Show simple item record

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
Descriptors: Genomic features
Descriptors: Wheats
Descriptors: Vegetation index
Descriptors: Regression analysis
Descriptors: Yields
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.
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.
Language: English
Publisher: BioMed Central
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place: United Kingdom
Journal: Plant Methods
Journal volume: 13:62
DOI: 10.1186/s13007-017-0212-4
Audicence: Researchers


Files in this item

Thumbnail

This item appears in the following Collection(s)

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

Show simple item record