High resolution climate spatial analysis of European winegrowing regions

Une étude préliminaire des situations météorologiques journalières a été réalisée pour l’Afrique du Sud et pour les mois de février (période de maturation des raisins dans la Province occidentale du Cap), à l’image de la classification synoptique réalisée aux latitudes tempérées en France (Jones & Davis, 2000), afin d’étudier les relations entre le climat et la viticulture à des latitudes plus basses.

Sforzato di Valtellina DOCG is a reinforced dry red wine produced in the mountain area of Valtellina alpine valley (North Italy), using ‘Nebbiolo’ grapes that undergo a withering process. This process impacts on the grape composition due to a sugar concentration and changes in secondary metabolism influencing volatile organic compounds (VOCs) and polyphenols.

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).

The mechanical strength or firmness of a fruit is considered an important parameter to characterize its state of maturity or conservation, as other parameters such as sugar level or color.

Aerial thermography has emerged as a promising tool for water stress detection in grapevines, but there are still challenges associated with this technology, particularly concerning the methodology employed to extract reliable canopy temperature values. This consideration is relevant especially in vertically trained vineyards, due to the presence of multiple surfaces which are captured by drone thermal cameras with high-resolution. To test the technology and the data analysis required, a field study was conducted during the 2022-2023 season in a model vineyard with multiple scions-rootstock combinations trained on a vertical shoot-positioning (VSP) system. Additionally, three irrigation regimes were implemented to introduce variability in water stress levels.