Assessment of Genetic Variability in Mid-altitude Sesame (Sesamum indicum L.) Collection of Ethiopia

Aim: The study was undertaken to assess the genetic variability and character association in 81 mid-altitude sesame accessions of Ethiopia based on important agronomic traits.

Study Design: A 9 x 9 Simple Lattice Design (SLD) with two replications was used.

Place and Duration of Study: Melkassa Agricultural Research Centre Ethiopia, during the July-December, 2011 main cropping seasons.

Methodology: The data recorded on 14 quantitative traits were analyzed for phenotypic and genotypic coefficient of variances, heritability and genetic advance, correlation coefficient, path coefficient analysis, principal component analysis and divergence analysis based on Mahalanobis statistics, using SAS 9.2. Statistical software to evaluate the pattern and extent of variation among 81 mid-altitude genotypes.

Results: Analysis of variance revealed significant difference among genotypes for all traits studied. Less than 50% heritability was noted in all traits studied. Moderate heritability coupled with moderate to high genetic advance was recorded for most of yield related traits, indicating that these traits are controlled by both additive and non-additive genes. Characters viz., number of capsules, biomass yield, harvest index and 1000 seed weight showed highly significant positive correlation with seed yield. Maximum positive direct effect on seed yield was exerted by number of capsules, biomass yield, days to maturity and harvest index, showing that these traits can be used for selection to improve the primary trait. Divergence analysis based on Mahalanobis statistics grouped the genotypes into seven different clusters. Genotypes were not grouped in relation to their geographical distribution. Maximum inter cluster distance was observed between cluster V and VII; hence, genotypes from these two clusters are suggested as parents for hybridization program to achieve promising recombinants.

Conclusion: The germplasm lines had sufficient level of genetic variability for seed yield and its components. Clustering was not associated with the geographical distribution instead genotypes were mainly grouped due to their morphological differences. Seed yield, biomass/plant, harvest index and number of capsules contributed highest towards genetic divergence. The use of these traits in sesame improvement program would increase yield.