Biotype diversity within the autochthonous ‘Bobal’ grapevine variety

Grapevine resilience to climate change has become one of the most pressing topics in the Viticulture & Enology field. Vineyard health demands understanding the mechanisms that explain the direct and indirect interactions between environmental stressors. The current climate change scenario, where drought and heat-wave are more frequent and intense, strongly demands improving our knowledge of environmental stresses. During a heatwave, the ambient temperature rises above the plant’s average tolerance threshold and, generally, above 35 oC plant’s adaptation to heat stress is activated.

Climate change is resulting in more dry and hot summers, accelerating grape ripening and increasing berry sugars concentration. This results in wines with a higher alcohol content, which has a negative impact on wine quality, as well as, on consumer health. Agronomic practices that minimize these effects on berry composition and, consequently, on wine quality must be defined. In this work, different management practices have been assessed on rainfed ‘Monastrell’ grapevines in Jumilla (Murcia, Spain) from 2021 to 2023 vintages. Mulching, shading, application of kaolin and different types of pruning were evaluated, among others field adaptation practices.

Less and less chemical plant protection products are approved by the E U. Plant pathogenic fungi become increasingly resistant to the active ingredients that have been around for a long time. Besides, there is a valid demand for effective products that can be applied in organic cultivation.
We examined Metschnikowia strains under laboratory conditions in order to find effective strains against B. cinerea. The antimicrobial mechanism of these yeasts is based on the competition for the ferric ions from the environment. Metschnikowia cells release the pulcherriminic acid which chelates with Fe3+, forming the pigment pulcherrimin.

Wine is one of the most representative components of Mediterranean diet. Moderate wine intake together with food, has been positively correlated with reduced risk of many chronic diseases. This beneficial effect seems to be ascribed to elevated polyphenolic content of wine [1]. Traditional approaches for the identification of wine biomarkers consumption include targeted metabolomics that focuses on the quantification of well-defined metabolites, losing a valuable information about a massive number of compounds. On the other hand, untargeted metabolomics can disclose a large quantity of signals corresponding to potential biomarkers in a single analysis with high sensitivity and resolution.

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).