Effects of different organic amendments on soil, vine, grape and wine, in a long-term field experiment in Chinon vineyard (France)

Vine genetics, fruit maturity, region and vineyard are perceived as factors that strongly influence Pinot noir grape and wine composition. Our study aims to understand the relationship between grape (and ultimately wine) composition and the physical appearance and performance characteristics of a vine (i.e. vine ideotype). Our experimental approach controlled these variables by

The European geographical indication (GI) Brandy Italiano is exclusively reserved to brandy obtained in Italy from the distillation of wine from grapes grown and vinified in the national territory [1].

From the standpoint of wine aroma oxidation there are two effects observed: aroma degradation of oxygen sensitive compounds (polyfunctional mercaptans) and the appearance of new substances with high aromatic power (acetaldehyde, methional, phenylacetaldehyde, sotolon, alkenals, isobutanal and 2, 3-metylbutanals) (1-5). According to our experience, Strecker aldehydes are compounds with highest sensory relevance in the oxidative degradation of many wines (5-7).

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