The impact of differences in soil texture within a vineyard on vine development and wine quality

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.

AIM: Lactiplantibacillus plantarum and Oenococcus oeni species is worldwide used as starter for malolactic fermentation [1, 2].

Vines are exposed to environmental conditions that cause abiotic stress on the plants (drought, nutrient and mineral deficits, salinity, etc.). Polyamines are growth regulators involved in various physiological processes, as in abiotic plant stress responses. Stressful conditions can modify grape’s composition, and in this work, we have focused on studying the effect of abiotic stress on the composition of polyamines and amino acids in grapes. In addition, the effect of grape variety on these compounds has been studied.

Pour prévoir et gérer chaque année les principales caractéristiques de la maturation en Champagne, le CIVC (Comité Interprofessionnel du Vin de Champagne) a développé un ensemble de moyens de prévision et d’information très performants qui permettent aux différents acteurs de la filière viti-vinicole de prendre en compte ces informations à l’échelle de chaque terroir communal pour la recherche d’une qualité optimale.

The Maule region hosts up to a third of the total area of vineyards in Chile, in an environment where ancient practices inherited from the colonial past coexist with modernity and dynamism that include technified irrigation and fine vines. In the dry land of Maule there is a viticulture that has subsisted with ancient vines and traditions transmitted over generations, and there is little clarity about the origin and classification of the Maule viticulture, giving rise to the use of different concepts as synonyms to describe the ancient, minority, patrimonial or Criollas vines. In order to characterize and protect the ancient material, we studied the genetic diversity of a territorial collection that covers 80% of the communes of the region, prioritizing plants established more than 40-60 years ago.