Título:	Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database.
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.
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.
Ano de publicação:	2022
Referência:	Science of the Total Environment, v. 825, n. 153982, p. 1-11, 2022.
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.
NAL Thesaurus:	Diet Linear models
Palavras-chave:	Empirical modeling Enteric methane GHG inventory Prediction equations
ISBN:	0048-9697
Digital Object Identifier:	https://doi.org/10.1016/j.scitotenv.2022.153982
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CPPSE)