AGRICULTURE
INVENTORY
ELABORATION
PART 2
3B.1
Status of national communications
from NAI Parties


By September 2003, 70 national communications
(NCs) from non-annex I (NAI) Parties had been
compiled and assessed by the UNFCCC secretariat
According to Compilation and Synthesis reports, the
problems encountered by NAI Parties in elaborating
their national inventories ranked:



activity data
emission factors
methods
93 per cent
64 per cent
11 per cent
3B.2
Status of national communications
from NAI Parties

NAI countries voluntarily submit their national GHG
inventories and NCs

By mid-2005, 117 NAI Parties had submitted their first
national communication; 3 NAI Parties had submitted their
second NC; 1 NAI Party did not include its national
inventory

Submitted inventories: 82 NAI Parties for 1 year (1994,
mainly); 12 NAI Parties for 2 years (1990/94); 18 NAI
Parties for 3–4 years; 12 NAI Parties for >4 years

100% NAI Parties included CO2; 99% included CH4 and
N2O; 20% included HFCs, PFCs or SF6
3B.3
Status of national communications
from NAI Parties


An important proportion of the problems mentioned
are related to LUCF
Eliminating this sector from the analysis, the
number of Parties mentioning problems
decreases substantially:



Problems only with LUCF: 13 per cent (9 countries)
Problems with LUCF and other sectors: 60 per cent
(42 countries)
Problems, excluding mention to LUCF: 27 per cent
(19 countries)
3B.4
Status of national communications
from NAI Parties

The Agriculture sector is second in terms of problems:
 Problems only with Agriculture: 0 per cent
 Problems with Agriculture and other sectors: 54 per
cent (38 countries)
 Problems excluding Agriculture: 46 per cent (32
countries)

Figures indicate that the Agriculture sector is less
problematic – with regard to elaboration of an accurate
GHG inventory – than is the LUCF sector
32 out of 70 NAI countries reported that Agriculture is
not a problem (19 NAI countries reported that the LUCF
sector is not a problem)

3B.5
INVENTORY ELABORATION

Previous activities undertaken in the framework
of national GHG inventories:




Preliminary key-source determination
Mass balance for crop residues and animal manure
Significance of sub-source categories (animal
species, anthropogenic N sources)
Livestock characterization, as part of specific source
category elaboration
3B.6
INVENTORY ELABORATION
Previous activities


Preliminary key-source determination
Two ways:


Using last year’s GHG inventory data
Applying tier 1 methods for all the
sectors for the year to be inventoried
3B.7
DETERMINATION OF KEY SOURCES
Steps





Enumeration of source categories (SC)
Ranking SC according to their emissions of CO2 equivalent
Estimating individual contributions of the SC to the total
national emissions by dividing the specific contribution by total
emissions and expresing the result in per cent
Calculating the accumulative contribution of the SC
Key sources, added together, should account for 95% of GHG
emissions
3B.8
DETERMINATION OF KEY SOURCES
CHILE, 1994 GHG inventory (Gg CO2 equivalent) (1)
SECTOR/subsector
CO2-
CH4
N2O
Gg/year
Gg/year
Gg/year
ENERGY
36227.0
1575.2
499.1
38301.3
- ENERGY INDUSTRIES
9439.8
21.2
31.0
9492.0
- PROCESSING INDUSTRIES AND CONSTRUCTION
9255.2
33.6
31.0
9319.8
- ROAD TRANSPORT
12695.3
44.1
310.0
13049.4
- RESIDENTIAL, COMMERCIAL, INSTITUTIONAL
4049.6
606.9
124.0
4780.5
- AGRICULTURE, FORESTRY, FISHING
787.1
14.7
3.1
804.9
TOTALS
- C MINING<<??coal??>>
195.3
195.3
- OIL AND NATURAL GAS
659.4
659.4
- OIL REFINING, FUEL STORAGE AND DISTRIBUTION
0.0
INDUSTRIAL PROCESSES
1870.0
- CEMENT
1021.1
44.1
248.0
2162.1
1021.1
- ASPHALT
0.0
- COPPER
0.0
- GLASS
0.0
- CHEMICAL PRODUCTS
44.1
248.0
292.1
- IRON AND STEEL
812.2
812.2
- IRONALLEYS<<?iron alloys?>>
36.7
36.7
- PULP/ PAPER; FOODS/DRINKS; COOLING/OTHERS
SOLVENT USE
0.0
0.0
0.0
0.0
0.0
3B.9
DETERMINATION OF KEY SOURCES
1994 GHG inventory of Chile (Gg CO2 equivalent) (Non-energy sectors)
AGRICULTURE:
0.0
6760.3
8661.3
15421.6
- RICE CULTIVATION
134.4
134.4
- ENTERIC FERMENTATION
5564.8
5564.8
- MANURE MANAGEMENT
1009.1
1304.8
2313.9
- AGRICULTURA SOILS: DIRECT EMISSIONS
4693.9
4693.9
- AGRICULTURAL SOILS: INDIRECT EMISSIONS
1495.9
1495.9
- AGRICULTURAL SOILS: PASTURE RANGE/PADDOCK
559.2
559.2
52.0
607.5
659.5
1560.3
206.7
1767.0
- AGRICULTURAL RESIDUE BURNING
WASTE:
0.0
- SEWAGE WATER TREATMENT:
- URBAN SOIL WASTES
3.2
3.2
1557.1
1557.1
- INDUSTRIAL SOLID WASTES
0.0
- UNTREATED SEWAGE WATER RUNOFF
206.7
- INDUSTRIAL LIQUID WASTES
TOTAL NATIONAL
202.9
38097.0
10142.8
206.7
202.9
9615.2
57854.9
3B.10
DETERMINATION OF KEY SOURCES
KEY SOURCES FOR THE 1994 GHG-Inventory of Chile
SECTOR/sub-sector
Gg/yr CO2-equiv.
Contribution
Individual
Cumulative
Sector
- Road transport
13049,4
22,6%
22,6%
Energy
- Energy industries
9492,0
16,4%
39,0%
Energy
- Processing industries and construction
9319,8
16,1%
55,1%
Energy
- Enteric fermentation
5564,8
9,6%
64,7%
Agriculture
- Residential, commercial, institutional
4780,5
8,3%
73,0%
Energy
- Agricultural soils, direct N2O
4693,9
8,1%
81,1%
Agriculture
- Urban solid wastes
1557,1
2,7%
83,8%
Residues
- Agricultural soils, indirect N2O
1495,9
2,6%
86,3%
Agriculture
- Manure management-N2O
1304,8
2,3%
88,6%
Agriculture
- Cement
1021,1
1,8%
90,4%
Energy
- Manure management-CH4
1009,1
1,7%
92,1%
Agriculture
- Iron and allow
812,2
1,4%
93,5%
Industrial Processes
- Agriculture, Forestry, Fishing
804,9
1,4%
94,9%
Energy
- Agricultural residue burning
659,5
1,1%
96,0%
Agriculture
- Oil and natural gas
659,4
1,1%
97,2%
Industrial Processes
- Agricultural soils, pasture range and paddock
559,2
1,0%
98,1%
Agriculture
- Chemical products
292,1
0,5%
98,7%
Industrial Processes
- Waste water runoff
206,7
0,4%
99,0%
Agriculture/Residues
- Industrial liquid residues
202,9
0,4%
99,4%
Residues
- C mining
195,3
0,3%
99,7%
Energy
- Rice production
134,4
0,2%
99,9%
Agriculture
3,2
0,0%
100,0%
Energy
- Sewage water
DETERMINATION OF KEY SOURCES
Contribution per sector
Contribution of sectors to
Chile's GHG emissions
3.4%
26.7%
Energy
Industrial Processes
Agriculture
Waste
3.1%
66.8%
GHG Inventory of Chile for 1994
3B.12
INVENTORY ELABORATION
Mass balance

Mass balance for crop residues:
 To be done for each crop species
 Example: wheat production in a country with three
agroecological units
 Characteristics of the agroecological units:
 A: Dessert climate, agriculture only under
irrigation
 B: Mediterranean climate with well-marked four
seasons; export agriculture under irrigation
 C: Rainy and rather cold climate with no dry
season; no irrigation
3B.13
INVENTORY ELABORATION
Mass balance

According to experts’ judgement:
END USE
ON-SITE
OFF-SITE
UNIT
TO FEED
ANIMALS
INCORPORATED IN
SOILS
MINERALIZED
BURNED
BURNED
(ENERGY)
BIOGAS
BRIQUETS
OTHERS
A
0.00
0.00
0.00
0.50
0.45
0.00
0.00
0.05
B
0.10
0.10
0.05
0.35
0.20
0.10
0.05
0.05
C
0.25
0.20
0.20
0.20
0.00
0.15
0.00
0.00
CROP
RESIDUES
BURNING
ENERY
ENERGY
TO BE
ACCOUNTED
UNDER
AGRICULTURAL
SOILS
3B.14
INVENTORY ELABORATION
Mass balance

Factors to be applied to total wheat residues:

Total wheat residues =
total productionunit i × (residue/production) factorunit i

Total residues burned in:
Unit A = total residuesunit A × 0.50
Unit B = total residuesunit B × 0.35
Unit C = total residuesunit C × 0.20
3B.15
INVENTORY ELABORATION
Mass balance

Mass balance for animal manure



Analysis at species level
First diversion, confinement and direct
grazing
Second diversion, under confinement,
according to the different manure treatment
systems
3B.16
INVENTORY ELABORATION
Mass balance

Example: non-dairy cattle population in the same country (same three
agroecological units already described)

First: disaggregation of the national population in agroecological unit
populations

Second: estimation of total manure produced per agroecological unit
Non-dairy cattle (experts' judgement)
Under confinement
Climatic
conditions
Direct
grazing
Unit A
Dessert
Unit B
Unit C
Unit
Anaerobic
Liquid
Solid
Daily
spread
Others
0.10
No
No
No
0.90
No
Mediterranean
0.75
0.10
No
0.10
0.05
No
Cold and
humid
0.35
0.35
No
0.20
0.10
No
3B.17
INVENTORY ELABORATION
Mass balance

Manure from non-dairy cattle, assigned to the different treatment systems:

Unit A: total manure producedunit A x Fi




Unit B: total manure producedunit A x Fj






If Fi is 0.90 = Anaerobic lagoon
If Fi is 0.10 = direct grazing
(Fi= 0 for the rest of the treatment systems)
If Fj is 0.75 = Direct grazing
If Fj is 0.10 = Anaerobic lagoon
If Fj is 0.20 = Solid systems
If Fj is 0.05 = Other systems
(Fj= 0 for the rest of the treatment systems)
Unit C: total manure producedunit A x Fk





If Fk is 0.35 = Direct grazing
If Fk is 0.35 = Anaerobic lagoon
If Fk is 0.20 = Solid systems
If Fk is 0.10 = Other systems
(Fk= 0 for the rest of the treatment systems)
3B.18
INVENTORY ELABORATION
Significance of sub-sources

Significance of animal species:



Example for CH4 linked to enteric fermentation and
manure management
CH4 emissions estimated by tier 1 method
Country as a whole, without division into
agroecological units
3B.19
INVENTORY ELABORATION
Significance of sub-sources

Steps:





Estimation of animal species population
As no national AD are available, the use of FAO
database is appropriate
Disaggregation between dairy and non-dairy cattle,
following experts’ judgement
Filling of Table 4-1s1 of IPCC software with the
population data and the default EFs
Estimation of individual contribution to the total
emissions of the source category
3B.20
Significance of sub-sources
MODULE
SUBMODULE
AGRICULTURE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
SHEET
4-1
1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC
FERMENTATION AND MANURE MANAGEMENT
STEP 1
A
Livestock Type
Number of
Animals
(1000s)
B
Emissions
Factor for
Enteric
Fermentation
(kg/head/yr)
STEP 2
C
D
Emissions
from Enteric
Fermentation
(t/yr)
Emissions
Factor for
Manure
Management
(kg/head/yr)
C = (A x B)
22%
Dairy Cattle
550
81
44.550
Non-dairy Cattle
2750
49
134.750
0
55
0
Sheep
2500
5
12.500
Goats
500
5
Camels
125
46
6%
2.500 <3%
<3%
5.750
Horses
75
18
1.350
Mules & Asses
25
10
250
Swine
5030
1
5.030
Poultry
15000
NE
NE
Buffalo
Totals
65% SIGN. 13
<3%
<3%
<3%
206.680
19
STEP 3
E
Emissions from
Manure
Management
(t/yr)
10.450
55,00
13%
35.750 43%
0,16
400
1,6
(Gg)
F =(C + E)/1000
0
1,9
Total Annual
Emissions
from
Domestic
Livestock
E = (A x D)
7
0,17
F
<1%
<1%
<1%
237,5
<1%
85
SIGN.170,50
0,00
12,90
2,59
5,99
120
1,47
0,9
22,5
7
35.210
<1%
0,27
43% SIGN.
0,018
270
82.545
40,24
<1%
NE
288,96
3B.21
INVENTORY ELABORATION

