Quantitatively Predicting Soil Carbon Across Landscapes

Budiman Minasny (1) , Alex B Mcbratney (2) , Brendan P Malone (3) , Marine Lacoste (4) , Christian Walter (5)

1 Soil Security Laboratory, Faculty of Agriculture and Environment
2 Faculty of Agriculture, Food & Natural Resources
3 Soil Security Laboratory, Department of Environmental Sciences, Faculty of Agriculture and Environment
4 USS - Unité de recherche Science du Sol
5 SAS - Sol Agro et hydrosystème Spatialisation

Quantitative prediction of soil carbon (C) in the landscape can be achieved by empirical or mechanistic models, or a combination of both. The empirical approach called digital soil mapping, usually involves: collection of a database of soil carbon observations over an area of interest; compilation of relevant covariates for the area; calibration or training of a spatial prediction function based on the observed dataset; interpolation and/or- extrapolation of the prediction function over the whole area; and finally validation using existing or independent datasets. The resulting digital maps of C can be used in landscape mechanistic models simulating soil organic C evolution laterally and vertically (within the profile). Here we demonstrate the two approaches in predicting C stock evolution in a landscape in Northwest of France. We introduce the pedogeomorphometry approach which can combine the two approaches to map soil carbon dynamics at the landscape scale.

Mots clés: soil maps; clorpt model; spatial prediction; dynamic soil carbon model

Domaines: Sciences de la Terre

Budiman Minasny, Alex B Mcbratney, Brendan P Malone, Marine Lacoste, Christian Walter. Quantitatively Predicting Soil Carbon Across Landscapes. Soil Carbon, Editions Springer, 2014, Progress in Soil Science, 978-3-319-04084-4. ⟨10.1007/978-3-319-04084-4_5⟩. ⟨hal-01209213⟩