Determinazione della frazione aromatica dei vini, quale strumento per-la valorizzazione del territorio viticolo

In a context of reducing herbicide use, the most part of French vineyards are developing permanent grass cover crops on inter-rows alleys, while under the row chemical weeding remains the general case. The setting up of a controlled grass cover crop under the vine row could be a complementary alternative to mechanical weeding – which one is very restrictive – interesting from a technical and economical point of view. The present study aimed at assessing agronomic impacts of grass cover crop under the row in different climatic conditions and production objectives.

Measuring the effect of oxygen consumption on the colour of wines as the level of dissolved oxygen decreases over time is very useful to know how much oxygen a wine is able to consume without significantly altering its colour. The changes produced in wine after being exposed to high oxygen concen-trations have been studied by different authors, but in all cases the wine has been analysed once the oxygen consumption process has been completed. This work presents the results obtained with the use of an equipment designed and made to measure simultaneously the level of dissolved oxygen and the spectrum of the wine, during the oxygen consumption process from saturation levels with air to very low levels, which indicate the total consumption of the dosed oxygen.

Pour son travail de cartographie et de caractérisation des terroirs, la Cellule Terroirs Viticoles utilise la méthode développée par l’Unité Vigne et Vin du Centre INRA d’Angers. Cette méthode reconnue au niveau international est appliquée dans les vignobles du Val de Loire à l’échelle du 1/10 000e et est valorisée par des éditions d’Atlas Viticoles à destination des viticulteurs et des organismes techniques.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

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.