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Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper‑spectral image data

Author: Montesinos-López, A.
Author: Montesinos-Lopez, O.A.
Author: Cuevas, J.
Author: Mata-López, W.A.
Author: Burgueño, J.
Author: Mondal, S.
Author: Huerta-Espino, J.
Author: Singh, R. P.
Author: Autrique, E.
Author: Gonzalez-Perez, L.
Author: Crossa, J.
Year: 2017
URI: http://hdl.handle.net/10883/19108
Abstract: Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1–8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1–23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information.
Format: PDF
Language: English
Publisher: BioMed Central
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Type: Article
Place of Publication: United Kingdom
Volume: 13:62
DOI: 10.1186/s13007-017-0212-4
Keywords: Hyperspectral Data
Keywords: Genomic Information
Keywords: Genotype × Environment Interaction
Keywords: Band × Environment Interaction
Keywords: Prediction Accuracy
Keywords: Bayesian Functional Regression
Keywords: Spline Regression
Keywords: Fourier Regression
Agrovoc: SPECTRAL ANALYSIS
Agrovoc: GENOMICS
Agrovoc: DATA ANALYSIS
Agrovoc: GENOTYPE ENVIRONMENT INTERACTION
Agrovoc: VEGETATION INDEX
Agrovoc: BAYESIAN THEORY
Agrovoc: REGRESSION ANALYSIS
Related Datasets: https://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRm
Journal: Plant Methods


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    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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