Measurements of methane emissions from a beef cattle feedlot using the eddy covariance technique

Abstract

The eddy covariance (EC) technique has been extensively used in several sites around the world to measure energy fluxes and CO2 exchange at the ecosystem scale. Recent advances in optical sensors have allowed the use of the EC approach to measure other trace gases (e.g. CH4, NH3 and N2O), which has expanded the use of eddy covariance for other applications, including measuring gas emissions from livestock production systems. The main objectives of this study were to assess the performance of a closed-path EC system for measuring CH4, CO2, and H2O fluxes in a beef cattle feedlot and to investigate the spatial variability of eddy covariance fluxes measured above the surface of a feedlot using an analytical flux footprint analysis. A closed-path EC system was used to measure CH4, CO2, and H2O fluxes. To evaluate the performance of this closed-path system, an open-path EC system was also deployed on the flux tower to measure CO2 and H2O exchange. The performance assessment of the closed-path EC system showed that this system was suitable for EC measurements. The frequency attenuations, observed for the closed-path system CO2 and CH4 cospectra in this study, are in agreement with results from previous instrument comparison studies. For the water vapor closed-path cospectra, larger attenuations were likely caused by water vapor molecule interaction with the sampling tube walls. Values of R2 for the relationship between H2O and CO2 fluxes, measured by open-path and closed-path systems, were 0.94–0.98, respectively. The closed-path EC system overestimated the CO2 by approximately 5% and underestimated the latent heat fluxes by about 10% when compared with the open-path system measurements. Measured CH4 and CO2 fluxes during the study period from the feedlot averaged 2.63μmolm−2s−1 and 103.8μmolm−2s−1, respectively. Flux values were quite variable during the field experiment and the footprint analysis was useful to interpret flux temporal and spatial variation. This study shows indication that consideration of atmospheric stability condition, wind direction and animal movement are important to improve estimates of CH4 emissions per pen surface or per head of cattle.