IDEAS home Printed from https://ideas.repec.org/p/ags/iaae15/212201.html
   My bibliography  Save this paper

How did farmers act? An ex-post validation of normative and positive mathematical programming for an agent-based sector model

Author

Listed:
  • Mack, Gabriele
  • Ferjani, Ali
  • Mohring, Anke
  • Zimmerman, Albert
  • Mann, Stefan

Abstract

This study evaluates normative (NMP) and positive (PMP) mathematical programming methods for the recursive dynamic agent-based sector model SWISSland, which determines production decisions for 3400 farm-level models for the ex-post period 2005 to 2012. This study clearly shows that PMP for crop production activities improves the forecasting performance of farm based agent-based models compared to NMP. It also shows that combining PMP and NMP could be a suitable approach for agent-based sector models. For short-term forecast PMP for all production activities and PMP combined with NMP lead to similar results. The results either show that PMP calibration based on revenues and PMP calibration based on the entropy approach lead to similar results. By combining PMP with NMP some limitations of PMP could be reduced. In branches where the adoption of new production activities is expected due to market, the NMP approach could be an appropriate solution.

Suggested Citation

  • Mack, Gabriele & Ferjani, Ali & Mohring, Anke & Zimmerman, Albert & Mann, Stefan, 2015. "How did farmers act? An ex-post validation of normative and positive mathematical programming for an agent-based sector model," 2015 Conference, August 9-14, 2015, Milan, Italy 212201, International Association of Agricultural Economists.
  • Handle: RePEc:ags:iaae15:212201
    DOI: 10.22004/ag.econ.212201
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/212201/files/Mack-How%20did%20farmers%20act%20An%20ex-post%20validation%20of%20normative%20and%20positive%20mathematical%20programming-886.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.212201?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wiborg, Torben & McCarl, Bruce A. & Rasmussen, Svend & Schneider, Uwe A., 2005. "Aggregation and Calibration of Agricultural Sector Models Through Crop Mix Restrictions and Marginal Profit Adjustments," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24567, European Association of Agricultural Economists.
    2. Thomas Heckelei & Hendrik Wolff, 2003. "Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 30(1), pages 27-50, March.
    3. Heckelei, Thomas & Britz, Wolfgang & Zhang, Yinan, 2012. "Positive Mathematical Programming Approaches – Recent Developments in Literature and Applied Modelling," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(1), pages 1-16, April.
    4. Argyris Kanellopoulos & Paul Berentsen & Thomas Heckelei & Martin Van Ittersum & Alfons Oude Lansink, 2010. "Assessing the Forecasting Performance of a Generic Bio‐Economic Farm Model Calibrated With Two Different PMP Variants," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(2), pages 274-294, June.
    5. Happe, Kathrin, 2004. "Agricultural policies and farm structures: Agent-based modelling and application to EU-policy reform," Studies on the Agricultural and Food Sector in Transition Economies, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), volume 30, number 14945.
    6. Kathrin Happe, 2005. "Agricultural policies and farm structures - agent-based simulation and application to EU-policy reform," Others 0504011, University Library of Munich, Germany.
    7. Cloé Garnache & Pierre R. Mérel, 2015. "What Can Acreage Allocations Say about Crop Supply Elasticities? A Convex Programming Approach to Supply Response Disaggregation," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(1), pages 236-256, February.
    8. Richard E. Howitt, 1995. "Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(2), pages 329-342.
    9. Gocht, Alexander, 2005. "Assessment of Simulation Behavior of Different Mathematical Programming Approaches," 89th Seminar, February 2-5, 2005, Parma, Italy 232598, European Association of Agricultural Economists.
    10. Judez, Lucinio & de Andres, Rosario & Ibanez, M. & De Miguel, J.M. & Miguel, J.L. & Urzainqui, Elvira, 2008. "Impact Of The Cap Reform On The Spanish Agricultural Sector," 109th Seminar, November 20-21, 2008, Viterbo, Italy 44830, European Association of Agricultural Economists.
    11. Mann, Stefan & Mack, Gabriele & Ferjani, Ali, 2003. "Können Produktionsentscheidungen als Investitionsentscheidungen modelliert werden?," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 52(07), pages 1-9.
    12. Gocht, Alexander & Britz, Wolfgang, 2011. "EU-wide farm type supply models in CAPRI--How to consistently disaggregate sector models into farm type models," Journal of Policy Modeling, Elsevier, vol. 33(1), pages 146-167, January.
    13. Lobianco, Antonello & Esposti, Roberto, 2010. "The Regional Multi-Agent Simulator (RegMAS): an open-source spatially explicit model to assess the impact of agricultural policies," MPRA Paper 25817, University Library of Munich, Germany.
    14. Graeme J. Doole & Dan K. Marsh, 2014. "Methodological limitations in the evaluation of policies to reduce nitrate leaching from New Zealand agriculture," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 58(1), pages 78-89, January.
    15. Pierre Mérel & Richard Howitt, 2014. "Theory and Application of Positive Mathematical Programming in Agriculture and the Environment," Annual Review of Resource Economics, Annual Reviews, vol. 6(1), pages 451-470, October.
