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The rise of sequencing technology brought about a surge of new methodologies that offered a new and deeper level of understanding of the role of the microbiome in the health and performance of livestock. This started a chain reaction in technology evolution, and a decade later, we have developed a new appreciation of a healthy gut and the role of early colonisation and nutrition in developing the microbiome, and its subsequent impact on animal productivity. An abundance of new products flooded the livestock supplement market with the promise of improving the health of intestinal microbiota. However, the impact of these products and any potential gains they might provide have not always been quantified or validated. Further to this, the potential interactions with the microbial community naturally occurring in the feed-base have not commonly been considered. We have recently shown that animal feed carries a complex microbial community that can have various impacts, including negating farm biosecurity measures. The ruminant animal provides an even greater level of complexity where physiological drivers act to maintain ruminal homeostasis. Despite many advances, numerous knowledge gaps remain, and the methodologies are not without their challenges with almost constant evolution in analysing and interpreting data. In this paper, we will discuss the benefits, challenges and shortfalls of microbiome science, its interfaces with multi-omics research and the strategies of its contribution to animal production science.

Water is an important nutrient, and its continuous provision is a critical welfare issue for cattle grazing arid and semiarid rangelands. Time and labor are needed to monitor water availability, and automated monitoring systems are a costly input on expansive rangeland pastures. The objective of this study was to evaluate the potential of detecting water system failures using Global Positioning System (GPS) tracking and accelerometers, assuming the data could be monitored in real or near-real time. Water system failure was simulated in a 1 096-ha pasture in Arizona by placing metal panels around the only drinker for 4 h (0800−1200) on three occasions in 2018 and two occasions in 2019. Randomly selected cows (10 in 2018 and 23 in 2019) of the 120 cows in the pasture were tracked with GPS collars, and 7 (2018) and 10 (2019) of the tracked cows were fitted with triaxial accelerometers. Movement intensity measured by accelerometers was greater (P = 0.03) on the day of simulated water failure than on control days with available water. During simulated water failure, cows remained closer to water (P = 0.01) after approaching the drinker (

Objectives GrandSchools is a new concept which co-locates retirement villages with secondary schools in one physical environment. Designed to enhance the health and well-being of both younger and older generations, this intergenerational-shared campus model promotes intergenerational inclusivity and active learning and living. In this paper, we explore stakeholder experts' perceptions of current opportunities and impediments to this proposed intergenerational learning and living model. Methods A qualitative study reporting on findings from an industry seminar (n = 50) and key interviews (n = 10) from stakeholders in education, health, higher education, the management and operation of retirement villages, and design firms. Results Three key themes summarised participants' assessment of the value, risks and what needs to change in order for intergenerational living and learning to become a reality. Conclusions By bringing younger and older generations together in one shared campus location, GrandSchools is a novel idea to promote intergenerational inclusivity, enhancing the health and well-being of our whole community.

This systematic review explores the use of on-animal sensors in sheep and their potential application in objective welfare monitoring. The key questions posed were: To what extent can current scientific knowledge inform a sensor-based approach to welfare evaluations? And, how might this knowledge shape development of commercial monitoring systems? These questions were explored through retrospective classification of published sensor applications using The Five Domains (FD) Model as a framework for animal welfare assessment. A total of 71 studies were reviewed. The results indicate studies specifically evaluating the use of sensors for welfare assessment are limited, though many experiments could still be related to some aspect of welfare. The assessment of sensor utilisation revealed the greatest proportion of applications within the ‘Behaviour’ Domain (90.1%; n = 64), and the lowest within the ‘Health’ (25.4%; n = 18) and ‘Mental state’ Domains (25.4%; n = 18). The review also highlights how different sensor types (location, motion or physiological) differ in their applicability for welfare assessment. This paper is the first to classify published sensor applications using the FD Model as a framework and highlights the potential for sensor technology in sheep welfare monitoring. The results suggest that any attempt to create a commercial sensor-based system for objective welfare assessment will require the integration of more than one sensor type, particularly if multiple Domains are to be addressed.

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 &lt, 0.25) at both spatial scales. Near the end of the study, cattle began to disperse likely in search of forages (p &lt, 0.01) and travelled farther (p &lt, 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.

