Sesame (Sesamum indicum L.) is produced worldwide, although more than 96% of the world sesame seed is produced in Africa and Asia. The objective of this study was to determine morphological properties and identify the genetic diversity of cultivated sesame genotypes grown in different parts of Ethiopia. Three hundred sesame genotypes collected from diverse ecologies of Ethiopia and introduced from different African and Asian countries, were used in this study. Genotypes showed wide variability for most morphological traits, except for plant growth type, leaf glands, anther filament colour, anther connective tip gland, and anthocyanin colouration of the capsule. Genetic divergence using Mahalanobis D2 statistics was computed, and the genotype lines were grouped into six different clusters. Clustering was not associated with the geographical distribution; instead genotypes were grouped mainly based on morphological differences. The lowest divergence was noticed between cluster I and V (10.06). Maximum inter-cluster distance was observed between clusters IV and VI (D2 =342.56, followed by clusters I and VI (D2 =217.9783), and III with IV (D2 =190.8707). Maximum genetic recombination and variation in the subsequent generation, is expected from crosses that involve parents from the clusters characterised by maximum distances. Thus, maximum distances or varation could maximise opportunities for transgressive segregation, since unrelated genotypes would contribute unique desirable alleles at different loci.
The present study was conducted to interpret Genotype main effect and GEI obtained by AMMI analysis and group the genotype having similar response pattern over all environments. Fifteen bread wheat genotypes were evaluated by RCBD using four replications at six locations in Ethiopia. The main effect differences among genotypes, environments, and the interaction effects were highly significant (P ≤ 0.001) for the total variance of grain yield. Results of AMMI analysis of mean grain yield for the six locations showed significant differences (P0.001) among the genotypes, environments and GEI. The environment had the greatest effect with the environmental sum of squares (35.28%) than the genotypes (33.46%) and GEI (31.45%) effect. The AMMI analysis for the IPCA1 captured 46.1% and the IPCA2 explained 28.6%. The two IPC cumulatively captured 74.7% of the sum of square the GEI of bread wheat genotypes, when the IPCA1 was plotted against IPCA2. The genotype ETBW8075, ETBW8070 and ETBW9470 were unstable as they are located far apart from the other genotypes in the biplot when plotted on the IPCA1 and IPCA2 scores. The ETBW8078, ETBW8459, Hidase and ETBW8311 were genotype located near to the origin of the biplot which implying that it was stable bread wheat genotypes across environments. There is closer association between Lemu and ETBW8065 which indicate similar response of the genotypes to the environment. The best genotype with respect to location Kulumsa was ETBW9470, ETBW8075 was the best genotype for Dhera, ETBW8070 was the best genotype for Holeta while ETBW9466 was the best genotype for Arsi Robe. Arsi Robe and Kulumsa is the most favorable environment for all genotypes with nearly similar yield response for grain yield.
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