Comparing non-linear mathematical models to describe growth of different animals/Avaliacao comparativa de modelos matematicos nao lineares para descrever o crescimento animal.
Traditionally, mathematical models have been applied to describe growth-age relationship in animals. One important feature of these models is their ability to describe the weight gain and evaluate some interesting biological parameters, such as the mature weight, the rate of maturing and the rate of gain. These parameters are useful tools to provide estimates of the daily feed requirements or to evaluate the influence of the environmental conditions on the weight gain of the animal. Growth models are also used to predict the optimum slaughter age. Therefore, mathematical models applied for animal growth can be considered as being important control and optimization instruments for the animal production (France & Thornley, 1984; France, Dijkstra, & Dhanoa, 1996; Lopez et al., 2000; Vazquez, Lorenzo, Fucinos, & Franco, 2012).
An appropriate growth function should summarize the information provided by experimental observations into a small set of parameters with biological meaning. Usually, these models consist of nonlinear functions and several studies including different mathematical models can be found in the literature. These models are usually applied for the evaluation of the growth kinetics of a wide range of animals, including birds (Aggrey, 2002; Sezer & Tarhan, 2005; Nahashon, Aggrey, Adefope, Amenyenu, & Wright, 2006), mammals (Curi, Nunes, & Curi, 1985; Silva, Alencar, Freitas, Packer, & Mourao, 2011; Franco et al., 2011), fishes (Hernandez-Llamas & Ratkowsky, 2004; Santos, Mareco, & Silva, 2013), reptiles (Bardsley, Ackerman, Bukhari, Deeming, & Ferguson, 1995) and amphibians (Rodrigues et al., 2007; Mansano, Stefani, Pereira, & Macente, 2013). Some mathematical functions commonly used in these studies include the Gompertz, Logistic, Brody, von Bertalanffy and Richards growth models (France et al., 1996).
The growth functions can be grouped into three main categories: those with a diminishing returns behavior (Brody model), those with sigmoidal shape and a fixed inflection point (Gompertz, Logistic and von Bertalanffy models) and those with a flexible inflection point (Richards model). The Logistic, Gompertz and von Bertalanffy models exhibit inflection points at about 50, 37 and 30% of the mature weight (asymptote), respectively. On the other hand, the Brody model does not exhibit an inflection point. The Richards model summarizes all the above growth functions in one function with a variable inflection point specified by the shape parameter (m) (Richards, 1959).
In this context, the aim of this study was to evaluate the influence of the shape parameter on growth curves and the five above mentioned models using experimental data of different animals, including mammals and birds, in order to identify the best growth model for each animal studied. The performance of the different models was compared using different goodness of fit statistics.
Material and methods
Growth data recorded for fourteen different datasets, all of them reported in the literature, were used for evaluation of the models. The raw growth data were collected from published articles by means of the GetData Graph Digitizer 2.24 software, as used by Vazquez, Lorenzo, Fucinos, and Franco (2012). The datasets are representative of the gain of body weight of mammals and birds (Table 1), with mature weights ranging from < 0.25 kg (Japanese quail) to > 1,000 kg (Holstein-Friesian bull). As usually adopted in similar studies, growth curves were based on means of weights of many individuals in order to minimize large variations that may occur in individual growth (Lopez et al., 2000; Vazquez et al., 2012).
Five nonlinear functions frequently used for the description of growth curves in animal production studies were analyzed: Brody, von Bertalanffy, Logistic, Gompertz, and Richards. The mathematical expressions associated to these functions are detailed in Table 2. In all equations presented, W stands for the body weight of the animal at age t, [W.sub.[infinity]] stands for the mature weight (asymptote) and [W.sub.0] stands for the birth weight. The parameter k is a constant that is directly related to the postnatal rate of maturing and can be interpreted as a maturing index, establishing the rate at which W approaches [W.sub.[infinity]]. Finally, m is the shape parameter in Richards? model. It determines the proportion of the mature weight at which the inflection point occurs (Perroto, Cue, & Lee, 1992; Gbangboche, Glelekakai, Salifou, Albuquerque, & Leroy, 2008).
