Spatiotemporal dynamics of agricultural resilience in China: a multidimensional assessment framework and analysis of systemic driving mechanisms

In the face of a changing environment, it is crucial to enhance the resilience of agricultural systems. This study explores this crucial issue by analyzing China’s agricultural resilience’s spatial and temporal evolution and its driving factors from 2012 to 2022. This study constructed an agricultural resilience evaluation index system based on three dimensions, including resistance to Resistance and Recovery Capacity, Adaptive Regulation Capacity Innovation and Transformation Capacity, and quantitatively analyzed it by using the entropy method, Dagum’s Gini coefficient, Kernel Density estimation, and GeoDetector model. The study found that (1) China’s agricultural resilience generally shows a fluctuating upward trend, and 2017 is an important turning point; (2) The spatial pattern is characterized by “high in the east, low in the west, and strong in the south and weak in the north,” but the regional disparity is gradually narrowing; (3) The contribution of agricultural resilience subsystems shows a nationally consistent “golden ratio” (49.7% for innovation and transformation, 35.5% for adaptive regulation, and 14.8% for resistance and recovery); (4) Per capita GDP and financial support policies are the most important factors in the agricultural resilience evaluation. GDP and fiscal policies to support agriculture are the most significant drivers, and the average annual temperature also has an important effect. (5) There is a nonlinear enhancement effect among most of the drivers, especially the interaction between economic development and other factors. This study reveals China's agricultural resilience's evolutionary pattern and formation mechanism, which provides a scientific basis for formulating precise and effective agricultural development strategies. This study improves the resilience of the agricultural system by synergistically regulating economic, policy, technological, and natural factors to cope with the challenges of future uncertainty in a localized manner.