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Sequential sampling: a novel method in farm animal welfare assessment

Published online by Cambridge University Press:  12 August 2015

C. A. E. Heath ,
D. C. J. Main ,
S. Mullan ,
M. J. Haskell  and
W. J. Browne
C. A. E. Heath*
Affiliation:
School of Veterinary Sciences, University of Bristol, Langford House, Langford, Bristol BS40 5D, United Kingdom
D. C. J. Main
Affiliation:
School of Veterinary Sciences, University of Bristol, Langford House, Langford, Bristol BS40 5D, United Kingdom
S. Mullan
Affiliation:
School of Veterinary Sciences, University of Bristol, Langford House, Langford, Bristol BS40 5D, United Kingdom
M. J. Haskell
Affiliation:
SRUC, West Mains Road, Edinburgh EH9 3JG, United Kingdom
W. J. Browne
Affiliation:
Graduate School of Education, University of Bristol, Helen Wodehouse Building, 35 Berkeley Square, Clifton, Bristol BS8 1JA, United Kingdom Centre for Multilevel Modelling, 2 Priory Road, Bristol BS8 1TX, United Kingdom
*
†E-mail: [email protected]
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Abstract

Lameness in dairy cows is an important welfare issue. As part of a welfare assessment, herd level lameness prevalence can be estimated from scoring a sample of animals, where higher levels of accuracy are associated with larger sample sizes. As the financial cost is related to the number of cows sampled, smaller samples are preferred. Sequential sampling schemes have been used for informing decision making in clinical trials. Sequential sampling involves taking samples in stages, where sampling can stop early depending on the estimated lameness prevalence. When welfare assessment is used for a pass/fail decision, a similar approach could be applied to reduce the overall sample size. The sampling schemes proposed here apply the principles of sequential sampling within a diagnostic testing framework. This study develops three sequential sampling schemes of increasing complexity to classify 80 fully assessed UK dairy farms, each with known lameness prevalence. Using the Welfare Quality herd-size-based sampling scheme, the first ‘basic’ scheme involves two sampling events. At the first sampling event half the Welfare Quality sample size is drawn, and then depending on the outcome, sampling either stops or is continued and the same number of animals is sampled again. In the second ‘cautious’ scheme, an adaptation is made to ensure that correctly classifying a farm as ‘bad’ is done with greater certainty. The third scheme is the only scheme to go beyond lameness as a binary measure and investigates the potential for increasing accuracy by incorporating the number of severely lame cows into the decision. The three schemes are evaluated with respect to accuracy and average sample size by running 100 000 simulations for each scheme, and a comparison is made with the fixed size Welfare Quality herd-size-based sampling scheme. All three schemes performed almost as well as the fixed size scheme but with much smaller average sample sizes. For the third scheme, an overall association between lameness prevalence and the proportion of lame cows that were severely lame on a farm was found. However, as this association was found to not be consistent across all farms, the sampling scheme did not prove to be as useful as expected. The preferred scheme was therefore the ‘cautious’ scheme for which a sampling protocol has also been developed.

Keywords

sequential samplingdairy cowslamenessanimal welfareprevalence
Type
Research Article
Information
animal , Volume 10 , Supplement 2 , February 2016 , pp. 349 - 356
Copyright
© The Animal Consortium 2015 

