Grapevine bud fertility under elevated carbon dioxide

Water use efficiency is one of the most valued objectives in vine growing in mediterranean climates (de la fuente et al., 2015). Due to this, the grape growers provide different adaptation strategies according to their efficient consumption against the presumable water deficit generated under these environmental conditions. The use of non-positioned shoot systems (like sprawl, bush, etc.) Can help to achieve this objective.

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 unique characteristics of terroir play a fundamental role in shaping the identity and quality of wines, influencing the aromatic complexity of young wines and their long-term aging potential. The volatile compounds responsible for these aromas are crucial to identifying and appreciating a given wine.

Postharvest dehydration is a widely employed technique in winemaking to enhance sugar concentration and secondary metabolites from grapes. Different grape varieties exhibit varying responses in terms of dehydration rate and the resulting chemical composition.

One of the main sensory characteristics of red wine is astringency, which can be defined as drying, puckering and roughing of the oral cavity after the exposure to tannin-rich wines. Tannins are the main responsible for the intensity of the sensation as well for the qualitative aspects of astringency. However, the total intensity of the sensation is not sufficient to fully characterize red wine astringency. Thirty-three different subqualities (Gawel et al. 2001) had been generated to describe the complexity of this multi perceptual phenomenon, which includes both tastes, tactile, and flavor sensations. So, how to feel tannins during tasting? In this study, we used a sensory method that combine the training for astringency subqualities with touch-standards and the CATA questions, usually applied in consumer science, to evaluate the astringency subqualities of different typologies of Sangiovese: commercial and experimental wines. Sangiovese wine represents a good model for the study of astringency because it is generally characterized by a high content of low and high molecular weight proantocyanidins. Commercial wines differed for percentage of Sangiovese (80-100 %) grapes used in winemaking and for designation (Toscana TS, Chianti Classico CH, Chianti Riserva CR, Morellino di Scansano MS). The astringency profile of wines changed as the percentage of Sangiovese increased. Positive subqualities as velvet, soft, mouthcoat, and rich highly characterized the Sangiovese wine belonging to TS and CR designations. Moreover, the astringency subqualities related to blending or wood aging, represented the drivers of quality of commercial Sangiovese wines.