Richards? model is a generalization of all growth models presented in Table 2, i.e., for m = -1, m = -1/3 and m = 1, it reduces to re-parameterized versions of Brody, von Bertalanffy and Logistic equations, respectively. In addition, it can be shown that it reduces to Gompertz model when one calculates [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. The parameter m is the unique parameter which has no direct biological meaning. However, it exerts great influence on the time to an individual to reach the mature weight and on the point of inflection of the growth curve, as will be shown in the next section.
Influence of the shape parameter (m) on growth curves
As previously mentioned, Richards growth model encompasses all the other models, for special values of the parameter m. In order to illustrate this important feature, it is adopted a procedure to present a possible meaning to this parameter. After some algebra, it is possible to obtain a mathematical expression describing the influence of the parameter m on the relation of the weight at the inflection point ([W.sub.inf]) to the asymptotic mature weight ([W.sub.[infinity]]) in equation 6:
[W.sub.inf]/[W.sub.[infinity]] = 1/[(1 + m).sup.m] (6)
Figure 1 presents simulations obtained with the use of equation 6 for different values of the parameter m. In all simulations performed, the values of [W.sub.0] and [W.sub.[infinity]] adopted were kept constant and equal to 10 g, and 150 g, respectively, and the values of k adopted were -0.0142 [d.sup.-1] for Brody, -0.0445 [d.sup.-1] for von Bertalanffy, -0.0739 [d.sup.-1] for Gompertz and -0.2143 [d.sup.-1] for Logistic model. The parameter m is the main responsible for the different shapes of the curves.
The weight at the inflection point is of significance because it is associated with a change in the acceleration of growth: for values of the weight lower than [W.sub.inf], the acceleration of growth is positive and for values the weight higher than [W.sub.inf] the acceleration of growth is negative. Thus, depending on the value of m, the time to the individual reaches the mature weight is lower, as observed for the Brody growth model.
The fitting procedures presented in this study were performed with the ?fit function? of the Curve Fitting Tool available in the Matlab R2011a software (MathWorks, Natick, USA), using the nonlinear least squares method. The starting value of each parameter model was based on visual inspection of the plots.
Statistical criteria for model selection
The performance of each model was evaluated by the calculation of the root mean square error, RMSE (Equation 7), and by the coefficient of determination, [R.sup.2] (Equation 8). In these expressions, [W.sub.exp] stands for the experimental data, [W.sub.cal] stands for the result fitted by the model, N represents the total number of experimental points, and K corresponds to the number of parameters of the model.
RMSE = [square root of 1/N - K [N.summation over (n = 1)][[[W.sub.exp](n) - [W.sub.cal](n)].sup.2] (7)
[R.sup.2] = 1 - [N.summation over (n = 1)][[[W.sub.exp](n) - [W.sub.cal](n)].sup.2]/[N.summation over (n = 1)][[[W.sub.exp](n) - 1/N[N.summation over (n = 1)][W.sub.exp](n)].sup.2] (8)
In order to obtain a more complete evaluation of the performance of the models, two additional criteria based on the information theory were applied to compare the goodness of fit of the models (Burnham & Anderson, 2002): the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Equations 9 and 10 present the corresponding mathematical expressions wherein SSE is the sum of the squared errors.
AIC = N log (SE/N) + 2K (9)
BIC = N log (SE/N) + K log (N) (10)
For the case in that the sample size is smaller than the number of model parameters (N/K < 40), the AIC might not be accurate. Therefore the corrected AIC (AICc in Equation 11) was used in the present study (Burnham & Anderson, 2002):
AIC = AIC + 2K (K + 1)/N - K - 1 (11)
Results and discussion
Fit with the use of Richards growth model
The growth curves obtained from regression analyses using Richards? model are presented in Figure 2 for birds and in Figure 3 for mammals. Table 3 shows the values of the parameter m estimated from regression and the value of the proportion of the mature weight at which the inflection point occurs (Equation 6) for each growth curve studied.
