Modeling soil organic matter dynamics as affected by soil water erosion

Environment International 30 (2004) 547 – 556 www.elsevier.com/locate/envint

Modeling soil organic matter dynamics as affected by soil water erosion V. Polyakov *, R. Lal Carbon Management and Sequestration Center, The Ohio State University, 422D Kottman Hall, 2021 Coffey Road, Columbus, OH 43210, USA Received 22 July 2003; accepted 27 October 2003

Abstract Soil organic carbon (SOC) stock is an important component of the global carbon (C) cycle, which has the potential to influence global climate. In this paper we presented an overview of soil organic matter (SOM) models in the context of soil erosion and discussed basic processes driving erosion-induced SOC loss. Although the mechanism of this loss is poorly understood, erosion influences SOC in two ways: redistribution of C within the watershed or ecosystem, and loss of C to the atmosphere. Erosion disperses soil, altering its microbiological activity as well as water, air and nutrient regimes. This, along with sediment enrichment, has an impact on greenhouse gas emission from soil. For most of agricultural settings, field studies suggest that cultivation along with soil erosion are the primary reasons for SOC loss. Tracing the fate of eroded C is a challenging task. Modeling is the approach taken most often. In this paper we discuss approaches used in various SOC models to assess erosion-induced C loss from soil in agricultural ecosystems. An example with Century model applied to meadow and corn – soybean rotation under chisel-till demonstrated the model’s ability to respond well to different erosion scenarios. It was estimated that at soil loss rate of 10 t ha
1 year
1 (value often considered a threshold for maintaining productivity) 19% of the total SOC loss would be attributed to erosion after 90 years of cultivation. D 2003 Elsevier Ltd. All rights reserved. Keywords: Soil carbon; Modeling; Erosion; Sequestration

1. Introduction Soil is an important element of the global carbon (C) cycle. Soil erosion from agricultural land results in considerable losses of soil organic matter (SOM). Considering that approximately 1.6 billion ha or 13% of the Earth surface is affected by human-induced erosion (GLASOD, 1990), and current annual soil loss on only the US cropland are as high as 1.9 billion tons per year (USDA, 1997), erosion-induced C displacement may be an important factor affecting CO2 concentration in the atmosphere (Lal, 1995). The overall impact of human-induced erosion on the global C cycle remains controversial. Assessment of this impact is likely to depend on the scale at which erosion is considered (vanNoordwijk et al., 1997). For example, on the field or watershed scale, it is generally recognized that loss of soil results in reduction of SOM concentration (Slater and Carleton, 1938; Pennock et al., 1994; Fahnestock et al.,

* Corresponding author. Fax: +1-614-292-5678. E-mail address: [email protected] (V. Polyakov). 0160-4120/$ - see front matter D 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.envint.2003.10.011

1995; Gregorich et al., 1998; Lal, 2003). However, when deposits such as colluvium, alluvium, aeolian as well as reservoir and lake sediment masses are considered, it is plausible that the erosion-induced burial of C is substantial, perhaps as high as 0.6 –1.5 Pg year
1 (Stallard, 1998). Although the question of the SOM fate during transition from sediment sources to sediment sinks has been raised in the literature (Lal, 1995) and some generalizations were made (Jacinthe and Lal, 2001), field data are scarce, investigations are limited to few pioneering studies (Bajracharya et al., 2000; Jacinthe et al., 2002), and the magnitude of CO2 release to the atmosphere from the sediment during transport phase is largely unknown (Jacinthe et al., 2001). Assessment of erosion-induced C losses are usually based on comparative observations between disturbed (either loss or accumulation) and undisturbed sites (Harden et al., 1999; Kimble et al., 2001). Often such comparisons are difficult to make when C loss due to wind erosion is considerable, such as in case of cultivated Chemozemic landscapes in Saskatchewan (Pennock et al., 1999). Due to a large number of soil types, climatic and topographical conditions at the studied sites and often lack of detailed site description, these relationships are difficult to generalize

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and extrapolate beyond the studied domain. To overcome the limitation of field observation studies, modeling of SOM loss due to water and wind erosion may be a viable option (Vanveen and Paul, 1981; Parton et al., 1987; Lee et al., 1996; Starr et al., 2000). Assessment and prediction of erosion-induced release of CO2 into the atmosphere requires integration of field results obtained at various scales and at different climatic and soil conditions with current C dynamics and soil erosion models. The objective of this article is to review modeling approaches to erosion-induced SOM losses and to summarize factors which have to be considered when incorporating an erosion component into a SOM model.

