Antociani ed acidi cinnamici per la caratterizzazione di vitigni in zone diverse della Toscana

Water is the main limiting factor for yield in viticulture. Improving drought adaptation in viticulture will be an increasingly important issue under climate change. Genetic variability of water deficit responses in grapevine partly results from the rootstocks, making them an attractive and relevant mean to achieve adaptation without changing the scion genotype. The objective of this work was to characterize the rootstock effect on the diurnal regulation of scion transpiration. A large panel of 55 commercial genotypes were grafted onto Cabernet Sauvignon. Three biological repetitions per genotype were analyzed. Potted plants were phenotyped on a greenhouse balance platform capable of assessing real-time water use and maintaining a targeted water deficit intensity. After a 10 days well-watered baseline period, an increasing water deficit was applied for 10 days, followed by a stable water deficit stress for 7 days. Pruning weight, root and aerial dry weight and transpiration were recorded and the experiment was repeated during two years. Transpiration efficiency (ratio between aerial biomass and transpiration) was calculated and δ13C was measured in leaves for the baseline and stable water deficit periods. A large genetic variability was observed within the panel. The rootstock had a significant impact on nocturnal transpiration which was also strongly and positively correlated with maximum daytime transpiration. The correlations with growth and water use efficiency related traits will be discussed. Transpiration data were also related with VPD and soil water content demonstrating the influence of environmental conditions on transpiration. These results highlighted the role of the rootstock in modulating water deficit responses and give insights for rootstock breeding programs aimed at identifying drought tolerant rootstocks. It was also helpful to better define the mechanisms on which the drought tolerance in grapevine rootstocks is based on.

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).

This study aims to create an updated, agile viticultural climate index (similar to the Winkler Index) by performing in-depth analyses of current and historical data from industry partners in several major winegrowing regions. The Winkler Index was developed in the early twentieth century based on analysis of various grape-growing regions in California. The index uses heat accumulation (i.e. Growing Degree Days) throughout the growing season to determine which grape varieties are best suited to each region. As viticultural regions are increasingly subject to the complexity and uncertainty of a changing climate, a more rigorous, agile model is needed to aid grape growers in determining which cultivars to plant where. For the first phase of this study, 21 industry partners throughout Napa Valley shared historical phenology, harvest, viticultural practice, and weather data related to their Cabernet sauvignon vineyard blocks. To complement this data, berry samples were collected throughout the 2021 growing season from 50 vineyard blocks located throughout 16 American Viticultural Areas that were then analyzed for basic berry chemistry and phenolics. These blocks have been mapped using a Geographic Information System (GIS), enabling analysis of altitude, vineyard row orientation, slope, and remotely sensed climate data. Sampling sites were also chosen based on their proximity to a weather station. By analyzing historical data from industry partners and data specifically collected for this study, it is possible to identify key parameters for further analysis. Initial results indicate extreme variability at a high spatial resolution not currently accounted for in modern viticultural climate indices and suggest that viticultural practices play a major role. Using the structure of data collection and analyses developed for the first phase, this project will soon be expanded to other wine regions globally, while continuing data collection in Napa Valley.

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.

The use of selected cultures of the species Lactiplantibacillus plantarum in Oenology has grown in prominence in recent years. While initial applications of this species centred very much around malolactic fermentation (MLF), there is strong evidence to show that certain strains can be harnessed for their bio-protective effects. Unwanted spontaneous MLF during alcoholic fermentation (AF), driven by rogue Oenococcus oeni, is a winemaking deviation that is very difficult to manage when it occurs. This work set out to determine the efficacy of one particular strain of Lactiplantibacillus plantarum(Viniflora® NoVA™ Protect), against this problem in Cabernet Sauvignon must. The work was carried out at commercial scale and in a winery environment and compared the bio-protective culture with the more traditional approach of reducing must pH by the addition of tartaric acid. The combination of both was also investigated. The concentration of both Oenococcus oeni and Lactiplantibacillus plantarum was determined using qPCR. The adventitious Oenococcus oeni showed the most growth during AF in the control wine, whereas in the wines treated with Lactiplantibacillus plantarum a bacteriostatic effect against this species was observed. This effect was comparable to the wines treated with tartaric acid. This has particular commercial relevance for controlling the flora in musts with high pH, or when the addition of tartaric acid is either not permitted or is prohibitive for other reasons.