Elsevier

Preventive Veterinary Medicine

Volume 162, 1 January 2019, Pages 56-66
Preventive Veterinary Medicine

Estimating and exploring the proportions of inter- and intrastate cattle shipments in the United States

https://doi.org/10.1016/j.prevetmed.2018.11.002Get rights and content

Abstract

Mathematical models are key tools for the development of surveillance, preparedness and response plans for the potential events of emerging and introduced foreign animal diseases. Creating these types of plans requires data; when data are incomplete, mathematical models can help fill in missing information, provided they are informed by the data that are available. In the United States, the most complete national-scale data available on cattle shipments are based on Interstate Certificates of Veterinary Inspection, which track the shipment of cattle between states; data on intrastate cattle shipments are lacking. Here we develop four new datasets on intrastate cattle shipments in the U.S., including an expert elicitation survey covering 19 states and territories and three state-level brand inspection data sets. The expert elicitation survey provides estimates on the proportion of shipments that travel interstate over multiple regions of the U.S. These survey data also identify differences in shipment patterns between regions, cattle commodity types, and sectors of the cattle industry. These survey data cover more states than any other source of intrastate data; however, one limitation of these data is the small number of participating experts in many of the states, only seven of the 19 responding states and territories had a group size of three or larger. The brand data sets include origin and destination information for both intra- and interstate shipments. These data, therefore, also provide detailed information on the proportion of interstate shipments in three Western states, including the temporal and geographic variation in shipments. Because the survey and brand data overlap in the Western U.S., they can be compared. We find that in the Western U.S. the expert estimates of the overall proportion of cattle shipments matched the brand data well. However, the experts estimated that there would be larger differences in beef and dairy shipments than the brand data show. This suggests the cattle industries in the West may be sending similar proportions of commodity specific cattle shipments over state lines. We additionally used the expert survey data to explore how differences in the proportion of interstate shipments can change predictions about cattle shipment patterns using the example of model-guided suggestions for targeted surveillance in Texas. Together these four data sets are the most extensive and geographically comprehensive information to date on intrastate cattle shipments. Additionally, our analyses on predicted shipment patterns suggest that assumptions about intrastate shipments could have consequences for targeted surveillance.

Introduction

Surveillance, tracing and response plans are critical aspects of preparedness and control for livestock diseases. Previous work has demonstrated that knowledge of livestock shipments is important for understanding disease spread and therefore, for improving the effectiveness of surveillance and outbreak planning and response activities (van Schaik et al., 2002, Green et al., 2006, Ortiz-Pelaez et al., 2006, Kao et al., 2007, Grear et al., 2014, Gorsich et al., 2018). Emerging and re-emerging livestock infections and the potential for an introduced foreign animal disease, require well-informed preparedness and response plans both in the United States (U.S.) and around the world. Despite this need, there is a limited amount of information on livestock shipments in the U.S. (Buhnerkempe et al., 2013; Lindström et al., 2013), and this is a considerable hindrance to disease preparedness activities. In particular, for the cattle industry in the U.S., within state shipment patterns are not well described.

In the U.S., the most extensive data on cattle shipments are the Interstate Certificates of Veterinary Inspection (ICVIs) that record interstate (between-state) shipments of livestock (Buhnerkempe et al., 2013, Portacci et al., 2013, Gorsich et al., 2016). These data have been used to build a national model for cattle shipments, called the United States Animal Movement Model (USAMM), that can be used to understand general cattle shipment patterns in the U.S. (Buhnerkempe et al., 2013; Lindström et al., 2013) and have also been used to predict movement of at-risk cattle (Grear et al., 2014, Gorsich et al., 2018). USAMM was also coupled with a disease simulation, called the United States Disease Outbreak Simulation (USDOS), to understand the potential for pathogen transmission and disease spread via animal shipments at a national-scale (Buhnerkempe et al., 2014). The USAMM model uses information on interstate shipments to estimate the within state patterns, but complete data to inform this process are lacking, and there is uncertainty in the relative contribution of within versus between state movement to disease spread (Lindström et al., 2013). The characterization of intrastate (within-state) shipment patterns and the relative number of shipments that occur within versus between states are key pieces of information for characterizing shipments at the state, regional or national scale.

