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

Because of its role in soil functioning, our ability to predict soil organic matter (SOM) dynamics, as influenced 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 SUM to agricultural practices at the soil-profile or small-plot scales. The 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 SUM dynamics must also be developed at an intermediate integrative level to better investigate the relative importance of transfer and transformation processes in SUM dynamics in agricultural landscapes. Predictive models at the landscape scale will facilitate the assessment of the impact of SUM 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 SUM at the landscape scale. Landscape-scale modeling involves specific land area representation and model requirements, which include: modeling SOM dynamics in the uncultivated elements of a landscape; simulating SUM distribution and differential dynamics along the soil profile; modeling SUM vertical and lateral fluxes linked to erosion, dissolved organic matter fluxes, 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 SUM modeling at the landscape level has still to be developed. This will only be possible with the design of a flexible, 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