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A novel system for on-farm fertility monitoring based on milk progesterone
Timely identification of a cow's reproduction status is essential to minimize fertility-related losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis, and the timely recognition of early embryonic death and ovarian problems. On-farm milk progesterone (P4) analysis c...
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Published in: | Journal of dairy science 2018-09, Vol.101 (9), p.8369-8382 |
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creator | Adriaens, Ines Saeys, Wouter Huybrechts, Tjebbe Lamberigts, Chris François, Liesbeth Geerinckx, Katleen Leroy, Jo De Ketelaere, Bart Aernouts, Ben |
description | Timely identification of a cow's reproduction status is essential to minimize fertility-related losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis, and the timely recognition of early embryonic death and ovarian problems. On-farm milk progesterone (P4) analysis can indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. The objective of this paper is to present a newly developed methodology for detecting luteolysis preceding estrus and give an indication of its on-farm use. The innovative monitoring system presented is based on milk P4 using the principles of synergistic control. Instead of using filtering techniques and fixed thresholds, the present system employs an individually on-line updated model to describe the P4 profile, combined with a statistical process control chart to identify the cow's fertility status. The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For 13 cows, no luteolysis was detected by the system within the 25 to 32 d after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, predicting the optimal insemination window, and establishing rules to detect problems based on deviating P4 patterns. |
doi_str_mv | 10.3168/jds.2017-13827 |
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This includes optimal estrus detection, pregnancy diagnosis, and the timely recognition of early embryonic death and ovarian problems. On-farm milk progesterone (P4) analysis can indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. The objective of this paper is to present a newly developed methodology for detecting luteolysis preceding estrus and give an indication of its on-farm use. The innovative monitoring system presented is based on milk P4 using the principles of synergistic control. Instead of using filtering techniques and fixed thresholds, the present system employs an individually on-line updated model to describe the P4 profile, combined with a statistical process control chart to identify the cow's fertility status. The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For 13 cows, no luteolysis was detected by the system within the 25 to 32 d after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, predicting the optimal insemination window, and establishing rules to detect problems based on deviating P4 patterns.</description><identifier>ISSN: 0022-0302</identifier><identifier>EISSN: 1525-3198</identifier><identifier>DOI: 10.3168/jds.2017-13827</identifier><identifier>PMID: 29935821</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Animals ; Cattle ; Farms ; Female ; Fertility - physiology ; Insemination, Artificial ; Luteolysis ; Milk - chemistry ; milk progesterone ; monitoring fertility ; on-line algorithm ; Pregnancy ; Progesterone - analysis ; statistical process control</subject><ispartof>Journal of dairy science, 2018-09, Vol.101 (9), p.8369-8382</ispartof><rights>2018 American Dairy Science Association</rights><rights>Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c384t-a8a0dc3e932412e98859ccebf4a042093e9b6ab6d70e03cc7ce522da8c50eadb3</citedby><cites>FETCH-LOGICAL-c384t-a8a0dc3e932412e98859ccebf4a042093e9b6ab6d70e03cc7ce522da8c50eadb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S002203021830585X$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3549,27924,27925,45780</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29935821$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Adriaens, Ines</creatorcontrib><creatorcontrib>Saeys, Wouter</creatorcontrib><creatorcontrib>Huybrechts, Tjebbe</creatorcontrib><creatorcontrib>Lamberigts, Chris</creatorcontrib><creatorcontrib>François, Liesbeth</creatorcontrib><creatorcontrib>Geerinckx, Katleen</creatorcontrib><creatorcontrib>Leroy, Jo</creatorcontrib><creatorcontrib>De Ketelaere, Bart</creatorcontrib><creatorcontrib>Aernouts, Ben</creatorcontrib><title>A novel system for on-farm fertility monitoring based on milk progesterone</title><title>Journal of dairy science</title><addtitle>J Dairy Sci</addtitle><description>Timely identification of a cow's reproduction status is essential to minimize fertility-related losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis, and the timely recognition of early embryonic death and ovarian problems. On-farm milk progesterone (P4) analysis can indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. The