Search Results (30)

Early diagnosis of heat load in beef cattle remains a challenge due to the limited understanding of behaviour-based indicators. This preliminary longitudinal study aimed to validate behavioural and physiological responses previously identified as heat load indicators. Black Angus steers were exposed to high environmental temperatures expected to cause heat load in the following sequence: an initial thermoneutral period, a hot period, and a recovery period. Changes in the positioning of key body parts, feeding behaviour, body maintenance, respiratory dynamics, and eye temperature were monitored. In the hot period, cattle increased their respiration rate, panting, and infrared eye temperature. Increased stepping by their left limbs suggested involvement of the right brain hemisphere in a stress response to high environmental temperatures. Cattle also held their heads more downward, ears backward, and their tail vertical, and reduced eating, grooming, and scratching during the hot period. Cattle responses to hot conditions were persistent in the recovery period, reflecting diagnostic relevance of the head, ear, and tail movements, stepping, especially by left limbs, and infrared eye temperature as non-invasive tools to identify heat load condition in cattle. The study reinforces our understanding of the specific behavioural and physiological responses to heat load condition, especially those involving left-limb stepping, ear and tail posture, and infrared eye temperature, are reliable indicators for identifying cattle experiencing high environmental temperature. Full article

The accurate recognition of cattle behaviours is crucial for improving animal welfare and production efficiency in precision livestock farming. However, existing methods pay limited attention to recognising behaviours under occlusion or those involving subtle interactions between cattle and environmental objects in group farming scenarios. To address this limitation, we propose a novel spatio-temporal feature extraction network that explicitly models the associative relationships between key body parts of cattle and environmental factors, thereby enabling precise behaviour recognition. Specifically, the proposed approach first employs a spatio-temporal perception network to extract discriminative motion features of key body parts. Subsequently, a spatio-temporal relation integration module with metric learning is introduced to adaptively quantify the association strength between cattle features and environmental elements. Finally, a spatio-temporal enhancement network is utilised to further optimise the learned interaction representations. Experimental results on a public cattle behaviour dataset demonstrate that our method achieves a state-of-the-art mean average precision (mAP) of 87.19%, outperforming the advanced SlowFast model by 6.01 percentage points. Ablation studies further confirm the synergistic effectiveness of each module, particularly in recognising behaviours that rely on environmental interactions, such as drinking and grooming. This study provides a practical and reliable solution for intelligent cattle behaviour monitoring and highlights the significance of relational reasoning in understanding animal behaviours within complex environments. Full article

The aim of this project was to co-create an animal welfare monitoring system that incorporated both positive and negative welfare measures that would contribute to best practice husbandry standards of farm animals in a real animal research setting. Researchers worked with nine staff to co-design six bespoke welfare assessment protocols to be conducted in addition to legally required welfare monitoring for adult cattle, calves, sheep, pigs, and goats in specific experimental environments. Four protocols were subsequently applied with variable frequency by three staff to cattle, goats, and two pig populations. Assessments were all observational, and included behavioural scan sampling, Qualitative Behaviour Assessment scores, visual analogue mood scores, and physical condition data. Two staff provided feedback on their views of the process. A key finding was that with facilitation, staff could generate protocols that included elements designed to encourage or evaluate interventions to promote positive emotions. However, data collection was sporadic, and although the staff who provided feedback reported that they valued the process highly, they noted that the primary challenge was finding the time to conduct the assessments. We therefore conclude that sustained support is likely to be required to maximise the benefits for the animals and staff of developing and conducting voluntary welfare monitoring of farm animals. Full article

The aim of this comprehensive review is to synthesize current knowledge on bovine respiratory disease (BRD), enhance diagnostic strategies, and support effective prevention and management practises. BRD remains a leading cause of morbidity and mortality in cattle, driven by a complex interplay of viral and bacterial pathogens, host factors, environmental stressors, and management conditions. Its prevalence (2.1% to 20.2%) varies across geographical regions, age groups, and diagnostic methods. BRD leads to significant economic losses through direct impacts such as mortality, reduced growth rates, and lighter carcass weights, as well as indirect costs like market restrictions and long-term productivity declines. Diagnosing BRD is challenging due to its non-specific clinical signs and frequent subclinical presentations. Traditional diagnostic tools like clinical respiratory scoring (CRS) systems provide structure but suffer from low sensitivity and subjectivity. Behavioural monitoring shows promise by detecting early changes in feeding, movement, and social behaviours. Thoracic auscultation is widely used but limited in accuracy. Thoracic ultrasonography (TUS) stands out as a more sensitive method for detecting subclinical disease and correlating with growth outcomes. Combining CRS with TUS enhances early and accurate detection. Advancing diagnostic approaches is critical for improving animal health and minimizing economic losses in cattle production systems. Full article

