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Modelling the growth of rearing cattle

Author

Listed:
  • Hanna Unterauer

    (Department of Integrative Biology and Biodiversity Research (DIBB), University of Natural Resources and Life Sciences (BOKU), Vienna, Austria)

  • Norbert Brunner

    (Department of Integrative Biology and Biodiversity Research (DIBB), University of Natural Resources and Life Sciences (BOKU), Vienna, Austria)

  • Manfred Kühleitner

Abstract

Scientific growth literature often uses the models of Brody, Gompertz, Verhulst, and von Bertalanffy. The versatile five-parameter Bertalanffy-Pütter (BP) model generalizes them. Using the least-squares method, we fitted the BP model to mass-at-age data of 161 calves, cows, bulls, and oxen of cattle breeds that are common in Austria and Southern Germany. We used three measures to assess the goodness of fit: R-squared, normalized root-mean squared error, and the Akaike information criterion together with a correction for sample size. Although the BP model improved the fit of the linear growth model considerably in terms of R-squared, the better fit did not, in general, justify the use of its additional parameters, because most of the data had a non-sigmoidal character. In terms of the Akaike criterion, we could identify only a small core of data (15%) where sigmoidal models were indispensable.

Suggested Citation

  • Hanna Unterauer & Norbert Brunner & Manfred Kühleitner, 2021. "Modelling the growth of rearing cattle," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 66(11), pages 441-449.
  • Handle: RePEc:caa:jnlcjs:v:66:y:2021:i:11:id:98-2021-cjas
    DOI: 10.17221/98/2021-CJAS
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    References listed on IDEAS

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    1. Tjørve, Kathleen M.C. & Tjørve, Even, 2017. "A proposed family of Unified models for sigmoidal growth," Ecological Modelling, Elsevier, vol. 359(C), pages 117-127.
    2. Norbert Brunner & Manfred Kühleitner & Katharina Renner-Martin, 2021. "Bertalanffy-Pütter models for avian growth," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-18, April.
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