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Modelling agricultural risk in a large scale positive mathematical programming model

Author

Listed:
  • Iván Arribas
  • Kamel Louhichi
  • Angel Perni
  • José Vila
  • Sergio Gómez-y-Paloma

Abstract

Mathematical programming has been extensively used to account for risk in farmers' decision making. The recent development of the positive mathematical programming (PMP) has renewed the need to incorporate risk in a more robust and flexible way. Most of the existing PMP-risk models have been tested at farm-type level and for a very limited sample of farms. This paper presents and tests a novel methodology for modelling risk at individual farm level in a large scale model, called individual farm model for common agricultural policy analysis (IFM-CAP). Results show a clear trade-off between including and excluding the risk specification. Albeit both alternatives provide very close estimates, simulation results shows that the explicit inclusion of risk in the model allows isolating risk effects on farmer behaviour. However, this specification increases three times the computation time required for estimation.

Suggested Citation

  • Iván Arribas & Kamel Louhichi & Angel Perni & José Vila & Sergio Gómez-y-Paloma, 2020. "Modelling agricultural risk in a large scale positive mathematical programming model," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 10(1), pages 2-32.
    • Handle: RePEc:ids:ijcome:v:10:y:2020:i:1:p:2-32
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    Cited by:

    1. Kamel Louhichi & Daël Merisier, 2023. "Potential impacts of the Income Stabilisation Tool on farmers' income and crop diversity: a French case study [Impacts potentiels de l'outil de stabilisation des revenus sur les revenus des agricul," Post-Print hal-04195630, HAL.
    2. Kamel Louhichi & Daël Merisier, 2024. "Potential impacts of the Common Agricultural Policy's Income Stabilisation Tool on farmers' incomes and crop diversity: A French case study," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(2), pages 716-739, June.

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