Valorization of wine lees for oenological interest by eco-responsible processes

Resilience and sustainability are two fundamental concepts in the sustainable development of the wine sector, being closely interconnected.

Le sol et le climat occupent une place prépondérante dans le concept de terroir, pour lequel l’OIV s’apprête à adopter une définition internationale. Les travaux de recherche qui ont été menés depuis une trentaine d’années sur ces thèmes et qui ont été, pour les plus importants, présentés dans les 7 premiers Congrès Internationaux des Terroirs Viticoles ont considérablement modifié les connaissances sur le fonctionnement des terroirs viticoles dans le monde et le comportement des consommateurs avertis par rapport aux vins de terroirs.

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

Grapevines (Vitis vinifera L.) are plants of great economic importance, with over 80% of grape production dedicated to wine production, yielding more than 258 million hectoliters annually [1].