Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data.

In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.

Measuring Methane Production from Ruminants.

Radiative forcing of methane (CH4) is significantly higher than carbon dioxide (CO2) and its enteric production by ruminant livestock is one of the major sources of greenhouse gas emissions. CH4 is also an important marker of farming productivity, because it is associated with the conversion of feed to product in livestock. Consequently, measurement of enteric CH4 is emerging as an important research topic. In this review, we briefly describe the conversion of carbohydrate to CH4 by the bacterial community within gut, and highlight some of the key host-microbiome interactions. We then provide a picture of current progress in techniques for measuring enteric CH4, the context in which these technologies are used, and the challenges faced. We also discuss solutions to existing problems and new approaches currently in development.

A unique in vivo approach for investigating antimicrobial materials utilizing fistulated animals.

Unique in vivo tests were conducted through the use of a fistulated ruminant, providing an ideal environment with a diverse and vibrant microbial community. Utilizing such a procedure can be especially invaluable for investigating the performance of antimicrobial materials related to human and animal related infections. In this pilot study, it is shown that the rumen of a fistulated animal provides an excellent live laboratory for assessing the properties of antimicrobial materials. We investigate microbial colonization onto model nanocomposites based on silver (Ag) nanoparticles at different concentrations into polydimethylsiloxane (PDMS). With implantable devices posing a major risk for hospital-acquired infections, the present study provides a viable solution to understand microbial colonization with the potential to reduce the incidence of infection through the introduction of Ag nanoparticles at the optimum concentrations. In vitro measurements were also conducted to show the validity of the approach. An optimal loading of 0.25 wt% Ag is found to show the greatest antimicrobial activity and observed through the in vivo tests to reduce the microbial diversity colonizing the surface.

Evaluating dispersion modeling options to estimate methane emissions from grazing beef cattle.

Enteric methane (CH) emission from cattle is a source of greenhouse gas and is an energy loss that contributes to production inefficiency for cattle. Direct measurements of enteric CH emissions are useful to quantify the magnitude and variation and to evaluate mitigation of this important greenhouse gas source. The objectives of this study were to evaluate the impact of stocking density of cattle and source configuration (i.e., point source vs. area source and elevation of area source) on CH emissions from grazing beef cattle in Queensland, Australia. This was accomplished using nonintrusive atmospheric measurements and a gas dispersion model. The average measured CH emission for the point and area source was between 240 and 250 g animal d over the entire study. There was no difference ( > 0.05) in emission when using an elevated area source (0.5 m) or a ground area source (0 m). For the point-source configuration, there was a difference in CH emission due to stocking density; likewise, some differences existed for the area-source emissions. This study demonstrates the flexibility of the area-source configuration of the dispersion model to estimate CH emissions even at a low stocking density.

Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing.

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.