Predicting enteric methane production from cattle in the tropics

Detalhes bibliográficos
Autor(a) principal: Ribeiro, R. S.
Data de Publicação: 2020
Outros Autores: Rodrigues, J. P.P., Maurício, R. M., Borges, A. L.C.C., Reis e Silva, R., Berchielli, T. T. [UNESP], Valadares Filho, S. C., Machado, F. S., Campos, M. M., Ferreira, A. L., Guimarães Júnior, R., Azevêdo, J. A.G., Santos, R. D., Tomich, T. R., Pereira, L. G.R.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1017/S1751731120001743
http://hdl.handle.net/11449/207971
Resumo: Accurate estimates of methane (CH4) production by cattle in different contexts are essential to developing mitigation strategies in different regions. We aimed to: (i) compile a database of CH4 emissions from Brazilian cattle studies, (ii) evaluate prediction precision and accuracy of extant proposed equations for cattle and (iii) develop specialized equations for predicting CH4 emissions from cattle in tropical conditions. Data of nutrient intake, diet composition and CH4 emissions were compiled from in vivo studies using open-circuit respiratory chambers, SF6 technique or the GreenFeed® system. A final dataset containing intake, diet composition, digestibility and CH4 emissions (677 individual animal observations, 40 treatment means) obtained from 38 studies conducted in Brazil was used. The dataset was divided into three groups: all animals (GEN), lactating dairy cows (LAC) and growing cattle and non-lactating dairy cows (GCNL). A total of 54 prediction equations available in the literature were evaluated. A total of 96 multiple linear models were developed for predicting CH4 production (MJ/day). The predictor variables were DM intake (DMI), gross energy (GE) intake, BW, DMI as proportion of BW, NDF concentration, ether extract (EE) concentration, dietary proportion of concentrate and GE digestibility. Model selection criteria were significance (P < 0.05) and variance inflation factor lower than three for all predictors. Each model performance was evaluated by leave-one-out cross-validation. The Intergovernmental Panel on Climate Change (2006) Tier 2 method performed better for GEN and GCNL than LAC and overpredicted CH4 production for all datasets. Increasing complexity of the newly developed models resulted in greater performance. The GCNL had a greater number of equations with expanded possibilities to correct for diet characteristics such as EE and NDF concentrations and dietary proportion of concentrate. For the LAC dataset, equations based on intake and animal characteristics were developed. The equations developed in the present study can be useful for accurate and precise estimation of CH4 emissions from cattle in tropical conditions. These equations could improve accuracy of greenhouse gas inventories for tropical countries. The results provide a better understanding of the dietary and animal characteristics that influence the production of enteric CH4 in tropical production systems.
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spelling Predicting enteric methane production from cattle in the tropicsbeefdairydigestibilityemissionsgreenhouse gasAccurate estimates of methane (CH4) production by cattle in different contexts are essential to developing mitigation strategies in different regions. We aimed to: (i) compile a database of CH4 emissions from Brazilian cattle studies, (ii) evaluate prediction precision and accuracy of extant proposed equations for cattle and (iii) develop specialized equations for predicting CH4 emissions from cattle in tropical conditions. Data of nutrient intake, diet composition and CH4 emissions were compiled from in vivo studies using open-circuit respiratory chambers, SF6 technique or the GreenFeed® system. A final dataset containing intake, diet composition, digestibility and CH4 emissions (677 individual animal observations, 40 treatment means) obtained from 38 studies conducted in Brazil was used. The dataset was divided into three groups: all animals (GEN), lactating dairy cows (LAC) and growing cattle and non-lactating dairy cows (GCNL). A total of 54 prediction equations available in the literature were evaluated. A total of 96 multiple linear models were developed for predicting CH4 production (MJ/day). The predictor variables were DM intake (DMI), gross energy (GE) intake, BW, DMI as proportion of BW, NDF concentration, ether extract (EE) concentration, dietary proportion of concentrate and GE digestibility. Model selection criteria were significance (P < 0.05) and variance inflation factor lower than three for all predictors. Each model performance was evaluated by leave-one-out cross-validation. The Intergovernmental Panel on Climate Change (2006) Tier 2 method performed better for GEN and GCNL than LAC and overpredicted CH4 production for all datasets. Increasing complexity of the newly developed models resulted in greater performance. The GCNL had a greater number of equations with expanded possibilities to correct for diet characteristics such as EE and NDF concentrations and dietary proportion of concentrate. For the LAC dataset, equations based on intake and animal characteristics were developed. The equations developed in the present study can be useful for accurate and precise estimation of CH4 emissions from cattle in tropical conditions. These equations could improve accuracy of greenhouse gas inventories for tropical countries. The results provide a better understanding of the dietary and animal characteristics that influence the production of enteric CH4 in tropical production systems.Bio-Engineering Department Federal University of São João Del ReiFederal University of Southern and Southeastern Pará (UNIFESSPA)Federal University of Minas Gerais State (UFMG)São Paulo State University (UNESP)Federal University of Viçosa (UFV)Brazilian Agricultural Research Corporation (EMBRAPA Dairy Cattle)Brazilian Agricultural Research Corporation (EMBRAPA Cerrados)State University of Santa CruzBrazilian Agricultural Research Corporation (EMBRAPA Semiárido)São Paulo State University (UNESP)Federal University of São João Del ReiFederal University of Southern and Southeastern Pará (UNIFESSPA)Universidade Federal de Minas Gerais (UFMG)Universidade Estadual Paulista (Unesp)Universidade Federal de Viçosa (UFV)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)State University of Santa CruzRibeiro, R. S.Rodrigues, J. P.P.Maurício, R. M.Borges, A. L.C.C.Reis e Silva, R.Berchielli, T. T. [UNESP]Valadares Filho, S. C.Machado, F. S.Campos, M. M.Ferreira, A. L.Guimarães Júnior, R.Azevêdo, J. A.G.Santos, R. D.Tomich, T. R.Pereira, L. G.R.2021-06-25T11:04:12Z2021-06-25T11:04:12Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articles438-s452http://dx.doi.org/10.1017/S1751731120001743Animal, v. 14, p. s438-s452.1751-732X1751-7311http://hdl.handle.net/11449/20797110.1017/S17517311200017432-s2.0-85091125775Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnimalinfo:eu-repo/semantics/openAccess2024-06-07T18:45:08Zoai:repositorio.unesp.br:11449/207971Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T00:09:48.995859Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Predicting enteric methane production from cattle in the tropics
title Predicting enteric methane production from cattle in the tropics
spellingShingle Predicting enteric methane production from cattle in the tropics
Ribeiro, R. S.
