Deficit irrigation and mechanical canopy management affect berry and wine phenolic and aroma composition of Syrah in Central California

Correlations between vineyard site and wine have, historically, been limited due to lack of uniformity in scion and rootstock clone and lack of controlled pilot-scale winemaking conditions, particularly temperature

One on three cases of cancer is associated with lifestyle and nutritional patterns, and the excessive intake of alcoholic beverages is a well established risk factor. Moderate drinking has been associated with reduced or increased risk of various types of cancer, but the clinical relevance of the risk rates has not been evaluated in ad hoc prospective investigations.

The wine industry is increasing the demand for indigenous yeasts adapted to the terroir to produce unique wines that reflect the distinctive characteristics of each region. In our group, we have identified and characterized 60 native yeast strains isolated from a vineyard in Maldonado-Uruguay, in which three strains stood out: Saccharomyces cerevisiae T193FS, Saturnispora diversa T191FS, and Starmerella bacillaris T193MS. Their oenological potential was evaluated at a semi-pilot scale in Tannat must vinification in the wine cellar to have a more precise and representative evaluation of the final product.

The analysis of the distribution of natural plant populations allows an ecological characterization of cultivated environments in thermal, water and trophic terms; it guides the choice or selection of plants (or grape varieties) to cultivate (Astruc et al ., 1984, 1987; Delpoux, 1971; Jacquinet and Astruc, 1979). This approach has given good results in areas where the topography is the determining factor in the ecological differentiation of the terroirs.

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.