The integration of broiler chicken threonine responses data into neural network models

Abstract

In making general recommendations for amino acids, researchers might survey various published data on the responses of poultry to amino acids. In this way, the use of appropriate mathematical and statistical approaches may help researchers draw appropriate conclusions. The purpose of this study was to develop artificial neural network (ANN)-based models to analyze data on the responses of broiler chickens [ADG and feed conversion ratio (FCR)] to protein and threonine from 21 to 42 d of age. A data set containing 92 dose-response treatments was extracted from the literature, compiled, and entered into the training and testing sets of the ANN models. The constructed models were subjected to a process of sensitivity analysis to evaluate the relative importance of the effects of dietary protein and threonine on ADG and FCR. Optimal values for the input variables (protein and threonine requirements) to maximize ADG and minimize FCR in birds were obtained by using the ANN models with an optimization algorithm. Based on the calculated goodness of fit criteria, it appeared that the platform of ANN-based models with the sensitivity analysis and optimization algorithms was an efficient tool for integrating published data on the responses of broiler chickens to threonine. The analyses of ANN models for ADG and FCR based on the compiled data set suggested that the dietary protein concentration was more important than the threonine concentration. The optimization algorithm revealed that diets containing 18.69% protein and 0.73% threonine could lead to optimal ADG, whereas the optimal FCR could be achieved with diets containing 18.71% protein and 0.75% threonine.

Effects of dietary L-threonine and <i>Saccharomyces cerevisiae</i> on performance, intestinal morphology and immune response of broiler chickens

“…L-threonine is added to the diet of pigs and poultry in order to exactly match the dietary amino acid balance to the unique nutritional requirements of the animal (Mikail et al, 2006). There are many reports on the threonine requirements of poultry (Rosa et al, 2001;Shan et al, 2003;Ahmadi & Golian, 2010), but less information is available on the effects of dietary L-threonine on the immune system and intestinal morphology of broilers. Threonine is involved in important metabolic processes such as uric acid formation and protein synthesis.…”

Role of Data Mining Techniques in Agriculture Improvement

“…Analyses of the artificial neural network models for weight gain and feed efficiency from a compiled data set suggested that the dietary protein concentration was more important than the threonine concentration. The results revealed that a diet containing 18.69% protein and 0.73% threonine may lead to producing optimal weight gain, whereas the optimal feed efficiency may be achieved with a diet containing 18.71% protein and 0.75% threonine [62].…”

A hybrid model of uniform design and artificial neural network for the optimization of dietary metabolizable energy, digestible lysine, and methionine in quail chicks

“…The sensitivity analysis indicates the relative importance of each variable in the model that could be defined as the ratio between the error with omission and the baseline error (Hunter et al, 2000). More important variables in the have higher variable sensitivity ratio (VSR) and VSR may be used to rank the input variables according to their importance (Ahmadi & Golian, 2010b).…”

“…Research has shown the suitability of the random search algorithm for nutrient optimization in poultry (Ahmadi & Golian, 2010b;Mehri, 2012). Random search algorithm was used in the present study to optimize the dietary inputs that maximize BWG and minimize FCR of quail chicks from 7 to 14 d of age (Table 4).…”

“…Artificial neural networks (ANN) have been employed to analyze data in poultry experiments Ahmadi & Golian, 2010a;Ahmadi and Golian, 2010b;Ahmadi & Golian, 2011;Mehri, 2012;Roush et al, 2006). Central composite design (CCD) has been successfully used to optimize dietary nutrients using response surface methodology and ANN approach (Ahmadi & Golian, 2011;Mehri, 2012).…”