2. Factors affecting erosion-induced soil organic matter turnover Wide variety of views on the role of erosion on C dynamics within ecosystems indicates that the processes involved in detachment, transport, deposition and mineralization of SOM are poorly understood. Discrepancies in estimates of erosion influence on soil C may point out to differences in SOM quality at various landscape positions (Schimel et al., 1985a) resulting from SOM transport and sorting by water. The SOM trapped in watershed deposits can be either sequestered or undergo increased rate of mineralization depending on local conditions. 2.1. Types of soil organic matter and its distribution in soil matrix The SOM consists of a great variety of organic compounds. For modeling purposes, however, it is usually classified into three pools based on the rate of mineralization and turnover (Stevenson, 1982; Parton et al., 1987). Labile or easily mineralizable compounds along with microbial and fungal biomass generally comprise about 5 – 15% of the total SOM. This pool has turnover rate of month to years and, perhaps, is of the greatest interest for SOM erosion modeling. Slow pool with turnover time of several decades comprises 20 –40% of the total SOM. Stable or recalcitrant pool has turnover time of hundreds to thousands of years and in most soils comprises remaining 60 – 70% of the total SOM (Rice, 2002). 2.2. Sediment enrichment Enrichment ratio of SOM is the ratio between concentrations of SOM in sediment to those in undisturbed soil. Enrichment ratio >1 is the result of preferential transport of either soil or SOM. Enrichment of eroded sediment with nutrients and SOM in particular has been widely reported (Sharpley, 1985; Owens et al., 2002). Its mechanism consists of two processes: dispersion of soil aggregates and their sorting during transport.

Raindrop impact is one of the primary erosive forces acting on soil. It was shown that upon impact with soil aggregate, raindrop removes its outer layer (slaking and peeling process), thus releasing microaggregates. The mechanism of enrichment is explained by the fact that the outer layers of soil aggregates have increased concentration of sorbed chemicals including SOM compared with the inner core (Ghadiri and Rose, 1991a). Raindrop impact causes the aggregates to slake and peel. As a result of this process eroded sediment not only have finer size characteristic than the original soil, but also SOM is unevenly distributed between coarse and fine particles (Palis et al., 1997). These structural factors, although difficult to quantify, need to receive more attention when modeling erosion-induced SOM dynamics. The sorting of material transported by water is caused by the differences in drag, gravitational, and cohesive forces acting on individual particles. The drag force is a function of the particle diameter and shape, and the gravity force is a function of particle mass. The enrichment ratio of SOM as high as 5 has been reported (Zobeck and Fryrear, 1986) for certain conditions. Although the amount of loose, poorly decomposed non-cohesive plant fragments in most agricultural soils is relatively small, its highly preferential transport may also have a significant impact on SOM redistribution (Ghadiri and Rose, 1991b). Enrichment ratio of SOM tends to be greater for more aggregated soils with higher concentration of clay than less aggregated and coarse-textured soils (Palis et al., 1997). The SOM concentration in various sizes of aggregates tends to decrease with the severity of erosion (Bajracharya et al., 1998). It also varies with rainfall duration. For example, in an experiment under simulated rainfall (Palis et al., 1997), it was shown that enrichment ratio of SOM in clay soil decreased from 1.62 to 0.88 within 35 min of rainfall. 2.3. Soil structural controls over decomposition of soil organic matter in disturbed soil While the effect of soil moisture and temperature regimes as well as management practices on the rate of SOM dynamics has received some attention (Rickman et al., 2002), soil structural controls over SOM decomposition have not been given the attention they deserve. Aggregate structure is one of the soil properties most significantly affected by erosion. Soil detachment and sediment transport alter aggregate structure which to a great extent controls decomposition of organic substances by microorganisms (Vanveen and Kuikman, 1990). The slaking and peeling process, described earlier, is an important factor in decomposition because aggregate breakdown occurs along intraaggregate pores, which are the preferable sites of sorption for SOM as well as other chemicals (Wan and El-Swaify, 1998). The amount of pores directly accessible to bacteria is estimated to be 5% (Vanveen and Kuikman, 1990), which suggests that much of substrate is physically protected from bacteria. Little data is available that quantifies how much of

