Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk
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 bovin...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/12/14/1830 |
| _version_ | 1797441262188494848 |
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| author | Lisa Rienesl Negar Khayatzdadeh Astrid Köck Christa Egger-Danner Nicolas Gengler Clément Grelet Laura Monica Dale Andreas Werner Franz-Josef Auer Julie Leblois Johann Sölkner |
| author_facet | Lisa Rienesl Negar Khayatzdadeh Astrid Köck Christa Egger-Danner Nicolas Gengler Clément Grelet Laura Monica Dale Andreas Werner Franz-Josef Auer Julie Leblois Johann Sölkner |
| author_sort | Lisa Rienesl |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T12:20:31Z |
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| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-09T12:20:31Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-17b097ef5c3c44dba8e6a0f260c083f12023-11-30T22:40:21ZengMDPI AGAnimals2076-26152022-07-011214183010.3390/ani12141830Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of MilkLisa Rienesl0Negar Khayatzdadeh1Astrid Köck2Christa Egger-Danner3Nicolas Gengler4Clément Grelet5Laura Monica Dale6Andreas Werner7Franz-Josef Auer8Julie Leblois9Johann Sölkner10Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, 1180 Vienna, AustriaDepartment of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, 1180 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaRegional Association for Performance Testing in Livestock Breeding of Baden-Wuerttemberg (LKV—Baden-Wuerttemberg), 70067 Stuttgart, GermanyWalloon Agricultural Research Center (CRA-W), 5030 Gembloux, BelgiumGembloux Agro-Bio Tech, Université de Liège (ULg), 5030 Gembloux, BelgiumGembloux Agro-Bio Tech, Université de Liège (ULg), 5030 Gembloux, BelgiumLKV Austria Gemeinnützige GmbH, 1200 Vienna, AustriaElevéo (Awé Groupe), 5590 Ciney, BelgiumDepartment of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, 1180 Vienna, AustriaMonitoring 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.https://www.mdpi.com/2076-2615/12/14/1830clinical mastitismid-infrared (MIR) spectroscopysomatic cell countdairy cowpartial least squares discriminant analysis |
| spellingShingle | Lisa Rienesl Negar Khayatzdadeh Astrid Köck Christa Egger-Danner Nicolas Gengler Clément Grelet Laura Monica Dale Andreas Werner Franz-Josef Auer Julie Leblois Johann Sölkner Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk Animals clinical mastitis mid-infrared (MIR) spectroscopy somatic cell count dairy cow partial least squares discriminant analysis |
| title | Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk |
| title_full | Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk |
| title_fullStr | Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk |
| title_full_unstemmed | Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk |
| title_short | Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk |
| title_sort | prediction of acute and chronic mastitis in dairy cows based on somatic cell score and mid infrared spectroscopy of milk |
| topic | clinical mastitis mid-infrared (MIR) spectroscopy somatic cell count dairy cow partial least squares discriminant analysis |
| url | https://www.mdpi.com/2076-2615/12/14/1830 |
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