreed on the matrix-heading: land uses, farm size and land ownership schemes, forage deficit (FD), grazing facilities, stocking, grazing management, economic performance, labor, and institutional factors. Productivity estimates were calculated on either by working unit (WU) or land unit.
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3.1. Main criteria and indicators Determining habitat and landscape features that lead to patterns of biodiversity is an important step for the assessment of the impact of extensification in agriculture. Local habitat factors for organisms are those influenced by management practices (Jeanneret et al., 2003a) and seminatural biotopes (Zebisch et al., 2004). The first authors stressed the influence of surrounding land use. There are no general models relating overall species diversity to landscape diversity, being the relationship depending on the organism examined. Land uses have been identified in our study areas as a requirement to assess biodiversity responses. These responses to landscape and habitat changes have to be identified by means of a multiindicator concept in different landscapes situations ( Jeanneret et al., 2003b). In the six study areas very different kinds of pastoral resources are used, but grazing and nongrazing land uses were differentiated. Intensity of use of grazing resources was also stressed either by accounting the level of use of potential resources or the spatiotemporal distribution of grazing use. In Northern Sapmi, winter and summer reindeer grounds are differentiated, partly within each country and partly across country borders, with Norway having excellent summer pastures in the suboceanic mountain ridge, and Sweden and Finland mostly winter pastures in the dry continental woods. Thus, the annual grazing cycle follows the directions of big river valleys, although borders’ barriers have curtailed the traditional migrations of Sa´mi herders between countries (Riseth et al., 2003). In the three mountainous areas (Tatra, Entlebuch, and Bavaria), land uses are differentiated by farm units (highland summer pastures and lowland private farms). Grasslands are dominant in both farming units but the degree of use and intensity are different with higher intensity in the lowland farm, especially in Entlebuch and Bavaria, and some risk of abandonment of upland pastures (Grunig et al., 2004). Nongrazing land uses corresponded mainly to alpine forest and some protected areas. In the two Iberian countries, pastoral resources encompassed a mixture of arable land resources (stubble and fallow land) and natural pastures linked to open oaks’ forest, the first being dominant in Castile-La Mancha and the latter in the Baixo Alentejo. Nongrazing land in these study areas are mainly more intensive cropped areas of vineyards, olives, and plots under irrigation, as well as some protected areas such as subsidized afforestation parcels or young tree plantations. Farm size and land ownership schemes were defined either in private grazing land or in common grazing land. The latter dominates in the Northern Sapmi study area and in the highland pastures of Bavaria. Private landholdings dominate in Entlebuch, Tatra, in the two Iberian study areas, and in the lowland farms of the three alpine areas. The size of the MU varied largely among study areas, as well as the size of farming units
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within study areas. Livestock farmers have legal grazing rights in the commons of Northern Sapmi and highland pastures of the alpine areas, private grazing rights in all study areas, and consuetudinary (customary) use-rights to arable pastoral resources in Castile-La Mancha. Grazing fees were applicable for allocation of grazing rights, either in commons or in private grazing land. Modeling the FD was defined by a mass balance of available grazing days provided by complementary forage resources (CFR) as compared with structural nongrazing days (Caballero, 1993, 2003). These are days along the year where grazing is hampered by lack of vegetative growth, presence of snow, or humid soils. The only way to avoid an FD under grazing conditions is long- or short-distance migration patterns (trashumancia or trasterminancia). The first migration pattern represents long-distance and horizontal movements and the latter, shorter, and vertical movements. The FD represents the forage coverage of CFR on structural nongrazing season (SNGS). Most areas of Northern Sapmi have operative long-distance migration patterns though present countries’ borders and grazing restrictions based on international border conventions, to a considerable extent, have shortened or stopped traditional patterns for reindeer herding. Particularly in Finland, with relatively stationary grazing patterns, supplementary feeding covers the FD. Unfortunately, we have no sufficient data to evaluate the implications of this fact. In the Tatra Mountains and in the two Alps’ study areas, summer grazing days in the highland pastures assures 100–130 grazing days and the potential FD may occur in the lowland farms. In the two latter study areas, the potential FD can be more acute as only a proportion of the livestock units (LU) goes to the highland pastures. Productivity of CFR and proportion of TAL devoted to forage conserves are key issues. In the Iberian study areas, with stationary grazing patterns and climatic constraints, an FD may appear if forage conserves are not provided for coverage of the SNGS. Across study areas and farm units within study areas, grazing facilities may differ greatly. Fences, barns, water points, milking or slaughtering facilities, haymaking or manure handling machinery, remoteness, accessibility paths, or herders’ shelters are important indicators of less hardworking conditions, mobility, and homogeneous grazing use. Grazing facilities are of the outmost importance in highland pastures, remote areas, or grazing units where the pastoralists have limited resources or rights to improve grazing facilities such as in the Tatra Mountains or in Castile-La Mancha. Stocking was defined as number of LU per hectare of available pastureland over grazing seasons or grazing units. In three of the six study areas, one grazing species is dominant: reindeer in Northern Sapmi, and sheep in the Tatra Mountains and the Castilian plain. In the other three study areas, different livestock species or type of animals are dominants in specific units. In the two alpine areas of Bavaria and Entlebuch, heifers are dominant in the alpine units
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and dairy cattle in the lowland farms. In Baixo Alentejo, meat cattle is the dominant specie followed by meat sheep, except in the holm oak Montado where the Alentejano pig has its mostly demanded grazing territory. In these three areas with mixed grazing, units of different species and types of animals should be converted to LU by standard tables of equivalence to obtain the size of the herd by grazing unit. Similarly, on each land unit, nongrazing land should be detracted from TAL to obtain available pastureland. In Northern Sapmi, different stocking and grazing distribution can be related to patterns of migration of reindeer herding. In the Tatra Mountains as well as in the Alps, differential stockings are related to lowland farm stocking, over most of the year, and summer stocking in the highland pastures. In Baixo Alentejo, cereal- or Montado-dominant areas may support differential stocking densities. In the southern Castilian plain, stocking can be related to land uses, either arable or nonarable land-linked resources. By comparing stocking across study areas and grazing units some insight on grazing distribution can be obtained. The question, however, of whether study areas or specific grazing units are over- or underused remained unchecked. This assessment would require a comparison between potential stocking (base stocking or carrying capacity) and real stocking. Estimation of potential stocking would be based on availability, seasonal productivity, and quality of corresponding pastoral resources by study area or grazing unit. However, the FD mass-balance model, applied to most study areas except Northern Sami, may provide some insight on the adjustment of forage supply to animals’ requirements. In Northern Sapmi, pasture surveys have been used regularly in the latest decades. Several studies indicate considerable overgrazing of lichen resources (particularly winter but to some extent also fall pastures) in Finland and in the Norwegian LACOPE area of western Finnmark as well as the adjacent Karasjok area of eastern Finnmark (Colpaert et al., 2003; Johansen and Karlsen, 1998; Moen and Danell, 2003). Under the heading of grazing management, mobility of livestock across grazing units was checked within study areas, as well as grazing days within the grazing units. Main schemes of seasonal reproduction (mating/calvinglambing/milking seasons) allowed relating the physiological status of main grazing species with seasonal grazing units. Grazing management also assessed the animal lots under grazing or indoor feeding by grazing season as well as main herding practices. The latter included whether herds/flocks were permanently conducted or only temporarily checked. Main grazing species, animals’ lots, animals’ breed, production objectives, and main indicators of animals’ performance were also recorded in this heading. Different production objectives were recorded across the six study areas and even within one specific study area. Animals’ performance indicators were required for estimating the value of production farming. For milking lots, marketed milk per dairy cow or per
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breeding ewe and for meat production, the mean live weight (LW) of animals at selling or slaughter weight in the case of Northern Sapmi were recorded. Some other productivity indicators such as milk production per WU were also recorded. Distribution of milk production over the year or main processing-marketing channels of important livestock products were also recorded in most study areas. Under this heading, some indicators of future trends in the grazing management of LSGS were also recorded. Current or predicted changes in LSGS management may have some ecological and economic effects. Changes in land uses that can promote better grazing practices and productivity, trends in grazing days, animals’ lots under grazing, or trends in spatial distribution of grazing over the MU were some management indicators recorded. It was also important to assess the trends in the extensive grazing operations in the face of the European debate between extensification and intensification of LSGS (Caravelli, 2000; Marriot et al., 2004; Pinto-Correa and Mascarenhas, 1999; von Boberfeld et al., 2002). Economic indicators were recorded with the aim of allowing a certain harmonization of reporting and comparison between study areas. Classical cost-benefit analysis was the main tool devised for analysis. The heading was divided in two main tiers: income structure and cost structure. The latter recorded external supply of feeding inputs, animal health expenditure and veterinary assistance, grazing fees, amortization and interests, labor (family or waged), and other costs such as transportation, animal acquisitions, or stock depreciation. Within the income tier, value of production farming, subsidies, and other income sources were recorded. Net profit or losses were calculated by detracting total cost from farming income, either with or without subsidies. Notwithstanding this common economic framework, we opted to maintain the traditional farm accounting of individual study areas instead of looking for a rather artificial harmonization. The main reason for this approach was that we were looking for a general picture of economic sustainability emerging from data of individual study areas rather than cross comparisons of individual study areas. These comparisons are still possible, taking into account particularities of farm accounting. Taking into account the high degree of heterogeneity of the farm structure between and within study areas, the definition and selection of a meaningful economic indicator is not straightforward. In our study areas we have a gradient of increasing ratio of capital demand to running costs with corresponding increase of imputed costs. The remuneration of the production factors family labor, own capital, own land, and own assets induce imputed costs. One factor determining the relevance of imputed costs is the productive orientation. Dairy operations, such as in the lowland farms of Entlebuch and Bavaria, have higher imputed costs than the ones focusing on meat production. In these areas, the imputed costs can be in the some order of magnitude as the running expenses.
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Most of the study areas depend greatly on family labor. Whether a farmer has to take into account this and other imputed costs depends on his/her attitude toward farming and his/her dependence on on-farm income. Only in Baixo Alentejo and Castile-La Mancha, family labor was valued due to the apparent trend of relying on waged labor. To correctly assess the economic sustainability of a given farm, one has to know the personal valuation of the farm-specific production factors. This would allow an adequate assessment of its imputed costs. Having these problems with the correct assessment of the imputed costs, we selected the cash flow as the main indicator for the cross-country comparison of the economic sustainability. The cash flow has the advantage that it can be traced back to ‘‘real’’ monetary transactions, reducing the potential assessment bias. However, a cash flow of a given magnitude does not imply the same degree of economic sustainability across the study areas. In one system, the imputed costs might be negligible due to the low capital demand of the system and the use of waged labor implying that the cash flow is nearly equal to the profit. In another, the imputed costs might even exceed the running expenses. For the study areas where these considerations play a role, these aspects will be addressed and discussed in the respective paragraphs. In addition, peculiarities of some running or imputed costs in specific study areas are described in the text. With respect to public handouts, the data depict the situation in EU countries before the 2003 CAP reform. Income and cost tiers were calculated for MU. In some study areas such as in Tatra, Entlebuch, and Bavaria, the whole MU is composed of two farm units: the highland pastures and the lowland farm, with different income, cost, and subsidy tiers attached to the respective unit. In Entlebuch, for example, grazing fees are a source of income instead of cost as owners of the external livestock, added to the alpine unit, pay a grazing fee to the owner/ manager of the alpine unit. In those cases, separated records were available for each farm unit and results can be combined to get a picture of the whole MU. For harmonization of reporting and comparison between study areas, results were recorded for LU of the respective type of animals. In the case of Northern Sapmi, official records were used to assess the economic performance in Norway, while Sweden and Finland data are mainly based on herder interviews as statistics are incomplete. Subsidies are an important tier in most extensive European livestock systems. A befuddled complex series of subsidy schemes are operative across study areas and even within one study area. Subsidies are awarded by EU, national and regional governments, or shared by both, and are allocated as direct payments, rural development schemes (agri-environment schemes, less-favored areas, and so on), specific grazing practices, and specific grazing units (a pool of public handouts). Notwithstanding this confusion, total subsidies were recorded by manager/owner of the MU and expressed as percentage of total value of production (OECD, 2001).
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Under the labor heading, number of WU was recorded per MU on each study area. Labor was differentiated as familiar or waged and as full- or part-time employment. Labor productivity was rated as value of production farming per WU or number of LU/WU. The working hour (wh) annual standard was 1800 Awh. Working intensity was classified as High (H), Medium (M), and Low (L), regarding care intensity and migration/stationary models of herding. Legal and institutional frameworks were important especially in those study areas where grazing rights are shared or regulated by some regulatory institution. Entitlement of property rights or renting contracts were also important for grazing regulation or subsidies’ allocation in some areas such as the Tatra Mountain. Government regulations of grazing rights’ and subsidies’ allocation were especially important in study areas dominated by common lands (Northern Sapmi) or landless pastoralists (Castile-La Mancha). Farmers’ opinions were recorded on the sustainability of the legal and institutional framework regulating the grazing operation, recent management trends, as well as the main destabilizing or limiting factors of the grazing systems.
3.2. Management units The reindeer husbandry matrix was based on the average economic data from the LACOPE target areas in Northern Sapmi. For Finland, only one district or MU was included in LACOPE. Economic data for this district were available. For Sweden, however, only mean regional data of national target areas were available. These regions included several districts: Northern Norrbotten Mountain Sa´mi (nine districts). In Norway, most data were available on district level, here used for western Finnmark (26 pasture districts). LACOPE districts are within these regions with one exception in Sweden. The numbers provided in the data matrix are average for these regions, as individual districts (siida in Norway/Sa´mi village in Sweden) data were not available. In these latter two countries, the MU was the husbandry unit within reindeer districts, where economic data were gathered and most subsidies allocated. National models (three matrices) are based on regions corresponding to each country (Norway, Sweden, and Finland). The national differences were larger in the income tiers than in the cost tiers as a consequence of differences in herder production strategies (Riseth, 2000, 2003), differential prices of reindeer meat, as well as national subsidy systems. In this study area, data were available for the three national models of reindeer herding (Norway, not a member of the EU; Sweden and Finland members of the EU). Two farming units constitute the grazing system in the Tatra Mountains. The whole MU is thus composed of the lowland farm and the clearing alps. In order to gain a more profound insight in the system, a specific questionnaire was designed for each farming unit that took into account their
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respective peculiarities. As a result of the breeding scheme (one lambing per year), the milking period corresponds to the summer season where traditional cheese making (bundz and oscypek cheeses) takes place. During the lowland farm period, marketed lambs and subsidies are the main sources of income. In Entlebuch, the whole MU is composed of two farming units, one in the valley as a private farm and one in the alpine pastures (67% as a private unit, 26% as tenant, and 7% in some private or public cooperative agreements). In Bavaria, the MU also encompassed two farming units. The lowland private farm, where animals stayed over the year. The land is in the ownership of the farmers or rented and is individually exploited. The second type of unit is the cooperative upland Allmende, where farmers sent their heifers for the summer grazing season. These units can be in the ownership of different bodies (private person, local authority, cooperative, and so on) and are jointly used and managed from the lowland private farmers. Although specific incentives are awarded for the use of upland pastures, only some 28% of lowland farmers send the totally of their heifers to the Allmende where they are raised under extensive grazing (Niemeyer and Rosenthal, 2003). The study area of Baixo Alentejo is the only one where the MU coincides with individual farm holdings. Individual holdings in Castile-La Mancha, mainly devoted to cereal cultivation, are grouped in large grazing allotments (polı´gonos de pastos) that encompass patches of diversity of resources such as cereal, annual legumes and sunflower stubble, shrubby-steppe vegetation (eriales), natural pastures, and fallow lands. The agricultural land of each municipality is divided, according to its size, into several polı´gonos and each small landowner, having a parcel within the polı´gonos, receive a per hectare grazing-fee paid by landless pastoralists who rented. More than 90% of sheep farmers rely on the polı´gonos de pastos and some of them add small parcels of owned or rented land outside the official system. As arable farming is the primary land use objective and crops are interspersed, the polı´gonos are unfenced, and sheep flocks should be permanently conducted with high working intensity. Individual polı´gonos corresponds to MU in this study area (Caballero, 2001).
3.3. Sampling process The matrix of data in Northern Sapmi was based on different public and private sources, and encompassed data from national reindeer models in Norway, Sweden, and Finland. The Norwegian reindeer husbandry administration publishes an annual economic report based on numbers and accounts from the reindeer herders and their supervising organizations ( konomisk Utvalg, 2004). In Sweden and Finland, there are no annual
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economic publications. The Swedish data are based on a joint publication issued by the Swedish bureau of statistic and Swedish reindeer-herding organization (SSR, 1999). The Finnish data were received from nonpublished sources given by the Association of reindeer herding cooperatives for the year 2003. Of a total population of 2751 farmers in the Tatra area, 40 lowland sheep farms were sampled. The results for the summer season corresponded to 17 flocks under the care of a main shepherd (baca-unit) and additional labor support by younger shepherds. Data gathered correspond to the year 2003. A main heading-based questionnaire was sent to the 230 owners and/or managers of the alpine units in the Entlebuch Reserve and 107 completed questionnaires were gathered. Effective rate of response to the different tiers of the questionnaire varied from 75% to 100% of the filled questionnaires. Other sources of reported information were used and recorded in the corresponding heading. Most managers and owners of the alpine unit (some 90%) have a farm unit in the valley. Information on these farm units was mostly recorded from BfS (2004) and AfS (2004). Data for both farm units corresponded to the year 2003. In the prealpine and alpine agrarian regions of Bavaria still exist around 1200 alps, in 155 thereof more than one farmer is involved in their exploitation (Allmende). For the purpose of this study, 56 farms participating in CLS and 34 Allmende were surveyed. Of the 56 farms, 38, 13, and 5 are located in the agricultural regions of the Alps, prealps, and prealpine moraine belt, respectively. Of those farms, 43 had entitlements to use the Allmende: 33 of those are located in the Alps and 10 in the prealps. The left 13 farms did not posses any entitlement but board their animals on the CLS. Average farm size increased from 29 LU in the Alps to 62 in the prealps and 82 in the prealpine moraine belt. In the same way, a number of 34 upland units were investigated. Most of them are located in the alpine region (25), 5 are situated in the prealpine area, 2 in the prealpine moraine belt, and 2 in the southern Bavarian foothills. Mean size of the upland Allmende unit is some 470 ha and ranges from 10 to 7400 ha. The average unit is more or less divided between forest and grazing land. Data for both units corresponded to the year 2003. In the study area of Baixo Alentejo, 15 mixed-operating farm holdings were sampled. Data corresponded to the agricultural year 2003–2004. Official records of sheep farmers in Castile-La Mancha, entitled of EU subsidies, amounted to some 8000. In the study area, 231 sheep farmers of the whole region were sampled with the criteria that the 5 provinces were to be represented by at least 5 farmers on each one of the 21 counties in the region. The survey tool was a questionnaire drafted according to main headings and totally 72 variables of quantitative and qualitative character. Surveyed sheep farmers were previously contacted for the local veterinary staff of the Animal Health Associations or Agrupaciones de Defensa Sanitaria
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(ADS) in its Spanish acronym. The staff concerted working meetings with their corresponding affiliates to explain the objectives of the survey and content of the questionnaire. In this way, the rate of response was almost 100%. Data gathered in this study area corresponded to the year 2002.
