The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials
By A. Kelly, Alison B. Smith, John A. Eccleston, Brian R. Cullis. 2007
175 citations recorded by the source corpus.
Subfields: Genetics, Management Science and Operations Research, Plant Science
View Paper: https://www.semanticscholar.org/paper/765ec14b5e542705a8e1eea78091ebf7289717b0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- Robin Thompson
- Brian R. Cullis
- Sue J. Welham
- Beverley J. Gogel
- Alison B. Smith
- P. Carneiro
- S. Ceccarelli
- Hans-Peter Piepho
In Management Science and Operations Research
- Brian R. Cullis
- Alison B. Smith
- Sue J. Welham
- Beverley J. Gogel
- Robin Thompson
- P. Carneiro
- R. M. Lark
- Hans-Peter Piepho
In Plant Science
- Brian R. Cullis
- Alison B. Smith
- Robin Thompson
- Sue J. Welham
- Beverley J. Gogel
- M. Pastina
- Isadora Cristina Martins Oliveira
- J. E. S. Carneiro
Citations in the corpus
- Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates (2020, methodology): “In MET datasets, these studies require the use of more flexible approaches, such as linear mixed models (Kelly et al., 2007; Resende and Thompson, 2004; Smith et al., 2005).”
- Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data (2018, unknown): “The FA structure has been found to perform extremely well in terms of providing a good fit to the data and a parsimonious model for VEI (Kelly et al. 2007).”
- A COMPARISON OF ANALYSIS METHODS FOR LATE‐STAGE VARIETY EVALUATION TRIALS (2010, background): “The most general form is an unstructured matrix constrained only to remain positive-definite (see also Kelly et al. 2007), but this can be difficult to fit for even moderately large numbers of varieties.”