Model-assisted analysis of the root traits underlying RSA genotypic diversity in Vitis: a promising approach for rootstock selection?

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.

The Agenda 2030 of the EU sets out the main guidelines for transitioning towards a resilient, green and safe economy. To this regard, the wine sector is experiencing an ecological transition in different ways such as increasing the production of ecological crops, or promoting the production of wines under more environmental-friendly and healthier (i.e., lower levels of SO2) products. These alternatives to conventional production are a smaller proportion of wines, in constant growth and demand, and follow alternative and minority practices, which range from sustainable to deeply philosophical thoughts. Among these methods there are organic, biodynamic and, more recently, natural wines.

Vines require proper light levels, temperature, and water availability, and climate change is modifying these factors, hampering yield and quality. Despite the large diversity of rootstocks, varieties, and clones, we still lack knowledge of their combined effects and potential role in a warmer and dryer future. Therefore, we aim to characterize some of the existing diversity of rootstocks and genotypes and their interaction at the eco-physiological level, combining stomatal conductance (gs) and chlorophyll a fluorescence analysis.

The use of grapevine genetic diversity is a way to mitigate the negative impacts of climate change on viticulture systems. Leaf epidermal flavonoids (including flavonols and anthocyanins) are involved in plant defense mechanisms against environmental stresses, like high temperatures or excessive solar radiation [1,2]. Among other factors, they modulate light absorption, which reduces photoinhibition processes in photosynthetic tissues [1]. Therefore, the identification of grapevine cultivars with an increased content on leaf epidermal flavonoids arises as a potential avenue to improve grapevine tolerance to some detrimental environmental stresses.

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).