Hormonal and associated metabolic changes in susceptible harvest-ripe grapes under asymptomatic and symptomatic Esca disease

In this video recording of the IVES science meeting 2025, Margot Berger (INRAE, UMR1287 EGFV, Institut des Sciences de la Vigne et du Vin, Villenave d’Ornon, France) speaks about epigenetics as an innovative lever for grapevine breeding in times of climatic changes. This presentation is based on an original article accessible for free on OENO One.

Sulfur-like off-odours are a problem caused by the presence of free forms of volatile sulphur compounds (VSCs). H2S is the most frequently found above its odour threshold

Recent regulations concerning alcoholic beverages have prompted producers to revise their product labels to incorporate nutritional information. In this context, qr codes containing such information, known as e-labels, are now being employed on wine labels for the first time.

The growing demand for non-alcoholic wines, along with issues related to waste disposal and environmental pollution amid military conflicts, natural disasters, and industrial emissions, necessitates the implementation of environmentally sustainable technologies in the winemaking industry.

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.