Precision livestock management has become a new field of study as the result of recent advancements in real-time global positioning system (GPS) tracking, accelerometer and other sensor technologies. Real-time tracking and accelerometer monitoring has the potential to remotely detect livestock disease, animal well-being and grazing distribution issues and notify ranchers and graziers so that they can respond as soon as possible. On-going research has shown that accelerometers can remotely monitor livestock behavior and detect activity changes that are associated with disease and parturition. GPS tracking can also detect parturition by monitoring the distance between a ewe and the remainder of the flock. Tracking also has the potential to detect water system failures. Combinations of GPS tracking and accelerometer monitoring may be more accurate than either device used by itself. Real-time GPS tracking can identify when livestock congregate in environmental sensitive areas which may allow managers the chance to respond before resource degradation occurs. Identification of genetic markers associated with terrain use, decreased cost of GPS tracking and novel tracking data processing should facilitate development of tools needed for genetic selection for cattle grazing distribution. Precision livestock management has potential to improve welfare of livestock grazing rangelands and forested lands, reduce labor costs and improve ranch profitability and improve the condition and sustainability of riparian areas and other environmental sensitive areas on grazing lands around the world.

Livestock vocalisations have been shown to contain information related to animal welfare and behaviour. Automated sound detection has the potential to facilitate a continuous acoustic monitoring system, for use in a range Precision Livestock Farming (PLF) applications. There are few examples of automated livestock vocalisation classification algorithms, and we have found none capable of being easily adapted and applied to different species’ vocalisations. In this work, a multi-purpose livestock vocalisation classification algorithm is presented, utilising audio-specific feature extraction techniques, and machine learning models. To test the multi-purpose nature of the algorithm, three separate data sets were created targeting livestock-related vocalisations, namely sheep, cattle, and Maremma sheepdogs. Audio data was extracted from continuous recordings conducted on-site at three different operational farming enterprises, reflecting the conditions of real deployment. A comparison of Mel-Frequency Cepstral Coefficients (MFCCs) and Discrete Wavelet Transform-based (DWT) features was conducted. Classification was determined using a Support Vector Machine (SVM) model. High accuracy was achieved for all data sets (sheep: 99.29%, cattle: 95.78%, dogs: 99.67%). Classification performance alone was insufficient to determine the most suitable feature extraction method for each data set. Computational timing results revealed the DWT-based features to be markedly faster to produce (14.81 – 15.38% decrease in execution time). The results indicate the development of a highly accurate livestock vocalisation classification algorithm, which forms the foundation for an automated livestock vocalisation detection system.

Livestock industries are increasingly embracing precision farming and decision support tools. As a result, sensors, weather stations, individual animal tracking, feed monitoring and other sources create large data volumes, much of which is used only for a single purpose. There are unrealised potential benefits of making on farm data interoperable and accessible and federating it with public data sources. We reviewed recent literature on precision livestock farming (PLF) technologies in relation to the use of public data, open standards and interoperability. Livestock farms produce rising volumes of disparate private datasets, reflecting a variety of information needs and technological opportunities, but typically lacking interoperable formats and metadata. These as well as large amounts of accessible public datasets are currently underutilised in decision support tools. Tools that demonstrate the use of interoperable standards and bring together public and private data for decision support can enhance the value proposition and help lower barriers to the sharing and re-use of data. This review of interoperable standards in extensive livestock farming systems concludes that there is a need for not only a new type of decision support tool, but also a consensus on data exchange standards to prove the value of shared data at farm scale (commercial benefit) and a regional scale (public good).

Livestock production in rangeland environments is often fraught with numerous challenges that can limit regular human-animal interaction. As such, cattle in these environments can remain unmonitored for days or months at a time. The integration of on-animal sensing systems in these environments could provide a means for producers to remotely monitor their animals without incurring the significant labour costs associated with manually checking on their livestock. These devices could be used to monitor critical times in a cow’s life, such as calving. Parturition represents a high-risk period in both a cow and calf’s life, where the risk of disease or death is greatly increased. Additional monitoring in this period is beneficial, such that any complications can be promptly detected and managed. On-animal sensing systems could be used to identify when or if calving has successfully occurred and to infer the status of the calf in the first few months of life. For a precise and well-integrated system to be developed, there needs to be a greater understanding of the data required at each stage. This study will provide a conceptual overview and systems design outlining how an on-animal sensor system could be used to detect calving and calf status and provide updates to the producer in the event of complications. This research is funded by Central Queensland University, Meat and Livestock Australia, and Telstra.

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.

