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Efficiency of augmented p-rep designs in multi-environmental trials

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The paper shows that unreplicated designs in multi-environmental trials are most efficient. If replication per environment is needed then augmented p-rep designs outperform augmented and replicated designs in triticale and maize.

Abstract

In plant breeding, augmented designs with unreplicated entries are frequently used for early generation testing. With limited amount of seed, this design allows to use a maximum number of environments in multi-environmental trials (METs). Check plots enable the estimation of block effects, error variances and a connection of otherwise unconnected trials in METs. Cullis et al. (J Agri Biol Environ Stat 11:381–393, 2006) propose to replace check plots from a grid-plot design by plots of replicated entries leading to partially replicated (p-rep) designs. Williams et al. (Biom J 53:19–27, 2011) apply this idea to augmented designs (augmented p-rep designs). While p-rep designs are increasingly used in METs, a comparison of the efficiency of augmented p-rep designs and augmented designs in the range between replicated and unreplicated designs in METs is lacking. We simulated genetic effects and allocated them according to these four designs to plot yields of a triticale and a maize uniformity trial. The designs varied in the number of environments, but have a fixed number of entries and total plots. The error model and the assumption of fixed or random entry effects were varied in simulations. We extended our simulation for the triticale data by including correlated entry effects which are common in genomic selection. Results show an advantage of unreplicated and augmented p-rep designs and a preference for using random entry effects, especially in case of correlated effects reflecting relationships among entries. Spatial error models had minor advantages compared to purely randomization-based models.

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Acknowledgments

Jens Möhring was supported by DFG Grant PI 377/13-1. We thank two anonymous reviewers for their helpful comments. The DArT marker data were created within research project 0315414A, funded by the Federal Ministry of Education and Research (BMBF), Germany.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Institute for Crop Science, Bioinformatics Unit, University of Hohenheim, Stuttgart, Germany

    Jens Moehring & Hans-Peter Piepho

  2. Statistical Consulting Unit, The Australian National University, Canberra, ACT, 0200, Australia

    Emlyn R. Williams

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  1. Jens Moehring
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  2. Emlyn R. Williams
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  3. Hans-Peter Piepho
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Correspondence to Hans-Peter Piepho.

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Communicated by I. Mackay.

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Moehring, J., Williams, E.R. & Piepho, HP. Efficiency of augmented p-rep designs in multi-environmental trials. Theor Appl Genet 127, 1049–1060 (2014). https://doi.org/10.1007/s00122-014-2278-y

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