Comportement du cépage Mourvèdre dans l’aire d’Appellation d’Origine Contrôlée de Bandol

Varietal thiols 3-sulfanylhexan-1-ol (3SH), 3-sulfanylhexyl acetate (3SHA) and 4-methyl-4-sulfanylpentan-2-one (4SMP) are essential for fruity aromas of Sauvignon Blanc wines. The concentration of varietal thiols in wines was thought to be related to the concentration of their precursors in grapes, however only a small proportion of precursors are released to varietal thiols during fermentation. New findings suggested that specific grape juice metabolites could significantly impact on the development of three major varietal thiols and other aroma compounds of Sauvignon Blanc wines.

Grapevine vegetative multiplication allows the accumulation of spontaneous mutations and increase intra-cultivar genetic diversity that can be exploited to maintain grape wine quality

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.

Aims: This study aimed to (1) characterise colour and phenolic profiles of commercial Australian Pinot noir wines, (2) understand regional drivers of sensory and volatile profiles of commercial Australian Pinot noir wines, and (3) generate a deeper understanding of where Australian Pinot noir wines profiles sit in an international context.

The current energy crisis means that interest in agrophotovoltaics has increased significantly. The reason behind this is that the system aims to combine agricultural production with energy production. During the three-year period from 2020 to 2022, the effects of photovoltaic panels on the vine, the yield and the quality of the must were studied in Walenstadt in northern Switzerland, an area with a cool, humid climate. 65 Pinot noir vines were planted in the 160m2 study area. Because of the large edge effects, only 3 repetitions with 4 vines each could be created. A significantly lower leaf infestation by Plasmopara viticola was observed among the panels in each of the three years.