Shapley values reveal the drivers of soil organic carbon stock prediction

Wadoux, Alexandre M. J.-C.; Saby, Nicolas P. A.; Martin, Manuel P.

doi:https://doi.org/10.5194/soil-9-21-2023

Articles | Volume 9, issue 1

https://doi.org/10.5194/soil-9-21-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/soil-9-21-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 9, issue 1

Original research article

|

11 Jan 2023

Original research article |

| 11 Jan 2023

Shapley values reveal the drivers of soil organic carbon stock prediction

Alexandre M. J.-C. Wadoux, Nicolas P. A. Saby, and Manuel P. Martin

Supplement

https://doi.org/10.5194/soil-9-21-2023-supplement

Data sets

French RMQS soil profile and monitoring dataset with related management practices data N. P. A. Saby, J.-P. Chenu, T. Szergi, A. Csorba, P. Bertuzzi, B. Toutain, C. Picaud, L. Gay, and R. Creamer https://doi.org/10.15454/AIQ9WS

Short summary

We introduce Shapley values for machine learning model interpretation and reveal the local and global controlling factors of soil organic carbon (SOC) stocks. The method enables spatial analysis of the important variables. Vegetation and topography determine much of the SOC stock variation in mainland France. We conclude that SOC stock variation is complex and should be interpreted at multiple levels.