Zoning of potential landscape and environment potential of the «Appellation d’Origine Contrôlée Costières de Nîmes»

Despite their significant impact on wine quality and stability, colloids in red wine remain relatively under-researched. A series of studies, developed in the context of the d-wines project, aimed to provide a comprehensive understanding of the structure, composition, and formation mechanisms of red wine colloids by studying monovarietal wines from 10 of the most significant Italian red grape varieties. Starting from the idea that proteins, polysaccharides, and tannins should be involved in colloid formation, 110 monovarietal red wines were analysed for these components, revealing high inter- and intra-varietal diversity [1].

The research examines how two characteristic cultivations of a territory like the vine and the hazelnut shape the identity of a unique terroir: Langhe (North West italy).

Methyl-jasmonate (MeJA) induces the production of at least 25 compounds with sesquiterpene- like mass spectra in ‘Cabernet sauvignon’. Tost effective concentration of MeJA in stimulating the production of sesquiterpenes was found to be 500 µM if added when the cell suspensions had a PCV of 35 %, and 1000 if added when the cell suspensions had a PCV of 70 %.

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.

Advanced knowledge on grapevine row orientation is required to improve establishment, management and outcomes of vineyards on terroirs with different environmental conditions (climate, soil, topography) and in view of a future change to more extreme climatic conditions. The purpose of this study was to determine the combined effect of row orientation, plant water status and ripeness level on the physiological and viticultural reaction of Shiraz/101-14 Mgt.