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Greenhouse gas emissions from beef cattle pen surfaces in North Dakota a
a
Shafiqur Rahman , Md Saidul Borhan & Kendall Swanson a
b
Agricultural and Biosystems Engineering Dept. , North Dakota State University , Fargo , USA
b
Department of Animal Sciences , North Dakota State University , Fargo , USA Accepted author version posted online: 30 Oct 2012.Published online: 15 Nov 2012.
To cite this article: Shafiqur Rahman , Md Saidul Borhan & Kendall Swanson (2013) Greenhouse gas emissions from beef cattle pen surfaces in North Dakota, Environmental Technology, 34:10, 1239-1246, DOI: 10.1080/09593330.2012.743598 To link to this article: http://dx.doi.org/10.1080/09593330.2012.743598
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Environmental Technology, 2013 Vol. 34, No. 10, 1239–1246, http://dx.doi.org/10.1080/09593330.2012.743598
Greenhouse gas emissions from beef cattle pen surfaces in North Dakota Shafiqur Rahmana∗ , Md Saidul Borhana and Kendall Swansonb a Agricultural
and Biosystems Engineering Dept., North Dakota State University, Fargo, USA; b Department of Animal Sciences, North Dakota State University, Fargo, USA
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(Received 8 August 2012; final version received 10 October 2012 ) There is a global interest to quantify and mitigate greenhouse gas (GHG) (e.g. methane-CH4 , nitrous oxide-N2 O and carbon dioxide-CO2 ) emissions in animal feeding operations. The goal of this study was to quantify GHG emissions from the feedlot pen surface under North Dakota climatic conditions. In this study gaseous flux from the pen surfaces was generated using a custom-made wind tunnel at different times of the year (summer, fall, winter and spring). Gaseous fluxes (air samples) were drawn in the Tedlar bags using a vacuum chamber and gas concentrations were measured using a gas chromatograph within 24 h of sampling. The CH4 concentrations and flux rates (FRs) or flux among the months were not significantly different. Overall CH4 , CO2 and N2 O concentrations over a 7-month period were 2.66, 452 and 0.67 ppm, respectively. Estimated overall CH4 , CO and N2 O FRs were 1.32, 602 and 0.90 g m−2 d−1 , respectively. Estimated emission rates using the wind tunnel were 38 g hd−1 d−1 , 17 kg hd−1 d−1 and 26 g hd−1 d−1 for CH4 , CO2 and N2 O, respectively. The emission factors for GHG estimated in the research for North Dakota climate were the first of its kind, and these emission estimates can be used as a basis for planning and implementing management practices to minimize GHG emissions. Keywords: feedlot; greenhouse gas, wind tunnel; flux rates; emission rates
1. Introduction Animal feeding operations (AFOs) provide benefits through better feed and production efficiency than grazing systems, with more consistent quality and quantity of product [1]. However, AFOs contribute a considerable amount of pollutant gases (e.g. ammonia and hydrogen sulphide) and greenhouse gases (e.g. methane, nitrous oxide and carbon dioxide), especially enteric methane and methane emission from manure handling and storage. Pollutant gas concentration might impact workers’ health and animal production efficiency, although limited data are available for livestock. On the other hand, greenhouse gases (GHGs) may contribute to global warming [2]. Pollutant gases and GHGs are generated at different stages of livestock production systems. For example, as manure is excreted, nitrogen may be lost in the form of ammonia (NH3 ) from excreta, during manure storage and following land application of manure. Similarly, livestock, (primarily ruminants) emit up to one-third of the emitted CH4 worldwide [2]. Methane (CH4 ) is produced through digestive processes (enteric fermentation in ruminant) and manure decomposition, and nitrous oxide (N2 O) is produced from manure storage as well as from land application of manure [1,3]. Within a feedlot, CH4 is emitted from enteric fermentation and manure degradation and N2 O is produced directly from manure [1]. Enteric ∗ Corresponding
author. Email: [email protected]
© 2013 Taylor & Francis
methane emissions from livestock contribute the major portion (25%) and manure contributes about 8% of the total CH4 emissions from anthropogenic activities [4]. The agricultural sector is reported to be the greatest contributor of N2 O and the third greatest contributor of CH4 in the United States [5,6]. Therefore, strategies need to be developed for reducing or minimizing net emissions of GHGs. There are broadly two main techniques commonly used to quantify gaseous emissions including GHGs from feedlot production facilities. The micrometeorological techniques include atmospheric dispersion modelling such as inverse dispersion, backward Lagrangian stochastic model, IPCC tiers I and II algorithm, and Blaxter and Clapperton algorithms are mathematical models based on the assumptions of biochemical reactions driven by animal size, feed intake and feed quality in confined conditions. Classical micrometeorological methods such as flux gradient, eddy covariance, relaxed eddy accumulation and boundary layer budgeting are also used to estimate emission rates [7]. In these techniques, the emission rates are calculated from the measured gas concentrations coupled with local micrometrical data, especially wind velocity profile data [8,9]. At the same time, it requires comparatively large source areas [7,10] and costly instruments [10]. In the non-micrometeorological techniques, flux chamber, wind tunnel, tracer ratio, mass balance, etc., have been used to
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estimate emission rates from AFOs. Most of the published GHG emissions data from feedlots used micrometeorological techniques coupled with monitoring devices often installed at 0–3 m above the target source depending on source type, model type and methodology applied [7]. Thus, pollutant concentration measurements at this height may not be a true value, since agriculture emissions are produced close to the emitting surface. The emissions from agricultural sources are different than industrial sources due to: (a) the source is at or near the ground; (b) the source may be of relatively large and areal extent; (c) the important receptor zone may be relatively close to the source of emission; and (d) the relatively low intensity of emission [11]. In non-micrometeorological techniques, benefits of using a wind tunnel over the traditional flux chamber are the larger surface area covered and the air exchange rates or air speed in the tunnel being more similar to ambient conditions [12]. When using a sampling device, flux chambers and wind tunnels are deployed on an emitting surface under some recommended operating conditions. Generally, the emission rate is estimated as the product of concentration and air flow through the device. Studies show that wind tunnels are a more reliable and suitable method for onfield NH3 volatilization measurement over static chambers, which tend to underestimate NH3 losses due to restricted air movement inside the chambers [13]. In addition, for point measurement of emissions, wind tunnels operate at a wind speed equivalent to ambient conditions [14]. Researchers have been using wind tunnels over the last several years to measure GHG emissions [15–17]. There is limited published literature reporting CH4 emissions from feedlot manure systems using atmospheric dispersion modelling (inverse dispersion, backward Lagrangian stochastic model, IPCC tiers I and II algorithm) to estimate emissions from a whole farm. Loh et al. [18] estimated summer CH4 emission rate (ER) data for two Australian feedlots using an open-path tunable near-infrared diode laser coupled with backward Lagrangian stochastic model of atmospheric dispersion. Methane ERs reported were 146 and 166 g hd−1 d−1 for Victoria and Queensland, respectively. Using the same techniques, the average CH4 emissions were 166 and 214 g CH4 hd−1 d−1 for feedlots in Queensland and Alberta, respectively [19]. Average daily CH4 emissions were estimated to be 323 g hd−1 d−1 for a large beef feedlot in western Canada using the inverse dispersion model [20]. Direct measurements using micrometeorological mass difference technique reported 70 g CH4 hd−1 d−1 emissions from a confined beef feedlot in Australia where animals were fed a highly digestible high grain diet [21]. Recently, Borhan et al. [4] examined GHG emissions from ground level area sources from a dairy and cattle feedlot operation in Texas, and they found that pen surface has significantly higher CO2 and N2 O emissions than compost pile and runoff pond. The median emission rates for CH4 , CO2 and N2 O were 3.8, 1399, 0.68 g hd−1 d−1 (1.7 kg CO2 e hd−1 d−1 ), respectively,
from the beef cattle feedlot pen surface measured with dynamic flux chamber [4]. However, limited published information quantifying GHG emissions from ground level sources in US feedlot production systems was found in the literature. Gas emissions from feedlot or manure storage vary throughout the years depending on weather, animal sizes and numbers, animal diets, manure accumulation on the pen surfaces, pen moisture content, manure storage conditions, etc. Cold ambient temperatures increase maintenance energy needs and feed intake in most ruminants, which can influence the extent of pollutant gas emissions. Boadi et al. [22] mentioned that diet formulation can influence the extent of CH4 released from manure. North Dakota has diversified animal feed grains (i.e. corn, barley, canola, peas, oats, soybeans and wheat) as well as by-products (i.e. DDGS, soybean hulls, soybean meals, canola meal, etc.), which are used as livestock feed. In North Dakota, feedlots are distributed throughout the state and consist of small to modest-sized yards. However, pollutant gas emissions data are lacking under North Dakota livestock operation and management practices. No published information quantifying GHG emissions from ground level sources in North Dakota feedlot production systems were found in the literature. However, there is limited published information on GHG emissions in the relatively warmer regions of the United States. The emissions from one region may not be directly applicable to other regions due to differences in weather, animal diets and management practices. To improve agricultural air quality research, the National Research Council (NRC) [23] highlighted the need for emission studies focusing on individual farm operations or sources within the farm. Thus, there is a need to quantify onfarm emissions under North Dakota livestock management and weather conditions that would be most reliable and relevant to establish emission rates in the state. Therefore, the purpose of this study was to quantify GHG emissions from feedlot pen surface under North Dakota climatic conditions.
