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Modelling agricultural risk in a large scale positive mathematical programming model
[Modélisation du risque agricole dans un modèle de programmation mathématique positive à grande échelle]

Author

Listed:
  • Iván Arribas

    (Universidad de Valencia VIU - Partenaires INRAE)

  • Kamel Louhichi

    (ECO-PUB - Economie Publique - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, JRC - European Commission - Joint Research Centre [Seville])

  • Angel Perni

    (JRC - European Commission - Joint Research Centre [Seville])

  • José Vila

    (Universidad de Valencia VIU - Partenaires INRAE)

  • Sergio Gomez y Paloma

    (JRC - European Commission - Joint Research Centre [Seville])

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 show 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 Gomez y Paloma, 2020. "Modelling agricultural risk in a large scale positive mathematical programming model [Modélisation du risque agricole dans un modèle de programmation mathématique positive à grande échelle]," Post-Print hal-02623168, HAL.
    • Handle: RePEc:hal:journl:hal-02623168
    DOI: 10.1504/IJCEE.2020.104136
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