Mgaloblishvili Rpv29 and Rpv31 loci reveal new insights on downy mildew resistance sources in Vitis vinifera

Winemakers are increasingly experimenting with new techniques, such as spontaneous fermentation, prolonged yeast contact, higher pH, minimal sulphur dioxid, filtration and clarification or oxidative ageing. Along with this, the risk of microbial spoilage increases, and so the off-flavour mousiness, long time underestimated, is becoming more frequent. Characteristic of the mousy off-flavour is the delayed perception after swallowing the wine. After a few seconds the flavour appears, reminiscent of a dirty mouse cage. There are three known compounds that cause mousy off-flavor: 2-ethyltetrahydropyridine, 2-acetyltetrahydopyridine, and 2-acetylpyrroline. Yeasts such as Dekkera/Brettanomyces and heterofermentative lactic acid bacteria like Lactobacillus hilgardii can release these compounds.

Studies suggest that ethanol (EtOH), triggers plant adaptation to various stresses at low concentrations (10 µM to 10 mM).

Viticulture and oenology face two major challenges today, climate change and the reduction in the use of inputs. Climate change induces low acidity and microbiologically less stable wines

Aim: To investigate the effects of different levels of temperature and light intensity on grapevine vegetative growth and bud fruitfulness, which includes the number and size of inflorescence primordia in primary buds.

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.