2.
Materials and methods
2.1. Study location This study was conducted at the Beef Cattle Research Complex at North Dakota State University (NDSU). This feedlot facility has six pens designed for a total capacity of 192 head of cattle. The length and width of each pen is 50 m × 16 m and has an overall aggregate slope 3%. Cattle in these pens were fed concentrated-based diets (90% concentrate) containing corn (finely or coarsely rolled), distillers grains plus solubles (20 or 40% of dry matter (DM), corn silage, hay, condensed corn distillers solubles, limestone, trace mineral premix, monensin and tylan. For the nutritional trial, four diets (Table 1) were fed to four groups of cattle in each individual pen using the Insentec feeding system [24–26].
Environmental Technology Table 1.
Level Level 1 Level 2 Level 3 Level 4
Diets constituents fed to the cattle during this trial.
Constituents CG corn and low DDGS FG corn and high DDGS CG corn and high DDGS FG corn and low DDGS
Mixing proportion
Formulated crude protein (%, DM basis)
60/20
13
40/40
17
40/40
17
68/20
13
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Note: CG = coarse grain; FG = fine grain; DDGS = dry distillers solubles; DM = Dry matter.
Because all four diets were fed within each pen, overall GHG emissions for animals fed the four mixed diets were measured from this feedlot, but not to see the impacts of diet on GHGs emission. Typically, manure was scraped every week from the pen surfaces and those pen surfaces were relatively dry. Manure samples were collected during each sampling event from 7–10 locations in each pen and mixed thoroughly. The composite sample was a mixture of fresh, dry and semi-dry manure and used for determining manure nutrients, fibre and volatile fatty acid contents. A total of 48 manure samples were collected during the study period.
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2.2. GHG sample collection and analysis Air samples were collected randomly at 3–6 locations in each pen corresponding to the total number of grid cells (e.g. 1–16 for a 4 m × 4 m grid). Air samplings were performed for 7 months from October 2011 to June 2012, covering typical climatic variations in North Dakota. A total of 152 air samples were collected from six pens of this feedlot. Air samples were collected from each pen surface in 5 L Tedlar bags using a portable wind tunnel (0.80 m × 0.40 m) and Vac-u-chamber (SKC Inc., Eighty Four, PA, USA) (Figure 1). A uniform air flow rate (1.75 m3 s−1 ) was maintained inside the tunnel throughout the sampling period using a DC motor regulator (to calculate emission rate). In this way duplicated air samples were collected from each pen during each sampling time at different times (summer, fall, winter and spring) of the years. Within 24 h of sampling, air samples were analysed for CH4 , CO2 and N2 O using a greenhouse gas chromatograph (GC) (Model No. 8610C, SRI Instruments, 20720 Earl St., Torrance, CA 90502) (Figure 1) equipped with a flame ionization detector (FID) and an electron capture detector (ECD). An air sample from the Tedlar bag was drawn into a 1 mL sample loop of the GC using an inbuilt vacuum pump interfaced with the GC system according to a prescheduled event programme. Before drawing any sample into the sample loop, the FID detector temperature was raised to 300◦ C and
Vacuum pump
Tedlar bag
Fan
Carbon filters
Sampling point
Pen surface
Figure 1.
Schematic diagram of a wind tunnel and greenhouse gas chromatography (GC) used in this study (drawing not to scale).
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Table 2.
Gas properties and calibration information.
GHGs
CASa No.
MWb (g gmol−1 )
Retention time (min)
Calibration equations
R2
MDLc
CH4 CO2 N2 O
74-82-8 124-38-9 10024-97-2
16.04 44.01 44.01
1.563 2.866 3.573
Y = 0.153(x) Y = 0.2448(x) Y = 0.0028(x)
0.99 0.99 0.98
82 918 66
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Note: a CAS No.- Chemical Abstracts Service Number; b MW = molecular weight; Y = concentration in ppm; and x = GC responses or area counts. c MDL – Minimum Detection Limit.
the ECD detector temperature was raised to 350◦ C. The system was operated on a nitrogen carrier at 20 PSI for the ECD hydrogen and air was supplied to the FID/methanizer at 20 PSI. In this system, the ECD detector detects N2 O, while the FID/methanizer detector detects both CH4 and CO2 . A detailed description of the GHG measurement procedure using GHG GC can be found elsewhere [27]. 2.3.
Emission calculation
Flux rates (FRs) (g m−2 d−1 ) from the tunnel were calculated using the average airflow through the wind tunnel multiplied by the concentration of a target gas and divided by the surface area covered by the wind tunnel. To compare the estimated ERs among the months, the measured volumetric concentrations were standardized at standard pressure and temperature. We have reported these to be consistent with the reporting of ERs of different air pollutants by the USEPA (1 atm and 25◦ C). Equations (1), (2) and (3) were used to calculate mass concentration of particular gas, FRs and ERs, respectively. Cppm × MWGHG 24.45 CMASS × VWT × 3600 × 24 = AWT × 1000 FRGHG × ASC = TNA
CMASS = FRGHG ERGHG
(1) (2) (3)
where: Cppm = Volumetric concentration of the given compound (ppm); CMASS = Mass concentration of the same compound (mg m−3 ); MWGHG = Molecular weight of the same compound (g gmol−1 ); 24.45 = Volume per mole of an ideal gas at standard temperature and pressure (L gmol−1 ); FRGHG = GHG emission flux rate from pen surface (g m−2 d−1 ); ERGHG = GHG emission rate from pen surface (g hd−1 d−1 ); VWT = Airflow rate through wind tunnel (m3 s−1 ); AWT = Surface area covered by the wind tunnel (0.4 × 0.8 m2 ); Asc = Surface area of the source (m2 ); TNA = Total number of cattle. 2.4. GC calibration and compound quantification Quantification of compounds contained in the sampled air was performed using certified calibration standard greenhouse gases (CH4 , CO2 and N2 O), and identification was
confirmed by matching retention times. To develop calibration equations (standard curves), three concentration levels of each calibration gas (0, 5, 10, 20 ppm for CH4 ; 0, 150, 1000, 5000 ppm for CO2 ; 0, 1, 5, 20 ppm for N2 O) with five to seven replicates were used. Regressions between the peak areas (GC responses) and different concentration levels of each compound through the origin were used to interpolate the total concentration of compounds contained in the sampled air. Minimum detection limits (MDLs) were calculated following USEPA guidelines [28] as the product of the standard deviation of seven replicates and the Student’s t value at the 99% confidence level. For seven replicates (6 degrees of freedom), a t value of 3.14 was used for MDLs calculation (Table 2). The coefficients of determination (R2 ) of each standard equation reflect the accuracy and reliability of the direct GHG measurements using a portable GHG GC (Table 2). The MDLs indicate the ability of the measurement system to accurately determine (with 99% confidence) CH4 , CO2 and N2 O concentrations as low as 82, 918 and 66 ppb, respectively. 2.5. Statistical analysis Measured GHG (CH4 , CO2 and N2 O) concentrations, FRs and estimated ERs during each month in the feedlot were compared through analysis of variance (ANOVA). The null hypothesis tested was that mean measure of GHG concentrations, FRs and ERs among various months were equal. All statistical analyses were performed in the SAS environment using the PROC Means procedures [29]. Mean concentrations, FRs, ERs were then separated using Duncan’s Multiple Range Test [30] at 5% significance level (p ≤ 0.05) if the main effect (month) was significant using F test at p ≤ 0.05. 3. Results and discussion 3.1. Background information Air samples were collected between 10:00 am and 1:00 pm, and manure samples were collected from the corresponding locations and a composite sample was prepared for each pen. Some of the preliminary manure characteristics are listed in Table 3. Ambient temperature during the monitoring period was downloaded from the nearest weather station maintained by North Dakota Agricultural Weather Network
