Influence of Fix Schedule on the Location Accuracy of a Low-Cost GPS Data Logger on Cattle

Global positioning system (GPS) data loggers are commonly used to track the movements and distribution of both wild and domestic animals. However, the expense often poses a challenge for researchers. Recently, there has been a rise in the utilization of affordable and user-friendly GPS data loggers for tracking animal movements, albeit with compromised accuracy. We aimed to identify factors influencing the accuracy of a low-cost GPS data logger (I-gotU GT-600) and to enhance its location accuracy. Initial investigations revealed that recording intervals impacted the location error of the GPS data logger. To elucidate the relationship between recording intervals and location accuracy, we conducted stationary and motion tests. Our findings indicated that recording intervals of less than 15 sec substantially enhances the location accuracy of the low-cost GPS data logger. Our results highlight the relationship between the fix schedule and location accuracy for these GPS data loggers. Our study provides information that enhances the quality of data for researchers using low-cost GPS data loggers for short-term studies in various settings, such as zoos and livestock facilities.

GIS-based methodology for tracking the grazing cattle site use

Interest in tracking and monitoring animals in livestock farming using wearable sensors has been steadily increasing. The use of these devices is particularly crucial in extensive livestock systems where direct interaction between animals and farmers is infrequent, necessitating strenuous efforts in long-distance herd monitoring. Internet of Things (IoT) technologies offer a promising solution to address the challenges posed by vast distances, enabling real-time and remote animal monitoring. In this study, an experimental trial was conducted using a custom-designed device, located in a Polyvinyl Chloride (PVC) case, specifically tailored to fit onto a collar. This case incorporates an integrated SigFox communication system, i.e., a Low Power Global Positioning System (LP-GPS) omnidirectional system, and a power supply. The trial took place in two grazing areas located in different territorial zones, designated as Case Study I and II. A LP-GPS collar was provided for each selected animal, and the data were recorded at 20-min intervals for Case Study I and 10-min intervals for Case Study II. The acquired data were then imported and analysed using Geographical Information Systems (GIS) software. Information was collected through a purpose-built web application (AppWeb). The objective was to analyze those territorial areas mostly occupied by animals within the two considered grazing areas by developing a GIS-based methodology. Specifically, customized algorithms such as Heatmap and Kernel Density Estimation (KDE) plugins were employed to conduct spatial analyses. The maps obtained through Heatmap plugin, showed the temporal-spatial distribution of animals within their grazing areas. Additionally, the KDE tool was used to classify preferred territorial areas, generating tailored charts for each animal in the sample. The individual Core Areas, determined through KDE evaluation for each animal, were overlaid to provide a comprehensive analysis of the monitored animals.The results achieved applying the GIS-based methodology facilitated the identification of animal positions and could be adopted to provide insights into feeding behavior and soil erosion, thereby aiding in the prevention of environmental issues.

Potential for automatic detection of calving in beef cows grazing on rangelands from Global Navigate Satellite System collar data

Dystocia is one of the main causes of calf death around calving. In addition, peripartum deaths may occur due to other factors, such as weather or predators, especially in the case of grazing animals. Precision Livestock Farming (PLF) tools aimed at the automatic detection of calving may be useful for farmers, allowing cow assistance in case of dystocia or checking the condition of the cow-calf pair after calving. Such PLF systems are commercially available for dairy cows, but these tools are not suitable for rangelands, mainly due to power and connectivity constraints. Thus, since most commercial PLF tools for rangelands are based on Global Navigate Satellite System (GNSS) technology, the objective of this study was to design and evaluate several indicators built from data gathered with GNSS collars to characterise their potential for the detection of calving on rangelands. Location data from 57 cows, 42 of which calved during the study, were curated and analysed following a standardised procedure. Several indicators were calculated using two different strategies. The first approach consisted of having indicators that could be computed using the data of a single GNSS collar (cow indicators). The second strategy involved the use of data from several animals (herd indicators), which requires more animals to be monitored, but may allow the characterisation of social behaviour. Several indicators, such as the length of the daily trajectory or the sinuosity of cow path, showed significant differences between the pre- and postpartum periods, but no clear differences between calving day and previous days. Herd indicators, such as the distance to herd centroid or to the nearest peer were superior in terms of the detection of calving day, as cows showed isolation behaviour from 24 hours before calving. Relative indicators, i.e., the value of cow or herd indicators for the calving cow in relation to the average value of the same indicators for its herdmates, provided additional information on cow behaviour. For instance, according to the relative indicator for the change in daily trajectory, pregnant cows had a differential exploratory behaviour up to 14 days before calving. In conclusion, data from commercial GNSS collars proved to be useful for the computation of several indicators related to the occurrence of calving on rangelands. Some of those indicators showed changes from baseline values on the day before calving, which could serve to predict the onset of parturition.

