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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.