Soil Temperature and Climate Change: Implications for Mediterranean Vineyards 

The Agenda 2030 of the EU sets out the main guidelines for transitioning towards a resilient, green and safe economy. To this regard, the wine sector is experiencing an ecological transition in different ways such as increasing the production of ecological crops, or promoting the production of wines under more environmental-friendly and healthier (i.e., lower levels of SO2) products. These alternatives to conventional production are a smaller proportion of wines, in constant growth and demand, and follow alternative and minority practices, which range from sustainable to deeply philosophical thoughts. Among these methods there are organic, biodynamic and, more recently, natural wines.

The choice of an adequate rootstock is a key tool to improve the performance of grapevine varieties in different ‘terroirs’, as rootstocks confer adaptation to soil characteristics

Ultrafiltration is a process that fractionates mixtures using semipermeable membranes, primarily on the basis of molecular weight. Depending on the nominal molecular weight cut-off (MWCO) specifications of the membrane, smaller molecules pass through the membrane into the ‘permeate’, while larger molecules are retained and concentrated in the ‘retentate’. This study investigated applications of ultrafiltration technology for enhanced wine quality and profitability. The key objective was to establish to what extent ultrafiltration could be used to manage phenolic compounds (associated with astringency or bitterness) and proteins (associated with haze formation) in white wine.

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.

Pulsed electric fields (pef) technology holds significant promise for the agrifood industry, considering the capacity of inducing cell electroporation, due to the disruption of cellular membranes. Pef-induced permeabilization is dependent of the chosen treatment protocol (i.e. Pulse shape, electrical field strength, specific energy) and of the matrix’s characteristics (i.e. Cell radii and size, ph, electrical conductivity).