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References

Aberle, DR, Adams, AM, Berg, CD, Black, WC, Clapp, JD, Fagerstrom, RM, Gareen, IF, Gatsonis, C, Marcus, PM, Sicks, JD and Team NLSTR 2011. Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine 365, 395–409.Google Scholar
Amory, JR, Barker, ZE, Wright, JL, Mason, SA, Blowey, RW and Green, LE 2008. Associations between sole ulcer, white line disease and digital dermatitis and the milk yield of 1824 dairy cows on 30 dairy cow farms in England and Wales from February 2003-November 2004. Preventive Veterinary Medicine 83, 381–391.Google Scholar
Ballard, RA, Truog, WE, Cnaan, A, Martin, RJ, Ballard, PL, Merrill, JD, Walsh, MC, Durand, DJ, Mayock, DE, Eichenwald, EC, Null, DR, Hudak, ML, Puri, AR, Golombek, SG, Courtney, SE, Stewart, DL, Welty, SE, Phibbs, RH, Hibbs, AM, Luan, XQ, Wadlinger, SR, Asselin, JM and Coburn, CE 2006. Inhaled nitric oxide in preterm infants undergoing mechanical ventilation. New England Journal of Medicine 355, 343–353.Google Scholar
Bang, YJ, Kim, YW, Yang, HK, Chung, HC, Park, YK, Lee, KH, Lee, KW, Kim, YH, Noh, SI, Cho, JY, Mok, YJ, Kim, YH, Ji, JF, Yeh, TS, Button, P, Sirzen, F, Noh, SH and CLASSIC trial Investigators 2012. Adjuvant capecitabine and oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): a phase 3 open-label, randomised controlled trial. Lancet 379, 315–321.Google Scholar
Barker, ZE, Leach, KA, Whay, HR, Bell, NJ and Main, DCJ 2010. Assessment of lameness prevalence and associated risk factors in dairy herds in England and Wales. Journal of Dairy Science 93, 932–941.Google Scholar
Booth, CJ, Warnick, LD, Grohn, YT, Maizon, DO, Guard, CL and Janssen, D 2004. Effect of lameness on culling in dairy cows. Journal of Dairy Science 87, 4115–4122.Google Scholar
Bussel, JB, Cheng, G, Saleh, MN, Psaila, B, Kovaleva, L, Meddeb, B, Kloczko, J, Hassani, H, Mayer, B, Stone, NL, Arning, M, Provan, D and Jenkins, JM 2007. Eltrombopag for the treatment of chronic idiopathic thrombocytopenic purpura. New England Journal of Medicine 357, 2237–2247.CrossRefGoogle ScholarPubMed
Chawg 2013. Dairy cow welfare strategy, year 1 progress against targets. Cattle Health and Welfare Group, Malmesbury, UK.Google Scholar
Clarkson, MJ, Downham, DY, Faull, WB, Hughes, JW, Manson, FJ, Merritt, JB, Murray, RD, Russell, WB, Sutherst, JE and Ward, WR 1996. Incidence and prevalence of lameness in dairy cattle. Veterinary Record 138, 563–567.CrossRefGoogle ScholarPubMed
DairyCo 2013. Dairy statistics an insiders guide 2013. The Agriculture and Horticulture Development Board, Kenilworth, UK.Google Scholar
D’Eath, RB 2012. Repeated locomotion scoring of a sow herd to measure lameness: consistency over time, the effect of sow characteristics and inter-observer reliability. Animal Welfare 21, 219–231.Google Scholar
Dember, LM, Beck, GJ, Allon, M, Delmez, JA, Dixon, BS, Greenberg, A, Himmelfarb, J, Vazquez, MA, Gassman, JJ, Greene, T, Radeva, MK, Braden, GL, Ikizler, TA, Rocco, MV, Davidson, IJ, Kaufman, JS, Meyers, CM, Kusek, JW, Feldman, HI and Dialysis Access Consortium Study Group 2008. Effect of clopidogrel on early failure of arteriovenous fistulas for hemodialysis: A randomized controlled trial. Journal of the American Medical Association 299, 2164–2171.Google Scholar
FAWC 2009. Farm animal welfare in Great Britain: past, present and future. Farm Animal Welfare Council, London, UK.Google Scholar
Garbarino, EJ, Hernandez, JA, Shearer, JK, Risco, CA and Thatcher, WW 2004. Effect of lameness on ovarian activity in postpartum Holstein cows. Journal of Dairy Science 87, 4123–4131.Google Scholar
Heath, CAE, Lin, Y, Mullan, S, Browne, WJ and Main, DCJ 2014. Implementing Welfare Quality® in UK assurance schemes: evaluating the challenges. Animal Welfare 23, 95–107.Google Scholar
Hoffman, AC, Moore, DA, Wenz, JR and Vanegas, J 2013. Comparison of modeled sampling strategies for estimation of dairy herd lameness prevalence and cow-level variables associated with lameness. Journal of Dairy Science 96, 5746–5755.Google Scholar
Kossaibati, MA and Esslemont, RJ 1997. The costs of production diseases in dairy herds in England. Veterinary Journal 154, 41–51.Google Scholar
Maertens, W, Vangeyte, J, Baert, J, Jantuan, A, Mertens, KC, De Campeneere, S, Pluk, A, Opsomer, G, Van Weyenberg, S and Van Nuffel, A 2011. Development of a real time cow gait tracking and analysing tool to assess lameness using a pressure sensitive walkway: the GAITWISE system. Biosystems Engineering 110, 29–39.Google Scholar
Main, DCJ, Barker, ZE, Leach, KA, Bell, NJ, Whay, HR and Browne, WJ 2010. Sampling strategies for monitoring lameness in dairy cattle. Journal of Dairy Science 93, 1970–1978.Google Scholar
Main, DCJ, Mullan, S, Atkinson, C, Bond, A, Cooper, M, Fraser, A and Browne, WJ 2012. Welfare outcomes assessment in laying hen farm assurance schemes. Animal Welfare 21, 389–396.Google Scholar
Manson, FJ and Leaver, JD 1988. The influence of dietary protein intake and of hoof trimming on lameness in dairy cattle. Animal production 47, 191–199.Google Scholar
Rushen, J, Pombourcq, E and de Passille, AM 2007. Validation of two measures of lameness in dairy cows. Applied Animal Behaviour Science 106, 173–177.Google Scholar
Rutherford, KMD, Langford, FM, Jack, MC, Sherwood, L, Lawrence, AB and Haskell, MJ 2009. Lameness prevalence and risk factors in organic and non-organic dairy herds in the United Kingdom. Veterinary Journal 180, 95–105.Google Scholar
Sorensen, JT, Rousing, T, Moller, SH, Bonde, M and Hegelund, L 2007. On-farm welfare assessment systems: what are the recording costs? Animal Welfare 16, 237–239.Google Scholar
Webster, AJF, Main, DCJ and Whay, HR 2004. Welfare assessment: indices from clinical observation. Animal Welfare 13, s93–s98.Google Scholar
Welfare Quality® 2009. Welfare Quality® assessment protocol for cattle. Welfare Quality® Consortium, Lelystad, The Netherlands.Google Scholar
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