Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental databaseTools Niu, Mutian, Kebreab, Ermias, Hristov, Alexander N., Oh, Joonpyo, Arndt, Claudia, Bannink, André, Bayat, Ali R., Brito, André F., Boland, Tommy, Casper, David, Crompton, Les A., Dijkstra, Jan, Eugène, Maguy A., Garnsworthy, P.C., Haque, Md Najmul, Hellwing, Anne L. F., Huhtanen, Pekka, Kreuzer, Michael, Kuhla, Bjoern, Lund, Peter, Madsen, Jørgen, Martin, Cécile, McClelland, Shelby C., McGee, Mark, Moate, Peter J., Muetzel, Stefan, Muñoz, Camila, O’Kiely, Padraig, Peiren, Nico, Reynolds, Christopher K., Schwarm, Angela, Shingfield, Kevin J., Storlien, Tonje M., Weisbjerg, Martin R., Yáñez-Ruiz, David R. and Yu, Zhongtang (2018) Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Global Change Biology, 24 (8). pp. 3368-3389. ISSN 1365-2486 Full text not available from this repository.AbstractEnteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.
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