Intra-block variations of vine water status in time and space

The challenges of the century for viticulture relate to coping with climate change and the loss of biodiversity in a downturning socio-economic context. Now more than ever, the vine and wine industry needs to be resilient to maintain and ensure a future for its heritage. An innovation of capital importance, in line with recently published research, deals with developing new methods of training our inherited and newly planted vineyards to better withstand environmental variations such as drought and heatwaves but also unevenly distributed rains and temperatures.

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

The wine industry is currently on the search for new aromas and less browning in their products. In the improvement process of wine, lower fermentation temperatures have been considered, however, the yeasts in the market cannot tolerate such temperatures

Agiorgitiko (Vitis vinifera L. cv.) is the most widely cultivated indigenous red grape variety in Greece, known for the production of Protected Designation of Origin Nemea wines.