Statistical methodologies for drawing causal inference from an unreplicated farmlet experiment conducted by the Cicerone Project
R. Murison A and J. M. Scott B C
+ Author Affiliations
- Author Affiliations
A School of Science and Technology, University of New England, Armidale, NSW 2351, Australia.
B School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
C Corresponding author. Email: dr.jimscott@gmail.com
Animal Production Science 53(8) 643-648 https://doi.org/10.1071/AN11331
Submitted: 1 December 2011 Accepted: 17 June 2012 Published: 10 July 2013
Journal Compilation © CSIRO Publishing 2013 Open Access CC BY-NC-ND
Abstract
The present paper explains the statistical inference that can be drawn from an unreplicated field experiment that investigated three different pasture and grazing management strategies. The experiment was intended to assess these three strategies as whole farmlet systems where scale of the experiment precluded replication. The experiment was planned so that farmlets were allocated to matched paddocks on the basis of background variables that were measured across each paddock before the start of the experiment. These conditioning variables were used in the statistical model so that farmlet effects could be discerned from the longitudinal profiles of the responses. The purpose is to explain the principles by which longitudinal data collected from the experiment were interpreted. Two datasets, including (1) botanical composition and (2) hogget liveweights, are used in the present paper as examples. Inferences from the experiment are guarded because we acknowledge that the use of conditioning variables and matched paddocks does not provide the same power as replication. We, nevertheless, conclude that the differences observed are more likely to have been due to treatment effects than to random variation or bias.
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