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2007 31: 131Progress in Physical GeographyVineet Yadav and George Malanson

sequestrationProgress in soil organic matter research: litter decomposition, modelling, monitoring and

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Progress in Physical Geography 31(2) (2007) pp. 131–154

© 2007 SAGE Publications DOI: 10.1177/0309133307076478

I IntroductionSoil organic matter (SOM) is an importantcomponent in Earth systems science. As agovernor of the fluxes of biogeochemicalcycles of carbon, nitrogen and other primarynutrients, the beneficial effects of SOM havebeen widely recognized for plant growth andmaintenance of pH status and structure insoils (Chen and Aviad, 1990; Stevenson andHe, 1990; Blanco-Canqui and Lal, 2004).Rising concerns about anthropogenic climate

change have led to increased emphasis oncomprehending the role and capacity of SOMas a potential sink of greenhouse gases.Presently, it is acknowledged as the largeststorehouse of terrestrial carbon that can befurther increased by proper land managementpractices (Amundson, 2001), althoughincreasing sequestration of SOM in soils can-not permanently resolve the problem ofgreenhouse warming. Nevertheless it still hasthe capacity to provide buffer periods of short

Progress in soil organic matter research:litter decomposition, modelling,monitoring and sequestration

Vineet Yadav* and George MalansonDepartment of Geography, The University of Iowa, Iowa City, Iowa, USA

Abstract: Retention and sequestration of soil organic matter is extremely important for themaintenance of soil structure, agricultural productivity and carbon sequestration. Research in soilorganic matter has advanced on many fronts in the last half century. During this timeunderstanding of the factors governing plant litter decomposition has increased considerablyresulting in the formulation of process and organism-based models. Remote sensing has beenshown to be useful for quickly monitoring stocks of soil organic carbon in the topsoil althoughmuch remains to be done to establish its efficacy. Fluxes of soil organic matter in the changingclimatic scenarios have been studied though outcomes remain debatable. In this paper an attemptis made to present these various aspects of soil organic matter cohesively. The focus is mainly onlitter decomposition, models and monitoring methods, role of soil aggregates and erosion, impactof climate change on long-term dynamics of soil organic matter and impending research themesneeding further attention.

Key words: climate change, erosion, litter decomposition, models, remote sensing, soilaggregates.

*Author for correspondence. Email: [email protected]

�

at LOUISIANA STATE UNIV on August 31, 2014ppg.sagepub.comDownloaded from


132 Progress in Physical Geography 31(2)

duration during which mitigation strategiescan be devised to reduce the overall impact ofatmospheric carbon emissions (Robert,2006). Thus SOM, while not a major focus ingeographical research, is relevant to geogra-phy due to the societal significance of its effects on (a) global greenhouse gasdynamics and (b) agricultural productivity.Understanding of various aspects of SOMhas increased substantially, but the reviewsare dated and disjoint, necessitating thedevelopment of a systematic and syntheticframework of SOM research.

Elementally, SOM primarily consists ofcarbon (C), oxygen (O), hydrogen (H) nitro-gen (N), phosphorus (P) and sulphur (S). It isdefined as the mixture of recognizable plantand animal parts and other material that hasbeen altered to the degree that it no longercontains its original structural configuration(Oades, 1989). Stabilized SOM is approxi-mately 58% carbon and 5 to 6% nitrogen(Nelson and Sommers, 1982). However, vari-ations from this mean value are expected, andare governed by onsite climate, soil, vegeta-tion, and management practices. For exam-ple, the major primary elements C, N, P, and Srequired by plants for growth in the soils havebeen found in the ratio of 110:10:1.4:1.2 inIowa (USA), whereas these respective ratioswere 194:10:1.2:1.4 in the case of Brazil(Neptune et al., 1975). None of the elementsmentioned above are present in SOM in afree state, but are bound together in differentchemical compounds like polysaccharides,proteins, amino acids, carbohydrates, oils,fats, sugars, waxes, humic substances, andother smaller molecules (Donahue et al.,1983). These organic constituents of the soil,during various stages of their microbialdecomposition, become a source of sustainingprimary nutrients for soil fauna and plants. Inmost soils, humus makes up the bulk of SOM,which also represents the intermediate recal-citrant stage of the decomposition continuumof plant and animal litter. Soil humus is a nat-urally occurring complex combination of sub-stances resultant of extensive biochemical

breakdown of plant and animal residues. Ithas still not been separated into its individualdiscrete components and the common frac-tionalization at present is based on solubility separations achieved in dilutesodium hydroxide (NaOH) solution. Namely,these fractions are humic acid, fulvic acid, andhumins. In NaOH solution, fulvic acidremains dissolved under all pH conditions.On the other hand, humic acid is only solubleat pH �2, while humins separate out in all pH environments (Maccarthy et al., 1990). Anumber of other ways exist to fractionalizeSOM, of which size and density based classi-ficatory groups like macro organic matter(MOM), particulate organic matter (POM),and light fraction carbon (LF) have gainedprevalence (Wander, 2004).

With this soil chemistry in the background,this study examines the process and models ofplant litter decomposition, remote sensingbased appraising methods of SOM, the func-tionality of soil aggregates and erosion in theflux of SOM, and the impact of climate changeon storage and turnover of the same. Thesethemes were selected for discussion from thestandpoint that a complete understanding ofthe field can only be accomplished by develop-ing awareness of the processes involved, fol-lowed by ways to model it and the factorsassociated with its flux. This standpoint callsfor a cross-disciplinary review with the objec-tive to include different perspectives (ie, geo-graphical and ecological) in a systematicaccount of the field. This kind of study also hasthe potential to unite often disparate areas ofgeography such as climate and biogeographywith agriculture and land-use change.

All the figures that illustrate the argumentput forward in the text come from the researchconducted in the Big Creek Basin (~13,000hectares) in Union and Pulaski Counties ofsouthern Illinois, USA. Certain terms appear-ing in the text are defined in the notes at theend of the paper. Decomposition/degradationmentioned from here onwards only meansplant litter decomposition. ‘Litter’and ‘residue’are used interchangeably in the paper, and



Vineet Yadav and George Malanson: Progress in soil organic matter research 133

wherever mentioned their context is limitedto plants unless otherwise stated.

II Processes of litter decompositionSubstantial progress has been made in decom-position studies in the last 30 years, but only asmall number of pathways are clearly under-stood. Generalizations about processes impact-ing decomposition across ecosystems arepossible, but arguments presented here onlyconsider below-ground changes in plant litter asit is accountable for �90% turnover of SOM.Disintegration of dead soil fauna is not dis-cussed here, though their role as principalagents responsible for the cycling of SOM isoutlined. Decomposition is a sequential processwhereby complicated organic compounds arecontinuously degraded into simpler substances,releasing nutrients as a byproduct of theirbreakdown. To comprehend its dynamics it isimportant to start with the chemical composi-tion of the plant litter, then identify significantmediators of change, and finally successivelyfollow the timeline of prominent rate regulatorsof substrate modification.

