Livestock Behavior Research Articles

PSLBI-25 Students’ perceptions of an artificial intelligence photo generation tool in upper-level animal science curriculum

Abstract Domesticated livestock behavior (DLB) is an advanced animal science lecture course that emphasizes behavior associated with domestication of various animal species such as livestock, equine, companion animals and exotic species. Meat Science (MS) is an introductory dual lecture and laboratory course that focuses on muscle biology, food-borne illnesses, humane livestock harvesting, and fabrication techniques. With the emerging usage of artificial intelligence (AI) generative technologies, an assignment was developed utilizing the free AI photo generation tool, Craiyon. Students in DLB and MS were tasked with completing an AI assignment, generating images relevant to either DLB or MS concepts, to expose students to AI technologies. Therefore, the objective of this study was to determine students’ perceptions, knowledge, concerns, and willingness to use AI generative tools and evaluate differences relative to course enrolled (DLB compared with MS). Students (n = 60) enrolled in DLB and MS completed a post-completion survey instrument that consisted of demographic and 5-point Likert-scale questions to gauge student perceptions of the AI assignment. Student survey responses were compiled online using Qualtrics and subsequent respondent data was analyzed using JMP. To give some demographic information relative to the population of students enrolled in DLB and MS, 81 and 76% of the students were female while 19 and 24% were male, respectively. Both courses contained students further along in their degree-seeking program (> 80% Junior or Senior classification). Approximately, 43% of respondents were first generation college students and 66% commute over 14 km daily. When asked about their previous experience using AI generative technologies, 75% of respondents reported no previous experience using AI generative programs. Students enrolled in MS showed a greater concern (P ≤ 0.03) of AI technologies limiting their opportunity to interact and socialize with others as compared with DLB students. Student experience, knowledge, and willingness to use AI tools was not impacted by course enrolled (P ≥ 0.14). Most students reported that they had a positive experience with the AI assignment and felt the experience would be useful in their future (53 and 73%, respectively). Additionally, a majority of students reported that the AI assignment allowed them to visualize concepts and offered a unique learning experience. Participants seemed to have a solid understanding of AI technologies reporting knowledge in tool limitations and inaccurate output. Students were neutral regarding concerns in becoming over-reliant on AI tools and AI use hindering the development of transferable skills. Students felt that AI image generation tools boosted creativity and could be useful in the realm of DLB and MS. Overall, students possessed a general knowledge of AI generative technologies and saw value in using AI image generation tools as it relates to domesticated animal behavior and meat science principles.

Animal Behaviour Packs a Punch: From Parasitism to Production, Pollution and Prevention in Grazing Livestock.

Behaviour is often the fundamental driver of disease transmission, where behaviours of individuals can be seen to scale up to epidemiological patterns seen at the population level. Here we focus on animal behaviour, and its role in parasite transmission to track its knock-on consequences for parasitism, production and pollution. Livestock face a nutrition versus parasitism trade-off in grazing environments where faeces creates both a nutritional benefit, fertilizing the surrounding sward, but also a parasite risk from infective nematode larvae contaminating the sward. The grazing decisions of ruminants depend on the perceived costs and benefits of the trade-off, which depend on the variations in both environmental (e.g., amounts of faeces) and animal factors (e.g., physiological state). Such grazing decisions determine the intake of both nutrients and parasites, affecting livestock growth rates and production efficiency. This impacts on the greenhouse gas costs of ruminant livestock production via two main mechanisms: (1) slower growth results in longer durations on-farm and (2) parasitised animals produce more methane per unit food intake. However, the sensitivity of behaviour to host parasite state offers opportunities for early detection of parasitism and control. Remote monitoring technology such as accelerometers can detect parasite-induced sickness behaviours soon after exposure, before impacts on growth, and thus may be used for targeting individuals for early treatment. We conclude that livestock host x parasite interactions are at the centre of the global challenges of food security and climate change, and that understanding livestock behaviour can contribute to solving both.

Temporal-spatial variability of grazing behaviors of yaks and the drivers of their intake on the eastern Qinghai-Tibetan Plateau.