Estimating Excreted Nutrients to Improve Nutrient Management for Grazing System Dairy Farms

Improving nutrient management in grazing system dairy farms requires determining nutrient flows through animals, the placement of cows within farms and potential for collection, and the re-use and loss of nutrients. We applied a model incorporating data collected at a range of temporal and spatial scales to quantify nutrient excretion in all locations that lactating herds visited on five days over a year on 43 conventional and organic grazing system dairy farms. The calculated nutrient loads excreted by cows in different places were highly skewed; while N, P and K deposited loads were consistent across the year, S, Ca and Mg loads varied between sampling times and seasons. The greatest mean and range in nutrient loads were deposited in paddocks, with the smallest amounts deposited in dairy sheds. All excreted nutrient loads increased with farm and herd sizes and milk production. Mean daily loads of 112, 15, 85, 11, 22 and 13 kg of N, P, K, S, Ca and Mg were deposited by the herds which, when standardised to a 305-day lactation, amounted to 24, 4, 20, 3, 5 and 3 t excreted annually, respectively. In addition to routine manure collection in dairy sheds, ensuring collection and recycling of nutrients excreted on feed pads and holding areas would decrease potential nutrient losses by 29% on average. Non-collected, recycled nutrients were disproportionately returned to paddocks in which cows spent time overnight, and except for S and Ca, nutrient loading rates were greater than rates applied as fertilisers. These data demonstrate the extent of excreted nutrients in grazing dairy systems and indicate the need to account for these nutrients in nutrient management plans for Australian dairy farms. We propose incorporating excretion data in current budgeting tools using data currently collected on most Australian grazing system dairy farms.

Evaluating Several Vegetation Indices Derived from Sentinel-2 Imagery for Quantifying Localized Overgrazing in a Semi-Arid Region of South Africa

Rangeland monitoring aims to determine whether grazing management strategies meet the goals of sustainable resource utilization. The development of sustainable grazing management strategies requires an understanding of the manner in which grazing animals utilize available vegetation. In this study, we made use of livestock tracking, in situ observations and Sentinel-2 imagery to make rangeland scale observations of vegetation conditions in a semi-arid environment, to better understand the spatial relationships between vegetation conditions and sheep movement patterns. We hypothesized that sheep graze more selectively under low stocking rates—resulting in localized overgrazing. We also assessed the importance of image spatial resolution, as it was assumed localized effects of grazing will be best explained by higher resolution imagery. The results showed that livestock tend to congregate along drainage lines where soils are deeper. The findings demonstrate how the spatial analysis of remotely sensed data can provide a landscape-scale overview of livestock movement patterns. This study illustrates that high-resolution normalized difference vegetation index (NDVI) data can be used as a grazing management tool to determine the spatial variability of productive areas across the semi-arid Upper Karoo rangelands and identify preferred grazing areas.

Spatial monitoring technologies for coupling the soil plant water animal nexus

Systems-level studies aimed at determining how soil properties are linked to plant production and ultimately animal response spatially are lacking. This study aims to identify if grazing pressure is linked to soil properties, terrain attributes, and above-ground plant accumulation and nutritive value in a silvopastoral (or integrated tree-livestock) system. Overall, cattle prefer grazing native grasses (2.81 vs. 1.24 h ha⁻¹ AU⁻¹) and udic (dry) landscape positions compared to aquic (wet) areas (2.07 vs. 1.60 h ha⁻¹ AU⁻¹). Greater grazing frequency occurs in udic soils with greater phosphorus and potassium contents and with accumulated forage with less lignin (P ≤ 0.05), which correspond to reduced elevation and greater tree height and diameter (shade) during summer mob grazing. Combining spatial monitoring technologies (both soil and animal) with forage allowance can optimize grazing systems management and sustainability spatially and temporally.

