Theoretical and Applied Genetics

, Volume 97, Issue 1, pp 195–201

Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures

  • Hans-Peter Piepho

DOI: 10.1007/s001220050885

Cite this article as:
Piepho, H. Theor Appl Genet (1998) 97: 195. doi:10.1007/s001220050885

Abstract

 Results of multi-environment trials to evaluate new plant cultivars may be displayed in a two-way table of genotypes by environments. Different estimators are available to fill the cells of such tables. It has been shown previously that the predictive accuracy of the simple genotype by environment mean is often lower than that of other estimators, e.g. least-squares estimators based on multiplicative models, such as the additive main effects multiplicative interaction (AMMI) model, or empirical best-linear unbiased predictors (BLUPs) based on a two-way analysis-of-variance (ANOVA) model. This paper proposes a method to obtain BLUPs based on models with multiplicative terms. It is shown by cross-validation using five real data sets (oilseed rape, Brassica napus L.) that the predictive accuracy of BLUPs based on models with multiplicative terms may be better than that of least-squares estimators based on the same models and also better than BLUPs based on ANOVA models.

Key words Genotype by environment interactionMixed modelMean squared error of predictionBrassica napus L.Cross-validationAdditive main effects multiplicative interaction (AMMI)Shifted multiplicative model (SHMM)Restricted maximum likelihood (REML)

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Hans-Peter Piepho
    • 1
  1. 1.Institut für Nutzpflanzenkunde, Universität-Gesamthochschule Kassel, Steinstrasse 19, 37213 Witzenhausen, GermanyDE