Cover crop influence on water relations, yield, grape and wine composition of Pinot noir

The nitrogen composition of the grapes directly affects the developments of alcoholic fermentation and influences the final aromatic composition of the wines. The aim of this study was to determine the effect and efficiency of foliar applications of urea on the nitrogen composition of grapes. This study was carried out during 2023 vintage and in the Chenin vineyard located in Estacion Experimental Mendoza (Argentina). Three urea concentrations 3, 6 and 9 Kg N/ha (C1, C2, and C3, respectively) and control (T) were applied in this vineyard at veraison.

Proteins play a significant role in the colloidal stability and clarity of white wines [1]. However, under conditions of high temperatures during storage or transportation, the proteins themselves can self-aggregate into light-dispersing particles causing the so-called protein haze [2]. Formation of these unattractive precipitates in bottled wine is a common defect of commercial wines, making them unacceptable for sale [3]. Previous studies identified the presence of phenolic compounds in the natural precipitate of white wine [4], contributing to the hypothesis that these compounds could be involved in the mechanism of protein haze formation.

The ‘fresh mushroom off-flavour’ has been recognized by the wine industry as an emerging defect since the 2000s. For many years, this off-flavour was not specifically characterized and rather grouped under ‘earthy’ and ‘musty’ taints. However, it has become increasingly problematic due to its rising prevalence. In some vineyards, incidents of this off-flavour now occur as frequently as once every five years. This trend may be associated with climatic changes affecting regions that are more prone to warm and wet seasons.

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.

Leaf removal in the fruit-zone has been employed to improve cluster light exposure and ventilation and therefore increase metabolite accumulation and reduce botrytis incidence in berries. When applied before flowering (early defoliation – ED), it can also decrease cluster compactness and regulate yield in high-yielding varieties. This study aimed to evaluate the impact of ED on the physiology and metabolism of Aragonez (syn. Tempranillo) berries along the ripening period. The experiment was set up in 2013 at a commercial vineyard located in the Lisbon winegrowing region.