The early lactation phase in dairy cows is characterised by significant metabolic and inflammatory changes. This study aimed to evaluate the relationship between serum nonesterified fatty acids (NEFAs), a marker of negative energy balance (NEB), and serum amyloid A (SAA), an indicator of systemic inflammation. Blood samples were collected from 71 Holstein cows during the transition period 17 (±3) DIM, and serum concentrations of NEFAs and SAA were measured. The results revealed a significant negative correlation between NEFAs and SAA (r = −0.441, p < 0.001), suggesting that increased fat mobilisation may suppress the inflammatory response, thereby increasing the susceptibility to metabolic and infectious diseases. The emerging research indicates a negative association between SAA levels and milk fat-to-protein ratio in dairy cows, particularly under inflammatory conditions. The research indicates that elevated levels of SAA, which is an inflammatory biomarker, are frequently associated with alterations in milk composition, including a reduced fat-to-protein ratio. This study examined the correlations among serum NEFAs, SAA, milk composition, and dairy cattle health. A strong positive correlation was identified between serum NEFAs and milk fat content (r = 0.459, p < 0.001), as well as between serum NEFAs and the milk fat-to-protein ratio (r = 0.516, p < 0.001). Cows with elevated serum NEFA levels (classified as II-NEFA) exhibited significantly higher milk fat content (4.20%) and milk fat-to-protein ratios (1.33) compared to cows with lower serum NEFA levels (I-NEFA class; 3.81% and 1.17, respectively). The data indicate that elevated serum NEFA levels are associated with an increased milk fat synthesis, likely driven by enhanced fat mobilisation during NEB. A significant negative correlation was observed between SAA and both milk fat content (r = −0.426, p < 0.001) and the milk fat-to-protein ratio (r = −0.535, p < 0.001), indicating that inflammation may impair milk fat production. Elevated SAA levels were also associated with increased cow activity (r = 0.382, p < 0.001), suggesting that inflammation may lead to behavioural changes driven by discomfort. Our findings suggest that milk composition reflects the metabolic and inflammatory status of dairy cows and could serve as a non-invasive alternative to blood sampling for assessing energy balance and health. NEB, which typifies early lactation, promotes fat mobilisation, resulting in elevated serum NEFA levels and an increased risk of metabolic disorders such as fatty liver syndrome and ketosis. Moreover, high serum NEFA levels adversely affect immune function, increasing vulnerability to infections such as mastitis. Monitoring milk composition may enable the early detection of NEB and inflammatory conditions, thereby supporting proactive health management. However, further research is necessary to elucidate the role of NEFAs and inflammation in the development of metabolic diseases in cattle. Full article

Wearable collar technologies have become integral to the advancement of precision livestock farming, revolutionizing how dairy cattle are monitored in terms of their behaviour, health status, and productivity. These devices leverage cutting-edge sensors, including accelerometers, RFID tags, GPS receivers, microphones, gyroscopes, and magnetometers, to provide non-invasive, real-time insights that enhance animal welfare, optimize resource use, and support decision-making processes in livestock management. This systematized review focuses on analyzing the sensors integrated into collar-based systems, detailing their functionalities and applications. However, significant challenges remain, including the high energy consumption of some sensors, the need for frequent recharging, and limited parameter coverage by individual devices. Future developments must focus on integrating multiple sensor types into unified systems to provide comprehensive data on animal behaviour, health, and environmental interactions. Additionally, advancements in energy-efficient designs, longer battery life, and cost-reduction strategies are essential to enhance the practicality and accessibility of these technologies. By addressing these challenges, wearable collar systems can play a pivotal role in promoting sustainable, efficient, and responsible livestock farming, aligning with global goals for environmental and economic sustainability. This paper underscores the transformative potential of wearable collar technologies in reshaping the livestock industry and driving the adoption of innovative farming practices worldwide. Full article