Grassland-livestock balance is an important principle of sustainable development of grassland livestock production and grassland ecosystem health. Grassland degradation becomes more serious at global scales and especially at the area that is sensitive to climate change and human activities. Decreases in pasture biomass and shifts in plant community composition in degraded grasslands can largely affect grazing behaviors of livestock. Up to date, however, it is unclear that whether livestock behaviors change across spatial and temporal scales and what key factors are to shape observed behavioral patterns of livestock. Here, yak behaviors including grazing, rumination and walking on the eastern Qinghai-Tibetan Plateau (QTP) were monitored by a continuous visual observation, to investigate temporal and spatial variations of grazing behavior of yaks (Bos grunniens); based on the data from public database in the past 18 years, a meta-analysis was conducted to examine the main factors that affect grazing behaviors and intake of yaks. We showed that grazing behaviors of yaks differed significantly within hours, among hours of each day and among days as well as across different observation sites. Intake rate of yaks was higher in the morning than in the afternoon, but walking speed showed an inverse trend compared with intake rate. Resting, altitude, the mean annual precipitation (MAP), the mean annual temperature (MAT), forage ash, yak age and season were the main predictors for yak intake, and forage and yak individual characteristics had direct effects on grazing behaviors and intake of yaks. The findings confirm that grazing behaviors of yaks can vary even at small temporal scales and regional scales, which is closely related to the shift in forage quality and biomass caused by environmental changes. The study suggests that multiple factors can be responsible for the variation in livestock behaviors and shifts in behavioral patterns may consequently lead to positive or negative feedback to grassland ecosystems through plant-animal interactions.

Behaviour recognition of housed sheep based on spatio-temporal information

ABSTRACT Livestock behavior is related to the healthy breeding and welfare level. Therefore, monitoring the behavior of sheep is helpful to predict the health status of sheep and thus safeguard the production performance of sheep. Taking semi-housed sheep as the research object, a behavior identification method for housed sheep based on spatio-temporal information is proposed. Firstly, video acquisition of housed sheep is carried out, and sheep detection and tracking is implemented based on the YOLOv5 coupled with a Deep-SORT algorithm, which detects sheep from the flock and labels their identity information; then, sheep posture estimation is done based on the Alphapose algorithm, which estimates the keypoints in the skeleton; finally, the detected keypoints are inputted into the trained spatio-temporal graph convolutional network model, and the spatio-temporal graph constructed from the keypoints and edge information is used for sheep behavior recognition. In the study, two keypoints selection proposals were made to implement sheep behavior recognition for non-contacted detection of ruminating, lying down and other behaviors. The results show the feasibility of sheep behavior recognition based on spatio-temporal information in semi-housed farming. The method provides a new solution idea for intelligent monitoring of sheep behavior and health assessment.

Optimizing Cattle Behavior Analysis in Precision Livestock Farming: Integrating YOLOv7-E6E with AutoAugment and GridMask to Enhance Detection Accuracy

Recently, the growing demand for meat has increased interest in precision livestock farming (PLF), wherein monitoring livestock behavior is crucial for assessing animal health. We introduce a novel cattle behavior detection model that leverages data from 2D RGB cameras. It primarily employs you only look once (YOLO)v7-E6E, which is a real-time object detection framework renowned for its efficiency across various applications. Notably, the proposed model enhances network performance without incurring additional inference costs. We primarily focused on performance enhancement and evaluation of the model by integrating AutoAugment and GridMask to augment the original dataset. AutoAugment, a reinforcement learning algorithm, was employed to determine the most effective data augmentation policy. Concurrently, we applied GridMask, a novel data augmentation technique that systematically eliminates square regions in a grid pattern to improve model robustness. Our results revealed that when trained on the original dataset, the model achieved a mean average precision (mAP) of 88.2%, which increased by 2.9% after applying AutoAugment. The performance was further improved by combining AutoAugment and GridMask, resulting in a notable 4.8% increase in the mAP, thereby achieving a final mAP of 93.0%. This demonstrates the efficacy of these augmentation strategies in improving cattle behavior detection for PLF.

