title: Response surface and neural network models for performance of broiler chicks fed diets varying in digestible protein and critical amino acids from 11 to 17 days of age.
abstract: Central composite design (CCD; 5 levels and 4 factors), response surface methodology (RSM), and artificial neural network-genetic algorithm (ANN-GA) were used to evaluate the response of broiler chicks [ADG and feed conversion ratio (FCR)] to dietary standardized ileal digestible protein (dP), lysine (dLys), total sulfur amino acids (dTSAA), and threonine (dThr). A total of 84 battery brooder units of 5 birds each were assigned to 28 diets of CCD containing 5 levels of dP (18-22%), dLys (1.06-1.30%), dTSAA (0.81-1.01%), and dThr (0.66-0.86%) from 11 to 17 d of age. The experimental results of CCD were fitted with the quadratic and artificial neural network models. A ridge analysis (for RSM models) and a genetic algorithm (for ANN-GA models) were used to compute the optimal response for ADG and FCR. For both ADG and FCR, the goodness of fit in terms of R(2) and MS error corresponding to ANN-GA and RSM models showed a substantially higher accuracy of prediction for ANN models (ADG model: R(2) = 0.99; FCR model: R(2) = 0.97) compared with RSM models (ADG model: R(2) = 0.70; FCR model: R(2) = 0.71). The ridge maximum analysis on ADG and minimum analysis on FCR models revealed that the maximum ADG may be obtained with 18.5, 1.10, 0.89, and 0.73% dP, dLys, dTSAA, and dThr, respectively, in diet, and minimum FCR may be obtained with 19.44, 1.18, 0.90, and 0.75% of dP, dLys, dTSAA, and dThr, respectively, in diet. The optimization results of ANN-GA models showed the maximum ADG may be achieved with 19.93, 1.06, 0.90, and 0.76% of dP, dLys, dTSAA, and dThr, respectively, in diet, and minimum FCR may be achieved with 18.63, 1.26, 0.84, and 0.69% of dP, dLys, dTSAA, and dThr, respectively, in diet. The results of this study revealed that the platform of CCD (for conducting growth trials with minimum treatments), RSM model, and ANN-GA (for experimental data modeling and optimization) may be used to describe the relationship between dietary nutrient concentrations and broiler performance to achieve the optimal target.
authors.affiliation: Center of Excellence in the Animal Sciences Department, Ferdowsi University of Mashhad, Mashhad, Iran. hahmadima@yahoo.com
authors.forename: H
authors.initials: H
authors.lastname: Ahmadi
authors.forename: A
authors.initials: A
authors.lastname: Golian
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000494:pharmacology*; D000821:Animal Feed / Q000032:analysis; D000818:Animals; D002645:Chickens / Q000235:genetics / Q000254:growth & development*; D004032:Diet / Q000662:veterinary; D004044:Dietary Proteins / Q000494:pharmacology*; D004063:Digestion / Q000502:physiology*; D004305:Dose-Response Relationship, Drug; D004334:Drug Administration Schedule; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer
chemical_list: D000596:Amino Acids; D004044:Dietary Proteins