Cattle health and production records (CHPR) are data collected by cattle producers and veterinarians in the form of measurements, observations, counts of events over time, and physiologic attributes that describe individual and group-level health and production. These data are useful to both veterinarians and cattle producers for making evidence-based decisions on cow-calf operations. Currently, there are no uniform, industry-wide methods of capturing and recording CHPR in the U.S. cow-calf industry. Although many cow-calf producers in the U.S. are thought to collect some form of CHPR, it is believed that relatively few are doing so in an electronic manner that facilitates optimal use and analysis of those records. Technology offers many opportunities to collect, record, and analyze CHPR for decision-making on cow-calf operations, with smartphones having great potential as a point-of-care CHPR collection device. Little is known regarding 1) barriers faced by producers to collecting and using CHPR, 2) interest of U.S. cow-calf producers in using technology such as smartphones for collecting and recording CHPR, and 3) the role of veterinarians in the collection and use of CHPR on U.S. cow-calf operations. The first study included in this dissertation was a survey of the cattle health and production record-keeping methods of cow-calf producers in Mississippi. The second study in this dissertation was a survey of cow-calf producers across the U.S. regarding their methods and opinions of cattle health and production record-keeping, their access to technology for record-keeping purposes, current types of data being collected on cow-calf operations, and the role of veterinarians in record-keeping on those cow-calf operations. The third study in this dissertation was a demonstration of common epidemiologic and biostatistical skills needed by veterinary practitioners to analyze CHPR and provide quality, evidence-based management recommendations to their cow-calf clients.

Ruminant animals comprise the greatest proportion of herbivores around the world, provide essential ecosystem services and human consumable protein by consuming grass and human inedible dietary fiber. Herbivory pressure alters plant communities and species diversity, effectively making grazing animals ecosystem engineers in dynamic ecosystems. Development of advanced computer processing power coupled with biometric and ecosystem sensors may be employed in the internet of things framework to create an integrated information system designed to inform understanding of grazing system function and animal energy balance. Towards this end, I utilized Bos indicus / Bos taurus crossbred steers (n = 20) across two study sites each in consecutive calendar years and fitted them with GPS and accelerometer collar systems. Steers were grazed in improved grass pastures containing Tall Fescue (Festuca arundinacea) and Bermudagrass (Cyanodon dactylon). Forage samples were collected in a 20-m grid pattern at 35-day intervals to test nutritional composition, and NDVI maps were created using remotely sensed data collected using a UAV mounted camera system. In the first chapter, I utilize the movement ecology framework to investigate metabolic theory and animal behavior on energy budgets, then explore available technology to utilize in an integrative information system. In Chapter 2, I tested preprocessing and behavior collection methods used to train a machine learning randomforest classification model to predict animal behavior using triaxial accelerometers. Landscape functional scale and optimal sampling density is the primary focus of Chapter 3, where I explored the complex relationship between sampling regime, interpolation strategy, and landscape complexity, demonstrating that sampling density is a product of desired accuracy and landscape complexity. Finally, I focused on animal growth in Chapter 4, demonstrating the functionality of a walk-over-weigh system, and identified robust regression as the most accurate smoothing method to identify and remove spurious animal weights.