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Modelling growth in lowland sheep using nonlinear functions.

Numan Sharif1,2, Fiona M McGovern2, Noirin McHugh3

  • 1School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, D04 V1W8, Ireland.

Translational Animal Science
|May 8, 2025
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Summary
This summary is machine-generated.

Growth modelling in sheep using various functions revealed that Gompertz and von Bertalanffy models best describe body weight profiles. These models offer good fit, convergence, and biologically sensible parameters for genetic evaluation.

Keywords:
correlationgrowth curvesgrowth functionsmature weightnonlinear regression

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Area of Science:

  • Animal Science
  • Quantitative Genetics
  • Mathematical Modelling

Background:

  • Accurate sheep growth modelling is crucial for understanding body weight changes and for genetic evaluation programs.
  • Serial body weight measures provide valuable data for deriving biologically significant parameters.

Purpose of the Study:

  • To evaluate the performance of six different mathematical functions (Brody, Gompertz, Logistic, negative exponential, Richards, and von Bertalanffy) for modelling lowland sheep growth.
  • To explore the relationships between model parameters within and across these functions.

Main Methods:

  • Fitted six growth functions to 158,463 body weight records from 13,090 female lowland sheep.
  • Evaluated model fit using coefficient of determination (R²), root mean square error (RMSE), and convergence rates.
  • Analyzed correlations between estimated growth parameters (A, B, K) across functions.

Main Results:

  • Gompertz and von Bertalanffy functions demonstrated excellent fit and convergence (100%), alongside biologically sensible parameters.
  • The Richards function showed the highest R² (0.98) but had lower convergence (82.39%).
  • Correlations between asymptotic weight (A) and maturity rate (K) parameters were generally negative across functions.

Conclusions:

  • Gompertz and von Bertalanffy functions are recommended for modelling Irish female lowland sheep growth profiles.
  • These models provide a robust framework for genetic parameter estimation in sheep breeding programs.
  • Model selection should consider fit, convergence, and biological interpretability of parameters.