The aim of this paper was model the amino acid intake of broiler chickens for optimum economic in different scenarios with MC how to example. The second objective was described in software R the scripts for modeling the optimum amino acid intake for broiler. In addition, we considered the price of whole birds and DL-methionine cost according to Economic scenario. The dilution technique was used to allow formulates the experimental diets with the same balance of amino acids for all treatments (Gous and Morris, 1985). This methodology is already consolidated and has been used in experiments with the concept of amino acid requirement (Fisher and Morris 1970; Gous and Morris 1985; Liebert 2009).
The results of the present study showed that the …show more content…
The gain in using this model in software R is to simulate a large random population with larger data sets, which enabling the variations between the experimental units (experimental error) as small as possible. Another advantage, is being able to simulate various amino acid intake levels for the same individual in the stipulated period, the increased of replicates number per treatment reduces the mean square error (MSE), desired factor in animal experimentation. With the procedure described in this paper, it is possible to study the responses of a large number of treatments with small additions in the simulated levels of DL-methionine, which is advantageous when applying mathematical models that follow the law of minimum returns. In practice this simulation would be impossible to reproduce in experimental sheds, for not having the required number of poultry facility, animals, as well …show more content…
This model is represented by an ascending line until reaching the plateau point, i.e. the point where there is no response to the addition of the nutrient. In this experiment, the parameters (Rmax, U, OMCI) in R software were estimated with the aim of minimizing the sum of squared deviations. This model has been used for various authors in experiments of amino acid requirement in broiler’s (Rosa and Pesti 2001; Robbins et al. 2006; Strathe et al. 2011) . The popularity of the broken line approach may be the consequence of the fact that one single point can be objectively defined (Rodehutscord and Pack 1999). The coefficient of correlation (R2) and mean square error (MSE) calculated by R software in this study, show the great adjust of the model by the simulate data, this is important because the lack of fit of model can make recommendations of MCi out of range the optimal
The results of the present study showed that the …show more content…
The gain in using this model in software R is to simulate a large random population with larger data sets, which enabling the variations between the experimental units (experimental error) as small as possible. Another advantage, is being able to simulate various amino acid intake levels for the same individual in the stipulated period, the increased of replicates number per treatment reduces the mean square error (MSE), desired factor in animal experimentation. With the procedure described in this paper, it is possible to study the responses of a large number of treatments with small additions in the simulated levels of DL-methionine, which is advantageous when applying mathematical models that follow the law of minimum returns. In practice this simulation would be impossible to reproduce in experimental sheds, for not having the required number of poultry facility, animals, as well …show more content…
This model is represented by an ascending line until reaching the plateau point, i.e. the point where there is no response to the addition of the nutrient. In this experiment, the parameters (Rmax, U, OMCI) in R software were estimated with the aim of minimizing the sum of squared deviations. This model has been used for various authors in experiments of amino acid requirement in broiler’s (Rosa and Pesti 2001; Robbins et al. 2006; Strathe et al. 2011) . The popularity of the broken line approach may be the consequence of the fact that one single point can be objectively defined (Rodehutscord and Pack 1999). The coefficient of correlation (R2) and mean square error (MSE) calculated by R software in this study, show the great adjust of the model by the simulate data, this is important because the lack of fit of model can make recommendations of MCi out of range the optimal