Nitrous Oxide Emissions from Southern High Plains Beef Cattle Feedyards: Measurement and Modeling

2017 ◽  
Vol 60 (4) ◽  
pp. 1209-1221 ◽  
Author(s):  
Heidi M. Waldrip ◽  
Kenneth D. Casey ◽  
Richard W. Todd ◽  
David B. Parker

Abstract. The Texas Panhandle produces approximately 42% of finished beef in the U.S., and cattle production is estimated to contribute 8 Tg carbon dioxide equivalents (CO2e) from nitrous oxide (N2O). Production of N2O in manure is largely a result of biochemical processes that are not static: N2O emission rates are dependent on numerous environmental and chemical factors. Process-based models that estimate N2O emissions from manure in open-lot cattle production systems typically rely on information derived from studies of soil biochemistry. Limited study has been conducted on manure-derived N2O in open-lot beef feedyards. The objectives of this study were to determine variables related to N2O losses from Texas Panhandle feedyards and develop empirical models to predict N2O emissions. Nitrous oxide flux data were collected from a series of 15 non-flow-through, non-steady-state (NFT-NSS) chamber studies (ten chambers per study) conducted from 2012 to 2014 on two commercial beef cattle feedyards. Manure samples (loose surface manure and the underlying manure pack) were analyzed for basic physicochemical properties, soluble carbon (C) and nitrogen (N), and ultraviolet-visible (UV-vis) spectral characteristics related to degree of organic matter (OM) stability and humification. Measured N2O emissions ranged from below detection to 101 mg m-2 h-1 (average 4.8 ±12 mg m-2 h-1) and were positively related to manure H2O content, temperature, and nitrate (NO3-) concentration (p < 0.01). Emissions were negatively related to manure OM, ammonia/ammonium (NH3/NH4+), dissolved C and dissolved N concentrations, and UV-vis parameters related to OM stability (p < 0.05). Based on these data, empirical models were developed and evaluated to predict manure-derived N2O emissions. Model predictions were not significantly different from observed N2O emissions (p < 0.05). The unbounded index of agreement (IA) indicated that model predictions were within 52% to 61% agreement with observations. Inclusion of OM characteristics improved model predictions of high (>30 mg m-2 h-1) N2O emissions but tended to overestimate low emission rates (<20 mg N2O m-2 h-1). This provides evidence for the importance of C stability in limiting manure N2O production. These models may improve parameterization of existing process-based models and are novel methods for predicting feedyard N2O emissions. Keywords: Beef cattle, Feedlot, Feedyard, Greenhouse gas, Manure, Modeling, Nitrous oxide, Organic matter, Urine, UV-vis spectroscopy.

2021 ◽  
Vol 64 (4) ◽  
pp. 1211-1225
Author(s):  
David B. Parker ◽  
Kenneth D. Casey ◽  
Will Willis ◽  
Beverly Meyer

HighlightsNitrous oxide and methane emissions were measured from a commercial beef feedyard following large rainfall events.Nitrous oxide emissions dropped below detection levels for ten days following a 77 mm rainfall event.Daily N2O and CH4 emissions followed a diel pattern, peaking at manure temperatures of 36°C to 38°C.Results will be used to refine empirical models for predicting GHG emissions from open-lot feedyards.Abstract. More than six million beef cattle are fed annually in feedyards on the semiarid Southern Great Plains (SGP). Manure deposited on the open-lot pen surfaces contributes to greenhouse gas (GHG) emissions. Nitrous oxide (N2O) and methane (CH4) are GHGs linked to climate change, and both have global warming potentials greater than carbon dioxide (CO2). Two sampling campaigns were conducted in 2019 to quantify N2O and CH4 emissions from open-lot pen surfaces. The occurrence of large, unforecast rainfall events during both campaigns provided an opportunity to compare GHG emissions from the dry manure before rainfall and from the wetted pen surface for one to two weeks following precipitation. Temporal variability was quantified by continuous sampling using six to eight automated flux chambers, a multiplexer system, and real-time analyzers. Spatial variability was quantified using a recirculating portable chamber on a 5 × 8 grid. Nitrous oxide emissions dropped below detection levels for ten days after the precipitation event. Nitrous oxide emissions were related to nitrification or other aerobic processes. Methane emissions dropped below detection levels for five days after the precipitation event and then increased to pre-rainfall levels by day 8. When present, N2O and CH4 emissions followed a diel pattern, with the highest emissions occurring during the afternoon when manure pack temperatures at the 25 mm depth were 36°C to 38°C and ambient temperatures were 31°C to 32°C. Average CH4 emissions from the feedyard pen surface were 96-fold lower than estimated enteric CH4 emissions. The results of this field research will be used to refine empirical models for predicting annual N2O and CH4 emissions from open-lot beef cattle feedyards on the semiarid SGP. Keywords: Beef cattle, Flux chamber, Greenhouse gas, Manure, Nitrous oxide, Rainfall.


