Improvement Pathways for Irrigation Water Use Efficiency in Large and Medium-Sized Irrigation Districts Based on Analysis of Influencing Factors: A Machine Learning Case Study in Anhui, China

Irrigation water use efficiency (IWUE) is a core indicator for assessing agricultural water use efficiency. However, existing studies predominantly focus on linear relationships between IWUE and individual correlates, with insufficient attention to the nonlinear interactions among multiple factors and the staged pathways of IWUE improvement. Taking 153 large- and medium-sized irrigation districts in Anhui Province as a case study, this research identifies seven key influencing factors—including canal lining rate (CLR), proportion of water-saving irrigation area (WSIR), and water price (WP)—and employs a random forest model coupled with SHAP (SHapley Additive exPlanations) interpretability analysis to uncover the driving mechanisms and enhancement pathways of IWUE. The results reveal that CLR, WSIR, and WP are the top three correlates, collectively contributing 67.80% to IWUE variation, with CLR being the most influential (28.75%). Their effects exhibit strong nonlinearity and threshold behavior: the marginal benefit of CLR diminishes significantly beyond approximately 75%; the optimal incentive range for WP lies between 0.09 and 0.14 CNY/m3; and precipitation exerts a persistent negative constraint. Moreover, IWUE improvement follows a sequential hierarchy: CLR serves as the foundational prerequisite; once CLR reaches a certain threshold, advancing WSIR becomes essential; and further gains require synergistic interaction between WSIR and WP after both attain sufficient levels. This study elucidates the nonlinear response mechanisms and stage-dependent driving patterns of IWUE, offering scientific insights and quantitative support for targeted, precision-oriented upgrades of irrigation infrastructure in Anhui Province and analogous humid/semi-humid regions, thereby contributing to sustainable agricultural water management.