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Bayesian network as a modelling tool for risk management in agriculture

Author

Listed:
  • Svend Rasmussen

    () (Department of Food and Resource Economics, University of Copenhagen)

  • Anders L. Madsen

    () (HUGIN EXPERT A/S
    Aalborg University)

  • Mogens Lund

    () (Department of Food and Resource Economics, University of Copenhagen)

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.

Suggested Citation

  • Svend Rasmussen & Anders L. Madsen & Mogens Lund, 2013. "Bayesian network as a modelling tool for risk management in agriculture," IFRO Working Paper 2013/12, University of Copenhagen, Department of Food and Resource Economics.
  • Handle: RePEc:foi:wpaper:2013_12
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    File URL: http://okonomi.foi.dk/workingpapers/WPpdf/WP2013/IFRO_WP_2013_12.pdf
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    References listed on IDEAS

    as
    1. Gilbert Nartea & Paul Webster, 2008. "Should farmers invest in financial assets as a risk management strategy? Some evidence from New Zealand ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(2), pages 183-202, June.
    2. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Bayesian network; Risk; Conditional probabilities; Stochastic simulation; Database; Farm account;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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