Predicting Greenhouse Gas Emissions from Beef Cattle Feedyard Manure.

Improved predictive models for nitrous oxide (N₂O) and methane (CH₄) are crucial for assessing the greenhouse gas (GHG) footprint of beef cattle production. Biochemical process-based models to predict GHG from manure rely on information derived from studies on soil and only limited study has been conducted on manure GHG. Little is known about specific factors that drive production and volatilization of N₂O and CH₄ from feedyard manure. We used GHG flux and weather data collected from non-flow-through non-steady-state chamber studies conducted from 2012 to 2014 on two beef cattle feedyards in the Texas Panhandle. Manure samples (unconsolidated surface manure and the underlying manure pack) were analyzed for basic physicochemical properties, soluble carbon (C) and nitrogen, and Ultraviolet-visible (UV-vis) spectral characteristics related to degree of decomposition and humification. Fluxes of CH₄ ranged from below detection to 25.5 mg m^-2 h^-1 (average 1.91 + 4.3 mg m^-2 h^-1). Correlation analyses indicated that CH₄ production increased with optical density at 254 nm (P < 0.001), a parameter which indicates higher manure organic matter complexity. Current process-based models include dissolved organic C (DOC) content in equations to predict CH₄ production; however, there were no correlations between CH₄ and DOC or any other variables studied. Nitrous oxide emissions ranged from below detection to 8.5 mg m^-2 h^-1 (average 1.1 + 2.2 mg m^-2 h^-1), and were positively related to water content, temperature, and nitrate concentrations (P < 0.01), and negatively related to OM content, ammonia concentration, DOC, dissolved N, and several UV-vis parameters related to degree of humification (P < 0.05). Based on these data, empirical models are being developed to predict manure-derived GHG emissions and will be evaluated against an independent dataset. These data will be used to improve parameterization of existing process-based models and develop new empirical models to predict feedyard GHG emissions.