Identification of the agronomical and landscape potentialities in Côtes du Rhône area (France)

La région viticole de Mendoza est la principale zone vitivinicole d’Argentine qui se compose de 3 oasis (Nord, Valle de Uco, Sud). La première zone vitivinicole, située dans l’oasis Nord, est composée par les département de Luján de Cuyo et Maipu. C’est la zone de production la plus ancienne et la plus reconnue pour la qualité de sa production. Ce travail se porte plus particulièrement sur le département de Luján de Cuyo qui constitue le lieu traditionnel de production du Malbec argertin. Ce travail propose de caractériser les terroirs et de mettre en avant leurs typicités.

The effect that some extraction techniques, such as ultrasound (Cacciola, Batllò, Ferraretto, Vincenzi, & Celotti, 2013; Povey & McClements, 1988) or microwaves (Carew, Close, & Dambergs, 2015; Carew, Gill, Close, & Dambergs, 2014) produce on the aroma of red wines, when applied to processes of extractive nature, such as pre-fermentative maceration or ageing with oak chips (Spanish oak – Quercus pyrenaica and French oak – Quercus robur) has been studied. The volatile profile was determined by means of gas chromatography coupled with olfactometric and mass spectrometric detection. A sensory analysis was also carried out. No indications were found to show that the pre-fermentative treatment with microwaves or ultrasound modified the sensory profile of the wines whereas the application of such energies during the ageing phase showed some positive trends at sensory level.

Disease resistant grapevine varieties (PIWI) are increasingly important for sustainable wine production, yet the impact of different yeasts on their wine profiles remains poorly studied. In this study, nine white interspecies varieties (i.e., caladris blanc, fleurtai, hibernal, johanniter, muscaris, sauvignon kretos, soreli, souvignier gris, and voltis) grown at the faculty of agriculture, university of Zagreb (Croatia) were vinified with three different saccharomyces cerevisiae yeasts (control strain, zymaflore x5, and zymaflore xarom).

Differences in wine flavour proceed primarily from grape quality. Environmental factors determined by the climate, soil and training systems modify many grape and wine quality traits. Metabolic profiling based on proton nuclear magnetic resonance (1H-NMR) spectra has been proved to be useful to study multifactorial effects of the vine environment on intricate grape quality traits. The capacity of this method to discriminate the environmental effects on wine has to be demonstrated.

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).