Climat-roche-sol-fromage. Cartographie fonctionnelle du terroir. Exemple de l’A.O.C. Comté

Many works have been carried out to identify the key aroma volatile compounds of oak wood (e.g., whisky-lactone, furfural, maltol, eugenol, guaiacol, vanillin) using conventional gas chromatography coupled with olfactometry and mass spectrometry (GC-O-MS). Inspired by recent untargeted approaches in the field of food “omics”, this work aims to extend our knowledge on the impact of cooperage process on the volatile composition of oak wood using two-dimensional comprehensive gas chromatography coupled with time of flight mass spectrometry (GCxGC-ToFMS).

Choosing the rootstock, the scion variety and the training system best suited to the local soil and climate are the key elements for an economically sustainable production of wine. The choice of the rootstock/scion variety best adapted to the characteristics of the soil is essential but, by changing climatic conditions, ongoing climate change disrupts the fine-tuned local equilibrium. Higher temperatures induce shifts in developmental stages, with on the one hand increasing fears of spring frost damages and, on the other hand, ripening during the warmest periods in summer. Expected higher water demand and longer and more frequent drought events are also major concerns. The genetic control of the phenotypes, by genomic information but also by the epigenetic control of gene expression, offers a lot of opportunities for adapting the plant material to the future. For complex traits, genomic selection is also a promising method for predicting phenotypes. However, ecophysiological modelling is necessary to better anticipate the phenotypes in unexplored climatic conditions Genetic approaches applied on parameters of ecophysiological models rather than raw observed data are more than ever the basis for finding, or building, the ideal varieties of the future.

Disease-resistant hybrid grape cultivars (DRHGCs) are hybrids of Vitis vinifera varieties with other Vitis species, and they are endowed with greater resistance to specific fungal diseases, enabling a potential reduction in the application of pesticides in the vineyard.

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.

The Grapevine Red Blotch Virus (GRBV) poses a critical challenge to the wine sector, lacking a uniquely identified vector. Current control methods involve costly and labor-intensive vine removal, emphasizing the urgency for targeted alternatives. The limited understanding of intricate host-virus interactions underscores the need for foundational knowledge to develop innovative disease control strategies. These include efforts to boost the plant’s RNA interference (RNAi) response, including RNA-based topical applications.