4. Results Typology of grazing systems with policy relevance can be addressed as a combination of analysis-related categories and systemic assessment of common features or trends. Even if a great deal of variation can be found for land use or grazing management indicators, both within or between systems, some common features may arise such as poor economic performance, scarce labor supply, abandonment, consolidation, legal or institutional drawbacks, and poorly devised subsidy schemes. From these main identified issues, policy actions can be derived although proper devising and implementation should be consistent with particularities of the individual systems. The interesting point in comparative typology of our six study areas is to untangle, if existing, these common features in the wide range of variation of most indicators. This report deals mainly with analysis of descriptive categories. In Section 5, however, we will try to uncover common features and trends to the six study areas. Between-system variability will be recorded in tables by indicating study areas’ averages of main indicators, and within-system variability of some significant indicators will be recorded in the text. For those study areas with two farming units per MU (Tatra, Entlebuch, and Bavaria), indicators will be differentiated or weighed either in tables or in the text.
4.1. Land uses Land uses were related by their potential contribution to the forage supply. In Northern Sapmi and the alpine study areas, nonarable land makes the most significant contribution to pastoral resources, mostly as natural grassland. In the two Iberian study areas, however, pastoral resources derived from arable land made a significant contribution to the feed supply (Table 2). In the study area of Northern Sapmi, boreal forest/open tundra and natural alpine grassland dominate while cultivated agricultural lands are limited to valley and fjord areas, in North Norway about 1% of the total land area (Statistics Norway, 2004). Most of grasslands and large proportion of forest/tundra can be used as summer and winter grounds, respectively, for reindeer herding. Imagining a scale from continuous outfields via plots of outfields and managed pastures to plots of infields and indoor feeding, the Northern Sapmi system is still to a very high extent based on feeding from continuous unmanaged pastures.
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Table 2 Land uses and pastoral significance in the six study areas (% TAL) Indicatora Arable land Nonarable Land Pastureland a
Northern Sapmi
Tatra
Entlebuch
Bavaria
Baixo Alentejo
Castile-La Mancha
1 99
3 97
0.5 99.5
1 99
40 60
65 35
80
93
99.5
80
75.3
85
Data corresponds to the lowland farm units in Tatra, Entlebuch, and Bavaria.
In the Tatra Mountains, land uses recorded in Table 2 corresponded to the lowland farm unit. In the alpine unit (sheep in bacas’ care for the summer grazing season), arable land was underrepresented but pastureland (clearings in the forest) accounted for some 30% of TAL. The rest were alpine forest. In the study area, clearings and forest were in the proportion of 1:1 in hectare. In Entlebuch, the data recorded corresponded to land in the lowland farm. In the alpine unit, unproductive land and forest take up some 40% of the land, the rest being pastureland of natural grasslands (51%), nature protected areas (5%), and grazing forest (4%). Similarly, land uses for Bavaria corresponded to the lowland farm unit. In this case, 60% of the farm hectare was composed of intensively managed land (intensive pastures plus arable land) and 20% corresponded to the extensive managed land (litter meadows and alpine pastures). Some 20% of the land managed in the lowland farms were composed of forest. Referring to the 1999 agricultural census the study area of Baixo Alentejo counted with more than 220,000 ha of TAL of which, 40% corresponded to arable land (temporary crops and fallow), 40% to area under permanent pastures and oak forests, and 20% of shrubland. Broadly, we can consider two different systems. The first, corresponded to cereal growing areas where residues (cereal stubble) are used by cattle and/or sheep and, the second, to the open-forest dominant areas (Montado) where cultivation was only occasional and where a mix of suckle cows, sheep, and Alentejano pigs were operating. Nongrazing land areas included shrubby invaders where only hunting may operate and some nongrazing cropland of vineyards, olives, and parcels under irrigation. In the southern Castilian plain, arable land was dominant, especially in the central part of the region (La Mancha) where arable land takes up some 80% of TAL. Nonarable land was more significant in the foothill and mountain areas surrounding the plain. In this study area, pastureland included cereal, legumes, and sunflower stubble in the arable land, and natural pastures, eriales (shrub-steppe vegetation), and grazing Mediterranean forest in nonarable land. Nongrazing land uses included mainly
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vineyards, olives, and irrigated parcels in the arable land part and dense Mediterranean forest in the nonarable part, encroached by shrubby invaders because of lack of grazing use. In most study areas, the coefficient of variation (CV) of many land use variables exceeded 0.8 or even 1, indicating very skewed distributions. For instance, in the sample of lowland farms of the Tatra Mountains, nonarable land per farm was 15 17 ha and area of natural grassland per farm was 13 16 ha. In the sample of Baixo Alentejo, the mean total pastured area was 379 322 ha, corresponding to 89% of the total area, while the proportion of arable land/TAL was 67 72%. In the sample of Castile-La Mancha, proportion of arable land over TAL was 65 28% (Table 2), and the proportion of natural pastures plus eriales over TAL was 17 17%. These two and all other land use variables showed asymmetrical distribution (absence of normality) as rated by the W-test of normality (Shapiro and Wilk, 1965).
4.2. Size of farm-holding, land prices, and grazing fees In Northern Sapmi, the farm-holding size was of less relevance as reindeer grazing is organized by pasture districts and husbandry units. In the western Finnmark area (Norway), with 24,290 km2 and 241 husbandry units, the mean size was some 10,000 ha per husbandry unit (Table 3) In the Tatra Mountains, the average renting price of pastureland (9 €/ha) and the grazing fee (4 €/ha) corresponded to the lowland farm unit (Table 3). In the alpine unit (sheep in bacas’ care), mean grazing fees were 4.8 €/ha in public lands (Tatra National Park) and 36 €/ha in private lands. The mean size of the alpine unit was 46 ha/baca flock. The price of land is currently under adaptation to the free market rules but the number of land transactions in the study area of Tatry and Podhale are very limited. Attachment to the land is part of the cultural character and, most frequently, land is transferred within the family. Even land lease is not based on written contracts and even long periods of occupancy do not mean any right for the leaseholder. Land transfer prices are much lower when ‘‘within the family’’ (some 2300 €/ha) than for ‘‘outsiders’’ (some 4000 €/ha). In other Carpathian regions, such as Beskid Niski, prices are much cheaper (some 1000 €/ha), transactions are more frequent, and do not carry so deep emotions. In Entlebuch, the mean size of the sampled lowland farm was 14 ha (Table 3) and the mean size of the alpine unit was 57 ha. Managers/owners of the lowland farms do not own enough animals to stock one unit of alpine pastures. They should rely on external animals to stock properly the alpine units. Owners of these external animals pay a grazing fee to alpine owners/managers (Wirz Handbuch, 2004). This side income represented the main income tier (59%), together with subsidies (41%), for the alpine unit operation. For this reason, luring external farmers to bring their animals
Table 3 Farm-holding structure, prices, and grazing fees in the study areas
a b
Indicator
Northern Sapmia
Tatra
Entlebuch
Bavaria
Baixo Alentejoa
Castile-La Mancha
Size of the farm holding (ha) Size of herd/flock (LU) Price of the lowland farm (€/ha) Rent of the lowland farm (€/ha) Grazing fees (€/ha) MU using LSGS (%)b
10,000 48 NA NA NA 100
15 12.7 4000 9 4 90
14 15.6 32,000 500 286 20
37 40.7 25,000 143 22.5 28
425 141 3250 50 17 NA
500 82 4900 50 3.2 90
In Northern Sapmi, mean size of the husbandry unit in western Finnmark (Norway). NA (not applicable). In Baixo Alentejo mean size based on the agriculture census was 65 ha. However, average LSGS involved larger farmers as it is represented in our sample (mean size 425 ha). Management units (MUs) using cooperative pastures or extensive grazing units (i.e., alpine units in Tatra, Entlebuch or Bavaria, rented pastures in Baixo Alentejo or polı´gonos de pastos in Castile-La Mancha). Approximately, the land price on the alpine unit of Entlebuch ¼ 10,000 €/ha.
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to the Alps for summer grazing was of paramount importance for the sustainability of the alpine system. In this case, the grazing fee stated in Table 3 was a mean estimation of external grazing fees and represented a source of income. Estimation1 was based on grazing fee paid by the most represented lot (1- to 2-year-old heifers). In Bavaria, the total hectare of the farms ranged from 3 to 124 ha with a mean size of 37 ha (Table 3). The cooperative upland Allmende ranged from 10 to 7400 ha with an average of 468 ha. The grazing fees in the Allmende were much lower as in the other alpine regions. Mean seasonal stocking (some 120 grazing days) on these units reached 0.9 LU/ha and mean grazing fee was 25 €/LU. Mean grazing fee in the alpine unit (Allmende) was thus some 22.5 €/ha (Table 3). However, it should be stressed that in a lot of cases (around 50% of the Allmende), no grazing fees were claimed. The other indicators represented in Table 3, for this study area, are mean sizes and prices in the lowland farms. In the Baixo Alentejo sample, the mean size of farm holdings was 425 256 ha, but only 72 178 ha corresponded to permanent natural pastures. According with expert knowledge information in all the reference area (Alentejo), it is frequent the acquisitions of grazing rights on a year basis (or part of the year) with the grazing fees ranging from 10 to 20 €/ha per year according with the quality of the land. In the few cases of our sample where the acquisition of grazing rights was reported, the grazing fees attained 17.5 €/ha. Renting land (some 9% of TAL) on a yearly basis or for larger period showed a wide variation, 7 €/ha as a minimum to 63 €/ha as maximum. In Baixo Alentejo study area the price of agriculture land ranged from 1500 to 5000 €/ha according with the quality of land. Considering the whole area of the Baixo Alentejo, this range would be enlarged if good clay soils of the Beja area (land price ranging from 4000 to 7500 €/ha), or if land located inside the perimeter from the new Alquevadam irrigation system (10,000 to 15,000 €/ha) were taken into consideration. In Castile-La Mancha, mean size of farm holdings (500 ha) corresponded to the MU ( polı´gonos de pastos) where individual sheep flocks are maintained. These MUs are aggregation of individual farm holdings with mean regional size of some 30 ha. In some counties and municipalities, the polı´gono may encompass the landholdings of up to 80 landowner cultivators. Grazing fee corresponded to the sheep allotments under public allocation of grazing rights ( polı´gonos parcelarios). Rented pastureland by private landowners was two to three times higher, although private landowners, who do not own a flock, rarely rent their land for sheep grazing.
1
External grazing fee in Entlebuch ¼ 0.9 LU/ha 2.5 heifers/LU 1.27 €/heifer per day 100 days ¼ 286 €/ha.
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Within study area variation for indicators of this heading was also large. In the Tatra Mountains, for example, mean size of farm holdings was 15 17 ha, and average area of bacas’ flock was 49 13 ha in the clearings of the alps. In Entlebuch, the altitude of 95 huts of sampled alpine units varied from 900 to 1600 m. Some huts were located in the high alpine area (up to 2500 m), with corresponding variation in size and land uses, land prices, and grazing fees, derived from differences in accessibility. In Bavaria, lowland farm size ranged from 3 to 124 ha in the sample and land price of agricultural land may reach a maximum of 32,000 €/ha (mean of 25,000 €/ha). Although mean size of the Allmende unit is some 470 ha, considering the nongrazing land (forest and wasteland) it may reach more than 7000 ha. In Castile-La Mancha, mean size of the polı´gonos was 499 513 ha or a CV of more than 100% and mean grazing fees 3.21 3.51 €/ha. Mean price of land for selling or renting showed less variation with values of 4900 1325 €/ha and 50 17 €/ha, respectively.
4.3. Institutional economics The investigated LSGS were closely linked to specific property rights, especially the ones organized in a collective form (in the following mentioned as CLS). The CLS studied manifest a number of institutional features. First, to some extent, CLS have accommodated a certain welfare institution within their own institutional limits by providing livelihood security to people with very limited alternative possibilities. Second, CLS provided access equity and conflicts resolution for its participants as a functional necessity. Third, there are complex relations between the institutional system and the mode of production including embedded cultural features making the production system viable. Fourth, CLS by mostly being based on some form of rotational and limited use of pastures contributed to resource preservation and ecological sustainability. Concluding on institutional properties, CLS had much in common with common-pool resources (CPR). The users have to make collective agreements and have to decide how the resource use can be arranged in such a fashion so that the benefit of each user is proportional to the effort of that user. Moreover, CLS had in cases served as a vehicle for the social distribution of goods among the deprived segments of the population and thus had a potential to contribute as a buffer to take care of the destitute parts of a population. A comparative analysis of the organization and structural form of the grazing systems presented the role different groups of actors play in these systems and their interrelationships (Gueydon et al., 2004). We could distinguish four main groups of roles: the landowners, resource owners, livestock owners, and pastoralists. The role of the landowner was to provide a part or all of the pastoral resource. The role of the resource owner was to hold the right to exploit a part or all of the resource. The role of the
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livestock owner was characterized by the fact that he/she owns some or all the animals grazing on the resource. Finally, the role of the pastoralist was to conduct on his own or together with others the herds on the resource. As shown in Table 4 the landowner can be a single entity for example the state such as in Northern Sapmi and Bavaria. It can be several individual persons acting independently like in the Tatra, in Baixo Alentejo, and also in some cases in Northern Sapmi and Bavaria. It can finally be a community of landowners like in Castile-La Mancha and sometimes in Bavaria or a formal legal entity like in Entlebuch and sometimes in Bavaria. In Northern Sapmi, Castile-La Mancha, and Entlebuch, and in the more frequent setting of Bavaria, community of people (landowners or livestock owners) jointly owned the resources. In Baixo Alentejo and in the Tatra Mountains the landowners were likewise the owners of the resource. They rent the land or sell the resource for grazing activities under market conditions. The land and resource’s ownership was separated in Northern Sapmi, Castile-La Mancha, and in some cases in Bavaria. The land belongs to individual landowners or the state but these entities do not have any statement to issue concerning the utilization or the distribution of the pastoral resource. In most cases the landowners received only limited revenues, if at all, for contributing their land to the system. In all cases, livestock was individually owned, implying that profits from the exploitation of the resource by selling marketable products, receiving subsidies related to the number and kind of livestock or related to the way the grazing is performed, were not shared. In most cases, except in Bavaria and Northern Sapmi, the direct utilization of the resource was under a single appropriator. Generally in cases of a single appropriator, the pastoralist was one of the livestock owners who may board animals of other livestock owners on his own account (Tatra and Entlebuch) or was being paid a fixed wage by livestock owners (alpine areas of Bavaria and sometimes in Entlebuch). In Baixo Alentejo, it was also frequent that the herdsmen combine a fix wage with the right to freely graze their own animals. In the prealpine region of Bavaria the livestock owners were also pastoralists as the work requirements for taking care of the livestock do not demand the employment of a herdsman. In Northern Sapmi, the pastoral-related work like herding and preparation of the herd for slaughtering was done cooperatively by the Sa´mi. In the Tatra Mountains, the pastoralist can be landowner and therefore also one of the resource owners and he frequently owns a significant part of the herded livestock himself. The clarification of the different groups of actors and their role gave indication on the action which are collectively undertaken and consequently help to systemize the notion of ‘‘cooperative systems’’ within the different regions. Two different types of activities were carried out together; these were the collective provision of the land and the collective utilization
Table 4 Actors involved in the cooperative livestock systems (CLS) Actors
Northern Sapmi
Tatra Mountains
Entlebuch
Bavaria
Landowner (resource provider)
Individual landowners
Individual landowners
Community of landowners (formal)
Individual landowner
Joint ownership of landowners
Community of landowners (informal or formal) State Individual landowner Joint ownership of landowners
Resource owner
Livestock owner Pastoralist (resource user) a b
State Collective private bodya Joint ownership of Sa´mi (siida)b or reindeer pasture ‘‘district’’
Jointly the members of siida
Individual pastoralist
Joint ownership of livestock owners Community of right holders Individual livestock owner Individual Same as ‘‘resource pastoralist owner’’ (or herdsmen)
Baixo Alentejo
Castilla-La Mancha
Individual landowners
(Community of) small landowners
Landowner
Joint ownership of sheep holders and landowners
Individual pastoralists
Individual pastoralist
Finnmark State and preliminary property during process of land reform (Sandberg, 2006), Norway; crown/state in Sweden/Finland but Sa´mi land claims. Norwegian expression. In Sweden it is the Sami village and in Finland Paliskunta (cooperative). But in essence it is the same form of organization among the three countries.
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of the resources. Table 5 gives an overview of the presence of these actions in the different grazing systems. One major reason to opt for collective action was to realize economies of scale or to reduce transaction costs (Gueydon et al., 2004). The degree of governmental involvement varied significantly between the different study areas. In Northern Sapmi, Castile-La Mancha, and Entlebuch, the systems were strongly regulated by external rules and specific public laws. In contrast, the authorities in the Tatra Mountains, Baixo Alentejo, and Bavaria were not involved in the management of the systems apart from general regulations dealing with ‘‘good agricultural practices.’’ These rules applied to all farmers or in the case of Baixo Alentejo to the farmers benefiting from the Zonal Plan of Castro Verde, which is an EU agri-environmental measure aimed at the preservation of steppe birds such as the Great Bustard (Otis tarda). Moreover, the investigated systems ranged from ones with a relatively rigid internal structure and rule system, like in Entlebuch and Bavaria, to others with a high degree of governmental involvement, like in Castile-La Mancha and in Northern Sapmi.
Table 5 Organization forms and collective actions in european grazing systems
Study area
Organization forms
N. Fennoscandia
Sa´mi pasture ‘‘district’’ (formal) Siida (cultural origin) Private property of the alpine meadows Private property under private or cooperative law Allmende Private property of agroforestry area and rarely transhumance to rented cereal areas Polı´gonos parcelarios
Tatra Mountains Entlebuch
Bavaria Baixo Alentejo
Castile-La Mancha
Source: Gueydon et al. (2004).
