Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping

• Autor(a) principal: Oliveira,Newton Tavares Escocard de
• Data de Publicação: 2019
• Outros Autores: Pozza,Paulo Cesar, Castilha,Leandro Dalcin, Pasquetti,Tiago Junior, Langer,Carolina Natali
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista ciência agronômica (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902019000100131
• topic: Chemical composition; Corn; Meta-analysis; Pigs; Regression models

ABSTRACT The use of predicted values of apparent metabolisable energy (AME), obtained from regression equations, can be useful for both research institutions and nutrition industries. However, there is a need to validate independent samples to ensure that the predicted equation for AME is reliable. In this study, data was collected in order to estimate the prediction equations of corn, sorghum and wheat bran for pig feed, based on the chemical composition, in addition to evaluating the validity of the stepwise selection procedure regressive method of non-parametric bootstrap resampling. Data from metabolism trials in pigs and the chemical composition of feedstuffs was collected from both Brazilian and international literature, expressed as dry matter. After the residue analysis, five models of multiple linear regression were adjusted to randomly generate 1000 bootstrap samples of equal size from the database via meta-analysis. The five estimated models were adjusted for all bootstrapped samples using the stepwise method. The highest percentage significance for regressor (PSR) value was observed for digestible energy (100%) in the AME1 model, and gross energy (95.7%) in the AME2 model, indicating high correlation of the regressive model with AME. The regressors selected for AME4 and AME5 resulted in a PSR of greater than 50%, and were validated for estimating the AME of pig feed. However, the percentage of joint occurrence of regressor models showed low reliability, with values between 2.6% (AME2) and 23.4% (AME4), suggesting that the stepwise procedure was invalid.