F. Alemayehu

Ethiopian Institute of Agricultural Research

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F. Gurum

Ethiopian Institute of Agricultural Research

Asrat Asfaw

International Institute of Tropical Agriculture

G. Feseha

Addis Ababa University

A. Trawford

Sidmouth Hospital

G. Ayele

United States Agency for International Development

R. A. Pearson

University College London

M. Getachew

Addis Ababa University

C. J. Muir

M. Tesfaye

M. Farrow

Garvan Institute of Medical Research

Cooperative Institutions

Ethiopian Institute of Agricultural Research

Addis Ababa University

Agricultural Research Center

Bahir Dar University

Wageningen University & Research

University of California, Davis

Africa Centres for Disease Control and Prevention

University of Washington

Mekelle University

International Center for Agricultural Research in the Dry Areas

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Analysis of Multi-environment Grain Yield Trials in Mung Bean Vigna radiate (L.) Wilczek Based on GGE Bipot in Southern Ethiopia

Asrat Asfaw F. Gurum F. Alemayehu Yayis Rezene

The objective of this research was to evaluate and to quantify the magnitude of the genotype × environment interaction effects on mung bean grain yield and to determine the winning genotype for the test environments. Seven mung bean genotypes were tested at three locations for over two years. The grain yield data for each environment (location year combination) was first subjected to analysis of variance using generalized linear model. Mean grain yields of genotypes for the environments were computed to generate a genotype and environment two-way table data for the GGE biplot analysis. The analysis revealed the presence of significant genotype x environment interactions for grain yield. Location effect explained more than 60% of the total grain yield variation. GGE biplot analysis depicted the adaptation pattern of genotypes at different environments and discrimination ability of testing environments. MH-96-4, shown to have the potential of combining high yield with stable performance, can be recommended for production in mung bean growing ecologies in southern Ethiopia.

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Mung bean

Ammi

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Citations (21)

Innovative haricot beans (Phaseolus vulgaris) seed system for smallholder farmers in Dale District, Southern Ethiopia: Experiences from IPMS

Y. Yilma Kahsay Berhe Dirk Hoekstra Moti Jaleta F. Alemayehu

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

Scientific Research and Essays (2009)

Asrat Asfaw F. Alemayehu F. Gurum Mulugeta Atnaf

Matching soybean variety selection with its production environment is often challenged by the occurrence of significant genotype-by-environment interactions (GEI) in the variety development process. Several statistical models have been proposed for increasing the chance of exploiting positive GEI and supporting breeding program decisions in variety 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 programs. The objective of this study was to assess the significance and magnitude of GEI effect on soybean 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 three cropping seasons (2002, 2003 and 2004) across the soybean production ecology in Ethiopia were used for this purpose. AMMI analysis showed that grain yield variation due to environments, genotypes and GEI were highly signifiscant (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 soybean varieties at multi-locations and over years. The first five bilinear AMMI model terms were highly significant (p<0.01) and of which the first two terms explained 67.5% of the GEI. According to the AMMI and SREG GGE biplots models, no single variety has superior performance in all the environments. However, the genotype TGx-1892-10F was overall winner in combining high yield with relatively less variable yield across environments. Application of AMMI and GGE biplots facilitated visual comparison and identification superior genotypes for each target set of environments. Key words: AMMI, GGE biplot, genotype-by-environment interaction, soybean, Ethiopia.

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Interaction

Additive model

10.5897/sre.9000148

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Reinvigorating bean seed system in Ethiopia: role of farmers and social institutions.

F. Alemayehu A. Asfaw Getamesay Demeke F Gurmu Shambel Maru

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