Response to powdery and downy mildew of varieties with disease resistance genes (PIWI)

All along the red winemaking process, many microorganisms develop in wine, some being beneficial and essential, others being feared spoilers. One of the most feared microbial enemy of wine all around the world is Brettanomyces bruxellensis. Indeed, in red wines, this yeast produces volatile phenols, molecules associated with a flavor described as “horse sweat”, “burnt plastic” or “leather”. To produce significant and detectable concentrations of these undesired molecules, the yeasts should first grow and become numerous enough. Even if the genetic group of the strain present and the cellar temperature may modulate the yeast growth rate¹ and thus the risk of spoilage, the main factor seems to be the wines themselves, some being much more permissive to B. bruxellensis development than others.

Yeast secondary metabolism is a complex network of biochemical pathways and the genetic profile of the yeast carrying out the alcoholic fermentation is obviously important in the formation of the metabolites conferring specific odors to wine. The aim of the present research was to investigate the relative expression of genes involved in flavor compound production in eight different Saccharomyces cerevisiae strains.
Two commercial yeast strains Sc1 (S.cerevisiae x S.bayanus) and Sc2 (S.cerevisiae) and six indigenous S. cerevisiae strains (Sc3, Sc4, Sc5, Sc6, Sc7, Sc8) isolated during spontaneous fermentations were inoculated in Assyrtiko and Vidiano grape must.

Grapevine yield and fruit quality are two major drivers of input allocation and, ultimately, revenue for grape producers. Because yield and fruit quality vary substantially from year-to-year and within a single block, opportunities exist for optimization via precision management activities that could lead to more profitable and sustainable grape production. Here, we review recent advances in the techniques and technology used to measure, estimate, and forecast grapevine yield and fruit quality. First, we discuss direct “measurement” of yield and quality (i.e. ground-truth data generation), with an emphasis on potential for scalability and automation. Second, we discuss technology and techniques that do not directly measure yield and quality, but use correlated measurements for their estimation.

When considering the application of a systemic approach to assess the intrinsic complexity of agricultural production, the following question immediately arises

Vines are one of the most pesticide-intensive crops in France, and reducing their use is a major challenge for both the environment and human health.