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

Iván Arribas, Kamel Louhichi, Angel Perni, Jose Vila () and Sergio Gomez Y Paloma

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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.

Keywords: agriculture; PMP; positive mathematical programming; risk and uncertainty; expected utility; highest posterior density; European common agricultural policy; programmation mathématique positive; risque et incertitude; modèle de ferme; politique agricole commune (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

Published in International Journal of Computational Economics and Econometrics, 2020, 10 (1), pp.2-32. ⟨10.1504/IJCEE.2020.104136⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02623168

DOI: 10.1504/IJCEE.2020.104136

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