Summary
Multilocation trials are important for the CIMMYT Bread Wheat Program in producing high-yielding, adapted lines for a wide range of environments. This study investigated procedures for improving predictive success of a yield trial, grouping environments and genotypes into homogeneous subsets, and determining the yield stability of 18 CIMMYT bread wheats evaluated at 25 locations. Additive Main effects and Multiplicative Interaction (AMMI) analysis gave more precise estimates of genotypic yields within locations than means across replicates. This precision facilitated formation by cluster analysis of more cohesive groups of genotypes and locations for biological interpretation of interactions than occurred with unadjusted means. Locations were clustered into two subsets for which genotypes with positive interactions manifested in high, stable yields were identified. The analyses highlighted superior selections with both broad and specific adaptation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abou-El-Fittouh HA, Rawlings JO, Miller PA (1969) Classification of environments to control genotype by environment interactions with an application to cotton. Crop Sci 9:135–140
Allen DM (1971) Mean square error of prediction as a criterion for selecting variables. Technometrics 13:469–475
Besag J, Kempton R (1986) Statistical analysis of field experiments using neighboring plots. Biometrics 42:231–251
Bradley JP, Knittle KH, Troyer AF (1988) Statistical methods in seed corn production selection. J Prod Agric 1:34–38
Bradu D, Gabriel KR (1978) The biplot as a diagnostic tool for models of two-way tables. Technometrics 20:47–68
Burr EJ (1970) Cluster sorting with mixed character types. II. Fusion strategies. Aust Comput J 2:98–103
Byth DE, Eisemann RL, De Lacy IH (1976) Two-way pattern analysis of a large data set to evaluate genotypic adaptation. Heredity 37:215–230
Crossa J (1988) A comparison of results obtained with two methods for assessing yield stability. Theor Appl Genet 75:460–467
Crossa J, Westcott B, Gonzalez C (1988a) The yield stability of maize genotypes across international environments: Full season tropical maize. Exp Agric 24:253–263
Crossa J, Westcott B, Gonzalez C (1988b) Analyzing yield stability of maize genotypes using a spatial model. Theor Appl Genet 75:863–868
Crossa J, Westcott B, Gonzalez C (1989) The yield stability of CIMMYT's maize germplasm. Euphytica 40:245–251
Crossa J, Gauch HG, Zobel RW (1990) Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci 30:493–500
Fox PN, Rosielle AA (1982) Reducing the influence of environmental main-effects on pattern analysis of plant breding environments. Euphytica 31:645–656
Fox PN, Skovmand B, Thompson BK, Braun H-J, Cormier R (1990) Yield and adaptation of hexaploid spring triticale. Euphytica 47:57–64
Freeman GH, Dowker BD (1973) The analysis of variation between and within genotypes and environments. Heredity 30:97–109
Gauch HG (1982a) Noise reduction by eigenvector ordinations. Ecology 63:1643–1649
Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 88:705–715
Gauch HG (1990) Full and reduced models for yield trials. Theor Appl Genet (in press)
Gauch HG, Zobel RW (1988) Predictive and postdictive success of statistical analyses of yield trials. Theor Appl Genet 76:1–10
Gauch HG, Zobel RW (1989) Accuracy and selection success in yield trial analyses. Theor Appl Genet 77:473–481
Kempton RA (1984) The use of the bi-plots in interpreting variety by environment interactions. J Agric Sci 103:123–135
Knight WE (1966) A computer method for calculating Kendall's Tau with ungrouped data. J Am Stat Assoc 61:436–439
Krzanowski WJ (1983) Cross-validatory choice in principal components analysis — some sampling results. J Stat Comp Simul 18:299–314
Patterson HD, Williams ER, Hunter EA (1978) Block designs for variety trials. J Agric Sci 90:395–400
Rajaram S, Skovmand B, Curtis BC (1984) Philosophy and methodology of an international wheat breeding program. In: Gustafson JP (ed) Gene manipulation in plant improvement. 16th Stadler Genet Symp, 1984, University of MissouriColumbia. Plenum Press, New York London, pp 33–60
Shorter R, Byth DE, Mungomery VE (1977) Genotype x environment interactions and environmental adaptation. II. Assessment of environmental contributions. Aust J Agric Res 28:223–235
Snee RD (1977) Validation of regression models: methods and examples. Technometrics 19:415–428
Westcott B (1987) A method of assessing the yield stability of crop genotypes. J Agric Sci 108:267–274
Wilkinson GN, Eckert SR, Hancock TW, Mayo O (1983) Nearest neighbor (NN) analysis of field experiments (with discussion). J Stat Soc, Ser B 45:152–212
Wold S (1978) Cross-validatory estimation of the number of components in factor and principal components models. Technometrics 20:397–405
Yates F (1936) A new method of arranging variety trials involving a large number of varieties. J Agric Sci 26:424–455
Zobel RW, Wright MJ, Gauch HJ (1988) Statistical analysis of a yield trial. Agron J 80:388–393
Author information
Authors and Affiliations
Additional information
Communicated by A. R. Hallauer
Rights and permissions
About this article
Cite this article
Crossa, J., Fox, P.N., Pfeiffer, W.H. et al. AMMI adjustment for statistical analysis of an international wheat yield trial. Theoret. Appl. Genetics 81, 27–37 (1991). https://doi.org/10.1007/BF00226108
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF00226108