International Journal of Applied Research
Vol. 2, Issue 7, Part M (2016)
Multivariate analysis of yield data of kinnow crop for optimizing productivity in Himachal Pradesh
Geeta Verma, PK Mahajan and Ashu Chandel
The paper deals with the usefulness of Discriminant and Principal Component analyses for determining the relative contribution of morphological and reproductive characters responsible in increasing the yield of kinnow. The technique Discriminant analysis was applied to formulate categorization rule for allocating the kinnow tree to ‘high’ and ‘low’ yielder groups. This Discriminant equation revealed that the characters plant girth (X2), plant spread (X3), leaves per branch (X4) and flower per branch (X6) are the most important characters that discriminated the two groups. The Principal Component Analysis was extracted for the assessment of relative contribution of morphological and reproductive characters responsible in increasing the yield of kinnow. In case of high yielders, three of the ten Principal Components (PCs) have Eigen values greater than unity (Gutman’s lower bound) which played the main role in the analysis. These components were Fruiting or Fruitfulness, Growth characteristics and Growth and Volume characteristics which explained 36.38%, 11.61% and 11.01% respectively and collectively 68.57% of the total variation of the original variables. In case of low yielders, three principal components had been retained for the analysis. These components were Fruiting and Vigour, Growth and Volume and Vigour characteristics, which explained 38.11%, 14.68% and 12.03% respectively and in aggregate, 64.83% of the total variation of original variables.
How to cite this article:
Geeta Verma, PK Mahajan and Ashu Chandel. Multivariate analysis of yield data of kinnow crop for optimizing productivity in Himachal Pradesh. International Journal of Applied Research. 2016; 2(7): 857-859.