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Modeling methane production from beef cattle using linear and non-linear approaches

J. L. Ellis^* , E. Kebreab, N. E. Odongo^*,, K. Beauchemin, S. McGinn, J. D. Nkrumah ^#, S. S. Moore^#, R. Christopherson^#, G. K. Murdoch¹, B. W. McBride^*, E. K. Okine^# and J. France^*

^* Centre for Nutrition Modeling, Department of Animal and Poultry Science, University of Guelph, Guelph, ON, N1G 2W1, Canada , National Centre for Livestock and Environment, Department of Animal Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada , Animal Production and Health Section, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, P. O. Box 100, Wagramer Strasse 5, A-1400 Vienna, Austria , Agriculture and Agri-Food Canada, Lethbridge Research Centre, Lethbridge, AB, T1J 4B1, Canada , ^# Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada ¹ Animal and Veterinary Science, University of Idaho, Moscow, ID, 83844

jellis{at}uoguelph.ca

Abstract

Canada is committed to reducing its greenhouse gas emissions to 6% below 1990 levels by 2008 to 2012, and methane is one of several greenhouse gases being targeted for reduction. Methane production from ruminants is one area where the agriculture sector can contribute to reducing our global impact. Through mathematical modeling, we can further our understanding of factors that control methane production, improve national or global greenhouse gas inventories, and investigate mitigation strategies to reduce overall emissions. The purpose of this study was to compile an extensive database of methane production values measured on beef cattle, and to generate linear and non-linear equations to predict methane production from variables that describe the diet. Extant methane prediction equations were also evaluated. The linear equation developed with the lowest root mean square prediction error (RMSPE) and residual variance (RV) was equation I: CH₄, MJ/d = 2.72 (± 0.543) + [0.0937 (± 0.0117) x MEI, MJ/d] + [4.31 (± 0.215) x Cellulose, kg/d] – [6.49 (± 0.800) x Hemicellulose, kg/d] – [7.44 (± 0.521) x Fat, kg/d] (RMSPE = 26.9 with 94% of MSPE was random error; RV = 1.13). Equations based on ratios of one diet variable to another were also generated, and equation P: CH₄, MJ/d = 2.50 (± 0.649) – [0.367 (± 0.0191) x [Starch/ADF]] + [0.766 (± 0.116) x DMI, kg/d] resulted in the lowest RMSPE values among these equations (RMSPE = 28.6 with 96% of MSPE from random error; RV = 1.35). Among the non-linear equations developed, equation W: CH₄, MJ/d = 10.8 (± 1.45) x (1 - e^{(-0.141 (± 0.0381) x DMI, kg/d)}) performed well (RMSPE = 29.9% of MSPE from random error, RV = 3.06) as did equation W₃: CH₄, MJ/d = 10.8 (± 1.45) x (1 - e^{(-(-0.034 x [NFC/NDF] + 0.228) x DMI, kg/d)}) (RMSPE = 28.0, 95% of MSPE from random error). Extant equations from a previous publication by the authors performed comparably, if not better in some cases, than the newly developed equations. Equation selection by users should be based on RV and RMSPE analysis, input variables available to the user, and the diet fed, as the equation selected must account for divergence from a ‘normal’ diet (e.g., high concentrate diets, high fat diets).

Key Words: beef cattle • greenhouse gas • methane production • modeling