Phenotypic Assessment and Cluster Analysis for Economic Traits in Advanced Lowland Rice (Oryza sativa L.) Breeding Lines
B. V. Sinchana *
Genetics and Plant Breeding, College of Agriculture, Navile, KSN University of Agricultural and Horticultural Sciences, Iruvakki, Shivamogga, Karnataka, India and Genetics and Plant Breeding, College of Agriculture, University of Agricultural Sciences, GKVK, Bengaluru, Karnataka, India.
Amoghappa Jakkaral Shridevi
AICRP on Rice, Zonal Agricultural and Horticultural Research Station, Brahmavar, KSN University of Agricultural and Horticultural Sciences, Iruvakki, Shivamogga, Karnataka, India.
Dushyanthakumar B. M.
Genetics and Plant Breeding, College of Agriculture, Navile, KSN University of Agricultural and Horticultural Sciences, Iruvakki, Shivamogga, Karnataka, India.
B. Halingali I.
Genetics and Plant Breeding, College of Agriculture, Navile, KSN University of Agricultural and Horticultural Sciences, Iruvakki, Shivamogga, Karnataka, India.
Girijesh G.K.
Genetics and Plant Breeding, College of Agriculture, Navile, KSN University of Agricultural and Horticultural Sciences, Iruvakki, Shivamogga, Karnataka, India.
Honnesh H. R.
Genetics and Plant Breeding, College of Agriculture, Navile, KSN University of Agricultural and Horticultural Sciences, Iruvakki, Shivamogga, Karnataka, India.
Anilkumar Lalasingh Chavan
Genetics and Plant Breeding, College of Agriculture, Navile, KSN University of Agricultural and Horticultural Sciences, Iruvakki, Shivamogga, Karnataka, India.
Prem Sagar S. P.
Genetics and Plant Breeding, College of Agriculture, Navile, KSN University of Agricultural and Horticultural Sciences, Iruvakki, Shivamogga, Karnataka, India and ICAR- Indian Institute of Rice Research, Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
Diversity for economic traits like yield is crucial in rice breeding programmes. In this context, the experiment aimed to identify genetically divergent rice genotypes for hybridization programs, analysing eleven yield and yield-related traits across 35 advanced breeding lines and four check varieties. Employing Mahalanobis’ D2 analysis, the study identified six clusters indicating significant genetic diversity, with the widest divergence observed between clusters VI and IV (203.42) and the closest proximity between clusters III and I (69.25). Traits such as the grain number per panicle and L/B ratio significantly contributed to genetic divergence. Notably, certain breeding line combinations, including KMLT-4 × KPR-2-4-3-1-1, JGL-1798 × KPR-2-7-2-3-4, KMLT-4 × KPR-2-1-7-1-2, KMLT-4 × KPR-2-2-3-1-2-4, and KMLT-4 × KPR-2-2-2-8-2-1-1-2-3, exhibited superior mean yield performance compared to the check varieties under lowland conditions. These promising genotypes offer potential as parental candidates for future hybridization endeavours, aiming to build elite rice cultivars with broader genetic bases, ultimately enhancing agricultural productivity and resilience.
Keywords: Rice, diversity, advanced breeding lines, economic traits
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References
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