As summarized in Table 3, all regression analysis of the Richards? model resulted in values of the parameter m lower or equal to one. This means that, for the animals studied, the inflection point at each growth curve is lower than 50% of the mature weight. In addition, it was observed that the value of m was lower than minus one only for two datasets (Nelore cattle and Angus cattle). This result seems to indicate that the Brody?s model is not feasible for most datasets studied.
It was also observed that the values of m were, in general, lower for females than for males (with the exception of Japanese quail--wild line and Karagouniko sheep). Therefore it can be assumed that, for most datasets studied, the males reached the mature weight before the females. In addition, according to Table 3, it can be seen that Karagouniko sheep and celta pigs reach the mature weight faster than the others.
Comparison between models
Tables 4, 5 and 6 summarize the goodness of fit statistics obtained for the five models studied. It was observed that for birds (Table 4), the Richards? and Logistic models provided the best fits. On the other hand, in what concerns mammals, different models provided best fits for different datasets. In this case, it was not possible to establish a model as being superior in relation to the others.
Table 7 summarizes comparison between pairs of models used in this study. Each entry of this table accounts for the number of times that the equation of the corresponding row provided better fit than the other equations. It can be seen that Richards? model exhibited a better performance than all the others.
As shown in Table 7, Logistic and Brody's model (Equations 3 and 1, respectively) exhibited the worst results in relation to the other models, since they provided a lower amount of better fits than the others. These poor results obtained for the Logistic and the Brody?s model can be ascribed to the symmetry of the inflection point and to the hyperbolic shape of the model, respectively. In order to illustrate this behavior, Figure 4 shows residuals obtained after fits of Logistic, Brody and Gompertz equations to Athens-Canadian chickens. As can be seen, the residuals are larger and not normally distributed for the Logistic and Brody models in relation to the Gompertz growth model.
In general, it was also observed that all models under investigation in this study exhibited high [R.sup.2] values (above 0.94), suggesting overall good fits to the data. According to [R.sup.2] values the Richards? model provided better results. Theoretically, the four parameter Richards model, is expected to give a higher [R.sup.2] than the three parameter models. However, due to the values of the shape parameter for some animals close to m = 0, m = -1/3, m = -1 or m = 1, the [R.sup.2] values are similar among Richards? and the three parameters models (Tables 4, 5 and 6).
The results obtained for Athens-Canadian chicken (m = 0.0541 for male and m = -0.0220 for female) and Guinea fowl (m = 0.0798 for male and m = -0.0760 for female) seem to indicate that the Gompertz model is appropriate to experimental data as previously pointed out by Aggrey (2002) and Nahashon et al. (2006), respectively. For Japanese quail m values showed deviations among lines and sex (Table 3). Only for the wild line, the parameter showed a similar value both for male (m = 0.2321) and female (m = 0.2342). The values of [W.sub.inf]/[W.sub.[infinity]] were between 0.39 and 0.46 for all Japanese quail lines. This justified that the statistical parameters indicated Richards? model as the most appropriated followed by Gompertz model (Table 4). None of the three parameters models generated inflection points between 39 and 46% of the mature weight, and the Gompertz model produced the value closest to this range (37%). These results are in accordance with the study of Sezer and Tarhan (2005), which discussed in details the fit of Richards? model to different lines of Japanese quail.
Among the mammals studied, only Californian rabbit (m = -0.0640) seems to indicate Gompertz model as the best one. Curi et al. (1985) fitted Logistic and Gompertz models to rabbit data for three lines and found that the Gompertz model was better than the Logistic model. However, for Norfolk and New Zealand lines, the von Bertalanffy model was the most appropriated one (Tables 3 and 6).
The results presented in Table 3 for Karagouniko sheep (m = 0.9436 for male and m = 0.9642 for female) and pig (m = 1.0184 for male) indicate Logistic model as the most appropriate. These animals showed the inflection point at growth curve close to 50% of the mature weight (Table 3). On the other hand, Gbangboche, Glele-kakai, Salifou, Albuquerque, and Leroy (2008) compared the goodness of fit of four non-linear growth models in West African Dwarf sheep and concluded that the Brody model provided the best fit.
