Zonazione e vitigni autoctoni nel sud della Basilicata: metodologie integrate per la caratterizzazione di ambienti di elezione di biotipi storici finalizzati a vini di territorio nella DOC “Terre dell’Alta Val d’Agri”

The Champagne vine-growing region has played a pioneering role in the delimitation of appellations of origin (AOC). The implementation of the Act of July, 22nd 1927 has led to drawing up lists of vine plots based on the criterion of vine cultivation antecedence.

Dating back to the early domestication period of grapevine (Vitis vinifera L.), expansion of human activity led to the creation of thousands of modern day genotypes that serve multiple purposes such as table and wine consumption. They also encompass a strong phenotypic diversity. Presently, viticulture faces various challenges, which include threatening climatic change scenarios and an historical track record of genetic erosion. Paritularly with regards to wine varieties, there is a pressing need to characterize the extant genetic diversity of modern varieties, as a means to delvier knowledge-based solutions under a rapidly evolving scenario, that may enable improved yields and profiles, resistance to pathogens, and increased resilience to climate change.

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.

Situated at the beginning of the 20th century on the territory of the Chablis municipality, delimited according to specialists of the time to plots of “kimmeridgian” origin, the vineyard producing Chablis

Carbon dioxide (CO2) is the gaseous species responsible for the sparkle in all sparkling wines, influencing their
visual appearance, aromas and mouthfeel.