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Measuring and Modeling Greenhouse Gas Emissions from Agroecosystems
Raymond L. DesjardinsAgriculture and Agri-Food Canada
Ottawa, ON Canada K1A [email protected]
Presented at the China Ecological Forum, June 12, 2009
Beijing, China
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Outline
•Greenhouse gas emissions from agroecosystems
•Flux meauring techniques
•Carbon dioxide
•Methane
•Nitrous oxide
.Modeling techniques
•Carbon dioxide exchange
•Methane emissions
•Nitrous oxide emissions
• Progress towards a more sustainable agriculture
•Greenhouse gas emissions from agroecosystems
•Flux meauring techniques
•Carbon dioxide
•Methane
•Nitrous oxide
.Modeling techniques
•Carbon dioxide exchange
•Methane emissions
•Nitrous oxide emissions
• Progress towards a more sustainable agriculture
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Atmospheric gases
Our dry atmosphere is made up of a mixture of gases, consisting of:
•78.1% N2;Nitrogen
•20.9% O2; Oxygen
•0.9% Ar; Argon
•0.1% all other gases including the greenhouse gases CO2 (carbon dioxide), CH4 (methane), N2O (nitrous oxide) and O3 (ozone)
Our dry atmosphere is made up of a mixture of gases, consisting of:
•78.1% N2;Nitrogen
•20.9% O2; Oxygen
•0.9% Ar; Argon
•0.1% all other gases including the greenhouse gases CO2 (carbon dioxide), CH4 (methane), N2O (nitrous oxide) and O3 (ozone)
On a wet basis, our atmosphere contains ≈ 1-4% H2O
On a wet basis, our atmosphere contains ≈ 1-4% H2O
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Atmospheric greenhouse gases (GHGs)
Atmospheric GHGs, including H2O, CO2, CH4, N2O and O3trap longwave radiation and maintain our climate at a temperature that can support life.
Atmospheric GHGs, including H2O, CO2, CH4, N2O and O3trap longwave radiation and maintain our climate at a temperature that can support life.
In recent years, human activities have led to an accumulation of GHGs in the atmosphere (mainly CO2, CH4 and N2O), resulting in an increased temperature on Earth through increased trapping of longwave radiation.
In recent years, human activities have led to an accumulation of GHGs in the atmosphere (mainly CO2, CH4 and N2O), resulting in an increased temperature on Earth through increased trapping of longwave radiation.
This is known as the enhanced greenhouse effect.This is known as the enhanced greenhouse effect.
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Recent trends in atmospheric greenhouse gas concentrations
Source: IPCC (2007)
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SoilSoil
AtmosphereAtmosphere
CH4 CH4 N2O CO2
Livestock ManureCrop
Management Plants
Agricultural GHG Emissions
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Global Warming Potential (GWP100)
•Greenhouse gases are not equal in their ability to trap radiation and on a mass basis over a 100 year time horizon, are indexed relative to CO2
•CH4 is 21 times more powerful than CO2
•N2O is 310 times more powerful than CO2
•Using the global warming potential (GWP) of each gas, GHG emissions are often expressed as CO2e, or ‘carbon dioxide equivalents’
•Greenhouse gases are not equal in their ability to trap radiation and on a mass basis over a 100 year time horizon, are indexed relative to CO2
•CH4 is 21 times more powerful than CO2
•N2O is 310 times more powerful than CO2
•Using the global warming potential (GWP) of each gas, GHG emissions are often expressed as CO2e, or ‘carbon dioxide equivalents’
310211 2422 ×+×+×= ONCHCOeCO SAR (1996)
298251 2422 ×+×+×= ONCHCOeCO AR4 (2007)
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Global sources of anthropogenic greenhouse gas emissions: Carbon Dioxide
GlobalCarbon Dioxide emissions from fossil fuel combustion
and cement amount to 7.2 Pg C as CO2 per year (1Pg = 1 billion tonnes).
Carbon Dioxide emissions from fossil fuel combustion and cement amount to 7.2 Pg C as CO2 per year
(1Pg = 1 billion tonnes).
Carbon Dioxide emissions from land use change (e.g. deforestation) amount to 1.6 Pg C as CO2 per year
Carbon Dioxide emissions from land use change (e.g. deforestation) amount to 1.6 Pg C as CO2 per year
Source: Denman et al. (2007)
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Global sources of anthropogenic greenhouse gas emissions: Methane and Nitrous Oxide
Global
Source: Denman et al. (2007)
Methane emissions from the energy, waste and agriculture sectors amount to about 350 Tg CH4 per
year (1Tg = 1 million tonnes).
Methane emissions from the energy, waste and agriculture sectors amount to about 350 Tg CH4 per
year (1Tg = 1 million tonnes).
Nitrous oxide emissions from all sectors amount to 6.7-8.1 Tg N2O-N per year
Nitrous oxide emissions from all sectors amount to 6.7-8.1 Tg N2O-N per year
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Agriculture’s contribution to global methane and nitrous oxide emissions
GlobalAgriculture is responsible for approximately 40-50% of
global methane emissions.Agriculture is responsible for approximately 40-50% of
global methane emissions.
Agriculture is responsible for approximately 50-70% of global nitrous oxide emissions.
Agriculture is responsible for approximately 50-70% of global nitrous oxide emissions.
Source: Denman et al. (2007)
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Agricultural Sources of Methane
Enteric fermentation (digestion) by ruminant animals 86 Tg CH4 per year
China: 8.9 Tg Ch4 per year
Enteric fermentation (digestion) by ruminant animals 86 Tg CH4 per year
China: 8.9 Tg Ch4 per year
Management of animal manures 18 Tg CH4 per year
China: 3.8 Tg CH4 per year
Management of animal manures 18 Tg CH4 per year
China: 3.8 Tg CH4 per year
Rice cultivation 60 Tg CH4 per year
China: 6.0 Tg CH4 per year
Rice cultivation 60 Tg CH4 per year
China: 6.0 Tg CH4 per year
Source: Denman et al. (2007); FAO (2006); Huang et al. (2006)
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Agricultural Sources of Nitrous Oxide
Manure Management – Direct emissions from manure storage 0.5 Tg N2O-N.
China: 0.15 Tg N2O-N
Manure Management – Direct emissions from manure storage 0.5 Tg N2O-N.
China: 0.15 Tg N2O-N
Source: US EPA (2006)
Agricultural soils – Direct and indirect emissions from application of synthetic/manure fertilizers, crop residue decomposition, waste deposition
by grazing animals and cultivation of organic soils 4.7 Tg N2O-N.
China: 1.2 Tg N2O-N
Agricultural soils – Direct and indirect emissions from application of synthetic/manure fertilizers, crop residue decomposition, waste deposition
by grazing animals and cultivation of organic soils 4.7 Tg N2O-N.
