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

Mitsunori Kayano¹, Tomoko Kataoka

¹Department of Animal and Food Hygiene, Obihiro University of Agriculture and Veterinary Medicine, Inada-Cho, Obihiro, Hokkaido 080-8555, Japan.

The Journal of Veterinary Medical Science

|June 16, 2015

Summary

This summary is machine-generated.

This study developed new diagnostic rules for ketosis in dairy cows using milk yield and composition. These models offer improved accuracy compared to traditional methods for identifying ketosis in both multiparous and primiparous cows.

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Area of Science:

Veterinary Medicine
Animal Science
Dairy Production

Background:

Ketosis is a metabolic disorder in dairy cows impacting milk production and health.
Accurate and early diagnosis of ketosis is crucial for effective management.
Existing diagnostic methods may have limitations in sensitivity and specificity.

Purpose of the Study:

To develop and validate novel diagnostic rules for ketosis in dairy cows.
To utilize milk yield and composition parameters for simultaneous ketosis diagnosis.
To compare the efficacy of new rules against traditional indicators like the protein-to-fat ratio.

Main Methods:

Multiple logistic regression analysis was performed on milk yield and composition data.
Data from 632 healthy and 61 ketotic cows were analyzed separately for multiparous and primiparous animals.
Diagnostic rules were formulated based on significant predictors identified for each parity group.

Main Results:

For multiparous cows, milk yield and protein-to-fat (P/F) ratio were significant predictors of ketosis (P<0.05).
For primiparous cows, lactose, solid not fat (SNF), and milk urea nitrogen (MUN) were significant predictors (P<0.01).
Developed diagnostic rules demonstrated high sensitivity (0.800-0.813) and specificity (0.729-0.730), outperforming the P/F ratio alone.

Conclusions:

Novel diagnostic rules integrating milk yield and composition parameters effectively diagnose ketosis in dairy cows.
The developed models offer superior diagnostic performance compared to the P/F ratio, especially when considering parity.
These findings provide valuable tools for improved ketosis management in dairy herds.