Temporal Changes in Association Patterns of Cattle Grazing at Two Stocking Densities in a Central Arizona Rangeland

Simple Summary

Monitoring changes in the utilization of forages across rangelands can be time consuming and difficult with untrained personnel. The use of real time positioning for cattle is becoming commercially available with the improvements in technology. The objective of this case study was to identify the changes in livestock social associations and spatial location at two stocking densities throughout a six-week grazing period. Both pastures used similar sized herds with 35 and 29 animals tracked with global positioning systems set at 30-min intervals. A half-weight index value was calculated for each pair of tracked cattle to determine the proportion of time that cattle were within 75 m and 500 m of each other. Throughout the study, forage utilization increased from 5 to 24% and from 10% to 20% and forage mass decreased from 2601 kg ha⁻¹ to 1828 kg ha⁻¹ and 2343 kg ha⁻¹ to 1904 kg ha⁻¹, in the high stocking density pasture and low stocking density pasture, respectively. Utilization of forages throughout the trial forced cattle to disperse and travel further from water sources to find new feeds. Real-time GPS tracking has the potential to remotely detect changes in animal spatial association, identify when cows disperse, and improve recognition for the need of pasture rotation to avoid rangeland degradation.

Abstract

Proper grazing management of arid and semi-arid rangelands requires experienced personnel and monitoring. Applications of GPS tracking and sensor technologies could help ranchers identify livestock well-being and grazing management issues so that they can promptly respond. The objective of this case study was to evaluate temporal changes in cattle association patterns using global positioning system (GPS) tracking in pastures with different stocking densities (low stocking density [LSD] = 0.123 animals ha⁻¹, high stocking density [HSD] = 0.417 animals ha⁻¹) at a ranch near Prescott, Arizona. Both pastures contained similar herd sizes (135 and 130 cows, respectively). A total of 32 cows in the HSD herd and 29 cows in the LSD herd were tracked using GPS collars at location fixes of 30 min during a 6-week trial in the summer of 2019. A half-weight index (HWI) value was calculated for each pair of GPS-tracked cattle (i.e., dyads) to determine the proportion of time that cattle were within 75 m and 500 m of each other. Forage mass of both pastures were relatively similar at the beginning of the study and forage utilization increased from 5 to 24% in the HSD pasture and increased from 10 to 20% in the LSD pasture. Cattle in both pastures exhibited relatively low mean association values (HWI < 0.25) at both spatial scales. Near the end of the study, cattle began to disperse likely in search of forages (p < 0.01) and travelled farther (p < 0.01) from water than during earlier periods. Real-time GPS tracking has the potential to remotely detect changes in animal spatial association (e.g., HWI), and identify when cows disperse, likely searching for forage.

Back to Nature With Fenceless Farms—Technology Opportunities to Reconnect People and Food

The development and application of the fence was one of the earliest forms of agricultural technology in action. Managing the supply of animal protein required hunter gatherer communities to be able to domesticate and contain wild animals. Over the ages the fence has become ingrained in the very fabric of society and created a culture of control and ownership. Garett Hardin's article titled “The Tragedy of the Commons” suggested that shared land, typified by access to a fenceless common resource, was doomed to failure due to a human instinct for mistrust and exploitation. Perhaps the fence has created an ingrained societal cultural response. While natural ecosystems do have physical boundaries, these are based on natural environmental zones. Landscapes are more porous and resilience is built up through animal's being able to respond to dynamic changes. This paper explores the opportunity for remote monitoring technologies to create open fenceless landscapes and how this might be integrated into the growing need for humans to access animal protein.

