Effect of Candida zemplinina oak chips biofilm on wine aroma profile

Champagne wines are produced via a two-step process: the first is an initial alcoholic fermentation of grape must that produces a still base wine, followed by a second fermentation in bottle – the prise de mousse – that produces the effervescence. This appellation produces non-vintage sparkling wines composed of still base wines assembled from different vintages, varieties, and regions. These base wines, or “reserve wines,” are typically conserved on their fine lies and used to compensate for quality variance between vintages (1). Continuously blending small amounts of these reserve wines into newer ones also facilitates preserving the producer’s “house style.”

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.

Context and Purpose of the Study. A deeper understanding of the relationship between weather conditions and wine quality is essential for assessing the impact of climate change and developing effective adaptation strategies.

Phenolic compounds are very importants in crop plants, which is why there have been the subject of a large number of studies

The main challenge of regulated deficit irrigation (RDI) research is to isolate the factors that come with RDI, the direct effect of plant water status from the indirect ones like increased radiation and temperature changes on the cluster zone. This study aims to isolate the effects of vine water status from the effects of increased radiation on the phenolic composition of grapes subjected to RDI.
A three-year study on an RDI experiment where radiation was controlled was implemented in a commercial vineyard of Cabernet Sauvignon in Chile. Four RDI treatments based on partial evapotranspiration (ET) irrigation were established. Irrigation treatments were 100% ET, 70% ET, 50-100% ET (50% ET before veraison and 100% ET afterward), and 35-100% ET (35% ET before veraison and 100% ET afterward).