Predicting the Digestive Tract Development and Growth Performance of Goat Kids Using Sigmoidal Models

Abstract

The transition from monogastric to rumination stage is crucial in ruminants’ growth to avoid stressors—weaning and neonatal mortalities. Poor growth of the digestive tract could adversely affect the performance of the animal. Modeling informative growth curves is of great importance for a better understanding of the effective development pattern, in order to optimize feeding management system, and to achieve more production efficiency. However, little is known about the digestive tract growth curves. For this reason, one big goat farm of Laiwu black breed was chosen as a basis of this study. Forty-eight kids belonging to eight-time points (1, 7, 14, 28, 42, 56, 70, and 84 d; 6 kids for each) were selected and slaughtered. The body weight, body size indices, rumen pH, and stomach parts were determined and fitted to the polynomial and sigmoidal models. In terms of goodness of fit criteria, the Gompertz model was the best model for body weight, body oblique length, tube, and rumen weight. Moreover, the Logistic model was the best model for carcass weight, body height, and chest circumference. In addition, the Quadratic model showed the best fit for dressing percentage, omasum weight, abomasum weight, and rumen volume. Moreover, the cubic model best fitted the ruminal pH and reticulum percentage. The Weibull model was the best model for the reticulum weight and omasum percentage, while the MMF model was the best model describing the growth of chest depth, rumen percentage, and abomasum percentage. The model parameters, R squared, inflection points, area under curve varied among the different dependent variables. The Pearson correlation showed that the digestive tract development was more correlated with age than body weight, but the other variables were more correlated with body weight than age. The study demonstrated the use of empirical sigmoidal and polynomial models to predict growth rates of the digestive tract at relevant age efficiently.

A systematic review of estimation of growth curve in goats

“…The results indicated that out of 25 articles Brazil (Pires et al 2017 ; Arré et al 2019 ; Araújo et al 2020 ; de Sousa et al 2021 ) and India (Das et al 2016 ; Paul et al 2016 ; Magotra et al 2021 ; Waiz et al 2019 ) had the high number of articles ( n = 4). The findings also showed that Germany (Waheed et al 2011 ), Vietnam (Trach and Phiovankham 2011 ), Bangladesh (Khan and Khatun 2013 ), Pakistan (Celik et al 2018 ), China (Abdelsattar et al 2021 ), Egypt (Rashad et al 2022 ), and South Africa (Tyasi et al 2022 ) had the least number of articles ( n = 1).

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“…Gompertz model is a type of non-linear mathematical model for a time series which was named after Benjamin Gompertz (1779–1865) that is a sigmoid function which describes growth as being slowest at the start and end of a given period and it is commonly used in animals and plants (Ghiasi et al 2018 ). This study revealed that the 40% of the articles recommended Gompertz model as the most suitable mathematical model for growth patterns of goats were originated in tropical environmental countries such as Vietnam (Trach and Phiovankham 2011 ), Bangladesh (Khan and Khatun 2013 ), Indonesia (Wiradarya et al 2020 ) and China (Abdelsattar et al 2021 ). Another 40% of articles were from mediterranean environmental countries such as Tunisia (Gaddour and Najari 2008 ; Najari et al 2010 ), Turkey (Özdemir and Dellal 2009 ; Cak et al 2017 ).…”

Growth curve analysis in different generations of Boer x Central Highland goats using alternative estimation models

“…However, Raji et al [11] compared five different growth models (Brody, Gompertz, Monomolecular, Richards, and Weibull) and found that Gompertz and Monomolecular functions were best for estimating the growth curve in Nigerian goats. On the other hand, Wiradarya et al [13] and Abdelsattar et al [14] reported that Gompertz growth curve was the bestfitted model for body weight in Kacang and Laiwu black goats, respectively. The variation of…”

“…Modeling the growth curve was attempted for a few goat breeds or populations using nonlinear growth models such as Gompertz, Logistic, Brody, Von Bertalanffy, Monomolecular, Negative exponential, and Richards [4,7,[10][11][12][13][14][15][16]. Nonetheless, breed, flock size, clusters, management level, physical environment, and selection attempts influence growth curves [17].…”