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

Abstract

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.

Model Pendugaan Total Digestible Nutrient (TDN) pada Hijauan Pakan Tropis menggunakan Komposisi Nutrien

“…MAD, RMSE dan MAPE merupakan alat validasi penting (De Oca et al, 2017) dalam tahap pemodelan persamaan. Semakin rendah insiden estimasi kesalahan, semakin dekat nilai estimasi dengan nilai yang diamati (Mariano et al, 2013). Validasi kesalahan yang lebih rendah menunjukkan persamaan prediksi dalam penelitian ini dapat digunakan untuk menentukan kualitas TDN untuk hijauan tropis.…”

Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs

“…The models selected in the present study -EqPred and committee neural network -were developed for the prediction of AMEn of both energetic and protein concentrate feedstuffs. The use of these models is still more appropriate to estimate the AMEn of feedstuffs such as maize, sorghum, soybean, soybean meal, wheat and wheat meal (Mariano et al, 2012(Mariano et al, , 2013. These models were indicated because these feedstuffs had greater representativeness in the dataset used in the development of the models.…”

“…Therefore, the committee model composed of N = 1,000 networks, considering as best estimator the empirical mode of the predictions, had a good generalization, and will be made available for use with new input data. For this, an easy to use tool, created by Mariano et al (2013), can be improved for nutritionists of the animal area.…”

“…Artificial neural network (ANN) models have recently been used in the prediction of energy values of feedstuffs for poultry (Perai et al, 2010;Mariano et al, 2013). Due to these models' great capacity of learning from examples and generalizing the learned information, as well as of giving coherent answers to unknown data, it is possible to state that the application of an ANN goes beyond simply mapping relations of input and output.…”

Validation of Prediction Equations of Energy Values of a Single Ingredient or Their Combinations in Male Broilers

“…The diversity of feedstuffs and by-products used for feeding broilers requires precise knowledge of the chemical composition and metabolizable energy (ME) of feed ingredients to allow for the formulation of nutritionally and economically balanced diets ( Mariano et al, 2012 ). Errors in diet formulation can limit broiler performance, causing costly losses for farmers and the meat industry.…”