Techniques of delimitation in France

Malbec grapes have been cultivated for 150 years in Argentina. In the last 20 years Argentinian Malbec wines have emerged as a commercial boom worldwide.

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Nitrogen (N) uptake by grapevine roots in forms like nitrate, ammonium, urea, or amino acids influences vegetative and generative growth, impacting grape quality and wine sensory profile. The study examined nitrogen’s influence on phenolic compounds in leaves, berries, and wine across different scales — hydroponics, soil culture, and vineyard trials. Nitrogen forms altered metabolite patterns in leaves and wine significantly, affecting aroma and flavor. Key nitrogen assimilation enzymes (NR, NiR, GS) in grapevine rootstocks responded to nitrogen forms and timing. Hydroponically grown rootstocks fertilized with various forms showed differences in enzyme expression and activity, suggesting rootstocks can assimilate amino acid glutamine (Gln).

Right after serving a sparkling wine into a glass, thousands of rising and bursting bubbles convey gas-phase CO2 and volatile organic compounds (VOCs) in the headspace above the champagne surface, thus progressively modifying the gaseous chemical space perceived by the consumer [1].

The phenolic compounds of red wines represent a source of numerous benefits for human health, which is why they are a constant subject of scientific research. Winemaking in Serbia has a growing economic significance, with particularly autochthonous varieties included [1]. This research identifies and quantifies phenolic compounds of Serbian red varietal wines of Prokupac and Black Tamjanika varieties. Quantification of the level of phenolics has been conducted, including molecular tannins [(+)-catechin, (-)-epicatechin, procyanidin dimers B1, B2, B3, B4], molecular anthocyanins, and the mean degree of polymerization of tannins by HPLC by UV detection, total antioxidant capacity via spectrophotometric methods and chromatic characteristics via CIELAB.