Toasting and grain effect on Tempranillo red wine aged in Quercus petraea barrels

The ongoing climate change implies an increasing mean air temperature, which is signified by weather extremes or sudden changes between drought and local heavy rainfalls. These changing conditions are especially challenging for the established grapevine varieties growing under cool climate conditions due to an increased risk for fungal diseases like downy mildew (DM) and Botrytis bunch rot (BBR) as well as for grape sunburn. To meet that demand, the scope of most grapevine breeding programs is the selection of mildew fungus-resistant and climatic adapted grapevines with balanced, healthy yield and outstanding wine quality.

The application of remote and proximal sensing is a fast and efficient method to monitor grapevine vegetative and physiological parameters and is considered valuable to derive information on associated yield and quality traits in the vineyard. Further details can be obtained by the application of molecular analysis at the gene expression level aiming at elucidating how pathways controlling the formation of different grape quality traits are influenced by spatial variability. This work aims at evaluating intra-vineyard variability in grape composition at harvest and at comparing this with remotely sensed canopy vegetation data and molecular-based approaches.

Sémillon is a cultivated grape variety known for contributing to dry and sweet white wine production. However, only seven approved clones have been officially recognized in France[1]. In this study, we aimed to characterize the genetic diversity and metabolomic profiles of a Sémillon clonal population, shedding light on the potential variations within this important grape variety.

By dissecting the root system architecture (RSA) into its underpinning components (e.g. root emission, axial growth, radial growth, branching, root direction or tropism) and identifying the relationships between them, functional-structural 3D root models are promising tools for analyzing the diversity and complexity of root system phenotypes with Genotype × Environment interactions. The model parameters are assumed to be synthetic traits, less influenced by the environment, and consequently with less polygenic architectures than the integrative RSA traits they drive. Root models can serve as a basis for in silico development of root system ideotypes by highlighting the developmental processes and parameters that most likely influence RSA fitness.

The plant rhizosphere microbial communities are an essential component of plant microbiota, which is crucial for sustaining the production of healthy crops. The main drivers of the composition of such communities are the growing environment and the planted genotype. Recent viticulture studies focus on understanding the effects of these factors on soil microbial composition since microbial biodiversity is an important determinant of plant phenotype, and of wine’s organoleptic properties. Microbial biodiversity of different wine regions, for instance, is an important determinant of wine terroir.