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CO2 evolution increase is attributed to the aggregate breakdown, although it is reasonable to hypothesize that this process occurs. Decomposition of SOM in liquid and soil phases was shown to have different rates (Vanveen and Paul, 1981), which suggests that soil architecture provides certain protection against decomposition. As an example, the turnover time of an amino acid in a soil mixture can vary by three orders of magnitude depending on the degree of protection (Sorensen, 1975).

the SOM from the colluvial source was decomposed during translocation or after deposition on a sandy soil in Northwest Germany (Beyer et al., 1993) where selective preservation of SOM in the colluvial sink was hypothesized. The erosion-induced losses of SOM into the atmosphere in the described cases are quite substantial considering the size of labile SOM pool reported in the literature for most of agricultural soils (Rice, 2002).

2.4. Sediment transport and sediment yield

3. Modeling

Studies, which investigated correlation of SOM with topographical position, usually report increased concentration of SOM at footslope locations (Bergstrom et al., 2001). Although increased SOM concentration may be partially attributed to greater SOM input due to local hydrological factors, the increase of C:N ratio in such locations (Schimel et al., 1985a) might suggest different organic matter quality probably resulting from selective transport of organic matter. After rainfall event most of displaced SOM remains within watershed. Large discrepancies were observed between amount of soil displaced from the field and amount of sediment delivered to the stream (Walling, 1983). Three ways of SOM loss associated with erosion are suggested: oxidation due to aggregate breakdown during detachment and transport of SOM (presumably more mobile and less dense and cohesive), transformation of SOM into more stable pool, and transfer of SOM into water bodies. Storage of sediment in digressional sites and fluvial plains can be substantial (Lowrance et al., 1986, 1988; Stallard, 1998; McCarty and Ritchie, 2002) thus allowing SOM from disrupted aggregates to be exposed to environmental factors for prolonged periods of time. Transport of sediment from watershed is a multistage process during which soil may be temporarily deposited on its way to the streams or permanent deposition sites. The CO2 flux from the disturbed aggregates is proportional to the time these aggregates reside on the landscape before being buried or delivered to an aquatic system. In most studies in natural conditions, enrichment of SOM in sediment distributed within watershed is reported to be higher than unity (Ghadiri and Rose, 1991b), which may not necessarily be the case with sediment leaving watershed. A study of sediment delivery into 41 impoundments in continental US (Avnimelech and McHenry, 1984) demonstrated decreased concentration of SOM compared to the soils of their origin in cases when these soils had high SOM concentration. These two seemingly contradictory observations indicate that displaced SOM accumulates within watershed boundary and perhaps substantial portion of it mineralizes before reaching water bodies (Jacinthe and Lal, 2001). This view is supported by rainfall simulation studies (Jacinthe et al., 2002) which demonstrated for a silt-loam soil from southwestern Ohio that 29 –46% of SOM transported in runoff was mineralized in 100 days. About 70% of

3.1. General structure of soil organic matter models Simulation models play an increasingly important role in C pools assessment at different spatial scales as well as in understanding of processes underlying C fluxes in ecosystems (Izaurralde et al., 1998). Models are also essential tools for devising and evaluating management practices intended to balance global C fluxes. The Soil Organic Matter Network (SOMNET) database (Molina and Smith, 1998) identified 33 SOM dynamics models available for use today and the database is being continuously updated. A great variety of models designed for different spatial scales and time steps can be classified into four groups according to the conceptual approach to SOM turnover in soil: (i) process-based (single or multicompartment), (ii) cohort, (iii) food-web chain, and (iv) combined (McGill, 1996; Smith, 2002). The majority of models are process-based multi-compartment models. The characteristic features of these models are: (i) subdivision of the SOM into several ‘‘homogeneous’’ pools each with its unique decomposition rate, (ii) assumption that decomposition of SOM follows first-order kinetics, (iii) defined relationship between the dynamics of C and N pools (Paustian, 1994). Different C pools of different properties combined with flows of C between the pools represent the structure of process-based models (Parton et al., 1994; Molina and Smith, 1998). From plants C is successively transferred into microbial biomass, SOM pools of various stability or CO2. The output from a component of the SOM system may be split or have a back loop to account for the process microbial succession (Molina and Smith, 1998). Modular structure of SOM models allows flexibility and ability to expand the model structure to accommodate new processes and flows as empirical data such as connection between microbial community and soil structure, structural effects on decomposition rates and effect of water, wind and tillage erosion becomes available. Each module occupies specific position in the C flow hierarchy, and is characterized by a decay rate. Most of SOM models do not explicitly specify C flows due to erosion, but allow including these flows if necessary. A general concept of SOM dynamics as affected by soil water erosion is presented in Fig. 1.

