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

Listed author(s):
  • Troost, Christian
  • Berger, Thomas
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    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.

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    File URL: http://purl.umn.edu/211929
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    Paper provided by International Association of Agricultural Economists in its series 2015 Conference, August 9-14, 2015, Milan, Italy with number 211929.

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    Date of creation: 2015
    Handle: RePEc:ags:iaae15:211929
    Contact details of provider: Web page: http://www.iaae-agecon.org/
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    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. 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.
    3. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    4. 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.
    5. 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, 06.
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