Environmental Technology Table 3. Main manure characteristics of beef cattle. Parameters
protein) amount (13–17%, Table 1), fed to the beef cattle during this trial at North Dakota were different than in the Texas study (20%; [4]). Similarly, feedlot pen surface in this research was mostly dry with portions of faeces and urine from April to June; however, it was moist and muddy during February and March. In this facility, the pen surfaces were cleaned once a week and maintained in this way during the study period. This difference was likely due to the moderate temperature of North Dakota compared with central Texas during summer months, as well as manure characteristics (Table 3). Average temperature of the summer months (June to October) ranged from 11–21◦ C. On the other hand, it was hotter (23.8–36.1◦ C) in central Texas than North Dakota. High temperature likely expedited the CH4 emissions from the Texas feedlot. Average atmospheric concentrations of CH4 , CO2 and N2 O are approximately ∼1.7, ∼379 and ∼0.3 ppmv, respectively, [2]. The overall CH4 concentration over a 7-month period was 2.71 ppm (Table 4). This research showed that overall CH4 concentration over 7 months measured from this facility was approximately 1.6 times higher than ambient concentration (1.7 ppm). The CO2 concentration in February was lowest (385 ppm, which was very close to the ambient concentration [2]); and in October, CO2 concentration was highest (554 ppm) among the months (Table 4). Average CO2 concentrations measured during October were significantly different and higher than those measured during other months. Carbon dioxide concentration in October was about 1.1, 1.4, 1.4, 1.2, 1.3 and 1.2 times higher than that measured in January, February, March, April, May and June, respectively. The CO2 concentrations measured during October, January, February, March, May and June were significantly different (p < 0.05). There were no significant differences in measured CO2 concentrations between April and May (p > 0.05). Similarly, the average CO2 concentrations between April and June were statistically similar (p > 0.05) and significantly different than other months. Average summer (June to October) CO2 concentration in this North Dakota feedlot was 507 ppm, which was approximately 1.7 times lower than that (852 ppm)
Amount
Total solids (%) Total volatile solids (%) Total carbon (%) Total phosphorus (%) Total nitrogen (%) Total potassium (%) Crude protein (%)
24.49 78.57 39.83 0.12 2.07 0.19 12.75
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(NDAWN). Table 4 provides ambient daily average air temperature ranges during the study period. 3.2. Greenhouse gas concentrations Methane concentrations in collected samples among the months did not differ greatly and were statistically similar (p > 0.05) with lowest concentrations in March and highest in April. Measured methane concentrations ranged from 2.27–3.17 ppm and were highly variable, as indicated by the standard deviation (Table 4). This was likely due to spatially variable manure loading rates, fluctuations in temperature and variations in populations of microorganisms that were responsible for gaseous emissions at the pen surfaces. Average summer (June to October) CH4 concentration at North Dakota was 2.6 ppm, which is lower (1.2 times) than that (3.1 ppm) measured at a large feedlot (910,252 m2 pen surface and 40,000 cattle) in Texas during August 2010 [4]. GHG emissions mainly depend on cleanliness of the pen, manure characteristics and the ambient temperature. The Texas feedlot was mostly dry, with portions containing scattered feed, faeces and urine. The manure solids on feedyard pens were scrapped after each growing cycle. The diets mainly consisted of forage (corn silage), grain (steam flaked corn), protein (wet distillers corn and condensed corn distillers), fat, beef finisher and trace elements (rumensin, tylan, vitamin A and D) were 19, 57.2, 20.1, 0.8, 2.1, 0.8%, respectively [4]. Diet constituents, especially protein (crude Table 4.
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Greenhouse gas (GHG) concentration measurements from a research feedlot at North Dakota State experiment station.
Sampling month Oct− 2011 Jan− 2012 Feb− 2012 Mar− 2012 April− 2012 May− 2012 June− 2012 Overall
Sampling number n =15 n =18 n =15 n =20 n =33 n =36 n =15 152
Daily average temperature (◦ C)
GHG concentration (ppm)
Average
Max
Min
CH4
CO2
N2 O
11 −7 −6 5 9 16 21
17 −2 −1 11 15 23 27
11 −7 −6 5 9 16 21
2.66 ± 1.32 2.34 ± 0.45 2.95 ± 0.69 2.27 ± 0.71 3.17 ± 1.93 2.74 ± 1.34 2.49 ± 1.54 2.71 ± 1.35
554a ± 36 496b ± 23 385f ± 40 408e ± 13 448cd ± 28 438d ± 24 460c ± 39 452 ± 53
0.62bc ± 0.15 0.44c ± 0.21 0.58bc ± 0.28 0.99a ± 0.49 0.80ab ± 0.42 0.65bc ± 0.32 0.47c ± 0.22 0.67 ± 0.37
Note: The values in a column, representing average concentration in different months followed by common letter(s) do not differ significantly at p ≤ 0.05.
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measured in a large feedlot in Texas during August 2010 [4]. This difference was due to cleanliness of the pen and manure characteristics. The overall CO2 concentration over a 7month period was 452 ppm which was slightly higher (1.2 times) than ambient concentration (379 ppm; [2]). Similar to CH4 concentrations, N2 O concentrations were also highly variable within and among the months, as indicated by the large standard deviation (Table 4). Average N2 O concentrations measured during March were significantly different and higher than those measured during other months. The highest N2 O concentrations were measured in March and April, although average concentrations between March and April were statistically similar (p < 0.05). Relatively drier pen surfaces during March and April may have facilitated aerobic nitrification which in turn increased N2 O emissions. There were no significant differences in N2 O concentrations measured during October, January, February, May and June (p > 0.05; Table 4). Nitrous oxide concentration measured in March was about 1.6, 2.3, 1.7, 1.2, 1.5 and 2.1 times higher than that measured in October, January, February, April, May and June, respectively. Average summer (June to October) N2 O concentration measured at this facility was 0.55 ppm, which was approximately 1.5 times greater than in the Texas feedlot (0.37 ppm [4]; and ambient concentrations (0.3 ppm) [2]). This difference may have been due to thawing of the pen surface in North Dakota.
3.3. Greenhouse FRs and ERs Similar to CH4 , CO2 and N2 O concentrations, FRs and ERs for CH4 , CO2 and N2 O showed a similar trend over the measurement period (Table 5). The CH4 concentrations, FRs and ERs among the months were not significantly different. Overall FRs estimated using the wind tunnel were 1.32, 602 and 0.90 g m−2 d−1 for CH4 , CO2 and N2 O, respectively (Table 5). These numbers for GHGs were much higher than those estimated using dynamic flux chamber from a large feedlot pen (901,252 m2 and 40,000 head) in the Texas Panhandle during the summer (August) of 2010 [4]. In the Table 5.
