Seasonal ammonia emissions from an intensive beef cattle feedlot in Victoria Australia.

Ammonia (NH3) emitted from concentrated animal feeding operations can cause environmental and health problems, and indirectly contribute to greenhouse gas emissions. Cattle feedlots are known to be large sources of NH3, but few studies have documented seasonal emissions from Australian feedlots. We conducted two field campaigns to measure NH3 emissions from an intensive beef cattle feedlot in southeast Australia, and these results were combined with previous measurements at the same feedlot to document seasonal variations in emissions and to derive annual feedlot emission factors (EFs). Emission rates were calculated with an inverse dispersion modelling (IDM) technique, based on NH3 concentrations measured at the feedlot with open-path lasers (OPLs). The average area emission rates in spring, summer, autumn and winter were 90.5, 167.4, 96.2 and 86.8 µg NH3 m-2 s-1 from the cattle pens, and 22.5, 18.1, 7.7 and 20.7 µg NH3 m-2 s-1 from the manure stockpile area, respectively. The total per-animal EFs ranged from 126.0 (autumn) to 190.2 g NH3 animal-1 d-1 (summer), representing a loss of 47.5-64.6% of the fed N. Seasonal variations in emissions were related to air temperature. Slight changes in crude protein content of the cattle diet may also have impacted seasonal variability. Taking seasonal variations into consideration, the average feedlot EF was 160.4 g NH3 animal-1 d-1, with 90% of the emissions coming from the cattle pens. Extrapolating the EF to all feedlot cattle in the country, the direct NH3 emissions from Australian feedlots amount to 65.2 Gg NH3 annually, or 3.7% of the national total. Our study benchmarks seasonal and annual EFs and N losses for Australian commercial feedlots, and provides a baseline for extrapolating the impacts of mitigation efforts.

Ammonia emissions and dispersion from broiler production.

Ammonia (NH3 ) has been used as a target gas for nuisance complaints to restrict or close poultry operations near encroaching rural development. There are conflicting data on NH3 emissions from broiler production across the United States. The purpose of this research is to compare emission rates from a Georgia broiler operation across seasons and with other geographical areas in the United States. Comparison of seasonal and geographical emission rates showed large seasonal variation in NH3 emissions for eastern U.S. sites but little seasonal variation in the semi-arid region of the United States. Differences in production management practices, ambient temperature, and animal density did not appear to explain differences in emissions between regions; however, the climatic influence of ambient humidity and litter management practices are thought to be key factors in the generation of emissions.

Applicability of Eddy Covariance to Estimate Methane Emissions from Grazing Cattle.

Grazing systems represent a significant source of enteric methane (CH), but available techniques for quantifying herd scale emissions are limited. This study explores the capability of an eddy covariance (EC) measurement system for long-term monitoring of CH emissions from grazing cattle. Measurements were made in two pasture settings: in the center of a large grazing paddock, and near a watering point where animals congregated during the day. Cattle positions were monitored through time-lapse images, and this information was used with a Lagrangian stochastic dispersion model to interpret EC fluxes and derive per-animal CH emission rates. Initial grazing paddock measurements were challenged by the rapid movement of cattle across the measurement footprint, but a feed supplement placed upwind of the measurements helped retain animals within the footprint, allowing emission estimates for 20% of the recorded daytime fluxes. At the water point, >50% of the flux measurement periods included cattle emissions. Overall, cattle emissions for the paddock site were higher (253 g CH m adult equivalent [AE] d, SD = 75) and more variable than emissions at the water point (158 g CH AE d, SD = 34). Combining results from both sites gave a CH production of 0.43 g kg body weight, which is in range of other reported emissions from grazing animals. With an understanding of animal behavior to allow the most effective use of tower placement, the combination of an EC measurement platform and a Lagrangian stochastic model could have practical applications for long-term monitoring of fluxes in grazing environments.

A Snapshot of Greenhouse Gas Emissions from a Cattle Feedlot.

Beef cattle feedlots emit large amounts of the greenhouse gases (GHG) methane (CH) and nitrous oxide (NO), as well as ammonia (NH), which contributes to NO emission when NH is deposited to land. However, there is a lack of simultaneous, in situ, and nondisturbed measurements of the major GHG gas components from beef cattle feedlots, or measurements from different feedlot sources. A short-term campaign at a beef cattle feedlot in Victoria, Australia, quantified CH, NO, and NH emissions from the feedlot pens, manure stockpiles, and surface run-off pond. Open-path Fourier transform infrared (OP-FTIR) spectrometers and open-path lasers (OP-Laser) were used with an inverse-dispersion technique to estimate emissions. Daily average emissions of CH, NO, and NH were 132 (± 2.3 SE), 0, and 117 (± 4.5 SE) g animal d from the pens and 22 (± 0.7 SE), 2 (± 0.2 SE), and 9 (± 0.6 SE) g animal d from the manure stockpiles. Emissions of CH and NH from the run-off pond were less than 0.5 g animal d. Extrapolating these results to the feedlot population of cattle across Australia would mean that feedlots contribute approximately 2% of the agricultural GHG emissions and 2.7% of livestock sector emissions, lower than a previous estimate of 3.5%.

Evaluating dispersion modeling options to estimate methane emissions from grazing beef cattle.

