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

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

A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.

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Bibliographic Details
Main Authors: Montesinos-López, Osval A., Montesinos-López, Abelardo, Crossa, Jose, Cuevas, Jaime, Montesinos-López, José Cricelio, Gutiérrez, Zitlalli Salas, Lillemo, Morten, Juliana, Philomin, Singh, Ravi
Other Authors: Shrestha, Rosemary
Format: Experimental data biblioteca
Language:English
Published: CIMMYT Research Data & Software Repository Network 2018
Subjects: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,
Online Access:https://hdl.handle.net/11529/10548141
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Summary:A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.

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