The appropriated models for Beetal goat were Brody model for male and Richards model for female (Table 5). Waheed et al. (2011) compared Brody and Gompertz models and concluded that both models efficiently explained the Beetal goat growth. An appropriated model for Beetal goat should exhibit an inflection point fixed close to 0.2 of the mature weight (Table 3).
Holstein-Friesian bull growth curve showed a m value equal to -0.223. Thus, the von Bertalanffy model could be a good choice for fitting the gain of body weight for this animal (Table 6). Vazquez et al. (2012) observed that this model was appropriate to describe the gain of the body weight of cattle. According to results obtained with Richards? model (Table 3), Nelore and Angus cattle exhibit no inflection point (m < -1) at their growth curves. Statistical parameters indicated the use of Brody model for both species (Table 6). Silva et al. (2011) evaluated five non-linear models for the gain of weight for cows of different biological types and found that Richards? and Brody models were the most appropriated for Nelore cow. Beltran et al. (1992) evaluated growth patterns of two lines of Angus cow using Brody and Richards? models and concluded that both models provided good results.
In general, all non-linear models demonstrated good capacities of fitting for describing the growth kinetics of several animals. Although the Richards model exhibited the highest [R.sup.2] than the three parameters models, the two criteria based on the information theory, AIC and BIC, indicated that Gompertz model was the best model for chickens. In what concerns mammals, the Logistic model was the best model for pigs and sheep, von Bertalanffy for rabbits and bulls and when experimental data showed hyperbolic profiles, like cows and goats, the most appropriated model was the Brody equation. According to AIC and BIC criteria, the Richards? model was the most appropriate only for Japanese quails, female pigs and female goats. However, it provided the best results in average when all animals studied were considered. The Richards? model may be preferable for data based on a sigmoidal behavior, due to the fact that the placement of the inflection point is flexible. If a fixed inflection point is preferred, any placement is possible by substitution of the parameter m, for any given value above-1.
The authors would like to express their gratitude to Conselho Nacional de Desenvolvimento Cientifico e Tecnologico--CNPq for financial support (Process 484037/2013-7).
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Received on March 19, 2016.
Accepted on August 23, 2016.
License information: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Jhony Tiago Teleken (1) *, Alessandro Cazonatto Galvao (2), Weber da Silva Robazza (2)
(1) Departamento de Engenharia Quimica e de Alimentos, Universidade Federal de Santa Catarina, 88040-900, Florianopolis, Santa Catarina, Brazil. (2) Departamento de Engenharia de Alimentos e Engenharia Quimica, Universidade do Estado de Santa Catarina, Pinhalzinho, Santa Catarina, Brazil. * Author for correspondence. E-mail: firstname.lastname@example.org
Caption: Figure 1. The influence of the parameter m on the inflection point of each growth curve: Brody (m = -1), von Bertalanffy (m = -1/3), Gompertz (m = 0), and Logistic (m = 1). The dots indicate the inflection points obtained for each growth model.
Caption: Figure 2. Birds growth kinetics fitted to the Richards? model.
Caption: Figure 3. Mammals growth kinetics fitted to the Richards? model.
Caption: Figure 4. Fits and residuals obtained for the Logistic, Brody and Gompertz models to Athens-Canadian chickens.