    16. Albert Zimmermann & Anke Möhring & Gabriele Mack & Ali Ferjani & Stefan Mann, 2015. "Pathways to Truth: Comparing Different Upscaling Options for an Agent-Based Sector Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-11.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Möhring, Anke & Mann, Stefan, 2020. "Causes and impacts of the mis-representation of agricultural policy—The case of food supply security payments in Switzerland," Journal of Policy Modeling, Elsevier, vol. 42(2), pages 466-482.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Viaggi, Davide & Raggi, Meri & Gomez y Paloma, Sergio, 2011. "Farm-household investment behaviour and the CAP decoupling: Methodological issues in assessing policy impacts," Journal of Policy Modeling, Elsevier, vol. 33(1), pages 127-145, January.
    2. Heckelei, Thomas & Britz, Wolfgang & Zhang, Yinan, 2012. "Positive Mathematical Programming Approaches – Recent Developments in Literature and Applied Modelling," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(1), pages 1-16, April.
    3. Arfini, Filippo & Donati, Michele & Marongiu, Sonia & Cesaro, Luca, 2012. "Farm production costs estimation trough PMP Models: an application in three Italian Regions," 2012 First Congress, June 4-5, 2012, Trento, Italy 124117, Italian Association of Agricultural and Applied Economics (AIEAA).
    4. Lee, Hwarang & Eom, Jiyong & Cho, Cheolhung & Koo, Yoonmo, 2019. "A bottom-up model of industrial energy system with positive mathematical programming," Energy, Elsevier, vol. 173(C), pages 679-690.
    5. Anke Möhring & Gabriele Mack & Albert Zimmermann & Maria Pia Gennaio & Stefan Mann & Ali Ferjani, 2011. "Modellierung von Hofübernahmeund Hofaufgabeentscheidungen in agentenbasierten Modellen," Journal of Socio-Economics in Agriculture (Until 2015: Yearbook of Socioeconomics in Agriculture), Swiss Society for Agricultural Economics and Rural Sociology, vol. 4(1), pages 163-188.
    6. Louhichi, Kamel & Ciaian, Pavel & Espinosa, Maria & Colen, Liesbeth & Perni, Angel & Gomez y Paloma, Sergio, 2015. "Farm-level economic impacts of EU-CAP greening measures," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205309, Agricultural and Applied Economics Association.
    7. Liu, Xuan & van Kooten, Gerrit Cornelis & Duan, Jun, 2020. "Calibration of agricultural risk programming models using positive mathematical programming," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(3), July.
    8. Jansson, Torbjörn & Heckelei, Thomas & Gocht, Alexander & Basnet, Shyam Kumar & Zhang, Yinan & Neuenfeldt, Sebastian, 2014. "Analysing impacts of changing price variability with estimated farm risk-programming models," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182665, European Association of Agricultural Economists.
    9. Louhichi, Kamel & Ciaian, Pavel & Espinosa, Maria & Colen, Liesbeth & Perni, Angel & Gomez y Paloma, Sergio, 2015. "EU-wide individual Farm Model for CAP Analysis (IFM-CAP): Application to Crop Diversification Policy," 2015 Conference, August 9-14, 2015, Milan, Italy 212155, International Association of Agricultural Economists.
    10. Petsakos, Athanasios & Rozakis, Stelios, 2015. "Calibration of agricultural risk programming models," European Journal of Operational Research, Elsevier, vol. 242(2), pages 536-545.
    11. Xuan Liu & Jun Duan & G. Cornelis van Kooten, 2018. "The impact of changes in the AgriStability program on crop activities: A farm modeling approach," Agribusiness, John Wiley & Sons, Ltd., vol. 34(3), pages 650-667, June.
    12. Wolfgang Britz & Linda Arata, 2019. "Econometric mathematical programming: an application to the estimation of costs and risk preferences at farm level," Agricultural Economics, International Association of Agricultural Economists, vol. 50(2), pages 191-206, March.
    13. Mack, Gabriele & Mohring, Anke & Zimmermann, Albert & Gennaio, Maria-Pia & Mann, Stefan & Ferjani, Ali, 2011. "Farm Entry Policy and Its Impact on Structural Change Analysed by and Agent-based Sector Model," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114374, European Association of Agricultural Economists.
    14. Umed Temurshoev & Marian Mraz & Luis Delgado Sancho & Peter Eder, 2015. "EU Petroleum Refining Fitness Check: OURSE Modelling and Results," JRC Research Reports JRC96207, Joint Research Centre.
    15. Garnache, Cloé & Mérel, Pierre R. & Lee, Juhwan & Six, Johan, 2017. "The social costs of second-best policies: Evidence from agricultural GHG mitigation," Journal of Environmental Economics and Management, Elsevier, vol. 82(C), pages 39-73.
    16. Umed Temurshoev & Fréderic Lantz, 2016. "Long-term petroleum product supply analysis through a robust modelling approach," Working Papers 2016-003, Universidad Loyola Andalucía, Department of Economics.
    17. Jonathan R. Sweeney & Richard E. Howitt & Hing Ling Chan & Minling Pan & PingSun Leung, 2017. "How do fishery policies affect Hawaii's longline fishing industry? Calibrating a positive mathematical programming model," Papers 1707.03960, arXiv.org.
    18. Siwa Msangi & Sarah Ann Cline, 2016. "Improving Groundwater Management for Indian Agriculture: Assessing Tradeoffs Across Policy Instruments," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 1-33, September.
    19. Cao, Zhaodan & Zhu, Tingju & Cai, Ximing, 2023. "Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia," Agricultural Water Management, Elsevier, vol. 277(C).
    20. Louhichi, Kamel & Ciaian, Pavel & Espinosa, Maria & Colen, Liesbeth & Perni, Angel & Paloma, Sergio, 2015. "The Impact of Crop Diversification Measure: EU-wide Evidence Based on IFM-CAP Model," 2015 Conference, August 9-14, 2015, Milan, Italy 211542, International Association of Agricultural Economists.

    More about this item

    Keywords

    Agricultural and Food Policy; Agricultural Finance;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:iaae15:212201. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/iaaeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.