Predicting provenance and grapevine cultivar implementing machine learning on vineyard soil microbiome data: implications in grapevine breeding

The utilization of non-Saccharomyces yeasts in the wine industry has increased significantly in recent years. Alternative species need commonly be employed in combination with Saccharomyces cerevisiae to avoid stuck fermentation, or microbial spoilage. The employment of more than one yeast starter can lead to interactions between different species with an impact on the outcome of wine fermentation. Previous studies[1] demonstrated that S. cerevisiae elicits transcriptional responses with both shared and species-specific features in co-culture with other yeast species.

The growing interest in minority grape varieties is due to their potential for adaptation to global warming and their oenological capabilities. However, the cultivation of these varieties has often been limited due to their low economic efficiency. One such example is Maturana Blanca, a recently recovered and authorized minority grape variety in the DOCa Rioja region, known for its remarkable oenological potential but low productivity. This study aimed to increase the yield of Maturana Blanca by implementing the vertical cordon training system, which allowed for a higher number of buds per plant and an increased cluster count per vine.

In the 19th century, devastating outbreaks of phylloxera (Daktulosphaira vitifoliae Fitch), almost brought European viticulture to its knees. Phylloxera does not only take energy in form of sugars from the vine, but also affects the up- and down- regulations of genes, acts as a carbon sink and reprograms the physiology of the grapevines, including nutrient uptake and the defense system [1]. A key trait of rootstocks is the ability to perform well under high lime conditions as about 30 % of the land surface has calcareous soil. Iron deficiency not only causes the well-known problems of lime-induced chlorosis and stunted growth, but also affects the entire plant metabolism.

Targeted analytical methods can overlook compounds that are a priori unknown to play a role in the mouthfeel sensations. This limitation can be overcome with the information provided by untargeted metabolomic analysis using UPLC‐QTOF-MS. To this end, an untargeted metabolomic approach applied to 42 red wines has allowed development of a model with predictive capacity by cross-validation for the “dry”, “oily” and “unctuous” sensations perceived by a sensory panel. The optimal PLS model for “dry” retained compounds with positive regression coefficients (≥ 0.17) including a trimer procyanidin, a peptide, and four anthocyanins.

In contrast to fruit and grape berry ripening, the biological processes causing ripening disorders are often much less understood, although shriveling disorders of fruits are manifold and contribute to yield losses and reduced fruit quality worldwide. Shrinking berries are a common feature for all shriveling disorders in grapevine although their timing of appearance during the berry ripening process and their underlying induction processes distinct them from each other. The sugar accumulation disorder Berry Shrivel (BS) is characterized by a suppression of sugar accumulation short after veraison resulting in berries low in sugar content and anthocyanins in berry skins, while the organic acid content is similar. Recent studies analyzed the biochemical, morphological and molecular processes affected in BS berries and linked early changes to the period of ripening onset [1,2].