Commercial Broiler Feed Additive Decision Support

By: Anders L. Madsen (HUGIN) and Jeffrey Hoorfar (DTU)
26 September 2016

Introduction

The model deployed on this page is an example illustrating the use of (limited-memory) influence diagrams to support decision making on the use of a feed additive in poultry production (commercial broiler). The decision considered by the farmer is whether or not to use a feed additive in commercial broiler production. This decision is assumed to be made after the house has been emptied and cleaned before filling the house with a new set of birds.

We assume the feed additive is available in crude and purified form. Furthermore, we assume the farmer can decide between providing the feed additive in different time frames, e.g., for the first two weeks or the last three days before slaughter. The decision alternative we consider are none (i.e., no use of a feed additive), crude for the first two weeks, crude for three days pre-slaughter, or purified for three days pre-slaughter. Each option will have a different expected impact on the level of campylobacter at slaughter.

The underlying model is a (limit-memory) influence diagram ([Kjaerulff and Madsen, 2013]) developed for the Danish market based on the work of ([Garcia et al, 2013,Garcia et al, 2016])].

The model deployed here implements Reward system 2 (Garcia et al, 2016])].

Interactive Front-end

Below are some HUGIN widgets for interacting with the model. This interface has been developed using the HUGIN Web Service API ([Madsen et al, 2013]).

The model is used under the assumption that the farmer at the time of decision knows the value of a set of risk factors related to farm characteristics, system variables and observations. The five risk factors included in the model are shown on the far left where the user is expected to select the corresponding value for each risk factore. The middle column shows the decision alternatives under the heading Feed additive. Below the decision alternatives the cost, reward and combined cost and reward are shown. On the far right, the expected impact of the selected decision alternative is shown.

Farm Characteristics, System Variables and Observations

Decision

The expected selling price at slaughter is .
The expected cost of the feed additive is .
This means that the expected profit is .

Decision Impact

The expected logs reduction is
The expected campylobacter level at slaughter is


Example Scenarios

[Garcia et al, 2016] define the best case scenario as follows:

Similarly, [Garcia et al, 2016] define the worst case scenario as follows:

Cost and Reward System

References

[Anon, 2010] Anon, 2010. The Joint Government And Industry Target To Reduce Campylobacter In Uk Produced Chickens By 2015 December 2010. Available online here.

[Crane et al, 2011] Crane, R., Davenport R., and Vaughan, R., 2011. Farm Business Survey 2009/2010. Poultry production in England. Available online here

[EFSA, 2010] Analysis of the baseline survey on the prevalence of Campylobacter in broiler batches and of Campylobacter and Salmonella on broiler carcasses in the EU, 2008, part A: Campylobacter and Salmonella prevalence estimates. EFSA Journal 2010; 8(03):1503. Available online here

[Lawes et al, 2012] Investigation of prevalence and risk factors for Campylobacter in broiler flocks at slaughter: results from a UK survey. Centre for Epidemiology and Risk Analysis, Animal Health and Veterinary Laboratories Agency, Surrey, UK. Epidemiol Infect. 2012 May 25:1-13. Lawes JR, Vidal A, Clifton-Hadley FA, Sayers R, Rodgers J, Snow L, Evans SJ, Powell LF. [Epub ahead of print].

[Madsen et al, 2013] Madsen, A. L., Karlsen, M., Barker, G. C., Garcia, A. B., Hoorfar, J., Jensen, F (2013). A Software Package for Web Deployment of Probabilistic Graphical Models. In Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence (SCAI), pages 175-184.

[Garcia et al, 2013] Garcia, A. B., Madsen, A. L. and Vigre, H. (2013). Integration of Epidemiological Evidence in a Decision Support Model for the Control of Campylobacter in Poultry Production, Agriculture, 3(3), pages 516-535.

[Garcia et al, 2016] Garcia, A.B., Madsen, A.L., Vigre, H. (2016). A decision support system for the control of Campylobacter in chickens at farm level using data from Denmark, Journal of Agricultural Science, 154, pages 720-731.

Useful references for those interested in BBN include:

[Kjærulff and Madsen, 2013] Kjærulff, U. B. and Madsen, A. L. (2013) Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer, Second Edition.

Contact information

For further details on the paper: Jeffrey Hoorfar

For further details on the use of Bayesian networks and web deployment of models contact: Anders L Madsen (alm(at)hugin(dot)com)

Disclaimer
HUGIN EXPERT A/S takes no responsibility whatsoever for examples and information in examples published on this web site. ALL EXAMPLES ARE FOR DEMONSTRATION PURPOSES ONLY.