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1. 1974
Abstract
Beef cattle feedlots emit large amounts of the greenhouse
gases (GHG) methane (CH4
) and nitrous oxide (N2
O), as well as
ammonia (NH3
), which contributes to N2
O emission when NH3
is deposited to land. However, there is a lack of simultaneous,
in situ, and nondisturbed measurements of the major GHG
gas components from beef cattle feedlots, or measurements
from different feedlot sources. A short-term campaign at a beef
cattle feedlot in Victoria, Australia, quantified CH4
, N2
O, and NH3
emissions from the feedlot pens, manure stockpiles, and surface
run-off pond. Open-path Fourier transform infrared (OP-FTIR)
spectrometers and open-path lasers (OP-Laser) were used with
an inverse-dispersion technique to estimate emissions. Daily
average emissions of CH4
, N2
O, and NH3
were 132 (± 2.3 SE), 0,
and 117 (± 4.5 SE) g animal-1
d-1
from the pens and 22 (± 0.7
SE), 2 (± 0.2 SE), and 9 (± 0.6 SE) g animal-1
d-1
from the manure
stockpiles. Emissions of CH4
and NH3
from the run-off pond were
less than 0.5 g animal-1
d-1
. Extrapolating these results to the
feedlot population of cattle across Australia would mean that
feedlots contribute approximately 2% of the agricultural GHG
emissions and 2.7% of livestock sector emissions, lower than a
previous estimate of 3.5%.
A Snapshot of Greenhouse Gas Emissions from a Cattle Feedlot
Mei Bai,* Thomas K. Flesch, Sean M. McGinn, and Deli Chen
Beef cattle feedlots are a large source of greenhouse
gases (GHG), which includes enteric methane (CH4
)
emissions coming directly from the cattle (Mathison et
al., 1998) and CH4
and nitrous oxide (N2
O) emitted during
manure decomposition (Rahman et al., 2013; Philippe and
Nicks, 2015). In addition, feedlots are large sources of ammo-
nia (NH3
), which when deposited to the surrounding landscape,
will lead to N2
O emissions (Denmead et al., 2008; Barrancos
et al., 2013). Although these emissions contribute significantly
to the GHG emission inventory, there are few direct feedlot
measurements from which to estimate the total. For example,
many approaches of measuring GHG emissions from cattle
have focused on only a single gas such as NH3
(Rhoades et al.,
2010; Todd et al., 2011), although Leytem et al. (2011) recently
studied multiple gases emissions of NH3
, CH4
, CO2
, and N2
O
from dairy farm. Consequently, there is a lack of simultaneous
in situ and nondisturbed measurements of the major GHG
gas components or measurements from the different feedlot
sources (pens, manure stockpiles, and run-off ponds). The objec-
tive of this study is to measure CH4
, N2
O, and NH3
emissions
from the three major emission sources at an Australian feed-
lot. Quantifying individual source emissions will help improve
understanding of the magnitude of emissions from Australian
feedlots for inventory and mitigation purposes and will also
allow development of models based on the underlying processes
that drive source emissions.
Materials and Methods
Experimental Site
Measurements were conducted at a commercial feedlot in
Victoria, 225 km northwest of Melbourne, Australia, between
2 and 20 Mar. 2015. The feedlot had an average gradient of 1.5
to 2%. The terrain around the feedlot was flat, with bare soil or
short grass cover in the surrounding fields. The average mini-
mum/maximum temperature was 11/27°C, with a range for
minimum and maximum temperatures of 8.4 to 12.6°C and 23.5
to 28.9°C, respectively. Precipitation during the experimental
period was 7.2 mm (BOM, 2015).
Abbreviations: GHG, greenhouse gases; OP-FTIR, open-path Fourier transform
infrared; OP-Laser, open-path lasers.
M. Bai and D. Chen, Faculty of Veterinary and Agricultural Sciences, The Univ.
of Melbourne, Parkville, VIC, 3010, Australia; T.K. Flesch, Dep. of Earth and
Atmospheric Sciences, Univ. of Alberta, Edmonton, AB T6G 2R3, Canada; S.M.
McGinn, Agriculture and Agri-Food Canada, Lethbridge, AB, T1J 4B1, Canada.
Assigned to Associate Editor Heidi Waldrip.
Copyright © 2015 American Society of Agronomy, Crop Science Society of America,
and Soil Science Society of America. 5585 Guilford Rd., Madison, WI 53711 USA.
All rights reserved.
J. Environ. Qual. 44:1974–1978 (2015)
doi:10.2134/jeq2015.06.0278
Received 13 June 2015.
Accepted 3 Sept. 2015.
*Corresponding author (mei.bai@unimelb.edu.au).
