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Zwygart S, Lutz B, Thomann B, Stucki D, Meylan M, Becker J. Evaluation of candidate data-based welfare indicators for veal calves in Switzerland. Front Vet Sci 2024; 11:1436719. [PMID: 39100759 PMCID: PMC11295006 DOI: 10.3389/fvets.2024.1436719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 08/06/2024] Open
Abstract
Welfare assessment protocols have been developed for dairy cows and veal calves during the past decades. One practical use of such protocols may be conducting welfare assessments by using routinely collected digital data (i.e., data-based assessment). This approach can allow for continuous monitoring of animal welfare in a large number of farms. It recognises changes in the animal welfare status over time and enables comparison between farms. Since no comprehensive data-based assessment for veal calves is currently available, the purposes of this review are (i) to provide an overview of single existing data-based indicators for veal calves and (ii) to work out the necessary requirements for data-based indicators to be used in a comprehensive welfare assessment for veal calves in Switzerland. We used the Welfare Quality Protocol® (WQ) for veal calves and the Terrestrial Animal Health Code from the World Organisation of Animal Health for guidance throughout this process. Subsequently, routinely collected data were evaluated as data sources for welfare assessment in Swiss veal operations. The four WQ principles reflecting animal welfare, i.e., 'good feeding', 'good housing', 'good health' and 'appropriate behaviour' were scarcely reflected in routinely available data. Animal health, as one element of animal welfare, could be partially assessed using data-based indicators through evaluation of mortality, treatments, and carcass traits. No data-based indicators reflecting feeding, housing and animal behaviour were available. Thus, it is not possible to assess welfare in its multidimensionality using routinely collected digital data in Swiss veal calves to date. A major underlying difficulty is to differentiate between veal calves and other youngstock using routine data, since an identifying category for veal calves is missing in official Swiss databases. In order to infer animal welfare from routine data, adaptations of data collection strategies and animal identification are required. Data-based welfare assessment could then be used to complement on-farm assessments efficiently and, e.g., to attribute financial incentives for specifically high welfare standards accordingly.
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Affiliation(s)
- Sibylle Zwygart
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Barbara Lutz
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
- Centre for Proper Housing of Ruminants and Pigs, Federal Food Safety and Veterinary Office, Agroscope, Ettenhausen, Switzerland
| | - Beat Thomann
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Dimitri Stucki
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Mireille Meylan
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Jens Becker
- Clinic for Ruminants, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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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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Colditz IG, Campbell DLM, Ingham AB, Lee C. Review: Environmental enrichment builds functional capacity and improves resilience as an aspect of positive welfare in production animals. Animal 2024; 18:101173. [PMID: 38761442 DOI: 10.1016/j.animal.2024.101173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 04/15/2024] [Accepted: 04/18/2024] [Indexed: 05/20/2024] Open
Abstract
The success of the animal in coping with challenges, and in harnessing opportunities to thrive, is central to its welfare. Functional capacity describes the capacity of molecules, cells, organs, body systems, the whole animal, and its community to buffer against the impacts of environmental perturbations. This buffering capacity determines the ability of the animal to maintain or regain functions in the face of environmental perturbations, which is recognised as resilience. The accuracy of physiological regulation and the maintenance of homeostatic balance underwrite the dynamic stability of outcomes such as biorhythms, feed intake, growth, milk yield, and egg production justifying their assessment as indicators of resilience. This narrative review examines the influence of environmental enrichments, especially during developmental stages in young animals, in building functional capacity and in its subsequent expression as resilience. Experience of enriched environments can build skills and competencies across multiple functional domains including but not limited to behaviour, immunity, and metabolism thereby increasing functional capacity and facilitating resilience within the context of challenges such as husbandry practices, social change, and infection. A quantitative method for measuring the distributed property of functional capacity may improve its assessment. Methods for analysing embedded energy (emergy) in ecosystems may have utility for this goal. We suggest functional capacity provides the common thread that links environmental enrichments with an ability to express resilience and may provide a novel and useful framework for measuring and reporting resilience. We conclude that the development of functional capacity and its subsequent expression as resilience is an aspect of positive animal welfare. The emergence of resilience from system dynamics highlights a need to shift from the study of physical and mental states to the study of physical and mental dynamics to describe the positive dimension of animal welfare.
