Impact of winemaking processes on wine polysaccharides, improving by qNMR

Knowledge of vine reaction to plant spacing under high potential soil conditions is restricted. This study was done to determine effects of vine spacing

Context and purpose of the study. Nitrogen (N) limitation enhances the production of phenolic compounds in grapes due to the downregulation of the flavonoid biosynthesis pathway.

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.

The integration of sensor systems in viticulture is significantly improving vineyard management by enabling faster, comprehensive crop data collection across the entire vineyard, supporting more informed viticultural decision-making, and as a result promoting sustainability.

Úbeda-Aguilera, C a, b, Peña-Neira, A.b Del Barrio-Galán, R.b, c a Biomedical Sciences Institute, Science Faculty, Universidad Autónoma de Chile, Chile. b Department of Agro-Industry and Enology, Faculty of Agronomical Sciences, University of Chile, Post Office Box 1004, Santa Rosa 11315, La Pintana, Santiago, Chile c Lallemand Inc. Chile y Compañía Limitada, Rosario Norte 407, piso 6, Las Condes, Santiago, Chile The wine is a complex matrix made up of several compounds which can interact among themselves throughout the wine ageing process, thereby modifying their sensorial characteristics. It is well known that during ageing of wines on lees, polysaccharides (mainly mannoproteins) can be released and can interact with the aromatic fraction modifying its volatility.