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| Registros recuperados : 3 | |
| 1. |  | ROCHA, A. A.; FARIAS, C. A.; COSTA, L. M. da; BARBIERI, R. S. Avaliação do efeito residual do herbicida fusilade-125 em solo. IN: CONGRESSO BRASILEIRO DE CIÊNÇIA DO SOLO, 25., 1995, Viçosa, MG. Resumos expandidos. Viçosa: UFV, 1995. v.1, p. 476-478.| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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| 2. | | FERNANDES, A. R.; MACHADO, C. H. C.; OLIVEIRA, G. R. A. e; LOPES, K. F.; OLIVEIRA, J. C. de; SILVA, A. M. dos S.; TEIXEIRA, R. B.; BARBIERI, R. S.; SILVA, L. C. M.; MORAES, C. R.; XAVIER, M. da C.; PANETTO, J. C. do C.; SILVA, M. V. G. B.; MACHADO, M. A.; VERNEQUE, R. da S.; MARTINS, M. F.; BORGES, C. A. V.; REIS, D. R. de L.; DOMINGUES, R. Programa Nacional de Melhoramento do Gir Leiteiro - 13ª prova de pré-seleção de touros: touros pré-selecionados por meio de avaliação genômica - maio 2022. Juiz de Fora: Embrapa Gado de Leite, 2022. 25 p. (Embrapa Gado de Leite. Documentos, 264).| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sul. |
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| 3. | | FERNANDES, A. R.; MACHADO, C. H. C.; OLIVEIRA, G. R. A. e; LOPES, K. F.; OLIVEIRA, J. C. de; SILVA, A. M. dos S.; TEIXEIRA, R. B.; BARBIERI, R. S.; XAVIER, M. da C.; PANETTO, J. C. do C.; SILVA, M. V. G. B.; MACHADO, M. A.; VERNEQUE, R. da S.; MARTINS, M. F.; BORGES, C. A. V.; REIS, D. R. de L.; DOMINGUES, R. Programa Nacional de Melhoramento do Gir Leiteiro - 14ª prova de pré-seleção de touros: touros pré-selecionados por meio de avaliação genômica - abril 2023. Juiz de Fora: Embrapa Gado de Leite, 2023. 23 p. (Embrapa Gado de Leite. Documentos, 274).| Biblioteca(s): Embrapa Gado de Leite. |
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| Registros recuperados : 3 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Cerrados; Embrapa Gado de Leite; Embrapa Semiárido. |
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Data corrente: |
21/09/2020 |
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Data da última atualização: |
22/09/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
RIBEIRO, R. S.; RODRIGUES, J. P. P.; MAURÍCIO, R. M.; BORGES, A. L. C. C.; REIS E SILVA, R.; BERCHIELLI, T. T.; VALADARES FILHO, S. C.; MACHADO, F. S.; CAMPOS, M. M.; FERREIRA, A. L.; GUIMARAES JUNIOR, R.; AZEVÊDO, J. A. G.; SANTOS, R. D. dos; TOMICH, T. R.; PEREIRA, L. G. R. |
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Afiliação: |
FERNANDA SAMARINI MACHADO, CNPGL; MARIANA MAGALHAES CAMPOS, CNPGL; ROBERTO GUIMARAES JUNIOR, CPAC; RAFAEL DANTAS DOS SANTOS, CPATSA; THIERRY RIBEIRO TOMICH, CNPGL; LUIZ GUSTAVO RIBEIRO PEREIRA, CNPGL. |
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Título: |
Predicting enteric methane production from cattle in the tropics. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Animal, 2020. |
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Páginas: |
15 p. |
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Idioma: |
Inglês
Português |
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Conteúdo: |
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. MenosAccurate 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-o... Mostrar Tudo |
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Palavras-Chave: |
Carne bovina; Emissões gases. |
|
Thesagro: |
Bovino; Carne; Digestibilidade; Efeito Estufa; Gado; Gado Leiteiro; Gás; Laticínio; Metano; Nutrição Animal. |
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Thesaurus NAL: |
Animal nutrition; Dairy cattle. |
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Categoria do assunto: |
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L Ciência Animal e Produtos de Origem Animal |
|
Marc: |
LEADER 03649naa a2200469 a 4500
001 2125038
005 2020-09-22
008 2020 bl uuuu u00u1 u #d
100 1 $aRIBEIRO, R. S.
245 $aPredicting enteric methane production from cattle in the tropics.$h[electronic resource]
260 $c2020
300 $a15 p.
520 $aAccurate 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.
650 $aAnimal nutrition
650 $aDairy cattle
650 $aBovino
650 $aCarne
650 $aDigestibilidade
650 $aEfeito Estufa
650 $aGado
650 $aGado Leiteiro
650 $aGás
650 $aLaticínio
650 $aMetano
650 $aNutrição Animal
653 $aCarne bovina
653 $aEmissões gases
700 1 $aRODRIGUES, J. P. P.
700 1 $aMAURÍCIO, R. M.
700 1 $aBORGES, A. L. C. C.
700 1 $aREIS E SILVA, R.
700 1 $aBERCHIELLI, T. T.
700 1 $aVALADARES FILHO, S. C.
700 1 $aMACHADO, F. S.
700 1 $aCAMPOS, M. M.
700 1 $aFERREIRA, A. L.
700 1 $aGUIMARAES JUNIOR, R.
700 1 $aAZEVÊDO, J. A. G.
700 1 $aSANTOS, R. D. dos
700 1 $aTOMICH, T. R.
700 1 $aPEREIRA, L. G. R.
773 $tAnimal, 2020.
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