Enteric methane (CH) emission from cattle is a source of greenhouse gas and is an energy loss that contributes to production inefficiency for cattle. Direct measurements of enteric CH emissions are useful to quantify the magnitude and variation and to evaluate mitigation of this important greenhouse gas source. The objectives of this study were to evaluate the impact of stocking density of cattle and source configuration (i.e., point source vs. area source and elevation of area source) on CH emissions from grazing beef cattle in Queensland, Australia. This was accomplished using nonintrusive atmospheric measurements and a gas dispersion model. The average measured CH emission for the point and area source was between 240 and 250 g animal d over the entire study. There was no difference ( > 0.05) in emission when using an elevated area source (0.5 m) or a ground area source (0 m). For the point-source configuration, there was a difference in CH emission due to stocking density; likewise, some differences existed for the area-source emissions. This study demonstrates the flexibility of the area-source configuration of the dispersion model to estimate CH emissions even at a low stocking density.

Optimal sensor locations for the backward lagrangian stochastic technique in measuring lagoon gas emission.

This study evaluated the impact of gas concentration and wind sensor locations on the accuracy of measuring gas emission rates from a lagoon environment using the backward Lagrangian stochastic (bLS) inverse-dispersion technique. Path-integrated concentrations (PICs) and three-dimensional (3D) wind vector data were collected at different locations within the lagoon landscape. A floating 45 m × 45 m perforated pipe network on an irrigation pond was used as a synthetic distributed emission source for the controlled release of methane. A total of 961 15-min datasets were collected under different atmospheric stability conditions over a 2-yr period. The PIC location had a significant impact on the accuracy of the bLS technique. The location of the 3D sonic anemometer was generally not a factor for the measured accuracies with the PIC positioned on the downwind berm. The PICs across the middle of the pond consistently produced the lowest accuracy with any of the 3D anemometer locations (<69% accuracy). The PICs located on the downwind berm consistently yielded the best bLS accuracy regardless of whether the 3D sonic anemometer was located on the upwind, side, or downwind berm (accuracies ranged from 79 to 108%). The accuracies of the emission measurements with the berm PIC-berm 3D setting were statistically similar to that found in a more ideal homogeneous grass field. Considering the practical difficulties of setting up equipment and the accuracies associated with various sensor locations, we recommend that wind and concentration sensors be located on the downwind berm.

Inference of emission rates from multiple sources using Bayesian probability theory.

The determination of atmospheric emission rates from multiple sources using inversion (regularized least-squares or best-fit technique) is known to be very susceptible to measurement and model errors in the problem, rendering the solution unusable. In this paper, a new perspective is offered for this problem: namely, it is argued that the problem should be addressed as one of inference rather than inversion. Towards this objective, Bayesian probability theory is used to estimate the emission rates from multiple sources. The posterior probability distribution for the emission rates is derived, accounting fully for the measurement errors in the concentration data and the model errors in the dispersion model used to interpret the data. The Bayesian inferential methodology for emission rate recovery is validated against real dispersion data, obtained from a field experiment involving various source-sensor geometries (scenarios) consisting of four synthetic area sources and eight concentration sensors. The recovery of discrete emission rates from three different scenarios obtained using Bayesian inference and singular value decomposition inversion are compared and contrasted.

The effect of biofuel production on swine farm methane and ammonia emissions.

Methane (CH) and ammonia (NH3) are emitted to the atmosphere during anaerobic processing of organic matter, and both gases have detrimental environmental effects. Methane conversion to biofuel production has been suggested to reduce CH4 emissions from animal manure processing systems. The purpose of this research is to evaluate the change in CH4 and NH3 emissions in an animal feeding operation due to biofuel production from the animal manure. Gas emissions were measured from swine farms differing only in their manure-management treatment systems (conventional vs. biofuel). By removing organic matter (i.e., carbon) from the biofuel farms' manure-processing lagoons, average annual CH4 emissions were decreased by 47% compared with the conventional farm. This represents a net 44% decrease in global warming potential (CO2 equivalent) by gases emitted from the biofuel farms compared with conventional farms. However, because of the reduction of methanogenesis and its reduced effect on the chemical conversion of ammonium (NH4+) to dinitrogen (N2) gas, NH3 emissions in the biofuel farms increased by 46% over the conventional farms. These studies show that what is considered an environmentally friendly technology had mixed results and that all components of a system should be studied when making changes to existing systems.

Estimating gas emissions from multiple sources using a backward Lagrangian stochastic model.

Manure storage tanks and animals in barns are important agricultural sources of methane. To examine the possibility of using an inverse dispersion technique based on a backward Lagrangian Stochastic (bLS) model to quantify methane (CH4) emissions from multiple on-farm sources, a series of tests were carried out with four possible source configurations and three controlled area sources. The simulated configurations were: (C1) three spatially separate ground-level sources, (C2) three spatially separate sources with wind-flow disturbance, (C3) three adjacent ground-level sources to simulate a group of adjacent sources with different emission rates, and (C4) a configuration with a ground level and two elevated sources. For multiple ground-level sources without flow obstructions (C1 and C3), we can use the condition number (K, the ratio of the uncertainty in the calculated emission rate to the uncertainty in the predicted ratio of concentration to emission rate) to evaluate the applicability of this inverse dispersion technique and a preliminary threshold of K <10 is recommended. For multiple sources with wind disturbance (C2) or an even more complex configuration including ground level and elevated sources (C4), a low kappa is not sufficient to provide reasonable discrete and total emission rates. The effect of flow obstructions can be neglected as long as the distance between the source and the measurement location is greater than approximately 10 times the height of the flow obstructions. This study shows that the bLS model has the potential to provide accurate discrete emission rates from multiple on-farm emissions of gases provided that certain conditions are met.