In economics, the perception of wine quality is not limited to sensorial characteristics: an indication of the region of production significantly affects the perception of quality and consumers’ WTP ([1]; [2]). However, [3] or more recently [4] show that even if a wine has an organic label, the taste of wine remains the predominant criterion in consumer preferences. The contribution of our experiment is to evaluate the impact of responsible attributes (organic label, Non Added Sulfites, HVE certification) on the appreciation of several red wines on the market. More than 280 consumers participated to the present study and they perform 25 tastings divided into 5 different sessions. 20 different red wines from Bordeaux Area are tasted.

In the context of global change, the environmental conditions are expected to be more stressful for viticulture. The choice of the rootstock may play a crucial role to improve the adaptation of viticulture to new biotic and abiotic threats (Ollat et al., 2016). However, the selection of interesting traits in rootstock breeding programs is complex because of the combination of multiple targets in a same ideotype. In this sense, the integration of studies about the genetic architecture for desired biotic and abiotic response traits allow us to identify genomic regions to combine and those with interesting pleiotropic effects.

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 industry, a key sector for the European Union’s economy, exhibits significant energy consumption, amounting to approximately 1,750 million kWh annually within this geographic context, with major contributions from Italy, France, Spain, and Portugal (Fuentes Pila et al., 2015).

The reaction between Strecker amino acids and α-dicarbonyls is a key pathway in the formation of Strecker aldehydes (SA), which are crucial oxidation-related odorants in wine [1].