Logsdon Muenich, RebeccaAryal, SanskritiAshworth, Amanda JBell, Michelle LBoudreau, Melanie RCunningham, Stephanie AFlynn, K ColtonHamilton, Kerry ALiu, TingMashtare, Michael LNelson, Natalie GRashid, BariraSaha, ArghajeetSchaffer-Smith, DanicaShowalter, CallieTchamdja, AurelianeThompson, Jada2025-03-112025-03-112025-02-11Logsdon Muenich, Rebecca, Sanskriti Aryal, Amanda J Ashworth, Michelle L Bell, Melanie R Boudreau, Stephanie A Cunningham, K Colton Flynn, et al. "Gaps in U.S. Livestock Data Are a Barrier to Effective Environmental and Disease Management". Environmental Research Letters 20, no. 3 (February 2025): 031001. https://doi.org/10.1088/1748-9326/adb050.http://doi.org/10.1088/1748-9326/adb050http://hdl.handle.net/11603/37752Livestock are a critical 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 depletion, antimicrobial resistance genes, and zoonotic disease outbreaks have all been linked to livestock operations. While many studies have examined these issues at depth at local scales, it has been difficult to complete studies at regional or national scales due to the dearth of livestock data, hindering pollution mitigation or response time for tracing and monitoring disease outbreaks. 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 leaving little inference that can be made at such a coarse spatiotemporal scale. While other data exist through some regulated permitting programs, there are significant data 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 accessibility and key limitations of currently available data. We then feature some recent work to improve livestock data availability through remote-sensing and machine learning, ending with our takeaways to address these data needs for the future of environmental and public health management.7 pagesen-USThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.Public Domainhttps://creativecommons.org/publicdomain/mark/1.0/Text