Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3389/fvets.2021.650248 http://hdl.handle.net/11449/221819 |
Resumo: | Microbial crude protein (MCP) produced in rumen could be estimated by a variety of protocols of experimental sampling and analysis. However, a model to estimate this value is necessary when protein requirements are calculated for small ruminants. This model could be useful to calculate rumen degradable protein (RDP) requirements from metabolizable protein (MP). Then, our objective was to investigate if there is a difference in MCP efficiency between sheep and goats, and to fit equations to predict ruminal MCP production from dietary energy intake. The database consisted of 19 studies with goats (n = 176) and sheep (n = 316), and the variables MCP synthesis (g/day), total digestible nutrients (TDN), and organic matter (OM) intakes (g/day), and OM digestibility (g/kg DM) were registered for both species. The database was used for two different purposes, where 70% of the values were sorted to fit equations, and 30% for validation. A meta-analytical procedure was carried out using the MIXED procedure of SAS, specie was considered as the fixed dummy effect, and the intercept and slope nested in the study were considered random effects. No effect of specie was observed for the estimation of MCP from TDN, digestible Organic Matter (dOM), or metabolizable energy (ME) intakes (P > 0.05), considering an equation with or without an intercept. Therefore, single models including both species at the same fitting were validated. The following equations MCP (g/day) = 12.7311 + 59.2956 × TDN intake (AIC = 3,004.6); MCP (g/day) = 15.7764 + 62.2612 × dOM intake (AIC = 2,755.1); and MCP (g/day) = 12.7311 + 15.3000 × ME intake (AIC = 3,007.3) presented lower values for the mean square error of prediction (MSEP) and its decomposition, and similar values for the concordance correlation coefficient (CCC) and for the residual mean square error (RMSE) when compared with equations fitted without an intercept. The intercept and slope pooled test was significant for equations without an intercept (P < 0.05), indicating that observed and predicted data differed. In contrast, predicted and observed data for complete equations were similar (P > 0.05). |
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Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small RuminantsbacteriagoatmicroorganismsrumensheepyieldMicrobial crude protein (MCP) produced in rumen could be estimated by a variety of protocols of experimental sampling and analysis. However, a model to estimate this value is necessary when protein requirements are calculated for small ruminants. This model could be useful to calculate rumen degradable protein (RDP) requirements from metabolizable protein (MP). Then, our objective was to investigate if there is a difference in MCP efficiency between sheep and goats, and to fit equations to predict ruminal MCP production from dietary energy intake. The database consisted of 19 studies with goats (n = 176) and sheep (n = 316), and the variables MCP synthesis (g/day), total digestible nutrients (TDN), and organic matter (OM) intakes (g/day), and OM digestibility (g/kg DM) were registered for both species. The database was used for two different purposes, where 70% of the values were sorted to fit equations, and 30% for validation. A meta-analytical procedure was carried out using the MIXED procedure of SAS, specie was considered as the fixed dummy effect, and the intercept and slope nested in the study were considered random effects. No effect of specie was observed for the estimation of MCP from TDN, digestible Organic Matter (dOM), or metabolizable energy (ME) intakes (P > 0.05), considering an equation with or without an intercept. Therefore, single models including both species at the same fitting were validated. The following equations MCP (g/day) = 12.7311 + 59.2956 × TDN intake (AIC = 3,004.6); MCP (g/day) = 15.7764 + 62.2612 × dOM intake (AIC = 2,755.1); and MCP (g/day) = 12.7311 + 15.3000 × ME intake (AIC = 3,007.3) presented lower values for the mean square error of prediction (MSEP) and its decomposition, and similar values for the concordance correlation coefficient (CCC) and for the residual mean square error (RMSE) when compared with equations fitted without an intercept. The intercept and slope pooled test was significant for equations without an intercept (P < 0.05), indicating that observed and predicted data differed. In contrast, predicted and observed data for complete equations were similar (P > 0.05).Instituto Nacional de Ciência e Tecnologia de Ciência AnimalSchool of Veterinary Medicine and Animal Science Universidade Federal da BahiaDepartment of Agricultural and Environmental Sciences Universidade Estadual de Santa CruzDepartment of Animal Science Instituto Federal de Educação Ciência e Tecnologia do Sul de Minas GeraisCenter of Agrarian Sciences Universidade Federal da ParaíbaDepartment of Plant and Animal Sciences Universidade Estadual do Sudoeste da BahiaDepartment of Animal Science Universidade Federal do CearáDepartment of Animal Science Universidade Federal de ViçosaDepartment of Animal Science Universidade Estadual PaulistaDepartment of Agricultural and Environmental Sciences Universidade Federal do Recôncavo da BahiaDepartment of Animal Science Universidade Estadual PaulistaUniversidade Federal da Bahia (UFBA)Universidade Estadual de Santa CruzCiência e Tecnologia do Sul de Minas GeraisUniversidade Federal da Paraíba (UFPB)Universidade Estadual do Sudoeste da BahiaUniversidade Federal do CearáUniversidade Federal de Viçosa (UFV)Universidade Estadual Paulista (UNESP)Universidade Federal do Recôncavo da BahiaSantos, Stefanie Alvarengade Carvalho, Gleidson Giordano PintoAzevêdo, José Augusto GomesZanetti, DiegoSantos, Edson MauroPereira, Mara Lucia AlbuquerquePereira, Elzania SalesPires, Aureliano José VieiraValadares Filho, Sebastião de CamposTeixeira, Izabelle Auxiliadora Molina de Almeida [UNESP]Tosto, Manuela Silva LibânioLeite, Laudi CunhaMariz, Lays Débora Silva2022-04-28T19:40:49Z2022-04-28T19:40:49Z2021-06-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3389/fvets.2021.650248Frontiers in Veterinary Science, v. 8.2297-1769http://hdl.handle.net/11449/22181910.3389/fvets.2021.6502482-s2.0-85108368655Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengFrontiers in Veterinary Scienceinfo:eu-repo/semantics/openAccess2022-04-28T19:40:49Zoai:repositorio.unesp.br:11449/221819Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:52:49.221374Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants |
