Four advanced lines: BR6855-3B-12 (G1), BR6855-3B-13 (G2), BR6848-3B-12 (G3) and BR6976-2B-11-1 (G4) along with BRRI dhan43 (G5) as check were tested in Gazipur (E1), Kapasia (E2), Noakhali Sadar (E3), Feni (E4), Sylhet (E5), Faridpur (E6), Magura Sadar(E7) and Kushtia (E8) during B. Aus 2014. The experiment was conducted in RCB design with three replications. The unit plot size for each entry was 15 m2 (3m x 5m). The time of seed sowing in main field at different locations was not same and the dates were within 15-30, April 2014. Direct seeding in line was done with 25 cm row spacing. Fertilizers urea, triple super phosphate, Muriate of potash, gypsum and zinc sulfate were applied @ 60, 10, 40, 10 and 4 kg N, P, K, S and Zn ha-1, respectively. All the fertilizers except urea were applied as basal and urea was applied in 3 equal splits at 20, 35 and 45 days after seeding (DAS). Standard and uniform crop management practices were followed in all the locations. Appropriate measures were taken to control insect but diseases were not controlled to identify susceptibility and tolerance level of the genotypes. Date of seeding, transplanting, flowering and maturity, plant population m-2, phenotypic acceptance at vegetative and ripening stage, plant height, lodging tolerance, yield and yield components, disease and insect incidence were recorded. Plant population (stand) m-2 was counted at 30 days after seeding (DAS). Feedback from farmers and extension personnel were also recorded. For yield estimation, 10 m2 sample area from each plot was harvested at maturity and grain yields were adjusted to 14% moisture content. Data were analyzed using the statistical software CropStat7.2. A combined analysis of variance was performed treating genotype as fixed effect and environment as random effect. The most recent statistical method is the AMMI (Additive Main Effect and Multiplicative Interaction) described by (Zobel et al., 1988) was used to investigate the main effects (G and E) and G×E interactions for grain yield of multi-environment data which includes analysis of variance and principal component analysis. A formula of AMMI stability value (ASV) developed by Purchase et al., (2000) based on the AMMI model’s IPC1 and IPC2 scores for each genotype was used to find the stable genotype.
Integrate both yield performance and stability in a single index namely yield stability index (YSI) statistic (Farshadfar et al., 2011) was applied for selecting high-yielding and stable genotypes. YSI was calculated based on the rank of mean yield of genotype (RY) and rank of ASV (RASV) in a single criterion (YSI) as:
YSI = RASV + RY
Ranks were assigned for mean yield and stability parameter, so that the genotype with the highest yield and the lowest estimated value for each statistic given a rank of 1 (Farshadfar et al., 2011; Roostaei et al., 2014). The genotypes with the lowest ASV value would be more stable. Also GGEbiplot is used to interpret the GE interaction of grain yield of multi-environment trials data (Yan, 2002; Yan et al., 2000). GGEbiplot analysis is a visualization method which graphically displays a GE interaction in two-way table for mega-environment analysis (e.g., “which-won where” pattern) that means specific genotypes can be recommended to specific mega-environments; the mean performance and stability (genotype evaluation) and environmental evaluation.