This systematic review explores the use of on-animal sensor technology in sheep research. A total of 71 peer-reviewed articles reporting on 82 independent experiments were reviewed, ranging in publication date from 1983 to 2017 and distributed across all populated continents. The findings demonstrate increasing numbers of published studies that validate the application of sensor technology to categorise and quantify sheep behaviour. The studies also used sheep sensors for environmental management, validation of data analysis methods and for health and welfare research. Whilst historically many applications of sensors in sheep research have been conducted over a short period with small numbers of experimental animals, this trend appears to be changing as technology develops and access improves. The literature suggests that many applications of sensors have already or are currently moving through a proof-of-concept stage, allowing future applications to focus on commercialisation of technology and potential integration with other technologies already in use (e.g. weather data).

An animal’s behaviour can be a useful indicator of their physiological and physical state. As resting, eating, walking and ruminating are the predominant daily activities of ruminant animals, monitoring these behaviours could provide valuable information for management decisions and individual animal health status. Traditional animal monitoring methods have relied on labour intensive, human observation of animals. Accelerometer technology offers the possibility to remotely monitor animal behaviour continuously 24/7. Commercially, an ear worn sensor would be the most suitable for the Australian sheep industry. Therefore, the aim of this current study was to determine the effectiveness of different methods of accelerometer deployment (collar, leg and eartag) to differentiate between three mutually exclusive behaviours in sheep: grazing, standing and walking. A subset of fourteen summary features were subjected to Quadratic Discriminant Analysis (QDA) with 94%, 96% and 99% of grazing, standing and walking events respectively, being correctly predicted from ear acceleration signals. These preliminary results are promising and indicate that an ear deployed accelerometer is capable of identifying basic sheep behaviours. Further research is required to assess the suitability of accelerometers for behaviour detection across different sheep classes, breeds and environments.

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.

We investigated relationship between pasture biomass and measures of height and NDVI (normalised difference vegetation index). The pastures were tall fescue (Festuca arundinacea), perennial ryegrass (Lolium perenne), and phalaris (Phalaris aquatica) located in Tasmania, Victoria and in the Northern Tablelands of NSW, Australia. Using the Trimble® GreenSeeker® Handheld active optical sensor (AOS) to measure NDVI, and a rising plate meter, the optimal model to estimate green dry biomass (GDM) during two years was a combination of NDVI and falling plate height index. The combined index was significantly correlated with GDM in each region during winter and spring (r2=0.62–0.77, P

Whilst agriculture is Australia’s fastest growing industry, the negative perception of career opportunities by high school students and the lack of educator confidence in teaching about food and fibre concepts is a major issue currently faced by the sector. The Teacher Farm Experience (TeacherFX), a joint program of Rabobank’s Western Australia Client Council and CQUniversity Australia, aims to increase awareness, knowledge and appreciation of the agricultural industry. This free two-day program designed for teachers entailed visiting four farms in the Great Southern region of WA on the first day and professional learning on the second day. Pre- and post- event surveys were conducted to gain baseline information on the participants, their perceptions of agriculture, quality of learning materials and reaction to the experience. Additional support in the form of professional development and networking opportunities was identified as required to assist teachers to implement learnings from TeacherFX. Event survey results were overwhelmingly positive, with 100% of teachers recommending their colleagues attend a future event. However, whether this positive result will translate to change in the classroom is unknown. Additional research needs to be conducted to measure the long-term impact of the program.

Within the agriculture sector the use of digital technologies is rapidly expanding. There is a growing shortage of skilled people considering a career within this sector to support the uptake of agricultural technology. The GPS Cows program is designed to improve secondary school student’s knowledge and skills of emerging agricultural technologies. It highlights a range of opportunities and potential career options available to students in agriculture. This collaborative project combines the expertise and passion of researchers, industry professionals and educators in both Australia and the USA. A pilot workshop was run with ten teachers from nine Queensland and New South Wales secondary schools. Teachers participated in lectures and practical workshops, developed data analysis skills and took part in a World Café style focus group. The focus group findings highlighted that for the GPS Cows program to be implemented in secondary school classrooms, excellent resources from the GPS Cows team are needed, combined with ongoing support and guidance. Nevertheless, the participating teachers felt that their students would both engage and enjoy participating in the GPS Cows program and realise the opportunities the agricultural sector offers.

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.