Table 1. Data sets used in this study to evaluate five different growth models. Data set Source Calo, Mcdowell, Vanvleck, and Miller, 1973 Holstein-Friesian bull (a) Table 1. Means and standard deviations for body weight, growth rates and degree of maturity of Holstein-Friesian bulls from 6 months to 8 years of age. Silva, Alencar, Freitas, Packer, and Mourao, 2011 Nelore cow (b) Figure 1. A) Estimation of weights based on age of Nelore females, observed and estimated by the models of Brody and von Bertalanffy. Beltran, Butts, Olson, and Koger, 1992 Angus cow (b) Figure 1. Growth curves of Lines A and K estimated with Brody model. Line least squares means for weight at fixed ages are used as reference for goodness of fit. Celta pig (b) Franco et al., 2011 (male and female) Figure 2. Growth curve for males and females of the variety Barcina slaughtered at 14 months. Goliomytis, Orfanos, Panopoulou, and Rogdakis, 2006 Karagouniko sheep (b) Figure 1. Growth curve and absolute (male and female) growth rate for body weight of the Karagouniko male sheep: estimate growth curve; observed mean; estimated absolute growth rate. Figure 2. Growth curve and absolute growth rate for body weight of the Karagouniko female sheep: estimate growth curve; observed mean; estimated absolute growth rate. Beetal goat (a) Waheed, Khan, Ali, and Sarwar, 2011 (male and female) Table 1. Means (kg) and standard deviations (SD) of growth traits of Beetal goats. New Zealand rabbit (a) Curi, Nunes, and Curi, 1985 Table 2. Body weight of Norkfolk Californian rabbit (a) rabbit. Table 3. Body weight of Californian Norfolk rabbit (a) rabbit. Table 4. Body weight of New Zealand rabbit. Athens-Canadian chicken (a) Aggrey, 2002 (male and female) Table 1. Means and standard deviations for body weight at different ages in Athens-Canadian random-bred chickens. Guinea fowl (a) Nahashon, Aggrey, Adefope, Amenyenu, and Wright, 2006 (male and female) Table 2. Means and standard for body Japanese quail--white line (a) weight at different ages in a (male and female) random-bred pearl guinea fowl population. Japanese quail--brown line (a) Sezer and Tarhan, 2005 (male and female) Table 1. The results of statistical Japanese quail--wild line (a) analyses for body weight ofJapanese (male and female) quail lines at different age (means [+ or -] standard errors). (a) Experimental data reported in the literature; (b) Experimental data taken from published figures by means of GetData Graph Digitizer 2.24. Table 2. Equations used to model the animal growth data. Model Equation Brody W(t) = [W.sub.[infinity]] [1 + [([W.sub.0]/ [W.sub.[infinity]] - 1] exp (-kt) (1) von Bertalanffy W(t) = [W.sub.[infinity]] [[1 + [[([W.sub.0]/ [W.sub.[infinity]]).sup.1/3] - 1] exp (-kt)].sup.3] (2) Logistic W(t) = [W.sub.[infinity]]/1 + [([W.sub.[infinity]]/[W.sub.0]) - 1] exp (-kt) (3) Gompertz W(t) = W(t) = [W.sub.[infinity]] exp [ln([W.sub.0]/[W.sub.[infinity]]) exp (-kt)] (4) Richards W(t) = [W.sub.[infinity]] x [[W.sub.0]/ [[W.sup.m.sub.0] + ([W.sup.m.sub.