1 Composition of plant litterThe most prominent organic chemicalconstituents in plant litter are water-solublecompounds, polymer carbohydrates1 such ascellulose2 and hemicellulose3 (labile) andlignin4 (recalcitrant), a complex polymer ofaromatic rings (Williams and Gray, 1974;Neher et al., 2003). Cellulose, hemicellulose,and lignin make up 20–30, 30–40, and 15–40%of the total litter mass, respectively (Killham,1994). The exact value between these rangesis governed by plant type and age, onsite con-ditions and yearly environmental variations(Berg and McClaugherty, 2003). Not all ofthese compounds are degraded at the samerate. After litter fall, the loss of soluble com-pounds is highest, primarily carried outthrough the process of leaching or rapid in-cellmetabolization by soil micro-organisms.Hemicellulose disappears next (Satchell,1974), followed by cellulose, and last to go islignin, the most resistant fraction of plant

litter. Typical decomposition time for thesecompounds varies from 14 days for hemicellu-lose to 500 days for lignin (Killham, 1994).Consequently, as mass loss advances the con-centration of lignin in putrefied SOMincreases and retards the rate of decomposi-tion. Therefore, an inverse relationship exists between lignin concentration and the rate of decomposition (Melillo et al., 1982).Analogously, breakdown rates are faster inagricultural ecosystems in comparison toforested environments as crop residues tendto have fewer resistant (lignin) compounds(Coleman and Crossley, 1996). Completemass loss of initial plant litter is never achievedin any ecological unit as some SOM getshumidified and becomes chemically protectedhaving turnover rates of thousands of years.Humidification as a process is not clearlyunderstood and the present theory explains itsformation as a result of microbial decomposi-tion or chemical and oxidative condensation oflignin and polyphenol compounds (Williamsand Gray, 1974; Vitousek et al., 2002).

2 Mediators of litter decompositionSoil organisms are the mediators whoseactions result in the decomposition of plantlitter. Size and cellular organization is the fore-most basis according to which they can bedivided. At the lower hierarchy of the foodchain and size class are soil microbes compris-ing bacteria, fungi, and algae. Next to comeare protozoa which feed on soil microbes andwhose size is �100 �m (Swift et al., 1979).Larger soil fauna comprise meso (100 �m to2 mm) and macro (2–20 mm) organisms andinclude earthworms, nematodes, arthropods,and mollusks (Dickinson and Pugh, 1974;Verhoef and Brussaard, 1990; Winegardner,1996). Relative occurrence of these groups oforganisms can be appreciated by understand-ing the approximate measure of their biomassin soils, which ranges between 3–8, 0–0.5,and 0–2 tons per hectare for microbes,protozoa, and meso and micro fauna, respec-tively (Killham, 1994). Micro-organisms arethe principal agents responsible for �90%




decomposition of plant litter (Zhang and Zak,1998). Yet this does not mean that meso andmicro fauna do not play a significant role inthis process, as their activities (mixing andcomminution) create conducive environ-ments for the microbial breakdown of SOM.Here, however, keeping in focus the roleofmicrobes in litter decomposition, theconcentration of the discussion is gearedtowards them, and avid readers interested inthe role of meso and macro fauna in decom-position are advised to consult Swift et al.(1979). Past and current research has beenmore centered on studying the role of fungi inthe breakdown of SOM; therefore, little isknown about bacterial degradation of plantlitter (Berg and McClaugherty, 2003).Nonetheless, both bacteria and fungi aresignificantly involved in the decomposition ofcellulose, hemicellulose, and lignin (Berg andLaskowski, 2006).

a Decomposition of cellulose: Cellulosefound in nature has a crystalline form and nosingle enzyme can hydrolize5 it. Few micro-organisms produce comprehensive sets ofenzymes necessary to degrade cellulose com-pletely. Aerobic and anaerobic microbes utilizedifferent strategies to break it into simple sug-ars (Schwarz, 2001). The main groups ofenzymes involved in cellulose degeneration arecellobiohydrolase and endoglucanase.Cellobiohydrolases release cellobiose from thenon-reducing ends of the cellulose chainwhereas endoglucanases cut polymeric cellu-lose chains at random positions around lesscrystalline regions, creating new ends (Nutt,2006). Cellobiose is further hydrolyzed by �-glucanase to glucose and then reduced byglucolytic reactions (Swift et al., 1979). Thedistinction between the roles of these twogroups of enzymes responsible for cellulosedegradation is not absolute, as some cellulasesdo attack polymer chains in both ways (Bayeret al., 1998). Micro-organisms that have beenstudied and found to be involved in cellulosedegradation are brown-rot, soft-rot, and

white-rot fungi and bacterial groups such asCytophaga, Cellulomonas, Pseudomonas,Cellvibrio, Ruminococcus, Caldocellulosirupto,Anaerocellum, Butyrivibrio, Bacteroides andClostridium (Kenyan et al., 1961; Schwarz,2001; Lynd et al., 2002; An et al., 2005).

b Decomposition of hemicellulose: Hemice-llulose is a heteropolymer composed of a het-erogeneous mixture of polysaccharides. It hasan intensively branched and amorphousstructure with little inherent strength. Bothbacteria and fungi are involved in the break-down of hemicellulose. Its degradation resultsfrom the action of extracellular enzymes, and hemicellulases such as �-glucosidase, �-xylosidase, �-cellobiosidase and �-glucosi-dase have been found to be involved in theprocess (Wittmann et al., 2004). However,since hemicelluloses are composed of hetero-geneous compounds connected by differentbonds, their hydrolysis requires numerousenzymes; hence, the role of any particularhemicellulase is not distinct (Eijsackers andZehnder, 1990).

c Decomposition of lignin: Lignin is primarilydecomposed metabolically and is not brokendown under anaerobic conditions (Kirk andFarrell, 1987; Falcon et al., 1995). Its degrada-tion by bacteria in comparison to fungi in ter-restrial environments is minimal (de Boeret al., 2005). Several fibre-degrading bacteriado exist, and it has been observed that bacte-rial decomposition only becomes importantwhen conditions for fungi growth are unfa-vorable (Berg and Laskowski, 2006). Largelyextracellular oxidative enzymes secreted bywhite-rot and soft-rot fungi are responsiblefor lignin decomposition in nature. Ligninperoxidase, manganese peroxidase, andlaccase are main ligninases involved in ligninbreakdown (Arora et al., 2002). Out ofthese three enzymes, lignin peroxidasedegrades non-phenolic lignin units whilemanganese peroxidase and laccase oxidizesphenol rings to phenoxy radicals leading to




the decomposition of lignin (Hatakka, 1994;Martinez et al., 2005).

