Estimators of variance components in the augmented block design with new treatments from one or more populations

This work compares by simulation estimates of variance components produced by the ANOVA (analysis of variance), ML (maximum likelihood), REML (restric ted maximum likelihood), and MIVQUE(0) (minimum variance quadratic unbiased estimator) methods for augmented block design with additional treatments ( progenies) stemming from one or more origins (crosses). Results showed the superiority of the MIVQUE(0) estimation. The ANOVA method, although unbiase d, showed estimates with lower recision. The ML and REML methods produced d ownwards biased estimates for error variance (sigma (2)(e)), and upwards bi ased estimates for genotypic variances (sigma (2)(g)), particularly the ML method. Biases for the REML estimation became negligible when progenies wer e derived from a single cross, and experiments were of larger size with rat ios sigma (2)(g)/sigma (2)(e) >0.5. This method, however, provided the wors t estimates for genotypic variances when progenies were derived from severa l crosses and the experiments were of small size (n< 120 observations).