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Replication Data for: Bayesian linear regression near infrared spectroscopy (NIR) to predict provitamin A carotenoids content in maize breeding programs

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Bayesian linear regression near infrared spectroscopy (NIR) to predict provitamin A carotenoids content in maize breeding programs
 
Identifier https://hdl.handle.net/11529/10548607
 
Creator Rosales, Aldo
Crossa, Jose
Cuevas, Jaime
Cabresa-Soto, Luisa
Dhliwayo, Thanda
Ndhela, Thokozile
Palacios-Rojas, Natalia
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description Vitamin A deficiency (VAD) is a public health problem worldwide. For countries with a high per capita consumption of maize, breeding varieties with higher provitamin A carotenoid content than normal yellow maize — biofortification — can be a viable strategy to reduce VAD. Selection for provitamin A carotenoid content uses molecular markers and phenotypic data generated using expensive and laborious wet lab analyses. Near-infrared spectroscopy (NIRS) could be a fast and cheap method to measure carotenoids. This dataset contains carotenoid and NIRS data from 1857 tropical maize samples used as a training set to predict provitamin A carotenoid content of an independent set of 650 tropical maize samples using Bayesian linear regression models. The datasets contain information about specific carotenoids measured and the NIRS values measured at different wavelengths. The results of the analysis are described in the accompanying article.
 
Subject Agricultural Sciences
Maize
Agricultural research
Plant Breeding
Zea mays
carotenoids
Grain provitamin A content
 
Language English
 
Date 2021-08-09
 
Contributor Dreher, Kate
Bill and Melinda Gates Foundation (BMGF)
Foreign, Commonwealth and Development Office (FCDO)
United States Agency for International Development (USAID)
CGIAR Research Program on Maize (MAIZE)
Agricultural Agreement Research Fund (JA)
Foundation for Research Levy on Agricultural Products (FFL)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
Global Maize Program (GMP)
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
CGIAR
 
Type Dataset