Multivariate Analyses of Yield and Its Components in Some Peanut Genotypes

Abstract

Aims: For proposing a statistical approach to select of the most promising genotypes for peanut breeding program. Place and Duration of Study: Twenty peanut genotypes were evaluated at Matana Agricultural Station Research, Luxor governorate, Egypt during 2018 and 2019. Study Design: In a randomized complete block design with three replications. Methodology: Analysis of variance (ANOVA), correlation coefficients, factor analysis, cluster method and some genetic parameters for seed yield and its components were calculated. Results: Results revealed that significant differences among the tested genotypes for the eight studied traits. Correlation coefficients indicated that seed yield was significantly correlated with all traits except plant height. Meanwhile, factor analysis was used to remove multi-collinearity problems, to simplify the complex relationships and to reduce variables number (into three extracted factors). 100-seed weight, number of branches/plant, 100-pod weight and seed oil content (%) with seed yield/plant traits which present in the 1st factor explained 42.039% of the total variance and recorded high heritability coupled with high genetic advance %. ANOVA results for factor scores obtained (native best multi-traits data) revealed that genotypes varied significantly. Conclusion: Factor and cluster analysis agreed in grouping Ismailia 2, Intr. 267, Intr. 182, Intr. 332 and Sohag 107 to be promising genotypes to increase peanut seed yield, whereas genotypes Intr. 504 and intr. 510 could be utilized to increase peanut seed oil content %. Then, the utilization of a factor score as a variable in ANOVA analysis was more appropriate rather than the original data. Consequently, factor scores (as a native data) would be more agreeable to selection and can be employed in plant breeding programs.