<![CDATA[Achieving national sugar self-sufficiency requires a nuanced understanding of regional production dynamics. This study employs a Geographically Weighted Regression (GWR) approach to analyze the spatial heterogeneity of sugarcane production in East Java Province, a critical region contributing approximately 40% of Indonesia's national output. Utilizing secondary data from 2019 to 2022 on production yield, harvested area, and key climatic variables, our GWR model reveals that harvested area is the most consistent and significant determinant of production across all regencies. In contrast, climatic factors such as rainfall and temperature exhibit localized, spatially varying effects. The model demonstrates high explanatory power, with a local R² value of up to 0.90, indicating it captures 90% of the spatial variation in production. These findings underscore the limitation of global regression models and affirm the superiority of the GWR method in providing location-specific insights. Consequently, this analysis offers a robust, spatially explicit foundation for policymakers to design targeted interventions aimed at optimizing regional productivity. Future research should integrate socioeconomic variables to further elucidate the linkage between localized production efficiency and the broader goal of national food security.]]>
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