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Abstract
Agricultural production relies to a great extent
on biological processes in natural environments. In addition
to volatile prices, it is thus heavily exposed to risks
caused by the variability of natural conditions such as rainfall,
temperature and pests. With a view to the apparently
lacking support of risky farm production program decisions
through formal planning models, the objective of this paper
is to examine whether, and eventually by how much, farmers’
“intuitive” program decisions can be improved through
formal statistical analyses and stochastic optimization models.
In this performance comparison, we use the results of
the formal planning approach that are generated in a quasi
ex-ante analysis as a normative benchmark for the empirically
observed ones. To avoid benchmark solutions that
would possibly exceed the respective farmer’s risk tolerance,
we limit the formal search to a subset of solutions that
are second-degree stochastically dominant compared to the
farmer’s own decision. We furthermore compare the suitability
of different statistical (time series) models to forecast
the uncertainty of single gross margins.