Temperature variability inside a wine production area and its effect on vine phenology and grape ripening. An example from the Saint-Emilion-Pomerol

The Mediterranean climate of The Priorat AOC, situated behind the coastal mountain range of Tarragona, tends towards continentality with very little precipitation during the vegetation cycle. The soil is poor, dry and rocky, largely composed of slate schist, known as “llicorella”. Vines primarily grow on steep slopes and terraces.

Vineyard nutrient management is crucial for reaching production-specific quality standards, yet timely evaluation of nutrient status remains challenging. The existing sampling protocol of collecting vine tissue (leaves and/or petioles) at bloom or veraison is time-consuming. Additionally, this sampling practice is too late for in-season fertilizer applications (e.g. N is applied well before bloom). Therefore alternative early-season protocols are necessary to predict the vine nutrient demand for the upcoming season. The main goals of this project are to 1) optimize existing tissue sampling protocols; 2) determine the amount of nutrients removed at the end of the growing season.

Water stress triggers functional compartmentalization in grapevines, influencing how resources are allocated to different plant organs.

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.

At a time when the world’s winegrowing industry is having to adapt to a number of challenges, winegrowers are wondering about the consequent changes they will have to make (grape varieties, changes in vineyard and cellar techniques). For winegrowers and consumers alike, there is also the question of how these changes will affect the taste of their wines. This research, based on the study of numerous sources and archives from the 20th century, some of which have never been published before, aims to show that, in the recent past, the winegrowing world has shown incredible resilience in the face of crises, and that the taste and perception of fine wines has changed considerably in 100 years.