Soil Temperature and Climate Change: Implications for Mediterranean Vineyards 

The global consumer demand for low or non-alcoholic wine is growing steadily in recent years, driven by health concerns, religious beliefs, and personal taste preferences etc.. Consequently, the removal of alcohol from wine can significantly alter its chemical and sensory properties, including color, aroma, and taste, which make a significant challenge for consumer to accept these products. Ethanol plays a crucial role in various chemical reactions and interactions that contribute to the development of wine’s characteristics.

The wine industry currently relies on a limited number of grapevine cultivars, comprised of numerous clones with slight differences in their viticultural, oenological, or stress-tolerance traits.

This study aims at evaluating the effects of different refermentation methods (Martinotti/Charmat vs. Classic) on the chemical composition, aroma profile and sensory characteristics of Ribolla Gialla sparkling wines; furthermore, certain winemaking practices (skin contact and use of pectolytic enzymes) were investigated considering the extraction of varietal aromas and aroma precursors. METHODS: Sparkling wines were produced at pilot-plant scale. Concerning refermentation methods, traditional Martinotti (MB – 30 days length), extended Martinotti (ML) with 4 months of aging on lees and Classic method (CL) with 11 months of aging on lees were compared; in a second trial, skin contact (MM), enzyme addition on must also subjected to maceration (ME), and enzyme addition on base wine (VE) were evaluated. All experimental trials were performed in triplicate. Basic chemical composition, varietal (terpenes and C13-norisoprenoids in free and bound form) and non-varietal aroma compounds were evaluated by LLE-GCMS analysis; finally, sensory analysis was also performed, by descriptive testing.

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 effects of climate change on viticulture are complex due to interactions among factors and cascading effects.