Intravaginal Devices and GNSS Collars with Satellite Communication to Detect Calving Events in Extensive Beef Production in Northern Australia

An automated system for cattle reproductive management under the IoT framework. Part I: the e-Synch system and cow responses

The objective of this manuscript was to present the e-Synch system, integrating an intravaginal electronically controlled hormone delivery and sensing device with an IoT platform for remote programming and monitoring. Secondary objectives were to demonstrate system functionality and cow responses to e-Synch. External components of e-Synch include a 3D-printed case with retention wings, a flexible wideband antenna, and silicone membrane for pressure balancing. Internal components include a central control board, battery, wireless charging coil, and two silicone hormone reservoirs connected to individual peristaltic pumps. An accelerometer and a high-accuracy temperature sensor are integrated in the custom printed circuit board (PCB). The IoT platform includes a gateway consisting of Raspberry PI 3 and a CC1352 radiofrequency module that collects sensor data at 915 mHz. Data is transferred to the Google Cloud utilizing the IoT Core service through TCP/IP, and then is pulled by the Pub/Sub service. After routing to a BigQuery table by the Dataflow service, data visualization is provided by Data Studio. Drug delivery protocols are selected using an IOS device app that connects to e-Synch through Bluetooth. Experiments with lactating Holsteins cows were conducted to demonstrate proof-of-concept system functionality and evaluate cow responses. Despite unstable communication and signal discontinuity because of signal strength attenuation by body tissue, devices (n=6) communicated with the IoT platform in 89% (24/27) of use instances. Temperature and accelerometer data were received for at least one 15 min period during an 8 h insertion period from all devices that communicated with the IoT platform. Variation in accelerometer data (± 8.565 m/s2) was consistent with cow activity during experimentation and mean vaginal temperature of 39.1 °C (range 38.6 to 39.5 °C) demonstrated sensor functionality. Hormone release was confirmed in all instances of device use except for one. Cow behavior evaluated through signs of discomfort and pain, and tail raising scores was mostly unaltered by e-Synch. Vaginal integrity and mucus scores also remained unaltered during and after device insertion. In conclusion, the e-Synch device integrated with a controlling app and IoT platform might be used to automate intravaginal hormone delivery and sensing for controlling the estrous cycle of cattle.

Perspectives on the Special Issue for Applications of Remote Sensing for Livestock and Grazingland Management

The use of geospatial sciences and technologies for the management of grazinglands has fostered a plethora of applications related to ecology, wildlife, vegetation science, forage productivity and quality, and animal husbandry. Some of the earliest use of remote sensing dates to the proliferation of aerial photography in the 1930s. Today, remote sensing using satellite imagery, global navigation satellite systems (GNSS), and internet-connected devices and sensors allow for real- and near real-time modeling and observation of grazingland resources. In this special issue of Remote Sensing, we introduce nine original publications focusing on varying aspects of grazingland management, such as animal health and telemetry, climate change, soil moisture, herbaceous biomass, and vegetation phenology. The work in this issue spans a diverse range of scale from satellite to unmanned aerial systems imagery, as well as ground-based measurements from mounted cameras, telemetry devices, and datalogging devices. Remote sensing-based technologies continue to evolve, allowing us to address critical issues facing grazingland management such as climate change, restoration, forage abundance and quality, and animal behavior, production, and welfare.

How to Predict Parturition in Cattle? A Literature Review of Automatic Devices and Technologies for Remote Monitoring and Calving Prediction

Cattle farming is facing an increase in number of animals that farmers must care for, together with decreasing time for observation of the single animal. Remote monitoring systems are needed in order to optimize workload and animal welfare. Where the presence of personnel is constant, for example in dairy farms with great number of lactating cows or with three milking/day, calving monitoring systems which send alerts during the prodromal stage of labor (stage I) could be beneficial. On the contrary, where the presence of farm personnel is not guaranteed, for example in smaller farms, systems which alert at the beginning of labor (stage II) could be preferred. In this case, time spent observing periparturient animals is reduced. The reliability of each calving alarm should also be considered: automatic sensors for body temperature and activity are characterized by a time interval of 6-12 h between the alarm and calving. Promising results have been shown by devices which could be placed within the vaginal canal, thus identifying the beginning of fetal expulsion and optimizing the timing of calving assistance. However, some cases of non-optimal local tolerability and cow welfare issues are reported. Future research should be aimed to improve Sensitivity (Se), Specificity (Sp) and Positive Predictive Value (PPV) of calving alert devices in order to decrease the number of false positive alarms and focusing on easy-to-apply, re-usable and well tolerated products.

The Relationship between Satellite-Derived Vegetation Indices and Live Weight Changes of Beef Cattle in Extensive Grazing Conditions

The live weight (LW) and live weight change (LWC) of cattle in extensive beef production is associated with pasture availability and quality. The remote monitoring of pastures and cattle LWC can be achieved with a combination of satellite imagery and walk-over-weighing (WoW) stations. The objective of the present study is to determine the association, if any, between vegetation indices (VIs) (pasture availability) and the LWC of beef cattle in an extensive breeding operation in Northern Australia. The study also tests a suite of VIs along with variables such as rainfall and Julian day to predict the LWC of breeding cows. The VIs were calculated from Sentinel-2 satellite imagery over a 2-year period from a paddock with 378 cattle. Animal LW was measured remotely using a weighing scale at the water point. The relationship between VIs, the LWC, and LW was assessed using linear mixed-effects regression models and random forest modelling. Findings demonstrate that all VIs calculated had a significant positive relationship with the LWC and LW (p < 0.001). Machine learning predictive modelling showed that the LWC of breeding cows could be predicted from VIs, Julian day, and rainfall information, with a Lin’s Concordance Correlation Coefficient of 0.62 when using the leave-one-month-out cross-validation. The LW and LWC were greater during the wet season when VIs were higher compared to the dry season (p < 0.001). Results suggest that the remote monitoring of pasture availability, the LWC and LW is possible under extensive grazing conditions. Further, the use of VIs and other readily available data such as rainfall can be used to predict the LWC of a breeding herd in extensive conditions. Such information could be used to increase the productivity and land management in extensive beef production. The integration of these data streams offers great potential to improve the monitoring, management, and productivity of grazing or cropping enterprises.