Factors Affecting Site Use Preference of Grazing Cattle Studied from 2000 to 2020 through GPS Tracking: A Review

Understanding the behaviour of grazing animals at pasture is crucial in order to develop management strategies that will increase the potential productivity of grazing systems and simultaneously decrease the negative impact on the environment. The objective of this review was to summarize and analyse the scientific literature that has addressed the site use preference of grazing cattle using global positioning systems (GPS) collars in the past 21 years (2000–2020) to aid the development of more sustainable grazing livestock systems. The 84 studies identified were undertaken in several regions of the world, in diverse production systems, under different climate conditions and with varied methodologies and animal types. This work presents the information in categories according to the main findings reviewed, covering management, external and animal factors driving animal movement patterns. The results showed that some variables, such as stocking rate, water and shade location, weather conditions and pasture (terrain and vegetation) characteristics, have a significant impact on the behaviour of grazing cattle. Other types of bio-loggers can be deployed in grazing ruminants to gain insights into their metabolism and its relationship with the landscape they utilise. Changing management practices based on these findings could improve the use of grasslands towards more sustainable and productive livestock systems.

Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

Simple Summary

Near-real-time monitoring of livestock using on-animal sensor technology has the potential to improve animal welfare and productivity through increased surveillance and improved decision-making capabilities. One potentially valuable application is for monitoring of lambing events in sheep. This research reports on the development of a machine learning classification algorithm for autonomous detection of lambing events. The algorithm uses data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data. Overall, four features of sheep behaviour were identified as having the greatest importance for lambing detection, including various measures of social distancing and frequency of posture change. Using these four features, the final algorithm was able to detect up to 91% of lambing events. This knowledge is intended to contribute to the development of commercially feasible lambing detection systems for improved surveillance of animals, ultimately improving methods of monitoring during critical welfare periods.

Abstract

In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

The Quantitative Estimation of Grazing Intensity on the Zoige Plateau Based on the Space-Air-Ground Integrated Monitoring Technology

Grazing intensity (GI) is an important indicator for grazing situations in pastoral areas. However, it has been difficult to be observed directly in the field, due to the randomness and dynamics of the grazing behavior of livestock. Consequently, the lack of actual GI information has become a common issue in studies on quantitatively estimating GI. In this paper, a novel quantitative estimation method is proposed based on the Space-Air-Ground integrated monitoring technology. It systematically integrates GPS tracking technology, Unmanned Aerial Vehicle (UAV) observation technology, and satellite remote sensing technology. Taking Xiangdong Village on the Zoige Plateau as a study area, the trajectory data and UAV images were acquired by the GPS tracking experiments and UAV observation experiments, respectively. The GI at paddock scale (PGI) was then generated with the Kernel Density Estimation (KDE) algorithm and the above data. Taking the generated PGI as training data, an estimation model of GI at region scale (RGI) was constructed by using the time-series satellite remote sensing images and random forest regression algorithm. Finally, the time-series RGI data with a spatial resolution of 10 m in Xiangdong Village were produced by the above model. The accuracy assessment demonstrated that the generated time-series RGI data could reflect the spatial-temporal heterogeneity of actual GI, with a mean absolute error of 0.9301 and r2 of 0. 8573. The proposed method provides a new idea for generating the actual GI on the ground and the time-series RGI data. This study also highlights the feasibility and potential of using the Space-Air-Ground integrated monitoring technology to generate time-series RGI data with high spatial resolution. The generated time-series RGI data would provide data support for the formulation of policies and plans related to the sustainable development of animal husbandry.

Response to resources and parasites depends on health status in extensively grazed sheep

A fundamental question in animal ecology is how an individual's internal state and the external environment together shape species distributions across habitats. The increasing availability of biologgers is driving a revolution in answering this question in a wide range of species. In this study, the position of sheep (Ovis aries) from Global Positioning System collars was integrated with remote sensing data, field sampling of parasite distributions, and parasite load and health measures for each tagged individual. This allowed inter-individual variation in habitat use to be examined. Once controlling for a positive relationship between vegetation productivity and tick abundance, healthier individuals spent more of their time at sites with higher vegetation productivity, while less healthy individuals showed a stronger (negative) response to tick abundance. These trends are likely to represent a trade-off in foraging decisions that vary between individuals based on their health status. Given the rarity of studies that explore how animal distributions are affected by health and external factors, we demonstrate the value of integrating biologging technology with remote sensing data, traditional ecological sampling and individual measures of animal health. Our study, using extensively grazed sheep as a model system, opens new possibilities to study free-living grazing systems.

