Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping
Main Author: | |
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Publication Date: | 2019 |
Other Authors: | , , , |
Format: | Article |
Language: | eng |
Source: | Revista ciência agronômica (Online) |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902019000100131 |
Summary: | 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. |
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Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrappingChemical compositionCornMeta-analysisPigsRegression modelsABSTRACT 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.Universidade Federal do Ceará2019-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902019000100131Revista Ciência Agronômica v.50 n.1 2019reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20190016info:eu-repo/semantics/openAccessOliveira,Newton Tavares Escocard dePozza,Paulo CesarCastilha,Leandro DalcinPasquetti,Tiago JuniorLanger,Carolina Natalieng2018-11-07T00:00:00Zoai:scielo:S1806-66902019000100131Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2018-11-07T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping |
title |
Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping |
spellingShingle |
Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping Oliveira,Newton Tavares Escocard de Chemical composition Corn Meta-analysis Pigs Regression models |
title_short |
Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping |
title_full |
Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping |
title_fullStr |
Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping |
title_full_unstemmed |
Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping |
title_sort |
Metabolisable energy prediction in energy feedstuffs and evaluation of the stepwise validation procedure using bootstrapping |
author |
Oliveira,Newton Tavares Escocard de |
author_facet |
Oliveira,Newton Tavares Escocard de Pozza,Paulo Cesar Castilha,Leandro Dalcin Pasquetti,Tiago Junior Langer,Carolina Natali |
author_role |
author |
author2 |
Pozza,Paulo Cesar Castilha,Leandro Dalcin Pasquetti,Tiago Junior Langer,Carolina Natali |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Oliveira,Newton Tavares Escocard de Pozza,Paulo Cesar Castilha,Leandro Dalcin Pasquetti,Tiago Junior Langer,Carolina Natali |
dc.subject.por.fl_str_mv |
Chemical composition Corn Meta-analysis Pigs Regression models |
topic |
Chemical composition Corn Meta-analysis Pigs Regression models |
description |
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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-03-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902019000100131 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902019000100131 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/1806-6690.20190016 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal do Ceará |
publisher.none.fl_str_mv |
Universidade Federal do Ceará |
dc.source.none.fl_str_mv |
Revista Ciência Agronômica v.50 n.1 2019 reponame:Revista ciência agronômica (Online) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Revista ciência agronômica (Online) |
collection |
Revista ciência agronômica (Online) |
repository.name.fl_str_mv |
Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
||alekdutra@ufc.br|| ccarev@ufc.br |
_version_ |
1750297489396727808 |