3 Rate regulators of the decomposition processThere are a number of nutrient supply relation-ships that considerably impact the rate, andlimit the loss of plant litter mass. Concentrationof nitrogen in soil is the most significant of all ofthem. High nitrogen content initially promotesdecomposition of the plant residue, although, asmass loss continues and fraction of ligninincreases in the remaining litter, nitrogen has arate suppressing effect due to the impedancecreated by its presence on lignin-degradingenzymes (Carreiro et al., 2000; Sjoberg et al.,2004). Litter C:N is initially greater than micro-bial C:N, but approximates it as CO2 is releasedby micro-organisms and available nitrogen isimmobilized. Hence, the larger the C:N of ini-tial litter the slower is its decay rate (Post et al.,1996). Contrary to the effects of nitrogen onlignin breakdown are the impacts of carbonsource other than lignin and manganese, whichstimulates growth of white-rot fungi and pro-motes enzymatic activity responsible for thedecomposition of the recalcitrant fraction ofSOM (Keyser et al., 1978; Berg andMcClaugherty, 2003). Thus, with respect tocyclical continuum of decomposition, in generalthe availability of N, P, and S has a significantimpact on the initial decomposition rates tillapproximately 30% mass loss is achieved(Taylor et al., 1989) after which nitrogen andlignin content become increasingly important.According to Berg and Laskowski (2006), dur-ing decomposition the concentration of N, P,and S increases with time; overall, however,early leaching might reverse this trend for P andS. Calcium concentration rises initially withsubsequent reduction contemporaneous withthe beginning of lignin degradation. Due to highmobility, the supply of potassium and magne-sium does not show any regular pattern andwaxes and wanes during the course of thedecomposition cycle. These nutrient patternsprove extremely useful in modelling theturnover of SOM with the relative proportion

of carbon and nitrogen (C:N) or lignin andnitrogen (lignin:N) in the plant residue, becom-ing the primary mode to divide falling litter intoSOM pools of various residence times.

Decomposition is second only to net primaryproduction as an ecosystem processes(Moorhead and Sinsabaugh, 2006). It plays animportant part in the terrestrial cycling of pri-mary nutrients. Even the current incompleteknowledge of the workings of litter decomposi-tion has helped considerably in the formulationof process based models which have predictedterrestrial turnover of primary nutrients of plantgrowth with approximate accuracy.

III Models of litter decompositionModels of SOM have largely adopted twomajor pathways to estimate stocks of carbonand nitrogen in the soil. The first and mostcommon of these two approaches modelsdeterminants controlling the movement andtransformation of matter and energy (processmodels) whereas the second method quanti-fies turnover of C and N by explicitlyaccounting for microbial population (foodweb models) responsible for decomposition ofplant litter. SOM models are important for anumber of reasons: first, they represent thebest scientific understanding of its dynamics;second, they can be studied within a largerecosystem framework; finally, they can beused to make projections about its contentunder changing land management and climate(Elliott et al., 1996). Models are the onlymeans for making predictions about SOMconcentration at places other than sites oflong-term experiments, which necessitatesunderstanding of their limitations and applica-bility (Powlson, 1996). With this focus theoverview in this section discusses approachesused to model SOM and prospective areas fortheir further development.

1 Process models of SOMSimply stated process models are compart-mental ecosystem entities based on linear, expo-nential decay, ordinary differential equations




(first order kinetics). These models aredevised on assumptions that SOM pools areparts of a chain of differential residence timeslinked by C flows, hence forming a catenarysequence of substrates (van Veen et al., 1981).These pools represent time-oriented move-ment of C from plant and animal detritus toSOM pools of varying stability. Not all modelscharacterize these flows as unidirectional lin-ear links and sometimes feedback loops areincluded to account for catabolic and anabolicprocesses and microbial succession (Molinaand Smith, 1998). Commonly included mod-els in the feedback category are Century(Parton et al., 1987; 1988), Roth C (Jenkinsonet al., 1990; Parshotam, 1996), Daisy (Muelleret al., 1996; Jensen et al., 1997), Candy(Franko, 1996; Franko et al., 1997), NCSOIL(Molina, 1996), and DNDC (Li, 1996; Li et al.,1997). Models in this group employ variedcompartmentalization mechanisms toschematize decomposition of litter, andturnover of C and N. Earliest process modelsused only a single compartment (Olson,1963); however, as research proved the exis-tence of different litter pools of diversedecomposition rates, the number of pools inthe models increased. For instance, ROTH Cdivides incoming plant material into five com-partments which are decomposable plantmaterial (labile – cellulose and hemicellulose),resistant plant material (recalcitrant – lignin),microbial biomass (soil microbes), humidifiedSOM (humus) and a constant organiccompartment that is inert to biological trans-formation (Coleman and Jenkinson, 1996).Procedurally, incoming litter first gets dividedinto decomposable or resistant plant materialpools with differential rate of decay and min-eralization. At the second step in the hierar-chy, litter in the resistant plant material pooleither gets mineralized or gets partitionedbetween microbial biomass or humidifiedorganic material compartments. Finally,humidified organic material also follows thecompartmental division schema of resistantplant material (Coleman et al., 1997). Pooldivision and number of pools vary among

models but are comparable with the exempli-fied division of ROTH C. Intermodel varia-tion is mainly the result of the rationale usedto divide input litter (C:N, lignin:N, or anempirical relationship), pool demarcations,6

number of pools, time step, and treatment ofrate constants for decay and mineralization.Overall the procedural conceptualizationused is similar. Interconnectivity among dif-ferent pools as stated earlier is not the onlyway to model litter decomposition.Experimental studies have used single tomulti-compartment decay models of C and Nwhere limit value is determined by lignifiedamount of litter (Couteaux et al., 1998; Bergand McClaugherty, 2003). Final stocks of Cand N in these empirical models are deter-mined by adding output from multiple pools ofdifferential decay rates. Mathematically, thiscan be expressed as:

(1)

where k1, k2, and k3 are rate constants for easilyto less easily degradable litter and O1, O2, andO3 are initial amounts of SOM content attime t � 0. Another class of models whichare not commonly used presently expressesSOM decomposition dynamics based on sec-ond order kinetics characterized by theMichaelis-Menten equation that is written as:

(2)

where � � reaction rate, Vmax is the maxi-mum reaction rate velocity, S is the initial sub-strate concentration, and Km is the Michaelisconstant, which is equivalent to half the Vmax(Tate III, 1987). Assuming that SOM is theprimary source of carbon and energy for soilmicrobes, any changes in its concentrationwill impact the complete soil food chain.Based on this presumption, the size of the soilpolysaccharide pool provides an overall meas-ure of SOM mineralization rate which can alsobe a substrate expressed in equation 2 (TateIII, 1987). Usage of the Michaelis-Menten

∂∂

= =+

st

V SK S

max

m

O O e O e O etk t k t k t= + +1 2 3

1 2 3




equation is not limited to studying soilpolysaccharide pools, and it is mostly appliedto model relationships between substrateconcentration and enzyme kinetics. The arbi-trary nature of pool demarcation and implicitconsideration of soil micro-organisms are thetwo major criticisms of the models based onfirst order kinetics (Christensen, 1996).Solutions put forward for overcoming the for-mer emphasizes the inclusion of functionallydefined (derived in laboratory) pool classes.Mostly, incorporation of gaseous, soluble andthree other extractable pools characterizedby isotope tracing of 15N and 13C in the soilhave been suggested (Arah and Gaunt, 2001).Explicit consideration of soil micro-organismsin the models has been undertaken but, with-out complete information about the enzymesand role of actual decomposers involved inthe breakdown of various SOM fractions, thismode of modeling has met with limited suc-cess and applicability.

