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The Agricultural Policy Simulator (Agripolis) An Agent-Based Model To Study Structural Change In Agriculture (Version 1.0)

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  • Happe, Kathrin
  • Balmann, Alfons
  • Kellermann, Konrad

Abstract

A central criticism common to agricultural economic modelling approaches for policy analy-sis is that they do not adequately take account of a number of characteristic factors of the agri-cultural sector. This concerns aspects like the immobility of land, heterogeneity of farms, in-teractions between farms, space, dynamic adjustment processes as well as dynamics of struc-tural change. In brief, modelling the complexity of the system has not been at the centre of interest. In terms of modelling complex economic systems, an agent-based modelling ap-proach is a suitable approach to quantitatively model and understand such systems in a more natural way. In the same way, this applies to the modelling of agricultural structures. In par-ticular, agent-based models of agricultural structures allow for carrying out computer experi-ments to support a better understanding of the complexity of agricultural systems, structural change, and endogenous adjustment reactions in response to a policy change. This paper pre-sents the agent-based model AgriPoliS (Agricultural Policy Simulator) which simultaneously considers a large number of individually acting farms, product markets, investment activity, as well as the land market, and a simple spatial representation. The ultimate objective of AgriPoliS is to study the interrelationship of rents, technical change, product prices, investments, pro-duction and policies, structural effects resulting from these, the analysis of the winners and losers of agricultural policy as well as the costs and efficiency of various policy measures. ZUSAMMENFASSUNG Ein oft genannter Kritikpunkt an vielen agrarökonomischen Politikanalysemodellen ist, dass diese nur ungenügend Bezug nehmen auf Aspekte wie die Immobilität von Boden, Hetero-genität der Akteure, Interaktionen zwischen Betrieben, räumliche Bezüge, dynamische An-passungsprozesse und Strukturwandel. Kurz, die Modellierung komplexer Wirkungszusam-menhänge steht weniger oder nicht im Zentrum des Interesses. Agentenbasierte Modelle stel-len einen Weg dar, das Verständnis komplexer ökonomischer Zusammenhänge zu verbessern bzw. zu quantifizieren. Insbesondere erlauben sie die Durchführung von einer Vielzahl von Computerexperimenten, mit denen Fragestellungen wie der Zusammenhang zwischen Poli-tikmaßnahmen und Strukturwandel untersucht werden können. Basierend darauf, stellt dieser Beitrag das agentenbasierte Modell AgriPoliS (Agricultural Policy Simulator) vor. AgriPoliS ist ein räumlich-dynamisches Modell einer Agrarstruktur, in dem eine Vielzahl individuell abgebildeter landwirtschaftlicher Unternehmen in einer vereinfacht dargestellten Agrarregion agiert und beispielsweise um begrenzt verfügbare landwirtschaftliche Flächen konkurriert.

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  • Happe, Kathrin & Balmann, Alfons & Kellermann, Konrad, 2004. "The Agricultural Policy Simulator (Agripolis) An Agent-Based Model To Study Structural Change In Agriculture (Version 1.0)," IAMO Discussion Papers 14886, Institute of Agricultural Development in Transition Economies (IAMO).
    • Handle: RePEc:ags:iamodp:14886
    DOI: 10.22004/ag.econ.14886
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    1. Happe, Kathrin, 2005. "Agent-Based Modelling and Sensitivity Analysis by Experimental Design and Metamodelling: An Application to Modelling Regional Structural Change," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24464, European Association of Agricultural Economists.
    2. Schilling, Chris & Kaye-Blake, William & Post, Elizabeth & Rains, Scott, 2012. "The importance of farmer behaviour: an application of Desktop MAS, a multi-agent system model for rural New Zealand communities," 2012 Conference, August 31, 2012, Nelson, New Zealand 136070, New Zealand Agricultural and Resource Economics Society.
    3. Lisa Baldi & Filippo Arfini & Sara Calzolai & Michele Donati, 2023. "An Impact Assessment of GHG Taxation on Emilia-Romagna Dairy Farms through an Agent-Based Model Based on PMP," Land, MDPI, vol. 12(7), pages 1-24, July.
    4. Tyler Freeman & James Nolan & Richard Schoney, 2009. "An Agent‐Based Simulation Model of Structural Change in Canadian Prairie Agriculture, 1960–2000," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 537-554, December.
    5. Alper Bayram & Antonino Marvuglia & Maria Myridinas & Marta Porcel, 2022. "Increasing Biowaste and Manure in Biogas Feedstock Composition in Luxembourg: Insights from an Agent-Based Model," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    6. Lisa Baldi & Arfini, Filippo & Calzolai, Sara & Donati, Michele, 2023. "CAP reform and GHG emissions: policy assessment using a PMP agent-based model," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK 334520, Agricultural Economics Society - AES.
    7. Freeman, Tyler & Schoney, Richard & Nolan, James, 2013. "Man vs. manure: Examining the effects of residential demand on dairy farming in rural America," Agricultural Systems, Elsevier, vol. 115(C), pages 129-136.
    8. Bert, Federico E. & Rovere, Santiago L. & Macal, Charles M. & North, Michael J. & Podestá, Guillermo P., 2014. "Lessons from a comprehensive validation of an agent based-model: The experience of the Pampas Model of Argentinean agricultural systems," Ecological Modelling, Elsevier, vol. 273(C), pages 284-298.
    9. Lobianco, Antonello & Roberto, Esposti, 2006. "Analysis of the impact of decoupling on two Mediterranean regions," MPRA Paper 1182, University Library of Munich, Germany.
    10. Panichelli, Luis & Gnansounou, Edgard, 2015. "Impact of agricultural-based biofuel production on greenhouse gas emissions from land-use change: Key modelling choices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 344-360.
    11. Zimmermann, Andrea & Heckelei, Thomas & Perez Dominguez, Ignacio, 2006. "Working paper: Literature Review of Approaches to Estimate Structural Change," Reports 9304, Wageningen University, SEAMLESS: System for Environmental and Agricultural Modelling; Linking European Science and Society.
    12. Bert, Federico E. & Podestá, Guillermo P. & Rovere, Santiago L. & Menéndez, Ángel N. & North, Michael & Tatara, Eric & Laciana, Carlos E. & Weber, Elke & Toranzo, Fernando Ruiz, 2011. "An agent based model to simulate structural and land use changes in agricultural systems of the argentine pampas," Ecological Modelling, Elsevier, vol. 222(19), pages 3486-3499.
    13. Lobianco, Antonello & Roberto, Esposti, 2006. "The regional model for Mediterranean agriculture," MPRA Paper 1181, University Library of Munich, Germany.
    14. Uthes, Sandra & Kiesel, Joachim, 2020. "Creating a synthetic landscape: Spatial allocation of non-spatial single farm data," Agricultural Systems, Elsevier, vol. 177(C).
    15. Albisser, Gregor & Lehmann, Bernard, 2006. "Modeling of Structural Adjustment Processes of Farming Enterprises: The Need for Implementation of Cooperation and Collaboration Strategies," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25250, International Association of Agricultural Economists.

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    More about this item

    Keywords

    Agricultural and Food Policy;

    JEL classification:

    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

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