Mining microbiome data to identify antagonists of grapevine downy mildew (Plasmopara viticola)

This study investigates how different pressing techniques impact on the sensory profile of Pinot Blanc wines sourced from different terroirs.

The project “Terroir Hesse” works out the main type and characteristics of soil-based terroirs and the resulting wine styles for the hessian wine-growing regions Rheingau and Hessian Bergstrasse.

Cover cropping in vineyard is a sustainable and alternative soil management system to conventional tillage that is gaining more and more importance among winegrowers and is being promoted, among other organizations, by the European Union through the eco-schemes of the Common Agricultural Policy.
However, the use of cover crops in Mediterranean viticultural environments is conditioned, to a large extent, by the availability of irrigation water which, in a context of global warming like the one we are experiencing, must be adjusted to savings strategies, supplying to the vine only what it needs in each moment.

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).

Aims: This work aimed i) to calibrate the accuracy of estimating vineyard water status by crop water stress index (CWSI) compared to stem water potential; ii) to determine the time interval during the day that best correlates to stem water potential and iii) to understand the its usefulness.