Simulation for:







Enteric fermentation – CH4 emissions
Manure management – CH4 and N2O emissions
Agricultural soils – N2O emissions
Prescribed burning of savannas – non-CO2 gas emissions
Burning of crop residues – non-CO2 gas emissions
Rice cultivation – CH4 emissions
When possible, analysis of different scenarios:



Less accurate scenario: No CS activity data (usual for non-collectable
data: factors, parameters)
Medium accurate scenario: No CS emission factors (very common fact)
Most accurate scenario: Availability of CS activity data and emission
factors
3B.22
Enteric
Fermentation
3B.23
Enteric fermentation

Hypothetical country with:

Two climate regions:
Warm (60% of surface)
 Temperate (40% of surface)
Domestic animal population:
 Cattle (dairy and non-dairy)
 Sheep
 Swine
 Poultry
 Some goats and horses


3B.24
Livestock characterization

Steps:




Identify and quantify existing livestock species
Review emission estimation methods for each
species
Identify the most detailed characterization
required for each species (i.e. ‘basic’ or
‘enhanced’)
Use same characterization for all sources
(‘Enteric Fermentation’, ‘Manure Management’,
‘Agricultural Soils’)
characterization detail will depend on whether the source
category is key source or not and on the relative
importance of the subcategory within the source category
3B.25
Enteric fermentation

Inventory simulation for three scenarios:



1) Low level of data availability
 no access to reliable statistics or other sources of AD, and
cannot use Country Specific (CS) EFs
2) Medium level of data availability
 detailed statistics on livestock activity, although some
Activity Data (AD2) are still required along with
default/regional EFs
3) High level of data availability
 good country-specific AD and EFs
3B.26
Low level of data availability
Animal population data from FAO database <www.fao.org>.
Open the web page; select “Statistical Databases”, “FAOSTAT-Agriculture”
and “Live Animals” in Agricultural Production (searching for country,
animal type and year):
Species/category
*
Dairy cattle*
Non-dairy cattle
Buffalo
Sheep
Goat
Camels
Horses
Mules and asses
Swine
Poultry
Number of animals (million)
1.0
5.0
0
3.0
0.05
0
0.01
0
1.5
4.0
Disaggregation between dairy and non-dairy cattle based on expert’s judgement.
3B.27
Determination of significant
sub-source categories


Species contributing to 25% or more of emissions
should have ‘enhanced’ characterization and tier 2
method should be applied
Perform a rough estimation of CH4 from enteric
fermentation applying tier 1 method



one way of screening species for their contribution to
emissions
estimation is to identify categories requiring application of
tier 2 method
use IPCC software, sheet ‘4-1s1’: fill in animal population
data, and collect default EF from Tables 4-3 and 4-4 of
Revised 1996 IPCC Guidelines, Vol. 3 (also taken from the
IPCC emission factor database (EFDB))
3B.28
Determining significant animal species
MODULE
SUBMODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1
SHEET
COUNTRY
YEAR
Livestock Type
A
Number of
Animals
(1000s)
Dairy Cattle
Non-dairy Cattle
Buffalo
Sheep
Goats
Camels
Horses
Mules & Asses
Swine
Poultry
Totals
AGRICULTURE
1000
5000
0
3000
50
0
10
0
1500
4000
1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC
FERMENTATION AND MANURE MANAGEMENT
Worksheet 4-1s1
Hypothetical
2003
STEP 1
B
Emissions
Factor for
Enteric
Fermentation
(kg/head/yr)
57
49
55
5
5
46
18
10
1.5
0
C
Emissions
from Enteric
Fermentation
(t/yr)
C = (A x B)
57,000.00
245,000.00
0.00
15,000.00
250.00
0.00
180.00
0.00
2,250.00
0.00
319,680.00
STEP 2
STEP 3
D
E
F
Emissions
Emissions from
Total Annual
Factor for
Manure
Emissions from
Manure
Management
Domestic
Management
Livestock
(kg/head/yr)
(t/yr)
(Gg)
E = (A x D)
F =(C + E)/1000
0.00
57.00
0.00
245.00
0.00
0.00
0.00
15.00
0.00
0.25
0.00
0.00
>25%
0.00
0.18
0.00
0.00
0.00
2.25
0.00
0.00
No other significant
species
0.00
319.68
Conclusion: Tier 2 method, supported by an enhanced characterization, for the non-dairy cattle.
3B.29
Enhanced characterization of
non-dairy cattle population

Enhanced characterization requires information additional to that
provided by FAO statistics. Consultation with local experts or
industry is valuable.

Assume that (using the above information sources) the inventory
team determines that the non-dairy cattle population is composed
of:




Cows – 40%
Steers – 40%
Young growing animals – 20%
Each of these categories must have an estimate of feed intake and
an EF to convert intake to CH4 emissions. Procedure is described
in IPCC Good Practice Guidance and Uncertainty Management in
National Greenhouse Gas Inventories (GPG2000)
(pages 4.10–4.20).
3B.30
Enhanced characterization of non-dairy cattle (1)
Parameter
Symbol
Weight (kg)
W
400
450
230
Table A-2, IPCC-GL V3
Weight gain (kg/day)
WG
0
0
0.3
Table A-2, IPCC-GL V3
Mature weight (kg)
MW
400
450
425
Table A-2, IPCC-GL V3
Ca
0.28
0.23
0.25
Table 4-5 GPG2000,
and expert’s judgment
Females giving birth
(%)
-
67
-
-
Table A-2, IPCC-GL V3
Feed digestibility (%)
DE
60
60
60
Table A-2, IPCC-GL V3
Maintenance
coefficient
Cfi
0.335
0.322
0.322
Table 4-4 GPG2000
Net energy
maintenance (MJ/day)
NEm
30.0
31.5
19.0
Calculated using
equation 4.1, GPG2000
Net energy activity
(MJ/day)
NEa
8.4
7.2
4.8
Calculated using
equation 4.2a,
GPG2000
Feeding situation
Cows Steers
Young
Comments
3B.31
Enhanced characterization of non-dairy cattle (2)
Parameter
Symbol
Cows
Steers
Young
Comments
Growth coefficient
C
-
-
0.9
p.4.15, GPG2000
Net energy growth
(MJ/day)
NEg
-
-
4.0
Calculated using
equation 4.3a,
GPG2000
CP
0.1
-
-
Table 4.7, GPG2000
Net energy
pregnancy (MJ/day)
NEP
3.0
-
-
Calculated using
equation 4.8, GPG2000
Portion of GE that is
available for
maintenance
NEma/DE
0.49
0.49
0.49
Calculated using
equation 4.9, GPG2000
Portion of GE that is
available for growth
NEga/DE
0.28
0.28
0.28
Calculated using
equation 4.10,
GPG2000
GE
139.3
130.4
117.7
Calculated using
equation 4.11,
GPG2000
Pregnancy coefficient
Gross energy intake
(MJ/day)
To check the estimates of GE, convert to kg/day of feed intake (by dividing GE by 18.45)
and divide by live weight. The result must be between 1% and 3 % of live weight.
3B.32
Tier 2 estimation of CH4 emissions from
enteric fermentation by non-dairy cattle



Enhanced characterization yielded AD (average
daily gross energy intake) for three types of nondairy cattle
These AD must be combined with emission
factors for each animal group to obtain emission
estimates
Determination of EFs requires selection of a
suitable value for methane conversion rate (Ym)

In this example (country with no CS data) a default
value for Ym can be obtained from GPG2000
3B.33
Tier 2 estimation of CH4 emissions from
enteric fermentation by non-dairy cattle
Parameter
Symbol
Cows
Steers
Young
Comments
Gross energy
intake (MJ/day)
(from enhanced
characterization)
GE
139.3
130.4
117.7
Calculated using
equation 4.11,
GPG2000
CH4 conversion
factor
Ym
0.06
0.06
0.06
Table 4.8, GPG2000,
and EFDB
Emission factor
(kg CH4/head/yr)
EF
54.8
51.3
46.3
Calculated using
equation 4.14,
GPG2000
Portion of group in
total population (%)
-
40
40
20
Population of group
(thousand heads)
-
2 000
2 000
1 000
CH4 emissions
(Gg CH4/yr)
-
110
103
46
Expert judgement,
industry data
3B.34
Tier 2 estimation of CH4 emissions from
enteric fermentation by non-dairy cattle

Tier 2 estimation for non-dairy cattle:


259 Gg CH4 (against 245 Gg CH4 for tier 1)
Weighted EF:

52 kg CH4/head/yr (againts the default value

of 49 kg CH4/head/yr)
This value should be used in the worksheet to
report emissions by non-dairy cattle
3B.35
Medium level of data availability



Assume that the country has good statistics on livestock
populations
Applying the same procedure as in previous example, the
country determines that non-dairy cattle category requires
enhanced characterization
National statistics + expert judgement allow disaggregation of
non-dairy cattle population by:



Two climate regions
Three systems of production
Three animal categories (same as in previous example)
3B.36
Medium Level of Data Availability
Climate
region
Warm
Temperate
Total
Production
system
Population (thousand heads)
Cows
Steers
Young
1 473
828
610
Intensive grazing
228
414
120
Feedlot
40
92
96
Extensive
grazing
348
201
161
Intensive grazing
150
275
75
Feedlot
15
31
32
2 254
1 841
1 094
Extensive
grazing
-
New total: 5,153,000 heads (against FAO: 5,000,000 heads).
3B.37
Tier 2 estimation of CH4 emissions from
enteric fermentation by non-dairy cattle



Enhanced characterization yielded AD (average
daily gross energy intake) for 18 classes of nondairy cattle
This AD must be combined with EFs for each
animal class to obtain 18 emission estimates
Next slides will show detailed calculations for
estimating gross energy intake for 6 of the 18
classes (three types of animals for ‘WarmExtensive Grazing’ and three for ‘TemperateIntensive Grazing’)
3B.38
Enhanced characterization, non-dairy cattle
Warm Climate, Extensive Grazing (1)
Parameter
Symbol
Cows
Steers
Young
Weight (kg)
W
420
380
210
Country-specific data
Weight gain (kg/day)
WG
0
0.2
0.2
Country-specific data
Mature weight (kg)
MW
420
440
430
Country-specific data
Ca
0.33
0.33
0.33
Table 4-5 GPG2000, and expert
judgement
-
60
-
-
Country-specific data
Feed digestibility (%)
DE
57
57
57
Country-specific data
Maintenance coefficient
Cfi
0.335
0.322
0.322
Table 4-4 GPG2000
Net energy maintenance
(MJ/day)
NEm
31.1
27.7
17.8
Calculated using equation 4.1,
GPG2000
Net energy activity
(MJ/day)
NEa
10.3
9.2
5.9
Calculated using equation 4.2a,
GPG2000
Feeding situation
Females giving birth (%)
Comments
Comments in green indicate improvements over previous example.
3B.39
Enhanced characterization, non-dairy cattle
Warm Climate, Extensive Grazing (2)
Parameter
Growth coefficient
Symbol
Cows
Steers
Young
Comments
C
-
1.0
0.9
p.4.15, GPG2000
Net energy growth (MJ/day)
NEg
-
3.4
2.4
Calculated using equation
4.3a, GPG2000
Pregnancy coefficient
CP
0.1
-
-
Table 4.7, GPG2000
Net energy pregnancy
(MJ/day)
NEP
3.1
-
-
Calculated using equation
4.8, GPG2000
Portion of GE available
for maintenance
NEma/DE
0.48
0.48
0.48
Calculated using equation
4.9, GPG2000
Portion of GE available for
growth
NEga/DE
0.26
0.26
0.26
Calculated using equation
4.10, GPG2000
GE
162.2
170.0
111.2
Calculated using equation
4.11, GPG2000
Gross energy intake
(MJ/day)
To check estimates of GE, convert to kg/day of feed intake (by dividing GE by 18.45)
and divide by live weight. The result must be between 1 and 3 % of live weight.
3B.40
Enhanced characterization, Non-Dairy Cattle,
Temperate Climate, Intensive Grazing (1)
Parameter
Symbol
Cows
Steers
Young
Weight (kg)
W
405
390
240
Country-specific data
Weight gain (kg/day)
WG
0.15
0.33
0.65
Country-specific data
Mature weight (kg)
MW
445
470
452
Country-specific data
Ca
0.17
0.17
0.17
Table 4-5 GPG2000, and
expert judgement
-
81
-
-
Country-specific data
Feed digestibility (%)
DE
72
72
72
Country-specific data
Maintenance coefficient
Cfi
0.335
0.322
0.322
Table 4-4 GPG2000
Net energy maintenance
(MJ/day)
NEm
30.2
28.3
19.6
Calculated using equation
4.1, GPG2000
Net energy activity
(MJ/day)
NEa
5.1
4.8
3.3
Calculated using equation
4.2a, GPG2000
Feeding situation
Females giving birth (%)
Comments
Comments in green indicate improvements over previous example.
3B.41
Enhanced characterization, Non-Dairy Cattle,
Temperate Climate, Intensive Grazing (2)
Parameter
Symbol
Cows Steer
Young
Comments
Growth coefficient
C
0.8
1.0
0.9
p.4.15, GPG2000
Net Energy Growth
(MJ/day)
NEg
3.0
5.7
9.2
Calculated using equation
4.3a, GPG2000
Pregnancy coefficient
CP
0.1
-
-
Table 4.7, GPG2000
Net Energy Pregnancy
(MJ/day)
NEP
3.0
-
-
Calculated using equation
4.8, GPG2000
Portion of GE that is
available for maintenance
NEma/DE
0.53
0.53
0.53
Calculated using equation
4.9, GPG2000
Portion of GE that is
available for growth.
NEga/DE
0.34
0.34
0.34
Calculated using equation
4.10, GPG2000
GE
120.1
123.9
121.5
Calculated using equation
4.11, GPG2000
Gross Energy Intake
(MJ/day)
To check estimates of GE, convert to kg/day of feed intake (by dividing GE by 18.45)
and divide by live weight. The result must be between 1 and 3 % of live weight.
3B.42
Medium level of data availability




