江苏省县域农业生态效率的时空演化特征及非线性驱动机制OA

Enhancing agricultural eco-efficiency was a crucial pathway for accelerating the comprehensive green transi-tion of agricultural development and achieving high-quality agricultural growth.Based on the super-efficiency slack-based measure(SBM)model with undesirable outputs,this research quantitatively assessed the agricultural eco-efficiency at the county level in Jiangsu Province from 2006 to 2022.The study meticulously examined its spatio temporal evolution pat-terns using the Dagum Gini coefficient decomposition method,and employed the eXtreme gradient boosting-sHapley addi-tive exPlanations(XGBoost-SHAP)model to probe into the nonlinear mechanisms driving the efficiency.The results indi-cated that during the study period,agricultural eco-efficiency showed an overall upward trend both at the provincial level and across three major regions,with Southern Jiangsu exhibiting a more rapid improvement than Central and Northern Jiangsu.Spatially,the distribution evolved from being predominantly characterized by low-efficiency agglomeration to high-efficiency agglomeration.With 2010 as the dividing line,the main source of the overall difference shifted from inter-regional differences to hypervariable density.Key driving factors of agricultural eco-efficiency included per capita sown ar-ea,cultivated land fragmentation index,multiple cropping index,and the degree of crop production concentration.Specif-ically,the per capita sown area and the multiple cropping index exhibited U-shaped impacts on efficiency,with threshold values of 1.9 hm2·person-1 and 1.1,respectively,initially suppressing and later promoting efficiency.Conversely,the cultivated land fragmentation index demonstrated an inverted U-shaped relationship,with a threshold of 0.28.The degree of crop production concentration showed an N-shaped relationship with thresholds at 0.01 and 0.03,characterized by an initial increase,followed by a decrease,and then a rebound.This study provided a scientific basis for Jiangsu Province to formulate policies for agricultural green transformation and to optimize the allocation of agricultural production factors.