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A simulation study of unreplicated and replicated designs for early generation plant breeding trials
by
Alison Kelly
Qld Department of Primary Industries
Coauthors: Brian Cullis (NSW Agriculture), John Eccleston (University of Qld)
The programs aim to progress material that is high yielding and so the statistical design and analysis should aim to improve the heritability of this trait. Selection for yield has improved through the use of statistical methods for spatial analysis of field trials (Cullis and Gleeson, 1991), and through advanced models for genotype by environment interaction (Smith, 1999).
Our study examines the impact of modelling spatial trend and genotype by environment interaction under differing replication regimes. Three designs are compared for bias in variance parameter estimates of the underlying mixed model, and for increase in heritability and selection response. The replicated design is a neighbour balanced design (Eccleston, 2001), and the unreplicated design has check entries interspersed at a frequency of 1:3. For the third design, we propose an improved layout for early generation trials that contains partial replication of the genotype set at each environment, balanced across environments, using the design package of Coombs (2002). It is found that this design type is superior to unreplicated designs in improving the heritability of the trait and equivalent in the level of bias in the variance parameters estimates from the mixed model. This layout is termed a super-replicated design.
References
Coombes, N. (2002) Developments in experimental design. Biometrics Meeting, NSW Agriculture, GOSFORD .
Cullis, B.R., and Gleeson, A.C. (1991) Spatial analysis of field experiments - an extension to two dimensions. Biometrics 47, 1449-1460.
Eccleston, J.A., Elliot, L.E., Chan, B., Martin, R.J., and Chauhan, N. (2001) Design of large field trials:spatial dependency, optimality and visualisation. In Proceedings of GENSTAT 2001. European Genstat Conference, Oxford, UK.
Smith, A.B. (1999) Multiplicative mixed models for the analysis of multi-environment trial data. PhD Thesis, Department of Statistics, University of Adelaide.
Date received: September 9, 2002
Copyright © 2002 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cajn-40.