Chemical composition of cool-climate Sauvignon blanc grape skins clones during ripening

When talking about wine, terroirs are never too far. The National Institute of Apellation d’Origine (INAO) defines it as a system inside of which interact a group of human factors, an agricultural production and a physical environment.

Wine tasting has been shown to provide emotions to tasters (Coste et al. 2018). How will expertise impact this emotional response? Burnham and Skilleås (2012) reported that the cultural, experiential, and aesthetic competencies characterize an expert in wine compared to a novice. Although there is no consensual definition of an aesthetic experience, Burnham and Skilleås (2012) reported that aesthetic appreciation is “disinterested, normative for others and communicable” in comparison to sensory pleasure.

The selection of ancient grapevine varieties aims to achieve genetic gains in several important traits that can make the variety more interesting for the objectives of the producers. Traditionally, yield and quality traits of the must have been considered for selection, but many others can be taken into account. Linear mixed models are fitted to the data to predict the empirical best linear unbiased predictors (EBLUPs) of genotypic effects for each evaluated trait, which will be the basis for selection.

In table grapes seedlessness is a crucial breeding target, mainly results from stenospermocarpy, linked to the Thompson Seedless variety. Several studies investigated the genetic control of seedlessness identifying AGL11, a MADS-box transcription factor, as a crucial gene.
We performed a deep investigation of the whole AGL11 gene sequence in a collection of grapevine varieties revealing three different promoter-CDS combinations. By investigating the expression of the three AGL11 alleles and evaluating their ability to activate the promoter region, we show that AGL11 regulates its transcription in a specific promoter-CDS manner. By a multi-AGL11 co-expression analysis we identified a methyl jasmonate esterase, an indole-3-acetate beta-glucosyltransferase, and an isoflavone reductase as top AGL11 candidate targets. In vivo experiments further confirmed AGL11 role in regulating these genes, demonstrating its significant influence in seed development and thus in seedlessness trait.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.