Climate, grapes, and wine: structure and suitability in a variable and changing climate

The timing of phenology is critical if grape quality potential is to be optimized. Phenological process based models are used to predict phenology. In this study, three different models

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.

Different separation techniques are frequently used during vinification process. Nowadays, clarification and microbiological stabilization of wine or beer can be done using precoat filters or crossflow filters to remove yeast and bacteria. Kieselguhr powders are the most used filter aids for precoat filtration. Their crystalline structure and their pulverulent nature induce ecotoxicological risks when used. Moreover, regeneration and reuse of these filter aids is not efficient and the filtration waste requires cost effective retreatment.

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.

La maturité optimale est définie en fonction du style de vin désiré, qui est fonction du marché. Le sol et le climat ont un effet sur la typicité des vins. Le niveau qualitatif des raisins et des vins, et le potentiel pour obtenir différents styles de vin est déterminé par l’association des caractéristiques naturel du terroir et les technologies mises en