Additives od aids? Evaluation of aroma compounds release from oenological tannins of different botanical origins.

Water nutrition is crucial for wine grape performance. Thus soil investigation aims at characterizing spatial and temporal variability of available water. A possible strategy

In winemaking, the appearance of turbidity in white and wine is a serious visual defect, which lowers significantly its commercial value. A major cause of the formation of turbidity in wine is attributed to the presence of temperature-sensitive proteins.

Extreme climatic events, such as prolonged drought followed by intense flooding, increasingly impact viticulture, affecting vine physiology, productivity, and grape composition.

The Agenda 2030 of the EU sets out the main guidelines for transitioning towards a resilient, green and safe economy. To this regard, the wine sector is experiencing an ecological transition in different ways such as increasing the production of ecological crops, or promoting the production of wines under more environmental-friendly and healthier (i.e., lower levels of SO2) products. These alternatives to conventional production are a smaller proportion of wines, in constant growth and demand, and follow alternative and minority practices, which range from sustainable to deeply philosophical thoughts. Among these methods there are organic, biodynamic and, more recently, natural wines.

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.