Journal of Environmental Quality SHORT COMMUNICATIONS
Core Ideas
• Open-path spectroscopy/inverse-dispersion technique is ap-
plied in this study.
• Quantifies CH4
, N2
O, and NH3
emissions from a large-scale, in-
tensive feedlot.
• Investigates emission contributions from pens, manure stock-
piles, and run-off ponds.
• Discusses the diurnal variation in CH4
, N2
O, and NH3
emissions.
• Estimates the total GHG emissions from the whole-feedlot.
Published November 6, 2015

2. Journal of Environmental Quality 1975
The three main sources of GHG emissions were cattle pens
(23.7 ha), a manure stockpile area (20 ha), and a manure run-
off pond (0.3 ha) (Fig. 1). The manure area consisted of manure
piles, compost rows, and sorted compost. During measurements,
the feedlot occupancy averaged 17,500 head of cattle. The cattle
were yearly (1–1.5 yr of age) European breeds predominately
Angus and Angus cross, with an average body weight of 396 ±
5.3 kg. The cattle were fed on a finishing diet of barley (Hordeum
vulgare L.) and grass hay and consumed an average of 10.2 kg
dry matter daily. The daily feeding time was twice a day at 0900
and 1600 h. Diet composition and animal intake and production
data are presented in Tables 1 and 2.
Methodologies
An inverse-dispersion technique (software WindTrax
2.0.0.8) (Flesch et al., 2004) was used to calculate emissions
based on the rise in concentration (over background levels) mea-
sured within or downwind of the feedlot emission sources. Two
open-path Fourier transform infrared (OP-FTIR) spectrometers
(Matrix-M IRcube, Bruker Optics) measured the atmospheric
concentrations of CH4
, N2
O, and NH3
. The OP-FTIR systems
were set up at location A and B (Fig. 1) for 10 d to measure emis-
sions from the manure stockpile area (2–12 March), and later
moved to location D and E for 8 d to calculate pen emissions
(12–20 March). Each OP-FTIR gave the average concentration
between the spectrometer and a retro-reflector, separated by
110 to 140 m (with a measurement path 1.65 m above ground
level). Two open-path lasers (OP-laser; Gas Finder 2.0, Boreal
Laser Inc.) were located at location C for 4 d to measure CH4
and NH3
concentrations adjacent to a small run-off pond (the
only run-off pond in use during our measurements) (14–18
March), using a 125- (CH4
) and 250-m (NH3
) path length. The
OP-laser measured CH4
concentrations over two paths via a pan-
tilt aiming motor (PTU D300, Directed Perceptions Inc.) that
gave an upwind and downwind concentration. Cross-calibration
of the OP-FTIR systems and OP-lasers were conducted on-site
before the measurement. A three-dimensional sonic anemometer
(CSAT3, Campbell Scientific) was located outside the feedlot
near location B for use with the manure and pond emission cal-
culations and inside the feedlot near location D for the pen cal-
culations (at 2.5 and 6.4 m above ground level). Concentration
and wind statistics were processed over 15-min intervals using
the software SAS (SAS 9.4, SAS Institute Inc.).
Themanurestockpilesemissioncalculationsusedonlyperiods
when the wind direction was from the southwest (to avoid pos-
sible “contamination” of the concentration measurements by pen
emissions). Pen calculations excluded periods when the wind was
from the northwest (i.e., potential contamination by the manure
stockpile) or from the west-southwest (i.e., potential contamina-
tion of the background measurement site by feedlot emissions).
Run-off pond calculations included only north-northeast wind
periods to avoid the emissions from pens and manure stockpiles.
Approximately 45, 20, and 20% of the measurement period were
used for calculation on manure stockpiles, feedlot pens, and
run-off pond, respectively. In our calculations, the ground area
of manure piles, pens, and the run-off pond were mapped and
designated as the emission sources.
Results and Discussion
Emission Rates
Cattle Pens
Methane emissions from the cattle pens followed a diel pat-
tern: emissions began to rise at about 0900 h, reached a peak in
the afternoon, and then fell to its lowest level near sunrise. This
corresponded to increased animal enteric fermentation and feed-
ing, reflecting the overwhelming contribution of enteric emis-
sions from this portion of the feedlot (Fig. 2). This pattern is
likely related to feeding times and can be found in other feedlot
studies (Loh et al., 2008; McGinn et al., 2008). The highest CH4
emission rate (based on the 17,500 cattle) observed was 154 kg
h-1
at 1800 h; the lowest was 50 kg h-1
at 0700 h. Average pen
emissions of 131.7 (± 2.3 SE) g CH4
animal-1
d-1
were greater
than the 110 g CH4
animal-1
d-1
found by Denmead et al.
