SSSAJ: Volume 74: Number 6 • November–December 2010

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Soil Sci. Soc. Am. J. 74:1847–1860Published online 30 Sept. 2010doi:10.2136/sssaj2009.0412Received 3 Nov. 2009.*Corresponding author (valerie.viaud@rennes.inra.fr).© Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USAAll rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Toward Landscape-Scale Modeling of Soil Organic Matter Dynamics in Agroecosystems

Review & Analysis

The relevance of a landscape-scale approach to addressing environmental con-cerns in agricultural areas is being increasingly recognized. Many issues relat-

ed to climate change, air quality, water quality, and soil conservation involve energy and matter fl uxes across space and time, and therefore require impact assessment and management options at integrated spatial scales. From that perspective, plan-ning activities and public policies (environmental assessment and management practices) are increasingly targeting integrated spatial scales rather than individual fi elds or farms, to account for interactions between sources and sinks through ei-ther natural or anthropogenic (mainly farm-scale) processes. For instance, the wa-tershed approach is widely used for implementing water-quality monitoring and planning programs in Europe (Gerrits and Edelenbos, 2004; Merot et al., 2008) as well as in North America (Maxted et al., 2009). Th is scale is also receiving increas-ing attention from soil scientists, especially for estimation of soil C stocks (Garten and Ashwood, 2002; Kravchenko et al., 2006).

Soil organic matter is key to soil functioning in (agro)ecosystems, both infl u-encing and being infl uenced by environmental conditions and fl uxes of matter and energy. First, SOM is a key component of soil quality because it directly aff ects soil chemical, physical, and biological properties and plays a crucial role in sustaining soil fertility (Tiessen et al., 1994) and environmental quality (Lal, 2009). Changes

Valérie Viaud*INRAUMR 1069 SASF-35000 Rennes, France

Denis A. AngersSoils and Crops Research and Development CentreAgriculture and Agri-Food CanadaQuebec City, Quebec, G1V 2J3 Canada

Christian WalterAgrocampus OuestUMR 1069 SASF-35000 Rennes, France

Because of its role in soil functioning, our ability to predict soil organic matter (SOM) dynamics, as infl uenced by natural and anthropogenic processes, is essential to mitigating soil degradation, ensuring food security, and improving the global environment. Numerous mathematical models have been developed to predict the response of SOM to agricultural practices at the soil-profi le or small-plot scales. Th e same models, coupled with spatial databases, have been applied to larger spatial extents, especially in response to the demand for national inventories of soil C sequestration potential. Modeling SOM dynamics must also be developed at an intermediate integrative level to better investigate the relative importance of transfer and transformation processes in SOM dynamics in agricultural landscapes. Predictive models at the landscape scale will facilitate the assessment of the impact of SOM dynamics on the environment and provide management guidelines at the farm and watershed levels. We review the existing approaches and outline the various needs toward an integrated modeling of SOM at the landscape scale. Landscape-scale modeling involves specifi c land area representation and model requirements, which include: modeling SOM dynamics in the uncultivated elements of a landscape; simulating SOM distribution and diff eren-tial dynamics along the soil profi le; modeling SOM vertical and lateral fl uxes linked to erosion, dissolved organic matter fl uxes, and litter transfer; and modeling the spatial distribution of organic matter input and management practices. Even though progress is being made toward all of these aspects, a fully integrated framework for SOM modeling at the landscape level has still to be developed. Th is will only be possible with the design of a fl exible, three-dimensional, spatially explicit representation of the landscape system and with the integration of functional interactions and organic matter transfer functions into the classical SOM modeling frameworks.

Abbreviations: DOC, dissolved organic carbon; DOM, dissolved organic matter; OM, organic matter; SOM, soil organic matter.

Published November, 2010

1848 SSSAJ: Volume 74: Number 6 • November–December 2010

in SOM content, composition, or dynamics can greatly modify nutrient availability (Agboola and Corey, 1973), soil aggrega-tion and structural stability (Tisdall and Oades, 1982), erodibil-ity (Le Bissonnais and Arrouays, 1997), porosity (Emerson and McGarry, 2003), water-holding capacity (Haynes and Naidu, 1998), and biological activity (Fonte et al., 2009). In addition to having a direct impact on the soil itself, SOM has implications in pollutant transfers to air and water bodies. It has a strong impact on the local and global C cycles (Smith et al., 1997a): even small changes in the soil organic C pool may change the global C cycle ( Johnston et al., 2004) and the increase in CO2 in the atmo-sphere during the past century has generated increased interest in the potential of agricultural soils to sequester C (Freibauer et al., 2004; Lal, 2004). In interactions with natural (redox condi-tions, water availability, etc.) or anthropogenic processes, SOM dynamics and stocks partly control the emission and discharge of dissolved nutrients, such as nitrates, and dissolved organic C (DOC). Conversely, SOM dynamics are strongly aff ected by natural and anthropogenic processes occurring at the landscape scale, such as soil redistribution in the lateral and vertical dimen-sions by tillage and erosion processes (Lal, 2005).

A number of mathematical models have been developed since the 1970s to predict the response of SOM stocks in the sur-face soil layer to agricultural management practices at the small plot scale (Powlson et al., 1996). A more fully integrated and more spatially explicit prediction of SOM dynamics and fl uxes across landscapes is required, however, to help manage soil prop-erties related to SOM stocks and mitigate pollutant fl uxes to air and water. A spatial perspective should make it possible to both identify landscape areas where selected management practices would have the potential to increase SOM, and predict future SOM dynamics related to changes in the nature of land uses and land management practices or in their spatiotemporal allocation on the landscape.

