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Thomsen PT, Shearer JK, Houe H. Prevalence of lameness in dairy cows: A literature review. Vet J 2023; 295:105975. [PMID: 36990338 DOI: 10.1016/j.tvjl.2023.105975] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/06/2023] [Accepted: 03/24/2023] [Indexed: 03/30/2023]
Abstract
Lameness in dairy cows has major negative impacts on animal welfare and production economy. While previous studies have evaluated the prevalence of lameness in single countries, the present literature review is the first overview of the prevalence of lameness in dairy cows globally. This literature review identified 53 studies reporting prevalence of lameness among representative samples of dairy cows and fulfilling a number of specified inclusion criteria (e.g., at least 10 herds and 200 cows, and locomotion scoring by trained observers). A total of 414,950 cows from 3945 herds were included in these 53 studies, which spanned a 30-year period (1989-2020) and included herds from six continents, with the majority from Europe and North America. Across the studies, the mean prevalence of lameness (typically defined as score 3-5 on a 1-5 scale) was 22.8% with a median of 22.0% and a range between studies from 5.1% to 45%, and a within herd range from 0% to 88%. The mean prevalence of severely lame cows (typically defined as score 4-5 on a 1-5 scale) was 7.0% with a median of 6.5% and a range between studies from 1.8% to 21.2%, and a within herd range from 0% to 65%. Over time, it appears that the prevalence of lameness has changed very little. Several different locomotion scoring systems and definitions of (severe) lameness were used across the 53 studies, and this may have affected the reported lameness prevalence. Sampling of herds and cows, inclusion criteria and representativeness also differed between studies. This review offers recommendations for the future capture of information on lameness in dairy cows and identifies potential knowledge gaps.
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Affiliation(s)
- Peter T Thomsen
- Department of Animal and Veterinary Sciences, Aarhus University, Tjele, Denmark.
| | - Jan K Shearer
- College of Veterinary Medicine, Iowa State University, Ames, IA, USA
| | - Hans Houe
- Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark
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2
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He W, Cardoso AS, Hyde RM, Green MJ, Scurr DJ, Griffiths RL, Randall LV, Kim DH. Metabolic alterations in dairy cattle with lameness revealed by untargeted metabolomics of dried milk spots using direct infusion-tandem mass spectrometry and the triangulation of multiple machine learning models. Analyst 2022; 147:5537-5545. [PMID: 36341756 PMCID: PMC9678129 DOI: 10.1039/d2an01520j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/18/2022] [Indexed: 07/29/2023]
Abstract
Lameness is a major challenge in the dairy cattle industry in terms of animal welfare and economic implications. Better understanding of metabolic alteration associated with lameness could lead to early diagnosis and effective treatment, there-fore reducing its prevalence. To determine whether metabolic signatures associated with lameness could be discovered with untargeted metabolomics, we developed a novel workflow using direct infusion-tandem mass spectrometry to rapidly analyse (2 min per sample) dried milk spots (DMS) that were stored on commercially available Whatman® FTA® DMPK cards for a prolonged period (8 and 16 days). An orthogonal partial least squares-discriminant analysis (OPLS-DA) method validated by triangulation of multiple machine learning (ML) models and stability selection was employed to reliably identify important discriminative metabolites. With this approach, we were able to differentiate between lame and healthy cows based on a set of lipid molecules and several small metabolites. Among the discriminative molecules, we identified phosphatidylglycerol (PG 35:4) as the strongest and most sensitive lameness indicator based on stability selection. Overall, this untargeted metabolomics workflow is found to be a fast, robust, and discriminating method for determining lameness in DMS samples. The DMS cards can be potentially used as a convenient and cost-effective sample matrix for larger scale research and future routine screening for lameness.
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Affiliation(s)
- Wenshi He
- Centre for Analytical Bioscience, Advanced Materials & Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK.
| | - Ana S Cardoso
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, LE12 5RD, UK
| | - Robert M Hyde
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, LE12 5RD, UK
| | - Martin J Green
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, LE12 5RD, UK
| | - David J Scurr
- Centre for Analytical Bioscience, Advanced Materials & Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK.
| | - Rian L Griffiths
- Centre for Analytical Bioscience, Advanced Materials & Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK.
| | - Laura V Randall
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, LE12 5RD, UK
| | - Dong-Hyun Kim
- Centre for Analytical Bioscience, Advanced Materials & Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK.