[infinity]] - [W.sup.m.sub.0] exp (-kt)].sup.1/m] for [not equal to] 0 (5) Table 3. Values of the parameter m estimated from regression analyses of Richards model and the relatives values of [W.sub.inf]/[W.sub.[infinity]] estimated from Equation 6. Animal m([W.sub.inf]/[W.sub.[infinity]] Male Female Athens-Canadian chicken 0.0541 (37.76%) -0.0220 (36.38%) Guinea fowl 0.0798 (38.21%) -0.0760 (35.34%) Japanese quail--White line 0.3549 (42.49%) 0.2321 (40.69%) Japanese quail--Brown line 0.6151 (45.87%) 0.1761 (39.81%) Japanese quail--Wild line 0.2321 (40.69%) 0.2342 (40.72%) Beetal goat -0.5030 (24.91%) -0.7070 (17.61%) Karagouniko sheep 0.9436 (49.45%) 0.9642 (49.64%) Celta pig 1.0184 (50.18%) 0.5275 (44.79%) Norfolk rabbit -0.2130 (32.48%) Californian rabbit -0.0640(35.58%) New Zeland rabbit -0.1960 (32.85%) Holstein-Friesian Bull -0.2230 (32. 26%) Nelore cattle -1.2780 (no inflection point) Angus cattle -1.1070 (no inflection point) Table 4. Goodness of fit statistics obtained from the growth models applied to the experimental data set of birds. Equations with the best goodness of fit are represented in bold. Animal Growth models (Male) Eq.(1) Eq.(2) Eq.(3) Eq.(4) Eq.(5) Athens-Canadian chicken [R.sup.2] 0.9860 0.9983 0.9964 0.9993 0.9993 RMSE 91.11 31.62 45.98 20.76 20.95 BIC 112.70 86.962 96.066 76.727 77.903 [AIC.sub.c] 115.35 89.620 98.725 79.385 81.853 Guinea fowl [R.sup.2] 0.9841 0.9982 0.9967 0.9991 0.9992 RMSE 72.48 24.16 32.99 16.85 16.98 BIC 88.259 66.310 72.536 59.110 60.124 [AIC.sub.c] 91.437 69.488 75.714 62.288 64.899 Japanese quail--White line [R.sup.2] 0.9840 0.9974 0.9986 0.9991 0.9996 RMSE 8.701 3.527 2.596 2.046 1.391 BIC 34.203 20.871 16.342 12.828 7.816 [AIC.sub.c] 38.358 25.025 20.497 16.983 14.227 Japanese quail--Brown line [R.sup.2] 0.9814 0.9961 0.9994 0.9984 0.9997 RMSE 8.957 4.120 1.667 2.636 1.233 BIC 34.631 23.163 9.804 16.573 6.038 [AIC.sub.c] 38.785 27.318 13.959 20.727 12.449 Japanese quail--Wild line [R.sup.2] 0.9886 0.9979 0.9980 0.9990 0.9992 RMSE 8.482 3.640 3.596 2.515 2.364 BIC 33.827 21.337 21.154 15.880 15.646 [AIC.sub.c] 37.981 25.492 25.309 20.035 22.058 Animal Growth models (Female) Eq.(1) Eq.(2) Eq.(3) Eq.(4) Eq.(5) [R.sup.2] 0.9879 0.9976 0.9950 0.9982 0.9982 RMSE 63.44 27.99 40.77 24.12 24.61 BIC 103.89 83.995 93.144 80.389 81.820 [AIC.sub.c] 106.55 86.654 95.802 83.048 85.770 Guinea fowl [R.sup.2] 0.9869 0.9977 0.9945 0.9980 0.9981 RMSE 66.88 28.07 43.10 25.83 26.32 BIC 86.654 69.309 77.876 67.650 68.869 [AIC.sub.c] 89.832 72.487 81.054 70.828 73.644 Japanese quail--White line [R.sup.2] 0.9886 0.9979 0.9980 0.9990 0.9992 RMSE 8.482 3.64 3.596 2.515 2.364 BIC 33.827 21.337 21.154 15.880 15.646 [AIC.sub.c] 37.981 25.492 25.309 20.035 22.058 Japanese quail--Brown line [R.sup.2] 0.9870 0.9979 0.9975 0.9987 0.9988 RMSE 8.867 3.603 3.870 2.817 2.818 BIC 34.482 21.185 22.241 17.548 18.238 [AIC.sub.c] 38.637 25.340 26.396 21.703 24.650 Japanese quail--Wild line [R.sup.2] 0.9881 0.9965 0.9966 0.9975 0.9976 RMSE 8.867 4.835 4.770 4.104 4.122 BIC 34.483 25.526 25.329 23.109 23.854 [AIC.sub.c] 38.638 29.681 29.484 27.263 30.266 Table 5. Goodness of fit statistics obtained from the growth models applied to the experimental data set of mammals. Equations with the best goodness of fit are represented in bold. Animal Growth models (Male) Eq.