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Affiliation(s)
- I G Colditz
- Agriculture and Food, CSIRO, Armidale, NSW 2350, Australia.
| | - D L M Campbell
- Agriculture and Food, CSIRO, Armidale, NSW 2350, Australia
| | - A B Ingham
- Agriculture and Food, CSIRO, St. Lucia, QLD 4067, Australia
| | - C Lee
- Agriculture and Food, CSIRO, Armidale, NSW 2350, Australia
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Cantor MC, Welk AA, Creutzinger KC, Woodrum Setser MM, Costa JHC, Renaud DL. The development and validation of a milk feeding behavior alert from automated feeder data to classify calves at risk for a diarrhea bout: A diagnostic accuracy study. J Dairy Sci 2024; 107:3140-3156. [PMID: 37949402 DOI: 10.3168/jds.2023-23635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 10/29/2023] [Indexed: 11/12/2023]
Abstract
The objective of this diagnostic accuracy study was to develop and validate an alert to identify calves at risk for a diarrhea bout using milk feeding behavior data (behavior) from automated milk feeders (AMF). We enrolled Holstein calves (n = 259) as a convenience sample size from 2 facilities that were health scored daily preweaning and offered either 10 or 15 L/d of milk replacer. For alert development, 132 calves were enrolled and the ability of milk intake, drinking speed, and rewarded visits collected from AMF to identify calves at risk for diarrhea was tested. Alerts that had high diagnostic accuracy in the alert development phase were validated using a holdout validation strategy of 127 different calves from the same facilities (all offered 15 L/d) for -3 to 1 d relative to diarrhea diagnosis. We enrolled calves that were either healthy or had a first diarrheal bout (loose feces ≥2 d or watery feces ≥1 d). Relative change and rolling dividends for each milk feeding behavior were calculated for each calf from the previous 2 d. Logistic regression models and receiver operator curves (ROC) were used to assess the diagnostic ability for relative change and rolling dividends behavior relative to alert d) to classify calves at risk for a diarrhea bout from -2 to 0 d relative to diagnosis. To maximize sensitivity (Se), alert thresholds were based on ROC optimal classification cutoff. Diagnostic accuracy was met when the alert had a moderate area under the ROC curve (≥0.70), high accuracy (Acc; ≥0.80), high Se (≥0.80), and very high precision (Pre; ≥0.85). For alert development, deviations in rolling dividend milk intake with drinking speed had the best performance (10 L/d: ROC area under the curve [AUC] = 0.79, threshold ≤0.70; 15 L/d: ROC AUC = 0.82, threshold ≤0.60). Our diagnostic criteria were only met in calves offered 15 L/d (10 L/d: Se 75%, Acc 72%, Pre 92%, specificity [Sp] 55% vs. 15 L/d: Se 91%, Acc 91%, Pre 89%, Sp 73%). For holdout validation, rolling dividend milk intake with drinking speed met diagnostic criteria for one facility (threshold ≤0.60, Se 86%, Acc 82%, Pre 94%, Sp 50%). However, no milk feeding behavior alerts met diagnostic criteria for the second facility due to poor Se (relative change milk intake -0.36 threshold, Se 71%, Acc 70%, and Pre 97%). We suggest that changes in milk feeding behavior may indicate diarrhea bouts in dairy calves. Future research should validate this alert in commercial settings; furthermore, software updates, support, and new analytics might be required for on-farm application to implement these types of alerts.