In this context, our objective was to review existing ap-proaches to the modeling of SOM dynamics at the landscape scale in agricultural ecosystems. We have focused on SOM stock changes in the medium term (from a decade to one or two centu-ries). We fi rst introduce the landscape concept and then consider the existing spatial modeling approaches for SOM. Finally, we outline and discuss the various needs and knowledge gaps that have to be taken into account in developing an integrated model-ing of SOM dynamics at the landscape scale.

DEFINITION AND RELEVANCE OF THE LANDSCAPE SCALE

As stated by Jenny (1941), soil is the result of the cumula-tive action of energy and matter fl uxes on parental material, and these fl uxes are themselves controlled by complex interactions between physical, chemical, and biological drivers. More specifi -cally, SOM dynamics depend on processes generally related to pedogenesis (Stewart and Cole, 1989). Soil organic matter can be greatly modifi ed (degraded or aggraded) by soil redistribu-tion, plant primary productivity, mineralization processes, land-

scape position, and land management practices (Gregorich et al., 1998; West and Marland, 2003). Management practices can infl uence the fate of SOM through various factors and processes: the quantity and quality of organic matter inputs into the soil, mineralization rates (by infl uencing organic matter quality or incorporation in the soil), and SOM losses through erosion or leaching processes (Magdoff and Weil, 2004).

Soil processes can be studied at a range of levels of integra-tion or levels of organization in a hierarchical perspective of agri-cultural systems (Rowe, 1961; Anderson et al., 1983). Studies on SOM dynamics and modeling have conventionally concentrated on the soil-profi le or small-plot scales. Th e profi le or plot scale corresponds to low levels of integration and are generally char-acterized by a single combination of soil, climate, and cropping system. Studies at this scale have made it possible to quantify the impact of various agricultural practices on SOM dynamics un-der a range of soil and climatic conditions (Haynes and Naidu, 1998; VandenBygaart et al., 2003; Ogle et al., 2005). Th ese ap-proaches, however, where agricultural systems are viewed as a set of independent fi elds, are not suffi cient to account for the entire complexity of soils. We address a more integrative organization level, namely the landscape scale.

As defi ned by landscape ecologists, landscape is the appro-priate spatial integration level for dealing with the mismatch between the scales of the biophysical processes and those of anthropogenic origin (Turner, 1989; Burel and Baudry, 2003). Landscape is an intermediate integration level between the fi eld and the physiographic region. Its exact extent, however, depends on the ecological process under study and on the spatial range of the biophysical and anthropogenic processes driving it (Forman, 1995). In Western Europe, considering the spatial scale for the biophysical and pedological processes controlling SOM dynam-ics (typically 0.01–1 km) and given the average size of farms (0.1–1 km2), the extent of the landscape can be considered to range from 1 to 100 km2 and would correspond to small wa-tersheds, for example. In areas like the North American Great Plains, where the landform and soils are generally more uniform than those in Western Europe, and where the farms are much larger, the spatial extent of the landscape can be much larger as well (100–1000 km2).

Working at the landscape level involves a specifi c descrip-tion of the portion of land that is being studied as, in essence, a complex heterogeneous system. Th e landscape concept de-signed by ecologists has been adapted to soil science in the form of landscape pedology, which considers soils from two view-points (Pennock and Veldkamp, 2006; Sommer, 2006): (i) the spatiotemporal variability of relatively static soil properties; and (ii) the spatiotemporal variability of dynamic processes, such as water and matter transport, with interactions and feedbacks. We here consider that the landscape system not only includes the soil by itself but also both anthropogenic (land use or land manage-ment practices) and natural factors that can potentially control the dynamics of SOM and may vary at diff erent temporal and spatial scales. Conceptually, the landscape may be represented by

SSSAJ: Volume 74: Number 6 • November–December 2010 1849

the combination of several layers, corresponding to lower level subsystems (Fig. 1), as follows:

· Parent material properties, topography, and broad soil types. Th ese control the long-term changes in SOM stocks and can explain their general spatial distribution.

· More-specifi c soil properties, such as drainage or texture. Th ese properties can vary in the short spatial range and explain short- and medium-distance variations in SOM dynamics.

· Land cover and land use. Th e features of interest are not only agricultural fi elds but also uncultivated landscape elements (e.g., buff er zones and set-asides), which may represent a signifi cant proportion of the landscape. While land use and land cover in the fi elds vary at the inter-annual time scale, most of the other landscape features vary more slowly according to changes in farm organization or ownership or other major land use changes (e.g., road and pipeline construction).

· Th e production unit or the farm. Decisions on management practices are made at the level of the farm (or group of farms), and as such, the fi elds are

not independent entities. In some farming systems, there might be a signifi cant spatial and temporal specialization or concentration of land use or land management practices, with potential direct consequences on SOM spatiotemporal patterns. In dairy production in Western Europe, for instance, grazed grasslands are preferentially located next to the farm headquarters, while the other crops are at a greater distance (Th enail, 2002).

CURRENT APPROACHES TO THE SPATIAL MODELING OF SOIL ORGANIC MATTER DYNAMICSScales of Soil Organic Matter Modeling

For modeling purposes, the spatial scale is defi ned by (i) the modeling extent, corresponding to the area of interest for the sim-ulation (fi eld, watershed, region, etc.), and (ii) the model resolu-tion or support unit, corresponding to the modeling elementary unit (plot, fi eld, pixel, etc.). At the local scale, the modeling ex-tent and the support unit are, in most cases, identical. Scaling up SOM models involves moving from a small support unit (a plot) to a larger one, and this aggregation is accompanied by a change in spatial extent, from the fi eld to a larger extent (region or nation).

Fig. 1. The components of an agricultural landscape system, with specifi c reference to soil organic matter (SOM) dynamics.

