Marketing terroir wines

Yeast protein extracts (ypes) have flocculating properties, allowing clarification of musts and wines. They are already authorized by oiv for fining purposes with a maximum dosage limit of 60 g/hl for red wines, and 30 g/hl for musts, white and rosè wines. The extraction of ypes from the cytoplasm of yeasts (saccharomyces spp) cells is defined by the resolution oiv oeno 452-2012, that indicate also some specification of the final product.

Climate change results in erratic weather conditions, which may lead for many crops including grapevine, to a reduced production and products of lower quality. Concerning grapevine, climate change results in shorter growing seasons and dates for budbreak, flowering and fruit maturity occur earlier in many regions. It also leads to an increase of various pests and diseases, as well as the vectors responsible for disease distribution.

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The presence of unstable proteins in wines can affect their stability and clarity. Before bottling, winemakers need to be sure that the wine is stable. A large number of stability tests have been proposed, usually based on heating a sample with a specific time-temperature couple. In practice, none is effective to accurately assess the risk of instability. Moreover, the interpretation of the results of these tests changes according to the region.

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.