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Current IssueEditorial BoardOnline First ArticlesArchive

volume 48 issue 5 (october 2014) : 452-458,   Doi: 10.5958/0976-0555.2014.00010.7

DETECTION OF DIFFICULT CALVINGS IN DAIRY COWS USING BOOSTED CLASSIFICATION TREES

D
D. Zaborski1*
W
W. Grzesiak1
K
K. Kotarska1
I
I. Szatkowska
M
M. Jedrzejczak
1Laboratory of Molecular Cytogenetics, Department of Ruminants Science, West Pomeranian University of Technology, Szczecin – 71 460, Poland
Cite article:- Zaborski1* D., Grzesiak1 W., Kotarska1 K., Szatkowska I., Jedrzejczak M. (2025). DETECTION OF DIFFICULT CALVINGS IN DAIRY COWS USING BOOSTED CLASSIFICATION TREES. Indian Journal of Animal Research. 48(5): 452-458. doi: 10.5958/0976-0555.2014.00010.7.

ABSTRACT

The aim of this study was to apply boosted classification trees to dystocia detection in dairy cattle and to indicate its most important predictors. A total of 1742 calving records were used. Proportions of correctly detected difficult and easy calvings in a test set were 75.0%, 92.0% and 75.0%, 77.3%, for heifers and multiparous cows, respectively. The key predictors of calving difficulty were: pregnancy length, body condition score index, calving age, proportion of Holstein-Friesian genes (for heifers) as well as calving and calving-to-conception intervals, mean daily milk yield, fat and protein content in milk and 4%-fat corrected milk yield (for cows). The data mining method used in this study allowed us to obtain the models of good quality, confirmed by their relatively high effectiveness of detecting dystotic animals.

REFERENCES

  1. Arthur, P.F., Archer, J.A. and Melville, G.J. (1999). Factors influencing dystocia and prediction of dystocia in Angus heifers selected for yearling growth rate. Crop Pasture Sci. 51:147–154.
  2. Atashi, H., Abdolmohammadi, A., Dadpasand, M. and Asaadi, A. (2012). Prevalence, risk factors and consequent effect of dystocia in Holstein dairy cows in Iran. Asian-Australas. J. Anim. Sci. 25:447–451.
  3. Barrier, A.C.M. (2012). Effects of a difficult calving on the subsequent health and welfare of the dairy cows and calves. Ph.D. thesis submitted to the University of Edinburgh, Edinburgh (UK).
  4. Basarab, J.A., Rutter, L.M. and Day, P.A. (1993). The efficacy of predicting dystocia in yearling beef heifers: II. Using discriminant analysis. J. Anim. Sci. 71:1372–1380.
  5. Enevoldsen, C. and Sørensen, J.T. (1992). Effects of dry period length on clinical mastitis and other major clinical health disorders. J. Dairy Sci. 75:1007–1014.
  6. Harañczyk, G. (2010). The ROC Curves - Evaluation of the Classifier Quality and Searching for the Optimum Cut-off Point. StatSoft, Kraków (Poland). pp. 79-89.
  7. Hill, T. and Lewicki, P. (2006). Statistics: Methods and Applications. StatSoft Inc., Tulsa, Oklahoma (USA).
  8. Ingvartsen, K.L., Dewhurst, R.J. and Friggens, N.C. (2003). 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. 83:277–308.
  9. Kolkman, I. (2010). Calving problems and calving ability in the phenotypically double muscled Belgian Blue breed. Ph.D. thesis submitted to Ghent Univeristy, Merelbeke (Belgium)
  10. Mee, J.F. (2008). Prevalence and risk factors for dystocia in dairy cattle: A review. Vet. J. 176:93–101.
  11. Mee, J.F., Berry, D.P. and Cromie, A.R. (2011). Risk factors for calving assistance and dystocia in pasture-based Holstein–Friesian heifers and cows in Ireland. Vet. J. 187:189–194.
  12. Meijering, A. (1984). Dystocia and stillbirth in cattle—A review of causes, relations and implications. Livest. Prod. Sci. 11:143–177.
  13. Micke, G. C., Sullivan, T.M., Rolls, P.J., Hasell, B., Greer, R.M., Norman, S.T. and Perry, V.E.A. (2010). Dystocia in 3- year-old beef heifers: Relationship to maternal nutrient intake during early-and mid-gestation, pelvic area and hormonal indicators of placental function. Anim. Reprod. Sci. 118:163–170.
  14. Morrison, D.G., Humes, P.E., Keith, N.K. and Godke, R.A. (1985a). Discriminant analysis for predicting dystocia in beef cattle. I. Comparison with regression analysis. J. Anim. Sci. 60:608–616.
  15. Morrison, D.G., Humes, P.E., Keith, N.K. and Godke, R.A. (1985b). Discriminant analysis for predicting dystocia in beef cattle. II. Derivation and validation of a prebreeding prediction model. J. Anim. Sci. 60: 617.
  16. Piwczyñski, D., Nogalski, Z. and Sitkowska, B. (2013). Statistical modeling of calving ease and stillbirths in dairy cattle using the classification tree technique. Livest. Sci. 154:19-27.
  17. Pogorzelska, P. and Nogalski, Z. (2010). Calving difficulty in cows and heifers of the Polish dairy cattle population in 2007-2008. Sci. Ann. Pol. Soc. Anim. Prod. 6:103-110.Schuenemann, G.M., Nieto, I., Bas, S., Galvão, K.N. and Workman, J. (2011). Assessment of calving progress and reference times for obstetric intervention during dystocia in Holstein dairy cows. J. Dairy Sci. 94:5494–5501.
  18. Szreder, T., Oprzadek, J., Zelazowska, B., Dymnicki, E. and Zwierzchowski, L. (2011). Polymorphism A/C in exon 7 of the bovine estrogen receptor alpha (ER alpha) gene and its association with functional and milk production traits in Red-and-White cattle. Anim. Sci. Pap. Rep. 29:281–291.
  19. Uematsu, M., Sasaki, Y., Kitahara, G., Sameshima, H. and Osawa, T. (2013). Risk factors for stillbirth and dystocia in Japanese Black cattle. Vet. J. 198:212–216.
  20. Uzamy, C., Kaya, I. and Ayyilmaz, T. (2010). Analysis of risk factors for dystocia in a Turkish Holstein herd. J. Anim. Vet. Adv. 9:2571–2577.
  21. Witten, I. H. and Frank, E. (2005). Data Mining. Practical Machine Learning Tools and Techniques. 2nd Ed. Elsevier, San Francisco, California (USA). pp. 321-323.
  22. Yang, X. Z., Lacroix, R. and Wade, K.M. (1999). Neural detection of mastitis from dairy herd improvement records. Trans. ASAE 42:1063–1071.
  23. Yildiz, H., Saat, N. and Simsek, H. (2011). An investigation on body condition score, body weight, calf weight and hematological profile in crossbred dairy cows suffering from dystocia. Pak. Vet. J. 31:125–128.
  24. Zaborski, D. and Grzesiak, W. (2011a). Detection of difficult calvings in dairy cows using neural classifier. Arch. Tierz. 54:477–489.
  25. Zaborski, D. and Grzesiak, W. (2011b). Detection of heifers with dystocia using artificial neural networks with regard to ERá-BglI, ERá-SnaBI and CYP19-PvuII genotypes. Acta Sci. Pol. Zootech. 10:105–116.
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Indian Journal of Animal Research
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    volume 48 issue 5 (october 2014) : 452-458,   Doi: 10.5958/0976-0555.2014.00010.7

