Título

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

Autor

Osval Antonio Montesinos-Lopez

Jaime Cuevas

Juan Burgueño

Suchismita Mondal

JULIO HUERTA-ESPINO

Ravi Singh

Lorena González Pérez

Jose Crossa

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by/4.0

Materias

Genomic features - (AGROVOC) Wheats - (AGROVOC) Vegetation index - (AGROVOC) Regression analysis - (AGROVOC) Yields - (AGROVOC) CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI)

Resumen o descripción

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.

Fecha de publicación

2017

Tipo de publicación

Artículo

Recurso de información

http://hdl.handle.net/10883/19108

Formato

application/pdf

Idioma

Inglés

Audiencia

Investigadores

Repositorio Orígen

Repositorio Institucional de Publicaciones Multimedia del CIMMYT

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