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Calibration of shadow values in constrained optimisation models of agricultural supply

Author

Listed:
  • Cloé Garnache
  • Pierre Mérel
  • Richard Howitt
  • Juhwan Lee

Abstract

This paper proposes a new information-based method to calibrate the shadow values of constraints in Positive Mathematical Programming models of agricultural supply. Shadow values are chosen so as to minimise model deviation from observed activity- and input-specific expenditures, enhancing the informational basis of the calibrated model. We provide an application to nitrogen policy in California using an agronomically and economically calibrated regionalised model. Regional shadow values of water are generally much higher than those suggested by the traditional method of Howitt, R. E. (1995b. American Journal of Agricultural Economics 77(2): 329–342). The implied statewide elasticity of demand for nitrogen is minimally affected by the choice of shadow values, however predicted environmental outcomes differ as this choice affects the distribution of nitrogen across regions and crops.

Suggested Citation

  • Cloé Garnache & Pierre Mérel & Richard Howitt & Juhwan Lee, 2017. "Calibration of shadow values in constrained optimisation models of agricultural supply," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 44(3), pages 363-397.
    • Handle: RePEc:oup:erevae:v:44:y:2017:i:3:p:363-397.
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    File URL: http://hdl.handle.net/10.1093/erae/jbx005
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    Citations

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    Cited by:

    1. 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).
    2. Weizhe Weng & Kelly M. Cobourn & Armen R. Kemanian & Kevin J. Boyle & Yuning Shi & Jemma Stachelek & Charles White, 2024. "Quantifying co‐benefits of water quality policies: An integrated assessment model of land and nitrogen management," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(2), pages 547-572, March.
    3. Weng, Weizhe & Cobourn, Kelly M. & Kemanian, Armen R. & Boyle, Kevin J. & Shi, Yuning & Stachelek, Joseph & White, Charles, 2020. "Quantifying Co-Benefits of Water Quality Policies: An Integrated Assessment Model of Nitrogen Management," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304667, Agricultural and Applied Economics Association.
    4. 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.
    5. Aghabeygi, Mona & Louhichi, Kamel & Gomez y Paloma, Sergio, 2022. "Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 11(1), April.
    6. 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.
    7. 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, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(2), pages 205-238.
    8. 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.
    9. Syed Shurid Khan & Shawn Arita & Richard Howitt & PingSun Leung, 2022. "Evaluating change in property tax regime on noncommercial food production using a modified positive mathematical programming model," SN Business & Economics, Springer, vol. 2(9), pages 1-20, September.

    More about this item

    Keywords

    positive mathematical programming; linear programming; calibration; shadow value; nitrogen policy;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

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