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V. Polyakov, R. Lal / Environment International 30 (2004) 547–556

Fig. 1. Soil organic matter fluxes as influenced by soil erosion.

3.2. Principal governing equation of erosion component in soil organic matter models Three major processes involved in erosion-induced SOM turnover can be defined as: (i) physical removal of SOM from slopes and convex landforms; (ii) deposition of SOM in depressions and on concave landforms; (iii) change in the rate of mineralization of displaced C (Fig. 1). Loss of C by SOM models is usually estimated in relation to soil loss (Gregorich et al., 1998). One of the most common approaches is a linear relationship (Starr et al., 2000): Closs ¼ A  CSOIL  Er

ð1Þ

where A = spatial average soil loss (t ha
1 year
1); CSOIL = concentration of organic C in soil (%); Er = enrichment ratio of eroded sediment relative to the original soil (dimensionless). Soil loss is included as a component in several SOM models such as Century (Parton et al., 1987) or EPIC (Sharpely and Williams, 1990; Goss et al., 2001). Century model employs the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978), which depends on rainfall to determine erosive energy. The USLE uses six erosion factors to compute the average annual soil loss on field slope as (Eq. (2)): A ¼ RKLSCP

ð2Þ

where R = rainfall erosivity factor, which includes runoff factor from snowmelt; K = soil erodibility factor, which is correlated with soil particle size distribution, OM content, permeability, etc.; L = slope length factor; S = slope steepness factor; C = cover and management factor, which describes the type of soil cultivation and the degree of soil protection by plant canopy; P = support practice factor related to the type of erosion protection practice such as terracing, stripcropping, etc. Another erosion prediction tool, MUSLE (Williams, 1975), is used in EPIC model (Williams, 1995). It has

structure similar to USLE, but employs runoff variables to estimate soil loss. This eliminates the need for a delivery ratio, increases prediction accuracy and enables to simulate single storm events. The limitation of the described soil loss prediction equations is their inability to account for soil deposition. The SOM turnover process in depositional areas is a key component of C balance in terrestrial ecosystems (Bajracharya et al., 1998). Although SOM mineralization in soil as affected by soil structure and degree of disturbance has been studied (Sorensen, 1981; Vanveen and Kuikman, 1990; Parton et al., 1993), attempts to incorporate the contribution of displaced sediment into CO2 flux were limited (Harden et al., 1999; West and Wali, 2002). The principal governing equation of C flux between SOM model components is first-order relationship of the form (Eq. (3)): dCs =dt ¼
kmpCSOIL þ h

ð3Þ

where CSOIL = concentration of organic C in soil; t = time; k = first-order decomposition coefficient; m, p = correction factors for soil temperature and moisture; h = additional rate independent of decomposition rate, such as erosion, deposition or net primary production as specified in greater detail in Eq. (7). One of the most significant changes, which occur with soil during transport, is change of texture of sediment relative to the soil from which it originates due to sorting of material. It has been shown that soil texture affects the mineralization of labile fraction of SOM (Sorensen, 1981) as well as has an influence on transformation of labile SOM into slow and recalcitrant SOM (Vanveen et al., 1984). Equations based on long-term incubation in soils with various clay mineralogy and soil texture were proposed for use in Century model (Parton et al., 1987), which quantify the impact of clay and silt contents on SOM turnover (Eqs. (4) and (5)): b ¼ að1
0:75SÞ