Collective provision of the land
Collective utilization of the resource
No
Yes
No
Yes
No
(Yes)
(Yes) No
Yes No
Yes
No
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4.4. Institutional and legal frameworks Present status of extensive grassland management systems is derived from land-use rules and institutions. Past traditional forms had to be adapted to the pressure of social changes and improved economic environment in most developed countries of our study areas. This may require new forms of production, sensible institutional and legal frameworks, and proper schemes of support in pursuit of economic, environmental, and social values. Whether these changes can be devised and implemented without losing the traditional values of these systems is a main challenge that can be addressed under a regeneration approach. The matrix headings compiled some farmers’ opinions on these issues already addressed by experts’ knowledge in previous work packages of LACOPE (Riseth et al., 2003). In Northern Sapmi, land-use policies and constraints regulating reindeer herding have been described by Riseth et al. (2003). In this area, land development had an impact on reindeer traditional pasturing areas. The UN report GLOBIO (UNEP, 2001) has researched the human impact and found that about 26% of the grazing areas in northern Norway have been lost in recent times and that there are serious impacts on about 50% of all grazing areas in Norway. The future prospects seem clearly negative and the situation is much similar in the whole Northern Sapmi. In the Tatra Mountains, farm protectionism has changed since the liberalization on July 1, 1989. Since 1992, an assistance program for agriculture and farms has been developed. The local government, called gminny, provides some farms assistance but barely for mountain sheep farming. LSGS in the Tatra Mountains was plagued with structural and legal problems between the main social actors: sheep owners ( gazdas), shepherds (with a main shepherd ¼ baca), and landowners. Improvement of the legal and institutional framework was probably the most limiting factor that should be addressed before introducing instruments of the new CAP policy (structural rural development and direct payments). Written documents of ownership are lacking and some land ownership ‘‘runs in a family’’ with most people respecting but without legal documents to prove it. There is a need to maintain traditional grazing practices as part of a cultural background but also to fulfill EU sanitary requirements for milk processing. Technical support and measures to stress cooperative behavior are also required. In Entlebuch, private property and private grazing rights were dominants (67%) in alpine units. One MU was usually composed of one farming unit in the lowland and one alpine unit. The livestock farmer managed both units during the summer grazing season driving down to the lowland valley farm for hay harvesting. Leasing of alpine units from private owners was also common in Entlebuch (26% of alpine units) while some farm cooperation by public or private law were less common in Entlebuch (some 7% of alpine
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units). In these cases only members of the whole local community or members of private law cooperation had a share of grazing rights. In Bavaria, the Allmende (CLS) system, in the sample, encompassed several cooperative forms depending on land property, legal form, and grazing rights. Land property comprised three categories: private (7% of whole surveyed hectare), public (71%), and cooperative (22%). In the surveyed study area, a short minority of Allmende (41% of the total number of sampled units) was organized as a collective organization with the land under the property of the cooperative. Also, frequent were types of public-owned land rented to livestock farmers (23% of units) and fractional ownership and management (23% of units). The most frequent legal form of the Allmende was a registered cooperative (42%) or registered association (9%). An important part of units (30%) did not present any registered legal form. Regarding the level of use of grazing rights, only 32% of upland units managed a full use of grazing rights. However, in 34% of the Allmende less than 60% of grazing rights were used. In those CLS an underuse was apparent. Grazing fees were paid in only some 50% of CLS. In these units, grazing fees per LU varied from 0 up to 90 €/LU with average of 25 €/LU and 75% quartile of 30 €/LU per annum. Many units charged no or low grazing fee due to the decline in the number of animals that are sent to the alpine pastures. The Allmende therefore created an incentive for the owners of boarding animals. Members have also additional ancillary rights for hunting, mowing, or wood collection although sampled farmers indicated insignificant use of such rights. Users can be classified as active or passive members, depending on whether they send their animals to the alpine pastures. Some users were not members and do not have entitlements. They are, however, allowed to bring their animals to the units and have to pay a compensatory fee. The average proportion of passive members in the surveyed Allmende amounted to 34.5%. Half of the Allmende, showed a proportion of 25% of passive members. Otherwise, the average active members by units decreased from 27 to 16.6 (38%) in the last 20 years, confirming a general trend to underuse of alpine pastures. Work overload, less family labor, and difficulties to organize the alpine labor were the main causes of abandonment of, especially, the less productive areas (e.g., calcareous mines) and the steep slopes of the Allmende, with subsequent forest encroachment. At the same time, an intensification trend was observed in the lowland private farms. First calving of highly productive breeds, mainly Brown Swiss and Simmentaler, was advanced to an earlier date and feeding conditions of heifers improved with the aim of improving milk performance. Under this trend, the significance of the Allmende for the lowland dairy farmers became partly redundant. In Baixo Alentejo, the institutional and legal framework did not have the same impact and importance as it happens in Castile-La Mancha. In fact, since the relations between landowners and livestock owners are regulated
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by the market and by the ordinary laws regulating all economic activities, livestock production shared the usual institutional and legal requirements of the other economic activities. Acquisitions, rental procedures, and grazing rights were subjected to the ordinary law, but many contracts were only informal, following gentlemen agreements between the landowners and the farmers or landless livestock owners. Other legal frameworks with incidence on the area resulted from the regulations involved on protected areas, as well as the voluntary adhesion to the Zonal Plan of Castro Verde. In Castile-La Mancha, 86% of surveyed farmers indicated that a regulatory institution for the management of the grazing polı´gonos and allocation of grazing rights was required. Of those in favor of a regulatory institution, 45% of the sheep farmers indicated that the Local Grazing Commissions (LGC) should be under the umbrella of the local council and 46% indicated that LGC should be independent (private associations of arable and sheep farmers). Regarding of grazing rights, 49% of the surveyed farmers favored direct allocation by LGC and 47% indicated allocation by inner consensus within pastoralists. Only 4% of sheep farmers favored allocation by free auction.
4.5. Forage deficit Availability of pastoral resources was seasonal in all study areas. Snowcapped land throughout winters in alpine areas and dry and hot summers, with soilmoisture deficit, in Mediterranean areas determine an SNGS, over the year. An FD may appear if available forage conserves do not meet the structural nongrazing days. The forage coverage model was based on a mass balance by comparing forage conserves availability with animals’ requirements. In extensive systems of grassland management it can be assumed that at least the basal diet of animals is met by on-farm forage supply and thus a strategy of forage conserves has to be implemented to meet the SNGS. If not, animals should be supplemented with marketed forage or concentrates during the SNGS. A large FD may thus indicate an LSGS disconnected from land-based resources and progressively relying on external feed sources (Caballero, 1993). A summary of the forage coverage model in the different study areas is recorded in Table 6. In Northern Sapmi, traditional migration patterns in the Sami area were hampered by between-countries border barrier and thus, FD was a serious concern for many of husbandry units practicing short migration or stationary patterns of reindeer herding. In Finland, CFR have become increasingly important from the 1980s onward (Kumpala, 2001). Coastal adaptation reindeer management in Norway also used some CFR under difficult winter conditions. The FD was of less relevance for reindeers herding under traditional full migration patterns. In this case, summer pasture grounds more nearer the coast and winter forest grounds in the interior
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Table 6 Estimation of the forage coverage in the study areas
Indicator SNGS (days)b Productivity of CFR (kg hay/ ha CFR)c CFR/TAL (ratio)d Stocking (LU/ha)e Days on CFRf Forage coverage (%) g a b c d e f g
Bavaria
Baixo Alentejo
CastileLa Mancha
290
290
125
125
5000
7500
7000
1440
4000
NA
0.5
0.65
0.65
0.15
0.0191
<0.005
0.85
1.42
1.1
0.2
0.2
NA
245
286
345
90
32
NA
111
99
115
90
74
Northern Sapmia
Tatra
Entlebuch
0–60
205
NA
Maximum in Finland. NA, not applicable. SNGS, structural nongrazing season. CFR, complementary forage resources. TAL, total agricultural land. LU, livestock units. Days met by CFR. Forage coverage ¼ [1 þ (CFR-SNGS)/365] 100 (100% would mean a balanced situation and thus absence of FD). Animals’ requirements for maintenance stated as 12-kg hay/LU per day.
can as a rule meet the forage supply over the year. However, in Sweden, CFR to some extent are used on migration and during the calving period (Aˆhman, 2002; A˚hman and Danell, 2001). Migratory reindeer herding in Finnmark Norway to some extent also used supplementary feeding in winter due to difficult grazing conditions with ice creation. In the mountainous study areas such as Tatra, Entlebuch, and Bavaria, the whole animal lot (sheep) in Tatra or a proportion of specific lots (mainly heifers) in Entlebuch and Bavaria are moved to the alpine units during the summer season. In these cases, the FD may apply only to the lowland farm over the year. In Entlebuch, only 20% of the lowland farmers sent their animals to the alpine units representing 8% of total LU in the study area. In Bavaria, most forage supply was provided by green forage or forage conserves, while pasturing representing a small fraction, even during the grasslands’ growing season. Under these conditions, a forage model has been developed by the Bavarian’ team which assess the forage supply-animal consumption balance of energy over the year (Roeder and Gueydon, 2005). This tool may provide an estimation of the potential versus real
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stocking of Bavaria lowland farms and an indication of level of stocking (under-, over-, or balanced supply of forage resources). In our estimation of the FD, grazing days was weighed by the proportion of LU (mainly heifers) that graze. In the Tatra Mountains, the summer grazing season represented 160 grazing days. It was assumed that the lowland farm does not provide grazing days, the SNGS amounted to 205 days. While sheep are on the alps for the summer season, 50% of TAL (CFR/TAL ¼ 0.5) can be mowed (one or two cuts) with mean productivity of 5000 kg of hay/ha CFR of available hay. Mean stocking in the lowland farm was some 5 ewes/ha or 0.85 LU/ha. Under this average scenario, days met by CFR would represent 245 days. As SNGS represented 205 days, an oversupply (111%) was apparent under average conditions and animals’ basal requirements of 12-kg hay/LU per day. Under these conditions, the FD model indicated that a minimum of 0.42 CFR/TAL (42% of TAL) devoted to forage conserves was required to meet a SNGS of 205 days.2 Similarly, if days provided by CFR equal the SNGS (null forage deficit), stocking could be increased to 1.01 LU/ha TAL without incurring in FD.3 Application of the FD model to Entlebuch and Bavaria lowland farms required some assumptions. In these cases, only a small part of the lowland farmers bring their animals to the alpine units. In these cases, we will consider a ‘‘mode’’ dairy farm situation on which farmers keep their animals in the lowland farm over the year, heifers being the only lot in the lowland pastures. In this case, grazing days should be weighed for the proportion of heifers-LU on the total lots. We assumed that heifers graze for 180 days and dairy cows are kept indoor. If heifers represented some 30% of total LU, the real number of grazing days was 60 or some 300 nongrazing days. An estimated ratio of CFR/TAL ¼ 0.65 was considered for both study areas. Stocking in Entlebuch was rated at 1.42 LU/ha TAL on farms at mean altitude of 800 m (BfS, 2004). For Bavaria, the stocking level was set to 1.1 LU/ha of TAL, as the average farm keeps 41 LU and has 29 ha of TAL under private ownership and an additionally attributed share of 9 ha under cooperative management. Mean yield of available CFR under medium-intensity farms was rated as 7500 kg/ha of hay-equivalent and 7000 kg/ha in Entlebuch and Bavaria, respectively. Some other estimations of hay yields for typical alpine regions are available (Gruber et al., 1999). Under these assumptions, days met by CFR amounted to 286 and 345 days, respectively.4 Maintenance requirements were 2
3
4
CFR required to meeting the SNGS in Tatra as a ratio to TAL. CFR/TAL ratio ¼ (205 0.85 12)/5000 ¼ 0.42 or 42% of TAL. As actual CFR/TAL is 50%, a small oversupply was apparent. Increasing potential stocking in Tatra for a balanced situation. Stocking ¼ (5000 0.5)/(205 12) ¼ 1.01 LU/ha TAL, up from 0.85 LU/ha of current average stocking. The forage deficit model in Entlebuch and Upper Bavaria (days met by CFR): Entlebuch, (7500 0.65)/ (1.42 12) ¼ 286 days; Upper Bavaria, (7000 0.65)/(1.1 12) ¼ 345 days.
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estimated as 12-kg hay/LU per day. Given within-farms variability, it should be stressed that applicability makes sense for individual farms. In Bavaria, for example, the forage supply-energy demand forage model stated a trend of FD on larger farms (more than 2000 GJ of NE of forage demand) and a forage oversupply on small farms. Nevertheless, this exercise illustrated that the average lowland farm was almost balanced in Entlebuch (99%) with a light oversupply (115%) in Bavaria. To estimate FD in Baixo Alentejo study area it was necessary to take into account the following assumptions. Even if the animals stay outside in the grazing plots all over the year relevant grazing days is far less than 365. Due to climatic conditions, we assumed that during two and half months in the winter period and from beginning of September to mid-October it was not possible to count on pasture to feed the animals. Therefore SNGS corresponded to 125 days. It was also assumed that, in average, we can count on 40% of the land as permanent grassland and the others 60% are involved in a 4-year rotation, meaning that CFR/TAL ratio is 0.15. Considering that the average of straw production is 2000 kg/ha, which is equivalent to 1440 kg/ha hay, and considering a stocking density of 0.2 LU/ha (Delgado, 2004), CFR corresponded to 90 days.5 The forage coverage (deficit) would be 90% or an additional 5.8% of TAL devoted to forage conserves to reach 100%. In Castile-La Mancha, the proportion of TAL devoted to forage conserves was much lower than in the alpine study areas, but also stocking on the MU ( polı´gonos) was much lower. CFR takes up land devoted to annual forage legumes (mainly vetches) and green cereals, both harvested as hay. But as landowner cultivators are not owners of the sheep flocks, they have little incentive in forage conserves, as these crops were not subsidized. Otherwise, landless pastoralists, who may have an incentive to increase the feed supply, were, for the most part, not owners of the land. However, a minority of them may own or rent some parcels for forage conserves cultivation. In the sample of 231 sheep farmers, CFR represented 1.91% of TAL (CFR/TAL ratio was 0.0191) and mean stocking was 1.17 ewes/ha TAL or 0.2 LU/ha TAL (1 ewe ¼ 0.17 LU). Mean number of grazing days stated by sheep farmers was 240 days or SNGS ¼ 125. With these data and mean productivity of available CFR of 4000-kg hay/ha, the estimated number of days provided by CFR amounted to 32 days5 and the CFR would only cover 26% of SNGS. Under these average conditions the CFR/TAL to cover the actual FD (93 days) would require an additional 5.6% of TAL devoted to forage conserves. This can be done by a trade-off to forage
5
The FD model in Baixo Alentejo and Castile-La Mancha (days met by CFR): Baixo Alentejo, (1440 0.15)/(0.2 12) ¼ 90 days; Castile-La Mancha, (4000 0.0191)/(0.2 12) ¼ 32 days.
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legumes and green cereals from plenty of available fallow land (some 20% of TAL). This average exercise conceals a great variation within farmers. For example, statistical measures of distribution for CFR/TAL and stocking showed values of 1.9 5.1% of TAL and 1.2 0.9 breeding ewes/ha of TAL, respectively. With these variations, it was clear that the model should be applied to individual farms. Nevertheless, the average model exercise illustrated that, in this study area, the forage coverage was only 74% and the FD situation was more common than in others. Tatra and Bavaria managed an oversupply of forage resources. One study area (Entlebuch) presented an almost balanced situation and in the two Iberian study areas (Baixo Alentejo and Castile-La Mancha) an FD was apparent, to a largest extent in Castile-La Mancha (Table 6).
4.6. Grazing infrastructure Extensive grazing management is usually a hardworking labor-intensive operation. In developed countries, where most of our study areas are located, availability of labor for these operations is in short supply and it is expensive. One of the main causes for the unsustainable state of the extensive grazing operations is that young European farmers are barely enthused toward the LSGS operation as they may find alternative labor opportunities in other sectors. This problem can be aggravated if grazing units lack minimum grazing infrastructures or, as it is some times the case, the land ownership structure or legal and institutional framework do not favor proper implementation of grazing facilities. In our six study areas, a wide range of situations were found. First of all, the systems can be differentiated between those requiring permanent or semipermanent herding, such as in Northern Sapmi (Norway), Tatra, and Castile-La Mancha, and those requiring only occasional care, such as in Baixo Alentejo and alpine units of Entlebuch and Bavaria. In the lowland units of the two latter study areas, even no herding of animals at all was needed as most lots were under indoor-feeding conditions. A scaled account of availability of main grazing infrastructures in the six study areas is shown in Table 7. In Northern Sapmi, permanent reindeer herding was dominant in Norway with high working intensity and less time for husbandry units’ leaders to be engaged in alternative sources of income (waged labor). Up to the late 1950s the migrating reindeer herders used to be full pastoralists moving the whole family with the herd year around. The sedentary process was promoted by obligatory schooling, making the families to settle in standard housing, often in fall areas, or on the border between fall and winter areas. The adult men follow the herd and live in huts and travel back to the family, by snowmobile in winter and by car in summer, as the collectivity of herding work often open for taking turns. In summer areas families often have second homes. In Northern Sapmi, fences are used for
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Table 7 Available grazing infrastructure in the six study areas
Indicatora Working intensity Fencing Water points Barns and shelters Milking facilities Accessibility Pattern of herding a
Bavaria
Baixo Alentejo
CastileLa Mancha
L/M
L/M
L/M
H
NA NA
A A
A A
A A
NA NA
A
A
A
A
A
A
NR
NA
A
A
NR
NA/A
L P
L/M P
M/H O
M O
M/H O
L P
Northern Sapmi
Tatra
Entlebuch
L/M/H
H
A A
L/M/H, Low, Medium, High; A, available; NA, not available; NA/A, not available dominant; NR, not relevant; P, permanent; O, occasional.
longitudinal divisions between districts and to some extent also between different season pastures and also as leading fences to corrals for herd roundups (for calf marking, herd divisions, slaughter, and so on). Low and medium working intensity were most common in Finland and Sweden, respectively, and related to stationary and short-distance migration patterns of reindeer herding. In the alpine units of the Tatra Mountains, fencing was not required as clearings have forest borders. However, almost permanent care of sheep was required, as shepherds have to move animals daily for milking, occasionally between rented clearings, and permanently because of predator problems mainly with bears and wolves. Although sheepfolds were available, most shepherds’ hut lacked electricity supply and milking facilities. There was a need of keeping traditional ways of milk processing, but fulfilling with EU sanitary requirements, which means some new equipment. Idle far-reaching clearings have problems of mobility. With increasing interest in summer grazing, these alpine pastures may be used only if transportation problems are solved. Most of lowland farm units were provided with electricity supply, but most dwellings, shelters, and barns were in most need of overhauling and repair. In the alpine units of Entlebuch, the lowland farmers, managing an alpine unit, go up with their animals (mostly heifers) for summer grazing and live in huts located in the alpine units. Temporarily, they may come down to the farm for hay supply. Most huts were located at a good accessible place. Some farmers (28%) owned more than one hut if they are managing alpine pastures located at different altitudes or they are managing heifers and dairy cattle. Most of alpine units (90%) had energy supply facilities, public
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electricity (60%) being the most common source of energy. Generators and solar panels were other sources. Many alpine units with public electricity also had solar panels for lighting and use wood and gas for cooking. According to Swiss standards, 45% of huts were in very good or good condition and 35% were in need of improvement in the medium term. Regarding the equipment of the stables, the situation was more or less the same, with more than 30% of the stables in need of renovation in the medium term. Although a minority of alpine managers operated with dairy cattle (some 22% of total LU in alpine pastures), a big majority of them had milking facilities. Of those alpine units included in the Entlebuch sample, some 88% had direct move by road and only 6% had move by paths. Incidentally, one alpine unit had access by a small cable car, but only for materials. In the lowland farms of Entlebuch, most dwellings and stables were in good condition as farmers may combine husbandry and tourism activities, and they received subsidies and free of interest loans for repairing and overhauling. In the alpine units of Bavaria, mobility to some areas of the Allmende was difficult and demands special equipment such as four-wheel drive vehicles. Other areas of difficult access such as steep slopes or less favored land areas such as calcareous mires tend to be less and less used. As a whole, mean time by car from the lowland farm to the Allmende units was some 15 min by road. alpine units were fenced and most dwellings were in good state of repair. In Baixo Alentejo, most private farms were fenced along large plots (mean size of 21 ha) with at least some infrastructure aimed to provide feed and water to the animals. The most modernized have automatic devices while the other only can count on more rustic facilities. Mobility does not pose any particular problem, unless when grazing parcels are far from the stables and other grazing areas. Daily movements varied according to the species: while suckle cows frequently do not return to the stables on a daily basis, sheep usually come to the stable or at least to a sheepfold near to the headquarters, the same pattern to the pigs. Manure was not a problem in these operations due to the low concentration of animals that only during the night achieve levels that could cause concern. Furthermore the traditional practice of rotating the location of the enclosures, determining also the rotation of the family orchards that took advantage of the fertilization provided by the animals, contributed to avoid excess manure. What caused concerns was what happens to the soil of the enclosures where the pigs are kept where not only almost all vegetations disappear but also where the soil structure is negatively affected due to the normal animal behavior when in circumscribed areas. In Castile-La Mancha, the grazing infrastructures and the location of the allocated polı´gono had an incidence on grazing use and spatial distribution of grazing. Usually, grazing use decreased as the distance between the center of the polı´gono and the village nucleus increased. On an average size municipality (some 8000 ha and 16 polı´gonos), the mean distance can be 4 km.
