Crop water stress index as a tool to estimate vine water status

The increase in the temperature and the more severe water stress conditions, trends observed in recent years as a consequence of climate change, are leading a mismatch between the technological and phenolic maturity of grapes

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.

The wine industry is currently shifting toward more sustainable production systems. There are many reasons for this as the interest of people over climate change and, consequently the wine consumer’s choice toward organic and biodynamic, reduced carbon-footprint, vegan and other environmentally friendly wines. While the viticultural effects of biodynamic and organic practices on wine grapes have been investigated, there is a lack in literature on the general effect on the final quality of wine

In the Burgundy vineyard, frequent tractor traffic and management of inter-rows alternating grass cover and chemical weed-control lead to structural soil contrast between row and inter-row. The aim of this study was to characterize and model water flow in relation with topsoil structure modifications induced by these practices. Void ratio of the different soil volumes were determined using bulk density measurements.

Polyphenols are very important compounds of red wines, serving as essential bioactive components and playing an important role in sensory properties. The determination of individual phenolic compounds in red wine is commonly performed by HPLC analysis, while the total polyphenols are quantified by spectrophotometric methods, usually by the method of absorbance at 280 nm (index of ribéreau-gayon) or the method of index of folin-ciocalteu. In this work, we pioneeringly proposed a new and fast method for simultaneous determination of individual and total polyphenols in red wines by direct-injection HPLC without sample preparation.