Texas study, estimated FRs for CH4 , CO2 and N2 O were 0.07, 57, 0.03 g m−2 d−1 , respectively, which were approximately 19, 11 and 30 times, respectively, lower than those estimated in this research feedlot using a wind tunnel. This difference was likely due to differences in the measurement system. Similarly, overall ERs estimated from the pen surfaces using a wind tunnel were 38 g hd−1 d−1 , 17 kg hd−1 d−1 and 26 g hd−1 d−1 for CH4 , CO2 and N2 O, respectively (Table 5). These numbers for GHG ERs were much higher than those estimated using a dynamic flux chamber from a large feedlot pen in the Texas Panhandle during the summer (August) of 2010 [4]. Estimated ERs for CH4 , CO2 and N2 O in the Texas study were 1.7, 1309, 0.57 g hd−1 d−1 , respectively, approximately 22, 13 and 46 times, respectively, lower than those estimated in this research using the wind tunnel. There is debate about the suitability and accurateness of using a flux chamber to quantify pollutant emissions at AFOs and other area sources due to the creation of microenvironments in the chamber and the small measurement footprint relative to the size of the source. Hudson et al. [9] compared odour emission rates from various sources using the two sampling methods and they reported that odour emissions rates from a wind tunnel were 60 to 240 times higher than those in a flux chamber. In contrast, the CH4 ER estimated from the pen surface (ground surface) in this study was much lower than those measured in Canada and Australia using different atmospheric dispersion models coupled with pollutant concentration measurements across the plume of the emitting surfaces by open-path tunable near-infrared diode sensors [19,21]. Harper et al. [21] reported 70 g hd−1 d−1 CH4 ER measured using a micrometeorological mass difference technique from a confined beef feedlot in Australia where animals were fed a highly digestible high grain diet. Similarly, CH4 ERs reported were 146 and 166 g hd−1 d−1 for beefyards in Victoria and Queensland, respectively [19]. These CH4 ERs using atmospheric dispersion models were estimated emissions on a whole-farm basis, which included
Estimated greenhouse gas (GHG) emission flux rates (FRs) and emission rates (ERs). GHG flux rates
GHG emission rates
Sampling month
CH4 (g m−2 d−1 )
CO2 (g m−2 d−1 )
N2 O (g m−2 d−1 )
CH4 (g hd−1 d−1 )
CO2 (kg hd−1 d−1 )
N2 O (g hd−1 d−1 )
Oct− 2011 Jan− 2012 Feb− 2012 Mar− 2012 April− 2012 May− 2012 June− 2012 Overall
1.30a ± 0.64 1.14a ± 0.22 1.44a ± 0.34 1.10a ± 0.34 1.54a ± 0.94 1.33a ± 0.65 1.19a ± 0.74 1.32 ± 0.66
740a ± 47 663b ± 31 514e ± 54 545d ± 17 598c ± 37 585c ± 32 608c ± 47 602 ± 71
0.83bc ± 0.20 0.58c ± 0.28 0.77bc ± 0.38 1.31a ± 0.66 1.06ab ± 0.56 0.87bc ± 0.42 0.63c ± 0.29 0.90 ± 0.50
37a ± 18 33a ± 6.3 41a ± 9.6 32a ± 9.9 44a ± 27.1 38a ± 19 34a ± 21 38 ± 19
21a ± 1.3 19b ± 1 15e ± 1.5 16d ± 0.5 17c ± 1 17c ± 0.9 17c ± 1.3 17 ± 2
24bc ± 6 17c ± 8 22bc ± 10.7 38a ± 19 30ab ± 16 25bc ± 12 18c ± 8 26 ± 14
Note: The values in a column, representing average FRs and ERs in different months followed by common letter(s) do not differ significantly at p ≤ 0.05.
Environmental Technology
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the emission contributions from composting area, runoff pond, silage piles and feed processing areas. In addition, sensors which measured pollutant concentration in this past research were placed at a certain height above the emitting source that might have captured emissions from the abovementioned sources along with CH4 released to ambient through animal breathing. 4. Summary and conclusions GHG emissions from a research feedlot with beef cattle were monitored with a view to investigate the variations in CH4 , CO2 , N2 O concentrations and to estimate ERs over a 7-month period in North Dakota climatic conditions. The temperature of the sampling days varied from −7–21◦ C. The measured gas concentrations within and among the months varied widely due to spatially variable manure loading rates and manure microbial activity at the pen surfaces. The following results and conclusions can be drawn from this research. • Measured CH4 , CO2 and N2 O concentration varied from 2.27–3.17 ppm, 385–554 ppm and 0.44– 0.99 ppm, respectively, over a 7-month period from October to June. • Overall CH4 , CO2 and N2 O concentrations over a 7-month period were 2.71, 452 and 0.67 ppm, respectively. • Estimated overall CH4 , CO2 and N2 O FRs were 1.32, 602, 0.90 g m−2 d−1 , respectively. • Overall ERs estimated using a wind tunnel were 38 g hd−1 d−1 , 17 kg hd−1 d−1 and 26 g hd−1 d−1 for CH4 , CO2 and N2 O, respectively. • The CH4 concentrations, FRs and ERs among the months were not significantly different. • Highest CH4 , CO2 and N2 O concentrations, FRs and ERs were measured during April, October and March respectively. 5. Recommendations for future work Further studies will be conducted with different feedlot sizes, management practices and climatic regions of North Dakota to represent seasonal trends. Also, the effect of different low and highprotein diets and bedding on manure characteristics and subsequent GHG emissions from a feedlot could be investigated. Acknowledgements This project was supported by the State Board of Agricultural Research and Education (SBARE), North Dakota.
References [1] S.K. Muir, Greenhouse gas emissions from Australian beef feedlot, Department of Agriculture and Food Systems, The University of Melbourne, Melbourne, 2011.
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[2] IPCC, Changes in atmospheric constituents and in radiative forcing, in Climate Change 2007: The Physical Science Basis; Contribution of Working Group I to the 4th Assessment Report of the Intergovernmental Panel on Climate Change, Chapter 2, S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller, eds., Cambridge University Press, New York, NY, USA, 2007, pp. 129–234. [3] J. Webb, Estimating the potential for ammonia emissions from livestock excreta and manures. Environmental pollution (Barking, Essex 1987), Environ. Pollut. 111 (2001), pp. 395–406. [4] M.S. Borhan, S. Capareda, S. Mukhtar, W.B. Faulkner, R. McGee, and C.B. Parnell, Greenhouse gas emissions from ground level area sources in dairy and cattle feedyard operations, Atmosphere 2 (2011), pp. 303–329. [5] R.T. Burns, H. Li, H. Xin, R.S. Gates, D.G. Overhults, J. Earnest, and L. Moody, Greenhouse gas (GHG) emissions from broiler houses in the Southeastern United States, Proceedings of the American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting, ASABE, St. Joseph, MI, 2008. [6] D.M. Sedorovich, C.A. Rotz, and T.L. Richard, Greenhouse gas emissions from dairy farms, Proceedings of the American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting, ASABE, St. Joseph, MI, 2007. [7] L.A. Harper, O.T. Denmead, and T.K. Flesch, Micrometeorological techniques for measurement of enteric greenhouse gas emissions. Anim. Feed Sci. Technol. 166–167 (2011), pp. 227–239. [8] S. Christensen, P. Ambus, J.R.M. Arah, H. Clayton, B. Galle, D.W.T. Griffith, K.J. Hargreaves, L. Klemedtsson, A.M. Lind, M. Maag, A. Scott, U. Skiba, K.A. Smith, M. Welling, and F.G. Wienhold, Nitrous oxide emission from an agricultural field: Comparison between measurements by flux chamber and micrometerological techniques, Atmos. Environ. 30 (1996), pp. 4183–4190. [9] N. Hudson, G.A. Ayoko, M. Dunlop, D. Duperouzel, D. Burrell, K. Bell, E. Gallagher, P. Nicholas, and N. Heinrich, Comparison of odour emission rates measured from various sources using two sampling devices, Bioresour. Technol. 100 (2009), pp. 118–124. [10] D. Gericke, A. Pacholski, and H. Kage, Measurement of ammonia emissions in multi-plot field experiments, Biosys. Eng. 108 (2011), pp. 164–173. [11] R.J. Smith, Dispersion of odours from ground level agricultural sources, J. Agric. Eng. Res. 54 (1993), 187–200. [12] K.D. Casey, J.R. Bicudo, D.R. Schmidt, A. Singh, S.W. Gay, R.S. Gates, L.D. Jacobson, and S.J. Hoff, Air quality and emissions from livestock and poultry production/waste management systems, Proceedings of the Animal Agriculture and the Environment: National Center for Manure and Animal Waste Management, ASABE, St. Joseph, MI, 2006, pp. 1–40. [13] E. Smith, R.Gordon, C. Bourque, and A. Campbell, Comparison of three simple field methods for ammonia volatilization from manure, Can. J. Soil Sci. 87 (2007), pp. 469–477. [14] N. Hudson and G.A. Ayoko, Comparison of emission rate values for odour and odorous chemicals derived from two sampling devices, Atmos. Environ. 43 (2009), pp. 3175– 3181. [15] O.T. Denmead, R. Leuning, D.W.T. Griffith, I.M. Jamie, M.B. Esler, L.A. Harper, and J.R. Freney, Verifying inventory predictions of animal methane emissions with meteorological measurements, Boundary Layer Meteorol. 96 (2000), pp. 187–209.
