Replication Data for: Sparse testing using genomic predication improves selection for breeding targets in elite spring wheat
CIMMYT Research Data & Software Repository Network Dataverse OAI Archive
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Title |
Replication Data for: Sparse testing using genomic predication improves selection for breeding targets in elite spring wheat
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Identifier |
https://hdl.handle.net/11529/10548639
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Creator |
Atanda, Sikiru Adeniyi
Govindan, Velu Singh, Ravi Robbins, Kelly R. Crossa, Jose Bentley, Alison R. |
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Publisher |
CIMMYT Research Data & Software Repository Network
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Description |
In multi-environment yield trials, the use of sparse testing genomic selection enables increasing selection intensity or testing environments. The data presented in this dataset were used in the evaluation of different sparse testing genomic selection strategies in the early yield testing stage of CIMMYT spring wheat breeding pipeline. Phenotypic, genotypic, and coefficient of parentage data are provided. The germplasm is made up of multiple populations each with small family sizes. The findings of the study are detailed in an accompanying article.
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Subject |
Agricultural Sciences
Wheat Triticum aestivum Agricultural research Plant breeding genotypes Grain yield Selection index Genetic gain Factor analytic Prediction accuracy Genomic best linear unbiased estimate Genotype x environment interaction |
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Language |
English
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Date |
2022
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Contributor |
Dreher, Kate
Foreign, Commonwealth and Development Office (FCDO) Bill and Melinda Gates Foundation (BMGF) Global Wheat Program (GWP) Genetic Resources Program (GRP) Biometrics and Statistics Unit (BSU) CGIAR Research Program on Wheat (WHEAT) CGIAR |
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Type |
Genotypic data
Phenotypic data Experimental data |
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