1850 SSSAJ: Volume 74: Number 6 • November–December 2010

Spatial approaches to SOM dynamics modeling can be of interest at a range of spatial extents, from farm to global scales, via the land-scape level, depending on the objectives of the modeling exercise. Th e demand from land managers and policymakers for forecasts of climate change and its consequences, however, has stimulated re-search at spatial extents larger than the landscape, namely regional (Sleutel et al., 2006), national (Howard et al., 1995; Arrouays et al., 2001), continental (Freibauer et al., 2004; Jones et al., 2005b), and global (Batjes, 1996; Jones et al., 2005a).

Modeling SOM dynamics at a larger levels than the plot in-volves representing the initial status of SOM, determining model input data for the whole spatial extent of the area under study, and evaluating SOM change through time in response to chang-es in land use or land management.

Two Modeling ApproachesTwo approaches are used to model SOM dynamics across

large spatial extents: the fi rst one consists in estimating and ap-plying empirical SOM change factors corresponding to specifi c land use and land management practices; the second strategy in-volves applying process-based SOM models across large extents.

Soil Organic Matter Change FactorsTh e annual changes in SOM stocks (the SOM change fac-

tor) are estimated from empirical regressions established on the basis of long-term fi eld experiments or derived from the litera-ture ( Janssens et al., 2005; Lettens et al., 2005; Meersmans et al., 2008). Regression precision levels vary greatly between studies.

Smith et al. (2000) estimated the increase in SOM stocks for the top 30 cm at the scale of the United Kingdom for a single arable agricultural land class and under several scenarios of land management change (e.g., an increase in organic amendment, ex-tensifi cation, or conversion to woodland). Th ey used statistical relationships established from long-term experiments from all of Europe (Smith et al., 1997a). Spatialization was based on maps of arable agricultural land at the national scale (resolution of 1 by 1 km), without accounting for local soil and climate variability.

Kern and Johnson (1993) used empirical regressions to pre-dict SOM change for the top 30 cm at the scale of the United States under several scenarios of conversion to conservation till-age. Regressions were applied to spatial databases allowing the inclusion of local variability in soil characteristics and climate. Similarly, the Intergovernmental Panel on Climate Change (2004) developed a tool based on empirical regressions, derived by compiling international literature references, to predict SOM stock change in the top 30 cm of cropland or grazing land.

Th is approach is mainly dedicated to spatial extents rang-ing from the regional to the national level. Provided that suitable spatial databases are available, SOM changes can be computed according to regional climate, soil type (World Reference Base or USDA classifi cation), land use, land management system (tillage for crops), and fertilizer input level (Easter et al., 2007).

Process-Based ModelsModels of SOM cycling, including C and N dynamics,

have been published since the 1940s (Hénin and Dupuis, 1945). Such models off er the possibility of simulating the processes in the soil and evaluating the probable eff ects of changes in agricul-tural practices on SOM dynamics and stocks. Th ese models have been compared and evaluated in many agricultural, pedologic, and climatic contexts (Powlson et al., 1996; Smith et al., 1997b; Falloon and Smith, 2002) and have been largely successful in simulating SOM dynamics under varying conditions at the plot or small-fi eld level.

For spatial modeling, input and output organic matter (OM) fl uxes are explicitly simulated by applying these models without changing their structure: the model formalism and the hierarchy between the processes remain the same, with no addi-tional processes taken into account. Model parameters and input data are estimated within homogeneous elementary units, and the model is run for each elementary unit. Th e most common-ly used models for that purpose are CENTURY (Falloon and Smith, 2002; Al-Adamat et al., 2007; Negra et al., 2008), RothC (Falloon et al., 2006), and DNDC (Liu et al., 2006; Sleutel et al., 2006). Th e models are fi rst calibrated using a limited number of plot-level situations with existing data on SOM changes and are then applied to make projections of SOM change throughout the simulation period. Other simplifi ed process-based models, designed for global or continental levels (thousands of square kilometers) were also developed by the global change scientifi c community: SOM dynamics modules are based on a single pool and a single fi rst-order decay rate dependent on soil temperature and soil hydrologic properties (Andrén and Kätterer, 1997; Cox et al., 2001; Jones et al., 2005a).

Th e SOM change factor approach has low data require-ments, is simple to apply, and is convenient for predicting the im-pact of major changes in land use and land management systems. A constant rate of SOM change during a given period is assumed but is not fully realistic, however, given that many studies have shown the nonlinearity of the process (rapid initial SOM change aft er land use change, followed by slower rates) (Falloon et al., 2002). Th e advantage of process-based modeling approaches is that the models can integrate numerous processes and factors controlling SOM dynamics.

Both empirical and process-based approaches are based and validated on a set of fi eld experiments considered to be equally valid and representative of a larger heterogeneous system, with no additional processes included.

Determining Input Data and ParametersOne of the critical issues in the spatial modeling of SOM

is the acquisition and formalization of model input data. Both the empirical and the process-based SOM modeling approaches described above require input data about current or initial SOM status and land use and land management change scenarios. Process-based models require additional soil parameters, climate data, and land management information.