2014 ◽  
Vol 7 ◽  
pp. ASWR.S12841 ◽  
Author(s):  
Orlando A. Aguilar ◽  
Ronaldo Maghirang ◽  
Charles W. Rice ◽  
Steven L. Trabue ◽  
Larry E. Erickson

Emission of greenhouse gases, including nitrous oxide (N2O), from open beef cattle feedlots is becoming an environmental concern; however, research measuring emission rates of N2O from open beef cattle feedlots has been limited. This study was conducted to quantify N2O emission fluxes as affected by pen surface conditions, in a commercial beef cattle feedlot in the state of Kansas, USA, from July 2010 through September 2011. The measurement period represented typical feedlot conditions, with air temperatures ranging from -24 to 39°C. Static flux chambers were used to collect gas samples from pen surfaces at 0, 15, and 30 minutes. Gas samples were analyzed with a gas chromatograph and from the measured concentrations, fluxes were calculated. Median emission flux from the moist/muddy surface condition was 2.03 mg m−2 hour−1, which was about 20 times larger than the N2O fluxes from the other pen surface conditions. In addition, N2O peaks from the moist/muddy pen surface condition were six times larger than emission peaks previously reported for agricultural soils.


2020 ◽  
Vol 63 (5) ◽  
pp. 1371-1384
Author(s):  
David B. Parker ◽  
Kenneth D. Casey ◽  
Kristin E. Hales ◽  
Heidi M. Waldrip ◽  
Byeng Min ◽  
...  

HighlightsNitrous oxide is a greenhouse gas emitted from feedyard pen surfaces.Experiments were conducted to quantify nitrous oxide emissions from precipitation, urine, and feces.Nitrous oxide emissions from urine were about 30% of those from equal amounts of precipitation.Regression equations were developed for empirical modeling of emissions.Abstract. The amount of moisture deposited annually as urine (~320 mm) and feces (~95 mm) on typical semi-arid Texas beef cattle feedyard pens is considerable compared to the regional 470 mm mean annual precipitation. Precipitation is a primary factor affecting nitrous oxide (N2O) emissions from manure, but specific effects of urine and feces deposition are unknown. The objectives of this research were to (1) quantify N2O emissions following precipitation, urine, and feces deposition on a dry feedyard manure surface, and (2) develop equations for future empirical modeling of these emissions. Four experiments (Exp.) were conducted using recirculating flux chambers to quantify N2O emissions. Exp. 1 had treatments (TRT) of water (W), artificial urine (AU), and two urines collected from beef cattle fed high-quality forage (FU) or corn-based concentrate (CU). Exp. 2 had TRT of W, AU, and two feces levels (Fx1 and Fx2). In Exp. 3, N2O emissions were quantified from fresh feces pats. In Exp. 4, the effect of rainfall pH on N2O emissions was evaluated. Results from Exp. 1 showed that the W TRT had the highest mean cumulative N2O emission, while AU, FU, and CU ranged from 31.0% to 70.0% of W on an equal volume-applied basis. There was little correlation between N2O emissions and urine or water nitrogen (N) content. In Exp. 2, W again had the highest cumulative N2O. Cumulative N2O emissions expressed per unit of water added were 29.0, 3.8, 4.5, and 5.1 mg N kg-1 water added for W, AU, Fx1, and Fx2, respectively. In Exp. 3, fresh feces pats emitted no direct N2O, but N2O originated from the dry manure beneath the feces pat due to wetting. In Exp. 4, the highest N2O emissions occurred at pH 5 and pH 8, with lower emissions at pH 6 and pH 7. This research has shown that the addition of moisture to the pen surface from urine and feces contributes considerably to N2O emissions as compared to precipitation alone. The following recommendations were developed for future empirical modeling purposes: (1) N2O emissions from urine should be calculated as 32.7% of those emissions from the equivalent mass deposition of water, and (2) N2O emissions resulting from the mass of water in feces should be calculated as 15.6% of those emissions from the equivalent mass deposition of water. Keywords: Beef cattle, Greenhouse gas, Manure, Nitrous oxide, Urine, Precipitation.