Potential for autonomous detection of lambing using global navigation satellite system technology

Context On-animal sensing systems are being promoted as a solution to the increased demand for monitoring livestock for health and welfare. One key sensor platform, global navigation satellite system (GNSS) positioning, provides information on the location and movement of sheep. This information could be used to detect partition in sheep, a key period of time when both ewes and lambs are at risk. The development of algorithms based on key behavioural features could provide alerts to sheep managers to enable intervention when problems arise. Aims To investigate the use of GNSS monitoring as a method for detecting behavioural changes in sheep in the period around parturition. Methods GNSS collars were attached to 40 late gestation ewes grazing a 3.09 ha paddock in New Zealand. Several metrics were derived: (i) mean daily speed, (ii) maximum daily speed, (iii) minimum daily speed, (iv) mean daily distance to peers, and (v) spatial paddock utilisation by 95% minimum convex polygon. Speed metrics and distance to peers were also evaluated at an hourly scale for the 12 h before and 12 h after lambing. Key results Minimum daily speed peaked on the day of parturition (P &lt; 0.001), suggesting animals may have been expressing more agitation and did not settle. Isolation was also evident during this time, with postpartum ewes located further from their peers than pre-partum ewes (P &lt; 0.001). Day of lambing was also evident by reduced spatial paddock utilisation (P &lt; 0.001). Conclusions This study demonstrates that GNSS technology can be used to detect parturition-related behaviours in sheep at a day scale; however, detection at the hour scale using GNSS is not possible. Implications This research highlights the opportunity to develop predictive models that autonomously detect behavioural changes in ewes at parturition using GNSS. This could then be extended to identify ewes experiencing prolonged parturition, for example dystocic birth enabling intervention which would improve both production and welfare outcomes for the sheep industry.

Prediction models, assessment methodologies and biotechnological tools to quantify heat stress response in ruminant livestock

Livestock industries have an important role in ensuring global food security. This review discusses the importance of quantifying the heat stress response of ruminants, with an emphasis on identifying thermo-tolerant breeds. There are numerous heat stress prediction models that have attempted to quantify the response of ruminant livestock to hot climatic conditions. This review highlights the importance of investigating prediction models beyond the temperature-humidity index (THI). Furthermore, this review highlights the importance of incorporating other climatic variables when developing prediction indices to ensure the accurate prediction of heat stress in ruminants. Prediction models, particularly the heat load index (HLI) were developed to overcome the limitations of the THI by incorporating ambient temperature (AT), relative humidity (RH), solar radiation (SR) and wind speed (WS). Furthermore refinements to existing prediction models have been undertaken to account for the interactions between climatic variables and physiological traits of livestock. Specifically, studies have investigated the relationships between coat characteristics, respiration rate (RR), body temperature (BT), sweating rate, vasodilation, body weight (BW), body condition score (BCS), fatness and feed intake with climatic conditions. While advancements in prediction models have been occurring, there has also been substantial advancement in the methodologies used to quantify animal responses to heat stress. The most recent development in this field is the application of radio frequency identification (RFID) technology to record animal behaviour and various physiological responses. Rumen temperature measurements using rumen boluses and skin temperature recording using infrared thermography (IRT) are making inroads to redefine the quantification of the heat stress response of ruminants. Further, this review describes several advanced biotechnological tools that can be used to identify climate resilient breeds of ruminant livestock.

A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd

Animal location technologies have evolved considerably in the last 60 years. Nowadays, animal tracking solutions based on global positioning systems (GPS) are commercially available. However, existing devices have several constraints, mostly related to wireless data transmission and financial cost, which make impractical the monitorization of all the animals in a herd. The main objective of this work is to develop a low-cost solution to enable the monitorization of a whole herd. An IoT-based system, which requires some animals of the herd being fitted with GPS collars connected to a Sigfox network and the rest with low-cost Bluetooth tags, has been developed. Its performance has been tested in two commercial farms, raising sheep and beef cattle, through the monitorization of 50 females in each case. Several collar/tag ratios, which define the cost per animal of the solution, have been simulated. Results demonstrate that a low collar/tag ratio enable the monitorization of a whole sheep herd. A larger ratio is needed for beef cows because of their grazing behavior. Nevertheless, the optimal ratio depends on the purpose of location data. Large variability has been observed for the number of hourly and daily messages from collars and tags. The system effectiveness for the monitorization of all the animals in a herd has been certainly proved.

