The increasing frequency of wildfires in grape-growing regions is seen as a significant risk for the grape and wine 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.

Wine aroma is shaped by the wine’s chemical compositions, in which both grape constituents and microbes play crucial roles. Although wine quality is influenced by the microbial communities, less is known about their population interactions.

The production of sparkling wines requires sugars for the second fermentation. The Solid Rectified Concentrated Must (SRCM) is a water free crystalline form of grape sugar, offering a purer, more stable, and easier-to-use alternative to the liquid Rectified Concentrated Must (RCM).

White wine protein haze can be prevented by removing the grape juice proteins, currently achieved by bentonite addition. To avoid wine volume loss and to minimizes aroma stripping, degrading haze-forming proteins in wine with proteases is a particularly interesting alternative to bentonite. In the present study, two fungal proteases treatments combined with different heating (50, 60, 72 °C) + refreshing steps, were applied on Gewürztraminer grape juice, and compared to bentonite treatments. The impact of these 19 treatments on the wine haze risks was determined by using two heat tests at 50 °C (heating during 30 to 120 min) and 80 °C (heating during 5 to 60 min). The protein contents and compositions were also estimated using the SDS-PAGE + densitometric integration techniques.