Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

The effect of a non-steroidal anti-inflammatory drug on the locomotor activity of reindeer (Rangifer tarandus tarandus) on their natural pastures after clamp castration-a pilot study

BACKGROUND

During seasonal round-ups, free-grazing reindeer are gathered from natural pastures. Reindeer bulls removed from breeding are clamp castrated, traditionally without analgesia, and then returned to the grazing grounds. The new Finnish Animal Welfare Act requires the use of analgesia in painful procedures. Our earlier studies have shown that a single dose of the non-steroidal anti-inflammatory drug (NSAID) meloxicam may maintain therapeutic plasma concentrations for 2-3 days in reindeer. No studies have been conducted on the effect of meloxicam on the locomotor activity of free-ranging castrated reindeer after castration. We installed GPS collars on 16 male reindeer (at least 5 years old, 130-160 kg), chosen to be castrated as a standard procedure during the round-up held on 5 Oct 2020. Of these, eight were randomly selected to receive approximately 0.5 mg/kg of meloxicam subcutaneously (NSAID group) and eight received no analgesia (TRAD group). The trackers were set to provide location twice per hour with 10 m accuracy. From the GPS data, we calculated the daily distances travelled by the reindeer during the 3 days after castration and analysed the differences between the treatments using a GEE model. Fixed factors were treatment (NSAID or TRAD), days (1-3) and hours, and the interactions between these variables. Our key presumption was that a meloxicam injection can reduce the pain related restless locomotion of newly clamp castrated reindeer.

RESULTS

The overall mean ± SE daily distances travelled by NSAID (n = 8) and TRAD (n = 8) reindeer did not differ (6.60 ± 0.67 km vs. 8.60 ± 1.54 km). However, all reindeer (n = 16) moved more on day 1 than day 3. TRAD reindeer travelled farther than NSAID on day 1 (11.67 ± 2.25 km vs. 7.08 ± 0.61 km, P < 0.05), but no differences were observed on days 2 or 3 due to high variation (10.19 ± 3.87 km vs. 6.59 ± 0.85 km and 5.35 ± 0.39 km vs. 6.17 ± 0.70 m, P > 0.1). NSAID movement remained stable between the days (P > 0.1), while TRAD activity declined (P = 0.002), levelling with NSAID by day 3. Daytime distances exceeded nighttime distances on days 2 and 3, with TRAD showing more disrupted daily rhythms.

CONCLUSIONS

Meloxicam may reduce restlessness in newly castrated reindeer, changing postoperative locomotor activity patterns in a way that suggests pain alleviation during the first 2-3 days following clamp castration. Further studies are needed on the use of analgesia and GPS collars for pain monitoring in freely grazing reindeer.

From tradition to precision: leveraging digital tools to improve cattle health and welfare

Traditional cattle production practices relied heavily on manual observation and empirical decision-making, often leading to inconsistent outcomes. In contrast, modern approaches leverage technology to achieve greater precision and efficiency. Advancement in technology has shifted to a new dimension of predictive and monitoring in cattle health management. This review aims at highlighting the available and current digital technologies in cattle health, evaluate their utility in practice, and identify possible future advancements in the field that can potentially bring even more changes to this industry. The paper highlights some of the barriers and disadvantages of using these technologies, such as data security issues, high capital investments, and skills gap. The integration of these advanced technologies is set to play a fundamental role in enabling the livestock industry to meet the rising global demand for high-quality, sustainably produced products. These technologies are essential for ensuring compliance with ethical standards and best practices in cattle care and well-being. In light of these advancements, the application of digital innovations will support the achievement of socially responsible cattle production, while simultaneously maintaining optimal levels of animal health and welfare.

Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior

This study classified cows' foraging behaviors using machine learning (ML) models evaluated through random test split (RTS) and cross-validation (CV) data partition methods. Models included Perceptron, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest (RF), and XGBoost (XGB). These models classified activity states (active vs. static), foraging behaviors (grazing (GR), resting (RE), walking (W), ruminating (RU)), posture states (standing up (SU) vs. lying down (LD)), and posture combinations with rumination and resting behaviors (RU_SU, RU_LD, RE_SU, and RE_LD). XGB achieved the highest accuracy for state classification (74.5% RTS, 74.2% CV) and foraging behavior (69.4% CV). RF outperformed XGB in other classifications, including GR, RE, and RU (62.9% CV vs. 56.4% RTS), posture (83.9% CV vs. 79.4% RTS), and behaviors-by-posture (58.8% CV vs. 56.4% RTS). Key predictors varied: speed and Actindex were crucial for GR and W when increasing and for RE and RU when decreasing. X low values were linked to RE_SU and RU_SU, while X and Z influenced RE_LD more. RTS showed higher accuracy in activity states classification while CV in foraging behaviors and by posture classification. These results emphasize CV in RF's reliability in managing complex behavioral patterns and the importance of continuous recording devices and movement data to monitor cattle behavior accurately.

