What metabolomics teaches us about wine shelf life

The vine is colonized by a multitude of micro-organisms (fungi, bacteria, oomycetes) mainly coming from the microbial reservoir constituted by the soil. These microorganisms have positive or negative effects on the vine (protection against pathogens, resistance to abiotic stress, nutrition, but also triggering of diseases) (Fournier, Pellan et al. 2022). In addition to these functional roles, they respond quickly to environmental changes (climate, cultural practices) which could make them good bioindicators of the functioning of the wine ecosystem.

Plusieurs caves coopératives de l’AOC Côtes du Rhône se servent des informations du zonage pour la sélection des vendanges en fonction du terroir d’origine, afin d’élaborer des «cuvées terroir» et d’exploiter ainsi le potentiel qualitatif de leurs secteurs.

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.

Vers la fin du XX siècle, la vitiviniculture argentine a subi une profonde transformation qualitative atteignant toute la filière. L’analyse de l’évolution de la superficie des vignobles, l’élaboration des vins, la consommation et les exportations, permet de mettre en évidence ces changements. Dans ce contexte, l’origine apparaît comme un outil de force, d’expansion et de succès sur les marchés.

Grapevine breeding focuses on high wine quality and climate-adapted grapevine varieties with fungal disease resistances to be cultivated in a pesticide-reduced and sustainable viticulture.