Physiological means to curb the enthusiasm of viruses from infecting grapevines  

The production of grapes with a balanced composition is one of the main goals that agronomists and oenologists pursue to produce premium quality wines.

Fossil fuel combustion continues to drive increases in atmospheric carbon dioxide, consequently elevating the global annual mean temperature and specifically increasing the growing season temperatures in many of the world’s most important wine growing regions (IPCC 2014; Jones et al 2005). Grapes are sensitive to changes in growing season temperatures, and past models have shown a direct link between warming temperatures and earlier harvest dates (Cook and Wolkovich 2016). Globally, there have been shifts of 1-2 weeks for wine growing regions (Wolkovich et al 2017 and references within). The phenological shifts resulting from growing season temperature increases are documented internationally, and models predicting phenology using temperature are becoming more precise (Parker et al 2011).

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.

Bentonite fining is commonly used in oenology to remove all or parts of white wine proteins, which are known to be involved in haze formation. This fining is effective, but has disadvantages: it is not selective, thus molecules responsible for aroma are also removed, it causes substantial volume losses, and finally it generates wastes. Over the last decades, the knowledge of wine proteins has increased: they have been identified, their structures are known, some of them have been crystallized.

The study of anthocyanins in wines and grapes has been the subject of numerous research works over the years due to their important role in enology regarding their contribution to wine sensory properties.