BiAF: research on dynamic goat herd detection and tracking based on machine vision

As technology advances, rangeland management is rapidly transitioning toward intelligent systems. To optimize grassland resources and implement scientific grazing practices, livestock grazing monitoring has become a pivotal area of research. Traditional methods, such as manual tracking and wearable monitoring, often disrupt the natural movement and feeding behaviors of grazing livestock, posing significant challenges for in-depth studies of grazing patterns. In this paper, we propose a machine vision-based grazing goat herd detection algorithm that enhances the streamlined ELAN module in YOLOv7-tiny, incorporates an optimized CBAM attention mechanism, refines the SPPCSPC module to reduce the parameter count, and improves the anchor boxes in YOLOv7-tiny to enhance target detection accuracy. The BiAF-YOLOv7 algorithm achieves precision, recall, F1 score, and mAP values of 94.5, 96.7, 94.8, and 96.0%, respectively, on the goat herd dataset. Combined with DeepSORT, our system successfully tracks goat herds, demonstrating the effectiveness of the BiAF-YOLOv7 algorithm as a tool for livestock grazing monitoring. This study not only validates the practicality of the proposed algorithm but also highlights the broader applicability of machine vision-based monitoring in large-scale environments. It provides innovative approaches to achieve grass-animal balance through information-driven methods, such as monitoring and tracking.

Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping

Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.

FloatingBlue: A Delay Tolerant Networks-Enabled Internet of Things Architecture for Remote Areas Combining Data Mules and Low Power Communications

Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the "FloatingBlue" architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE's low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, "FloatingBlue" demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 µJ in sleep mode and 11.8 µJ in transmission mode. Our results highlight "FloatingBlue" as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches.

Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning Techniques

Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.

Distance examination of livestock with drones - an effective method for assessing health and welfare

Distance examination is an important part of veterinary investigation into ruminant herd health and welfare. The Department of Primary Industries and Regional Development (DPIRD) explored the use of drones to conduct assessments of the health and welfare status of sheep and cattle. Three methods of distance examination were compared comprising observations; from a vehicle, a "micro" category drone and a "very small" category drone. The disturbance and behavioural reactions caused by the methods were compared. Assessments of adverse health and welfare conditions by each method were compared to observations made at yarding. The preferred method was the use of the very small drone which had the best sensitivity for detection of conditions potentially associated with adverse health or welfare and the best optics at a distance that did not disturb the animals. The optics of the very small drone enabled distance examination without disturbance in both cattle and sheep. Cattle were more sensitive to the presence of the drones than sheep. The micro drone was unable to approach cattle close enough to allow undisturbed distance examination. All methods had similar specificity, however, sensitivity varied markedly. The very small drone had the best sensitivity 86% which was statistically greater than the micro drone (44%, P = 0.05) and better than the vehicle observations, which had sensitivity of 77% (not statistically significant). The selection of an appropriate drone model is essential for accurate distance examination. Distance examination of livestock with drones of suitable optic quality and resolution represents an effective method for assessing animal health and welfare.

Using Activity Measures and GNSS Data from a Virtual Fencing System to Assess Habitat Preference and Habitat Utilisation Patterns in Cattle

There has been an increased focus on new technologies to monitor habitat use and behaviour of cattle to develop a more sustainable livestock grazing system without compromising animal welfare. One of the currently used methods for monitoring cattle behaviour is tri-axial accelerometer data from systems such as virtual fencing technology or bespoke monitoring technology. Collection and transmission of high-frequency accelerometer and GNSS data is a major energy cost, and quickly drains the battery in contemporary virtual fencing systems, making it unsuitable for long-term monitoring. In this paper, we explore the possibility of determining habitat preference and habitat utilisation patterns in cattle using low-frequency activity and location data. We achieve this by (1) calculating habitat selection ratios, (2) determining daily activity patterns, and (3) based on those, inferring grazing and resting sites in a group of cattle wearing virtual fencing collars in a coastal setting with grey, wooded, and decalcified dunes, humid dune slacks, and salt meadows. We found that GNSS data, and a measure of activity, combined with accurate mapping of habitats can be an effective tool in assessing habitat preference. The animals preferred salt meadows over the other habitats, with wooded dunes and humid dune slacks being the least preferred. We were able to identify daily patterns in activity. By comparing general trends in activity levels to the existing literature, and using a Gaussian mixture model, it was possible to infer resting and grazing behaviour in the different habitats. According to our inference of behaviour the herd predominantly used the salt meadows for resting and ruminating. The approach used in this study allowed us to use GNSS location data and activity data and combine it with accurate habitat mapping to assess habitat preference and habitat utilisation patterns, which can be an important tool for guiding management decisions.

