Conservation: the best valorisation strategy for wine growing areas

In this work, these four red varieties were characterized in terms of phenolic composition. Thus, the anthocyanin accumulation and the extractability evolution during ripening were compared.

The evaluation of new grapevine genotypes regarding their potential to produce high quality wines is the time limiting factor in the process of grapevine breeding. Hence, the development of quality-related markers useable in marker-assisted selection (MAS) as well as in prediction models for this bottleneck trait will tremendously enhance breeding efficiency. In extensive studies a training set of a segregating white wine F1 population (150 F1 genotypes = POP150; `Calardis Musqué´ x `Villard Blanc´) was deeply phenotyped and genotyped for model development and QTL analysis.

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.

A major topic in viticultural research is the analysis of the relationships between climate on one side, and grape and wine quality on the other. It is well known that climatic conditions

The balance between the different flavours of a wine largely determines its perception and appreciation by the consumers. In white wines, sweetness and sourness are usually the two poles balancing the taste properties. The bitter flavour, on the other hand, is frequently associated with a loss of equilibrium and all white wines (dry and sweet, young and aged) are affected.
Several bitter compounds are already well-described in wines.