Under-vine management effects on grapevine vegetative growth, gas exchange and rhizosphere microbial diversity

The root and shoot abscisic acid (ABA) accumulation in response to water deficit and its relation with stomatal conductance is longtime known in grapevine. ABA-dependent and ABA-independent signalling response to osmotic stress coexist in sessile plants. In grapevine, the signaling role of ABA in response to water stress conditions and its influence on berry quality is critical to manage grapevine acclimation to climate change.

The arrangements of flowers and wine counterparts are inextricably linked. Whether a fundamental aspect of tablescaping or acolytes to broader entertainment rituals, they have an entangled history since ancient times. The aim of this contribution is to verify the influence of visually delicate and robust flower arrangements on individual description of wines. Changes in the sensory description of wines were investigated during subjects’ (thirty-two participants) exposure to three different conditions: the presence of delicate, robust, or totally absent flower arrangements. In each condition, the same two wines were blind tasted: a wine previously defined as delicate – a Pinot Noir from Australia, and a wine known for its robust character – a Tannat from Uruguay.

Drought is one of the main challenges for viticulture in the context of global change. The choice of rootstock could be leveraged for vineyard adaptation to drought as we can improve plant performance without modifying the scion variety. However, most of the existing rootstocks, selected over a century ago, have a narrow genetic background which could compromise their adaptive potential.

Wine contains secondary metabolites derived from aromatic amino acids (AADC), which can determine quality, stability and bioactivity. Several yeast species, as well as some lactic acid bacteria (LAB), can contribute in the production of these aromatic compounds. Winemaking should be studied as a series of microbial interactions, that work as an interconnected network, and can determine the metabolic and analytical profiles of wine. The aim of this work was to select microorganisms (yeast and LAB) based on their potential to produce AADC compounds, such as tyrosol and hydroxytyrosol, and design a microbial consortium that could increase the production of these AADC compounds in wines.

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).