Climate change impact study based on grapevine phenology modelling

Currently, the greatest challenge for vine growers is to improve the yield and quality of grapes by minimizing costs and environmental impacts. This goal can be achieved through a better knowledge of vineyard spatial variability. Traditional platforms such as airborne, satellite and unmanned aerial vehicles (UAVs) solutions are useful investigation tools for vineyard site specific management.

The Douro Demarcated Region, in Northern Portugal, offers outstanding wines with unique characteristics. Due to the today’s globalized marketplace, local producers often need to further develop their cultivation techniques to remain competitive. The Association of Viticultural Development in the Douro Valley (ADVID) works as a unit of experimentation, offering services and training to meet the demands of the Douro’s vitiviniculturists.

High-power ultrasounds have been related to an accelerated aging of wines, an effect that has been associated to the formation of radical species caused by the cavitation phenomenon [1]. This phenomenon consists of the formation of bubbles in the liquid medium that, when they collapse, cause high-pressure hot spots and temperatures of up to 4800 k [2], notably increasing the reactivity in the medium.

In the last years the application of genomic tools to the analysis of gene expression during grape berry development generated a huge amount of transcriptomic data

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.