What triggers the decision to ripen 

A foresight study was carried out by a group of experts from INRAE, universities, INAO and FranceAgriMer from 2014 as part of the multidisciplinary “laccave” project intended to anticipate climate change in the French wine industry. The initial objective was to initiate an interdisciplinary dialogue between researchers and to feed their questions in a more systemic way. The scenario development method made it possible to build possible futures for the wine sector in the face of climate change. It began by drafting four adaptation strategies, combining different possible intensities of innovation and relocation of the vineyard.

Nearly one third of the Herault vineyard (south of France) is planted on soils very sensitive to water runoff and erosion

Water resource is a major limiting factor impacted by climate change that threatens grapevine production and quality. Understanding the ecophysiological mechanisms involved in the response to water deficit is crucial to select new varieties more drought tolerant. A major bottleneck that hampers such advances is the lack of methods for measuring fine functioning traits on thousands of plants as required for genetic analyses. This study aimed at investigating how water deficit affects the trade-off between carbon gains and water losses in a large panel representative of the Vitis vinifera genetic diversity. 250 genotypes were grown under 3 watering scenarios (well-watered, moderate and severe water deficit) in a high-throughput phenotyping platform.

Managing extraction of tannins and green aroma compounds attributed from methoxypyrazines in winemaking is crucial for producing high quality Pinot noir wine. This study1 investigated the impact of leaf removal on concentrations of anthocyanins, tannins, and methoxypyrazines in Pinot noir grapes and resultant wines.

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.