Estimated GE values are used for calculation of EF
(using equation 4.14, GPG2000)
Calculation of EF required to select a value for
methane conversion rate (Ym), that is, the fraction of
energy in feed intake that is converted to energy in
methane
In this example we assume the country uses a
default value (Ym =0.06, from Table 4.8, GPG2000)
18 estimates of EF were obtained (next slide)
3B.43
Medium level of data availability
Climate
region
Warm
Temperate
Production
system
EF (kg CH4/head/yr)
Cows
Steers
Young
Extensive
grazing
63.8
66.9
43.8
Intensive grazing
47.7
51.5
48.4
Feedlot
41.5
49.3
52.8
Extensive
grazing
61.5
66.7
49.5
Intensive grazing
47.3
48.8
47.8
Feedlot
41.5
49.3
52.8
3B.44
Medium level of data availability

Weighted EF (tier 2, country-specific AD):
57 kg CH4/head/yr (range: 42-67 kg CH4/head/yr)



EF for tier 1: 49 kg CH4/head/yr
EF for tier 2 (with default AD): 52 kg CH4/head/yr
Multiplication of EF with cattle population in each
class yielded 18 estimates of annual emissions of
methane from enteric fermentation, with a total of
294 Gg CH4/year


Total for tier 1: 245 Gg CH4/year
Total for tier 2 (with default AD): 259 Gg CH4/year
3B.45
Medium level of data availability
MODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1
SHEET
COUNTRY
YEAR
Livestock Type
A
Number of
Animals
(1000s)
Dairy Cattle
Non-dairy Cattle
Buffalo
Sheep
Goats
Camels
Horses
Mules & Asses
Swine
Poultry
Totals
AGRICULTURE
SUBMODULE
1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC
FERMENTATION AND MANURE MANAGEMENT
Worksheet 4-1s1
Hypothetical
2003
STEP 1
B
Emissions
Factor for
Enteric
Fermentation
(kg/head/yr)
1000
57
5153
0
3000
50
0
10
0
1500
4000
57
55
5
5
46
18
10
1.5
0
C
Emissions
from Enteric
Fermentation
(t/yr)
C = (A x B)
57,000.00
293,721.00
0.00
15,000.00
250.00
0.00
180.00
0.00
2,250.00
0.00
368,401.00
STEP 2
STEP 3
D
E
F
Emissions
Emissions from
Total Annual
Factor for
Manure
Emissions from
Manure
Management
Domestic
Management
Livestock
(kg/head/yr)
(t/yr)
(Gg)
E = (A x D)
F =(C + E)/1000
0.00
57.00
0.00
293.72
0.00
0.00
0.00
15.00
0.00
0.25
0.00
0.00
0.00
0.18
0.00
0.00
0.00
2.25
0.00
0.00
0.00
368.40
3B.46
Highest level of data availability

Activity data could be improved by:




more accurate national statistics on livestock population and
uncertainties
further disaggregation of cattle population (e.g. by race and
animal age, or by subdividing climate region by administrative
units, soil type, forage quality, etc.)
implementation of geographically explicit AD and cattle
traceability systems
development of local research to obtain better estimates of
parameters used for livestock characterization
(e.g. coefficients for maintenance, growth, activity or
pregnancy)
3B.47
Highest level of data availability

EFs could be improved by:




developing local capacities for measuring CH4
emissions by cattle
characterizing diverse feeds by their CH4
conversion factors for different animal types
development of local research to improve
understanding of locally relevant factors affecting
methane emissions
adapting international information (scientific
literature, EFDB, etc.) from areas with conditions
similar to those of the country
3B.48
Highest level of data availability

Numerical example not developed here

Few, if any, developing countries are currently in
the position of having access to this level of
information

With high level of data availability, countries would
be able to implement tier 3 methods (still not
proposed by IPCC)
3B.49
Example of development of local
capacity in Uruguay




Almost 50% of GHG emissions in
Uruguay come from enteric
fermentation
A project was implemented by the
National Institute of Agricultural
Research co-funded by US-EPA to
improve local capacity to measure
CH4
First results indicate that IPCC
default EF used so far in
preparation of inventories may be
too high
A similar project is being
conducted in Brazil by EMBRAPA
3B.50
Estimation of Uncertainties

It is good practice to estimate and report uncertainties of
emission estimates, which implies estimating uncertainties of AD
and EF

According to IPCC, EFs used in a tier 1 method might have an
uncertainty of 30–50%, and default AD might have even higher
values

Application of a tier 2 method with country-specific AD can
substantially reduce uncertainty levels compared to a tier 1
method with default AD/EF

Priority should be given to improve the quality of AD estimates
3B.51
Manure
Management:
CH4 Emissions
3B.52
Manure management – CH4


We will continue with the assumptions relating to the
same hypothetical country
Again, tier 1 method will be applied to assess the
significance of the different species for this source
category



with the purpose of identifying the need for enhanced
characterization
in practice, this should be done as a first step in inventory
elaboration, considering that it is good practice to use the
same characterization for all categories (it is presented
here for training purposes only)
Numerical examples for countries with different levels of
data availability will be developed
3B.53
Livestock characterization
From FAO database <www.fao.org>, then “Statistical Databases”,
“FAOSTAT-Agriculture”, and “Live Animals” in Agricultural Production
(searching for the country, animal type and year):
Species/category
Dairy cattle*
Non-dairy cattle
Buffalo
Sheep
Goat
Camels
Horses
Mules and Asses
Swine
Poultry
*
Number of animals (million)
1.0
5.0
0
3.0
0.05
0
0.01
0
1.5
4.0
Disaggregation between dairy and non-dairy cattle, based on expert`s judgement.
3B.54
Livestock characterization
MODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1
SHEET
COUNTRY
YEAR
Livestock Type
A
Number of
Animals
(1000s)
Dairy Cattle
Non-dairy Cattle
Buffalo
Sheep
Goats
Camels
Horses
Mules & Asses
Swine
Poultry
Totals
Worksheet 4-1s1
AGRICULTURE
SUBMODULE
1000
5153
0
3000
50
0
10
0
1500
4000
1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC
FERMENTATION AND MANURE MANAGEMENT
Hypothetical
2003
STEP 1
B
Emissions
Factor for
Enteric
Fermentation
(kg/head/yr)
57
57
55
5
5
46
18
10
1.5
0
STEP 2
C
Emissions
from Enteric
Fermentation
(t/yr)
C = (A x B)
57,000.00
293,721.00
0.00
15,000.00
250.00
0.00
180.00
0.00
2,250.00
0.00
368,401.00
D
Emissions
Factor for
Manure
Management
(kg/head/yr)
1.6
1.6
1.6
0.196
0.2
2.32
1.96
1.08
1.6
0.021
E
Emissions from
Manure
Management
(t/yr)
E = (A x D)
1,600.00
8,244.80
0.00
588.00
10.00
0.00
19.60
0.00
2,400.00
84.00
12,946.40
STEP 3
F
Total Annual
Emissions from
Domestic
Livestock
(Gg)
F =(C + E)/1000
58.60
301.97
0.00
15.59
0.26
0.00
0.20
0.00
4.65
0.08
381.35
Significant
species
3B.55
Livestock characterization

The non-dairy cattle sub-source is the most significant,
and deserves enhanced characterization and
application of a tier 2 method for CH4 from manure
management

Swine account for 20% of total emissions, and the
country considers it appropriate to develop an
enhanced characterization and apply a tier 2 method
for this species as well
3B.56
Enhanced characterization of swine
population (1)

Estimation of CH4 emissions from manure management
requires two types of activity data:



animal population
manure management system usage
Swine population: GPG2000 recommends
disaggregation into at least three categories (sows,
boars and growing animals)



However, neither IPCC-GL nor GPG2000 provides default
EFs for these categories
EFDB only provides EFs for European conditions (not
suitable for our example in Latin America)
Therefore, for the case of a country that lacks CS AD, we
assume that the swine population is not classified into
subcategories
3B.57
Enhanced characterization of swine
population (2)

Manure management system (MMS): we make
the following assumptions for the inventory
simulation for a country lacking CS AD:




swine population is equally distributed among the
two climate regions (i.e. 60% in warm area, 40% in
temperate area)
90% of manure is managed as a solid
10% is managed in liquid-based systems
it is not possible to discriminate between MMS by
climate regions
3B.58
Low level of data availability: CH4
emissions by non-dairy cattle, swine

Tier 2 method requires determination of three
parameters to estimate EF:




For low level of data:



VS (kg): mass of volatile solids excreted
Bo (m3/kg of VS): max. CH4 producing capacity;
MCF: CH4 conversion factor
default AD derived from FAO database and expert
judgement.
default EF from IPCC-GL and GPG2000
Examples for non-dairy cattle, swine in next slides
3B.59
Low level of data availability: CH4 emissions from
manure management for non-dairy cattle (default AD and EF) (1)
Parameter
Symbol
Cows
Steers
Young
GE
139.3
130.4
117.7
Calculated using
equation 4.11, GPG2000 *
Energy intensity of feed
(MJ/kg)
-
18.45
18.45
18.45
IPCC default value
Feed intake
(kg dm/day)
-
7.55
7.07
6.38
Calculated
DE
60
60
60
Table A-2, IPCC-GL V3
Ash content of manure
(%)
ASH
8
8
8
IPCC-GL V3, p. 4.23
Volatile solid excretion
(kg dm/day)
VS
2.78
2.60
2.35
Calculated using equation
4.16, GPG2000
Maximum CH4
producing capacity of
manure (m3CH4/kg VS)
Bo
0.10
0.10
0.10
Table B-1, p.4.40,
IPCC-GL V3
Gross energy intake
(MJ/day)
(from the enhanced
characterization)
Feed digestibility (%)
Comments
* GE is used for determining VS. If these data are not available, default VS
values are provided in Table B-1, p. 4.40 IPCC-GL.
3B.60
Low level of data availability: CH4 emissions from
manure management for non-dairy cattle (default AD and EF) (2)
Parameter
Methane conversion
factor (%)
Symbol
Cows Steer
Young
Comments
MCF
1.8
1.8
1.8
Table 4-8, p.4.25, IPCC-GL
V3 (data for
pasture/range/paddock
system, weighted by climate
region)
EF
1.22
1.14
1.03
Calculated using equation
4.17, GPG2000
Population (thousand
heads)
-
2 000
2 000
1 000
FAO database, local experts,
industry
CH4 emissions
(Gg CH4 /yr)
-
2.45
2.29
1.03
Total emissions:
5.8 Gg CH4 /yr
Emission factor
(kg CH4/head/yr)
Total emissions estimated here are lower than those using Tier 1 (8.2 Gg CH4/yr).
Weighted EF derived from this table is 1.2 kg CH4/head/yr, and this value should be used
instead of the default (1.6 kg CH4/head/yr) in IPCC Software
3B.61
Low level of data availability: CH4 emissions from
manure management for Swine (default AD and EF) (1)
Symbol
Warm
solid
Warm
liquid
Temp.
solid
Temp.
liquid
GE
13.0
13.0
13.0
13.0
Default value, Table B-2, p.
4.42, IPCC-GL V3
Energy intensity of feed
(MJ/kg)
-
18.45
18.45
18.45
18.45
IPCC default value
Feed intake
(kg dm/day)
-
0.7
0.7
0.7
0.7
Calculated
DE
50
50
50
50
IPCC-GL V3, p. 4.23
Ash content of manure
(%)
ASH
8
8
8
8
IPCC-GL V3, p. 4.23
Volatile solid excretion
(kg dm/day)
VS
0.34
0.34
0.34
0.34
Calculated using equation
4.16, GPG2000
Max. CH4 producing
capacity of manure
(m3CH4/kg VS)
Bo
0.29
0.29
0.29
0.29
Table B-2, p.4.42, IPCC-GL
V3
Parameter
Gross energy intake
(MJ/day)
(from the enhanced
characterization)
Feed digestibility (%)
Comments
3B.62
Low level of data availability: CH4 emissions from
manure management for Swine (default AD and EF) (2)
Warm
solid
Warm
liquid
Temp
solid
Temp
liquid
MCF
2
65
1.5
35
Table 4-8, p.4.25, IPCC-GL V3 *
EF
0.5
15.6
0.4
8.4
Calculated using equation 4.17,
GPG2000
Population (thousand
heads)
-
810
90
540
60
FAO Database, local experts,
industry
CH4 emissions
(Gg CH4 /yr)
-
0.39
1.40
0.19
0.50
Parameter
Methane conversion
factor (%)
Emission factor
(kg CH4/head/yr)
Symbol
Comments
Total emissions:
2.5 Gg CH4 /yr
* Liquid/slurry was assumed to be the only system used. GPG2000 provides slightly
different default values (Table 4.10), as well as a formula for accounting for recovery,
flaring, and use of biogas.
Total emissions estimated were similar to those using tier 1 (2.4 Gg CH4/yr).
Weighted EF derived from this table is 1.7 kg CH4/head/yr, and this value should be
used instead of the default (1.6 kg CH4/head/yr) in IPCC Software,
3B.63
Low level of data availability: results
MODULE
SUBMODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1
SHEET
COUNTRY
YEAR
Livestock Type
A
Number of
Animals
(1000s)
Dairy Cattle
Non-dairy Cattle
Buffalo
Sheep
Goats
Camels
Horses
Mules & Asses
Swine
Poultry
Totals
AGRICULTURE
1000
5153
0
3000
50
0
10
0
1500
4000
1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC
FERMENTATION AND MANURE MANAGEMENT
Hypothetical
2003
STEP 1
B
Emissions
Factor for
Enteric
Fermentation
(kg/head/yr)
57
57
55
5
5
46
18
10
1.5
0
STEP 2
C
Emissions
from Enteric
Fermentation
(t/yr)
C = (A x B)
57,000.00
293,721.00
0.00
15,000.00
250.00
0.00
180.00
0.00
2,250.00
0.00
368,401.00
D
Emissions
Factor for
Manure
Management
(kg/head/yr)
E
Emissions from
Manure
Management
1.6
(t/yr)
E = (A x D)
1,600.00
1.2
1.6
0.196
0.2
2.32
1.96
1.08
6,183.60
0.00
588.00
10.00
0.00
19.60
0.00
1.7
0.021
2,550.00
84.00
11,035.20
STEP 3
F
Total Annual
Emissions from
Domestic
Livestock
(Gg)
F =(C + E)/1000
58.60
299.90
0.00
15.59
0.26
0.00
0.20
0.00
4.80
0.08
379.44
3B.64
Medium level of data availability