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[16] M.S. Mkhabela, R. Gordon, D. Burton, E. Smith, and A. Madani, The impact of management practices and meteorological conditions on ammonia and nitrous oxide emissions following application of hog slurry to forage grass in Nova Scotia, Agric. Ecosyst. Environ. 130 (2009), pp. 41–49. [17] Q. Zhang, X.J. Zhou, N. Cicek, and M. Tenuta, Measurement of odour and greenhouse gas emissions in two swine farrowing operations, Can. Biosyst. Eng. 49 (2007), pp. 6.13–6.20. [18] Z. Loh, D. Chen, M. Bai, T. Naylor, D. Griffith, J. Hill, T. Denmead, S. McGinn, and R. Edis, Measurement of greenhouse gas emissions from Australian feedlot beef production using open-path spectroscopy and atmospheric dispersion modelling, Aust. J. Exp. Agric. 48 (2008), pp. 244–247. [19] S.M. McGinn, D. Chen, Z. Loh, J. Hill, K.A. Beauchemin, and O.T. Denmead, Methane emissions from feedlot cattle in Australia and Canada, Aust. J. Exp. Agric. 48 (2008), pp. 183–185. [20] R.P. van Haarlem, R.L. Desjardins, Z. Gao, T.K. Flesch, and X. Li, Methane and ammonia emissions from a beef feedlot in western Canada for a twelve-day period in the fall, Can. J. Anim. Sci. 88 (2008), pp. 641–649. [21] L.A. Harper, O.T. Denmead, J.R. Freney, and F.M. Byers, Direct measurements of methane emissions from grazing and feedlot cattle, J. Anim. Sci. 77 (1999), pp. 1392–1401. [22] D.A. Boadi, K.M. Wittenberg, S.L. Scott, D. Burton, K. Buckley, J.A. Small, and K.H. Ominski, Effect of low and high forage diet on enteric and manure pack greenhouse gas emissions from a feedlot, Can. J. Anim. Sci. 84 (2004), pp. 445–453.
[23] NRC, Air Emissions from Animal Feeding Operations: Current Knowledge, Future Needs, The National Academies Press, Washington, DC, 2003. [24] C.J. Mader, Y.R. Montanholi, Y.J. Wang, S.P. Miller, I.B. Mandell, B.W. McBride, and K.C. Swanson, Relationships among measures of growth performance and efficiency with carcass traits, visceral organ mass, and pancreatic digestive enzymes in feedlot cattle, J. Anim. Sci. 87 (2009), pp. 1548– 1557. [25] Y.R. Montanholi, K.C. Swanson, R. Palme, F.S. Schenkel, B.W. McBride, D. Lu, and S.P. Miller, Assessing feed efficiency in beef steers through feeding behavior, infrared thermography and glucocorticoids, Animal 4 (2010), pp. 692–701. [26] K.M. Wood, H. Salim, P.L. Mcewen, I.B. Mandell, S.P. Miller, and K.C. Swanson, The effect of corn or sorghum dried distillers grains plus solubles on growth performance and carcass characteristics of cross-bred beef steers, Anim. Feed Sci. Technol. 165 (2011), pp. 23–30. [27] S. Rahman, Lin, and J. Zhu, Greenhouse gas (GHG) emissions from mechanically ventilated deep pit swine gestation operation, J. Civ. Environ. Eng. 2 (2012), p. 104. [28] USEPA, Definition and Procedure for the Determination of the Method Detection Limit-Revision 1.11; 40CFR Part 136, U.S.E.P. Agency, U.S. Government Printing Office, Washington, DC, 1995. [29] SAS, SAS User’s Guide Statistics, Ver 8, SAS Institute Inc., Cary, NC, 1999. [30] R.C.D. Steel, J.H. Torrie, and D.A. Dickey, Principles and Procedures of Statistics: A Biometrical Approach, McGrawHill, Singapore, 1997.
Environmental Technology Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tent20
Greenhouse gas emissions from beef cattle pen surfaces in North Dakota a
a
Shafiqur Rahman , Md Saidul Borhan & Kendall Swanson a
b
Agricultural and Biosystems Engineering Dept. , North Dakota State University , Fargo , USA
b
Department of Animal Sciences , North Dakota State University , Fargo , USA Accepted author version posted online: 30 Oct 2012.Published online: 15 Nov 2012.
To cite this article: Shafiqur Rahman , Md Saidul Borhan & Kendall Swanson (2013) Greenhouse gas emissions from beef cattle pen surfaces in North Dakota, Environmental Technology, 34:10, 1239-1246, DOI: 10.1080/09593330.2012.743598 To link to this article: http://dx.doi.org/10.1080/09593330.2012.743598
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Environmental Technology, 2013 Vol. 34, No. 10, 1239–1246, http://dx.doi.org/10.1080/09593330.2012.743598
Greenhouse gas emissions from beef cattle pen surfaces in North Dakota Shafiqur Rahmana∗ , Md Saidul Borhana and Kendall Swansonb a Agricultural
and Biosystems Engineering Dept., North Dakota State University, Fargo, USA; b Department of Animal Sciences, North Dakota State University, Fargo, USA
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(Received 8 August 2012; final version received 10 October 2012 ) There is a global interest to quantify and mitigate greenhouse gas (GHG) (e.g. methane-CH4 , nitrous oxide-N2 O and carbon dioxide-CO2 ) emissions in animal feeding operations. The goal of this study was to quantify GHG emissions from the feedlot pen surface under North Dakota climatic conditions. In this study gaseous flux from the pen surfaces was generated using a custom-made wind tunnel at different times of the year (summer, fall, winter and spring). Gaseous fluxes (air samples) were drawn in the Tedlar bags using a vacuum chamber and gas concentrations were measured using a gas chromatograph within 24 h of sampling. The CH4 concentrations and flux rates (FRs) or flux among the months were not significantly different. Overall CH4 , CO2 and N2 O concentrations over a 7-month period were 2.66, 452 and 0.67 ppm, respectively. Estimated overall CH4 , CO and N2 O FRs were 1.32, 602 and 0.90 g m−2 d−1 , respectively. Estimated emission rates using the wind tunnel were 38 g hd−1 d−1 , 17 kg hd−1 d−1 and 26 g hd−1 d−1 for CH4 , CO2 and N2 O, respectively. The emission factors for GHG estimated in the research for North Dakota climate were the first of its kind, and these emission estimates can be used as a basis for planning and implementing management practices to minimize GHG emissions. Keywords: feedlot; greenhouse gas, wind tunnel; flux rates; emission rates
1. Introduction Animal feeding operations (AFOs) provide benefits through better feed and production efficiency than grazing systems, with more consistent quality and quantity of product [1]. However, AFOs contribute a considerable amount of pollutant gases (e.g. ammonia and hydrogen sulphide) and greenhouse gases (e.g. methane, nitrous oxide and carbon dioxide), especially enteric methane and methane emission from manure handling and storage. Pollutant gas concentration might impact workers’ health and animal production efficiency, although limited data are available for livestock. On the other hand, greenhouse gases (GHGs) may contribute to global warming [2]. Pollutant gases and GHGs are generated at different stages of livestock production systems. For example, as manure is excreted, nitrogen may be lost in the form of ammonia (NH3 ) from excreta, during manure storage and following land application of manure. Similarly, livestock, (primarily ruminants) emit up to one-third of the emitted CH4 worldwide [2]. Methane (CH4 ) is produced through digestive processes (enteric fermentation in ruminant) and manure decomposition, and nitrous oxide (N2 O) is produced from manure storage as well as from land application of manure [1,3]. Within a feedlot, CH4 is emitted from enteric fermentation and manure degradation and N2 O is produced directly from manure [1]. Enteric ∗ Corresponding
author. Email: [email protected]
© 2013 Taylor & Francis
methane emissions from livestock contribute the major portion (25%) and manure contributes about 8% of the total CH4 emissions from anthropogenic activities [4]. The agricultural sector is reported to be the greatest contributor of N2 O and the third greatest contributor of CH4 in the United States [5,6]. Therefore, strategies need to be developed for reducing or minimizing net emissions of GHGs. There are broadly two main techniques commonly used to quantify gaseous emissions including GHGs from feedlot production facilities. The micrometeorological techniques include atmospheric dispersion modelling such as inverse dispersion, backward Lagrangian stochastic model, IPCC tiers I and II algorithm, and Blaxter and Clapperton algorithms are mathematical models based on the assumptions of biochemical reactions driven by animal size, feed intake and feed quality in confined conditions. Classical micrometeorological methods such as flux gradient, eddy covariance, relaxed eddy accumulation and boundary layer budgeting are also used to estimate emission rates [7]. In these techniques, the emission rates are calculated from the measured gas concentrations coupled with local micrometrical data, especially wind velocity profile data [8,9]. At the same time, it requires comparatively large source areas [7,10] and costly instruments [10]. In the non-micrometeorological techniques, flux chamber, wind tunnel, tracer ratio, mass balance, etc., have been used to
