Impact of polyclonal selection for abiotic stress tolerance on the yield and must quality traits of grapevine varieties

For sustainable viticulture, the substitution of chemical inputs with biocontrol products has become one of the most considered strategies. This strategy is based on elicitor-triggered immunity that requires a deep understanding of the molecular mechanisms involved in plant defense activation. Plant immune responses are triggered through the perception of conserved microbe-associated molecular patterns (MAMPs) which are recognized by pattern recognition receptors (PRRs) at the plasma membrane.

Brettanomyces yeast can negatively impact the quality and stability of wines, posing a significant challenge to winemakers. [1] This study aims to develop novel management practices to limit Brettanomyces impact on wines by evaluating the effectiveness of electrodialysis (ED) technology in removing magnesium (Mg2+) from wine to prevent the development of Brettanomyces yeast. The ED technique utilizes charged membranes to extract ions from the wine, and it is considered an alternative to cold stabilization that requires less energy. [2]

Climate change is altering water balances, thereby compromising water availability for crops. In grapevine, the strategic selection of genotypes more tolerant to soil water deficit can improve the resilience of the vineyard under this scenario. Previous studies demonstrated that root anatomical and morphological traits determine vine performance under water deficit conditions. Therefore, 13 ungrafted rootstock genotypes, 6 commercial (420 A, 41 B, Evex 13-5, Fercal, 140 Ru y 110 R), and 7 from new breeding programs (RG2, RG3, RG4, RG7, RG8, RG9 and RM2) were evaluated in pots during 2021 and 2022.

Proper soil health assessments are crucial for sustainable cropland. Among the widely employed approaches, evaluating nematode community structure is particularly suitable. Traditionally, the taxonomic characterization of soil nematodes has relied on time-consuming morphology-based methods requiring experienced experts. However, molecular tools like high-throughput sequencing have emerged as efficient alternatives. In this study, we performed a metataxonomic analysis of soil samples collected from 57 vineyards in the DOCa Rioja region of Northern Spain, focusing on the impact of organic viticulture and cover cropping compared to integrated pest management (IPM) and tilling practices.

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).