Beef cattle abnormal behaviour recognition based on dual-branch frequency channel temporal excitation and aggregation

The behaviour of beef cattle, especially abnormal behaviours such as mounting, fighting, and running, provides valuable information regarding their health status. Recently, existing methods based on deep convolutional networks have achieved state-of-the-art performance in beef cattle behaviour recognition. However, these methods focus only on the basic motion behaviours of a single cow (e.g. lying and standing) and ignore the abnormal behaviours of group-housed cattle, which further limits their application in an actual farm environment. In this study, we collected a realistic dataset of beef cattle abnormal behaviour called Beef Cattle Abnormal Actions, which was captured in different light environments and on different behavioural area scales. With the proposed dataset, we proposed a Dual-Branch Temporal Excitation and Aggregation with Frequency Channel Attention (DB-TEAF) method. First, a sampling strategy based on differences in image RGB information was proposed to extract representative motion-salient frames from redundant videos. Second, the temporal excitation and aggregation branch with frequency channel attention (TEAF) was introduced to focus attention on the key channels of short- and long-range temporal features. The spatial branch is incorporated into the TEAF branch to obtain the representative spatio-temporal features. In addition, focal loss was used to train the proposed model, which made the learning process aware of valuable samples from abnormal behaviour. Testing with the newly collected dataset verified that the proposed DB-TEAF method achieved superior performance compared to other state-of-the-art approaches. The findings of this study would provide support for recognising the abnormal behaviour of livestock during precision farming.

Evaluation of experimental error in accelerometer monitoring: Variation among individual animals versus variation among devices

Application of sensors is becoming prevalent in research and production settings. With increasing battery power, improved case and component durability, and consistent data connectivity, precision technologies, such as accelerometers, can help identify changes in livestock behavior. The objective of this study is to identify the variation among individual animals and among different accelerometer devices. A repeated 4 × 4 Latin-square design was utilized to identify differences between accelerometer, animal, and week. Twelve ewes separated into three age groups were randomly assigned to 4 different accelerometers deployed as an ear tag weekly over the course of 4 weeks. Manual behavior observations were paired to calculated accelerometer metrics and were used for training and validation dataset to predict animal behavior using random forest machine learning. Movement variation had the greatest importance in predicting behaviors. Across the four week study, differences were found for animal and week through each of the calculated metrics. Differences in accelerometers were detected for 80 % of the calculated metrics. This study shows the importance to account for variation among individual animals and accelerometer devices in experimental designs.

Forage taste agents manage plant communities through modifying grazing behavior of yak in alpine meadow

The use of taste agents to regulate the grazing behavior of livestock is a new attempt in pasture management, but the effects on grassland plant communities are not clear at present. Therefore, the following scientific questions need to be addressed: (1) how do different taste agents affected plant community structure by changing feed intake? (2) What was the mechanism of this effect? We proposed the following hypotheses: (1) Salt and sweetener increased feed intake of livestock and decreased the biomass of plant community, while bitters did the opposite. (2) Taste agents can regulate the relationship between plant species, and different taste agents can enhance or weaken the competitiveness of the different plants. In order to test the hypothesis, a grazing experiment with yaks was conducted in the alpine meadows of the Tibetan Plateau. Denatonium benzoate (Bitterant), NaCl (Salt), and sodium cyclamate (Sweetener) were sprayed onto the meadows twice a year, along with a control treatment of tap water. The results showed that (1) Salt increased the feed intake of yak significantly; bitterant decreased the feed intake of livestock and increased the biomass of plant community. (2) Salt increased the Pielou index of the plant community significantly. (3) The stability of plant community ranking from high to low is as follows: Control > Bitterant > Sweetener > Salt. (4) Bitterant and salt improved grazing tolerance of grassland and salt reduced the edibility of grassland. (5) The use of taste agents reduced the correlation between dominant species and led to the fragmentation of the relationship chain. The results of this study will provide a theoretical basis for using taste agents to regulate the community, species biodiversity management, restoration of degraded grassland, promoting utilization of grassland though controlling livestock selectivity.

Genetic parameters of behavior traits of beef cattle classified using wearable devices.