China: 1.2 Tg N2O-N
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Agricultural greenhouse gas emissions
Global
Methane Emissions
Nitrous oxide Emissions
China
120-180 Tg CH4 1.7-4.8 Tg N2O-N
Source: Denman et al. (2007); People’s Republic of China (1994)
18 Tg CH4 0.5 Tg N2O-N
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Space and Time Scale of Measurement Techniques
Balloon
1 hour
1 Day
1 Month
1 Year
Aircraft
Laser
1 m2 1 Hectare 1 km2
Representative Area of Measurement
10 km2
Chamber
Rep
rese
ntat
ive
Tim
e of
Mea
sure
men
t
Tower
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Non-Flow Through Non-Steady State (NFT-NSS) chambers: principles of operation
Time since deployment
Gas
co
ncen
tratio
n in
he
ad s
pace
•Insert collar into soil, affix chamber to collar
•Gas accumulates in head space, no replacement of air
•Gas concentration in the chamber rises continually
•Sample periodically, typically at intervals of a few minutes andfor periods of 15-30 minutes
•Gas samples returned to the lab and analyzed with e.g. gas chromatography
•Most common type of chamber because large concentration change is possible, mechanically simple, no need for power and no need for gas analysis on-line
•Insert collar into soil, affix chamber to collar
•Gas accumulates in head space, no replacement of air
•Gas concentration in the chamber rises continually
•Sample periodically, typically at intervals of a few minutes andfor periods of 15-30 minutes
•Gas samples returned to the lab and analyzed with e.g. gas chromatography
•Most common type of chamber because large concentration change is possible, mechanically simple, no need for power and no need for gas analysis on-line
collar
chamber
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Flux calculation:
Fg = flux density of gasV = volume of head spaceA = area of land enclosedC = gas concentration in head spacet = time
Flux calculation:
Fg = flux density of gasV = volume of head spaceA = area of land enclosedC = gas concentration in head spacet = time
⎟⎠⎞
⎜⎝⎛×⎟
⎠⎞
⎜⎝⎛=
dtdC
AVFg
Non-Flow Through Non-Steady State (NFT-NSS) chambers: Closed Chambers
Because the head space is small and confined, the resolution for these measurements are much higher than using any other technique.
Because the head space is small and confined, the resolution for these measurements are much higher than using any other technique.
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y = 0.2024x + 0.844R2 = 0.9608
y = -0.006x2 + 0.3465x + 0.4868R2 = 0.9959
0
1
2
3
4
5
6
0 5 10 15 20 25
Deployment time (min)
N2O
con
cent
ratio
n (祄
ol m
ol-1
)
y = 0.2024x + 0.844R2 = 0.9608
y = -0.006x2 + 0.3465x + 0.4868R2 = 0.9959
0
1
2
3
4
5
6
0 5 10 15 20 25
Deployment time (min)
N2O
con
cent
ratio
n (祄
ol m
ol-1
)dc/dt in non flow-throughNSS chamber
- 8%
Concentration Change Over Time in ClosedChambers
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y = 0.2024x + 0.844R2 = 0.9608
y = -0.006x2 + 0.3465x + 0.4868R2 = 0.9959
0
1
2
3
4
5
6
0 5 10 15 20 25
Deployment time (min)
N2O
con
cent
ratio
n (祄
ol m
ol-1
)
y = 0.2024x + 0.844R2 = 0.9608
y = -0.006x2 + 0.3465x + 0.4868R2 = 0.9959
0
1
2
3
4
5
6
0 5 10 15 20 25
Deployment time (min)
N2O
con
cent
ratio
n (祄
ol m
ol-1
)
- 25%
dc/dt in non flow-throughNSS chamber
Concentration Change Over Time in ClosedChambers
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y = 0.2024x + 0.844R2 = 0.9608
y = -0.006x2 + 0.3465x + 0.4868R2 = 0.9959
0
1
2
3
4
5
6
0 5 10 15 20 25
Deployment time (min)
N2O
con
cent
ratio
n (祄
ol m
ol-1
)
y = 0.2024x + 0.844R2 = 0.9608
y = -0.006x2 + 0.3465x + 0.4868R2 = 0.9959
0
1
2
3
4
5
6
0 5 10 15 20 25
Deployment time (min)
N2O
con
cent
ratio
n (祄
ol m
ol-1
)
- 38%
dc/dt in non flow-throughNSS chamber
Concentration Change Over Time in ClosedChambers
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y = 0.6153x + 396.62R2 = 0.9964
y = -0.0005x2 + 0.7369x + 391.61R2 = 0.999
350
370
390
410
430
450
470
490
510
530
550
0 50 100 150 200 250
Deployment time (s)
Hea
dspa
ce C
O2 c
once
ntra
tion
(祄ol
mol
-1)
y = 0.6153x + 396.62R2 = 0.9964
y = -0.0005x2 + 0.7369x + 391.61R2 = 0.999
350
370
390
410
430
450
470
490
510
530
550
0 50 100 150 200 250
Deployment time (s)
Hea
dspa
ce C
O2 c
once
ntra
tion
(祄ol
mol
-1)
dc/dt in flow-through NSS chamber
- 17%
dC/dt
Concentration Change Over Time in Open Chambers
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Criteria for reliable soil flux measurements
Rochette and Eriksen-Hamel (2007) evaluated the quality of soil N2O emissions that have been collected using closed chambers and has suggested the confidence level in 50% of recent (2005-2007) N2O flux measurements is low or very low owing to poor methodologies or incomplete reporting. They proposed the following series of requirements to ensure a minimum standard and confidence in chamber measurements:
Rochette and Eriksen-Hamel (2007) evaluated the quality of soil N2O emissions that have been collected using closed chambers and has suggested the confidence level in 50% of recent (2005-2007) N2O flux measurements is low or very low owing to poor methodologies or incomplete reporting. They proposed the following series of requirements to ensure a minimum standard and confidence in chamber measurements:
1. Use insulated and vented base and chamber design2. Avoid chamber heights lower than 10 cm3. Have a minimum collar insertion depth of 5 cm4. Avoid plastic syringes for sample storage5. Take a minimum of 3 samples, including 1 at time t=06. Test non-linearity of changes in headspace gas concentration
1. Use insulated and vented base and chamber design2. Avoid chamber heights lower than 10 cm3. Have a minimum collar insertion depth of 5 cm4. Avoid plastic syringes for sample storage5. Take a minimum of 3 samples, including 1 at time t=06. Test non-linearity of changes in headspace gas concentration
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Mass Balance Technique – Micrometeorological Mass Difference approach
Flux from within the enclosed source area can be calculated as the difference between the total gas fluxes across the upwind and downwind boundaries of the space, as follows:
Where, = the gas concentration across each boundary= wind vectors perpendicular to the boundary
Flux from within the enclosed source area can be calculated as the difference between the total gas fluxes across the upwind and downwind boundaries of the space, as follows:
Where, = the gas concentration across each boundary= wind vectors perpendicular to the boundary
( ) ( )dxdzvuF zgzgzzgzg
z x
zMMD .1.3.2.40 0
ρρρρ −+−= ∫ ∫
ρvu,
However, the MMD technique is limited by the fact that it can only be applied at a scale of perhaps 10’s of meters, it computationally demanding and it requires a significant amount of set up.
