Identification of the agronomical and landscape potentialities in Côtes du Rhône area (France)

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.

Wine is often described with emotional terms, such as surprising, disappointing or pleasant. However, very little has been done to really characterize this link between emotions and wine. Can it really bring emotions to wine tasters? Many studies have looked at the extrinsic factors that can improve the emotional

Major factors involved in wine quality and typicity are soil type, climatic conditions, plant material (rootstock and cultivar), vineyard management practices and winemaking conditions.

Today the knowledge in terms of molecular composition of the colloidal matrix is ​​not enough in order to control its stability, according to the number of winemaking and wine stabilization processes. The physico-chemical processes during the winemaking change the composition and quantity of wine macromolecules. The goal today is to determine which analytical techniques will allow to discriminate these winemaking processes in order to better understand their impact on colloidal matrix stability as well as which molecules are responsible for its instabilities. METHODS: Wines obtained after conventional winemaking were subjected to different fining and chemical stabilization treatments. Different methods were used to investigate the wine macromolecular composition and stability after chemical stabilization, including quantitative and qualitative analyzes of total soluble polysaccharides by extraction under acidified ethanol, and by size exclusion separation as well as qNMR metabolomics. RESULTS: Observation of a slight difference at the quantitative level using classical analysis between the winemaking processes was observed as well as a strong discrimination by qNMR metabolomics.

Free amino acid concentrations in must of 7 Vitis vinifera cultivars (Cabernet Franc, Syrah, Merlot, Montepulciano, Sangiovese, Cesanese d’Affile, Carmenere) grown in the Latium region (Italy) were monitored from 2003 to 2005. The cultivars were located in a homogeneous soil and climatic zone and with the same training system (Cordon Spur).