College of Agriculture

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As the foundation of the land grant mission at Montana State University, the College of Agriculture and the Montana Agricultural Experiment Station provide instruction in traditional and innovative degree programs and conduct research on old and new challenges for Montana’s agricultural community. This integration creates opportunities for students and faculty to excel through hands-on learning, to serve through campus and community engagement, to explore unique solutions to distinct and interesting questions and to connect Montanans with the global community through research discoveries and outreach.

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Author: search.filters.author.Davis, Ryan

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    A quantitative approach for integrating multiple lines of evidence for the evaluation of environmental health risks
    (2015-01) Schleier, Jerome Joseph III; Marshall, Lucy; Davis, Ryan; Peterson, Robert K. D.
    Decision analysis often considers multiple lines of evidence during the decision making process. Researchers and government agencies have advocated for quantitative weight-of-evidence approaches in which multiple lines of evidence can be considered when estimating risk. Therefore, we utilized Bayesian Markov Chain Monte Carlo to integrate several human-health risk assessment, biomonitoring, and epidemiology studies that have been conducted for two common insecticides (malathion and permethrin) used for adult mosquito management to generate an overall estimate of risk quotient (RQ). The utility of the Bayesian inference for risk management is that the estimated risk represents a probability distribution from which the probability of exceeding a threshold can be estimated. The mean RQs after all studies were incorporated were 0.4386, with a variance of 0.0163 for malathion and 0.3281 with a variance of 0.0083 for permethrin. After taking into account all of the evidence available on the risks of ULV insecticides, the probability that malathion or permethrin would exceed a level of concern was less than 0.0001. Bayesian estimates can substantially improve decisions by allowing decision makers to estimate the probability that a risk will exceed a level of concern by considering seemingly disparate lines of evidence.
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