2 Organism-based or food web models of SOMOrganism-based models of SOM focus uponlinking the flow of matter and energy throughsoil food webs (Heal and Maclean, 1975).The principle of energy transformationexpressed in its most basic form in Einstein’sequation (E � mc2) is the key to understand-ing these models. For a biological system(such as a soil food web) the first law of ther-modynamics is the summation of all knownmass and energy inputs and outputs, whichcan be written as:

(3)

where u is the net change in the internalenergy, � is the amount of mass and energyflowing across the system and W is theenergy cost incurred by the system (Gallucci,1973).

Thermodynamic equilibrium representedas a mass conservation equation in food web

dudt

d dWii ii= +∑ ∑φ

models includes the descriptive pathways asshown below (Jørgensen, 2000):

Net Production � Intake of Food

Respiration Excretion

Wasted Food (4)

In terms of energy, the above equation can also be expressed as (Jørgensen andSvirenzhev, 2004):

FI � EA CEF CEG HER (5)

where FI � food intake, EA � energy assimi-lated by plants, CEF � chemical energy offaeces, CEG � chemical energy of growth,and HER � heat energy of respiration. In afood web there is always a close relationshipbetween energy flow rates and organism size.Thus, small microbes are mainly responsiblefor most of the respiration whereas biomassremains locked in larger organisms (Jørgensenand Svirenzhev, 2004). This also impliesreduction in the amount of available exergy7

at higher trophic levels.Organism-based models often employ

accounting methodology to compartmentalizethe flow of energy (equation 5) through foodweb components, which are organized eitherfunctionally or taxonomically. Type andpopulation sizes of the functional groups andfeeding rates among the same are determinedon the basis of imposed environmentalconstraints. C and N flows in the models aresimulated by balancing the rate of change inbiomass to the rate of material loss from thesystem (de Ruiter and Faassen, 1994; deRuiter et al., 1996). Respiration rates arederived from temperature adjusted empiricalrelationships between body size and oxygenutilization patterns. Ratios between consump-tion, respiration, productivity, and defecationare used to derive energy balance equations(Heal and Maclean, 1975). Inclusion of addi-tional information about assimilation effi-ciency and C:N ratios of incoming litter anddecomposers permits to include parallel path-ways of C and N in the soil (Paustian, 1994).




Mathematically, this relationship is exemplifiedin equation 6 for a particular trophic level in asoil food web from de Ruiter and Faassen’s(1994) model for agro-ecosystems. In thismodel, nitrogen mineralization is given as:

(6)

where Nmin � nitrogen mineralization rateresulting from a trophic interaction (kg N ha1

wk1), CNi � C:N ratio of the prey,CNj � C:N ratio of the predator, BP � bio-mass production per unit assimilated carbon,ACj � assimilated carbon per unit consumedcarbon, and Fij � feeding rate of group j ongroup i. Models which fall in this category arePhoenix (McGill et al., 1981a; 1981b), the grass-land models of Hunt and others (Hunt, 1977;Hunt et al., 1987; 1991), and the fungal growthresponse model of Paustian et al. (1990).

The necessity of enhanced knowledgeabout the functioning of microbes in litterbreakdown makes it harder to model nutrientpathways based on detailed consideration oftheir groupings. Outcome of the organismmodels rely on biomass measurements ofmicro-organisms which show high spatial andtemporal variability (Paustian, 1994).Moreover, these models remain highly sensi-tive to number of assumptions such as miner-alization rates, food web structure, amount ofphysically protected SOM, and element par-titioning (C:N) of incoming residue which aredifficult to predict for field conditions(Paustian, 1994). Nonetheless they are highlyuseful in exemplifying the role of microbes inlitter decomposition in different ecosystems.Additionally, they can prove to be extremelyadvantageous in global change studies, as theimpact of concomitant transformations infood web structure and decompositiondynamics due to climatic alteration can beanalyzed organismically.

3 Prospective areas of model developmentRecent years have seen a major emphasis injoining both process and organism oriented

N ACCN

BPCN

Fmin ji j

ij= 1

models. Theoretical schemes of litter decayand microbial interaction have been pro-posed, but they still rely on first order kineticsand non-linearity, and stochasticity of theprocesses has hardly been examined(Christensen, 1996; Somaratne et al., 2005).Conceptual pools in the current representa-tions do not correspond with experimentallymeasurable fractions in the laboratory.Therefore, a greater semblance between themodelled pools and measurable fractionswould enhance the opportunities to verifyresults as well as prove beneficial in under-standing SOM dynamics (Elliott et al., 1996).The great advantage of such an achievementwould be that SOM turnover can be meas-ured directly in order to deduce the residencetime of different compartments.

Incorporation of additional decompositionrate determinants like erosion (Stallard, 1998)and enzyme activity (Schimel and Weintraub,2003) can further fine tune the existing mod-els. Presently, in all the models, absence ofspatial connectivity of sites at different reso-lution acts as a hindrance in studying flux ofcarbon across the landscape. Merging ofphysiochemical processes of erosion, trans-port, and volatilization (van Veen et al., 1984)with plant production and soil food web com-ponents in an extended representation will goa long way in amending this deficiency. Thiswould also entail inclusion of changes in soilquality over time, a fact completely ignored inpresent-day exemplifications (McGill, 1996).Finally, quick monitoring mechanisms arerequired which can detect shifts in the con-centration of SOM in the face of changingland management decisions. This can beachieved through remote sensing, thoughresearch in this field is still evolving and robustmethodologies have yet to be established.

