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Multi-phase variety trials using both composite and individual replicate samples: a model-based design approach

  • A. B. SMITH (a1), D. G. BUTLER (a2), C. R. CAVANAGH (a3) and B. R. CULLIS (a1) (a4)

Summary

The present paper provides an approach for the design and analysis of variety trials that are used to obtain quality trait data. These trials are multi-phase in nature, comprising a field phase followed by one or more laboratory phases. Typically the laboratory phases are costly relative to the field phase and this imposes a limit on the number of samples that can be tested. Historically, this has been achieved by sacrificing field replication, either by testing a single replicate plot for each variety or a single composite sample, obtained by combining material from several field replicates. An efficient statistical analysis cannot be applied to such data so that valid inference and accurate prediction of genetic effects may be precluded. A solution that has appeared recently in the literature is the use of partial replication, in which some varieties are tested using multiple field replicates and the remainder as single replicates only. In the present paper, an approach is proposed in which some varieties are tested using individual field replicate samples and others as composite samples. Replication in the laboratory is achieved by splitting a relatively small number of field samples into sub-samples for separate processing. It is shown that, if necessary, some of the composite samples may be split for this purpose. It is also shown that, given a choice of field compositing and laboratory replication strategy, an efficient design for a laboratory phase may be obtained using model-based techniques. The methods are illustrated using two examples. It is demonstrated that the approach provides more accurate variety predictions compared with the partial replication approach and that the gains can be substantial if the field variation is large relative to the laboratory variation.

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Copyright

Corresponding author

*To whom all correspondence should be addressed. Email: alismith@uow.edu.au

References

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Brien, C. J., Harch, B. D., Correll, R. L. & Bailey, R. A. (2011). Multiphase experiments with at least one later laboratory phase. I. Orthogonal designs. Journal of Agricultural, Biological and Environmental Statistics 16, 422450.
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Butler, D. G. (2013). On the Optimal Design of Experiments under the Linear Mixed Model. Ph.D. Thesis, School of Mathematics and Physics, University of Queensland, Australia.
Butler, D. G., Cullis, B. R., Gilmour, A. R. & Gogel, B. J. (2009). Analysis of Mixed Models for S Language Environments: ASReml-R Reference Manual Release 3. Training and Development Series, No QE02001. Brisbane, QLD, Australia: QLD Department of Primary Industries and Fisheries.
Cullis, B. R., Smith, A. B. & Coombes, N. E. (2006). On the design of early generation variety trials with correlated data. Journal of Agricultural, Biological and Environmental Statistics 11, 381393.
Mrode, R. (2005). Linear Models for the Prediction of Animal Breeding Values, 2nd edn. Wallingford, UK: CABI Publishing.
R Development Core Team (2011). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
Smith, A. B., Lim, P. & Cullis, B. R. (2006). The design and analysis of multi-phase plant breeding experiments. Journal of Agricultural Science, Cambridge 144, 393409.
Smith, A. B., Thompson, R., Butler, D. G. & Cullis, B. R. (2011). The design and analysis of variety trials using mixtures of composite and individual plot samples. Journal of the Royal Statistical Society, Series C: Applied Statistics 60, 437455.

Multi-phase variety trials using both composite and individual replicate samples: a model-based design approach

  • A. B. SMITH (a1), D. G. BUTLER (a2), C. R. CAVANAGH (a3) and B. R. CULLIS (a1) (a4)

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