Assume the country has good statistics on livestock
population to develop an enhanced characterization with
CS AD, but has to use default EFs
Non-Dairy Cattle: Same 18 classes as for enteric
fermentation


Assume that 50% of manure from feedlot has liquid/slurry
management system, and 50% anaerobic lagoons
Swine: 18 classes are identified and quantified, based
on combination of:



Two climate regions
Three manure management systems
Three swine population categories
3B.65
Medium level of data availability (Swine)
Climate
region
Warm
Population (thousand heads)
Sows
Boars
Young
121
30
490
Liquid/slurry
8
3
40
Anaerobic lagoon
2
2
9
130
36
555
Liquid/slurry
5
1
24
Anaerobic lagoon
8
1
40
-
274
73
1 158
Pasture/range/
paddock
Temperate
Total
Manure
management
system
Pasture/range/
paddock
New Total: 1,505,000 heads (FAO: 1,500,000)
3B.66
Tier 2 estimation of CH4 from manure
management by non-dairy cattle, swine

Next slides will show examples of detailed
calculations for tier 2 method estimation of CH4
emissions from manure management by:


Non-dairy cattle under ‘Warm Region–Extensive
Grazing’ system
Swine under ‘Temperate–Liquid/Slurry’ system
3B.67
Medium level of data availability: CH4 manure management,
non-dairy cattle under ‘Warm, Intensive Grazing’ (CS-AD) (1)
Parameter
Symbol
Cows
Steers
Young
GE
121.2
130.8
123.0
Country-specific values
calculated using equation
4.11, GPG2000 *
Energy intensity of feed
(MJ/kg)
-
18.45
18.45
18.45
IPCC default value
Feed intake
(kg dm/day)
-
6.57
7.09
6.67
Calculated
DE
68
68
68
Country-specific data
Ash content of manure (%)
ASH
8
8
8
IPCC-GL V3, p. 4.23
Volatile solid excretion (kg
dm/day)
VS
1.93
2.09
1.96
Calculated using equation
4.16, GPG2000
Maximum CH4 producing
capacity of manure (m3
CH4/kg VS)
Bo
0.12
0.12
0.12
IPCC default values
adjusted by local expert
judgement.
Gross energy intake
(MJ/day)
(from the enhanced
characterization)
Feed digestibility (%)
Comments
* GE is used for determining VS. If these data are not available, default VS
values are provided in Table B-1, p. 4.40 IPCC-GL.
3B.68
Medium level of data availability: CH4 manure management,
non-dairy cattle under ‘Warm, Intensive Grazing’ (CS-AD) (2)
Parameter
Methane conversion
factor (%)
Symbol
Cows Steers
Young
Comments
MCF
2.0
2.0
2.0
Table 4-8, p.4.25, IPCC-GL
V3
EF
1.14
1.23
1.15
Calculated using equation
4.17, GPG2000
Population (thousand
heads)
-
228
414
120
Country-specific data
CH4 emissions
(Gg CH4 /yr)
-
0.26
0.51
0.14
Emission factor
(kg CH4/head/yr)
In this case, the country has its own estimation for feed/gross energy intake, feed
digestibility, and animal population for each of the different classes of non-dairy cattle.
For Bo, even though the country has no locally developed studies, IPCC default was
adjusted for local conditions following expert judgement. For other factors (ASH, MCF),
IPCC default values were used.
3B.69
Medium level of data availability: CH4 manure management,
swine under ‘Warm, Liquid/Slurry’ (CS-AD) (1)
Parameter
Symbol
Sows
Boars
Young
GE
9.0
9.0
13.0
Country-specific data (or
from the enhanced
characterization)
Energy intensity of feed
(MJ/kg)
-
18.45
18.45
18.45
IPCC default value
Feed intake
(kg dm/day)
-
0.49
0.49
0.70
Calculated
DE
49
49
49
Country-specific data
Ash content of manure
(%)
ASH
4
4
4
IPCC-GL V3, p. 4.23
Volatile solid excretion
(kg dm/day)
VS
0.23
0.23
0.23
Calculated using equation
4.16, GPG2000
Maximum CH4
producing capacity of
manure (m3 CH4/kg VS)
Bo
0.29
0.29
0.29
IPCC default values
adjusted by local expert
judgement
Gross energy intake
(MJ/day)
(from the enhanced
characterization)
Feed digestibility (%)
Comments
3B.70
Medium level of data availability: CH4 manure management,
swine under ‘Warm, Liquid/Slurry’ (CS-AD) (2)
Parameter
Methane conversion
Factor (%)
Symbol
Sows Boars
Young
MCF
72
72
72
EF
11.7
11.7
16.9
Population (thousand
heads)
-
8
3
40
CH4 emissions
(Gg CH4 /yr)
-
0.09
0.04
0.68
Emission factor
(kg CH4/head/yr)
Comments
Table 4-8, p.4.25, IPCC-GL V3
Calculated using equation
4.17, GPG2000
Country-specific data
In this case, the country has its own estimation for feed/gross energy intake, feed
digestibility, and animal population for each of the different classes of non-dairy cattle.
For Bo, even though the country has no locally developed studies, IPCC default was
adjusted for local conditions following expert judgement.
For other factors (ASH, MCF), IPCC default values were used.
3B.71
Medium level of data availability: EFs estimated by
tier 2 for non-dairy cattle, with CS AD
Climate
region
Warm
Temperate
Production
system
EF (kg CH4/head/yr)
Cows
Steers
Young
Extensive
grazing
1.7
1.8
1.2
Intensive grazing
1.1
1.2
1.2
Feedlot
28.8
34.2
36.6
Extensive
grazing
1.2
1.3
0.9
Intensive grazing
0.7
0.8
0.8
Feedlot
23.2
27.6
29.6
Weighted EF: 3.2 kg CH4/head/yr
Use this value in IPCC Software
3B.72
Medium level of data availability: swine,
EF estimated by tier 2, with CS AD
Climate
region
Warm
Temperate
Manure
management
system
EF (kg CH4/head/yr)
Sows
Boars
Young
Pasture/range/
paddock
0.3
0.3
0.5
Liquid/slurry
11.7
11.7
16.8
Anaerobic lagoon
14.3
14.3
21.5
Pasture/range/
paddock
0.3
0.3
0.4
Liquid/slurry
7.3
7.3
10.6
Anaerobic lagoon
14.3
14.3
21.5
Weighted EF: 1.9 kg CH4/head/yr
Use this value in IPCC Software
3B.73
Medium level of data availability: results
MODULE
SUBMODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1
SHEET
COUNTRY
YEAR
Livestock Type
Weighted EF
Dairy Cattle
Non-dairy Cattle
Buffalo
Sheep
Goats
Camels
Horses
Mules & Asses
Swine
Poultry
Totals
AGRICULTURE
A
Number of
Animals
(1000s)
1000
5153
0
3000
50
0
10
0
1505
4000
1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC
FERMENTATION AND MANURE MANAGEMENT
Worksheet 4-1s1
Hypothetical
2003
STEP 1
B
Emissions
Factor for
Enteric
Fermentation
(kg/head/yr)
57
57
55
5
5
46
18
10
1.5
0
STEP 2
C
Emissions
from Enteric
Fermentation
(t/yr)
C = (A x B)
57,000.00
293,721.00
0.00
15,000.00
250.00
0.00
180.00
0.00
2,257.50
0.00
368,408.50
D
Emissions
Factor for
Manure
Management
(kg/head/yr)
E
Emissions from
Manure
Management
1.6
(t/yr)
E = (A x D)
1,600.00
3.2
1.6
0.196
0.2
2.32
1.96
1.08
16,489.60
0.00
588.00
10.00
0.00
19.60
0.00
1.9
0.021
2,859.50
84.00
21,650.70
STEP 3
F
Total Annual
Emissions from
Domestic
Livestock
(Gg)
F =(C + E)/1000
58.60
310.21
0.00
15.59
0.26
0.00
0.20
0.00
5.12
0.08
390.06
3B.74
Manure
Management:
N2O Emissions
3B.75
Manure management – N2O

Only tier 1 provided for this source. Steps:







characterization of livestock population
determination of average N excretion rate for each defined
livestock category
determination of fraction of N excretion that is managed in
each MMS identified
determination of an EF for each MMS
multiplication of total N excretion by EF, and summation of all
estimates
We will continue with the assumption of a hypothetical
country in Latin America, with same animal characterization
used for CH4 from manure management (and also for enteric
fermentation)
One numerical example, developed here
3B.76
Livestock characterization to estimate N2O
emissions from manure management

Assume that only a small fraction of the manure produced in the
country undergoes some form of management

Dairy and non-dairy cattle: mostly grazing, with urine/faeces
deposited directly on soil (N2O emissions accounted under
“Agricultural Soils”)



Cattle in feedlots assumed to have liquid/slurry (50%)
and anaerobic lagoon (50%) management systems
Swine: a small fraction as liquid/sslurry or anaerobic lagoons (Table
4.22 IPCC-GL V3)
Poultry: all manure managed (60% with / 40% without bedding)
(Table 4.13 GPG2000)
3B.77
Livestock characterization to estimate N2O
emissions from manure management
Livestock
Dairy cattle
Climate
Warm
Temperate
Non-dairy
cattle
Warm
Temperate
Swine
Warm
Temperate
Poultry
All
Population
(1000s)
Fraction of
Total Pop.(%)
Liquid/slurry
60
6.0
Anaerobic lagoon
60
6.0
Liquid/slurry
40
4.0
Anaerobic lagoon
40
4.0
Liquid/slurry
114
2.2
Anaerobic lagoon
114
2.2
Liquid/slurry
39
0.8
Anaerobic lagoon
39
0.8
Liquid/slurry
51
3.4
Anaerobic lagoon
13
0.9
Liquid/slurry
30
2.0
Anaerobic lagoon
49
3.3
With bedding
1 600
40
Without bedding
2 400
60
AWMS
In case the country does not have this information, IPCC-GL provides default
AD for different animal waste management systems (AWMS) in different regions
(Table 4-21 V3).
3B.78
Determination of average N excretion per
head for identified livestock categories