Tracking the movement of grazing Merino ewes using Global Navigational Satellite Systems (GNSS) provided data which could be used to determine the behavioural characteristics exhibited during different physiological states and times-within-day. GNSS location and the resulting movement calculations from five foraging Merino ewes of known lambing date were partitioned into four different 24 h physiological states (PS): (1) seven days before lambing (L − 7), (2) day of lambing (L), (3) three days after lambing (L + 3) and (4) seven days after lambing (L + 7). Within each of these four PS the data were further partitioned into three time-within-day (TWD) intervals based on behavioural patterns: (1) 0500 to 1159 h morning foraging; (2) 1200 to 1859 h afternoon foraging and (3) 1900 to 0459 h night camping. Ewe locations were recorded at 10 min intervals over a 43 day period. Distances moved (m) and rates of travel (m/s) were analysed to characterise free-ranging ewe spatio-temporal activities within the four PS and three TWD periods. Physiological state and TWD had significant (P 0.05). Ewe rate of travel was significantly (P < 0.001) affected by PS and TWD and there was a significant (P < 0.01) two-way interaction. Ewes at L spent 13.2% less time foraging than ewes in other PS (mean 53.2%), while time spent in stationary behaviour accounted for 59.5% and ewes in other PS spent a mean of 45.2% of their time stationary. Distance moved and rate of travel during foraging were significantly (P < 0.001) affected by PS and TWD. GNSS collars were satisfactory to characterise behaviour of foraging Merino ewes at different PS and TWD. However, using ewe behaviour alone to identify when lambing occurs may not be a satisfactory husbandry option.

Bovine Ephemeral Fever affects livestock across Africa, Asia, and Australia with symptoms of fever, lameness, and inappetence. Currently, cattle producers must visually observe symptoms to detect Bovine Ephemeral Fever. The aim of this study is to determine the potential for accelerometer sensors deployed on the animal to identify when cattle are becoming ill with Bovine Ephemeral Fever. Two of 8 heifers fitted with 3-axis accelerometers became ill and were diagnosed with Bovine Ephemeral Fever by a veterinarian. Movement intensity was calculated from the x, y, z axes of the accelerometer readings recorded at 25 Hz and then averaged into 1-hour epochs. Accelerometer data from the two heifers that became ill were compared to three different sets of two randomly selected healthy heifers 2 days before and during the 24-hours before the onset of visually observable symptoms. Movement intensity of diagnosed heifers was lower (P ≤ 0.01) during the 24-hours before the symptoms were observed compared control heifers and levels observed 2 days before diagnosis. Accelerometers provided a clear signal that heifers were becoming ill before symptoms were observed by the manager and before diagnosis by a veterinarian. This case study shows that on-animal accelerometer sensors have the potential to remotely detect a disease such as Bovine Ephemeral Fever.

On-animal sensor systems provide an opportunity to monitor ewes during parturition, potentially reducing ewe and lamb mortality risk. This study investigated the capacity of machine learning (ML) behaviour classification to monitor changes in sheep behaviour around the time of lambing using ear-borne accelerometers. Accelerometers were attached to 27 ewes grazing a 4.4 ha paddock. Data were then classified based on three different ethograms: (i) detection of grazing, lying, standing, walking; (ii) detection of active behaviour; and (iii) detection of body posture. Proportion of time devoted to performing each behaviour and activity was then calculated at a daily and hourly scale. Frequency of posture change was also calculated on an hourly scale. Assessment of each metric using a linear mixed-effects model was conducted for the 7 days (day scale) or 12 h (hour scale) before and after lambing. For all physical movements, regardless of the ethogram, there was a change in the days surrounding lambing. This involved either a decrease (grazing, lying, active behaviour) or peak (standing, walking) on the day of parturition, with most values returning to either pre-partum or near-pre-partum levels (all P < 0.001). Hourly changes also occurred for all behaviours (all P < 0.001), the most marked being increased walking behaviour and frequency of posture change. These findings indicate ewes were more restless around the time of parturition. Further application of this research should focus on development of algorithms that can be used to identify onset of lambing and/or time of parturition in pasture-based ewes.

The application of accelerometer sensors for automated animal behaviour monitoring is becoming increasingly common. Despite the rapid growth of research in this area, there is little consensus on the most appropriate method of data summation and analysis. The objective of this current study was to explore feature creation and machine learning (ML) algorithm options to provide the most accurate behavioural classification from an ear-borne accelerometer in extensively grazed sheep. Nineteen derived movement features, three epochs (5, 10 and 30 s) and four ML-algorithms (Classification and Regression Trees (CART), Linear Kernel Support-Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)) were assessed. Behaviour classification was also evaluated using three different ethograms, including detection of (i) grazing, lying, standing, walking; (ii) active and inactive behaviour; and (iii) body posture. Detection of the four mutually-exclusive behaviours (grazing, lying, standing and walking) was most accurately performed using a 10 s epoch by an SVM (76.9%). Activity was most accurately detected using a 30 s epoch by a CART (98.1%). LDA and a 30 s epoch was superior for detecting posture (90.6%). Differentiation relied on identification of disparities between behaviours rather than pattern recognition within a behaviour. The choice of epoch and ML algorithm will be dependent on the application purpose, with different combinations of each more accurate across the different ethograms. This study provides a crucial foundation for development of algorithms which can identify multiple behaviours in pasture-based sheep. This knowledge could be applied across a number of contexts, particularly at times of change in physiological or mental state e.g. during parturition or stress-inducing husbandry procedures.

