Behavioral Adaptations of Nursing Brangus Cows to Virtual Fencing: Insights from a Training Deployment Phase.

Virtual fencing systems have emerged as a promising technology for managing the distribution of livestock in extensive grazing environments. This study provides comprehensive documentation of the learning process involving two conditional behavioral mechanisms and the documentation of efficient, effective, and safe animal training for virtual fence applications on nursing Brangus cows. Two hypotheses were examined: (1) animals would learn to avoid restricted zones by increasing their use of containment zones within a virtual fence polygon, and (2) animals would progressively receive fewer audio-electric cues over time and increasingly rely on auditory cues for behavioral modification. Data from GPS coordinates, behavioral metrics derived from the collar data, and cueing events were analyzed to evaluate these hypotheses. The results supported hypothesis 1, revealing that virtual fence activation significantly increased the time spent in containment zones and reduced time in restricted zones compared to when the virtual fence was deactivated. Concurrently, behavioral metrics mirrored these findings, with cows adjusting their daily travel distances, exploration area, and cumulative activity counts in response to the allocation of areas with different virtual fence configurations. Hypothesis 2 was also supported by the results, with a decrease in cueing events over time and increased reliance with animals on audio cueing to avert receiving the mild electric pulse. These outcomes underscore the rapid learning capabilities of groups of nursing cows in responding to virtual fence boundaries.

Real-Time Monitoring of Grazing Cattle Using LORA-WAN Sensors to Improve Precision in Detecting Animal Welfare Implications via Daily Distance Walked Metrics.

Animal welfare monitoring relies on sensor accuracy for detecting changes in animal well-being. We compared the distance calculations based on global positioning system (GPS) data alone or combined with motion data from triaxial accelerometers. The assessment involved static trackers placed outdoors or indoors vs. trackers mounted on cows grazing on pasture. Trackers communicated motion data at 1 min intervals and GPS positions at 15 min intervals for seven days. Daily distance walked was determined using the following: (1) raw GPS data (RawDist), (2) data with erroneous GPS locations removed (CorrectedDist), or (3) data with erroneous GPS locations removed, combined with the exclusion of GPS data associated with no motion reading (CorrectedDist_Act). Distances were analyzed via one-way ANOVA to compare the effects of tracker placement (Indoor, Outdoor, or Animal). No difference was detected between the tracker placement for RawDist. The computation of CorrectedDist differed between the tracker placements. However, due to the random error of GPS measurements, CorrectedDist for Indoor static trackers differed from zero. The walking distance calculated by CorrectedDist_Act differed between the tracker placements, with distances for static trackers not differing from zero. The fusion of GPS and accelerometer data better detected animal welfare implications related to immobility in grazing cattle.

The Use of Spatial and Spatiotemporal Modeling for Surveillance of H5N1 Highly Pathogenic Avian Influenza in Poultry in the Middle East.

Since 2005, H5N1 highly pathogenic avian influenza virus (HPAIV) has severely impacted the economy and public health in the Middle East (ME) with Egypt as the most affected country. Understanding the high-risk areas and spatiotemporal distribution of the H5N1 HPAIV in poultry is prerequisite for establishing risk-based surveillance activities at a regional level in the ME. Here, we aimed to predict the geographic range of H5N1 HPAIV outbreaks in poultry in the ME using a set of environmental variables and to investigate the spatiotemporal clustering of outbreaks in the region. Data from the ME for the period 2005-14 were analyzed using maximum entropy ecological niche modeling and the permutation model of the scan statistics. The predicted range of high-risk areas (P > 0.60) for H5N1 HPAIV in poultry included parts of the ME northeastern countries, whereas the Egyptian Nile delta and valley were estimated to be the most suitable locations for occurrence of H5N1 HPAIV outbreaks. The most important environmental predictor that contributed to risk for H5N1 HPAIV was the precipitation of the warmest quarter (47.2%), followed by the type of global livestock production system (18.1%). Most significant spatiotemporal clusters (P < 0.001) were detected in Egypt, Turkey, Kuwait, Saudi Arabia, and Sudan. Results suggest that more information related to poultry holding demographics is needed to further improve prediction of risk for H5N1 HPAIV in the ME, whereas the methodology presented here may be useful in guiding the design of surveillance programs and in identifying areas in which underreporting may have occurred.