Now showing items 1-7 of 7
IBFIELDBOOK, an integrated breeding field book for plant breeding
(Sociedad Mexicana de Fitogenética, 2013)
The development of an integrated breeding field book (IBFieldbook) for different crops involves the generation, handling and analysis of large amounts of data. Managing the integration of environmental, pedigree, and ...
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in ...
Genomic prediction enhanced sparse testing for multi-environment trials
(Genetics Society of America, 2020)
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 ...
Genomic prediction of gene bank wheat landraces
(Genetics Society of America, 2016)
This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including ...
A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years
(Crop Science Society of America (CSSA), 2016)
In agriculture and plant breeding, multienvironment trials over multiple years are conducted to evaluate and predict genotypic performance under different environmental conditions and to analyze, study, and interpret ...
Genomic-enabled prediction in maize using kernel models with genotype x environment interaction
(Genetics Society of America, 2017)
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: ...