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| Registros recuperados : 14 | |
| 9. |  | MEJIA, N.; GEBAUER, M.; MUNOZ, L.; HEWSTONE, N.; MUNOZ, C.; HINRICHSE, P. Identification of QTLs for seedlessness, Berry Size, and ripening date in a seedless x seedless table grape progeny. American Journal of Enology and Viticulture, v. 58, n. 4, p. 499-507, 2007.| Biblioteca(s): Embrapa Semiárido. |
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| 11. | | GOMEZ, M. S.; MUNOZ, C. G.; SOARES, J. P. G.; JUNQUEIRA, A. M. R.; CARVALHO, J. M. Café orgánico, fortalezas y limitaciones: el caso de Colombia y Brasil. In: CONGRESSO DA SOCIEDADE BRASILEIRA DE ECONOMIA, ADMINISTRAÇÃO E SOCIOLOGIA RURAL, 52., 2014, Goiânia. Heterogeneidade e suas implicações no rural brasileiro: anais. Goiânia: Sober, 2014. não paginado Sober 2014.| Biblioteca(s): Embrapa Cerrados. |
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| 12. | | SOLARTE GÓMEZ, M. G.; GUZMÁN MUÑOZ, C. M.; SOARES, J. P. G.; JUNQUEIRA, A. M. R.; CARVALHO, J. M. Café orgánico, fortalezas y limitaciones: el caso de Colombia y Brasil. In: CONGRESSO DA SOCIEDADE BRASILEIRA DE ECONOMIA, ADMINISTRAÇÃO E SOCIOLOGIA RURAL, 52., 2014, Goiânia. Heterogeneidade e suas implicações no rural brasileiro: anais. Goiânia: SOBER, 2014.| Biblioteca(s): Embrapa Cerrados. |
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| 13. | | CONGIO, G. F. S.; BANNINK, A.; MAYORGA, O. L.; RODRIGUES, J. P. P.; BOUGOUIN, A.; KEBREAD, E.; SILVA, R. R.; MAURÍCIO, R. M.; SILVA, S. C. DA; OLIVEIRA, P. P. A.; MUÑOZ, C.; PEREIRA, L. G. R.; GÓMEZ, C.; ARIZA-NIETO, C.; RIBEIRO-FILHO, H. M. N.; CASTELÁN-ORTEGA, O. A.; ROSERO-NOGUERA, J. R.; TIERI, M. P.; RODRIGUES, P. H. M.; MARCONDES, M. I.; ASTIGARRAGA, L.; ABARCA, S.; HRISTOV, A. N. Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database. Science of the Total Environment, v. 825, n. 153982, p. 1-11, 2022.| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| 14. | | CONGIO, G. F. DE. S.; BANNINK, A.; MAYORGA MOGOLLÓN, O. L.; JAURENA, G.; GONDA, H.; GERE, J. I.; CERÓN-CUCCHI, M. E.; ORTIZ-CHURA, A.; TIERI, M. P.; HERNÁNDEZ, O.; RICCI, P.; JULIARENA, M. P.; LOMBARDI, B.; ABDALLA, A. L.; ABDALLA-FILHO, A. L.; BERNDT, A.; OLIVEIRA, P. P. A.; HENRIQUE, F. L.; MONTEIRO, A. L. G.; BORGES, L. I.; RIBEIRO-FILHO, H. M. N.; PEREIRA, L. G. R.; TOMICH, T. R.; CAMPOS, M. M.; MACHADO, F. S.; MARCONDES, M. I.; MERCADANTE, M. E. Z.; SAKAMOTO, L. S.; ALBUQUERQUE, L. G.; CARVALHO, P. C. DE. F.; ROSSETTO, J.; SAVIAN, J. V.; RODRIGUES, P. H. M.; PERNA JÚNIOR, F.; MOREIRA, T. S.; MAURÍCIO, R. M.; RODRIGUES, J. P. P.; BORGES, A. L. DA