Nonlinear mixed models to describe the growth of Columbia sheep in Mexico

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ID: 321386
2026
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Abstract
Abstract Our objective in this study was to identify the nonlinear mixed model that best describes the growth curve of Columbia sheep within the central region of Mexico. The study was conducted using a total of 1,886 live body weight records (birth to 450 days of age) from 174 Columbia Sheep (90 males and 84 females). Only animals with at least four weight records were considered in the study. The sheep were fed twice daily with a ration of corn silage and oat hay. Animals were weaned at an average age of 75 days. Growth curves were fitted using four nonlinear mixed models: Brody, Logistic, Von Bertalanffy, and Gompertz. In all models, the asymptotic body weight (A), integration constant (b), and maturity rate (k) were estimated, with A and k modeled as both fixed and random effects. Sex was included as a fixed effect affecting only the A and k parameters. A model comparison was performed using the Akaike Information Criterion (AIC), which states that lower AIC indicates better fit. Among the evaluated models, the Von Bertalanffy model provided the best fit for the data. Estimated parameters for females were A = 40.26 kg, b = 0.516, and k = 0.011 day−1, while the sex effects for males were +9.55 kg for A and −0.001 for k. These estimates indicate that males reach higher mature body weights while maturing slightly more slowly than females. Including random effects for A and k improved model accuracy by reducing residual variance. These results provide useful information on growth characterization may help define age or weight targets for further evaluation of slaughter strategies in Columbia sheep.
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openalex_W7169610215 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ricardo Martínez-Rocha, Rodolfo Ramírez-Valverde, Rafael Núñez-Domínguez, Joel Domínguez-Viveros, Víctor Díaz Sánchez, Jonathan Valerio-Hernandez, Katherine Scheflen, Jorge Hidalgo
Journal Translational animal science
Year 2026
DOI
10.1093/tas/txag100
URL
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