A physical geography approach to predict vineyard occurrence using statistical methods in the Taurasi DOCG Terroir (Avellino Province, southern Italy)

This study applies a geographical-physical and statistical methodology to predict vineyard distribution in the Taurasi DOCG terroir, southern Italy. Integrating morpho-topographical, climatic, and pedological data through GIS-based logistic regression, it aims to refine vineyard site selection—traditionally guided by local expertise—via scientifically validated predictive tools. The Taurasi territory, marked by pronounced lithological and topographic heterogeneity and a viticulture-favorable climate, serves as an ideal case study. The model was developed using environmental variables, optimized through stepwise selection and Variance Inflation Factor (VIF) analysis, and validated using the Receiver Operating Characteristic (ROC) curve. The resulting suitability map identifies areas most conducive to viticulture, emphasizing the importance of altitude, slope, aspect, and temperature in shaping vineyard potential. Despite sensitivity to environmental data quality, the approach demonstrates the value of integrating geospatial and statistical methods for informed spatial planning. The study reinforces the role of data-driven strategies in optimizing and sustainably managing viticultural landscapes.