Full proceedings at: http://www.extension.org/72909 Accurate estimation of greenhouse gas emissions, including nitrous oxide and methane, from open beef cattle feedlots is an increasing concern given the current and potential future reporting requirements for GHG emissions. Research measuring emission fluxes of GHGs from open beef cattle feedlots, however, has been very limited. Soil and environmental scientists have long used various chamber based techniques, particularly non-flow-through - non-steady-state (NFT-NSS) chambers for measuring soil fluxes. Adaptation of this technique to feedyards presents a series of challenges, including spatial variability, presence of animals, chamber base installation issues, gas sample collection and storage, concentration analysis range, and flux calculations.

1. Measuring Nitrous Oxide and
Methane from Feedlot Pen
Surface: Experience with NFT-
NSS Chamber Technique
Ken Casey1, Heidi Waldrip2, Rick Todd2, Andy Cole2
1 Texas A&M AgriLife Research – Amarillo, TX
2 USDA-ARS CPRL – Bushland, TX

2. Feedlot GHG Emissions
• Emissions inventory is dominated by Enteric
emissions.
• Emissions inventory for the feedlot manure
management system is dominated by methane in
mass terms but by nitrous oxide in CO2e terms.
– Little research has been undertaken on CH4 and N2O
emissions from feedlot manure management systems and
particularly under climatic conditions and management
strategies representative of the major US cattle feeding
region.
• IPCC Workbook (& EPA Reporting Rule)
– N2O Emission Factor = 0.02 N2O-N/kg Kjdl N excreted

3. Goals and Objectives
• The overall goal is to develop a mechanistic understanding
of factors influencing the emissions of greenhouse gases
from cattle feedyards.

4. Specific Objectives
1. Examine the magnitude, spatial and temporal
variability of emissions of N2O and CH4 from the
manure management system from cattle
feedyards in the Texas High Plains.
2. Develop a functional understanding of the
environmental variables that influence and
control the emissions of N2O and CH4 from the
manure management system from cattle
feedyards in the Texas High Plains. Contribute this
data to the developers of larger scale, process
based models to improve their predictive
capability for the conditions prevailing on typical
cattle feedyards in the Texas High Plains.

5. Specific Objectives
1. Examine the magnitude, spatial and temporal variability of
emissions of N2O and CH4 from the manure management
system from cattle feedyards in the Texas High Plains.
2. Develop a functional understanding of the environmental
variables that influence and control the emissions of N2O
and CH4 from the manure management system from cattle
feedyards in the Texas High Plains. Contribute this data to
the developers of larger scale, process based models to
improve their predictive capability for the conditions
prevailing on typical cattle feedyards in the Texas High
Plains.

6. GHG Emission Measurements
• Non-Flow Through – Non-Steady State Chambers
– Widely used in soil and environmental science
• Gracenet & other protocols
– Measure relatively small area
• Influence of spatial and temporal variability
– Potential to influence emission rate if poorly conducted
• Chamber environment (T & BP)
• Chamber base installation
• Chamber base effect
– Non-real time measurements
• Gas Chromatograph
– Automated Chambers
• Real time measurements
– Gas Chromatograph
– FTIR Spectrometer
– Closed path Tuned Diode Laser
– Cavity Ring Down Spectrometer
– Quantum Cascade Laser
• Area integrating measurement techniques
– Open-Path measurements
• OP-FTIR Spectrometer
– Eddy Covariance
• Closed path Tuned Diode Laser
• Cavity Ring Down Spectrometer
• Quantum Cascade Laser

7. Non-Flow Through – Non-Steady State
(NFT-NSS) Chambers

8. NFT-NSS Chamber Measurements
• Non-flow-through, non-steady-state (NFT-NSS) chambers
(Hutchinson and Mosier, 1981) are used to sample GHG
emissions from the feedyard pen surfaces.
• Greenhouse gas sampling measurements for all studies
are performed starting at 12:00 h US Central Standard
Time (CST).
• Ten chamber bases are installed in a recently emptied pen
in two rows on a Friday afternoon
• GHG measurements are conducted from the following
Monday to Friday for each study
• Four samples collected over 30 min period (0, 10, 20, 30)
• Quadratic flux calculation procedure

