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Replication Data for: Sparse multi-trait genomic prediction under incomplete block designs

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Sparse multi-trait genomic prediction under incomplete block designs
 
Identifier https://hdl.handle.net/11529/10548787
 
Creator Montesinos-López, Osval A.
Mosqueda-González, Brandon Alejandro
Salinas-Ruiz, Josafhat
Montesinos-López, Abelardo
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description The efficiency of genomic selection methodologies can be increased by sparse testing where a subset of materials are evaluated in different environments. Seven different multi-environment plant breeding datasets were used to evaluate four different methods for allocating lines to environments in a multi-trait genomic prediction problem. The results of the analysis are presented in the accompanying article.
 
Subject Agricultural Sciences
Plant Breeding
Agricultural research
Triticum aestivum
Wheat
Groundnuts
Rice
 
Language English
 
Date 2022
 
Contributor Dreher, Kate
CGIAR Research Program on Wheat (WHEAT)
Genetic Resources Program (GRP)
Global Wheat Program (GWP)
Bill and Melinda Gates Foundation (BMGF)
United States Agency for International Development (USAID)
CGIAR Research Program on Maize (MAIZE)
Biometrics and Statistics Unit (BSU)
CGIAR
Agricultural Agreement Research Fund (JA)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
Foundation for Research Levy on Agricultural Products (FFL)
Foreign, Commonwealth and Development Office (FCDO)
 
Type Experimental data
Phenotypic data
Genotypic data