Comprehensive Assessment of Genetic Variability, Association Analysis, and Elucidation of Direct and Indirect Effects of Yield and Yield Contributing Traits From Diverse Exogenous Soybean (Glycine max L.) Genotypes in Jimma District, Southwest Ethiopia

Abstract

Based on its valuable protein and oil content, soybean is one of the most crucial legume crop with a key role to play in achieving sustainable agriculture and improving the food security of Ethiopia. Thus, identifying traits with important influence on yield and quality, ensuring efficient selection in breeding programmes to improve productivity and adaptability are some of major factors of soybean that can be well addressed by studying variability, heritability, correlation, and path coefficient analysis. Therefore, an investigation was conducted in Jimma district where 64 soybean genotypes including two checks were grown in an 8 × 8 simple lattice design with two replications to provide valuable insights about the genetic variability, heritability, correlation, and path coefficient analysis for ~12 quantitative traits. The analysis of variance exhibited significant differences among the genotypes for all the traits studied indicating considerable variability among the tested genotypes. Highest genotypic and phenotypic coefficient of variation (PCV) values were observed for traits, namely, number of seeds pod−1, pod length, number of branches plant−1, 100 seed weight, and grain yield whereas moderate genotypic and PCV values were recorded by harvest index and number of pods plant−1. Essentially, these high values of genotypic and PCV suggest that the observed differences in those traits are more pronounced, making them potentially important for selection and improvement of soybean crop. Traits like the number of branches plant−1, pod length, number of seeds pod−1, and 100 seed weight showed high heritability accomplished with high genetic advance as percent of mean, suggesting that such traits are likely to respond well to breeding efforts aimed at enhancing their expression in future generations. Additionally, it was perceived from the results that number of seeds plant−1, 100 seed weight, harvest index, plant height, number of pods plant−1, and number of branches plant−1 displayed positive and significant genotypic as well as phenotypic correlation with grain yield, whereas, on the other hand, path coefficient analysis revealed that the number of seeds plant−1, plant height 100 seed weight, and harvest index showed the highest positive direct effect with grain yield. The traits identified through correlation and path coefficient analysis are crucial for targeted breeding efforts because they imply that improving certain traits could lead to enhanced grain yield in soybean.