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Neculai-Valeanu AS, Ariton AM, Radu C, Porosnicu I, Sanduleanu C, Amariții G. From Herd Health to Public Health: Digital Tools for Combating Antibiotic Resistance in Dairy Farms. Antibiotics (Basel) 2024; 13:634. [PMID: 39061316 PMCID: PMC11273838 DOI: 10.3390/antibiotics13070634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/04/2024] [Accepted: 07/06/2024] [Indexed: 07/28/2024] Open
Abstract
The emergence of antimicrobial resistance (AMR) is a significant threat to global food security, human health, and the future of livestock production. Higher rates of antimicrobial use in dairy farming and the sheer lack of new antimicrobials available for use focused attention on the question of how the dairy production sector contributed to the development of AMR and paved the path toward taking action to curtail it on the targeted type of farms. This paper aims to provide an introduction to a phenomenon that has gained considerable attention in the recent past due to its ever-increasing impact, the use of antimicrobial drugs, the emergence of antimicrobial resistance (AMR) on dairy farms, and seeks to discuss the possibilities of approaches such as digital health monitoring and precision livestock farming. Using sensors, data, knowledge, automation, etc., digital health monitoring, as well as Precision Livestock Farming (PLF), is expected to enhance health control and minimize disease and antimicrobial usage. The work presents a literature review on the current status and trends of AMR in dairy farms, an understanding of the concept of digital health monitoring and PLF, and the presentation and usefulness of digital health monitoring and PLF in preventing AMR. The study also analyses the strengths and weaknesses of adopting and incorporating digital technologies and artificial intelligence for dairy farming and presents areas for further study and level of use.
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Affiliation(s)
- Andra-Sabina Neculai-Valeanu
- Research and Development Station for Cattle Breeding Dancu, 707252 Iasi, Romania; (A.-S.N.-V.); (A.-M.A.)
- The Academy of Romanian Scientists, Str. Ilfov No. 3, Sector 5, 050045 Bucharest, Romania
| | - Adina-Mirela Ariton
- Research and Development Station for Cattle Breeding Dancu, 707252 Iasi, Romania; (A.-S.N.-V.); (A.-M.A.)
| | - Ciprian Radu
- Research and Development Station for Cattle Breeding Dancu, 707252 Iasi, Romania; (A.-S.N.-V.); (A.-M.A.)
| | - Ioana Porosnicu
- Research and Development Station for Cattle Breeding Dancu, 707252 Iasi, Romania; (A.-S.N.-V.); (A.-M.A.)
- The Academy of Romanian Scientists, Str. Ilfov No. 3, Sector 5, 050045 Bucharest, Romania
- Faculty of Veterinary Medicine, Iasi University of Life Science, 700490 Iasi, Romania
| | - Catalina Sanduleanu
- Research and Development Station for Cattle Breeding Dancu, 707252 Iasi, Romania; (A.-S.N.-V.); (A.-M.A.)
- Faculty of Food and Animal Resources, Iasi University of Life Science, 700490 Iasi, Romania
| | - Gabriela Amariții
- Research and Development Station for Cattle Breeding Dancu, 707252 Iasi, Romania; (A.-S.N.-V.); (A.-M.A.)
- Faculty of Food and Animal Resources, Iasi University of Life Science, 700490 Iasi, Romania
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Sundman ER, Dewell GA, Dewell RD, Johnson AK, Thomson DU, Millman ST. The welfare of ill and injured feedlot cattle: a review of the literature and implications for managing feedlot hospital and chronic pens. Front Vet Sci 2024; 11:1398116. [PMID: 38799724 PMCID: PMC11117431 DOI: 10.3389/fvets.2024.1398116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/12/2024] [Indexed: 05/29/2024] Open
Abstract
By definition, ill and injured animals are on the negative valence of animal welfare. For beef cattle kept in feedlot settings, advances in cattle health management have resulted in a greater understanding and prevention of illness and injury. However, the management of cattle once they become ill and injured is an understudied area, and there are gaps in knowledge that could inform evidence-based decision-making and strengthen welfare for this population. The aim of this review is to provide a comprehensive overview of the acquired knowledge regarding ill and injured feedlot cattle welfare, focusing on existing knowledge gaps and implications for hospital and chronic pen management and welfare assurance. Ill and injured feedlot cattle consist of acutely impaired animals with short-term health conditions that resolve with treatment and chronically impaired animals with long-term health conditions that may be difficult to treat. A literature search identified 110 articles that mentioned welfare and ill and injured feedlot cattle, but the population of interest in most of these articles was healthy cattle, not ill and injured cattle. Articles about managing ill and injured cattle in specialized hospital (n = 12) or chronic (n = 2) pens were even more sparse. Results from this literature search will be used to outline the understanding of acutely and chronically ill and injured feedlot cattle, including common dispositions and welfare considerations, behavior during convalescence, and strategies for identifying and managing ill and injured cattle. Finally, by working through specific ailments common in commercial feedlot environments, we illustrate how the Five Domains Model can be used to explore feelings and experiences and subsequent welfare state of individual ill or injured feedlot cattle. Using this approach and our knowledge of current industry practices, we identify relevant animal-based outcomes and critical research questions to strengthen knowledge in this area. A better understanding of this overlooked topic will inform future research and the development of evidence-based guidelines to help producers care for this vulnerable population.
