Replication Data for: Optimizing sparse testing for genomic prediction of plant breeding crops
CIMMYT Research Data & Software Repository Network Dataverse OAI Archive
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Title |
Replication Data for: Optimizing sparse testing for genomic prediction of plant breeding crops
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Identifier |
https://hdl.handle.net/11529/10548813
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Creator |
Montesinos-López, Osval A.
Saint Pierre, Carolina Mosqueda-González, Brandon Alejandro Bentley, Alison Montesinos-López, Abelardo Beyene, Yoseph Gowda, Manje Crespo Herrera, Leonardo Abdiel Crossa, Jose |
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Publisher |
CIMMYT Research Data & Software Repository Network
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Description |
In plant breeding, sparse testing methods have been suggested to improve the efficiency of the genomic selection methodology. The data provided in this dataset were used to evaluate four methods for allocating lines to environments for sparse testing in multi-environment trials. The analysis was conducted using a multi-trait and uni-trait framework. The accompanying article describes the results of the evaluation as well as a cost-benefit analysis to identify the benefits that can be obtained using sparse testing methods.
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Subject |
Agricultural Sciences
Zea mays Plant Breeding Agricultural research Triticum aestivum Maize Wheat Plant height Days to Silking Plant height Grain yield Heading time Maturity time Germination |
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Language |
English
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Date |
2022
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Contributor |
Dreher, Kate
Genetic Resources Program (GRP) Global Wheat Program (GWP) Global Maize Program (GMP) Biometrics and Statistics Unit (BSU) Bill and Melinda Gates Foundation (BMGF) United States Agency for International Development (USAID) Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG) Stress Tolerant Maize for Africa (STMA) CGIAR Agricultural Agreement Research Fund (JA) Foundation for Research Levy on Agricultural Products (FFL) Foreign, Commonwealth and Development Office (FCDO) |
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Type |
Experimental data
Phenotypic data Genotypic data |
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