Preliminary characterisation of mannoproteins from different wine yeast strains and impact on wine properties

The main issues in viticulture are to highly decrease the use of phytochemicals. Electrolyzed water (EW) is one of the possible alternative when illness pressure is not too high

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.

Grape quality potential for wine production is strongly influenced by environmental parameters such as climate and agronomic factors such as rootstock. Several studies underline the effect of rootstock on vegetative growth of the scions [1] and on berry composition [2, 3] with an impact on wine quality. Rootstocks are promising agronomic tools for climate change adaptation and in most grape-growing regions the potential diversity of rootstocks is not fully used and only a few genotypes are planted. Little is known about the effect of rootstock genetic variability on the aromatic composition in wines; thus further investigations are needed.

Currently, grapevine is host to a large number of pathogenic agents, including 65 viruses, five viroids and eight phytoplasmas. Needless to say, these pathogens, especially viruses responsible for several ‘infectious degeneration’ or ‘decline’ cause great distress to wine makers and grape growers, let alone the large economic losses incurred by the wine industry. A recent addition to this wide repertoire of grapevine viruses is a new viral disease known as Red Blotch in viticulture parlance. Its causal organism, Grapevine red blotch associated virus (GRBaV), discovered in 2008 is a newly identified virus of grapevines and a putative member of a new genus within the family Geminiviridae.

The study was carried out in 2006 with Merlot grapes from vines grown using the trellis system, where four treatments were compared with different levels of water stress.