Viticultural and enological strategies for the prevention of Botrytis cinerea- induced quality losses

This study evaluated the physicochemical parameters of grape juices produced in the serra gaúcha from the 2019 and 2020 harvests. To do this, 43 juice samples were analyzed, and divided into four distinct categories: juices made exclusively from bordô grapes (sb), juices made from bordô and niágara grapes (sbn), juices combining bordô and isabel grapes, and juices made from cuts of several grape varieties.

Context and Purpose of the study. Arbuscular mycorrhizal fungi (AMF) symbiosis with grapevines is a key component of vineyard ecosystems.

The interactive effects of three major enological variables were evaluated on the quality of Schiava wine up to one year of storage in bottle.

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