ANALYSIS OF COVARIANCE IN AUGMENTED RANDOMIZED BLOCK DESIGN WITH A SINGLE EXPLANATORY VARIABLE: AN EMPIRICAL EVIDENCE ON AGRONOMIC CROP

Analysis of Covariance

Abstract

The augmented randomized block design is a robust and versatile framework that maximizes the use of treatments, not readily available in conjunction with treatments, making it a crucial tool for researchers looking to draw valid and significant findings from the experimental design. This study proposed an analysis of the covariance (ANCOVA) model in augmented randomized block design to incorporate the effect of a concomitant variable. The estimation procedure of the parameters in the covariance model (ANCOVA) has been developed using maximum likelihood estimation. The testing procedure for treatment effects has been proposed for the analysis of covariance (ANCOVA) in augmented randomized block design. Also, a field experiment for maize cultivation using augmented randomized block design has been performed to compare the proposed design (ANCOVA) with the results of the analysis of variance (ANOVA) in augmented randomized block design and simple linear regression. The comparison of AIC values among ANOVA, regression, and ANCOVA models reveals that the proposed analysis of covariance (ANCOVA) in augmented randomized block design is more efficient and informative, which is likely to provide a new horizon in agricultural research.