Identification of riboflavin low producer yeasts to prevent the light-struck taste in white wines

The Earth’s system is undergoing major changes through a wide range of spatial and temporal scales as a response to growing anthropogenic radiative forcing, which is pushing the whole system far beyond its natural variability. Sources of greenhouse gases largely exceed their sinks, thus leading to a strengthened greenhouse effect. More energy is thereby being supplied to the system, with inevitable shifts in climatic patterns and weather regimes. Over the last decades, these modifications have been manifested in the full statistical distributions of the atmospheric variables, with dramatic changes in the frequency and intensity of extremes. Natural hazards, such as severe droughts, floods, forest fires, or heatwaves, are being triggered by extreme atmospheric events worldwide, thus threatening human activities. Viticultculture is not only exposed to changing climates but is also highly vulnerable, as grapevine phenology and physiological development are strongly controlled by atmospheric conditions. Therefore, the assessment of climate change projections for a given region is critical for climate change adaptation and risk reduction in viticulture. By adopting timely and suitable measures, the future sustainability and resiliency of the sector can be fostered. Climate-grapevine chain modelling is an essential tool for better planning and management. However, the accuracy of the resulting projections is limited by many uncertainties that must be duly taken into account when transferring knowledge to stakeholders and decision-makers. Climate-smart viticulture will comprise ensembles of locally tuned strategies, envisioning both adaptation and mitigation, assisted by emerging technologies and decision-support systems.

In the context of sustainable viticulture, modern and efficient techniques to determine water status are required to optimize irrigation practices. Proximal techniques such as thermography and spectroscopy have shown promising results. When these techniques are incorporated into mobile systems is possible to evaluate the water status on-the-go, offering the possibility to generate variability maps. However, in most cases, complex protocols of data acquisition and analysis are required. Also, the inherent physiological behaviour of the plants under certain water stress conditions needs to be considered. Therefore, the aim of this study was to evaluate the effect of scion-rootstock combinations on the performance of a predefined plant-based method based on proximal near-infrared (NIR) spectroscopy.

Context and purpose of the study. Modern conventional agriculture, including viticulture, relies greatly on the use of chemical inputs, especially synthetic pesticides.

Drought and heat waves deriving from climate change have been affecting grapevine plants and altering wine characteristics in the past years, and effects are expected to get worst. Innovative approaches to address this problem have been undertaken in several varieties, that consist in exploring intravarietal variability to identify genotypes that are tolerant to abiotic stress. Such is the case of the variety Arinto, where an experimental population of 165 clones installed according to a resolvable row-column design with 6 replicates, was scanned for several parameters, including surface leaf temperature (SLT). Linear mixed models were fitted to the data of the traits evaluated, and the empirical best linear unbiased predictors (EBLUPs) of genotypic effects for each trait were obtained as well as the coefficient of genotypic variation (CVG) and broad sense heritability.

The use of elicitors to promote the biosynthesis of secondary metabolites in grapes has been tackled in several reports, however its study linked to nanotechnology is less developed.