Effect of nitrogen content on fermentation kinetics and aroma profile of assyrtiko wine

The structural diversity is one of the most remarkable characteristics of proanthocyanidins (PA). Indeed, PA in wines may vary in the B-ring and C-ring substitutes, the C-ring stereochemistry, the degree of polymerization (DP) and the linkage between the monomers. Knowing in detail the structural characteristics of the PA of a wine can help us to understand and modulate several sensorial characteristics of the wine, such as color, antioxidant properties, flavor, and mouthfeel properties. In the last years was discovered and confirmed the presence of sulfonated monomeric and oligomeric flavan-3-ols in wine [1], as well as was pointed out their importance in wine quality [1,2].

Nella realtà viticola Italiana il Prosecco è uno degli esempi più evidenti di un percorso storico che ha saputo valorizzare lo stretto legame tra vitigno e territorio d’origine.

For a long time, cool climate grapevine breeding has striven for early ripening cultivars to adapt to the former climate conditions.

Dans le vignoble champenois, le risque thermique associé au gel des bourgeons au printemps et en hiver est très mal connu et ne peut être envisagé qu’à l’échelle locale, en raison d’une variabilité spatiale forte. L’objectif de l’étude est d’appréhender ce risque de façon fiable et pluri locale en utilisant le réseau de stations météos récemment implanté.

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.