This study focused on a group of 49 high-yielding dairy cows (primarily Holstein Friesians) and how their interactions with wall-mounted automated brushes correlated to their mobility (also described as lameness) score (AHDB 0–3 system. Of the 49 animals in the study, 48 were mobility scored with a sample lameness prevalence of 14.6% (n = 22 score 0, n = 19 score 1, n = 6 score 2 and n = 1 score 3 (score 2 and 3 combined due to low numbers identified)). There was no statistical difference in the number of visits between the lame (score 2 and 3) and sound cows (score 0 and 1); however, there was a statistically relevant decrease in the duration that the lame cows spent brushing per visit (sound 91.7 ± 6.06 s compared to lame 63.0 ± 9.22 s, p = 0.0097). No significant difference was identified in how the lame cows interacted with the brushes (i.e., which body part) when compared to the group. The group, in general, showed a significant preference towards interacting with the brush with their head area (63.95% of interactions observed over the 72 h involved the head). In conclusion, monitoring brush use (duration of use per visit) could aid with the identification of clinically lame animals. Full article

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. Full article

Two experiments were conducted to assess the effects of water medication technology on beef cattle behaviour and performance in tropical conditions. Experiment 1 involved 30 Droughtmaster yearling steers monitored over seven days in a controlled environment. Feed and water consumptions were monitored with Smart Feed Pro^® systems, with three water treatments administered via uDOSE^® technology. The results indicated an average water intake of 13.6 L/head/d. Experiment 2 had 120 yearling steers from four genetic groups grazing on an extensive pasture system. Throughout four 24-day periods, forage availability and chemical composition were measured once monthly. Experiment 2 revealed a variation in water intake, ranging from 16.2 L/head/d down to 4.75 L/head/d. Notably, the lower intake coincided with a rainfall event documented during the fourth experimental period. Overall, results from both experiments indicated that water medication did not alter cattle water preference. There was no preference for treated water sources in Experiment 1, while differences in Experiment 2 appeared to be influenced by external factors like weather and prior habits. These experiments demonstrate the feasibility of water medication for beef cattle without disruption of their natural behaviour. Full article

The aim of this study was to monitor the behaviour of purebred and crossbred beef cattle reared in the same optimal environmental conditions according to Classyfarm^®. Thirty-yearling beef 11.5 months old, including 10 Limousines (LMS), 10 Sardo-Bruna (SRB), and 10 crossbred Limousine × Sardo-Bruna (LMS × SRB), balanced for sex and body weight, were used. Animals were evaluated for five months by two trained operators by SCAN (“sternal resting”, “lateral resting”, “ central or peripheral position in the pen”, standing”, “walking”, “feeding”, “drinking”, and “ruminating) and FOCUS (“displacement for space”, “displacement for feed or water”, “play-fighting”, “self-grooming”, “allo-grooming”, “stereotyping”, and “mounting”) protocols. Feeding behaviour was monitored by a CCTV system. The application of the SCAN sampling evidenced that SRB animals preferred the “standing” activity over the LMS animals, while the LMS × SRB did not differ from them. The “standing” and “ ruminating “activities were observed mostly in females than males (p < 0.05). For behaviour parameters assessed by the FOCUS methodology, the n-events of “allo-grooming” were higher (p < 0.05) in SRB than in LMS and LMS × SRB genetic types. Males showed higher (p < 0.05) n-events than females for “play-fighting”. For feeding behaviour, the “eating concentrate” activity (expressed as n-events) was higher (p < 0.05) in SRB than LMS × SRB and LMS being intermediate (p < 0.05). The duration of “eating concentrate” (expressed in minutes) was higher (p < 0.05) in females than males. In conclusion, behaviour indicators of animal welfare did not evidence substantial differences among genetic types and between sexes reared in the same “optimal” environmental conditions. Female beef and the autochthon’s cattle breed of Sardinia, although typically hardy, showed a wide behavioural repertoire. Full article

Over the past four decades the dietary needs of the global population have been elevated, with increased consumption of animal products predominately due to the advancing economies of South America and Asia. As a result, livestock production systems have expanded in size, with considerable changes to the animals’ management. As grazing animals are commonly grown in herds, economic and labour constraints limit the ability of the producer to individually assess every animal. Precision Livestock Farming refers to the real-time continuous monitoring and control systems using sensors and computer algorithms for early problem detection, while simultaneously increasing producer awareness concerning individual animal needs. These technologies include automatic weighing systems, Radio Frequency Identification (RFID) sensors for individual animal detection and behaviour monitoring, body temperature monitoring, geographic information systems (GIS) for pasture evaluation and optimization, unmanned aerial vehicles (UAVs) for herd management, and virtual fencing for herd and grazing management. Although some commercial products are available, mainly for cattle, the adoption of these systems is limited due to economic and cultural constraints and poor technological infrastructure. This review presents and discusses PLF applications and systems for grazing animals and proposes future research and strategies to improve PLF adoption and utilization in today’s extensive livestock systems. Full article

