Master of Science / Department of Agricultural Economics / Kevin C. Dhuyvetter / A Microsoft Excel based budget was developed to find the cost of becoming National Animal Identification Systems (NAIS) compliant in the U.S for beef cow-calf producers. This budget was turned into a stochastic budget by using different distributions for five key variables. From these distributions 10,000 observations were simulated using Latin Hypercube sampling. From the comprehensive budget, a second, more simple budget was constructed for obtaining NAIS cost. This Microsoft Excel based model gives beef cow-calf producers an estimate and a prediction interval associated with the estimated cost of adopting a cattle ID system that is compliant with the National Animal Identification Systems quickly and conveniently, requiring only six inputs. Both the comprehensive and the quick budget are available online. An Ordinary Least Squares regression was estimated using the simulated observations to find marginal effects associated with key variables. The driving factor of total cost per head was eID tag price for operations that tag and eID tag price and chute costs for non-tagging operations. For producers with five or less animals, it was cheaper to hire third parties to tag animals. From the sample data generated, smaller operations pay significantly more than larger operations on a per head basis, as the minimum cost was $2.08 for the larger operations and the maximum cost to small operations was $17.56. The estimated overall average cost per head for the cow/calf industry was $6.26, with a standard deviation of $4.12. Costs were on a per breeding female basis. The Excel spreadsheet budget and model can be downloaded at http://www.agmanager.info/ for producers who wish to estimate NAIS costs specific to their operations.

The primary objective of this study is to evaluate the industry impact of a hypothetical Foot and Mouth Disease (FMD) outbreak in the Texas High Plains using alternative animal tracing levels. To accomplish this objective, an epidemiological disease spread model, AUSSPREAD, is used to simulate the FMD outbreak and an economic model is used to examine the impacts of different animal identification levels in cattle. The different levels of animal identification relate to the model?s ability to trace back the subsequent infected and/or dangerous contacts with which the initial outbreak herd has been in contact. The study examines direct disease management costs (slaughter, euthanasia, disposal, surveillance, and cleaning disinfection), forgone income, and other indirect costs (indemnity payments and welfare slaughter) for outbreaks originating from a large beef operation, a feedlot, and a saleyard across subsequent tracing periods from 1 to 10 days. Welfare slaughter and quarantine costs were estimated for the best and worst outbreaks from the feedlot operation. It is noteworthy that total direct costs of a FMD outbreak would be more extensive than the current study's calculations, which only analyzed the direct disease management costs. The increased days to trace dangerous contacts presented overall increases in outbreak losses over each outbreak scenario. Although outcome averages appear insensitive at times under the assumptions applied, the epidemiological model presented the possibility that traceability could reduce the risk of extreme outcomes in respect to the overall distribution of losses. For each cattle operation, the outbreaks stayed consistent or marginally increased with their respective average costs, but their maximum losses rose steadily, across the trace periods examined. The impact of increased traceability and decreased outbreak length can be justified in affecting FMD outbreak costs in a positive manner. The results provide the industry with estimations of different outbreak scenarios which can be used to inform the decision on the NAIS system. Longer tracing periods, larger simulations (by iteration), and further study of the model is necessary in order to more accurately imitate FMD outbreaks within the Texas High Plains and its detrimental effects.