C. C.; SILVA, R. R. E.; LAGE, H. F.; REIS, R. A.; RUGGIERI, A. C.; CARDOSO, A. DA. S.; SILVA, S. C. DA; CHIAVEGATO, M. B.; VALADARES-FILHO, S. DE. C.; SILVA, F. A. DE. S.; ZANETTI, D.; BERCHIELLI, T. T.; MESSANA, J. D.; MUÑOZ, C.; ARIZA-NIETO, C. J.; SIERRA-ALARCÓN, A. M.; GUALDRÓN-DUARTE, L. B.; MESTRA-VARGAS, L. I.; MOLINA-BOTERO, I. C.; BARAHONA-ROSALES, R.; ARANGO, J.; GAVIRIA-URIBE, X.; VALDERRAMA, L. A. G.; ROSERO-NOGUERA, J. R.; POSADA-OCHOA, S. L.; ABARCA-MONGE, S.; SOTO-BLANCO, R.; KU-VERA, J. C.; JIMÉNEZ-OCAMPO, R.; FLORES-SANTIAGO, E. DEL. J.; CASTELÁN-ORTEGA, O. A.; VÁZQUEZ-CARRILLO, M. F.; BENAOUDA, M.; GÓMEZ-BRAVO, C. A.; BOLOVICH, V. I. A.; CÉSPEDES, M. A. D.; ASTIGARRAGA, L.; HRISTOV, A. N. Enteric methane mitigation strategies for ruminant livestock systems in the Latin America and Caribbean region: a meta-analysis. Journal of Cleaner Production, v.312, 127693, 2021 13 p.| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| Registros recuperados : 14 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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Data corrente: |
16/09/2022 |
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Data da última atualização: |
16/09/2022 |
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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: |
CONGIO, G. F. S.; BANNINK, A.; MAYORGA, O. L.; RODRIGUES, J. P. P.; BOUGOUIN, A.; KEBREAD, E.; SILVA, R. R.; MAURÍCIO, R. M.; SILVA, S. C. DA; OLIVEIRA, P. P. A.; MUÑOZ, C.; PEREIRA, L. G. R.; GÓMEZ, C.; ARIZA-NIETO, C.; RIBEIRO-FILHO, H. M. N.; CASTELÁN-ORTEGA, O. A.; ROSERO-NOGUERA, J. R.; TIERI, M. P.; RODRIGUES, P. H. M.; MARCONDES, M. I.; ASTIGARRAGA, L.; ABARCA, S.; HRISTOV, A. N. |
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Afiliação: |
GUILHERMO F. S. CONGIO, UNIVERSIDADE DE SÃO PAULO; ANDRÉ BANNINK, WAGENINGEN UNIVERSITY & RESEARCH; OLGA L. MAYORGA, COLOMBIAN CORPORATION FOR AGRICULTURAL RESEARCH; JOÃO P. P. RODRIGUES, UNIVERSIDADE FEDERAL DO SUL E SUDESTE DO PARÁ; ADELINE BOUGOUIN, UNIVERSITY OF CALIFORNIA; ERMIAS KEBREAD, UNIVERSITY OF CALIFORNIA; RICARDO R. SILVA, UNIVERSIDADE FEDERAL DE MINAS GERAIS; ROGÉRIO M. MAURÍCIO, UNIVERSIDADE FEDERAL DE SÃO JOÃO DEL REI; SILA C. DA SILVA, UNIVERSIDADE DE SÃO PAULO; PATRICIA PERONDI ANCHAO OLIVEIRA, CPPSE; CAMILA MUÑOZ, INSTITUTO DE INVESTIGACIONES AGROPECUARIAS; LUIZ GUSTAVO RIBEIRO PEREIRA, CNPGL; CARLOS GÓMEZ, NATIONAL AGRARIAN UNIVERSITY LA MOLINA; CLAUDIA ARIZA-NIETO, COLOMBIAN CORPORATION FOR AGRICULTURAL RESEARCH; HENRIQUE M. N. RIBEIRO-FILHO, UNIVERSIDADE ESTADUAL DE SANTA CATARINA; OCTAVIO A. CASTELÁN-ORTEGA, AUTONOMOUS UNIVERSITY OF THE STATE OF MEXICO; JAIME R. ROSERO-NOGUERA, UNIVERSITY OF ANTIOQUIA; MARIA P. TIERI, NATIONAL INSTITUTE OF AGRICULTURAL TECHNOLOGY; PAULO H. M. RODRIGUES, UNIVERSIDADE DE SÃO PAULO; MARCOS I. MARCONDES, WASHINGTON STATE UNIVERSITY; LAURA ASTIGARRAGA, UNIVERSITY OF THE REPUBLIC OF URUGUAY; SERGIO ABARCA, NATIONAL INSTITUTE OF INNOVATION AND AGRICULTURAL TECHNOLOGY TRANSFER; ALEXANDER N. HRISTOV, THE PENNSYLVANIA STATE UNIVERSITY. |