However, the MMD technique is limited by the fact that it can only be applied at a scale of perhaps 10’s of meters, it computationally demanding and it requires a significant amount of set up.
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1
23
4
u vU
Mass Balance Technique – Micrometeorological Mass Difference approach
Anemometer tower
Enclosed source of
emissions
PumpIntegrator
Site selector valve
Trace gas analyzer
z
x
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Instrumentation
Open-path lasers and retroreflectorOpen-path lasers and retroreflector
• Gas detector (NH3, CO2, CH4 and H2S) for area sources• On board calibration cell and datalogger• Narrow wavelength minimizes interference with CO2 and H2O• Path length up to 1000 m (depends on target gas)• Detection limit for CH4 of 2.0 ppm-m• e.g. Boreal Laser GasFinder, PKL Spectra-1
• Gas detector (NH3, CO2, CH4 and H2S) for area sources• On board calibration cell and datalogger• Narrow wavelength minimizes interference with CO2 and H2O• Path length up to 1000 m (depends on target gas)• Detection limit for CH4 of 2.0 ppm-m• e.g. Boreal Laser GasFinder, PKL Spectra-1
Boreal GasFinder PKL Spectra-1
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Mass Balance Technique – Modified MicrometeorologicalMass Difference approach
•Simplified approach, does not require air sampling on all four sides, only upwind and downwind
•Width of laser line should be at least 6 times the width the of the source area
•Limited by wind direction, which should not be more than 45º to the laser lines
•Simplified approach, does not require air sampling on all four sides, only upwind and downwind
•Width of laser line should be at least 6 times the width the of the source area
•Limited by wind direction, which should not be more than 45º to the laser lines
Laser Tower Reflector Tower
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Mass Balance Technique – Modified MicrometeorologicalMass Difference approach
U
Source of emissions
Upwind laser reflectors
Downwind laser reflectors
Downwind open-path lasers
Upwind open-path lasers
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If a laser based measurement technique is used, such that the width of the laser line is much greater than the width of the plume of gas being measured and that the wind speed at any given height for all points along the laser line is uniform, then
Where,
= the length of the laser path
= the wind speed normal to the boundaries
= the scalar concentration and the subscripts d and b denote the downwind and background, respectively
If a laser based measurement technique is used, such that the width of the laser line is much greater than the width of the plume of gas being measured and that the wind speed at any given height for all points along the laser line is uniform, then
Where,
= the length of the laser path
= the wind speed normal to the boundaries
= the scalar concentration and the subscripts d and b denote the downwind and background, respectively
( )dzUXFZ
ggng bzdz∫ −=0
,,ρρ
X
nU
gρ
Mass Balance Technique: Integratedhorizontal flux method
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Recovery from Synthetic Gas Release
0
1
2
3
4
5
6
7
0 20 40 60
Recovered methane (mg CH4 m-1 s-1)
Heig
ht (m
)
0
1
2
3
4
5
6
7
0 20 40 60
Recovered methane (mg CH4 m-1 s-1)
Heig
ht (m
)
Measured flux of CH4 and standard errors as a function
of height based on three releases of 79 mg CH4 s-1
97% recovery
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The MMD technique was tested using a synthetic tracer (CH4) release to simulate on farm release by cattle.
The MMD technique was tested using a synthetic tracer (CH4) release to simulate on farm release by cattle.
Testing the MMD technique with a synthetictracer release
Methane recovery slightly exceeded methane release and was foundthat it could identify 10% changes in the rate of emission, provided the rate of emission was greater than about 40 mg CH4 s-1, equivalent to roughly 10 dairy cattle.
Methane recovery slightly exceeded methane release and was foundthat it could identify 10% changes in the rate of emission, provided the rate of emission was greater than about 40 mg CH4 s-1, equivalent to roughly 10 dairy cattle.
Source: Desjardins et al. (2004)
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CQ
• Calculate trajectories upwind from C, using e.g. the Windtrax model*• Efficient & simple to find the rate of emission, Q from an area source• C-Q relationship given by “touchdowns”
• Calculate trajectories upwind from C, using e.g. the Windtrax model*• Efficient & simple to find the rate of emission, Q from an area source• C-Q relationship given by “touchdowns”
Trajectories affected by average wind and turbulence, i.e., different touchdown pattern during day (unstable) and night (stable).
Trajectories affected by average wind and turbulence, i.e., different touchdown pattern during day (unstable) and night (stable).
windwind
touchdowntouchdown
sourcesource
Micrometeorological tools: bLS modelling
*See: http://www.thunderbeachscientific.com/windtrax.html for more details
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+ simple measurements + no operation disruption+ total emissions + remote measurement+ simple measurements + simple measurements + no operation disruption+ no operation disruption+ total emissions + total emissions + remote measurement+ remote measurement
Inverse Dispersion Modeling
• Dispersion model relates concentration C to emission rate Q (“C-Q relationship” ) for prevailing winds
• Measure C then infers Q
• Dispersion model relates concentration C to emission rate Q (“C-Q relationship” ) for prevailing winds
• Measure C then infers Q
Advantages
Wind CQ (g s-1)
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Open path lasers and the backwards Lagrangian Stochasticmodelling technique to estimate CH4 emissions
Advantages:
•Non-invasive measurement technique
•Simple measurement set-up (one open path laser, one sonic anemometer)
•Effective for a range of emission sources (e.g. manure tank, beef feedlot, whole farm)
•Non-invasive measurement technique
•Simple measurement set-up (one open path laser, one sonic anemometer)
•Effective for a range of emission sources (e.g. manure tank, beef feedlot, whole farm)
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Open path lasers and the backwards Lagrangian Stochasticmodelling technique to estimate CH4 emissions
Disadvantages:
•Ineffective during light wind conditions and periods of extreme stability
•Obstructions to wind flow must be accounted for by placing the lasers downwind about 10-20 times the height of the wind obstruction
•Open path lasers are not available for all gas species of interest (e.g. N2O) therefore this technique cannot be used for such gases
•Ineffective during light wind conditions and periods of extreme stability
•Obstructions to wind flow must be accounted for by placing the lasers downwind about 10-20 times the height of the wind obstruction
•Open path lasers are not available for all gas species of interest (e.g. N2O) therefore this technique cannot be used for such gases
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Micrometeorological tools: bLS modeling
simL
bLbLS QC
CCQ
)/()( −
= ⎟⎟⎠
⎞⎜⎜⎝
⎛= ∑∑
= 01
211)/(wNP
QCP
isimL
Using a line measurement:
where,QbLS = Estimated emission rate
Q = Uniform but unknown emission rate
CL = Line concentration
Cb = Background concentration
P = Point Concentrations evenly spaced along the path
N = Total number of particles released at each point
wo = Vertical touchdown velocities
* The inner summation refers only to touchdowns within the source
In practice, C is measured along a line with open path lasers
In practice, C is measured along a line with open path lasers
Wind speed and direction is measured using a sonic anemometer and friction velocity, u*, surface roughness, z0 and Obukhov stability length, L are calculated, from which the vertical touchdown velocity, w0 can be estimated.