Application of accelerometers to record drinking behaviour of beef cattle

Accelerometers have been used to record many cattle postures and behaviours including standing, lying, walking, grazing and ruminating but not cattle drinking behaviour. This study explores whether neck-mounted triaxial accelerometers can identify drinking and whether head-neck position and activity can be used to record drinking. Over three consecutive days, data were collected from 12 yearling Brahman cattle each fitted with a collar containing an accelerometer. Each day the cattle were herded into a small yard containing a water trough and allowed 5 min to drink. Drinking, standing (head up), walking and standing (head down) were recorded. Examination of the accelerometer data showed that drinking events were characterised by a unique signature compared with the other behaviours. A linear mixed-effects model identified two variables that reflected differences in head-neck position and activity between drinking and the other behaviours: mean of the z- (front-to-back) axis and variance of the x- (vertical) axis (P &lt; 0.05). Threshold values, derived from Kernel density plots, were applied to classify drinking from the other behaviours using these two variables. The method accurately classified drinking from standing (head up) with 100% accuracy, from walking with 92% accuracy and from standing (head down) with 79% accuracy. The study shows that accelerometers have the potential to record cattle drinking behaviour. Further development of a classification method for drinking is required to allow accelerometer-derived data to be used to improve our understanding of cattle drinking behaviour and ensure that their water intake needs are met.

Use of GPS tracking collars and accelerometers for rangeland livestock production research

Over the last 20 yr, global positioning system (GPS) collars have greatly enhanced livestock grazing behavior research. Practices designed to improve livestock grazing distribution can now be accurately and cost effectively monitored with GPS tracking. For example, cattle use of feed supplement placed in areas far from water and on steep slopes can be measured with GPS tracking and corresponding impacts on distribution patterns estimated. Ongoing research has identified genetic markers that are associated with cattle spatial movement patterns. If the results can be validated, genetic selection for grazing distribution may become feasible. Tracking collars have become easier to develop and construct, making them significantly less expensive, which will likely increase their use in livestock grazing management research. Some research questions can be designed so that dependent variables are measured by spatial movements of livestock, and in such cases, GPS tracking is a practical tool for conducting studies on extensive and rugged rangeland pastures. Similarly, accelerometers are changing our ability to monitor livestock behavior. Today, accelerometers are sensitive and can record movements at fine temporal scales for periods of weeks to months. The combination of GPS tracking and accelerometers appears to be useful tools for identifying changes in livestock behavior that are associated with livestock diseases and other welfare concerns. Recent technological advancements may make real-time or near real-time tracking on rangelands feasible and cost-effective. This would allow development of applications that could remotely monitor livestock well-being on extensive rangeland and notify ranchers when animals require treatment or other management.

Effects of number of animals monitored on representations of cattle group movement characteristics and spatial occupancy

The number of animals required to represent the collective characteristics of a group remains a concern in animal movement monitoring with GPS. Monitoring a subset of animals from a group instead of all animals can reduce costs and labor; however, incomplete data may cause information losses and inaccuracy in subsequent data analyses. In cattle studies, little work has been conducted to determine the number of cattle within a group needed to be instrumented considering subsequent analyses. Two different groups of cattle (a mixed group of 24 beef cows and heifers, and another group of 8 beef cows) were monitored with GPS collars at 4 min intervals on intensively managed pastures and corn residue fields in 2011. The effects of subset group size on cattle movement characterization and spatial occupancy analysis were evaluated by comparing the results between subset groups and the entire group for a variety of summarization parameters. As expected, more animals yield better results for all parameters. Results show the average group travel speed and daily travel distances are overestimated as subset group size decreases, while the average group radius is underestimated. Accuracy of group centroid locations and group radii are improved linearly as subset group size increases. A kernel density estimation was performed to quantify the spatial occupancy by cattle via GPS location data. Results show animals among the group had high similarity of spatial occupancy. Decisions regarding choosing an appropriate subset group size for monitoring depend on the specific use of data for subsequent analysis: a small subset group may be adequate for identifying areas visited by cattle; larger subset group size (e.g. subset group containing more than 75% of animals) is recommended to achieve better accuracy of group movement characteristics and spatial occupancy for the use of correlating cattle locations with other environmental factors.

