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Gge Biplot Analysis for Yield Stability in Multi-environment Trials of Promising Hybrid Rice (oryza Sativa L.)

Anowara Akter
Scientific Officer
Hybrid Rice Division Bangladesh Rice Research Institute Bangladesh, Gazipur-1701

M Jamil Hasan
Principal Scientific Officer
Hybrid Rice Division Bangladesh Rice Research Institute Bangladesh, Gazipur-1701

M Umma Kulsum
Scientific Officer
Hybrid Rice Division Bangladesh Rice Research Institute Bangladesh, Gazipur-1701

M Hafizar Rahman
Scientific Officer
Hybrid Rice Division Bangladesh Rice Research Institute Bangladesh, Gazipur-1701

Mahmuda Khatun
Principal Scientific Officer
Plant Breeding Division Bangladesh Rice Research Institute Bangladesh, Gazipur-1701

M Rafiqul Islam
Scenior Scientific Officer
Plant Breeding Division Bangladesh Rice Research Institute Bangladesh, Gazipur-1701

Abstract/ Executive Summary:

The GGE [genotype main effect (G) and genotype by environment interaction (GE)] biplot model is an excellent tool for visual MET data analysis was investigated on grain yield of six rice genotypes (3 tested, 1 released hybrids and 2 inbred check varieties) in five environments. The combined analysis of variance for grain yield data indicated that the differences among all sources of variation were highly significant (P<0.001). Environment (E), Genotype (G) and GxE interaction effects accounted for 12.49, 76.51and 10.21% of the total sum of squares, respectively. The first two principal components (PC1 and PC2) were used to display a two-dimensional GGE biplot. Thus, genotypic PC1 scores>0 classified the high yielding genotypes while PC1 scores<0 identified low yielding genotypes. In this study, the polygon view of GGE biplot showed that the vertex genotypes were BRRI1A/BR168R (G1) , BRRI 10A/BRRI10R (G2) and BRRI dhan28 (G5) having the largest distance from the origin which was most discriminated genotypes with unstable. These vertex genotypes BRRI1A/BR168R (G1) and BRRI 10A/BRRI10R (G2) gave higher yield (PC1 scores>0) while another vertex genotype BRRI dhan28 (G5) was low yield (PC1 score<0). Hence, the vertex genotype BRRI 10A/BRRI10R (G2) was high yielding for all environments and this genotype fell into section 1 following to IR58025A/BRRI10R (G3) and BRRI hybrid dhan1 (G4). Mean yield and stability performance over environments of each genotype is explored by using the average environment (tester) coordinate (AEC) methods. This methods showing that the genotypes BRRI 10A/BRRI10R (G2), IR58025A/BRRI10R (G3) and BRRI hybrid dhan1 (G4) had higher stability as well as higher mean yield while the genotype IR58025A/BRRI10R (G3) had the highest stability out of these three genotypes. The ideal genotype biplot suggest that the closer to ‘ideal” genotype was IR58025A/BRRI10R (G3) followed by G2 and G4 being more desirable than other genotypes. Similarly, the environment Barisal (E3) was “ideal” environment followed by E1, E2 and E5. Hence, the environment Barisal (E3) is more stable and suitable for all genotypes following to Satkhira (E5) because it has large PC1 and small PC2 score but Rangpur (E4) is a discriminating environment because it has large PC2 score. The interrelationship among the environments according to the small angles of test environments between them were highly positively correlated such as Gazipur (E1), Comilla (E2), Barisal (E3) and Satkhira (E5) were closely correlated with small angles but Rangpur (E4) had medium long angles. Comparison between two genotypes showed that BRRI10A/BRRI10R (G2) and IR58025A/BRRI10R (G3) were high yielder in test environments. Thus, the difference between G2 and G3 was relatively small in test environments.

Keywords: GGE biplot analysis, Yield stability, Multi-environments, Hybrid rice.

Location: The experiments were conducted at five districts namely Gazipur (E1), Comilla (E2), Barisal (E3), Rangpur (E4) and Satkhira(E5)

Start Date: 15-11-2007

End Date: 15-05-2008

Program Area: Variety and Species

Non-commodity: Performance

Objectives:

The main objectives of the present study are to identify the best performing high yielding stable promising hybrids for selection environments, the identification of mega-environments and analysis of the ideal genotype and environment by GGE biplot method.

