A procedure for the zoning of grapevine in a hilly area (Collio, North-Eastern Italy) using simulation models and GIS

Viticulture in Greece is the oldest, but in recent years there has been a reduction of areas intended for the production of wine products. The article contains data on viticulture in Greece. Over time, the land of Greek vineyards is fluctuating. There is a trend towards a decrease in areas in connection with the quota of products from the EU.

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.

Winemaking is a globally significant industry in the field of food technology (218 mhL of wine estimated for 2024 harvest) [1], which activity produces tons of by-products annually, including pomace (pulp, stems, seeds, skins), lees, organic acids, CO2, and water [2].

This study aimed to determine mineral composition in red wine obtained from cv. Teran (Vitis vinifera L.), autochtonous Croatian grape variety. Six different vinification treatments, including the control treatment (7-day standard maceration), were performed to study the effects of: 48-hour pre-fermentative mash cooling (8 °C) followed by prolonged post-fermentative maceration of 13 days (C15), 28 days (C30), and saignée technique (juice runoff) proceeded with prolonged post-fermentative maceration of 13 days (CS15); and effect of 48-hour heating (50 °C) followed by prolonged post-fermentative maceration of 13 days (H15) and 28 days (H30) on macro- and microelements in wine.

Drought and heat stress will pose challenges for the future of viticulture and wine quality, as grapevine biological processes are pushed beyond their optimum conditions. Efforts are increasing to study and predict the effects of drought spells and heatwaves on grapevine physiology and resulting harvest quality. This calls for the development of adequate systems to induce and manipulate the required stress, especially in open field trials where conditions are more difficult to control. We present a semi-controlled system for studying drought and heat stress in grapevine in the field.