Now showing items 1-10 of 19
Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat
(Genetics Society of America, 2017)
Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal ...
Genome-wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes
(Springer; Springer Verlag, 2018)
Leaf rust (LR), stripe rust (YR) and tan spot (TS) are some of the important foliar diseases in wheat (Triticum aestivum L.). To identify candidate resistance genes for these diseases in CIMMYT’s (International Maize and ...
Wheat quality improvement at CIMMYT and the use of genomic selection on it
The International Center for Maize and Wheat Improvement (CIMMYT) leads the Global Wheat Program, whose main objective is to increase the productivity of wheat cropping systems to reduce poverty in developing countries. ...
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 ...
Genetic gains for grain yield in CIMMYT’s semi-arid wheat yield trials grown in suboptimal environments
(Crop Science, 2018)
Wheat (Triticum aestivum L.) is a major staple food crop grown worldwide on >220 million ha. Climate change is regarded to have severe effect on wheat yields, and unpredictable drought stress is one of the most important ...
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 ...
Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones
(Nature Publishing Group, 2016)
Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental ...
Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
(Crop Science Society of America, 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 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 ...