Study of broiler chicken responses to dietary protein and lysine using neural network and response surface models

Faridi, A.; Golian, Abolghasem; France, J.; Mousavi, Amin

doi:10.1080/00071668.2013.803517

Cited by 8 publications

(10 citation statements)

References 33 publications

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“…In addition, it is the second limiting amino acid for broilers fed with rations formulated on the basis of corn and soybean meal (Waldroup;Jiang and Fritts, 2005;Faridi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Digestible lysine for broiler chickens with lower genetic potential grown on free-range system

et al. 2016

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Determining the actual lysine requirement is crucial as it is an essential amino acid and plays animportant role in the metabolism of broilers. In addition, it is the second limiting amino acid for broilers fed with rations formulated basedon corn and soybean meal. The aim of this study was to determine the digestible lysine requirement for male broilers with lower genetic potential for growth in a semiconfined system as well as to evaluate the effects of different values of digestible lysine on performance and carcass characteristics. A total of 300 broiler chickensfrom a commercial lineage EMBRAPA 041 were used with an average initial weight of 987 g. The broilers were distributed into 20 experimental units in a completely randomized design with five treatments (0.586; 0.746; 0.906; 1.066, and 1.226% digestible lysine), four replicates and 15 broilers per experimental unit.In both periods, there were linear effect of digestible lysine values on the lysine intake and lysine utilization efficiency and quadratic effect on final body weight, weight gain and feed: gain ratio.There was a quadratic effect on the absolute weight of carcasses, breast, thigh + drumstick and income gizzard.For rearing of male broilers with lesser genetic growth potential reared in semi-confinement, rations with 1.040% and 1.103% digestible lysine can be recommended for maximum weight gain in periods from 35 to 70 and 35 to 84 days of age, respectively.Index terms: Amino acids; nutritional requirements; performance; yield. RESUMOA determinação da real exigência de lisina é fundamental, por ser um aminoácido essencial e desempenhar importantes funções no metabolismo dos frangos de corte. Além disto, apresenta-se como segundo aminoácido limitante para frangos de corte alimentados com rações formuladas a base de milho e farelo de soja. O objetivo deste estudo foi determinar a exigência de lisina digestível para frangos de corte machos de menor potencial genético para crescimento em sistema semiconfinado, bem como avaliar os efeitos de diferentes valores de lisina digestível sobre o desempenho e características de carcaça. Foram utilizados 300 frangos de corte, da linhagem comercial EMBRAPA 041, com pesoinicial médio de 987 g. Os frangos foram distribuídos em 20 unidades experimentais em um delineamento inteiramente casualizado, com cinco tratamentos (0,586; 0,746; 0,906; 1,066 e 1,226% de lisina digestível), quatro repetições e quinze frangos por unidade experimental. Nos dois períodos avaliados, verificaram-se efeito linear dos níveis de lisina digestível sobre a ingestão e eficiência na utilização de lisina, e efeito quadrático sobre peso vivo final, ganho de peso e a conversão alimentar. Verificou-se efeito quadrático sobre os pesos absolutos da carcaça, peito, coxa+sobrecoxa e rendimento da moela. Podem ser recomendadas, respectivamente, para máximo ganho de peso rações com 1,040% e 1,103% de lisina digestível nos períodos de 35 aos 70 e 35 aos 84 dias de idade.Termos para indexação: Aminoácidos; exigência nutricional; desempenho...

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“…In addition, it is the second limiting amino acid for broilers fed with rations formulated on the basis of corn and soybean meal (Waldroup;Jiang and Fritts, 2005;Faridi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Digestible lysine for broiler chickens with lower genetic potential grown on free-range system

et al. 2016

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“…Ahmadi and Golian, ; Faridi et al., ). In a recent study, 16 profiles of lysine dose–response data were taken from the literature then analysed using the random search method (Faridi et al., ). Their results were in general agreement with Vazquez and Pesti () who analysed the same 16 lysine dose–response profiles using linear and quadratic regression analysis.…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, neural networks (NNs) have been employed in poultry nutrition although they are still considered a fairly new modelling tool. Neural network modelling has been a subject of interest in predicting the response of broilers to dietary nutrients, especially AAs (Faridi et al., , ). Recently, Ahmadi and Golian () composited the literature data on broiler response to Thr and analysed these data using NN models.…”

Section: Introductionmentioning

confidence: 99%

Broiler responses to digestible threonine at different ages: a neural networks approach

