Gaps in U.S. livestock data are a barrier to effective environmental and disease management

Authors: MUENICH, REBECCA - University Of Arkansas; Ashworth, Amanda; BELL, MICHELLE - Yale University; BOUDREAU, MELANIE - Mississippi State University; Flynn, Kyle; HAMILTON, KERRY - Arizona State University; LIU, TING - University Of Arkansas; MASHTARE, MICHAEL - Pennsylvania State University; NELSON, NATALIE - North Carolina State University; OBENOUR, DANIEL - North Carolina State University; BARIRA, RASHID - University Of Arkansas; ARGHAJEET, SAHA - University Of Arkansas; SCHAFFER-SMITH, DANICA - Us Fish And Wildlife Service; THOMPSON, JADA - University Of Arkansas

Submitted to: Environmental Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/17/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary: Despite the innovation occurring towards improving livestock data, there is a strong need to overhaul livestock data in the U.S. to improve environmental and public health assessments. Without the most recent, timely, and detailed livestock data, it is difficult to assess how well livestock systems are being managed. It also makes tracking and tracing disease outbreaks difficult, especially with facilities going “under the radar” of existing regulations and databases. A team of researchers therefore set out to address this critical need for revolutionizing livestock data. In this article, researchers provide two solutions aimed at addressing the critical need for improved livestock data collection and analysis: 1) object-oriented classification techniques for better estimating animal counts and 2) thermal remote sensing bands for detecting animal presence and duration. While this research is focused on the U.S., many of these sentiment apply to global livestock production as we recognize that livestock are part of the global supply chain. Addressing the identified gaps in livestock data in the U.S. will require changes to policies and regulations which may be a difficult and slow process, but a necessary one to address and future issues caused by the growing livestock industry.

Technical Abstract: Livestock are a critical and important part of our food systems, yet their abundance globally has been cited as a driver of many environmental and human health concerns. Issues such as soil, water, and air pollution, greenhouse gas emissions, aquifer overdraft, antimicrobial resistance, and zoonotic disease outbreaks have all been linked to livestock operations. While many studies have examined these issues at depth, it has been difficult to complete larger, regional studies due to the dearth of livestock data in the U.S. and world. This also hinders response time for tracing and monitoring disease outbreaks or pollution emissions. In the U.S., the National Agricultural Statistics Service completes a census once every 5 years that includes livestock, but data are only available at the county level. While other data exists through some regulated permitting programs, there are significant gaps in where livestock are raised, how many livestock are on site at a given time, and how these livestock and, importantly, their waste emissions, are managed. In this perspective, we highlight the need for better livestock data, then discuss the availability and key limitations of currently available data. We then feature some recent work to improve the available livestock data through remote-sensing and machine learning, ending with our takeaways to address these data needs for the future of environmental and public health management.