A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data
CIMMYT Research Data & Software Repository Network Dataverse OAI Archive
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Title |
A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data
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Identifier |
https://hdl.handle.net/11529/10548141
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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 |
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Publisher |
CIMMYT Research Data & Software Repository Network
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Description |
A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.
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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 |
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Language |
English
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Date |
2018
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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 |
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Type |
Experimental data
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