Climate change strongly affects the wine-growing sector which increasingly requires in situ adaptation strategies aimed at preserving the sustainability of production. Investigating microclimate becomes crucial in comprehending environmental pressures on plants. The microclimatic investigation conducted in the Orvieto PDO (central Italy) allowed us to highlight the climatic dynamics occurring in the last 25 years and the frequency and intensity of abiotic stresses. Two management strategies for the canopy were identified: early defoliation (ELR) and foliar application of Basalt Flour ® (FB) compared to the ordinary management (C) of the company (bud selection and topping). The effects on plant vigour indices (LAI), resource allocation in terms of carbon stored in the above-ground organs of the vine, and the microclimate of the canopy and the berry were evaluated. In particular, microclimate was evaluated through a network of sensors connected wirelessly (Wireless Sensor Network), dedicated to collecting information on temperature and humidity in the canopy and clusters.

Aromatic composition contributes mainly to the quality aroma of white wine. A natural and gradual evolution of the aroma in the bottle occurs over storage with a very low oxygen content.

L’étude du comportement physiologique et agronomique de la vigne (cv. Chasselas) a été réalisée en 2001 par la Station fédérale de recherches en production végétale de Changins sur divers terroirs viticoles vaudois (Suisse), dans le cadre d’un projet d’étude des terroirs viticoles vaudois en collaboration avec le bureau I LETESSIER (SIGALES) à Grenoble et l’École polytechnique fédérale de Lausanne (EPFL).

In recent years, heightened awareness of the environmental impact has led to sustainability as a key issue for the winemaking sector.

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.