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.
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.
the Creative Commons Attribution 4.0 License.
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
CORRESPONDING AUTHOR
Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia
Nicolas P. A. Saby
INRAE, Unité de Recherche Info&Sols, Orléans, France
Manuel P. Martin
INRAE, Unité de Recherche Info&Sols, Orléans, France
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Soil organic carbon (SOC) is has an heterogeneous nature and varies in chemistry, stabilization mechanisms and persistence in soil. In this study we mapped the stocks of SOC fractions with different characteristics and turnover rate (presumably PyOC >= MAOC > POC) across Australia, combining spectroscopy and digital soil mapping. The SOC stocks (0–30 cm) were estimated as 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC.
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Soil organic carbon (SOC) is has an heterogeneous nature and varies in chemistry, stabilization mechanisms and persistence in soil. In this study we mapped the stocks of SOC fractions with different characteristics and turnover rate (presumably PyOC >= MAOC > POC) across Australia, combining spectroscopy and digital soil mapping. The SOC stocks (0–30 cm) were estimated as 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC.
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Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.
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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.
We introduce Shapley values for machine learning model interpretation and reveal the local and...
