Alternative fate of varietal thiols in wine: identification, formation, and enantiomeric distribution of novel 1,3-oxathianes

Climate change critically challenges viticulture. Among other threats, extreme and increasingly frequent heatwaves cause irreversible burns on leaves and bunches. A series of observations and experiments was conducted to better understand how leaf burns originate and whether genetics or management practices can mitigate them. In 2019, a panel of 279 potted cultivars of Vitis vinifera L. grown outdoors suffered a heat peak and a genetic origin of leaf burn variability was demonstrated. To deeper explore this variability, fourteen cultivars were selected for their contrasting responses to high temperatures, and detached leaves were submitted to a controlled increase in temperature up to 50 °C in a growth chamber.

Climate change is increasing water needs in most of the wine growing regions while reducing the availability and quality of water resources for irrigation. In this context, the sustainability of Mediterranean viticulture depends on grapevine responses to the combinations of water and salt stress. With this aim, this work studies the effects of deficit irrigation and salinity on the physiology of the Tempranillo cultivar (Vitis vinifera L.) grafted onto a drought and salinity tolerant rootstock (1103 Paulsen).

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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.

Plant water stress affects grape (Vitis vinifera L.) berry composition and is variable in space due to variations in the physical environment at the growing site. Could we use water status maps as a sensitive tool to discriminate between harvest zones?