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
| 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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Cited
25 citations as recorded by crossref.
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- Understanding the risks of peri-urbanization to food systems to help establish sustainable agriculture near cities X. Wang et al. 10.1016/j.eiar.2024.107777
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- A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling W. Cui et al. 10.1016/j.jag.2025.104542
- Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica R. Siqueira et al. 10.1016/j.catena.2023.107677
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- Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland J. Skála et al. 10.1016/j.jenvman.2025.125035
- Determination of soil organic carbon by conventional and spectral methods, including assessment of the use of biostimulants, N-fertilisers, and economic benefits J. Rukaitė et al. 10.1016/j.jafr.2024.101434
- Biplots for understanding machine learning predictions in digital soil mapping S. van der Westhuizen et al. 10.1016/j.ecoinf.2024.102892
- Artificial intelligence in soil science A. Wadoux 10.1111/ejss.70080
- Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning B. Hu et al. 10.1016/j.catena.2024.108635
- Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI) H. Li et al. 10.1016/j.scitotenv.2024.170330
- Assessment of the spatial and temporal heterogeneity of available phosphorus content in arable soil on the scale of one field (using the example of the Republic of Tatarstan, Russia) I. Sakhabiev et al. 10.1051/e3sconf/202562302014
- Exploring the monthly contribution of drivers on European summer wildfires with explainable artificial intelligence (XAI) H. Li et al. 10.1016/j.ecolind.2025.113605
- Impact of Updating Vegetation Information on Land Surface Model Performance M. Ruiz‐Vásquez et al. 10.1029/2023JD039076
- Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands A. Helfenstein et al. 10.1038/s43247-024-01293-y
- Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening H. Huang et al. 10.3390/su17073173
- Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia A. Chinilin & I. Savin 10.1016/j.ejrs.2023.07.007
- Drivers of Soil Organic Carbon Spatial Distribution in the Southern Ural Mountains: A Machine Learning Approach A. Suleymanov et al. 10.1134/S1064229324602014
25 citations as recorded by crossref.
- Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone S. Azamat et al. 10.1016/j.geodrs.2024.e00855
- Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning Y. Dong et al. 10.1016/j.geoderma.2025.117225
- Understanding the risks of peri-urbanization to food systems to help establish sustainable agriculture near cities X. Wang et al. 10.1016/j.eiar.2024.107777
- Biotic Predictors of Carbon Stock Variation in Light-Textured Forest Soils A. Kuznetsova et al. 10.1134/S1062359024613211
- Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach J. Rohmer et al. 10.5194/soil-10-679-2024
- Mapping of cropland humus content of the Bryansk region using machine learning methods L. Konoplina et al. 10.55959/MSU0137-0944-17-2024-79-4-130-140
- Soil organic carbon retrieval using a machine learning approach from satellite and environmental covariates in the Lower Brazos River Watershed, Texas, USA B. Tikuye & R. Ray 10.1016/j.acags.2025.100252
- Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al. 10.1017/eds.2024.2
- Unraveling drivers of maize (Zea mays L.) yield variability in Ghana: A machine learning approach A. Kouame et al. 10.1016/j.compag.2025.110647
- A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling W. Cui et al. 10.1016/j.jag.2025.104542
- Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica R. Siqueira et al. 10.1016/j.catena.2023.107677
- Mapping of Cropland Humus Content of Bryansk Oblast Using Machine-Learning Methods L. Konoplina et al. 10.3103/S0147687424700479
- Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland J. Skála et al. 10.1016/j.jenvman.2025.125035
- Determination of soil organic carbon by conventional and spectral methods, including assessment of the use of biostimulants, N-fertilisers, and economic benefits J. Rukaitė et al. 10.1016/j.jafr.2024.101434
- Biplots for understanding machine learning predictions in digital soil mapping S. van der Westhuizen et al. 10.1016/j.ecoinf.2024.102892
- Artificial intelligence in soil science A. Wadoux 10.1111/ejss.70080
- Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning B. Hu et al. 10.1016/j.catena.2024.108635
- Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI) H. Li et al. 10.1016/j.scitotenv.2024.170330
- Assessment of the spatial and temporal heterogeneity of available phosphorus content in arable soil on the scale of one field (using the example of the Republic of Tatarstan, Russia) I. Sakhabiev et al. 10.1051/e3sconf/202562302014
- Exploring the monthly contribution of drivers on European summer wildfires with explainable artificial intelligence (XAI) H. Li et al. 10.1016/j.ecolind.2025.113605
- Impact of Updating Vegetation Information on Land Surface Model Performance M. Ruiz‐Vásquez et al. 10.1029/2023JD039076
- Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands A. Helfenstein et al. 10.1038/s43247-024-01293-y
- Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening H. Huang et al. 10.3390/su17073173
- Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia A. Chinilin & I. Savin 10.1016/j.ejrs.2023.07.007
- Drivers of Soil Organic Carbon Spatial Distribution in the Southern Ural Mountains: A Machine Learning Approach A. Suleymanov et al. 10.1134/S1064229324602014
Latest update: 26 Jun 2025
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...
