Livestock Health Alerts Through Community Sensor Networks

Johan Bergström

Livestock Health Alerts Through Community Sensor Networks

Author(s)	Prof. Johan Bergström
Country	United States
Abstract	Delayed recognition of animal-health problems can increase production losses, welfare impairment, treatment complexity, and the risk that infectious conditions spread before veterinary attention is mobilized. Continuous sensing technologies can detect changes in activity, rumination, temperature, feeding, location, and environmental conditions earlier than intermittent observation in some livestock contexts, but the value of these technologies depends on more than sensor accuracy. Small and geographically dispersed livestock holdings may lack continuous connectivity, individual-animal sensors may be financially impractical, and automated alerts may have little operational value when no trained person is available to interpret and respond to them. This study develops a community sensor-network framework that combines livestock sensing with shared digital infrastructure, locally available animal-health responders, participatory surveillance, and veterinary escalation. Because authenticated sensor streams, clinical diagnoses, and community veterinary records were not supplied, the quantitative component is explicitly simulation based. A synthetic environment comprising 180 livestock-keeping households is assessed across routine visual observation, stand-alone animal sensing, community-networked alerts, and integrated community–veterinary response. A Community Livestock Health Alert Network Index is developed from sensor coverage, signal reliability, connectivity continuity, individualized baseline modeling, alert interpretability, community response capacity, and veterinary escalation. Simulated analysis indicates that the proportion of emerging health events identified or acted upon within twelve hours rises from 35% under routine visual observation to 67% with stand-alone sensors and 83% when sensors are embedded within a community response network. The study emphasizes that behavioral and physiological anomalies are screening signals rather than diagnoses. Effective systems therefore require species-specific validation, false-alert management, clinical confirmation, data governance, equitable access, and clearly defined responder responsibilities. Community sensor networks can strengthen livestock-health surveillance most effectively when technology accelerates attention to animals requiring examination without displacing farmer observation or professional veterinary judgment.
Keywords	livestock health monitoring, community sensor networks, precision livestock farming, animal-health alerts, wireless sensor networks, participatory epidemiology, early disease detection, veterinary decision support
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-27

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Livestock Health Alerts Through Community Sensor Networks

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