Application of Precision Technologies to Characterize Animal Behavior: A Review

This study aims to evaluate the state of precision livestock farming (PLF)'s spread, utilization, effectiveness, and evolution over the years. PLF includes a plethora of tools, which can aid in a number of laborious and complex tasks. These tools are often used in the monitoring of different animals, with the objective to increase production and improve animal welfare. The most frequently monitored attributes tend to be behavior, welfare, and social interaction. This study focused on the application of three types of technology: wearable sensors, video observation, and smartphones. For the wearable devices, the focus was on accelerometers and global positioning systems. For the video observation, the study addressed drones and cameras. The animals monitored by these tools were the most common ruminants, which are cattle, sheep, and goats. This review involved 108 articles that were believed to be pertinent. Most of the studied papers were very accurate, for most tools, when utilized appropriate; some showed great benefits and potential.

How do grazing beef and dairy cattle respond to virtual fences? A review

Virtual fencing (VF) is a modern fencing technology that requires the animal to wear a device (e.g., a collar) that emits acoustic signals to replace the visual cue of traditional physical fences (PF) and, if necessary, mild electric signals. The use of devices that provide electric signals leads to concerns regarding the welfare of virtually fenced animals. The objective of this review is to give an overview of the current state of VF research into the welfare and learning behavior of cattle. Therefore, a systematic literature search was conducted using two online databases and reference lists of relevant articles. Studies included were peer-reviewed and written in English, used beef or dairy cattle, and tested neck-mounted VF devices. Further inclusion criteria were a combination of audio and electrical signals and a setup as a pasture trial, which implied that animals grazed in groups on grassland for 4 h minimum while at least one fence side was virtually fenced. The eligible studies (n = 13) were assigned to one or two of the following categories: animal welfare (n studies = 8) or learning behavior (n studies = 9). As data availability for conducting a meta-analysis was not sufficient, a comparison of the means of welfare indicators (daily weight gain, daily lying time, steps per hour, daily number of lying bouts, and fecal cortisol metabolites [FCM]) for virtually and physically fenced animals was done instead. In an additional qualitative approach, the results from the welfare-related studies were assembled and discussed. For the learning behavior, the number of acoustic and electric signals and their ratio were used in a linear regression model with duration in days as a numeric predictor to assess the learning trends over time. There were no significant differences between VF and PF for most welfare indicators (except FCM with lower values for VF; P = 0.0165). The duration in days did not have a significant effect on the number of acoustic and electric signals. However, a significant effect of trial duration on the ratio of electric-to-acoustic signals (P = 0.0014) could be detected, resulting in a decreasing trend of the ratio over time, which suggests successful learning. Overall, we conclude that the VF research done so far is promising but is not yet sufficient to ensure that the technology could not have impacts on the welfare of certain cattle types. More research is necessary to investigate especially possible long-term effects of VF.

Rumination Detection in Sheep: A Systematic Review of Sensor-Based Approaches

The use of sensors to analyze behavior in sheep has gained increasing attention in scientific research. This systematic review aims to provide an overview of the sensors developed and used to detect rumination behavior in sheep in scientific research. Moreover, this overview provides details of the sensors that are currently commercially available and describes their suitability for sheep based on the information provided in the literature found. Furthermore, this overview lists the best sensor performances in terms of achieved accuracy, sensitivity, precision, and specificity in rumination detection, detailing, when applicable, the sensor position and epoch settings that were used to achieve the best results. Challenges and areas for future research and development are also identified. A search strategy was implemented in the databases PubMed, Web of Science, and Livivo, yielding a total of 935 articles. After reviewing the summaries of 57 articles remaining following filtration (exclusion) of repeated and unsuitable articles, 17 articles fully met the pre-established criteria (peer-reviewed; published between 2012 and 2023 in English or German; with a particular focus on sensors detecting rumination in sheep) and were included in this review. The guidelines outlined in the PRISMA 2020 methodology were followed. The results indicate that sensor-based systems have been utilized to monitor and analyze rumination behavior, among other behaviors. Notably, none of the sensors identified in this review were specifically designed for sheep. In order to meet the specific needs of sheep, a customized sensor solution is necessary. Additionally, further investigation of the optimal sensor position and epoch settings is necessary. Implications: The utilization of such sensors has significant implications for improving sheep welfare and enhancing our knowledge of their behavior in various contexts.

