Data-driven Method for the Delimitation of Viticultural Zones: Application in the Mendoza River Oasis, Argentina

In viticulture, understanding the spatial variability of natural factors influencing vineyard potential is essential for terroir characterization. In the present study, we present a data-driven protocol that integrates climate, geomorphology, and soil data to delineate viticultural zones. The method combines spatial layers with statistical tools to partition a region into areas with similar characteristics. The protocol comprises: 1) rescaling multiple spatial data layers, 2) applying spatial multivariate analysis to group spatial units, and 3) using machine-learning algorithms to identify key zoning drivers. The approach was applied to the Mendoza River oasis in Argentina. Climate and geomorphology layers were used first, as they varied at a broader spatial scale than soil data. Two climatic zones were identified, mainly differentiated by elevation and thermal indices. Subsequent soil-based zoning within each climatic zone revealed five distinct edaphoclimatic zones. These zones showed statistically significant differences in environmental variables and exhibited spatial coherence aligned with landscape features. Results showed that this protocol facilitates the integration of diverse data sources and supports a deeper understanding of the uniqueness of vineyard zones in wine-producing regions. Highlights: • A data-driven protocol integrates climate, soil, and geomorphology for vineyard zoning. • Spatial principal components and clustering ensure geographically coherent zones. • Nested zoning distinguishes broad climatic patterns and finer edaphic variability. • Random Forest identifies key environmental drivers defining each viticultural zone. • The method supports terroir analysis, vineyard management, and regional planning.