SSSAJ: Volume 74: Number 6 • November–December 2010 1851

At the larger spatial extent, the input data cannot realistical-ly be determined for each fi eld in the area under study. Th erefore, the standard approach to large-scale studies is to divide the simulation spatial extent into irregular polygons, corresponding to homogeneous soil landscape units, for which a unique set of driving variables is derived (soil properties, SOM initial state, land use, land management, land use change, and land manage-ment change). Th e Soil and Terrain Digital Database (SOTER) methodology is generally applied to compile soil and terrain at-tribute data and delimit homogeneous polygon units with regard to model inputs (van Engelen and Wen, 1995). Soil organic mat-ter models are linked to spatial soil, climate, land use, and land management databases specifying soil, climate, land use, and land management characteristics for each elementary spatial unit. Th e SOM modeling system developed by the Global Environment Facility Soil Organic Carbon (GEFSOC) Project provides user interfaces to facilitate data exchange between spatial databases and SOM models (Easter et al., 2007; Milne et al., 2007). An al-ternative approach is to divide the area into regular cells to better capture soil and land use or management heterogeneity (Ardo and Olsson, 2003).

Once spatial subdivisions have been defi ned, data required for the models have to be estimated for each support unit in the simulation area. Because these data are available at a given spatial support (soil profi le, fi eld, etc.) and for specifi c locations, scaling involves extrapolating data to the whole support unit, aggregat-ing data to integrate information available on smaller support units, or both. Models, even process-based ones, are thereaft er run independently in each support unit. No SOM transfers be-tween support units are considered.

Soil Data: Initial Soil Organic Matter and Other Soil Properties

A common procedure to obtain SOM data is to extrapo-late the available information from representative soil profi les by soil type, using existing soil maps (1:250,000 or 1:500,000 soil maps in most cases) or soil and land use maps (Table 1). A single combination of values for soil parameters required as SOM model inputs (initial SOM content, texture, soil density, and soil depth) is assigned to each soil (Falloon et al., 2006) or soil and land use class (Bhattacharyya et al., 2007). Th ese values result either from a single representative profi le or from average values calculated from several soil profi les (Falloon et al., 2002). Sleutel et al. (2006) used a generalized soil map giving the domi-nant soil texture class for each municipality. Based on this map, they calculated weighted averages for clay content, silt content, and soil density for each textural class. If data for one of the soil parameters required by the model are missing, soil or landscape units are excluded from the data set (Falloon et al., 2006) or the missing soil properties are estimated using pedotransfer func-tions (Sleutel et al., 2006).

Several comments can be made on the soil data upscaling procedure. First, soils are oft en considered to a depth of 20 to 30 cm, assuming that most changes will occur in this soil layer.

Second, when soil data are available for several soil profi les per soil or landscape class, the variability of the soil parameter values is almost never considered, and its impact on model prediction is not addressed. Th e homogeneity of soils in the support unit is, at a fi rst approximation, an acceptable assumption for spatial integration at this scale. Because process-based models include nonlinear relationships between some soil properties and SOM dynamics, aggregation may introduce bias in the prediction of SOM change. For instance, the eff ect of averaging soil texture at a range of support unit sizes was shown by Burke et al. (1990).

Land Use and Land Management PracticesCurrently, the spatial distribution of crops at large scales

is derived mainly from remote sensing data (Table 1). In the European context, land use inputs are most often derived from the CORINE Land Cover database (scale 1:100,000, minimum mapping unit 25 ha). With this database, research-ers often use the second level of CORINE nomenclature to define land use classes and consider a limited number of land use classes, corresponding to arable cropland, grassland, for-ests, and other land use (Falloon and Smith, 2002; Lettens et al., 2005; Schaldach and Alcamo, 2006). This classifica-tion makes it possible to test the impact on SOM dynamics of gross land use changes, such as conversion of cropland to grassland or afforestation. Inputs can be derived from agri-cultural census data providing the average area of each crop or crop rotation or of the dominant crop or crop rotation per support unit (Sleutel et al., 2006). A crop management prac-tice also can be derived from expert opinion, statistical data-bases, or farmer interviews. A uniform management of each land use type is generally considered for the entire area.

CHALLENGES IN MODELING SOIL ORGANIC MATTER DYNAMICS AT THE LANDSCAPE SCALE

So far, the spatial modeling of SOM stocks has mostly been done at spatial extents larger than the landscape level and is most oft en used for regional or national inventory purposes, thus ac-counting for variations in land use, land management, and soil type at this scale. In most cases, the methodology used is quite standard, allows comparison of broad situations, and lays the foundation for public policies regarding C sequestration at the scale of administrative entities (region or nation) and continent. From a methodological point of view, advances made in coupling geographic information system databases and SOM process models, in estimating the soil initial state and in combining soil and land use information, are directly transferable to landscape-scale modeling.

Landscape models essentially need highly accurate and spatially explicit information, however, to simulate SOM dy-namics and variations in SOM stocks (Fig. 2): (i) the relevant features of the landscape, i.e., patterns of soil-landscape hetero-geneity, need to be described in detail, (ii) SOM transfers with-in the soil in both the lateral and vertical dimensions should be

1852 SSSAJ: Volume 74: Number 6 • November–December 2010

added to the modeling framework, (iii) patterns of OM input into the soil should be modeled ac-curately, and (iv) relevant procedures for model calibration, validation, and uncertainty assess-ment should be developed.

Considering Relevant Features of the Landscape Soil-landscape Heterogeneity in the Lateral Dimension: Accounting for Nonagricultural Landscape Elements

One of the challenges in the landscape-level modeling of SOM dynamics is the inclusion not only of agricultural production areas as such (ar-able land or grassland), as is already done in the plot- and large-scale approaches, but also of other landscape features, oft en permanently vegetated and interspersed among the agricultural fi elds. Such features include set-aside plots, woodlots, hedgerows, riparian wetlands, depression wet-lands, and grass strips.