IPCC-GL (Table 4-20, V3) and GPG2000 (Table 4.14)
provide default values for Nex(T) for different livestock
species. Use of country-specific values is recommended
County specific values can be obtained from scientific
literature or industry sources, or be calculated from N
intake and N retention data according to equation 4.19
(GPG2000)
Assume the country decides to use country-specific
values to estimate Nex(T) for non-dairy cattle only, and
that default values are used for all other categories
3B.79
Determination of country-specific average N
excretion per head for non-dairy cattle



Assume that the country has information about
crude protein content of feed for the different
classes identified
Crude protein data are combined with feed
intake data (from the same livestock
characterization used for estimating CH4
emissions) to obtain N intake
Assume that the country uses IPCC default
value for N retention in body and products (0.07
for non-dairy cattle, GPG2000, Table 4.15)
3B.80
Livestock characterization for estimating N2O
emissions from manure management
Climate
region
MMS*
Livestock
category
Pop.
(1000s)
Feed
intake
(kg/day)
Crude
protein
(%)
N intake
(kg/head/yr)
Warm
L/S
Cows
20
5.7
15
50
0.07
47
Steers
46
6.8
15
60
0.07
55
Young
48
7.3
15
64
0.07
59
Cows
20
5.7
15
50
0.07
47
Steers
46
6.8
15
60
0.07
55
Young
48
7.3
15
64
0.07
59
Cows
7
5.7
16
53
0.07
50
Steers
16
6.8
16
63
0.07
59
Young
16
7.3
16
68
0.07
63
Cows
7
5.7
16
53
0.07
50
Steers
16
6.8
16
63
0.07
59
Young
16
7.3
16
68
0.07
63
AL
Temp
L/S
AL
N
N excretion
retention (kg/head/yr)
* MMS = Manure management system
L/S = Liquid/slurry
AL = Anaerobic lagoon
3B.81
Determination of average N excretion
per head for non-dairy cattle

Values estimated for Nex(T), using a combination of
country-specific and default data, ranged between 47 and
63 kg N/head/yr for a population of non-dairy cattle in
feedlots, with a weighted average of 56 kg N/head/yr.
This value should be introduced in IPCC software


This value is higher than the IPCC default for Latin America
(40 kg N/head/yr), which is based on grazing cattle
Default values were used for the other species
3B.82
N2O from manure management: use of IPCC
software to estimate total N excretion (1)
MODULE
AGRICULTURE
SUBMODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1 (SUPPLEMENTAL)
SPECIFY AWMS
SHEET
COUNTRY
YEAR
Livestock Type
ANAEROBIC LAGOONS
NITROGEN EXCRETION FOR ANIMAL WASTE MANAGEMENT SYSTEM
Hypothetical
2003
A
Number of Animals
B
Nitrogen Excretion
Nex
(# of animals)
(kg//head/(yr)
C
Fraction of Manure
Nitrogen per AWMS
(%/100)
(fraction)
D
Nitrogen Excretion per
AWMS, Nex
Non-dairy Cattle
5153000
Estimated  56
0.03
(kg/N/yr)
D = (A x B x C)
8,657,040.00
Dairy Cattle
Poultry
Sheep
Swine
1000000
4000000
3000000
1500000
IPCC Default  70
0.1
0
0
0.042
7,000,000.00
0.00
0.00
1,008,000.00
TOTAL
0.00
16,665,040.00
IPCC Default  16
Others
Data from livestock characterization
3B.83
N2O from manure management: use of IPCC
software to estimate total N excretion (2)
MODULE
AGRICULTURE
SUBMODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1 (SUPPLEMENTAL)
SPECIFY AWMS
SHEET
COUNTRY
YEAR
Livestock Type
LIQUID SYSTEMS
NITROGEN EXCRETION FOR ANIMAL WASTE MANAGEMENT SYSTEM
Hypothetical
2003
A
Number of Animals
B
Nitrogen Excretion
Nex
(1000s)
(kg//head/(yr)
C
Fraction of Manure
Nitrogen per AWMS
(%/100)
(fraction)
D
Nitrogen Excretion per
AWMS, Nex
(kg/N/yr)
D = (A x B x C)
Non-dairy Cattle
5153000
Dairy Cattle
1000000
Poultry
Calculated 
56
0.03
8,657,040.00
IPCC Default  70
0.1
7,000,000.00
4000000
0
0.00
Sheep
3000000
0
0.00
Swine
1500000
0.054
1,296,000.00
IPCC Default  16
Others
0.00
TOTAL
16,953,040.00
Data from livestock characterization
3B.84
N2O from manure management: use of IPCC
software to estimate total N excretion (3)
MODULE
SUBMODULE
WORKSHEET
SPECIFY AWMS
SHEET
COUNTRY
YEAR
Livestock Type
AGRICULTURE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
4-1 (SUPPLEMENTAL)
OTHER (POULTRY MANURE WITH BEDDING)
NITROGEN EXCRETION FOR ANIMAL WASTE MANAGEMENT SYSTEM
Hypothetical
2003
A
Number of Animals
B
Nitrogen Excretion
Nex
(1000s)
(kg//head/(yr)
C
Fraction of Manure
Nitrogen per AWMS
(%/100)
(fraction)
D
Nitrogen Excretion per
AWMS, Nex
(kg/N/yr)
D = (A x B x C)
Non-dairy Cattle
0.00
Dairy Cattle
0.00
Poultry
4000000
IPCC Default  0.6
0.6
1,440,000.00
Sheep
0.00
Swine
0.00
Others
0.00
TOTAL
1,440,000.00
Data from livestock characterization
3B.85
N2O from manure management: use of IPCC
software to estimate total N excretion (4)
MODULE
AGRICULTURE
SUBMODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1 (SUPPLEMENTAL)
SPECIFY AWMS
SHEET
COUNTRY
YEAR
Livestock Type
OTHER (POULTRY MANURE WITHOUT BEDDING)
NITROGEN EXCRETION FOR ANIMAL WASTE MANAGEMENT SYSTEM
Hypothetical
2003
A
Number of Animals
B
Nitrogen Excretion
Nex
(1000s)
(kg//head/(yr)
C
Fraction of Manure
Nitrogen per AWMS
(%/100)
(fraction)
Non-dairy Cattle
Dairy Cattle
Poultry
D
Nitrogen Excretion per
AWMS, Nex
(kg/N/yr)
D = (A x B x C)
0.00
4000000
IPCC Default  0.6
0.4
0.00
960,000.00
Sheep
0.00
Swine
Others
0.00
0.00
TOTAL
960,000.00
Data from livestock characterization
3B.86
Use of IPCC software for estimating N2O
from manure management
MODULE
AGRICULTURE
SUBMODULE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC
FERMENTATION AND MANURE MANAGEMENT
WORKSHEET
4-1
SHEET
COUNTRY
YEAR
2 OF 2 NITROUS OXIDE EMISSIONS FROM ANIMAL PRODUCTION
EMISSIONS FROM ANIMAL WASTE MANAGEMENT SYSTEMS (AWMS)
Hypothetical
2003
STEP 4
A
Nitrogen Excretion
Nex(AWMS)
Animal Waste
Management System
(AWMS)
(kg N/yr)
B
Emission Factor For
AWMS
EF3
(kg N2O–N/kg N)
C
Total Annual Emissions
of N2O
(Gg)
Anaerobic lagoons
16,665,040.00
IPCC Default 
0.001
C=(AxB)[44/28] / 1 000 000
0.03
Liquid systems
Daily spread
16,953,040.00
960,000.00
IPCC Default 
0.001
0.03
Poultry manure with bedding
Pasture range and paddock
1,440,000.00
0.00
IPCC Default 
0.02
0.05
Poultry manure w/o bedding
960,000.00
36,978,080.00
IPCC Default 
0.005
0.01
Total
Total
0.11
IPCC defaults obtained from Table 4-22, IPCC-GL V3, and Tables 4.12 and 4.13, GPG2000.
Note: cells corresponding to poultry were manually altered to accommodate
these new categories from GPG2000, not included in IPCC-GL.
3B.87
Direct N2O Emissions
from Agricultural Soils
3B.88
Mineral fertilizers
Animal manures
Anthropogenic
N inputs to soils
Fraction of …
(from the mass balance)
Crop residues
Sewage sludges
N-fixing crops
Other practices
dealing with soil N
Histosols cultivation
3B.89
AGRICULTURAL SOILS
Assess individual contribution of different N sources to determine
ones (subcategories) which are significant for the source category
(25% or more of source category N2O emissions)
For this, apply Tier 1a method and default values to get an
economic emission estimate
For the significant subcategories, the best efforts should be invested to
apply Tier 1b along with country-specific AD1 and AD2 (parameters) and
country-specific emission factors
For non-significant subcategories, Tier 1a, along with country-specific
AD1, default AD2 (parameters) and default emission factors, is acceptable
It is also acceptable to mix Tiers 1a and 1b for different N sources, which will
depend on the activity data availability
3B.90
Direct N2O – Agricultural soils

Assumption of the same hypothetical country

We will assume that the country has the following AD:





usage of synthetic N fertilizers (FAO database)
usage of synthetic N fertilizers for barley crop (industry source)
estimate of EF1 for N applied to barley crops (local research), which
due to improved practices in this crop (e.g. fractioning of N applications),
is lower than the IPCC default EF
N excretion from different animal categories under
pasture/range/paddock AWMS (data from previous example of N2O
from manure management)
area devoted to N-fixing crops (FAO database)

The country has no organic soils (histosols)

Direct N2O emissions are estimated using a combination of Tier 1a
(for most of the sources) and Tier 1b (for use of N fertilizers in barley
crop and N in crop residues)
3B.91
Use of N fertilizers
From the FAO database:
Crop
Area
(1000 ha)
Crop yield
(kg/ha)
Use of N fertilizer
(1000 t N)
Wheat
824
1 545
n/a
Barley 1
356 (371)
1 488 (1400)
19.1
1 225
2 233
n/a
Rice
98
4 800
n/a
Soybeans
231
1 982
n/a
Potatoes
25
18 000
n/a
2 779
--
130
Maize
Total
1
Barley data from industry sources, shown in parentheses.
3B.92
Direct N2O – Agricultural soils


From FAO database, only total country data for fertilizer use are available.
Therefore, only Tier 1a method could be used
Data from barley industry/research can be used to apply Tier 1b method:






to ensure consistency, it is recommended to compare crop area and crop yield
data from FAO with data from local industry
in this case, the two sources reasonably matched in terms of area and yield, and it
can be assumed that the industry estimation of N fertilizer usage is compatible with
the FAO N fertilizer data
from previous table, it can be derived that 19,000 t N fertilizer were applied to
barley crops, and 111,000 t N fertilizer to the rest (130,000 minus 19,000)
from local research, EF1 was estimated to be 0.9% for fertilizer applied to barley
crops in the country
Since there are no organic soils in the country, EF2 is not needed
Emissions from grazing livestock are included here. Note that the GPG2000
includes this source under manure management
3B.93
Synthetic fertilizers:
determination of FSN and EF1

FSN: annual amount of fertilizer N applied to soils, adjusted by amount of N that
volatilizes as NH3 and NOx

To adjust for volatilization, use IPCC default value from Table 4-17, IPCC-GL, V2:
0.1 kg (NOx+NH3)-N/kg fertilizer-N

It is determined that:





EF1 is 0.9% for barley (country specific) and 1.25% for the other crops (Table 4.17,
GPG2000)
For the purpose of filling the IPCC software sheet 4-5s1, a weighted EF1 is calculated
as follows:


FSN = 19,000 (1-0.1) = 17,100 t fertilizer-N (barley)
FSN = 111,000 (1-0.1) = 99,900 t fertilizer-N (all other
crops)
Total fertilizer-N = 117,000 t fertilizer-N
EF1 = weighted average = 17.1/117 (0.9) + 99.9/117 (1.25) = 1.20%
From worksheet 4-5s1, the annual emission of N2O-N from use of synthetic fertilizer
was estimated as 1.40 Gg N2O-N
3B.94
Emissions of N2O from synthetic fertilizers
MODULE
AGRICULTURE
SUBMODULE
AGRICULTURAL SOILS
WORKSHEET
4-5
SHEET
COUNTRY
YEAR
Type of N input to soil
Combined EF
and default)
1 OF 5 DIRECT NITROUS OXIDE EMISSIONS FROM
AGRICULTURAL FIELDS, EXCLUDING CULTIVATION OF(CS
HISTOSOLS
Hypothetical
2003
STEP 1
A
Amount of N
Input
STEP 2
(kg N/yr)
Synthetic fertiliser (FSN)
Animal waste (FAW)
C
Direct Soil
Emissions
(kg N2O–N/kg N)
(Gg N2O-N/yr)
C = (A x B)/1 000 000
117,000,000.00
0.012
1.40
65,793,280.00
0.0125
0.82
0.0125
0.00
0.0125
0.00
Total
2.23
N-fixing crops (FBN)
Crop residue (FCR)
B
Factor for
Direct Emissions
EF1
0.00
3B.95
Manure applied to soils:
determination of FAM





