Path Coefficient, Eigenvalues, and Genetic Parameters in Egyptian Rice (Oryza Sativa L.) Under Aerobic Conditions

Abstract

The genetic parameters, eigenvalues in principal components, correlation, and path coefficient in Egyptian rice (Oryza sativa L.) proceeded to understand the interrelationship among studied traits, which helps develop selection strategies in genetic breeding programs under aerobic conditions. Twelve rice genotypes planted under the aerobic condition in a randomized complete block design included three replications during the 2020 and 2021 seasons. The analysis of variance revealed significant differences among studied genotypes, indicating a broad genetic diversity among its genotypes. Eigenvalues used helped to understand the data structure and trait relations and divided the 21 traits into two components. The highly positive phenotypic and genotypic correlations appeared in 16 characteristics, i.e., leaf area index, total chlorophyll, and antioxidant traits, indicating that any improvement of these traits will improve rice grain yield under aerobic conditions. The path structure showed that panicle weight, the number of panicles hill-1, catalase, superoxide dismutase, root length, and flag leaf area had increased grain yield. The current results indicated that Egyptian hybrid1, GZ9399, and Giza179 could recommend for cultivation under aerobic conditions and serve as donors in breeding programs to improve tolerance to drought under aerobic conditions.

Genetic Diversity and Path Analysis in Rice Germplasms (Oryza sativa L.)

“…Similar results were observed by Vennela et al [6]; Ramachary et al [7]; Sanchika et al [8]; Pushkarnath et al [9]; Gayathri, NK, & Padmalatha Y. [10]; Heera et al [11]; Zayed et al [12]; Kujur et al [13]; Renuprasath et al [14].…”

Path Coefficient Analyses of Introduced Rice Varieties Under Different Planting Distances

“…Knowledge about the genotypic and phenotypic correlations is vital in diagnosing the traits most closely related to grain yield (Shrestha et al, 2014;Al-Shugeairy et al,2014;Len et al, 2015). The correlation of morphological traits with the yield is of great importance in determining the components of the yield; however, it does not reveal the direct and indirect effects of each trait on the yield, so the path coefficient analysis provides for further assessment (Alhassan and Musa, 2021;Zayed et al, 2023). The path analysis is critical in studying the direct and indirect effects of the various yield components to determine the importance of these traits as selection criteria in managing the grain yield (Nadali et al, 2011;Dutt et al, 2020).…”

Principal Component Analysis and Estimated Breeding Values for Selecting Suitable Parental Genotypes in Rice (Oryza Sativa L.)

“…It works to keep the relevant portions with the data that differs more and eliminate the unnecessary parts. An orthogonal transformation process known as the principal component analysis (PCA) transforms a collection of characters with potentially correlated values into a set of observations with linearly uncorrelated characters (Mohan et al, 2015;Alshugeairy et al, 2023;Zayed et al, 2023). Genotypes or breeding lines attain grouping using cluster analysis, such that genotypes of the same type are in the same group (referred to as a cluster), and genotypes of different kinds are in distinct groups.…”