PSIX-21 Evaluation of Methane Emissions Predictions from Observed Methane Emissions Data in Beef Steers, Heifers and Bulls

Abstract

Abstract Enteric methane emissions from cattle are the largest source of agricultural methane emissions; however, measuring enteric methane emissions is costly and infeasible in many environments. Consequently, prediction of enteric methane emissions for greenhouse gas emissions inventories for reporting and carbon market purposes is an important alternative to measurement. The objective of the current experiment was to evaluate the relationship between feed intake and methane emissions, and to compare observed methane emissions with commonly used enteric methane emissions prediction equations. Cattle (n = 194; steers n = 99, heifers n = 58, bulls n = 37) were housed at the Colorado State University Climate Smart Research pens equipped with Smartfeed feed intake measurement and Greenfeed emissions monitoring systems (C-Lock, Rapid City, SD). Body weight, body weight gain, dry matter intake, and methane emissions were collected for 37 days for each individual animal. All summary statistics and univariate fit analyses were conducted in JMP Pro 15.0 (SAS Institute Inc. Cary, NC). Observed methane emissions were compared with three extant methane prediction equations: IPCC tier 2 (IPCC), Moraes et al. (2014) animal-level (MAL), and Mills et al. (2003) non-linear equation 2 (MNL). Mean observed methane emissions were 185.3 g· animal-1· day-1 [standard deviation (SD) = 31.1], 135.6 g· animal-1· day-1 (SD = 29.3), and 177.8 g· animal-1· day-1 (SD = 20.1), for steers, heifers, and bulls, respectively. Across all 194 animals mean feed intake was 6.33 kg/d (SD = 1.65) and mean methane emissions were 169 g· animal-1· day-1 (SD = 36.2; Pearson correlation coefficient r = 0.65). When comparing observed vs. predicted methane emissions, MNL over predicted methane emissions (mean = 182.7 g· animal-1· day-1), whereas MAL (mean 119.3 g· animal-1· day-1) and IPCC (mean = 136.6 g· animal-1· day-1) underpredicted methane emissions (P < 0.01). Of the 3 prediction equations, MAL had the lowest root mean square error (RMSE) of 20.9 (R square = 0.50), followed by IPCC (RMSE = 27.2, R square = 0.42), and MNL (RMSE = 32.8, R square = 0.42). The current preliminary data suggests further refinement of enteric methane emissions prediction equations could improve inventories and estimates of enteric methane emissions on commercial cattle operations.