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A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

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Title A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data
 
Identifier https://hdl.handle.net/11529/10548141
 
Creator Montesinos-López, Osval A.
Montesinos-López, Abelardo
Crossa, Jose
Cuevas, Jaime
Montesinos-López, José Cricelio
Gutiérrez, Zitlalli Salas
Lillemo, Morten
Juliana, Philomin
Singh, Ravi
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.
 
Subject Agricultural Sciences
Agricultural research
Maize
Zea mays
Wheat
Triticum aestivum
Genomic selection
Bayesian multi-output regressor stacking
Genomic best linear unbiased prediction
GBLUP
Multi-trait
Multi-environment
Breeding programs
 
Language English
 
Date 2018
 
Contributor Shrestha, Rosemary
Global Maize Program (GMP)
Global Wheat Program (GWP)
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
CGIAR Research Program on Maize (MAIZE)
CGIAR Research Program on Wheat (WHEAT)
CGIAR
 
Type Experimental data