ð4Þ

e ¼ ð0:85
0:68SÞ

ð5Þ

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where b = texture correction factor, relating SOM decomposition rate to soil texture; a = linear coefficient; S = silt and clay fraction, %; e = SOM stabilization factor, relating the efficiency of stabilizing active SOM into slow SOM to soil texture. Correction factors b and e are used to adjust the first-order decomposition coefficient k (Eq. (3)). The SOM decomposition process in soil depends on soil depth. Temperature and moisture reduction coefficients (Voroney et al., 1981) are included in SOM transformation equation (Eq. (1)) to accommodate for differences in these variables with soil depth. Majority of SOM dynamics models treat soil profile as a combination of layers with different decomposition rates (Molina and Smith, 1998). This subdivision on layers has an effect on how erosion component of the model operates. A scheme was proposed (Schimel et al., 1985b; Bouwman, 1989) in which the upper soil layer, which becomes thinner due to the loss of soil, is compensated from the second layer. An amount of soil equal to the one lost to erosion is transferred from the second (lower) soil layer into the first (upper). As a result, the SOM in the upper layer becomes diluted. Enrichment ratio of SOM in sediment is usually logarithmically related to sediment loss (Massey and Jackson, 1952; Sharpley, 1980, 1985; Ghadiri and Rose, 1991b) (Eq. (6)): Er ¼ bAd

ð6Þ

where Er = enrichment ratio; n, d = coefficients (dimensionless); A = soil loss (t ha
1). Coefficients n and d vary widely with type and texture of soil, which complicates the incorporation of the logarithmic equation into erosion component of SOM dynamics models. Many models use constant values for enrichment ratio (Voroney et al., 1981; Bouwman, 1989; Lee et al., 1996), because sediment delivery is estimated as an average annual sediment yield (such as by USLE) while for the logarithmic relationship to be employed sediment yield data for a specific event is needed. Logarithmic relationship though is used in models such as CREAMS (Silburn and Loch, 1989) and EPIC (Williams, 1995) which have more powerful hydrological component capable of generating stochastic events. Because rare large storms are responsible for most of the soil loss from watersheds (Edwards and Owens, 1991), logarithmic expression for Er needs to be incorporated into SOM dynamics models. Enrichment ratio also tends to decrease with the rainfall duration (Palis et al., 1997) in a logarithmic fashion. Although such a relationship was observed only for an artificially simulated rainfall in a laboratory conditions and no mathematical formulation was proposed, it seems to be in agreement with the theoretical framework of enrichment mechanics (Wan and El-Swaify, 1997).

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3.3. Examples of soil organic matter models with soil erosion component Century is a process-based model originally developed to simulate long-term SOM dynamics in grassland ecosystems (Parton et al., 1987). It was later adapted to simulate forest and agricultural systems in various climatic conditions (Kelly et al., 1997). The Century model uses a monthly step and consists of several submodels such as plant production, SOM decomposition, weather and water budget, and soil erosion, and is described in details elsewhere (Parton et al., 1993). Model’s principal driving variables are: air temperature, precipitation, soil texture, type of crop and management practice (Table 1). Soil erosion and deposition submodel (Fig. 2) allows to simulate the effect of soil loss based on RUSLE (Renard et al., 1997). Modifications to the Century model enabling it to calculate the impact of deposited sediment on SOM turnover have been attempted in earlier versions (Harden et al., 1999; Pennock and Frick, 2001) and the new Version 5 of the model is now able to do it explicitly (NREL, 2001). Deposition events can be added using the output from erosional events from previous simulations. Also, depositional and erosional events are allowed to be modeled simultaneously in the same time step (NREL, 2001). The change in C budget over time on eroding site can be generalized as follows (Harden et al., 1999) (Eq. (7)): dCS =dt ¼ CNPP
ks CSL
kE CEROS þ CLHZ

ð7Þ

where CNPP = net primary production (input) of C; CSL = amount of C in the top layer of soil; CEROS = amount of C eroded from the site; CLHZ = amount of C, which is incorporated into upper layer from the lower layer of soil; kS and kE = decomposition rates of soil and sediment C. Monreal et al. (1997) demonstrated using Century that erosion rate on Chernozemic and Gray Luvisolic soils was linearly correlated with SOM change. Similar trend was also observed in field measurements of SOM and soil losses for two watersheds in Ohio (Starr et al., 2000). The authors (Monreal et al., 1997) estimated that depending on management practice 12 – 46 years was required to achieve steady-state level of SOM when erosion was included into simulation, which was 5– 50% longer than when erosion component was not included. Simulated SOM losses were in agreement with those obtained directly assuming 1.18 SOM enrichment factor. Gregorich et al. (1998) used Century model to simulate the effect of erosion on soil C on a black chernozem under a wheat-fallow rotation and a thin black chernozem in Canada. The soil loss estimated from the model was verified using 137Cs technique (Ritchie and McHenry, 1990). Out of 67% of total C lost from the cultivated field after 71 years of agricultural activity (comparing to undisturbed prairie), 47% was attributed to loss by soil erosion. When erosion component was not included into the simu-

