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An Efficient Single&mdash;Person Technique for Milk Sampling from Laboratory Mice
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Fitting milk production curves through nonlinear mixed models.

Monica Piccardi1, Raúl Macchiavelli2, Ariel Capitaine Funes3

  • 1Cátedra de Estadística y Biometría de la Facultad de Ciencias Agropecuarias de la Universidad Nacional de Córdoba.

The Journal of Dairy Research
|March 29, 2017
PubMed
Summary
This summary is machine-generated.

Comparing lactation curve models, the Wood model effectively fits expected milk production. Including cow random effects improved model prediction but not key production indicators like peak yield.

Keywords:
comparison criteriaestimationlactation curvesrandom effect

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Area of Science:

  • Dairy Science
  • Animal Husbandry
  • Statistical Modeling

Background:

  • Understanding lactation curve dynamics is crucial for dairy farm management and individual cow performance assessment.
  • Accurate modeling aids in optimizing decisions related to feeding, breeding, and herd health.
  • Lactation curves provide insights into milk production trends at both population and individual levels.

Purpose of the Study:

  • To fit and compare three non-linear models (Wood, Milkbot, diphasic) for dairy cow lactation curves.
  • To evaluate the impact of including a cow random effect on model performance and parameter estimation.
  • To assess the practical utility and predictive potential of different lactation curve models.

Main Methods:

  • Utilized PROC NLMIXED in SAS for fitting non-linear models to 2167 complete lactations (first and third lactations).
  • Compared models with and without the inclusion of a cow random effect.
  • Evaluated model performance using information criteria and assessed effects on production indicators.

Main Results:

  • The diphasic model was found to be computationally complex and impractical.
  • While information criteria favored the Milkbot model, the Wood model provided a good fit for expected lactation curves with no significant difference in production indicator estimation.
  • All models demonstrated improved fit and predictive potential when the cow random effect was incorporated.

Conclusions:

  • The Wood model is a practical and effective choice for modeling the expected value of lactation curves in dairy cows.
  • Incorporating a cow random effect enhances the predictive accuracy of lactation curve models, particularly concerning milk production magnitude.
  • Despite improved predictive power, the cow random effect did not significantly alter key production indicators derived from the fitted curves.