@proceedings {JRN55359,
	title = {Digital ranching as aspirational system for resilient ranching on Southwestern US rangelands},
	year = {2022},
	month = {2/8/2022},
	abstract = {The Sustainable Southwest Beef Project led by New Mexico State University is strategically partnering with ranchers, researchers, extension specialist, educators and industry stakeholders to pioneer a Digital Ranching Platform for improving sustainability outcomes on ranches of the Southwestern US. The goals of the platform are to improve the operational efficiency of managing beef production systems for profitability, climate-resilience, and adaptive capacity. Ultimately, end-users will implement near realtime tracking and scouting of livestock across large pastures, rapid assessments of animal welfare, remote monitoring of rain gauge tipping buckets and tracking of water levels in cattle drinking troughs dispersed across the ranch. Another module is providing unobtrusive scoring of cattle body condition using support vector machine classifiers feeding on video imagery collected by automated infrared depth cameras. Methodologically, our approach fuses traditional statistics and smart analytics with novel dashboard tools to rapidly provide management indicators computed from streams of real-time data which are concurrently collected, logged and transmitted through a network of high throughput sensors, gateways routers and cloud computing services. The IoT infrastructure includes field sensors and accelerometer and GPS sensors on animals, and operates on a Long Range Wide Area Network (LoRa WAN) using solar or grid power and Ethernet, WiFi backhaul, or GSM communication. Our software engineering and IT team is developing a unified web-based server and dashboard application that facilitates the aggregation, visualization and retrieval of computed data, and configurations of sensors. Current analytics seek to enhance the harmonization (i.e. common feature representation) and curation of data using preprocessing, cleansing and normalization steps prior to implementing machine learning variants for classification and predictions objectives. Pilot case studies suggest several advantages of the system along with areas for potential improvement of existing sensors, network infrastructure and engineering, which may enhance future applications of the system on cooperating commercial ranches. },
	keywords = {aspirational system, digital ranching, resilient ranching, Southwestern US rangelands},
	author = {S. A. Utsumi and S. Nyamuryekung{\textquoteright}e and Matthew M. McIntosh and Andr{\'e}s F. Cibils and Richard E. Estell and S. Spiegal and G. Duff and Huiping Cao and LE Boucheron and H. Chen and Le, T. and Winkler, Z. and Rahman, S. and Qixu Gong and Andrew Cox and Craig Gifford and John P Ragosta and V. Gouvea and Carolina Brandani and Tony Waterhouse and J P Holland and Emile H. Elias and Skye Aney and Brandon T Bestelmeyer and Jean Steiner}
}
