Knowledge Center Catalog

Local cover image
Local cover image

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

By: Contributor(s): Material type: ArticleArticleLanguage: English Publication details: United Kingdom : Biomed Central, 2017.Subject(s): Online resources: In: Plant Methods v. 13, art. 62Summary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Article CIMMYT Knowledge Center: John Woolston Library CIMMYT Staff Publications Collection Available
Total holds: 0

Peer review

Open Access

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.

Wheat CRP FP3 - Global partnership to accelerate genetic gain in farmers field

Text in English

Click on an image to view it in the image viewer

Local cover image

International Maize and Wheat Improvement Center (CIMMYT) © Copyright 2021.
Carretera México-Veracruz. Km. 45, El Batán, Texcoco, México, C.P. 56237.
If you have any question, please contact us at
CIMMYT-Knowledge-Center@cgiar.org