beef
dairy
digestibility
emissions
greenhouse gas
title_short Predicting enteric methane production from cattle in the tropics
title_full Predicting enteric methane production from cattle in the tropics
title_fullStr Predicting enteric methane production from cattle in the tropics
title_full_unstemmed Predicting enteric methane production from cattle in the tropics
title_sort Predicting enteric methane production from cattle in the tropics
author Ribeiro, R. S.
author_facet Ribeiro, R. S.
Rodrigues, J. P.P.
Maurício, R. M.
Borges, A. L.C.C.
Reis e Silva, R.
Berchielli, T. T. [UNESP]
Valadares Filho, S. C.
Machado, F. S.
Campos, M. M.
Ferreira, A. L.
Guimarães Júnior, R.
Azevêdo, J. A.G.
Santos, R. D.
Tomich, T. R.
Pereira, L. G.R.
author_role author
author2 Rodrigues, J. P.P.
Maurício, R. M.
Borges, A. L.C.C.
Reis e Silva, R.
Berchielli, T. T. [UNESP]
Valadares Filho, S. C.
Machado, F. S.
Campos, M. M.
Ferreira, A. L.
Guimarães Júnior, R.
Azevêdo, J. A.G.
Santos, R. D.
Tomich, T. R.
Pereira, L. G.R.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Federal University of São João Del Rei
Federal University of Southern and Southeastern Pará (UNIFESSPA)
Universidade Federal de Minas Gerais (UFMG)
Universidade Estadual Paulista (Unesp)
Universidade Federal de Viçosa (UFV)
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
State University of Santa Cruz
dc.contributor.author.fl_str_mv Ribeiro, R. S.
Rodrigues, J. P.P.
Maurício, R. M.
Borges, A. L.C.C.
Reis e Silva, R.
Berchielli, T. T. [UNESP]
Valadares Filho, S. C.
Machado, F. S.
Campos, M. M.
Ferreira, A. L.
Guimarães Júnior, R.
Azevêdo, J. A.G.
Santos, R. D.
Tomich, T. R.
Pereira, L. G.R.
dc.subject.por.fl_str_mv beef
dairy
digestibility
emissions
greenhouse gas
topic beef
dairy
digestibility
emissions
greenhouse gas
description Accurate estimates of methane (CH4) production by cattle in different contexts are essential to developing mitigation strategies in different regions. We aimed to: (i) compile a database of CH4 emissions from Brazilian cattle studies, (ii) evaluate prediction precision and accuracy of extant proposed equations for cattle and (iii) develop specialized equations for predicting CH4 emissions from cattle in tropical conditions. Data of nutrient intake, diet composition and CH4 emissions were compiled from in vivo studies using open-circuit respiratory chambers, SF6 technique or the GreenFeed® system. A final dataset containing intake, diet composition, digestibility and CH4 emissions (677 individual animal observations, 40 treatment means) obtained from 38 studies conducted in Brazil was used. The dataset was divided into three groups: all animals (GEN), lactating dairy cows (LAC) and growing cattle and non-lactating dairy cows (GCNL). A total of 54 prediction equations available in the literature were evaluated. A total of 96 multiple linear models were developed for predicting CH4 production (MJ/day). The predictor variables were DM intake (DMI), gross energy (GE) intake, BW, DMI as proportion of BW, NDF concentration, ether extract (EE) concentration, dietary proportion of concentrate and GE digestibility. Model selection criteria were significance (P < 0.05) and variance inflation factor lower than three for all predictors. Each model performance was evaluated by leave-one-out cross-validation. The Intergovernmental Panel on Climate Change (2006) Tier 2 method performed better for GEN and GCNL than LAC and overpredicted CH4 production for all datasets. Increasing complexity of the newly developed models resulted in greater performance. The GCNL had a greater number of equations with expanded possibilities to correct for diet characteristics such as EE and NDF concentrations and dietary proportion of concentrate. For the LAC dataset, equations based on intake and animal characteristics were developed. The equations developed in the present study can be useful for accurate and precise estimation of CH4 emissions from cattle in tropical conditions. These equations could improve accuracy of greenhouse gas inventories for tropical countries. The results provide a better understanding of the dietary and animal characteristics that influence the production of enteric CH4 in tropical production systems.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
2021-06-25T11:04:12Z
2021-06-25T11:04:12Z
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/10.1017/S1751731120001743
Animal, v. 14, p. s438-s452.
1751-732X
1751-7311
http://hdl.handle.net/11449/207971
10.1017/S1751731120001743
2-s2.0-85091125775
url http://dx.doi.org/10.1017/S1751731120001743
http://hdl.handle.net/11449/207971
identifier_str_mv Animal, v. 14, p. s438-s452.
1751-732X
1751-7311
10.1017/S1751731120001743
2-s2.0-85091125775
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Animal
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv s438-s452
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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