Wind speed and direction is measured using a sonic anemometer and friction velocity, u*, surface roughness, z0 and Obukhov stability length, L are calculated, from which the vertical touchdown velocity, w0 can be estimated.
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bLS Modeling Software: WindTrax
•WindTrax is a software tool for simulating short-range atmospheric dispersion•Operates in backward and forward Lagrangian Stochastic modes to estimate the concentration and downwind concentration for ground level sources•Operates with point or line averaged concentrations
•WindTrax is a software tool for simulating short-range atmospheric dispersion•Operates in backward and forward Lagrangian Stochastic modes to estimate the concentration and downwind concentration for ground level sources•Operates with point or line averaged concentrations
WindTrax can be downloaded from: http://www.thunderbeachscientific.com/windtrax.html
Graphical display to enter instrument and emissions source locations.
Graphical display to enter instrument and emissions source locations.
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Wind Direction
Open path laser
Upwind Reflector
Background CH4
concentration (Cb)
Estimate CH4 concentration in air using open path lasers
Open path laser
Downwind Reflectors
Downwind CH4concentration
(CL)
Source of gas emissions: Pure compressed CH4 released through a grid
Experimental setup for pure CH4 release to test the bLS technique
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Testing the bLS technique under complex conditions: Methane release in a barn
h = 6mGao et al. 2006. Evaluating the backward Lagrangian Stochastic (bLS) technique for estimating methane emissions from a barn. In preparation.
0
0.2
0.4
0.6
0.8
1
1.2
5h 10h 15h 20h 25h 30h
Fetch to building height ratio (D:h)
QbL
S/Q
Downwind distance from the barn at which measurements were made (m)
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Measuring methane emissions from beef feedlot
Canada
Study site:
•Near to Vegreville, Canada
•Beef feedlot: 17,000 head
Study site:
•Near to Vegreville, Canada
•Beef feedlot: 17,000 head
Source: Van Haarlem et al. (2008)300 m
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backwards Lagrangian Stochastic modelling: CH4emissions from whole farm feedlots
Diurnal Cycle in CH4 emissions, corresponding with feeding times
Diurnal Cycle in CH4 emissions, corresponding with feeding times
Source: Van Haarlem et al. (2008)
Average daily emission rate: 320 g CH4 animal-1 d-1
The CH4 emission factor estimated using the IPCC methodology is
approximately 240 g CH4 animal-1 d-1
Average daily emission rate: 320 g CH4 animal-1 d-1
The CH4 emission factor estimated using the IPCC methodology is
approximately 240 g CH4 animal-1 d-1
EFCH4 = GEI × YmEFCH4 = GEI × Ym
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Reynolds:
Flux:
,xxx ′+= ,0=′x yxyxxy ′′+=.
qwqwwqF ′′+==
.
x
x′
x0=w
assuming stationarity and horizontal homogeneity!
The Eddy Covariance Method
0 10 20 30 40
Time (with tower) or distance (with aircraft)
verti
cal w
ind
velo
city
scal
ar
vertical wind velocityscalar
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Measuring Changes in Soil Organic Carbon
CO2 - flux data have the potential to provide high resolution measurements of changes in C sequestration on the scale of hectares.
CO2 - flux data have the potential to provide high resolution measurements of changes in C sequestration on the scale of hectares.
e.g. ChinaFlux; Carbo Europe;
AmeriFlux; Fluxnet - Canada
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CO2
Soil organic matter
product
energy
Ecosystemboundary
0.1
1.5
1.5
1.5
4
7
The carbon cycle in agroecosystems
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43
Measurement of Soil C Gain
0
10
20
30
40
50
60
70
80
Initial
Increase with improved management
VariabilityAnalytical Spatial
Soil
C (M
g C
ha-
1 )
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44
Flux Underestimation
From the basic energy balance equation,
Qn – QG – ΔQS = QE + QH
However, experimentally it has generally been found that
Qn – QG – ΔQS > QE + QH
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Flux Underestimation – A persistent Problem
Therefore, the eddy covariance method generally underestimates the turbulent energy fluxes by 5-30 % as
compared to available energy
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Dryer
Dz ~ 0.5 -1m
TDL resolution ~ 20 ppt
10 s/level - 10 Hz
Air sample under vacuum
pump
Tower-based N2O Flux
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Small Dewar~12-16 h
=> vibration - free environment Sample cell 1.5m long
Tunable Diode Laser
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The aerodynamic method, stable and unstableconditions
The aerodynamic method can be extended to stable and unstable atmospheric conditions as follows:
where, ψ1 and ψ2 are corrections for stability. In stable conditions,
In unstable conditions,
and
Where,
The aerodynamic method can be extended to stable and unstable atmospheric conditions as follows:
where, ψ1 and ψ2 are corrections for stability. In stable conditions,
In unstable conditions,
and
Where,
( )( )( ) ( )[ ] ( ) ( )[ ]⎥
⎦
⎤⎢⎣
⎡−−⎟⎟
⎠
⎞⎜⎜⎝
⎛⎥⎦
⎤⎢⎣
⎡−−⎟⎟
⎠
⎞⎜⎜⎝
⎛−−
=
12221
21121
1
2
12122
lnln zzzzzz
zz
CCuukFg
ψψψψ
Lz521 −==ψψ
( ) ( )2
tan22
1ln2
1ln2 12
1πψ +−⎥
⎦
⎤⎢⎣
⎡ ++⎥⎦
⎤⎢⎣⎡ +
= − xxx ( )⎥⎦
⎤⎢⎣
⎡ +=
21ln2
2
2xψ
41
161 ⎟⎠⎞
⎜⎝⎛ −=
Lzx
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49Canada
-50
0
50
100
150
200
N 2O
Flu
x (n
g N 2
O m
-2 s
-1)
Feb 1 Mar 1 Apr 1 May 1
Nitrous oxide emissions measured in 1996 over a soybean field in Ottawa Canada, using a tower-based system
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50
Tower based N2O flux estimates over corn, 2001
-50
0
50
100
150
200
250
300
350
400
15-Mar 25-Mar 4-Apr 14-Apr 24-Apr 4-May 14-May 24-May 3-Jun 13-Jun
N2O
Em
issi
ons
(g N
2O-N
ha-1
d-1)
Tower - Corn
Canada
Source: Desjardins et al (2009)
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Tower based N2O flux estimates over spring wheat, 2004
-50
0
50
100
150
200
250
15-Mar 25-Mar 4-Apr 14-Apr 24-Apr 4-May 14-May 24-May 3-Jun 13-Jun
