DefinePK

Research Objective

To discover the prevailing genetic variation among soybean genotypes for diverse characters for breeding purposes, utilizing agro-morphological parameters.

Methodology

One hundred soybean genotypes (97 genotypes and 3 varieties) were evaluated in a field experiment using a 10 x 10 simple lattice design at Uke, East Wollega, Western Ethiopia, during the 2018 Meher cropping season. Thirteen agro-morphological traits were measured, including phenological data (e.g., days to maturity), plant characteristics (e.g., plant height, primary branches), and yield components (e.g., seed yield, harvest index). Data analysis involved ANOVA (Proc GLM), standardization (mean zero, variance one), Mahalanobis D2 statistics for genetic distance calculation, agglomerative hierarchical clustering (UPGMA method using Proc CUSTER), and Principal Component Analysis (PCA) using SAS software.

Methodology Flowchart

                        graph TD; A[Field Experiment: 100 Genotypes in 10x10 Lattice Design] --> B[Data Collection: 13 Agro-morphological Traits]; B --> C[Data Standardization]; C --> D[Genetic Distance CalculationMahalanobis D2]; D --> E[Clustering Analysis UPGMA]; D --> F[Principal Component Analysis PCA]; E --> G[Identify Divergent Clusters & High-Yield Clusters]; F --> H[Determine Major Contributing Variables]; G & H --> I[Conclusion & Breeding Recommendations];                    

Discussion

The existence of significant genetic diversity among the tested soybean genotypes suggests potential for improvement through selection and hybridization. High inter-cluster distances (like between VI & IX, and III & VI) confirm the presence of genetically divergent parents that can maximize heterosis and generate desirable segregants. Clusters II and VII, despite having few members, possess superior yield traits, making them valuable sources for gene selection or direct variety development. PCA confirmed that yield-related traits contribute significantly to the overall variation, with PC1 capturing the most variance related to productivity components.

Key Findings

The 100 genotypes were clustered into ten distinct clusters based on D2 analysis. Cluster IX contained the largest number of genotypes (23), followed by Cluster IV (22) and Cluster V (19). Clusters II and VII contained the minimum number of genotypes (1 each). The maximum inter-cluster distance (397.8) was observed between Cluster VI and IX, and between Cluster III and VI, indicating highly divergent genotypes suitable for hybridization. Cluster II and Cluster VII contained the highest mean values for key yield characters, including seed yield (2897 kg ha-1 and 2574 kg ha-1, respectively). The first four principal components accounted for 61.96% of the total variation. PC1 was primarily influenced by seed yield, number of primary branches/plant, harvest index, biological yield, and number of pods/plant.

Conclusion

Sufficient genetic variability exists among the tested soybean genotypes for desirable traits in future breeding programs. Genotypes within Cluster IX, IV, and V are numerous, while the highest divergence is found between Clusters VI/IX and III/VI. Clusters II and VII are identified as superior sources for yield characters, making them promising for direct variety development or as parents in hybridization programs.

Fact Check

1. The study evaluated 100 soybean genotypes (97 genotypes and 3 varieties).
2. The first four principal components accounted for 61.96% of the total variation.
3. The maximum inter-cluster distance recorded was 397.8, occurring between Cluster VI and IX, and Cluster III and VI.

Mind Map