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Total Economic Impact of Aflatoxin: Models of Economic Loss and Industry Learning


To estimate the total economic impacts of aflatoxin to U.S. food industries.

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Approach: An empirical economic model will be developed, using as inputs numerous cost categories to various stakeholders in the food industry (including consumers). The output will be cost to particular sectors and to particular industries (e.g., pistachio industry). Specifically, the economic impact of aflatoxin content on each commodity will be predicted by using the following categories of losses and gains that will be considered in the model: Market rejection costs of contaminated commodities, export market losses, sampling and testing costs, costs to food processors and consumers, and associated health costs. The model will be implemented in standard software systems (readily used by the cooperating scientist). Additionally, organizational theory will be used for the benefit of food industries in determining the most cost effective strategy for dealing with aflatoxin contamination problems. Specifically, we wish to answer the question: Which aflatoxin reduction/prevention methods and technologies make the most sense for industry to adopt; given costs, expected benefits, organizational design and required training? Organizational change in the corn, cottonseed, peanut, and tree nut industries must occur to accommodate ever-stricter aflatoxin standards. Organizational change typically occurs through "organizational learning" or education, although not all learning and not all changes end up being beneficial to the organization. Options for the above-mentioned industries in terms of newly available aflatoxin-elimination technologies and methods are numerous, which makes the task of choosing which ones to adopt both more hopeful and more difficult. Different behavioral change models will be combined and adapted: ORGAHEAD and the hybrid model of technology adoption. ORGAHEAD is an organizational learning model designed to test how different forms of organizations optimally respond to new information of many types.

University of Pittsburgh
USDA - Agricultural Research Service
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