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Affiliation(s)
- Emiline R. Sundman
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States
| | - Grant A. Dewell
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States
| | - Renee D. Dewell
- Center for Food Security and Public Health, College of Veterinary Medicine, Iowa State University, Ames, IA, United States
| | - Anna K. Johnson
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - Daniel U. Thomson
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - Suzanne T. Millman
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States
- Department of Biomedical Sciences, College of Veterinary Medicine, Iowa State University, Ames, IA, United States
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Bushby EV, Thomas M, Vázquez-Diosdado JA, Occhiuto F, Kaler J. Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data. Sci Rep 2024; 14:9737. [PMID: 38679647 PMCID: PMC11056383 DOI: 10.1038/s41598-024-58206-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/26/2024] [Indexed: 05/01/2024] Open
Abstract
Previous research shows that feeding and activity behaviours in combination with machine learning algorithms has the potential to predict the onset of bovine respiratory disease (BRD). This study used 229 novel and previously researched feeding, movement, and social behavioural features with machine learning classification algorithms to predict BRD events in pre-weaned calves. Data for 172 group housed calves were collected using automatic milk feeding machines and ultrawideband location sensors. Health assessments were carried out twice weekly using a modified Wisconsin scoring system and calves were classified as sick if they had a Wisconsin score of five or above and/or a rectal temperature of 39.5 °C or higher. A gradient boosting machine classification algorithm produced moderate to high performance: accuracy (0.773), precision (0.776), sensitivity (0.625), specificity (0.872), and F1-score (0.689). The most important 30 features were 40% feeding, 50% movement, and 10% social behavioural features. Movement behaviours, specifically the distance walked per day, were most important for model prediction, whereas feeding and social features aided in the model's prediction minimally. These results highlighting the predictive potential in this area but the need for further improvement before behavioural changes can be used to reliably predict the onset of BRD in pre-weaned calves.
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Affiliation(s)
- Emily V Bushby
- School of Veterinary Medicine and Science, Sutton Bonington Campus, University of Nottingham, Leicestershire, LE12 5RD, UK
| | - Matthew Thomas
- School of Veterinary Medicine and Science, Sutton Bonington Campus, University of Nottingham, Leicestershire, LE12 5RD, UK
| | - Jorge A Vázquez-Diosdado
- School of Veterinary Medicine and Science, Sutton Bonington Campus, University of Nottingham, Leicestershire, LE12 5RD, UK
| | - Francesca Occhiuto
- School of Veterinary Medicine and Science, Sutton Bonington Campus, University of Nottingham, Leicestershire, LE12 5RD, UK
| | - Jasmeet Kaler
- School of Veterinary Medicine and Science, Sutton Bonington Campus, University of Nottingham, Leicestershire, LE12 5RD, UK.
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Kamel MS, Davidson JL, Verma MS. Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview. Animals (Basel) 2024; 14:627. [PMID: 38396598 PMCID: PMC10885951 DOI: 10.3390/ani14040627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/24/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Despite significant advances in vaccination strategies and antibiotic therapy, bovine respiratory disease (BRD) continues to be the leading disease affecting the global cattle industry. The etiology of BRD is complex, often involving multiple microbial agents, which lead to intricate interactions between the host immune system and pathogens during various beef production stages. These interactions present environmental, social, and geographical challenges. Accurate diagnosis is essential for effective disease management. Nevertheless, correct identification of BRD cases remains a daunting challenge for animal health technicians in feedlots. In response to current regulations, there is a growing interest in refining clinical diagnoses of BRD to curb the overuse of antimicrobials. This shift marks a pivotal first step toward establishing a structured diagnostic framework for this disease. This review article provides an update on recent developments and future perspectives in clinical diagnostics and prognostic techniques for BRD, assessing their benefits and limitations. The methods discussed include the evaluation of clinical signs and animal behavior, biomarker analysis, molecular diagnostics, ultrasound imaging, and prognostic modeling. While some techniques show promise as standalone diagnostics, it is likely that a multifaceted approach-leveraging a combination of these methods-will yield the most accurate diagnosis of BRD.