title |
Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants |
spellingShingle |
Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants Santos, Stefanie Alvarenga bacteria goat microorganisms rumen sheep yield |
title_short |
Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants |
title_full |
Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants |
title_fullStr |
Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants |
title_full_unstemmed |
Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants |
title_sort |
Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants |
author |
Santos, Stefanie Alvarenga |
author_facet |
Santos, Stefanie Alvarenga de Carvalho, Gleidson Giordano Pinto Azevêdo, José Augusto Gomes Zanetti, Diego Santos, Edson Mauro Pereira, Mara Lucia Albuquerque Pereira, Elzania Sales Pires, Aureliano José Vieira Valadares Filho, Sebastião de Campos Teixeira, Izabelle Auxiliadora Molina de Almeida [UNESP] Tosto, Manuela Silva Libânio Leite, Laudi Cunha Mariz, Lays Débora Silva |
author_role |
author |
author2 |
de Carvalho, Gleidson Giordano Pinto Azevêdo, José Augusto Gomes Zanetti, Diego Santos, Edson Mauro Pereira, Mara Lucia Albuquerque Pereira, Elzania Sales Pires, Aureliano José Vieira Valadares Filho, Sebastião de Campos Teixeira, Izabelle Auxiliadora Molina de Almeida [UNESP] Tosto, Manuela Silva Libânio Leite, Laudi Cunha Mariz, Lays Débora Silva |
author2_role |
author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal da Bahia (UFBA) Universidade Estadual de Santa Cruz Ciência e Tecnologia do Sul de Minas Gerais Universidade Federal da Paraíba (UFPB) Universidade Estadual do Sudoeste da Bahia Universidade Federal do Ceará Universidade Federal de Viçosa (UFV) Universidade Estadual Paulista (UNESP) Universidade Federal do Recôncavo da Bahia |
dc.contributor.author.fl_str_mv |
Santos, Stefanie Alvarenga de Carvalho, Gleidson Giordano Pinto Azevêdo, José Augusto Gomes Zanetti, Diego Santos, Edson Mauro Pereira, Mara Lucia Albuquerque Pereira, Elzania Sales Pires, Aureliano José Vieira Valadares Filho, Sebastião de Campos Teixeira, Izabelle Auxiliadora Molina de Almeida [UNESP] Tosto, Manuela Silva Libânio Leite, Laudi Cunha Mariz, Lays Débora Silva |
dc.subject.por.fl_str_mv |
bacteria goat microorganisms rumen sheep yield |
topic |
bacteria goat microorganisms rumen sheep yield |
description |
Microbial crude protein (MCP) produced in rumen could be estimated by a variety of protocols of experimental sampling and analysis. However, a model to estimate this value is necessary when protein requirements are calculated for small ruminants. This model could be useful to calculate rumen degradable protein (RDP) requirements from metabolizable protein (MP). Then, our objective was to investigate if there is a difference in MCP efficiency between sheep and goats, and to fit equations to predict ruminal MCP production from dietary energy intake. The database consisted of 19 studies with goats (n = 176) and sheep (n = 316), and the variables MCP synthesis (g/day), total digestible nutrients (TDN), and organic matter (OM) intakes (g/day), and OM digestibility (g/kg DM) were registered for both species. The database was used for two different purposes, where 70% of the values were sorted to fit equations, and 30% for validation. A meta-analytical procedure was carried out using the MIXED procedure of SAS, specie was considered as the fixed dummy effect, and the intercept and slope nested in the study were considered random effects. No effect of specie was observed for the estimation of MCP from TDN, digestible Organic Matter (dOM), or metabolizable energy (ME) intakes (P > 0.05), considering an equation with or without an intercept. Therefore, single models including both species at the same fitting were validated. The following equations MCP (g/day) = 12.7311 + 59.2956 × TDN intake (AIC = 3,004.6); MCP (g/day) = 15.7764 + 62.2612 × dOM intake (AIC = 2,755.1); and MCP (g/day) = 12.7311 + 15.3000 × ME intake (AIC = 3,007.3) presented lower values for the mean square error of prediction (MSEP) and its decomposition, and similar values for the concordance correlation coefficient (CCC) and for the residual mean square error (RMSE) when compared with equations fitted without an intercept. The intercept and slope pooled test was significant for equations without an intercept (P < 0.05), indicating that observed and predicted data differed. In contrast, predicted and observed data for complete equations were similar (P > 0.05). |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-10 2022-04-28T19:40:49Z 2022-04-28T19:40:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3389/fvets.2021.650248 Frontiers in Veterinary Science, v. 8. 2297-1769 http://hdl.handle.net/11449/221819 10.3389/fvets.2021.650248 2-s2.0-85108368655 |
url |
http://dx.doi.org/10.3389/fvets.2021.650248 http://hdl.handle.net/11449/221819 |
identifier_str_mv |
Frontiers in Veterinary Science, v. 8. 2297-1769 10.3389/fvets.2021.650248 2-s2.0-85108368655 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Frontiers in Veterinary Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128577065975808 |