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 < 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 < 0.001). Day of lambing was also evident by reduced spatial paddock utilisation (P < 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.

Simple Summary Monitoring livestock farmed under extensive conditions is challenging and this is particularly difficult when observing animal behaviour at an individual level. Lameness is a disease symptom that has traditionally relied on visual inspection to detect those animals with an abnormal walking pattern. More recently, accelerometer sensors have been used in other livestock industries to detect lame animals. These devices are able to record changes in activity intensity, allowing us to differentiate between a grazing, walking, and resting animal. Using these on-animal sensors, grazing, standing, walking, and lame walking were accurately detected from an ear attached sensor. With further development, this classification algorithm could be linked with an automatic livestock monitoring system to provide real time information on individual health status, something that is practically not possible under current extensive livestock production systems. Abstract Lameness is a clinical symptom associated with a number of sheep diseases around the world, having adverse effects on weight gain, fertility, and lamb birth weight, and increasing the risk of secondary diseases. Current methods to identify lame animals rely on labour intensive visual inspection. The aim of this current study was to determine the ability of a collar, leg, and ear attached tri-axial accelerometer to discriminate between sound and lame gait movement in sheep. Data were separated into 10 s mutually exclusive behaviour epochs and subjected to Quadratic Discriminant Analysis (QDA). Initial analysis showed the high misclassification of lame grazing events with sound grazing and standing from all deployment modes. The final classification model, which included lame walking and all sound activity classes, yielded a prediction accuracy for lame locomotion of 82%, 35%, and 87% for the ear, collar, and leg deployments, respectively. Misclassification of sound walking with lame walking within the leg accelerometer dataset highlights the superiority of an ear mode of attachment for the classification of lame gait characteristics based on time series accelerometer data.

The ability to identify areas of pasture that are more likely to support damaging levels of the soil-borne, redheaded cockchafer (Adoryphorus couloni) (Burmeister) (RHC) would allow farmers to target expensive control measures. This study explored soil properties, measured via electromagnetic surveys (EM38), pasture biomass via active optical sensors (CropCircle™) and topography via GPS elevation survey as potential indicators of RHC population density. A combination of these variables was used to produce risk maps with an accuracy of 88 % at predicting likely RHC density-categories on a dairy property in the Gippsland region of Victoria, Australia. This risk mapping protocol could be used to improve sampling programs and direct site-specific pest management.

The amount of photosynthetically active radiation (PAR, 0.4–0.7 μm) absorbed by plants for photosynthesis relative to incident radiation is defined as the fraction of absorbed photosynthetically active radiation (fAPAR). This is an important variable in both plant biomass production and plant growth modeling. This study investigates the application of a newly developed, linear irradiance sensor (LightScout Quantum Bar Sensor, LightScout, Spectrum Technologies, Inc. USA), to quantify fAPAR for a demonstrator crop, Triticale (X Triticosecale Wittmack). A protocol was devised for sensor placement to determine reflected PAR components of fAPAR and to determine the optimal time of day and sensor orientation for data collection. Coincident, top of canopy, normalized difference vegetation index (NDVI) measurements were also acquired with a CropCircle™ ACS-210 sensor and measurements correlated with derived fAPAR values. The optimum height of the linear irradiance sensor above soil or plant canopy was found to be 0.4 m while measuring reflected PAR. Measurement of fAPAR was found to be stable when conducted within 1 h of local solar noon in order to avoid significant bidirectional effects resulting from diurnal changes of leaf orientation relative to the vertically-placed sensor. In the row crop studied, averaging fAPAR readings derived from the linear irradiance sensor orientated across and along the plant row provided an R2 = 0.81 correlation with above-canopy NDVI. Across row sensor orientation also gave a similar correlation of R2 = 0.76 allowing the user to reduce sampling time.

Here, a Web of Things case study for agriculture focuses on an experimental smart farm that uses a range of environmental sensors and livestock monitoring technologies. An ontology-enabled architecture that permits personal alert specification was applied on the farm and the results analyzed; the observations are published as a linked data cube, which supports longer-term analysis and data sharing from a local to national scale. This case study highlights the business opportunities for such technology for agriculture in the context of a national broadband rollout.