Accounting for nonagricultural landscape ele-ments is relevant for two main reasons. Th e fi rst is that these features are becoming more common in agricultural landscapes because, in many jurisdic-tions, environmental regulations for natural re-sources and wildlife protection oft en promote the introduction or preservation of natural or perma-nent vegetation in agricultural landscapes. In the United States and Canada, for example, the man-agement of grass strips and riparian buff er zones for water-quality protection has been enhanced for the past 30 yr by guidelines and regulations es-tablished by diff erent local and state or provincial regulatory agencies (NRCS, 1994; Goupil, 1998; National Research Council, 2002). Similarly, in Europe, the Common Agricultural Policy provides subsidies for the establishment of permanently vegetated structures such as set-asides, hedge-rows, or grass strips along river banks as part of its agroenvironmental policies for the protection of water quality and biodiversity. Another important example is agroforestry systems, which represent a substantial proportion of the world agricultural land and is a mixture in space of cultivated fi elds and wooded landscape elements (Nair et al., 2009).

Th e second reason is that many of these land-scape features may behave diff erently in terms of SOM dynamics. Locally high SOM stocks can originate from two main processes: (i) higher pri-mary plant biomass production and consequently higher OM input to the soil compared with ar-able land because of the presence of permanent and sometimes wooded vegetation (e.g., hedge-

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SSSAJ: Volume 74: Number 6 • November–December 2010 1853

rows or set-asides); and (ii) low mineralization rates linked, for instance, to anaerobic conditions (e.g., wetlands and the bottoms of slopes). As a consequence, regardless of their gener-ally small spatial extent, the nonagricultural landscape features might have strong impacts on both SOM fl uxes and the SOM budget at the landscape scale. For example, Walter et al. (2003) estimated the fraction of SOC stock attributed to hedgerows to be between 13 and 38% of the total stock in hedgerow landscapes of Western Europe. Similarly, in a hummocky land-scape of the Canadian Prairies, Bedard-Haughn et al. (2006) estimated that uncultivated wetlands account for about 20% of the SOC stored to a depth of 45 cm yet occupy 11% of the landscape. Some landscape elements can release OM, however: riparian wetlands, for instance, can episodically play the role of a C source when pulses of DOC leach to stream water and leave the soil-landscape system (Bishop et al., 1994).

Incorporating nonagricultural landscape elements into an integrated SOM modeling framework raises several methodolog-

ical issues. Th e fi rst is the appropriate numerical representation of the landscape to account for nonagricultural landscape features in SOM models. Th e choice of landscape subdivision is a critical point (Lindenmayer et al., 2007). A fl exible representation of the landscape consistent with the variable processes occurring in the various landscape elements must be designed. While a discon-tinuous representation of space as an assemblage of polygons is commonly used for subregional approaches, a grid-based contin-uous representation of space can be more suitable for modeling soil variation at the landscape level. Two options therefore ex-ist: using a single resolution for the whole landscape extent, one that is detailed enough to represent hot-spot processes, or using a landscape representation supporting multiple resolutions, such as irregular pixels or voxels (Vivoni et al., 2005).

Another important issue is the ability of the existing classi-cal SOM models to simulate specifi c SOM dynamics observed in nonagricultural features, which can range from wetlands to wooded areas. Th is issue may be especially signifi cant for small

Fig. 2. Hypothetical representation of soil profi les at different locations of the landscape under varying land use and management practices and associated soil organic matter (SOM) and dissolved organic C (DOC) fl uxes. The relative size of arrows indicates the intensity of the processes and fl uxes.

1854 SSSAJ: Volume 74: Number 6 • November–December 2010

wetlands or in the neighborhood of hedgerows, where SOM decomposition is hampered by soil waterlogging: most of the classical process-based models integrate a moisture correction factor that takes into consideration a reduction in decomposi-tion on soil drying but do not simulate a possible reduction in decomposition due to excess water (or reduced aeration) (Shibu et al., 2006). Th e DNDC model is one of the few able to simu-late SOM dynamics in poorly drained soils (Zhang et al., 2002). Again, the issue of the modularity and fl exibility of the modeling approach at the landscape level arises.

A fi rst approach to addressing this problem is to use a single model that is wide ranging enough to represent the entire vari-ety of soil processes under diff erent landscape conditions. Th e challenge is then to generalize existing classical SOM models to extend their domain of applicability. An alternative approach is to develop a single modular modeling framework composed of several specifi c SOM modules simulating SOM dynamics in the various landscape features. Each module can be a stand-alone SOM model based on existing models: forest models for SOM dynamics simulation in wooded elements (e.g., ROMUL [Chertov et al., 2001] or Yasso [Liski et al., 2005]), specifi c wetland models (e.g., Zhang et al., 2002; van Huissteden et al., 2006), classical SOM models for well-drained arable land, etc. Th is latter approach is increasingly used in ecosystem modeling (Voinov et al., 2004). Th e challenge, however, is to make the individual modules communicate by accounting for the fl uxes between the spatial units (e.g., litterfall in the neighborhood of a hedge) and to ensure the consistency of the simulation re-sults at the edge of the diff erent simulation units in order to produce a truly integrated evaluation of SOM dynamics at the landscape scale.

Heterogeneity of the Soil Profi le: Explicitly Accounting for Soil Organic Matter Vertical Distribution within the Soil Profi le

In a number of contexts, signifi cant amounts of C can be stored below the top 20 to 30 cm (Stallard, 1998; Quine and Zhang, 2002; Chan et al., 2009). Soil profi les in agricultural landscapes may have A horizons deeper than 30 cm, where OM was incorporated into the soil as a result of deeper tillage such as plowing to a greater depth, the burial of former surface layers by depositional processes (Fig. 2e), deep rooting in wooded areas (Fig. 2a), long-term and massive addition of organic fertilizers (Blume and Leinweber, 2004), or the mixing process caused by soil fauna. Deeper mineral soil layers may also have signifi cant SOM content owing to the macropore transport of particulate SOM or the percolation of dissolved OM (DOM). Th e entire depth of soil containing OM has to be taken into account.

