Detection of Dairy Herd Management Issues Using Fatty Acid Profiles Predicted by Mid-Infrared Spectrometry

This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predic...

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Published in	Animals (Basel) Vol. 15; no. 11; p. 1575
Main Authors	Franceschini, Sébastien, Fastré, Claire, Nickmilder, Charles, Santschi, Débora E., Warner, Daniel, Bahadi, Mazen, Bertozzi, Carlo, Veselko, Didier, Dehareng, Frédéric, Gengler, Nicolas, Soyeurt, Hélène
Format	Journal Article Web Resource
Language	English
Published	Switzerland MDPI AG 28.05.2025 MDPI
Subjects	Analysis Animal production & animal husbandry Animal welfare Body fat Breastfeeding & lactation cow Dairy cattle dairy cows Dairy industry Decision-making detection disease fatty acid Fatty acids Fermentation Fourier transforms FT-MIR spectrometry herd management Humidity infrared Infrared spectroscopy Life sciences Machine learning Metabolism Milk Milk production Nutrition Productions animales & zootechnie Proteins Sciences du vivant Scientific imaging unsupervised hierarchical clustering Belgium Canada Quebec dairy cows herd management fatty acids unsupervised hierarchical clustering FT-MIR spectrometry
Online Access	Get full text
ISSN	2076-2615 0030-6835 2076-2615
DOI	10.3390/ani15111575

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Abstract	This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. In this research, these profiles were summarized using 31 fatty acids or groups of fatty acids. The methodology consists of four steps: hierarchical clustering to detect patterns in a Belgian spectral dataset (N = 774,781), interpretation of the identified seven clusters, development of predictive models applied to a Canadian dataset (N = 670,165), and validation using management information collected from Canadian farms. The identified clusters revealed significant relationships with feeding management strategies and temporal evolutions, highlighting the potential to develop automated alert systems that assist farmers and advisors in herd monitoring.
AbstractList	This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. In this research, these profiles were summarized using 31 fatty acids or groups of fatty acids. The methodology consists of four steps: hierarchical clustering to detect patterns in a Belgian spectral dataset (N = 774,781), interpretation of the identified seven clusters, development of predictive models applied to a Canadian dataset (N = 670,165), and validation using management information collected from Canadian farms. The identified clusters revealed significant relationships with feeding management strategies and temporal evolutions, highlighting the potential to develop automated alert systems that assist farmers and advisors in herd monitoring. This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. In this research, these profiles were summarized using 31 fatty acids or groups of fatty acids. The methodology consists of four steps: hierarchical clustering to detect patterns in a Belgian spectral dataset ( = 774,781), interpretation of the identified seven clusters, development of predictive models applied to a Canadian dataset ( = 670,165), and validation using management information collected from Canadian farms. The identified clusters revealed significant relationships with feeding management strategies and temporal evolutions, highlighting the potential to develop automated alert systems that assist farmers and advisors in herd monitoring. Farms generate increasing amounts of data each year. One example is the bulk tank milk composition, predicted through spectrometry, which is routinely measured for milk payment purposes. Among the different milk components, the fatty acid profile is of utmost importance because it is closely linked to animal status and farm management practices. This research aims to explore a novel application of these fatty acid profiles by developing a practical herd-monitoring tool for farmers and advisors. The methodology developed consists of an unsupervised learning method that identifies meaningful patterns in fatty acid profiles, combined with an expert-driven interpretation of those patterns. The analysis was performed using a Belgian bulk tank milk