Wines produces without SO2 addition: which impact on their colour? An approach at the global and pigments levels

High concentration of sugars in grapes and alcohols in wines is one of the consequences of climate change on viticulture production in several wine regions. One of the options to alleviate this potential problem is to perform severe shoot trimming of the vines to limit the production of carbohydrates. Two different studies were performed in order to investigate the effects of severe shoot trimming on the composition of Merlot grapes; in a first study severe shoot trimming was performed at three different phenological stages (at berry set, at the beginning of veraison and at the end of veraison), while in a second study two trimming treatments (standard shoot trimming and severe shoot trimming performed at the end of veraison) were combined with two shoot densities in order to evaluate the relative impact of these treatments on Merlot grape composition.

A two-bite compression test was applied on Cabernet franc grapes during two harvest seasons. The evolution of the texture parameters from véraison to harvest was studied and a new mechanical ripeness notion was introduced.

To make some particular wine styles (e.g., Amarone), grapes are harvested and stored in dehydrating rooms before vinification, in a process called withering

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.

Grapevine response to water deficit has been extensively studied. Nevertheless, debate still exists regarding some physiology adoption under drought