The movement distances of merino sheep were monitored over a 24 h period using global positioning system tracking collars and correlated with their faecal egg counts. A linear relationship between the logarithm of faecal egg count and the mean distance moved per time step was found. The results suggest that animal behaviour and productivity outcomes might be influenced even at low faecal egg count levels. The considerable variability of the observations about this linear model suggests that it might be useful for identifying those animals inherently resistant to internal parasites. Such technology could be used as part of a breeding program aimed at improving stock resistance to internal parasites.

The use of precision farming technology for enhanced farm management will play an essential role in future Australian livestock farming systems. Currently a gap exists between the potential perceived by precision technology developers and the on-farm benefits achieved by farmers. The concept of technological innovation systems is used in this paper to discuss the current challenges for successful precision farming in practice through case studies of farmers using the technologies in the dairy, beef, and sheep sectors. These case studies highlight the need for greater emphasis on the “user” phase of the technology development cycle. The concept of technological innovation systems provides a path forward for agricultural engineers where a more holistic approach is taken to technology development, and where the processes of learning, knowledge creation, and ongoing adaptation occur alongside new technology design. The value proposition for farmers needs to be clear to encourage them to invest time an...

Highlights? A high-powered, LED crop sensor of NDVI tested over 80ha field. ? Altitudes ranging from 15 to 45 m above the canopy. ? A detailed on-ground NDVI survey indicated high correlations. ? Testing exemplar NDVI-based algorithms indicate no significant effect on predictions. Recently reported testing of active, optical crop sensors in low-level aircraft have demonstrated a new class of airborne sensing system that can be deployed under any ambient illumination conditions, including at night. A second-generation, high-powered, light-emitting diode system has been assembled and tested over a 80ha field of wheat (Triticum aestevum) by mapping the normalised difference vegetation index (NDVI) at altitudes ranging from 15 to 45 m above the canopy; significantly higher altitudes than existing systems. Comparisons with a detailed on-ground NDVI survey indicated the aerial sensor values were highly correlated to the on-ground sensor (0.79

The aims of this work were to (1) develop a low-cost equine movement tracking collar based on readily available components, (2) conduct preliminary studies assessing the effects of both paddock size and internal fence design on the movements of domestic horses, with and without foals at foot, and (3) describe distances moved by mares and their foals. Additional monitoring of free-ranging feral horses was conducted to allow preliminary comparisons with the movement of confined domestic horses. A lightweight global positioning system (GPS) data logger modified from a personal/vehicle tracker and mounted on a collar was used to monitor the movement of domestic horses in a range of paddock sizes and internal fence designs for 6.5-day periods. In the paddocks used (0.8-16 ha), groups of domestic horses exhibited a logarithmic response in mean daily distance travelled as a function of increasing paddock size, tending asymptotically towards approximately 7.5 km/day. The distance moved by newborn foals was similar to their dams, with total distance travelled also dependent on paddock size. Without altering available paddock area, paddock design, with the exception of a spiral design, did not significantly affect mean daily distance travelled. Feral horses (17.9 km/day) travelled substantially greater mean daily distances than domestic horses (7.2 km/day in 16-ha paddock), even when allowing for larger paddock size. Horses kept in stables or small yards and paddocks are quite sedentary in comparison with their feral relatives. For a given paddock area, most designs did not significantly affect mean daily distance travelled.

Operational airborne and satellite remote sensing in agriculture remains constrained by matching platform availability to suitable daytime weather and illumination conditions, crop development, and availability of ground staff. An ultra low-level aircraft carrying an active NIR/Red CropCircle(TM) sensor was successfully deployed to record and subsequently map crop vigour via the simple ratio (SR) index over a field of sorghum. Given the logging frequency of ~20Hz and the presence of alternate rows of bare soil, the Moire effect reduced the contrast between crop and bare soil skip-rows. Such effects would not be expected to occur in non-skip-row crops. The ultra low-level airborne (ULLA)-SR map derived from the 20m transect records compared favorably with the SR map derived from a meter-resolution airborne digital multispectral image that was re-sampled to a similar spatial resolution. This case study, involving a CropCircle(TM) sensor mounted in a low-level aircraft demonstrates another deployment option for users of this class of sensor. Moreover, an ULLA configuration offers the potential for greater flexibility in scheduling compared to airborne imaging, given it can be flown at any sun-angle, under cloud, at night, and may easily be incorporated into aircraft already conducting low-level operations, for example crop dusting and reconnaissance, over agricultural fields.

