Incidences of the climate, the soil and the harvest date on Colombard aromatic potential in Gascony

Mandilaria, a red grape variety indigenous to the Aegean islands, is well known for its robust tannins and pronounced astringency, which can challenge the palatability and marketability of its wines. The aim of this study was the reduction of astringency in wines made exclusively from mandilaria grapes through dehydrations practices and targeted winery applications.

In view of sustainability and zero-waste initiatives, the valorisation of sidestreams is a key emerging topic in the wine industry.

Background: The Flavan-3-ol extraction from grape skin and seed during red-winemaking and their retention into wines depend on many factors, some of which are modified in the winemaking of blend wines. Recent research shows that Marselan, have grapes with high proportion of skins with high concentrations of flavanols, but produces red-wines with low proportion of skin derived flavanols, differently to the observed in Syrah or Tannat. But the factors explaining these differences are not yet understood.

Polyphenols, bioactive secondary metabolites abundantly found in various grapevine components such as stalks, skins, and seeds, have attracted considerable attention in recent decades due to their potential health benefits. These compounds, including flavan-3-ols, flavanols, flavones, and stilbenes, are known for their antioxidant and anti-inflammatory properties.

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.