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Production evaluation techniques based on lactation curves

C P Van Tassell1, L R Jones, S W Eicker

  • 1Cornell University, Ithaca, NY 14853, USA.

Journal of Dairy Science
|February 1, 1995
PubMed
Summary
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Monte Carlo simulation generated dairy cow milk yield data. Analyzing yield deviations and transformed data provided the most uniform probability distributions for evaluating lactation effects.

Area of Science:

  • Animal Science
  • Dairy Cattle Genetics
  • Statistical Modeling

Background:

  • Accurate estimation of dairy cow performance is crucial for genetic selection and herd management.
  • Test day milk yield data are influenced by various factors including lactation stage and seasonality.
  • Evaluating the statistical properties of different analytical approaches is essential for reliable data interpretation.

Purpose of the Study:

  • To assess the uniformity of probability distributions generated from simulated test day milk yield data.
  • To compare the effectiveness of different statistical measures in detecting simulated lactation effects.
  • To evaluate the impact of cow effects on the analysis of test day yield data.

Main Methods:

  • Monte Carlo simulation generated milk yield data for 1000 herds (30 cows/herd, 2 years).

Related Experiment Videos

  • Simulated scenarios included increasing test day and seasonality effects.
  • Calculated statistics included yield deviations, transformed deviations, 305-day mature equivalent estimates, and changes from previous test days.
  • ANOVA models were used to test for month of lactation effects, with and without cow effects.
  • Chi-square tests assessed the uniformity of generated probability values.

    • Main Results:

    • Probability values associated with the change in test day deviations showed the most uniform distribution.
    • Results remained similar when cow effects were removed from the analysis model.
    • Transformed variables also yielded fairly uniform probability distributions, but were more difficult to interpret.
    • Tests based on 305-day mature equivalent records were found to be oversensitive.

    Conclusions:

    • Analyzing changes in test day yield deviations offers a robust method for evaluating lactation effects in dairy cattle.
    • Statistical transformations can aid in data analysis but require careful interpretation.
    • The inclusion of cow effects in ANOVA models did not significantly alter the uniformity of probability distributions for key variables.
    • Oversensitivity of mature equivalent tests suggests caution in their application for detecting subtle lactation effects.