Water is the most abundant active compound in wine!

Microbiomes have crucial roles in maintaining life on Earth, and their functions drive human, animal, plant and environmental health. The microbiome research landscape is developing rapidly and is performed in many different science fields using similar concepts but mostly one (eco)system at-a-time. Thus, we are only starting to unravel and understand the interconnectedness of microbiomes across the (eco)systems.

The presence of grapevine trunk diseases (GTDs) related pathogens leads to severe economic losses in wine‐growing regions all over the world

Plasmopara viticola, the causal agent of downy mildew (DM), and Uncinula necator, the causal agent of powdery mildew (PM), are two of the main phytopathogenic microorganisms causing major economic losses in the primary sector, especially in the wine sector, by wilting bunches and leaves with a consequent decrease in the photosynthetic rate of the plant and in the annual yield. Currently, the most widespread methods for planning spraying are based on the 3-10 rule, which states that the first application should take place when: (i) the air temperature is greater than 10°C; (ii) shoots are equal or greater than 10 cm; and (iii) a minimum of 10 mm rainfall within 24–48 hours has occurred, or at the beginning of the bud break with periodic applications according to the manufacturer’s instructions.

Corporate Social Responsibility (CSR), as defined by the European Commission, is a strategic framework through which companies integrate social, environmental, and economic sustainability into their operations (European Commission, 2001).

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.