Stakeholder Challenges and Opportunities of GPS Shock Collars to Achieve Optimum Welfare in a Conservation or Farm Setting

Virtual fences for livestock facilitated by a GPS shock collar (GPS-SC) and phone app were introduced to the UK in cattle herd trials in 2020. Technology which uses aversive shocks to control livestock movement on farms and in other settings poses a significant risk to livestock welfare. There are currently no welfare protocols in place in the UK to ensure the ethical use of GPS-SCs. The objective of this study was to understand how GPS-SCs were being used in practice in the UK and gather data to assist researchers and policymakers in the future research and development of a welfare protocol for the UK. We studied how the technology performs in terms of welfare challenges and opportunities, covering extensive livestock production, conservation settings, "rewilding", and regenerative farming practices, where the technology is currently being applied. Semistructured interviews were conducted with key stakeholders. In-depth interviews (n = 8) supported the previous literature that the use of GPS-SCs in restricted grazing settings poses a risk to animal welfare. This is due to the wavering virtual fence boundary line (which is affected by satellite movements), a lack of visual markers, and, in some "rewilding" and conservation settings, livestock keepers, which require training and support to enable optimal welfare in practice and prevent misuse of the technology. Results also indicated that there are opportunities for enhancing livestock welfare with GPS-SCs in very extensive farm settings, where targeted care can be facilitated by using the data to monitor and track livestock using GPS-SCs, and which can also prevent cattle injury or fatality through virtual pastures designed to protect livestock from hazards such as roads or bogs. Future research is needed to focus on minimising shocks in the training period and to better understand the value of visual electric fences in the training process.

Opportunities for Regulatory Authorities to Assess Animal-Based Measures at the Slaughterhouse Using Sensor Technology and Artificial Intelligence: A Review

Animal-based measures (ABMs) are the preferred way to assess animal welfare. However, manual scoring of ABMs is very time-consuming during the meat inspection. Automatic scoring by using sensor technology and artificial intelligence (AI) may bring a solution. Based on review papers an overview was made of ABMs recorded at the slaughterhouse for poultry, pigs and cattle and applications of sensor technology to measure the identified ABMs. Also, relevant legislation and work instructions of the Dutch Regulatory Authority (RA) were scanned on applied ABMs. Applications of sensor technology in a research setting, on farm or at the slaughterhouse were reported for 10 of the 37 ABMs identified for poultry, 4 of 32 for cattle and 13 of 41 for pigs. Several applications are related to aspects of meat inspection. However, by European law meat inspection must be performed by an official veterinarian, although there are exceptions for the post mortem inspection of poultry. The examples in this study show that there are opportunities for using sensor technology by the RA to support the inspection and to give more insight into animal welfare risks. The lack of external validation for multiple commercially available systems is a point of attention.

Non-invasive estimation of in vivo optical properties and hemodynamic parameters of domestic animals: a preliminary study on horses, dogs, and sheep

Biosensors applied in veterinary medicine serve as a noninvasive method to determine the health status of animals and, indirectly, their level of welfare. Near infrared spectroscopy (NIRS) has been suggested as a technology with this application. This study presents preliminary in vivo time domain NIRS measurements of optical properties (absorption coefficient, reduced scattering coefficient, and differential pathlength factor) and hemodynamic parameters (concentration of oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin, and tissue oxygen saturation) of tissue domestic animals, specifically of skeletal muscle (4 dogs and 6 horses) and head (4 dogs and 19 sheep). The results suggest that TD NIRS in vivo measurements on domestic animals are feasible, and reveal significant variations in the optical and hemodynamic properties among tissue types and species. In horses the different optical and hemodynamic properties of the measured muscles can be attributed to the presence of a thicker adipose layer over the muscle in the Longissimus Dorsi and in the Gluteus Superficialis as compared to the Triceps Brachii. In dogs the absorption coefficient is higher in the head (temporalis musculature) than in skeletal muscles. The smaller absorption coefficient for the head of the sheep as compared to the head of dogs may suggest that in sheep we are indeed reaching the brain cortex while in dog light penetration can be hindered by the strongly absorbing muscle covering the cranium.

Validation of a methodology for characterization of rumination, lying, standing, and performing non-nutritive oral behaviors and behavioral patterns in Holstein dairy calves