Fig. 1. Locations of instruments and sources in this study. The
aerial photograph does not reflect the actual conditions during
measurements (e.g., ponds).
Table 1. Average diet composition fed to finishing cattle during
experimental period.†
Dry matter (DM) (g kg-1
) 744
Crude protein (g kg-1
DM) 126
Fat (g kg-1
DM) 46
Ash (g kg-1
DM) 50
Crude fiber (g kg-1
DM) 100
Neutral detergent fiber (g kg-1
DM) 242
Nitrogen free extract (g kg-1
DM) 679
Metabolizable energy (MJ kg-1
DM) 13
† Ration constituents (g kg-1
DM): steam flaked barley 519; cereal hay
162; barley silage 106; solvent extracted canola meal 61; bread waste
61; molasses 48; mineral and vitamins 25; vegetable oil 18.
Table 2. Average production data for the feedlot during experimental
period from 28 Feb. to 20 Mar. 2015.
No. of
animals†
Live weight Weight gain dMI‡ N intake§
kg animal-1
— kg animal-1
d-1
— kg d-1
17,500 396.5 (5.3)¶ 1.5 10.2 (0.3)¶ 3830.5 (122.1)¶
† Average animals over the last few months. Cattle were European
breeds, predominately Angus and Angus cross.
‡ Dry matter intake.
§N intake = 17,500 (no. of animals) × 10.2 kg DMI animal-1
d-1
× 0.134
kg crude protein kg-1
DMI × 1 kg N 6.25 kg-1
crude protein.
¶ Average (SE) based on the 17,500 cattle in the feedlot.

3. 1976 Journal of Environmental Quality
(2013) at another Australian feedlot but lower than values found
in North American feedlots (214–323 g CH4
animal-1
d-1
)
(McGinn et al., 2008; van Haarlem et al., 2008). Our emissions
correspond to a yield of 13 g CH4
kg-1
dry matter intake (Table
3), at the low range of reported values (10-30 g CH4
kg-1
dry
matter intake) (Grainger and Beauchemin, 2011).
We found no measureable N2
O emissions from the cattle
pens during our study. This was reasonable as the feedlot surface
was relatively dry due to lack of precipitation (Flessa et al., 1995).
However, this was in contrast to the N2
O emissions of 0.7 to 5.0
g animal-1
d-1
from cattle pens found in other studies (Borhan et
al., 2011; Denmead et al., 2013).
Ammonia emissions from the pens displayed a clear diel pat-
tern related to ambient temperature: lowest near sunrise and
highestnearnoon.Amultiple rvalueforregressionofNH3
emis-
sion and air temperature of 0.55 (P < 0.0001) was obtained (Fig.
3). There was also a strong positive correlation between emis-
sions and wind (friction velocity) u*
, with r = 0.75, P < 0.0001
(Fig. 4). The highest emission rate we observed was 283 kg h-1
,
which occurred during late morning (1000 h) when winds were
very high (u*
= 0.77 m s-1
). The lowest emission rate was 15 kg
h-1
and was observed in the early morning. This diel pattern
associated with animal activity has been reported in other stud-
ies (Flesch et al., 2007; Bai et al., 2015).
Manure Stockpiles
Emissions of CH4
, N2
O, and NH3
from the manure each
showed a strong diel pattern, with less emission during the night
and maximum values in the afternoon. The peak afternoon rates
were 25 (± 1.5 SE), 5 (± 0.3 SE), and 15 (± 1.1 SE) kg h-1
for
CH4
, N2
O, and NH3
, respectively. While the night-time CH4
emissions remained relatively large (~10 kg h-1
), the emissions
of N2
O and NH3
declined to near zero. Higher afternoon emis-
sions of these gases may relate to higher air temperatures or to
daily management activity that occurs through the work day
(e.g., turning of compost piles, moving of manure stockpiles)
(Flesch et al., 2007; Petersen et al., 2013).
The average CH4
emissions from the manure (21.5 g animal-1
d-1
)weregreaterthanthe14.5ganimal-1
d-1
reportedbyHusted
(1994) or the 3.8 g animal-1
d-1
reported by Borhan et al. (2011).
Dividing manure emissions by the ground area covered by the
manure piles (72,000 m2
) gives emissions of 3.9 g CH4
–C m-2
d-1
and 0.37 g N2
O–N m-2
d-1
. These are higher than the 2.6 g
CH4
–C m-2
d-1
and 0.1 g N2
O–N m-2
d-1
found by Hao et al.
