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Predictive and postdictive success of statistical analyses of yield trials

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The accuracy of a yield trial can be increased by improved experimental techniques, more replicates, or more efficient statistical analyses. The third option involves nominal fixed costs, and is therefore very attractive. The statistical analysis recommended here combines the Additive main effects and multiplicative interaction (AMMI) model with a predictive assessment of accuracy. AMMI begins with the usual analysis of variance (ANOVA) to compute genotype and environment additive effects. It then applies principal components analysis (PCA) to analyze non-additive interaction effects. Tests with a New York soybean yield trial show that the predictive accuracy of AMMI with only two replicates is equal to the predictive accuracy of means based on five replicates. The effectiveness of AMMI increases with the size of the yield trial and with the noisiness of the data. Statistical analysis of yield trials with the AMMI model has a number of promising implications for agronomy and plant breeding research programs.

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  • Aitchison J, Dunsmore IR (1975) Statistical prediction analysis. Cambridge University Press, Cambridge

    Google Scholar 

  • Blackburn S (1973) Reason and prediction. Cambridge University Press, Cambridge

    Google Scholar 

  • Bradu D (1984) Response surface model diagnosis in two-way tables. Commun Stat Theory Methods 13:3059–3106

    Google Scholar 

  • Bradu D, Gabriel KR (1978) The biplot as a diagnostic tool for models of two-way tables. Technometrics 20:47–68

    Google Scholar 

  • Burchfield RW (1982) A supplement to the Oxford English Dictionary. Oxford University Press, Oxford

    Google Scholar 

  • Cady FB, Allen DM (1972) Combining experiments to predict future yield data. Agron J 64:211–214

    Google Scholar 

  • Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14:742–754

    Google Scholar 

  • Freeman GH (1973) Statistical methods for the analysis of genotype-environment interactions. Heredity 31:339–354

    Google Scholar 

  • Gauch HG (1982) Noise reduction by eigenvector ordinations. Ecology 63:1643–1649

    Google Scholar 

  • Gauch HG (1985) Integrating additive and multiplicative models for analysis of yield trials with assessment of predictive success, Mimeo 85-7. Department of Agronomy, Cornell University, Ithaca/NY

    Google Scholar 

  • Gauch HG (1987) MATMODEL. Microcomputer Power, Ithaca/NY

  • Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics (in press)

  • Gollob HF (1968) A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33:73–115

    Google Scholar 

  • Gregorius HR, Namkoong G (1986) Joint analysis of genotypic and environmental effects. Theor Appl Genet 72:413–422

    Google Scholar 

  • Gusmão L (1985) An adequate design for regression analysis of yield trials. Theor Appl Genet 71:314–319

    Google Scholar 

  • Gusmão L (1986) Inadequacy of blocking in cultivar yield trials. Theor Appl Genet 72:98–104

    Google Scholar 

  • Harrison PJ, Stevens CF (1976) Bayesian forecasting. J R Stat Soc Ser B 38:205–247

    Google Scholar 

  • Kempton RA (1984) The use of biplots in interpreting variety by environment interactions. J Agric Sci 103:123–135

    Google Scholar 

  • Mandel J (1971) A new analysis of variance model for non-additive data. Technometrics 13:1–18

    Google Scholar 

  • Snedecor GW, Cochran WG (1980) Statistical Methods, 7th edn. Iowa State University Press, Ames/IA, pp 44–45, 264–265

    Google Scholar 

  • Student (1923) On testing varieties of cereals. Biometrika 15271–293

    Google Scholar 

  • Talbot M (1984) Yield variability of crop varieties in the UK. J Agric Sci 102:315–321

    Google Scholar 

  • Wood CL, Cady FB (1981) Intersite transfer of estimated response surfaces. Biometrics 37:1–10

    Google Scholar 

  • Wright AJ (1971) The analysis and prediction of some two factor interactions in grass breeding. J Agric Sci 76: 301–306

    Google Scholar 

  • Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agron J (in press)

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Communicated by A. R. Hallauer

This research was supported by the Rhizobotany Project of the USDA-ARS

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Gauch, H.G., Zobel, R.W. Predictive and postdictive success of statistical analyses of yield trials. Theoret. Appl. Genetics 76, 1–10 (1988).

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