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

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. 2009 Elsevier Ltd. All rights reserved.

authors

• Richardson, James

published proceedings

• AGRICULTURAL SYSTEMS

author list (cited authors)

• Lien, G., Hardaker, J. B., van Asseldonk, M., & Richardson, J. W.

citation count

• 12

complete list of authors

• Lien, Gudbrand||Hardaker, J Brian||van Asseldonk, Marcel APM||Richardson, James W

publication date

• June 2009

publisher

• Elsevier  Publisher

published in

• Agricultural Systems  Journal

keywords

• Kernel Smoothing
• Latin Hypercube Sampling
• Risk Programming
• Sparse Data
• States Of Nature

Digital Object Identifier (DOI)

• 10.1016/j.agsy.2009.03.001

start page

• 42

end page

• 48

volume

• 101

issue

• 1-2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.agsy.2009.03.001