Theoretical and Applied Genetics

, Volume 76, Issue 1, pp 1–10 | Cite as

Predictive and postdictive success of statistical analyses of yield trials

  • H. G. GauchJr.
  • R. W. Zobel


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.

Key words

AMMI Genotype-environment interaction Prediction Soybean Yield trials 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aitchison J, Dunsmore IR (1975) Statistical prediction analysis. Cambridge University Press, CambridgeGoogle Scholar
  2. Blackburn S (1973) Reason and prediction. Cambridge University Press, CambridgeGoogle Scholar
  3. Bradu D (1984) Response surface model diagnosis in two-way tables. Commun Stat Theory Methods 13:3059–3106Google Scholar
  4. Bradu D, Gabriel KR (1978) The biplot as a diagnostic tool for models of two-way tables. Technometrics 20:47–68Google Scholar
  5. Burchfield RW (1982) A supplement to the Oxford English Dictionary. Oxford University Press, OxfordGoogle Scholar
  6. Cady FB, Allen DM (1972) Combining experiments to predict future yield data. Agron J 64:211–214Google Scholar
  7. Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14:742–754Google Scholar
  8. Freeman GH (1973) Statistical methods for the analysis of genotype-environment interactions. Heredity 31:339–354Google Scholar
  9. Gauch HG (1982) Noise reduction by eigenvector ordinations. Ecology 63:1643–1649Google Scholar
  10. 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/NYGoogle Scholar
  11. Gauch HG (1987) MATMODEL. Microcomputer Power, Ithaca/NYGoogle Scholar
  12. Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics (in press)Google Scholar
  13. Gollob HF (1968) A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33:73–115Google Scholar
  14. Gregorius HR, Namkoong G (1986) Joint analysis of genotypic and environmental effects. Theor Appl Genet 72:413–422Google Scholar
  15. Gusmão L (1985) An adequate design for regression analysis of yield trials. Theor Appl Genet 71:314–319Google Scholar
  16. Gusmão L (1986) Inadequacy of blocking in cultivar yield trials. Theor Appl Genet 72:98–104Google Scholar
  17. Harrison PJ, Stevens CF (1976) Bayesian forecasting. J R Stat Soc Ser B 38:205–247Google Scholar
  18. Kempton RA (1984) The use of biplots in interpreting variety by environment interactions. J Agric Sci 103:123–135Google Scholar
  19. Mandel J (1971) A new analysis of variance model for non-additive data. Technometrics 13:1–18Google Scholar
  20. Snedecor GW, Cochran WG (1980) Statistical Methods, 7th edn. Iowa State University Press, Ames/IA, pp 44–45, 264–265Google Scholar
  21. Student (1923) On testing varieties of cereals. Biometrika 15271–293Google Scholar
  22. Talbot M (1984) Yield variability of crop varieties in the UK. J Agric Sci 102:315–321Google Scholar
  23. Wood CL, Cady FB (1981) Intersite transfer of estimated response surfaces. Biometrics 37:1–10Google Scholar
  24. Wright AJ (1971) The analysis and prediction of some two factor interactions in grass breeding. J Agric Sci 76: 301–306Google Scholar
  25. Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agron J (in press)Google Scholar

Copyright information

© Springer-Verlag 1988

Authors and Affiliations

  • H. G. GauchJr.
    • 1
  • R. W. Zobel
    • 1
  1. 1.Department of AgronomyCornell UniversityIthacaUSA

Personalised recommendations