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estimate emission rates from AFOs. Most of the published GHG emissions data from feedlots used micrometeorological techniques coupled with monitoring devices often installed at 0–3 m above the target source depending on source type, model type and methodology applied [7]. Thus, pollutant concentration measurements at this height may not be a true value, since agriculture emissions are produced close to the emitting surface. The emissions from agricultural sources are different than industrial sources due to: (a) the source is at or near the ground; (b) the source may be of relatively large and areal extent; (c) the important receptor zone may be relatively close to the source of emission; and (d) the relatively low intensity of emission [11]. In non-micrometeorological techniques, benefits of using a wind tunnel over the traditional flux chamber are the larger surface area covered and the air exchange rates or air speed in the tunnel being more similar to ambient conditions [12]. When using a sampling device, flux chambers and wind tunnels are deployed on an emitting surface under some recommended operating conditions. Generally, the emission rate is estimated as the product of concentration and air flow through the device. Studies show that wind tunnels are a more reliable and suitable method for onfield NH3 volatilization measurement over static chambers, which tend to underestimate NH3 losses due to restricted air movement inside the chambers [13]. In addition, for point measurement of emissions, wind tunnels operate at a wind speed equivalent to ambient conditions [14]. Researchers have been using wind tunnels over the last several years to measure GHG emissions [15–17]. There is limited published literature reporting CH4 emissions from feedlot manure systems using atmospheric dispersion modelling (inverse dispersion, backward Lagrangian stochastic model, IPCC tiers I and II algorithm) to estimate emissions from a whole farm. Loh et al. [18] estimated summer CH4 emission rate (ER) data for two Australian feedlots using an open-path tunable near-infrared diode laser coupled with backward Lagrangian stochastic model of atmospheric dispersion. Methane ERs reported were 146 and 166 g hd−1 d−1 for Victoria and Queensland, respectively. Using the same techniques, the average CH4 emissions were 166 and 214 g CH4 hd−1 d−1 for feedlots in Queensland and Alberta, respectively [19]. Average daily CH4 emissions were estimated to be 323 g hd−1 d−1 for a large beef feedlot in western Canada using the inverse dispersion model [20]. Direct measurements using micrometeorological mass difference technique reported 70 g CH4 hd−1 d−1 emissions from a confined beef feedlot in Australia where animals were fed a highly digestible high grain diet [21]. Recently, Borhan et al. [4] examined GHG emissions from ground level area sources from a dairy and cattle feedlot operation in Texas, and they found that pen surface has significantly higher CO2 and N2 O emissions than compost pile and runoff pond. The median emission rates for CH4 , CO2 and N2 O were 3.8, 1399, 0.68 g hd−1 d−1 (1.7 kg CO2 e hd−1 d−1 ), respectively,
from the beef cattle feedlot pen surface measured with dynamic flux chamber [4]. However, limited published information quantifying GHG emissions from ground level sources in US feedlot production systems was found in the literature. Gas emissions from feedlot or manure storage vary throughout the years depending on weather, animal sizes and numbers, animal diets, manure accumulation on the pen surfaces, pen moisture content, manure storage conditions, etc. Cold ambient temperatures increase maintenance energy needs and feed intake in most ruminants, which can influence the extent of pollutant gas emissions. Boadi et al. [22] mentioned that diet formulation can influence the extent of CH4 released from manure. North Dakota has diversified animal feed grains (i.e. corn, barley, canola, peas, oats, soybeans and wheat) as well as by-products (i.e. DDGS, soybean hulls, soybean meals, canola meal, etc.), which are used as livestock feed. In North Dakota, feedlots are distributed throughout the state and consist of small to modest-sized yards. However, pollutant gas emissions data are lacking under North Dakota livestock operation and management practices. No published information quantifying GHG emissions from ground level sources in North Dakota feedlot production systems were found in the literature. However, there is limited published information on GHG emissions in the relatively warmer regions of the United States. The emissions from one region may not be directly applicable to other regions due to differences in weather, animal diets and management practices. To improve agricultural air quality research, the National Research Council (NRC) [23] highlighted the need for emission studies focusing on individual farm operations or sources within the farm. Thus, there is a need to quantify onfarm emissions under North Dakota livestock management and weather conditions that would be most reliable and relevant to establish emission rates in the state. Therefore, the purpose of this study was to quantify GHG emissions from feedlot pen surface under North Dakota climatic conditions.
2.
Materials and methods
2.1. Study location This study was conducted at the Beef Cattle Research Complex at North Dakota State University (NDSU). This feedlot facility has six pens designed for a total capacity of 192 head of cattle. The length and width of each pen is 50 m × 16 m and has an overall aggregate slope 3%. Cattle in these pens were fed concentrated-based diets (90% concentrate) containing corn (finely or coarsely rolled), distillers grains plus solubles (20 or 40% of dry matter (DM), corn silage, hay, condensed corn distillers solubles, limestone, trace mineral premix, monensin and tylan. For the nutritional trial, four diets (Table 1) were fed to four groups of cattle in each individual pen using the Insentec feeding system [24–26].
Environmental Technology Table 1.
Level Level 1 Level 2 Level 3 Level 4
Diets constituents fed to the cattle during this trial.
Constituents CG corn and low DDGS FG corn and high DDGS CG corn and high DDGS FG corn and low DDGS
Mixing proportion
Formulated crude protein (%, DM basis)
60/20
13
40/40
17
40/40
17
68/20
13
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Note: CG = coarse grain; FG = fine grain; DDGS = dry distillers solubles; DM = Dry matter.
Because all four diets were fed within each pen, overall GHG emissions for animals fed the four mixed diets were measured from this feedlot, but not to see the impacts of diet on GHGs emission. Typically, manure was scraped every week from the pen surfaces and those pen surfaces were relatively dry. Manure samples were collected during each sampling event from 7–10 locations in each pen and mixed thoroughly. The composite sample was a mixture of fresh, dry and semi-dry manure and used for determining manure nutrients, fibre and volatile fatty acid contents. A total of 48 manure samples were collected during the study period.
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2.2. GHG sample collection and analysis Air samples were collected randomly at 3–6 locations in each pen corresponding to the total number of grid cells (e.g. 1–16 for a 4 m × 4 m grid). Air samplings were performed for 7 months from October 2011 to June 2012, covering typical climatic variations in North Dakota. A total of 152 air samples were collected from six pens of this feedlot. Air samples were collected from each pen surface in 5 L Tedlar bags using a portable wind tunnel (0.80 m × 0.40 m) and Vac-u-chamber (SKC Inc., Eighty Four, PA, USA) (Figure 1). A uniform air flow rate (1.75 m3 s−1 ) was maintained inside the tunnel throughout the sampling period using a DC motor regulator (to calculate emission rate). In this way duplicated air samples were collected from each pen during each sampling time at different times (summer, fall, winter and spring) of the years. Within 24 h of sampling, air samples were analysed for CH4 , CO2 and N2 O using a greenhouse gas chromatograph (GC) (Model No. 8610C, SRI Instruments, 20720 Earl St., Torrance, CA 90502) (Figure 1) equipped with a flame ionization detector (FID) and an electron capture detector (ECD). An air sample from the Tedlar bag was drawn into a 1 mL sample loop of the GC using an inbuilt vacuum pump interfaced with the GC system according to a prescheduled event programme. Before drawing any sample into the sample loop, the FID detector temperature was raised to 300◦ C and
Vacuum pump
Tedlar bag
Fan
Carbon filters
Sampling point
Pen surface
Figure 1.
Schematic diagram of a wind tunnel and greenhouse gas chromatography (GC) used in this study (drawing not to scale).
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Table 2.
Gas properties and calibration information.
GHGs
CASa No.
