Determination of Factors Affecting Mastitis in Holstein Friesian and Brown Swiss by Using Logistic Regression Analysis

The aim of this study was to determine subclinical mastitis with the help of logistic regression of milk quality determined factors and some features the research material consisted of 204 (145 Holstein, 59 Brown Swiss) dairy cattle raised in a private cattle farm in Konya Province, Turkey. The independent variables considered for the detection of subclinical mastitis are breed, somatic cell number (SCC), color values (L, a, b, H, C), freezing point (FP), pH, elec- trical conductivity (EC), milking day (MD), lactation order (LO). The depend- ent variable of logistic regression was CMT score. According to the results of the study, the spescifity was 95.7% and the sensitivity was 57.6%. In general, the predicted value of the accuracy of all data was 83.3%.

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Akin M, Hand C, Eyduran E, Reed BM (2018). Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees. Pl. Cell Tissu. OrganCult.,132:545-559.

https://doi.org/10.1007/s11240-017-1353-x

Alpar R (2011). Applied Multivariate Statistical Methods, 3rd Edition, Detay Publishing, Ankara.

Aytekin İ, Boztepe S (2014). Somatic Cell Count, Importance and Effect Factors in Dairy Cattle. Turkish Journal of Agriculture-Food Science and Technology, 2(3), 112-121.

Bircan H (2004). Logistic regression analysis: An application on medical data. Kocaeli University Journal of Social Sciences, (8), 185-208.

Boztepe S, Aytekin İ, Zulkadir U (2015). Dairy Cattle, 1st Edition, Selcuk University Publishing, Konya.

Duval J (1969). Treating mastitis without antibiotics,” EAP Publication 69, 1969.

Eyduran E, Ozdemir T, Çak B, Alarslan E (2005). Using of logistic regression in Animal Science. Applied Sci, 5(10), 1753-1756.

Eyduran E (2008). Usage of penalized maximum likelihood estimation method in medical research: An alternative to maximum likelihood estimation method. J. Res. Med. Sci, 13(6), 325-330.

Korkmaz M, Güney S, Yiğiter Ş (2012). The importance of logistic regression implementations in the Turkish livestock sector and logistic regression implementations/fields. Harran Journal of Agriculture and Food Sciences, 16(2), 25-36.

Menard S (2002). Applied logistic regression analysis (Vol. 106). Sage.

Mammadova N, Keskin İ (2013). Application of the support vector machine to predict subclinical mastitis in dairy cattle. The Scientific World Journal.

Sanford CJ, Keefe GP, Sanchez J, Dingwell RT, Barkema HW, Leslie KE, Dohoo IR (2006). Test characteristics from latent-class models of the California Mastitis Test. Preventive Veterinary Medicine 77, 96–108.

Schalm O, Noorlander D (1957). Experiments and observations leading to the development of Califor-nia mastitis test. Journal of American Veterinary Medical Association 130, 199–204.

Şahin A, Yıldırım A (2014). The Mastitis Case in Wa-ter Buffalo. Turkish Journal Of Agriculture- Food Science And Technology, 3(1), 1-8.

Tatlıdil H (1996). Uygulamalı Çok Değişkenli İstatiksel Analiz. Ankara, Cem Web Ofset.

Tekeli T (2005). Mastitis: Quality milk production and somatic cell count in the process of the European Union. Guzelis Pub. Co., Konya.
Selcuk Journal of Agriculture and Food Sciences-Cover
  • ISSN: 2458-8377
  • Yayın Aralığı: Yılda 3 Sayı
  • Başlangıç: 2002