Calf behavior is closely related to its early growth, production performance, and health performance. Continuous behavior recording is the most accurate but also time-consuming method used for monitoring animal behaviors, so the instantaneous sampling method is often adopted to minimize the time required to quantify behavioral observations in animal studies. Moreover, the optimal sampling intervals required to yield accurate information for estimating Holstein dairy calves' behaviors are still unknown. Our primary objective was to determine the most optimal sampling intervals for monitoring behaviors of Holstein dairy calves during preweaning and weaning periods to improve efficiency while maintaining reliability. The secondary objective was to describe their behavioral patterns. Rumination, lying, standing, and non-nutritive oral behavior (NNOB) data of 18 calves (observation time: 360 h/calf, 6,480 h in total) were continuously recorded for 15 d (3 d at 1, 3, 6, 9, and 12 wk of age). The continuous behavioral data were compared with instantaneous sampling at 5 s, 10 s, 15 s, 30 s,1 min, 3 min, 5 min, 10 min, 15 min, 30 min, and 60 min intervals. Sampling intervals were considered accurate if they met 4 criteria: coefficient of determination ≥0.90 (i.e., strongly related to true values), slope = 1, intercept = 0 (i.e., they did not over- or underestimate true values), and relative error <10%. The most optimal sampling interval was considered the highest sampling interval among the 11 sampling intervals that meet the criteria for accurate monitoring. As expected, the strength of the linear relationship between the continuous recording and instantaneous sampling decreased as the sampling intervals increased. The results varied across the different behaviors, with rumination, lying, standing, and NNOB being reliable at instantaneous recordings of 3 min, 10 min, 10 min, and 1 min for the preweaning period (1, 3, and 6 wk of age) and 10 min, 10 min, 15 min, and 3 min for the postweaning period (9 and 12 wk of age). In terms of behavioral patterns, lying time decreased, whereas rumination, standing, and NNOB time increased with age. After weaning, no significant changes in time spent performing these behaviors. Additionally, the rumination behavioral pattern becomes stable after wk 6 with decreasing after the morning feeding and occurring mainly in the morning. In conclusion, instantaneous sampling is a reliable method for monitoring the behaviors of dairy calves, but the optimal sampling intervals should be selected based on different ages and management conditions.

Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation

This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in 'shy feeder' cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.

Review: Using animal welfare to frame discussion on dairy farm technology

The use of technology on dairy farms has increased dramatically over the last half-century. The ways that scientists describe the potential benefits and risk of technology are likely to affect if it is accepted for use on farms. The aim of our study was to identify papers that describe a linkage between technologies used on dairy farms and the welfare of dairy cattle. Our systematic review identified 380 papers, of which 28 met our inclusion criteria and were used to describe the technologies examined, the welfare-relevant measures used, and the ways in which authors framed welfare benefits and risks associated with the technologies. The large majority (27 of 28 papers) used positive frames, considering how the technology could improve welfare. Some authors carefully articulated the logic linking the specific measures to specific welfare-related outcomes (such as the use of accelerometer data to draw inferences about changes in lying times), but others made more general inferences (about health and welfare) that were not (and perhaps could not) be assessed. We conclude that much of the framing focused on animal welfare is biased toward welfare benefits and that future work should strive to address both potential benefits and harms; more balanced coverage may better inform solutions to the problems encountered by the people and animals affected by the technology. Welfare is a complex and multifaced concept, and it is unlikely that any one technology (or perhaps even a combination of technologies) can adequately capture this complexity. Thus, we encourage authors to restrict their claims to specific, welfare-relevant measures that can be assessed independently and thus validated. More general claims about welfare should be treated with skepticism.

Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems

Animal welfare is becoming an increasingly important requirement in the livestock sector to improve, and therefore raise, the quality and healthiness of food production. By monitoring the behaviour of the animals, such as feeding, rumination, walking, and lying, it is possible to understand their physical and psychological status. Precision Livestock Farming (PLF) tools offer a good solution to assist the farmer in managing the herd, overcoming the limits of human control, and to react early in the case of animal health issues. The purpose of this review is to highlight a key concern that occurs in the design and validation of IoT-based systems created for monitoring grazing cows in extensive agricultural systems, since they have many more, and more complicated, problems than indoor farms. In this context, the most common concerns are related to the battery life of the devices, the sampling frequency to be used for data collection, the need for adequate service connection coverage and transmission range, the computational site, and the performance of the algorithm embedded in IoT-systems in terms of computational cost.

The Development of Smart Dairy Farm System and Its Application in Nutritional Grouping and Mastitis Prediction

In order to study the smart management of dairy farms, this study combined Internet of Things (IoT) technology and dairy farm daily management to form an intelligent dairy farm sensor network and set up a smart dairy farm system (SDFS), which could provide timely guidance for dairy production. To illustrate the concept and benefits of the SDFS, two application scenarios were sampled: (1) Nutritional grouping (NG): grouping cows according to the nutritional requirements by considering parities, days in lactation, dry matter intake (DMI), metabolic protein (MP), net energy of lactation (NEL), etc. By supplying feed corresponding to nutritional needs, milk production, methane and carbon dioxide emissions were compared with those of the original farm grouping (OG), which was grouped according to lactation stage. (2) Mastitis risk prediction: using the dairy herd improvement (DHI) data of the previous 4 lactation months of the dairy cows, logistic regression analysis was applied to predict dairy cows at risk of mastitis in successive months in order to make suitable measurements in advance. The results showed that compared with OG, NG significantly increased milk production and reduced methane and carbon dioxide emissions of dairy cows (p < 0.05). The predictive value of the mastitis risk assessment model was 0.773, with an accuracy of 89.91%, a specificity of 70.2%, and a sensitivity of 76.3%. By applying the intelligent dairy farm sensor network and establishing an SDFS, through intelligent analysis, full use of dairy farm data would be made to achieve higher milk production of dairy cows, lower greenhouse gas emissions, and predict in advance the occurrence of mastitis of dairy cows.