In the majority of U.S. states, intrastate shipments of cattle are not recorded; however, it is generally assumed that the majority of cattle shipments occur within states (USDA, 2009). Because there is not a national source of information on intrastate cattle shipments, data describing this process need to be compiled from different sources. Previous studies on cattle shipments have used data compiled from questionnaires and expert opinion to describe intrastate cattle shipments at a local level (Bates et al., 2001, Liu et al., 2012); however, the scale of these studies makes it difficult to extrapolate regional or even state-level patterns. The main source of directly observed data on intrastate shipments are brand inspection data, which some states use when ownership of animals is transferred or when animals are shipped. Largely collected in the Western U.S., brand inspection data capture both intrastate shipments and interstate shipments; however, because these are state-level data, the type of shipments tracked, the information tracked, geographic coverage and the accessibility of the data (i.e. paper versus electronic) vary from state to state. Despite the differences in data accessibility, and the type of data recorded, brand inspection data provide consistently tracked state-level data on intrastate shipments.

The brand inspection data provide detailed information on cattle shipments traveling within and between states in the Western U.S. Despite the brand inspection data being limited to a subset of states, it most likely provides the best data available on intrastate shipments. The differences in cattle infrastructure and regional management practices in the cattle industry make it probable that differences will also be present in shipment patterns across the U.S. Therefore, information gathered from brand inspection data, though invaluable in states where brand inspection is available, may not provide accurate estimates for states in other regions of the U.S. where production systems can be very different (e.g. many small farms or areas with a predominance of dairy production). To fill these gaps in knowledge, we implement an expert elicitation survey to explore differences in intra- and interstate cattle shipments across the U.S. The comparison between brand inspection data and expert elicitation estimates in the Western U.S. can provide information on the accuracy of expert estimates. We combine the novel survey data with brand inspection data from three Western states (California, Wyoming and Montana), and one market data set from Montana to provide the first regional estimates of intrastate cattle shipments for the U.S. We also use the expert survey data to explore how changing estimates of the proportion of interstate shipments can alter predictions about cattle shipments, and therefore, targeted surveillance of cattle imported to Texas.

Section snippets

Expert elicitation survey development and implementation

The survey was developed and implemented as a modified Delphi group process. This method was chosen because it is the most commonly used survey method in ecology and veterinary epidemiology and could be adapted to the large number of expert groups required for this study (Kuhnert et al., 2005, Kuhnert et al., 2010, Gustafson et al., 2010, Gustafson et al., 2013). The goal of this survey was to develop data on intrastate cattle shipments with good geographic coverage of the continental U.S.

Our

Expert elicitation survey

In total, 51 experts from 19 states and territories participated in the survey (Table B1). The median response rate from the ten focal states (including experts who where invited and those who responded to the general announcement) was 0.29 (range: 0.1–0.5) and the median final group size from the focal states was 2.5 (range: 1–8) (Table B1). In total, we had seven states with expert group sizes of three or more; these states were Iowa, Minnesota, New York, Oklahoma, Tennessee, Texas and

Conclusions

The development of and comparisons among these four data sets is an important step for improving our understanding of intrastate cattle shipments in the United States. Our results both corroborate existing literature that predicts U.S. cattle shipments and indicate that regional differences exist in cattle shipments as well as highlight potential gaps in current knowledge about cattle shipment patterns and industry practices. As we demonstrate with our application of expert data to targeted

Acknowledgements

This work is supported by the Department of Homeland Security Science and Technology Directorate's Homeland Security Advanced Research Projects Agency under contract number HSHQDC-13-C-B0028 and the United States Department of Agriculture under cooperative agreement 13-9208-0344-CA. We thank Dave Dargatz, Lori Gustafson, and Jason Lombard for invaluable feedback on the design and implementation of this survey. We also thank Courtney Larson, Evan Rosenlieb and Benjamin Abbey for assistance with

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  • Cited by (0)

    1

    Present address: National Wildlife Health Center, United States Geological Survey, 6006 Schroeder Rd, Madison, WI 53711, USA.

    2

    Present address: Agricultural and Resource Economics Department, Colorado State University, 1172 Campus Delivery, Fort Collins, CO 80523, USA.

    3

    Present address: Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, United Kingdom.

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