Multivariate Analysis for Yield and Its Component Traits in Experimental Maize Hybrids

Ramesh Kumar, G. K. Chikkappa, S. B. Singh, Ganapati Mukri, J. Kaul, A. K. Das, D. Sravani, D. S. Olakh, D. Bhatia

Abstract


Crop yields of major cereal including maize are not increasing at the targeted growth rates to feed the rising demands stemming from increase in the human population. To increase maize grain yield, there should be continuous improvement of cultures which are actively utilized by the plant breeders. Variability in germplasm is always the key to improvement and to assess the extent of variation is never ending process in a plant breeding program. Out of several methods available for assessing the variability, multivariate analysis is one of the most important and widely used methods. In the present study, 27 hybrids (including three checks) were evaluated for yield and yield contributing traits at three different locations during rabi 2013-14. Analysis of variance revealed significant variations among hybrids for all the traits. Based on Principal Component Analysis, 76.81% of the total variance in the data was accounted for by first four principal components (PC). Cluster analysis based on PC grouped the 27 hybrids into two major groups named as A and B. The group A further contained three sub-groups named as A1, A2, and A3 with two hybrids falling in each group. Similarly group B contained four subgroups classified as B1 to B4 with 2, 7, 5 and 7 hybrids falling in each subgroup respectively. The hybrids falling in two major groups contained more diversity than those falling in subgroups within a group. Selection of hybrids from the different groups would facilitate exploiting significant heterosis. Therefore, multivariate analysis including Principal component analysis followed by cluster analysis could be a reliable approach for assessing the extent of variability on in the germplasm and making its use in a suitable direction.


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DOI: https://doi.org/10.5539/jas.v9n3p219

Copyright (c) 2017 Ganapati Mukri

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Journal of Agricultural Science   ISSN 1916-9752 (Print)   ISSN 1916-9760 (Online)  E-mail: jas@ccsenet.org

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