MWb (g gmol−1 )
Retention time (min)
Calibration equations
R2
MDLc
CH4 CO2 N2 O
74-82-8 124-38-9 10024-97-2
16.04 44.01 44.01
1.563 2.866 3.573
Y = 0.153(x) Y = 0.2448(x) Y = 0.0028(x)
0.99 0.99 0.98
82 918 66
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Note: a CAS No.- Chemical Abstracts Service Number; b MW = molecular weight; Y = concentration in ppm; and x = GC responses or area counts. c MDL – Minimum Detection Limit.
the ECD detector temperature was raised to 350◦ C. The system was operated on a nitrogen carrier at 20 PSI for the ECD hydrogen and air was supplied to the FID/methanizer at 20 PSI. In this system, the ECD detector detects N2 O, while the FID/methanizer detector detects both CH4 and CO2 . A detailed description of the GHG measurement procedure using GHG GC can be found elsewhere [27]. 2.3.
Emission calculation
Flux rates (FRs) (g m−2 d−1 ) from the tunnel were calculated using the average airflow through the wind tunnel multiplied by the concentration of a target gas and divided by the surface area covered by the wind tunnel. To compare the estimated ERs among the months, the measured volumetric concentrations were standardized at standard pressure and temperature. We have reported these to be consistent with the reporting of ERs of different air pollutants by the USEPA (1 atm and 25◦ C). Equations (1), (2) and (3) were used to calculate mass concentration of particular gas, FRs and ERs, respectively. Cppm × MWGHG 24.45 CMASS × VWT × 3600 × 24 = AWT × 1000 FRGHG × ASC = TNA
CMASS = FRGHG ERGHG
(1) (2) (3)
where: Cppm = Volumetric concentration of the given compound (ppm); CMASS = Mass concentration of the same compound (mg m−3 ); MWGHG = Molecular weight of the same compound (g gmol−1 ); 24.45 = Volume per mole of an ideal gas at standard temperature and pressure (L gmol−1 ); FRGHG = GHG emission flux rate from pen surface (g m−2 d−1 ); ERGHG = GHG emission rate from pen surface (g hd−1 d−1 ); VWT = Airflow rate through wind tunnel (m3 s−1 ); AWT = Surface area covered by the wind tunnel (0.4 × 0.8 m2 ); Asc = Surface area of the source (m2 ); TNA = Total number of cattle. 2.4. GC calibration and compound quantification Quantification of compounds contained in the sampled air was performed using certified calibration standard greenhouse gases (CH4 , CO2 and N2 O), and identification was
confirmed by matching retention times. To develop calibration equations (standard curves), three concentration levels of each calibration gas (0, 5, 10, 20 ppm for CH4 ; 0, 150, 1000, 5000 ppm for CO2 ; 0, 1, 5, 20 ppm for N2 O) with five to seven replicates were used. Regressions between the peak areas (GC responses) and different concentration levels of each compound through the origin were used to interpolate the total concentration of compounds contained in the sampled air. Minimum detection limits (MDLs) were calculated following USEPA guidelines [28] as the product of the standard deviation of seven replicates and the Student’s t value at the 99% confidence level. For seven replicates (6 degrees of freedom), a t value of 3.14 was used for MDLs calculation (Table 2). The coefficients of determination (R2 ) of each standard equation reflect the accuracy and reliability of the direct GHG measurements using a portable GHG GC (Table 2). The MDLs indicate the ability of the measurement system to accurately determine (with 99% confidence) CH4 , CO2 and N2 O concentrations as low as 82, 918 and 66 ppb, respectively. 2.5. Statistical analysis Measured GHG (CH4 , CO2 and N2 O) concentrations, FRs and estimated ERs during each month in the feedlot were compared through analysis of variance (ANOVA). The null hypothesis tested was that mean measure of GHG concentrations, FRs and ERs among various months were equal. All statistical analyses were performed in the SAS environment using the PROC Means procedures [29]. Mean concentrations, FRs, ERs were then separated using Duncan’s Multiple Range Test [30] at 5% significance level (p ≤ 0.05) if the main effect (month) was significant using F test at p ≤ 0.05. 3. Results and discussion 3.1. Background information Air samples were collected between 10:00 am and 1:00 pm, and manure samples were collected from the corresponding locations and a composite sample was prepared for each pen. Some of the preliminary manure characteristics are listed in Table 3. Ambient temperature during the monitoring period was downloaded from the nearest weather station maintained by North Dakota Agricultural Weather Network
Environmental Technology Table 3. Main manure characteristics of beef cattle. Parameters
protein) amount (13–17%, Table 1), fed to the beef cattle during this trial at North Dakota were different than in the Texas study (20%; [4]). Similarly, feedlot pen surface in this research was mostly dry with portions of faeces and urine from April to June; however, it was moist and muddy during February and March. In this facility, the pen surfaces were cleaned once a week and maintained in this way during the study period. This difference was likely due to the moderate temperature of North Dakota compared with central Texas during summer months, as well as manure characteristics (Table 3). Average temperature of the summer months (June to October) ranged from 11–21◦ C. On the other hand, it was hotter (23.8–36.1◦ C) in central Texas than North Dakota. High temperature likely expedited the CH4 emissions from the Texas feedlot. Average atmospheric concentrations of CH4 , CO2 and N2 O are approximately ∼1.7, ∼379 and ∼0.3 ppmv, respectively, [2]. The overall CH4 concentration over a 7-month period was 2.71 ppm (Table 4). This research showed that overall CH4 concentration over 7 months measured from this facility was approximately 1.6 times higher than ambient concentration (1.7 ppm). The CO2 concentration in February was lowest (385 ppm, which was very close to the ambient concentration [2]); and in October, CO2 concentration was highest (554 ppm) among the months (Table 4). Average CO2 concentrations measured during October were significantly different and higher than those measured during other months. Carbon dioxide concentration in October was about 1.1, 1.4, 1.4, 1.2, 1.3 and 1.2 times higher than that measured in January, February, March, April, May and June, respectively. The CO2 concentrations measured during October, January, February, March, May and June were significantly different (p < 0.05). There were no significant differences in measured CO2 concentrations between April and May (p > 0.05). Similarly, the average CO2 concentrations between April and June were statistically similar (p > 0.05) and significantly different than other months. Average summer (June to October) CO2 concentration in this North Dakota feedlot was 507 ppm, which was approximately 1.7 times lower than that (852 ppm)
Amount
Total solids (%) Total volatile solids (%) Total carbon (%) Total phosphorus (%) Total nitrogen (%) Total potassium (%) Crude protein (%)
24.49 78.57 39.83 0.12 2.07 0.19 12.75
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(NDAWN). Table 4 provides ambient daily average air temperature ranges during the study period. 3.2. Greenhouse gas concentrations Methane concentrations in collected samples among the months did not differ greatly and were statistically similar (p > 0.05) with lowest concentrations in March and highest in April. Measured methane concentrations ranged from 2.27–3.17 ppm and were highly variable, as indicated by the standard deviation (Table 4). This was likely due to spatially variable manure loading rates, fluctuations in temperature and variations in populations of microorganisms that were responsible for gaseous emissions at the pen surfaces. Average summer (June to October) CH4 concentration at North Dakota was 2.6 ppm, which is lower (1.2 times) than that (3.1 ppm) measured at a large feedlot (910,252 m2 pen surface and 40,000 cattle) in Texas during August 2010 [4]. GHG emissions mainly depend on cleanliness of the pen, manure characteristics and the ambient temperature. The Texas feedlot was mostly dry, with portions containing scattered feed, faeces and urine. The manure solids on feedyard pens were scrapped after each growing cycle. The diets mainly consisted of forage (corn silage), grain (steam flaked corn), protein (wet distillers corn and condensed corn distillers), fat, beef finisher and trace elements (rumensin, tylan, vitamin A and D) were 19, 57.2, 20.1, 0.8, 2.1, 0.8%, respectively [4]. Diet constituents, especially protein (crude Table 4.
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Greenhouse gas (GHG) concentration measurements from a research feedlot at North Dakota State experiment station.