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Rienesl L, Khayatzdadeh N, Köck A, Egger-Danner C, Gengler N, Grelet C, Dale LM, Werner A, Auer FJ, Leblois J, Sölkner J. Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk. Animals (Basel) 2022; 12:ani12141830. [PMID: 35883377 PMCID: PMC9312168 DOI: 10.3390/ani12141830] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/12/2022] [Accepted: 07/13/2022] [Indexed: 12/03/2022] Open
Abstract
Simple Summary Mid-infrared (MIR) spectroscopy is the method of choice to determine milk components like fat, protein and urea. We examined the potential of MIR spectra analyses for the prediction of clinical mastitis events of dairy cows additionally, or alternatively, to somatic cell count, which is routinely used as an indicator for mastitis monitoring. Prediction models based on MIR spectra and a somatic cell count-derived score (SCS) were developed and compared. A model based on MIR spectra and SCS proved more accurate at predicting mastitis than models based on either indicator alone. Consequently, MIR spectra analyses add extra value in the prediction of clinical mastitis, making them potentially useful for dairy farm management and as an auxiliary trait for the genetic evaluation of udder health. Abstract Monitoring for mastitis on dairy farms is of particular importance, as it is one of the most prevalent bovine diseases. A commonly used indicator for mastitis monitoring is somatic cell count. A supplementary tool to predict mastitis risk may be mid-infrared (MIR) spectroscopy of milk. Because bovine health status can affect milk composition, this technique is already routinely used to determine standard milk components. The aim of the present study was to compare the performance of models to predict clinical mastitis based on MIR spectral data and/or somatic cell count score (SCS), and to explore differences of prediction accuracies for acute and chronic clinical mastitis diagnoses. Test-day data of the routine Austrian milk recording system and diagnosis data of its health monitoring, from 59,002 cows of the breeds Fleckvieh (dual purpose Simmental), Holstein Friesian and Brown Swiss, were used. Test-day records within 21 days before and 21 days after a mastitis diagnosis were defined as mastitis cases. Three different models (MIR, SCS, MIR + SCS) were compared, applying Partial Least Squares Discriminant Analysis. Results of external validation in the overall time window (−/+21 days) showed area under receiver operating characteristic curves (AUC) of 0.70 when based only on MIR, 0.72 when based only on SCS, and 0.76 when based on both. Considering as mastitis cases only the test-day records within 7 days after mastitis diagnosis, the corresponding areas under the curve were 0.77, 0.83 and 0.85. Hence, the model combining MIR spectral data and SCS was performing best. Mastitis probabilities derived from the prediction models are potentially valuable for routine mastitis monitoring for farmers, as well as for the genetic evaluation of the trait udder health.
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Affiliation(s)
- Lisa Rienesl
- Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, 1180 Vienna, Austria; (L.R.); (N.K.)
| | - Negar Khayatzdadeh
- Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, 1180 Vienna, Austria; (L.R.); (N.K.)
| | - Astrid Köck
- ZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, Austria; (A.K.); (C.E.-D.)
| | | | - Nicolas Gengler
- Regional Association for Performance Testing in Livestock Breeding of Baden-Wuerttemberg (LKV—Baden-Wuerttemberg), 70067 Stuttgart, Germany;
| | - Clément Grelet
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium;
| | - Laura Monica Dale
- Gembloux Agro-Bio Tech, Université de Liège (ULg), 5030 Gembloux, Belgium; (L.M.D.); (A.W.)
| | - Andreas Werner
- Gembloux Agro-Bio Tech, Université de Liège (ULg), 5030 Gembloux, Belgium; (L.M.D.); (A.W.)
| | | | | | - Johann Sölkner
- Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, 1180 Vienna, Austria; (L.R.); (N.K.)
- Correspondence: ; Tel.: +43-1-476-549-3201
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Silva SR, Araujo JP, Guedes C, Silva F, Almeida M, Cerqueira JL. Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition. Animals (Basel) 2021; 11:2253. [PMID: 34438712 PMCID: PMC8388461 DOI: 10.3390/ani11082253] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 01/28/2023] Open
Abstract
Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.
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Affiliation(s)
- Severiano R. Silva
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
| | - José P. Araujo
- Escola Superior Agrária do Instituto Politécnico de Viana do Castelo, Rua D. Mendo Afonso, 147, Refóios do Lima, 4990-706 Ponte de Lima, Portugal;
- Mountain Research Centre (CIMO), Instituto Politécnico de Viana do Castelo, Rua D. Mendo Afonso, 147, Refóios do Lima, 4990-706 Ponte de Lima, Portugal
| | - Cristina Guedes
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
| | - Flávio Silva
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
| | - Mariana Almeida
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
| | - Joaquim L. Cerqueira
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
- Escola Superior Agrária do Instituto Politécnico de Viana do Castelo, Rua D. Mendo Afonso, 147, Refóios do Lima, 4990-706 Ponte de Lima, Portugal;
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Bahadi M, Ismail AA, Vasseur E. Fourier Transform Infrared Spectroscopy as a Tool to Study Milk Composition Changes in Dairy Cows Attributed to Housing Modifications to Improve Animal Welfare. Foods 2021; 10:450. [PMID: 33670588 PMCID: PMC7922570 DOI: 10.3390/foods10020450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/10/2021] [Accepted: 02/13/2021] [Indexed: 11/16/2022] Open
Abstract
Animal welfare status is assessed today through visual evaluations requiring an on-farm visit. A convenient alternative would be to detect cow welfare status directly in milk samples, already routinely collected for milk recording. The objective of this study was to propose a novel approach to demonstrate that Fourier transform infrared (FTIR) spectroscopy can detect changes in milk composition related to cows subjected to movement restriction at the tie stall with four tie-rail configurations varying in height and position (TR1, TR2, TR3 and TR4). Milk mid-infrared spectra were collected on weekly basis. Long-term average spectra were calculated for each cow using spectra collected in weeks 8-10 of treatment. Principal component analysis was applied to spectral averages and the scores of principal components (PCs) were tested for treatment effect by mixed modelling. PC7 revealed a significant treatment effect (p = 0.01), particularly for TR3 (configuration with restricted movement) vs. TR1 (recommended configuration) (p = 0.03). The loading spectrum of PC7 revealed high loadings at wavenumbers that could be assigned to biomarkers related to negative energy balance, such as β-hydroxybutyrate, citrate and acetone. This observation suggests that TR3 might have been restrictive for cows to access feed. Milk FTIR spectroscopy showed promising results in detecting welfare status and housing conditions in dairy cows.
