Agronomical assessment of a vine « terroir » map: first results in the « AOC » Minervois region

White wines contain macromolecules such as proteins, phenolic compounds and polysaccharides. On a sensory
level, these compounds contribute to the ‘mouthfeel’ that differentiates the white wines worldwide [1].

Global climate change is exerting a notable influence on viticulture sector and grape composition. The increase in temperature and the changes in rainfall pattern are causing a gap between phenolic and technological grape maturities [1]. As a result, the composition of grapes at harvest time and, consequently, that of wines are being affected, especially with regards to phenolic composition. Hence, wine quality is decreasing due to changes in the organoleptic properties, such as color and astringency, making necessary to implement new adaptive technologies in wineries to modulate these properties in order to improve wine quality.

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65

In an experiment located at Quinta da Cavadinha, Sabrosa, Douro Region the behaviour of the varieties Touriga Nacional (TN), Tinta Barroca (TB), Touriga Franca