    DETECTION OF DIFFICULT CALVINGS IN DAIRY COWS USING BOOSTED CLASSIFICATION TREES

    D
    D. Zaborski1*
    W
    W. Grzesiak1
    K
    K. Kotarska1
    I
    I. Szatkowska
    M
    M. Jedrzejczak
    1Laboratory of Molecular Cytogenetics, Department of Ruminants Science, West Pomeranian University of Technology, Szczecin – 71 460, Poland
    Cite article:- Zaborski1* D., Grzesiak1 W., Kotarska1 K., Szatkowska I., Jedrzejczak M. (2025). DETECTION OF DIFFICULT CALVINGS IN DAIRY COWS USING BOOSTED CLASSIFICATION TREES. Indian Journal of Animal Research. 48(5): 452-458. doi: 10.5958/0976-0555.2014.00010.7.

    ABSTRACT

    The aim of this study was to apply boosted classification trees to dystocia detection in dairy cattle and to indicate its most important predictors. A total of 1742 calving records were used. Proportions of correctly detected difficult and easy calvings in a test set were 75.0%, 92.0% and 75.0%, 77.3%, for heifers and multiparous cows, respectively. The key predictors of calving difficulty were: pregnancy length, body condition score index, calving age, proportion of Holstein-Friesian genes (for heifers) as well as calving and calving-to-conception intervals, mean daily milk yield, fat and protein content in milk and 4%-fat corrected milk yield (for cows). The data mining method used in this study allowed us to obtain the models of good quality, confirmed by their relatively high effectiveness of detecting dystotic animals.

