From genes to vineyards: system biology and new breeding technologies for water stress tolerance in grapevines

Among the different management techniques in Viticulture, which have
been developed with the purpose of optimizing the interception of sunlight, the photosynthetic capacity of
the plant and the microclimate of the clusters, especially in varieties that show excess vigor, the management of defoliation presents great importance. The defoliation consists of the removal of leaves that cover or that are in direct contact with the curls, which can cause physical damages in the berries, and aims to balance the relation between part area and number of fruits, providing the aeration and insolation in the interior of the vineyard, as well as reduce the incidence of rot in order to achieve greater efficiency in phytosanitary treatments and quality musts.

L’utilisation de levures sélectionnées est généralement considérée comme le moyen d’éviter les problèmes fermentaires. Néanmoins de nombreux viticulteurs pensent que ces levures sont à l’origine d’une standardisation des vins et militent pour le respect d’une flore indigène (Bourguignon, 1992).

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.

Aroma is one of the most important attributes that determine consumer’s perception of the sensory quality of wine and varietal typicity.

The tartaric salts precipitation is one of the main issues regarding wine instability 1. In addition to the well-known and deeply studied phenomena of potassium hydrogentartrate precipitation (KHT), the last decade has been increased the phenomena of calcium tartrate (CaT) precipitation, that is a concern for the wine industry 2.