DOI
https://doi.org/10.18849/ve.v4i4.205Abstract
Objective:
The particular interest of this study was to identify the relative impacts of magnitude and category of high SCC on ‘recovery’, once other factors were taken into account.
Background:
A high SCC (≥200 thousand cells/ml) is commonly associated with intra-mammary infection (IMI), one of the most economically important diseases faced by the dairy industry. High SCC is important in its own right because the bulk milk tank SCC is a determinant of the price farmers receive for their milk.
Evidentiary value:
A high SCC may be transient, returning (‘recovering’) to low SCC at the next monthly milk recording. Better understanding of factors influencing the probability of ‘recovery’ from high SCC, could improve decision making on how to manage cows with high SCCs in the most effective manner to minimise both production loss and unnecessary interventions that may include antimicrobial use.
Methods:
This research used monthly milk recording data to explore how the probability of ‘recovery’ from high somatic cell count (SCC) is associated with different factors such as parity, stage of lactation, the magnitude of SCC and category of high SCC (e.g. new or chronic). Different factors (SCC magnitude; category of high SCC, e.g. new or chronic; parity; and lactation stage) were analysed using a multilevel, mixed effect logistic regression model to assess the association with recovery probability. Data of 12,250 full lactation records from 499 milk-recorded UK dairy herds were used, from which 30,080 high SCC ‘cases’ were analysed.
Results:
In line with established evidence, increasing parity, increasing days in lactation and increasing magnitude of SCC are associated with decreased probability of recovery. The important result is that the category of high SCC is the most influential factor, with probability of recovery after two consecutive high SCC being around half or less the probability of recovery after just one high SCC.
Conclusion:
The results provide evidential support for category of high SCC to be used ahead of magnitude of SCC when advising farmers about prioritising high SCC cases for investigation and possible treatment, drying-off or culling.
Application:
Dairy herd management software companies should work with farmers and their advisors to ensure SCC data are presented in the most useful way, allowing easy interpretation and translation into the most effective interventions that can increase the efficiency of the dairy industry as well as avoiding needless or ineffectual interventions, that may include use of antimicrobials.
References
Archer, S.C., Mc Coy, F., Wapenaar, W. & Green, M.J. (2013) Association between somatic cell count early in the first lactation and the lifetime milk yield of cows in Irish dairy herds. Journal of Dairy Science 96, 2951–2959. DOI: https://doi.org/10.3168/jds.2012-6294
Baayen, R.H., Davidson, D.J. & Bates, D.M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. J. Mem. Lang. 59, 390–412. DOI: https://doi.org/10.1016/j.jml.2007.12.005
Biggs, A. (2017a). Update on dry cow therapy 1. Antibiotic v non-antibiotic approaches. In Practice, 39, 328–333. DOI: http://dx.doi.org/10.1136/inp.j3107
Biggs, A. (2017b). Update on dry cow therapy 2. Measuring dry period performance. In Practice, 39, 363–371.DOI: http://dx.doi.org/10.1136/inp.j3592
Bradley, A. & Green, M. (2005). Use and interpretation of somatic cell count data in dairy cows. In Practice, 27, 310–315. DOI: https://doi.org/10.1136/inpract.27.6.310
Deluyker, H.A., Van Oye, S.N. & Boucher, J.F. (2005). Factors Affecting Cure and Somatic Cell Count After Pirlimycin Treatment of Subclinical Mastitis in Lactating Cows. J. Dairy Sci. 88 (2), 604–614. DOI: https://doi.org/10.3168/jds.S0022-0302(05)72724-7
Dohoo, I.R. & Leslie, K.E. (1991). Evaluation of changes in somatic cell counts as indicators of new intramammary infections. Preventive Veterinary Medicine, 10, 225–237. DOI: https://doi.org/10.1016/0167-5877(91)90006-N
Edmondson, P. & Blowey, R. (1998). Record analysis in mastitis investigation. In Practice, 20, 297–303. DOI: https://doi.org/10.1136/inpract.20.6.297
Goldstein, H. (2003). Multilevel Statistical Models (Third Edition), Wiley Series in Probability and Statistics. John Wiley & Sons, Ltd, Chichester, UK. DOI: https://doi.org/10.1002/9780470973394
Halasa, T., Huijps, K., Østerås, O. & Hogeveen, H. (2007). Economic effects of bovine mastitis and mastitis management: A review. Veterinary Quarterly 29, 18–31. DOI: https://doi.org/10.1080/01652176.2007.9695224
Hanks, J. & James, A. (2011). An analysis of somatic cell counts between 2005 and 2010 and the contribution of chronic high cell count cows. UK VET - Livestock 16, (6), 29–34. DOI: https://doi.org/10.1111/j.2044-3870.2011.00067.x
Hanks, J. & Kossaibati, M. (2014). Key performance indicators for the UK national dairy herd. A study of herd performance in 500 Holstein/Friesian herds for the year ending 31st August 2014 Ed Veterinary Epidemiology and Economics Research Unit (VEERU). School of Agriculture Policy & Development, University of Reading.
Hanks, J. & Kossaibati, M. (2018). Key Performance Indicators for the UK national dairy herd. A study of herd performance in 500 Holstein/Friesian herds for the year ending 31st August 2018. Ed Veterinary Epidemiology and Economics Research Unit (VEERU). School of Agriculture Policy & Development, University of Reading.
Herlekar, D.A., Shashikant, C.S., Gurjar, A.A. & Jayarao, B.M. (2013). Presence of viral and bacterial organisms in milk and their association with somatic cell counts. Journal of Dairy Science 96, 6336–6346. DOI: https://doi.org/10.3168/jds.2013-6631
Morris, M.J., Kaneko, K., Uppal, S.K., Walker, S.L., Jones, D.N., Routly, J.E., Smith, R.F. & Dobson, H. (2013). Effects of high somatic cell counts in milk on reproductive hormones and oestrus behaviour in dairy cows with special reference to those with concurrent lameness. Animal Reproduction Science 141, 20–25. DOI: https://doi.org/10.1016/j.anireprosci.2013.07.004
Schepers, A.J., Lam, T.J., Schukken, Y.H., Wilmink, J.B. & Hanekamp, W.J. (1997). Estimation of variance components for somatic cell counts to determine thresholds for uninfected quarters. J. Dairy Sci. 80 (8), 1833–1840. DOI: https://doi.org/10.3168/jds.S0022-0302(97)76118-6
Sol, J., Sampimon, O.C., Snoep, J.J. & Schukken, Y. H. (1997). Factors associated with bacteriological cure during lactation after therapy for subclinical mastitis caused by Staphylococcus aureus. J. Dairy Sci. 80, 2803–2808. DOI: https://doi.org/10.3168/jds.S0022-0302(97)76243-X
Quené, H. & van den Bergh, H. (2004). On multi-level modeling of data from repeated measures designs: a tutorial. Speech Commun. 43, 103–121. DOI: https://doi.org/10.1016/J.SPECOM.2004.02.004
Van Soest, F.J.S., Santman-berends, L.M.G.A., Lam, T.J.G.M. & Hogeveen, H. (2016). Failure and preventive costs of mastitis on Dutch dairy farms. J. Dairy Sci. 99, 8365–8374. DOI: http://dx.doi.org/10.3168/jds.2015-10561
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