DOI QR코드

DOI QR Code

The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle

  • Zaborski, Daniel (Department of Ruminants Science, West Pomeranian University of Technology) ;
  • Proskura, Witold S. (Department of Ruminants Science, West Pomeranian University of Technology) ;
  • Grzesiak, Wilhelm (Department of Ruminants Science, West Pomeranian University of Technology)
  • 투고 : 2017.10.20
  • 심사 : 2018.03.21
  • 발행 : 2018.11.01

초록

Objective: The aim of this study was to verify the usefulness of artificial neural networks (ANN), multivariate adaptive regression splines (MARS), naïve Bayes classifier (NBC), general discriminant analysis (GDA), and logistic regression (LR) for dystocia detection in Polish Holstein-Friesian Black-and-White heifers and cows and to indicate the most influential predictors of calving difficulty. Methods: A total of 1,342 and 1,699 calving records including six categorical and four continuous predictors were used. Calving category (difficult vs easy or difficult, moderate and easy) was the dependent variable. Results: The maximum sensitivity, specificity and accuracy achieved for heifers on the independent test set were 0.855 (for ANN), 0.969 (for NBC), and 0.813 (for GDA), respectively, whereas the values for cows were 0.600 (for ANN), 1.000 and 0.965 (for NBC, GDA, and LR), respectively. With the three categories of calving difficulty, the maximum overall accuracy for heifers and cows was 0.589 (for MARS) and 0.649 (for ANN), respectively. The most influential predictors for heifers were an average calving difficulty score for the dam's sire, calving age and the mean yield of the farm, where the heifer was kept, whereas for cows, these additionally included: calf sex, the difficulty of the preceding calving, and the mean daily milk yield for the preceding lactation. Conclusion: The potential application of the investigated models in dairy cattle farming requires, however, their further improvement in order to reduce the rate of dystocia misdiagnosis and to increase detection reliability.

