Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis

Detalhes bibliográficos
Autor(a) principal: Bougouin, Adeline
Data de Publicação: 2022
Outros Autores: Hristov, Alexander, Zanetti, Diego, Filho, Sebastiao C.V., Rennó, Lucianna N., Menezes, Ana C.B., Silva, Jarbas M., Alhadas, Herlon M., Mariz, Lays D.S., Prados, Laura F. [UNESP], Beauchemin, Karen A., McAllister, Tim, Yang, WenZhu Z., Koenig, Karen M., Goossens, Karen, Yan, Tianhai, Noziere, Pierre, Jonker, Arjan, Kebreab, Ermias
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1093/jas/skac150
http://hdl.handle.net/11449/248659
Resumo: Manure N from cattle contributes to nitrate leaching, nitrous oxide, and ammonia emissions. Measurement of manure N outputs on commercial beef cattle operations is laborious, expensive, and impractical; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were to 1) collate an international dataset of N excretion in feces and urine based on individual observations from beef cattle; 2) determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and 3) develop robust and reliable N excretion prediction models based on individual observation from beef cattle consuming various diets. A meta-analysis based on individual beef data from different experiments was carried out from a raw dataset including 1,004 observations from 33 experiments collected from 5 research institutes in Europe (n = 3), North America (n = 1), and South America (n = 1). A sequential approach was taken in developing models of increasing complexity by incrementally adding significant variables that affected fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models with experiment as a random effect. Simple models including dry matter intake (DMI) were better at predicting fecal N excretion than those using only dietary nutrient composition or body weight (BW). Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI. A model including DMI and dietary component concentrations led to the most robust prediction of fecal and urinary N excretion, generating root mean square prediction errors as a percentage of the observed mean values of 25.0% for feces and 25.6% for urine. Complex total manure N excretion models based on BW and dietary component concentrations led to the lowest prediction errors of about 14.6%. In conclusion, several models to predict N excretion already exist, but the ones developed in this study are based on individual observations encompassing larger variability than the previous developed models. In addition, models that include information on DMI or N intake are required for accurate prediction of fecal, urinary, and total manure N excretion. In the absence of intake data, equations have poor performance as compared with equations based on intake and dietary component concentrations.
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spelling Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysisbeef cattlenitrogen excretionprediction modelsManure N from cattle contributes to nitrate leaching, nitrous oxide, and ammonia emissions. Measurement of manure N outputs on commercial beef cattle operations is laborious, expensive, and impractical; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were to 1) collate an international dataset of N excretion in feces and urine based on individual observations from beef cattle; 2) determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and 3) develop robust and reliable N excretion prediction models based on individual observation from beef cattle consuming various diets. A meta-analysis based on individual beef data from different experiments was carried out from a raw dataset including 1,004 observations from 33 experiments collected from 5 research institutes in Europe (n = 3), North America (n = 1), and South America (n = 1). A sequential approach was taken in developing models of increasing complexity by incrementally adding significant variables that affected fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models with experiment as a random effect. Simple models including dry matter intake (DMI) were better at predicting fecal N excretion than those using only dietary nutrient composition or body weight (BW). Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI. A model including DMI and dietary component concentrations led to the most robust prediction of fecal and urinary N excretion, generating root mean square prediction errors as a percentage of the observed mean values of 25.0% for feces and 25.6% for urine. Complex total manure N excretion models based on BW and dietary component concentrations led to the lowest prediction errors of about 14.6%. In conclusion, several models to predict N excretion already exist, but the ones developed in this study are based on individual observations encompassing larger variability than the previous developed models. In addition, models that include information on DMI or N intake are required for accurate prediction of fecal, urinary, and total manure N excretion. In the absence of intake data, equations have poor performance as compared with equations based on intake and dietary component concentrations.U.S. Department of AgricultureNational Institute of Food and AgricultureFondo Nacional de Desarrollo Científico, Tecnológico y de Innovación TecnológicaDepartment of Animal Science University of CaliforniaDepartment of Animal Science The Pennsylvania State UniversityDepartment of Animal Sciences Universidade Federal de Viçosa, Minas GeraisDepartment of Animal Science Universidade Estadual Paulista (UNESP), JaboticabalAgriculture and Agri-Food Canada Lethbridge Research and Development CentreAnimal Sciences Unit Flanders Research Institute for Agriculture Fisheries and Food (ILVO), ScheldewegSustainable Agri-Food Sciences Division Agri-Food and Biosciences Institute, County DownINRAE Université Clermont Auvergne - VetAgroSup UMR 1213 Unité Mixte de Recherche sur les Herbivores Centre de recherche Auvergne Rhône-Alpes TheixAgResearch Grasslands Research CentreDepartment of Animal Science Universidade Estadual Paulista (UNESP), JaboticabalNational Institute of Food and Agriculture: 1000803Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica: 1191476National Institute of Food and Agriculture: 2014-67003-21979National Institute of Food and Agriculture: 2019-67019-29400University of CaliforniaThe Pennsylvania State UniversityUniversidade Federal de Viçosa (UFV)Universidade Estadual Paulista (UNESP)Lethbridge Research and Development CentreFisheries and Food (ILVO)Agri-Food and Biosciences InstituteTheixGrasslands Research CentreBougouin, AdelineHristov, AlexanderZanetti, DiegoFilho, Sebastiao C.V.Rennó, Lucianna N.Menezes, Ana C.B.Silva, Jarbas M.Alhadas, Herlon M.Mariz, Lays D.S.Prados, Laura F. [UNESP]Beauchemin, Karen A.McAllister, TimYang, WenZhu Z.Koenig, Karen M.Goossens, KarenYan, TianhaiNoziere, PierreJonker, ArjanKebreab, Ermias2023-07-29T13:50:08Z2023-07-29T13:50:08Z2022-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1093/jas/skac150Journal of Animal Science, v. 100, n. 9, 2022.1525-31630021-8812http://hdl.handle.net/11449/24865910.1093/jas/skac1502-s2.0-85151990202Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Animal Scienceinfo:eu-repo/semantics/openAccess2023-07-29T13:50:08Zoai:repositorio.unesp.br:11449/248659Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T13:50:08Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis
title Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis
spellingShingle Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis
Bougouin, Adeline
beef cattle
nitrogen excretion
prediction models
title_short Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis
title_full Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis
title_fullStr Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis
title_full_unstemmed Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis
title_sort Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis
author Bougouin, Adeline
author_facet Bougouin, Adeline
Hristov, Alexander
Zanetti, Diego
Filho, Sebastiao C.V.
