The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches

By Alison B. Smith, Brian R. Cullis, Robin Thompson. Journal of Agricultural Sciences, 2005

556 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Plant Science

View Paper: https://www.semanticscholar.org/paper/54a5427cb6ee7f2d0173b8bfdc1ef88240c8fe23

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In Management Science and Operations Research

In Plant Science

Citations in the corpus

The design and analysis of multi-phase plant breeding experiments (2006, background): “Email : alison.smith@dpi.nsw.gov.au Journal of Agricultural Science (2006), 144, 393–409. f 2006 Cambridge University Press 393 doi:10.1017/S0021859606006319 Printed in the United Kingdom reader is referred to Smith et al. (2005) for a more comprehensive discussion of this issue.”
Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction (2014, background): “…in understanding the major environmental, socioeconomic and emerging scientific trends expected to impact and shape the likely paths that will unfold in the future of global agriculture (Evans 1998; Smith et al. 2005a; Tuberosa et al. 2005; Edgerton 2009; Boyer et al. 2013; Grassini et al. 2013).”
Perspectives for Genomic Selection Applications and Research in Plants (2015, background): “In plant breeding, the use of mixed models is more recent and not yet as widespread (Smith et al., 2005; Piepho et al., 2007).”
Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, background): “At this stage, the breeding lines or cultivars may be reasonably considered as fi xed [but as discussed earlier, Smith et al. (2005) argued against this consideration].”
BLUP for phenotypic selection in plant breeding and variety testing (2008, methodology): “BLUP of variety effects based on such models is now routinely used in the analysis of crop variety evaluation data in Australia (Smith et al. 2001a, 2005).”
Quantitative trait loci analysis of zinc efficiency and grain zinc concentration in wheat using whole genome average interval mapping (2008, methodology): “In the analysis of multi-environment trials, this matrix is taken to be a factor analytic (FA) structure (Smith et al. 2005).”
Modeling genotype × environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes (2006, methodology): “Furthermore, sites may form clusters showing higher genotypic correlations than other site groups, as shown by Crossa et al. (2004), who used factor analytic mixed model theory to confirm clusters of sites formed by SREG with negligible crossover GE. Smith et al. (2005) gave a general formulation ”
The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials (2007, methodology): “the consequences of fi tting cultivars as fi xed or random can be found in Smith et al. (2005) .”
On the design of early generation variety trials with correlated data (2006, result): “Note that the assumption of random (rather than fixed) variety effects is consistent with the aim of EGVTs, namely selection (also see Smith et al. (2005)).”
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).”