IDEAS home Printed from https://ideas.repec.org/p/ags/iaae18/277475.html
   My bibliography  Save this paper

Calibration of Agricultural Risk Programming Models Using Positive Mathematical Programming

Author

Listed:
  • Liu, S.
  • Duan, J.
  • Van Kooten, G.C.

Abstract

Beginning in the 1960s, agricultural economists used mathematical programming methods to examine producers responses to policy changes. Today, positive mathematical programming (PMP) employs observed average costs and crop allocations to calibrate a nonlinear cost function, thereby modifying a linear objective function to a nonlinear one to replicate observed allocations. The standard PMP approach takes into account producers risk aversion, which is not a very satisfying outcome because it intricately entangles the cost parameters and the producer s attitudes biophysical aspects of production and human behavior are intertwined so that one cannot study the impact of policy on one in the absence of the other. Several approaches that calibrate both the risk coefficient and cost function parameters have been proposed. In this paper, we discuss two methods mentioned in literature one based on constant absolute risk aversion (exponential utility function) and the other on decreasing absolute risk aversion (logarithmic utility function). We compare these methods to an approach that employs maximum entropy method. Then we use historical data from a region in Alberta s southern grain belt to compare the different outcomes to which the three approaches lead. We find that the latter approach is robust and easier to employ. Acknowledgement :

Suggested Citation

  • Liu, S. & Duan, J. & Van Kooten, G.C., 2018. "Calibration of Agricultural Risk Programming Models Using Positive Mathematical Programming," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277475, International Association of Agricultural Economists.
  • Handle: RePEc:ags:iaae18:277475
    DOI: 10.22004/ag.econ.277475
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/277475/files/912.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.277475?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Arata, Linda & Donati, Michele & Sckokai, Paolo & Arfini, Filippo, 2017. "Incorporating risk in a positive mathematical programming framework: a dual approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 61(2), April.
    2. Barichello, Richard R., 1995. "Overview Of Canadian Agricultural Policy Systems," Proceedings of the 1st Agricultural and Food Policy Systems Information Workshop, 1995: Understanding Canada\United States Grain Disputes 16747, Farm Foundation, Agricultural and Food Policy Systems Information Workshops.
    3. Onal, Hayri & McCarl, Bruce A, 1989. "Aggregation of Heterogeneous Firms in Mathematical Programming Models," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 16(4), pages 499-513.
    4. Moschini, Giancarlo & Hennessy, David A., 2001. "Uncertainty, risk aversion, and risk management for agricultural producers," Handbook of Agricultural Economics, in: B. L. Gardner & G. C. Rausser (ed.), Handbook of Agricultural Economics, edition 1, volume 1, chapter 2, pages 88-153, Elsevier.
    5. Hayri Önal & Bruce A. McCarl, 1991. "Exact Aggregation in Mathematical Programming Sector Models," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 39(2), pages 319-334, July.
    6. Thomas Heckelei & Hendrik Wolff, 2003. "Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 30(1), pages 27-50, March.
    7. Paris, Quirino & Drogué, Sophie & Anania, Giovanni, 2011. "Calibrating spatial models of trade," Economic Modelling, Elsevier, vol. 28(6), pages 2509-2516.
    8. 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.
    9. Ottmar Röhm & Stephan Dabbert, 2003. "Integrating Agri-Environmental Programs into Regional Production Models: An Extension of Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 254-265.
    10. Richard E. Howitt, 1995. "Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(2), pages 329-342.
    11. Johnston, Craig M.T. & van Kooten, G. Cornelis, 2017. "Impact of inefficient quota allocation under the Canada-U.S. softwood lumber dispute: A calibrated mixed complementarity approach," Forest Policy and Economics, Elsevier, vol. 74(C), pages 71-80.
    12. Petsakos, Athanasios & Rozakis, Stelios, 2015. "Calibration of agricultural risk programming models," European Journal of Operational Research, Elsevier, vol. 242(2), pages 536-545.
    13. Arata, Linda & Donati, Michele & Sckokai, Paolo & Arfini, Filippo, 2014. "Incorporating risk in a positive mathematical programming framework: a new methodological approach," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182659, European Association of Agricultural Economists.
    14. Pierre Mérel & Santiago Bucaram, 2010. "Exact calibration of programming models of agricultural supply against exogenous supply elasticities," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 37(3), pages 395-418, September.
    15. 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.
    16. Xiaoguang Chen & Hayri Önal, 2012. "Modeling Agricultural Supply Response Using Mathematical Programming and Crop Mixes," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(3), pages 674-686.
    17. Paris,Quirino, 2011. "Economic Foundations of Symmetric Programming," Cambridge Books, Cambridge University Press, number 9780521123020, October.
    18. Kamel Elouhichi & Maria Espinosa Goded & Pavel Ciaian & Angel Perni Llorente & Bouda Vosough Ahmadi & Liesbeth Colen & Sergio Gomez Y Paloma, 2018. "The EU-Wide Individual Farm Model for Common Agricultural Policy Analysis (IFM-CAP v.1): Economic Impacts of CAP Greening," JRC Research Reports JRC108693, Joint Research Centre (Seville site).
    19. Severini, Simone & Cortignani, Raffaele, 2011. "Modeling farmer participation to a revenue insurance scheme by means of Positive Mathematical Programming," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 116001, European Association of Agricultural Economists.
    20. Petsakos, Athanasios & Rozakis, Stelios, 2011. "Integrating risk and uncertainty in PMP models," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114762, European Association of Agricultural Economists.
    21. Reidsma, Pytrik & Janssen, Sander & Jansen, Jacques & van Ittersum, Martin K., 2018. "On the development and use of farm models for policy impact assessment in the European Union – A review," Agricultural Systems, Elsevier, vol. 159(C), pages 111-125.
    22. Quirino Paris & Richard E. Howitt, 1998. "An Analysis of Ill-Posed Production Problems Using Maximum Entropy," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 124-138.
    23. 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.
    24. Pierre Mérel & Leo K. Simon & Fujin Yi, 2011. "A Fully Calibrated Generalized Constant-Elasticity-of-Substitution Programming Model of Agricultural Supply," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(4), pages 936-948.
    25. Jeder, Houcine & Sghaier, Mongi & Louhichi, Kamel & Reidsma, Pytrik, 2014. "Bio-economic modelling to assess the impact of water pricing policies at the farm level in the Oum Zessar watershed, southern Tunisia," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 15(2), pages 1-19.
    26. Bruce A. McCarl, 1982. "Cropping Activities in Agricultural Sector Models: A Methodological Proposal," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 64(4), pages 768-772.
    27. Paris,Quirino, 2011. "Economic Foundations of Symmetric Programming," Cambridge Books, Cambridge University Press, number 9780521194723, October.
    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. Athanasios Petsakos & Stelios Rozakis, 2022. "Models and muddles: comment on ‘Calibration of agricultural risk programming models using positive mathematical programming’," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(3), pages 713-728, July.
    2. Jon Duan & G. Cornelis van Kooten & A. T. M. Hasibul Islam, 2023. "Calibration of Grid Models for Analyzing Energy Policies," Energies, MDPI, vol. 16(3), pages 1-21, January.
    3. Wang, Shuping & Tan, Qian & Zhang, Tianyuan & Zhang, Tong, 2022. "Water management policy analysis: Insight from a calibration-based inexact programming method," Agricultural Water Management, Elsevier, vol. 269(C).

