Introducing heterogeneity measurements in terroir studies. Application in the região demarcada do douro (n portugal)

Insufficient levels of malate and lack of acidity in commercial grape cultivars (V.vinifera) hinders the quality of fruit grown in warm climates. Conversely, excessive levels of malate and sourness in wild Vitis grape, leads to unpalatable fruit and complicates the introgression of valuable disease resistant alleles through breeding. Nonetheless, albeit decades of research, knowledge regarding the molecular regulation of malate levels in grape remains limited.

Botrytis cinerea (Bc) is one of the main pathogens affecting the cultivated grapevine. A key role in grapevine tissue colonization is played by cell wall (CW) remodeling driven by CW Modifying Enzymes (CWMEs), expressed both by the host and the pathogen. Their action can impact CW integrity and trigger specific immune signaling, thus influencing Bc infection outcome. To further characterize the role of the CW in the grapevine response to Bc, two contrasting genotypes in their resistance to the fungus were artificially inoculated at full bloom. RNA-seq analysis and biochemical characterization of the CW and its modification in samples collected at 24 hours post-inoculation highlighted significant differences between genotypes.

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, is one of the most destructive diseases of Vitis vinifera worldwide. All V. vinifera cultivars are susceptible to P. viticola infection, and epidemics can spread across an entire vineyard within a matter of weeks. Severe outbreaks cause substantial reductions in yield and fruit quality. Tracking GDM spread by manual scouting is time-consuming and unfeasible over large spatial extents.

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.

Noble rot sweet wines are reputed wines, traditionally elaborated according to a singular vinification process involving the harvesting of overripe grapes under the action of the ascomycete fungus Botrytis cinerea.