Ripening of Mencía grape cultivar in different edaphoclimatic situations (D.O. Ribeira Sacra, Spain)

Grape quality potential for wine production is strongly influenced by environmental parameters such as climate and agronomic factors such as rootstock. Several studies underline the effect of rootstock on vegetative growth of the scions [1] and on berry composition [2, 3] with an impact on wine quality. Rootstocks are promising agronomic tools for climate change adaptation and in most grape-growing regions the potential diversity of rootstocks is not fully used and only a few genotypes are planted. Little is known about the effect of rootstock genetic variability on the aromatic composition in wines; thus further investigations are needed.

The wine sector is greatly threatened by climate change, but is also one of its contributors.

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.

Hydroxytyrosol (HT) is a phenolic compound with extensive bioactive properties. It is present in olives, olive oil and wines. Its occurrence in wines is partly due to yeast synthetise tyrosol from tyrosine by the Ehrlich pathway, which is subsequently hydroxylated to .
The aim of the present work is to study how different yeast strains can influence in the HT production and, how the different nitrogen consumption of each strain can interfere the production of bioactive compounds.

a) wine, a qualitative and user-friendly product, favors a visual support, even for a scientific study because it refers to the image of the terroir, in particular by its visible landscape. b) the vineyard landscape, which is fairly open by definition, favors this type of approach. c) the framework of the Terroir Test conducted by the URVV (INRA – Angers) comprises 15 micro-plots of 100 strains, and requires at this scale precise surveys of the environment, hence systematic shots, of the center of the plot, over 360°, at 50 mm intervals, at 1.70 m from the ground and horizontally.