The pedoclimatic conditions impact the yeast assimilable nitrogen concentration in the grapevine must and the valorisation of foliarnitrogen fertilisation

Polyphenols, bioactive secondary metabolites abundantly found in various grapevine components such as stalks, skins, and seeds, have attracted considerable attention in recent decades due to their potential health benefits. These compounds, including flavan-3-ols, flavanols, flavones, and stilbenes, are known for their antioxidant and anti-inflammatory properties.

Pesticides are considered one of the main causes for arthropod decline in agriculture which in turn may affect ecosystem services such as natural pest control and soil fertility.

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.

En raison des vanations locales d’exposition et de déclivité, l’évaluation climatique des vignobles et des régions viticoles est très important pour la culture des raisins.

Aims: The objective of this research was to analyse the spatial and temporal variability of vine phenology of the Tempranillo variety in the Toro Designation of Origen (DO) related to climatic conditions at present and under future climate change scenarios.