Évolution de la surmaturation du cépage Chenin dans différents terroirs des Coteaux du Layon en relation avec les variables agroviticoles

Climate changes are impacting viticultural regions throughout the world with temperature increases being most prevalent.1 These changes will not only impact the regions capable of growing grapes, but also
the grapes that can be grown.2 As temperatures rise the growing degree days increase and with it the sugar accumulation within the berries and subsequent alcohol levels in wine. Consequently, viticultural
practices need to be examined to decrease the levels of sugars.

Kokumi is a complex sensation perceived as enhanced palatability. Under the influence of kokumi substances, foods/beverages tastes become more flavorful with increased intensity, spread, continuity, richness, harmony, and punch which are the six related characteristics corresponding to the Kokumi sensory concept [1].

Vineyard site selection and determination of adapted grape varieties for a site are the most fundamental factors contributing to vineyard success, but can be challenging to ascertain

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 measurement of carbon isotopic discrimination of musts (δ13C) at harvest is an integrated assessment of water status during ripening of grapevine. It is an alternative to traditional measurements of water status in the field, which is crucial for understanding spatial variability of plant physiology at the vineyard scale, proven useful for delineation of management zones in precision viticulture. The aim of this work was to attune the method for the first time to California conditions across a range of areas and cultivars with different hydric behavior, and to evaluate its efficiency in delineating management zones for selective harvest in commercial vineyards.