Techniques for sunburn reduction in bunches in Vitis vinifera L. cv. Graciano

The objective of the present work was to study the influence of the foliar application of seaweed (Laminaria japonica), on the bunch and on the must in the ‘Cabernet Sauvignon’ grape. The experiment was carried out in the years 2021/2022, in a 21-year-old commercial vineyard, in the municipality of “Dom Pedrito” – “Rio Grande do Sul” (RS). A completely randomized experimental design was used, with 4 treatments and 4 replications (7 plants per replication). The treatments were: T1- control treatment; T2- Exal Powder 5 g L-1; T3- Hidro Exal 15 ml L-1; T4- Exal Powder 5 g L-1+ Hidro Exal 15 ml L-1.

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.

AIM: Firm tissues of grapes and yeast are the major sources of lipids in wine.

Grapevine (Vitis vinifera) and its derived products, in terms of cultivated area and economic volume, constitute the most relevant fruit crop in the world (7.5 million cultivated hectares). In the current context of climate change, the wine sector faces unprecedented challenges to satisfy a growing demand for wines of greater quality through sustainable viticulture. Global warming threatens quality wine production in Mediterranean wine regions in particular. The increase in heatwaves and drought episodes accelerate the vine phenology and alter the ripening and composition of grapes and wine. Extreme abiotic stress episodes compromise grape production and plant survival, intensifying the pressure on the use of limited resources like water. Abscisic acid (ABA) is an important hormone in the ripening of certain fruits and in plant response to abiotic stress.

Grapevine nitrogen (N) monitoring is essential for efficient N management plans that optimize fruit yield and quality while reducing fertilizer costs and the risk of environmental contamination. Unlike traditional vegetative-tissue sampling methods, remote sensing technologies, including hyperspectral imaging, have the potential to allow monitoring of the N status of entire vineyards at a per-vine resolution. However, differential N partitioning, variable spectral properties, and complex canopy structures hinder the development of a robust N retrieval algorithm. The present study aimed to establish a solid understanding of vine spectroscopic response at leaf and canopy levels by evaluating the different nitrogen retrieval approaches, including the radiative transfer model.