Precision Livestock Farming: Technologies for Improving Animal Health, Welfare and Production

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 24627

Special Issue Editors

Institute for Future Farming Systems, School of Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD 4702, Australia
Interests: agriculture, land and farm management; agricultural spatial analysis and modelling
Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA
Interests: use of technologies to monitor animal welfare; precision livestock management

Special Issue Information

Dear Colleagues,

Precision livestock technologies are being promoted across many industries as a revolution in animal management. There are several animal industries that have already seen the development and relative widespread adoption of some of these technologies with a focus on a range of production-related applications. While production-related outcomes, particularly increased output and reduced costs, are critical to sustainable businesses, there is growing interest in the development of systems for monitoring the health and welfare of livestock for the purpose of a social license. The growing demand amongst consumers to be informed with regard to the conditions under which the animal products they use are being raised will be a key driver of the future development of precision livestock technologies. This Special Issue will include a range of papers that explore the use of precision livestock technologies to manage health and welfare and their benefits in terms of production and social license.

Dr. Mark Trotter

Prof. Dr. Derek Bailey

Guest Editors

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Published Papers (9 papers)

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Research

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10 pages, 4984 KiB  
Article
Interactions between Ewes and Rams during Mating Can Be Used to Predict Lambing Dates Accurately, but Not Sire
by Kirsty Cunningham, Andrew Van Burgel, Khama R. Kelman, Claire M. Macleay, Beth L. Paganoni and Andrew N. Thompson
Animals 2022, 12(13), 1707; https://doi.org/10.3390/ani12131707 - 01 Jul 2022
Viewed by 1450
Abstract
Ewes often lamb over extended periods so the level of nutrition during pregnancy and lambing may be suboptimal for ewes that conceived later during mating. Predicting lambing dates would allow cohorts of ewes with similar gestational ages to be managed more precisely to [...] Read more.
Ewes often lamb over extended periods so the level of nutrition during pregnancy and lambing may be suboptimal for ewes that conceived later during mating. Predicting lambing dates would allow cohorts of ewes with similar gestational ages to be managed more precisely to achieve targets for ewe nutrition, feed on offer, mob sizes and access to shelter to improve lamb survival. The interactions between ewes and rams during mating have been used to predict the time of oestrus and lambing dates successfully, but this has not been tested at a commercial scale. In this study, proximity sensors were used to measure interactions between inexperienced Merino ewes (n = 317) and experienced rams (n = 9) during a 27-day mating period under commercial production conditions. When the gestation length was assumed to be 150 days, 91% of lambing dates were predicted within ±6 days of the actual birth date of lambs and 84% of lambing dates were predicted within ±3 days. The use of proximity sensors during mating was an effective means of predicting lambing dates, and there was no significant difference in accuracy for single bearing verses multiple bearing ewes. However, DNA parentage data showed the ram corresponding with the maximum daily interactions ratio was the sire for only 16% of all progeny, suggesting they could not be used to indicate the sire of the progeny. Full article
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13 pages, 1152 KiB  
Article
Early Detection of Respiratory Diseases in Calves by Use of an Ear-Attached Accelerometer
by Nasrin Ramezani Gardaloud, Christian Guse, Laura Lidauer, Alexandra Steininger, Florian Kickinger, Manfred Öhlschuster, Wolfgang Auer, Michael Iwersen, Marc Drillich and Daniela Klein-Jöbstl
Animals 2022, 12(9), 1093; https://doi.org/10.3390/ani12091093 - 23 Apr 2022
Cited by 8 | Viewed by 2263
Abstract
Accelerometers (ACL) can identify behavioral and activity changes in calves. In the present study, we examined the association between bovine respiratory disease (BRD) and behavioral changes detected by an ear-tag based ACL system in weaned dairy calves. Accelerometer data were analyzed from 7 [...] Read more.
Accelerometers (ACL) can identify behavioral and activity changes in calves. In the present study, we examined the association between bovine respiratory disease (BRD) and behavioral changes detected by an ear-tag based ACL system in weaned dairy calves. Accelerometer data were analyzed from 7 d before to 1 d after clinical diagnosis of BRD. All calves in the study (n = 508) were checked daily by an adapted University of Wisconsin Calf Scoring System. Calves with a score ≥ 4 and fever for at least two consecutive days were categorized as diseased (DIS). The day of clinical diagnosis of BRD was defined as d 0. The data analysis showed a significant difference in high active times between DIS and healthy control calves (CON), with CON showing more high active times on every day, except d −3. Diseased calves showed significantly more inactive times on d −4, −2, and 0, as well as longer lying times on d −5, −2, and +1. These results indicate the potential of the ACL to detect BRD prior to a clinical diagnosis in group-housed calves. Furthermore, in this study, we described the ‘normal’ behavior in 428 clinically healthy weaned dairy calves obtained by the ACL system. Full article
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13 pages, 2215 KiB  
Article
Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods
by Yongfeng Li, Hang Shu, Jérôme Bindelle, Beibei Xu, Wenju Zhang, Zhongming Jin, Leifeng Guo and Wensheng Wang
Animals 2022, 12(9), 1060; https://doi.org/10.3390/ani12091060 - 20 Apr 2022
Cited by 11 | Viewed by 2295
Abstract
The behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data [...] Read more.
The behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification. Full article
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11 pages, 964 KiB  
Article
Sensor-Based Detection of Predator Influence on Livestock: A Case Study Exploring the Impacts of Wild Dogs (Canis familiaris) on Rangeland Sheep
by Caitlin A. Evans, Mark G. Trotter and Jaime K. Manning
Animals 2022, 12(3), 219; https://doi.org/10.3390/ani12030219 - 18 Jan 2022
Cited by 2 | Viewed by 2092
Abstract
In Australia, wild dogs are one of the leading causes of sheep losses. A major problem with managing wild dogs in Australia’s rangeland environments is that sheep producers are often unaware of their presence until injuries or deaths are observed. One option for [...] Read more.
In Australia, wild dogs are one of the leading causes of sheep losses. A major problem with managing wild dogs in Australia’s rangeland environments is that sheep producers are often unaware of their presence until injuries or deaths are observed. One option for earlier detection of wild dogs is on-animal sensors, such as Global Positioning System (GPS) tracking collars, to detect changes in the behaviour of sheep due to the presence of wild dogs. The current study used spatio-temporal data, derived from GPS tracking collars, deployed on sheep from a single rangeland property to determine if there were differences in the behaviour of sheep when in the presence, or absence, of a wild dog. Results indicated that the presence of a wild dog influenced the daily behaviours of sheep by increasing the daily distance travelled. Differences in sheep diurnal activity were also observed during periods where a wild dog was present or absent on the property. These results highlight the potential for on-animal sensors to be used as a monitoring tool for sheep flocks directly impacted by wild dogs, although further work is needed to determine the applicability of these results to other sheep production regions of Australia. Full article
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10 pages, 388 KiB  
Article
Strategy to Predict High and Low Frequency Behaviors Using Triaxial Accelerometers in Grazing of Beef Cattle
by Rafael N. Watanabe, Priscila A. Bernardes, Eliéder P. Romanzini, Larissa G. Braga, Thaís R. Brito, Ronyatta W. Teobaldo, Ricardo A. Reis and Danísio P. Munari
Animals 2021, 11(12), 3438; https://doi.org/10.3390/ani11123438 - 02 Dec 2021
Cited by 8 | Viewed by 2175
Abstract
Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim of this study was to provide [...] Read more.
Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim of this study was to provide strategies for predicting more and less frequent beef cattle grazing behaviors. The behavior activities observed were grazing, ruminating, idle, water consumption frequency (WCF), feeding (supplementation) and walking. Three Machine Learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) and two resample methods: under and over-sampling, were tested. Overall accuracy was higher for RF models trained with the over-sampled dataset. The greatest sensitivity (0.808) for the less frequent behavior (WCF) was observed in the RF algorithm trained with the under-sampled data. The SVM models only performed efficiently when classifying the most frequent behavior (idle). The greatest predictor in the NBC algorithm was for ruminating behavior, with the over-sampled training dataset. The results showed that the behaviors of the studied animals were classified with high accuracy and specificity when the RF algorithm trained with the resampling methods was used. Resampling training datasets is a strategy to be considered, especially when less frequent behaviors are of interest. Full article
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14 pages, 2644 KiB  
Article
Temporal Changes in Association Patterns of Cattle Grazing at Two Stocking Densities in a Central Arizona Rangeland
by Colin T. Tobin, Derek W. Bailey, Mitchell B. Stephenson and Mark G. Trotter
Animals 2021, 11(9), 2635; https://doi.org/10.3390/ani11092635 - 08 Sep 2021
Cited by 3 | Viewed by 2100
Abstract
Proper grazing management of arid and semi-arid rangelands requires experienced personnel and monitoring. Applications of GPS tracking and sensor technologies could help ranchers identify livestock well-being and grazing management issues so that they can promptly respond. The objective of this case study was [...] Read more.
Proper grazing management of arid and semi-arid rangelands requires experienced personnel and monitoring. Applications of GPS tracking and sensor technologies could help ranchers identify livestock well-being and grazing management issues so that they can promptly respond. The objective of this case study was to evaluate temporal changes in cattle association patterns using global positioning system (GPS) tracking in pastures with different stocking densities (low stocking density [LSD] = 0.123 animals ha−1, high stocking density [HSD] = 0.417 animals ha−1) at a ranch near Prescott, Arizona. Both pastures contained similar herd sizes (135 and 130 cows, respectively). A total of 32 cows in the HSD herd and 29 cows in the LSD herd were tracked using GPS collars at location fixes of 30 min during a 6-week trial in the summer of 2019. A half-weight index (HWI) value was calculated for each pair of GPS-tracked cattle (i.e., dyads) to determine the proportion of time that cattle were within 75 m and 500 m of each other. Forage mass of both pastures were relatively similar at the beginning of the study and forage utilization increased from 5 to 24% in the HSD pasture and increased from 10 to 20% in the LSD pasture. Cattle in both pastures exhibited relatively low mean association values (HWI < 0.25) at both spatial scales. Near the end of the study, cattle began to disperse likely in search of forages (p < 0.01) and travelled farther (p < 0.01) from water than during earlier periods. Real-time GPS tracking has the potential to remotely detect changes in animal spatial association (e.g., HWI), and identify when cows disperse, likely searching for forage. Full article
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Review

