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DIVISION S-5—PEDOLOGY

Spatio-Temporal Simulation of the Field-Scale Evolution of Organic Carbon over the Landscape

^a ENSA-INRA Rennes, 65 rue de St Brieuc, CS 84215, 35042 Rennes, France
^b Faculty of Agriculture, Food and Natural Resources, The University of Sydney, NSW 2006, Australia

^* Corresponding author (cwalter{at}roazhon.inra.fr).

The spatial or temporal variability of soil has been extensively considered in the literature using either experimental or modeling approaches. However, only a few studies integrate both spatial and temporal dimensions. The aim of this paper is to present a method for field-scale simulations of the spatio-temporal evolution of topsoil organic C (OC) at the landscape scale over a few decades and under different management strategies. A virtual landscape with characteristics matching part of Brittany (France) was considered for the study. Stochastic simulations and regression analysis were used to simulate spatial fields with known spatial structures: short-range, medium-range, and long-range variability. These were then combined using an additive model of regionalization. Agricultural land use was simulated considering four different land uses: permanent pasture, temporary pasture, annual cereal crops, and maize (Zea mays L.). Land use evolution over time was simulated using transition matrices. Evolution of soil organic matter was estimated each year for each pixel through a rudimentary balance model that accounts for land use and the influence of soil waterlogging on mineralization rates. This spatio-temporal simulation approach at the landscape level allowed the simulation of several scales of soil variability including within-field variability. Spatial variability decreased drastically over time when only the influence of land use was considered. This effect on soil variability over the landscape may have implications for site-specific soil management and precision agriculture. The presence of redoximorphic conditions was found to maintain soil spatial variability.

Abbreviations: DEM, digital elevation model • HI, hydromorphic index • OC, organic C • RF, random function • SOC, soil organic C • VQT, variance quadtree algorithm

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