A Review on Comparison of Complete and Incomplete Block Designs

Habtamu Kefale Sewenet

doi:10.7176/jbah/9-9-03

Abstract

The study of strategies for efficient plans for the collection of data, which lead to proper estimates of parameters relevant to the researcher’s objective is known as experimental design. Field-based agronomic and genetic research is a decision-based process and many of decisions are required to decide the type of design used to conduct a field experiment, collect, analyze data and interpret the results. Many researchers are locked into particular dimensions that are partly determined by the size or capacity of planting or harvesting equipment, available resources, the size and shape of fields available for research, and colleagues’ perceptions, or perhaps even peer pressure. A properly designed experiment for a particular research objective is the basis of all successful experiments. Experimental designs are classified in to complete block and incomplete block designs based on the treatment numbers found in a block. In case of complete block designs, all treatments are found in a block while some treatments found in incomplete block designs. Some of complete block designs are, completely randomized design (CRD), completely randomized block design (RCBD, Latin square (LSD), split plot design (SPD) whereas incomplete block designs are lattice design (LD) and Augmented designs (AD) are most known. Most of the time complete block design is widely used by researchers more than the balanced incomplete block designs because the missing data are computed before analysis. Each research designs had their own level of precision and conditions. Therefore, no single design is best all over designs. Due to this, the present review highlights the basic comparison concepts of complete and incomplete experimental designs and the focus of selection criteria. Keywords: Complete block, Experimental design and Incomplete block DOI : 10.7176/JBAH/9-9-03 Publication date :May 31 st 2019

Highlights

Experimentation plays a momentous role in the field of agriculture
Most of the time complete block design is widely used by researchers more than the balanced incomplete block designs because the missing data are computed before analysis (Kelechi, 2012)
According to the Author, results of the second dataset signify that Lattice design increases the precision of experiment by 17% and shows less coefficient of variation than Randomized Complete Block Design (RCBD) which implies that lattice design is again more efficient

Summary

INTRODUCTION

Experimentation plays a momentous role in the field of agriculture. A good experiment is the one which involves good planning, accurate data collection, proper data analysis and precise interpretation of the data (Masood et al, 2008). The common incomplete block designs are: Lattice design: - Balanced lattice design (BLD), Partially balanced lattice design (PBLD), they are so diverse: the two replications, the partially balanced lattice design is referred to as a simple lattice; with three replications, a triple lattice; with four replications, a quadruple lattice; and Augmented designs (group balanced block design) – with two groups, a simple augmented design; with three groups, a triple augmented design; with four groups, a quadruple augmented design; and so on Such flexibility in the choice of the number of replications results in a loss of symmetry in the arrangement of treatments over blocks (i.e., some treatment pairs never appear together in the same incomplete block). Because there is more than one level of precision for comparing treatment means, data analysis becomes more complicated (Gomez and Gomez, 1984)

COMPLETE AND INCOMPLETE BLOCK DESIGN COMPARISON

Resource requirement

Findings

Incomplete block designs