Sci. Agri. 12 (1), 2015: 34-39 © PSCI Publications Scientia Agriculturae www.pscipub.com/SA E-ISSN: 2310-953X / P-ISSN: 2311-0228 DOI: 10.15192/PSCP.SA.2015.12.1.3439 GGE bipot analysis of genotype x environment interaction in rice (oryza sativa l.) genotypes in Bangladesh Hasina Khatun 1 , Rafiqul Islam 1 , Mohammad Anisuzzaman 1 , Helal Uddin Ahmed 2 , Maksudul Haque 1* 1. Scientific Officer, Plant Breeding Division, Bangladesh Rice research Institute (BRRI), Gazipur-1701, Bangladesh 2. Chief Scientific Officer, Plant Breeding Division, Bangladesh Rice research Institute (BRRI), Gazipur-1701, Bangladesh *Corresponding auother email: maksudulhq@gmail.com Paper Information A B S T R A C T Received: 18 June, 2015 Accepted: 27 September, 2015 Published: 20 October, 2015 Citation Khatun H, Islam R, Anisuzzaman M, Ahmed HU, Haque M. 2015. GGE bipot analysis of genotype x environment interaction in rice (oryza sativa l.) genotypes in Bangladesh. Scientia Agriculturae, 12 (1), 34-39. Retrieved from www.pscipub.com (DOI: 10.15192/PSCP.SA.2015.12.1.3439) The GGE genotype main effect (G) and genotype by environment interaction (GE) biplot graphical tool was applied to analyze multi- environment trials (MET) data. The first two principal components (IPC1 and IPC2) were used to create a two-dimensional GGE biplot that accounted percentages of 49.4% and 39.1% respectively of sums of squares of the GE interaction. The combined ANOVA analysis for grain yield data indicated that the differences among all sources of variation were highly significant (P<0.01). Environment (E), Genotype (G) and G x E interaction effects accounted for 23.60, 16.27 and 24.89% of the total sum of squares, respectively. The GGE biplot suggested the existence of two rice mega-environments with wining genotypes G2 and G4. According to the ideal-genotype biplot, genotype G4 was the better genotype demonstrating high mean yield and high stability of performance across test locations. The average tester coordinate view indicated that genotype G1 had the highest average yield, and genotype G4 recorded the best stability. Among the six environments, E6 and E4 were most discriminating (informative) and E1 and E3 were most representative. The G4 is adaptable for a wide range of environments of Bangladesh. © 2015 PSCI Publisher All rights reserved. Key words: GGE, genotype, environment, interaction, rice (oryza sativa l.) Introduction Rice is an important cereal crop which is receives the most attention of specialists in plant breeding and production. However, its production is limited by the adverse environmental conditions. Therefore, Multi-environment trials (MET) are conducted to evaluate yield stability performance of genetic materials under varying environmental conditions (Delacy et al. 1996, Yan et al. 2000, Yan and Rajcan 2002). A genotype grown in different environments will frequently show significant fluctuations in yield performance. These changes are influenced by the different environmental conditions and are referred to as genotype-by-environment (GE) interaction (Allard and Bradshow, 1964, Kang 2004). But, GE interaction reduces the genetic progress in plant breeding programs through minimizing the association between phenotypic and genotypic values (Comstock and Moll 1963). Hence, GE interaction must be either exploited by selecting superior genotype for each specific target environment or avoided by selecting widely adapted and stable genotype across wide range of environments (Kaya, 2006 and Mitrovic et al. 2012). Numerous methods such as regression coefficient (Finlay and Wilkinson 1963), sum of squared deviations from regression (Eberhart and Russel 1966), stability variance (Shukla 1972), coefficient of determination (Pinthus 1973), coefficient of variability (Francis and Kanneberg 1978) and additive main effects and multiplicative interaction (AMMI) (Gauch and Zobel, 1988, Zobel et al. 1988, Gauch 1992) have been commonly used to analyze MET data to reveal patterns of GE interaction. Yan et al. (2000) proposed another methodology known as GGE-biplot for graphical display of GE interaction pattern of MET data with many advantages. A GGE biplot as a data visualization tool is able to graphically demonstrate a GE interaction pattern. It is an effective tool to identify a mega-environment, genotype evaluation based on the both yield and stability; and evaluation of test environments from a discrimination aspect. The objectives of this study were to apply a GGE biplot to evaluate the magnitude of the effect of GE interaction on grain yield of six genotypes tested across six locations, determine the best performing genotypes for selection locations, the identification of mega-environments and analysis of the ideal genotype and environment for rice production areas in Bangladesh. Materials and Methods