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Table 1 Comparison of SOM models, which include soil erosion component (modified from Smith, 2002) Model

Time step Basic inputs

Century (Parton et al., 1987)

Month

EPIC (Williams, 1995)

Day

Bouwman’s model (Bouwman, 1989) REM (West and Wali, 2002)

Month

Modified VanVeen’s model (Voroney et al., 1981)

Month

P, AT; W, Tx, OM, pH, N; Rot, Ti, Fert, Man, Res, Irr, AtN P, AT; Lay, Imp, W, Tx, OM, pH, BD, Wi; Rot, Ti, Fert, Man, Res, Irr, AtN P, AT; OM, Lay, Tx; Ti P, AT; W, Tx, OM, pH, N; Rot, Ti, Fert, Man, Res, Irr, AtN P, ST; Tx, OM, W, Lay; Rot, Ti, Res

Soil outputs

Developed for Erosion Model features included equation Deposition Stochastic SOM component erosion enrichment events in eroded sediment

C, BioC, 13C, 14C, N, W, ST, Gas C, BioC, N, W, ST

Grassland, cropland, forest Cropland, grassland, forest

RUSLE

C

Cropland

C, BioC, 13C, Rehabilitated 14C, N, W, land ST, Gas C Grassland, cropland

CO2 flux from displaced sediment

Yes

No

Yes

No

RUSLE, No MUSLE

Yes

Yes

No

USLE

No

No

Yes

No

RUSLE

No

No

No

Yes

USLE

No

No

No

No

Key: 13C = 13C dynamics, 14C = 14C dynamics, AT = air temperature, AtN = atmospheric nitrogen input, BD = soil bulk density, BioC = biomass carbon, C = soil carbon content, Fert = inorganic fertilizer application, Gas = gaseous losses, Imp = depth of impermeable layer, Irr = irrigation, Lay = soil layers, Man = manure application, N = soil nitrogen content, OM = organic matter content, P = precipitation, pH = pH, Res = residue management, Rot = rotation, ST = soil temperature, Ti = tillage, Tx = soil texture, W = soil water characteristics, Wi = wilting point.

lation, soil C content stabilized after approximately 20 years, while with 40 t ha
1 of annual soil loss soil C continued to decline even after 70 years of cultivation. The erosion component of the model was sensitive to the management practice. Harden et al. (1999) in the modeling of soil C dynamic in Mississippi loess incorporated deposition component into Century simulation (Eq. (7)). The authors investigated scenarios under which displaced C underwent same decomposition rate as original soil or was protected from decomposition (sequestered). Depending on the scenario, the landscape could be turned from C source into C sink. This simulation stressed the importance of investigating the fate of the displaced C in depositional areas, which is largely unknown (Lal, 1995, 2003). Simulation showed that all of the original SOM could be lost to erosion in 127 years of cultivation.

Manies et al. (2001) tested Century model using three scenarios with no erosion, moderate erosion and severe erosion on loess cropland in western Iowa. Simulation was run for 140 years and the results were compared to field data. Although the model does not account for sediment deposition on site, the authors used method proposed by Harden et al. (1999) to account for emission from displaced SOM and examined the effect of various decomposition rates in sediment on total C loss. Century output agreed well with field measurements on ridgetop and midslope positions and demonstrated the importance of accounting for mineralization of C in sediment. While net amount of soil C on the studied area decreased 29– 56% during past 140 years of cultivation, it was shown that as much as 93 – 172% of the original C has actually been lost. These results indicate continuous replacement of C from vegetation and, perhaps the most important, substantial CO2

Fig. 2. Schematic of soil C flows in erosion – deposition component of the Century model (NREL, 2001).

