Correlation and Path Coefficient Analysis of Yield and Its Contributing Traits in Red Rice (Oryza sativa L.)
Nanda K. P. *
Department of Genetics and Plant Breeding, College of Agriculture, Junagadh Agricultural University, Junagadh 362001, Gujarat, India.
Vavdiya P. A.
Hill Millet Research Station, Navsari Agricultural University, Waghai (Dangs) 394730, Gujarat, India.
Jadav R. V.
Department of Genetics and Plant Breeding, College of Agriculture, Junagadh Agricultural University, Junagadh 362001, Gujarat, India.
Sorathia A. M.
Department of Genetics and Plant Breeding, B. A. College of Agriculture, Anand Agricultural University, Anand 388110, Gujarat, India.
Mungra K. S.
Main Rice Research Centre, Navsari Agricultural University, Navsari 396450, Gujarat, India.
Naghera Y. V.
Department of Biotechnology, College of Agriculture, Junagadh Agricultural University, Junagadh 362001, Gujarat, India.
*Author to whom correspondence should be addressed.
Abstract
Aims: This study was conducted to examine the relationship and degree of connection between grain yield and its various components and to determine the main traits that affect grain yield in red rice (Oryza sativa L.) using correlation and path coefficient analysis.
Study Design: A Randomized Block Design (RBD) with three replications was used and the experiment was carried out during a single cropping season.
Place and Duration of Study: The experiment was conducted during kharif-2024 at the Main Rice Research Centre, Navsari Agricultural University, Navsari, Gujarat, India.
Methodology: We looked at forty different types of red rice to see how they differed in sixteen different agro-morphological and biochemical traits. We looked at how traits were related to each other by calculating the genotypic and phenotypic correlation coefficients with grain yield per plant. Path coefficient analysis was used to break down the observed correlations into direct and indirect effects. This helped to identify traits that really affect grain yield.
Results: Grain yield per plant showed very strong and positive correlations with straw yield per plant and harvest index at both the genotypic and phenotypic levels (rg = 0.837 and 0.863; rp = 0.505 and 0.793, respectively). This shows that they play a big role in improving yield. There were also positive correlations between productive tillers per plant, 100-grain weight and zinc content. This suggests that it is possible to improve both yield and nutritional quality at the same time. Path coefficient analysis showed that the harvest index had the biggest positive direct effect on grain yield. The straw yield per plant, the length/breadth ratio and the kernel breadth were next. On the other hand, traits like kernel length and plant height had negative direct effects on grain yield, even though they had positive indirect effects through other traits. This suggests that directly selecting for these traits may not be the best way to increase yield.
Conclusion: The study clearly shows that the harvest index and straw yield per plant are the two most important factors that affect the grain yield of red rice. These can be used as reliable selection criteria. Focusing on traits that have strong positive direct effects would make it easier to choose the best red rice breeding programs for improving yield.
Keywords: Yield components, trait association, direct and indirect effects, nutritional traits, zinc content and selection indices