Meso-scale future climate modeling (5 km resolution): application over French wine regions under the SRES A2 scenario (2041-2050)

The amount of synthetic pesticides applied in viticulture is relatively high compared to other agricultural crops, due to the high sensitivity of grapevine to diseases such as downy mildew (Plasmopora viticola). Alternatives to reduce fungicides are utterly needed to promote a sustainable vineyard-ecosystems and meet consumer acceptance. Essential oils (EOs) are amongst the most promising natural plant protection agents and have shown their antifungal properties previously. However, the efficiency of EOs depends highly on timing and application technique.

While SO2 is one of the oldest and widest additive used in enology for its well-known antioxidant, anti-laccase and antimicrobial properties, it can cause health problems in some individuals.

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 colour is the first quality characteristic to be assessed, especially regarding red wines. Anthocyanins are very well known to be the main responsible compounds for red wine colour. Red cultivars can synthesize and accumulate anthocyanins in berry skin to express their colour. However, anthocyanin accumulation is often influenced by a series of factors, such as genetic regulation, phytohormones, environmental conditions and viticultural management.