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Theoretically, access to the polı´gonos by shepherds conducting their flocks is mandatory and regulated in the Local Grazing Management Plans (Ordenanzas Locales de Pastos in Spanish). In practice, in many municipalities the Ordenanzas are not up-to-date and many drove paths have fallen to the plough by increasing intensity of cultivation. Most arable farmers abide by the law allowing access of flock to grazing parcels. However, they do not show cooperative behavior claiming that sheep flocks interrupt land practices or harm land infrastructure or crops. Additionally, grazing (cereal, legumes and sunflower’s stubble, fallow lands, eriales, and natural pastures) and nongrazing parcels (irrigation, vineyards, and olive orchards) are interspersed within the polı´gonos, hampering movement to specific parcels. Some 56% of sheep farmers had their sheepfold located in the polı´gonos and the rest (44%) in the villages or their surroundings. This last group of sheep farmers, when operating a milking flock, has to move the flock on grazing days from the sheepfold to the polı´gonos and back for sheltering, milking, and water supply. Under these grazing practices, sheep flocks should be permanently conducted and a trend toward less grazing days and heterogeneous distribution of grazing use (far-reaching parcels less used than those near the village) was apparent. Most sheepfolds either in the village or in the polı´gonos had water supply (85%) and feed storing facilities (89%), but only 10% of them had proper manure disposal facilities, and 45% of sheep farmers arranged machinery for handling manure. Milk- and meat-oriented sheep flock were more or less evenly distributed in the region and in the sample. Of those milk-oriented in the sample, 55% had milking facilities and 45% milked by hand. The question of manure disposal was of great environmental significance. Those sheep farmers with the sheepfold near the village may accumulate heaps of manure near the villages until disposal by interested cropping farmers, with sanitary risks and hazards of leaching to aquifers or runoff to surface waters.
4.7. Labor The extensive systems of grassland management in Europe can still be considered a family-business operation. Most of these systems represent a hardworking operation carried out in remote and LFAs within a much more favorable general economic environment. The labor constraint is thus, at present, one important limitation for the sustainability of extensive systems of grassland management in Europe. In some study areas, such as Northern Sapmi, reindeer herding was a source of social cohesion for the Sa´mi families and households and the general social rating of the reindeer farmers is high. In this area, most laboring was family job, although some farmers may have an extra source of income with occasional wage labor. Sa´mi lifestyle was centered on reindeer herding and their annual cycle of work tasks were organized in
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accordance to the pasture cycle. Within the Sa´mi society, being a herdsman (badjeolmmus) has a high societal status reinforced by the revival of Sa´mi language and culture (Riseth, 2006). In the Tatra Mountains, family labor was dominant in the lowland farm, but 7 out of the 40 sampled farms had waged labor. For the summer grazing season, sheep of several lowland owners are gathered in large flocks and conducted by the baca shepherd. The supporting labor of the baca shepherd is paid in cash. The baca shepherd net income was the difference between income from cheese production and costs from grazing maintenance and additional supporting labor. Usually, it is counted as 100 sheep per shepherd and they owned some 1250 € per grazing season (some 4 months in the mountains) plus food and accommodation. Grazing management was harsh work by lack of grazing infrastructures and technical improvements, but in this less-developed study area, young farmers have few options to alternative jobs. In Entlebuch, family was also the main source of labor in the lowland farm, but waged labor support was required for managing alpine grazing units. In this case, external animals in the alpine unit required permanent (dairy cows) or occasional caring and waged labor was required. Farming job was standardized by WU (Arbeistkrafteinheit, AK) depending on animal species and on sloping land. Most alpine units (63%) had between one and three WU and 23% less than one. Caring of dairy cows (less dominant in alpine pastures) was permanent while caring of heifers (dominant livestock) was occasional, lowland farmers managing an alpine unit need help during summer from family labor or employees and labor was usually the main tier of costs. The decreasing interest of young people to work as farmers was mostly caused by high labor inputs, isolation on the Alps, hard work, and poor economic perspectives. However, stabilizing factors in this area such as off-farming income from tourism, intact family life, regard by the regional community, and good social integration were incentives for social sustainability. In Bavaria also, most farming job in the lowland farm was carried out by the family, while most job in the alpine unit (Allmende) was waged (some 80% of working hours). Part-time job dominated in smaller farms of the Alps agrarian region with some 50% of the farm in this region (n ¼ 38) having less than 50% of the household income derived from farming. The profit estimates were based on labor reimbursement of 10 €/h for labor on the Allmende. Mean labor demand on the Almende, which were mainly grazed by heifers, sums up 9 h per grazing season and LU. Although most of the farms in Baixo Alentejo were family farms, hired labor was more relevant in larger farms, which use most of the area. Nevertheless, the declining trend of the wage labor was evident from the observation of the census. Herdsmen jobs are nonattractive from a sociocultural point of view. In these extensive farming systems, it has been
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possible to increase the substitution of labor for capital (fences, automatic feeding and drinking devices, machinery, and so on), making labor easier and less constraining. In our sample (n ¼ 15), the mean WU per farm was 3.4 1.7. Family and wage work corresponded to (60.4 26.4)% and (39.6 26.4)%, respectively, of labor demand. In this sample, two cases only used family labor, five used essentially family labor, four used more permanent wage work than family labor, and four used wage work but only part time. In the cost structure of surveyed farms, family job was accounted for by the real wages of the region. In Castile-La Mancha, family labor was dominant in smaller flocks while a combination family and waged labor support was dominant in larger flocks. In this case, most waged labor was carried out from immigrant population. With mean regional flock size of 485 396 breeding ewes, mean provincial numbers of WU per flock were 1.55 0.75 (n ¼ 51); 1.28 0.58 (n ¼ 41); 1.83 1.08 (n ¼ 49); 1.56 0.88 (n ¼ 39); 1.52 0.78 (n ¼ 51) for the provinces of Albacete, Ciudad Real, Cuenca, Guadalajara, and Toledo, respectively. The mode for all sheep flocks was 1 WU per sheep flock. Proportions of the only waged or both family waged flocks have increased in the last years with decreasing number of flocks and increasing flock size. Some 30 years ago more than 90% of sheep flocks were operated with family labor only. Immigrant implication is filling the void of barely enthused young Castilian farmers toward the sheep operation. In this study area, the social rating of the shepherd job was low even within their own farming communities although, paradoxically, the production of the Manchego cheese was promoted as a seal of regional identity. Grazing infrastructure and management should be improved and higher professional rating of the shepherd job enhanced to achieve higher social integration. Higher labor productivity in the two Iberian study areas was mostly the result of larger herd/flock size (Table 3).
4.8. Productivity estimates Extensive systems of grazing management are characterized by low output related to the farmed area. In the EU, most of these systems also are located in areas with physical environmental constraints such as poor and dry soils or steep slopes in mountain areas. In our six study areas, the most contrasting biogeographical European regions were represented from the alpine-boreal zone in Northern Sapmi to the Mediterranean zone in Baixo Alentejo and Castile-La Mancha. But within specific study areas, some contrasting trends of intensification were also represented. In Northern Sapmi, for example, different patterns of reindeer migration can be found. In Norway, a full migration-extensive pattern was more common with full-time reindeer husbandry and subsidies allocated to husbandry leaders. In Sweden and
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Finland, the subsidies were allocated to reindeer owners who are paid by meat production. They had an incentive for part-time job and more intensive forms of production (short migration or stationary patterns of herding). Productivity estimates were required to assess economic performance. In Entlebuch and Bavaria, two farming units (alpine and lowland pastures) were integrated in the whole MU with very different levels of intensification. A common feature of these two systems is a combination of one unit with regulated stocking and a function of nature conservation (alpine unit) with an intensive use in the lowland farm unit with high use of farm machinery (indoor feeding of grassland conserves dominant). In the Tatra Mountains, however, although the system was based on the use of the same two units (alpine and lowland units), more extensive forms of production were dominant. In Baixo Alentejo and Castile-La Mancha, climatic and soil constraints limit the intensification and productive outputs. In fact, these two latter systems can be considered as modified forms of past traditional systems dating from before the introduction of farm machinery in the early 1960s. The crop subsystems have evolved in response to new technologies (crop varieties, mineral fertilizers, and farm machinery) as requirement for cutting costs and less labor demanding operations. The livestock subsystems, however, had changed less and can still be considered as open grazing and extensive operations. In Northern Sapmi, productivity was estimated for the Norwegian part based on public statististics ( konomisk Utvalg, 2004; Reindriftsforvaltningen, 2005) and the numbers in Table 8 are regional average numbers for western Finnmark (Norway). The numbers are based on 1.8 WU per husbandry unit (mainly family labor), 8.33 reindeer per LU, and 350 reindeer per husbandry unit. Accordingly, the average labor productivity was 23.3 LU/WU. Compared to the other study areas land productivity was extremely low (Table 8). The main reason was very low-intensive land use (3.5 animals per km2). Labor productivity was at a low medium level both in livestock and in income.6 In the Tatra Mountains, labor productivity was estimated as a weighed mean (9 LU/WU) of those in the lowland farm (3 LU/WU) and sheep in the alpine unit (17 LU/WU). Productivity of labor7 was weighed for days in the lowland farm (205) and days in the alpine unit (160). Land productivity was valued by estimating a weighed stocking (5.88 ewes/ha) and value added by ewe resulting of the addition of lamb selling in the lowland farm (19 €/ewe) and processed milk in the alpine unit (47 liters/ewe and 24 €/ewe). Total value was 43 €/ewe and land productivity was 253 €/ha. Lambs are marketed at mean LW of 11.6 kg and, the number of sold lambs by breeding ewes was 0.6. If labor productivity is estimated as €/WU, the
6
7
For internal comparison, the best performing region of Sa´mi reindeer husbandry (South Trondelag/ Hedmark) had these indicators outcome: €/ha ¼ 2.7; LU/WU ¼ 41.5; €/WU ¼ 33667. Productivity of labor in Tatra ¼ (17 0.44) þ (3 0.56) ¼ 9 LU/WU.
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Table 8 Main productivity indicators in the six study areas
Indicatora
Northern Sapmi
3.3 Land productivity (€/ha) 23.3 Labor productivity (LU/WU) 15,912 Labor productivity (€/WU) a
CastileLa Mancha
Tatra
Entlebuch
Bavaria
Baixo Alentejo
253
1570
1500
188
162
9
17.9
31.5
43
41
2276
28,016
31,500
22,914
39,073
In Northern Sapmi data represented only Norway (western Finnmark). In Tatra, Entlebuch, and Bavaria data represented the combined alpine and lowland units. Productivity related to only farming income.
estimation8 would be 2276 €/WU. While sheep are on the alps, it was estimated one shepherd per 100 sheep (17 LU). For our mean sample of 340 sheep per baca flock corresponded to 3.4 shepherds. In Entlebuch, the mean altitude of the lowland farm was 800 m (range 600–1000 m) and the mean altitude of alpine pastures was 1300 m. Mean growth rate of younger cattle in the lowland farm was 0.62 kg LW/day. However, in some alpine pastures located between 1700–2600 m growth rate may decrease at 0.1 kg LW/day. For a grazing season of 100 days this difference represented some 50-kg LW. Similarly, milk productivity of dairy cattle in the lowland farm was 6000 liters/cow per year, but for the small number of dairy cattle in the alpine pastures, productivity may decrease to an equivalent of 15 liters/cow per day (100 days on summer pastures). Usually, the small number of dairy cattle in the alpine pastures used the best quality pastures, although of the 230 farming units of alpine pastures in Entlebuch (2003), only 17 maintained dairy cattle and only 7 produced alpine cheese. Productivity data recorded in Table 8 corresponded to an average MU (LBL, 2004b). Other productivity estimators for upland Swiss pastures are available (Mayer et al., 2003). Roughly, a mode MU consists of a lowland farm and the alp, on the 18 LU use 14.6 ha while on the alp 34.2 ha of rough pasture are used by the equivalent of 12.5 LU. Production income9 per MU, including 7366 € of grazing fees, was nearly 48,000 € of which over 70% are derived from milk sales. Based on this mode MU, the standardized productivity equals 993 €/ha, 1570 €/LU, or 28,016 €/WU (Table 8). 8 9
Productivity of labor in Tatra ¼ (43 €/ewe/0.17 LU/ewe) 9 LU/WU ¼ 2276 €/WU. Production income per lowland farm in Entlebuch: (6000 12.5 0.464) ¼ €34800 for milk sales plus (420 5 3.25) ¼ €6825 for meat sales or a total of €41625 per farm or 2312 €/LU.
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For Bavaria, the calculations of the revenues are based on the information on the production amounts of each farm and the average Bavarian prizes in autumn 2004. Dairy farms represent 79% of the farms by production objective and 91% of LU in the sample. The average farm realizes market revenues of 42,500 €. In case of an average farm 75% of these revenues can be attributed to milk and 25% to meat sales. This corresponds to roughly 1500 €/ha, 1000 €/LU, 31,500 €/WU, and 13 €/Awh. In the Baixo Alentejo, sample the average total pastureland was 379 322 ha and mean of 141 96 LU/farm or 0.44 0.30 LU/ha corresponding to a total income of 1289 1040 €/LU with costs of 943 662 €/LU. The system was strongly dependent on subsidies that represented, in average, 588 294 €/LU or 196 159 €/ha. The weighed average labor productivity was 43 21 LU/WU or 22,914 17,403 €/WU without subsidies. Finally, land productivity was 188 148 €/ha without subsidies. In Castile-La Mancha, a mean polı´gono of 500 ha (mean of 82% pastureland) stocked at a rate of 1.17 animals/ha (1 breeding ewe per ha). Animals’ productivity was some 100 liters of marketed milk per breeding ewe per year, which was sold at mean price of 0.9 €/liter. Similarly, milking oriented flocks sold 1.4 lambs per ewe at a mean LW of 13.2 kg/lamb and mean price of 3.9 €/kg LW. Total sales per breeding ewe were 90 €/ewe for milk and 72 €/ewe for meat or a total of 162 €/ewe and roughly the same 162 €/ha. Mean labor productivity was different for milk-oriented sheep flocks (242 ewes/WU) or meat-oriented sheep flocks (367 ewes/WU). In milkoriented flock the labor productivity was equivalent to some 41 LU/WU (0.17 LU/ewe). Taking into account this equivalence, the value added by ewe (162 €/ewe) was equivalent to 953 €/LU and productivity by WU can be estimated at 39,073 €/WU. Averaging productivity indicators by study area illustrated differences between study areas but concealed a great deal of variation within study areas. In Entlebuch, for example, farm size ranged from 0 to 1 ha (2% of farms) to more than 30 ha (4% of lowland farms) with farms between 10 and 20 ha representing some 48% of total lowland farms. Size of farms may affect productivity indicators as well as lowland farms using or not an alpine unit (20% of lowland farmers using). Of those using an alpine unit, some 16% bring dairy cattle to the alpine unit and only seven out of 230 alpine units in Entlebuch produced alpine cheese. Similarly, mean LU per lowland farm was only 16 but the mean alpine farming units allotted 27 LU of which 52% were heifers, 23% dairy cattle, 9% sheep and 7% suckle cows. The workload also varied depending on whether the lowland farmers also operated an alpine unit. In this case, WU in the lowland farm should be supplemented with full-time family or waged labor, if the manager brings dairy cattle to the Alps, or part-time family waged labor for heifers, suckle cows, and sheep. Similarly, land productivity showed a decreasing gradient with increasing
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altitude. In the lowland farms at mean altitude of 800 m (range of 600–1000 m), mean stocking was 1.42 LU/ha (97% of TAL was grassland) and mean grassland productivity was 7500-kg hay/ha. In the alpine unit at mean altitude of 1300 m stocking was 0.5 LU/ha TAL (only 60% of TAL is pastureland) and mean land productivity was 3000-kg hay/ha. At higher altitudes of 1800–2000 m productivity decreased to some 1500-kg hay/ha. In Bavaria, differences in productivity between lowland and alpine units are also acute. Year-round stocking in the lowland farms was some 1.4 LU/ha TAL (if just privately managed TAL is considered), but only 0.4 LU/ha in the Allmende. Within the farmers’ sample differences in productivity were related to farm location. Sampling units are located in three agrarian regions with increasing levels of land productivity—Alps (Alpen), prealps (Alpenvorland), and prealpine moraine belt (Voralpines Hu¨gelland). The intensively managed grassland and arable land represented more than 80% of land uses in the two latter areas. In contrast, it represented only 50% of land use for the farms located in the Alps. Increasing land intensity and mineral nitrogen application were also related, with 13%, 46%, and 60% of farms located in the Alps, prealps, and prealpine moraine belt using mineral nitrogen, respectively. These differences in land structure and intensity had an impact on land productivity. While similar in overall average farm size, including forests, farms in the three areas stocked a mean of 29, 62, and 82 LU per farm, respectively. Mean productivity of grasslands in the whole sample was 41 MJ NE for lactation per hectare but values ranged from a minimum of 14 to a maximum of 78 MJ of NE for lactation per hectare. In Baixo Alentejo, large variation of productivity was observed. The results from our small sample showed high figures for standard deviations suggesting that larger samples will provide even large variation, and this happens either on animal, work, or land productivity. In Castile-La Mancha, mean values of animals’ productivity concealed a large variation. Ewes’ yield of marketed milk presented a CV of 58% (98 57 liters per ewe per year). Data presented in Table 8 represented means of milk-oriented sheep flocks (n ¼ 112 in a whole sample of n ¼ 230) for harmonization of reporting. Production objective, however, was a main cause of variation within this study area. Flock size in milk- or meat-oriented sheep flocks presented values of 428 430 (n ¼ 112) and 540 354 (n ¼ 118) breeding ewes per flock, respectively. LW of lambs at selling had values of 13.1 4 and 20.0 4.4 kg LW, respectively, and corresponding labor productivity showed values of 249 99 ewes/WU and 367 150 ewes/WU, respectively. Despite the fact that all systems are regarded as marginal, within the national and regional context, big differences in the market revenues per hectare indicated large differences in the productivity of the systems. The market revenues per WU, however, showed less difference for the study areas located in the EU-15. Only for Tatra it was significantly lower.
