Livestock Behavior Research Articles

Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques.

Sensor technologies allow ethologists to continuously monitor the behaviors of large numbers of animals over extended periods of time. This creates new opportunities to study livestock behavior in commercial settings, but also new methodological challenges. Densely sampled behavioral data from large heterogeneous groups can contain a range of complex patterns and stochastic structures that may be difficult to visualize using conventional exploratory data analysis techniques. The goal of this research was to assess the efficacy of unsupervised machine learning tools in recovering complex behavioral patterns from such datasets to better inform subsequent statistical modeling. This methodological case study was carried out using records on milking order, or the sequence in which cows arrange themselves as they enter the milking parlor. Data was collected over a 6-month period from a closed group of 200 mixed-parity Holstein cattle on an organic dairy. Cows at the front and rear of the queue proved more consistent in their entry position than animals at the center of the queue, a systematic pattern of heterogeneity more clearly visualized using entropy estimates, a scale and distribution-free alternative to variance robust to outliers. Dimension reduction techniques were then used to visualize relationships between cows. No evidence of social cohesion was recovered, but Diffusion Map embeddings proved more adept than PCA at revealing the underlying linear geometry of this data. Median parlor entry positions from the pre- and post-pasture subperiods were highly correlated (R = 0.91), suggesting a surprising degree of temporal stationarity. Data Mechanics visualizations, however, revealed heterogeneous non-stationary among subgroups of animals in the center of the group and herd-level temporal outliers. A repeated measures model recovered inconsistent evidence of a relationships between entry position and cow attributes. Mutual conditional entropy tests, a permutation-based approach to assessing bivariate correlations robust to non-independence, confirmed a significant but non-linear association with peak milk yield, but revealed the age effect to be potentially confounded by health status. Finally, queueing records were related back to behaviors recorded via ear tag accelerometers using linear models and mutual conditional entropy tests. Both approaches recovered consistent evidence of differences in home pen behaviors across subsections of the queue.

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding.

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.

Knowledge co‐production with traditional herders on cattle grazing behaviour for better management of species‐rich grasslands

Abstract The research gap between rangeland/livestock science and conservation biology/vegetation ecology has led to a lack of evidence needed for grazing‐related conservation management. Connecting scientific understanding with traditional ecological knowledge of local livestock keepers could help bridge this research and knowledge gap. We studied the grazing behaviour (plant selection and avoidance) of beef cattle (c. 33,000 bites) on species‐rich lowland pastures in Central Europe and traditional herding practices. We also did >450 outdoor interviews with traditional herders about livestock behaviour, herders' decisions to modify grazing behaviour and effects of modified grazing on pasture vegetation. We found that cattle grazing on species‐rich pastures displayed at least 10 different behavioural elements as they encountered 117 forage species from highly desired to rejected. The small discrimination error suggests that cattle recognize all listed plants ‘by species’. We also found that herders had broad knowledge of grazing desire and they consciously aimed to modify desire by slowing, stopping or redirecting the herd. Modifications were aimed at increasing grazing intensity in less‐desired patches and decreasing grazing selectivity in heterogenous swards. Synthesis and applications. The traditional herd management practices presented here have significant conservation benefits, such as avoiding under‐ and overgrazing, and targeted removal of pasture weeds, litter and encroaching bushes, tall competitive plants and invasive species. We argue that knowledge co‐production with traditional herders who belong to another knowledge system could help connect isolated scientific disciplines especially if ecologists and rangeland scientists work closely with traditional herders, co‐designing research projects and working together in data collection, analysis and interpretation. Stronger links between these disciplines could help develop evidence‐based, specific conservation management practices while herders could contribute with their practical experiences and with real‐world testing of new management techniques.

