Ihab Mostafa Shaat, Alaa Alhamadani , Khafan Al-Shargi, Rashid Al-Habsi , Talal Al-Sedeiri , Asila Al Naabi, Asko Maki-Tanila

Statistical modelling of growth curve for longitudinal data on a feeding trial in goat breeds

Introduction

Statistical modelling of growth curve for longitudinal data on a feeding trial in goat breeds . Statistical modelling of goat growth curves from longitudinal feeding trial data. Examines concentrate supplement effects on Sahrawi and Jabbali breeds using mixed models in R.

Abstract

The growth curve parameters were estimated extending a linear regression to higher degree polynomial with attempts to use also the biologically appealing Gombertz function. The data contained monthly weight records from weaning to the age of 14 months of kids in two Omani goat breeds Sahrawi and Jabbali used in a feeding trial assessing the effect of concentrate supplement (14% crude protein). The growth curve parameters were estimated within the fixed effects breed and feed concentrate level (2 or 3% of live body weight) with an extension to mixed models using animals as random effects. The parameters and model contrasting were performed with ML or REML as appropriate with relative comparison relying on AIC and significance testing of estimated parameters. The mixed model analyses were performed with publicly available R software package programs. The fixed effect cubic regression with linear random effect model fitted into the data. The higher level of concentrates improved the immediate post-weaning growth in the same way in the breeds while the subsequent growth curve differed with more pronounced weight increments in the Jabbali breed.

Review

This manuscript presents a thorough statistical analysis of growth curves in two Omani goat breeds subjected to a feeding trial, utilizing longitudinal monthly weight data. The authors adopt a robust methodological approach, exploring both linear regression extended to higher degree polynomials and the biologically significant Gompertz function. The decision to incorporate both fixed effects (breed and concentrate level) and mixed models, accounting for individual animal variability as random effects, is a strong point, reflecting a sophisticated understanding of longitudinal data analysis in animal science. Methodologically, the study effectively employed standard techniques such as ML or REML for parameter estimation and AIC for model selection, ensuring appropriate statistical rigor. The finding that a fixed effect cubic regression with a linear random effect model best fitted the data indicates a careful evaluation of model complexity and goodness-of-fit. The key results are impactful: a higher level of concentrate universally improved immediate post-weaning growth, while subsequent growth patterns diverged, with the Jabbali breed demonstrating superior weight increments over time. This highlights crucial breed-specific responses to nutritional interventions. Overall, this paper appears to be a well-executed study that provides valuable insights into the growth dynamics of Omani goat breeds under varying nutritional conditions. For the complete manuscript, it would be beneficial to further elaborate on the specific reasons for selecting the cubic regression over the Gompertz function, perhaps with a comparative discussion of their biological interpretations and statistical fit. Additionally, a detailed discussion of the practical implications of the observed breed-by-nutrition interaction for sustainable goat production and breeding strategies in the region would enhance the paper's overall contribution and impact.

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