Varieties and rootstocks: an important mean for adaptation to terroir

Ultra-premium champagne wines are characterized by a long stay on laths. The goal of the winemaker is to use all possible oenological techniques to keep the aromatic freshness of the future products. To that purpose, some champagne base wines can be aged on lees in oak barrels. However, if it is now acknowledged that such ageing practices contribute to the oxidative stability of dry white wines, no study has been done on Chardonnay champagne base wines designed for a long ageing on laths [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.

Wine production is a complex biochemical process that involves a heterogeneous microbiota consisting of different microorganisms such as yeasts, bacteria, and filamentous fungi. Among these microorganisms, yeasts play a predominant role in the chemistry of wine, as they actively participate in alcoholic fermentation, a biochemical process that transforms the sugars in grapes into ethanol and carbon dioxide while producing additional by-products. The quality of the final product is greatly influenced by the microbiota present in the grape berry, and the demand for indigenous yeast starters adapted to specific grape must and reflecting the biodiversity of a particular region is increasing. This supports the concept that indigenous yeast strains can be associated with a “terroir”.

An emblematic name of the burgundy wine region, a few kilometers from dijon, gevrey-chambertin stands out as a small wine town of international renown in the heart of a prestigious red wine vineyard listed as a unesco world heritage site.

Using electromagnetic conductivity mapping and GIS technology, we identified two unique soil zones within a 0.8-hectare Cabernet Franc block in central Virginia, USA.