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Affiliation(s)
- Mohamed S. Kamel
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
- Department of Medicine and Infectious Diseases, Faculty of Veterinary Medicine, Cairo University, Giza 12211, Egypt
| | - Josiah Levi Davidson
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47907, USA
| | - Mohit S. Verma
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
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Hu S, Reverter A, Arablouei R, Bishop-Hurley G, McNally J, Alvarenga F, Ingham A. Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data. Animals (Basel) 2024; 14:301. [PMID: 38254470 PMCID: PMC11154254 DOI: 10.3390/ani14020301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/11/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.
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Affiliation(s)
- Shuwen Hu
- Agriculture and Food, CSIRO, Saint Lucia, QLD 4067, Australia; (A.R.); (G.B.-H.); (J.M.); (A.I.)
| | - Antonio Reverter
- Agriculture and Food, CSIRO, Saint Lucia, QLD 4067, Australia; (A.R.); (G.B.-H.); (J.M.); (A.I.)
| | | | - Greg Bishop-Hurley
- Agriculture and Food, CSIRO, Saint Lucia, QLD 4067, Australia; (A.R.); (G.B.-H.); (J.M.); (A.I.)
| | - Jody McNally
- Agriculture and Food, CSIRO, Saint Lucia, QLD 4067, Australia; (A.R.); (G.B.-H.); (J.M.); (A.I.)
| | - Flavio Alvarenga
- NSW Department of Primary Industries, Armidale, NSW 2350, Australia;
| | - Aaron Ingham
- Agriculture and Food, CSIRO, Saint Lucia, QLD 4067, Australia; (A.R.); (G.B.-H.); (J.M.); (A.I.)
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Dißmann L, Reinhold P, Smith HJ, Amon T, Sergeeva A, Hoffmann G. Evaluation of a Respiration Rate Sensor for Recording Tidal Volume in Calves under Field Conditions. SENSORS (BASEL, SWITZERLAND) 2023; 23:4683. [PMID: 37430597 DOI: 10.3390/s23104683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 07/12/2023]
Abstract
In the assessment of pulmonary function in health and disease, both respiration rate (RR) and tidal volume (Vt) are fundamental parameters of spontaneous breathing. The aim of this study was to evaluate whether an RR sensor, which was previously developed for cattle, is suitable for additional measurements of Vt in calves. This new method would offer the opportunity to measure Vt continuously in freely moving animals. To measure Vt noninvasively, the application of a Lilly-type pneumotachograph implanted in the impulse oscillometry system (IOS) was used as the gold standard method. For this purpose, we applied both measuring devices in different orders successively, for 2 days on 10 healthy calves. However, the Vt equivalent (RR sensor) could not be converted into a true volume in mL or L. For a reliable recording of the Vt equivalent, a technical revision of the RR sensor excluding artifacts is required. In conclusion, converting the pressure signal of the RR sensor into a flow equivalent, and subsequently into a volume equivalent, by a comprehensive analysis, provides the basis for further improvement of the measuring system.
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Affiliation(s)
- Lena Dißmann
- Department Sensors and Modeling, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany
| | - Petra Reinhold
- Institute of Molecular Pathogenesis, "Friedrich-Loeffler-Institut" (Federal Research Institute for Animal Health), Naumburger Str. 96a, 07743 Jena, Germany
| | - Hans-Jürgen Smith
- Research in Respiratory Diagnostics, Bahrendorfer Straße 3A, 12555 Berlin, Germany
| | - Thomas Amon
- Department Sensors and Modeling, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany
- Department of Veterinary Medicine, Institute of Animal Hygiene and Environmental Health, Freie Universität Berlin, Robert-von-Ostertag-Str. 7-13, 14163 Berlin, Germany
| | - Alisa Sergeeva
- System Modeling Group, Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Königsweg 67, 10117 Berlin, Germany
| | - Gundula Hoffmann
- Department Sensors and Modeling, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany
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