Identification of important genomic regions controlling resistance to biotic and abiotic stresses in Vitis sp. through QTL meta-analysis

Global changes and sustainability challenge researchers in saving water and nutrients. The response of woody crops, which can be forced at facing more drought events during their life, is particularly important. Vitis vinifera can be an important model for its relevance in countries subjected to climate changes and its breeding, requiring cuttings plantation and strong pruning.

In order to study the impact of climate change on Bordeaux grape varieties and to assess the behavior of candidate grape varieties potentially better adapted to the new climatic conditions, an experimental vineyard composed of 52 grape varieties was planted in 2009 at the INRAE Bordeaux Aquitaine center[1]. Among the many parameters studied since 2012, berry weight for each variety was measured weekly from mid-veraison to maturity, with four independent replicates. The kinetics obtained allowed to study berry growth, a key parameter in grape composition and yield.

Grapevine cultivation is threatened by the global warming, which combines high temperatures and reduced rainfall, impacting in wine quality and even plant survival. Breeding for varieties resilient to these challenges must address plant traits such as tolerance to supraoptimal temperatures and optimized water use efficiency while minimizing productivity and quality losses. Stomatal abundance (SA) determines the maximum leaf potential for transpiration and thus water loss and cooling. Since SA results from a developmental process during leaf emergence and growth, knowledge on the genetic control of this process would provide specific targets for modification.

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).

Calcium (Ca) is an important nutrient for plants which plays key signaling and structural roles. It has been observed that exogenous Ca application favors the pectin accumulation and inhibition of polygalacturonase enzymes, minimizing fruit spoilage. Silicon (Si) is a non-essential element which has been found to be beneficial for improving crop yield and quality, as well as plant tolerance to diverse abiotic and biotic stress factors. The effect of Si supply to grapevine has been assessed in few investigations, which reported positive changes in grape quality and must composition.