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Mathematical approaches to detect low concentrations in progesterone profiles

Published online by Cambridge University Press:  18 November 2013

R. von Leesen ,
J. Tetens ,
W. Junge  and
G. Thaller
R. von Leesen*
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Hermann-Rodewald-Straße 6, D-24118 Kiel, Germany
J. Tetens
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Hermann-Rodewald-Straße 6, D-24118 Kiel, Germany
W. Junge
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Hermann-Rodewald-Straße 6, D-24118 Kiel, Germany
G. Thaller
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Hermann-Rodewald-Straße 6, D-24118 Kiel, Germany
*
E-mail: rvleesen@tierzucht.uni-kiel.de
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Abstract

There is a general need for higher objectivity and accuracy in describing the physiological fertility performance of dairy cows. To develop the alternative meaningful starting points for the selection of genetically superior dairy cows, this study focused on the detection of low progesterone concentrations, which are indicative of estrus events. Three mathematical approaches were used: one based on the exponentially weighted moving average control chart, and two threshold methods, which were developed in-house. Data were collected from one data set that included 97 insemination data of first-lactating Holstein dairy cows, and a second set that included 160 inseminations of primiparous and multiparous Holstein dairy cows. On the basis of these 2 data sets, and using a threshold of 1.2 ng progesterone/ml skimmed milk, the sensitivity of the 3 models was high and ranged between 100% and 93.13%, with an error rate between 4% and 22.17%. The specificity varied between 97.92% and 99.93%. The average concentration levels of true-positive–detected progesterone measures were low and ranged between 0.18 and 0.28 ng progesterone/ml skimmed milk (first data set) and 0.21 to 0.26 ng progesterone/ml skimmed milk (second data set). False-positive–detected low progesterone concentrations during estrus events were closely related to progesterone values around the 1.2 ng progesterone/ml skimmed milk threshold and the detecting rules of the control chart. Thus, we suggest that a threshold of 0.8 ng progesterone/ml skimmed milk is indicative for luteal activity in defatted foremilk. By means of the three methods used, the detection of low progesterone concentrations was possible and it can be assumed that this is a good starting point for further studies (such as interval calculation) in this area.

Keywords

approachesanalysisprogesteronetime seriescattle
Type
Physiology and functional biology of systems
Information
animal , Volume 7 , Issue 12 , December 2013 , pp. 2008 - 2015
Copyright
Copyright © The Animal Consortium 2013 

