Genetic Variability, Correlation Co-efficient and Path Analysis in Soybean (Glycine max L. Merr.) Genotypes

Md. Anisur Rahaman *

State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany and Department of Genetics and Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.

Anik Sarker

Department of Genetics and Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.

Sourav Modak

Institute of Agricultural Tropics, University of Hohenheim, Stuttgart, Germany and Department of Horticulture, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.

Md. Rasal-Monir

Department of Horticulture, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.

Naheed Zeba *

Department of Genetics and Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.

*Author to whom correspondence should be addressed.


Abstract

Soybean (Glycine max L. Merr.) is an important oil seed and grain legume crop in the world and a perspective high potential crop in Bangladesh. It belongs to the family Leguminosae, sub-family papilionaceae and genus Glycine. An experiment was conducted with 14 genotypes of soybean (Glycine max L. Merr.) in a randomized complete block design (RCBD) to study the variability, correlation coefficient, and path analysis. Analysis of variance for each trait showed significant differences among the genotypes. The genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were close to each other for all the characters except the leaf area index, indicating the minor environmental influence on the expression of these characters. High heritability associated with high genetic advance in percent of mean was observed for leaf area index, number of seeds per plant, 100 seed weight, and yield, which pointed out that selection for these characters, would be effective. The significant positive correlation with seed yield was found in pod length and 100 seed weight which revealed that selection based on these traits would improve yield ultimately. Path coefficient analysis evidenced that 50% flowering, pod length, days to 1st pod maturity, number of seed per plant and 100 seed weight had a positive direct effect on yield per plant. Therefore, importance must be given to these characters in further breeding programs to improve soybean yield.

Keywords: Genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), path coefficient analysis


How to Cite

Rahaman, M. A., Sarker, A., Modak, S., Rasal-Monir, M., & Zeba, N. (2022). Genetic Variability, Correlation Co-efficient and Path Analysis in Soybean (Glycine max L. Merr.) Genotypes. Asian Journal of Advances in Agricultural Research, 19(2), 46–55. https://doi.org/10.9734/ajaar/2022/v19i2372

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