Estimation of stomatal conductance and chlorophyll fluorescence in Croatian grapevine germplasm under water deficit    

The Txakoli, a white wine produced in the Basque Country (North of Spain), has recently gained popularity due to wine quality improvement and increase in both acreages of production and wine consumption. The aim of this study was to characterize the chemical and microbiological differences between Txakoli wines made with grapes from different sites.

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.

This study investigates how the sensory profile of Pinot Blanc is affected from different maceration and pressing techniques. Grapes were sourced from four vineyards in the village Tramin in South Tyrol. For the experiment 200 kg of grapes from each vineyard site were hand picked the day before harvest for the commercial winery took place. Grapes were stored over night at 4°C, homogenized and processed in the experimental winery at Laimburg research centre the day after harvest. Four different pressing techniques were applied in duplicates of 100kg each.

Since the approval in 2019 of the use of high-power ultrasound (US) in winemaking to support extractive processes from grape to must, the study of this technology in red winemaking has increased significantly, with laboratory and semi-industrial scale studies.

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.