Application to the wine sector of European Convention on the landscapes

Insufficient acidity in grapes from warm climates/vintages is commonly corrected through addition of tartaric acid during vinification, and less so with other organic acids. An alternative approach involves bio-acidification with the yeast Lachancea thermotolerans (LT) via lactic acid production during fermentation.

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.

Climate change and harvest date decisions have led to the evolution of must quality over the last decades. Increases in must sugar concentrations are among the most obvious consequences, quantitatively. Saccharomyces cerevisiae is a robust and acid tolerant organism. These properties, its sugar to ethanol conversion rate and ethanol tolerance make it the ideal production organism for wine fermentations. Unfortunately, high sugar concentrations may affect S. cerevisiae and lead to growth inhibition or yeast lysis, and cause sluggish or stuck fermentations. Even sublethal conditions cause a hyperosmotic stress response in S. cerevisiae which leads to increased formation of fermentation by-products, including acetic acid, which may exceed legal limits in some wines.

Bud fruitfulness is a key component of reproductive performance of grapevine. It plays a significant role in annual yield variation of vineyards as it is a prerequisite of crop production in the following season. Various exogenous and endogenous factors influencing the development of inflorescence primordia (IP) have been studied. However, the research on molecular genetic control of bud fruitfulness, especially how it interacts with environmental factors is still lacking. This study aims to investigate the molecular mechanism of effects of temperature and light on grapevine bud fruitfulness during initiation and differentiation of IP.

In the current scenario of climatic variability, even though the vine (Vitis vinifera) is a species generally considered very fertile, the process of bud differentiation is particularly influenced by the weather trend not only of the current year but also of the previous one.