One approach in modeling SOM with depth consists in esti-mating SOM stocks or concentration down the soil profi le based on the stocks or concentration in the surface layer (Hilinski, 2001; Jenkinson and Coleman, 2008). Th e location of SOM in the soil profi le aff ects its dynamics, a fact that supports the neces-sity of explicitly considering several soil layers and the distribu-

tion of SOM within them. In deep soil layers, SOM turnover is diff erent than in topsoil layers, especially because of diff erent soil environmental conditions, potentially lower SOM content, and diff erences in fresh OM inputs (Lorenz and Lal, 2005; Fontaine et al., 2007).

Basically, the above-mentioned considerations suggest that the soil profi le should be divided into several functional soil lay-ers and that the defi nition of the soil layer limits must be allowed to vary laterally (Fig. 2). In the cultivated elements of the land-scape, we suggest that SOM dynamics in the top few centimeters of the soil (0–2.5 or even 5 cm) be simulated apart from the rest of the soil profi le because the top layer plays a critical role in the estimation of total SOM dynamics. Under no-till or grassland systems, there are steep gradients of SOM concentration near the soil surface (Angers and Eriksen-Hamel, 2008; Don et al., 2009). Additionally, in any location in the landscape, some soil func-tions related to SOM, e.g., soil erodibility, aggregate stability, or soil infi ltration capacity, are controlled mainly by soil properties very near the soil surface (Le Bissonnais and Arrouays, 1997). In uncultivated areas, specifi c attention should also be paid to sur-face organic horizons, when they exist, and the delimitation of the fi rst layer may be fi tted to the depth of O horizons (Fig. 2a). In conventionally tilled soils, a second layer limit would be the actual soil tillage depth or residue burial depth. Burial of residues results in a concentration of OM at the bottom of the plow layer, with substantial consequences for SOM stocks at the soil-profi le scale (Angers and Eriksen-Hamel, 2008). A third limit is related to the deepest former tillage depth, if one exists. A further limit is the A horizon depth, where little fresh OM is added and where SOM dynamics are specifi c. Th e additional layers can be delin-eated on the basis of the pedological horizons.

Hence, similarly to the lateral dimension, the representa-tion of soil depth in an integrated modeling approach for soil and landscape has to either be fl exible or be systematic and fi ne enough to capture the key features of soil profi le heterogeneity.

Modeling Lateral and Vertical Soil Organic Matter Fluxes in the Soil

Landscape-scale approaches require dealing with the trans-fer of SOM (in solid or soluble form), which may have substan-tial eff ects on SOM dynamics.

Soil organic matter transfers include the following: SOM redistribution within the soil profi le by tillage practices or biopedoturbation processes; SOM transport associated with erosion and deposition; and DOM fl uxes from the topsoil into the deeper soil horizons or directly to stream water with surface runoff . Soil organic matter transfers lead to the redistribution of SOM within the soil-landscape system or to the fi nal export of SOM beyond the system boundaries. As shown in Fig. 2, the type and magnitude of SOM fl uxes can vary greatly between one location and another in the landscape, depending mainly on land use and land management practices (tillage and crop rotation), landforms (deposition vs. erosion areas), and connectivity be-tween landscape elements.

SSSAJ: Volume 74: Number 6 • November–December 2010 1855

Soil Organic Matter Redistribution across the Landscape Associated with Erosion and Deposition Processes

Erosion and deposition processes caused by wind, water, or tillage have signifi cant impacts on soil C cycling because they af-fect the SOM net budget, the SOM spatial distribution across the landscape, and SOM dynamics. In terms of the overall eff ects of erosion on SOM stocks, recent studies have been contradicto-ry as to whether soil erosion does or does not constitute a C sink (Lal and Pimentel, 2008; Van Oost et al., 2008). Deep burial of autochthonous or allochthonous OM may enhance SOM stabi-lization, whereas physical or chemical breakdown particles dur-ing detachment and transport may enhance SOM mineraliza-tion (Van Oost et al., 2007). Most SOM models do not explicitly specify OM fl uxes due to erosion (Polyakov and Lal, 2004). In the CENTURY model, erosion is modeled with the Universal Soil Loss Equation (USLE) (Parton et al., 1994). Th e EPIC model simulates soil losses with the modifi ed USLE (MUSLE) equation (Williams, 1995). Both models focus only on the ero-sion part of the process (i.e., soil loss from point locations) and do not account for transport and deposition processes.

Th e spatial modeling of SOM fl uxes and the temporal mod-eling of SOM dynamics progressively converge as geomorphic and SOM models are combined. For example, Yoo et al. (2006) developed a mass balance model that is dedicated to long-term simulations and explicitly includes soil formation from bedrock and sediment transport. Van Oost et al. (2006) combined the spatially distributed geomorphological soil erosion–deposition model SPEROS with the ICBM model to examine the interac-tion between soil redistribution and C fl uxes to the atmosphere in agricultural landscapes. Th ose approaches are promising ways to better account for the feedback of erosion and deposition processes on SOM dynamics. Further developments are still re-quired, however. Erosion processes and SOM dynamics involve a range of time scales, from the rainfall event scale to the long-term time scale (several decades) where geomorphologic processes oc-cur. Th e consistency between the processes modeled at the dif-ferent potential scales therefore must be considered. Moreover, specifi c attention has to be paid to the infl uence of landscape structures such as banks, grass strips, and hedgerows, whose spa-tial arrangement has a strong impact on erosion–deposition pat-terns across the landscape (Follain et al., 2007).

