Relationships between the Fregoni bioclimatic index (IF) and wine quality

During alcoholic fermentation, when yeasts grow simultaneously, they often do not coexist passively and in most cases interact with each others

A major goal of modern grapevine (Vitis vinifera L.) breeding programs is the introgression of resistance genes along with desirable traits for better adaptation to climate change. Developmental stages have an impact on yield components and berry composition and are expected to shift towards earlier dates in the future. We investigated the genetic determinism of phenological stages in the progeny of a cross between two grapevine hybrids, each carrying several quantitative trait loci (QTL) for downy mildew and powdery mildew resistance.

The planet is warmer than at any time in our recorded past and increasing greenhouse emissions and persistence in the climate system means that continued warming is highly likely. Climate change has already altered the basic framework of growing grapes for wine production worldwide and will likely continue to do so for years to come. The wine sector can continue to play an important role in leading the agricultural sector in addressing climate change. From developing on…

With its Farm-to-Fork Strategy, which is a part of the European Green Deal, the European Union aims at reducing the amount of pesticides used in agriculture by 50% until 2030. As viticulture uses around 70% of the fungicides in the EU, there is substantial pressure on winemakers to reduce their pesticide input. On top of the political goal, winegrowers face increased pressure from the public demanding a more sustainable production of wine.

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.