Molecular characterization of a variegated grapevine mutant cv Bruce’s Sport

Today’s viticulture is facing a new climatic scenario with temperature increases and rainfall deficits, generated by the effect of climate change. As a result of these new conditions, there are earlier harvests, increased plant water stress and higher disease risk in wetter wine-growing regions.

Le Dipartimento di Progettazione dell ‘Architettura del Politecnico di Milano et l’Istituto di Viticoltura della Facoltà d’Agraria di Piacenza dell’Università Cattolica del Sacra Cuore, ont elaboré une proposition pour réaliser, dans l’aire de colline de la province de Piacenza, un Parco Produttivo Agrovitivinicolo.

The Valpolicella area (Veneto Region, Italy) is famous for its high quality wines: Amarone and Recioto, both obtained from partial post-harvest dehydrated red grapes. The main cultivars used for these wines are Corvina and Corvinone. In this Region hundreds of years ago a particular training system (Pergola, cordon/cane with horizontal shoot-positioning) was developed. In the last 20 years the Guyot have been introduced in the area; now Pergola and Guyot are equally widespread in the Valpolicella area. In two different environmental conditions (hill and floodplain) two vineyards, one for each type of training system, were studied along two years (2011-2012).

Smoke impact in wines is caused by a wide range of volatile phenols found in wildfire smoke. These compounds are absorbed and accumulate in berries, where they may also become glycosylated. Both volatile and glycosylated forms eventually end up in wine where they can cause off-flavors. The impact on wine aroma is mainly attributed to volatile phenols, while in-mouth hydrolysis of glycosylated forms may be responsible for long-lasting “ashy” aftertastes (1).

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.