Methodology:

Experimental Design and Plant Materials: The experiments were conducted at five districts namely Gazipur (E1), Comilla (E2), Barisal (E3), Rangpur (E4) and Satkhira (E5) representing five different agro-ecological zones (AEZ) of Bangladesh during Boro season 2007-08. Six (6) genotypes consisting of 3 advanced lines (BRRI1A/BRRI168R (G1), BRRI10A/ BRRI10R (G2) and IR58025A/BRRI10R (G3), one released hybrid (BRRI hybrid dhan1 (G4) and 2 inbreed check varieties (BRRI dhan28 (G5) and BRRI dhan29 (G6)) were used as experimental materials. The experiment was laid out in a randomized complete block design with three replications (RCBD), with three replications. Thirty days old seedlings were transplanted in 20 square meter plot using single seedling per hill at a spacing of 20 cm x 15 cm. Fertilizers were applied @ 270:130:1200:70:10 kg/ha Urea, TSP, MP, Gypsum and ZnSO₄, respectively. Standard agronomic practices were followed and plant protection measures were taken as required following the recommendation of Adhunik Dhaner chash, BRRI (2008). Two border rows were used to minimize the border effects. The grain yield (tha^-1) data was collected at 14% moisture level. Data were collected followed by standard method as described by Yoshida et al. (1976). Statistical analysis: The grain yield data for six (6) genotypes in five (5) environments were subjected to combined analysis of variance (ANOVA) to determine the effects of environment (E), genotype (G) and their interactions. The data were graphically analyzed for interpreting GE interaction using the GGE biplot software (Yan, 2001). GGE biplot methodology, which is composed of two concepts, the biplot concept (Gabriel, 1971) and the GGE concept (Yan et al., 2000), was used to visually analyze the rice genotypes MET data. This methodology uses a biplot to show the factors (G and GE) that are important in genotype evaluation and that are also the sources of variation in GE interaction analysis of MET data (Yan, 2001). The graphs generated based on (i) The polygon view of GGE biplot to identification of winning genotypes and their mega environments by “which-won-where” pattern, (ii) Ranking of Genotypes based on yield and stability performance, (iii) Evaluation of genotypes relative to an ideal genotypes, (iv) Evaluation of environments relative to ideal environments, (v) Relationship among environments, (vi) Comparison between two genotypes.

Source of Information: Bangladesh Rice J. 19(1): 1-8, 2015.

Article Link (Online Journal):

Funding Source:

1. Government

Budget:

Total Budget (Tk.) :

Achievement/Findings:

The GGE biplot model is an excellent tool for visual MET data analysis (Mohammadi et al., 2011). In this study, the combined analysis of variance (ANOVA) indicated that the genotype (G), environments (E) and GxE interaction variance were highly significant at P<0.001. On the other hand, the vertex genotype G₂ was high yielding genotype for all environments and this genotype fell into section 1 following to G₃ and G₄. Other two vertex genotypes G₁and G₅ gave medium high yield and poor yield with poor adaptation to five testing environments. Mean yield and stability performance over environments of each genotype is explored by using the average environment (tester) coordinate (AEC) methods. This methods showing that the genotypes G₂, G₃ and G₄ had higher stability as well as higher mean yield while the genotype G₃ had the highest stability out of these three genotypes. The ideal genotype biplot suggest that the closer to ‘ideal” genotype was G₃ followed by G₂ and G4 being more desirable than other genotypes. Similarly, the environment E₃ was “ideal” environment followed by E₁, E₂ and E₅. Hence, the environment E₃ is more stable and suitable for all genotypes following to E₅ because it has large PC1 and small PC2 score but E₄ is a discriminating environment because it has large PC2 score. The interrelationship among the environments according to the small angles of test environments between them were highly positively correlated such as E₁, E₂, E₃ and E₅ were closely correlated with small angles but E₄ had medium long angles. Comparison between two genotypes showed that G₂ and G₃ had higher yield in test environments. Thus, the difference between G₂ and G₃ was relatively small in test environments.

Knowledge Management: Journal