(2011) for composting feedlot manure over a 100-d period. Our
rates would be reduced if we could calculate emissions per unit
of manure surface area as Hao et al. (2011) reported. A similar
calculation for NH3
gives emissions of 1.8 g NH3
–N m-2
d-1
,
Fig. 2. Emission rates of CH4
, N2
O, and NH3
from (A) feedlot pens, (B)
manure stockpiles, and (C) CH4
and NH3
from the run-off pond plotted
with time of day. Each data point is a 15-min average emission rate.
Table 3. daily average emissions of CH4
, N2
O, and NH3
from feedlot pens, manure stockpiles, and run-off pond and CH4
yield, N loss, and CO2
–
equivalent (CO2
–e) in this study from 2 to 20 March 2015.
Feedlot pens Manure stockpiles Run-off pond Total emission CH4
yield N loss as % of fed N CO2
–e†
———— g animal-1
d-1
———— g kg-1
DMI % Gg yr-1
CH4
131.7 (2.3)‡ 21.5 (0.7)‡ 0.32 (0.03)‡ 153.5 (37.5)§ 12.9 – 27.5¶
N2
O 0# 2.4 (0.2) –†† 2.4 (2.8)§ – 0.8 4.1¶
NH3
116.9 (4.5) 9.1 (0.6) 0.35 (0.03) 126.4 (73.6)§ – 47.5 2.8¶‡‡
† Global warming potential (GWP): CH4
is 28, and N2
O is 265.
‡ Average (SE) based on the 17,500 cattle.
§Standard deviation of sum.
¶ Total greenhouse gas emission as CO2
–e (Gg yr-1
) = 17,500 (no. of animal) × emission rate (g animal-1
d-1
) × 365 (d) × GWP ×10-9
.
# Actual calculation was -0.1 g animal-1
d-1
.
†† –, no measurement.
‡‡ Calculation assumes 1% of the emitted NH3
–N is deposited and re-emitted as N2
O–N.

4. Journal of Environmental Quality 1977
much less than the 15 g NH3
–N m-2
d-1
(per ground area)
found by Sommer et al. (2004) from feedlot manure over 7 d
after removal. That our rate in our study is reduced from Sommer
et al. (2004) is not surprising given the freshness of the manure
studied by Sommer et al. (2004) or that water was added to their
manure before measurements.
Run-Off Pond
Methane emissions from the run-off pond ranged from 0.06
to 0.7 kg h-1
, with no obvious diurnal pattern in emissions. This
is small compared with emissions from the pens and the manure
stockpiles, less than 1 and 2%, respectively. Ammonia emissions
were also small: less than 1% of that from the pens. This agrees
with the results of Flesch et al. (2007), who showed run-off
ponds contributed approximately 2% of total NH3
emissions at
a Texas feedlot. The relatively low CH4
and NH3
emission rates
were expected given the lack of rainfall and run-off before the
study (and thus the small size of the run-off pond).
Total Feedlot Emissions
Total emissions from the three feedlot sources (Table 3)
were 154 g CH4
animal-1
d-1
, 2.4 g N2
O animal-1
d-1
, and 126
g NH3
animal-1
d-1
. Using global warming potential values
of 28 for CH4
and 265 for N2
O, and assuming that 1% of the
NH3
–N emissions are ultimately deposited downwind of the
feedlot and then lost as N2
O–N emissions (Klein et al., 2006),
we estimate a total GHG emission rate of 34.3 Gg yr-1
CO2
–
equivalent (CO2
–e) from the feedlot. The largest GHG com-
ponent is the CH4
emissions from the cattle pens, accounting
for 80% of the total emissions. It is interesting that the direct
N2
O emissions and indirect NH3
emissions contributed 20%
of total emissions; of this, 8% is from the indirect contribution
of NH3
emissions.
With a national feedlot cattle population of 875,000, we
estimate that Australian feedlots (1.7 Mt CO2
–e) contribute
2.0% of Australian agricultural emissions (87.4 Mt CO2
–e)
(NIR, 2014) and 2.7% from the livestock sector (62.6 Mt
CO2
–e) (Hegarty, 2001). This value is lower than previous
estimates of 3.5% of livestock emissions (Muir, 2011). However,
this extrapolation is uncertain given that our short-term study
cannot represent seasonal variation in emissions. Indeed, our
results are useful for understanding snapshot emissions from the
feedlot. Longer-term measurements are required to investigate
seasonal contributions to emission sources and ultimately
understand the whole-feedlot emission variability.
Acknowledgments
We thank Meat & Livestock Australia and CSIRO Sustainable
Agriculture Flagship Program for funding. We also acknowledge Trevor
W. Coates for technical assistance. Many thanks to the soil group staff
members Dr. Shu Kee Lam, Dr. Jianlei Sun, and Dr. Jianlin Shen for
their help during the experiment.
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