In this paper, we develop innovative digital twins of cattle status that are powered by artificial intelligence (AI). The work is built on a farm IoT system that remotely monitors and tracks the state of cattle. A digital twin model of cattle based on Deep Learning (DL) is generated using the sensor data acquired from the farm IoT system. The physiological cycle of cattle can be monitored in real time, and the state of the next physiological cycle of cattle can be anticipated using this model. The basis of this work is the vast amount of data that is required to validate the legitimacy of the digital twins model. In terms of behavioural state, this digital twin model has high accuracy, and the loss error of training reach about 0.580 and the loss error of predicting the next behaviour state of cattle is about 5.197 after optimization. The digital twins model developed in this work can be used to forecast the cattle’s future time budget. Full article

Cattle behaviour is a significant indicator of cattle welfare. With the advancements in electronic equipment, monitoring and classifying multiple cattle behaviour patterns is becoming increasingly important in precision livestock management. The aim of this study was to detect important cattle physiological states using a neural network model and wearable electronic sensors. A novel long short-term memory (LSTM) recurrent neural network model that uses two-way information was developed to accurately classify cattle behaviour and compared with baseline LSTM. Deep residual bidirectional LSTM and baseline LSTM were used to classify six behavioural patterns of cows with window sizes of 64, 128 and 256 (6.4 s, 12.8 s and 25.6 s, respectively). The results showed that when using deep residual bidirectional LSTM with window size 128, four classification performance indicators, namely, accuracy, precision, recall, and F1-score, achieved the best results of 94.9%, 95.1%, 94.9%, and 94.9%, respectively. The results showed that the deep residual bidirectional LSTM model can be used to classify time-series data collected from twelve cows using inertial measurement unit collars. Six aim cattle behaviour patterns can be classified with high accuracy. This method can be used to quickly detect whether a cow is suffering from bovine dermatomycosis. Furthermore, this method can be used to implement automated and precise cattle behaviour classification techniques for precision livestock farming. Full article

Monitoring and classification of dairy cattle behaviours is essential for optimising milk yields. Early detection of illness, days before the critical conditions occur, together with automatic detection of the onset of oestrus cycles is crucial for obviating prolonged cattle treatments and improving the pregnancy rates. Accelerometer-based sensor systems are becoming increasingly popular, as they are automatically providing information about key cattle behaviours such as the level of restlessness and the time spent ruminating and eating, proxy measurements that indicate the onset of heat events and overall welfare, at an individual animal level. This paper reports on an approach to the development of algorithms that classify key cattle states based on a systematic dimensionality reduction process through two feature selection techniques. These are based on Mutual Information and Backward Feature Elimination and applied on knowledge-specific and generic time-series extracted from raw accelerometer data. The extracted features are then used to train classification models based on a Hidden Markov Model, Linear Discriminant Analysis and Partial Least Squares Discriminant Analysis. The proposed feature engineering methodology permits model deployment within the computing and memory restrictions imposed by operational settings. The models were based on measurement data from 18 steers, each animal equipped with an accelerometer-based neck-mounted collar and muzzle-mounted halter, the latter providing the truthing data. A total of 42 time-series features were initially extracted and the trade-off between model performance, computational complexity and memory footprint was explored. Results show that the classification model that best balances performance and computation complexity is based on Linear Discriminant Analysis using features selected through Backward Feature Elimination. The final model requires 1.83 ± 1.00 ms to perform feature extraction with 0.05 ± 0.01 ms for inference with an overall balanced accuracy of 0.83. Full article

In this paper, a method to classify behavioural patterns of cattle on farms is presented. Animals were equipped with low-cost 3-D accelerometers and GPS sensors, embedded in a commercial device attached to the neck. Accelerometer signals were sampled at 10 Hz, and data from each axis was independently processed to extract 108 features in the time and frequency domains. A total of 238 activity patterns, corresponding to four different classes (grazing, ruminating, laying and steady standing), with duration ranging from few seconds to several minutes, were recorded on video and matched to accelerometer raw data to train a random forest machine learning classifier. GPS location was sampled every 5 min, to reduce battery consumption, and analysed via the k-medoids unsupervised machine learning algorithm to track location and spatial scatter of herds. Results indicate good accuracy for classification from accelerometer records, with best accuracy (0.93) for grazing. The complementary application of both methods to monitor activities of interest, such as sustainable pasture consumption in small and mid-size farms, and to detect anomalous events is also explored. Results encourage replicating the experiment in other farms, to consolidate the proposed strategy. Full article