IV Remote sensing based monitoringmethods for SOM appraisalThe content of SOM in soils typically rangesfrom 2 to 5% and is dispersed over particlesizes of 2 �m to �250 �m (Chefetz et al.,2002). Most of the SOM is distributed within




the top 1 m of the soil profile. Opacity of soilsto electromagnetic radiation makes it difficultto determine its concentration from remotesensing (RS). Surrogate measures for esti-mating SOM from RS exist, but they can onlytell relative differences and actual content isnot deemed determinable until accompaniedby supporting field data. As soils rich in SOMare identified by their dark appearance(Schulze et al., 1993), research has focused oncorrelating reflectance spectra with SOMconcentration (Chen et al., 2000). Thismethod proves to be useful in areas withmoderate to high SOM levels but is ineffec-tive elsewhere (Sullivan et al., 2005). Thecorrelation between reflectance and SOM isalso unproductive over large geographicregions due to confounding effects of mois-ture and underlying parent material (Sudduthand Hummel, 1991; Hummel et al., 2001).Spectrally, visible, near-infrared and mid-infrared regions have demonstrated their use-fulness for SOM evaluation, with longervisible wavelengths from 500 to 750 nm hav-ing the highest predictive capability (Mulders,1987; Sudduth and Hummel, 1991; Ben-Doret al., 1999). In the ultraviolet and visibleportion of the spectrum, humic acid hasminimum and maximum absorption of0.260–0.275 �m and 0.225–0.230 �m,respectively, whereas in the near- and mid-infrared wavebands it absorbs strongly within2.86–3.03 �m and 3.49–3.57 �m (Curranet al., 1990). Fulvic acid has similar spectra;however, comparatively it is a powerful selec-tive reflector with peaks occurring in greenand red wavelengths (Curran et al., 1990).

Hyperspectral RS platforms like AVIRISare useful for detecting minute differences inthe spectral signature of soils, which hasturned out to be effective in determininggrades of SOM (Martin and Aber, 1997;Palacios-Orueta et al., 1999; Ben-Dor, 2002).Methodologically, stepwise regression, par-tial least square analysis and artificial neuralnetworks are used as primary modeling tech-niques (Uno et al., 2005). However, despitethe promise shown by RS, it can only be used

without field measurements to approximategrades of differences (dark-colored soils havemore SOM vis-à-vis light-colored soils)between the SOM content of exposed soils,which are uncommon. To overcome these hin-drances, geographic information systems (GIS)methodology has been devised. GIS tech-niques for SOM approximation grew in the mid1990s and involve combining various databasesof soils, terrain description, climatic parame-ters, and land cover with RS-derived inputs(Huete and Escadafal, 1991; Bliss et al., 1995).Furthermore, a calibrated regression modelbetween these variables and field measure-ments is used to predict SOM levels regionally.

The need for prompt information aboutSOM for large areas necessitates the develop-ment of reliable RS and GIS techniques. In thefuture, availability of high-resolution multispec-tral RS output accompanied by GIS databaseswill considerably improve our capacity topredict variations in SOM caused by processesresponsible for its flux over the landscape.

V Role of erosion and soil aggregates in the flux of SOMErosion is the fundamental process account-able for the flux of SOM. It is not the onlyprocess, as leaching responsible for the move-ment of SOM in soluble forms across the soilprofile also plays a minor role. Here, a compre-hensive mechanistic description of the impactsof erosion on soil structure and translocationof SOM is developed. Discussion in this sec-tion proceeds by outlining the theory behindthe formation of SA; following this, the role oferosion in the breakdown of aggregates andmovement of SOM is evaluated.

1 Soil aggregates: the physical basisSoil aggregates (SA) are parts of soil structure(size and arrangement of particles and poresin soils) made up of primary soil particles.They are held together by soil organic carbon(SOC), biota, iron oxides, clay, and carbon-ates (Bronick and Lal, 2005). SA may beformed naturally, such as peds, or result from tillage, such as crumbs and clods. SA




physically protect SOM, affect microbialcommunity structure, limit diffusion of oxy-gen, regulate water flow into soil, reducerunoff and erosion, and determine adsorptionand desorption of nutrients (Six et al., 2004).Altogether, these associated impacts of SAhave an overwhelming influence on SOMdynamics and nutrient cycling. SA are classi-fied on the basis of size into different micro-aggregate (�250 �m) and macro-aggregate(�250 �m) fractions (Rillig and Mummey,2006). Numerous theories have been pro-posed for the formation of SA. The majorityof these claim soil structure to be a dynamicarena where aggregates of different sizes arecontinuously formed and broken down(Blanco-Canqui and Lal, 2004). Over time,hierarchical formation of SA has gainedacceptance, although various modifications inthe original conceptual organization havebeen proposed. Accordingly, at an initial stagemacro-aggregates break into micro-aggre-gates which on recombining result in the for-mation of new macro-aggregates (Oades,1984). Binding agents are responsible for thestabilization and arrangement of aggregates;these are classified into temporary (such asroots and fungal hyphae), transient (mainlypolysaccharides), and persistent (comprisingresistant aromatic components associatedwith polyvalent metal cations and stronglysorbed polymers) (Tisdall and Oades, 1982).These agents generally join �250 �m,10–50 �m, and �10 �m aggregates, respec-tively (Blanco-Canqui and Lal, 2004).Hierarchy theory and its various offshoots donot explain aggregate formation in all soilsand remain valid only where SOM is themajor binding agent (Six et al., 2000).

Texture, clay mineralogy, composition ofexchangeable ions, and SOM content are thedominant factors influencing aggregate pack-ing in a soil matrix. Aggregate packing within asoil matrix significantly affects sequestrationof organic matter in soils. Turnover of SOMdecreases from macro- to micro-aggregates,implying that there is greater physical protec-tion of SOM in micro-aggregates resulting in

its sequestration (Jastrow and Miller, 1998).This claim is further strengthened from thefact that SOM in micro-aggregates is older incomparison to macro-aggregates owing tolonger storage time and reduced disruption(Blanco-Canqui and Lal, 2004). Land man-agement has a profound influence on SA. In comparison to no-till (NT) agricultural systems, a lower level of aggregation is seen in conventionally tilled plots (Paustian et al.,2000). Tillage brings subsurface soil to thesurface, exposing it to weathering, therebyincreasing the susceptibility of aggregates todisruption (Beare et al., 1994; Paustian et al.,1997). Ploughing alters the soil matrix/condi-tions and increases the decomposition rates oflitter (Cambardella and Elliott, 1993). In NTagricultural systems, residue accrues at thesurface where decomposition rate is loweredas drier conditions reduce contact betweensoil micro-organisms and litter (Salinas-Garciaet al., 1997). Moreover, the amount of themicrobial biomass is generally higher in NT incomparison to conventional tillage (CT)(Ohalloran et al., 1986; Frey et al., 1999),which contributes to macro-aggregate forma-tion and stabilization (Tisdall and Oades,1982). Translocation of SOM over the land-scape primarily results from erosion, the ratesof which are controlled by the level of soilaggregation which is itself considerably deter-mined by land cover and management.

