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SUMMARYThe goal of the present study was to characterize the growth of body parts and composition of the growing empty body to infer how these aspects relate to the long-term growth of goat wethers from dairy breeds. Animals were slaughtered at several ages from birth to maturity (≅900 days old). All body parts were weighed and sampled to determine chemical constituent dry matter, crude protein, crude fat, ash and specific energy. The monomolecular (Brody), Gompertz, and Richards models, a biphasic model formed by the combined Brody and Gompertz functions, and a simple linear model were fitted to the growth profiles with different variance functions and were all evaluated using likelihood-information criteria. The effect of breed (genotype) was accounted for in all models but the resulting models were not more likely than the models without the breed effect. Remarkable differences were observed regarding inflection points, growth rates and trends for all body parts and chemical constituents of the body. The biphasic model did not supplant the monomolecular, Gompertz, Richards or the linear model in terms of likelihood-information criteria. Therefore, body parts and chemical constituents of the empty body presented monomolecular, sigmoid and linear time-trends. The growth profiles of fat, protein and energy of the empty body did not scale isometrically with the empty body proper. In addition, the variance was heteroscedastic along the time scale and was better represented by both an exponential variance over time or by a power function of the mean.

We studied the ontogenetic growth of goat wethers (castrated male goats) of the Saanen and Swiss Alpine breeds based on a large range of intraspecific body mass (BM). The body parts and the chemical constituents of the empty body were described by the allometric function by using BM and the empty body mass (EBM) as the predictors for morphological traits and chemical composition, respectively. We fitted the allometric scaling function by applying the SAS NLMIXED procedure, but to evaluate assumptions regarding variances in morphological and compositional traits, we combined the scaling function with homoscedastic (MOD1), and the heteroscedastic exponential (MOD2) and power-of-the-mean (MOD3) variance functions. We also predicted the ontogenetic growth by using the traditional log-log transformation and back-transformed results into the arithmetic scale (MOD4). We obtained predictions from MOD4 in the arithmetic scale by a two-step process, and evaluated MOD1, MOD2 and MOD3 by a model selection framework, and compared MOD4 with MOD1, MOD2 and MOD3 based on goodness-of-fit measures. Based on information criteria for model selection, heterogeneous variance functions were more likely to describe 10 over 36 traits with a low level of model selection uncertainty. One trait was predicted by averaging the MOD1 and MOD2 variance functions; and nine traits were better described by averaging the MOD2 and MOD3 variance functions. The predictions for other 16 traits were averaged from MOD1, MOD2 and MOD3. However, MOD4 better described 11 traits according to the goodness-of-fit measures. Depending on the variable being analyzed, the body parts and the chemical amounts exhibited the three types of allometric behavior with respect to BM and EBM, that is, positive, negative and isometric ontogenetic growth. Reference BMs, that is, 20, 27, 35 and 45 kg, were used to compute the net protein and energy requirements based on the first derivative of the scaling function, and the results were presented in reference to the EBM and EBM0.75. Both the net protein and energy requirements scaled to EBM0.75 increased from 20 to 45 kg of BM.

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-The objective of this study was to simulate total dry matter intake and cost of diets optimized by nonlinear programming to meet the nutritional requirements of dairy does and growing doelings. The mathematical model was programmed in a Microsoft Excel ® spreadsheet. Increasing values of body mass and average daily weight gain for growing doelings and increasing body mass values and milk yield for dairy does were used as inputs for optimizations. Three objective functions were considered: minimization of the dietary cost, dry matter intake maximization, and maximization of the efficiency of use of the ingested crude protein. To solve the proposed problems we used the Excel ® Solver ® algorithm. The Excel ® Solver ® was able to balance diets containing different objective functions and provided different spaces of feasible solutions. The best solutions are obtained by least-cost formulations; the other two objective functions, namely maximize dry matter intake and maximize crude protein use, do not produce favorable diets in terms of costs.

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