Amyndeon‐naoussa: the two faces of Xinomavro

ln the Mid-Loire Valley, in France, for the fast twenty years a network of experimental plots has been used to analyse the terroir effect on the behaviour of the Cabernet franc variety of grape. The study of the primary leaf area (SFI) for several vintages shows that it differs greatly from one terroir to another.

Synthetic pesticides are widely used in viticulture to ensure steady harvest quality and quantity. Fungicides are primarily used to control grapevine diseases but insecticides and herbicides are likewise used. Pesticide residues in viticultural areas currently represent a strong societal concern, but may also affect different trophic chains in such areas. In this project we wish to analyse honeybees collected from hives placed in different vineyards, their natural predator (the invasive hornet Vespa velutina), as well as the honey, grape berries, and wines produced.

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).

Grapevine (Vitis vinifera L.) exhibits a high level of genetic and phenotypic diversity among the approximately 6000 cultivars recorded. This perennial crop is highly vulnerable to numerous fungal diseases, including esca, which is a complex vascular pathology that poses a significant threat to the wine sector, as there is currently no cost-efficient curative method[1]. In this context, an effective approach to mitigate the impact of such diseases is by leveraging the crop’s genetic diversity. Indeed, susceptibility to esca disease appears to vary between cultivars, under artificial or natural infection. However, the mechanisms and varietal characteristics underlying cultivar susceptibility to esca are still unknown.

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.