V. Polyakov, R. Lal / Environment International 30 (2004) 547–556

emission, which otherwise (without simulation modeling) was unaccounted for. Another widely used model capable of simulating erosion-educed C dynamics is Erosion Productivity Impact Calculator (EPIC) (Sharpely and Williams, 1990). The principal components of the model are: nutrient cycle, plant growth, management practices, weather simulation, hydrology, pesticide and nutrient cycle, soil temperature and moisture, and erosion and deposition processes (Table 1). It is a continuous simulation model with daily time step. Water erosion process is modeled by several equations depending on conditions. The equations used are USLE (Wischmeier and Smith, 1978), described earlier, and its two modifications: MUSLE (Williams, 1975) and USLE modified by Onstad and Foster (1975). These modified equations include runoff variables into the formulation of erosive energy, thus increasing the accuracy of erosion prediction. The EPIC employs a stochastic generation of weather variables such as precipitation, air temperature, solar radiation, humidity, and wind speed, which enables it to model rare large events given that simulation time is substantial. Stochastic approach is an advantage over Century model, where only year or month average soil loss is calculated. EPIC simulates SOM turnover in 10 soil horizons, including the top one of 1 cm depth, which is important for erosion being a surface process. Lee et al. (1996) used the EPIC model to determine the sensitivity of soil C to variation in temperature and precipitation in US Corn Belt region. It was shown that during a 100-year simulation total C stock in soil decreased by 27%, of which half was transported off-site by erosion. Erosion rate was estimated at 8 t ha
1 year
1. Somewhat smaller decrease, 10 –11%, of total C during a 100-year simulation was attributed to erosion in a study on Illinois cropland sites with conventional tillage and erosion rate of 10 t ha
1 year
1 (Phillips et al., 1993). Emission of CO2 from displaced sediment could not be explicitly calculated by EPIC, which explains the differences in estimates comparing to Century simulations discussed earlier and suggests that erosion-induced losses could be even higher if increased decomposition due to disturbance of sediment was assumed. A model describing SOM dynamics on Black Chernosemic soil in Canadian prairie was proposed by Voroney et al. (1981). Erosion component of the model was based on RUSLE (Renard et al., 1997). The model was capable of assessing microbial decay and growth, efficiency of C utilization for biosynthesis. Soil loss due to erosion did not include loss from snowmelt. Although soil erosion on the cultivated site was only 1.7 t ha
1 year
1, the rate of SOM depletion was significant. After 70 years of cultivation, SOM decreased by 36%. This correlated well with actual measurement on native prairie. While without erosion loss SOM concentration attained equilibrium after this initial 70 years, with soil erosion component the model indicated continuous decline of SOM. After 200 years of

553

cultivation with erosion, the model predicted a loss of 60% of the original organic C in crop-fallow rotation. While continuously cropped and crop-fallow rotations were not much different in terms of SOM loss when erosion factor was not included, an addition of soil loss into the model greatly increased the rate of C loss on crop-fallow rotation relative to continuously cropped plot. A model by Bouwman (1989) was developed and tested on two tropical soils. The model employs the same basic principles as Century or EPIC models. It differentiates between four SOM pools: easily decomposable or active material, resistant material, microbial biomass, and inert organic matter. Soil profile is divided into three layers: 0– 15, 15 –40, and 40 – 80 cm and the transfer of soil between layers is allowed. Governing transformation equation is the first-order kinetics with correction factors for soil moisture, temperature and soil texture. Erosion component of the model uses USLE model allowing for SOM enrichment (arbitrary defined by the user). A 100-year simulation showed that erosion, in general, caused the initial C losses to increase compared with non-eroded sites during initial years of cultivation. The soil texture effects on SOM decomposition was less pronounced where erosion was severe. On average within 100 years 25% of SOM loss was attributed to erosion on 5% slope, and as much as much as 50% of SOM loss on 10% slope. Rehabilitation Ecosystem Model (REM) was proposed by West and Wali (2002) for use on surface-mined lands. REM is based largely on Century model with one SOM pool, a single soil layer, and a monthly time step. Erosion component is represented by RUSLE. A characteristic feature of the model is its ability to account for the fate of displaced C by modeling CO2 evolution from displaced sediment. A decomposition factor (Parton et al., 1993) is applied to displaced sediment by interacting with nutrient cycle submodel. Using this approach, it was estimated that for barren lands in Ohio C efflux from displaced SOM could reach 10% of the total C efflux from the study area. We used previously published data (Hao et al., 2002) for North Appalachian Experimental Watershed to simulate the effect of erosion on SOM using Century 5 model. Different erosion scenarios were tested on Coshocton silt loam soil (Watershed 109), which has been under corn (Zea mays L.) – soybean (Glycina max L.) rotation with chisel tillage beginning from 1984. The watershed was chiseled in April to the depth of 25 cm at 30 cm spacing. Corn was planted in April at 76 cm spacing, herbicides applied and interrow cultivation conducted early in the season. Soybean was drilled with row spacing of 18 cm. The crop was harvested in October. Management history of the study area is described in greater details elsewhere (Owens et al., 2002). The model was initialized with soil (texture, bulk density, nutrient regime, hydrological characteristics), climate (location, monthly precipitation and temperature) and management (crop, tillage, growth season, cultivation, harvest and residue practice) data. Soil profile for the simulation was

