Early detection of clinical mastitis from electrical conductivity data in an automatic milking system
Momena Khatun A C , Cameron E. F. Clark A , Nicolas A. Lyons B , Peter C. Thomson A , Kendra L. Kerrisk A and Sergio C. García AA School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2570, Australia.
B Intensive Livestock Industries, NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Menangle, NSW 2568, Australia.
C Corresponding author. Email: mkha3293@uni.sydney.edu.au
Animal Production Science 57(7) 1226-1232 https://doi.org/10.1071/AN16707
Submitted: 14 July 2016 Accepted: 2 December 2016 Published: 23 February 2017
Abstract
Mastitis adversely affects profit and animal welfare in the Australian dairy industry. Electrical conductivity (EC) is increasingly used to detect mastitis, but with variable results. The aim of the present study was to develop and evaluate a range of indexes and algorithms created from quarter-level EC data for the early detection of clinical mastitis at four different time windows (7 days, 14 days, 21 days, 27 days). Historical longitudinal data collected (4-week period) for 33 infected and 139 healthy quarters was used to compare the sensitivity (Se; target >80%), specificity (Sp; target >99%), accuracy (target >90%) and timing of ‘alert’ by three different approaches. These approaches involved the use of EC thresholds (range 7.5– 10 mS/cm), testing of over 250 indexes (created ad hoc), and a statistical process-control method. The indexes were developed by combining factors (and levels within each factor), such as conditional rolling average increase, percentage of variation, mean absolute deviation, mean error %; infected to non-infected ratio, all relative to the rolling average (3–9 data points) of either the affected quarter or the average of the four quarters. Using EC thresholds resulted in Se, Sp and accuracy ranging between 47% and 92%, 39% and 92% and 51% and 82% respectively (threshold 7.5 mS/cm performed best). The six highest performing indexes achieved Se, Sp and accuracy ranging between 68% and 84%, 60% and 85% and 56% and 81% respectively. The statistical process-control approach did not generate accurate predictions for early detection of clinical mastitis on the basis of EC data. Improved Sp was achieved when the time window before treatment was reduced regardless of the test approach. We concluded that EC alone cannot provide the accuracy required to detect infected quarters. Incorporating other information (e.g. milk yield, milk flow, number of incomplete milking) may increase accuracy of detection and ability to determine early onset of mastitis.
Additional keywords: dairy cows, indexes, statistical process control, thresholds.
References
Bar D, Tauer LW, Bennett G, González RN, Hertl JA, Schukken YH, Schulte HF, Welcome FL, Gröhn YT (2008) The cost of generic clinical mastitis in dairy cows as estimated by using dynamic programming. Journal of Dairy Science 91, 2205–2214.| The cost of generic clinical mastitis in dairy cows as estimated by using dynamic programming.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXmsVWmt7o%3D&md5=581595edf8a3373a9a9d84b47224b765CAS |
Chagunda MGG, Friggens NC, Rasmussen MD, Larsen T (2006) A model for detection of individual cow mastitis based on an indicator measured in milk. Journal of Dairy Science 89, 2980–2998.
| A model for detection of individual cow mastitis based on an indicator measured in milk.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xns1CltL8%3D&md5=364ac72996a8407c9df462d7e794e95aCAS |
Claycomb RW, Johnstone PT, Mein GA, Sherlock RA (2009) An automated in-line clinical mastitis detection system using measurement of conductivity from foremilk of individual udder quarters. New Zealand Veterinary Journal 57, 208–214.
| An automated in-line clinical mastitis detection system using measurement of conductivity from foremilk of individual udder quarters.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1MrivFCrsw%3D%3D&md5=9a7ed6dd3602e070b888f18063adca81CAS |
Dairy Australia (2017) Mastitis focus report. Available at http://www.dairyaustralia.com.au/Animal-management/Mastitis/Countdown-resources-and-tools-2/Mastitis-Focus-Report.aspx [Verified 24 January 2017]
de Mol RM, Ouweltjes W (2001) Detection model for mastitis in cows milked in an automatic milking system. Preventive Veterinary Medicine 49, 71–82.
| Detection model for mastitis in cows milked in an automatic milking system.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD3M7ntVWqsA%3D%3D&md5=b6942f8ed0f39d47fde63cbb1b8628caCAS |
de Vries A, Conlin BJ (2003) Design and performance of statistical process control charts applied to estrous detection efficiency. Journal of Dairy Science 86, 1970–1984.
| Design and performance of statistical process control charts applied to estrous detection efficiency.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXks1GitL4%3D&md5=f0abec88a54bf7d2e25446f4f00ed775CAS |
Halasa T, Nielen M, De Roos APW, Van Hoorne R, de Jong G, Lam TJGM, van Werven T , Hogeveen H (2009) Production loss due to new subclinical mastitis in Dutch dairy cows estimated with a test-day model. Journal of Dairy Science 92, 599–606.
| Production loss due to new subclinical mastitis in Dutch dairy cows estimated with a test-day model.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXit1Kis7w%3D&md5=c4b2df402935a27eb554c4f3a82ee22fCAS |
Hertl JA, Schukken YH, Bar D, Bennett GJ, González RN, Rauch BJ, Welcome FL, Tauer LW, Gröhn YT (2011) The effect of recurrent episodes of clinical mastitis caused by gram-positive and Gram-negative bacteria and other organisms on mortality and culling in Holstein dairy cows. Journal of Dairy Science 94, 4863–4877.
| The effect of recurrent episodes of clinical mastitis caused by gram-positive and Gram-negative bacteria and other organisms on mortality and culling in Holstein dairy cows.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXht1eisb7P&md5=b5a4d19aca6006eebbedb5f0203bcf1aCAS |
Hogeveen H, Kamphuis C, Steeneveld W, Mollenhorst H (2010) Sensors and clinical mastitis-the quest for the perfect alert. Sensors 10, 7991–8009.
