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Article
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture
(BioMed Central, 2018)
Background: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. ...
Article
Correction to: bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture
(BioMed Central, 2018)
Unfortunately, in the original version [1] of this article, a funder note was missed out in the acknowledgement. Te corrected acknowledgement is given below: Acknowledgements Te authors thank all the feld and lab assistants ...
Article
Genomic prediction of genotype x environment interaction kernel regression models
(Crop Science Society of America, 2016)
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this ...
Article
Bayesian genomic prediction with genotype x environment interaction kernel models
(Genetics Society of America, 2017)
The phenomenon of genotype · environment (G · E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G · E have been ...
Article
Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data
(BioMed Central, 2017)
Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, ...
Article
Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers
(Crop Science Society of America (CSSA), 2012)
Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. ...
Article
Genomic prediction in maize breeding populations with genotyping-by sequencing
(Genetics Society of America, 2013)
Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, ...
Article
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
(Springer, 2013)
In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in ...
Article
Extending the marker x environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat
(Crop Science Society of America (CSSA), 2016)
The marker ´ environment interaction (M´E) genomic model can be used to generate predictions for untested individuals and identify genomic regions in which effects are stable across environments and others that show ...
Article
Use of hyperspectral image data outperforms vegetation indices in prediction of maize yield
(Crop Science Society of America (CSSA), 2017)
Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data ...