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Nonlinear Threshold Effects of Agricultural Inputs on Crop Production in China: Insights from XGBoost-SHAP and Spatiotemporal Analysis
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DOI：10.3390/agriculture16131472.png)
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Abstract
En 中文
Understanding the spatiotemporal relationship between agricultural inputs and crop production is essential for sustainable agricultural management. Using provincial panel data from China from 2000 to 2022, this study integrates spatiotemporal analysis with the XGBoost-SHAP model to examine the nonlinear effects of agricultural machinery, fertilizers, pesticides, and plastic films on soybean, cereal, and tuber yields. The results show that China’s agricultural input system shifted around 2015 from input-intensive growth toward green transformation, with fertilizer, pesticide, and plastic-film use declining after this inflection point. Spatially, agricultural inputs and crop production show clear agglomeration and path dependence: machinery is concentrated in northern China, fertilizers and pesticides in eastern intensive farming regions, and plastic-film use in arid and cold regions, while soybean, cereal, and tuber production are mainly concentrated in Northeast China, the Northeast-Huang-Huai-Hai region, and Southwest China, respectively. The SHAP results reveal distinct crop-specific importance rankings and nonlinear threshold patterns. For soybean yield prediction, agricultural plastic film use contributes most strongly to the model output, followed by fertilizer application, pesticide use, and machinery power; its SHAP contribution turns negative beyond approximately 112.4 thousand tons. For cereal yield prediction, machinery power ranks first, followed by fertilizer application, pesticide use, and plastic-film use; its contribution becomes positive beyond approximately 28.34 million kW and then gradually levels off. For tuber yield prediction, fertilizer application is the dominant predictor, followed by pesticide use, machinery power, and plastic-film use; its contribution turns negative beyond approximately 1.35 million tons. These findings indicate that agricultural inputs have crop-specific nonlinear effects, and that input regulation should prioritize the most influential factors for each crop while considering their threshold ranges. The study provides a scientific basis for differentiated, crop-specific, and regionally adaptive agricultural input management.
Keywords:
agricultural inputs
crop yields
spatial heterogeneity
sustainable agriculture
XGBoost-SHAP model
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