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

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

Identifying livestock behavior patterns based on accelerometer dataset

In large livestock farming it would be beneficial to be able to automatically detect behaviors in animals. In fact, this would allow to estimate the health status of individuals, providing valuable insight to stock raisers. Traditionally this process has been carried out manually, relying only on the experience of the breeders. Such an approach is effective for a small number of individuals. However, in large breeding farms this may not represent the best approach, since, in this way, not all the animals can be effectively monitored all the time. Moreover, the traditional approach heavily rely on human experience, which cannot be always taken for granted. To this aim, in this paper, we propose a new method for automatically detecting activity and inactivity time periods of animals, as a behavior indicator of livestock. In order to do this, we collected data with sensors located in the body of the animals to be analyzed.In particular, the reliability of the method was tested with data collected on Iberian pigs and calves. Results confirm that the proposed method can help breeders in detecting activity and inactivity periods for large livestock farming.

Method for Classifying Behavior of Livestock on Fenced Temperate Rangeland in Northern China

Different livestock behaviors have distinct effects on grassland degradation. However, because direct observation of livestock behavior is time- and labor-intensive, an automated methodology to classify livestock behavior according to animal position and posture is necessary. We applied the Random Forest algorithm to predict livestock behaviors in the Horqin Sand Land by using Global Positioning System (GPS) and tri-axis accelerometer data and then confirmed the results through field observations. The overall accuracy of GPS models was 85% to 90% when the time interval was greater than 300–800 s, which was approximated to the tri-axis model (96%) and GPS-tri models (96%). In the GPS model, the linear backward or forward distance were the most important determinants of behavior classification, and nongrazing was less than 30% when livestock travelled more than 30–50 m over a 5-min interval. For the tri-axis accelerometer model, the anteroposterior acceleration (–3 m/s2) of neck movement was the most accurate determinant of livestock behavior classification. Using instantaneous acceleration of livestock body movement more precisely classified livestock behaviors than did GPS location-based distance metrics. When a tri-axis model is unavailable, GPS models will yield sufficiently reliable classification accuracy when an appropriate time interval is defined.

Effects of Climatic Conditions on the Lying Behavior of a Group of Primiparous Dairy Cows.

Simple SummaryDairy cow welfare has become a significant topic in recent years. Lying (down) behavior is considered a useful indicator for dairy cow health, welfare, reproductive and productive status. The study evaluated the interaction of climatic conditions on the lying behavior of a group of dairy cows. The developed model seems helpful to identify and predict this important indicator of the welfare of the herd. The prediction model developed, with automatic monitoring of cow behavior, could be a valid early warning system to identify any deviation from the expected behavior, and could be also used to evaluate the goodness of management and to evaluate the heat stress mitigation strategy.Currently, lying behavior can be assessed using continuous observations from sensors (e.g., accelerometers). The analysis of digital data deriving from accelerometers is an effective tool for studying livestock behaviors. Despite the large interest in the lying behavior of dairy cows, no reference was found in literature regarding the prediction of lying behavior as a function of the interaction of environmental parameters. The present study aimed to evaluate the influence of climatic conditions (temperature-humidity index, solar radiation, air velocity and rainfalls) on the lying behavior of a group of primiparous dairy cows, using data from accelerometers, and develop a prediction model to identify and predict the lying behavior of dairy cows as a function of the effects of environmental conditions. Results from the. GLM Procedure (SAS) showed that the model was highly significant (p < 0.001) and the r2 was 0.84. All of the effects in the model resulted in being highly significant (p < 0.001). This model, if validated properly, could be a valid early warning system to identify any deviation from the expected behavior, and to assess the effectiveness of thermal stress mitigation strategies.

Evaluation of pre-processing methods for the prediction of cattle behaviour from accelerometer data

