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Bayesian network as a modelling tool for risk management in agriculture

Svend Rasmussen (), Anders L. Madsen () and Mogens Lund ()
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Svend Rasmussen: Department of Food and Resource Economics, University of Copenhagen
Anders L. Madsen: HUGIN EXPERT A/S
Mogens Lund: Department of Food and Resource Economics, University of Copenhagen

No 2013/12, IFRO Working Paper from University of Copenhagen, Department of Food and Resource Economics

Abstract: The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools. In this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be efficiently used to estimate conditional probabilities, which are the core elements in Bayesian network models. We further show how the Bayesian network model RiBay is used for stochastic simulation of farm income, and we demonstrate how RiBay can be used to simulate risk management at the farm level. It is concluded that the key strength of a Bayesian network is the transparency of assumptions, and that it has the ability to link uncertainty from different external sources to budget figures and to quantify risk at the farm level.

Keywords: Bayesian network; Risk; Conditional probabilities; Stochastic simulation; Database; Farm account (search for similar items in EconPapers)
JEL-codes: C11 C63 D81 Q12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-agr, nep-ecm and nep-rmg
Date: 2013-05
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