Coping with extreme climatic events: some lessons from recent work on grapevine under heat peak

Chitin is the main structural component of a large number of organisms (i.e., mollusks, insects, crustaceans, fungi, algae), and marine invertebrates including crabs and shrimps. The main derivative of chitin is chitosan (CH), produced by N-deacetylation of chitin in alkaline solutions. Over the past decade, the OIV/OENO 338A/ 2009 resolution approved the addition of allergen-free fungoid CH to must and wine as an adjuvant for microbiological control, prevention of haziness, metals chelation and ochratoxins removal (European Commission. 2011). Despite several studies on application of CH in winemaking, there are still very limited and controversial data on its interaction with acidic components in wine (Colan-gelo et al., 2018; Castro Marin et al., 2021).

Damage during the budbreak period due to spring season frosts remains one of the most significant weather-related challenges to viticulture around the world. For example, in 2021, €2bn of estimated damage was reported in france while >50% of vineyards were badly affected in the UK in 2017.

The wine market interest for autochthonous varieties, particularly from less known wine regions, has significantly raised in the past few years. In that context, Slovenia, a small country from central Europe with a long winemaking tradition, is getting more and more attention, particularly through its range of unique regional varieties. Among them, Zelen, meaning “green” in Slovene, can only be found in the Vipava valley region, located on the western side of the country, near the border with Italy. When they are young, Zelen wines display very singular aromas reminiscent of rosemary, sage and white fruit. Despite its uniqueness, Zelen wine aromatic typicality is poorly documented in the literature.

On the market there are several wine grapes which are tolerant to the main fungal diseases. These varieties, commonly defined “resistant”, were developed in the grapevine breeding programs carried out mainly in Germany, France, Hungary and Italy. Some of these cultivars have been included in the national catalogues of wine grape varieties and have sometimes been allowed for specific kinds of wine. The VEVIR project, aimed at the enological evaluation of resistant vines, involves 33 cultivars achieved at the State Institute for Viticulture Freiburg in Germany, the Research Institute of Viticulture and Enology Pecs in Hungary and the Fondazione Edmund Mach S. Michele all’Adige (FEM) in Italy.

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.