Monitoring livestock behaviour can be a useful way to improve farm animal management and to detect individual health events. The use of automated systems that predict several daily behaviours from accelerometer data is growing and studies have often focused on the comparison of datamining classification methods. However, few studies have attempted to evaluate the effect of the step preceding the classification, namely accelerometer signal pre-processing. This study aimed to evaluate the effect of several pre-processing methods on the prediction of dairy cows behaviour from accelerometer data. Ten Holstein cows equipped with a three-dimensional accelerometer fixed on a neck collar were simultaneously observed by two observers. Observed behaviours were predicted with decision tree using pre-processed accelerometer data as inputs. Different procedures were evaluated for each of these following pre-processing steps: signal filtering, signal segmentation and feature calculation. For signal filtering, low-pass filters and high-pass filters were considered. For signal segmentation, various window sizes and percentages of overlap between windows were implemented. Sixty-one features were computed. This resulted in 150 different combinations of pre-processing steps. For each combination a decision tree model predicting the observed behaviours was trained. The performance of each model to predict the observed behaviours was compared based on accuracy and F-score measures. The relative importance of each pre-processing configuration on the performance of prediction was evaluated with a linear regression model. The best configurations led to an accuracy of 0.95 and a F-score of 0.96 against 0.76 and 0.65 respectively with the worse combinations. The best combinations included a window size of 20 s and 30 s, with an overlap of 90% and no high-pass filter. High-pass filter had the most significant effect on the classification (P < 0.001), with a substantial decrease when it was applied. Performance of the classification was also significantly improved when increasing the percentage of overlapping (P < 0.001). The decline of the accuracy observed for the window size of 20 s without overlap was prevented when an overlap higher than 50% was applied. This suggested that overlap should be considered when there is not sufficient data available. No impact was found for the low-pas filter. In conclusion, the way of pre-processing the accelerometer signal deserves to be considered in the classification and adapted according to the aim and design of the study.

A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming.

Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to “concept drift”, which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions.

Evaluación productiva y económica de un sistema silvopastoril intensivo en bovinos doble propósito en Michoacán, México

Se evaluó el comportamiento productivo de bovinos doble propósito en un sistema silvopastoril intensivo (SSPi) en Apatzingán, Michoacán. La actividad se llevó a cabo mediante seguimiento técnico-económico. Asimismo, se monitorearon variables productivas y económicas (consumo de forraje, producción de leche, peso, ingresos y egresos) de 60 vacas de la raza Gyr analizadas mediante estadística descriptiva y presupuestos por actividad. La carga animal empleada fue de 2.06 UA ha-1 y la producción de leche obtenida fue 9.15 kg vaca-1 día-1. El manejo de vacas Gyr en un SSPi favorece la producción de leche y carne y el ingreso económico a la unidad de producción (UP).

Modeling Large Carnivore and Ranch Attribute Effects on Livestock Predation and Nonlethal Losses

Predator-livestock interactions are a major concern for both agriculture and conservation globally. Using retrospective survey data from 274 ranches in Wyoming, United States, we used information theory to model how ranch attributes and large carnivores influenced the timing, duration, and severity of livestock predation. We then used constrained ordination to understand how 1) landscape, weather, and animal features influence predation and 2) how livestock behavior and nonlethal loss relate to ranch attributes and large carnivores. Timing, duration, and severity of livestock predation were generally not explained by ranch size or number of counties but were explained by livestock type, livestock parturition (either timing or duration), and documented large carnivore loss. Addition of the large carnivore loss variable to global models always improved Akaike information criterion scores. Rangelands characterized as rough, forested, shrubby, or a public grazing allotment reportedly increased predation risk, in part, due to large carnivore exposure. Approximately two-thirds of participants noticed livestock nervousness if a predator was nearby, half of participants noted changes in livestock distribution patterns, and a quarter of participants noted a reduction in livestock grazing time. Nonlethal losses such as lower weight gains, lower conception rates, lower birth rates, and delayed birth season were reported by 27%, 19%, 12%, and 11% of participants, respectively. Ordination revealed separation between behavioral changes and nonlethal losses, attributed to large carnivore exposure. Parturition relative to livestock type was also strongly correlated to timing and duration of predation for cattle-only operations but not for operations with sheep. The predictive cattle predation-parturition model suggests that for each additional month of calving, producers should anticipate 21 additional d of predation. Understanding predator-livestock interactions relative to ranch and rangeland features, parturition, large carnivore exposure, and losses that extend beyond mortalities can assist in developing novel strategies to mitigate lethal and nonlethal losses.

Multi-dimensional Precision Livestock Farming: a potential toolbox for sustainable rangeland management.

