|CASEY, KENNETH - Texas A&M Agrilife|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/28/2015
Publication Date: 11/16/2015
Citation: Waldrip, H., Casey, K., Todd, R.W., Cole, N.A. 2015. Predicting greenhouse gas emissions from beef cattle feedyard manure [abstract]. In: ASA-CSSA-SSSA Annual Meeting. Emissions from Livestock Production: I. November 16, 2015, Minneapolis, MN. 96-3.
Technical Abstract: Improved predictive models for nitrous oxide and methane 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 nitrous oxide and methane 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 and nitrogen, and Ultraviolet visible (UV vis) spectral characteristics related to degree of decomposition and humification. Fluxes of methane ranged from below detection to 25.5 mg per square meter per hour (average 1.91 mg methane per square meter per hour). Correlation analyses indicated that methane production increased with optical density at 254 nm (P less than 0.001), a parameter which indicates higher manure organic matter complexity. Current process based models include dissolved organic carbon (DOC) content in equations to predict methane production; however, there were no correlations between methane and DOC or any other variables studied. Nitrous oxide emissions ranged from below detection to 8.5 mg nitrous oxide per square meter per hour (average 1.1 mg nitrous oxide per square meter per hour), and were positively related to water content, temperature, and nitrate concentrations (P less than 0.01), and negatively related to OM content, ammonia concentration, DOC, dissolved nitrogen, and several UV vis parameters related to degree of humification (P less than 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.