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divided on three layers with depth of 0– 10, 10– 30, and 30 –45 cm. Three management scenarios were simulated for the study area: meadow, corn –soybean rotation with actually measured erosion (0.828 t ha
1 year
1), and corn – soybean rotation with increased erosion (10 t ha
1 year
1). Measured enrichment ratio of SOM in the sediment was 1.7 (Owens et al., 2002). The model well represented soil organic carbon concentration in 45 cm layer for meadow (5190 g m
2) after 15 years and slightly underestimated it for corn –soybean rotation (3850 g m
2) after 35 years (Fig. 3). Relatively small erosion rate observed in the field was related to the residue management used when more than 80% of the soil surface was protected (Hao et al., 2001). If erosion rate is increased to 10 t ha
1 year
1 (a value often considered a threshold for maintaining productivity (USDA, 1997), the model estimated additional 19% (after 90 years) loss of soil organic carbon comparing to the current practice. We studied the response of the model to soil loss at various levels of soil organic carbon enrichment in runoff sediment for the same crop and management practice (Fig. 4). The equilibrium value of soil organic carbon was reached within 75 – 90 years of cultivation. The model showed a linear relationship between soil loss and soil organic carbon remaining in the soil at enrichment ratio of one, which agreed well with previous studies (Monreal et al., 1997). Exponential decline character of the curve at higher enrichment ratios suggests that surface soil layer in this scenario becomes depleted with soil organic carbon and its further removal has progressively lesser affect on SOM stock in the whole soil profile. Relative depletion of the surface layer on severely eroded sites was reported in the literature (Woods and Schuman, 1988). These simulations emphasized that moderate and severe soil erosion in its contribution to the depletion of SOM is comparable to cultivation-induced SOM loss and biomass removal through harvesting. The limitation of the model is that it represents only processes occurring at a point scale

Fig. 3. Century model output of the effect of various erosion intensities and cropping schemes (CS, corn – soybean) on the level of soil organic carbon in Coshocton – Rayne silt loam.

Fig. 4. Effect of erosion intensity and enrichment ratio (Er) on simulated remaining soil organic carbon in the soil after 90 years of cultivation (equilibrium level).

without regard of the fate of the eroded C. This makes it difficult to interpret simulation output in a larger CO2 budget context.

4. Conclusion The SOM is preferentially transported by flow due to the differences in bulk density. In most studies in natural conditions, enrichment of SOM in sediment distributed within watershed is reported to be higher than unity. Aggregate structure is one of the soil properties most significantly affected by erosion. Soil detachment and sediment transport alter aggregate structure, which to a great extent controls decomposition of organic substances by microorganisms. Because field measurement of erosion impact on SOM remains a difficult task, modeling of this process is a viable option. Our review showed that from over 30 SOM models developed and in use today only few include erosion component into their routine. These erosion components are based on empirical equations such as USLE and unable to represent erosion-induced SOM loss and deposition as spatially distributed phenomena. Modeling exercise presented in this paper as well as in previous research by various authors showed the ability of SOM models to adequately simulate only on-site erosioninduced C dynamics. Using Century model, we demonstrated that SOM content is sensitive only to moderate and severe erosion, while at low erosion the soil –plant system is able to replenish lost SOM. It was found that the magnitude of erosion-induced SOM depletion (at moderate or severe stages of erosion) is comparable with that caused by cultivation and biomass removal through harvesting. These simulation results are in compliance with field measurements reported widely in the literature. It is important to put erosion-induced SOM loss into context of the greenhouse gas emission. Despite successes

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in modeling, the fate of the displaced C remains largely unresolved. Further development of erosion component in SOM models is hampered by the lack of reliable data, required for validation, and great variety of environmental conditions in which transport of sediment occurs. A comprehensive model of C dynamics in soil is needed and would require description of SOM transport in runoff and SOM decomposition in the displaced sediment.

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