| Sensors and clinical mastitis-the quest for the perfect alert.Crossref | GoogleScholarGoogle Scholar |
Hovinen M, Pyörälä S (2011) Invited review: udder health of dairy cows in automatic milking. Journal of Dairy Science 94, 547–562.
| Invited review: udder health of dairy cows in automatic milking.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXls1Ggs7k%3D&md5=0dc7309f3b3ba7b67d1ffdbf1c36a17bCAS |
Huijps K, Lam TJ, Hogeveen H (2008) Costs of mastitis: facts and perception. The Journal of Dairy Research 75, 113–120.
| Costs of mastitis: facts and perception.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXitVOmu7g%3D&md5=65674df349321208d9adbc0a394929e4CAS |
Huybrechts T, Mertens K, De Baerdemaeker J, De Ketelaere B, Saeys W (2014) Early warnings from automatic milk yield monitoring with online synergistic control. Journal of Dairy Science 97, 3371–3381.
| Early warnings from automatic milk yield monitoring with online synergistic control.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXmtFamt7g%3D&md5=10ba8f60388d974911fba87e86db118bCAS |
Kamphuis C, Mollenhorst H, Heesterbeek JAP, Hogeveen H (2010) Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. Journal of Dairy Science 93, 3616–3627.
| Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXht1GgtbfJ&md5=93bfa2e6340a5511245ed661c5cd2378CAS |
Kitchen BJ (1981) Review of the progress of dairy science: bovine mastitis: milk compositional changes and related diagnostic tests. The Journal of Dairy Research 48, 167–188.
| Review of the progress of dairy science: bovine mastitis: milk compositional changes and related diagnostic tests.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaL3MXhtF2nu7Y%3D&md5=86274a4e76a22daf590e9db03e7c588dCAS |
Lukas JM, Reneau JK, Linn JG (2008) Water intake and dry matter intake changes as a feeding management tool and indicator of health and estrus status in dairy cows. Journal of Dairy Science 91, 3385–3394.
| Water intake and dry matter intake changes as a feeding management tool and indicator of health and estrus status in dairy cows.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtFaltbbP&md5=302b156a2181e123753df5e41cabf161CAS |
Lukas JM, Reneau JK, Wallace R, Hawkins D, Munoz-Zanzi C (2009) A novel method of analyzing daily milk production and electrical conductivity to predict disease onset. Journal of Dairy Science 92, 5964–5976.
| A novel method of analyzing daily milk production and electrical conductivity to predict disease onset.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhsFWisbfK&md5=b1acce6e48707cd911f48221658f25b5CAS |
Mollenhorst H, van der Tol PPJ, Hogeveen H (2010) Somatic cell count assessment at the quarter or cow milking level. Journal of Dairy Science 93, 3358–3364.
| Somatic cell count assessment at the quarter or cow milking level.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtVOhtrfL&md5=e2934cda01da16e411ae45837f0b6e89CAS |
Mollenhorst H, Rijkaart LJ, Hogeveen H (2012) Mastitis alert preferences of farmers milking with automatic milking systems. Journal of Dairy Science 95, 2523–2530.
| Mastitis alert preferences of farmers milking with automatic milking systems.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XlvFyhtLg%3D&md5=c50070bc26b5e5907441aa5d6003e102CAS |
Mucchetti G, Gatti M, Neviani E (1994) Electrical conductivity changes in milk caused by acidification: determining factors. Journal of Dairy Science 77, 940–944.
| Electrical conductivity changes in milk caused by acidification: determining factors.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2cXjt1Wks7s%3D&md5=7b59a79d13be1959855d338e5a11a3c2CAS |
Nielen M, Deluyker H, Schukken YH, Brand A (1992) Electrical conductivity of milk: measurement, modifiers, and meta analysis of mastitis detection performance. Journal of Dairy Science 75, 606–614.
| Electrical conductivity of milk: measurement, modifiers, and meta analysis of mastitis detection performance.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaK383hvVOjuw%3D%3D&md5=aeac4a8b3e4328f2c06b15a4069206ceCAS |
Norberg E, Hogeveen H, Korsgaard IR, Friggens NC, Sloth KHMN, Løvendahl P (2004) Electrical conductivity of milk: ability to predict mastitis status. Journal of Dairy Science 87, 1099–1107.
| Electrical conductivity of milk: ability to predict mastitis status.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXivFyqs7w%3D&md5=de65f8c3082e5d2a6a8dbdb3437739a2CAS |
Pyörälä S (2003) Indicators of inflammation in the diagnosis of mastitis. Veterinary Research 34, 565–578.
| Indicators of inflammation in the diagnosis of mastitis.Crossref | GoogleScholarGoogle Scholar |
Rasmussen MD, Bjerring M (2005) Visual scoring of milk mixed with blood. The Journal of Dairy Research 72, 257–263.
| Visual scoring of milk mixed with blood.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXltFWqsro%3D&md5=1479d18b92078c8929c2d5e2a7e1ff87CAS |
Steeneveld W, van der Gaag LC, Ouweltjes W, Mollenhorst H, Hogeveen H (2010) Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems. Journal of Dairy Science 93, 2559–2568.
| Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtVahs7jL&md5=2b21932879e438a5dff4e16b07b9e44dCAS |
Wachs S (2010) What is a CUSUM chart and when should i use one? (Statistician Integral Concepts, Inc.: West Bloomfield, MI) Available at http://www.integral-concepts.com/docs/What%20is%20a%20CUSUM%20Chart%20and%20When%20Should%20I%20Use%20One.pdf [Verified 24 January 2017]