FAM: annual amount of manure N applied to soils, adjusted by amount
of N that volatilizes as NH3 and NOx
To calculate amount of manure N applied to soils, use total amount of
manure produced (using livestock characterization previously applied
to other sources) and subtract the amounts used for fuel, feed and
construction (here assumed to be zero) and those deposited on soils
by grazing livestock (whose emissions are reported separately as
direct emissions)
To adjust for volatilization, use IPCC default value from Table 4-17,
IPCC-GL, V2: 0.2 kg (NOx+NH3)-N/kg animal manure N
It is determined that:
 FAM = 24,924 t animal manure N applied to soils
Next two slides illustrate the use of IPCC software to estimate FAM
(named as FAW in IPCC-GL) and estimation of an annual emission of
N2O-N from application of animal manure to soil of 0.31 Gg N2O-N
3B.96
Emissions of N2O from animal manure (1)
MODULE
AGRICULTURE
SUBMODULE
AGRICULTURAL SOILS
WORKSHEET
4-5A (SUPPLEMENTAL)
SHEET
1 OF 1 MANURE NITROGEN USED
COUNTRY
Hypothetical
YEAR
2003
A
Total Nitrogen
Excretion
B
Fraction of Nitrogen
Burned for Fuel
(kg N/yr)
(fraction)
249,240,080.00
C
Fraction of Nitrogen
Excreted During
Grazing
(fraction)
0
0.7
D
Fraction of Nitrogen
Excreted Emitted as
NOX and NH3
(fraction)
E
Sum
(fraction)
F = 1 - (B + C + D)
0.2
0.10
F
Manure Nitrogen Used
(corrected for NOX and
NH3 emissions), FAW
(kg N/yr)
F = (A x E)
24,924,008.00
Country’s estimate
Data from livestock
characterization
From Table 4-17
IPCC Guidelines V2
3B.97
Emissions of N2O from animal manure (2)
MODULE
AGRICULTURE
SUBMODULE
AGRICULTURAL SOILS
WORKSHEET
4-5
SHEET
COUNTRY
YEAR
Type of N input to soil
1 OF 5 DIRECT NITROUS OXIDE EMISSIONS FROM
AGRICULTURAL FIELDS, EXCLUDING CULTIVATION OF
HISTOSOLS
Hypothetical
2003
STEP 1
A
Amount of N
Input
(kg N/yr)
STEP 2
B
Factor for
Direct Emissions
EF1
C
Direct Soil
Emissions
(kg N2O–N/kg N)
(Gg N2O-N/yr)
C = (A x B)/1 000 000
0.012
1.40
0.0125
0.31
N-fixing crops (FBN)
0.0125
0.00
Crop residue (FCR)
0.0125
0.00
Total
1.72
Synthetic fertiliser (FSN)
Animal waste (FAW)
117,000,000.00
24,924,008.00
IPCC default
3B.98
N-fixing crops:
determination of FBN







FBN: amount of N fixed by N-fixing crops cultivated annually (in our case,
soybeans)
To calculate amount of N fixed, we assume that there are no crop-specific
values for grain/biomass ratio or for moisture content of biomass; therefore,
default data are used
Grain production is estimated from FAO statistics (457,842 t/yr)
N content of biomass (FracNCRBF) is obtained from Table 4.16 (GPG2000):
0.023 kg N/kg dry biomass
Residue/crop product ratio is 2:1, and dry matter fraction is 0.85 (from
same table as above)
It is determined (by using equation 4.26, GPG2000) that:
 FBN= 27,748 t fixed-N
This value is introduced in IPCC software worksheet 4-4s1 to estimate an
annual emission of N2O-N from N-fixing crops of 0.35 Gg N2O-N
3B.99
Emissions of N2O from N-fixing crops
MODULE
AGRICULTURE
SUBMODULE
AGRICULTURAL SOILS
WORKSHEET
4-5
SHEET
COUNTRY
YEAR
1 OF 5 DIRECT NITROUS OXIDE EMISSIONS FROM
AGRICULTURAL FIELDS, EXCLUDING CULTIVATION OF
HISTOSOLS
Hypothetical
2003
STEP 1
A
Amount of N
Input
Type of N input to soil
(kg N/yr)
Synthetic fertiliser (FSN)
Animal waste (FAW)
N-fixing crops (FBN)
Crop residue (FCR)
Estimated
activity data
STEP 2
B
Factor for
Direct Emissions
EF1
C
Direct Soil
Emissions
(kg N2O–N/kg N)
(Gg N2O-N/yr)
C = (A x B)/1 000 000
117,000,000.00
0.012
1.40
24,924,008.00
0.0125
0.31
0.0125
0.35
0.0125
0.00
Total
2.06
27748000
IPCC default
3B.100
Crop residues:
determination of FCR




FCR: amount of N in crop residues returned to soil annually
It is estimated by adjusting the total amount of crop residue N produced
to account for the fraction that is burned in the field and for the fraction
that is removed from the field
We assume that the country has enough data to apply Tier 1b method
(equation 4.29 in GPG2000)
It is determined that:


FCR = 37,934 t N in crop residues that are
returned to soils
This value is introduced in sheet 4-5s1 of the IPCC software to estimate
an annual emission of N2O-N from N in crop residues of 0.47 Gg N2O-N

IPCC Software worksheet was designed for Tier-1a method, and use of Tier 1b
requires manually altering sheet 4-5s1, cell C23
3B.101
Crop residues: determination of FCR
Crop
Crop
(1000 t)
(1)
Res/Crop
FracDM
FracNCR
FracBURN
FracFUEL
FracFOD
(2)
(2)
(2)
(3)
(3)
(3)
Eq. 4.29
GPG
(t N20-N)
Wheat
1,273
1.3
0.85
0.0028
0.2
0
0.1
2,757
Barley
148
1.2
0.85
0.0043
0.2
0
0.1
456
Maize
2,735
1.0
0.78
0.0081
0
0.2
0.2
10,369
Rice
470
1.4
0.90
0.0067
0
0
0
3,971
Soybean
458
2.1
0.85
0.023
0
0
0
18,797
Potatoes
450
0.4
0.80
0.011
0
0
0
1,584
---
---
---
---
---
---
---
37,934
Total
(1)
(2)
(3)
Source: FAO statistics
Source: Table 4.16, GPG2000 (except FracDM for potatoes, which was estimated by experts)
Source: Country-specific data
FCR
3B.102
N2O emissions from N in crop residues
MODULE
AGRICULTURE
SUBMODULE
AGRICULTURAL SOILS
WORKSHEET
4-5
SHEET
COUNTRY
YEAR
Type of N input to soil
1 OF 5 DIRECT NITROUS OXIDE EMISSIONS FROM
AGRICULTURAL FIELDS, EXCLUDING CULTIVATION OF
HISTOSOLS
Hypothetical
2003
STEP 1
A
Amount of N
Input
(kg N/yr)
STEP 2
B
Factor for
Direct Emissions
EF1
C
Direct Soil
Emissions
(kg N2O–N/kg N)
(Gg N2O-N/yr)
C = (A x B)/1 000 000
117,000,000.00
0.012
1.40
Animal waste (FAW)
24,924,008.00
0.0125
0.31
N-fixing crops (FBN)
27748000
0.0125
0.35
0.0125
0.47
Total
2.54
Synthetic fertiliser (FSN)
Crop residue (FCR)
37,934,124.00
IPCC default
Total direct N2O emissions (excluding pasture, range and paddock): 2.54 Gg N2O-N/yr
3B.103
N excretion from pasture/range/paddock
MODULE
SUBMODULE
WORKSHEET
SPECIFY AWMS
SHEET
COUNTRY
YEAR
Livestock Type
AGRICULTURE
METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK
ENTERIC FERMENTATION AND MANURE MANAGEMENT
4-1 (SUPPLEMENTAL)
PASTURE RANGE AND PADDOCK
NITROGEN EXCRETION FOR ANIMAL WASTE MANAGEMENT SYSTEM
Hypothetical
2003
A
Number of Animals
B
Nitrogen Excretion
Nex
(1000s)
(kg//head/(yr)
C
Fraction of Manure
Nitrogen per AWMS
(%/100)
(fraction)
D
Nitrogen Excretion per
AWMS, Nex
(kg/N/yr)
D = (A x B x C)
195,814,000.00
Non-dairy Cattle
5153000
40
0.95
Dairy Cattle
Poultry
Sheep
1000000
4000000
3000000
70
0.2
14,000,000.00
0.00
0.00
Swine
Others
1500000
16
0.102
2,448,000.00
0.00
212,262,000.00
TOTAL
Default values
3B.104
N2O emissions from pasture/range/paddock
MODULE
AGRICULTURE
SUBMODULE
AGRICULTURAL SOILS
WORKSHEET
4-5
SHEET
COUNTRY
YEAR
3 OF 5 NITROUS OXIDE SOIL EMISSIONS FROM GRAZING ANIMALS PASTURE RANGE AND PADDOCK
Hypothetical
2003
A
STEP 5
B
C
Animal Waste
Nitrogen Excretion
Emission Factor for
Emissions Of N2O from
Management System
(AWMS)
Nex(AWMS)
AWMS
EF3
(kg N2O–N/kg N)
(kg N/yr)
Grazing Animals
(Gg)
C = (A x B)[44/28]/1 000 000
Pasture range & paddock
212,262,000.00
0.02
6.67
From Table 4-8
IPCC Guidelines V2
3B.105
Indirect N2O Emissions
from Agricultural Soils
3B.106
Indirect N2O – Agricultural soils

We will continue with the assumption of a hypothetical country in
Latin America

We will assume that the country only covers the following sources:
 N2O(G): from volatilization of applied synthetic fertilizer and animal
manure N, and its subsequent deposition as NOx and NH4
 N2O(L): from leaching and runoff of applied fertilizer and animal
manure

Indirect N2O emissions are estimated using Tier 1a method and
IPCC default emission factors