BackgroundPrecision Livestock Farming (PLF) is a promising approach to minimize the conflicts between socio-economic activities and landscape conservation. However, its application on extensive systems of livestock production can be challenging. The main difficulties arise because animals graze on large natural pastures where they are exposed to competition with wild herbivores for heterogeneous and scarce resources, predation risk, adverse weather, and complex topography. Considering that the 91% of the world’s surface devoted to livestock production is composed of extensive systems (i.e., rangelands), our general aim was to develop a PLF methodology that quantifies: (i) detailed behavioural patterns, (ii) feeding rate, and (iii) costs associated with different behaviours and landscape traits.MethodsFor this, we used Merino sheep in Patagonian rangelands as a case study. We combined data from an animal-attached multi-sensor tag (tri-axial acceleration, tri-axial magnetometry, temperature sensor and Global Positioning System) with landscape layers from a Geographical Information System to acquire data. Then, we used high accuracy decision trees, dead reckoning methods and spatial data processing techniques to show how this combination of tools could be used to assess energy balance, predation risk and competition experienced by livestock through time and space.ResultsThe combination of methods proposed here are a useful tool to assess livestock behaviour and the different factors that influence extensive livestock production, such as topography, environmental temperature, predation risk and competition for heterogeneous resources. We were able to quantify feeding rate continuously through time and space with high accuracy and show how it could be used to estimate animal production and the intensity of grazing on the landscape. We also assessed the effects of resource heterogeneity (inferred through search times), and the potential costs associated with predation risk, competition, thermoregulation and movement on complex topography.DiscussionThe quantification of feeding rate and behavioural costs provided by our approach could be used to estimate energy balance and to predict individual growth, survival and reproduction. Finally, we discussed how the information provided by this combination of methods can be used to develop wildlife-friendly strategies that also maximize animal welfare, quality and environmental sustainability.

Comparison of grazing behaviour of sheep on pasture with different sward surface heights using an inertial measurement unit sensor

Grazing is the most important activity that ruminant livestock undertake daily. A number of studies have used motion sensors to study the grazing behaviour of ruminant livestock. However, few have attempted to validate their approaches against various sward surface heights (SSH). The objectives of our study were to: (1) identify and compare the effects of different SSH on the grazing behaviour of sheep by analyzing data collected by a collar mounted Inertial Measurement Unit (IMU) sensor; (2) calculate the relative importance of the extracted features on grazing identification and compare the consistency of the selected features across various SSH; (3) validate the robustness by using classifiers trained from the dataset with specific SSH to distinguish the grazing activity on the datasets from different SSH; and (4) visualize the classification results of grazing versus non-grazing activities on various SSH. Linear Discriminant Analysis (LDA) was chosen as the classification method, while Probabilistic Principal Component Analysis (PPCA) was used to reduce dimensionality of the feature space for visualization of the results. Experimental results revealed that (1) our approach achieved high classification accuracy of grazing behaviour (over 95%) on all the epochs regardless of SSH; (2) Mean of accelerometer Z-axis, Entropy of accelerometer Y-axis, Entropy of accelerometer Z-axis, Mean of gyroscope X-axis and Mean of gyroscope Y-axis were the top 5 features that contributed most in classifying the grazing versus non-grazing activities and there were consistent trends in features across the three SSH; (3) there was enough robustness when the trained LDA classifier on a specific SSH was used to classify behaviour on different SSH; and (4) there existed a clear linear boundary between the data points representing grazing and those of non-grazing behaviour. Overall, our research confirmed that IMU sensors can be a very effective tool for identifying the grazing behaviour of sheep and there is enough robustness to use a trained LDA classifier on a specific pasture SSH to classify grazing behaviour at different SSH pastures.

An agent-based model of cattle grazing toxic Geyer's larkspur.

