Exploiting somaclonal variability to increase drought stress tolerance in grapevine 

Terroir is increasingly important today in wine markets. In a large wine production area such as the Loire Valley, the whole territories/terroirs can be distinguished according to different combinations of geological, soil, climatic and landscape features but are also characterized by their differences and likenesses in terms of combinations of terroir units and practices.

Context and purpose of the study. Conventional viticulture relies heavily on synthetic inputs (fertilizers, pesticides), as well as mechanization to manage pests, weeds, and diseases and maximize yields.

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.

The foundation of wine production lies in the use of high-quality grapes. To produce wines that meet the highest standards, a fast and reliable analytical assessment of grape quality is essential. Many wineries currently employ Fourier-Transform Middle-Infrared Spectroscopy (FTIR) for this purpose.

Œnococcus oeni is a wine-associated lactic acid bacterium performs the malolactic fermentation, which improves the taste and aromatic complexity of many wine.