Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China

Balancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial data from 77 county-level units and employing spatial autocorrelation analysis, multiple linear regression, and mixed geographically weighted regression (MGWR), this study reveals the spatial heterogeneity and key driving factors of non-grainization. The results indicate strong spatial dependence, with persistent high–high clusters in economically developed southern/coastal areas and low–low clusters in northern regions. Furthermore, the driving mechanism shifted significantly over the two decades. Early constraints from natural endowments (e.g., elevation’s positive impact significantly weakened post 2010) and individual economics diminished with technological progress, while macroeconomic development became dominant, influencing both scale and structure. Infrastructure improvements (reflected by rural electricity use) consistently limited non-grainization; some factors showed phased effects, and annual mean precipitation emerged as a significant influence in 2020. MGWR revealed substantial, dynamic spatial heterogeneity in these drivers’ impacts across different periods. These findings highlight the importance of geoinformation tools in managing regional disparities. Integrating spatial and socio-economic analysis offers practical insights for policymakers to develop targeted strategies that balance food security with agricultural diversification.