With the development of wearable devices, it is now possible to monitor livestock behavior 24 h a day. In this study, we estimated the genetic parameters of the daily duration of six behaviors (feeding, moving, lying, standing, ruminating while lying, and ruminating while standing) in beef cattle, automatically classified using wearable devices. The devices were attached to 332 Japanese beef cattle at two stations for approximately 5months. We compared repeatability, Poisson regression, and random regression models using the deviance information criterion. Poisson regression models were selected for all traits at each station, probably because of the non-normal distribution of the phenotypes. The heritability estimates by the Poisson regression models were moderate at each station: 0.67 and 0.68 for feeding, 0.68 and 0.53 for moving, 0.47 and 0.55 for lying, 0.45 and 0.40 for standing, 0.51 and 0.59 for ruminating while lying, and 0.37 and 0.45 for ruminating while standing. The genetic correlations between these traits were all negative at both stations, whereas the residual correlations showed different directions depending on the station. Although validation studies with larger populations are needed to confirm these findings, this study provides fundamental knowledge of the genetic basis of daily behavior in beef cattle.

Estimation of gestating sows’ welfare status based on machine learning methods and behavioral data

Estimating the welfare status at an individual level on the farm is a current issue to improve livestock animal monitoring. New technologies showed opportunities to analyze livestock behavior with machine learning and sensors. The aim of the study was to estimate some components of the welfare status of gestating sows based on machine learning methods and behavioral data. The dataset used was a combination of individual and group measures of behavior (activity, social and feeding behaviors). A clustering method was used to estimate the welfare status of 69 sows (housed in four groups) during different periods (sum of 2 days per week) of gestation (between 6 and 10 periods, depending on the group). Three clusters were identified and labelled (scapegoat, gentle and aggressive). Environmental conditions and the sows’ health influenced the proportion of sows in each cluster, contrary to the characteristics of the sow (age, body weight or body condition). The results also confirmed the importance of group behavior on the welfare of each individual. A decision tree was learned and used to classify the sows into the three categories of welfare issued from the clustering step. This classification relied on data obtained from an automatic feeder and automated video analysis, achieving an accuracy rate exceeding 72%. This study showed the potential of an automatic decision support system to categorize welfare based on the behavior of each gestating sow and the group of sows.

How does grazing pressure affect feed intake and behavior of livestock in a meadow steppe in northern China and their coupling relationship

Livestock feeding behavior and intake play a crucial role in influencing grassland health and productivity. A comprehensive investigation into livestock feeding behavior and intake can effectively elucidate the interactions and impacts of livestock and grasslands, providing scientific evidence and technical support for the formulation and implementation of sustainable grassland development strategies. Based on a long-term controlled grazing experiment platform conducted over 13 years, the feeding behavior and forage intake of cattle under different grazing intensities were observed and analyzed. Additionally, we used GPS sensors to study cattle grazing behavior trends. Using Mantel's test, we analyzed the relationship between cattle movement distance, forage intake, and environmental factors. The results demonstrated that cattle forage intake decreased with increasing grazing intensity. Forage intake peaked at the end of July and beginning of August, with the highest efficiency observed in August. Moreover, under light grazing intensity, cattle exhibited greater fluctuations in forage intake than those under moderate and heavy grazing intensity. Cattle movement levels increased with higher grazing intensity, and during the period of lush grass growth, cattle displayed significantly higher movement levels than during grass senescence. The accuracy of the behavior determination model based on cattle velocity ranged from 60 to 80 %. Using this model, we found that under heavy grazing conditions, cattle spent significantly more time roaming than under light and moderate grazing. Conversely, under light grazing conditions, cattle spent significantly more time feeding. A negative correlation was identified between cattle forage intake and movement distance. Cattle's forage intake was significantly positively correlated with grass height and grass biomass and significantly negatively correlated with stocking rate and movement distance. Thorough research on livestock feeding behavior and intake offers scientific evidence and technical support for formulating and implementing sustainable grassland development strategies.

Seasonal resource selection of free-ranging Zhongwei goats in the semi-arid grassland.

