Malaysian Applied Biology Journal

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40-2-03

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Malays. Appl. Biol. (2011) 40(2):19–26

ANALYSIS OF GENOTYPE x ENVIRONMENT INTERACTION OF GROUNDNUT (Arachis hypogaea L.)

MAKINDE, S.C.O.1* and ARIYO, O.J.2

1Department of Botany, Faculty of Science, Lagos State University, Ojo Campus, P.O. Box 001, LASU Post Office Ojo, Lagos, Nigeria
2Department of Plant Breeding and Seed Technology, College of Plant Science, University of Agriculture Abeokuta, P.M.B2240 Abeokuta, Ogun State, Nigeria
*E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

ABSTRACT

Twenty two groundnut genotypes collected from International Crops Research Institute for Semi Arid Tropics (ICRISAT) and local sources were cultivated in two different locations during 2003, 2004 and 2005 planting seasons. Data on yield was subjected to the additive main effect and multiplicative interaction (AMMI) and joint regression analysis. Differences between the genotypes and environments accounted for 58% and 28% of the total variance respectively while genotype x environment interaction accounted for 14% of the total variance. The first, second and third interaction axes captured 56%, 16% and 6% respectively of the total variation due to GxE interaction. The AMMI plot accounted for 96% of the total sum of squares. The environment differed in main and interaction effects. The first (E1) and fourth (E4) environments had positive interaction effects while the second (E2), third (E3) and fifth (E5) had negative interaction effects. First environment and second environment had the highest (3.52) and least (-0.15) interaction effects respectively. Groundnut genotypes used in this study exhibited similar response to different environment except for ICG-6402. ICG-4998 was the most favoured genotype in all the environments. However, most genotypes recorded highest yield during the 2003 planting in first environment (E1).
Key words: Additive Main Effect and Multiplicative Interaction model, biplot, joint regression analysis, Principal Component Analysis

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