Electrochemical diversity of italian white wines

In the face of climate change, new grapevine varieties will have to show an adaptive phenotypic plasticity to maintain production with erratic water resources, and still ensure the quality of the final product. Their selection requires a better knowledge of the genetic basis of those traits and of the elementary processes involved in their variability. ‘PlastiVigne’, an emblematic project of the Vinid’Occ key challenge, funded by the Occitanie Region (France), tackles this issue with innovative genomic and physiological tools implemented on a unique panel of grape genetic resources representing the genetic diversity of Vitis vinifera. A graph-pangenome is developed from a representative set of high-quality genomes to study the extent and impact of structural genome variations and chromosomal rearrangements in the rapid adaptation capacity of grapevine.

Various molecular compounds are responsible for the complex mixture of fragrances that give wine its aroma. In particular, the ‘cooked fruit’ aroma found in red wines from hot and/or dry vintages or from the vinification of late harvested grapes has been intensively investigated in recent years. Lactones and especially γ-nonalactone were found to be responsible for the ‘cooked fruit’

The risk of wildfires is increasing as the frequency and severity of drought and heat waves continue to rise. Wildfires are associated with the combustion of plant materials and emit smoke. In the atmosphere, smoke may spread readily across large areas. Smoke is composed of solid and liquid phase particulates and gases and has been identified as a causal agent of “smoke taint” in wine. On a smoky day, the intensity of direct light decreases because these particulates scatter sunlight. Even though this effect is frequently assumed to decrease plant photosynthesis, this assumption ignores the potential changes in diffuse light and may be based on scant evidence.

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

Pour prévoir et gérer chaque année les principales caractéristiques de la maturation en Champagne, le CIVC (Comité Interprofessionnel du Vin de Champagne) a développé un ensemble de moyens de prévision et d’information très performants qui permettent aux différents acteurs de la filière viti-vinicole de prendre en compte ces informations à l’échelle de chaque terroir communal pour la recherche d’une qualité optimale.