Spatiotemporal Dynamics and Predictors of Cropland Change in Hunan Province, China: An XGBoost-SHAP Approach

This study examines the spatiotemporal patterns of cropland change in Hunan Province and identifies the factors associated with net cropland decrease from 2000 to 2020. Using land-use transition analysis, spatial autocorrelation, Lorenz curves, Gini coefficients, and an interpretable XGBoost-SHAP model, this study analyzed cropland outflow, cropland inflow, net cropland change, and their associated explanatory patterns. The results show that: (1) cropland outflow was mainly concentrated in central and western Hunan, whereas cropland inflow was relatively more evident in central Hunan, but also occurred in parts of western and eastern Hunan. Total cropland area increased by 2961.73 km2 from 2000 to 2020, but decreased by 1467.91 km2 after peaking in 2009, indicating an inverted U-shaped trajectory; (2) the Gini coefficient of cropland outflow decreased from 0.4024 to 0.2891, while that of cropland inflow decreased from 0.3780 to 0.2538, indicating stronger spatial concentration of cropland outflow, although its spatial imbalance weakened over time; and (3) XGBoost-SHAP results showed that mechanical efficiency, gross domestic product (GDP), and fiscal conditions made the highest contributions to net cropland decrease, with mean absolute SHAP values of 0.21, 0.17, and 0.16, respectively. Overall, cropland change exhibited clear spatial heterogeneity, and socioeconomic and human-activity factors were dominant factors associated with net cropland decrease. These findings provide support for differentiated cropland protection and sustainable land-use management in major grain-producing regions.