Phenotypic correlation, path coefficient and multivariate analysis for yield and yield-associated traits in groundnut accessions

Abstract

Yield is a complex quantitative trait largely influenced by the environment. Direct selection for grain yield is less efficient in improving groundnut productivity. The selection efficiency can be enhanced by exploiting the relationship between yield and its related traits. Moreover, the use of genetically diverse parents is essential to generate genetic variation for successful selection of genotypes in a breeding program. Therefore, the study aimed at analysing the relationship between grain yield and its related traits and determining the morphological diversity among selected groundnut genotypes under natural rosette disease (GRD) infestation. The genotypes were evaluated in a 7 × 4 alpha lattice design with three replications. Data were collected on yield and yield-related traits. Correlation, path coefficient and multivariate analyses were done. The results revealed that yield was directly associated with plant height, number of pods per plant, hundred seed weight, GRD incidence and number of secondary branches. Therefore, these traits should be considered in selection when improving groundnut for yield. Cluster analysis revealed existence of diversity among the evaluated groundnut genotypes with no influence of geographical origin to the clustering pattern. The Principal Components Analysis (PCA) biplot was effective in showing the genetic distance among the genotypes and the results were comparable with those of the cluster analysis. Moreover, Shannon-Weaver diversity indices revealed existence of high diversity among the genotypes, an implication that groundnut improvement for yield is possible through selection in breeding.

Keywords

• groundnut
• correlation
• path analysis
• diversity
• cluster
• principal component analysis