Bud death due to cold damage is a recurrent and major economic issue with Vitis vinifera L. in the Northeastern U.S. winegrowing regions. Primary buds – and sometimes secondary and tertiary buds – are often damaged by fluctuating temperatures in the winter and early spring. To maintain balanced vegetative and reproductive growth of a vine, pruning practices need to be adjusted to account for bud damage. Conventional bud damage assessment requires growers to sample canes/spurs, cut nodes with a razor blade, and then visually assess bud damage. This process is laborious and becomes a major barrier for damage-compensated pruning decision-making, leading to too few live buds per vine and the associated excessive vigor and low yield that result. The overarching goal of this study was to develop an active thermographic system for non-destructive detection of bud damage in the vineyard.

The Bull’s Blood of Eger (‘Egri Bikavér’) is one of the most reputed red wines in Hungary and abroad, produced in the Northeastern part of the country.

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.

In the course of this study, stabilisation (lees stirring in unclarified must) with skin contact and classic white wine vinification were compared for the Sauvignon blanc and Traminer varieties in Austria. The test wines were analysed for the volatile substances esters, free monoterpenes and fruity thiols

The construction of vineyard landscapes along the Burgundy Côte is the result of geological processes and of human labour. Substratum diversity in this vineyard is the result of a very long history explained by the diversity of Jurassic sedimentary facies and Tertiary tectonic activity. The nature and thickness of Quaternary deposits (Weichselian scree debris and alluvial fans) reflect sediment dynamics concurrent with the last glaciation.