The effect of different irrigation regimes on the indigenous Cypriot grape variety Xynisteri and comparison to Sauvignon blanc

«L’Observatoire Grenache» est un réseau de parcelles qui a été mis en place par l’Institut Rhodanien en Vallée du Rhône sur les millésimes de 1995 à 1999. Composé de 24 parcelles de Vitis vinifera L. cv Grenache noir, ce réseau vise à étudier l’influence du terroir (sol, climat et vigneron) sur la qualité des vins. Les parcelles ont été choisies afin de représenter différentes situations géographiques et géopédologiques de la vallée du Rhône. Le matériel végétal (clone, porte-greffe), la taille (cordon de Royat), la densité et l’âge de la parcelle ont été encadrées. Ainsi les conditions de milieu (sol, climat) et les pratiques du vigneron étaient les principales sources de variations.

Le climat a de fortes implications sur le bon fonctionnement physiologique de la vigne et a besoin d’être quantifié afin de déterminer l’aptitude des régions à la culture de la vigne. Une méthode, qui pourrait éventuellement servir à prévoir l’aptitude des régions à la culture de la vigne, est proposée.

Wine quality is strongly linked to climate. Quality scores are often driven by climate variation across different winegrowing regions and years, but also influenced by other aspects of terroir, including variety. While recent work has looked at the relationship between quality scores and climate across many European regions, less work has examined New World winegrowing regions. Here we used scores from three major rating systems (Wine Advocate, Wine Enthusiast and Wine Spectator) combined with daily climate and phenology data to understand what drives variation across wine quality scores in major regions of the Western US, including regions in California, Oregon and Washington. We examined effects of variety, region, and in what phenological period climate was most predictive of quality. As in other studies, we found climate, based mainly on growing degree day (GDD) models, was generally associated with quality—with higher GDD associated with higher scores—but variety and region also had strong effects. Effects of region were generally stronger than variety. Certain varieties received the highest scores in only some areas, while other varieties (e.g., Merlot) generally scored lower across regions. Across phenological stages, GDD during budbreak was often most strongly associated with quality. Our results support other studies that warmer periods generally drive high quality wines, but highlight how much region and variety drive variation in scores outside of climate.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

As the quality and typicity of wine are influenced by the climate, it is essential to have a good knowledge of climate variability, especially with regard to temperature, which has a great impact on vine behavior and grape ripening.