Étude intégrée et allégée des terroirs viticoles en Anjou: caractérisation et zonage de l’unité terroir de base, en relation avec une enquête parcellaire

Aims: Shiraz is the most widely cultivated grape variety in Australia, and is grown under a range of viticultural and climatic conditions. Given its importance to the Australian wine sector, a number of studies have been conducted in recent years which involved a comprehensive assessment of grape composition, in order to objectively predict wine quality and style outcomes.

The universalization in wine production has been restricting the imprint of terroir in regional wines, resulting in loss of typicity. Microbes are the main driving force in wine production, conducting fermentation and originating a myriad of metabolites that underly wine aroma. Grape berries harbor an ecological niche composed of filamentous fungi, yeasts and bacteria, which are influenced by the ripening stage, cultivar and region. The research project GrapeMicrobiota gathers a consortium from University of Zaragoza, University of Minho and University of Tours and aims at the isolation of native yeast strains from berries of the wine region Douro, UNESCO World Heritage, towards the production of wines that stand out in the market for their authenticity and for reflecting their region of origin in their aroma.

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.

The 1365 ha of the Genevese vineyard are located at the south-western corner of the Swiss plateau, between 395m and 505 m altitude.

En vue d’une production économique de qualités des raisins optimales une connaissance des informations les plus différentes est importante. Les nouvelles technologies, telles qu’un SIG permettent de réunir les informations sur le terrain, la nature du sol, le danger d’érosion, le climat, la végétation, l’hydrographie, l’apparition de nuisible et de maladies, etc. Sur la base de cartes topographiques un SIG permet une vaste analyse, une appréciation des rapports complexes ainsi qu’une représentation cartographique. Sur la base de modélisations en trois dimensions du terrain avec le SIG, les ensembles de données saisies ainsi que leur classification au niveau local peuvent être utilisés dans la production de zonages régionaux.