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4.9. Economic performance Two main assumptions supporting EU policy toward extensive systems of grassland management are that these systems are not economically sustainable but, as they may deliver some social and environmental functions not properly factored in farm prices, they should be awarded some public handouts. Neither of both assumptions has been properly tested. In this section, we will address the first part of the assumption (economic sustainability) by applying a classical cost-benefit analysis to the economic data gathered in our six study areas. Given the wide variations in production objectives, farming practices, and general economic environment between the six study areas, economic sustainability should be addressed within each study area. After this, a general picture of sustainability may or may not appear. The data gathering process was agreed on within LACOPE (Caballero and Ferna´ndez-Santos, 2004). This previous coordination effort facilitated comparisons and harmonization of reporting. Profit or losses of the farming operations were estimated either with or without subsidies, and main data were related to the same unit (€/LU) for comparisons. The implication of public handouts was valued as the ratio of total subsidies to value of production farming (Table 9). In three study areas (Tatra, Entlebuch, and Bavaria), the MU or operating unit was composed of two farming units (the alpine unit and the lowland unit), each one with their own structure, practices, and subsidies. Field economic data were recorded for each farming unit but an economic appraisal was also performed for the whole operating unit. One of the main problems for proper economic appraisal was recording of subsidies. Many policy schemes were operating in the six study areas. Subsidies can be awarded by the EU or by national and regional governments, and being allocated as production subsidies, environmental schemes or direct headage payments (by head of animals). All were accounted as public handouts. In our income accounting (Table 9), we will refer only to farming-derived sources of income (value of production farming). In some areas other sources of income such as tourism will be reported in the text. Data recorded correspond to the year 2004 in Tatra and Baixo Alentejo; to 2003 in Northern Sapmi, Entlebuch, and Bavaria; and to 2002 in Castile-La Mancha. In Northern Sapmi, data reported in Table 9 are divided into specific columns for each of the three countries due to the diversity of economic structure. The Norwegian part corresponds to the region of western Finnmark and represented the average of 241 husbandry units, 84,200 reindeer in the area, and 1279 reindeer owners. These data represented means of 350 reindeer per husbandry unit, 66 reindeer per owner, and 5 owners per husbandry unit (Reindriftsforvaltningen, 2005). For Sweden and Finland, the main data are herder interviews material from one district in each country. In the north of Sweden (province), reindeer husbandry master
Table 9 Main economic results of operating units in the six study areas Northern Sapmi
a b c d e
c
d
d
a
b
a
a
Baixo Alentejo
Castile-La Mancha
Indicator
Norway
Sweden
Farming income (A) e Total costs Total subsidies (B) Net cash flow without subsidies Net cash flow with subsidies Ratio (B)/(A) (%) Total subsidies (€/ha) Year of records
790 443 656 347
120 169 33 –49
419 196 167 223
(€/LU) 253 509 176 256
1606 650 784 956
1291 820 270 471
701 943 588 242
953 865 141 88
1003
16
390
80
1740
741
346
229
83 3 2003
27 0.1 2003
40 0.4 2003
70 105 2004
49 491 2003
21 380 2004
84 196 2003
15 140 2002
Finland
Tatra
Entlebuch
Bavaria
In the case of Tatra, Entlebuch, and Bavaria a combined MU is considered (lowland farms plus alpine pastures). Lowland farms only in Entlebuch had farming income of 2312 €/LU and total subsidies of 1060 €/LU. This profit conceals a great of variation within individual samples with 35% of milk-oriented sheep flocks having losses without subsidies. Subsidies in €/ha correspond to the cultivator, not to the landless pastoralists. Based on public statistics ( konomisk utvalg, 2004; Reindriftsforvaltningen, 2005). Based on herder interviews in one MU each country. In Finland, cost of supplementary feeding is not included. Family labor not included in Northern Sapmi, Tatra, Entlebuch, and Bavaria.
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(unit or household) was 332 husbandry masters and 56,522 reindeers. Reindeer owners numbered 1249 and each husbandry master had in average 170 reindeers. In Finland Ka¨savarren paliskunta (district) reindeer husbandry dollu (unit or household), 128 husbandry dollu and 10,000 reindeer were in the area. Reindeer owners numbered 168. The EU agricultural subsidy programs do not involve the reindeer industry in Sweden. The argument is that the Swedish’s reindeer husbandry operation is an exclusive right for the Sa´mi people and thereby not open to all European citizens ( Jernsletten and Klokov, 2002; Ulvevadet and Klokov, 2004). We noted that both income and cost tiers were clearly higher in Norway than in Sweden and Finland, which limits the differences in the level of profit without subsidies. In Sweden and Finland, animal stock increases are not included in the income tier due to uncertainty of data. Particularly in Sweden this probably means an underestimation of farming income. In Finland, an additional uncertain factor is the cost of supplementary feeding, which is not included in the calculation due to the uncertainty of extent, cost, and its coverage. Strikingly, the subsidy level is very high in Norway and intermediate in Sweden and Finland. Compared to other study areas, two national operations in Northern Sapmi (Norway and Finland) turn a profit without subsidies. With subsidies, the profit is high in Norway, Finland operates at an intermediate level and Sweden showed a loss. While data for Norway are confirmed, we have indications that farming income is underestimated for Sweden and costs for Finland. Notwithstanding these uncertainties, we have preferred to show the trends in the three countries. The low to very low level of subsidies per area in the three countries is a good indication of very low intensity per area of the reindeer production. In the Tatra Mountains, main source of income came from processed milk (marketed cheese) during the summer grazing season, while sheep on the alps (24 €/ewe). Marketed lambs on the lowland farm represented some 19 €/ewe to a total income from production of some 43 €/breeding ewe. Total costs included 70 €/ewe in the lowland farm and 10 €/ewe on the alps or a total of some 80 €/ewe. Subsidies were only allocated to sheep in the lowland farm and amounted to some 30 €/breeding ewe. In this case, the sheep business was unprofitable even if accounting subsidies in total farm income. Only by adding nonfarming income sources such as tourism services (equivalent to some 23 €/breeding ewe) can the farming enterprise turn profitable. It should be noted that the summer period of sheep on the alps was profitable even without subsidies due to the fact that the milking period occurred during this season and sheep gathered in large flocks accounted for higher labor productivity than in the winter period in the lowland farm. In the farms of Entlebuch, farming income and cost tiers were derived from the lowland and the alpine farming units, providing that the livestock
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farmers bring their animals to the Alps. If this is the case, we considered an average lowland unit of 14.6 ha and 18 LU with total sales of 41,625 € per lowland farm (see productivity tier) plus 7366 € per alpine unit in grazing fees. Total farming income in the whole operating MU of 48,991 or 1606 €/LU for a standard MU stocking 30.5 LU (Table 9). A main cost tier not included in Table 9 is the remuneration of family labor. Taken a full cost approach, labor represent some 50% of total costs (Ho¨ltschi, 2006), if one takes the standard rate of 15.7 €/wh. For an average MU with 30.5 LU, the remuneration of family labor would imply additional costs of 1583 €/LU. Subsidies in the alpine units of Entlebuch represented 80 €/LU as sheep and 200 €/LU as heifers. For a standard unit stocked with 80% heifers and 20% sheep, represented 176 €/LU. For stocking of 0.8 LU/ha and an alpine unit with 34.2 ha of pastureland, subsidies valued 141 €/ha and total of 4822 € per alpine unit. According to government allocation, subsidies for the average lowland farm in Entlebuch comprised general area-payment subsidies, compensation for harsh production conditions, and contribution to environmental performance, for a total of 19,087 € per farm (14.6 ha) or 1307 €/ha. Subsidies in the lowland farm were more than 10 times higher than in the alpine units. Weighed mean for an average MU in Entlebuch10 with 34.2 ha (70%) of pastureland in the alpine unit and 14 ha (30%) lowland farm would receive 491 €/ha and 784 €/LU (Table 9). For an average situation in the Swiss alpine region,11 the average lowland farm was 18.6 ha and received 31,098 or 1672 €/ha (Swiss Federal Office for Agriculture, 2004). If combined with an alpine unit of 37 ha of pastureland (receiving 141 €/ha), the weighed average would be 646 €/ha. In Bavaria, the 56 farms surveyed in the sample participated in 17 different Allmende. Of those farms, 43 had CLS locally inherited entitlements and 13 were boarding farms, paying a grazing fee. In this case, however, livestock farmers were less lured to send their heifers to the Alps than in the study area of Entlebuch, and contribution of grazing fees by external animals was of much less significance. In most CLS, most of the required labor was waged. For the Allmende, the main source of revenues was public handouts. For this study area, data showed in Table 9 corresponded to economic performance of the mean lowland farm, without taking into account only the labor and fodder saving of using the alpine pastures. For the average farm, the total amount of public handouts amounted to 400 €/ha but only 8.800 € per farm since the larger farms received lower 10
11
Weighed average farm support in Entlebuch (30% lowland unit and 70% alpine unit) ¼ (1307 0.3) þ (141 0.70) ¼ 491 €/ha. Weighed average farm support in the whole Swiss alpine region (33% lowland farm and 67% alpine unit) ¼ (1672 0.33) þ (141 0.67) ¼ 646 €/ha.
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public handouts per hectare. Farming revenues for lowland farm summed up to 50.800 €, not including side income by agrotourism and forestry. Total costs, excluding family labor but including capital cost and depreciation, amounted to 51.400 € per farm. Of these total costs roughly a third are depreciation for buildings and machinery. The depreciation was calculated based on standard costs, which might underestimate the maximum useful life of these facilities especially for the very small farms, which are very frequent in the sample. The figures presented in Table 9 deviate to some extent from the data just presented. This is for two reasons. First of all, no depreciation is included in the stated costs. Second, larger farm, measured in LU, are more profitable than smaller ones, therefore if the economic indicators are related to LU the farm they show has a slightly more positive picture. Taking into account these assumptions, the average farm made a loss even before remunerating family labor. These mean results for the average farm in the sample (n ¼ 56) of Bavaria conceals a great deal of variability between larger and small farms and between agrarian regions. Only 26 of the 56 farms in total had profits. Especially, 24 of the 38 farms in the Alps showed losses. However most farms had a positive cash flow, and only four small farms showed negative calculated cash flow per hectare. These negative cash flows can be largely attributed to the conservative assessment of the revenues. Especially direct marketing was more common in the small farms in the Alps. Further, it should be kept in mind that these farms continued farming for noneconomic reasons. As a further general pattern it can be seen that the cash flow increased with increasing farm size. This holds especially for the farms outside the Alps. The farms in the Alps often compensated small herd sizes with the large quantities of land, which are eligible for nature conservancy or compensatory payments and which can be managed at low costs. As stated before, some of the economic agricultural activities of especially small farms were omitted in the calculation. Therefore, the real cash flow and profits of these farms per hectare could be a few hundred higher than stated. The profits per hectare as well as the cash flow per hectare showed the same general patterns in relation to land use intensity. Farms with a higher average stocking rate per hectare had a higher profit as well as cash flows. Since the cash flow was positive for most farms at least short term sustainability seems to be assured. The cash flow as well as the profits showed that farming was most profitable in the prealpine moraine belt and least in the Alps. The processed costs in the farm were very high if they are compared with other study areas. Some authors have related these high costs with intensive use of farm machinery and low incentives to raise suckle cows and heifers under grazing conditions (von Boberfeld et al., 2002). Economic results from the Baixo Alentejo sample showed a clearly divergent situation before and after subsidies. Only three farms in the sample
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showed positive results before subsidies and, interestingly, all of them produced Alentejano pig. Furthermore, the only producer specialized in Alentejano pig was the most successful before and after subsidies. Results of the sample (n ¼ 15) without subsidies ranged from 810 €/LU to 841 €/LU, the mean was 242 436. Considering the situation with subsidies the panorama was much better, but three farmers still presented losses. The results ranged from 1659 €/LU to 254 €/LU with a mean 346 489 €/LU. The ratio of subsidies to total income (including subsidies) was 46 21% and the ratio of subsidies to value of production (without subsidies) was 84 55%. The great variation observed in this small sample suggests that, at least, identical variability could be found in larger samples. The number of observations was too low to try to extract statistical evidence correlating the results with any kind of variable, namely productive orientation. In Castile-La Mancha, farm economic results were mostly dependent on the production objective (milk- or meat-oriented sheep flocks), and the size of the flocks. Data gathered in Table 9 corresponded to an average of milkoriented sheep flock (n ¼ 112). In this case, farm market revenues were coming from milk sales to Manchego cheese-making facilities and meat sales of early-weaned lambs. Total farm revenues represented 162 €/breeding ewe (953 €/LU). Total costs represented 147 €/breeding ewe (865 €/LU) of which the two main tiers were labor (45%) and supplementary feeding (39%). The average milk-oriented sheep flock would make a small profit of 16 €/breeding ewe. However, data recorded for meat-oriented sheep flocks (n ¼ 118 in the sample) showed a net loss without subsidies of 12 €/breeding ewe. Subsidies awarded to the sheep operation included direct headage payment plus a top-up payment to LFAs, amounting to 24.04 €/ewe for milk-oriented flocks and 30.05 €/ewe for meat-oriented flocks. When subsidies were included, the average farm turns a small profit. These data, however, concealed a great deal of variation within the sample. Farm profit of milk-oriented sheep flock (n ¼ 112) showed mean values of 16 41 €/breeding ewe with 35% of milk-oriented sheep flock showing losses without subsidies. When subsidies were included, mean farm profit showed values of 40 41 € per ewe and even 17% of individual farms showing a loss. Similarly, in meat-oriented sheep flocks, mean profit without subsidies showed values of 12 38 €/breeding ewe with 58% of farm showing losses. When subsidies were included net results were 18 39 €/ewe with yet 24% of meat-oriented flocks with losses. As expected, the economic results among the six study areas varied greatly in the total income (farming revenues plus subsidies), as well as in the total costs and proportion of public handouts on farming revenues. Farming income varied from 1606 €/LU in the MU of Entlebuch to 253 €/LU in the whole MU of Tatra. In this latter study area, total costs were much higher than farming income, although farmers may have side-income from tourism
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services. If in Entlebuch one would only take the figures for the lowland farm, farming income would account to 2312 €/LU, the highest of all study areas. The proportion of subsidies to value of production farming also varied greatly from 15% in Castile-La Mancha to around 85% in Norway and Baixo Alentejo. It was illustrating how in those study areas (Tatra and Entlebuch), where the low-input units were included in the analysis, showed the poorest economic performance if family job were included. In the case of Tatra, the alpine unit was unsupported. In the case of Entlebuch, the alpine unit had much less agricultural output (only external grazing fees) and agricultural support than the lowland farm unit and all jobs (family or waged) were rated at standard rate. The combined operation was unprofitable mainly because families accept a lower wage rate for working in the Alps. This idealism, however, may have a limit.