objective of this paper is to present a newly developed methodology for detecting luteolysis preceding estrus and give an indication of its on-farm use. The innovative monitoring system presented is based on milk P4 using the principles of synergistic control. Instead of using filtering techniques and fixed thresholds, the present system employs an individually on-line updated model to describe the P4 profile, combined with a statistical process control chart to identify the cow's fertility status. The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For 13 cows, no luteolysis was detected by the system within the 25 to 32 d after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, predicting the optimal insemination window, and establishing rules to detect problems based on deviating P4 patterns.</description><subject>Animals</subject><subject>Cattle</subject><subject>Farms</subject><subject>Female</subject><subject>Fertility - physiology</subject><subject>Insemination, Artificial</subject><subject>Luteolysis</subject><subject>Milk - chemistry</subject><subject>milk progesterone</subject><subject>monitoring fertility</subject><subject>on-line algorithm</subject><subject>Pregnancy</subject><subject>Progesterone - analysis</subject><subject>statistical process control</subject><issn>0022-0302</issn><issn>1525-3198</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kLtPwzAQhy0EoqWwMiKPLCl-5GGPVUV5qBILzJZjXyqXJC52Wqn_PS4tbEznk7_7ne5D6JaSKaeleFjbOGWEVhnlglVnaEwLVmScSnGOxoQwlhFO2AhdxbhOLWWkuEQjJiUvBKNj9DrDvd9Bi-M-DtDhxgfs-6zRIb0hDK51wx53vneDD65f4VpHsAnBnWs_8Sb4FaTB4Hu4RheNbiPcnOoEfSwe3-fP2fLt6WU-W2aGi3zItNDEGg6Ss5wykEIU0hiom1yTnBGZfupS16WtCBBuTGWgYMxqYQoC2tZ8gu6PuWn51zZtV52LBtpW9-C3UaUTZUoqS5nQ6RE1wccYoFGb4Dod9ooSdfCnkj918Kd-_KWBu1P2tu7A_uG_whIgjgCkC3cOgorGQW_AugBmUNa7_7K_Aakaf2o</recordid><startdate>201809</startdate><enddate>201809</enddate><creator>Adriaens, Ines</creator><creator>Saeys, Wouter</creator><creator>Huybrechts, Tjebbe</creator><creator>Lamberigts, Chris</creator><creator>François, Liesbeth</creator><creator>Geerinckx, Katleen</creator><creator>Leroy, Jo</creator><creator>De Ketelaere, Bart</creator><creator>Aernouts, Ben</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>201809</creationdate><title>A novel system for on-farm fertility monitoring based on milk progesterone</title><author>Adriaens, Ines ; Saeys, Wouter ; Huybrechts, Tjebbe ; Lamberigts, Chris ; François, Liesbeth ; Geerinckx, Katleen ; Leroy, Jo ; De Ketelaere, Bart ; Aernouts, Ben</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c384t-a8a0dc3e932412e98859ccebf4a042093e9b6ab6d70e03cc7ce522da8c50eadb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Animals</topic><topic>Cattle</topic><topic>Farms</topic><topic>Female</topic><topic>Fertility - physiology</topic><topic>Insemination, Artificial</topic><topic>Luteolysis</topic><topic>Milk - chemistry</topic><topic>milk progesterone</topic><topic>monitoring fertility</topic><topic>on-line algorithm</topic><topic>Pregnancy</topic><topic>Progesterone - analysis</topic><topic>statistical process control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adriaens, Ines</creatorcontrib><creatorcontrib>Saeys, Wouter</creatorcontrib><creatorcontrib>Huybrechts, Tjebbe</creatorcontrib><creatorcontrib>Lamberigts, Chris</creatorcontrib><creatorcontrib>François, Liesbeth</creatorcontrib><creatorcontrib>Geerinckx, Katleen</creatorcontrib><creatorcontrib>Leroy, Jo</creatorcontrib><creatorcontrib>De Ketelaere, Bart</creatorcontrib><creatorcontrib>Aernouts, Ben</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of dairy science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adriaens, Ines</au><au>Saeys, Wouter</au><au>Huybrechts, Tjebbe</au><au>Lamberigts, Chris</au><au>François, Liesbeth</au><au>Geerinckx, Katleen</au><au>Leroy, Jo</au><au>De Ketelaere, Bart</au><au>Aernouts, Ben</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel system for on-farm fertility monitoring based on milk progesterone</atitle><jtitle>Journal of dairy science</jtitle><addtitle>J Dairy Sci</addtitle><date>2018-09</date><risdate>2018</risdate><volume>101</volume><issue>9</issue><spage>8369</spage><epage>8382</epage><pages>8369-8382</pages><issn>0022-0302</issn><eissn>1525-3198</eissn><abstract>Timely identification of a cow's reproduction status is essential to minimize fertility-related losses on dairy farms. 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The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For 13 cows, no luteolysis was detected by the system within the 25 to 32 d after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. 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subjects | Animals Cattle Farms Female Fertility - physiology Insemination, Artificial Luteolysis Milk - chemistry milk progesterone monitoring fertility on-line algorithm Pregnancy Progesterone - analysis statistical process control |
title | A novel system for on-farm fertility monitoring based on milk progesterone |
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