Vine performance benchmarking of indigenous Cypriot grape varieties Xynisteri and Maratheftiko

A rapid and reproducible microwave (MW)-assisted acid digestion protocol was developed to determine the elemental composition of grapevine bark samples using ICP-MS.

In this video recording of the IVES science meeting 2024, Silvina Dayer (PhD in Agronomy, Les Sanctuaires du Mirazur-Groupe Mauro Colagreco, Menton, France) speaks about grapevine drought response to increase vineyard sustainability. This presentation is based on an original article accessible for free on IVES Technical Reviews.

Rootstocks play a key role in the grapevine’s adaptation to the increasing soil water scarcity related to climate change. A pot experiment carried out in 2022 aimed at assessing the physiological responses of seven ungrafted rootstocks to a progressive soil water deficit and a subsequent recovery to field capacity.

Context and purpose of the study. `Grüner Veltliner´ is the most important Austrian white quality wine variety, which is mainly used to produce primary fruity wines.

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).