Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning

Abstract

• Integrating spatial–temporal continuous crop residue coverage (CRC) enhances SOC mapping accuracy. • SHAP analysis identified a CRC threshold (>0.3) was linked to increased SOC, with temperature and clay also as key factors. • An interpretable machine learning framework is developed for SOC mapping in the black soil of Northeast China. Accurate regional mapping of soil organic carbon (SOC) in croplands is essential for assessing soil carbon sequestration potential. However, accurate SOC mapping of cropland at a regional scale is challenging due to numerous natural and anthropogenic management factors. The impact of covered crop residue remains undervalued when mapping surface SOC, despite the significant impact of crop residue coverage (CRC) on SOC. In particular, the agricultural management practice of returning crop residues to the soil significantly alters the spatio temporal patterns of SOC in northeast China. Given these issues, we used the Shapley Additive exPlanations (SHAP) approach to interpret the influence of natural and anthropogenic factors on SOC estimation using the random forest model. Our results show the high SHAP values of air temperature, CRC, and clay content due to their significant influence on SOC estimation. Interestingly, our analysis showed a significant increase in SHAP values when the CRC reached 0.30, which refers to the CRC threshold of conservation tillage. Furthermore, our results revealed that integrating crop residue coverage significantly improved the accuracy of SOC mapping as the Lin Concordance Correlation Coefficient (LCCC) increased from 0.75 to 0.83 and the root mean squared error (RMSE) decreased from 6.70 g kg −1 to 5.60 g kg −1 . This study provides actionable insights for optimizing CRC management practices for SOC sequestration in Northeast China.

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