Electronic Journal of Plant Breeding

Open Access
SCOPUS
  • Year: 2023
  • Volume: 14
  • Issue: 3

Unravelling genetic variability and trait association studies in red sorghum (Sorghum bicolor L. Moench) genotypes

  • Author:
  • Tanisha Nayak1, R. Chandirakala1,*, D. Kavithamani2, N. Manikanda Boopathi3, K. Chandrakumar4
  • Total Page Count: 7
  • Page Number: 1111 to 1117

1Department of Genetics and Plant Breeding, TNAU, Coimbatore

2Department of Millets, CPBG, TNAU, Coimbatore - 641 003

3Centre for Plant Molecular Breeding and Biotechnology, TNAU, Coimbatore

4Department of Renewable Energy and Engineering, TNAU, Coimbatore

Abstract

Sorghum (Sorghum bicolor L. Moench) is a significant crop known for its resilience, versatility, and nutritional value. The research aimed to enhance yield and nutritional quality through the assessment of genetic variability, heritability, correlations, and causal relationships among traits. Variance component analysis in the F2 population of red sorghum genotypes from the cross between Paiyur 2 and IS 21731 revealed the influence of the environment on trait expression. High variability in flag leaf traits and single plant yield was observed. Traits such as plant height, stem girth, and flag leaf length exhibited high heritability and genetic advancement which indicated that these traits are influenced by additive genetic factors and are suitable for selection processes. Positive correlations were identified between single plant yield and traits like panicle weight and primary branches and hence selection based on these characteristics could directly contribute to improved yield in sorghum. Path analysis highlighted the direct and indirect effects of traits on grain yield with panicle weight emerging as a major driver. This study provided valuable insights into effective breeding strategies emphasizing traits such as panicle weight, primary branches, and seed index for enhancing both yield and nutritional quality in red sorghum genotypes.

Keywords

Red sorghum, Nutritional profiling, Genetic variability, Correlations