Doctor of Philosophy / Department of Agronomy / Eduardo Alvarez Santos / Measurements of methane (CH₄) emissions from livestock production could provide invaluable data to reduce uncertainties in the global CH₄ budget and to evaluate mitigation strategies to lower greenhouse gas (GHG) emissions. The eddy covariance (EC) technique has recently been applied as an alternative to measure CH₄ emissions from livestock systems, but heterogeneities in the source area and fetch limitations impose challenges to EC measurements. The main objectives of this study were to: 1) assess the performance of a closed-path EC system for measuring CH₄, CO₂, and H₂0 fluxes; 2) investigate the spatial variability of the EC fluxes in a cattle feedlot using flux footprint analysis; 3) estimate CH₄ emission rates per animal (Fanimal) from a beef cattle feedlot using the EC technique combined with two footprint models: an analytical footprint model (KM01) and a parametrization of a Lagrangian dispersion model (FFP); and 4) compare CH₄ emissions obtained using the EC technique and a footprint analysis with CH₄ emission estimates provided by a well-stablished backward-Lagrangian stochastic (bLS) model. A closed-path EC system was used to measure CH₄, CO₂, and H₂0 fluxes. To evaluate the performance of this closed-path system, a well-stablished open-path EC system was also deployed on the flux tower to measure CO₂ and H₂0 exchange. Methane concentration measurements and wind data provided by that system were used to estimate CH₄ emissions using the bLS model. The performance assessment that included comparison of gas cospectra and measured fluxes from the two EC systems showed that the closed-path system was suitable for the EC measurements. Flux values were quite variable during the field experiment. A one-dimensional flux footprint model was useful to interpret some of the flux temporal and spatial dynamics. Then, a more comprehensive data analysis was carried out using two-dimensional footprint models (FFP and KM01) to interpret fluxes and scale fluxes measured at landscape to animal level. The monthly average Fanimal, calculated using the footprint weighed stocking density ranged from 83 to 125 g animal⁻¹ d⁻¹ (KM01) and 75–114 g animal⁻¹ d⁻¹ (FFP). These emission values are consistent with the results from previous studies in feedlots however our results also suggested that in some occasions the movement of animals on the pens could have affected CH₄ emission estimates. The results from the comparisons between EC and bLS CH₄ emission estimates show good agreement (0.84; concordance coefficient) between the two methods. In addition, the precision of the EC as compared to the bLS estimates was improved by using a more rigorous fetch screening criterion. Overall, these results indicate that the eddy covariance technique can be successfully used to accurately measure CH₄ emissions from feedlot cattle. However, further work is still needed to quantify the uncertainties in Fanimal caused by errors in flux footprint model estimates and animal movement.

A better way to improve understanding and quantification of nitrous oxide (N₂O) emitted from intensive maize cropping systems is to develop an advanced emissions measurement method This study developed an open path (OP) method to measure N₂O emissions from four adjacent maize plots managed by tillage practices of no-till (NT) and chisel plow (ChP), and different nitrogen (N) treatments from 2014 to 2016. Anhydrous ammonia (220 kg NH₃-N ha^-1) was applied in once or equally split (full vs. split rate) and applied in different timing (Fall vs. Spring). The spring N application occurred either before planting (pre-plant) or in season (side-dress). Emissions measurements were conducted by using the OP method (the scanning OP Fourier transform infrared spectrometry (OP-FTIR) + the gas point-sampling system + a backward Lagrangian stochastic (bLS) dispersion model) and static closed chamber methods. The performance and feasibility of the OP measurements were assessed by a sensitivity analysis, starting with errors associated with the OP-FTIR for calculating N₂O concentrations, and then errors associated with the bLS model for estimating N₂O emissions. The quantification of N₂O concentrations using the OP-FTIR spectrum was influenced by ambient humidity, temperature, and the path length between a spectrometer and a retro-reflector. The optimal quantitative method mitigated these ambient interference effects on N₂O quantification. The averaged bias of the calculated N₂O concentrations from the spectra acquired from wide ranges of humidity (0.5 – 2.0 % water vapor content), temperature (10 – 35 °C), and path length (100 – 135 meters) was 1.4 %. The precision of the OP-FTIR N₂O concentrations was 5.4 part per billion(3σ) in a stationary flow condition for a 30-minute averaging period. The emissions measurement from multiple sources showed that the field of interest was likely interfered by adjacent fields. Fields with low emission rates were more sensitive to the adjacent fields with high emissions, resulting in substantial biases and uncertainties. The minimum detection limit of the N₂O emission rates was 1.2 µg m^-2 s^-1 (MDL; 3σ). The OP measurements showed that the NT practice potentially reduced N₂O emission compared with ChP. Under the long-term NT treatments, the split-N rate application (110 kg NH₃-N ha^-1 in the fall and spring) resulted in lower N₂O emissions than the full application (220 kg NH₃-N ha^-1 in the fall). The management of NT coupled with split-N rate application minimized N₂O emissions among treatments in this study, resulting in N₂O-N losses of 3.8, 13.2, and 6.6 N kg ha^-1 over 9-, 35-, and 20-days after the spring NH₃ application in 2014, 2015, and 2016, respectively. The spring pre-plant N application in 2015 also resulted in higher N₂O emissions than the spring side-dress application in 2016, and the increased N₂O-N loss was corresponding to lower N recovery efficiency in 2015 measurements. A comparison of chamber and OP measurements showed that soil N₂O emissions were likely underestimated by 10x without considering the wind-induced effect on gas transport at the ground-atmospheric interface. This study showed that the OP method provides a great opportunity to study agricultural N₂O emissions as well as management optimization for the sustainability of the agroecosystems.