(1) Eq.(2) Eq.(3) Eq.(4) Eq.(5) Beetal goat [R.sup.2] 0.9969 0.9978 0.9938 0.9972 0.9979 RMSE 0.3948 0.3334 0.5632 0.3749 0.3445 BIC -8.632 -10.54 -4.622 -9.216 -9.654 [AIC.sub.c] -3.308 -5.217 0.703 -3.891 -1.110 Karagouniko sheep [R.sup.2] 0.9623 0.9693 0.9728 0.9711 0.9728 RMSE 6.223 5.611 5.289 5.447 5.488 BIC 29.253 27.725 26.851 27.288 28.081 [AIC.sub.c] 33.408 31.880 31.006 31.443 34.492 Celta pig [R.sup.2] 0.9825 0.9920 0.9962 0.9942 0.9962 RMSE 6.479 4.376 3.014 3.746 3.092 BIC 40.019 32.179 24.730 29.072 26.093 [AIC.sub.c] 43.197 35.357 27.908. 32.250 30.868 Animal Growth models (Female) Eq.(1) Eq.(2) Eq.(3) Eq.(4) Eq.(5) Beetal goat [R.sup.2] 0.9992 0.9991 0.9943 0.9983 0.9995 RMSE 0.1845 0.1944 0.4953 0.2733 0.1523 BIC 17.222 -16.63 -6.074 12.786 -18.87 [AIC.sub.c] 11.897 -11.30 -0.749 -7.462 -10.32 Karagouniko sheep [R.sup.2] 0.9456 0.9501 0.9520 0.9511 0.9520 RMSE 5.616 5.380 5.278 5.326 5.477 BIC 27.737 27.105 26.821 26.956 28.051 [AIC.sub.c] 31.892 31.260 30.975 31.111 34.462 Celta pig [R.sup.2] 0.9841 0.9946 0.9970 0.9966 0.9975 RMSE 5.607 3.239 2.438 2.601 2.297 BIC 37.131 26.167 20.495 21.789 20.153 [AIC.sub.c] 40.309 29.345 23.673 24.967 24.929 Table 6. Goodness of fit statistics obtained from the growth models applied to the experimental data set of mammals. Equations with the best goodness of fit are represented in bold. Animals Growth models Eq.(1) Eq.(2) Eq.(3) Eq.(4) Eq.(5) Norfolk rabbit [R.sup.2] 0.9925 0.9991 0.9950 0.9990 0.9992 RMSE 106.8 37.30 87.57 39.84 36.83 BIC 75.373 58.922 72.266 59.953 59.444 [AIC.sub.c] 79.322 62.870 76.214 63.901 65.500 Californian rabbit [R.sup.2] 0.9882 0.9972 0.9943 0.9976 0.9976 RMSE 110.6 53.98 77.26 49.91 51.45 BIC 75.917 64.701 70.307 63.477 64.667 [AIC.sub.c] 79.866 68.650 74.255 67.425 70.723 New Zeland rabbit [R.sup.2] 0.9909 0.9985 0.9942 0.9984 0.9986 RMSE 103.3 42.07 82.27 43.15 41.88 BIC 74.851 60.803 71.289 61.201 61.450 [AIC.sub.c] 78.799 64.752 75.238 65.150 67.506 Holstein-Friesian Bull [R.sup.2] 0.9958 0.9988 0.9953 0.9986 0.9988 RMSE 17.54 9.57 18.61 10.02 9.60 BIC 67.549 53.886 68.892 54.910 54.872 [AIC.sub.c] 70.395 55.641 71.738 57.756 59.117 Nelore cow [R.sup.2] 0.9912 0.9832 0.9641 0.9781 0.9922 RMSE 14.84 20.55 30.01 23.41 14.81 BIC 29.856 33.243 37.193 34.603 30.294 [AIC.sub.c] 35.6187 39.006 42.955 40.365 39.691 Angus cow [R.sup.2] 0.9981 0.9921 0.9766 0.9878 0.9982 RMSE 8.992 18.26 31.47 22.68 9.955 BIC 14.188 18.497 21.804 19.813 14.778 [AIC.sub.c] 25.653 29.961 33.268 31.277 39.397 Table 7. Comparison between pairs of models used in this study. Eq.(1) Eq.(2) Eq.(3) Eq.(4) Eq.(5) Total Eq.(1) 3 5 3 2 13 Eq.(2) 19 13 7 5 44 Eq.(3) 17 9 5 3 34 Eq.(4) 19 15 17 9 60 Eq.(5) 20 17 19 13 69
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|Author:||Tiago Teleken, Jhony; Cazonatto Galvao, Alessandro; da Silva Robazza, Weber|
|Publication:||Acta Scientiarum. Animal Sciences (UEM)|
|Date:||Jan 1, 2017|
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