2 Translocation of SOM: role of erosion and depositionErosion and its counterpart, weathering, arethe main processes responsible for aggregatebreakdown and transportation of detachedsediments. Slaking, differential swelling, phys-iochemical dispersion, and raindrop impactare the four main mechanisms by whichaggregates are broken down (Stefano andFerro, 2002). The process of aggregate break-down results in individual particles and micro-aggregates whose transport across hillslopesis chiefly governed by overland flow. Erosionprimarily moves the smaller, lighter particlesof soil which are relatively enriched in SOM




(Lal et al., 2004; Polyakov and Lal, 2004).This phenomenon is defined in terms ofenrichment ratio (ER) which is expressed asthe proportion of SOM in transported sedi-ment (SOMts) to that of SOM in uneroded soil(SOMur) (Teixeira and Misra, 2005), ie:

(7)

An ER of �1 indicates that sediment is richerin SOM than uneroded soil, whereas an ER of�1 denotes the opposite. Enrichment ratioslargely range between 1.5 and 4.5 (Quintonet al., 2006). During a rainfall event ERdeclines as sediment yield increases over time(Palis et al., 1990). Furthermore, as gradientand ER rise, soils with higher clay contentretain more SOM in comparison to soils withrelatively low clay content, but as erosionincreases this difference declines (Gregorichet al., 1998). This can be explained by the factthat clay content in soils is associated withaggregate formation and higher energy isrequired to erode aggregated soils. As sever-ity of rainfall increases, however, this factordiminishes in importance owing to thebreakdown of stabilized aggregates. ER ofSOM under different tillage practices differslittle with time but, as tillage affects erosion,total SOM lost varies substantially (Owenset al., 2002). Knowledge of ER allows us toestimate cumulative or single-event SOMloss due to erosion, which can be computedby multiplying soil loss with near surfaceSOM concentration and ER (Starr et al.,2000), expressed as:

SOM loss � Soil loss

� SOM concentration � ER (8)

Sediments translocated due to erosion eitherget deposited at footslopes/toeslopes or fur-ther downstream or become part of theocean through stream transportation. TheSOM sequestration potential of depositedsediments in a watershed, however, remainsdebatable and two contrasting viewpoints

ESOMSOMR

ts

ur

=

exist in this regard. The first of these arguesthat the mineralization potential of SOMgenerally increases with decreasing particlesize and supports its claim by emphasizing therole of light fraction SOM (including exposedphysically protected SOM inside micro-aggregates), which is preferentially trans-ported and deposited and is biologically moreactive than source site SOM (Gregorich et al.,1998). Contrastingly, it is argued that accu-mulation increases sequestration of SOM.This happens because SOM is transported todepositional basins and is buried andsequestered (Polyakov and Lal, 2004).Besides, onsite SOM depletion is replenishedby equilibrating returns from vegetation. Theprincipal inconsistency between these twoarguments results from the accepted assump-tions concerning the amount of oxidation ofsettling SOM (Lal, 2006). The magnitude ofthis depends on the composition of POM.Some of the POM gets re-aggregated in asediment accruing site and gets physicallyprotected (Gregorich et al., 1998), but thebulk of the easily decomposable labile fractionis mineralized (Beyer et al., 1993), whilehumins are mostly preserved (Hatcher andSpiker, 1988). Hence, depending on theamount of oxidation, a depositional site canprove to be a net source or sink of SOM andin turn greenhouse gases (GHGs) such asCO2 and N2O. Increasing GHGs in theatmosphere become the forerunner of climatechange which has considerable capacity toalter the dynamics of SOM and therebynutrient cycling in almost all the ecosystems.

VI Climate change and soilorganic matterClimate has an overarching control on theturnover of SOM; consequently, its changehas the capacity to alter rates of litter decom-position. Increase in global temperatures willunquestionably affect SOM storage. Whetherit will augment or reduce it depends on thelocation, the magnitude of temperatureand precipitation change, and net primaryproduction under increased CO2 levels.




Several other factors will also indirectly deter-mine the answer to this question: primaryamong them are above- and below-groundbiodiversity, soil food webs, and rate of futureGHG emissions regulated by current landmanagement practices and fossil fuel usage.

1 Impacts of climate change on turnover and storage of SOMNumerous pathways of climatic impacts onSOM dynamics are recognized. Most ofthese are interlinked to, or are controlled by,above-surface changes in vegetation growthand composition. Net primary productivity(NPP) will increase8 owing to rising CO2 lev-els if not constrained by nutrient and moistureavailability (Scholes et al., 1997). Plant com-munity structure will also change owing tothe differential response of individual speciesto enhanced concentration of CO2. Relativelygreater proportion of fixed carbon will be allo-cated below ground than above ground(Young et al., 1998). Furthermore, water-useefficiency will rise because of decreasedstomatal conductance and transpiration rates(Scholes et al., 1997). Yet certainty of thedirection of change in SOM storage, in light ofthese and other above-ground adjustmentsand climatic alterations, is far from clear,though some outcomes are generallyaccepted. Thus, the amount of C and N insoils is positively associated with precipitation(Kirschbaum, 1995) and negatively linked totemperature as substantiated by theArrhenius equation9 (Dalias et al., 2003). Withreference to a Q10 relationship,10 this translatesinto an upsurge in soil respiration rates by a fac-tor of 2 for every 10°C rise in temperature(Kirschbaum, 1995). Numerically, release of agigaton of soil carbon is expected for every0.03°C rise in average annual temperature,with greatest losses occurring in tropicalregions (Jenkinson et al., 1991). Large annualto decadal precipitation variability mightbecome more common, causing increaseddrying and wetting of soils (Young et al.,1998). This will exacerbate breakdown of soil aggregates resulting in mineralization of

physically protected SOM. Additionally,areas and time periods witnessing intensifiedrainfall regimes will have to confront aggra-vated leaching effects that will result in flush-ing of nutrients from the soil profile. Litterquality is also anticipated to change owing torising CO2 levels (Cotrufo et al., 1998;Couteaux et al., 1999). Reduction in litterC:N ratio is projected owing to higher carbonthan nitrogen assimilation by plants (Norbyet al., 2001). Lack of nitrogen in plants willincrease polyphenol and lignin concentrations(Scholes et al., 1997), which will producelower initial decomposition rates; however,the trend will reverse in the case of lignolyticfraction (Berg and Laskowski, 2006). In addi-tion, N available for plant uptake will reduce,creating a negative feedback for NPP.Functional shifts among the soil communityare also likely to occur, but how precisely andto what extent these shifts will affect soilfood webs is not known. Redundancy existsin detrital food webs and loss of somemicrobes will have no impact on SOMdynamics (Hunt and Wall, 2002), althoughexactly which microbes will disappear isunclear. Better moisture availability mighteffect replacement of fungal food webs bybacterial bottom-up controls with bloomingof fauna which are dependent on moisture fortheir survival (Wardle et al., 1998). Therelationship between above-ground changesin plant community structure and litterdecomposition, however, remains obscure(Hattenschwiler et al., 2005). Regarding therespiration rates of labile and resistant SOMpools under increased temperatures, opinionis conflicting, with some suggesting similarresponse (Fang et al., 2005; Knorr et al.,2005a) and others challenging it (Knorr et al.,2005b). However, as the resistant fraction ofSOM is recycled over centuries, these respi-ration rates might not be as important withrespect to global warming on timescales ofcurrent importance.