N2O
Em
issi
ons
(g N
2O-N
ha-1
d-1)
Tower - Spring wheat
Canada
Source: Desjardins et al (2009)
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Aircraft Instrumentation for Regional Flux Measurement
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Comparing Tower and Aircraft Flux Measurements
QE QE QE
QH QH QH
SGP project 1997
SGP project 1997
USA
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Relaxed Eddy Accumulation (REA)
• Alternate to eddy covariance technique to measure fluxes of trace gases for which there are no fast-response analyzers
• Air samples from updrafts and downdrafts are collected in two separate reservoirs for later analysis
• In EA, sample flow rate is proportional to w; this requirement is ‘relaxed’ in REA (i.e., full flow into up or down reservoir depending on the direction of the vertical wind)
• Alternate to eddy covariance technique to measure fluxes of trace gases for which there are no fast-response analyzers
• Air samples from updrafts and downdrafts are collected in two separate reservoirs for later analysis
• In EA, sample flow rate is proportional to w; this requirement is ‘relaxed’ in REA (i.e., full flow into up or down reservoir depending on the direction of the vertical wind)
( )F w wχ χ σ χ χ= −' ' = A Up Down
Vent (Dead band)
PTFESample Bag
DC Power supply
3-wayValve
Mass-FlowController
2-μmFilter
Reliefvalve
DiaphragmPump 12 l/min
Inlet
UP
DOWN
¼” PTFEtubing
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Relaxed eddy accumulation technique
• Easier to apply than eddy accumulation• Fast-response anemometer measures w and controls a simple valving
system• Air is sampled at a constant rate and diverted into ‘up’ and ‘down’ bins
depending on the direction of w
• Easier to apply than eddy accumulation• Fast-response anemometer measures w and controls a simple valving
system• Air is sampled at a constant rate and diverted into ‘up’ and ‘down’ bins
depending on the direction of w
( )dnupwg CCbF −= σ
Where,b = empirical coefficient that varies with the dead bandσw and σC = standard deviation of vertical velocity (w) and concentration (C)r = correlation coefficient between w and C
Theory and experiments have suggested that b is constant and independent of stability, ≈ 0.6.
Where,b = empirical coefficient that varies with the dead bandσw and σC = standard deviation of vertical velocity (w) and concentration (C)r = correlation coefficient between w and C
Theory and experiments have suggested that b is constant and independent of stability, ≈ 0.6.
Cwg rF σσ=
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-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0-0.8-0.6-0.4-0.20.0
CO2 Flux (EC) (mg CO2 m-2 s-1)
CO
2 Flu
x (R
EA) (
mg
CO
2 m
-2 s
-1)
FREA = (0.84 +/- 0.10) FEC - (0.03 +/- 0.05)
R2 = 0.83
• Forest and agricultural fields
• Stainless steel canisters, with in-line magnesium percholateto remove water
• Forest and agricultural fields
• Stainless steel canisters, with in-line magnesium percholateto remove water
Comparison of EC and REA Flux Estimates of CO2
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57
ΔN2O (pptv) σw = 0.3 m s-1 σw = 0.9 m s-1
10 1.6 5.5 g N2O-N ha-1 d-1
FN2O = ρN2O × 0.56 × σw × ΔN2O
Resolution of the REA system for N2O flux measurement
μ=-1.1 sc=17
-40
-30-20
-100
10
2030
40
ΔN
20 =
up
-dow
n (p
ptv)
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Morewood
Casselman
N
0 5 km5 km
Morewood
Casselman
N
0 5 km5 km
N
0 5 km5 km
Crop types in the aircraft footprint
soy
cereals
pasture/grass
alfalfa
forest
corn
town
LEGEND
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59
0
10
20
30
40N
o D
ata
Con
ifero
us F
ores
t
Mix
ed F
ores
t
Bro
adle
af F
ores
t
Low
Den
sity
For
est
Bar
e S
oil
Cor
n
Soy
bean
Cer
eals
Alfa
lfa
Ope
n Fi
eld
Wat
er
Bui
ltup
Are
as
Wet
land
% o
f Ave
rage
Foo
tprin
t
CasselmanMorewood
Land use information in 2001 within footprint of aircraft transects
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N2O emissions during and right after snowmelt at the Eastern Canada study sites in 2001
Each data point represents the average of 3 samples, collected during two consecutive 10 km flight legs (total flight distance for one data point is ≈ 20 km)
Each data point represents the average of 3 samples, collected during two consecutive 10 km flight legs (total flight distance for one data point is ≈ 20 km)
-25
0
25
50
75
100
12515
-Mar
25-M
ar
4-A
pr
14-A
pr
24-A
pr
4-M
ay
14-M
ay
24-M
ay
3-Ju
n
13-J
un
N2O
Em
issi
ons
(g N
2O-N
ha-1
d-1
) CasselmanMorewood
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N2O emissions right after snowmelt and after plantingat the Eastern Canada study sites in 2003
-25
0
25
50
75
100
125
15-M
ar
25-M
ar
4-A
pr
14-A
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24-A
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ay
14-M
ay
24-M
ay
3-Ju
n
13-J
un
N2O
Em
issi
ons
(g N
2O-N
ha-1
d-1
)
CasselmanMorewood
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62
-25
25
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125
175
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ar
25-M
ar
4-A
pr
14-A
pr
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pr
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ay
14-M
ay
24-M
ay
3-Ju
n
13-J
un
TowerMorewoodCasselman
-25
25
75
125
175
15-M
ar
25-M
ar
04-A
pr
14-A
pr
24-A
pr
04-M
ay
14-M
ay
24-M
ay
03-J
un
13-J
un
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25
75
125
175
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ar
25-M
ar
04-A
pr
14-A
pr
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pr
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14-M
ay
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ay
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un
13-J
un
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75
125
175
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ar
25-M
ar
04-A
pr
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pr
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04-M
ay
14-M
ay
24-M
ay
03-J
un
13-J
un
N2O
Flu
x (g
N2O
-N h
a-1
d-1 )
2000 2001
2003 2004
Peak, April 14, 360 g N2O-N ha-1 d-1
