Predicting Metabolizable Energy of Normal Corn from its Chemical Composition in Adult Pekin Ducks

Zhang, Feng; Zhang, H. F.; Hou, Shuisheng; Zhang, Z. Y.

doi:10.3382/ps.2007-00494

Cited by 26 publications

(22 citation statements)

References 15 publications

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“…Our results indicated that, in the MLR models, crude fibre was negatively correlated with AME, AME n , TME, and TME n , while regression coefficients for CP, EE, and ash were not statistically significant (Table 3). The same findings were reported by Zhao et al (2008). SVR models are known as universal approximations of any function to a desired degree of accuracy (Kecman, 2005).…”

Section: Calibrationsupporting

confidence: 75%

“…Data used to develop the SVR and ANN models for AME, AME n , and TME n were taken from Zhao et al (2008), and information reported by Zhao et al (2008) and Zhou et al (2010) was used to develop the TME prediction models. There were 36 records of observations for AME, AME n and TME n and 42 for TME.…”

Section: Data Sourcesmentioning

confidence: 99%

“…A number of studies have been conducted to estimate the metabolizable energy (ME) content of corn based on its physical characteristics and chemical composition (e.g. Leeson et al, 1993;Zhao et al, 2008). The energy content of feedstuffs depends strongly on their chemical composition.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

Faridi

Golian

Mottaghitalab³

et al. 2013

Span. j. agric. res.

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IntroductionCorn (Zea mays L.) is the main energy source in diets for intensively reared avian species (broilers and ducks), therefore accurate information on its effective energy content is of importance to nutritionists. A number of studies have been conducted to estimate the metabolizable energy (ME) content of corn based on its physical characteristics and chemical composition (e.g. Leeson et al., 1993;Zhao et al., 2008). The energy content of feedstuffs depends strongly on their chemical composition. Nutritionists are interested in using models that predict the nutritive value of poultry feedstuffs accurately. Recently, artif icial neural network (ANN) models have received attention among poultry nutritionists, e.g. for estimating the ME of poultry offal meal (Ahmadi et al., 2008) and sorghum grain (Sedghi et al., 2011) AbstractSupport vector regression (SVR) is used in this study to develop models to estimate apparent metabolizable energy (AME), AME corrected for nitrogen (AME n ), true metabolizable energy (TME), and TME corrected for nitrogen (TME n ) contents of corn fed to ducks based on its chemical composition. Performance of the SVR models was assessed by comparing their results with those of artificial neural network (ANN) and multiple linear regression (MLR) models. The input variables to estimate metabolizable energy content (MJ kg -1 ) of corn were crude protein, ether extract, crude fibre, and ash (g kg -1 ). Goodness of fit of the models was examined using R 2 , mean square error, and bias. Based on these indices, the predictive performance of the SVR, ANN, and MLR models was acceptable. Comparison of models indicated that performance of SVR (in terms of R 2 ) on the full data set (0.937 for AME, 0.954 for AME n , 0.860 for TME, and 0.937 for TME n ) was better than that of ANN (0.907 for AME, 0.922 for AME n , 0.744 for TME, and 0.920 for TME n ) and MLR (0.887 for AME, 0.903 for AME n , 0.704 for TME, and 0.902 for TME n ). Similar findings were observed with the calibration and testing data sets. These results suggest SVR models are a promising tool for modelling the relationship between chemical composition and metabolizable energy of feedstuffs for poultry. Although from the present results the application of SVR models seems encouraging, the use of such models in other areas of animal nutrition needs to be evaluated.Additional key words: maize; poultry; nutritive value; chemical composition; artificial neural network; multiple linear regression. Abbreviations used: AME (apparent metabolizable energy); AME n (apparent metabolizable energy corrected for nitrogen); ANN (artificial neural network); CF (crude fibre); CP (crude protein); EE (ether extract); ME (metabolizable energy); MLR (multiple linear regression); SVM (support vector machine); SVR (support vector regression); TME (true metabolizable energy); TME n (true metabolizable energy corrected for nitrogen); VIF (variance inflation factor).

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Section: Calibrationsupporting

confidence: 75%

Section: Data Sourcesmentioning

confidence: 99%

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Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

Faridi

Golian

Mottaghitalab³

et al. 2013

Span. j. agric. res.

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“…The prediction of energy values by using regression models considers just chemical composition variables (Zhao et al, 2008;Wan et al, 2009;Mariano et al, 2012). The MLP can be fit by using other factors in addition to chemical composition, which is a great advantage.…”

Section: Resultsmentioning

confidence: 99%

“…A variety of feedstuffs and their by-products are used in diets, and it is important to know accurately the dietary nutrients that each contains. The energy content of feedstuffs may be determined using metabolic bioassays (Rodrigues et al, 2001;Zhao et al, 2008;Wan et al, 2009), which are onerous and timeconsuming. Alternative ways to obtain these values include using the composition of feedstuffs and nutritional composition tables, and prediction equations based on the chemical composition of the feedstuffs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Mariano

Paixão

Lima

et al. 2013

Animal

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Several researchers have developed prediction equations to estimate the metabolisable energy (ME) of energetic and protein concentrate feedstuffs used in diets for broilers. The ME is estimated by considering CP, ether extract, ash and fibre contents. However, the results obtained using traditional regression analysis methods have been inconsistent and new techniques can be used to obtain better estimate of the feedstuffs' energy value. The objective of this paper was to implement a multilayer perceptron network to estimate the nitrogen-corrected metabolisable energy (AMEn) values of the energetic and protein concentrate feeds, generally used by the poultry feed industry. The concentrate feeds were from plant origin. The dataset contains 568 experimental results, all from Brazil. This dataset was separated into two parts: one part with 454 data, which was used to train, and the other one with 114 data, which was used to evaluate the accuracy of each implemented network. The accuracy of the models was evaluated on the basis of their values of mean squared error, R 2 , mean absolute deviation, mean absolute percentage error and bias. The 7-5-3-1 model presented the highest accuracy of prediction. It was developed an Excel R AMEn calculator by using the best model, which provides a rapid and efficient way to predict the AMEn values of concentrate feedstuffs for broilers.Keywords: avian production, broilers, metabolisable energy, multilayer perceptron ImplicationsIt is difficult and expensive to estimate the nitrogencorrected metabolisable energy (AMEn) values of the feedstuffs used for broilers. The results of this study demonstrate that the implementation of multilayer perceptron networks (MLP) in a meta-analysis is suitable to estimate these energy values. Furthermore, a calculator was created on the basis of the results of the MLP, which allows an efficient way of predicting the AMEn values.

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Energetic values of feedstuffs for broilers determined with in vivo assays and prediction equations

Alvarenga

Rodrigues

Zangerônimo

et al. 2011

Animal Feed Science and Technology

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Predicting Metabolizable Energy of Normal Corn from its Chemical Composition in Adult Pekin Ducks

Cited by 26 publications

References 15 publications

Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Energetic values of feedstuffs for broilers determined with in vivo assays and prediction equations

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