Déterminisme de l’effet terroir: influence de la surface foliaire primaire de la vigne en début de cycle sur le potentiel vendange

The dynamic changes of non-structural carbohydrates (NSC) in grapevines during the growing season is driven by phenological events and environmental factors.

Climate is a key variable for grapevine development and berry ripening processes. At mesoscale level, climate spatial variations are often determined empirically, as weather station networks are generally not dense enough to account for local climate variations.

Las condiciones heliotérmicas en España son en general favorables a alcanzar una elevada producción de azúcares en las bayas de prácticamente todas las variedades que se cultivan en nuestro país.

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.

The influence of dipping pretreatments on drying of ‘Pedro Ximenez’ grapes, destined to the elaboration of sweet wines, in chamber at controlled temperature, has been studied. Changes in color and phenolic composition of raisins produced were observed during the process of raisining.