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Sensitivity (Stability) Analysis of Multiple Variety Trials, with Special Reference to Data Expressed as Proportions or Percentages

Published online by Cambridge University Press:  03 October 2008

G. V. Dyke ,
P. W. Lane  and
J. F. Jenkyn
G. V. Dyke
Affiliation:
AFRC Institute of Arable Crops Research, Rothamsted Experimental Station, Harpenden, Herts AL5 2JQ, England
P. W. Lane
Affiliation:
AFRC Institute of Arable Crops Research, Rothamsted Experimental Station, Harpenden, Herts AL5 2JQ, England
J. F. Jenkyn
Affiliation:
AFRC Institute of Arable Crops Research, Rothamsted Experimental Station, Harpenden, Herts AL5 2JQ, England
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Summary

The literature on ‘stability analysis’, which is better called ‘sensitivity analysis’, leaves much to be desired. This paper attempts to clarify the issues, particularly the effect of the non-linearity of the standard model relating variety yields to mean yields over sites. The model is extended from its original applications in the analysis of crop yields to the analysis of measurements that cannot be considered Normally distributed, such as disease assessments expressed as percentages or proportions. Modern computer software offers a method that is theoretically sound and can be applied routinely to data of many different distributional types. Graphical methods of displaying the results of the analysis of non-Normal data are discussed.

Análisis de la sensibilidad de ensayos con variedades

Type
Research Article
Information
Experimental Agriculture , Volume 31 , Issue 1 , January 1995 , pp. 75 - 87
Copyright
Copyright © Cambridge University Press 1995

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References

Digby, P. G. N. (1979). Modified joint regression analysis for incomplete variety environment data. Journal of Agricultural Science, Cambridge 93:81–86.Google Scholar
Dyke, G. V. & Patterson, H. D. (1952). Analysis of factorial arrangements when the data are proportions. Biometrics 8:1–12.Google Scholar
Eberhart, S. A. & Russell, W. A. (1966). Stability parameters for comparing varieties. Crop Science 6:36–40.Google Scholar
Eskridge, K. M., Bryne, P. F. & Crossa, J. (1991). Selection of stable varieties by minimizing the probability of disaster. Field Crops Research 27:169–181.Google Scholar
Finlay, K. W. & Wilkinson, G. N. (1963). The analysis of adaptation in a plant-breeding programme. Australian journal of Agricultural Research 14:742–54.CrossRefGoogle Scholar
Finney, D. J. (1971). Probit Analysis, Third Edition. Cambridge: Cambridge University Press.Google Scholar
Lane, P. W. & Ryder, K. (1994). Procedure RJOINT. In Genstat 5 Procedure Library Manual 3 [1], 291–294 (Eds Payne, R. W., Arnold, G. M. and Morgan, G. W.). Oxford: Numerical Algorithms Croup.Google Scholar
Lin, C. S., Binns, M. R. & Lefkovitch, L. P. (1986). Stability analysis: where do we stand? Crop Science 26:899–900.Google Scholar
McCullagh, P. & Neider, J. A. (1989). Generalized Linear Models, Second Edition. London: Chapman and Hall.Google Scholar
Mead, R., Riley, J., Dear, K. & Singh, S. P. (1986). Stability comparison of intercropping and monocropping systems. Biometrics 42:253–266.CrossRefGoogle Scholar
Mutsaers, H. J. W. & Walker, P. (1990). Farmers' maize yields in SW Nigeria and the effect of variety and fertilizer: an analysis of variability in on-farm trials. Field Crops Research 23:265–278.Google Scholar
Nelder, J. A. & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society A 135:370–84.Google Scholar
Patterson, H. D. & Silvey, V. (1980). Statutory and recommended list trials of crop varieties in the United Kingdom. Journal of the Royal Statistical Society A 143:219–252.CrossRefGoogle Scholar
Priestley, R. H. (1978). Detection of increased virulence in populations of wheat yellow rust. In Plant Disease Epidemiology, 63–70 (Ed. by Scott, P. R. and Bainbridge, A.). Oxford: Blackwell Scientific.Google Scholar
Saltelli, A., Andres, T. H. & Homma, T. (1993). Sensitivity analysis of model output: an investigation of new techniques. Computational Statistics and Data Analysis 15:211–238.CrossRefGoogle Scholar
Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika 61:439–997.Google Scholar
Yates, F. & Cochran, W. G. (1938). The analysis of groups of experiments. journal of Agricultural Science, Cambridge 28:556–580.CrossRefGoogle Scholar