Using Expert Knowledge to Understand Biosecurity Adoption Aimed at Reducing Tier 1 Disease Risks in the U.S. Livestock Industry

Qianrong Wu, Lee Schulz and Glynn Tonsor

ISU General Staff Papers from Iowa State University, Department of Economics

Abstract: Using primary data from a survey of swine, beef cattle, and dairy industry experts in the United States, this study provides insights into adoption of biosecurity measures aimed at reducing Tier 1 disease risks. Experts believe the swine industry would see the highest and the beef cattle industry would see the lowest biosecurity adoption in the first year of a large Tier 1 disease outbreak. Risk reduction has a positive marginal effect on biosecurity adoption, and a firm’s own risk reduction matters as well as their closest neighbor’s risk reduction. Costs have a negative marginal effect on biosecurity adoption. A key reason explaining partial adoption might be that experts believe industry-wide biosecurity investment would likely bring benefits primarily to downstream sectors in the supply chain and producers would bare most of the costs. More educational materials available to explain Tier 1 disease risks and the benefits of risk mitigating biosecurity measures is found to be the least important factor for adoption and implementation of new, additional biosecurity measures. A producer or neighbor having personally experienced a Tier 1 disease on their operation, a producer’s view on their own likelihood of experiencing a Tier 1 disease given their current situation, and a producer’s view on effectiveness in reducing Tier 1 disease risks are found to be the most important factors. Understanding how several factors might impact biosecurity adoption aimed at reducing Tier 1 disease risks is necessary for the development of practices and policies that could reduce the impact of such disease incursions.

Date: 2018-01-01
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Journal Article: Using Expert Knowledge to Understand Biosecurity Adoption Aimed at Reducing Tier 1 Disease Risks in the U.S. Livestock Industry (2017)
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