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

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  • 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
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    File URL: http://ageconsearch.umn.edu/record/260071/files/ajar12023withCorr.pdf
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    References listed on IDEAS

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    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. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    11. 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.
    12. 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.
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    14. Johansson, Robert & Peters, Mark & House, Robert, 2007. "Regional Environment and Agriculture Programming Model," Technical Bulletins 184314, United States Department of Agriculture, Economic Research Service.
    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. 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.
    17. 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.
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    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.

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