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Affiliation(s)
- M C Cantor
- Department of Animal Science, The Pennsylvania State University, College Park, PA 16803; Department of Population Medicine, University of Guelph, Guelph, ON, Canada N1G 2W1.
| | - A A Welk
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada N1G 2W1
| | - K C Creutzinger
- Department of Animal and Food Science, University of Wisconsin-River Falls, River Falls, WI 54022
| | - M M Woodrum Setser
- Department of Animal and Food Sciences, University of Kentucky, Lexington, KY 40546
| | - J H C Costa
- Department of Veterinary and Animal Sciences, University of Vermont, Burlington, VT 05405
| | - D L Renaud
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada N1G 2W1
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Dineva K, Atanasova T. Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud. Animals (Basel) 2023; 13:3254. [PMID: 37893978 PMCID: PMC10603760 DOI: 10.3390/ani13203254] [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: 09/07/2023] [Revised: 10/12/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.
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Affiliation(s)
- Kristina Dineva
- Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 2, 1113 Sofia, Bulgaria;
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6
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Riley BB, Duthie CA, Corbishley A, Mason C, Bowen JM, Bell DJ, Haskell MJ. Intrinsic calf factors associated with the behavior of healthy pre-weaned group-housed dairy-bred calves. Front Vet Sci 2023; 10:1204580. [PMID: 37601764 PMCID: PMC10435862 DOI: 10.3389/fvets.2023.1204580] [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: 04/12/2023] [Accepted: 07/03/2023] [Indexed: 08/22/2023] Open
Abstract
Technology-derived behaviors are researched for disease detection in artificially-reared calves. Whilst existing studies demonstrate differences in behaviors between healthy and diseased calves, intrinsic calf factors (e.g., sex and birthweight) that may affect these behaviors have received little systematic study. This study aimed to understand the impact of a range of calf factors on milk feeding and activity variables of dairy-bred calves. Calves were group-housed from ~7 days to 39 days of age. Seven liters of milk replacer was available daily from an automatic milk feeder, which recorded feeding behaviors and live-weight. Calves were health scored daily and a tri-axial accelerometer used to record activity variables. Healthy calves were selected by excluding data collected 3 days either side of a poor health score or a treatment event. Thirty-one calves with 10 days each were analyzed. Mixed models were used to identify which of live-weight, age, sex, season of birth, age of inclusion into the group, dam parity, birthweight, and sire breed type (beef or dairy), had a significant influence on milk feeding and activity variables. Heavier calves visited the milk machine more frequently for shorter visits, drank faster and were more likely to drink their daily milk allowance than lighter calves. Older calves had a shorter mean standing bout length and were less active than younger calves. Calves born in summer had a longer daily lying time, performed more lying and standing bouts/day and had shorter mean standing bouts than those born in autumn or winter. Male calves had a longer mean lying bout length, drank more slowly and were less likely to consume their daily milk allowance than their female counterparts. Calves that were born heavier had fewer lying and standing bouts each day, a longer mean standing bout length and drank less milk per visit. Beef-sired calves had a longer mean lying bout length and drank more slowly than their dairy sired counterparts. Intrinsic calf factors influence different healthy calf behaviors in different ways. These factors must be considered in the design of research studies and the field application of behavior-based disease detection tools in artificially reared calves.
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Affiliation(s)
- Beth B. Riley
- Scotland's Rural College (SRUC), Edinburgh, United Kingdom
- Clinical Sciences, The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Alexander Corbishley
- Dairy Herd Health and Productivity Service, University of Edinburgh, Edinburgh, United Kingdom
- Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Colin Mason
- Scotland's Rural College (SRUC), Edinburgh, United Kingdom
| | - Jenna M. Bowen
- Scotland's Rural College (SRUC), Edinburgh, United Kingdom
| | - David J. Bell
- Scotland's Rural College (SRUC), Edinburgh, United Kingdom
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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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Silva FG, Conceição C, Pereira AMF, Cerqueira JL, Silva SR. Literature Review on Technological Applications to Monitor and Evaluate Calves' Health and Welfare. Animals (Basel) 2023; 13:ani13071148. [PMID: 37048404 PMCID: PMC10093142 DOI: 10.3390/ani13071148] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 03/08/2023] [Accepted: 03/16/2023] [Indexed: 04/14/2023] Open
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 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.
