Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 551850
Title Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
Author(s) Lingen, Henk J. van; Niu, Mutian; Kebreab, Ermias; Valadares Filho, Sebastião C.; Rooke, John A.; Duthie, Carol Anne; Schwarm, Angela; Kreuzer, Michael; Hynd, Phil I.; Caetano, Mariana; Eugène, Maguy; Martin, Cécile; McGee, Mark; O'Kiely, Padraig; Hünerberg, Martin; McAllister, Tim A.; Berchielli, Telma T.; Messana, Juliana D.; Peiren, Nico; Chaves, Alex V.; Charmley, Ed; Cole, N.A.; Hales, Kristin E.; Lee, Sang Suk; Berndt, Alexandre; Reynolds, Christopher K.; Crompton, Les A.; Bayat, Ali R.; Yáñez-Ruiz, David R.; Yu, Zhongtang; Bannink, André; Dijkstra, Jan; Casper, David P.; Hristov, Alexander N.
Source Agriculture, Ecosystems and Environment 283 (2019). - ISSN 0167-8809
Department(s) WIAS
Animal Nutrition
Animal Nutrition
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Dietary variables - Empirical modeling - Forage content - Geographical region - Methane emission

Enteric methane (CH4) production attributable to beef cattle contributes to global greenhouse gas emissions. Reliably estimating this contribution requires extensive CH4 emission data from beef cattle under different management conditions worldwide. The objectives were to: 1) predict CH4 production (g d−1 animal−1), yield [g (kg dry matter intake; DMI)−1] and intensity [g (kg average daily gain)−1] using an intercontinental database (data from Europe, North America, Brazil, Australia and South Korea); 2) assess the impact of geographic region, and of higher- and lower-forage diets. Linear models were developed by incrementally adding covariates. A K-fold cross-validation indicated that a CH4 production equation using only DMI that was fitted to all available data had a root mean square prediction error (RMSPE; % of observed mean) of 31.2%. Subsets containing data with ≥25% and ≤18% dietary forage contents had an RMSPE of 30.8 and 34.2%, with the all-data CH4 production equation, whereas these errors decreased to 29.3 and 28.4%, respectively, when using CH4 prediction equations fitted to these subsets. The RMSPE of the ≥25% forage subset further decreased to 24.7% when using multiple regression. Europe- and North America-specific subsets predicted by the best performing ≥25% forage multiple regression equation had RMSPE of 24.5 and 20.4%, whereas these errors were 24.5 and 20.0% with region-specific equations, respectively. The developed equations had less RMSPE than extant equations evaluated for all data (22.5 vs. 23.2%), for higher-forage (21.2 vs. 23.1%), but not for the lower-forage subsets (28.4 vs. 27.9%). Splitting the dataset by forage content did not improve CH4 yield or intensity predictions. Predicting beef cattle CH4 production using energy conversion factors, as applied by the Intergovernmental Panel on Climate Change, indicated that adequate forage content-based and region-specific energy conversion factors improve prediction accuracy and are preferred in national or global inventories.

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