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Research article - Peer-reviewed, 2023

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

Congio, Guilhermo F. S.; Bannink, Andre; Mayorga, Olga L.; Rodrigues, Joao P. P.; Bougouin, Adeline; Kebreab, Ermias; Carvalho, Paulo C. F.; Berchielli, Telma T.; Mercadante, Maria E. Z.; Valadares-Filho, Sebastiao C.; Borges, Ana L. C. C.; Berndt, Alexandre; Rodrigues, Paulo H. M.; Ku-Vera, Juan C.; Molina-Botero, Isabel C.; Arango, Jacobo; Reis, Ricardo A.; Posada-Ochoa, Sandra L.; Tomich, Thierry R.; Castelan-Ortega, Octavio A.;
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Abstract

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended prac-tices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the pre-dictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (similar to 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % >= DFC >= 54 %), and low-forage (50 % >= DFC) diets. Feed intake and average daily gain (ADG) were the main pre-dictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Sim-ple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equa-tions to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Keywords

Dietary nutrients; Greenhouse gas; Linear regression; Livestock; Methane conversion factor; Model cross-validation

Published in

Science of the Total Environment
2023, volume: 856, number: Part 2, article number: 159128

Authors' information

Congio, Guilhermo F. S.
Universidade de Sao Paulo
Bannink, Andre
Wageningen University and Research
Mayorga, Olga L.
Corporacion Colombiana de Investigacion Agropecuaria, AGROSAVIA
Rodrigues, Joao P. P.
Universidade Federal Rural do Rio de Janeiro (UFRRJ)
Bougouin, Adeline
University of California Davis
Kebreab, Ermias
University of California Davis
Carvalho, Paulo C. F.
Universidade Federal do Rio Grande do Sul
Berchielli, Telma T.
Universidade Estadual Paulista
Mercadante, Maria E. Z.
São Paulo Agribusiness Technology Agency
Valadares-Filho, Sebastiao C.
Universidade Federal de Vicosa
Borges, Ana L. C. C.
Universidade Federal de Minas Gerais
Berndt, Alexandre
Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA)
Rodrigues, Paulo H. M.
Universidade de Sao Paulo
Ku-Vera, Juan C.
Universidad Autonoma de Yucatan
Molina-Botero, Isabel C.
University Nacional Agraria La Molina
Arango, Jacobo
Alliance
Reis, Ricardo A.
Universidade Estadual Paulista
Posada-Ochoa, Sandra L.
Universidad de Antioquia
Tomich, Thierry R.
Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA)
Castelan-Ortega, Octavio A.
Universidad Autonoma del Estado de Mexico
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UKÄ Subject classification

Environmental Sciences related to Agriculture and Land-use
Animal and Dairy Science

Publication Identifiers

DOI: https://doi.org/10.1016/j.scitotenv.2022.159128

URI (permanent link to this page)

https://res.slu.se/id/publ/119227