All these myriad responses of plants andSOM dynamics are mostly the result of sin-gle-factor response studies, but ecosystems




are complex interwoven assemblies and largeuncertainty exists regarding the nature anddirection this complexity will take towardsequilibration in the face of climate change.Uncertainty increases risks and hampers pre-paredness to cope with sudden jolts and evengradual changes brought about by climaticalterations; this necessitates the adoption andimprovement of strategies capable of reduc-ing these future risks for sustainable survival.

2 Agricultural practices and SOMLand-use and land-cover change have been acontinuing response to human populationgrowth. Largely native vegetation has beencleared for agriculture to support the rise inhuman numbers. This replacement mostlyresults in the reduction of SOM. Reversion tonatural vegetation is an alternative tosequester more SOM, but is not an availablechoice owing to human pressure that directsus to take steps at better management ofagricultural lands to do the same.

Tillage of soil in almost all circumstancesincreases rates of SOM decomposition, lead-ing to a significant decline in its concentrationin the soil. This occurs due to the breakdownof soil aggregates which expose physicallyprotected SOM making it amenable formicrobial decomposition (Blanco-Canqui andLal, 2004). Moreover, higher temperatures ofthe uncovered dark soil with lower albedopromotes enhanced microbial activity whichagain acts as a positive feedback for degrada-tion (Amundson, 2001). This is clearly depictedin Figure 1, where time series of changes insoil organic carbon (SOC) due to the impactof agriculture in southern Illinois for a singlesoil type are shown (modelled fromCENTURY 4.0). Exclusion and inclusion oferosion (approximates field measurements ofSOC) respectively is a major differentiatingfactor between the two curves in Figure 1.The increase in SOC after 1970 in the figurecoincides with changes in agriculturalmanagement practices such as increase inmulch tillage and adoption of no-till farmingfrom 1990s.

Sequestration of SOM can only increasesubstantially by embracing strategies thatreduce tillage and erosion and enhance theamount of crop residue returned to the soil.Such is the impact of this change that seques-tration of approximately 337 kg C ha1 yr1 isachieved in the soils of the USA alone by theadoption of NT agricultural practices. This,however, does come at an increased price ofC emissions from agricultural inputs (Westand Marland, 2002). Even after these consid-erations a net additional sequestration of200 kg C ha1 yr1 is achieved in comparisonto conventional agriculture (West andMarland, 2002). This number can be furtherimproved if agricultural energy usage is opti-mized in areas like tillage, fertilizer applica-tion, and irrigation, which can lead to atheoretical reduction of 10–40% of currentenergy requirements (Sauerbeck, 2001). The above estimates result only from Cbudgeting and little is known about the com-parative release potential of other GHGs withrespect to different agricultural practices.Considerations of overall comprehensiveGHG emissions are important as some gaseshave more potential for global warming thanothers. Thus, N20 and methane have 270 and21 (Robert, 2006) times higher global warm-ing potential than CO2 on a molecular basis.The outcome of the limited research regard-ing N20 production from agriculture suggeststhat its emissions from NT are similar orslightly higher than CT (Robertson et al.,2000), and are controlled by the type (broad-cast urea, anhydrous ammonia, and ureaammonium nitrate) and application methods(surface or injection) of fertilizers (Ventereaet al., 2005). On the other hand, methane(CH4) is mostly released from continuouslyflooded rice fields. Draining of these fields atspecific times can decrease these emissionsby 50% without reducing the rice yield (Coleet al., 1997). Mitigation can also be achievedby appropriate selection of fertilizers (Lindauet al., 1993) and optimizing rice cultivars andother management practices (Neue, 1992;Sass et al., 1992).




To summarize, considerable reduction inGHG emissions can be achieved by carefulselection and improvement of agriculturalmanagement practices. Scope for such achange is considerable in developing countrieswhere most of the human populace resides.

VII Research domains of the futureThe role of SOM in regulating globalnutrient cycles is now clearly established.Understanding of SOM mechanics is alsoimportant because it regulates ecosystemfunctioning and health of the soil, thereby hav-ing a strong effect on agricultural productivityand global climate. However, several knowl-edge gaps remain and the current comprehen-sion of processes cannot completely explainthe underlying complexities involved in SOMcycling, sequestration, monitoring, andtranslocation. This necessitates the pressingcommitment of resources for both experimen-tation and modelling to eliminate these lacunae.Other than procedural gaps, substantial infor-mation gaps also exist vis-à-vis the regionaldynamics of SOM. This implies that even thecurrent state of knowledge or models have notbeen substantially tested/applied in different

ecosystems and regions of the world. Theissue of translocation is extremely importantand understanding of SOM dynamics over alandscape can only be achieved by studyingboth onsite and offsite relocation and mineral-ization processes.

Each aspect of SOM discussed in this paperhas its own fundamental questions that shouldbe addressed. Most of these research areasare interlinked, and advancement in one willsimultaneously improve our understandingabout other dimensions of SOM dynamics.Except in the case of the process of litterdecomposition, almost all the areas have to becomprehensively researched regionally todevelop a fairly complete picture about them.Further research should be geared towardsdeveloping a better understanding of the bio-chemistry of decomposition, which needs tobe explicitly implemented in the SOM models.Similarly, enhancement of theoreticalknowledge about soil aggregate formation andmanagement effects on soil structure willalso prove to be beneficial. With respect toclimate change, studies detailing its impacton soil fauna, below-ground biodiversity(bacteria and fungi), and multifactor response

Figure 1 Timeline of SOC Changes (with and without erosion)




research linking the effect of changing litterquality, below-ground food webs, increasingtemperature, and precipitation fluctuationsand NPP on SOM will improve our under-standing of its future fluxes. Development ofRS/GIS based methodologies is also requiredto enhance the capability to monitor andmeasure SOM at regular intervals.

One area which deserves foremost atten-tion is the issue of erosion/deposition and itsinfluence on transportation and mineraliza-tion of SOM on the landscape. Such is theimportance of topography, soils, and overlandflow that Thompson and Kolka (2005) wereable to explain ~70% variation in the concen-tration of SOC on the landscape by means ofterrain-derived attributes such as upslopearea, dispersal area, topographic wetnessindex, stream power index, sediment trans-port capacity, curvature, and distance tostreams. This goes on to show the relativeimportance of rainfall-runoff relationship overterrain in determining densities of SOM. Inpast and to an extent current research in sed-iment transport, modelling is mostly gearedtowards determining the magnitude of erosion(Lane et al., 1997; Sun et al., 2002; Gonzalez-Bonorino and Osterkamp, 2004; Lu et al.,2004), with deposition being relegated as anarea of no concern. Current process-basedmodels such as WEPP (Flanagan andNearing, 1995) and EUROSEM (Morganet al., 1998) do give estimates of both erosionand deposition, but are mostly designed forthe study of individual hill slopes or smallcatchments of the size of a few hectares andtherefore are not adequate for large basin-based analysis. This is not a flaw with theprocess models itself but is more related toparameter uncertainty brought about byaggregation at higher scales both in spatial(hillslopes to watershed) and temporal (sum-mation of composite rainstorms based trans-portation over an year) dimensions. Forexample, aggregation of output from routingwater through every conceivable flowpath in awatershed from GeoWEPP (Renschler, 2003;Renschler and Lee, 2005) as depicted in

