From genes to vineyards: system biology and new breeding technologies for water stress tolerance in grapevines

Dolcetto (Vitis vinifera L.) is one of the traditionally cultivated varieties in Piedmont (north-east Italy). Dolcetto wines have long been associated with local consumption and they are little known internationally. In particular, the Ovada area (south-east Piedmont), even if it represents a small share of the regional PDO Dolcetto production, is one of the oldest and vocated territory, giving wine also suitable for aging. In this study, the basic composition and phenolic content of Dolcetto grapes for Ovada DOCG wines have been investigated in three different vintages (2020-2022), as well as the main aspects of the derived commercial and experimental wines (basic parameters, phenolics, volatile compounds, sensory properties).

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).

Remote sensing, using Near Infra Red wavelength, can characterize within-vineyard variability using vegetation index. Between 2007 and 2009, a study was led on the vineyards of a cooperative winery, in Fitou area (France) aiming at characterizing vineyard oenological potential. A vegetation index, green leaf cover, developed on crops (wheat, rice, corn…) was implemented on vineyards.

Wine storage ensuring the quality and correct aging is one of the issues that wineries, wine traders and consumers encounter after wine bottling.

The ongoing climate change results in increasing mean air temperature, which is manifested by weather extremes or sudden changes between drought and local heavy rainfalls. These changing conditions are especially challenging for the established grapevine varieties growing under cool climate conditions due to an increased biotic infection pressure. Thus, the scope of most grapevine breeding programs is the selection of mildew fungus-resistant and climatic adapted grapevines with balanced, healthy yield and outstanding wine quality. Since no resistances or candidate genes have yet been described for Botrytis bunch rot (BBR), physical-mechanical traits like berry size and thick, impermeable berry cuticles phenotyped with high-throughput sensors represent novel effective parameters to predict BBR.