Goats rarely move and forage randomly. They tend to move in ways generally influenced by biotic and abiotic factors, respectively. However, few studies have explored the foraging behaviour of goats in the absence of predation and human disturbance. Based on step selection function modelling framework, Normalised Difference Vegetation Index, vegetation surveys, and Global Positioning System tracking of 124 free-ranging domestic adult male Zhongwei goats over one year (2016-2017) were used to assess how biotic and abiotic environmental factors affected their spatiotemporal distribution, and developed a conceptual model to represent the goats' trade-off between forage quantity and preference at different seasons, in the semi-arid grassland of Loess Plateau of 1178 hectare. The results showed that spatial distributions of goats responded to spatiotemporal variation of biotic factors rather than abiotic factors of elevation, slope and solar radiation, which indicated that biotic factors were of priority to abiotic factors in the foraging process for the goats. According to the season changing, the goats positively used areas with higher forage quantity in the spring and winter, areas of higher forage quantity and preferred species in summer, and areas of abundance of preferred species in autumn. We developed a model to describe the phenomenon that the goats selected areas with higher preferred species only when the forage quantity was plentiful, otherwise they selected areas with higher forage quantity. Better understanding of the patterns and drivers of spatiotemporal distribution of the goats can improve our ability to predict foraging behaviour of livestock in heterogeneous environment and lead to better management practices and policies for the sustainability of these semi-arid landscapes and associated ecosystem services.

A two-stage recognition method based on deep learning for sheep behavior

Recently, computer vision has been widely used in livestock behavior detection. In animal multi-behavior detection, there is a common problem of low recognition accuracy. To address this, a deep learning-based method for sheep-behavior detection is proposed in this paper. Six types of sheep behavior can be recognized by the method. Among these behaviors, standing, feeding, and lying are normal physiological activities. Attacking, biting, and climbing are disruptive behaviors that could result in ranch losses and require immediate concern from the breeder. The method consists of two stages: the detection stage and the classification stage. In the detection stage, a detection network is used to determine whether every sheep's behavior belongs to normal physiological activities or disruptive behaviors respectively. Based on a classic network, the multi-scale feature aggregation, attention mechanism and depthwise convolution module are imported to it, which makes the network trades off between detection accuracy and model size. These improvements make the network more suitable for sheep-behavior detection. In the classification stage, the VGG network is utilized to classify the behavior of each sheep specifically. Experimental results demonstrate that the two-stage method achieves desirable results in sheep-behavior recognition. In the detection stage, the mAP of the two types of behaviors exceeds 98%. In the classification stage, the classification accuracy of all behaviors exceeds 94%. At the same time, the memory of the detection model is less than 130 MB.

Grazing-induced cattle behaviour modulates the secondary production in a Eurasian steppe ecosystem

Livestock-grassland interactions are among the most important relationships in grazed grassland ecosystems, where herbivores play a crucial role in plant community and ecosystem functions. However, previous studies primarily have focused on the responses of grasslands to grazing, with few focussing on the effects of livestock behaviour that in turn would influence livestock intake and primary and secondary productivity. Through a 2-year grazing intensity experiment with cattle in Eurasian steppe ecosystem, global positioning system (GPS) collars were used to monitor animal movements, where animal locations were recorded at 10-min intervals during the growing season. We used a random forest model and the K-means method to classify animal behaviour and quantified the spatiotemporal movements of the animals. Grazing intensity appeared to be the predominant driver for cattle behaviour. Foraging time, distance travelled, and utilization area ratio (UAR) all increased with grazing intensity. The distance travelled was positively correlated with foraging time, yielding a decreased daily liveweight gain (LWG) except at light grazing. Cattle UAR showed a seasonal pattern and reached the maximum value in August. In addition, the canopy height, above-ground biomass, carbon content, crude protein, and energy content of plants all affected cattle behaviour. Grazing intensity and the resulting change in above-ground biomass and forage quality jointly determined the spatiotemporal characteristics of livestock behaviour. Increased grazing intensity limited forage resources and promoted intraspecific competition of livestock, which induced longer travelling distance and foraging time, and more even spatial distribution when seeking habitat, which ultimately led to a reduction in LWG. In contrast, under light grazing where there were sufficient forage resources, livestock exhibited higher LWG with less foraging time, shorter travelling distance, and more specialized habitat occupation. These findings support the Optimal Foraging Theory and the Ideal Free Distribution model, which may have important implications for grassland ecosystem management and sustainability.