Multi-year comparison of aircraft and tower basedestimates of N2O emissions
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Multi-year comparison of aircraft and model estimates of N2O emissions
-25
25
75
125
175
15-M
ar
25-M
ar
4-A
pr
14-A
pr
24-A
pr
4-M
ay
14-M
ay
24-M
ay
3-Ju
n
13-J
un
-25
25
75
125
175
15-M
ar
25-M
ar
4-A
pr
14-A
pr
24-A
pr
4-M
ay
14-M
ay
24-M
ay
3-Ju
n
13-J
un
-25
25
75
125
175
15-M
ar
25-M
ar
4-A
pr
14-A
pr
24-A
pr
4-M
ay
14-M
ay
24-M
ay
3-Ju
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DNDCCasselman
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25
75
125
175
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ar
25-M
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N2O
Flu
x (g
N2O
-N h
a-1
d-1 )
2000 2001
2003 2004
Total emissions (kg N2O-N ha-1)DNDC: 0.34Aircraft: 0.53
Total emissions (kg N2O-N ha-1)DNDC: 0.76Aircraft: 0.55
Total emissions (kg N2O-N ha-1)DNDC: 1.44Aircraft: 1.87
Total emissions (kg N2O-N ha-1)DNDC: 1.11Aircraft: 1.77
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6464
ΔCH4(ppbv) σw = 0.3 m s-1 σw = 0.9 m s-1
0.2 2 6 mg m-2 d-1
0.2 700 2100 kg km-2 y-1
FCH4 = 0.56 × σw × ΔCH4
Resolution of the REA system for CH4 flux measurement
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
dCH
4= U
P - D
OW
N (p
pbv)
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65
Estimated Agricultural CH4 Emissions
Canada
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66
6 284 kg km-2 yr-1
7 252 kg km-2 yr-118 119 kg km-2 yr-1
9 209 kg km-2 yr-1
7 114 kg km-2 yr-1
10 373 kg km-2 yr-1
6 992 kg km-2 yr-1
6 631 kg km-2 yr-1
Regional CH4 Emissions, Sept. 3 and 18, 2003
Canada
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67
Develop ‘Model’
Develop ‘Model’
MeasureGHG’s
MeasureGHG’s
Verified Model
Verified Verified Model Model
timetime
. . .
Process of Model Development
Learn Learn BuildBuild ApplyApply
MeasureMeasure
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68
Nutrient and energy cycles in agricultural ecosystems
OrganicOrganicmattermatter
nutrients
NN22OO
CHCH44 COCO22export
import
energy
Ecosystem boundary
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69
Influence of time on net GHG emissions
0
Time
Cha
nge
in n
et G
HG
E
mis
sion
s
+
- BMP 1
BMP 2
BMP 3The relative benefit of adopting any one beneficial management practice (BMP) may change over time
The relative benefit of adopting any one beneficial management practice (BMP) may change over time
Other factors may change over time that can affect GHG emissions. For example:
•Temperature
•Atmospheric CO2
•Crop types
Other factors may change over time that can affect GHG emissions. For example:
•Temperature
•Atmospheric CO2
•Crop types
•Technology
•Economics
•Land use
•Government policy
•Population
•Social value of farms
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70
Interactions between management decisionsand GHG emissions
Management decision
Grow leguminous forage crops
Grow leguminous forage crops
Implications
Residue return to soil
Residue return to soil
Forages fed to cattle
Forages fed to cattle
Increased manure production
Increased manure production
Decreased synthetic fertilizer
Decreased synthetic fertilizer
Effect on GHG emissions
Decreased CO2Decreased CO2
Increased CH4Increased CH4
Increased CH4and N2O
Increased CH4and N2O
Decreased N2O and CO2
Decreased N2O and CO2
What is the cumulative effect of this management decision on net greenhouse gas emissions?
What is the cumulative effect of this management decision on net greenhouse gas emissions?
Models are necessary to answer these types of questionsModels are necessary to answer these types of questions
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71
CO2
Soil organic matter
product
energy
Ecosystemboundary
The carbon cycle in agroecosystems
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72
Soil
carb
on
Initialcultivation
ΔC<0 ΔC≈ 0 ΔC>0
Effect onatmospheric CO2 ~
Managementchange
agriculture
Management-Induced Carbon Change on Agricultural Land
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73
Estimating C factors
• Factors from empirical data and/or Century • Century used extensively because need to obtain
time effects (rate, duration) of C change
• Factors from empirical data and/or Century • Century used extensively because need to obtain
time effects (rate, duration) of C change
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74
5000
5200
5400
5600
5800
6000
6200
6400
6600
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080
Time (yr)
Tota
l Soi
l C (g
m-2
)
Land management change(switch to perennial crops)in 2000
CENTURY simulation withoutland management change
CENTURY simulation withland management change
Estimating soil C factors
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75
-200
0
200
400
600
800
1000
1200
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080
Time (yr)
Cha
nge
in S
oil C
(g m
-2) Curve fit to C change
ΔC = 1336 x [1 – exp(-0.024 x t)]
Estimating soil C factors
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76
Zone LMC
K (/ year)
ΔCLMCm
ax(Mg/ha)
Final Yearof Effect
after LMC
Mean annual Linear Coefficient over Duration of Effect of LMC (Mg/ha
per year)
Mean Annual Linear Coefficient over First 20 years
after LMC(Mg/ha per year)
IT to NT 0.0261 4.9 63 0.06 0.10
IT to RT 0.0188 2.3 30 0.03 0.04
RT to NT 0.0222 2.5 37 0.04 0.05
Decrease fallow 0.0305 13.1 91 0.14 0.30
Increase perennial 0.0281 26.1 120 0.21 0.56
Semi-Arid Prairies
Soil Carbon Change Factors in Canada
Reducing tillage, decreasing fallow and converting to permanent cover all increase soil carbon
Canada
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77
Soil organic matter
N2
fertilizer
manure
NO3,NH4
legumesN2,N2O
The nitrogen cycle in agroecosystems
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78
DNDC – DeNitrification and DeCompositionmodel for estimating soil N2O emissions
Thermal-hydraulic Sub-model
DenitrificationSub-model
Decomposition Sub-model
Plant Sub-model
•Hourly N2O •Soil microbial respiration•Hourly N2O•C decomposition•NH3 volatilization
•daily root respiration
•N utilization
Soil properties
