Can early defoliation improve fruit composition of Tempranillo grapevines in the semi-arid terroir of Utiel-Requena, Spain?

Ellagitannins consist the main extractible phenolic compounds in oak wood.

The evolution of wine during bottle ageing has been of great interest to ensure consistent quality over time. While the role of wine closures on the amount of oxygen is well-known [1], closures could also play other roles such as the scalping phenomenon of flavour compounds. Flavour scalping has been described as the sorption of flavour compounds by the packaging material, which could result in losses of flavour intensity. It has been reported in the literature that volatile sulphur compounds (VSC) can be scalped on wine closures depending on the type of closure (traditional and agglomerated cork, screw-cap, synthetic [2]).

AIM: Firm tissues of grapes and yeast are the major sources of lipids in wine.

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

Aims: Establishing an image of fine wine through the Geographical Indication (GI) system is of interest to the Australian wine sector. Beyond provenance, the sensory experience of fine wine is often linked to consumption with appropriate foods. For this purpose, studies were undertaken to understand consumer perceptions of what