Influence de la nutrition potassique sur le manque d’acidité des vins issus du cépage Negrette

Aging wine on lees is a consolidated practice during which some yeast components (e.g., polysaccharides,
proteins, peptides) are released and solubilized in wine thus, affecting its stability and quality.
Apart from the widely studied mannoproteins, the role of other yeast components in modulating wine
characteristics is still scarce. Wine peptides have been studied for their contribution to taste, antioxidant,
and antihypertensive potentials. However, the peptides detected in wine can be influenced by the
interaction between yeasts and grape components.

Planting vineyards in cooler climates has been used over recent years as
a strategy to counter the climatic shifts caused by climate change. A move towards higher altitudes in hilly and mountainous wine regions may provide a solution to deleterious effects that increased ambient temperatures have on wine quality. Until now, the influences of higher altitudes and their climates, as well as their effect on vine growing cycles, still holds a lot of scientific uncertainty. The transnational EU-funded project REBECKA (Interreg V-A IT-AT: ITAT1002, duration: 2017-2019) has the objective to develop a regional valuation method to rate the suitability for viticulture in South Tyrol (Italy) and Carinthia (Austria). Preliminary surveys were performed regarding the effects of altitude on ripening performance of the cultivar Pinot Noir.

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.

In the Champagne vineyards, spring frosts are the cause of significant variations in the volume of the harvest which are very penalizing for the trade. This variability is reflected both in time (years without frost alternating with years with severe frosts) and in space. Certain sectors of the vineyard are in fact statistically more susceptible to frost than others, but each year no municipality can consider itself immune to this climatic accident. The objective of the study is precisely to analyze the spatial distribution of frost and to determine its main mechanisms, linked to the topography of the hillsides, their orientation but also to regional meteorological variables.

Estimation of vineyard leaf area index (LAI) is an important aspect for the winegrowers. However, tracking and monitoring are difficult tasks due to time constraints. Satellite and unmanned aerial vehicle (UAV) imaging have become a practical monitoring method for LAI. Nevertheless, for a proper LAI determination, the image’s spatial resolution is a key factor, since low-resolution images are incapable of distinguishing between adjacent vines due to the large area covered in each pixel, this leads to misinterpretation or generalisation of vineyard information.