The sensitivity to ABA affects the cross-talk between scion/rootstock in tolerant grapevines to drought stress

The effect of two rootstocks of different drought tolerance (1103 Paulsen and 3309 Couderc) on sap flow, water relations and gas exchange of cv. Xinomavro (Vitis vinifera L.) was investigated during the 2005 season in Naoussa, Greece. Soil was maintained at field capacity for both rootstock treatments until mid July when a restricted water regime was applied by irrigation cutoff. Sap flow diurnals for the Xinomavro-1103P combination showed a rapid decrease of flow after midday, under water stress conditions.

Enhancing the sensory profile of wine by the use of different microorganism has been always a challenge in winemaking. The aim of our work was to evaluate the impact of different fermentation schemes by using mixed and pure cultures of different Saccharomyces species to Sauvignon blanc wine chemical composition and sensory profile.

During cold storage, grapes undergo changes that affect their visual, mechanical, and organoleptic properties, potentially impacting quality and negatively influencing consumer acceptance. Key parameters include uniform color, crunchiness, and flesh consistency. We evaluated the influence of two distinct packaging methods on the chromatic characteristics, hardness, and pedicel detachment resistance of fourteen new seedless white and red grape varieties during cold storage. These factors are crucial for maintaining the quality of the product and extending its shelf-life. The novel grape varieties were obtained through a breeding program at CREA-VE of Turi, Southern Italy.

Nuclear magnetic resonance spectroscopy (NMR) is a high-tech analytical method that recently found its way into the field of wine analysis with special focus on wine authentication.

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.