PRReSTo – the pest risk reduction scenario tool

PRReSTO models pest infestation risk scenarios in commodities for trade and guides the selection of effective biosecurity measures

PRReSTo guides the complex balancing act of enabling trade while preventing the spread of pests and disease.

Many factors can influence whether traded goods could pose biosecurity risks in receiving markets.

PRReSTo is a modelling tool that enables industry and regulators to systematically assess these factors and test various risk management scenarios.

PRReSTo puts the Risk Framework and Menu of Measures to work! The model enables pest exposure in the field and factors affecting host vulnerability to be considered to assess background risk, then explores how pest infestation risk could be further reduced if various measures from the menu were applied.

PRReSTo quantifies biosecurity risk by calculating the likely percentage of fruit in a consignment that may be infested with pests based on the background risks, mitigation measures and inspection steps proposed in the scenario. The likelihood that the pest would be detected by the selected inspection options is also assessed. Risk reducing measures and inspection methods can be added or adjusted as required until the acceptable risk level for a target market is achieved.

The model solves a major challenge for biosecurity stakeholders. Until now, it has been difficult to quantify the biosecurity risks of horticultural trade and evaluate the effects of risk-reducing measures – particularly when combinations of measures are proposed.

PRReSTo addresses this gap and offers a valuable tool to quantitatively assess pest infestation risks in fresh produce for trade. The tool can help regulators set entry conditions that are better calibrated to the level of risk, enabling industry to operate under risk mitigation measures that are least restrictive to trade.

Note that in its current form, the model assesses insect pest risks in fresh fruit or vegetables and is limited to pests that can be monitored using trap-based surveillance programs. In future, the model could be expanded further to address other commodities and biosecurity hazards.

How to use PRReSTo

The PRReSTo shiny app provides an interface for the model. User guidelines will be available soon – until then, some background knowledge of biosecurity risk regulation will be needed to navigate the app. Contact our team to arrange a demonstration of the model.

In the first two columns, use the sliders and drop-down menus to set the background risk factors of pest abundance and host vulnerability. The expected host infestation rate will then calculate in the graph at the top of the third column.

Then adjust for risk-reducing factors, such as

  • pest monitoring
  • in-field pest management
  • treatments to kill or inactivate pests
  • or quality measures such as grading

Finally, various inspection options to detect infested fruit can be considered.

The risk threshold that is accepted in the target market can also be set.

As each factor is adjusted, the model’s outputs are updated. These can be seen in the final column. PRReSTo calculates:

  • the likelihood that a certain percentage of fruit in a consignment may be infested
  • the likelihood that infested fruit would be detected
  • and whether acceptable risk levels are exceeded for the target market

Alternate scenarios can be tested by adding or adjusting management or inspection measures until an acceptable level of risk is achieved.

How are the parameters set?

The parameters for each factor or measure can be set based on scientific evidence or expert judgement.

When there is scientific evidence to support the setting of each parameter, PRReSTo can indicate the likely efficacy of a proposed set of risk management measures. This analysis can contribute to the preparation of a supporting data package for market access proposals.

Even when expert judgements are used, PRReSTo offers a powerful way to explore which measures have the best potential to reduce biosecurity risks – and to prioritise research to address data gaps.

How can we have confidence in the model?

The rigour of the PRReSTo model has been peer-reviewed as part of its publication as an article in the international journal Crop Protection. Of course, like any model, the results generated by PRReSTo rest on underlying assumptions. Confidence in the results from the model is generally higher where there is strong supporting evidence for assumptions. The accuracy of the model’s estimates can also be empirically tested for different pest-host systems.

We first applied PRReSTo to support the development and validation of a phytosanitary systems approach protocol for domestic trade of cherries within Australia. This helped regulators to understand and value the approach. At the request of Australian regulators, the model has since been applied to other risk assessments and reviews of entry conditions applied to regulate domestic trade.