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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
 
Identifier https://hdl.handle.net/11529/10548813
 
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
 
Publisher CIMMYT Research Data & Software Repository Network
 
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.
 
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
 
Language English
 
Date 2022
 
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)
 
Type Experimental data
Phenotypic data
Genotypic data