Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-25T06:51:32.387Z Has data issue: false hasContentIssue false
  • English
  • Français

Adjustment for competition between varieties in plant breeding trials

Published online by Cambridge University Press:  27 March 2009

R. A. Kempton
R. A. Kempton
Affiliation:
Plant Breeding Institute, Trumpington, Cambridge
Get access Rights & Permissions [Opens in a new window]

Summary

A method is proposed for correcting for competition effects in yield trials by joint regression of plot yields on to the yields of neighbours. Estimation of the variety effects and competition coefficient along with tests of significance are described for a sugar-beet trial with single-row plots where competition effects are assumed to extend only to plants in immediately adjacent rows. For designs which are balanced for neighbouring varieties it is feasible to estimate separate varietal competition coefficients which may be partitioned into components for sensitivity and aggressiveness. An example is given of this extended model fitted to a competition diallel of seven species. While species differed in their sensitivity to competition there was no essential difference between inter- and intra-species behaviour. The model is used to assess comparative varietal performance in monocultures from performance in small plot trials.

(Note that the general term ‘variety’ is used throughout this paper to refer to progenies at any stage in a selection programme.)

Type
Research Article
Information
The Journal of Agricultural Science , Volume 98 , Issue 3 , June 1982 , pp. 599 - 611
Copyright
Copyright © Cambridge University Press 1982

