Advanced row-column designs for animal feed experiments

PRAKASH KUMAR; KRISHAN LAL; ANIRBAN MUKHERJEE; UPENDRA KUMAR PRADHAN; MRINMOY RAY; OM PRAKASH

doi:10.56093/ijans.v88i4.78895

Authors

PRAKASH KUMAR Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
KRISHAN LAL Ex-Principal Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
ANIRBAN MUKHERJEE PhD Scholar, Division of Agricultural Extension, ICAR-IARI, New Delhi
UPENDRA KUMAR PRADHAN Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
MRINMOY RAY Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
OM PRAKASH Principal Scientist, Division of Agricultural Extension, ICAR-IARI, New Delhi

https://doi.org/10.56093/ijans.v88i4.78895

Keywords:

Animal feed experiment, Asymmetric factorial experiment, Balanced factorial experiments (BFE), Confounding using equation method

Abstract

Inappropriate statistical designs may misinterpret results of animal feed experiments. Thus complete statistical designs can make animal feed research more appropriate and cost effective. Usually factorial row-column designs are used when the heterogeneity in the experimental material is in two directions and the experimenter is interested in studying the effect of two or more factors simultaneously. Attempts have been to develop the method of construction of balanced nested row column design under factorial setup. Factorial experiments are used in designs when two or more factors have same levels or different levels. The designs that are balanced symmetric factorials nested in blocks are called block designs with nested row-column balanced symmetric factorial experiments. These designs were constructed by using confounding through equation methods.Construction of confounded asymmetrical factorial experiments in row-column settings and efficiency factor of confounded effects was worked out. The design can be used in animal feed experiment with fewer resources by not compromising the test accuracy.

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