Developing decision support tools for rangeland management by combining state and transition models and Bayesian belief networks

Bashari, H., Smith, C. and Bosch, O. J. H. (2009) Developing decision support tools for rangeland management by combining state and transition models and Bayesian belief networks. Agricultural Systems, 99 1: 23-34. doi:10.1016/j.agsy.2008.09.003


Author Bashari, H.
Smith, C.
Bosch, O. J. H.
Title Developing decision support tools for rangeland management by combining state and transition models and Bayesian belief networks
Journal name Agricultural Systems   Check publisher's open access policy
ISSN 0308-521X
Publication date 2009
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.agsy.2008.09.003
Volume 99
Issue 1
Start page 23
End page 34
Total pages 12
Editor J W Hansen
P B M Berentsen
P K Thornton
Place of publication Netherlands
Publisher Elsevier BV
Language eng
Subject C1
830403 Native and Residual Pastures
070101 Agricultural Land Management
Abstract State and transition models provide a simple and versatile way of describing vegetation dynamics in rangelands. However, state and transition models are traditionally descriptive, which has limited their practical application to rangeland management decision support. This paper demonstrates an approach to rangeland management decision support that combines a state and transition model with a Bayesian belief network to provide a relatively simple and updatable rangeland dynamics model that can accommodate uncertainty and be used for scenario, diagnostic, and sensitivity analysis. A state and transition model, developed by the authors for subtropical grassland in south-east Queensland, Australia, is used as an example. From the state and transition model, an influence diagram was built to show the possible transitions among states and the factors influencing each transition. The influence diagram was populated with probabilities to produce a predictive model in the form of a Bayesian belief network. The behaviour of the model was tested using scenario and sensitivity analysis, revealing that selective grazing, grazing pressure, and soil nutrition were believed to influence most transitions, while fire frequency and the frequency of good wet seasons were also important in some transitions. Overall, the integration of a state and transition model with a Bayesian belief network provided a useful way to utilise the knowledge embedded in a state and transition model for predictive purposes. Using a Bayesian belief network in the modelling approach allowed uncertainty and variability to be explicitly accommodated in the modelling process, and expert knowledge to be utilised in model development. The methods used also supported learning from monitoring data, thereby supporting adaptive rangeland management.
Keyword Rangeland management
State and transition model
Queensland
Bayesian belief network
Adaptive Management
Decision support
Q-Index Code C1
Q-Index Status Provisional Code
Additional Notes Expanded Year of Publication Rule applied for HERDC 2010

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Agriculture and Food Sciences
Ecology Centre Publications
 
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Created: Thu, 03 Sep 2009, 09:09:23 EST by Mr Andrew Martlew on behalf of School of Integrative Systems