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PLANNING CLONAL SELECTION PROGRAMMES FOR PERENNIAL CROPS

Published online by Cambridge University Press:  28 January 2018

FRANK OWUSU-ANSAH  and
ROBERT N. CURNOW
FRANK OWUSU-ANSAH*
Affiliation:
Social Science and Statistics Unit, Cocoa Research Institute of Ghana, P.O. Box 8, New Tafo-Akim, Ghana
ROBERT N. CURNOW
Affiliation:
Mathematics and Statistics, School of Mathematical and Physical Sciences, University of Reading, Reading, Berkshire, UK
*
‡Corresponding author. Email: [email protected]
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Summary

A formula is developed for calculating the expected gain when a first-order autoregressive repeated measures model for the plot errors is assumed. Using examples from our earlier papers, the similarities of the conclusions about the best selection programme from using simulation of an unstructured model and from using the autoregressive formula for expected gain are presented. The autoregressive formula is then used to derive optimal programmes when the number of plots or plot years is fixed for a range of values for the variance of the interactions of clone effects with years relative to the variance of the clone effects and for the variances and covariances between years of the plot residuals. In general, there are advantages in studying many clones at low replication rather than fewer clones at high replication.

Type
Research Article
Information
Experimental Agriculture , Volume 54 , Issue 6 , December 2018 , pp. 957 - 965
Copyright
Copyright © Cambridge University Press 2018 

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References

REFERENCES

Curnow, R. N. (1961). Optimal programmes for varietal selection (with discussion). Journal of the Royal Statistical Society B 23:282–318.Google Scholar
Finney, D. J. (1958a). Statistical problems of plant selection. Bulletin of the International Statistical Institute 36:242–268.Google Scholar
Finney, D. J. (1958b). Plant selection for yield improvement. Euphytica 7:83–106.Google Scholar
Gauch, H. G. and Zobel, R. W. (1996). Optimal replication in selection experiments. Crop Science 36:838–843.Google Scholar
Gilmour, A. R., Cullis, B. R. and Verbyla, A. P. (1997). Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological, and Environmental Statistics 2 (3):269–293.Google Scholar
MacCulloch, C. E., Searle, S. R. and Neuhaus, J. M. (2008). Generalized, Linear, and Mixed Models. Hoboken, New Jersey: John Wiley and Sons, Inc.Google Scholar
Maxwell, S. E. and Delaney, H. D. (1990). Designing Experiments and Analyzing Data: A Model Comparison Perspective. Belmont, California: Wadsworth.Google Scholar
Mead, R., Gilmour, S. G. and Mead, A. (2012). Statistical Principles for the Design of Experiments: Applications to Real Experiments. New York: Cambridge University Press.Google Scholar
Owusu-Ansah, F., Curnow, R. N. and Adu-Ampomah, Y. (2013). Optimal planning of cocoa clonal selection programmes. Experimental Agriculture 49:574–584.Google Scholar
Owusu-Ansah, F., Curnow, R. N. and Adu-Ampomah, Y. (2017). Optimal planning of cocoa clonal selection programmes – Corrigendum. Experimental Agriculture 1–3; doi:10.1017/S0014479717000175.Google Scholar
Pearson, E. and Hartley, H. (1972). Biometrika Tables for Statisticians, Version 2. London: Cambridge University Press.Google Scholar
Richter, C. and Kroschewski, B. (2006). Analysis of a long-term experiment with repeated-measurement models. Journal of Agronomy and Crop Science 192 (1):55–71.Google Scholar
Stroup, W., Baenziger, P. and Mulitze, D. (1994). Removing spatial variation from wheat yield trials: A comparison of methods. Crop Science 34:62–66.Google Scholar