Cumulative effects of repeated drought stress on berry composition, and phenolic profile: Field experiment insights

Malolactic fermentation (MLF)¹, the decarboxylation of L-malic acid into L-lactic acid, is performed by lactic acid bacteria (LAB). MLF has a deacidifying effect that may compromise freshness or microbiological stability in wines² and can be inhibited by fumaric acid [E297] (FA). In wine, can be added at a maximum allowable dose of 0.6 g/L³. Its inhibition with FA is being studied as an alternative strategy to minimize added doses of SO₂⁴. In addition, wine yeasts are capable of metabolizing and storing small amounts of FA and during alcoholic fermentation (AF).

 The manipulation of the genetic basis controlling grapevine adaptation and phenotypic plasticity can be performed either by classical genetics or biotechnologies.

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

At the end of the 19th century, with the development of the chemical industry and the arrival of sulfite products that were easy to add throughout the winemaking process, SO2 volumes tended to increase in wines.