Comportement hydrique des sols viticoles et leur influence sur le terroir

Valpolicella is a famous wine producing region in the province of Verona owing its fame above all to the production of two Protected Designation of Origins (PDOs) withered wines: Amarone and Recioto. In recent years, however, the wineries have been interested in the enhancement and qualitative increase of another PDO, Valpolicella Superiore. All the Valpolicella PDOs wines are produced with a unique grape blend, mainly Corvina, Corvinone, Rondinella and a range of other minor varieties.

In order to study the impact of climate change on Bordeaux grape varieties and to assess the adaptation capacities of candidates to the grape varieties of this wine region to the new climatic conditions, an experimental block design composed of 52 grape varieties was set up in 2009 at the INRAE Bordeaux Aquitaine center. Among the many parameters studied, the three main phenological stages of the vine (budburst, flowering and veraison) have been closely monitored since 2012. Observations for each year, stage and variety were carried out on four independent replicates. Precocity indices have been calculated from the data obtained over the 2012-2021 period (Barbeau et al. 1998). This work allowed to group the phenological behaviour of the grapevine varieties, not only based on the timing of the subsequent developmental stages, but also on the overall precocity of the cycle and the total length of the cycle between budburst and veraison. Results regarding the variability observed among the different grape varieties for these phenological stages are presented as heat maps.

La maîtrise de la typicité du vin s’élabore au niveau local ou communal d’une exploitation viticole et/ou d’une cave, unité de vinification. La cave coopérative de Saint-Hilaire­-d’Ozilhan (AOC Côtes-du-Rhône), dont le territoire communal s’étend sur une superficie de 1 670 ha, couvre près de 310 ha cultivés en vigne. Elle réalise des vinifications «au terroir», en utilisant des regroupements d’unités de sol en 9 unités de terroir potentiellement viticoles, définies en s’appuyant sur la parenté des substrats. Diverses sélections d’une même unité peuvent aboutir aussi à des vins différents, ce qui suggère une hétérogénéité spatiale de certaines unités définies.

The light exposure of white wine is responsible for several reactions leading to changes on colour, flavours and, consequently, affecting the sensory profile.

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.