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Affiliation(s)
- Flávio G Silva
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
- Mediterranean Institute for Agriculture, Environment and Development (MED), Universidade de Évora Pólo da Mitra, Apartado, 94, 7006-554 Évora, Portugal
| | - Cristina Conceição
- Mediterranean Institute for Agriculture, Environment and Development (MED), Universidade de Évora Pólo da Mitra, Apartado, 94, 7006-554 Évora, Portugal
| | - Alfredo M F Pereira
- Mediterranean Institute for Agriculture, Environment and Development (MED), Universidade de Évora Pólo da Mitra, Apartado, 94, 7006-554 Évora, Portugal
| | - Joaquim L Cerqueira
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), Escola Superior Agrária do Instituto Politécnico de Viana do Castelo, Rua D. Mendo Afonso, 147, 4990-706 Ponte de Lima, Portugal
| | - Severiano R Silva
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
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9
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Donlon JD, Mee JF, McAloon CG. Prevalence of respiratory disease in Irish preweaned dairy calves using hierarchical Bayesian latent class analysis. Front Vet Sci 2023; 10:1149929. [PMID: 37124570 PMCID: PMC10133517 DOI: 10.3389/fvets.2023.1149929] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/27/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction Bovine respiratory disease (BRD) has a significant impact on the health and welfare of dairy calves. It can result in increased antimicrobial usage, decreased growth rate and reduced future productivity. There is no gold standard antemortem diagnostic test for BRD in calves and no estimates of the prevalence of respiratory disease in seasonal calving dairy herds. Methods To estimate BRD prevalence in seasonal calving dairy herds in Ireland, 40 dairy farms were recruited and each farm was visited once during one of two calving seasons (spring 2020 & spring 2021). At that visit the prevalence of BRD in 20 calves between 4 and 6 weeks of age was determined using thoracic ultrasound score (≥3) and the Wisconsin respiratory scoring system (≥5). Hierarchical Bayesian latent class analysis was used to estimate the calf-level true prevalence of BRD, and the within-herd prevalence distribution, accounting for the imperfect nature of both diagnostic tests. Results In total, 787 calves were examined, of which 58 (7.4%) had BRD as defined by a Wisconsin respiratory score ≥5 only, 37 (4.7%) had BRD as defined by a thoracic ultrasound score of ≥3 only and 14 (1.8%) calves had BRD based on both thoracic ultrasound and clinical scoring. The primary model assumed both tests were independent and used informed priors for test characteristics. Using this model the true prevalence of BRD was estimated as 4%, 95% Bayesian credible interval (BCI) (1%, 8%). This prevalence estimate is lower or similar to those found in other dairy production systems. Median within herd prevalence varied from 0 to 22%. The prevalence estimate was not sensitive to whether the model was constructed with the tests considered conditionally dependent or independent. When the case definition for thoracic ultrasound was changed to a score ≥2, the prevalence estimate increased to 15% (95% BCI: 6%, 27%). Discussion The prevalence of calf respiratory disease, however defined, was low, but highly variable, in these seasonal calving dairy herds.