9. NFT-NSS Chamber Measurements
• Ten chamber bases are installed in a recently emptied
pen.
• Emission rate varies spatially
• Greater chamber numbers yield a better average
and allows exploration of different areas within
pen
• Compromise with labor and resource availability
• One operator can manage 10 chambers
• Recently Emptied Pen
• Animals would disturb bases & chamber caps
and/or potentially be injured
• Cannot practically ‘guard’ bases for any length of
time while animals are present

10. NFT-NSS Chambers
• 8 in. dia PVC Pipe Cap.
• Septa equipped sampling port
• 1/8 in. dia balance tube.
• 8 in dia steel pipe Base
• Lower edge sharpened to aid installation
• Installed 3 – 4 in. into manure pack
• Rubber skirt rolled up to seal Cap to Base.
• GHG measurements were conducted from the following
Monday to Friday for each study study.

11. NFT-NSS Chamber

12. Deployed NFT-NSS Chamber

13. NFT-NSS Chambers Installation
• Installation of bases can fracture soil, resulting in
temporarily enhanced emissions
• Gracenet Protocol
• Wait at least 24 hours before taking measurements
• Testing confirmed an enhanced flux following installation
of chamber bases in feedlot pens
• Wait at least 48 hours before taking measurements
• Chamber bases are installed on a Friday afternoon
• GHG measurements are conducted from the
following Monday to Friday for each study

14. Installing Chamber Bases

15. Installing Chamber Bases

16. NFT-NSS Chambers Installation
• 8 in dia steel pipe Base
• Installed 3 – 4 in. into manure pack
• Testing evaluated installation of chamber bases in feedlot
pens
• Steel pipe Base driven 3-4 in. into manure pack
• Manure and/or sand piled around chamber
• Plastic skirt w/wo weights attached to chamber
• Measured fluxes were higher and more consistent
with driven bases –> leakage was occurring with
other base systems

17. Flux Calculation Procedure
• Linear Regression (LR)
• Quadratic Method (Quad)
• Hutchinson and Mosier (H/M)
• Pedersen HMR (HMR)
• …udy.

18. Linear Response

19. Non-Linear Response

20. Methane and N2O flux rates were determined by fitting a quadratic regression equation to
the measured concentrations at 0, 10, 20 and 30 minutes and solving for the slope at time
zero.

21. Typical Chamber Layout – 2 rows of 5

22. Pen#:L-15
FYD: A
Date Installed: 07-11-2014
Chamber # Soil condition Chamber
position
1
2
3
4
5
6
7
8
9
10
N
5
3
× ×
Trough
×
10
6
10 2020 40
195
80
1
92
4 5
6
31
31
Met
Stn

23. Gas samples injected into evacuated 12 ml exetainers for storage, transport and analysis

24. Sample Storage

25. Vial Preparation

26. Gas concentration analysis was conducted on a Varian 450 gas chromatograph with a
CombiPal auto-sampler equipped with a flame ionization detector for CH4, an electron
capture detector for N2O and a thermal conductivity detector for CO2.

27. Measured gas concentrations – 1000+ analyses from chambers in cropping and rangeland

28. Measured gas concentrations – 4400 analyses from chambers in feedyards and dairies

29. GHG Calibration Standards

30. GHG Calibration Issues
• Electron Capture Detectors (N2O) are not linear
• May be treated as linear by some analysts
over limited range.
• A lab running cropping samples may not
know their linear range!
• Our system is linear up to about 10 ppm
• Analyze using low range calibration curve
• Reprocess any chromatograms with
indicated concentration > 10 ppm

31. System Minimum Detection Limits and Fluxes

34. Summary Outcomes
• Unique Issues in measuring GHG emissions from waste
• Small ‘insignificant’ variations from protocol turn your
results to ‘manure’!
• Good measurement strategy and equipment
• Sample collection and storage
• Sample analysis
• Know your system MDL
• Know your concentration range
• Flux calculation
• Temporally Variable Results
• Large influence of rainfall events
• Fast response to changing microclimate
• Challenges in data analysis study.

35. Questions?