키워드

참고문헌

  1. McHugh N, Kearney JF, Berry DP. The effect of dystocia on subsequent performance in dairy cows. Moorepark Res Rep 2011 2011:15.
  2. Atashi H, Abdolmohammadi AR, Asaadi A, et al. Using an incomplete gamma function to quantify the effect of dystocia on the lactation performance of Holstein dairy cows in Iran. J Dairy Sci 2012;95:2718-22. https://doi.org/10.3168/jds.2011-4954
  3. Heald CW, Kim T, Sischo WM, Cooper JB, Wolfgang DR. A computerized mastitis decision aid using farm-based records: an artificial neural network approach. J Dairy Sci 2000;83:711-20. https://doi.org/10.3168/jds.S0022-0302(00)74933-2
  4. Balshi MS, McGuire AD, Duffy P, et al. Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach. Glob Change Biol 2009;15:578-600. https://doi.org/10.1111/j.1365-2486.2008.01679.x
  5. Rish I. An empirical study of the naive Bayes classifier. In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI) 2001; 2001 Aug 4 - 10; Seattle, Washington, USA: Menlo Park, American Association for Artificial Intelligence; 2001. p. 41-6.
  6. Thirunavukkarasu M, Kathiravan G. Predicting the probability of conception in artificially inseminated bovines-A logistic regression analysis. J Anim Vet Adv 2006;5:522-7.
  7. Basarab JA, Rutter LM, Day PA. The efficacy of predicting dystocia in yearling beef heifers: II. Using discriminant analysis. J Anim Sci 1993;71:1372-80. https://doi.org/10.2527/1993.7161372x
  8. Zaborski D, Grzesiak W. Detection of difficult calvings in dairy cows using neural classifier. Arch Tierz-Arch Anim Breed 2011;54:477-89. https://doi.org/10.5194/aab-54-477-2011
  9. Zaborski D, Grzesiak W. Detection of heifers with dystocia using artificial neural networks with regard to $ER{\alpha}$-BglI, $ER{\alpha}$-SnaBI and CYP19-PvuII genotypes. Acta Sci Pol Zootech 2011;10:105-16.
  10. Zaborski D, Grzesiak W, Kotarska K, Szatkowska I, Jedrzejczak M. Detection of difficult calvings in dairy cows using boosted classification trees. Indian J Anim Res 2014;48:452-8. https://doi.org/10.5958/0976-0555.2014.00010.7
  11. Morrison DG, Humes PE, Keith NK, Godke RA. Discriminant analysis for predicting dystocia in beef cattle. I. Comparison with regression analysis. J Anim Sci 1985;60:608-16. https://doi.org/10.2527/jas1985.603608x
  12. Morrison DG, Humes PE, Keith NK, Godke RA. Discriminant analysis for predicting dystocia in beef cattle. II. Derivation and validation of a prebreeding prediction model. J Anim Sci 1985;60:617-21. https://doi.org/10.2527/jas1985.603617x
  13. Arthur PF, Archer JA, Melville GJ. Factors influencing dystocia and prediction of dystocia in Angus heifers selected for yearling growth rate. Aust J Agric Res 2000;51:147-54. https://doi.org/10.1071/AR99070
  14. Johnson SK, Deutscher GH, Parkhurst A. Relationships of pelvic structure, body measurements, pelvic area and calving difficulty. J Anim Sci 1988;66:1081-8. https://doi.org/10.2527/jas1988.6651081x
  15. Piwczynski D, Nogalski Z, Sitkowska B. Statistical modeling of calving ease and stillbirths in dairy cattle using the classification tree technique. Livest Sci 2013;154:19-27. https://doi.org/10.1016/j.livsci.2013.02.013
  16. Liu J, Neerchal NK, Tasch U, Dyer RM, Rajkondawar PG. Enhancing the prediction accuracy of bovine lameness models through transformations of limb movement variables. J Dairy Sci 2009;92:2539-50. https://doi.org/10.3168/jds.2008-1301
  17. Barrier ACM. Effects of a difficult calving on the subsequent health and welfare of the dairy cows and calves [dissertation]. Edinburgh, UK: University of Edinburgh; 2012.
  18. Atashi H, Zamiri MJ, Sayadnejad MB. The effect of maternal inbreeding on incidence of twinning, dystocia and stillbirth in Holstein cows of Iran. Iran J Vet Res IJVR 2012;13:93-9.
  19. Ghavi Hossein-Zadeh N. Effect of dystocia on the productive performance and calf stillbirth in Iranian Holsteins. J Agric Sci Technol 2013;16:69-78.
  20. Berry DP, Cromie AR. Associations between age at first calving and subsequent performance in Irish spring calving Holstein-Friesian dairy cows. Livest Sci 2009;123:44-54. https://doi.org/10.1016/j.livsci.2008.10.005
  21. Mee JF, Berry DP, Cromie AR. Risk factors for calving assistance and dystocia in pasture-based Holstein-Friesian heifers and cows in Ireland. Vet J 2011;187:189-94. https://doi.org/10.1016/j.tvjl.2009.11.018
  22. Eaglen SAE, Bijma P. Genetic parameters of direct and maternal effects for calving ease in Dutch Holstein-Friesian cattle. J Dairy Sci 2009;92:2229-37. https://doi.org/10.3168/jds.2008-1654
  23. Grohn Y, Erb HN, McCulloch CE, et al. Epidemiology of reproductive disorders in dairy cattle: associations among host characteristics, disease and production. Prev Vet Med 1990;8:25-39. https://doi.org/10.1016/0167-5877(90)90020-I
  24. Fiedlerova M, Rehak D, Vacek M, et al. Analysis of non-genetic factors affecting calving difficulty in the Czech Holstein population. Czech J Anim Sci 2008;53:284-91. https://doi.org/10.17221/355-CJAS
  25. Ghanem ME, Higuchi H, Tezuka E, et al. Mycoplasma infection in the uterus of early postpartum dairy cows and its relation to dystocia and endometritis. Theriogenology 2013;79:180-5. https://doi.org/10.1016/j.theriogenology.2012.09.027
  26. Dhakal K, Maltecca C, Cassady JP, et al. Calf birth weight, gestation length, calving ease, and neonatal calf mortality in Holstein, Jersey, and crossbred cows in a pasture system. J Dairy Sci 2013;96:690-8. https://doi.org/10.3168/jds.2012-5817
  27. Murray CF, Leslie KE. Newborn calf vitality: Risk factors, characteristics, assessment, resulting outcomes and strategies for improvement. Vet J 2013;198:322-8. https://doi.org/10.1016/j.tvjl.2013.06.007
  28. Ingvartsen KL, Dewhurst RJ, Friggens NC. On the relationship between lactational performance and health: is it yield or metabolic imbalance that cause production diseases in dairy cattle? A position paper. Livest Prod Sci 2003;83:277-308. https://doi.org/10.1016/S0301-6226(03)00110-6
  29. Grohn YT, Rajala-Schultz PJ, Allore HG, et al. Optimizing replacement of dairy cows: modeling the effects of diseases. Prev Vet Med 2003;61:27-43. https://doi.org/10.1016/S0167-5877(03)00158-2

피인용 문헌

  1. The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records vol.11, pp.3, 2018, https://doi.org/10.3390/ani11030721
  2. Classification of environmental factors potentially motivating for dairy cows to access shade vol.88, pp.3, 2021, https://doi.org/10.1017/s0022029921000509