Rennó, Lucianna N.
Menezes, Ana C.B.
Silva, Jarbas M.
Alhadas, Herlon M.
Mariz, Lays D.S.
Prados, Laura F. [UNESP]
Beauchemin, Karen A.
McAllister, Tim
Yang, WenZhu Z.
Koenig, Karen M.
Goossens, Karen
Yan, Tianhai
Noziere, Pierre
Jonker, Arjan
Kebreab, Ermias
author_role author
author2 Hristov, Alexander
Zanetti, Diego
Filho, Sebastiao C.V.
Rennó, Lucianna N.
Menezes, Ana C.B.
Silva, Jarbas M.
Alhadas, Herlon M.
Mariz, Lays D.S.
Prados, Laura F. [UNESP]
Beauchemin, Karen A.
McAllister, Tim
Yang, WenZhu Z.
Koenig, Karen M.
Goossens, Karen
Yan, Tianhai
Noziere, Pierre
Jonker, Arjan
Kebreab, Ermias
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv University of California
The Pennsylvania State University
Universidade Federal de Viçosa (UFV)
Universidade Estadual Paulista (UNESP)
Lethbridge Research and Development Centre
Fisheries and Food (ILVO)
Agri-Food and Biosciences Institute
Theix
Grasslands Research Centre
dc.contributor.author.fl_str_mv Bougouin, Adeline
Hristov, Alexander
Zanetti, Diego
Filho, Sebastiao C.V.
Rennó, Lucianna N.
Menezes, Ana C.B.
Silva, Jarbas M.
Alhadas, Herlon M.
Mariz, Lays D.S.
Prados, Laura F. [UNESP]
Beauchemin, Karen A.
McAllister, Tim
Yang, WenZhu Z.
Koenig, Karen M.
Goossens, Karen
Yan, Tianhai
Noziere, Pierre
Jonker, Arjan
Kebreab, Ermias
dc.subject.por.fl_str_mv beef cattle
nitrogen excretion
prediction models
topic beef cattle
nitrogen excretion
prediction models
description Manure N from cattle contributes to nitrate leaching, nitrous oxide, and ammonia emissions. Measurement of manure N outputs on commercial beef cattle operations is laborious, expensive, and impractical; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were to 1) collate an international dataset of N excretion in feces and urine based on individual observations from beef cattle; 2) determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and 3) develop robust and reliable N excretion prediction models based on individual observation from beef cattle consuming various diets. A meta-analysis based on individual beef data from different experiments was carried out from a raw dataset including 1,004 observations from 33 experiments collected from 5 research institutes in Europe (n = 3), North America (n = 1), and South America (n = 1). A sequential approach was taken in developing models of increasing complexity by incrementally adding significant variables that affected fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models with experiment as a random effect. Simple models including dry matter intake (DMI) were better at predicting fecal N excretion than those using only dietary nutrient composition or body weight (BW). Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI. A model including DMI and dietary component concentrations led to the most robust prediction of fecal and urinary N excretion, generating root mean square prediction errors as a percentage of the observed mean values of 25.0% for feces and 25.6% for urine. Complex total manure N excretion models based on BW and dietary component concentrations led to the lowest prediction errors of about 14.6%. In conclusion, several models to predict N excretion already exist, but the ones developed in this study are based on individual observations encompassing larger variability than the previous developed models. In addition, models that include information on DMI or N intake are required for accurate prediction of fecal, urinary, and total manure N excretion. In the absence of intake data, equations have poor performance as compared with equations based on intake and dietary component concentrations.
publishDate 2022
dc.date.none.fl_str_mv 2022-09-01
2023-07-29T13:50:08Z
2023-07-29T13:50:08Z
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/10.1093/jas/skac150
Journal of Animal Science, v. 100, n. 9, 2022.
1525-3163
0021-8812
http://hdl.handle.net/11449/248659
10.1093/jas/skac150
2-s2.0-85151990202
url http://dx.doi.org/10.1093/jas/skac150
http://hdl.handle.net/11449/248659
identifier_str_mv Journal of Animal Science, v. 100, n. 9, 2022.
1525-3163
0021-8812
10.1093/jas/skac150
2-s2.0-85151990202
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Animal 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)
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