Prefermentative CO₂ saturation of grape must to obtaining white wines with low SO₂ content

Temperature extremes affect grapevine physiology, as well as grape quality and production. In most grape growing regions, frost or heat wave events are rare and as such conducting a risk analysis using robust statistics makes the use of long term daily data necessary.

The use of elicitors to promote the biosynthesis of secondary metabolites in grapes has been tackled in several reports, however its study linked to nanotechnology is less developed.

To alter the vineyard microclimate and produce quality wine under a semi-arid climate, black geotextile inter-row mulch (M) was applied for two vintages (2016-2017). The grapes were sampled at three growing stages to conduct the untargeted metabolome and transcriptome analysis. The upregulated genes related to photosynthesis and heat shock proteins confirmed that M weakened the total light exposure and grapes suffered severe heat stress, resulting in lower sugar and higher acids at harvest. The integration of metabolome and transcriptome analysis identified the key genes responsible for the enhancements in phenylalanine, glutamine, ornithine, arginine, and C6 alcohol concentrations, and the downward trend in ε-viniferin, anthocyanins, flavonols, terpenes and norisoprenoids concentrations in M grapes.

The aim of this work was to reduce the alcohol content of Tempranillo wine. Tempranillo wines were produced by grapes harvested at different ripening dates (August 11 which was 21 oBrix and September 28 with 25 oBrix). At the second date, the Tempranillo wines were elaborated as follows: grapes were destemmed, crushed and collected into 50 L stainless-steel vats. Before preferementative maceration in cold, 50 % (M1) and 70 % (M2) of the must have been replaced by the same percentage of must from the first harvest. In addition, a control wine (C) was performed with only grapes from the second harvest.

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.