Climate variability and its effects in the Penedès vineyard region (NE Spain)

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.

The wine sector is one of the most significant industries worldwide, with Spain being a leading country in wine production and export. A key factor in wine quality is its aroma, which is directly influenced by the volatile compounds present in the grape, with terpenes being among the most significant contributors.

Climate change is a major challenge in wine production. The IPCC (2014) projected that by the end of the 21st century average temperatures will increase by 1-3.7°C. Consequently, harvest dates could advance by approximately 30 days. A general observed trend is the increase in berry sugar content and decrease in organic acids, posing challenges for winegrowers. Variability among cultivars is a precious resource to adapt viticulture to a changing environment.

Quel est l’intérêt des cartes en viticulture? Celles-ci répondent à plusieurs usages.
Formalisation au sein d’un référentiel codifié et normalisé de la connaissance relative au milieu, aux observations biologiques et aux pratiques culturales.

Climate is considered one of the main factors that determines the aptitude of a specific location for growing grapes and producing high quality wine, being in that sense one of the main elements defining the concept of terroir