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Ideally, a phytosanitary performance standard would be defined as a probabilistic tolerance. For treatments such as solid wood pasteurization, this could be operationalized by stating with a specific degree of confidence that the treatment failure rate for a sentinel pest should be less than a defined level (e.g., X% confidence that the wood heat treatment failure rate for pest Y does not exceed Z%). This article illustrates a probabilistic approach to developing a phytosanitary performance standard, using heat treatment of the wood-inhabiting fungus Postia placenta as an example. The uncertainty about the proportion of wood blocks in which P. placenta survives after treatment is characterized by the Beta distribution, subject to the biological constraint that survival should decrease monotonically with increased time and temperature. Monte Carlo simulation techniques are then used to generate a probabilistic response surface relating proportion survival to treatment time and temperature. This modeling approach relaxes the parametric assumptions associated with traditional statistical methods for fitting response surfaces and is more flexible than conventional methods, resulting in a better fit to the observed data.

A Model for Probabilistic Assessment of Phytosanitary Risk Reduction Measures