Context and Purpose of the Study. In many countries, the preservation of grape varieties with heritage value is ensured by genebanks of outstanding significance, which allow for the study of these varieties and the assessment of their future roles in response to environmental, market, and social challenges.

Les enzymes d’oxydation (polyphénoloxydase, peroxydase) des raisins sont d’origine génétique dépendantes des facteurs climatiques et agrotechniques (Sapis et al, 1983). Dans le processus technologique de l’obtention du moût de raisins, ces enzymes catalysent l’oxydation de certains composés phénoliques naturellement présents dans le raisin, produisant ainsi des modifications indésirables de la couleur et de l’arôme du vin.

Late spring frost is a major challenge for various winegrowing regions across the world, its occurrence often leading to important yield losses and/or plant failure. Despite a significant increase in minimum temperatures worldwide, the spatial and temporal evolution of spring frost risk under a warmer climate remains largely uncertain. Recent projections of spring frost risk for viticulture in Europe throughout the 21st century show that its evolution strongly depends on the model approach used to simulate budburst. Furthermore, the frost damage modelling methods used in these projections are usually not assessed through comparison to field observations and/or frost damage reports.
The present study aims at comparing frost risk projections simulated using six spring frost models based on two approaches: a) models considering a fixed damage threshold after the predicted budburst date (e.g BRIN, Smoothed-Utah, Growing Degree Days, Fenovitis) and b) models considering a dynamic frost sensitivity threshold based on the predicted grapevine winter/spring dehardening process (e.g. Ferguson model). The capability of each model to simulate an actual frost event for the Vitis vinifera cv. Chadonnay B was previously assessed by comparing simulated cold thermal stress to reports of events with frost damage in Chablis, the northernmost winegrowing region of Burgundy. Models exhibited scores of κ > 0.65 when reproducing the frost/non-frost damage years and an accuracy ranging from 0.82 to 0.90.
Spring frost risk projections throughout the 21st century were performed for all winegrowing subregions of Bourgogne-Franche-Comté under two CMIP5 concentration pathways (4.5 and 8.5) using statistically downscaled 8×8 km daily air temperature and humidity of 13 climate models. Contrasting results with region-specific spring frost risk trends were observed. Three out of five models show a decrease in the frequency of frost years across the whole study area while the other two show an increase that is more or less pronounced depending on winegrowing subregion. Our findings indicate that the lack of accuracy in grapevine budburst and dehardening models makes climate projections of spring frost risk highly uncertain for grapevine cultivation regions.

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

Wine aroma descriptors are important tools for wine evaluation. The present well-known wine aroma descriptor system was created and based on Western culture, which makes difficult for Chinese consumers to recognize and learn wine. AIM: The aim of this study was to update the wine aroma descriptor system for Chinese.

Methods: Fifty-four wine aroma descriptors of ‘Le nez du vin’ was used as substitution candidates. Firstly, a survey on unfamiliar aromas was distributed to 150 untrained Chinese wine consumers. Twenty attributors, such as blackcurrent buds, quince, linden, were selected as the most 17 unfamiliar. Then, a descriptive analysis was performed by trained tasting panel to substitute the targeted twenty aromas perfume. Furthermore, reference standards were looked and new le nez du vin were made. Finally, a substitution analysis was performed to replace the unknown wine aroma to the Chinese local aromas.