Exploiting somaclonal variability to increase drought stress tolerance in grapevine 

Grapevine phenology is advancing with increased temperatures associated with climate change. This may result in higher fruit sugar concentrations at harvest and/or earlier compressed harvests and changes in the synchrony of sugar with other fruit metabolites. One adaptation strategy that growers may use to maintain typicity of wine style is to change cultivars. This approach may enable fruit

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

The maturation patterns process has been very studied. In particular the modelization of the sugars and titratable acidity during the ripening period was an important approach, in particular for the prediction of harvest date (Barillere et al., 1988; Jourion et al.,1987; Maujean et al., 1983; Scienza, 1989). In Oltrepò Pavese, the widest viticultural district of Lombardy – Northern Italy – (about 15000 hectares), grape maturation trends shows high variability, due to the large variation in environmental characteristics of vineyards (altitude, exposure, soil type, mesoclimate) and to “cultivar x environment” interaction.

Correlations between vineyard site and wine have, historically, been limited due to lack of uniformity in scion and rootstock clone and lack of controlled pilot-scale winemaking conditions, particularly temperature

The quality of wine is difficult to define. This is most certainly accredited to everyone´s different perception of quality. Some of the indicators of high-quality wines are color complexity and balance. Color is one of the most crucial attributes of quality, not only for the obvious implications for their perception but also because they are indicators of other aspects related to its aroma and taste.