Quad bikes (all-terrain vehicles) are the leading cause of nonintentional farm injury deaths in Australia. Approximately 50% result from rollovers of the vehicle, with death frequently due to crush injury/asphyxiation. Crush protection devices fitted to quads have been demonstrated to enhance safety. This exploratory study aimed to assess feedback from a small group of dairy farmers (N = 11) that had crush protection devices fitted to their quads. A mixed-methods design was adopted consisting of pre- and post-intervention surveys in addition to focus groups/interviews for this exploratory study. Results indicate that the crush protection device was viewed positively by the majority of participants, with virtually no impact on the reported performance of the quad in terms of braking, steering, suspension, and getting on/off. The most problematic issue was some contact with overhead objects, although the majority reported no issues at all. Some issues were also identified with attaching trailers, carrying loads, and noise from rattling. The participants reported that the likely potential benefits of a crush protection device clearly outweighed these issues. Some 4 to 6 months following the study, 10 of the 11 farmers continued to have the crush protection device fitted to their quad. The major barrier to adoption was the manufacturers' resistance and negative public statements regarding the effectiveness of crush protection devices. Participants suggested that there are steps that can be taken to increase the uptake of crush protection devices by dairy farmers. Further lag in promoting these potentially life-saving devices will only add to the death and injury burden associated with quad bikes.

Medication use is high in the older population. The prevalence of polypharmacy and complex medication regimes coupled with age-related changes makes the older adult prone to experience adverse drug reactions and drug interactions. Changes in pharmacokinetic and pharmacodynamic parameters with age ne

In temperate and mediterranean regions of Australia, utilisation of pastures by grazing animals can often be as low as thirty percent. Feed budgeting is a critical strategy for improving feed utilisation and there are now pasture evaluation and monitoring programs available to farmers across Australia to enable them to estimate Pasture Growth Rate (PGR) and feed availability or Feed On Offer (FOO). Unfortunately, many farmers do not have the confidence or the time available to make regular accurate field estimates across large and remote paddocks. It is also extremely difficult to measure the spatial variation of FOO and PGR. Both satellite image-based systems, such as that derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and on-ground, active sensors and spectroradiometers have limitations. The MODIS sensor, although offering a daily acquisition interval, has a spatial resolution of 6.25ha which does create an issue in describing higher resolution spatial variation in fields and is susceptible to the unwanted artefacts associated with non-forageable vegetation such as trees. On-ground (proximal) sensors such as the Crop Circle™ instrument, can be integrated with with global positioning systems (GPS) and dataloggers and operated ‘on-the-go’, but field-coverage require a vast number of samples that in turn require tedious analysis and particularly if done at frequent intervals. The aim of this paper is to test whether the MODIS and Crop Circle™ data can be intergrated to provide the benefit of both high spatial and temporal resolution. Such information would be useful when determining feed on offer and pasture growth rate information at weekly or strategic times from MODIS acquisitions including other biophysical and physical relevant attributes. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

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.

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.

The predation of sheep (Ovis aries) by wild and domestic dogs (Canis lupis) is a major issue in Australia, causing serious welfare issues to inflicted animals. The estimated cost of sheep and cattle production losses caused by wild dogs when combined with an extensive range of control measures, costs the Australian economy AU$66 million annually. Spatio-temporal data derived from global navigation satellite system (GNSS) devices were used to quantify the behavioural responses of two flocks of 15 Merino ewes ranging from 2 to 8 years old (average 4.5 years) during simulated dog predation events. Each sheep was fitted with a GNSS collar, and the behavioural responses of the sheep were video recorded during six trials (three per flock). The behavioural data collated from video recordings were then compared with the movement metrics derived from the GNSS collars. Derived metrics include the spatial distribution of flock members, speed of animal movement and specific behavioural changes including centripetal rotation (circling behaviour of the flock, with individual sheep seeking the centre). While the spatial distribution data did not appear to be specific enough to enable identification of a predation event, the velocity of sheep was higher (P < 0.001) during compared with before and after a simulated dog predation event. Centripetal rotation occurred in 80% of the simulated predation events during this study, and may provide a means for identifying predation. The spatio-temporal data from GNSS devices have potential as a research tool to assist in understanding sheep movement patterns during a dog attack. While further research and mathematical modelling of predation events is clearly required, the application of remote sensing technology has the potential to improve future livestock monitoring.

