Fast, and full microbiological wine analysis using triple cellular staining.

Sforzato di Valtellina DOCG is a reinforced dry red wine produced in the mountain area of Valtellina alpine valley (North Italy), using ‘Nebbiolo’ grapes that undergo a withering process. This process impacts on the grape composition due to a sugar concentration and changes in secondary metabolism influencing volatile organic compounds (VOCs) and polyphenols.

Bois noir is the most widespread phytoplasma grapevine disease in Europe. It is associated with ‘Candidatus Phytoplasma solani’. In symptomatic grapevines cv. ‘Zweigelt’ infected with ‘Ca. P. solani’ compared with uninfected grapevines, metabolic pathways associated with phosphorylated sugar production were induced both at the transcriptional level and at the level of activity of the corresponding enzymes (Dermastia et al., 2021, Int. J. Mol. Sci. 22: 3531). In particular, the expression of gene coding for phosphoglucoisomerase was upregulated, resulting in increased phosphoglucoisomerase enzyme activity.

Since 1996, we study the soil in viticulture, specially in the South of France. In the field, we delimit soil units and observe soil profiles and take samples to analyse its physical, mineral, organic and microbial mass composition

The Mediterranean basin is one of the most vulnerable regions to Climate Change effects. According to unanimous forecasts, the vineyards of Castilla-La Mancha will be among the most adversely affected by rising temperatures and water scarcity during the vine’s vegetative period. One potential strategy to mitigate the negative impacts of these changes involves the identification of grapevine varieties with superior water use efficiency, while ensuring satisfactory yields and grape 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.