Application of nitrogen forms such as nitrate, urea, and amino acids effects on leaf and berry physiology and wine quality

Using grapevine genetic diversity is one of the strategies to adapt viticulture to climate change. In this sense, assessing the plasticity of cultivars in their responses to environmental conditions is essential. For this purpose, the drought tolerance of Grenache, Tempranillo and Semillon cultivars grafted onto SO4 was evaluated at two experimental vineyards, one located in Valencia (Spain) and the other in Bordeaux (France). This was done by assessing gas exchange parameters, water relations and leaf hydraulic traits at the end of the season.

The construction of vineyard landscapes along the Burgundy Côte is the result of geological processes and of human labour. Substratum diversity in this vineyard is the result of a very long history explained by the diversity of Jurassic sedimentary facies and Tertiary tectonic activity. The nature and thickness of Quaternary deposits (Weichselian scree debris and alluvial fans) reflect sediment dynamics concurrent with the last glaciation.

‘Timorasso’ is an autochthonous white grape variety from southern Piedmont (Italy) used for producing wines in the Colli Tortonesi product designation of origin (PDO). Over the last decade, there has been a notable rise in its production, due to the increased interest of wine enthusiasts who prized its wine distinctive ageing notes [1].

The Occitanie Region is the first vine-growing area in France: 270 000 hectares of vineyard and an annual production of 15 million hectoliters. Its annual income reaches 1 900 million euros, of which 900 million euros in export.The vine and wine sector is facing many issues: inputs reduction, adaptation to climate change, maintaining the production competitiveness, digital tools integration in production and transformation processes, and the production of quality wines meeting the consumer demand.

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).