Matériel végétal et valorisation des terroirs viticoles

Current environmental, ecological and economic issues require a better vineyard production management. In fact, a poor use of fertilizing could lead to harmful impact on environment. Another issue concerns the cultures themselves which couldn’t use fertilizers efficiently, leading to a loss of income or too much expense for farmers. Presently, estimation of fertilization’s needs is realized by the laboratory analysis of leaves selected through a random sampling. The present study aims at optimizing fertilization’s management by using a map of biophysical parameters estimated from satellite images.

Non-targeted analysis is applied in many different domains of analytical chemistry such as metabolomics, environmental and food analysis. In contrast to targeted analysis, non-targeted approaches take information of known and unknown compounds into account, are inherently more comprehensive and give a more holistic representation of the sample composition.

the aim of this study was the optimization and validation of a robust and comprehensive method for the determination of volatile carbonyl compounds (VCCs) in wines

Antão Vaz is a Portuguese white grapevine variety grown mainly in the wine-growing regions of Southern Portugal, particularly in the Alentejo, Lisbon and Setúbal peninsula regions. It is a very vigorous and productive variety, giving the wines a strong identity. It needs heat and sunlight and prefers deep and dry soils, which makes it tolerant to scald caused by the high summer temperatures of Southern Portugal. However, this variety is very susceptible to downy mildew, caused by plasmopara viticola, a very destructive disease in years with rainy springs.

The introduction of ai-driven tools in digital content creation represents a significant shift in the landscape of marketing, particularly for industries reliant on rich visual storytelling such as the wine sector. The development of ai models like openai’s sora, runway’s gen-2 or google’s lumiere, which can generate realistic video content from textual descriptions, offers promising new avenues for enhancing brand narrative and consumer engagement. This research explores the potential of text-to-video (t2v) ai models to revolutionize wine marketing by creating dynamic, engaging content that captures the essence of vineyards and their products without the need for traditional video production processes.