Spatial variability of grape berry maturation program at the molecular level 

In the old-world viticulture autochthonous varieties are an important inheritance because they can provide wines with authenticity and distinction. Cultivar Mandó is an almost extinguished variety from the south-east

The color is the first attribute perceived by consumers and a major factor determining the quality of red wines. This depends mainly on the content of grape anthocyanins and their extraction into the juice/wine during winemaking. Furthermore, these compounds can undergo reactions that influence the chemical and sensory characteristics of the wine. Monomeric forms are prone to oxidation and adsorption on solid parts.

Global changes and sustainability challenge researchers in saving water and nutrients. The response of woody crops, which can be forced at facing more drought events during their life, is particularly important. Vitis vinifera can be an important model for its relevance in countries subjected to climate changes and its breeding, requiring cuttings plantation and strong pruning.

Le manque de connaissances concernant la physiologie de la maturation du raisin a longtemps interdit d’interpréter l’effet du terroir ou du millésime sur la qualité des vendanges en termes moléculaires. L’hypothèse selon laquelle c’est la perméabilité membranaire qui contrôlerait le sens comme l’intensité du stockage des acides est pourtant déjà ancienne (1). L’étude du transport des acides organiques et de son coût énergétique permet d’avancer certaines hypothèses concemant les sites potentiels de la régulation du contenu en sucres et acides du raisin sous l’effet de paramètres environnementaux.

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.