Principal Component Analysis for Evaluation of Guinea grass (Panicum maximum Jacq.) Germplasm Accessions
Abstract
The present study was conducted to study the variability among the genotypes by Principal Component Analysis (PCA) in order to select those that are most suitable for breeding programme. This study included ten quantitative traits. The result of principal component analysis showed that the first four principal components with Eigen value greater than 0.88 contributed about 76.10 per cent of total variation in the population. The variability of the genotypes was interpreted based on four principal components, the first principal component described the yield level, the second principal component described the productivity and quality and the last two principal components described the quality of the fodder which indicating that the identified traits within the axes exhibited great influence on the phenotype and this could be effectively used for selection among the tested entries for further development of Guinea grass varieties with improved fodder yield and quality.
Keywords
Download Options
Introduction
Among the grasses, Guinea grass (Panicum maximum Jacq.) is an important forage grass of tropical and semi tropical regions, largely apomictic and predominantly exist in tetraploid form. It is also endowed with virtues like profuse tillering, high leafiness, thin stems, short duration, etc., all of which contributes towards high biomasss production and better palatability. Being tolerance to shade, it is largely cultivated in coconut gardens. It is extensively cultivated under irrigated condition on fairly rich soils and is popular with dairy farmers. At present in India there is a deficit of 64 per cent of green fodder, and hence there is a need of over production of quality fodder especially the range grasses which could rejuvenate the fast degrading grasslands. In order to improve the productivity, adaptability and quality of Guinea grass, it is important to understand the genetic variability that exist in the population which also helps in their conservation and germplasm management (Tiwari and Chandra, 2010).
The major resource of plant breeders is the genetic variability in gene pool accessible to the crop of interest. The successes of crop improvement programs are highly reliant on the efficient manipulation of that genetic variability. Morphological markers have played an essential role in crop improvement since the beginning of modern breeding programs. A large number of variables are often measured by plant breeders, some of which may not be of sufficient discriminatory power for germplasm evaluation, characterization, and management. In such case, Principal Component Analysis (PCA) used to reveal patterns and eliminate redundancy in data sets (Adams, 1995; Amy and Pritts, 1991) as morphological and physiological variations routinely occur in crop. Knowledge of the nature, extent and organization of this variation could be useful for genetic improvement of crop species. Until a collection has been properly evaluated and its attributes become known to breeders, it has little practical use. Therefore, the present investigation was undertaken to study the Principal Component Analysis among 60 germplasm accessions of Guinea grass (Panicum maximum Jacq.) for characterization and preliminary evaluation, and also for further evaluation aimed at yield and quality improvement in this crop.
Conclusion
In the present study, Principal Component Analysis showed that cumulative variance of 76.10 per cent by the first four axes with eigen v alue of greater than 0.88 indicates that the identified traits within the axes exhibited great influence on the phenotype of the tested entries. The variability of the genotypes was interpreted based on four principal components, the fir st one described th e yield level, the second described the productivity and quality level and the last two principal components described the quality of the fodder which indicating that the identified traits within the axes exhibited great influence on the phenotype and this could be effectively used for selection among the tested entries for suitable breeding programme. The variability that are strongly associated in the component traits may share some underlying biological relationship, and these associations are often usef ul for generating hypothesis for better understanding of behaviour of complex traits that would allow breeders to maximize their knowledge.