Air temperature/ precipitationManagement
practices
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DNDC-MFT Interface
Simulate default crop rotations, soil and climate
User can choose or modify management practices and soil
data as required
DNDC-MFT Interface
Simulate default crop rotations, soil and climate
User can choose or modify management practices and soil
data as required
DNDCSimulation processes
DNDCSimulation processes
OutPut
Estimated N2O, CO2and Net GHG
emission factors ecodistrict level
for each managementpractice and rotation
OutPut
Estimated N2O, CO2and Net GHG
emission factors ecodistrict level
for each managementpractice and rotation
EcodistrictDatabase
EcodistrictDatabase
DailyMeteorological
Data
SoilProperties
Crop Rotations
Validate and compare against Tier 2 IPCC
factors
Validate and compare against Tier 2 IPCC
factors
Canada
Integrating Process-based and Empirical Models
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Estimated N2O and CO2 emissions as influenced by agricultural practices in Canada
Combined CO2 & N2O coefficients (Mg CO2 equiv. ha-1 y-1)
No-tillage ReducedFallow
150%Fertilizer
50%Fertilizer
FallFertilizer
PermanentGrassland
Brown Chern. -0.33 -0.43 0.04 0.01 0.03 -0.97
Dark Brown Chern. -0.64 -0.80 -0.03 0.12 0.14 -1.33
Black Chern. -0.72 0.19 0.01 0.28 -3.44
Dark Gray Luvisol -0.80 -0.61 0.26 -0.09 0.46 -4.24
Gray Brown Luvisol -0.54 -0.11 0.33 -2.56
Gray Luvisol -0.55 -0.27 0.39 -2.13
Gleysolic -0.40 0.21 0.12 -2.36
Minus sign represents a net reduction in GHG
• Century and DNDC
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Methane emissions calculations, IPCC Methodology
I - Methodology - OverviewI - Methodology - Overview
Sources
Levels
Calculations
Tier 2 Tier 2Tier 1
Enteric Fermentation Manure management
Cattle Other All Animal Categories
GEI VS
(Bo x MCF x MS)(Ym)x x
CH4 (kg/head/day) CH4 (kg/head/day)
GEI = Gross Energy IntakeYm = methane conversion
rate (% of feed energyconverted to CH4)
GEI = Gross Energy IntakeYm = methane conversion
rate (% of feed energyconverted to CH4)
VS = daily Volatile Solidexcreted (kg of dry matter)
Bo = maximum methaneproducing capacity(m3CH4/kg of VS) for manure produced by animal
MCF = MethaneConversion Factors for each manuremanagement system
MS = fraction of animal type's manure handledusing a definedManure System
VS = daily Volatile Solidexcreted (kg of dry matter)
Bo = maximum methaneproducing capacity(m3CH4/kg of VS) for manure produced by animal
MCF = MethaneConversion Factors for each manuremanagement system
MS = fraction of animal type's manure handledusing a definedManure System
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82
CH4 is a molecule containing energy
The amount of energy lost though CH4 emissions is : ym
So:
GEI
GEI = Gross Energy IntakeYm = methane conversion rate
(% of feed energy converted to CH4)
CH4
Calculation of the Emission Factor
(EF)
EF(Gg/hd/yr) = (GEI x ym) x 36555.65 (Mj/kgCH4)
Methane emissions from enteric fermentation
Depending on the type and quality of feed, Ym can vary from about 2 to 12% of GEI. Recent research from Canadian feedlots suggests Ym is about 4% compared to the IPCC default Ym value for feedlot cattle of 3% (6.5% for grazing cattle). About10% of Canadian beef cattle are housed in feedlots.
Depending on the type and quality of feed, Ym can vary from about 2 to 12% of GEI. Recent research from Canadian feedlots suggests Ym is about 4% compared to the IPCC default Ym value for feedlot cattle of 3% (6.5% for grazing cattle). About10% of Canadian beef cattle are housed in feedlots.
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Other (heat, urine, CH4)
Digestible Energy
Net Energy
NEm(maintenance)
NEa(activity)
NEg(growth)
NEl(lactation)
NEp(pregnancy)
Liveweight
Weightgain
Matureweight
Liveweight
Fatcontent
Milkprod.
Feedingsituation
% femalethat give
birth
Category (female, castrate, bull)
+ +++
+
GEI
(NEm + NEa + NEl + NEp + NEg)GEI (MJ/day) =
x F
DE (%GEI)
Digestible Energy (DE MJ/day) = Net Energy x F
/ DE(%)
GEI
Fecal Energy
Manure
Enteric fermentation
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Methane emissions from manure management
EF = VS x Bo x Σ(MCF x MS) x 0.67 x 365EF = VS x Bo x Σ(MCF x MS) x 0.67 x 365
EF = annual emission factor (kgCH4/hd/yr)
VS = daily Volatile Solid excreted (kg of dry matter)
Bo = maximum methane producing capacity (m3CH4/kg of VS) for manure produced by animal
MCF = Methane Conversion Factors for each manure management system
MS = fraction of animal type's manure handled using a defined Manure System
0.67 = conversion factor of m3 CH4 to kilograms CH4 (kg CH4 /m3 CH4)
EF = annual emission factor (kgCH4/hd/yr)
VS = daily Volatile Solid excreted (kg of dry matter)
Bo = maximum methane producing capacity (m3CH4/kg of VS) for manure produced by animal
MCF = Methane Conversion Factors for each manure management system
MS = fraction of animal type's manure handled using a defined Manure System
0.67 = conversion factor of m3 CH4 to kilograms CH4 (kg CH4 /m3 CH4)
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Greenhouse Gas Emissions in Canada, 1990-2006
0
100
200
300
400
500
600
700
800
900
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Gre
enho
use
Gas
Em
issi
ons
(Mt C
O2e
)
Agricultural Emissions
Kyoto Objective: -6% compared to 1990Kyoto Objective: -6% compared to 1990
+28%+28%
Agricultural emissions represent about 8.5% of national emissionsAgricultural emissions represent about 8.5% of national emissions
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86
ModelModel
MethodMethod
Sources of Sources of emissionsemissions
Holistic Approach for Estimating GHG Emissions from the Dairy Sector
CH 4 N2O CO 2
Feed ration Population - Dairy cows
Synthetic Nitrogen Fertilizers
Farm Fieldwork
CH4 emissionsIPCC TII
Crop Complex For dairy cows
Nitrogen fertilizerFor the crop complex
N2O EmissionsIPCC TII
Manure
Enteric Fermentation
Manure Mgmnt
Fertilizer Manufacture
CO2 EmissionsFossil Energy F4E2 Model
: Inputs
: Calculation
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PrinciplePrinciple
Yields kg / haYields kg / ha
Silage
Straw
Barley
Pasture
Corn
Others(wheat, soybean,
etc.)
