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Some Design Problems in Crop Experimentation. III. Non-Orthogonality

Published online by Cambridge University Press:  03 October 2008

S. C. Pearce
Affiliation:
ASRU Ltd, University of Kent, Canterbury CT2 7NF, England

Summary

Ideally each block of an experiment should be made up in the same way with respect to treatments, that is, the design should be ‘orthogonal’. In practice that can be difficult to achieve, especially if the blocks have been chosen to fit the fertility pattern of the field. Sometimes it is impossible, in which case each block will have to contain its own selection of treatments. A number of simple and useful possibilities exist.

Whatever non-orthogonal design is chosen some of the contrasts of interest (perhaps all of them) will be evaluated less efficiently, but that can be compensated by the smaller error mean-square given by a better blocking system. Also, where blocks do differ in their content, comparing their means will provide additional information about treatment effects. Sometimes the information may be worth the trouble of recovery.

Special attention is given in this paper to total balance (including balanced incomplete block designs), supplemented balance, square and rectangular lattices and alpha-designs. The reinforcement of a design is explained and the advantages considered.

Problemas de diseño en la experimentación con cultivos. III

Type
Research Article
Copyright
Copyright © Cambridge University Press 1995

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References

REFERENCES

Federer, W. T. (1955). Experimental Design: Theory and Application. New York: Macmillan Book Company.Google Scholar
Finney, D. J. (1946). Orthogonal partitions of the 6 × 6 Latin squares Annals of Eugenics 13:184196.CrossRefGoogle Scholar
Fisher, R. A. & Yates, F. (1938). Statistical Tables for Biological, Agricultural and Medical Research. Edinburgh: Oliver and Boyd.Google Scholar
Harschbarger, B. (1946). Preliminary report on the rectangular lattices. Biometrics Bulletin 2:115119.CrossRefGoogle Scholar
Harschbarger, B. (1949). Triple rectangular lattices. Biometrics 5:113.CrossRefGoogle Scholar
Mathon, R. & Rosa, A. (1985). Tables of parameters of BIBDs with r ≥ 41 including existence, enumeration, and resolvability results. Annals of Discrete Mathematics 26:275308.Google Scholar
Patterson, H. D. & Williams, E. R. (1976). A new class of resolvable incomplete block designs. Biometrika 63:8392.CrossRefGoogle Scholar
Patterson, H. D., Williams, E. R. & Hunter, E. A. (1978). Block designs for variety trials. Journal of Agricultural Science, Cambridge 90:395400.CrossRefGoogle Scholar
Pearce, S. C. (1963). The use and classification of non-orthogonal designs (with discussion). Journal of the Royal Statistical Society, A 126:353377.CrossRefGoogle Scholar
Pearce, S. C. (1976). Concurrences and quasi-replications: An alternative approach to precision in designed experiments. Biometrische Zeitschrift 18:105112.Google Scholar
Pearce, S. C. (1992). Data analysis in agricultural experimentation. I. Contrasts of interest. Experimental Agriculture 28:245253.CrossRefGoogle Scholar
Pearce, S. C. (1994). Reinforced lattices. Journal of the Royal Statistical Society B56:469476.Google Scholar
Pearce, S. C. (1995). Some design problems in crop experimentation I. The use of blocks. Experimental Agriculture 31:191203.CrossRefGoogle Scholar
Yates, F. (1936). Incomplete randomized blocks. Annals of Eugenics 7:121140.CrossRefGoogle Scholar