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

dc.creatorMontesinos-López, A.
dc.creatorMontesinos-Lopez, O.A.
dc.creatorCuevas, J.
dc.creatorMata-López, W.A.
dc.creatorBurgueño, J.
dc.creatorMondal, S.
dc.creatorHuerta-Espino, J.
dc.creatorSingh, R. P.
dc.creatorAutrique, E.
dc.creatorGonzalez-Perez, L.
dc.creatorCrossa, J.
dc.date2018-01-04T18:22:32Z
dc.date2018-01-04T18:22:32Z
dc.date2017
dc.date.accessioned2023-07-17T20:01:49Z
dc.date.available2023-07-17T20:01:49Z
dc.identifierhttp://hdl.handle.net/10883/19108
dc.identifier10.1186/s13007-017-0212-4
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7511031
dc.descriptionModern 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.
dc.formatPDF
dc.languageEnglish
dc.publisherBioMed Central
dc.relationhttps://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRm
dc.rightsCIMMYT 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.
dc.rightsOpen Access
dc.source13:62
dc.sourcePlant Methods
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectHyperspectral Data
dc.subjectGenomic Information
dc.subjectGenotype × Environment Interaction
dc.subjectBand × Environment Interaction
dc.subjectPrediction Accuracy
dc.subjectBayesian Functional Regression
dc.subjectSpline Regression
dc.subjectFourier Regression
dc.subjectSPECTRAL ANALYSIS
dc.subjectGENOMICS
dc.subjectDATA ANALYSIS
dc.subjectGENOTYPE ENVIRONMENT INTERACTION
dc.subjectVEGETATION INDEX
dc.subjectBAYESIAN THEORY
dc.subjectREGRESSION ANALYSIS
dc.titleGenomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper‑spectral image data
dc.typeArticle
dc.coverageUnited Kingdom


Este ítem pertenece a la siguiente institución