4.10. Grazing management and trends Within this tier, main trends in grazing management were recorded by study area regarding specially the following subjects—animals’ lots under grazing, trends in spatial distribution of grazing and trends in grazing days. Some others study-area related indicators were also recorded. Grazing management trends were recorded by comparing the present situation with available data of some 20–30 years ago. Long displacements of herds (trashumancia) across grazing units and seasons were operative only in Northern Sapmi, but only under full migration patterns. Short displacements (trasterminancia) of some animals’ lots between lowland farms and highland pastures were operative in the Tatra Mountains and the two Alps study areas. In Baixo Alentejo and Castile-La Mancha, grazing patterns were mostly stationary across the year with herds/ flocks displacements between seasonal resources within one specific MU. In Castile-La Mancha, a small proportion of sheep flocks (some 2%) in the sample still practiced the old trashumancia across the can˜adas from summer pastures in the north of the region to winter pastures in the south. Currently, lorries or trains displace sheep. Also in Baixo Alentejo, a very small proportion of cattle and sheep herds still practice the trashumancia to cereal stubble fields over the summer season unless occasional sanitary rules restrict displacements. Indigenous livestock breeds were dominant in most study areas except in Entlebuch and Bavaria where more productive breeds are dominant, indicating an increasing level of intensification, specially in the lowland farms. In Tatra, indigenous sheep breed (Polska owca go´rska) is dominant for the production of regional cheeses. In Entlebuch, Original Brown and Simmentaler Fleckvieh dairy breeds are indigenous, but more productive breeds are increasing (Brown Swiss and Red Holstein). In Bavaria, highly productive breeds such as Brown Swiss and Simmentaler are dominant. In Baixo Alentejo,
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indigenous sheep (Merino) and pig (Alentejano) are dominant, but cattle crossing with foreign breeds is frequent and the same in Castile-La Mancha (indigenous Manchego sheep). The importance of autochthonous livestock breeds for the sustainability of extensive livestock systems has been stressed (Blanc et al., 2004). Herd-conducting patterns were very different across the study areas depending on availability of grazing infrastructures, type and distribution of grazing resources, labor availability and cost, and grazing behavior of the specie. In Northern Sapmi, the reindeer husbandry has changed in the last 20–30 years from a subsistence pastoralism to a motorized and marketoriented industry. With the motorized vehicles, the Sa´mi could keep much bigger herds and, in recent years, the number of animals has increased considerably in several of the regions (Kautokenio, Karasjok, and Finnish Lapland) of Northern Sapmi. The implications included decreasing animal weights, reduced offspring rates, increasing predators, emergent dependence on artificial feeding, increasing socioeconomic stress, and avoiding traditional long-migration patterns. The level of support and policy schemes varied between the three countries but headage payments and direct price support played a great role. Reindeer herders have an incentive to increase stocking density with these systems of support and overgrazing may result on specific areas. At the same time, and as a consequence of access restrictions across borders, traditional migration patterns were hampered and some pasture areas are unused or underused due to grazing prohibition. Dissolution of national borders between Norway, Sweden, and Finland is required and new area-payment schemes of subsidies devised and implemented in favor of long-term pattern of seasonal land use and pasture adaptation. In the Tatra Mountain area, the number of sheep has decreased from some 300,000 to 60,000 in the last 30 years, especially in the downturn of change since the liberalization of the Polish economy in 1989 and lower demand and prices for meat, milk, and wool. This lower size of regional sheep flocks is having an influence on stocking of alpine pastures over the summer grazing season. Far-reaching grazing meadows in the clearings of the forest are becoming less used or abandoned on mobility problems. The alpine pasture lack of a proper system of subsidy support mainly because property and grazing rights are not clearly defined. A rehearsal of the grazing system should start with a proper legal and institutional framework with appropriate claim of property and grazing rights. Currently, mean number of owners claiming property on private clearings (mean size of some 5 ha) was 32. Some indicators to assess the degree of intensification/extensification in the Carpathian region are available (Fereniec, 1999; Krajcovic, 1990; Krynski, 1976; Manteuffel, 1981; Statistical Yearbook, 2003). In Entlebuch, the same trend toward abandonment of the far-reaching alpine units was observed. Here, however, only 12% of alpine units did not have road but, care of animals in far-reaching alpine units represented a
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harsh working and expensive operation due to high standard wage-labored cost of Switzerland. Animal care was restricted by labor cost and thus, dairy cows, requiring permanent herding and care, was the least present lot in the alpine units. Generally, heifers’ care was at least once a week, but nearly one third was cared for twice a day. Care intensity depends on weather conditions. The care of suckle cows and sheep was more occasional. Stocking density in alpine pastures is law-regulated (So¨mmerungsverordung). Organic fertilizers, as dung and liquid manure, are allowed only if produced in the stable of alpine units and spread during dry weather. Only Phosphor and Kali are allowed as chemical fertilizers and use of other chemicals require an especial permission. N-fertilizers are not allowed since 1970s, when common use of N-fertilizers promoted the spread of weeds in alpine pastures. Currently, most alpine units (95%) used dung and a big majority (78%) used liquid manure usually close-by the alpine huts. Underuse is a regular trend in alpine pastures with shrub and forest encroachment. These areas have to be cleaned by hardworking and costly operations. In the sampled lowland farms of Bavaria, most (80%) were dairyoriented. Only one farm in the sample was not keeping any cattle at all. The farm’s productive orientation influenced the number of animals kept. Usually dairy farms were the largest. The dominant cattle breeds were Simmental (53% of farms) and Brown Swiss (dominant in 29% of farms), with old regional breeds present in 15% of the farms (mainly part-time farms). Regional breeds were usually maintained in small herds of some 16 LU by part-time farmers. Heifers on the Allmende were mostly from dominant breeds. Of the 56 sampled farms, only 14 applied N-mineral fertilizers to grassland, and the rest were under the K34 Bavarian agrienvironmental scheme that does not allow the application of N-mineral fertilizers. Seven farms complied with organic farm standards. Complying with organic standards was easier for small than for larger farms but the first have less comparative advantages. Organic farming implied a surplus income of 5%. Winter-spring calving was the most frequent calving season. This breeding scheme allowed to meeting the higher energy demands for milk production with spring flushing meadows. Three to four harvests per plot were common with a high percentage (some 65%) of land uses devoted to conservation as hay or silage. Plots under grazing were usually used by heifers or suckle cows. Outside the Alps, dairy cattle barely grazed at all. In Baixo Alentejo, livestock production knew important shifts in the last 20 years but showing an oscillating pattern according to the variations of the determinants of change that is socioeconomic factors such as subsidies and availability of wage labor. The first movement corresponded to the increase of meat sheep production compared to the others species. This was followed by a declining trend of the sheep production that was being substituted for meat cattle during the last 10–15 years. This shift was essentially provoked
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by a more attractive animal premium. In this last period, it was also observed an important increase of extensive Alentejano pig production, mainly in oakholm Montados, where the acorn allows premium prices to these animals. Differently from other livestock, pig production determinants were essentially market driven. In Castile-La Mancha, 46% of the milk-oriented flocks kept the milking lot under indoor feeding for some 4–5 months, with only maintenance and gestating ewes (four first months) under grazing. In meat-oriented sheep flocks, ewes are kept indoor usually 1 month before and 1 month after lambing. Sheep farmers used all kind of available resources but a trend was observed for less grazing days and heterogeneity of spatial distribution of grazing. A large majority of sheep farmers (77%) indicated less outdoor grazing feeding than 20 years ago and only 3% more grazing days. The main reason given by farmers was lack of grazing resources in the polı´gonos due to increasing intensity of cultivation in fallow and stubble-land. Lack of grazing resources, hardworking conditions and daily drove of flocks from near-the-village sheepfolds to the polı´gonos are promoting less use or abandonment of far-reaching polı´gonos or parcels and some overuse of plots near the villages. A consolidation trend was apparent in the regional flock. In the last 20 years the numbers of flocks decreased at a rate of some 3% per year, but the number of sheep remained almost the same at the regional level. Average flock size increased from some 200-breeding ewes in the 1970s to more than 400 at present.
4.11. Main limiting factors Livestock farmers were questioned on main factors that may hinder present use of land-based resources on LSGS or may favor stabilization of the grazing systems. For Northern Sapmi, we based our assessment of limiting factors on our total material. Generally, the different adaptations (migration types) within Sa´mi reindeer management face various concrete problems. Tables 8 and 9 showed that Sa´mi reindeer management is a low-profit industry and a lowintensive land-user. We noted that the industry was subsidized not only from public budgets, but also from other income (Karlstad et al., 2002; Labba and Riseth, 2007). The low-profit situation does not seem to be limiting as the cultural valuation of staying in business seems to be very important (Ciuryk and Niemeyer, 2003; Labba and Riseth, 2007; Riseth et al., 2005; Riseth, 2006). However, on a general basis much of the problems could be traced much back to: (1) an encroachment/disturbance problem and (2) a seasonal pasture balance problem. If these problems were reduced, clear overgrazing problems also might be scaled down/brought under better control. Both problems have external reasons as (1) depends on the property rights situation and the general development of society’s
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technical infrastructure while (2) in addition to nature conditions depends on national borders, border closures, and bilateral conventions (Riseth et al., 2004). In the Tatra Mountains, the small size of the plots in the lowland farms and large number of owners of clearings in alpine units makes difficult the implementation of efficient grazing systems. Surveyed farmers also indicated a lack of cooperation between stakeholders but specially sheep owners and shepherds (gazdas and bacas) and, some time, landowners. Long-term renting contracts would contribute to stability and much easier management. Technical assistance was also cited as a main limiting factor especially required for milk processing facilities and animal transportation to farreaching alpine units, presently underused. Finally, the most cited limiting factor was a lack of proper legal and institutional framework. Without proper allocation of property rights and grazing rights it was difficult to devise and much less to implement grazing rights contracts or even a sensible scheme of policy support for the alpine pastures. In Entlebuch, a majority of surveyed farmers (78%) rated as good or medium the future of the alpine unit operation. When questioned about the main conditions for increasing stability, the most cited responses were the luring of external farmers to bring animals to the Alps (32%) and increasing subsidies for alpine pastures (32%). Other less cited responses included nature contract, cooperation with tourist facilities or improving marketing of products. It seems that luring lowland and external farmers to bring their animals to the Alps was a main condition for long-term stability of the system. Notwithstanding this response, 9.9% of farmers abandoned the operation between 1985 and 1996 and 12% of farms gave up between 1997 and 2003. In Bavaria present incentives for luring farmers to bring their animals to the Allmende (CLS) are higher for small farms located in the Alps agricultural area. Most farmers derived less than 10% of the forage supply and less than 5% of the revenues from the CLS. Present scheme of subsidies do not incentive the use of CLS. Nevertheless, for the average surveyed farm, the granted amount of public handouts per ha dropped from 380 €/ha of agricultural land to 340 €/ha, if corresponding share of the CLS was included. This situation is changing in the course of the implementation of 2003 CAP reform. Until 2004, direct payments played a minor role and the lowland dairy farmers have little incentive to comply with stocking limits. Under new cross-compliance rules for direct payments, dairy farmers may have an incentive to comply with stocking limits by outsourcing heifers to the Allmende. In Baixo Alentejo, the most frequently referred limitations to livestock farming were the market price of meat products, the scarcity of labor, the soil and climatic constraints of the region, and the lack of land to buy or rent. Therefore, farmers adjust by favoring livestock species with better
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market prices, more favorable public handouts, and less labor demand. The rejection of the herdsmen profession forces an adaptation of the workload through new forms of management and better infrastructure and machinery that allow them to bypass the need of permanent care of the animals (Vicente et al., 2005). Other constraints, besides the ones that result from the soil and climatic conditions, result from the lack of cooperation between farmers. If there are some signs of cooperation concerning marketing and livestock sanitarymedical help that was enforced by the government and primarily funded by the EU. All the other dimensions of cooperation are totally absent or only represent incipient attempts. In Castile-La Mancha, main limiting factors cited by sheep farmers included cooperation with landowners–cultivators, improving grazing infrastructures for less hardworking conditions and overhauling the legal and institutional framework. These conditions had no clear arrow of causality. Landless pastoralists may expect better cooperation by landowners but landowners-cultivators do not have proper economic incentives to facilitate the grazing use of their lands, although most of them abide by the law. Hardworking conditions are to some extent inherent to the structure of land-based resources (unfenced polı´gonos), but also stressed by lack of grazing infrastructure. As a result, young Castilian farmers are barely enthused toward the sheep grazing operation. The rehearsal of grazing law ( JCCM, 2000) has not derived a general social consensus.
4.12. Interface to biodiversity On this research, the current socioeconomic status, limiting factors, and main trends of the surveyed LSGS were to be addressed. However, this research had also the subsequent objective of paving the way for further interdisciplinary research between the socioeconomic and ecology groups within the LACOPE project. For this reason, some grazing management indicators of ecological significance are stressed on this heading. In Northern Sapmi, three questions connected to border restrictions are at stake from an interdisciplinary point of view: (1) The relatively stationary coastal reindeer herding in Norway have very limited winter pastures and would gain much of increased access to winter pastures in continental Sweden and Finland. (2) The stationary adaptation in continental Finland leads to trampling of lichen resources and increased dependence of supplementary feeding. Change to cross-boundary migrations would increase support the double herd size without supplementary feeding and increase productivity by one third due to access to cool mountains in summer. (3) Migratory reindeer herding in northern Sweden have limited access to summer pastures in Norway and have their summer pastures in areas better suited for fall pasturing (Riseth et al., 2004). Eliminating these restrictions would increase reindeer
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summer pasturing in the Scandinavian mountain ridge and thereby promote grazing at biodiversity hot-spots of arctic-alpine plant rarities dependent of disturbance by grazing (Fuelling et al., 2004; Olofsson and Oksanen, 2005). In the Tatra Mountains, the summer grazing season with sheep in bacas’ care in the alpine units (clearings) was of the outmost importance for economic results of the whole MU as well as for cultural assets and indigenous product (cheese processing in the alps). The ecological effects of abandonment of the summer grazing operation in the clearings are at the stake. In the Swiss Entlebuch UNESCO Biosphere Reserve, the use of the lowland farm seems not to be at stake, as is sustained by generous handouts. However, not all the managers/owners of lowland farms take their animals to the alpine units. In this case, we have assessed the economic effect of the MU with or without operating an alpine unit or the economic effect of MU operating alpine units but with or without external grazing fees. The question of abandonment of the alpine unit, with much lesser support, is more acute in this study area, with corresponding lower grazing use in the upland pastures. In Bavaria, the number of entitled farmers sending their animals on the Allmende tends to decline. The alternative of valuing the economic status of the lowland farms with or without using alpine pastures seems sensible and feasible with present economic data. The question of abandonment of far-reaching and low-productive grazing grounds in upland pastures may have ecological significance. In Baixo Alentejo, one controversial question was the foundation of subsidies granted to cereal production on the grounds of environmental schemes. On the one hand, this practice is easily understandable in the restricted area of the Zonal Plan of Castro Verde where the cereal-fallow rotation is of importance as habitat for steppe birds. Differently is subsidizing wheat in other areas that looks more as a way to substitute coupled subsidies that no longer can be granted. Another question was the existence of contradictory policy measures with impact on the competition for the land use, which have great impact on grazing and in biodiversity. In fact, subsidies to afforestation of agricultural and pasture land are not only contradictory with the needed open spaces for target birds, such as Great Bustard, but also reduces the area devoted to grazing and increases the risks of fire. These practices may contribute to decreasing biodiversity levels. In the southern Castilian plain the mixed cereal and sheep operation is hindered by scarcity or poor mobility to grazing resources, lack of grazing infrastructures, and harsh-working operations. Cultivators–pastoralists relationships were not cooperative and current scheme of subsidies promoted a divergence of interests between the two social groups. The grazing operation based on the use of agricultural residues in arable land can be considered as a secondary option of land use and thus the landless shepherding operation based on the use of the polı´gonos (MU) is under risk. Under the present
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decoupling scheme for direct payments, rated as 75% for cereal and 50% for sheep in the Mid-Term Review of the CAP, 2003, cereal farming’ consolidation can be expected with corresponding increase of tilling intensity and less available resources for the sheep operation. Under this scenario, it would be sensible to assess the interdisciplinary effects of mixing cereal and sheep versus growing cereal as the only operation or the effects of crop rotations with declining tilling intensity (unploughed fallow land) and increasing hectare of annual legumes. These latter cropping and management alternatives, apart from their effects on soil quality and cereal yields (Lacasta and Meco, 2006), may have an incidence on the habitat suitability of target steppe birds such as Great Bustard. Some 50% of the world population of this specie is concentrated in the Spanish cereal regions (Alonso et al., 2003). One common ground for most study areas, except Baixo Alentejo, was the presence of problems of mobility of herds/flocks and/or problems of access to specific grazing grounds. The first were more apparent in Tatra (transport to far-reaching clearings in the Alps), and to some extent in Entlebuch and Bavaria and the second in Northern Sapmi (country borders barriers). Castile-La Mancha was a case study of both mobility and access problems. Landless pastoralists drove animals on grazing days from sheepfolds near the villages to the grazing allotments ( polı´gonos), and back for sheltering, watering or milking. Accessibility, even to far-reaching polı´gonos, was allowed, but mobility was hampered of lack of drove paths and interspersion of nongrazing parcels. On the other side, accession was only allowed to particularly allocated polı´gonos, and access to nongrazing grounds (growing cereals, vineyards, olive orchards, and irrigation parcels) was mandatory prohibited. For all study areas and from an ecological point of view, occasional overgrazing in specific parcels can be hazardous, but a lesser magnitude than heterogeneity in spatiotemporal distribution of grazing-use.
5. Discussion One of the main findings of this comparative typology was to assess that the intensification and abandonment threats were not totally unrelated. Strong attachment to traditional forms of production may bear these systems more prone to be abandoned. Our real goal is to devise management plans for these systems without losing their main assets. The six analyzed systems encompassed a wide range of variation in environmental and structural conditions within corresponding categories on the main headings-indicators analyzed. However, within this ample variation, some common trends aroused.