Dissolved Organic Matter FluxesTh e signifi cance of DOM in SOM dynamics in natural

or semi-natural ecosystems is now widely recognized (Tipping et al., 2007). Despite being generally present at low concentra-tions, soluble substrates are utilized much more readily by mi-croorganisms. Th e movement of DOM through the soil profi le contributes to SOM redistribution in landscapes, DOM fl uxes can contribute a substantial amount of C to deep mineral soil layers, and DOM is a vector of C losses out of the landscape sys-tem. Dissolved organic matter leaching may also depend on land use and land management practices, but this relationship is still

poorly understood and is likely to vary greatly under various soil and climatic conditions.

Th ree principal approaches to DOM modeling can be dis-tinguished. Th e fi rst one is centered on biogeochemistry and fo-cuses on DOM production processes from SOM at the soil-pro-fi le scale. Both the CENTURY (Parton et al., 1994) and DAISY (Gjettermann et al., 2008) models present such a feature. Th e second approach is primarily hydrologic and focuses on DOM transport at the watershed scale. Th e topology of DOC fl uxes are represented explicitly but only some of the biogeochemical processes controlling DOM production and cycling are included (Boyer et al., 2000). Th e third one provides a better coupling be-tween both biogeochemical processes and DOC transfers. Th e soil DOC model of Neff and Asner (2001) considers a multilay-ered soil and uses the CENTURY model to simulate decomposi-tion, with DOC being generated by assigning solubility charac-teristics to diff erent C pools, and with a soil hydrology model simulating DOC transport. Th e DyDOC model presents similar characteristics but diff ers slightly in terms of C pool defi nition (Michalzik et al., 2003).

For the modeling of DOM in a landscape framework, there is a strong need to unify the existing approaches to be able to simulate the critical factors controlling DOM production and transport at this scale. Moreover, further research is required to better capture and model DOM dynamics in cropland and their interaction with management practices (Chantigny, 2003).

Soil Organic Matter Vertical Relocation within the Soil Profi le by Tillage Operations: Incorporation of Fresh Organic Matter

In conventionally tilled soils, tillage operations largely de-termine the depth of incorporation of fresh organic matter, in-cluding crop residues, roots, and organic amendments. Explicitly modeling the impact of tillage on organic matter location ap-pears essential to an adequate modeling of SOM under various tillage and management systems. A surprisingly small number of models, however, explicitly consider the variable residue par-titioning and varying decomposition rates with depth. Th e ver-tical transfer of SOM by leaching, the action of soil fauna, or both was implemented empirically in a modifi ed version of the CENTURY model, but burial of crop residues or native SOM by tillage was not considered (De Gryze et al., 2007). By con-trast, the CQESTR model was specifi cally developed to account for OM location in the soil profi le and the impact of tillage on fresh OM burial (Rickman et al., 2001; Liang et al., 2009). Four compartments are considered: surface, buried, and root residues and SOM. Organic matter is transferred from the aboveground to the buried-residue or SOM compartments in each tillage operation; however, the dynamics of SOM itself are modeled in a simple way with a single decomposition rate.

More work is therefore required toward a more explicit modeling of mixing processes and vertical movements of SOM between soil layers. We also need better knowledge of SOM stocks and characteristics in subsurface soil organic and mineral

1856 SSSAJ: Volume 74: Number 6 • November–December 2010

layers, an improved understanding of deep SOM dynamics and their controlling factors, and better knowledge of SOM inputs at diff erent depths (roots, etc.).

Landscape Variability of Organic Matter Inputs into the SoilInput Related to Agricultural Activity

Large amounts of organic materials from various sources are handled as part of farming activities: organic amendment, crop harvesting, and animal feeding involve large fl uxes of C. Th ese OM transfers are organized in space and time depending on the farming system.

Th e application of exogenous sources of OM or the input of crop residues most oft en results in an increase in the C content of soils. Th is was demonstrated in a number of fi eld-scale studies comparing, for example, manured and unmanured treatments. Nevertheless, the transfer of plant biomass associated with crop residue management, with the transfer of wood residues from wooded areas to cropland, or with manure production has more complex implications for the C balance at the landscape scale. For instance, Schlesinger (2000) argued that greater SOM levels in manured fi elds are associated with lower inputs of plant resi-dues on a proportionally larger area of land from which the fod-der and bedding were obtained. In this case, the use of farmyard manure may not increase net atmospheric C sequestration at the farm or larger spatial integration levels. Th erefore, the transfer of OM, in the form of crop residues, animal waste, or wood-derived residues, from the landscape has to be explicitly taken into ac-count because it can greatly modify the balance of C at a more fully integrated scale. Th is issue is especially signifi cant when dif-ferent cropping systems (e.g., livestock farms and crop farms) co-exist within a single area. Biomass fl uxes and fertility transfer can occur between two fi elds of a single farm but also between two fi elds belonging to diff erent farms. In intensive livestock pro-duction regions such as Brittany, France (Lopez-Ridaura et al., 2009), or Réunion Island (Guerrin and Paillat, 2003), strategies based on the collective management of animal waste are being developed in response to soil fertility loss or the risk of pollution resulting from overuse or misuse of manures.

Th erefore, in complex situations like rural landscapes, the capacity to model the variability of anthropogenic OM inputs and their spatiotemporal distribution over the area under con-sideration needs to be improved. Advances have been made and are still in progress toward spatially explicit modeling of land use and farm practices, from very detailed, process-based mod-els that consider anthropogenic drivers and the functioning of individual farms (Guerrin, 2001; Berntsen et al., 2003; Bakker and van Doorn, 2009, Chardon et al., 2009) to less detailed, sta-tistically based models (Gaucherel et al., 2006; Castellazzi et al., 2008). Such approaches need to be combined with SOM models to fully evaluate the impact of farm management strategies and biomass fl uxes between management units on SOM stocks at the landscape scale.

