Monte Carlo Approach to Genotype by Environment Interaction Models

Oyamakin S Oluwafemi and Durojaiye M Olalekan
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Durojaiye M Olalekan: Department of Statistics, University of Ibadan, Nigeria

Biostatistics and Biometrics Open Access Journal, 2020, vol. 10, issue 1, 5-10

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.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:10:y:2020:i:1:p:5-10

DOI: 10.19080/BBOAJ.2020.10.555777

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