Effect of vineyard management strategy on the nutritional status of irrigated « Tempranillo » vineyards grown in semi-arid areas

Enological tannins are a diversified group of winemaking products that vary in several aspects such as chemical composition, botanical origin, and production method. In consideration of their richness in phenolic compounds, one of their main application in vinification is related to their antioxidant capacity, in particular their ability to consume oxygen during red wine maturation.

Canopy-management practices are applied in viticulture to improve berries composition and quality, having a great impact on primary and secondary grape metabolism. Among these techniques, cluster zone leaf removal (defoliation) is widely used to manage air circulation, temperature and light radiation of grape bunches and close environment. Since volatiles are quantitatively and qualitatively influenced by the degree of fruit ripeness, the level of solar exposure, and the thermal environment in which grapes ripen, leaf removal has been shown to affect volatile composition of grape berries [1].

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.

Nearly one third of the Herault vineyard (south of France) is planted on soils very sensitive to water runoff and erosion

Red grape pomace can be an important source of polysaccharides, but currently they are little studied and even less with viable and environmental extraction processes (green extraction). These green techniques must be able to break the cell wall so that the compounds contained in the cells, including polysaccharides, are released and can have a great influence on extraction yields, the chemical structure of polysaccharides and applications in wines. Amongst the emerging green techniques most applied to the extraction of bioactive compounds, such as polysaccharides, high-power ultrasound (US) and enzyme-assisted extraction stand out.