Now showing items 1-10 of 116
Genomic-enabled prediction with classification algorithms
(Springer Nature, 2014)
Pearson’s correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression ...
A general Bayesian estimation method of linear-bilinear models applied to plant breeding trials with genotype × environment interaction
(Springer Verlag; American Statistical Association; International Biometrics Society, 2012)
Statistical analyses of two-way tables with interaction arise in many different fields of research. This study proposes the von Mises-Fisher distribution as a prior on the set of orthogonal matrices in a linear-bilinear ...
Genomic prediction models for count data
(Springer Verlag; American Statistical Association; International Biometrics Society, 2015)
Whole genome prediction models are useful tools for breeders when selecting candidate individuals early in life for rapid genetic gains. However, most prediction models developed so far assume that the response variable ...
Identification of drought, heat, and combined drought and heat tolerant donors in maize
(Crop Science Society of America (CSSA), 2013)
Low maize (Zea maysL.) yields and the impacts of climate change on maize production highlight the need to improve yields in eastern and southern Africa. Climate projections suggest higher temperatures within drought-prone ...
Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper‑spectral image data
(BioMed Central, 2017)
Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ...
Unlocking the genetic diversity of Creole wheats
(Nuture Publishing Group, 2016)
Climate change and slow yield gains pose a major threat to global wheat production. Underutilized genetic resources including landraces and wild relatives are key elements for developing high-yielding and climate-resilient ...
Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
(Crop Science Society of America (CSSA), 2017)
Genotype × environment (G × E) interaction can be studied through multienvironment trials used to select wheat (Triticum aestivum L.) lines. We used spring wheat yield data from 136 international environments to evaluate ...
Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data
(BioMed Central, 2017)
Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, ...