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Título: |
Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database. |
|
Ano de publicação: |
2022 |
|
Fonte/Imprenta: |
Science of the Total Environment, v. 825, n. 153982, p. 1-11, 2022. |
|
ISBN: |
0048-9697 |
|
DOI: |
https://doi.org/10.1016/j.scitotenv.2022.153982 |
|
Idioma: |
Inglês |
|
Conteúdo: |
ABSTRACT: Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d−1) and yield [g kg−1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries. MenosABSTRACT: Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d−1) and yield [g kg−1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had... Mostrar Tudo |
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Palavras-Chave: |
Empirical modeling; Enteric methane; Equação de previsão; GHG inventory; Inventário de GEE; Metano entérico; Modelagem empírica; Modelo linear; Prediction equations. |
|
Thesagro: |
Dieta; Gado Leiteiro; Metano; Produção; Rendimento. |
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Thesaurus NAL: |
Diet; Linear models. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1146525/1/PredictionOfEntericMethaneProductionAndYieldInDairyCattleUsingALatinAmericaAndCaribbeanDatabase.pdf
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Marc: |
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001 2146525
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022 $a0048-9697
024 7 $ahttps://doi.org/10.1016/j.scitotenv.2022.153982$2DOI
100 1 $aCONGIO, G. F. S.
245 $aPrediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database.$h[electronic resource]
260 $c2022
520 $aABSTRACT: Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d−1) and yield [g kg−1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.
650 $aDiet
650 $aLinear models
650 $aDieta
650 $aGado Leiteiro
650 $aMetano
650 $aProdução
650 $aRendimento
653 $aEmpirical modeling
653 $aEnteric methane
653 $aEquação de previsão
653 $aGHG inventory
653 $aInventário de GEE
653 $aMetano entérico
653 $aModelagem empírica
653 $aModelo linear
653 $aPrediction equations
700 1 $aBANNINK, A.
700 1 $aMAYORGA, O. L.
700 1 $aRODRIGUES, J. P. P.
700 1 $aBOUGOUIN, A.
700 1 $aKEBREAD, E.
700 1 $aSILVA, R. R.
700 1 $aMAURÍCIO, R. M.
700 1 $aSILVA, S. C. DA
700 1 $aOLIVEIRA, P. P. A.
700 1 $aMUÑOZ, C.
700 1 $aPEREIRA, L. G. R.
700 1 $aGÓMEZ, C.
700 1 $aARIZA-NIETO, C.
700 1 $aRIBEIRO-FILHO, H. M. N.
700 1 $aCASTELÁN-ORTEGA, O. A.
700 1 $aROSERO-NOGUERA, J. R.
700 1 $aTIERI, M. P.
700 1 $aRODRIGUES, P. H. M.
700 1 $aMARCONDES, M. I.
700 1 $aASTIGARRAGA, L.
700 1 $aABARCA, S.
700 1 $aHRISTOV, A. N.
773 $tScience of the Total Environment$gv. 825, n. 153982, p. 1-11, 2022.
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Registro original: |
Embrapa Pecuária Sudeste (CPPSE) |
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