Molecular characterization of a variegated grapevine mutant cv Bruce’s Sport

Today’s viticulture is facing a new climatic scenario with temperature increases and rainfall deficits, generated by the effect of climate change. As a result of these new conditions, there are earlier harvests, increased plant water stress and higher disease risk in wetter wine-growing regions.

The brown marmorated stink bug (BMSB, Halyomorpha halys Stal) is an invasive pentatomid native to eastern Asia that is spreading rapidly worldwide, notably through human-mediated activities. Globally, it was reported in the USA, Canada, Italy, Hungary, and other European countries. BMSB has a broad host range that includes over 170 plants, many of agricultural importance, including various fruit, vegetables, row crops, and ornamentals. When present in the vineyard, the pest can affect yield and quality by directly feeding on berries resulting in fruit collapse and necrosis. Additional damage occurs when BMSB are carried into the winery within the grape clusters. The presence of BMSB during wine processing can affect juice and wine quality through the release of volatile compounds produced as a stress response. The major secretes compounds are tridecane and trans-2-decenal. Tridecane is an odorless compound and its effect on wine quality is currently unknown. Trans-2-decenal is an unsaturated aldehyde considered to be the main component of BMSB taint with strong green, coriander, and musty-like aromas. Its threshold value in wine was estimated at about 5 µg/L.

The growing region of Barolo DOCG certified wines is topographically complex. The region is famous for this complexity and for the associated terroir driven Nebbiolo grapes and wines derived distinctly from this varietal. Although it is recognized that the Barolo area is unusual topographically and it is assumed that this unusual topography lends to the inherit terroir, the specifics of this relationship are less well defined.

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.

Grapevine leaf removal (LR) in the cluster area is typically done between fruit set and cluster closure to create an unfavorable microclimate for fungal diseases, such as Botrytis cinerea and powdery mildew. Grape growers are now turning their attention to pre-flowering LR, which has additional benefits under certain conditions. When applied before flowering, LR strongly affects fruit set and thus the number of berries per cluster. It is therefore a good yield control tool, replacing time-consuming manual cluster thinning (Poni et al. 2006). It also improves berry structure, that is, skin thickness, skin-to-pulp ratio, and berry composition (total soluble solids, titratable acidity, and polyphenols) (Palliotti et al. 2012; Komm and Moyer 2015). By exacerbating competition for assimilates between reproductive and vegetative organs, pre-flowering LR also poses some risks. Excessive yield loss at the same year’s harvest due to a too low fruit set rate is the main concern: intensive pre-flowering LR (100% of the cluster area) can induce up to 50% yield loss in potted vines (Poni et al. 2005). Other parameters, such as cool climatic conditions during flowering, also affect fruit set rate and make it difficult to predict potential yield at harvest. Repeated and overly intensive preflowering LR can have repercussions over time and induce a decline in bud fruiting and plant vigor (Risco et al. 2014).