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.
Highlights
Knowledge of the apparent metabolisable energy (AME) of feedstuffs is essential for formulating balanced rations (SAKOMURA; ROSTAGNO, 2016), as nutrient requirements are expressed in terms of energy levels of feed, which affect feed intake and pig performance (ROSTAGNO et al, 2011)
In order to suggest high reliability of the estimates, the coefficients of determination (R2) obtained needed to be high and show that the estimated equations explained more than 90% of the variation in the AME data as a function of the chemical composition of corn, sorghum and wheat bran, as obtained from the Brazilian and international literature data (Table 1)
Except for the AME5 model, the results showed that the corn and sorghum data can be combined into a single database, because the estimates for corn and sorghum, provided by the AME, AME, AME
Summary
Knowledge of the apparent metabolisable energy (AME) of feedstuffs is essential for formulating balanced rations (SAKOMURA; ROSTAGNO, 2016), as nutrient requirements are expressed in terms of energy levels of feed, which affect feed intake and pig performance (ROSTAGNO et al, 2011).The direct determination of AME values for pig feed is time consuming, labour intensive and costly, as it involves performing metabolic tests. The presence of outliers, due to experimental errors or errors in the determination of chemical composition of feed, combined with model adjustment strategies, can change the parameter estimates of the equations from the ordinary least squares method, resulting in variation of the predicted AME values for pig feed. This can compromise the validity of the model, related to the stability and reasonableness of the regression coefficients, and the usefulness of the model for giving accurate predictions for new data samples (CASTILHO et al, 2015; OREDEIN; OLATAYO; LOYINMI, 2011)
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