By killing cattle and otherwise complicating management, the many species of larkspur (Delphinium spp.) present a serious, intractable, and complex challenge to livestock grazing management in the western United States. Among the many obstacles to improving our understanding of cattle-larkspur dynamics has been the difficulty of testing different grazing management strategies in the field, as the risk of dead animals is too great. Agent-based models (ABMs) provide an effective method of testing alternate management strategies without risk to livestock. ABMs are especially useful for modeling complex systems such as livestock grazing management, and allow for realistic bottom-up encoding of cattle behavior. Here, we introduce a spatially-explicit, behavior-based ABM of cattle grazing in a pasture with a dangerous amount of Geyer’s larkspur (D. geyeri). This model tests the role of herd cohesion and stocking density in larkspur intake, finds that both are key drivers of larkspur-induced toxicosis, and indicates that alteration of these factors within realistic bounds can mitigate risk. Crucially, the model points to herd cohesion, which has received little attention in the discipline, as playing an important role in lethal acute toxicosis. As the first ABM to model grazing behavior at realistic scales, this study also demonstrates the tremendous potential of ABMs to illuminate grazing management dynamics, including fundamental aspects of livestock behavior amidst ecological heterogeneity.

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

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

Addressing the Challenges of Conducting Observational Studies in Sheep Abattoirs.

Simple SummaryCollecting data, particularly data on animal behavior, on-site at abattoirs, can be hindered by a series of challenges. These challenges are summarized and recommendations are offered for those planning similar studies. Particular emphasis is placed on examining interactions between dogs, handlers and sheep in animal-processing facilities because this is a significantly under-researched area in the literature. There is significant merit in collecting data via video-recording software, but the subsequent potential for hardware issues and sampling difficulties must be recognized and addressed.The competing needs of maintaining productivity within abattoirs, and maintaining high standards of animal welfare, provide fertile grounds for applied research in animal behavior. However, there are challenges involved in capturing useful behavioral data from the supply chain (from paddock to processing plant). The challenges identified in this report are based on a review of the scientific literature as well as field study observations. This article describes those challenges as they relate to collecting behavioral data on livestock-herding dogs, humans and livestock as they interact in abattoirs, and provides insights and recommendations for others embarking on animal studies in confined spaces, as well as in commercial settings. Direct observation of livestock behavior permits animal-welfare assessments and evaluations of the efficacy of operations in unfamiliar and high-pressure contexts, such as abattoirs. This brief report summarizes the factors that must be considered when undertaking in situ studies in abattoirs. There is merit in passive behavioral data-collection using video-recording equipment. However, the potential for hardware issues and sampling difficulties must be anticipated and addressed. Future research directions and recommendations to avoid such issues are discussed. This information will be highly beneficial to future abattoir studies focusing on efficiency and animal welfare at commercial abattoirs. Furthermore, it may also be relevant to any analyses involving large cohorts of animals in a confined environment.

The Behavioural Responses of Beef Cattle (Bos taurus) to Declining Pasture Availability and the Use of GNSS Technology to Determine Grazing Preference

Combining technologies for monitoring spatial behaviour of livestock with technologies that monitor pasture availability, offers the opportunity to improve the management and welfare of extensively produced beef cattle. The aims of the study were to investigate changes to beef cattle behaviour as pasture availability changed, and to determine whether Global Navigation Satellite System (GNSS) technology could determine livestock grazing preference and hence improve pasture management and paddock utilisation. Data derived from GNSS collars included distance travelled and location in the paddock. The latter enabled investigation of individual animal interactions with the underlying Normalised Difference Vegetation Index (NDVI) and pasture biomass of the paddock. As expected, there was a significant temporal decrease in NDVI during the study and an increase in distance travelled by cattle (P &lt; 0.001; r2 = 0.88). The proportion of time budget occupied in grazing behaviour also increased (P &lt; 0.001; r2 = 0.71). Cattle showed a partial preference for areas of higher pasture biomass/NDVI, although there was a large amount of variation over the course of the study. In conclusion, cattle behaviour changed in response to declining NDVI, highlighting how technologies that monitor these two variables may be used in the future as management tools to assist producers better manage cattle, to manipulate grazing intensity and paddock utilisation.

