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

Author

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  • Troost, Christian
  • Berger, Thomas
Abstract
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
    DOI: 10.22004/ag.econ.211929
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    References listed on IDEAS

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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. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    3. 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.
    4. Gibbons, J.M. & Wood, A.T.A. & Craigon, J. & Ramsden, S.J. & Crout, N.M.J., 2010. "Semi-automatic reduction and upscaling of large models: A farm management example," Ecological Modelling, Elsevier, vol. 221(4), pages 590-598.
    5. 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.
    6. 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.
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    Cited by:

    1. Yamashita, Ryohei & Hoshino, Satoshi, 2018. "Development of an agent-based model for estimation of agricultural land preservation in rural Japan," Agricultural Systems, Elsevier, vol. 164(C), pages 264-276.
    2. Winter, Eva & Grovermann, Christian & Aurbacher, Joachim & Messmer, Monika M., 2021. "Analysing Interventions in the Seed and Breeding System for Organic Carrot Seed Use in Germany - a Multi-Agent Value Chain Approach," 2021 Conference, August 17-31, 2021, Virtual 314959, International Association of Agricultural Economists.
    3. Troost, Christian & Huber, Robert & Bell, Andrew R. & van Delden, Hedwig & Filatova, Tatiana & Le, Quang Bao & Lippe, Melvin & Niamir, Leila & Polhill, J. Gareth & Sun, Zhanli & Berger, Thomas, 2023. "How to keep it adequate: A protocol for ensuring validity in agent-based simulation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 159, pages 1-21.

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    Agribusiness; International Development;

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