The next slides show calculations as performed by IPCC Software
3B.107
Indirect N2O emissions from atmospheric depositions
MODULE AGRICULTURE
SUBMODULE AGRICULTURAL SOILS
WORKSHEET 4-5
SHEET 4 OF 5 INDIRECT NITROUS OXIDE EMISSIONS FROM ATMOSPHERIC DEPOSITION OF NH 3 AND NOX
COUNTRY Hypothetical
YEAR 2003
Type of
Deposition
A
Synthetic
Fertiliser N
B
Fraction of
Synthetic
C
Amount of
Synthetic N
D
Total N
Excretion by
STEP 6
E
Fraction of
Total Manure N
Applied to
Soil, NFERT
Fertiliser N
Applied that
Volatilizes
FracGASFS
(kg N/kg N)
Applied to Soil
that Volatilizes
Livestock
NEX
Excreted that
Volatilizes
(kg N/yr)
130000000
0.1
G
Emission Factor
EF4
H
Nitrous Oxide
Emissions
(kg N2O–N/kg N)
(Gg N2O–N/yr)
Volatilizes
FracGASM
(kg N/kg N)
(kg N/yr)
(kg N/kg N)
(kg N/kg N)
C = (A x B)
Total
F
Total N Excretion
by Livestock that
13,000,000.00 249,240,080.00
F = (D x E)
0.2
49,848,016.00
H = (C + F) x G /1 000 000
0.01
0.63
From Table 4.18
GPG2000
Default value
From Table 4-17
IPCC Guidelines V2
3B.108
Indirect N2O emissions from leaching and runoff
MODULE
AGRICULTURE
SUBMODULE
AGRICULTURAL SOILS
WORKSHEET
4-5
SHEET
COUNTRY
YEAR
I
Synthetic Fertiliser
Use NFERT
5 OF 5 INDIRECT NITROUS OXIDE EMISSIONS FROM LEACHING
Hypothetical
2003
J
Livestock N
Excretion NEX
STEP 7
K
Fraction of N That
Leaches
L
Emission Factor
EF5
M
Nitrous Oxide Emissions
From Leaching
STEP 8
N
Total Indirect
Nitrous Oxide
Emissions
(Gg N2O–N/yr)
M = (I + J) x K x L/1 000 000
(Gg N2O/yr)
N = (H + M)[44/28]
FracLEACH
(kg N/yr)
(kg N/yr)
130,000,000.00
249,240,080.00
From Table 4-17
IPCC Guidelines V2
(kg N/kg N)
0.3
0.025
2.84
5.46
From Table 4.18
GPG2000
3B.109
Field Burning of
Crop Residues
3B.110
Burning of crop residues
Main issues derived from the decision tree
•
•
•
•
If not occurring, then emission estimates are “NO”
If occurring, then emissions must be estimated using
worksheet 4-4 sheets 1-2-3 (IPCC software)
Only one method is available to estimate emissions from
this source category
If key source, then country-specific values for noncollectable AD and emission factors must preferrably be
used (default values for key sources are possible if the country cannot
provide the required AD or financial resources are lacking)
•
If country-specific values are used, they must be reported
in a transparent manner
3B.111
Burning of crop residues
• Activity data required to estimate emissions:
• collected by statistics agencies: annual crop
production (alternate way is FAO database)
• not collected by statistics agencies:
• residue to crop ratio
• dry matter fraction of biomass
• fraction of crop residues burned in field
• fraction of crop residues oxidized
• C fraction in dry matter
• Nitrogen/carbon ratio
• Emission factors: C-N emission ratios as CH4, CO, N2O,
NOX
• Other constants (conversion ratios):
• C to CH4 or CO (16/12; 28/12, respectively)
• N to N2O or NOX (44/28; 46/14, respectively)
3B.112
1. OPEN THE IPCC SOFTWARE AND CHOOSE THE YEAR OF THE INVENTORY
2. CLICK 0N “SECTORS” IN THE MENU BAR, AND THEN CLICK ON AGRICULTURE
3. OPEN SHEET 4-4s2
MODULE
SUBMODUL
E
WORKSHE
ET
SHEET
COUNTRY
YEAR
AGRICULTURE
FIELD BURNING OF AGRICULTURAL RESIDUES
4-4
1 OF 3
FICTICIOUS LAND
2002
STEP 1
Crops
A
B
C
(specify locally
Annual
Residue to
Quantity of
important
Production
Crop Ratio
Residue
Main residue-producing crops:
Cereals (wheat, barley, oats, rye, rice,
maize, sorghum)
Sugarcane
STEP
2
STEP 3
Pulses
(peas,
D
E
F beans, Glentils)
H
Potatoes,
peanut,
others
Dry Matter
Quantity of
Fraction
Fraction
Total Biomass
Fraction
Dry Residue
crops)
Burned in
Oxidised
Burned
Fields
(Gg crop)
(Gg biomass)
(Gg dm)
(Gg dm)
C = (A x B)
E = (C x D)
H = (E x F xG)
0,00
0,00
0,00
Wheat
15750
1,3
20.475,00
0,85
17.403,75
0,75
0,9
11.747,53
Maize
5200
1
5.200,00
0,5
2.600,00
0,5
0,9
1.170,00
Rice
1050
1,4
1.470,00
0,85
1.249,50
0,85
0,9
955,87
.
Identify the
existing residueproducing crops
0,00
0,00
0,00
3B.113
Field burning of crop residues
Worksheet 4-4, sheet 1
Flowchart to be applied to each crop
Priority order for
non-collectable AD2:
1. CS values - research
2. CS values - expert
judgement
3. Values from countries
with similar conditions
4. Default values
(search EFDB)
A. Annual crop
production
(Gg)
B. Residue/crop
ratio
Priority order for
collectable AD1:
1. Values collected from
published statistics
2. If not available,
values can be
derived from:
a) crop area (in kha)
b) crop yield
(in tonne/ha)
3. From FAO DB
C. Quantity of
residues
(Gg biomass)
3B.114
Field burning of crop residues
Worksheet 4-4, sheet 1
Flowchart to be applied to each crop
C. Quantity of
residue
(Gg biomass)
from previous slide
D. Dry matter
Fraction
Priority order for
non-collectable AD:
1. CS values - research
2. CS values - expert
judgement
3. Values from countries
with similar conditions
4. IPCC default values
(search EFDB)
E. Total quantity of
dry residue
(Gg dm)
3B.115
Field burning of crop residues
Worksheet 4-4, sheet 1
Flowchart to be applied to each crop
Priority order for
non-collectable AD:
1. CS values - research
2. CS values - expert
judgement
3. Values from countries
with similar conditions
(no default values)
To avoid double
counting, a mass balance
of crop residue biomass must
be internally performed:
Fburned= Total biomass –
(Fremoved from the field+
Featen by animals+
Fother uses)
E. Quantity of
dry residue
(Gg dm)
from previous slide
F. Fraction burned
in fields
For default values,
search EFDB as
combustion efficiency
G. Fraction
oxidized
H. Total biomass
burned
(Gg dm burned)
3B.116
4. OPEN SHEET 4-4s2 OF “AGRICULTURE” UNDER “SECTORS”
MODULE
AGRICULTURE
SUBMODULE
FIELD BURNING OF AGRICULTURAL RESIDUES
WORKSHEET
4-4
SHEET
COUNTRY
YEAR
2 OF 3
FICTICIOUS LAND
2002
STEP 4
Crops
STEP 5
I
J
K
L
Carbon
Total Carbon
Nitrogen-
Total Nitrogen
Fraction of
Released
Carbon Ratio
Released
Residue
(Gg C)
(Gg N)
J = (H x I)
L = (J x K)
0,00
0,00
Wheat
0,48
5.638,82
0,012
67,67
Maize
0,47
549,90
0,02
11,00
Rice
0,41
391,91
0,014
5,49
.
0,00
0,00
3B.117
Field burning of crop residues
Worksheet 4-4, sheet 2
Flowchart to be applied to each crop
H. Biomass burned
(Gg dm burned)
from previous slide
J. C released
(Gg C)
Total C and N released
are obtained by
addding the values
obtained per each
individual crop
I. C fraction
in residue
K. N/C ratio
Priority order for
non-collectable AD:
1. CS values - research
2. CS values - expert
judgement
3. Values from countries
with similar conditions
4. Default values
(search EFDB)
L. N released
(Gg N)
3B.118
5. OPEN SHEET 4-4s3 OF “AGRICULTURE” UNDER “SECTORS”
Worksheet 4-4, sheet 3
SUBMODULE
Total emission
estimates
FIELD BURNING OF AGRICULTURAL RESIDUES
WORKSHEET
4-4
MODULE
SHEET
COUNTRY
YEAR
AGRICULTURE
3 OF 3
FICTICIOUS LAND
2002
STEP 6
M
N
O
P
Emission Ratio
Emissions
Conversion Ratio
Emissions
from Field
Burning of
Agricultural
Residues
(Gg C or Gg N)
(Gg)
N = (J x M)
P = (N x O)
CH4
0,005
32,90
16/12
43,87
CO
0,06
394,84
28/12
921,29
N = (L x M)
P = (N x O)
N2O
0,007
0,59
44/28
0,93
NOx
0,121
10,18
46/14
33,46
3B.119
6. GO TO THE “OVERVIEW” MODULE
7. OPEN THE WORHSHEET 4-S2
TABLE 4 SECTORAL REPORT FOR
AGRICULTURE
(Sheet 2 of 2)
Total emission
estimates
SECTORAL REPORT FOR NATIONAL GREENHOUSE GAS
INVENTORIES
(Gg)
GREENHOUSE GAS SOURCE AND SINK CATEGORIES
CH4
N2O
NOx
CO
NMVOC
B Manure Management (cont...)
10 Anaerobic
0
11 Liquid Systems
0
12 Solid Storage and Dry Lot
0
13 Other (please specify)
0
C Rice Cultivation
0
1 Irrigated
0
2 Rainfed
0
3 Deep Water
0
4 Other (please specify)
D Agricultural Soils
E Prescribed Burning of Savannas
F Field Burning of Agricultural Residues (1)
0
1
0
2
36
44
1
33
921
1 Cereals
2 Pulse
3 Tuber and Root
4 Sugar Cane
5 Other (please specify)
G Other (please specify)
3B.120
Field burning of crop residues
Worksheet 4-4, sheet 3
Flowchart to be applied to aggregated figures
Total C released
(Gg C from all crops)
from previous slide
M
Non-CO2
emission rates
(search EFDB)
Total N released
(Gg N from all crops)
from previous slide
EFs:
If no CS values,
use defaults
(Table 4-16, Reference Manual,
Rev. 1996 IPCC Guidelines)
P
CH4 emitted
(Gg CH4)
P
CO emitted
(Gg CO)
C-N
emitted
(Gg C emitted as
CH4 or CO;
Gg N emitted as
N2O or NOX)
O
Conversion
ratios
P
N2O emitted
(Gg N2O)
P
NOX emitted
(Gg NOX)
3B.121
Field burning of crop residues
Emission factors
3B.122
Field burning of crop residues
Emission estimates using country-specific values
Wheat residues (1 of 3)
MODULE
AD from
national statistics
AGRICULTURE
SUBMODUL
E
FIELD BURNING OF AGRICULTURAL RESIDUES
WORKSHEE
T
4-4
SHEET
COUNTRY
YEAR
CS activity data,
from research and
monitoring
1 OF 3
FICTICIOUS
2002
STEP 1
STEP 2
STEP 3
Crops
A
B
C
D
E
F
G
H
(specify
locally
Annual
Residue to
Quantity of
Dry
Matter
Quantity of
Fraction
Fraction
Total
Biomass
important
Production
Crop Ratio
Residue
Fraction
Dry
Residue
Burned in
Oxidised
Burned
crops)
Fields
(Gg crop)
Wheat
18.350,50
1,50
(Gg biomass)
(Gg dm)
(Gg dm)
C = (A x B)
E = (C x D)
H = (E x F
xG)
27.525,8
0,90
24.773,2
0,12
0,96
2.735,0
3B.123
Field burning of crop residues
Emission estimates using country-specific values
Wheat residues (2 of 3)
MODULE
AGRICULTURE
SUBMODULE
FIELD BURNING OF AGRICULTURAL RESIDUES
WORKSHEET
4-4
SHEET
COUNTRY
YEAR
2 OF 3
FICTICIOUS
2002
STEP 4
Crops
Wheat
STEP 5
I
J
K
L
Carbon
Total Carbon
Nitrogen-
Total Nitrogen
Fraction of
Released
Carbon Ratio
Released
Residue
0,45
(Gg C)
(Gg N)
J = (H x I)
L = (J x K)
1.230,7
0,0032
3,94
CS activity data,
from research and
monitoring
3B.124
Field burning of crop residues
Emission estimates using country-specific values
Wheat residues (3 of 3)
MODULE
AGRICULTURE
SUBMODULE
FIELD BURNING OF AGRICULTURAL RESIDUES
WORKSHEET
4-4
SHEET
COUNTRY
YEAR
3 OF 3
FICTICIOUS
2002
STEP 6
Gas
M
N
O
P
Emission Ratio
Emissions
Conversion Ratio
Emissions
(Gg C or Gg N)
(Gg)
N = (J x M)
P = (N x O)
CH4
0,00311
3,83
16/12
5,10
CO
0,06
73,84
28/12
172,30
N = (L x M)
P = (N x O)
N2O
0,018
0,07
44/28
0,11
NOx
0,121
0,48
46/14
1,57
CS values for CH4/N2O
D for CO/NOX
3B.125
Field burning of crop residues
Emission estimates using default values
Wheat residues (1 of 3)
MODULE
SUBMODULE
WORKSHEET
AD:
SHEET
1. from
national statistics, or
COUNTRY
2. from FAO database:
YEAR
(www.fao.org, then “FAOSTATAgriculture” and “Crops
primary”)
STEP
1
AGRICULTURE
FIELD BURNING OF AGRICULTURAL RESIDUES
4-4
1 OF 3
FICTICIOUS
2002
STEP 2
STEP 3
Crops
A
B
C
D
E
F
G
H
(specify
locally
Annual
Residue to
Quantity
of
Dry Matter
Quantity
of
Fraction
Fraction
Total
Biomass
important
Production
Crop Ratio
Residue
Fraction
Dry
Residue
Burned
in
Oxidised
Burned
crops)
Fields
(Gg crop)
Wheat
18.350,5
(Gg
biomass)
EF ID=
43555
C = (A x
B)
1,30
23.855,7
(Gg dm)
EF ID=
43636
0,83
Activity data,
taken from EFDB
(Gg dm)
EF ID=
45941
E = (C x
D)
19.800,2
0,12
0,94
H = (E x F
xG)
2.140,4
CS value,
from monitoring or
expert judgement
3B.126
Field burning of crop residues
Emission estimates using default values
Wheat residues (2 of 3)
MODULE
AGRICULTURE
SUBMODULE
FIELD BURNING OF AGRICULTURAL RESIDUES
WORKSHEET
4-4
SHEET
COUNTRY
YEAR
2 OF 3
FICTICIOUS
2002
STEP 4
Crops
Wheat
STEP 5
I
J
K
L
Carbon
Total Carbon
Nitrogen-
Total Nitrogen
Fraction of
Released
Carbon Ratio
Released
Residue
0,48
(Gg C)
(Gg N)
J = (H x I)
L = (J x K)
1.027,4
EF ID= 43716
0,012
12,33
EF ID= 43796
Default activity data,
from EFDB
3B.127
Field burning of crop residues
Emission estimates using CS values
Wheat residues (3 of 3)
MODULE
AGRICULTURE
SUBMODULE
FIELD BURNING OF AGRICULTURAL RESIDUES
WORKSHEET
4-4
SHEET
COUNTRY
YEAR
3 OF 3
FICTICIOUS
2002
STEP 6
M
N
O
P
Emission Ratio
Emissions
Conversion Ratio
Emissions
(Gg C or Gg N)
(Gg)
N = (J x M)
P = (N x O)
CH4
0,005
5,14
16/12
6,85
CO
0,06
61,64
28/12
143,83
N = (L x M)
P = (N x O)
N2O
0,007
0,09
44/28
0,14
NOx
0,121
1,49
46/14
4,90
EF ID=
43583, 43548,
43543, 43549
Default values,
from EFDB
3B.128
Field burning of crop residues
Differences in emission estimates
if country-specific or default values are used
Emissions
Emissions
Per cent
Gg gas
Gg gas
of
using
using
difference
CS values
Defaults
CH4
5,10
6,85
-25%
CO
172,30
143,83
20%
N2O
0,11
0,14
-18%
NOx
1,57
4,90
-68%
Gas emitted
3B.129
Prescribed
Burning of
Savannas
3B.130
PRESCRIBED BURNING OF SAVANNAS
Main issues derived from the Decision-tree
• If not occurring, then no emission estimates
• If occurring, then emissions must be are estimated
using Worksheet 4-3, sheets 1-2-3 (IPCC software)
• Only one methods is available to estimate emissions
from this source category
• If key source, country-specific non-collectable activity
data and emission factors must be preferred to be used
(use of default values for key source is possible, if the country cannot
provide the required AD or resources are jeopardised)
• If CS values are used, they must be reported in a
transparent manner
3B.131
PRESCRIBED BURNING OF SAVANNAS
• Activity data required to estimate emissions:
• collected by statistics agencies:
• division of savannas into categories
• area per savanna category
• not collected by statistics agencies:
• biomass density (kha) (column A in worksheets)
•
•
•
•
•
•
•
dry matter fraction of biomass (ton DM/ha) (column B)
fraction of biomass actually burned (column D)
fraction of living biomass actually burned (column F)
fraction oxidised of living and dead biomass (column I)
C fraction of living and dead biomass (column K)
Nitrogen/carbon ratio
Emision factors: C-N emission ratios as CH4, CO, N2O, NOX
• Other constants (conversion ratios):
• C to CH4 or CO (16/12; 28/12, respectively)
• N to N2O or NOX (44/28; 46/14, respectively)
3B.132
1.
2.
3.
4.
OPEN THE IPCC SOFTWARE AND CHOOSE THE YEAR OF THE INVENTORY
GO TO THE MENU BAR AND CLICK IN “SECTORS” AND THEN IN “AGRICULTURE”
OPEN THE SHEET 4-3s1
FILL IN WITH THE DATA
MODULE
AGRICULTURE
SUBMODULE
PRESCRIBED BURNING OF SAVANNAS
WORKSHEET
4-3
SHEET
COUNTRY
YEAR
1 OF 3
FICTICIOUS LAND
2002
STEP 1
STEP 2
A
B
C
D
E
F
G
H
Area Burned
by Category
(specify)
Biomass
Density of
Savanna
Total Biomass
Exposed to
Burning
Fraction
Actually
Burned
Quantity
Actually
Burned
Fraction of
Living
Biomass
Burned
Quantity of
Living Biomass
Burned
Quantity of
Dead Biomass
Burned
(k ha)
(t dm/ha)
(Gg dm)
The first 3 steps is
to determine:
15,5
1. the categories of
savannas existing per
ecological unit
2. the area burned
per category
3. the biomass density
per category
C = (A x B)
7
108,50
Sources for AD on categories of savannas and
(Gg dm)
(Gg dm)
(Gg dm)
area covered by category:
E =1.
(C xNational
D)
G = (E x F)
H = (E - G)
statistics
0,85 2. National
92,23 mapping
0,45 systems
41,50
50,72
Sources for AD on biomass density:
0,00
0,00
0,00
1. National
statistics
2. National vegetation surveys and mapping 0,00
3. National expert judgement
4. Data provided by third countries with similar features
5. IPCC defaults (Table 4-14, Reference Manual, 1996
3B.133
Revised Guidelines)
PRESCRIBED BURNING OF SAVANNAS
Flow chart to estimate non-CO2 emissions
To be applied to each savanna category
B
Biomass density
(ton dm/ha)
A
Area burned
(k ha)
C
Total biomass
exposed to burning
(Gg dm)
D
F actually burned
E
Biomass actually
Burned
(Gg dm)
F
F of living
biomass burned
G
Living biomass
actually burned
(Gg dm)
Ideally, CS values
based on measurements.
If not, CS values based
on expert judgement.
If not, default values
(search EFDB)
H
Dead biomass
actually burned
(Gg dm)
3B.134
5. GO SHEET 4-3s2 IN “SECTORS/AGRICULTURE” OF THE IPCC SOFTWARE
6. FILL IT WITH THE DATA
MODULE
AGRICULTURE
SUBMODULE
PRESCRIBED BURNING OF SAVANNAS
WORKSHEET
4-3
SHEET
COUNTRY
YEAR
2 OF 3
FICTICIOUS LAND
2002
STEP 3
I
Fraction
of living
Oxidised
and dead
J
K
L
Total Biomass
Oxidised
Carbon Fraction
of Living & Dead
Biomass
Total Carbon
Released
biomass
(Gg dm)
(Gg C)
Living: J = (G x I)
Dead: J = (H x I)
L = (J x K)
Living
0,9
37,35
0,45
16,81
Dead
0,95
48,19
5
240,94
Living
0,00
0,00
Dead
0,00
0,00
3B.135
PRESCRIBED BURNING OF SAVANNAS
Flow chart to estimate non-CO2 emissions
Applicable per each savanna category
G
Living biomass
actually burned (Gg dm)
If no CS values,
defaults in EFDB, as
combustion efficiency
I1
Fraction of living
biomass oxidised
(Gg dm)
from previous slide
J1
Oxidised living
biomass
(Gg dm)
H
Dead biomass
actually burned (Gg dm)
from previous slide
I2
Fraction of dead
biomass oxidised
(Gg dm)
K1
C fraction of
living biomass
J2
Oxidised dead
biomass
(Gg dm)
K2
C fraction of
dead biomass
N
Total N released
(Gg N)
M
N/C ratio
L
Total C released
(Gg C)
L1
C released from
living biomass
(Gg C)
L2
C released from
dead biomass
(Gg C)
3B.136
7. GO TO SHEET 4.3s3 IN “SECTORS/AGRICULTURE”
8. FILL IT GO THE DATA
MODULE
AGRICULTUR
E
SUBMODULE
PRESCRIBED BURNING OF SAVANNAS
WORKSHEET
4-3
SHEET
COUNTRY
YEAR
TOTAL EMISSION
ESTIMATES
3 OF 3
FICTICIOUS LAND
2002
STEP 4
STEP 5
L
M
N
O
P
Q
R
Total Carbon
Released
NitrogenCarbon Ratio
Total Nitrogen
Content
Emissions
Ratio
Emissions
Conversion
Ratio
Emissions from
Savanna Burning
(Gg C)
257,75
0,015
(Gg N)
(Gg C or Gg N)
(Gg)
N = (L x M)
P = (L x O)
R = (P x Q)
0,004
1,03
16/12
CH4
1,37
0,06
15,46
28/12
CO
36,08
3,87
P = (N x O)
R = (P x Q)
0,007
0,03
44/28
N2O
0,04
0,121
0,47
46/14
NOx
1,54
3B.137
9. GO TO “OVERVIEW” MODULE
8. OPEN THE WORKSHEET 4S2
TABLE 4 SECTORAL REPORT FOR
AGRICULTURE
(Sheet 2 of 2)
SECTORAL REPORT FOR NATIONAL GREENHOUSE GAS
INVENTORIES
(Gg)
GREENHOUSE GAS SOURCE AND SINK CATEGORIES
CH4
N2O
NOx
CO
NMVOC
B Manure Management (cont...)
10 Anaerobic
0
11 Liquid Systems
0
12 Solid Storage and Dry Lot
0
Total emission
estimates
13 Other
(please specify)
From Savanna
Burning
C Rice Cultivation
0
0
1 Irrigated
0
2 Rainfed
0
3 Deep Water
0
4 Other (please specify)
D Agricultural Soils
E Prescribed Burning of Savannas
F Field Burning of Agricultural Residues (1)
0
1
0
2
36
44
1
33
921
1 Cereals
2 Pulse
3 Tuber and Root
4 Sugar Cane
5 Other (please specify)
G Other (please specify)
3B.138
PRESCRIBED BURNING OF SAVANNAS
Applicable to aggregated figures
O
N2O & NOx
emission rates
N
Total N released
(Gg N)
from previous slide
If no CS EFs,
defaults in EFDB
P
N2O-N released
(Gg N)
R
NOx emitted
(Gg NOX)
P
NOx-N released
(Gg N)
Q
N2O & NOx
conversion rates
O
CH4 & CO
emission rates
L
Total C released
(Gg C)
R
N2O emitted
(Gg N2O)
P
CH4-C released
(Gg C)
R
CO emitted
(Gg CO)
from previous slide
P
CO-C released
(Gg C)
R
CH4 emitted
(Gg CH4)
Q
CH4 & CO
conversion rates
3B.139
PRESCRIBED BURNING OF SAVANNAS
Examples of default emission factors
3B.140
PRESCRIBED BURNING OF SAVANNAS





