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Incorporating risk in a positive mathematical programming framework: a dual approach

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  • Linda Arata
  • Michele Donati
  • Paolo Sckokai
  • Filippo Arfini

Abstract

In this study we develop a new methodological proposal to incorporate risk into a farm-level positive mathematical programming (PMP) model. We estimate simulta-neously the farm nonlinear cost function and a farmer-specific coefficient of absolute risk aversion as well as the resource shadow prices. The model is applied to a sample of representative arable crop farms from the Emilia-Romagna region in Italy. The estimation results confirm the calibration ability of the model and reveal the values of the individual risk aversion coefficients. We use the model to simulate different scenarios of crop price volatility, in order to explore the potential risk management role of an agri-environmental scheme.
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Suggested Citation

  • Linda Arata & Michele Donati & Paolo Sckokai & Filippo Arfini, 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), pages 265-284, April.
    • Handle: RePEc:bla:ajarec:v:61:y:2017:i:2:p:265-284
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    File URL: http://hdl.handle.net/10.1111/1467-8489.12199
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    1. Basnet, Shyam Kumar & Jansson , Torbjorn & Heckelei, Thomas, 2021. "A Bayesian econometrics and risk programming approach for analysing the impact of decoupled payments in the European Union," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(03), January.
    2. Riccardo D’Alberto & Matteo Zavalloni & Meri Raggi & Davide Viaggi, 2018. "AES Impact Evaluation With Integrated Farm Data: Combining Statistical Matching and Propensity Score Matching," Sustainability, MDPI, vol. 10(11), pages 1-24, November.
    3. 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.
    4. Sapino, Francesco & Pérez-Blanco, C. Dionisio & Gutiérrez-Martín, Carlos & García-Prats, Alberto & Pulido-Velazquez, Manuel, 2022. "Influence of crop-water production functions on the expected performance of water pricing policies in irrigated agriculture," Agricultural Water Management, Elsevier, vol. 259(C).
    5. 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.
    6. Lapierre, Margaux & Le Velly, Gwenolé & Bougherara, Douadia & Préget, Raphaële & Sauquet, Alexandre, 2023. "Designing agri-environmental schemes to cope with uncertainty," Ecological Economics, Elsevier, vol. 203(C).
    7. Henderson, Benjamin & Cacho, Oscar & Thornton, Philip & van Wijk, Mark & Herrero, Mario, 2018. "The economic potential of residue management and fertilizer use to address climate change impacts on mixed smallholder farmers in Burkina Faso," Agricultural Systems, Elsevier, vol. 167(C), pages 195-205.
    8. Mingze Wu & Yueji Zhu, 2023. "Does Farmers’ Lottery Participation Affect Technical Efficiency of Banana Production in Rural China?," Agriculture, MDPI, vol. 13(4), pages 1-17, March.
    9. 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.
    10. 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.
    11. 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).
    12. Qiuzhuo Ma & Krishna P Paudel & Liting Gu & Xiaowei Wen, 2018. "An application of a cardinality-constrained multiple benchmark tracking error model on a plant enterprise selection problem," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(5), pages 677-721.

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