Historical Development and Practical Application of Correlation and Path Coefficient Analysis in Agriculture

Abstract

Correlation coefficient measures the mutual relationship between various plant characters and determines the component characters on which selection can be based for the improvement in associated complex character. Several correlation coefficients based on different statistical hypothesis are used today: Pearson correlation coefficient, Spearman ranks correlation coefficient and Spearman semi-quantitative correlation coefficient, Kendall tau-a, -b and -c correlation coefficients, Gamma correlation coefficient. In quantitative genetics there are three types of correlations and these are phenotypic, genotypic and environmental correlations. Path coefficient analysis splits the correlation coefficients into the measures of direct and indirect effects of a set of independent variables on the dependent variable and estimated using a standardized partial regression coefficient known as path coefficient analysis, as suggested by Dewey and Lu in 1959.Based on this, data from national variety trial conducted at  Holetta agricultural research center was analyzed both for correlation coefficient(phenotypic and genotypic) and path coefficient. Phenotypic correlation coefficient between yield and plant height, ear height and ear per plant positive and significant and reveals that the relation is due to environmental influence. However, there is no genotypic correlation between yield and other studied trait which indicting that selecting one trait will not improve the other which refers most of the traits were influenced by environment more relatively. Phenotypic path coefficient analysis between, EH (0.406139) and PH (0.402022) showed positive and high direct effects, whereas genotypic path coefficient analysis the direct effect of EH (0.511255(and PH (0.511028) on GY was considerably higher.

Keyword: correlation coefficient, path coefficient, phenotype, genotype, variance, models