Screening for ketosis using multiple logistic regression based on milk yield and composition

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著者

    • KAYANO Mitsunori KAYANO Mitsunori
    • Department of Animal and Food Hygiene, Obihiro University of Agriculture and Veterinary Medicine, Inada-Cho, Obihiro, Hokkaido 080–8555, Japan
    • KATAOKA Tomoko KATAOKA Tomoko
    • Faculty of Animal Husbandry, Obihiro University of Agriculture and Veterinary Medicine, Inada-Cho, Obihiro, Hokkaido 080–8555, Japan

抄録

Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (<i>P</i><0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/d<i>l</i>) were significantly associated with ketosis (<i>P</i><0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF − 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively.

収録刊行物

  • The Journal of Veterinary Medical Science

    The Journal of Veterinary Medical Science 77(11), 1473-1478, 2015

    公益社団法人 日本獣医学会

各種コード

  • NII論文ID(NAID)
    130005112094
  • NII書誌ID(NCID)
    AA10796138
  • 本文言語コード
    ENG
  • ISSN
    0916-7250
  • NDL 記事登録ID
    026964844
  • NDL 請求記号
    Z18-350
  • データ提供元
    NDL  J-STAGE 
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