The effects of global navigation satellite system (GNSS) collars on cattle (Bos taurus) behaviour

The use of Global Navigation Satellite System (GNSS) collars has become an increasingly important research tool to study the behaviour of domestic livestock species in grazing conditions. However, relatively little is known about the effects on livestock behaviour of livestock “wearing” such collars. The aims of the present research were to determine if GNSS collars affect the behaviour of beef cattle at pasture, and whether an habituation period is required before the animal is accustomed to wearing the device. Behaviour observations were conducted on 20 Charolais cows which were maintained as a single herd in an 8.9ha paddock. Behaviour was recorded during 13 of 15days using a scan sampling technique every 5min for a total of 8h daily. The trial was divided into three, 5-d periods: before, with and after wearing GNSS collars. During the “with collar” period, 10 randomly selected cows were fitted with a GNSS collar (CD cows) weighing 0.61kg (0.1% of liveweight) whereas the remaining 10 cows were not fitted with GNSS collars (NC cows). Over the course of the 15days, 12 mutually exclusive behaviours were recorded for all cows: stand stationary, graze, walk, run, drink, stand ruminate, lie ruminate, rest/idle, social, self-directed, other and out of view. No significant behavioural differences were found between CD and NC cows, with the exception of stand stationary which was greater in CD (9.7%) than NC cows (7.3%). However, there was no interaction with Collar by Period, Day and Hour for any behaviours. During the first hour during the “with collar” period there was no significant effect on behaviour between CD and NC cows when behaviour:hour of day combinations were analysed. Therefore, it would appear that the presence of GNSS collars did not modify any cow behaviours with the exception of stand stationary over the course of this trial, and for future trials an habituation period to the collars is not necessary. This knowledge validates the use of GNSS technology for studying cattle grazing behaviour, and is thus important for future research involving the application of these remote sensing devices to study livestock behaviour.

Towards evenly distributed grazing patterns: including social context in sheep management strategies.

Background. A large proportion of natural grasslands around the world is exposed to overgrazing resulting in land degradation and biodiversity loss. Although there is an increasing effort in the promotion of sustainable livestock management, rangeland degradation still occurs because animals’ foraging behaviour is highly selective at different spatial scales. The assessment of the ecological mechanisms modulating the spatial distribution of grazing and how to control it has critical implications for long term conservation of resources and the sustainability of livestock production. Considering the relevance of social interactions on animals’ space use patterns, our aim was to explore the potential effects of including animals’ social context into management strategies using domestic sheep grazing in rangelands as case study.Methods. We used GPS data from 19 Merino sheep (approximately 10% of the flock) grazing on three different paddocks (with sizes from 80 to 1000 Ha) during a year, to estimate resource selection functions of sheep grazing in flocks of different levels of heterogeneity. We assessed the effects of sheep class (i.e., ewes, wethers, and hoggets), age, body condition and time since release on habitat selection patterns.Results. We found that social rank was reflected on sheep habitat use, where dominant individuals (i.e., reproductive females) used more intensively the most preferred areas and low-ranked (i.e., yearlings) used less preferred areas. Our results showed that when sheep grazed on more heterogeneous flocks, grazing patterns were more evenly distributed at all the paddocks considered in this study. On the other hand, when high-ranked individuals were removed from the flock, low-ranked sheep shifted their selection patterns by increasing the use of the most preferred areas and strongly avoided to use less preferred sites (i.e., a highly selective grazing behaviour).Discussion. Although homogenization and segregation of flocks by classes are common practices to increase flock productivity, we are proposing an alternative that employs behavioural interactions in heterogeneous flocks to generate more evenly distributed grazing patterns. This practice can be combined with other practices such as rotational grazing and guardian dogs (to decrease mortality levels that may be generated by sheep grazing on more risky habitats). This does not imply any modifications of livestock stocking rates and densities or any additional investments for labour and materials. Considering livestock behaviour is critical for the design of sustainable management practices that balance landscape conservation and livestock productivity.

A brief review of the application of machine vision in livestock behaviour analysis

A brief review of the application of machine vision in livestock behaviour analysis