Random regression models in the evaluation of the growth curve of Simbrasil beef cattle

Mota, Rodrigo Reis; Marques, Luiz Fernando Aarão; Lopes, Paulo Sávio; Silva, L. P. da; Hidalgo, A.M.; Leite, Carla Daniela Suguimoto; Torres, R.A.

doi:10.4238/2013.february.27.2

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…The growth of Bos indicus (Riley et al, 2007;Boligon et al, 2012;Lopes et al, 2012;Scalez et al, 2014;Bertipaglia et al, 2015), Bos taurus (Meyer, 2005a,b;Mota et al, 2013b), and crossbred beef cattle (Sánchez et al, 2008;Baldi et al, 2010;Mota et al, 2013a;Scalez et al, 2014) has been modeled by random regression models. Legendre polynomials are the typical choice for most researchers for fitting mean growth trajectories and also additive and permanent environmental effects.…”

Section: Introductionmentioning

confidence: 99%

Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests

et al. 2018

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The objective of this study was to estimate genetic parameters for body weight of beef cattle in performance tests. Different random regression models with quadratic B-splines and heterogeneous residual variance were fitted to estimate covariance functions for body weights of Nellore and crossbred Charolais × Nellore bulls. The criteria −2 residual log-likelihood (−2RLL), Akaike Information Criterion (AIC), and consistent AIC (CAIC) were used to choose the most appropriate model. For Nellore bulls, residual variance was modeled with six classes of age, and direct additive genetic and permanent environment effects were modeled with quadratic B-splines with two and one intervals, respectively. For crossbred bulls, quadratic B-splines with one interval fitted direct additive genetic and permanent environment effects and nine classes of age were needed to fit residual variance. Pooling classes of age with up to 40% in difference of residual variances does not compromise the fit of the model. Heritability for body weight in performance tests are moderate (>0.25, for crossbred bulls) to high (>0.5, for Nellore bulls) and genetic correlation between weights over the test are also high (>0.65). Then, selection of young bulls in performance test is an efficient tool to increase body weight in beef cattle.

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Section: Introductionmentioning

confidence: 99%

Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests

et al. 2018

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“…In this case, the hierarchical model shared many features with the growth data random regression model, where fixed effects of regression were fitted to show the overall growth curve. Random effects were used to show deviation of each individual from the overall growth curve (5,12,13). The accuracy of predicting the growth curve of Iranian Lori-Bakhtiari sheep was studied using nonlinear growth functions (von Bertalanffy, Gompertz, logistic, and Brody) to accompany hierarchical modeling.…”

Section: Introductionmentioning

confidence: 99%

Simple hierarchical and general nonlinear growth modeling in sheep

Ghaderi–Zefrehei¹,

Rafeie²,

Behzadi³

et al. 2018

Turk J Vet Anim Sci

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Differential equations and advanced statistical models have been used to predict growth phenomena. In the present study, general nonlinear growth functions such as von Bertalanffy, Gompertz, logistic, and Brody, along with hierarchical modeling were applied to investigate the phenotypic growth pattern of Iranian Lori-Bakhtiari sheep. Growth data from 1410 Lori-Bakhtiari lambs were used in the present study. The results showed that the Brody function outperformed the other three nonlinear growth functions. In addition, including hierarchical growth modeling results allowed the adoption of many random effect structures, suggesting that hierarchical growth modeling has a useful role in growth data modeling. This method provides an estimation of growth parameters based on individual animals, improving individual growth selection. The results suggest this approach for growth modeling. Combining the strength of individual growth modeling with general growth modeling, e.g., von Bertalanffy, Gompertz, logistic, and Brody would be deeply appealing in the future. In this regard, dealing with sheep growth phenomenon using pure mathematical models, i.e. grey system theory models that could be new powerful prediction tools for breeders and experts, has not been done yet. However, running the analysis on large datasets will require significantly higher computational power than is ordinarily available.

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“…According to FAO (2021), domestic cattle (this paper focuses on Bos taurus) are "the most common and widespread species of large ruminant livestock and are raised primarily to produce milk, meat and hides and to provide draft power". In comparison with other livestock, the reproduction rate is low, and calves need much maternal care, whence the rearing of cattle is costly and there is an interest in growth modelling to improve selection and increase the efficiency of production (Arango and Van Vleck 2002;Mota et al 2013;Goldberg and Ravagnolo 2015). Commonly used models, aside from simple linear or exponential growth, include Brody's model and the sigmoidal models (with S-shaped growth curves) of Gompertz, Richards, Verhulst, and von Bertalanffy (Nogales et al 2017).…”

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confidence: 99%

Modelling the growth of rearing cattle

Unterauer¹,

Brunner²,

Kühleitner³

2021

CZECH J ANIM SCI

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Scientific growth literature often uses the models of Brody, Gompertz, Verhulst, and von Bertalanffy. The versatile five-parameter Bertalanffy-Pütter (BP) model generalizes them. Using the least-squares method, we fitted the BP model to mass-at-age data of 161 calves, cows, bulls, and oxen of cattle breeds that are common in Austria and Southern Germany. We used three measures to assess the goodness of fit: R-squared, normalized root-mean squared error, and the Akaike information criterion together with a correction for sample size. Although the BP model improved the fit of the linear growth model considerably in terms of R-squared, the better fit did not, in general, justify the use of its additional parameters, because most of the data had a non-sigmoidal character. In terms of the Akaike criterion, we could identify only a small core of data (15%) where sigmoidal models were indispensable.

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Random regression models in the evaluation of the growth curve of Simbrasil beef cattle

Cited by 5 publications

References 17 publications

Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests

Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests

Simple hierarchical and general nonlinear growth modeling in sheep

Modelling the growth of rearing cattle

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