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Risk programming and sparse data: how to get more reliable results

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  • Hardaker, J. Brian
  • Lien, Gudbrand D.
  • Van Asseldonk, Marcel A.P.M.
  • Richardson, James W.
  • Hegrenes, Agnar

Abstract

Because relevant historical data for farms are inevitably sparse, most risk programming studies rely on few observations. We discuss how to use available information to derive an appropriate multivariate distribution function that can be sampled for a more complete representation of the possible risks in riskbased models. For the particular example of a Norwegian mixed livestock and crop farm, the solution is shown to be unstable with few states, although the cost of picking a sub-optimal plan declines with increases in number of states by Latin Hypercube sampling.

Suggested Citation

  • Hardaker, J. Brian & Lien, Gudbrand D. & Van Asseldonk, Marcel A.P.M. & Richardson, James W. & Hegrenes, Agnar, 2008. "Risk programming and sparse data: how to get more reliable results," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44051, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaae08:44051
    DOI: 10.22004/ag.econ.44051
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    2. Komarek, Adam M. & MacAulay, T. Gordon, 2013. "Farmer responses to changing risk aversion, enterprise variability and resource endowments," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(3).
    3. Komarek, Adam M. & Bell, Lindsay W. & Whish, Jeremy P.M. & Robertson, Michael J. & Bellotti, William D., 2015. "Whole-farm economic, risk and resource-use trade-offs associated with integrating forages into crop–livestock systems in western China," Agricultural Systems, Elsevier, vol. 133(C), pages 63-72.
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    8. Kandulu, John, 2011. "Assessing the potential for beneficial diversification in rain-fed agricultural enterprises," 2011 Conference (55th), February 8-11, 2011, Melbourne, Australia 100568, Australian Agricultural and Resource Economics Society.
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