Abstract
Designing balanced rations for broilers depends on precise knowledge of nitrogen-corrected apparent metabolizable energy (AMEn) and the chemical composition of the feedstuffs. The equations that include the measurements of the chemical composition of the feedstuff can be used in the prediction of AMEn. In the literature, there are studies that obtained prediction equations through multiple regression, meta-analysis, and neural networks. However, other statistical methodologies with promising potential can be used to obtain better predictions of energy values. The objective of the present study was to propose and evaluate the use of Bayesian networks (BN) to the prediction of the AMEn values of energy and protein feedstuffs of vegetable origin used in the formulation of broiler rations. In addition, verify that the predictions of energy values using this methodology are the most accurate and, consequently, are recommended to Animal Science professionals area for the preparation of balanced feeds. BN are models that consist of graphical and probabilistic representations of conditional and joint distributions of the random variables. BN uses machine learning algorithms, being a methodology of artificial intelligence. The bnlearn package in R software was used to predict AMEn from the following covariates: crude protein, crude fiber, ethereal extract, mineral matter, as well as food category, i.e., energy (corn, corn by-products, and others) or protein (soybean, soy by-products, and others) and the type of animal (chick or cockerel). The data come from 568 feeding experiments carried out in Brazil. Additional data from metabolic experiments were obtained from the Federal University of Lavras (UFLA) – Lavras, Minas Gerais, Brazil. The model with the highest accuracy (mean squared error = 66529.8 and multiple coefficients of determination = 0.87) was fitted with the max-min hill climbing algorithm (MMHC) using 80% and 20% of the data for training and test sets, respectively. The accuracy of the models was evaluated based on their values of mean squared error, mean absolute deviation, and mean absolute percentage error. The equations proposed by a new methodology in avian nutrition can be used by the broiler industry in the determination of rations.
Highlights
Production of low-cost high protein chicken meat through intensively reared broiler chickens has high economic importance at national and international levels
Another way of obtaining the values of apparent metabolizable energy (AMEn) is the prediction equations established according to the chemical composition of the feedstuffs, which is usually easy and quick to obtain (Rodrigues et al, 2001, 2002)
To obtain the equations via Bayesian networks (BN), data from the meta-analysis were used, referring to the experiments conducted in Brazil in the period from 1967 to 2007, resulting in 568 experiments (Nascimento et al, 2009; Nascimento et al, 2011), among them which refer to the values of AMEn and chemical composition of energy (n = 370) and protein (n = 198) feedstuffs, of vegetable origin, commonly used in the formulation of broiler diets
Summary
Production of low-cost high protein chicken meat through intensively reared broiler chickens has high economic importance at national and international levels. The productive efficiency of birds is directly related to the adequate supply of dietary energy, which, in turn, depends on the nitrogen-corrected apparent metabolizable energy (AMEn) of the foods. The energy values feedstuffs can be obtained in biological tests, with the execution is time-consuming and of high cost, or by the composition tables of the feedstuffs (Albino, 1980). Another way of obtaining the values of AMEn is the prediction equations established according to the chemical composition of the feedstuffs, which is usually easy and quick to obtain (Rodrigues et al, 2001, 2002). Another way of obtaining the values of AMEn is the prediction equations established according to the chemical composition of the feedstuffs, which is usually easy and quick to obtain (Rodrigues et al, 2001, 2002). Zhao et al (2008) developed prediction equations using multiple regression to estimate the energy values using the chemical composition of the feedstuffs; their results have been inconsistent or applicable only to one feedstuff group (Alvarenga et al, 2011). Nascimento et al (2009, 2011) and Mariano et al (2012) used meta-analyses to better predict AMEn. Perai et al (2010), Ahmadi et al (2007, 2008), and Mariano et al (2013) used neural networks (NN), and the latter used a larger number of foods and in vivo trials
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