Sampling month Oct− 2011 Jan− 2012 Feb− 2012 Mar− 2012 April− 2012 May− 2012 June− 2012 Overall
Sampling number n =15 n =18 n =15 n =20 n =33 n =36 n =15 152
Daily average temperature (◦ C)
GHG concentration (ppm)
Average
Max
Min
CH4
CO2
N2 O
11 −7 −6 5 9 16 21
17 −2 −1 11 15 23 27
11 −7 −6 5 9 16 21
2.66 ± 1.32 2.34 ± 0.45 2.95 ± 0.69 2.27 ± 0.71 3.17 ± 1.93 2.74 ± 1.34 2.49 ± 1.54 2.71 ± 1.35
554a ± 36 496b ± 23 385f ± 40 408e ± 13 448cd ± 28 438d ± 24 460c ± 39 452 ± 53
0.62bc ± 0.15 0.44c ± 0.21 0.58bc ± 0.28 0.99a ± 0.49 0.80ab ± 0.42 0.65bc ± 0.32 0.47c ± 0.22 0.67 ± 0.37
Note: The values in a column, representing average concentration in different months followed by common letter(s) do not differ significantly at p ≤ 0.05.
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measured in a large feedlot in Texas during August 2010 [4]. This difference was due to cleanliness of the pen and manure characteristics. The overall CO2 concentration over a 7month period was 452 ppm which was slightly higher (1.2 times) than ambient concentration (379 ppm; [2]). Similar to CH4 concentrations, N2 O concentrations were also highly variable within and among the months, as indicated by the large standard deviation (Table 4). Average N2 O concentrations measured during March were significantly different and higher than those measured during other months. The highest N2 O concentrations were measured in March and April, although average concentrations between March and April were statistically similar (p < 0.05). Relatively drier pen surfaces during March and April may have facilitated aerobic nitrification which in turn increased N2 O emissions. There were no significant differences in N2 O concentrations measured during October, January, February, May and June (p > 0.05; Table 4). Nitrous oxide concentration measured in March was about 1.6, 2.3, 1.7, 1.2, 1.5 and 2.1 times higher than that measured in October, January, February, April, May and June, respectively. Average summer (June to October) N2 O concentration measured at this facility was 0.55 ppm, which was approximately 1.5 times greater than in the Texas feedlot (0.37 ppm [4]; and ambient concentrations (0.3 ppm) [2]). This difference may have been due to thawing of the pen surface in North Dakota.
3.3. Greenhouse FRs and ERs Similar to CH4 , CO2 and N2 O concentrations, FRs and ERs for CH4 , CO2 and N2 O showed a similar trend over the measurement period (Table 5). The CH4 concentrations, FRs and ERs among the months were not significantly different. Overall FRs estimated using the wind tunnel were 1.32, 602 and 0.90 g m−2 d−1 for CH4 , CO2 and N2 O, respectively (Table 5). These numbers for GHGs were much higher than those estimated using dynamic flux chamber from a large feedlot pen (901,252 m2 and 40,000 head) in the Texas Panhandle during the summer (August) of 2010 [4]. In the Table 5.
Texas study, estimated FRs for CH4 , CO2 and N2 O were 0.07, 57, 0.03 g m−2 d−1 , respectively, which were approximately 19, 11 and 30 times, respectively, lower than those estimated in this research feedlot using a wind tunnel. This difference was likely due to differences in the measurement system. Similarly, overall ERs estimated from the pen surfaces using a wind tunnel were 38 g hd−1 d−1 , 17 kg hd−1 d−1 and 26 g hd−1 d−1 for CH4 , CO2 and N2 O, respectively (Table 5). These numbers for GHG ERs were much higher than those estimated using a dynamic flux chamber from a large feedlot pen in the Texas Panhandle during the summer (August) of 2010 [4]. Estimated ERs for CH4 , CO2 and N2 O in the Texas study were 1.7, 1309, 0.57 g hd−1 d−1 , respectively, approximately 22, 13 and 46 times, respectively, lower than those estimated in this research using the wind tunnel. There is debate about the suitability and accurateness of using a flux chamber to quantify pollutant emissions at AFOs and other area sources due to the creation of microenvironments in the chamber and the small measurement footprint relative to the size of the source. Hudson et al. [9] compared odour emission rates from various sources using the two sampling methods and they reported that odour emissions rates from a wind tunnel were 60 to 240 times higher than those in a flux chamber. In contrast, the CH4 ER estimated from the pen surface (ground surface) in this study was much lower than those measured in Canada and Australia using different atmospheric dispersion models coupled with pollutant concentration measurements across the plume of the emitting surfaces by open-path tunable near-infrared diode sensors [19,21]. Harper et al. [21] reported 70 g hd−1 d−1 CH4 ER measured using a micrometeorological mass difference technique from a confined beef feedlot in Australia where animals were fed a highly digestible high grain diet. Similarly, CH4 ERs reported were 146 and 166 g hd−1 d−1 for beefyards in Victoria and Queensland, respectively [19]. These CH4 ERs using atmospheric dispersion models were estimated emissions on a whole-farm basis, which included
Estimated greenhouse gas (GHG) emission flux rates (FRs) and emission rates (ERs). GHG flux rates
GHG emission rates
Sampling month
CH4 (g m−2 d−1 )
CO2 (g m−2 d−1 )
N2 O (g m−2 d−1 )
CH4 (g hd−1 d−1 )
CO2 (kg hd−1 d−1 )
N2 O (g hd−1 d−1 )
Oct− 2011 Jan− 2012 Feb− 2012 Mar− 2012 April− 2012 May− 2012 June− 2012 Overall
1.30a ± 0.64 1.14a ± 0.22 1.44a ± 0.34 1.10a ± 0.34 1.54a ± 0.94 1.33a ± 0.65 1.19a ± 0.74 1.32 ± 0.66
740a ± 47 663b ± 31 514e ± 54 545d ± 17 598c ± 37 585c ± 32 608c ± 47 602 ± 71
0.83bc ± 0.20 0.58c ± 0.28 0.77bc ± 0.38 1.31a ± 0.66 1.06ab ± 0.56 0.87bc ± 0.42 0.63c ± 0.29 0.90 ± 0.50
37a ± 18 33a ± 6.3 41a ± 9.6 32a ± 9.9 44a ± 27.1 38a ± 19 34a ± 21 38 ± 19
21a ± 1.3 19b ± 1 15e ± 1.5 16d ± 0.5 17c ± 1 17c ± 0.9 17c ± 1.3 17 ± 2
24bc ± 6 17c ± 8 22bc ± 10.7 38a ± 19 30ab ± 16 25bc ± 12 18c ± 8 26 ± 14
Note: The values in a column, representing average FRs and ERs in different months followed by common letter(s) do not differ significantly at p ≤ 0.05.
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the emission contributions from composting area, runoff pond, silage piles and feed processing areas. In addition, sensors which measured pollutant concentration in this past research were placed at a certain height above the emitting source that might have captured emissions from the abovementioned sources along with CH4 released to ambient through animal breathing. 4. Summary and conclusions GHG emissions from a research feedlot with beef cattle were monitored with a view to investigate the variations in CH4 , CO2 , N2 O concentrations and to estimate ERs over a 7-month period in North Dakota climatic conditions. The temperature of the sampling days varied from −7–21◦ C. The measured gas concentrations within and among the months varied widely due to spatially variable manure loading rates and manure microbial activity at the pen surfaces. The following results and conclusions can be drawn from this research. • Measured CH4 , CO2 and N2 O concentration varied from 2.27–3.17 ppm, 385–554 ppm and 0.44– 0.99 ppm, respectively, over a 7-month period from October to June. • Overall CH4 , CO2 and N2 O concentrations over a 7-month period were 2.71, 452 and 0.67 ppm, respectively. • Estimated overall CH4 , CO2 and N2 O FRs were 1.32, 602, 0.90 g m−2 d−1 , respectively. • Overall ERs estimated using a wind tunnel were 38 g hd−1 d−1 , 17 kg hd−1 d−1 and 26 g hd−1 d−1 for CH4 , CO2 and N2 O, respectively. • The CH4 concentrations, FRs and ERs among the months were not significantly different. • Highest CH4 , CO2 and N2 O concentrations, FRs and ERs were measured during April, October and March respectively. 5. Recommendations for future work Further studies will be conducted with different feedlot sizes, management practices and climatic regions of North Dakota to represent seasonal trends. Also, the effect of different low and highprotein diets and bedding on manure characteristics and subsequent GHG emissions from a feedlot could be investigated. Acknowledgements This project was supported by the State Board of Agricultural Research and Education (SBARE), North Dakota.
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