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Ontology-based simulation in agricultural systems modeling


  • Beck, Howard
  • Morgan, Kelly
  • Jung, Yunchul
  • Grunwald, Sabine
  • Kwon, Ho-young
  • Wu, Jin


A methodology and applications of ontology-based simulation are presented. An environment for building simulations based on the Lyra ontology management system is described which includes web-based visual design tools for constructing models and automatically generating simulation code. The ontology is used for representing all equations and all symbols appearing in these equations that are needed to describe a model. The example applications presented are models of soil, water, and nutrient management in citrus and sugarcane. Results thus far show that the ontology-based approach has advantages for representing the model structure, equations, and symbols, that complex models can be described in this format, and that efficient simulation code can be generated automatically from the ontology definition of the model. Potential applications, not yet fully explored, include ability to automatically connect models and data sources, using the ontology to organize model bases containing many models and model components, and using ontology reasoners to search for models, automatically discover model similarities and differences, and generate model instances from general principles.

Suggested Citation

  • Beck, Howard & Morgan, Kelly & Jung, Yunchul & Grunwald, Sabine & Kwon, Ho-young & Wu, Jin, 2010. "Ontology-based simulation in agricultural systems modeling," Agricultural Systems, Elsevier, vol. 103(7), pages 463-477, September.
  • Handle: RePEc:eee:agisys:v:103:y:2010:i:7:p:463-477

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    References listed on IDEAS

    1. van Ittersum, Martin K. & Ewert, Frank & Heckelei, Thomas & Wery, Jacques & Alkan Olsson, Johanna & Andersen, Erling & Bezlepkina, Irina & Brouwer, Floor & Donatelli, Marcello & Flichman, Guillermo & , 2008. "Integrated assessment of agricultural systems - A component-based framework for the European Union (SEAMLESS)," Agricultural Systems, Elsevier, vol. 96(1-3), pages 150-165, March.
    2. Moore, A.D. & Holzworth, D.P. & Herrmann, N.I. & Huth, N.I. & Robertson, M.J., 2007. "The Common Modelling Protocol: A hierarchical framework for simulation of agricultural and environmental systems," Agricultural Systems, Elsevier, vol. 95(1-3), pages 37-48, December.
    3. Caldwell, Robert M. & Fernandez, Anthony A. J., 1998. "A generic model of hierarchy for systems analysis and simulation," Agricultural Systems, Elsevier, vol. 57(2), pages 197-225, June.
    4. Kwon, Ho-Young & Grunwald, Sabine & Beck, Howard W. & Jung, Yunchul & Daroub, Samira H. & Lang, Timothy A. & Morgan, Kelly T., 2010. "Ontology-based simulation of water flow in organic soils applied to Florida sugarcane," Agricultural Water Management, Elsevier, vol. 97(1), pages 112-122, January.
    5. Jones, J. W. & Keating, B. A. & Porter, C. H., 2001. "Approaches to modular model development," Agricultural Systems, Elsevier, vol. 70(2-3), pages 421-443.
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    1. repec:eee:ecomod:v:222:y:2011:i:19:p:3486-3499 is not listed on IDEAS
    2. Robert, Marion & Dury, Jérôme & Thomas, Alban & Therond, Olivier & Sekhar, Muddu & Badiger, Shrini & Ruiz, Laurent & Bergez, Jacques-Eric, 2016. "CMFDM: A methodology to guide the design of a conceptual model of farmers' decision-making processes," Agricultural Systems, Elsevier, vol. 148(C), pages 86-94.


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