Analysis Related to Breeding Researches

Abstract

Variability is one of the major concerns of any research. In a real-life situation, we are to deal with a number of variables at a time, so co-variability plays an important role. In social, agricultural, and other fields, the biophysical features in any experiment rarely behave independently; rather these are found to be functionally related to each other. There are several examples where the analysis of covariance can be used effectively in augmenting the precession of the experimental results. For example, in yield component analysis of paddy, the yield components, namely, the number of hills per unit area, the number of effective tillers per hill, and the number of grains per panicles, can be used as covariates or concomitant variables. In a study of health drinks on the growth and physique of school-going children, initial body weight, height, age, physical agility, etc., can be taken as concomitant variables during the analysis of covariance. In the analysis of covariance, there are two types of variables: the characteristic of the main interest and the information on the secondary or auxiliary interest or the covariates. In the analysis of covariance, the expected (true) value of the response is the resultant of two components, one because of the linear combination of the values of the concomitant variables which are functionally related with the response and another one already obtained in the analysis of variance. Thus, the analysis of covariance is the synthesis of the analysis of variance and the regression. Similar to that of the partitioning of variances into different components, one can also partition the covariance among the variables into different components like genotypic and environmental.