Farm
NN22OO
CHCH44COCO22
NN22OOCHCH44
COCO22
“Crop Complex”
Feed Rations Feed Rations
Crops (kg/anl-yr)Wheat 50
Oats 30Barley 870
Corn 440Dry peas 5Soybean 90
Canola 125
Straw-Silage CensusPasture
Dairy cows (2001)
Defining the Crop Complex
Intensity IndicatorIntensity Indicator
Areas (ha)Areas (ha)
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Dairy Population and Milk Production
0.0
0.5
1.0
1.5
2.0
1981 1986 1991 1996 2001 2006
Dai
ry C
ow P
opul
atio
n (M
hea
d)
0
10
20
30
40
Milk
Pro
duct
ion
(kg
hd-1
d-1)
PopulationMilk Production
Met
hane
Em
issi
on F
acto
r (k
g C
H4
head
-1ye
ar-1
)
0
25
50
75
100
125
150
175
200
1981 1986 1991 1996 2001 2006
Methane Emission Factor
Increasing milk production per head has resulted in an increase in the methane emission factor for dairy cattleIncreasing milk production per head has resulted in an increase in the methane emission factor for dairy cattle
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0
0.2
0.4
0.6
0.8
1
1.2
1.4
1981 1986 1991 1996 2001 2006GH
G E
mis
sion
s pe
r kilo
gram
of m
ilk p
rodu
ced
(kg
CO
2e)
GHG emissions intensity: Dairy Sector
1981-2006: 26% reduction in emissions intensity
1991-2006: 18% reduction in emissions intensity
1981-2006: 26% reduction in emissions intensity
1991-2006: 18% reduction in emissions intensity
Source: Vergé et al (2007); Dyer et al (2008)
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GHG Emissions Intensity in Canada
0
2
4
6
8
10
12
14
16
18
Dairy Beef Pork Poultry -broiler meat
Poultry - eggs
GH
G e
mis
sion
s pe
r kilo
gram
of m
ilk o
r liv
e w
eigh
t pr
oduc
ed o
r doz
en e
ggs
1981 1986 1991
1996 2001 2006
-26 %
-56 %
-56 %-19 % -8 %
Improved breeds, adoption of BMPsfeeding of leguminous crops have led todairy,
such as no-tillage and increased a reduction in emissions intensity
beef, pork and poultry production in Canada.
Improved breeds, adoption of BMPsfeeding of leguminous crops have led todairy,
such as no-tillage and increased a reduction in emissions intensity
beef, pork and poultry production in Canada.
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System Boundaries for Emissions Calculations
Dairy FarmInputs Milk ProcessingTransportation
WholesaleRetail
Transportation
Transportation
Consumer
Transportation
Inputs
InputsInputsDepending on how the system boundaries are defined, emissions per
unit of product can change significantly. Life Cycle Analysis must define the system boundaries which then ensures the consistency
and comparability of studies.
Depending on how the system boundaries are defined, emissions per unit of product can change significantly. Life Cycle Analysis must define the system boundaries which then ensures the consistency
and comparability of studies.
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An estimate of the carbon footprint of milk production in Canada
Crop production
Milk production Processing Packaging
Transport/distribution Retail
GH
G E
mis
sion
s (M
t CO
2e)
0
1
2
3
4
5
6
7
8
All emissions, CO2 (estimated)Electricity, CO2
Manure management, CH4 + N2OEnteric fermentation, CH4
Fuel use, CO2
Fertilizer supply, CO2
Soil, N2OCrop and milk production represent
about 80% of total emissionsCrop and milk production represent
about 80% of total emissions
Assuming a similar proportion of off-farm emissions as the US:
Total CN emissions: 12.42 Mt CO2e per year, or about 1.24 kg CO2e per kg of milk
produced
Assuming a similar proportion of off-farm emissions as the US:
Total CN emissions: 12.42 Mt CO2e per year, or about 1.24 kg CO2e per kg of milk
produced
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Modeling tools for producers
Knowledge gained during the Model Farm program has been synthesized in a user friendly computer program, Holos, which estimates whole farm GHG emissions.
Knowledge gained during the Model Farm program has been synthesized in a user friendly computer program, Holos, which estimates whole farm GHG emissions.
Holos – Greek, meaning whole or complete
Holos – Greek, meaning whole or complete
“Model Farm” was a Canadian agricultural research program that lasted from 2002-2006 and focused on improving estimates of GHG emissions from Canadian farms, and finding methods of mitigating those GHG emissions.
“Model Farm” was a Canadian agricultural research program that lasted from 2002-2006 and focused on improving estimates of GHG emissions from Canadian farms, and finding methods of mitigating those GHG emissions.
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Holos: What is it?
1. A simple, user friendly ecosystem model that estimates net-greenhouse gas emissions from individual Canadian farms
2. Describes best understood biophysical processes and their interconnections
3. Mathematical equations combined with expert knowledge and findings from experiments for a variety of farming practices
4. Allows for the evaluation of mitigation practices
5. Allows farmers to experiment with ‘what if?’ scenarios to reduce on-farm GHG emissions.
1. A simple, user friendly ecosystem model that estimates net-greenhouse gas emissions from individual Canadian farms
2. Describes best understood biophysical processes and their interconnections
3. Mathematical equations combined with expert knowledge and findings from experiments for a variety of farming practices
4. Allows for the evaluation of mitigation practices
5. Allows farmers to experiment with ‘what if?’ scenarios to reduce on-farm GHG emissions.
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Holos output
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Summary
•Process-based models provide useful information on GHG emissions from agroecosystems
•Empirical models are sometimes used because our knowledge of processes is still fairly poor
•Management practices are changing rapidly in agriculture, hence we continuously need new measurements to improve the models
•Much progress has been achieved in reducing the GHG emission intensities from major animal production sectors
•Tools like Holos should help producers reduce GHG emissions on their farms
•Process-based models provide useful information on GHG emissions from agroecosystems
•Empirical models are sometimes used because our knowledge of processes is still fairly poor
•Management practices are changing rapidly in agriculture, hence we continuously need new measurements to improve the models
•Much progress has been achieved in reducing the GHG emission intensities from major animal production sectors
•Tools like Holos should help producers reduce GHG emissions on their farms
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Conclusions
•Substantial progress has been made in measuring GHG emissions from agroecosystems
•Models have also been improved substantially
•Management practices are continuously changing, hence the need to measure emissions and modify our models accordingly
•Tools like Holos are increasing awareness among producers and are helping to reduce GHG emissions from farms
•Because of increasing demand for food, the greatest progress one can expect is a reduction in GHG emission intensities
•Substantial progress has been made in measuring GHG emissions from agroecosystems
•Models have also been improved substantially
•Management practices are continuously changing, hence the need to measure emissions and modify our models accordingly
•Tools like Holos are increasing awareness among producers and are helping to reduce GHG emissions from farms
•Because of increasing demand for food, the greatest progress one can expect is a reduction in GHG emission intensities
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