Ellis J L, Kebreab E, Odongo N E, Beauchemin K, McGinn S, Nkrumah J D, Moore S S, Christopherson R, Murdoch G K, McBride B W, Okine E K, France J
Centre for Nutrition Modeling, Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario, N1G 2W1, Canada.
J Anim Sci. 2009 Apr;87(4):1334-45. doi: 10.2527/jas.2007-0725. Epub 2008 Dec 19.
Canada is committed to reducing its greenhouse gas emissions to 6% below 1990 amounts between 2008 and 2012, and methane is one of several greenhouse gases being targeted for reduction. Methane production from ruminants is one area in which 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 nonlinear equations to predict methane production from variables that describe the diet. Extant methane prediction equations were also evaluated. The linear equation developed with the smallest root mean square prediction error (RMSPE, % observed mean) and residual variance (RV) was Eq. I: CH(4), MJ/d=2.72 (+/-0.543) + [0.0937 (+/-0.0117) x ME intake, 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 mean square prediction error (MSPE) being random error; RV=1.13]. Equations based on ratios of one diet variable to another were also generated, and Eq. P, CH(4), MJ/d=2.50 (+/-0.649) - [0.367 (+/-0.0191) x (Starch:ADF)] + [0.766 (+/-0.116) x DMI, kg/d], resulted in the smallest RMSPE values among these equations (RMSPE=28.6%, with 93.6% of MSPE from random error; RV=1.35). Among the nonlinear equations developed, Eq. W, CH(4), MJ/d=10.8 (+/-1.45) x (1-e([-0.141 (+/-0.0381) x DMI, kg/d])), performed well (RMSPE=29.0%, with 93.6% of MSPE from random error; RV=3.06), as did Eq. W(3), CH(4), MJ/d=10.8 (+/-1.45) x [1-e({-[-0.034 x (NFC/NDF)+0.228] x DMI, kg/d})] (RMSPE=28.0%, with 95% of MSPE from random error). Extant equations from a previous publication by the authors performed comparably with, if not better than in some cases, 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, because the equation selected must account for divergence from a "normal" diet (e.g., high-concentrate diets, high-fat diets).
加拿大致力于在2008年至2012年间将其温室气体排放量降至比1990年的水平低6%，甲烷是被列为减排目标的几种温室气体之一。反刍动物产生的甲烷是农业部门能够为减少全球影响做出贡献的一个领域。通过数学建模，我们可以进一步了解控制甲烷产生的因素，完善国家或全球温室气体清单，并研究减排策略以减少总体排放。本研究的目的是汇编一个关于肉牛甲烷产生量测量值的广泛数据库，并生成线性和非线性方程，以便根据描述日粮的变量预测甲烷产生量。同时还对现有的甲烷预测方程进行了评估。以最小的均方根预测误差（RMSPE，%观测均值）和残差方差（RV）建立的线性方程为方程I：CH₄，MJ/d = 2.72（±0.543）+ [0.0937（±0.0117）× 代谢能摄入量，MJ/d] + [4.31（±0.215）× 纤维素，kg/d] - [6.49（±0.800）× 半纤维素，kg/d] - [7.44（±0.521）× 脂肪，kg/d] [RMSPE = 26.9%，其中94%的均方预测误差（MSPE）为随机误差；RV = 1.13]。还生成了基于一种日粮变量与另一种日粮变量比值的方程，方程P，CH₄，MJ/d = 2.50（±0.649）- [0.367（±0.0191）×（淀粉:酸性洗涤纤维）] + [0.766（±0.116）× 干物质采食量，kg/d]，在这些方程中其RMSPE值最小（RMSPE = 28.6%，93.6%的MSPE来自随机误差；RV = 1.35）。在建立的非线性方程中，方程W，CH₄，MJ/d = 10.8（±1.45）×（1 - e^([-0.141（±0.0381）× 干物质采食量，kg/d])）表现良好（RMSPE = 29.0%，93.6%的MSPE来自随机误差；RV = 3.06），方程W(3)，CH₄，MJ/d = 10.8（±1.45）× [1 - e^({-[-0.034 ×（非纤维性碳水化合物/中性洗涤纤维）+ 0.228] × 干物质采食量，kg/d})] （RMSPE = 28.0%，95%的MSPE来自随机误差）也表现良好。作者之前发表的现有方程在某些情况下即便不比新建立的方程更好，至少也与之相当。用户在选择方程时应基于RV和RMSPE分析、用户可获取的输入变量以及所饲喂的日粮，因为所选方程必须考虑到与“正常”日粮（例如高浓缩日粮、高脂肪日粮）的差异。
