Applications pratiques du zonage vitivinicole

Water deficit and salinity are increasingly affecting the viticulture and wine industry. These two stresses are intimately related; understanding the physiological and metabolic responses of grapevines to water deficit, salinity and combined stress is critical for developing strategies to mitigate the nega- tive impacts of these stresses on wine grape production. These strategies can include selecting more tolerant grapevine cultivars and graft combinations, improving irrigation management, and using soil amendments to reduce the effects of salinity. For this purpose, understanding the response of grape- vine metabolism to altered water balance and salinity is of pivotal importance.

In recent years, there has been a growing interest in characterizing the ecological environment of vineyard production, and the growing need to delimit and characterize with precision the different homogeneous viticultural units. This has allowed the development of new studies which have as their objective the Vineyard Zoning. The delimitation and characterization of wine-growing areas poses specific problems in Spain, not only linked to the specific characteristics of the territory, but also to the size, distribution and index of viticultural occupation in the designations of origin.

An organoleptic defect, called fresh mushrooms off-flavor (FMOff), appeared in wines and spirits since the 2000’s. Numerous researches demonstrated that octen-3-one, 3-octanol and octen-3-ol (C8 compounds) were involved in the mushroom off-flavor in wines

In this video recording of the IVES science meeting 2021, Benjamin Bois (Institut Universitaire de la Vigne et du Vin – IUVV, Université de Bourgogne, Dijon, France) speaks about zoning the climatic potentialities and risk of vineyards & wine production regions. This presentation is based on an original article accessible for free on OENO One

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.