Better understanding on the fungal chitosan and derivatives antiseptic effect on Brettanomyces bruxellensis in wine.

The fungicidal effect of UV-C radiation (100-280 nm wavelength) is well known, but its applicability for the control of pathogenic molds of grapes is conditioned by its effect on the host and by the risks inherent in its handling[1].
As an alternative, the effect in vitro of UV-B radiation (280-315 nm) on the main pathogenic molds of grapes has been studied: Botrytis cinerea, Aspergillus niger, Penicillium expansum and Rhizopus stolonifer.

Knowledge of the nitrogen nutrition status of grapevines is essential for the sustainable management of their nutrition for the production of quality grapes. The measurement of the chlorophyll index is a rapid, non-destructive and relatively inexpensive method that provides a good approximation of the nitrogen nutrition status of the vine during the season. Interpretation thresholds are currently insufficient or non-existent for some chlorophyll meters. Ideally, they should be available for each variety and each phenological stage. In order to popularize the use of chlorophyll-meters, measurements were carried out at Agroscope in Switzerland to establish the correlation between the indices obtained by the devices N-tester and SPAD 502.

Addressing the opportunities and challenges of genomics methods in grapevine (Vitis vinifera L.) requires the development of a comprehensive and accurate reference genome and annotation. We aimed to create a new gene annotation for the PN40024 grapevine reference genome by integrating the highly accurate and complete T2T assembly and the manually curated PN40024.v4 annotation. Here, we present a novel workflow to enhance the annotation of the T2T genome by incorporating past community input found in PN40024.v4. The pipeline’s containerization will improve the workflow’s reproducibility and flexibility, facilitating its inclusion as a shared workflow on the Grapedia portal, the grapevine genomics encyclopedia.

The biodiversity of cultivated vines has been significantly reduced due to a series of factors that have favoured the cultivation of a limited number of varieties and clones over time. In veneto, since 1980, a series of important actions have been implemented to counter this process. These actions have focused on the conservation of germplasm identified in the territory and the recovery of varieties historically present in the region, which were in danger of being abandoned and disappearing.

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).