Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material.

Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide.

Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye.

Hyperspectral and genomic data are effective predictors of biomass yield in winter rye. Variable selection procedures can improve the informativeness of reflectance data. Integrating cutting-edge technologies is imperative to sustainably breed crops for a growing global population.

Monitoring cellular C:N ratio in phytoplankton by means of FTIR-spectroscopy.

Statistical growth rate modelling can be applied in a variety of ecological and biotechnological applications. Such models are frequently based on Monod or Droop equations and, especially for the latter, require reliable determination of model input parameters such as C:N quotas. Besides growth rate modelling, a C:N quota quantification can be useful for monitoring and interpretation of physiological acclimation to abiotic and biotic disturbances (e.

FTIR spectra of algal species can be used as physiological fingerprints to assess their actual growth potential.

Fourier transform infrared (FTIR) spectra were measured from cells of Microcystis aeruginosa and Protoceratium reticulatum, whose growth rates were manipulated by the availability of nutrients or light. As expected, the macromolecular composition changed in response to the treatments. These changes were species-specific and depended on the type of perturbation applied to the growth regime.