Department of Basic Sciences, Dr YS Parmar University of Horticulture and Forestry Nauni, Solan, 173230, Himachal Pradesh, India
The paper deals with the usefulness of discriminant and principal component analysis for determining the relative contribution of morphological and reproductive characters responsible in increasing the yield of kinnow. For this purpose a field experiment was conducted during 2014–15 at kinnow orchards of farmers in Indpur block of Kangra district as this area represents the main kinnow growing belt of the state. An optimum sample size of 96 kinnow trees was selected randomly for the study. The technique discriminant analysis was applied to formulate categorization rule for allocating the kinnow trees to ‘high’ and ‘low’ yielder groups. This discriminant equation revealed that the characters plant height (X1), number of leaves per plant (X4) and fruit weight (X8) were the most important characters that discriminated the two groups. Principal component analysis in high yielder group showed that three of the ten principal components had eigen values greater than unity (Gutman's lower bound) which played the main role in the analysis. These components were fruiting and growth and vigour and growth characteristics which explained 40.59, 13.52 and 11.98 per cent respectively and collectively 66.09 per cent of the total variation of the original variables. In case of low yielders four principal components were retained for the analysis. These were fruiting, growth and fruiting and growth characteristics. These principal components explained 24.70, 18.36, 14.91 and 11.95 per cent of the total variation and in aggregate 69.92 per cent of original variables.
Discriminant analysis, Gutman's lower bound, kinnow, principal component analysis