database. Seven distinct patterns were identified; among these, three were associated with good management practices, three indicated potential risks, and one highlighted a metabolic disorder probably related to management practices. Most of these patterns were also observed in a Canadian bulk tank milk dataset, demonstrating that the method is generalizable across different regions and farming conditions. Moreover, the probabilities associated with each pattern can serve as a reliable foundation for creating practical alerts to support on-farm decision making, thereby enhancing farm management and contributing positively to animal welfare. This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. In this research, these profiles were summarized using 31 fatty acids or groups of fatty acids. The methodology consists of four steps: hierarchical clustering to detect patterns in a Belgian spectral dataset ( N = 774,781), interpretation of the identified seven clusters, development of predictive models applied to a Canadian dataset ( N = 670,165), and validation using management information collected from Canadian farms. The identified clusters revealed significant relationships with feeding management strategies and temporal evolutions, highlighting the potential to develop automated alert systems that assist farmers and advisors in herd monitoring. Farms generate increasing amounts of data each year. One example is the bulk tank milk composition, predicted through spectrometry, which is routinely measured for milk payment purposes. Among the different milk components, the fatty acid profile is of utmost importance because it is closely linked to animal status and farm management practices. This research aims to explore a novel application of these fatty acid profiles by developing a practical herd-monitoring tool for farmers and advisors. The methodology developed consists of an unsupervised learning method that identifies meaningful patterns in fatty acid profiles, combined with an expert-driven interpretation of those patterns. The analysis was performed using a Belgian bulk tank milk database. Seven distinct patterns were identified; among these, three were associated with good management practices, three indicated potential risks, and one highlighted a metabolic disorder probably related to management practices. Most of these patterns were also observed in a Canadian bulk tank milk dataset, demonstrating that the method is generalizable across different regions and farming conditions. Moreover, the probabilities associated with each pattern can serve as a reliable foundation for creating practical alerts to support on-farm decision making, thereby enhancing farm management and contributing positively to animal welfare. This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. In this research, these profiles were summarized using 31 fatty acids or groups of fatty acids. The methodology consists of four steps: hierarchical clustering to detect patterns in a Belgian spectral dataset (N = 774,781), interpretation of the identified seven clusters, development of predictive models applied to a Canadian dataset (N = 670,165), and validation using management information collected from Canadian farms. The identified clusters revealed significant relationships with feeding management strategies and temporal evolutions, highlighting the potential to develop automated alert systems that assist farmers and advisors in herd monitoring.This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. In this research, these profiles were summarized using 31 fatty acids or groups of fatty acids. The methodology consists of four steps: hierarchical clustering to detect patterns in a Belgian spectral dataset (N = 774,781), interpretation of the identified seven clusters, development of predictive models applied to a Canadian dataset (N = 670,165), and validation using management information collected from Canadian farms. The identified clusters revealed significant relationships with feeding management strategies and temporal evolutions, highlighting the potential to develop automated alert systems that assist farmers and advisors in herd monitoring.
Audience	Academic