Faridi

Gitoee

Donato

et al. 2015

Animal Physiology Nutrition

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Three experiments were conducted with broiler chickens to evaluate the effects of digestible threonine (DThr) and crude protein (CP) on their performance at three different phases of age: 1-14, 15-28 and 29-42 days. The measured traits included the following: average daily gain (ADG), feed intake (FI), feed conversion ratio (FCR), carcass crude protein (CCP), body lipid (BL), feather weight gain (FWG), protein deposited in feather (FCP), carcass plus feather protein (CFCP), carcass Thr deposition (CDThr) and nitrogen excretion (NE). A dilution technique was used to create seven diets (with eight replicates) increasing the DThr content from 1.5 to 10 g/kg of diet for phase 1, 1.3-8.9 g/kg of diet for phase 2, and 1.2-8.2 g/kg of diet for phase 3. Data measured were imported into neural networks (NNs) to: (i) predict the measured traits in response to DThr and CP, (ii) rank the importance of DThr and CP on these traits through sensitivity analysis and (iii) find the optimal levels of DThr and CP that lead to the desired (maximum or minimum) responses. For each trait investigated, 50 different random groups of data were generated using a bootstrapping method. These 50 data groups were then used to develop 50 separate NNs which were subsequently combined to construct the final ensemble NN model. In general, accuracy of the models constructed was acceptable, although models of high (ADG, FCR, CFCP, BL, DThr and NE; 0.64 ≤ R(2) ≤ 0.99) and low (CCP, FWG and FCP; 0.26 ≤ R(2) ≤ 0.79) accuracy were obtained. All models developed showed the greatest sensitivity to DThr. This may be explained by the dilution technique diet preparation used in these experiments. Optimization results showed decreases in optimal values of DThr and CP with increasing age for all traits. The highest level of DThr was suggested for minimum BL, followed by minimum FCR, maximum ADG, maximum CFCP, minimum NE and maximum CCP respectively. Results showed that the optimal values of DThr for minimum FCR in phases 1-3 were 8.5, 7.4 and 6.4 g/kg of diet, while these values for maximum ADG were 8.2, 7.2 and 6.4 g/kg of diet respectively.

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“…The use of such self-organizing networks has led to their successful application in a broad range of areas in engineering, science, and economics (Seginer et al, 1994;Vallejo-Cordoba et al, 1995;Roush et al, 1996). The GMDH-type NNs have been used in poultry science for the prediction of broiler performance (Faridi et al, 2011;2013b), turkey performance (Mottaghitalab et al, 2010), egg production of broiler breeder hens (Faridi et al, 2012;2013a) and true metabolizable energy content in feather meal and poultry offal meal (Ahmadi et al, 2008).…”

Section: Model Developmentmentioning

confidence: 99%

Predicting methionine and lysine contents in soybean meal and fish meal using a group method of data handling-type neural network

Mottaghitalab¹,

Nikkhah²,

Darmani-Kuhi³

et al. 2015

Span. j. agric. res.

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Artificial neural network models offer an alternative to linear regression analysis for predicting the amino acid content of feeds from their chemical composition. A group method of data handling-type neural network (GMDH-type NN), with an evolutionary method of genetic algorithm, was used to predict methionine (Met) and lysine (Lys) contents of soybean meal (SBM) and fish meal (FM) from their proximate analyses (i.e. crude protein, crude fat, crude fibre, ash and moisture). A data set with 119 data lines for Met and 116 lines for Lys was used to develop GMDH-type NN models with two hidden layers. The data lines were divided into two groups to produce training and validation sets. The data sets were imported into the GEvoM software for training the networks. The predictive capability of the constructed models was evaluated by their abilities to estimate the validation data sets accurately. A quantitative examination of goodness of fit for the predictive models was made using a number of precision, concordance and bias statistics. The statistical performance of the models developed revealed close agreement between observed and predicted Met and Lys contents for SBM and FM. The results of this study clearly illustrate the validity of GMDH-type NN models to estimate accurately the amino acid content of poultry feed ingredients from their chemical composition.

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Study of broiler chicken responses to dietary protein and lysine using neural network and response surface models

Cited by 8 publications

References 33 publications

Digestible lysine for broiler chickens with lower genetic potential grown on free-range system

Digestible lysine for broiler chickens with lower genetic potential grown on free-range system

Broiler responses to digestible threonine at different ages: a neural networks approach

Predicting methionine and lysine contents in soybean meal and fish meal using a group method of data handling-type neural network

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