Climate change impact study based on grapevine phenology modelling

Postharvest losses of fruits may reach in some cases 40% in developed countries. This food waste has a significant carbon footprint and makes a major contribution toward greenhouse gas emissions so sustainable postharvest strategies are being investigated.
Melatonin, a well-known mammalian neurohormone, has been investigated as a priming agent to slow down fungal decay progression in postharvest climacteric and some non-climacteric fruits. However, the molecular and metabolic mechanisms responsible for such enhancement of disease tolerance are largely unknown.

Valpolicella is a famous wine producing region located in the north of Verona close to Garda lake and owes its fame above all to the production of two Protected Designation of Origins (PDOs) withered wines: Amarone and Recioto. Nowadays the production of another PDO, Valpolicella Superiore is gaining more attention by the consumers, increasing the interest of the wineries to improve the quality of this wines

Wine dealcoholisation has been practised since the early 1900s and has gained importance due to climate change
and shifting consumer preferences for lower-alcohol beverages. Rising temperatures are accelerating grape
ripening, increasing sugar content and, consequently, raising the alcohol strength of wines.

One of the most concerning trends associated with increasing heat and water stress is advanced ripening of grapes, which leads to harvesting fruit at higher sugar concentrations but lacking optimal phenolic (i.e. color and mouthfeel) and aromatic maturity. Mitigation techniques for this phenomenon have been studied for many years and practices to delay sugar accumulation have been identified, including antitranspirants, delayed pruning and late-source-limitation techniques. Evaluation of the efficacy of these vineyard practices has occurred across a wide range of environments, vintages, varieties and growing conditions. To assess the broader efficacy of these three vineyard practices, which are easy-to-implement and cost-effective, a meta-analytic approach was adopted using data retrieved from 43 original studies.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).