Interpretable machine learning reveals spatial drivers and regional gradients of soil organic matter across Anhui Province, China

Abstract

Abstract Soil organic matter (SOM) is a crucial indicator of soil quality and carbon sequestration capacity. In regions with complex topographic and climatic gradients such as Anhui Province, the spatial patterns of SOM and its environmental controls remain insufficiently characterized. This study collected 151 soil samples in 2010 across Anhui Province and derived environmental covariates using GIS and RS technologies. Random Forest, Gradient Boosting, Extra Trees, and LightGBM models were constructed and compared. The Extra Trees model achieved the best performance, with a test-set R² of 0.79, RMSE of 4.55 g/kg, and MAE of 3.66 g/kg. In four-block spatial cross-validation, the R² decreased to 0.60, indicating moderate but limited spatial transferability. The predicted SOM pattern was higher in the southern mountainous region and lower in the northern plains. Nitrogen had the largest model contribution (56.8%). Mean annual precipitation was positively associated with SOM below 1400 mm, and higher predicted SOM occurred at soil pH values of 5.5-7.0. Topographic variables showed terrain-dependent associations, with positive effects in mountainous areas and weaker or negative effects in plains. The calibrated model was applied to environmental covariates representing 2010 and 2024 to produce a 2010 spatial prediction and an exploratory 2024 projection. The projection indicated a small increase in mean SOM from 22.34 to 23.90 g/kg. Because independent SOM observations were unavailable for 2024, the projection should be treated as a covariate-driven scenario rather than a validated estimate. Integrating SHAP with spatial analysis helped identify the modeled environmental controls on SOM variability and may inform soil management in topographically heterogeneous areas.