Abstract
The study on genotypes by environment interaction (GEI) and stability analysis was conducted to determine the G, E, and GEI variance magnitudes. The experiment was carried out at three locations in two consecutive years on 26 soybean genotypes using randomized complete block design (RCBD) design with three replications. The objectives were to (i) estimate the magnitudes of G, E, and GEI effects, (ii) stability analysis of 26 genotypes, and (iii) to identify the highest yielding genotypes for both specific and wide adaptability. The combined analysis of variance (ANOVA) of seed yield data was confirmed strongly significant (pi‚£0.001) for G, E, and GEI variances. At Kamash, the yield was increased by 47.6% as compared to Begi might be due to soil factors differences. The soybean plants therefore grew more produced, more yield where soil fertility is the highest as compared to poorest areas. The G, E, and GEI effects contributed 15.1, 51.6, and 30.2%, respectively. Such that the main variability is due to E and GEI variances being the largest proportions of the total treatment sum of square (TTSS). The genotypes main effect and genotypes by environment interaction (GGE) biplot is therefore the most appropriate recently used model's for stability analysis in efficiently utilizing and exploiting the existed GEI SS. The first two PC (PC1 and PC2) axes were used to create the two dimensional GGE biplots that explained 40.35 and 26.38% of GGE TSS, respectively. The biplots polygons vertex genotypes were categorized as the strongest and weakest as well as stable and unstable genotypes. The result of GGE biplot for G3 and G5 providing the best niche at A15, B15 and B16, G5, and G4 the highest at A16 and K16, while G4 and G12 are also best at K15. The highest and specifically performing polygon vertex genotypes contributed maximum MS for GEI SS. The highest scores for PC1, near zero absolute values for PC2, and the highest means were recorded from G5, G6, G19, G17, and G25 contributing nothing or little MS for GEI SS. These consistently performing genotypes showed high stability based on GGE biplots analysis growing vigorously in producing maximum means without changing their ranking across all sites for this economically interesting trait. Key words: Genotypes main effect and genotypes by environment interaction (GGE) biplot, genotypes by environment interaction (GEI), seed yield, soybean genotypes, stability analysis.
Highlights
Soybean (Glycine max (L.) Merr.) is categorized under Fabaceae family, genus Glycine, and sub-genus Soja (Lackey, 1977)
The genotypes main effect and genotypes by environment interaction (GGE) model is more preferable for crossover-type GEI describing via visual displaying of the which-won-where, and high mean vs. stability (Yan, 2001; Ding et al, 2007)
The analysis of variance (ANOVA) showed that environments have significantly (p 0.001) affected the seed yield of 26 tested genotypes (Table 2)
Summary
Soybean (Glycine max (L.) Merr.) is categorized under Fabaceae family, genus Glycine, and sub-genus Soja (Lackey, 1977). Ethiopia is endowed with 18 main and 32 subagro-ecologies This wide agro-ecological variability is the major challenges for field crops which resulted in high genotypes by environment interaction (GEI) effect. The ultimate goal of stability analysis is developing of consistently responding superior genotypes for broad adaptability (Kang, 1998) Achieving of these objectives is generally difficult due to the probability of significant GEI effect (Gauch and Zobel, 1996). The genotypes main effect and genotypes by environment interaction (GGE) model is more preferable for crossover-type GEI describing via visual displaying of the which-won-where, and high mean vs stability (Yan, 2001; Ding et al, 2007). The objectives of the study were (i) to determine the G, E, and GEI variances magnitudes, (ii) estimate the stability of 26 genotypes for seed yield, and (iii) to identify the highest yielding genotypes for both specific and broad sense adaptability
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