Data

Web app for: Pest Risk Reduction Scenario Tool (PRReSTo) for quantifying trade-related plant pest risks and benefits of risk-reducing measures

Commonwealth Scientific and Industrial Research Organisation
Beeton, Nick ; Froese, Jens ; Murray, Justine ; Van Klinken, Rieks
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://hdl.handle.net/102.100.100/448452?index=1&rft.title=Web app for: Pest Risk Reduction Scenario Tool (PRReSTo) for quantifying trade-related plant pest risks and benefits of risk-reducing measures&rft.identifier=http://hdl.handle.net/102.100.100/448452?index=1&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=A Shiny web application accompanying: “Froese, JG, Murray, JV, Beeton, NJ and van Klinken, RD (2022). The Pest Risk Reduction Scenario Tool (PRReSTo) for quantifying trade-related plant pest risks and benefits of risk-reducing measures. Manuscript submitted for publication (epublish submission ID EP2022-3533).” The app supports a specific implementation of the generic PRReSTo method, which was parameterised for quantifying infestation rates of horticultural produce by insect pests that can be trapped. The app is provided as an open access service to facilitate adoption as a scenario analysis and decision-support tool by industry and regulators.\nLineage: The app was developed using Shiny and R&rft.creator=Beeton, Nick &rft.creator=Froese, Jens &rft.creator=Murray, Justine &rft.creator=Van Klinken, Rieks &rft.date=2022&rft.edition=v2&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2022.&rft_subject=Bayesian network&rft_subject=pest risk analysis&rft_subject=pest risk assessment&rft_subject=pest risk management&rft_subject=phytosanitary measures&rft_subject=scenario analysis&rft_subject=Horticultural production not elsewhere classified&rft_subject=Horticultural production&rft_subject=AGRICULTURAL, VETERINARY AND FOOD SCIENCES&rft_subject=Ecological applications not elsewhere classified&rft_subject=Ecological applications&rft_subject=ENVIRONMENTAL SCIENCES&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 4.0 International Licence
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Data is accessible online and may be reused in accordance with licence conditions

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Brief description

A Shiny web application accompanying: “Froese, JG, Murray, JV, Beeton, NJ and van Klinken, RD (2022). The Pest Risk Reduction Scenario Tool (PRReSTo) for quantifying trade-related plant pest risks and benefits of risk-reducing measures. Manuscript submitted for publication (epublish submission ID EP2022-3533).” The app supports a specific implementation of the generic PRReSTo method, which was parameterised for quantifying infestation rates of horticultural produce by insect pests that can be trapped. The app is provided as an open access service to facilitate adoption as a scenario analysis and decision-support tool by industry and regulators.
Lineage: The app was developed using Shiny and R

Available: 2022-11-03

Subjects
Agricultural, Veterinary and Food Sciences | Bayesian network | Environmental Sciences | Ecological Applications | Ecological Applications Not Elsewhere Classified | Horticultural Production | Horticultural Production Not Elsewhere Classified | pest risk analysis | pest risk assessment | pest risk management | phytosanitary measures | scenario analysis |

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