Can Sound Alone Act as a Virtual Barrier for Horses? A Preliminary Study

Virtual fencing is an innovative alternative to conventional fences. Different systems have been studied, including electric-impulse-free systems. We tested the potential of self-applied acoustic stimulus in deterring the horses from further movement. Thirty warmblood horses were individually introduced to a designated corridor leading toward a food reward (variant F) or a familiar horse (variant S). As the subject reached a distance of 30, 15 or 5 m from a finish line, an acute alarming sound was played. Generally, a sudden and unknown sound was perceived by horses as a threat causing an increase in vigilance and sympathetic activation. Horses' behaviour and barrier effectiveness (80% for F vs. 20% for S) depended on motivator (F/S), while the cardiac response indicating some level of stress was similar. The motivation for social interactions was too strong to stop the horses from crossing a designated boundary. Conversely, the sound exposure distance did not vary the barrier effectiveness, but it differentiated HRV responses, with the strongest sympathetic activation noted at a distance of 5 m. Thus, the moment of a sound playback has important welfare implications. Due to the limited potential of sound as a virtual barrier, auditory cues cannot be used as an alternative for conventional fencing.

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.

Opportunities to monitor animal welfare using the five freedoms with precision livestock management on rangelands

Advances in technology have led to precision livestock management, a developing research field. Precision livestock management has potential to improve sustainable meat production through continuous, real-time tracking which can help livestock managers remotely monitor and enhance animal welfare in extensive rangeland systems. The combination of global positioning systems (GPS) and accessible data transmission gives livestock managers the ability to locate animals in arduous weather, track animal patterns throughout the grazing season, and improve handling practices. Accelerometers fitted to ear tags or collars have the potential to identify behavioral changes through variation in the intensity of movement that can occur during grazing, the onset of disease, parturition or responses to other environmental and management stressors. The ability to remotely detect disease, parturition, or effects of stress, combined with appropriate algorithms and data analysis, can be used to notify livestock managers and expedite response times to bolster animal welfare and productivity. The “Five Freedoms” were developed to help guide the evaluation and impact of management practices on animal welfare. These freedoms and welfare concerns differ between intensive (i.e., feed lot) and extensive (i.e., rangeland) systems. The provisions of the Five Freedoms can be used as a conceptual framework to demonstrate how precision livestock management can be used to improve the welfare of livestock grazing on extensive rangeland systems.

Emphasis on resilience in dairy cattle breeding: Possibilities and consequences

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Vision-Based Module for Herding with a Sheepdog Robot

Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms.

Livestock Management on Grazing Field: A FANET Based Approach

In recent times, designated grazing areas/fields or routes for livestock grazing are usually defined. Hence, their herding activities’ success relies on data extracted from aerial photographs. As such, a direct and cost-effective way of monitoring livestock for perimeter coverage and in other natural situations is required. This paper presents a coverage solution involving multiple interacting unmanned aerial vehicles. The presented approach is built on a graph, with geographic coordinates set such that several Unmanned Aerial Vehicles (UAVs) can successfully cover the area. The maximum flying time determines the number of UAVs employed for coverage. The proposed solution is thought to solve some practical problems encountered during the execution of the task with actual UAVs. It is suitable for long-term monitoring of animal behavior under various weather conditions and observing the relationship between livestock distribution and available resources on a grazing field. The simulation was carried out using MATLAB and aerial images from Google Earth.

Twelve Threats of Precision Livestock Farming (PLF) for Animal Welfare

Research and development of Precision Livestock Farming (PLF) is booming, partly due to hopes and claims regarding the benefits of PLF for animal welfare. These claims remain largely unproven, however, as only few PLF technologies focusing on animal welfare have been commercialized and adopted in practice. The prevailing enthusiasm and optimism about PLF innovations may be clouding the perception of possible threats that PLF may pose to farm animal welfare. Without claiming to be exhaustive, this paper lists 12 potential threats grouped into four categories: direct harm, indirect harm via the end-user, via changes to housing and management, and via ethical stagnation or degradation. PLF can directly harm the animals because of (1) technical failures, (2) harmful effects of exposure, adaptation or wearing of hardware components, (3) inaccurate predictions and decisions due to poor external validation, and (4) lack of uptake of the most meaningful indicators for animal welfare. PLF may create indirect effects on animal welfare if the farmer or stockperson (5) becomes under- or over-reliant on PLF technology, (6) spends less (quality) time with the animals, and (7) loses animal-oriented husbandry skills. PLF may also compromise the interests of the animals by creating transformations in animal farming so that the housing and management are (8) adapted to optimize PLF performance or (9) become more industrialized. Finally, PLF may affect the moral status of farm animals in society by leading to (10) increased speciesism, (11) further animal instrumentalization, and (12) increased animal consumption and harm. For the direct threats, possibilities for prevention and remedies are suggested. As the direction and magnitude of the more indirect threats are harder to predict or prevent, they are more difficult to address. In order to maximize the potential of PLF for improving animal welfare, the potential threats as well as the opportunities should be acknowledged, monitored and addressed.

