A robust initialization method for accurate soil organic carbon simulations

Kanari, Eva; Cécillon, Lauric; Baudin, François; Clivot, Hugues; Ferchaud, Fabien; Houot, Sabine; Levavasseur, Florent; Mary, Bruno; Soucémarianadin, Laure; Chenu, Claire; Barré, Pierre

doi:https://doi.org/10.5194/bg-19-375-2022

Articles | Volume 19, issue 2

https://doi.org/10.5194/bg-19-375-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-19-375-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 2

Research article

|

24 Jan 2022

Research article |

| 24 Jan 2022

A robust initialization method for accurate soil organic carbon simulations

Eva Kanari, Lauric Cécillon, François Baudin, Hugues Clivot, Fabien Ferchaud, Sabine Houot, Florent Levavasseur, Bruno Mary, Laure Soucémarianadin, Claire Chenu, and Pierre Barré

Supplement

https://doi.org/10.5194/bg-19-375-2022-supplement

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

Download

Article (909 KB)
Full-text XML

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 PARTY_SOC, 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.