Cover crops under-vine impact on grapevine performance and vineyard soil microorganisms is highly affected by edaphoclimatic conditions at a regional scale 

On peut être surpris de l’existence d’un vignoble de vins liquoreux, le vignoble des Coteaux du Layon, dans une zone septentrionale à la limite Nord de la culture de qualité de la vigne et ce d’autant plus que le cépage de ce vignoble, le Chenin ou Pineau de la Loire, est un cépage semi tardif. La première explication est à rechercher au niveau des facteurs naturels (données climatiques et géopédologiques) permettant la réalisation de ce type de produit. Il est nécessaire de souligner ici l’importance de chaque paramètre du terroir pris dans im sens large (géopédologique et climatique) et que toute variation de l’un d’entre eux, même non perceptible en première analyse à l’homme, peut avoir des incidences déterminantes sur la qualité des vins.

The integration of digital technologies in vineyard management offers substantial opportunities for enhancing sustainability, efficiency, and grape quality.

The past three decades of terrestrial remote sensing research have delivered unprecedented insights into our fundamental ability to detect, quantify, and differentiate plant disease (Gold 2021). However, much of our fundamental knowledge in this domain has come from studies in non-agricultural systems and until recently, most agricultural studies, when extant, have focused on tree crops where canopy closure and large plot and plant size facilitate stress detection at low spatial resolution. Recent engineering innovations and advancements in constellation architecture design have refined the accuracy and scalability of airborne and spaceborne sensing platforms, enabling us to monitor diverse specialty crops, including grapevine, planted in smaller, spatially varied fields.

The evaluation of new grapevine genotypes regarding their potential to produce high quality wines is the time limiting factor in the process of grapevine breeding. Hence, the development of quality-related markers useable in marker-assisted selection (MAS) as well as in prediction models for this bottleneck trait will tremendously enhance breeding efficiency. In extensive studies a training set of a segregating white wine F1 population (150 F1 genotypes = POP150; `Calardis Musqué´ x `Villard Blanc´) was deeply phenotyped and genotyped for model development and QTL analysis.

The creation of disease-resistant varieties adapted to climate change is a key challenge for the future of the wine industry. At present, the selection of these new varieties is essentially based on screening for genetic markers of resistance and agronomic criteria, due to the small number of vines available per genotype. Integrating screening for oenological criteria into the early stages of selection would speed up this process.