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Process-based simulation of regional agricultural supply functions in Southwestern Germany using farm-level and agent-based models


  • Troost, Christian
  • Berger, Thomas


In combination with crop growth models, farm-level models allow an in-depth, process-based analysis of farmer adaptation to climate change and agricultural policy. Evaluated for all farms in an area and extended by interactions, farm-level models become agent-based models that allow simulating aggregate regional production and structural change. Confined to a local or regional scope, however, they cannot directly incorporate price feedbacks that play out at global scale. In this contribution, we use experimental designs to evaluate a non-connected agent-based model for the full space of potential future price developments. We discuss and compare the use of standard regression analysis and non-parametric, automatic methods (MARS and Kriging) to summarize supply behavior over the simulated price ranges. Estimated supply functions constitute a surrogate model for the original agent-based model and could be used to iterate detailed regional analysis with national or global market models in an efficient way.

Suggested Citation

  • Troost, Christian & Berger, Thomas, 2015. "Process-based simulation of regional agricultural supply functions in Southwestern Germany using farm-level and agent-based models," 2015 Conference, August 9-14, 2015, Milan, Italy 211929, International Association of Agricultural Economists.
  • Handle: RePEc:ags:iaae15:211929

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    References listed on IDEAS

    1. John M. Antle & Susan M. Capalbo, 2001. "Econometric-Process Models for Integrated Assessment of Agricultural Production Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(2), pages 389-401.
    2. repec:eee:ecomod:v:221:y:2010:i:4:p:590-598 is not listed on IDEAS
    3. Berger, Thomas & Schreinemachers, Pepijn & Woelcke, Johannes, 2006. "Multi-agent simulation for the targeting of development policies in less-favored areas," Agricultural Systems, Elsevier, vol. 88(1), pages 28-43, April.
    4. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    5. Aurbacher, Joachim & Parker, Phillip S. & Calberto Sánchez, Germán A. & Steinbach, Jennifer & Reinmuth, Evelyn & Ingwersen, Joachim & Dabbert, Stephan, 2013. "Influence of climate change on short term management of field crops – A modelling approach," Agricultural Systems, Elsevier, vol. 119(C), pages 44-57.
    6. Thomas Berger & Christian Troost, 2014. "Agent-based Modelling of Climate Adaptation and Mitigation Options in Agriculture," Journal of Agricultural Economics, Wiley Blackwell, vol. 65(2), pages 323-348, June.
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    1. repec:eee:agisys:v:164:y:2018:i:c:p:264-276 is not listed on IDEAS

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    Agribusiness; International Development;

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