Development of a model based on Bayesian networks to estimate the probability of sheep lice presence at shearing
B. J. Horton A F , D. L. Evans B , P. J. James C and N. J. Campbell D EA DPIW Tasmania, PO Box 46, Kings Meadows, Tas. 7249, Australia.
B Department of Agriculture & Food WA, PO Box 609, Denmark, WA 6333, Australia.
C Animal Research Institute, Department of Primary Industries and Fisheries, 665 Fairfield Road, Yeerongpilly, Qld 4105, Australia.
D Department of Primary Industries Victoria, 475 Mickleham Road, Attwood, Vic. 3049, Australia.
E Present address: 5 Anka Close, Eltham, Vic. 3095, Australia.
F Corresponding author. Email: brian.horton@dpiw.tas.gov.au
Animal Production Science 49(1) 48-55 https://doi.org/10.1071/EA07179
Submitted: 13 June 2007 Accepted: 8 August 2008 Published: 5 January 2009
Abstract
This paper describes the development of a model, based on Bayesian networks, to estimate the likelihood that sheep flocks are infested with lice at shearing and to assist farm managers or advisers to assess whether or not to apply a lousicide treatment. The risk of lice comes from three main sources: (i) lice may have been present at the previous shearing and not eradicated; (ii) lice may have been introduced with purchased sheep; and (iii) lice may have entered with strays. A Bayesian network is used to assess the probability of each of these events independently and combine them for an overall assessment. Rubbing is a common indicator of lice but there are other causes too. If rubbing has been observed, an additional Bayesian network is used to assess the probability that lice are the cause. The presence or absence of rubbing and its possible cause are combined with these networks to improve the overall risk assessment.
Acknowledgements
Funding for development of this model was provided by Australian wool producers and the Australian Government through Australian Wool Innovation Limited. Don Moir, Department of Agriculture & Food WA provided a decision tree on which the rubbing network was based. Michael Horton (School of Computing, University of Tasmania) provided assistance in development of the Bayesian network and comparison with other decision support systems.
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