Author	Dehareng, Frédéric Warner, Daniel Bertozzi, Carlo Veselko, Didier Nickmilder, Charles Soyeurt, Hélène Santschi, Débora E. Bahadi, Mazen Gengler, Nicolas Franceschini, Sébastien Fastré, Claire
AuthorAffiliation	1 TERRA Research and Teaching Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; sfranceschini@uliege.be (S.F.); claire.fastre@uliege.be (C.F.); charles.nickmilder@uliege.be (C.N.) 2 Lactanet, Saint-Anne-de-Bellevue, QC H9X 3R4, Canada 4 Comité du Lait, 4651 Herve, Belgium 3 Walloon Breeders Association, 5590 Ciney, Belgium 5 Walloon Agricultural Research Centre, 5030 Gembloux, Belgium
AuthorAffiliation_xml	– name: 2 Lactanet, Saint-Anne-de-Bellevue, QC H9X 3R4, Canada – name: 1 TERRA Research and Teaching Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; sfranceschini@uliege.be (S.F.); claire.fastre@uliege.be (C.F.); charles.nickmilder@uliege.be (C.N.) – name: 4 Comité du Lait, 4651 Herve, Belgium – name: 5 Walloon Agricultural Research Centre, 5030 Gembloux, Belgium – name: 3 Walloon Breeders Association, 5590 Ciney, Belgium
Author_xml	– sequence: 1 givenname: Sébastien orcidid: 0000-0001-6298-5149 surname: Franceschini fullname: Franceschini, Sébastien – sequence: 2 givenname: Claire orcidid: 0009-0006-0498-1098 surname: Fastré fullname: Fastré, Claire – sequence: 3 givenname: Charles orcidid: 0000-0001-5235-2145 surname: Nickmilder fullname: Nickmilder, Charles – sequence: 4 givenname: Débora E. surname: Santschi fullname: Santschi, Débora E. – sequence: 5 givenname: Daniel orcidid: 0000-0001-8576-6985 surname: Warner fullname: Warner, Daniel – sequence: 6 givenname: Mazen orcidid: 0000-0001-7210-1861 surname: Bahadi fullname: Bahadi, Mazen – sequence: 7 givenname: Carlo surname: Bertozzi fullname: Bertozzi, Carlo – sequence: 8 givenname: Didier surname: Veselko fullname: Veselko, Didier – sequence: 9 givenname: Frédéric orcidid: 0000-0002-6733-4334 surname: Dehareng fullname: Dehareng, Frédéric – sequence: 10 givenname: Nicolas orcidid: 0000-0002-5981-5509 surname: Gengler fullname: Gengler, Nicolas – sequence: 11 givenname: Hélène orcidid: 0000-0001-9883-9047 surname: Soyeurt fullname: Soyeurt, Hélène
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Cites_doi	10.1016/j.tifs.2022.02.005 10.1038/s41598-021-02600-9 10.3168/jds.2007-0656 10.3168/jds.S0022-0302(06)72437-7 10.3168/jds.S0022-0302(06)72192-0 10.3168/jds.2016-12046 10.3168/jds.S0022-0302(06)72409-2 10.3402/fnr.v52i0.1821 10.1038/s41598-017-01120-9 10.3168/jds.S0022-0302(02)74079-4 10.3168/jds.2014-9148 10.1051/animres:2000117 10.3390/ani12091202 10.3168/jds.2022-21975 10.3168/jds.2010-3408 10.3168/jds.2014-8039 10.3390/ani11020533 10.1007/s00357-014-9161-z 10.18637/jss.v028.i05 10.3168/jds.S0022-0302(93)77727-9 10.1016/j.anifeedsci.2006.06.017 10.3390/rs15071890 10.3168/jds.S0022-0302(06)72343-8 10.1016/j.ymeth.2020.07.012 10.3168/jds.2016-10920 10.1016/S0301-6226(01)00196-8 10.3168/jds.S0022-0302(93)77508-6 10.3168/jds.2016-10998 10.3168/jds.2014-8764 10.1016/j.jpba.2020.113372
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Snippet	This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier... Farms generate increasing amounts of data each year. One example is the bulk tank milk composition, predicted through spectrometry, which is routinely measured...
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SubjectTerms	Analysis Animal production & animal husbandry Animal welfare Body fat Breastfeeding & lactation cow Dairy cattle dairy cows Dairy industry Decision-making detection disease fatty acid Fatty acids Fermentation Fourier transforms FT-MIR spectrometry herd management Humidity infrared Infrared spectroscopy Life sciences Machine learning Metabolism Milk Milk production Nutrition Productions animales & zootechnie Proteins Sciences du vivant Scientific imaging unsupervised hierarchical clustering
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Title	Detection of Dairy Herd Management Issues Using Fatty Acid Profiles Predicted by Mid-Infrared Spectrometry
URI	https://www.ncbi.nlm.nih.gov/pubmed/40509041 https://www.proquest.com/docview/3217685727 https://www.proquest.com/docview/3218473886 http://orbi.ulg.ac.be/handle/2268/334010 https://pubmed.ncbi.nlm.nih.gov/PMC12153918 https://doaj.org/article/5224e9e0063a48d09970c3963a78fa9c
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