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High resolution 3D modeling of soil organic carbon in a complex agricultural landscape using continuous depth functions

Abstract : Soil organic carbon (SOC) is a key element of agroecosystems functioning and has a crucial impact on global carbon storage at world scale. SOC spatial variability and temporal dynamics are strongly affected by natural and anthropogenic processes occurring at the landscape scale, such as soil redistribution in the lateral and vertical dimensions by tillage and erosion processes. This study aims at modeling SOC distribution in A-horizons, at high spatial resolution, for an area of 1 000 ha in a complex agricultural landscape (NW France). The study site is characterized by high short distance heterogeneity due to an important diversity of soils (with varying redoximorphic conditions, depth), soil parent material (Aeolian loam cover, granite, hard and soft schist), topography, land use (annual crops, temporary or permanent grasslands) and hedge density. We used learning methods based on soil point data, characterized by soil description, SOC content and bulk density measurements. 200 points were selected using conditioned Latin hypercube sampling in order to cover the whole range of ancillary variables (elevation, Modified Compound Topographic Index, K emissions and land use). This sampling strategy enables to select a limited number of sampling sites coverering the study site heterogeneity. Additive sampling was designed to investigate SOC distribution near hedges (112 points sampled at fixed distances along 14 transects crossing hedges). Predictive environmental data consisted in the data used in the conditioned Latin hypercube sampling, at which were added topographic attributes derivated from the DEM and geological variables. We will discuss the ability of our model to capture and predict the SOC, considering the general SOC distribution trend at the landscape scale and the finer SOC distribution in landscape, at hedgerow proximity. The SOC 3-D map obtained will be used as soil data input in a soil evolution model, coupling SOC dynamics and soil erosion modeling.
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Contributor : Migration Prodinra <>
Submitted on : Wednesday, June 3, 2020 - 7:44:21 AM
Last modification on : Thursday, February 11, 2021 - 3:04:12 PM
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  • HAL Id : hal-02744647, version 1
  • PRODINRA : 274507


Marine Lacoste, Budiman Minasny, Alex B Mcbratney, Christian Walter, Valérie Viaud, et al.. High resolution 3D modeling of soil organic carbon in a complex agricultural landscape using continuous depth functions. Pedometrics 2011, Aug 2011, Trest, Czech Republic. ⟨hal-02744647⟩



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