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Arnold, M. H. & Kempton, R. A. (1979). Estimating the performance of sugar beet varieties. In Proceedings 42nd Winter Congress Institut International de Recherches Betteravieres, 1979, pp. 189–203.Google Scholar
Austin, R. B. & Blackwell, R. D. (1980). Edge and neighbour effects in cereal yield trials. Journal of Agricultural Science, Cambridge 94, 731–734.CrossRefGoogle Scholar
Besag, J. E. (1974). Spatial interaction and the statistical analysis of lattice systems (with Discussion). Journal of the Royal Statistical Society B 36, 192–236.Google Scholar
Bleasdale, J. K. A. (1967). Systematic designs for spacing experiments. Experimental Agriculture 3, 73–85.CrossRefGoogle Scholar
Breese, E. L. & Hill, J. (1973). Regression analysis of interactions between competing species. Heredity 31, 181–200.CrossRefGoogle Scholar
Cannell, M. G. R., Njuguna, C. K., Ford, E. D., Smith, R. & Ross-Parker, H. M. (1977). Variation in yield among competing individuals within mixed genotype stands of tea: a selection problem. Journal of Applied Ecology 14, 969–985.Google Scholar
Cliff, A. D. & Ord, J. K. (1981). Spatial Processes. London: Pion.Google Scholar
Cleaver, T. J., Greenwood, D. J. & Wood, J. T. (1970). Systematically arranged fertilizer experiments. Journal of Horticultural Science 45, 457–469.CrossRefGoogle Scholar
Digby, P. G. N. (1979). Modified joint regression analysis for incomplete variety × environment data. Journal of Agricultural Science, Cambridge 93, 81–86.CrossRefGoogle Scholar
Draper, N. R. & Guttman, I. (1980). Incorporating overlap effects from neighbouring units into response surface models. Applied Statistics 29, 128–134.CrossRefGoogle Scholar
Dyke, G. V. & Shelley, C. F. (1976). Serial designs balanced for effects of neighbours on both sides. Journal of Agricultural Science, Cambridge 87, 303–305.CrossRefGoogle Scholar
Finlay, K. W. & Wilkinson, G. N. (1963). The analysis of adaption ina plant breeding programme. Australian Journal of Agricultural Research 14, 742–754.CrossRefGoogle Scholar
Finney, D. J. & Outhwaite, A. D. (1956). Serially balanced sequences in bioassay. Proceedings of the Royal Society B 145, 493–507.Google ScholarPubMed
Fischer, R. A. (1979). Are your results confounded by intergenotypic competition? In Proceedings of Vth International Wheat Genetics Symposium, Vol. 2, pp. 767–777. New Delhi, 1978.Google Scholar
Freeman, G. H. (1979 a). Some two-dimensional designs balanced for nearest neighbours. Journal of the Royal Statistical Society B 41, 88–95.Google Scholar
Freeman, G. H. (1979 b). Complete latin squares and related experimental designs. Journal of the Royal Statistical Society B 41, 253–262.Google Scholar
Freeman, G. H. (1981). Further results on quasi complete latin squares. Journal of the Royal Statistical Society B 43, 314–320.Google Scholar
Gomez, K. A. (1972). Border effects in rice experimental plots. II. Varietal competition. Experimental Agriculture 8, 295–298.CrossRefGoogle Scholar
Hald, A. (1948). The Decomposition of a Series of Observations. G.E.C. Gads Forlag, Copenhagen.Google Scholar
Hogg, R. V. & Craig, A. T. (1965). Introduction to Mathematical Statistics, 2nd edn.New York: Macmillan.Google Scholar
Jacquard, P. (1970). Study of the social relations between seven forage species at two trophic levels. In Proceedings Uth International Grassland Congress, Australia, pp. 657–662.Google Scholar
Jensen, N. F. & Federer, W. T. (1964). Adjacent row competition in wheat. Crop Science 4, 641–645.CrossRefGoogle Scholar
Kempton, R. A. & Howes, C. W. (1981). The use of neighbouring plot values in the analysis of variety trials. Applied Statistics 30, 59–70.CrossRefGoogle Scholar
Kendall, M. G. & Stuart, A. (1976). The Advanced Theory of Statistics, Vol. 3, 3rd edn.London: Griffin.Google Scholar
Lazenby, A. & Rogers, H. H. (1964). Selection criteria in grass breeding. II. Effect, on Lolium perenne, of differences in population density, variety and available moisture. Journal of Agricultural Science, Cambridge 62, 285–298.CrossRefGoogle Scholar
Mead, R. (1967). A mathematical model for the estimation of inter-plant competition. Biometrics 23, 189–205.CrossRefGoogle ScholarPubMed
Mead, R. & Riley, J. (1981). A review of statistical ideas relevant to intercropping research (with Discussion). Journal of the Royal Statistical Society A 144, 462–509.Google Scholar
Nelder, J. A. (1962). New kinds of systematic designs for spacing experiments. Biometrics 18, 283–307.CrossRefGoogle Scholar
Ord, J. K. (1975). Estimation methods for models of spatial interaction. Journal of the American Statistical Association 70, 120–126.CrossRefGoogle Scholar
Patterson, H. D. & Silvey, V. (1980). Statutory and recommended list trials of crop varieties in the UK (with Discussion). Journal of the Royal Statistical Society A 143, 219–252.Google Scholar
Ross, G. J. S. (1980). Maximum Likelihood Program. Rothamsted Experimental Station, Harpenden, U.K.Google Scholar
Trenbath, B. R. (1978). Models and interpretation of mixture experiments. In Plant Relations in Pastures (ed. Wilson, J. R.), pp. 145–162. Melbourne: C.S.I.R.O.Google Scholar
Walker, D. I. T. & Simmonds, N. W. (1981). Comparisons of the performance of sugar cane varieties in trials and in agriculture. Experimental Agriculture 17, 137–144.CrossRefGoogle Scholar
Williams, E. J. (1962). The analysis of competition experiments. Australian Journal of Biological Science 15, 509–525.Google Scholar
Williams, R. M. (1952). Experimental designs for serially correlated observations. Biometrika 39, 152–167.CrossRefGoogle Scholar
Wright, A. J. (1971). The analysis and prediction of some two factor interactions in grass breeding. Journal of Agricultural Science, Cambridge 76, 301–306.Google Scholar
Yates, F. (1938). The comparative advantages of systematic and randomised arrangements in the design of agricultural and biological experiments. Biometrika 30, 440–466.CrossRefGoogle Scholar
Yates, F. & Cochran, W. G. (1938). The analysis of groups of experiments. Journal of Agricultural Science, Cambridge 28, 556–580.Google Scholar