GNSS technology and its application for improved reproductive management in extensive sheep systems

The behaviour of Merino ewes during non-oestrus and oestrus were quantified using Global Navigation Satellite System (GNSS) tracking devices and direct visual observation. GNSS devices were attached to neck collars and deployed on mixed-age ewes (38 maiden and 40 experienced ewes) following hormonal oestrus synchronisation. The positional accuracy of the GNSS data was validated through a comparative study of GNSS estimates of each animal’s location compared with direct visual observations. Positional accuracy was estimated at 90–94%, for a 4-m and 6-m-buffer radius, respectively. Ewe speed of movement was calculated from the GNSS data and plotted against hour of the day to determine diurnal activity patterns during non-oestrus and oestrus days. Ewes showed increased speed of movement during the early morning of the anticipated day of oestrus compared with the non-oestrus day (P < 0.001). In addition, ewes that increased their speed of movement by 0.05 m/s received 1.4–28.4 times more mounts depending on the hour of the day (P = 0.02). Ewes also displayed an increased speed of movement in the period leading up to maximum sexual activity, defined as the hour in which ewes received the maximum number of mounts. Thereafter, ewe activity decreased. No difference in sexual activity was detected between maiden and experienced ewes. The present study has demonstrated a change in ewe diurnal activity at oestrus, suggesting the onset of sexual activity can be identified as a period of increased speed of movement followed by a return to ‘normal’ activity. The development of commercial remote autonomous monitoring technologies such as GNSS tracking to detect this change in behaviour could facilitate improved reproductive management of sheep in extensive systems.

Using UHF proximity loggers to quantify male-female interactions: a scoping study of estrous activity in cattle

Reproductive efficiency is an important determinant of profitable cattle breeding systems and the success of assisted reproductive techniques (ART) in wildlife conservation programs. Methods of estrous detection used in intensive beef and dairy cattle systems lack accuracy and remain the single biggest issue for improvement of reproductive rates and such methods are not practical for either large-scale extensive beef cattle enterprises or free-living mammalian species. Recent developments in UHF (ultra high frequency) proximity logger telemetry devices have been used to provide a continuous pair-wise measure of associations between individual animals for both livestock and wildlife. The objective of this study was to explore the potential of using UHF telemetry to identify the reproductive cycle phenotype in terms of intensity and duration of estrus. The study was conducted using Belmont Red (interbred Africander Brahman Hereford-Shorthorn) cattle grazing irrigated pasture on Belmont Research Station, northeastern Australia. The cow-bull associations from three groups of cows each with one bull were recorded over a 7-week breeding season and the stage of estrus was identified using ultrasonography. Telemetry data from bull and cows, collected over 4 8-day logger deployments, were log transformed and analyzed by ANOVA. Both the number and duration of bull-cow affiliations were significantly (P<0.001) greater in estrous cows compared to anestrus cows. These results support the development of the UHF technology as a hands-off and noninvasive means of gathering socio-sexual information on both wildlife and livestock for reproductive management.

The use of GNSS technology to identify lambing behaviour in pregnant grazing Merino ewes

This current study investigated whether pre-lambing behavioural changes could be identified with the use of global navigation satellite system (GNSS) technology. GNSS devices were deployed on 20 pregnant Merino ewes grazing a 1.6 ha paddock and their lambing activity was compared with the metrics derived from the spatial data. The aims were to evaluate the lambing event using the following three separate metrics: (1) mean daily speed (MDS) of ewes 7 days before and to 7 days after lambing, inclusive (n = 12); (2) mean hourly speed (MHS) 12 h before and 12 h after lambing, inclusive (n = 9); and (3) the mean distance the lambing ewe to her peers in the 7 days before and the 7 days after lambing (mean distance to peers (MDP); n = 9), inclusive. There was a significant (P < 0.01) difference between pre- and post-lambing MDS with average ± se MDS pre-lambing being faster than post-lambing (0.051 ± 0.0004 vs 0.047 ± 0.0005 m/s). Pre- and post-lambing MHS differed significantly (P < 0.05), with mean ± s.e. MHS pre-lambing being faster than post-lambing (0.049 ± 0.002 vs 0.038 ± 0.002 m/s). Mean distance to peers indicated that at the time of lambing, ewes were significantly (P < 0.01) further from their peers than at either pre- or post-lambing (83.6 ± 14.59 vs 35.2 ± 2.82 vs 35.6 ± 1.68 m). Despite MDS and MHS metrics indicating significant changes pre- and post-lambing, neither metric was able to identify the time of lambing. The MDP metric could not identify differences pre- and post-lambing but was useful at predicting lambing. The current study found that MDS and MHS metrics have the potential to determine a ‘trigger’ point that could identify parturition and therefore could be used to determine the day of lambing. Therefore, further research is required to determine if a combination of these metrics could identify pre-lambing activity that would enable informed management decisions to be made.