Monitoring pasture growth rate is an important component of managing grazing livestock production systems. In this study, we demonstrate that a pasture growth rate (PGR) model, initially designed for NOAA AVHRR normalised difference vegetation index (NDVI) and since adapted to MODIS NDVI, can provide PGR at spatial resolution of ~2 m with an accuracy of ~2 kg DM/ha.day when incorporating in-situ sensor data. A PGR model based on light-use efficiency (LUE) was combined with in-situ measurements from proximal weather (temperature), plant (fraction of absorbed photosynthetically active radiation, fAPAR) and soil (relative moisture) sensors to calculate the growth rate of a tall fescue pasture. Based on an initial estimate of LUEmax for the candidate pasture, followed by a process of iterating LUEmax to reduce prediction errors, the model was capable of estimating PGR with a root mean square error of 1.68 kg/ha.day (R2 = 0.96, P-value ≈ 0). The iterative process proved to be a convenient means of estimating LUE of this pasture (1.59 g DM/MJ APAR) under local conditions. The application of the LUE-PGR approach to developing an in-situ pasture growth rate monitoring system is discussed.

Nutrient-use efficiency is a key issue for grazing systems in Australia. Spatial variability in soil pH and nutrients at the sub-paddock scale may affect the efficiency of utilisation of, and provide an opportunity for, site-specific management (SSM) of fertiliser and soil ameliorants. However, there has been little research exploring the potential for SSM in grazing systems. This study examines the spatial variability of soil test pH, phosphorus (P), potassium (K) and sulfur (S) in two typical pasture fields (a native and an improved) on the Northern Tablelands of New South Wales and evaluates the potential for SSM based on a comparison with critical values. In both fields, the overall paddock mean from a grid survey containing >80 samples for pH, P, K and S (0–10 cm) exceeded the critical values, suggesting that the addition of fertiliser or lime was not required. However, considerable sub-paddock-scale variability was observed, with CV ranging from 35% to 66% for the key nutrients (P, K and S). The Sprengel–Liebig Law of the Minimum was applied to evaluate the proportion of each field constrained by one or more soil characteristics. Up to 55% of the improved paddock and 78% of the native pasture was potentially responsive to amendments. The results of this study suggest that SSM of fertilisers and ameliorants could provide substantial improvements in productivity and possibly reductions in fertiliser use. The development and application of appropriate systems and tools to effectively quantify this spatial variability remain a challenge, coupled with management strategies that optimise the placement of amendments and account for the variability in other production limiting factors.

Scattered paddock trees occur across agricultural landscapes in Australia. However, in the temperate regions of Australia their numbers are rapidly declining and they may be lost across much of the landscape in 200 years. Here we examined the spatial distribution of green (GDB), senescent (SDB) and total (TDB) dry pasture biomass, and nutrient status of the GDB fraction around scattered Eucalyptus trees on three parent materials (basalt, granite and meta-sediment) in native and sown pastures across a range of grazed temperate landscapes in northern New South Wales. We used a combination of destructive harvests and a handheld active optical canopy reflectance sensor (AOS) with an integrated GPS to examine pasture biomass around scattered trees. The harvested pasture biomass data indicated that under grazed conditions the presence of scattered trees did not introduce significant radial trends in TDB or GDB out to a distance of 3.5 canopy radii regardless of tree species or parent material. The red and near-infrared reflectance-based Normalised Difference Vegetation Index (NDVI), as measured by the AOS, did indicate a consistent azimuthal trend with larger GDB on the southern side of the tree and lower GDB on the northern side in the native pasture. However, this observation must be qualified as the regression coefficient for the relationship between NDVI and GDB was significant but weak (best r2 = 0.42), and SDB reduced its predictive capacity. We also found a higher percentage of GDB under the canopy than in the open paddock. We suggest that the combination of these results may indicate higher grazing pressure under trees than in the open paddock. Pasture nutrient concentration (P, K and S) was higher in both native and sown pastures beneath the tree canopy compared with the open paddock. This study indicates that, in this temperate environment, scattered trees do not adversely affect pasture production, and that they can improve some pasture nutrients.

The use of global satellite navigation system tracking as a research tool for monitoring livestock activity is increasing. Commercial systems are being developed for the livestock industry. This paper reports on the development of a low-cost, store-on-board Global Positioning System collar suitable for large-scale deployment in livestock herds. A robust collar design that avoids the necessity of external cables has been designed and was tested on beef cattle in western New South Wales. Configured for alternating wake and sleep modes to conserve battery life, the collars obtained a positional fix on 99.9% of attempts. Numerous alternatives for presenting extracted data, based on average diurnal activity, mean daily velocity, Livestock Residence Index and dry sheep equivalent maps are introduced and discussed.