Terroir and precision viticulture: are they compatible?

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.

Aims: The current study seeks to demonstrate that premium Shiraz wines from different Australian geographic indications (GI) can be distinguished by their volatile compound composition. 

Environmental factors like soil and climate influence grape quality potential. Their impact is often mediated through vine water and nitrogen status. Depending on the color of the grapes (red or white) and the type of wine produced, the desired level of vine water and nitrogen status for optimum wine quality is different. Little investigation has been carried out concerning these factors and their potential influence on sparkling wine quality on two vintages. In this study vine water and nitrogen status were assessed at a very high density and related to grape composition and berry weight. Through statistical analyses, the major factors driving grape quality potential on Pinot noir in Champagne were highlighted.

Les paysages viticoles méditerranéens présentent une originalité qui ne se retrouve nulle part ailleurs : ils associent des garrigues très odoriférantes à des parcelles de vignes souvent qualitatives. La connaissance empirique des vins du Languedoc par leurs dégustateurs a conduit la Chambre d’Agriculture de l’Hérault à supposer que les arômes de la garrigue environnante peuvent se retrouver dans les vins (arômes de ciste, de genévrier, … )

Un grand nombre de travaux ont été consacrés à la mise en éyidence et à la quantification de l’effet du climat sur la qualité de la production viticole. IIs ont permis de caractériser les grands types de production à une large échelle géographique, et d’en évaluer les variations interannuelles au niveau des millésimes.