High resolution climate spatial analysis of European winegrowing regions

One of the major threats to viticulture is represented by fungal pathogens. Plasmopara viticola, an oomycete causing grapevine downy mildew, is one of the principal causes of grape production losses. The most efficient management strategies are represented by a combination of agronomical practices, fungicides’ applications, and use of resistant varieties. Plant resistance is conferred by the presence of resistance (R) genes. Opposed to them, susceptibility (S) genes are encoded by plants and exploited by pathogens to promote infection. Loss or mutation of S genes can limit the ability of pathogens to infect the host. By exploiting post-transcriptional gene silencing, known as RNA intereference (RNAi), it is possible to knock-down the expression of S genes, promoting plant resistance.

‘Cabernet sauvignon’ and ‘Merlot’ are among the most recognized red wine grape cultivars. This work is aimed at investigating the proanthocyanidin composition of skins and seeds to determine the grape variety and the vintage effects on the phenolic composition of Bordeaux grapes.

The ripening of grapevine berries encompasses complex morphological and physiological processes, especially at veraison. Berry shrivel (BS) is a ripening physiological disorder affecting grape berries with visible symptoms appearing short after veraison. The main symptoms of BS are a strong reduction in sugar accumulation, inhibited anthocyanin biosynthesis and high pH values. The most popular red grape cultivar in Austria “Blauer Zweigelt” (Vitis vinifera L.) is specifically prone to develop the BS ripening disorder and up to date a no specific cause or causes could be identified. Recently omics approaches have identified and characterized key processes during grapevine ripening. Among them a small subset of genes, called SWITCH, have been described as markers for the onset of the ripening process in fruits.

Volatile organic compounds (VOCs) are very important for the characterisation and quality of the final white wine. An oenological practice to increase the extraction of aroma compounds is the cold pre-fermentative maceration [1,2], although it may also release phenolic compounds that confer darker chromatic traits to white wines, not appreciated by consumers. This practice could be improved by the use of enzymes in order to facilitate the release of the odorous molecules. In this study, the effect of different enzyme treatments during skin contact on the chromatic characteristics and volatile composition of white musts from four winegrape varieties was evaluated.

Knowledge on the reflectance spectrum of soil is potentially useful since it carries information on soil chemical composition that can be used to the planning of agricultural practices. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on soils from two vineyards in south Brazil. The vineyards are close to each other, are on different geological formations, but were subjected to the same management. The objective was to detect spectral differences between the two areas, correlating these differences to variations in their chemical composition, to assess the technique’s potential to predict soil attributes from reflectance data.To that end, soil samples were collected from ten selected vine parcels. Chemical analysis yield data on concentration of twenty-one soil attributes, and spectroradiometry was performed on samples. Chemical differences significant to a 95% confidence level between the two studied areas were found for six soil attributes, and the average reflectance spectra were separated by this same level along most of the observed spectral domain. Correlations between soil reflectance and concentrations of soil attributes were looked for, and for ten soil traits it was possible to define wavelength domains were reflectance and concentrations are correlated to confidence levels from 95% to 99%. Partial Least Squares Regression (PLSR) analyses were performed comparing measured and predicted concentrations, and for fifteen out of 21 soil traits we found Pearson correlation coefficients r > 0.8. These preliminary results, which have to be validated, suggest that variations of concentration in the investigated soil attributes induce differences in reflectance that can be detected by spectroradiometry. Applications of these observations include the assessment of the chemical content of soils by spectroradiometry as a fast, low-cost alternative to chemical analytical methods.