Analysis of Driving Factors of Cropland Productivity in Northeast China Using OPGD-SHAP Framework
Year: 2025
Type: article
Abstract: In the context of climate change and ecological degradation, enhancing cropland productivity in Northeast China is essential for ensuring national food security. This study adopted an integrated framework combining the optimal parameter-based geographical detector (OPGD) and SHapley Additive exPlanations (SHAP) to identify key drivers of average and total cropland productivity at the county level from 2001 to 2020. Growing-season-based cropland Net Primary Productivity (NPP) was estimated using the CASA model to represent cropland productivity. Results indicated that natural and ecological factors significantly dominated the spatial variation of cropland productivity, with their interactions amplified through dual-factor or nonlinear enhancements. Various machine learning models were fine-tuned and compared, and optimal models were selected for subsequent SHAP analysis. The findings revealed that erosion intensity exhibited the most significant impact on cropland productivity, whereas the effect of precipitation shifted from negative to positive, with a clear threshold of around 400 mm—matching the boundary between China’s semi-arid and semi-humid regions. Low-elevation plains (<300 m) and gentle slopes (<0.5°) predominately promoted total cropland productivity. Interactions between erosion and fertilizer intensity highlighted the need for moderate fertilization to prevent ecological degradation in severely eroded counties. These findings provide scientific support for targeted cropland management aimed at achieving sustainable agriculture in Northeast China.
Source: Land
DOI: 10.3390/land14051010
Authors: Runzhao Gao, Hongyan Cai, Xinliang Xu
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G3325834653
OpenAlex ID: W4410191914