  Farmers use pesticides to reduce yield losses. The efficacies of pesticide
treatments are often evaluated by analyzing the average treatment effects and
risks. The stochastic efficiency with respect to a function is often employed
in such evaluations through ranking the certainty equivalents of each
treatment. The main challenge of using this method is gathering an adequate
number of observations to produce results with statistical power. However, in
many cases, only a limited number of trials are replicated in field
experiments, leaving an inadequate number of observations. In addition, this
method focuses only on the farmer's profit without incorporating the impact of
disease pressure on yield and profit. The objective of our study is to propose
a methodology to address the issue of an insufficient number of observations
using simulations and take into account the effect of disease pressure on yield
through a quantile regression model. We apply this method to the case of
strawberry disease management in Florida.
