Cover crops under-vine impact on grapevine performance and vineyard soil microorganisms is highly affected by edaphoclimatic conditions at a regional scale 

The wine industry is increasingly focused on carbon accounting due to consumer’s demand and the industry’s goal of reducing carbon emissions.

The nutrient reserves in the grapevine perennial structure perform a critical role in supplying the grapevine with nutrients

Cis-acting regulatory elements are DNA sequences that can be bound by transcription factors to regulate the expression of genes in a condition-dependent and tissue-specific way. It is nowadays possible to search for DNA motives and sequences that a given transcription factor is binding or at least can, but it is still hard to have a glance at all the transcription factors that are contemporaneously located at the same locus. Inspired by an existing technique that uses the CRISPR-Cas system in mammal cells, we are trying to develop a protocol to study such regulation in Vitis vinifera. Using the highly sequence-specific binding capacity of a catalytically inactive Cas9 protein (dCas9), our idea is to set up a system to target a desired sequence and precipitate all the crosslinked proteins and distantly interacting chromatin at this locus and analyze them.

Differences in wine flavour proceed primarily from grape quality. Environmental factors determined by the climate, soil and training systems modify many grape and wine quality traits. Metabolic profiling based on proton nuclear magnetic resonance (1H-NMR) spectra has been proved to be useful to study multifactorial effects of the vine environment on intricate grape quality traits. The capacity of this method to discriminate the environmental effects on wine has to be demonstrated.

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.