Articles | Volume 19, issue 2
Biogeosciences, 19, 375–387, 2022
https://doi.org/10.5194/bg-19-375-2022
Biogeosciences, 19, 375–387, 2022
https://doi.org/10.5194/bg-19-375-2022

Research article 24 Jan 2022

Research article | 24 Jan 2022

A robust initialization method for accurate soil organic carbon simulations

Eva Kanari et al.

Data sets

Modeling soil organic carbon evolution in long-term arable experiments with AMG model H. Clivot, J. C. Mouny, A. Duparque, J. L. Dinh, P. Denoroy, S. Houot, F. Vertès, R. Trochard, A. Bouthier, S. Sagot, and B. Mary https://doi.org/10.1016/j.envsoft.2019.04.004

The simple AMG model accurately simulates organic carbon storage in soils after repeated application of exogenous organic matter F. Levavasseur, B. Mary, B. T. Christensen, A. Duparque, F. Ferchaud, T. Kätterer, H. Lagrange, D. Montenach, C. Resseguier, and S. Houot https://doi.org/10.1007/s10705-020-10065-x

Model code and software

lauric-cecillon/PARTYsoc: Second version of the PARTYsoc statistical model Lauric Cécillon https://doi.org/10.5281/zenodo.4446138

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Short summary
Soil organic carbon (SOC) is crucial for climate regulation, soil quality, and food security. Predicting its evolution over the next decades is key for appropriate land management policies. However, SOC projections lack accuracy. Here we show for the first time that PARTYSOC, an approach combining thermal analysis and machine learning optimizes the accuracy of SOC model simulations at independent sites. This method can be applied at large scales, improving SOC projections on a continental scale.
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