Terroir and precision viticulture: are they compatible?

Aim: In recent times the export of Beaujolais wines has been jeopardised due to a limit of manganese content (Mn) in wine implemented by China (2 mg/L), related to suspicions of potassium permanganate fraud. Nevertheless, soil Mn content may be high in some soil types in Beaujolais. The aim of this study was to improve knowledge of manganese transfer from soil to vine and wine because data on this subject is scarce.

Wine grape berries respond to postharvest treatments with specific gaseous elicitors in terms of metabolic changes and composition. Short-term (3 days) high (30 KPa) CO2 treatment affects phenol compound concentration in skins of ‘Trebbiano toscano’ berries.

A common problem in wineries is haze formation after bottling, mainly caused by unstable proteins present in white wine. The most used material to eliminate these proteins is bentonite.

Depuis la création des outils d’amélioration et de suivi de la qualité, le CREDO développe et réalise des zonages de potentialités viticoles.

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).