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Updated: May 12, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Accounting for variability among individual pigs in deterministic growth models.
B Vautier1, N Quiniou, J van Milgen
1INRA, UMR1348 Pegase, F-35590 Saint-Gilles, France.
Animal : an International Journal of Animal Bioscience
|April 5, 2013
Summary
Realistic pig growth models require understanding parameter covariances. This study analyzed feed intake and growth data to develop a median covariance matrix, improving virtual population generation for better performance variability representation.
Area of Science:
- Animal Science
- Quantitative Genetics
- Nutritional Modeling
Background:
- Deterministic nutritional models often simplify parameter variation.
- Realistic simulation requires understanding covariance structures among growth and feed intake parameters.
- Previous models lacked accurate representation of individual pig parameter relationships.
Purpose of the Study:
- To analyze the mean and covariance structure of growth and feed intake parameters in pigs.
- To develop a realistic covariance matrix for pig growth modeling.
- To improve the generation of virtual pig populations reflecting performance variability.
Main Methods:
- Analyzed feed intake and body weight data from 1288 crossbred pigs.
- Characterized individual pigs using five key growth and feed intake parameters.
- Computed and compared covariance matrices, evaluating similarity using Flury hierarchy.
- Developed a median covariance matrix accounting for subpopulation sizes.
Main Results:
- Sex and batch significantly affected most parameters; crossbreed influenced PDm.
- Observed significant interactions between sex and crossbreed for PDm and DFI₁₀₀.
- Identified unique covariance structures for different subpopulations (batch, sex, crossbreed combinations).
- The median covariance matrix provided the most accurate estimation of observed covariance.
Conclusions:
- A median covariance matrix accurately represents pig growth parameter variability.
- This matrix enables the generation of virtual pig populations with realistic performance distributions.
- Improved modeling enhances the understanding of pig growth dynamics and nutritional requirements.
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