Agriculture and Horticulture

Biography

Biography: Mesfin Hailemariam Habtegebriel

Abstract

Soybean (Glycine max (L.) Merrill] is that recently introduced crop in Ethiopia and also getting importance over time. However, production is affected by environment interaction and lack of stable genotypes across locations. Hence, this experiment was conducted with the objectives of estimating the genotype by environment interaction through stability parameters and to study the interrelationship among stability parameters. Twenty-four soybean genotypes were planted at six soybeans major growing agroecologies of Ethiopia (Asosa, Bako, Dimtu, Jimma, Metu and Pawe) with RCBD in three replications in 2015/2016 cropping season. Among the nine traits subjected to the combined analysis all are showed a highly significant (P Ë‚ 0.01) environment, genotype and GEI that claims the need of stability analysis. Similarly, combined AMMI ANOVA for grain yield revealed that there were a very highly significant (PË‚ 0.01) difference among genotypes, environments and genotype by environment interactions and accounted 15.3%, 47.32% and 14.24% of the total variations respectively. The high percentages of the environments are an indication that the major factor that influences the yield performance of soybean grain in Ethiopia is the environment. In addition, the first two IPCAs are significant and accounted for 70.34 form a total of interaction sum squares. Nine stability measures viz.,Additive Main Effects and Multiplicative Interactions (AMMI), AMMI stability value (ASV), Francis and Kannenberg’s Coefficient of Variability (CVi), The Environmental Variance (S2i), Wricke’s Ecovalence Analysis (Wi), Shukla’s Stability Variance (σ2i), Finlay and Wilkenson (bi ), Eberhart and Russell's (bi and S2di), Lin and Binns's cultivar performance measure (Pi) and Genotype plus GEI (GGE) bi-plot analysis were used to identify the high yielding and stable genotypes across the testing environments. Genotypes Hang dou No-1 and Spry were the most stable genotypes by stability measures such as ASV, Shukla's stability variance, Wricke's ecovalence, Finlay and Wilkinson's, environmental variance, Eberhart and Russell's and, Lin and Binns's cultivar performance measure. The total correspondence for significance Spearman’s rank correlation was used to see the level of association among stability measures. Pi showed a positive highly significant rank correlation(r=0.97**) with mean grain yield and it did not show any correlation with other stability measures except S2i (r=-0.85**) and bi (r=0.88**). From AMMI model, genotypes SCS-1, AFGAT and Clarck-63k were selected as best varieties for Asosa, AFGAT, SCS-1 and Clarck-63k for Bako; SCS-1, ks4895, and Clarck-63k for Dimtu; SCS-1, ks4895 and AGS-7-1 for Jimma; AFGAT, Clarck-63k and Motte for Metu; SCS-1, AFGAT and Clarck-63k for Pawe that suit to a specific environment. AMMI1 biplot showed Pawe is ideal environment; Bako is favorable environment; Asosa average environment; and the rest environments viz., Dimtu, Jimma, and Metu as unfavorable environments. Whereas AMMI-2 biplot analysis genotypes Prichard, Spry, Delsoy 4710 and Croton 3.9 were identified as stable genotypes. Bako and Metu were identified as the most discriminating environments this may due the effects of climate change called El Niño. Mega environments and the best yielding soybean genotypes on each mega environment were revealed by GGE bi-plots analysis model. The genotypes SCS-1 and AGS-7-1 were stable across soybean growing environments and it recommended for mega environment production.