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References

Benneyan, JC 1998. Use and interpretation of statistical quality control chart. International Journal for Quality and Health Care 10, 69–73.Google Scholar
Capilla, C 2008. Time series analysis and identification of trends in a mediterranean urban area. Global and Planetary Change 63, 275–281.Google Scholar
Close, B and Zurbenko, I 2011. kza: Kolmogorov-Zurbenko adaptive filters. Retrieved October 24, 2011, from http://CRAN.R-project.org/package=kzaGoogle Scholar
Darling, JAB, Laing, JA and Harkness, RA 1974. A survey of the steroids in cows' milk. Journal of Endocrinology 62, 291–297.Google Scholar
Eradus, WJ, Scholten, H and Udink Ten Cate, AJ 1996. An optimized fuzzy inference system for oestrus detection in dairy cattle. In 1st International Symposium on Neuro-Fuzzy Systems (ed. International Symposium on Neuro-Fuzzy Systems), pp. 171–176. I.E.E.E. Press, Lausanne, Switzerland.Google Scholar
Firk, R, Stamer, E, Junge, W and Krieter, J 2003. Improving oestrus detection by combination of activity measurements with information about previous oestrus cases. Livestock Production Science 82, 97–103.Google Scholar
Friggens, NC and Chagunda, MGG 2005. Prediction of the reproductive status of cattle on the basis of milk progesterone measures: model description. Theriogenology 64, 155–190.Google Scholar
Friggens, NC, Bjerring, M, Ridder, C, Højsgaard, S and Larsen, T 2008. Improved detection of reproductive status in dairy cows using milk progesterone measurements. Reproduction in Domestic Animals 43, 113–121.Google Scholar
Galanis, G, Chu, PC and Kallos, G 2011. Statistical post processes for the improvement of the results of numerical wave prediction models. A combination of Kolmogorov-Zurbenko and Kalman filters. Journal of Operational Oceanography 4, 23–31.Google Scholar
Gorzecka, J, Codrea, MC, Friggens, NC and Callesen, H 2011. Progesterone profiles around the time of insemination do not show clear differences between of pregnant and not pregnant dairy cows. Animal Reproduction Science 123, 14–22.Google Scholar
Hayes, JF, Cue, RI and Monardes, HG 1992. Estimates of repeatability of reproductive measures in canadian holsteins. Journal of Dairy Science 75, 1701–1706.Google Scholar
Heap, RB, Gwyn, M, Laing, JA and Walters, DE 1973. Pregnancy diagnosis in cows; changes in milk progesterone concentration during the oestrous cycle and pregnancy measured by rapid radioummunoassay. The Journal of Agricultural Science 81, 151–157.Google Scholar
Hunter, JS 1986. Exponentially weighted moving average. Journal of Quality Technology 18, 203–210.Google Scholar
Johannesson, T, Bjornsson, H and Office IM 2009. Stineman, a consistently well behaved method of interpolation. Retrieved October 18, 2011, from http://cran.r-project.org/web/packages/stinepack/stinepack.pdfGoogle Scholar
Jønsson, RI, Björgvinsson, T, Blanke, M, Poulsen, NK, Højsgaard, S and Munksgaard, L 2008. Oestrus detection in dairy cows using likelihood ratio tests, 6.-11.7.2008. In 17th World Congress of the International Federation of Automatic Control, Seoul, Korea, pp. 658–663.Google Scholar
Krieter, J, Stamer, E and Junge, W 2006. Control charts and neural networks for oestrus detection in dairy cows. In Land- und Ernährungswirtschaft im Wandel: Aufgaben und Herausforderungen für die Agrar- und Umweltinformatik (ed. K-O Wenkel, P Wagner, M Morgenstern, K Luzi and P Eisermann), pp. 133–136. Referate der 26. GIL Jahrestagung, Potsdam, Germany.Google Scholar
Lamming, GE and Darwash, AO 1998. The use of milk progesterone profiles to characterise components of subfertility in milked dairy cows. Animal Reproduction Science 52, 175–190.CrossRefGoogle ScholarPubMed
Marti, CF and Funk, DA 1994. Relationship between production and days open at different levels of herd production. Journal of Dairy Science 77, 1682–1690.Google Scholar
McCoy, MA, Lennox, SD, Mayne, CS, McCaughey, WJ, Verner, M, Catney, DC, Wylie, ARG, Kennedy, B and Gordon, AW 2001. An investigation into the relationship between milk progesterone concentrations in fore-milk and composite milk samples. In Fertility in the high producing dairy cow (ed. MG Diskin), 2(26), pp. 471–473. British Society of Animal Science occasional publication, UK.Google Scholar
Montgomery, DC 2009. The exponentially weighted moving average control chart. In Statistical quality control – a modern introduction, international student version, 6th edition (ed. DC Montgomery), pp. 419–420. John Wiley & Sons Inc., River Street, Hoboken, NJ.Google Scholar
Opsomer, G, Gröhn, YT, Hertl, J, Coryn, M, Deluyker, H and de Kruif, A 2000. Risk factors for post partum ovarian dysfunction in high producing dairy cows in Belgium: a field study. Theriogenology 53, 841–857.Google Scholar
Pennington, JA, Spahr, SL and Lodge, JR 1981. Influences on progesterone concentration in bovine milk. Journal of Dairy Science 64, 259–266.CrossRefGoogle ScholarPubMed
Pope, AL and Swinburne, JK 1980. Reviews of the progress of dairy science: hormones in milk: their physiological significance and value as diagnostic aids. Journal of Dairy Research 47, 427–449.Google Scholar
Pryce, JE, Veerkamp, RF, Thompson, R, Hill, WG and Simm, G 1997. Genetic aspects of common health disorders and measures of fertility in holstein friesian dairy cattle. Animal Science 65, 353–360.Google Scholar
R Development Core Team 2012. R: a language and environment for statistical computing. The R foundation for statistical computing, Vienna, Austria.Google Scholar
Renner, E 1970. Messfehler von Bestimmungsmethoden. In Mathematisch-statistische Methoden in der praktischen Anwendung (ed. E Renner), pp. 79–80. Verlag Paul Parey, Berlin und Hamburg.Google Scholar
Royal, M, Mann, GE and Flint, APF 2000a. Strategies for reversing the trend towards subfertility in dairy cattle. The Veterinary Journal 160, 53–60.CrossRefGoogle ScholarPubMed
Royal, M, Darwash, AO, Flint, APF, Webb, R, Wooliams, JA and Lamming, GE 2000b. Declining fertility in dairy cattle: changes in traditional and endocrine parameters of fertility. Animal Science 70, 487–501.Google Scholar
SAS 2004. Institute Inc. 2002–2008. SAS/STAT Userʼs guide. Version 9.0. SAS Institute Inc., Cary, NC.Google Scholar
Schiavo, JJ, Matuszczak, RL, Oltenacu, EB and Foote, RH 1975. Milk progesterone in postpartum and pregnant cows as a monitor of reproductive status. Journal of Dairy Science 58, 1713–1716.Google Scholar
Schwalm, JW and Tucker, A 1978. Glucocorticoids in mammary secretions and blood serum during reproduction and lactation and distributions of glucocorticoids, progesterone, and estrogens in fractions of milk. Journal of Dairy Science 61, 550–560.Google Scholar
Shrestha, HK, Nakao, T, Suzuki, T, Higaki, T and Akita, M 2004. Effects of abnormal ovarian cycles during pre-service period postpartum on subsequent reproductive performance of high-producing Holstein cows. Theriogenology 61, 1559–1571.Google Scholar
Stineman, RW 1980. A consistently well-behaved method of interpolation. Creative Computing 6, 54–57.Google Scholar
Waldmann, A, Ropstad, E, Landsverk, K, Sørensen, K, Sølverød, L and Dahl, E 1999. Level and distribution of progesterone in bovine milk in relation to storage in the mammary gland. Animal Reproduction Science 56, 79–91.CrossRefGoogle ScholarPubMed
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