Investigating the Ancient Egyptian wines: The wine jars database

Viticulture’s adaptation to the harmful effects of climate change is globally the biggest challenge of the near future. Short, extremely intensive rainfalls and longer periods of drought are getting more frequent in the Tokaj Wine Region, where the majority of the vineyards are cultivated on steep slopes. Hence, erosion has high risk, especially when combined with the loess-based soils on about ten percent of the region. The environmentally beneficial cover crop and mulch usage can effectively reduce the risk of erosion, according to research done by the Tokaj Wine Region Research Institute of Viticulture and Oenology.

The context of climate crisis leads to the acceleration of technological ripening of grapes, with unsuitable loss of acidity, so various vineyard management alternatives are being considered to delay the grape ripening. The delay of the vegetative cycle towards a period of milder temperatures affects ripening, but vine behavior can vary according to the area, conduction, watering, variety, etc. A work is proposed to know the response to the green pruning of shoots, executed in fruit set and in pea size, in cv. Verdejo.

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.

In the context of global change, the environmental conditions are expected to be more stressful for viticulture. The choice of the rootstock may play a crucial role to improve the adaptation of viticulture to new biotic and abiotic threats (Ollat et al., 2016). However, the selection of interesting traits in rootstock breeding programs is complex because of the combination of multiple targets in a same ideotype. In this sense, the integration of studies about the genetic architecture for desired biotic and abiotic response traits allow us to identify genomic regions to combine and those with interesting pleiotropic effects.

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).