Analysis of the Spatio-Temporal Evolution and Influencing Factors of Crops at County Level: A Case Study of Rapeseed in Sichuan, China

Exploring the spatio-temporal evolution patterns of rapeseed production at the county level in Sichuan Province, China, and analyzing the influence of natural conditions and socioeconomic development based on regional spatial characteristics, can help guide the rational distribution of crop production and provide a reference for the high-quality and sustainable development of the local rapeseed industry. Based on panel data from 2001 to 2023, this study employs GIS spatial analysis to examine the spatio-temporal evolution of rapeseed production in Sichuan and applies a Geodetector model to identify factors influencing its spatial and temporal variations. The results reveal that rapeseed production in Sichuan is concentrated in three main production areas: the northeastern Sichuan region, the middle Sichuan hilly region, and the Chengdu Plain. The dynamic evolution exhibits a composite pattern characterized by the stability and expansion of core areas, alongside breakthroughs and growth in peripheral regions, with increased production observed across 134 counties. The spatial center of rapeseed production shows short-range fluctuations and distinct regional anchoring, oscillating among Santai County, Shehong City, and Daying County, tracing a “Z”-shaped trajectory. Over the 23-year period, the global Moran’s I index ranged from 0.464 to 0.558, indicating a significant spatial clustering trend in rapeseed output among adjacent counties. Local spatial autocorrelation patterns were predominantly H-H, L-L, and L-H clusters. Factor detection identifies labor force availability, fertilizer application intensity, and effective irrigated area as the most influential factors. Interaction detection results consistently exhibit a two-factor enhancement effect. To enhance the rapeseed industry’s performance and efficiency, it is recommended to stabilize production capacity in the three core production areas, leverage central regions to strengthen radiation to the surrounding counties, optimize resource allocation based on clustering patterns, and focus on improving key factors such as labor and irrigation, as well as their synergistic effects.