“Terroir” and climate change in Franconia / Germany

Maturation of wine on lees (often referred as sur lie) is a common practice applied by many winemakers around the world. In the past this method was applied mainly on white and/or sparkling wine production but recently also to red wine production. In our experiment, we matured red wine on wine lees of two origins: a) Light wine lees, collected after the completion of the alcoholic fermentation, b) Heavy lees, collected after the completion of the malolactic fermentation. The lees were free of off-odors and were added in the red wine in percentage 3% and 8%, simulating common winemaking addition. The maturation lasted in total six months and samples were collected for analysis after one, three and six months. During storage the lees were stirred.

INTRODUCTION: Foam stability of sparkling wines is significantly favored by the presence of surface active agents such as proteins and polysaccharides [1]. For that reason, the renowned sparkling wines are aged after the second fermentation in contact with the lees for several months (even years). Thereby wines are enriched in these macromolecules due to yeast autolysis. Since this practice is slow and costly, winemakers are seeking for alternative procedures to increase their concentration in base wines. In that sense, the supplementation with inactive yeast during alcoholic fermentation has been proposed [2]. The aim of this study was to determine whether this new strategy is really useful for enriching base wines in macromolecules and for improving foam properties of the base wines.

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.

The appearance of the Phylloxera pest in the 19th century in Europe caused dramatical damages in grapevine diversity. To mitigate these losses, grapevine growers resorted to using crosses of different Vitis species, such as 110 Richter (110R) (V. berlandieri x V. rupestris), which has been invaluable for studying adaptations to stress responses in vineyards. Recently, a high quality chromosome scale assembly of 110R was released, but the available gene models were predicted without using as evidence transcriptional sequences obtained from roots, that are crucial organs in rootstock, and they may express certain genes exclusively. Therefore, we employed RNA sequencing reads of 110R roots under different stress conditions to predict new gene models in each haplotype of 110R under different stresses.

Soil plays an important role in wine quality, especially its water holding capacity because it affects the balance between vigour and grape yield. The aim of this work was to study the influence of different soil types on the must quality in a vineyard at DO Ca Rioja