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

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  • Lien, Gudbrand
  • Hardaker, J. Brian
  • Asseldonk, Marcel A.P.M. van
  • Richardson, James W.

Abstract

Because relevant historical data for farms are inevitably sparse, most risk programming studies rely on few observations of uncertain crop and livestock returns. We show the instability of model solutions with few observations and 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 risk-based models. For the particular example of a Norwegian mixed livestock and crop farm, the solution is shown to be unstable with few states of nature producing a risky solution that may be appreciably sub-optimal. However, the risk of picking a sub-optimal plan declines with increases in number of states of nature generated by Latin hypercube sampling.

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Bibliographic Info

Article provided by Elsevier in its journal Agricultural Systems.

Volume (Year): 101 (2009)
Issue (Month): 1-2 (June)
Pages: 42-48

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Handle: RePEc:eee:agisys:v:101:y:2009:i:1-2:p:42-48

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Web page: http://www.elsevier.com/locate/agsy

Related research

Keywords: Risk programming States of nature Sparse data Kernel smoothing Latin hypercube sampling;

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References

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  1. Flaten, O. & Lien, G., 2007. "Stochastic utility-efficient programming of organic dairy farms," European Journal of Operational Research, Elsevier, Elsevier, vol. 181(3), pages 1574-1583, September.
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  3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
  4. Lence, Sergio H. & Hayes, Dermot J., 1995. "Land Allocation in the Presence of Estimation Risk," Staff General Research Papers 995, Iowa State University, Department of Economics.
  5. G Lien & JB Hardaker, 2001. "Whole-farm planning under uncertainty: impacts of subsidy scheme and utility function on portfolio choice in Norwegian agriculture," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 28(1), pages 17-36, March.
  6. J. Brian Hardaker & Louise H. Patten & David J. Pannell, 1988. "Utility‐Efficient Programming For Whole‐Farm Planning," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 32(2-3), pages 88-97, 08-12.
  7. Torkamani, Javad, 2005. "Using a whole-farm modelling approach to assess prospective technologies under uncertainty," Agricultural Systems, Elsevier, Elsevier, vol. 85(2), pages 138-154, August.
  8. Nanseki, Teruaki & Morooka, Yoshinori, 1991. "Risk preference and optimal crop combinations in upland java, Indonesia: an application of stochastic programming," Agricultural Economics: The Journal of the International Association of Agricultural Economists, International Association of Agricultural Economists, International Association of Agricultural Economists, vol. 5(1), January.
  9. Dorward, Andrew, 1999. "A Risk Programming Approach for Analysing Contractual Choice in the Presence of Transaction Costs," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 26(4), pages 479-92, December.
  10. David J. Pannell, 2006. "Flat Earth Economics: The Far-reaching Consequences of Flat Payoff Functions in Economic Decision Making," Review of Agricultural Economics, Agricultural and Applied Economics Association, Agricultural and Applied Economics Association, vol. 28(4), pages 553-566.
  11. Nanseki, Teruaki & Morooka, Yoshinori, 1991. "Risk preference and optimal crop combinations in upland Java, Indonesia: An application of stochastic programming," Agricultural Economics, Blackwell, Blackwell, vol. 5(1), pages 39-58, January.
  12. Lien, Gudbrand D. & Hardaker, J. Brian & Richardson, James W., 2006. "Simulating Multivariate Distributions with Sparse Data: A Kernal Density Smoothing Procedure," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25449, International Association of Agricultural Economists.
  13. Richardson, James W. & Klose, Steven L. & Gray, Allan W., 2000. "An Applied Procedure For Estimating And Simulating Multivariate Empirical (Mve) Probability Distributions In Farm-Level Risk Assessment And Policy Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 32(02), August.
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Cited by:
  1. Kandulu, John, 2011. "Assessing the potential for beneficial diversification in rain-fed agricultural enterprises," 2011 Conference (55th), February 8-11, 2011, Melbourne, Australia, Australian Agricultural and Resource Economics Society 100568, Australian Agricultural and Resource Economics Society.
  2. Acs, Szvetlana & Berentsen, Paul & Huirne, Ruud & van Asseldonk, Marcel, 2009. "Effect of yield and price risk on conversion from conventional to organic farming," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, Australian Agricultural and Resource Economics Society, vol. 53(3), September.

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