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Regarding land use, overstocking was more occasional than usual, being inconsistent with a frequent concern stressed in the policy schemes and agri-environmental measures (On˜ate et al., 1998; Primdahl et al., 2003). Land-based grazing resources were not usually in short supply, but we have found heterogeneous distribution of grazing lots over the available resources with widespread abandonment of the far-reaching or less-quality grazing grounds. This trend may have consequences on the succession vegetation changes and environmental assets of these LFAs. This general trend was coupled, in some study areas, such as Entlebuch, Bavaria, and Castile-La Mancha, with high-nutrients-demanding lots, such as milking animals, unlinked to land-based resources. This again, may have consequences for the maintenance of autochthonous breeds and indigenous quality of regional products. All areas faced also climatic constraints limiting the duration of the grazing season. Strategies of forage conserves or CFR were devised to meet the corresponding FD. In the cases of Northern Sapmi, Tatra, Entlebuch, and Bavaria, the main strategy was to move some lots of animals to different grazing units to remove grazing pressure in the lowland farms over the summer season. This was coupled with a supply of forage conserves in the lowland farms, which help to meet the structural FD. In the Iberian systems, however, animals stayed over the year in the same grazing units. In this case, the only strategy was to devise a supply of forage conserves for the SNGS. Our results showed that, especially in Castile-La Mancha, such a strategy was not fully in operation, with correspondent regional FD. The same occurred in Baixo Alentejo where farmers deal largely by buying external feed supplies. Another common ground of these systems was their poor economic performance and the need to support the systems with public handouts. This research, however, has not addressed the issue of whether this poor performance is structural or there could be some management alternatives that may improve the economic results without affecting their main environmental assets. This is of importance as the two main assumptions in support of public handouts to these systems are the structural unprofitability, and the one that presumes that these systems may deliver environmental assets not factored in farm prices. The first assumption relates to the content of this report. Our results showed that, under current land use and grazing management practices, the repeal of subsidies would render these systems economically unsustainable. To test the hypothesis of structural unprofitability, the individual farming data collected on each study area should be of use for further modeling on management alternatives. What this research revealed is that subsidies make the difference between gain and loss for many farmers in the study areas and thus, under present schemes, farmers do not have any incentive to look for alternatives that are not yet devised and much less implemented. Further results will show that these alternatives may exist
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and that current policy schemes would need to be adjusted for the new practices being implemented (Roeder et al., 2005). Notwithstanding these common grounds, large differences between study areas were found regarding the policy schemes in practice and the total amounts of handouts diverted to the systems. Although it is common knowledge on the largesse of the EU, our results revealed that the two nonEU countries (Norway and Switzerland) supported our two study areas with larger handouts. In the case of the Tatra Mountains (Poland), the situation regarding public handouts is at an impasse. Current policy schemes of support were devised after liberalization of agricultural markets in Poland, but new schemes, under the EU umbrella, are in the process of being devised and implemented. In this study area, subsidies represented a large amount when stated in relative terms, but this was mostly the effect of a low value of production farming in the Tatra Mountains. Our results showed the limitation of comparing public handouts in relative terms (OECD, 2001), both between or within study areas. Tatra and Entlebuch, for example, had similar ratio of total subsidies to value of production farming. However, the MU in Entlebuch can be supported as seven times higher when valued in €/ha. In this latter study area, the lowland farm unit and the alpine unit presented similar ratio (around 50%). However, the lowland farm was supported at 10 times higher per area when, paradoxically, it is the alpine unit that concentrates most natural values. Even value of production farming can be artificially inflated by higher prices (an indirect subsidy or handout), as it is the case of milk price in Entlebuch (60% higher than in Bavaria). In this case, an indirect support can be rated as value of production, deflating the ratio. The main issue regarding public handouts is the lack of a methodological approach at the European level to test how these schemes should be devised, implemented, and controlled. This is a big issue as large amounts of taxpayers money is at stake and farmers’ decisions on land use and practices are mostly driven by policy schemes, with corresponding environmental and social consequences. Farming practices within specific systems are restricted by physical constraints such as climate and soil factors, but policy schemes cannot have the same category. We may find sensible alternative farming practices, with economic, environmental, or social assets that cannot be implemented because they are not favored by proper policy schemes. In short, policy schemes should be devised only after proper knowledge of structural, social, and environmental constraints of extensive grazing systems. Our report can be a good example of this approach. Although we have found a common ground of economic unsustainability, the specific schemes for overhauling should be regionally tailored and adapted to specific conditions of our study areas. We also found differential grounds regarding social inclusiveness and cooperation between the surveyed grazing systems that are usually related to
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money poured into the systems. In Northern Sapmi, Entlebuch, and, to a lesser extent, Bavaria, the main functions of the systems were supported and entrenched by the whole population. In other systems, in addition to poor economic performance, we have found a social fragility with institutional and legal frameworks that do not accomplish their functions (Castile-La Mancha) or were almost nonexistent (Tatra Mountains). This makes a point for this sustainability tier to be taken into account when devising policy frameworks (RDP, 2005). Faltering institutions and legal systems should be rebuilt and economic infrastructure improved on these fragile systems. Farmers’ attitudes toward their grazing operations were not monolithic across our study areas. European young farmers are barely enthused about extensive livestock operations, but family turnover was more assured in those study areas such as Northern Sapmi (Norway) and Entlebuch which poured more money into their systems and operated with better grazing infrastructures. In the case of Northern Sapmi, a further cause of stability is the isolation of the area and the cultural ties to the livelihood. Alternative jobs, if available, are often used as supplements (Labba and Riseth, 2007; Nordin, 2006; Riseth, 2006). Just the opposite was occurring in Castile-La Mancha, where the vicinity of Madrid lures many young farmers to alternative jobs in the Spanish capital. Our results showed that, let to their own, these systems may be swept away by the market forces operating under a surrounding environment of better economic and social conditions. This finding supports the EU approach of pouring handouts, but does not support the current policy framework in its functions of integrating these systems in the mainstream economic of developed countries, and stabilizing the rural population in the LFAs of the EU. Other more market-oriented experts and institutions (Anderson, 2004) support the view of repealing handouts and trade barriers altogether. Our results, however, showed that our main objective should not be simply to eliminate the supply of subsidies but rather to undermine the demand for it. Subsidies should be redirected to the extensive livestock systems and their HNV farmland under Rural Development Policy (RDP) guidelines, and in support of sensible management alternatives that may render these systems more sustainable in the future (Moreira, 2004). Of the two main threats facing European livestock systems, intensification and abandonment, the latter was more apparent in our study areas. In some study areas, such as Entlebuch and Bavaria, we have found an intensification trend in the lowland farm units coupled with a related abandonment of the low-input and extensive alpine units. In any case, in Europe, there is a common knowledge and corresponding extensive literature on the negative impact of intensification on nature values (Benton et al., 2002; Chamberlain et al., 2000; Donald et al., 2002; Newton, 2004; Ormerod and Watkinson, 2000; Watkinson and Ormerod, 2001). Important policy schemes, such as agri-environmental measures, have been devised and
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implemented to mitigate this impact (Oglethorpe and Sanderson, 1999; On˜ate et al., 1998; Primdahl et al., 2003), although with mixed effects (Critchley et al., 2004; Kleijn et al., 2006). But what our results showed is that abandonment is the first concerning risk in our less favored study areas. In this case, agri-environment schemes are less suited and rural development plans should be devised with the aims of improving economic profitability and social cohesion for these LFAs. Paradoxically, these areas concentrate a great deal of European HNV farmland (Baldock et al., 1996) and receive much less support than agricultural intensive areas, as shown by our results. Our results thus showed that the LFAs of the MU were less supported than more intensively used grounds. In Entlebuch, the alpine unit was much less supported than the lowland farms. In Bavaria, premium by area was higher in the lowland farms than in the Allmende, although support by wh or LU was not so different In the Tatra Mountains, sheep farmers received handouts for their lowland operation but any incentive for using the clearings of the alpine unit. In Castile-La Mancha, cultivators and pastoralists were paid separately, although they use the same land unit. As being the sheep operation in most danger of abandonment, pastoralists are supported to a much lesser extent than arable farmers. These and many other inconsistencies plagued the EU current schemes of support, mainly because these schemes are horizontal in scale, sector-oriented, and have not being devised taking into account specific structure as well as values and constraints of particular grazing systems. Rather than seeking to improve a simple rigid general model of policy support, we should be encouraging a more diverse range of economic practices and respecting the different values that they reflect. However, for communication to be transparent, we must all be speaking a common language of economic, environmental, and social assets. From our results, several management goals emerged in support of the main framework: 1. To improve mobility, accessibility, and grazing infrastructures. The main goals of this management scheme would be to facilitate grazing management, relieve pressure on harsh-working conditions, improve spatial distribution of grazing, and fulfill with EU rules on animal management and sanitary rules of slaughters and milk-processing. Our results illustrated that most of our study areas showed constraints regarding these goals, although to a different level. The questions of mobility, accessibility, and poor spatial distribution of grazing were shared at a higher level than the constraints on grazing infrastructure or poor sanitary conditions in milk processing. 2. Promoting proper legal and institutional frameworks. In this case, we found different level of social fragility in our grazing systems, from Northern Sapmi, where social inclusiveness, cooperation, and support attitudes were apparent, to the Tatra Mountains, where even land
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property rights or grazing rights were not properly documented or assured. The latter situation was also present in Castile-La Mancha where, although a management institution (LGC) was legally in charge, its operation was failing by lack of managerial and technical support. Our results showed different categories in our study areas regarding natural assets and product values, but also divergences in social values. In this latter case, categories were not an asset but one hazard. 3. Encouraging professional labor supply. Structural harsh environments, on most of our analyzed systems, and failings in grazing infrastructures rendered these extensive systems hardworking. Family-labor and familybusiness turnover was not assured. Progressively, these systems are more and more relying on external waged labor. Professionalism of present and newcomers should be upgraded to improve the social rating of the herders/shepherds jobs and assure labor supply. This scheme may include the implementation of grazing schools within study areas (Agricultural Education and Extension Center LBBZ Schu¨pfheim, mountain School Hondrich, and Plantahof GR in Switzerland are good examples). 4. A management plan in support of regional products. Up to now, pouring taxpayers’ money to these systems has been justified on the basis of environmental and social assets. However, the perception of these values by the society is somewhat blurred. The general high social rating of these systems could be exploited for marketing of premium products. For this aim to be reached, quality assurance practices should be devised and implemented on maintenance of autochthonous breeds, extensive production methods and fulfillment of EU sanitary, and welfare rules for animals and processing products. Regulatory Councils can be established in pursuits of these practices and promotion of grazing systems and regional products of potential links with nature conservation. Good examples of this development plan are the productions of reindeer meat in Northern Sapmi, alpine cheeses in Entlebuch and Berner Alpka¨se AOC in Switzerland (cows’ milk), Tatra cheeses (ewes’ milk), Alentejano pigs products and cattle and sheep meat in Baixo Alentejo, and Manchego cheese (ewes’ milk) in Castile-La Mancha. But the strong dependence on this pillar of income is very risky since the economic viability of the systems greatly depends on external decisions. A fine example of the low predictability of the political decisions in this sector is the recent development of the EU’s rural development policies. Within the framework of the 2003 CAP reform the council intended to strengthen the funding for rural development, but not even 2 years later in fall of 2005 the EU funds for rural development in the EU-15 were greatly reduced (CEU, 2005). Current state of development for these regional products was far from the objectives devised in this plan. In the Tatra Mountains processing of
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alpine ewes’ milk did not comply with EU sanitary rules. In Entlebuch, only 7% of alpine units processed cows’ milk, and in Castile-La Mancha, although a regulatory council was functioning, attachment to production rules (indigenous breeds and linking to land-based resources) were not assured. Of these two main pillars for indigenous products’ assurance, the latter is more at risk. Our results showed a progressive detachment of production methods from land-based grazing resources, as consequence of harsh working conditions on grazing units and higher costs of changing to wage from family labor conditions. The cost of bringing animals to grazing grounds, including waged labor and grazing fees should be lower than income, including value of production and subsidies. 5. An assessment tool that allow, on the basis of available geographic data (CORINE data, biotope mapping, digital terrain models), save upper and lower bounds for acceptable stocking levels for each MU. This would allow to emphasizing the linkage between public payments and the provision of environmental services. In many mountain areas the productivity of pastures varied significantly and to establish a minimum stocking (0.5 LU/ha) makes little sense. Large-scale extensive systems in developed countries can be categorized as losers within a hyper-competitive economic environment. In dealing with losers, we may assess whether these systems are ‘‘born losers’’ (structural or chronic) or there are some alternatives to improve their economic and social performance. Our results supported the view that, although these systems are plagued with structural and physical constraints (harsh climatic conditions, poor soils, mobility, accessibility, steep slopes), much can be done to correct many other nonstructural constraints (insensible policy schemes is probably the most important) in support of alternative management plans. Most pundits would agree on the requirement to overhaul and modernizing the extensive livestock systems in Europe to refrain the abandonment trend. The question is what path should be chosen to that end. If we remain strongly attached to traditional production rules, more farmers may detach. But if loose rules are devised modernizing would mean increasing number of farmers at the expense of lack of fidelity to extensive principles. At the end, both paths can take place, but customers and taxpayers should be aware of the differences with proper discriminating rules and information channels. It is in this latter task where the modernizing path cannot be questioned.
REFERENCES AfS. (2007). Statistisches Jahrbuch des Kantons Luzern 2007, Amt f u¨r Statistik, Lucerne, Switzerland, www.lustat.ch. ˆ hman, B. (2002). ‘‘Utfording av renear.’’ p. 32. SSR, Lulea¨. A
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Index
A Abscisic acid (ABA), 146–147, 157 Acacia nilotica (L.), 230–231, 236 Acetoclastic methanogens, 10–12 Acetotrophic methanogens, 9, 13 Acetylcoenzyme A (acetyl-CoA), 9, 11 Acetylene (C2H2), 258 Acetyl-P, 9 Agroecosystems, 250, 279 Agropyron elongatum, 214 Agrosilvopastoral system, 360 Al-activated malate transporter gene, 100, 102 Alanine (CH3CH(NH2) COOH), 92 Albizia lebbeck (L.), 230–231 AlCl3-NaOH system, 88 Al-detoxifying agents in soils, plant-originated chemistry and behavior of, 89–94 phytotoxic Al in soils, 87–89 Alentejano pig, 360, 364 Alfalfa. See Medicago sativa L. Allmende, 360 Allmende (CLS) system, 380 ALMT1–1 cRNA, 100 ALMT1 gene. See Al-activated malate transporter gene Al-(oxalate) complexes, 92 Alphaproteobacteria, 24 Alquevadam irrigation system, 374 AltBH gene. See Al-toxicity tolerance gene Al toxicity mechanism symptoms calcium, 96 callose formation, 97 cell division, 94 cell wall, 95–96 hormone, 96–97 oxidative stress, 97–98 plasma membrane (PM), 94–95 tolerance ALMT1 gene, 100 exudation regulation of organic acids, from root apex, 98–100 genes in major crops, 102–103 gene transformation, 101–102 internal Al-detoxification mechanism, 101 malate exudation from root tips and, 99 Al-toxicity stress, 86 mechanism, 94–98
plant-originated Al-detoxifying agents in soils, Al chemistry and, 87–94 tolerance mechanisms, 98–103 Al-toxicity tolerance gene, 102–103 Ammonia-oxidizing strains, 255 Ammonia volatilization, 251 Ammonium monooxygenase (AMO), 39 amoA gene coding for, 31, 33 AMO Cluster I, 36 Anaerobic degradation, of organic matter to methane, 3 methanogenic pathway of, 4, 6 Anaeromyxobacter spp., 25 Anammox, 251 Andropogon bladhii, 316 Animals’ performance indicators, 364–365 ANME clusters, 33 Annual grazing cycle, 362 Anoxia, AQP activity and, 176–180 Anoxic–oxic interface, at soil surface, 6 Anthropogenic greenhouse effect, 254 AQP gating, 163–168 Aquaporins (AQPs), 134–135 discovery of, 146 plant (See plant AQPs) Arabidopsis thaliana, 75–76, 85–86, 148, 157, 170, 176, 179 Arachis hypogaea, 107 Arbuscular mycorrhizal (AM) fungi, 312 Artemisia tridentata, 319 Asphyxiation, 254 AtALMT1 (At1g08430), 100 A. thaliana FRO2 encoding protein (AtFRO2), 75 Atmospheric methane, oxidation of, 41–42 AtNRAMP1/3/4, 76 Atriplex species, 222, 235 Avena sativa L., 209 B baca flock, 358 Bahia grass. See Paspalum notatum Baixo Alentejo, Portugal, 360–361, 374 Barley. See Hordeum vulgare Bavaria, Germany, 359–360, 374 Begonia evansiana, 92 Biodiversity loss, ecosystem responses to, 313–315 linkages of communties effect on plants, 315
421
422
Index