Invited Review: Applications of unsupervised machine learning in livestock behavior: Case studies in recovering unanticipated behavioral patterns from precision livestock farming data streams

The objective of this article is to review fun- damental differences between model-dependent and mod- el-free approaches to data analysis, and to explore the potential advantages of more open-ended machine learn- ing approaches in recovering complex behavioral patterns from precision livestock farming data streams. Case studies using simulated data were de- signed to mimic a real-world scenario. Data from a feeding trial in an organic dairy were reanalyzed using the Live- stock Informatics Toolkit. Case studies using simulated data are used to demonstrate how incomplete information about the management system can prohibit the development of an appropriate model for information compression, allow- ing aggregation bias to mask important behavioral indi- cators of compromised welfare. These hidden behavioral patterns are then recovered using unsupervised machine learning approaches that are able to leverage the intrinsic behavioral codependencies of group-housed animals. This simulated case study is then extended to demonstrate how model-based approaches can also overlook causes of com- promised welfare when the link between environmental factors and behavioral responses is strong but nonlinear, whereas model-free information-theoretic tools can easily recover and characterize such complex dynamics. Finally, in an empirical case study with data from a commercial organic dairy, the Livestock Informatics Toolkit is used to recover from milk parlor metadata complex associations between herd age structure, levels of milk production, and order of milking. Model-free machine learning algorithms provide a more open-ended approach to knowledge discovery that require fewer up-front as- sumptions about the management system. This can yield more comprehensive insights into large precision livestock farming data sets now commonly encountered in on-farm research trials and in applied data auditing scenarios.

Evaluation of Chute Exit, Novelty and Human Approach Tests in Mertolenga Beef Cattle

Livestock behavior and welfare are increasingly recognized to be related, not only to the animals' handling, but also with productivity. The present work was carried out at the Mertolenga Breed Testing Center and its objective was to evaluate the behavior of Mertolenga breed young bulls when exiting the chute, the reaction to novelty and human approach tests, as well as to understand if the responses between these tests are related. Twenty-nine Mertolenga-bred young bulls from 16 different farms, aged between 8 and 13 months, entered the study farm from the end of May to the beginning of June 2021. Data was collected on six different days and analyzed with the SAS® 9.4 software. Older animals showed a tendency to leave the chute more calmly, take longer to touch the novelty ball and to touch the ball less often, with a consequent reduction in the likelihood of playing with it. In the human approach test, animals that came out of the chute more calmly allowed the human to come closer. These behavior tests should be further studied on potential sires, so as to increase docility and manageability of autochthonous beef breeds.

An Investigation on Real Time Monitoring System for Livestock and Agriculture using IoT

Farmers used to calculate the soil's readiness and assess the impact of their doubts before producing a particular type of yield. A real-time monitoring system for livestock and agriculture describes the utilization of the Internet of Things (IoT) technology. The system comprises of various sensors and devices that collect data related to the environmental conditions, health status, and behaviour of livestock, as well as the soil moisture level, temperature, and humidity of crops. The data collected from these devices is transmitted to a cloud-based platform where it is processed. The system also includes alerts and notifications to farmers in case of any anomalies or deviations from the expected norms, enabling them to take prompt corrective action. Overall, the real-time monitoring system using IoT technology has the potential to improve livestock and crop management practices, increase productivity, and enhance the overall efficiency of the agriculture industry.

The effect of perceived value on farmers' livestock manure resource utilization behavior: Evidence from Shandong, China.

The rapid development of animal husbandry has brought many problems such as ecological environmental pollution and public health damage. The resource utilization of livestock manure is the key way to deal with the above crisis and turn waste into treasure. Based on the theory of perceived value, this paper uses multi-group structural equation model to explore the driving mechanism of perceived value on the resource utilization behavior of livestock manure. The results showed that: (1) The resource utilization behavior of livestock manure followed the logic of "cognitive level → cognitive trade-off → perceived value → behavioral intention → behavioral performance." Perceived benefit and perceived risk have positive and reverse driving effects on perceived value, respectively. Perceived value has a positive driving effect on behavioral intention. The behavioral intention has a positive driving effect on utilization behavior. (2) Among the observed variables of perceived benefits, ecological benefits have the greatest impact; Among the observed variables of perceived risk, economic risk has the greatest impact. Among the observed variables of perceived value, Significance cognition has the greatest influence. Among the observed variables of behavioral intention, utilization intention has the greatest influence. (3) The perceived value has a differential effect on the utilization behavior of livestock manure resources of different part-time farmers, and the driving effect is more obvious for full-time farmers. Therefore, it is necessary to improve the resource utilization system of livestock manure, increase the channel for realizing the output of manure resources, strengthen technical assistance and policy subsidies, and implement policies according to local conditions to improve the overall perceived value of farmers.