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Affiliation(s)
- Mazen Bahadi
- McGill IR Group, McGill University, Sainte Anne de Bellevue, QC H9X 3V9, Canada;
| | - Ashraf A. Ismail
- McGill IR Group, McGill University, Sainte Anne de Bellevue, QC H9X 3V9, Canada;
| | - Elsa Vasseur
- Department of Animal Science, McGill University, Sainte Anne de Bellevue, QC H9X 3V9, Canada;
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Impact of Nutrients on the Hoof Health in Cattle. Animals (Basel) 2020; 10:ani10101824. [PMID: 33036413 PMCID: PMC7600182 DOI: 10.3390/ani10101824] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/01/2020] [Accepted: 10/03/2020] [Indexed: 12/12/2022] Open
Abstract
Lameness is currently one of the most important and economically demanding diseases in cattle. It is manifested in a change in locomotion that is associated with lesions, especially the pelvic limbs. The disease of the hoof is painful, affecting the welfare of dairy cows. Important factors that influence the health of the limbs include nutrition, animal hygiene, stable technology, and genetic and breeding predispositions. Nutrition is one of the basic preventive factors affecting the quality and growth of the hoof horn, and the associated prevalence of hoof disease. The strength and structure of the hoof horn are affected by the composition of the feed ration (amino acids, minerals, vitamins, and toxic substances contaminating the feed ration, or arising in the feed ration as metabolites of fungi).
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Grelet C, Dardenne P, Soyeurt H, Fernandez JA, Vanlierde A, Stevens F, Gengler N, Dehareng F. Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions. Methods 2020; 186:97-111. [PMID: 32763376 DOI: 10.1016/j.ymeth.2020.07.012] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 06/12/2020] [Accepted: 07/27/2020] [Indexed: 12/17/2022] Open
Abstract
Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic evaluation, and milk quality control. In the recent years, the research was very active to predict new phenotypes from the mid-infrared (MIR) analysis of milk. Models were developed to predict phenotypes such as fine milk composition, milk technological properties or traits related to cow health, fertility and environmental impact. Most of models were developed within research contexts and often not designed for routine use. The implementation of models at a large scale to predict new traits of interest brings new challenges as the factors influencing the robustness of models are poorly documented. The first objective of this work is to highlight the impact on prediction accuracy of factors such as the variability of the spectral and reference data, the spectral regions used and the complexity of models. The second objective is to emphasize methods and indicators to evaluate the quality of models and the quality of predictions generated under routine conditions. The last objective is to outline the issues and the solutions linked with the use and transfer of models on large number of instruments. Based on partial least square regression and 10 datasets including milk MIR spectra and reference quantitative values for 57 traits of interest, the impact of the different factors is illustrated by evaluating the influence on the validation root mean square error of prediction (RMSEP). In the displayed examples, all factors, when well set up, increase the quality of predictions, with an improvement of the RMSEP ranging from 12% to 43%. This work also aims to underline the need for and the complementarity between different validation procedures, statistical parameters and quality assurance methods. Finally, when using and transferring models, the impact of the spectral standardization on the prediction reproducibility is highlighted with an improvement up to 86% with the tested models, and the monitoring of individual spectrometer stability over time appears essential. This list inspired from our experience is of course not exhaustive. The displayed results are only examples and not general rules and other aspects play a role in the quality of final predictions. However, this work highlights good practices, methods and indicators to increase and evaluate quality of phenotypes predicted at a large scale. The results obtained argue for the development of guidelines at international levels, as well as international collaborations in order to constitute large and robust datasets and enable the use of models in routine conditions.
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Affiliation(s)
- C Grelet
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - P Dardenne
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - H Soyeurt
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium.
| | - J A Fernandez
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - A Vanlierde
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - F Stevens
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - N Gengler
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium.
| | - F Dehareng
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
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