Extensive Sheep and Goat Production: The Role of Novel Technologies towards Sustainability and Animal Welfare

Simple Summary

New technologies have been recognized as valuable in controlling, monitoring, and managing farm animal activities. It makes it possible to deepen the knowledge of animal behavior and improve animal welfare and health, which has positive implications for the sustainability of animal production. In recent years, successful technological developments have been applied in intensive farming systems; however, due to challenging conditions that extensive pasture-based systems show, technology has been more limited. Nevertheless, awareness of the available technological solutions for extensive conditions can increase the implementation of their adoption among farmers and researchers. In this context, this review addresses the role of different technologies applied to sheep and goat production in extensive systems. Examples related to precision livestock farming, omics, thermal stress, colostrum intake, passive immunity, and newborn survival are presented; biomarkers of metabolic diseases and parasite resistance breeding are discussed.

Abstract

Sheep and goat extensive production systems are very important in the context of global food security and the use of rangelands that have no alternative agricultural use. In such systems, there are enormous challenges to address. These include, for instance, classical production issues, such as nutrition or reproduction, as well as carbon-efficient systems within the climate-change context. An adequate response to these issues is determinant to economic and environmental sustainability. The answers to such problems need to combine efficiently not only the classical production aspects, but also the increasingly important health, welfare, and environmental aspects in an integrated fashion. The purpose of the study was to review the application of technological developments, in addition to remote-sensing in tandem with other state-of-the-art techniques that could be used within the framework of extensive production systems of sheep and goats and their impact on nutrition, production, and ultimately, the welfare of these species. In addition to precision livestock farming (PLF), these include other relevant technologies, namely omics and other areas of relevance in small-ruminant extensive production: heat stress, colostrum intake, passive immunity, newborn survival, biomarkers of metabolic disease diagnosis, and parasite resistance breeding. This work shows the substantial, dynamic nature of the scientific community to contribute to solutions that make extensive production systems of sheep and goats more sustainable, efficient, and aligned with current concerns with the environment and welfare.

Evaluation of an Active LF Tracking System and Data Processing Methods for Livestock Precision Farming in the Poultry Sector

Tracking technologies offer a way to monitor movement of many individuals over long time periods with minimal disturbances and could become a helpful tool for a variety of uses in animal agriculture, including health monitoring or selection of breeding traits that benefit welfare within intensive cage-free poultry farming. Herein, we present an active, low-frequency tracking system that distinguishes between five predefined zones within a commercial aviary. We aimed to evaluate both the processed and unprocessed datasets against a “ground truth” based on video observations. The two data processing methods aimed to filter false registrations, one with a simple deterministic approach and one with a tree-based classifier. We found the unprocessed data accurately determined birds’ presence/absence in each zone with an accuracy of 99% but overestimated the number of transitions taken by birds per zone, explaining only 23% of the actual variation. However, the two processed datasets were found to be suitable to monitor the number of transitions per individual, accounting for 91% and 99% of the actual variation, respectively. To further evaluate the tracking system, we estimated the error rate of registrations (by applying the classifier) in relation to three factors, which suggested a higher number of false registrations towards specific areas, periods with reduced humidity, and periods with reduced temperature. We concluded that the presented tracking system is well suited for commercial aviaries to measure individuals’ transitions and individuals’ presence/absence in predefined zones. Nonetheless, under these settings, data processing remains a necessary step in obtaining reliable data. For future work, we recommend the use of automatic calibration to improve the system’s performance and to envision finer movements.