Taste agents as modulators of the feeding behaviour of grazing yaks in alpine meadows

Feeding behaviour plays a significant role in promoting good animal health and welfare. It is also reflective of the quality and quantity of available feed. In fact, grazing livestock do not select their feed randomly, rather their behaviour is influenced by the texture, taste, and smell of each pasture species. Although taste agents are often used to modify feed intake for captive livestock, the effect on the feeding behaviour of grazing livestock has not yet been extensively evaluated in native grasslands. To address this gap in knowledge, herein, we sprayed three types of taste agents—salty (SA), sweet (SW), and bitter (BT)—on alpine meadows to investigate their effect on the grazing behaviour of yaks (Bos Grunniens) on the Qinghai-Tibetan Plateau (QTP). Behavioural observations showed that grazing was concentrated primarily in the morning and afternoon, while ruminating/resting peaked at noon; however, the diurnal behavioural patterns of grazing yaks were not affected by the taste agents. Application of the SA agent significantly increased the yaks’ grazing time, bites per minute, bites per step, time per feeding station, and steps per feeding station, while significantly reducing walking time, steps per minute, and number of feeding stations per minute. Meanwhile, application of the SW agent significantly increased the yaks’ time per feeding station, however, significantly reduced the steps per minute and number of feeding stations per minute. In contrast, the BT agent significantly increased the yaks’ walking time, steps per minute, and number of feeding stations per minute, while significantly reducing grazing time, bites per minute, bites per step, and time per feeding station. Application of the SA agent also significantly increased the intake of favoured, edible, and inedible forage, while the SW agent improved inedible forage intake, however, had a more subtle effect on favoured and edible forage intake. Meanwhile, the BT agent had an inhibitory effect on grazing intake. Hence, the structural equation model suggested that taste agents may directly or indirectly influence grazing behaviour by regulating feeding behaviour. Our findings provide a theoretical basis for using taste agents in grasslands to control the grazing behaviour of livestock and provide a method to promote the stability of grassland communities, while mitigating the degradation of grasslands in the QTP.

Microclimate Drives Shelter-Seeking Behaviour in Lambing Ewes

Silvopastoral agroforestry and the strategic placement of trees and hedgerows offers potential to improve livestock welfare and production efficiency through the provision of shelter in livestock farming systems. The aim of this study was to investigate the relationship between shelter-seeking behaviour of ewes during the lambing period and the microclimate influenced by landscape shelter features. Artificial and natural shelter was provided to Aberfield ewes (n = 15) on an upland sheep farm in Wales, UK, which were then continuously monitored for 14 days using global positioning system tracking devices. Modelling of microclimate influenced by topographical shelter features at the test site was used to generate a 1 m resolution wind field for geospatial statistical analysis of localised wind speed. Ewes demonstrated an increased preference for natural (3.4-fold; p < 0.01) and artificial (3.0-fold; p < 0.05) shelter zones five times the height of the shelter, compared to the exposed area of the trial site. Wind-chill and modelled local-scale wind speeds were found to have the greatest influence on shelter-seeking behaviour, with temperature and field-scale wind speed significantly influencing livestock behaviour. Mean wind-chill temperature during the trial was 3.7 °C (min −5.3 °C; max 13.1 °C), which is within the cold stress temperature threshold (−3 and 8 °C) that requires thermoregulatory strategies such as shelter-seeking behaviour. An improved understanding of the relationship between microclimate and shelter-seeking behaviour in sheep, demonstrated through the agent-based model developed in this project, shall better inform the economic incentives (e.g., reduction in lamb mortality and forage requirements) behind silvopastoral practices that benefit farm productivity, livestock welfare and the environment.