Reframing terroir as a predictive system: A multi-scale data-driven and machine learning approach to viticultural complexity and product identity

Abstract

Traditional terroir studies often rely on static environmental descriptors. However, the increasing complexity of viticultural systems under climate change demands a paradigm shift toward dynamic, predictive modeling. This study presents a high-resolution data-driven framework developed within the Global WineTerroir Analysis (GWA NEXT 4.0) System, leveraging Data Science and ML-based analytical architectures to process a global database of over 620,000 georeferenced vineyards.

By integrating multi-scale environmental variables with wine quality indicators and market data, we redefine terroir as a measurable and predictive system. Our methodology employs ML algorithms to identify non-linear relationships between “Nature” and “Human” factors, revealing previously unquantified “self-compensating” resilience mechanisms in terroir sub-factors. Analytical AI-driven simulations allow for long-term suitability forecasting and the identification of structural terroir analogies across disparate regions.

Furthermore, we introduce the “market footprint”—a multi-factorial model linking vineyard-scale characteristics to wine quality, price formation, critic scores, and the complex interplay of market positioning and perceived identity.  This approach transitions terroir from a descriptive narrative to an operational tool for strategic decision-making, providing a robust scientific foundation for managing resilient wine ecosystems – the integrated union of wine biosphere, technosphere, and market – even under fast-changing climatic and market conditions.