Fresh Organic Matter Fluxes between Landscape Elements

Another example of OM fl uxes is the direct transfer from one landscape element to another, which can occur, for instance, between a permanently vegetated area and an adjacent crop fi eld. In agroforestry systems, for example, direct and non-negligible transfers of organic residues occur between a hedgerow and ad-jacent fi elds across a distance of up to >10 m through litterfall and the lateral growth of roots, thus quantitatively and qualita-tively modifying OM input to the soil of the adjacent crop fi eld (Th evathasan and Gordon, 2004; Oelbermann and Voroney, 2007; Gupta et al., 2009). To our knowledge, these OM transfers are not currently being taken into consideration in modeling.

Model Calibration, Validation, andUncertainty Assessment

Th e fi rst issue of concern in the application of a model at a spatial extent larger than the plot is obtaining appropriate data against which to calibrate and test the model (Bouma et al., 1998). Currently, plot-level models are usually sensitive to and well calibrated for variations in OM inputs. Consequently, modeling and validating the impact of anthropogenic trans-fers of OM between units should be feasible, as should cali-brating vertical translocation within the soil profi le, assuming that evaluations made at the soil-profi le level remain valid at larger spatial extents. Th e more diffi cult issue, very specifi c to the landscape level, is how to validate other functional relation-ships, such as lateral translocation of SOM, for which fewer data are available and further measurement approaches prob-ably have to be developed.

Landscape modeling needs detailed local data sets, includ-ing spatial data on farming activities, land use, land cover, en-vironmental variables, and soil information. Th e averaged data currently used for regional approaches are oft en not suffi cient to represent the local spatial and temporal variability of SOM dynamics. Part of the required information (land use and topog-raphy) can be derived from high-resolution remote sensing data. Another essential requirement is that all elements or activities be assigned to a spatial location, i.e., that a map location be recorded and a spatial database, which provides input data for the model, be developed. Specifi c sampling strategies have to be designed to cover the spatial variability of SOM stocks and the other envi-ronmental variables in the area of interest. Recent methodologi-cal advances in soil spatial statistics and optimal sampling theory can be useful for this purpose (e.g., Minasny et al., 2007). Th e last point is that the validation of dynamics models requires several data sets in time. In the case of spatial models, a critical issue is that sampling locations may diff er with time, especially when ex-isting soil survey data are used.

One last major problem is the quantifi cation of uncertain-ties, particularly from the perspective of using the model for a pre-dictive or prospective purpose. Complex spatial models involve numerous input data and parameters and are subjected to many sources of uncertainty, including errors in measurement, inad-

SSSAJ: Volume 74: Number 6 • November–December 2010 1857

equate sampling resolution, positional uncertainty, and uncer-tainty in model specifi cations (Heuvelink et al., 2007). Th e error may be very diffi cult to quantify in practice, especially for some environmental variables or some data related to anthropogenic activities. But although uncertainty analysis remains challenging for any complex model, promising methodological frameworks and soft ware programs are currently being developed with the goal of accounting for spatial variables more eff ectively (Crosetto and Tarantola, 2001; Brown and Heuvelink, 2007; Lesschen et al., 2007) or dealing with the computational costs of the complex models (Marrel et al., 2008).

CONCLUSIONSChanges in soil properties with time result from complex

interactions between natural and anthropogenic processes. Integrated landscape modeling, as considered here, should provide a relevant tool for improving our understanding of the relative importance of transfer and transformation processes in SOM dynamics in agricultural landscapes, as well as of the nu-merous and complex interactions among landscape elements.

Although lessons can be learned from the large-spatial-extent (regional to global) modeling of SOM dynamics and from progress made in integrated modeling in other areas of science such as hydrology or ecology, the modeling of SOM dy-namics at the landscape level remains challenging. Some of the required methodological knowledge and data exist, but they re-main scattered and need to be connected in a relevant way. As a result of our review process, three main gaps with respect to the landscape modeling of SOM dynamics stand out. Th e fi rst one is the need to develop an optimal, fl exible, three-dimensional, spatially explicit representation of the landscape system both vertically and laterally, one that is precise enough to represent landscape heterogeneity but at the same time simple enough to be feasible in practice. Th e latter point is less critical now with the recent advances in computational power and capacity. Th e second major research need is the integration of functional interactions and SOM transfers into the classical SOM model-ing frameworks. Progress has been made recently in some of those relationships (e.g., erosion, DOM, and depth eff ects), but there remains a need to develop a framework integrat-ing all these aspects. Th is point is not trivial, considering the functional complexity and diversity of soils and the numerous natural and anthropogenic factors controlling SOM dynamics. Th e third critical limitation is the availability of adequate data sets to implement landscape SOM models. Part of the required data, corresponding to data already used for classical modeling, does exist, but landscape-scale modeling involves further fi eld measurements for satisfactory calibration and validation of the relationships among landscape elements.

Th e landscape modeling of SOM dynamics is complemen-tary to local- and large-extent modeling. Landscape-scale model-ing can be used as a decision-support system to test the impact of local spatial scenarios of landscape management on SOM dy-namics and thus help planners and policymakers achieve sustain-

ability and environmental-quality goals. Moreover, landscape SOM models can be useful for improving regional or national predictions of SOM stock changes by estimating the statistical distribution of SOM fl uxes and stocks within each modeling unit and by accounting for the impact of sensitive ecosystems. A current and much-debated example of this is the role or signifi -cance of erosion processes at the landscape scale with respect to the global C cycle (Kuhn et al., 2009).

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