Biodiversity loss, ecosystem responses to (cont.) resource quality homogeneity, 314 Biological denitrification, 253 Biological nitrogen cycle, 250 Biotite (K(FeII,Mg)3Si3AlO10(OH)2), 70 Blue baby syndrome, 254 BnALMT1/2, ALMT1 homologues, 100 Boreal-alpine biogeographical region, Northern Sapmi, 354 Brachiaria mutica, 224 Brassica napus, 93, 100, 171, 220 Bromus tectorum, 316 Brown Swiss, 401 Brunoniella acaulis, 217 bundz and oscypek cheeses, 368 C Cajanus cajan, 107 Calcite (CaCO3), 207 Calliandra calothyrsus, 217 Camellia sinensis, 101 Campo Branco open fields, 354 Capsicum annuum, 168 Carbohydrates, methanogenic degradation of, 15 Carbon cycling and methane emission, methanogens and methanotrophs role in, 3–8 Carica papaya, 101 Cassia tora, 98 Castile-La Mancha, Spain, 361 Castor. See Ricinum communis Castro Verde zonal plan, cereal-fallow rotation and, 407 Catechol. See 1,2-dihydroxybenzene Cation exchange capacity (CEC), 201 cDNA microarray analysis, 79 Centaurea jacea, 314 Cereal-sheep system in Spain, 354 CFB group. See Cytophaga-FlavobacteriumBacteroides group CFR. See Complementary forage resources Channel index (CI), 323–324 Chara corallina, 167 Chemolithoautotrophic acetogens. See Homoacetogenic bacteria Citrate synthase, 101 Climatic changes, effects on soil biota concentrations of CO2 studies, 318–320 fungal-feeding taxa sensitivity, 319 soil conditions, 320 temperature changes, 317–318 Clostridium Cluster XIVa, 15 Clostridium spp., 24 CLS. See Cooperative livestock system C/N ratios, 23, 275–276, 326, 332 Cohesion-tension (CT) theory, 136 Colocasia esculenta, 98
Common Agricultural Policy (CAP) of EU, 354 Complementary forage resources, 363 Connemara west of Ireland study area, 355 Cooperative livestock systems, 359 actors in, 377 actors involved, 377 Cotton. See Gossypium hirsutum L. Crenothrix polyspora, 33 Crop management, CH4 emission and, 43 Crotalaria juncea, 325 CS. See Citrate synthase Cucumis melo, 168 Cultivators-pastoralists relationships, 407 Cynodon dactylon (L.), 209 Cytochrome, 252 Cytophaga-Flavobacterium-Bacteroides group, 15 CytoplasmicpH, 165 D Dalbergia sissoo, 222, 231, 235 Daucus carota, 110 DCCD. See Dicyclohexylcarbodiimide Deferriferrioxamine B, 81 Dehydrogenase activity (DHA), in soils, 229 Denitrification, 250–251, 253 activity, 280–282 effect of crops on, 267 inhibition/stimulation of, 280 inorganic and organic fertilizer effects on, 273 negative effects of heavy metals on, 283 nitrogen application rates and poor soil drainage on, 273 for agriculture, 253 cascade/electron acceptor, 251, 255 impact, on environment and human health, 254 measurement acetylene inhibition method, 258–259 isotope N-labeled methods, 259 rates, 250 total annual for global agricultural area, 253 Denitrification, impact of fertilization on denitrifier communities and, 277–279 inorganic and organic fertilizer effects on, 274–276 nitrous oxide emissions and, 276–277 secondary effects and heavy metal content effect, 273 Denitrification, natural factors causing variations in dry–wet cycles, 265–266 freeze–thaw cycles, 263–265 temperature and water, 262–263 Denitrifier communities, 260, 277, 283, 286 freeze–thaw effects on, 264–265 Denitrifiers, 255
423
Index
culture-independent molecular and fingerprinting techniques, 260 diversity of, 259 most probable number (MPN) and molecular method for quantification, 260–261 Denitrifiers, effect of environmental pollution on heavy metals, 283–285 influence of pesticides on, 280–283 organic pollutants, 279–280 Denitrifying microorganisms, 255–258 Denitrifying strains. See Denitrifying microorganisms 20 deoxymugineic acid, 77, 79, 81 Desulfotomaculum, 20 Detritivore food web, 310–311 Diazotrophs, 251, 255 Dicyclohexylcarbodiimide, 79 Diethylenetriaminepentaacetic acid, 81, 83 1,2-dihydroxybenzene, 67 Discolaimus arenicolus, 336 Dissimilatory NO 3 reduction to ammonium, 251 Dissimilatory NO 3 reduction to ammonium (DNRA), 251 DMA. See 20 deoxymugineic acid DTPA. See Diethylenetriaminepentaacetic acid E Echinochloa colona (L.), 225 E. crusgalli (L.), 235 Ectomycorrhizal fungi, 337 Effective cation exchange capacity (ECEC), 202 Eleusine coracana (L.), 225 Enrichment index (EI), 323–324 Entlebuch, sampled lowland farm, 372–373 Entomopathogenic nematodes, 337 Environment conservation, sodic and saline-sodic soils and, 230–233 Eriales, shrub-steppe vegetation, 371 Escherichia coli, 340 ESF. See Exchangeable sodium fraction ESP. See Exchangeable sodium percentage Ethylenediaminetetraacetic acid (EDTA), 81 EUagricultural subsidy programs, 397 Eucalyptus tereticornis Sm., 230 Euphorbia esula, 316 European grazing systems, organization forms and actions, 378 European livestock systems threats, 411 Euryarchaeota, 8 Exchangeable sodium fraction, 201 Exchangeable sodium percentage, 201–202 ranges in soils, 235 Exotic soil organisms, 316 Extensification process, 354
F FD mass-balance model, 364 Fe-deficiency stress, 69 Fe acquisition, in plants, 73–85 Fe chemistry, in soils, 70–72 genetic improvement of Fe-acquisition, in plants, 85–86 Ferrihydrite (Fe2O32FeOOH2.6H2O), 25, 70–71, 80 Fertilization effects with Fe, S, and N, 25–28 Fertilizers, inorganic and organic, 273 Festuca arundinacea (L.), 221 Finland, regional data of reindeer husbandry family, 358 Finnmark Norway, Migratory reindeer herding, 382 Fluorescent pseudomonads, 255 Folsomia quadrioculata, 339 Food web dynamics modeling application of, 332–335 consumption coefficient, 330 death of consumers, and consumption of detritus study, 330 energy flux, 331 estimates of parameter values used, 333 functional and energy web using feeding rates, 329–330 modeling flux of energy, 329 species richness and linkages, 328 strength to speed of energy flow, interaction, 331 theory of, 328–332 two-channel food web, 331–332 Forage coverage model, 381 FRO2 genes, 75–76, 85 G gazdas, 358 Genetically modified (GM) crops, 313 Gene transformation, Al-toxicity tolerance and, 101–102 Geobacter spp., 25 Gibberelic acid (GA), 157 Gibbsite (g-Al(OH)3), 83 Global warming, 254 Globobulimina pseudospinescens, 258 Glycine max, 76, 151 Glycyrrhiza glabra L., 235 GM cropping systems Bt-corn variety, 321 endophytic and rhizosphere microbial communities, effect on, 320 glyphosate tolerant cropping, 321 herbicide-tolerant varieties, 322 insecticidal Cry protein, 321 transgenic proteinase inhibitor I, 320
424
Index
Goethite (aFeOOH), 70–71, 79 Gossypium hirsutum L., 209 Grazing fee and grazing rights, 374 Grazing infrastructure, 385–388 Grazing management, 364 and biodiversity, 406–408 economic performance of, 395–401 infrastructure of, 385–388 labor in, 388–390 limiting factors of, 404–406 productivity estimation of, 390–394 trends in, 401–404 Grazing systems typology, 370 Great Bustard, 407 Greenhouse effect, 2 Greenhouse gases, 250–251 Gypsum (CaSO42H2O), 207–211, 216, 219, 225 H Hedychium gardnerianum, 316 Helianthus annuus, 146 Hematite (a-Fe2O3), 70–71, 80 Herbivore food web, 310–311 High nature value (HNV) farmland, 352 Holcus lanatus, 314 Homoacetogenic bacteria, 3 Homoacetogenic microorganisms, 14 Hordeum distichon, 139 Hordeum marinum, 174, 176 Hordeum vulgare, 77, 157, 208, 267 Hornblende, Ca2(Mg,Fe,Al)5(Si,Al)8 O22(OH)2, 70 HS-HTP. See N-7-mercaptoheptanoyl-Ophospho-L-threonine HvYS1 gene, 84 Hydrangea macrophylla, 101 Hydraulic conductance, water movement and, 137–138 Hydraulic conductivity of roots (Lpr) changes in, 146–147 water movement and, 138–140 Hydrogenotrophic methanogens, 9, 12–14 Gibbs free energy (DG) of, 19 Hydroxyaluminum (HyA), 105 20 -hydroxyavenic acid A, 77 3-hydroxy-20 -deoxymugineic acid, 77 I Ilmenite (FeTiO3), 70 Intergovernmental Panel on Climate Change (IPCC), 254 Iron-containing components, in soils, 70 Iron-reducing bacteria, 25 Iron-regulated transporter 1 (IRT1), 75–76
K Karasjok area of eastern Finnmark, 364 Kochia scoparia L., 235 Kyoto protocol, 250 L LACOPE study areas, 355, 357 Land use coefficient of variation (CV), 372 Large-scale grazing systems, 352–353 Leaching, 213, 251, 258, 276 for calcareous sodic and saline-sodic soils, 216 of Naþ, 210, 215, 219, 223 NO3- and, 258, 276 LeIRT1, 76 Lepidocrocite (g-FeOOH), 70–71, 80 Leptochloa fusca (L.), 210 Less favored areas, 353 Leucaena leucocephala, 236 Leucanthemum vulgare, 314 LFAs. See Less favored areas LGC. See Local Grazing Commissions Linum usitatissimum, 171 Local Grazing Commissions, 381 Lolium perenne, 77 Lotus japonicus, 146 LSGS. See Large-scale grazing systems Lupinus albus, 100, 146 Lupinus angustifolius, 139 Lycopersicon esculentum, 76 M MA. See Mugineic acid Maas–Hoffman equation, 233 Maghemite (gFe2O3), 70, 80 Magnetite (Fe3O4), 70, 80 Maireana, 235 Maize. See Zea mays Malate dehydrogenase, 108 M. albus, 208 Management unit (MU), 354 Maturity index (MI), 323 MBC. See Microbial biomass carbon mcrA genes, 10, 33 MDH. See Malate dehydrogenase Medicago sativa L., 209 Melilotus indicus L., 208 N-7-mercaptoheptanoyl-O-phospho-Lthreonine, 9 Mercuric chloride, 164–165, 167–167 Mesembranthemum crystallinum, 163 Methane emission, from rice fields carbon cycling, methanogens and methanotrophs role in, 3–8 mitigation of, 42–43 and production, microbiological explanations
425
Index
fertilization with Fe, S, and N, effects of, 25–28 methanogenesis, sequential reduction and initiation of, 16–22 organic amendment effect, 23–25 plants effect, 29–30 short-term drainage effect, 22–23 temperature effect, 28–29 from rice fields, global methane budget and processes controlling, 2–3, 6 Methane monooxygenase, 31, 33, 38–39 Methanobacteriales, 13 Methanobacterium, 12 Methanobrevibacter spp., 12 Methanofuran, 10 Methanogenesis, sequential reduction and initiation of, 16–22 Methanogenic substrates, microorganisms and, 14–16 Methanogens, microbial ecology diversity, habitats, and ecological niches, 10–16 methane production and emission, 16–30 physiology and phylogeny of, 8–10 Methanomicrobiales, 12–14, 24 Methanosaetaceae, 9 Methanosaeta spp., 9–12, 22–23 Methanosarcinaceae, 9, 12–14, 20, 24 Methanosarcina spp., 9–12, 20, 22, 30 Methanospirillum spp., 12 Methanothermobacter spp., 24 Methanotrophic microorganisms, 2 Methanotrophs microbial ecology atmospheric methane oxidation, 41–42 bulk rice field soil, 35–36 niche differentiation, 34–35 nitrogen fertilization effect, 38–41 physiology and phylogeny, 31–34 rice roots, 36–38 soil surface, 36 Methemoglobinemia, 254 l-Methionine, 77, 79 Methyl-CoM reductase, 8–9, 33 Methylobacter, 31, 35 Methylocaldum, 31, 35 Methylocapsa, 31 Methylocella, 31, 34 Methylococcus, 31, 35 Methylocystis, 31, 35 Methylohalobius, 31, 34 Methylomicrobium, 31, 35 Methylomonas, 31, 35 Methylosarcina, 31 Methylosinus, 31, 35 Methylosphaera, 31 Methylothermus, 31, 34 MFR. See Methanofuran Microbial biomass carbon, 229–230 Microbial respiratory process, 250
Milk- and meat-oriented sheep flock, 388 Mimosa pudica, 165 1-M KCl, 87 MMO. See Methane monooxygenase Molybdoenzymes, 251 Mononchus aquaticus, 336 Montado of Baixo Alentejo, 361 Montado system, 354, 360 Mucuna pruriens, 93 Mugineic acid, 81–83 chemical structures, 78 interactions to Fe minerals, 80–81 Multicopper homodimeric N2O reductase, 252 Multiindicator concept and landscapes situations, 362 Mycorrhizal fungi, 311, 337 N NAAR. See Net acid addition rate naat genes, 86 National Oceanic and Atmospheric Administration, 2 Neanurum muscorum, 339 Nematode faunal analysis, 323 functional groups of soil nematodes, 325 improvement in utility and sensitivity of, 327–328 indices, interpret nematode community shifts, 323–325 soil biodiversity and, 326–327 soil nutrient status and food web condition for, 326 theory of, 323–325 Net acid addition rate, 217 Neutral lipid fatty acid, 339 Niche differentiation, of aerobic methanotrophs, 34–35 Nicotiana tabacum, 85, 151 NIPs. See Nodulin-like intrinsic proteins Nitrate-reducing bacteria, 26 Nitrification, 251 Nitrifiers, 255 Nitrogen fertilization, rice fields treatment and, 38–41 Nitrogen fertilizers, 273 Nitrogen, oxidation forms, 250 Nitrosomonas spp., 27, 36, 255 Nitrosospira spp., 27, 255 Nitrospira spp., 36 Nitrous oxide (N2O), 250–252 emissions, 254, 258, 262, 273 fertilization effects on, 276–277 freeze–thaw effects on, 263–265 NLFA. See Neutral lipid fatty acid NLFA:PLFA ratios, 339 N-(1-napthyl)phtalamic acid (PA), 107 N2/N2O ratio, 262
426
Index
NOAA. See National Oceanic and Atmospheric Administration Nodulin-like intrinsic proteins, 148, 150 NO3 reducers, 255 Northern Sapmi, Fennoscandia, 355–358 Norway, coastal adaptation reindeer management, 381 Norwegian LACOPE area, 364 NtAQP1, 170 O Oat. See Avena sativa L. O2-detoxifying enzymes, 17, 30 Olivine ((Mg,FeII)2SiO4), 70 Opuntia acanthocarpa, 168 Organic acids exudation, 98–99 regulation, from root apex, 99–100 Organic carbon effects, on CH4 production and emission, 23–25 Oryza sativa, 76 OsPTF1, 110 Oxide-reductive reactions, 69 P P acquisition in plants, mechanism of organic acids secretion, 108–109 phosphatase secretion, 109 P transporters, enhanced expression of, 109–110 root architecture alteration, 106–108 Pan-European coordinate socioeconomic research, 353 Para grass. See Brachiaria mutica Parkinsonia aculeata L., 236 Partial pressure of CO2, in root zone, 212–216 Paspalum notatum, 221 P. cineraria (L.), 236 P-deficiency stress, 103 P acquisition in plants, mechanism of, 106–110 plant-originated P-dissolving agents in soils, chemistry of P and, 104–106 tolerance, genetic improvement in plants AND, 110–111 Peanut. See Arachis hypogaea Pectin, photochemical decomposition of, 29 Pelotomaculum, 16 PEPC. See Phosphoenolpyruvate carboxylase Pesticides denitrification activity and, 280–282 on size and structure of denitrifier community, 282–283 Phaseolus coccineus, 139 Phaseolus vulgaris, 93, 98, 146 Phosphoenolpyruvate carboxylase, 108 Phosphogypsum, 25–26 Phospholipid fatty acids, 14–15, 24, 35, 339
Phytoremediation, of sodic and saline-sodic soils, 206–208 comparative efficiency of, 223 environment conservation, 230–233 soil amelioration, zone of, 228–230 soil sodicity amelioration, 224–228 historical perspective, 208–211 mechanism and processes partial pressure of CO2, in root zone, 212–216 proton, release by plant roots, 216–218 roots, physical effects of, 218–222 salt and Naþ uptake, by shoots, 222–223 plant species for, 233–236 Phytosiderophore-Fe3þ complex uptake, by roots, 83–85 Phytosiderophores in graminaceous plants, biosynthesis of, 77–79 low-solubility Fe, solubilization of, 79–83 secretion to rhizosphere, 79 Pigeon pea. See Cajanus cajan PIPs. See Plasma membrane intrinsic proteins Plantago lanceolata, 314 Plant AQPs, 147 control of water permeability and, 152–168 role, in root water transport expression and transformation studies, 170–171 inhibition studies, 168–170 radial water flow and, 171–172 selectivity, 150–151 structure of, 148–150 Plant roots, proton release by, 216–218 Plants, Fe acquisition in genetic improvement of, 85–86 mechanism of strategy I, 73–76 strategy II, 77–85 Plasma membrane intrinsic proteins, 148–150, 156–157, 164, 170, 176, 179 Plasma membrane (PM) P-type ATPase, 75 Pleiotropic effects, 320 PLFA. See Phospholipid fatty acids PM28A, 163–164 pmoA gene coding, for pMMO, 31, 33, 35 Poa pratensis, 77 Poiseuille-Hagen equation, 142 Polysaccharides degradation, 21 Populus tremuloides, 147, 165, 169 Portulaca oleracea L., 235 Potential Denitrification Activity (PDA), 281 Predatory nematodes, 312 Prosopis juliflora, 222, 231, 235 Prosthetic metals, 252 Proteobacteria, 30 Pseudoazurin, 252 Pseudomonas aeruginosa, 101 Pseudomonas fluorescens, 259
427
Index
Pulse labeling, of plants, 4 Purple lupin (Lupinus pilosus), 100 Pyrite (FeS2), 70 Pyroxene [augite (Ca,Mg,FeII,Al,Ti)2 (Si,Al)2O6], 70 Q Q. rotundifolia Lamk, 360 Quantitative trait locus (QTL) analysis, 103 Quercus suber L., 360 Quinol pool, 252 R Radial water flow, parallel pathways for, 144–146 Raphanus sativus (Radish), 141 RDP. See Rural Development Policy Reindeer grazing system, 354 Reindeer management culture, by Sa´mi herders, 355 Rhizobial bacteria, 311 Rhizosphere effect, 266, 271 Rhizosphere of crops, denitrification in barley crop and maize plants, 267–268 crop species/cultivars impact on microorganisms, 270–271 effects, 271–272 factors regulating, 266–267 plant effect on denitrifier community, 268–270 selective advantage for denitrifiers in, 270 transgenic crop’s impact on, 272–273 Rice. See Oryza sativa Rice Cluster I (RC-I), 12–14, 17, 20, 24 thermophilic methanogens, 29 Rice field ecosystems, 10–11 carbon cycling in, 23 iron cycling and methane suppression in, 25 methanotrophic biomass in, 35 Rice field soil, aerobic methanotrophs and, 35–36 Rice plants, CH4 emission and, 29–30 Rice roots, aerobic methanotrophs habitat and, 36–38 Rice straw, microbial colonization of, 23–24 Ricinum communis, 107, 146 Root anatomy, 141 axial pathway, 142 radial pathway, 142–146 Root-feeding arthropods, 337 Root water transport, plant AQPs role in expression and transformation studies, 170–171 inhibition studies, 168–170 radial water flow and, 171–172 16S rRNA genes, 8, 10–12, 14, 22, 33, 35 molecular analyses of, 15 rice straw and, 23 Rural Development Policy, 352 guidelines, 411
S Saccharomyces cerevisiae, 75 Saccharum spontaneum L., 210 Salicornia bigelovii, 235 Salt and Naþ uptake, by shoots, 222–223 SAR. See Sodium adsorption ratio Scandinavian peninsula Northern Fennoscandia, 355 Scapteriscus spp., 317 Sesbania bispinosa, 210 Sesbania sesban (L.), 236 Shallow oxic soil surface layer, 6 Short-term drainage, of flooded rice fields, 22–23 Sinorhizobium meliloti, 320 SIPs. See Small basic intrinsic proteins Sisymbrium officinale, 93 Small basic intrinsic proteins, 148 Smithella, 16 Snapbean. See Phaseolus vulgaris SNGS. See Structural nongrazing season Sodic and saline-sodic soils, 201–203 degradation processes in, 203–206 phytoremediation of, 206–208 comparative efficiency of, 223–233 historical perspective, 208–211 mechanism and processes, 212–223 plant species for, 233–236 Sodium adsorption ratio, 201–202 Soil amelioration nutrient dynamics during, 228–230 zone of, 228 Soil biodiversity, 308 Soil biota, 312–313 Soil ecosystems, 254 Soil food web, 309–313 approaches, organism status, 323–335 components of detritivore and herbivore food webs integration, 336–338 resolution, 335–336 role of technology, 338–340 dynamics, human activities and, 313 biodiversity loss, 313–315 climate change, 317–320 GM cropping systems, 320–322 invasive species, 315–317 Soil-plant-atmosphere continuum, 136–137 Soil redox potential (Eh), 16–17, 21 Soils, Fe chemistry in iron-containing components, 70 secondary Fe minerals, 70–72 Soil slurries, 259 Soil sodicity amelioration, 224–228 Soils, phytotoxic Al in, 87–89 Soils, plant-originated P-dissolving agents, chemistry of P and, 104–106
428
Index
Soil surface, aerobic methanotrophs habitat and, 36 Soil texture, 312 Soluble nitrogen oxides, 250 Sorghum bicolor, 93, 107 Soybean. See Glycine max SPAC. See Soil-plant-atmosphere continuum Spinacia oleracea (Spinach), 163–164 Structural nongrazing season, 363 Structure index (SI), nematode community shifts and, 323–324 Stylosanthes seabrana, 217 Sulfate reducers, methanogenic activity and, 26 Sunflower. See Helianthus annuus Sweden, regional data of reindeer husbandry family, 358 Sweet clover. See Melilotus indicus L. Swiss Entlebuch UNESCO Biosphere reserve, 407 Symbiotic microorganisms, 310 Symbiotic organisms, 337 Syntrophic microorganisms, 3 Syntrophobacter, 16 T Tajo and Guadiana rivers, 361 Tamarix dioica, 236 Taro. See Colocasia esculenta Tatra mountains, Poland, 358–359 Temperature, effect on CH4 emission, 28–29 Terminalia arjuna, 230–231, 236 Tetrahydromethanopterin (H4MPT), 10 T3238fer, FER function, 76 Themeda triandra, 217 Thermodynamic analysis, hydrogenotrophic methanogenesis and, 17 Thermophilic methanogens proliferation, 28 Titanomagnetite (Fe3-xTixO4), 70 Tobacco. See Nicotiana tabacum Tomato. See Lycopersicon esculentum Tonoplast intrinsic proteins (TIPs), 148–149 Total agricultural land (TAL), 361 Tradescantia fluminensis, 316 2,3,5-triiodobenzoic acid (TIBA), 107 Triticum aestivum, 139, 141, 219 Typha latifolia, 25
U Ulex europaeus, 316 UNESCO Biosphere Entlebuch, Switzerland, 359 UN report GLOBIO, 379 Urea, CH4 emission by, 27 V Velvetbean. See Mucuna pruriens Verrucomicrobia, 15 W Water flow, root characteristics and root anatomy, 142–146 root growth and water uptake, influencing factors, 140–141 Waterlogging anoxia, AQP activity and, 176–180 O2 effect, in rhizosphere, 172–173 root growth, effect on, 173–175 water use, effect on, 175–176 Water management, CH4 emission and, 42 Water movement, through plant driving forces, 135–137 hydraulic conductance, 137–138 hydraulic conductivity of roots (Lpr), 138–140 Water permeability control, AQPs and AQP abundance, changes in, 156–162 patterns of expression in roots, AQP abundance and, 152–156 posttranslational regulation, 162–168 Water potential (c), 135–137 Wheat. See Triticum aestivum White lupin. See Lupinus albus White sweet clover. See M. albus World Heath Organization, 254 X Xenopus laevis oocytes, 84, 100, 150, 156, 164 Z Zea mays, 79, 93, 139 ZmPIP2;5, 152 ZmYS1 gene, 84
