CH4 and N2O emissions from China's beef feedlots with ad libitum and restricted feeding in fall and spring seasons

Validation study of WindTrax reverse dispersion model coupled with a sensitivity analysis of model-specific settings

In last years, atmospheric dispersion models have reached considerable popularity in environmental research field. In this regard, given the difficulties associated to the estimation of emission rate for some kind of sources, and due to the importance of this parameter for the reliability of the results, Backward dispersion models may represent promising tools. In particular, by knowing a measured downwind concentration in ambient air, they provide a numerical value for the emission rate. This paper discusses a critical validation of the WindTrax Backward model: the investigation does not only deal with the strict reliability of the model but also assesses under which conditions (i.e. stability class, number, and location of the sensors) the model shows the greatest accuracy. For this purpose, WindTrax results have been compared to observed values obtained from available experimental datasets. In addition, a sensitivity study regarding model-specific parameters required by WindTrax to replicate the physics and the random nature of atmospheric dispersion processes is discussed. This is a crucial point, since, for these settings, indications on the numerical values to be adopted are not available. From this study, it turns out that the investigated model specific settings do not lead to a significant output variation. Concerning the validation study, a general tendency of the model to predict the observed values with a good level of accuracy has been observed, especially under neutral atmospheric conditions. In addition, it seems that WindTrax underestimates the emission rate during unstable stratification and overestimates during stable conditions. Finally, by the definition of alternative scenarios, in which only a portion of the concentration sensors was considered, WindTrax performance appears better than acceptable even with a small number of concentration sensors, as long as the positioning is in the middle of the plume and not in the strict vicinity of the source.

Trade-offs of gaseous emissions from soils under vegetable, wheat-maize and apple orchard cropping systems applied with digestate: An incubation study

Land application of digestate from anaerobic digestion causes various gaseous emissions. A soil core incubation experiment was carried out in the laboratory to investigate the trade-offs of NH₃, N₂O and CH₄ emissions from soils collected from vegetable, arable and orchard cropping systems. Digestate derived from liquid cattle manure was applied to the soil cores through the surface (SA) and incorporation application (IA) methods under three soil moisture conditions (40%, 60%, and 80% water-filled pore space, WFPS). Gaseous emissions from vegetable soil were significantly greater (P< .05) than those from soils under the other two cropping systems under similar conditions, particularly under a high moisture condition. The greenhouse gas emissions (GHG, in term of CO₂-equivalents) of all soils increased with the increasing soil moisture contents, mainly due to rapidly increasing N₂O emissions. Trade-offs in the emissions of these three gases were observed between SA and IA. As expected, SA was characterized by greater NH₃ and CH₄ but lower N₂O emissions compared to IA. The increase in GHG under IA could be offset only somewhat by the reduced NH₃ (and this reduced indirect N₂O) and CH₄ emissions under lower moisture conditions (<60% WFPS), which indicates a requirement for other strategies to control gaseous emissions from wet soils applied with digestate. In conclusion, an environmentally friendly strategy for digestate application should consider the soil moisture, types of soils and application methods, and all the presented suggestions need to be verified in the field in the future.Implications: This study shows that digestate incorporation can decrease NH3 but increase GHG emissions verse the surface application method, where the increased GHG could only be offset by the NH3 reductions at relatively dry soil condition, indicating an urgent requirement to mitigating GHG emissions under moist soil condition.

Micrometeorological Methods for Measuring Methane Emission Reduction at Beef Cattle Feedlots: Evaluation of 3-Nitrooxypropanol Feed Additive

It is highly desirable to test agricultural emission mitigation strategies in a whole-farm environment to ensure that all aspects of management and production operations are included. However, the large spatial scale of commercial operations makes the dual measurements of control and treatment(s) difficult. We evaluated the application of two micrometeorological methods, a novel concentration ratio method and an inverse dispersion method, where both were used to measure methane (CH) emission reductions in cattle fed the compound 3-nitrooxypropanol compared with cattle fed just the basal diet. In total, there were 1344 cattle used that were located in six pens (∼222 animals per pen). Three adjacent pens to the east and three to the west were designated as the treatment and control blocks, respectively. Underlying the emission reduction method was the assumption of site symmetry between the treatment and control pen blocks in the feedlot. There was, on average, a large CH emission reduction of ∼70% (±18%) due to the additive as found by both micrometeorological methods. Both methods also show a change in the diel distribution (peak emissions after initial morning feeding) and seasonal pattern (a decrease in emission reduction of 7.5 and 26.1% over 90 d). The simplicity of the developed concentration ratio method is expected to have applications for evaluating other mitigation strategies at large commercial scales (e.g., the application of manure additives to pens to reduce odors and ammonia emissions).

Impacts of feedlot floor condition, deposition frequency, and inhibitors on N2O and CH4 emissions from feedlot dung and urine patches

Patches of dung and urine are major contributors to the feedlot gas emissions. This study investigated the impacts of dung deposition frequency (partly reflecting animal stocking density of a feedlot), dairy feedlot floor conditions (old floor indicated with the presence of consolidated manure pad [CMP] vs. new floor with the absence of consolidated manure pad [CMPn]), and application of dicyandiamide (DCD) and hydroquinone (HQ) on nitrous oxide (N₂O) and methane (CH₄) emissions from patches in the laboratory, and the integrative impacts were expressed in terms of global warming potential (CO₂-equivalent). Dung deposition frequency, feedlot floor condition, and application of inhibitors showed inverse impacts on N₂O and CH₄ emissions from patches. Greenhouse gas (GHG) emissions from the dung, urine, and dung+urine patches on the CMP feedlot surface were approximately 7.48, 87.35, and 7.10 times those on the CMPn feedlot surface (P < 0.05). Meanwhile, GHG emissions from CMP and CMPn feedlot surfaces under high deposition frequency condition were approximately 10 and 1.7 times those under low-frequency condition. Moreover, application of HQ slightly reduced the GHG emission from urine patches, by 14.9% (P > 0.05), while applying DCD or DCD+HQ significantly reduced the GHG, by 60.3% and 65.0%, respectively (P < 0.05). Overall, it is necessary to include feedlot management such as animal stocking density and feedlot floor condition to the process of determining emission factors for feedlots. In the future, field measurements to quantitatively evaluate the relative contribution of nitrification and denitrification to the N₂O emissions of feedlot surfaces are highly required for effective N₂O control.

IMPLICATIONS

This study shows that feedlot CH₄ and N₂O emissions inversely respond to the dicyandiamide (DCD) application. Applying DCD significantly reduces GHG emissions of feedlot urine patches. Feedlot floor condition and stocking density strongly impact feedlot GHG emissions. Including feedlot floor condition and stocking density in the feedlot EF determining process is necessary.