Figures 2 and 3 results in accurate estimates oferosion and deposition in only restricted areas.The output obtained is also highly dependenton the resolution of the elevation data and thedemarcated hillslopes cross the length of unitstandard plot of 72.6 ft, prescribed by USDAfor erosion measurements within three pixelsin the case of a 10 m DEM. Basin-scale formu-lations of sediment transport based on unitstream power theory (Mitasova, 1996) alsoprovides only coarse estimates over the land-scape and again is DEM resolution dependent.Inherent variability in the output of the erosionmodels also result from the fact that the gov-erning equations, like the ones for sedimenttransport capacity of overland flow, vary frommodel to model, which raises questions aboutthe theoretical support for the particularchoices made (Julien, 1995; Prosser andRustomji, 2000). Moreover, with respect toSOM, obscurity in estimates results from notknowing the ER of SOM in sediment transportwhich itself is governed by soil type, land coverand intensity, and duration of individual rainstorms. All this parametric uncertainty makesit extremely difficult to accurately determineSOM estimates and in most circumstancesonly an approximation of real scenarios can beachieved. Furthermore, the complexityincreases substantially if we start consideringthe fate of eroded and deposited sediments inrelation to magnitude and time taken formineralization.

Within the current realm of SOM models,only CENTURY and EPIC (Williams, 1990)explicitly include terms to account for erosion,of which only CENTURY has the capability torepresent both processes of erosion and depo-sition. Modelling of movement of SOM acrosslandscapes has not been effectively realized inany SOM model and CENTURY or EPIC areno exceptions to that. ER based SOM removalhas been parameterized in CENTURY but, asER remains fixed for the duration of simulation,time-varying changes in its magnitude withmodified erosion potential cannot be imple-mented. More remains to be done to under-stand changes in SOM in depositional zones




and at present there hardly exists any researchwhich has looked at this aspect. This gap inknowledge can only be filled when an attemptis made to quantify pathways through whichoverland flow moves through catchments andresults in transport of SOM-laden sediments.Thus, endeavours to link cell-based sedimenttransport models with SOM models arerequired to enhance the understanding of theflux of SOM over the landscape.

SOM is a biophysical parameter, which isalso directly linked to above-ground land useand land cover. Magnitude of SOM changesover a parcel of land on the temporal scale ofdecades is an unambiguous portrayal ofhuman-intervened land-cover/use modifica-tions. Currently changes in land use and landcover and variations in SOM are studied and

modelled separately. However, both are con-joined and should be seen as part of a cascadingecosystem framework. This is true not only forSOM but also for other biophysical parameterswhich in this anthropocenic (Crutzen, 2002)world are chiefly governed by human activities.For instance, in terms of SOM the two promi-nent questions we should ask are as follows. (a)What was and will be the impact of land-usechange on SOM? (b) What measures can beinvoked to reduce and make up for the loss ofSOM induced since human modification ofnatural land use and land cover? Answeringthese questions will require study of alternativestates of management of different land useshistorically and futuristically (mainly withrespect to agriculture). This will help in devel-oping an integrated policy for managing land

Figure 2 Onsite erosion and deposition in a small watershed in the Big Creek Basin




resources in a nested watershed framework.The time has therefore come for linking modelsof LULC change with those of SOM turnoverto examine impacts of human (primary agentsof change) decision-making on global climateand ecosystem functioning. This is necessaryas a complete picture has to be developed of abio-complex whole/system. Discernment ofindividual parts is important but a clear picturewill never emerge if the focus is only compo-nent-driven. Application of this kind of anapproach will require full integration of RS andGIS with SOM models. Attempts in thisregard are forthcoming but the research is stillin its nascent stages. This necessitates addedfocus on monitoring and measurement of bio-physical parameters such as SOM that aredirectly responsible for the alteration of theearth’s ecosystem.

AcknowledgementsThis research is an outcome of the financialassistance provided under a subcontract toGeorge Malanson, from NSF grant 0410187to Christopher Lant. We also thank MarcLinderman and Dave Bennett for theirconstructive suggestions.

Notes1. Polymer carbohydrates or polysaccharides are

complex carbohydrates composed of simplesugar building blocks bonded together in longchains.

2. Cellulose is the most common carbohydrate onearth. It is a highly soluble polysaccharide usedby plants as their major supporting material andis a principal structural component of the cellwall that encloses plant cell membranes.

3. Hemicellulose is a heteropolymer present incell walls of plants along with cellulose.

Figure 3 Histogram of soil loss of the watershed depicted in Figure 2. Note: thenumber of pixels decreases substantially towards the lower and higher ends of the x axis, because of which they are not visible. Moreover, as we move away from thenarrow band around the mean, estimates of erosion and deposition become moreinaccurate. However, the total area of these pixels in the watershed is much less




Hemicellulose is composed of many sugarmonomers such as xylose, mannose, andgalactose.

4. Lignin is an organic substance that binds cellu-lose fibres in plants and hardens the cell walls.Thus, it is an integral part of the plant’s cellwall. Lignin is most common substance onearth after cellulose and chitin.

5. Hydrolysis is a chemical reaction in which theaction of water or its ions breaks down a sub-stance into smaller molecules. There are twomain types of hydrolysis: acid and enzymatic.

6. Laboratory-derived portions of SOM arecalled fractions, whereas in the case of modelsthey are called pools.

7. Energy which is useful and can do work. Bymeasuring the energy that can do work,exergy expresses energy with a built-in meas-ure of quality, for example the chemicalenergy in biomass.

8. Plants can be classified into three groups onthe basis of the photosynthetic pathways.These three groups are C3, C4, and crassu-lacean acid metabolism (CAM). Rise in NPPis mostly predicted for C3 plants (CalvinCycle) which make up ~85% of the earth’splant species. C4 (Hatch–Slack pathway)plants (mainly grasses) which evolved whenCO2 concentration was significantly lesserthan current levels will not respond similarlyto rising CO2. Finally, CAM plants do notrespond at all to increasing CO2 levels.

9. The Arrhenius equation is a formula thatexpresses temperature dependence of achemical reaction rate. Mathematically it isexpressed as:

where K is a constant derived from the firstorder ordinary differential equation, Ae is theactivation energy, G is the gas constant, and kis the reaction rate constant.

10. Q10 is an empirical coefficient that describesrate of change of a biological system as aconsequence of increasing the temperature by10°C. It is analogous to the Arrheniusequation and is a dimensionless measure.

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progress in soil organic matter research: litter decomposition, modelling, monitoring and...

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