GPS observation of shelter utilisation by Merino ewes

The present study examined how shelter availability, altitude and temperature influence paddock utilisation by pregnant and lambing Merino ewes. Global positioning systems (GPS) attached to collars allowed continuous tracking of ewes’ positions within two paddocks, and environmental conditions were also monitored throughout this time using temperature loggers. Animal tracking devices (UNEtracker GPS collars) were used in the spring (September–November) of 2008 (51 days) and 2009 (43 days), 14 days post-shearing, to monitor movement of pregnant grazing fine-wool Merino ewes (5 per paddock per year). The data were used to examine sheep use of lone trees, interior shelter, perimeter shelter and remainder of the paddock during three distinct diurnal activity periods, namely night camping, morning grazing and afternoon grazing. Regular use of shelter was consistently recorded in the two experimental years and in both paddocks. The ewes consistently used sheltered areas and both the leeward and windward sides of shelter, particularly during high sheep chill days. The sheep used the sheltered areas significantly more often than they used the remainder of the paddock, which was devoid of shelter except for lone trees. Night camping did not occur at the highest altitude, but predominantly where shelter was also located. The present study has demonstrated the consistent use of shelter by sheep. The shelter-seeking behaviour of the ewes a month post-shearing suggests that these animals are more sensitive to weather conditions than has been previously reported.

Adaptation behaviour of local and rangeland cattle relocated to a temperate agricultural pasture

Relocating cattle from rangeland properties to agricultural pastures in southern Western Australia allows producers to improve year-round continuity of feed supply in their beef cattle businesses, and can reduce substantially the time taken to grow animals to meet market specifications. In this study the behaviour and growth of two groups of young cattle that were sourced from different locations was evaluated after they were transferred to a new extensive grazing system. In Experiment 1, 122 Limousin-cross heifers that were raised in the agricultural region of Western Australia (AR cattle) were transferred to the experimental site from a neighbouring property (~10 km away). In Experiment 2, 95 Brahman-cross heifers that were raised in the rangelands of Western Australia (RR cattle) were transferred to the experimental site. Animal growth and behaviour were analysed across time and differences in the time-course of behavioural changes between the groups were compared. Rate of liveweight gain in the AR cattle remained consistent (~1.2 kg/day) during the experiment. There was an increase in horizontal (16%; P < 0.001) and vertical (12%; P = 0.002) head movement in AR cattle over the first several weeks after relocation, indicative of increased grazing activity, but there was little change in other behaviours over the duration of the experiment. In contrast, the RR cattle had reduced growth during the first 4 weeks after relocation (P < 0.001). From weeks 2–4 weight gain in RR cattle was 0.31 kg/day, approximately one-quarter of their average daily gain attained 4 weeks later. During the first 6 weeks, RR cattle showed behavioural changes indicative of adaptation, including a 61% increase in horizontal head movements, suggesting more grazing activity. The paddock area utilised daily by RR cattle was 32% higher in week 6 compared with week 1, and during daylight hours (0600–1900 hours) they began to travel more (23%) and spent more time active (16%). We conclude that rangeland-raised Bos indicus heifers take from 4 to 6 weeks to adapt from their previous large paddocks/natural plant environment to a new temperate agricultural environment. Our results suggest that the cause of lost productivity in rangeland cattle when they are relocated to a temperate pasture is at least in part due to initial lower grazing activity as they become familiar with the new environment.