IDEAS home Printed from https://ideas.repec.org/a/ags/aareaj/260071.html
   My bibliography  Save this article

Methodological limitations in the evaluation of policies to reduce nitrate leaching from New Zealand agriculture

Author

Listed:
  • Doole, Graeme J.
  • Marsh, Dan K.

Abstract

The land-use optimisation framework, NZFARM, has been promoted as a tool that can be used to assess the economic and environmental impacts of policy on regional land use. This paper outlines how methodological limitations presently restrict its capacity to provide meaningful insight into the relative value of alternative land-use configurations. The model is calibrated using positive mathematical programming, which has been shown in the literature to result in models that yield arbitrary output outside of the calibrated baseline. There is a high likelihood that this is the case, as no validation appears to have been carried out. Significant model development will be required before NZFARM outputs can be used with any confidence to inform future policy development. We conclude with suggestions on how NZFARM and models of its kind can be further developed to improve their capacity for meaningful simulation.

Suggested Citation

  • Doole, Graeme J. & Marsh, Dan K., 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), January.
  • Handle: RePEc:ags:aareaj:260071
    DOI: 10.22004/ag.econ.260071
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/260071/files/ajar12023withCorr.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.260071?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. McCarl, Bruce A. & Apland, Jeffrey, 1986. "Validation Of Linear Programming Models," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 18(2), pages 1-10, December.
    2. Doole, Graeme & Pannell, David J., 2011. "Evaluating environmental policies under uncertainty through application of robust nonlinear programming," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(4), pages 1-18.
    3. Nordblom, T.L. & Christy, B.P. & Finlayson, J.D. & Roberts, A.M. & Kelly, J.A., 2010. "Least cost land-use changes for targeted catchment salt load and water yield impacts in south eastern Australia," Agricultural Water Management, Elsevier, vol. 97(6), pages 811-823, June.
    4. Doole, Graeme J. & Vigiak, Olga & Pannell, David J. & Roberts, Anna M., 2013. "Cost-effective strategies to mitigate multiple pollutants in an agricultural catchment in North Central Victoria, Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(3).
    5. Samarasinghe, Oshadhi & Daigneault, Adam J. & Greenhalgh, Suzie & Munguia, Oscar Montes de Oca & Walcroft, Jill, 2012. "Impacts of Farmer Attitude on the Design of a Nutrient Reduction Policy – a New Zealand Catchment Case Study," 2012 Conference (56th), February 7-10, 2012, Fremantle, Australia 124439, Australian Agricultural and Resource Economics Society.
    6. Thomas Heckelei & Wolfgang Britz, 2000. "Positive Mathematical Programming with Multiple Data Points: A Cross-Sectional Estimation Procedure," Cahiers d'Economie et Sociologie Rurales, INRA Department of Economics, vol. 57, pages 27-50.
    7. Graeme J. Doole & David J. Pannell, 2012. "Empirical evaluation of nonpoint pollution policies under agent heterogeneity: regulating intensive dairy production in the Waikato region of New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(1), pages 82-101, January.
    8. Tom Nordblom & Iain Hume & Andrew Bathgate & Michael Reynolds, 2006. "Mathematical optimisation of drainage and economic land use for target water and salt yields ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 50(3), pages 381-402, September.
    9. 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.
    10. 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.
    11. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    12. Doole, Graeme J. & Pannell, David J., 2013. "A process for the development and application of simulation models in applied economics," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(1), pages 1-25.
    13. 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.
    14. Richard E. Howitt, 1995. "Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(2), pages 329-342.
    15. Landry, Maurice & Malouin, Jean-Louis & Oral, Muhittin, 1983. "Model validation in operations research," European Journal of Operational Research, Elsevier, vol. 14(3), pages 207-220, November.
    16. Johansson, Robert & Peters, Mark & House, Robert, 2007. "Regional Environment and Agriculture Programming Model," Technical Bulletins 184314, United States Department of Agriculture, Economic Research Service.
    17. Heckelei, Thomas & Britz, Wolfgang, 2005. "Models Based on Positive Mathematical Programming: State of the Art and Further Extensions," 89th Seminar, February 2-5, 2005, Parma, Italy 234607, European Association of Agricultural Economists.
    18. Nordblom, Thomas L. & Hume, Iain H. & Bathgate, Andrew D. & Reynolds, Michael, 2006. "Mathematical optimisation of drainage and economic land use for target water and salt yields," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 50(3), pages 1-22, September.
    19. 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.
    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. Doole, Graeme J. & Marsh, Dan K., 2014. "Use of positive mathematical programming invalidates the application of the NZFARM model: Response to Daigneault et al. (2014)," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 58(2), April.
    2. Spicer, E. Anne & Swaffield, Simon & Moore, Kevin, 2021. "Agricultural land use management responses to a cap and trade regime for water quality in Lake Taupo catchment, New Zealand," Land Use Policy, Elsevier, vol. 102(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. 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.
    2. 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).
    3. 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.
    4. Frahan, Bruno Henry de, 2005. "PMP, Extensions and Alternative Methods: Introductory Review of the State of the Art," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24537, European Association of Agricultural Economists.
    5. Arfini, Filippo & Donati, Michele & Grossi, L. & Paris, Quirino, 2008. "Revenue and Cost Functions in PMP: a Methodological Integration for a Territorial Analysis of CAP," 107th Seminar, January 30-February 1, 2008, Sevilla, Spain 6636, European Association of Agricultural Economists.
    6. Arfini, Filippo & Donati, Michele & Paris, Quirino, 2008. "Innovation in Estimation of Revenue and Cost Functions in PMP Using FADN Information at Regional Level," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44008, European Association of Agricultural Economists.
    7. Heckelei, Thomas & Britz, Wolfgang, 2005. "Models Based on Positive Mathematical Programming: State of the Art and Further Extensions," 89th Seminar, February 2-5, 2005, Parma, Italy 234607, European Association of Agricultural Economists.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. CARPENTIER, Alain & GOHIN, Alexandre & SCKOKAI, Paolo & THOMAS, Alban, 2015. "Economic modelling of agricultural production: past advances and new challenges," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement (RAEStud), Institut National de la Recherche Agronomique (INRA), vol. 96(1), March.
    13. 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.
    14. 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.
    15. 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.
    16. Kamel Louhichi & Pascal Tillie & Aymeric Ricome & Sergio Gomez y Paloma, 2020. "Modelling Farm-household Livelihoods in Developing Economies Insights from three country case studies using LSMS-ISA data [Modélisation des moyens de subsistance des ménages agricoles dans les écon," Working Papers hal-02544905, HAL.
    17. Doole, Graeme J. & Marsh, Dan K., 2014. "Use of positive mathematical programming invalidates the application of the NZFARM model: Response to Daigneault et al. (2014)," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 58(2), April.
    18. 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.
    19. 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.
    20. Kamel Louhichi & Pascal Tillie & Aymeric Ricome & Sergio Gomez y Paloma, 2020. "Modelling Farm-household Livelihoods in Developing Economies Insights from three country case studies using LSMS-ISA data [Modélisation des moyens de subsistance des ménages agricoles dans les écon," Post-Print hal-02544905, HAL.

    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:aareaj:260071. 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/aaresea.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.