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International Journal of Plant Breeding and Genetics
  Year: 2014 | Volume: 8 | Issue: 3 | Page No.: 139-152
DOI: 10.3923/ijpbg.2014.139.152
 
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Comparative Performance of Forty-Eight Rice Genotypes in Diverse Environments Using the Ammi and GGE Biplot Analyses
S.A. Ogunbayo, M. Sie, D.K. Ojo, A.R. Popoola, O.A. Oduwaye, I.O. Daniel, K.A. Sanni, M.G. Akinwale, B. Toulou, A. Shittu, G.B. Gregorio and E.F. Mercado

Abstract:
The study of GenotypexEnvironment Interaction (GEI) is critical for accurate cultivar evaluation in large multi-environment trials. Cultivars that exhibit high levels of mean performance and stability across a wide range of environmental conditions are desirable for rice production. The goal of this study was to examine the relative discriminatory abilities of AMMI and GGE stability models in selection for grain yield and stability among lowland rice genotypes. Forty-eight rice genotypes were tested for genetic variability and stability of performance in twelve environments in Nigeria, Benin Republic and Togo between 2008 and 2009. Statistical analysis was performed using Additive Main effect and Multiplicative Interaction (AMMI) and Genotype+GenotypexEnvironment (GGE) biplot models. The analysis of variance revealed significant (p = 0.05) GEI effect. Mean grain yield of the rice genotypes ranged from 2148 kg ha-1 for genotype TOG 5681 to 4469 kg ha-1 for NERICA-L28. Ouedeme environments in Benin Republic were the most stable and ideal for rice cultivation while Ibadan sites were the most variable. Mega-traits and the best yielding rice genotypes in each mega-environment were revealed by the GGE biplot analysis. Furthermore, NERICA-L12, NERICA-L33, NERICA-L42 and NERICA-L56 were established as the most promising and stable genotypes across the test environments. FKR 19, NERICA-L49 and NERICA-L48 performed best for the grain thickness and 1000 grain weight mega traits while FARO 51 (CISADANE) performed best for grain width mega-trait. The best genotype for the grain length was NERICA-L55. Stability model of GGE biplot was observed to be more effective and informative in mega-environment analysis compared to AMMI analysis.
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How to cite this article:

S.A. Ogunbayo, M. Sie, D.K. Ojo, A.R. Popoola, O.A. Oduwaye, I.O. Daniel, K.A. Sanni, M.G. Akinwale, B. Toulou, A. Shittu, G.B. Gregorio and E.F. Mercado, 2014. Comparative Performance of Forty-Eight Rice Genotypes in Diverse Environments Using the Ammi and GGE Biplot Analyses. International Journal of Plant Breeding and Genetics, 8: 139-152.

DOI: 10.3923/ijpbg.2014.139.152

URL: https://scialert.net/abstract/?doi=ijpbg.2014.139.152

 
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