Equações de predição para estimar valores da energia metabolizável de alimentos concentrados energéticos para aves utilizando meta-análise

Nascimento, Germano Augusto Jerônimo do; Rodrigues, Paulo Borges; Freitas, Rilke Tadeu Fonseca de; Neto, Rafael Vilhena Reis; Lima, Renato Ribeiro de; Allaman, Ivan Bezerra

doi:10.1590/s0102-09352011000100032

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…In the selected equations, the best fit was due to the higher adjusted R 2 and R 2 , in agreement with Nascimento et al (2011), who also reported an increase in R 2 with the increase in the number of variables in the equation. The use of prediction equations for ME of corn that are based on composition of DE was proposed by Noblet and Perez (1993) and Castilha et al (2011), although they used fewer variables.…”

Section: Resultssupporting

confidence: 86%

Development and ex post validation of prediction equations of corn energy values for growing pigs

et al. 2015

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The aim of this study was to determine and validate prediction equations for digestible (DE) and metabolizable energy (ME) of corn for growing pigs. The prediction equations were developed based on data on the chemical composition, digestible and metabolizable energy of corn grain (30 samples) evaluated in experiments in Embrapa Suínos e Aves, Brazil. The equations were evaluated using regression analysis, and adjusted R² was the criterion for selection of the best models. Two equations were tested for DE and ME, each. To validate the equations, 1 experiment with 2 assays was performed to determine the values of DE and ME of 5 corn cultivars. In each assay, we used 24 growing pigs with initial average weight of 54.21 ± 1.68 kg in complete randomized block design with 6 treatments and 4 replicates. Treatments consisted of a reference diet and 5 ration tests composed of 60% of the reference diet and 40% of corn (1 of the 5 cultivars). Based on the results of the metabolic experiment and predicted values obtained in the equations, the validation of the equations was conducted using the lowest prediction error (p e ) as a criterion for selection. The equations that produced the most accurate estimates of DE and ME of corn were as follows: DE = 11812 -1015. como critério para selecionar os melhores modelos. Para a validação das equações, foi realizado um experimento com dois ensaios para a determinação dos valores de ED e EM dos grãos de milho de cinco cultivares. Em cada ensaio, 24 leitões machos castrados com peso médio inicial de 54,21±1,68 kg foram distribuídos em um delineamento em blocos ao acaso, com 6 tratamentos e 4 repetições. Os tratamentos foram uma ração referência e cinco rações testes, compostas por 60% da ração referência e 40% do milho de cada um dos cinco diferentes cultivares. A partir dos resultados do experimento e dos valores preditos pelas equações, procedeu-se a validação das equações, sendo o critério de seleção o menor erro de predição. As equações que melhor se ajustaram para os A partir dos dados de composição química, foi possível determinar equações de predição para os valores de ED e EM do milho para suínos, sendo estas validadas pelo menor erro de predição para maior acurácia dos modelos. Palavras-chave: Energia digestível, energia metabolizável, modelos matemáticos

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Section: Resultssupporting

confidence: 86%

Development and ex post validation of prediction equations of corn energy values for growing pigs

et al. 2015

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“…Durante muito tempo, prevaleceu a ideia de que a ave procura o alimento para satisfazer suas exigências em energia. No entanto, muitos trabalhos têm demonstrado que o controle do consumo de alimento é regulado pela densidade energética da ração (NASCIMENTO et al, 2011).…”

Section: Introductionunclassified

Desempenho E Morfometria Duodenal De Frangos De Corte Submetidos a Diferentes Níveis De Energia E Programas De Alimentação De 42 a 57 Dias De Idade

Duarte¹,

Junqueira

Borges

et al. 2012

Ci. Anim. Bras.

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RESUMOO experimento foi realizado com o objetivo de avaliar diferentes níveis de energia e programas de alimentação sobre o desempenho, as características de carcaça e a morfometria da mucosa do duodeno de frangos de corte de 42 a 57 dias de idade. Foram utilizados 1.600 pintos machos "Cobb 500", em um delineamento inteiramente ao acaso, em esquema fatorial 2X4, sendo dois níveis de energia (3.200 e 3.600 kcal EM/kg) e quatro programas de alimentação. Os níveis energéticos ou os diferentes programas de alimentação não determinaram diferenças expressivas nas características de carcaça que justifiquem a escolha de um nível de energia ou de um programa alimentar, devendo prevalecer o nível energético e as recomendações de aminoácidos que determinem o maior custo-benefício. Observou-se que o nível 3.600 kcal EM/kg proporcionou melhoria no desempenho das aves e na morfometria da mucosa intestinal e o fracionamento das exigências de aminoácidos digestíveis em dois períodos piorou os resultados de desempenho e de altura de vilosidade.

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“…To obtain the equations via 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. The data used to validate the proposed equations were obtained by Alvarenga et al (2011) .…”

Section: Methodsmentioning

confidence: 99%

“… Zhao et al (2008) developed prediction equations using multiple regression to estimate the energy values using the chemical composition of the feedstuffs; however, 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.…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian networks to the prediction of the AMEn: a new methodology in broiler nutrition

Alvarenga

Lima

Filho

et al. 2021

Translational Animal Science

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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.

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Equações de predição para estimar valores da energia metabolizável de alimentos concentrados energéticos para aves utilizando meta-análise

Cited by 10 publications

References 8 publications

Development and ex post validation of prediction equations of corn energy values for growing pigs

Development and ex post validation of prediction equations of corn energy values for growing pigs

Desempenho E Morfometria Duodenal De Frangos De Corte Submetidos a Diferentes Níveis De Energia E Programas De Alimentação De 42 a 57 Dias De Idade

Application of Bayesian networks to the prediction of the AMEn: a new methodology in broiler nutrition

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