Review: Precision Livestock Farming technologies in pasture-based livestock systems

Precision Livestock Farming (PLF) encompasses the combined application of single technologies or multiple tools in integrated systems for real-time and individual monitoring of livestock. In grazing systems, some PLF applications could substantially improve farmers' control of livestock by overcoming issues related to pasture utilisation and management, and animal monitoring and control. A focused literature review was carried out to identify technologies already applied or at an advanced stage of development for livestock management in pastures, specifically cattle, sheep, goats, pigs, poultry. Applications of PLF in pasture-based systems were examined for cattle, sheep, goats, pigs, and poultry. The earliest technology applied to livestock was the radio frequency identification tag, allowing the identification of individuals, but also for retrieving important information such as maternal pedigree. Walk-over-weigh platforms were used to record individual and flock weights. Coupled with automatic drafting systems, they were tested to divide the animals according to their needs. Few studies have dealt with remote body temperature assessment, although the use of thermography is spreading to monitor both intensively reared and wild animals. Global positioning system and accelerometers are among the most applied technologies, with several solutions available on the market. These tools are used for several purposes, such as animal location, theft prevention, assessment of activity budget, behaviour, and feed intake of grazing animals, as well as for reproduction monitoring (i.e., oestrus, calving, or lambing). Remote sensing by satellite images or unmanned aerial vehicles (UAVs) seems promising for biomass assessment and herd management based on pasture availability, and some attempts to use UAVs to monitor, track, or even muster animals have been reported recently. Virtual fencing is among the upcoming technologies aimed at grazing management. This system allows the management of animals at pasture without physical fences but relies on associative learning between audio cues and an electric shock delivered if the animal does not change direction after the acoustic warning. Regardless of the different technologies applied, some common constraints have been reported on the application of PLF in grazing systems, especially when compared with indoor or confined livestock systems. Battery lifespan, transmission range, service coverage, storage capacity, and economic affordability were the main factors. However, even if the awareness of the existence and the potential of these upcoming tools are still limited, farmers' and researchers' demands are increasing, and positive outcomes in terms of rangeland conservation, animal welfare, and labour optimisation are expected from the spread of PLF in grazing systems.

Individual Monitoring of Behavior to Enhance Productivity and Welfare of Animals in Small-Scale Intensive Cattle Grazing Systems

To enhance productivity and welfare of individual animals maintained as a group, management based on individual behavioral tendencies is essential, which requires individual monitoring of animal behavior. Several behavior monitoring systems are currently available to livestock producers. The data obtained from these systems are analyzed to detect significantly high or low frequencies or intensities of behavior associated with estrus, calving and poor health conditions based on thresholds or past trends of the monitored individual. However, because behavior under grazing is more complex and changeable than under confinement, behavioral symptoms are more difficult to detect, and on-farm monitoring of individual animal behavior has been less validated and utilized in grazing systems. Nevertheless, individual monitoring of all animals in a herd is more feasible and cost-effective in small-scale intensive grazing systems because these systems pursue high productivity at the individual level with smaller herd size than large-scale extensive systems. Individually tailored management to enhance productivity and welfare will be possible by focusing on inter-individual differences in behavior within a herd. Behavior of an individual can be analyzed and understood in more detail by comparing it with those of the herd mates. Higher or lower levels of specific activities than the other animals can be associated with health disorders, temporal changes in physiological states, or productivity- or welfare-related traits. More sensitive monitoring and detection of behavioral responses of individuals to changes in nutritional, physical and social environments will lead to more efficient and welfare-conscious management that better meets the needs of individuals.

RFID and Drones: The Next Generation of Plant Inventory

Collection of plant inventory (i.e., count, grade, plant size, yield) data is time-consuming, costly, and can be inaccurate. In response to increasing labor costs and shortages, there is an increased need for the adoption of more automated technologies by the nursery industry. Growers, small and large, are beginning to adopt technologies (e.g., plant spacing robots) that automate or augment certain operations, but greater strides must be taken to integrate next-generation technologies into these challenging unstructured agricultural environments. The main objective of this work is to demonstrate merging specific ground and aerial-based technologies (Radio Frequency Identification (RFID), and small Unmanned Aircraft System (sUAS)) into a holistic systems approach to address the specific need of moving toward automated on-demand plant inventory. This preliminary work focuses on evaluating different RFID tags with respect to their distance and orientation to the RFID reader. Fourteen different RFID tags, five distances (1.5 m, 3.0 m, 4.5 m, 6.0 m, and 7.6 m), and four tag orientations (the front of the tag (UP), back of the tag (DN), tag at sideways left (SL), and tag at sideways right (SR)) were assessed. Results showed that the tag upward orientation resulted in the highest scanning total for both the laboratory and field experiments. Two orientations (UP and SR) had significant effect on the scan total of tags. The distance between the reader and the tags at 1.5 m and 6.0 m did not significantly affect the scanning efficiency of the RFID system in horizontally fixed (p-value > 0.05) position regardless of tags. Different tag designs also produced different scan totals. Overall, since most of the tags were scanned at least once (except for Tag 6F), it is a very promising technology for use in nursery inventory data acquisition. This work will create a unique inventory system for agriculture where locations of plants or animals will not present a barrier as the system can easily be mounted on a drone. Although these experiments are focused on inventory in plant nurseries, results for this work has potential for inventory management in other agricultural sectors.