Abstract
Grasslands are important terrestrial carbon sinks and play a critical role in the global carbon cycle and climate change mitigation. China has implemented a range of grassland restoration and management practices in recent decades. However, their relative importance, nonlinear relationships with net ecosystem productivity (NEP), and interactions with environmental factors remain insufficiently understood at the national scale. This study integrates long-term remote sensing, environmental, and grassland management practice datasets and applies the XGBoost-SHAP modeling to investigate the spatiotemporal patterns and drivers of grassland NEP across China. The results indicated that NEP increased across 73.44% of China’s grasslands, with the most pronounced increases occurring in the northeastern grasslands and the eastern Qinghai-Tibet Plateau. Grassland management variables accounted for part of the variation in NEP captured by the model, with grazing intensity (GI) identified as the most important management predictor. GI showed a significant nonlinear association with grassland NEP. Across different ecological regions, GI interacted most strongly with elevation, temperature, vapor pressure deficit, and slope, highlighting marked spatial heterogeneity in the effects of grassland management. These findings underscore the critical role of grassland management in regulating grassland NEP and provide a scientific basis for optimizing region-specific management strategies and safeguarding ecological security.