AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia

Asfaw, A. and Alemayehu, F. and Gurum, F. and Atnaf, M. (2009) AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia. Scientific Research and Essays, 4 (11). pp. 1322-1330.

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Matching soyabean cultivar selection with its production environment is often challenged by the occurrence of significant genotype-by-environment interactions (GEI) in the cultivar development process. Several statistical models have been proposed for increasing the chance of exploiting positive GEI and supporting breeding programme decisions in cultivar selection and recommendation for target set of environments. Additive main effects and multiplicative interactions (AMMI) and site regression (SREG) genotype plus genotype-by-environment interaction (GGE) models are among the models that effectively capture the additive (linear) and multiplicative (bilinear) components of GEI and provide meaningful interpretation of multi-environment data set in breeding programmes. The objective of this study was to assess the significance and magnitude of GEI effect on soyabean grain yield and exploit the positive GEI effect using AMMI and SREG GGE biplot analysis. Grain yield data of 11 genotypes evaluated at 4 sites for 3 cropping seasons (2002, 2003 and 2004) across the soyabean production ecology in Ethiopia were used for this purpose. AMMI analysis showed that grain yield variation due to environments, genotypes and GEI were highly significant (P<0.01). Environments explained the greater proportion (61.08%) of total yield variation followed by GEI (34.13%) and genotypes (4.79%), indicating the necessity for testing soyabean cultivars at multi-locations and over years. The first 5 bilinear AMMI model terms were highly significant (P<0.01) and of which the first 2 terms explained 67.5% of the GEI. According to the AMMI and SREG GGE biplots models, no single cultivar has superior performance in all the environments. However, the genotype TGx-1892-10F was the overall winner in combining high yield with relatively less variable yield across environments. Application of AMMI and GGE biplots facilitated visual comparison and identification of superior genotypes for each target set of environments

Item Type: Article
Additional Information: The authors would like to acknowledge the financial support provided by Ethiopian Institute of Agricultural Research via soybean national project for conducting the field trials.
Uncontrolled Keywords: AMMI, GGE biplot, genotype-by-environment interaction, soybean, Ethiopia
Author Affiliation: Awassa ARC, P. O. Box 6, Awassa, Ethiopia.
Subjects: Statistics and Experimentation
Crop Improvement
Divisions: Soyabean
Depositing User: Mr Balakrishna Garadasu
Date Deposited: 25 Jun 2013 12:32
Last Modified: 25 Jun 2013 12:32
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