Identifying physiological and genetic bases of grapevine adaptation to climate change with maintained quality: Genome diversity as a driver for phenotypic plasticity  (‘PlastiVigne’ project)

Vitis vinifera L., is considered one of the world’s most important cultivated fruit crops. In agriculture, bunch morphology is a grapevine-specific trait, which directly impacts fruit quality and health.
Bunch size, shape, and compactness are major aspects of bunch morphology, with the degree of compactness emerging as an important trait for grapevine genetic enhancement and vineyard management. The importance of this trait stems from its impact on disease susceptibility, berry ripening, and other grape quality properties. However, current knowledge of the genes controlling it remains limited.

Atmospheric and statistical models were used to increase understanding of potential climatic impacts, resulting from mesoscale physical processes that cause significant temperature variability for viticulture within the Stellenbosch Wine of Origin district. Hourly temperature values from 16 automatic weather stations and 40 tinytag data loggers located in the vineyards were analysed.

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).

In the context of climate change, excessive bitterness and astringency in wines have become increasingly prevalent. While variety selection and viticultural practices offer long-term solutions, they require considerable time before yielding practical results. In contrast, fining remains an accessible and immediate tool for winemakers.

 Temperatures are increasing due to climate change leading to advances in grapevine phenology and sugar accumulation in grape berries.