Now showing items 1-10 of 27
Mejoramiento de la calidad de trigo
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 ...
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 ...
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 ...
A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model
(Springer Nature, 2019)
Today, breeders perform genomic-assisted breeding to improve more than one trait. However, frequently there are several traits under study at one time, and the implementation of current genomic multiple-trait and ...
Genetic yield gains in CIMMYT’s international elite Spring Wheat yield trials by modeling the Genotype X environment interaction
(Crop Science Society of America (CSSA), 2017)
We calculated the annual genetic gains for grain yield (GY) of wheat (Triticum aestivum L.) achieved over 8 yr of international Elite Spring Wheat Yield Trials (ESWYT), from 2006–2007 (27th ESWYT) to 2014–2015 (34th ESWYT). ...
Genetic gains for grain yield in CIMMYT’s semi-arid wheat yield trials grown in suboptimal environments
(Crop Science Society of America (CSSA), 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 ...
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 ...
Single-step genomic and pedigree genotype x environment interaction models for predicting wheat lines in international environments
(Crop Science Society of America, 2017)
Genomic prediction models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individuals. However, pedigree information for an entire breeding population is frequently ...
Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model
(Genetics Society of America, 2015)
Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype · environment interaction( G·E). ...