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Plant pest risks posed by trade in agricultural commodities are managed through pest risk analysis which involves assessing risk and selecting phytosanitary measures to manage unrestricted risk if it is unacceptably high. Diverse measures can be used to reduce risk. However, calibrating risk management protocols with the level of unrestricted risk is challenging. Agreed methods and user-friendly tools for comparatively evaluating risk-reducing measures in different pest-host systems are lacking. Here, we describe the Pest Risk Reduction Scenario Tool (PRReSTo), a generic Bayesian network model that estimates likely infestation rates of host items in consignments. PRReSTo considers host items' vulnerability to pest infestation, the pest abundance to which host items are exposed during production, and the individual and cumulative effects of risk-reducing measures aimed at either minimising host vulnerability, pest exposure or infestation rates. We apply a PRReSTo implementation developed for assessing trappable insect pests in horticultural produce to test its versatility. First, we evaluate likely (unrestricted) host infestation rates under different levels of pest exposure and host vulnerability. Next, we evaluate the adequacy of risk-reducing measures under different host infestation scenarios and acceptability thresholds. We find that equivalent risk reduction may be achieved by highly effective individual measures or combinations of measures in a phytosanitary systems approach. The current implementation of PRReSTo is supported by an open access web application to facilitate adoption as a scenario analysis and decision-support tool by industry and regulators. • Here we present and test the new Pest Risk Reduction Scenario Tool (PRReSTo). • PRReSTo estimates likely infestation rates of host items in consignments. • PRReSTo allows for evaluating the individual or combined effects of risk-reducing measures. • The current implementation of PRReSTo is supported by an open access web application. • PRReSTo may support trading partners in trialling innovative risk-based protocols. [ABSTRACT FROM AUTHOR]