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25 pages, 4494 KiB  
Review
Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare
by Flávio G. Silva, Cristina Conceição, Alfredo M. F. Pereira, Joaquim L. Cerqueira and Severiano R. Silva
Animals 2023, 13(7), 1148; https://doi.org/10.3390/ani13071148 - 24 Mar 2023
Cited by 4 | Viewed by 2635
Abstract
Precision livestock farming (PLF) research is rapidly increasing and has improved farmers’ quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and [...] Read more.
Precision livestock farming (PLF) research is rapidly increasing and has improved farmers’ quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves’ research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves’ health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves’ health, performance, and welfare, if commercial applications are available, which, from the authors’ knowledge, are not at the moment. Full article
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11 pages, 254 KiB  
Review
Automated Tracking Systems for the Assessment of Farmed Poultry
by Suresh Neethirajan
Animals 2022, 12(3), 232; https://doi.org/10.3390/ani12030232 - 19 Jan 2022
Cited by 12 | Viewed by 5731
Abstract
The world’s growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance [...] Read more.
The world’s growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animals on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of artificial intelligence (AI)-assisted technology individualised, and per-herd assessments of livestock became possible and accurate. Various studies have shown that cameras linked with specialised systems of AI can properly analyse flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on recent advancements, this review explores the aspects of AI in the detection, counting, and tracking of poultry in commercial and research-based applications. Full article

Other

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13 pages, 1891 KiB  
Case Report
A Case Study Using Accelerometers to Identify Illness in Ewes following Unintentional Exposure to Mold-Contaminated Feed
by Sara C. Gurule, Victor V. Flores, Kylee K. Forrest, Craig A. Gifford, John C. Wenzel, Colin T. Tobin, Derek W. Bailey and Jennifer A. Hernandez Gifford
Animals 2022, 12(3), 266; https://doi.org/10.3390/ani12030266 - 21 Jan 2022
Cited by 4 | Viewed by 1584
Abstract
Sensor technologies can identify modified animal activity indicating changes in health status. This study investigated sheep behavior before and after illness caused by mold-contaminated feed using tri-axial accelerometers. Ten ewes were fitted with HerdDogg biometric accelerometers. Five ewes were concurrently fitted with Axivity [...] Read more.
Sensor technologies can identify modified animal activity indicating changes in health status. This study investigated sheep behavior before and after illness caused by mold-contaminated feed using tri-axial accelerometers. Ten ewes were fitted with HerdDogg biometric accelerometers. Five ewes were concurrently fitted with Axivity AX3 accelerometers. The flock was exposed to mold-contaminated feed following an unexpected ration change, and observed symptomatic ewes were treated with a veterinarian-directed protocol. Accelerometer data were evaluated 4 days before exposure (d −4 to −1); the day of ration change (d 0); and 4 days post exposure (d 1 to 4). Herddogg activity index correlated to the variability of minimum and standard deviation of motion intensity monitored by the Axivity accelerometer. Herddogg activity index was lower (p < 0.05) during the mornings (0800 to 1100 h) of days 2 to 4 and the evening of day 1 than days −4 to 0. Symptomatic ewes had lower activity levels in the morning and higher levels at night. After accounting for symptoms, activity levels during days 1 to 4 were lower (p < 0.05) than days −4 to 0 the morning after exposure. Results suggest real-time or near-real time accelerometers have potential to detect illness in ewes. Full article
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