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Affiliation(s)
- John D. Donlon
- School of Veterinary Medicine, University College Dublin, Dublin, Ireland
- Animal and Bioscience Research Department, Teagasc, Animal and Grassland Research Centre, Grange, Dunsany, Meath, Ireland
- *Correspondence: John D. Donlon
| | - John F. Mee
- Animal and Bioscience Research Department, Teagasc, Moorepark Research Centre, Fermoy, Co. Cork, Ireland
| | - Conor G. McAloon
- School of Veterinary Medicine, University College Dublin, Dublin, Ireland
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Puig A, Ruiz M, Bassols M, Fraile L, Armengol R. Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms. Animals (Basel) 2022; 12:ani12192623. [PMID: 36230364 PMCID: PMC9558517 DOI: 10.3390/ani12192623] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/25/2022] [Accepted: 09/27/2022] [Indexed: 11/07/2022] Open
Abstract
Simple Summary The inclusion of remote automatic systems that use continuous learning technology are of great interest in precision livestock cattle farming, since the average size of farms is increasing while time for individual observation is decreasing. Bovine respiratory disease is a main concern in both fattening and heifer rearing farms due to its impact on antibiotic use, loss of performance, mortality, and animal welfare. Much scientific literature has been published regarding technologies for continuous learning and monitoring of cattle’s behavior and accurate correlation with health status, including early detection of bovine respiratory disease. This review summarizes the up-to-date technologies for early diagnosis of bovine respiratory disease and discusses their advantages and disadvantages under practical conditions. Abstract Classically, the diagnosis of respiratory disease in cattle has been based on observation of clinical signs and the behavior of the animals, but this technique can be subjective, time-consuming and labor intensive. It also requires proper training of staff and lacks sensitivity (Se) and specificity (Sp). Furthermore, respiratory disease is diagnosed too late, when the animal already has severe lesions. A total of 104 papers were included in this review. The use of new advanced technologies that allow early diagnosis of diseases using real-time data analysis may be the future of cattle farms. These technologies allow continuous, remote, and objective assessment of animal behavior and diagnosis of bovine respiratory disease with improved Se and Sp. The most commonly used behavioral variables are eating behavior and physical activity. Diagnosis of bovine respiratory disease may experience a significant change with the help of big data combined with machine learning, and may even integrate metabolomics as disease markers. Advanced technologies should not be a substitute for practitioners, farmers or technicians, but could help achieve a much more accurate and earlier diagnosis of respiratory disease and, therefore, reduce the use of antibiotics, increase animal welfare and sustainability of livestock farms. This review aims to familiarize practitioners and farmers with the advantages and disadvantages of the advanced technological diagnostic tools for bovine respiratory disease and introduce recent clinical applications.
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Affiliation(s)
- Andrea Puig
- Department of Animal Science, ETSEA, University of Lleida, 25198 Lleida, Spain
| | - Miguel Ruiz
- Department of Animal Science, ETSEA, University of Lleida, 25198 Lleida, Spain
| | - Marta Bassols
- Department of Animal Science, ETSEA, University of Lleida, 25198 Lleida, Spain
| | - Lorenzo Fraile
- Department of Animal Science, ETSEA, University of Lleida, 25198 Lleida, Spain
- Agrotecnio Research Center, ETSEA, University of Lleida, 25198 Lleida, Spain
| | - Ramon Armengol
- Department of Animal Science, ETSEA, University of Lleida, 25198 Lleida, Spain
- Correspondence: ; Tel.: +34-973-706-451
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11
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Nelis JLD, Bose U, Broadbent JA, Hughes J, Sikes A, Anderson A, Caron K, Schmoelzl S, Colgrave ML. Biomarkers and biosensors for the diagnosis of noncompliant pH, dark cutting beef predisposition, and welfare in cattle. Compr Rev Food Sci Food Saf 2022; 21:2391-2432. [DOI: 10.1111/1541-4337.12935] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 02/02/2022] [Accepted: 02/09/2022] [Indexed: 11/29/2022]
Affiliation(s)
| | - Utpal Bose
- CSIRO Agriculture and Food St Lucia Australia
| | | | | | - Anita Sikes
- CSIRO Agriculture and Food Coopers Plains Australia
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Colditz IG. Competence to thrive: resilience as an indicator of positive health and positive welfare in animals. ANIMAL PRODUCTION SCIENCE 2022. [DOI: 10.1071/an22061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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