A DNA-free editing approach to help viticulture sustainability: dual editing of DMR6-1 and DMR6-2 enhances resistance to downy mildew 

To characterize in depth the enological productions according to the origin territories and to provide modern tools for the qualitative raising of the assorted typologies of wine produced, Veneto Agricoltura (the regional agency for the agriculture, forestry and food industry development), the Regional Government of Veneto (north-eastern Italy) and various Consortia of Producers have undertaken since 2002 a systematic classification of the viticultural territories by agro-ecological zoning to achieve a strategic project aimed to set Veneto as the first Italian region to have completed in a systematic and scientifically rigorous way the zoning of most of its Denomination of Origin areas.

Remote sensing, using Near Infra Red wavelength, can characterize within-vineyard variability using vegetation index. Between 2007 and 2009, a study was led on the vineyards of a cooperative winery, in Fitou area (France) aiming at characterizing vineyard oenological potential. A vegetation index, green leaf cover, developed on crops (wheat, rice, corn…) was implemented on vineyards.

In modern viticulture, sustainability must be considered not only into the winery, but in the vineyard as well, being that with the most attentive interventions in order to protect the environment. In this context, the new “fungi resistant” varieties represent a valid option for reducing the negative environmental impact of agrochemicals used in viticulture, including those ones used in organic farming (given the copper accumulation into soils). Several application studies have demonstrated the enological validity of many resistant varieties, both in price and as a blend. Also, under the production point of view, the feasibility and economical sustainability of the new resistant varieties was verified. The aim of this work was to deepen the knowledge on the organoleptic characteristics of wines obtained from the Fleurtai and Soreli varieties and to compare them with the wine obtained from Tocai Friulano, the mother variety in the area destined for the production of the Lison Classico DOCG appellation. The purpose of the work is then to verify the possibility of introducing resistant varieties into the DOCG while maintaining the wine name of the appellation linked to the territory.

The “GuIturnio dei Colli Piacentini” V.Q.PR.D. results from the vinification of Barbera (55-70%) and Bonarda (30-40%) cultivars, grown in the hilly area of the Piacenza district, identified by the DM 31-07-93 art. 3.
The present work concerns the “zonation” of this area, constituted by 3 valleys Tidone (A), Nure (B) and Arda (C )

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.