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Monte Carlo Approach to Genotype by Environment Interaction Models

Author

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  • Oyamakin S Oluwafemi
  • Durojaiye M Olalekan

    (Department of Statistics, University of Ibadan, Nigeria)

Abstract

Understanding the implication of Genotype-by-Environment (GE×) interaction structure is an important consideration in plant breeding programs. Traditional statistical analyses of yield trials provide little or no insight into the particular pattern or structure of the GE× interaction. In this study, efforts were made to solve these problems under different level of data occurrence. We employed the simulation process of Monte Carlo in generating since use of a real-life data may pose a serious difficulty. In this paper, we simulated for two data Types of Balance and Unbalance designs with different Levels of generations (33×,77×, 1010× and 37×, 73× , 710× , 107×respectively). We therefore check the performance of interaction on four different models (AMMI, FW, GGE and Mixed model), and also their stability and adaptability. The findings revealed that, when the assumption was maintained, AMMI outperformed Finlay-Wilkinson model, GGE Biplot model and Mixed model.

Suggested Citation

  • Oyamakin S Oluwafemi & Durojaiye M Olalekan, 2020. "Monte Carlo Approach to Genotype by Environment Interaction Models," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 10(1), pages 5-10, February.
    • Handle: RePEc:adp:jbboaj:v:10:y:2020:i:1:p:5-10
    DOI: 10.19080/BBOAJ.2020.10.555777
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