Application of DEXI PM Vigne sustainability tool to the assessment of alternative vineyard protection strategies

Wine is the result of the alcoholic fermentation (AF) of grape must. Besides AF, wine can also undergo the malolactic fermentation (MLF) driven out by lactic acid bacteria (LAB). Among LAB, Oenococcus oeni and Lactiplantibacillus plantarum are the dominant species in wine. Even if O. oeni is the most common LAB undergoing MLF in wine, due to its high tolerance to wine conditions, L. plantarum can be used to undergo MLF in must. The moderate tolerance of L. plantarum to low pH and ethanol, may compromise the fermentative process in harsh wines.

The context of climate crisis leads to the acceleration of technological ripening of grapes, with unsuitable loss of acidity, so various vineyard management alternatives are being considered to delay the grape ripening. The delay of the vegetative cycle towards a period of milder temperatures affects ripening, but vine behavior can vary according to the area, conduction, watering, variety, etc. A work is proposed to know the response to the green pruning of shoots, executed in fruit set and in pea size, in cv. Verdejo.

The effects of climate change in viticulture are currently a major concern, with heat waves and drought affecting yield, wine quality, and in extreme cases, even plant survival. Ancient grapevine varieties have high intravarietal genetic variability that so far has been explored successfully to improve yield and must quality. Currently, there is little information available on intravarietal variability regarding responses to stress. In the current work, the intravarietal genetic variability of several Portuguese varieties was studied for yield, must quality, and tolerance to abiotic stress, through indirect, rapid, and nondestructive measurements carried out in the field.

The wine industry produces an enormous amount of waste every year. A wider inclusion of disregarded by-products in the human diet or its use as a source of bioactive compounds is a good strategy for reducing waste. It will not only introduce an added value to a waste product but also come upon the European Union and United Nations’ demands towards more sustainable agricultural approaches and circular economy.

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).