    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. Kooten, G. Cornelis van, 2013. "Modeling Forest Trade in Logs and Lumber: Qualitative and Quantitative Analysis," Working Papers 149182, University of Victoria, Resource Economics and Policy.
    2. van Kooten, G. Cornelis & Johnston, Craig, 2014. "Global impacts of Russian log export restrictions and the Canada–U.S. lumber dispute: Modeling trade in logs and lumber," Forest Policy and Economics, Elsevier, vol. 39(C), pages 54-66.
    3. Kamel Elouhichi & Maria Espinosa Goded & Pavel Ciaian & Angel Perni Llorente & Bouda Vosough Ahmadi & Liesbeth Colen & Sergio Gomez Y Paloma, 2018. "The EU-Wide Individual Farm Model for Common Agricultural Policy Analysis (IFM-CAP v.1): Economic Impacts of CAP Greening," JRC Research Reports JRC108693, Joint Research Centre (Seville site).
    4. Kamel Louhichi & Pavel Ciaian & Maria Espinosa & Angel Perni & Sergio Gomez y Paloma, 2018. "Economic impacts of CAP greening: application of an EU-wide individual farm model for CAP analysis (IFM-CAP)," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 45(2), pages 205-238.
    5. 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.
    6. Britz, Wolfgang & Linda, Arata, "undated". "How Important Are Crop Shares In Managing Risk For Specialized Arable Farms? A Panel Estimation Of A Programming Model For Three European Regions," 56th Annual Conference, Bonn, Germany, September 28-30, 2016 244801, German Association of Agricultural Economists (GEWISOLA).
    7. 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 (Seville site).
    8. Johnston, Craig M.T. & van Kooten, G. Cornelis, 2014. "Modelling Bi-lateral Forest Product Trade Flows: Experiencing Vertical and Horizontal Chain Optimization," Working Papers 197898, University of Victoria, Resource Economics and Policy.
    9. 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.
    10. 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.
    11. Petsakos, Athanasios & Rozakis, Stelios, 2015. "Calibration of agricultural risk programming models," European Journal of Operational Research, Elsevier, vol. 242(2), pages 536-545.
    12. Arata, Linda & Donati, Michele & Sckokai, Paolo & Arfini, Filippo, 2014. "Incorporating risk in a positive mathematical programming framework: a new methodological approach," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182659, European Association of Agricultural Economists.
    13. 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.
    14. Athanasios Petsakos & Stelios Rozakis, 2022. "Models and muddles: comment on ‘Calibration of agricultural risk programming models using positive mathematical programming’," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(3), pages 713-728, July.
    15. 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.
    16. Affuso, Ermanno & Hite, Diane, 2013. "A model for sustainable land use in biofuel production: An application to the state of Alabama," Energy Economics, Elsevier, vol. 37(C), pages 29-39.
    17. 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.
    18. Torbjörn Jansson & Staffan Waldo, 2022. "Managing Marine Mammals and Fisheries: A Calibrated Programming Model for the Seal-Fishery Interaction in Sweden," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 81(3), pages 501-530, March.
    19. 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.
    20. Franz Sinabell & Martin Schönhart & Erwin Schmid, 2015. "Austrian Agriculture 2010-2050. Quantitative Effects of Climate Change Mitigation Measures – An Analysis of the Scenarios WEM, WAM and a Sensitivity Analysis of the Scenario WEM," WIFO Studies, WIFO, number 58400, February.

    More about this item

    Keywords

    Risk and Uncertainty;

    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:iaae18:277475. 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.