Now showing items 1-10 of 33
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, ...
Genomic prediction using phenotypes from pedigreed lines with no marker data
(Crop Science Society of America (CSSA), 2016)
Until now, genomic prediction (GP) in plant breeding has only used information from individuals that have been genotyped. Information from nongenotyped relatives of genotyped individuals can also be used. Single-step GP ...
Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat
The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies ...
Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers
(Crop Science Society of America (CSSA), 2012)
Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. ...
Hybrid wheat prediction using genomic, pedigree, and environmental covariables interaction models
(Crop Science Society of America, 2019)
In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted ...
Applications of machine learning methods to genomic selection in breeding wheat for rust resistance
(Crop Science Society of America, 2018)
New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus ...
Partner survey for the CGIAR Research Program WHEAT: national and international priorities and engagement
This document analyzes 92 responses to a 2012 survey sent by the CGIAR Research Program WHEAT to its more than 200 partners regarding their institutional priorities, engagement and activities under WHEAT strategic initiatives, ...
Utilizing genomics and phenomics in CIMMYT wheat breeding