Multivariate Analysis of Yield Data of Apple Crop for Optimizing Productivity in Himachal Pradesh.

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 apple. The technique Discriminant analysis was applied to formulate categorization rule for allocating the apple tree to 'high' and 'low' yielder groups. This Discriminant equation revealed that the characters canopy spread (X2), Fruit set (X7), LD Ratio (X9) and Fruit weight (X10) 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 apple. 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 vegetative characteristics, Plant vigour and flowering characteristics and Fruiting characteristics which explained 33.29%, 22.24% and 13.55% respectively and collectively 69.08% 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 Plant Vigour, Yield and Flowering Characteristics and Fruiting characteristics which explained 35.68%, 22.22% and 12.09% respectively and in aggregate, 69.99% of the total variation of original variables. [ABSTRACT FROM AUTHOR]