The Accuracy of Varietal Selection Using Factor Analytic Models for Multi-Environment Plant Breeding Trials

Kelly, Alison; Smith, Alison; Eccleston, J. A.; Cullis, Brian R

doi:10.2135/cropsci2006.08.0540

Cited by 166 publications

(211 citation statements)

References 16 publications

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“…Thus candidate models can be assessed and selected for MET data analyses, which result in high accuracy when estimating and testing varietal effects. Although there are already some general reviews of crop breeding analysis and variety evaluation trials (Davidoff & Selim, 1988;Smith et al, 2001Smith et al, , 2005, as well as studies on the analysis of MET data using the mixed models (Bartlett, 1978;Piepho, 1997;Kelly et al, 2007;Piepho & Möhring, 2010;Stefanova & Buirchell, 2010), most references just contain some examples for demonstration, or contain just one specific type of mixed model in data analysis.…”

Section: Methodsmentioning

confidence: 99%

Mixed model with spatial variance–covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment

Negash

Mwambi

Zewotir

et al. 2014

Span. j. agric. res.

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The most common procedure for analyzing multi-environmental trials is based on the assumption that the residual error variance is homogenous across all locations considered. However, this may often be unrealistic, and therefore limit the accuracy of variety evaluation or the reliability of variety recommendations. The objectives of this study were to show the advantages of mixed models with spatial variance-covariance structures, and direct implications of model choice on the inference of varietal performance, ranking and testing based on two multi-environmental data sets from realistic national trials. A model comparison with a χ 2 -test for the trials in the two data sets (wheat data set BW00RVTI and barley data set BW01RVII) suggested that selected spatial variance-covariance structures fitted the data significantly better than the ANOVA model. The forms of optimally-fitted spatial variance-covariance, ranking and consistency ratio test were not the same from one trial (location) to the other. Linear mixed models with single stage analysis including spatial variance-covariance structure with a group factor of location on the random model also improved the real estimation of genotype effect and their ranking. The model also improved varietal performance estimation because of its capacity to handle additional sources of variation, location and genotype by location (environment) interaction variation and accommodating of local stationary trend.

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Section: Methodsmentioning

confidence: 99%

Mixed model with spatial variance–covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment

Negash

Mwambi

Zewotir

et al. 2014

Span. j. agric. res.

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show abstract

“…Following the approach of Smith et al 26 , a factor analytic form for the genetic variance /covariance matrix was adopted, and independence between genotypes assumed. This model allows for heterogeneous genetic variances between sites and different covariances between each pair of sites, two assumptions deemed necessary to appropriately model the genetic variance across environments for these measurements 17 .…”

Section: Resultsmentioning

confidence: 99%

Measurement of Barley Grain Rheology for the Quality Selection of Breeding Material

Osborne

Fox

Kelly³

et al. 2007

Journal of the Institute of Brewing

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The Single-Kernel Characterization System (SKCS) 4100 was used to test whole grain barley from a replicated breeding trial across two environments and two seasons. Hardness was calculated from a crush response profile of the resistance to shell and endosperm crushing as a function of time. This rheological data recovered was averaged over 300 grains for each sample then partitioned into genetic, environmental and error components in order to calculate the genetic components and heritability. The results demonstrated a high level of genetic variance for all rheological parameters at all sites and years. In addition, the heritability was also moderate to high (68% to 90%) for the SKCS hardness and the rheological parameters that had been previously shown to provide information about endosperm modification during malting. Since the rheological information is recovered for individual grains within a sample, the SKCS 4100 may provide a new tool for screening breeding material for uniformity of quality.

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“…Cereal Research Communications 44, 2016 covariance matrix (Smith et al 2001;Kelly et al 2007). The best linear unbiased estimators (BLUEs) for the fixed effects and the best linear unbiased predictors (BLUPs) of the random effects in model (2) were utilized to calculate adjusted GR means on average locations using the algorithm described by Welham et al (2004).…”

Section: Derejko Et Al: a Comparison Of Wheat Cultivar Rankingsmentioning

confidence: 99%

A comparison of winter wheat cultivar rankings in groups of Polish locations

Derejko

Studnicki

Mądry

et al. 2016

Cereal Research Communications

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The grouping of locations from local-scale multi-environmental trials (METs) into megaenvironments has been criticized. Some European countries, e.g. the Czech Republic, Poland and Germany, have been characterized as possessing homogeneous environmental conditions. For aligned environmental conditions, it has been assumed that cultivar rankings will be similar and consequently cannot be used to designate mega-environments. An example of METs at the local scale is the Polish Post Registration Variety Testing System. The objective of this study was to determine groups of test sites within 16 Polish regions which are characterized by similar yield ranking of 50 winter wheat cultivars over three growing seasons (2011)(2012)(2013). The compatibility of these cultivar yield rankings across regions was evaluated using Pearson correlation coefficients. Thereby, the 16 regions were divided into six groups (mega-environments) of locations. Regions within each group have similar cultivar rankings, whereas between groups, we observed different cultivar rankings, indicating crossover interactions. Besides similar cultivar yield responses the regions within megaenvironments were characterized also by similar environmental (soil and/or climate) conditions.

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The Accuracy of Varietal Selection Using Factor Analytic Models for Multi-Environment Plant Breeding Trials

Abstract: Abbreviations: BLUP, best linear unbiased prediction; E-BLUP, empirical best linear unbiased prediction; FA, factor analytic; MET, multi-environment trial; MSEP, mean squared error of prediction; US, unstructured variance.

Cited by 166 publications

References 16 publications

Mixed model with spatial variance–covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment

Mixed model with spatial variance–covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment

Measurement of Barley Grain Rheology for the Quality Selection of Breeding Material

A comparison of winter wheat cultivar rankings in groups of Polish locations

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