2019 ◽  
Author(s):  
David B. Parker ◽  
Kenneth D. Casey ◽  
Erin L. Cortus ◽  
Byeng R. Min ◽  
Heidi M. Waldrip ◽  
...  

2016 ◽  
Author(s):  
Philipp Porada ◽  
Ulrich Pöschl ◽  
Axel Kleidon ◽  
Christian Beer ◽  
Bettina Weber

Abstract. Nitrous oxide is a strong greenhouse gas and atmospheric ozone-depleting agent, which is largely emitted by soils. Recently, also lichens and bryophytes have been shown to release significant amounts of nitrous oxide. This finding relies on empirical relationships between nitrous oxide emissions, respiration and net primary productivity of lichens and bryophytes, which are combined with ecosystem-scale values of their productivity. Here we obtain an alternative estimate of nitrous oxide emissions which is based on a global process-based non-vascular vegetation model of lichens and bryophytes. The model quantifies photosynthesis and respiration of lichens and bryophytes directly as a function of environmental conditions, such as light and temperature. Nitrous oxide emissions are then derived from simulated respiration assuming a fixed relationship between the two fluxes. This approach yields a global estimate of 0.27 (0.19–0.35) Tg yr−1 of nitrous oxide released by lichens and bryophytes. This is lower than previous estimates, but corresponds to about 50 % of the atmospheric deposition of N2O into the oceans or 25 % of the atmospheric deposition on land. Uncertainty in our simulated estimate results from large variation in emission rates due to both physiological differences between species and spatial heterogeneity of climatic conditions. To constrain our predictions, field observations of respiration in combination with a more process-based approach for relating nitrous oxide emissions to respiration may be helpful.


2021 ◽  
Author(s):  
Jarno Rouhiainen ◽  
Dorothee Neukam ◽  
Rene Dechow ◽  
Rima Rabah Nasser ◽  
Henning Kage

<div> <div> <div> <p>Nitrous oxide is an important greenhouse gas. In Germany, around 50% of annual nitrous oxide emissions originate from managed agricultural land. Among other options, the mitigation of nitrous oxide emissions from arable land is one important measure to reduce greenhouse gas emissions of the agricultural sector. Several mitigation options have been examined including reduced application of nitrogen fertilizers, timing of fertilizer applications, crop residue management, pH management or application of nitrification inhibitors. Depending on the underlying natural conditions (soil, climate), these measures vary in their mitigation efficiency.</p> <p>Suitable methods are required to evaluate and quantify mitigation strategies for nitrous oxide emissions at a regional and national scale. For this purpose, several model approaches have been developed ranging from simple stochastic equations to sophisticated process-based models. Because of their reduced input requirements, stochastic approaches like emission factor approaches are common to quantify nitrous oxide emissions and mitigation effects while process based models are promising tools to describe interactions of natural conditions and anthropogenic activities. They have the potential to be more accurate and informative.</p> <p>However, due to the complex nature of N2O producing processes in croplands and the high spatial and temporal variability of N2O fluxes the portability of model developments from one site to another site or the validity of upscaling methods are questionable. We collected available field experimental data measuring nitrous oxide emissions to improve and analyze the prediction accuracy of model approaches in Germany, recently with data of 19 sites and 1251 site years in total and focus on the crop types wheat, maize and rape.</p> <p>Here, we present this data set and show results of model applications and a multi-site sensitivity analyses with the process based model DNDCv.Can. Contrary to other DNDC versions, DNDCvCAN allows to modify a range of internal parameters.</p> <p>We performed sensitivity analyses based on the Morris method by varying 45 model parameters. Each participating site was modeled for a three years period and the simulations were repeated for each parameter 500 times, resulting to 23000 simulations per site. Highest impact on N2O emissions were caused by soil concentrations of humads, humus and black carbon and their related C/N ratios. Surprisingly, N2O emissions showed only minor sensitivites in general on hydrological parameters and</p> </div> </div> </div><div> <div> <div> <p>on parameters related to N cycling in soil profile. Parameters controling macropore flow, nitrifier growth and denitrifier growth made here an exception. Sets of ranked most sensitive parameters varied between sites showing that multi-site sensitivity analyses might be helpful to identify global and local parameters for model calibration and help to assess regional mitigation effects.</p> </div> </div> </div>


2017 ◽  
Vol 81 (6) ◽  
pp. 1537-1547 ◽  
Author(s):  
Ben W. Thomas ◽  
Xinlei Gao ◽  
Jessica L. Stoeckli ◽  
Ryan Beck ◽  
Kui Liu ◽  
...  

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