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Title: Statistical methodologies for drawing causal inference from an unreplicated farmlet experiment conducted by the Cicerone Project
Contributor(s): Murison, Robert D  (author); Scott, Jim M  (author)
Publication Date: 2013
Open Access: Yes
DOI: 10.1071/AN11331Open Access Link
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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.
Publication Type: Journal Article
Source of Publication: Animal Production Science, 53(7-8), p. 643-648
Publisher: CSIRO Publishing
Place of Publication: Melbourne, Australia
ISSN: 1836-5787
Field of Research (FOR): 010401 Applied Statistics
Socio-Economic Objective (SEO): 830311 Sheep - Wool
830406 Sown Pastures (excl. Lucerne)
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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