    REFERENCES

    1. Arthur, P.F., Archer, J.A. and Melville, G.J. (1999). Factors influencing dystocia and prediction of dystocia in Angus heifers selected for yearling growth rate. Crop Pasture Sci. 51:147–154.
    2. Atashi, H., Abdolmohammadi, A., Dadpasand, M. and Asaadi, A. (2012). Prevalence, risk factors and consequent effect of dystocia in Holstein dairy cows in Iran. Asian-Australas. J. Anim. Sci. 25:447–451.
    3. Barrier, A.C.M. (2012). Effects of a difficult calving on the subsequent health and welfare of the dairy cows and calves. Ph.D. thesis submitted to the University of Edinburgh, Edinburgh (UK).
    4. Basarab, J.A., Rutter, L.M. and Day, P.A. (1993). The efficacy of predicting dystocia in yearling beef heifers: II. Using discriminant analysis. J. Anim. Sci. 71:1372–1380.
    5. Enevoldsen, C. and Sørensen, J.T. (1992). Effects of dry period length on clinical mastitis and other major clinical health disorders. J. Dairy Sci. 75:1007–1014.
    6. Harañczyk, G. (2010). The ROC Curves - Evaluation of the Classifier Quality and Searching for the Optimum Cut-off Point. StatSoft, Kraków (Poland). pp. 79-89.
    7. Hill, T. and Lewicki, P. (2006). Statistics: Methods and Applications. StatSoft Inc., Tulsa, Oklahoma (USA).
    8. Ingvartsen, K.L., Dewhurst, R.J. and Friggens, N.C. (2003). 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. 83:277–308.
    9. Kolkman, I. (2010). Calving problems and calving ability in the phenotypically double muscled Belgian Blue breed. Ph.D. thesis submitted to Ghent Univeristy, Merelbeke (Belgium)
    10. Mee, J.F. (2008). Prevalence and risk factors for dystocia in dairy cattle: A review. Vet. J. 176:93–101.
    11. Mee, J.F., Berry, D.P. and Cromie, A.R. (2011). Risk factors for calving assistance and dystocia in pasture-based Holstein–Friesian heifers and cows in Ireland. Vet. J. 187:189–194.
    12. Meijering, A. (1984). Dystocia and stillbirth in cattle—A review of causes, relations and implications. Livest. Prod. Sci. 11:143–177.
    13. Micke, G. C., Sullivan, T.M., Rolls, P.J., Hasell, B., Greer, R.M., Norman, S.T. and Perry, V.E.A. (2010). Dystocia in 3- year-old beef heifers: Relationship to maternal nutrient intake during early-and mid-gestation, pelvic area and hormonal indicators of placental function. Anim. Reprod. Sci. 118:163–170.
    14. Morrison, D.G., Humes, P.E., Keith, N.K. and Godke, R.A. (1985a). Discriminant analysis for predicting dystocia in beef cattle. I. Comparison with regression analysis. J. Anim. Sci. 60:608–616.
    15. Morrison, D.G., Humes, P.E., Keith, N.K. and Godke, R.A. (1985b). Discriminant analysis for predicting dystocia in beef cattle. II. Derivation and validation of a prebreeding prediction model. J. Anim. Sci. 60: 617.
    16. Piwczyñski, D., Nogalski, Z. and Sitkowska, B. (2013). Statistical modeling of calving ease and stillbirths in dairy cattle using the classification tree technique. Livest. Sci. 154:19-27.
    17. Pogorzelska, P. and Nogalski, Z. (2010). Calving difficulty in cows and heifers of the Polish dairy cattle population in 2007-2008. Sci. Ann. Pol. Soc. Anim. Prod. 6:103-110.Schuenemann, G.M., Nieto, I., Bas, S., Galvão, K.N. and Workman, J. (2011). Assessment of calving progress and reference times for obstetric intervention during dystocia in Holstein dairy cows. J. Dairy Sci. 94:5494–5501.
    18. Szreder, T., Oprzadek, J., Zelazowska, B., Dymnicki, E. and Zwierzchowski, L. (2011). Polymorphism A/C in exon 7 of the bovine estrogen receptor alpha (ER alpha) gene and its association with functional and milk production traits in Red-and-White cattle. Anim. Sci. Pap. Rep. 29:281–291.
    19. Uematsu, M., Sasaki, Y., Kitahara, G., Sameshima, H. and Osawa, T. (2013). Risk factors for stillbirth and dystocia in Japanese Black cattle. Vet. J. 198:212–216.
    20. Uzamy, C., Kaya, I. and Ayyilmaz, T. (2010). Analysis of risk factors for dystocia in a Turkish Holstein herd. J. Anim. Vet. Adv. 9:2571–2577.
    21. Witten, I. H. and Frank, E. (2005). Data Mining. Practical Machine Learning Tools and Techniques. 2nd Ed. Elsevier, San Francisco, California (USA). pp. 321-323.
    22. Yang, X. Z., Lacroix, R. and Wade, K.M. (1999). Neural detection of mastitis from dairy herd improvement records. Trans. ASAE 42:1063–1071.
    23. Yildiz, H., Saat, N. and Simsek, H. (2011). An investigation on body condition score, body weight, calf weight and hematological profile in crossbred dairy cows suffering from dystocia. Pak. Vet. J. 31:125–128.
    24. Zaborski, D. and Grzesiak, W. (2011a). Detection of difficult calvings in dairy cows using neural classifier. Arch. Tierz. 54:477–489.
    25. Zaborski, D. and Grzesiak, W. (2011b). Detection of heifers with dystocia using artificial neural networks with regard to ERá-BglI, ERá-SnaBI and CYP19-PvuII genotypes. Acta Sci. Pol. Zootech. 10:105–116.
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