Example based in a ficticious country having
three ecological regions: north, centre, south
Northern zone: shortest drought period
Southern zone: longest drought period
Central zone: intermediate situation
Two scenarios:


use of country-specific values for the majority
of the ADs and EFs
use of default values for all the ADs and EFs
3B.141
PRESCRIBED BURNING OF SAVANNAS
Emission estimates using CS values
STEP 1
Savann
a
categor
y
STEP 2
A
B
C
D
E
F
G
H
Area
Burned
by
Category
(specify)
Biomass
Density
of
Savanna
Total
Biomass
Exposed to
Burning
Fraction
Actually
Burned
Quantity
Actually
Burned
Fraction
of
Living
Biomass
Burned
Quantity of
Living
Biomass
Burned
Quantity of
Dead
Biomass
Burned
(k ha)
(t dm/ha)
(Gg dm)
(Gg dm)
(Gg dm)
(Gg dm)
C = (A x B)
E = (C x D)
G = (E x F)
H = (E - G)
15,5
7,00
108,50
0,85
92,23
0,55
50,72
North
41,50
145,8
5,00
729,00
0,95
692,55
0,50
346,28
Centre
346,28
22,0
4,00
88,00
1,00
88,00
0,45
39,60
South
48,40
436,60
Totals
436,18
AD from national statistics
(census, surveys, mapping)
CS values
(field measurements, expert’s
judgement)
3B.142
PRESCRIBED BURNING OF SAVANNAS
Emission estimates using CS values
STEP 3
Savanna
category
North
Centre
South
Totals
Biomass
type
I
J
K
L
Fraction
Oxidised of living
and dead biomass
Total Biomass
Oxidised
Carbon Fraction
of Living & Dead
Biomass
Total Carbon
Released
(Gg dm)
(Gg C)
Living: J = (G x I)
Dead: J = (H x I)
L = (J x K)
Living
0,9
37,35
0,4
14,94
Dead
0,95
48,19
0,45
21,68
Living
0,9
324,77
0,4
129,91
Dead
0,95
280,48
0,45
126,22
Living
0,9
41,38
0,4
16,55
Dead
0,95
35,74
0,45
16,08
Living
403,50
Dead
364,41
325,39
CS values
(field measurements, lab
analysis, expert’s judgement)
3B.143
PRESCRIBED BURNING OF SAVANNAS
Emission estimates using CS values
SUBMODULE
PRESCRIBED BURNING OF SAVANNAS
WORKSHEET
4-3
SHEET
CS values for CH4 & N2O
D values for CO & NOx
COUNTRY
YEAR
3 OF 3
CHILE
2002
STEP 4
STEP 5
M
N
O
P
Q
R
NitrogenCarbon Ratio
Total Nitrogen
Content
Emissions
Ratio
Emissions
Conversi
on
Ratio
Emissions from
Savanna Burning
0,0142
(Gg N)
(Gg C or
Gg N)
(Gg)
N = (L x M)
P = (L x O)
R = (P x Q)
0,006
2,06
16/12
CH4
2,75
0,06
20,62
28/12
CO
48,11
4,88
R = (P x
Q)
P = (N x O)
0,006
0,03
44/28
N2O
0,05
0,121
0,59
46/14
NOx
1,94
3B.144
PRESCRIBED BURNING OF SAVANNAS
Emission estimates using default values
STEP 1
STEP 2
A
B
C
D
E
F
G
H
Area Burned
by Category
(specify)
Biomass
Density of
Savanna
Total
Biomass
Exposed to
Burning
Fraction
Actually
Burned
Quantity
Actually
Burned
Fraction of
Living
Biomass
Burned
Quantity of
Living
Biomass
Burned
Quantity of
Dead
Biomass
Burned
(k ha)
(t dm/ha)
(Gg dm)
(Gg dm)
(Gg dm)
(Gg dm)
C = (A x B)
E = (C x D)
G = (E x F)
H = (E - G)
15,50
7,00
108,50
EF ID= 43475
145,80
6,00
4,00
EF ID= 43480
103,08
EF ID= 43485
874,80
EF ID= 43445
22,00
0,95
0,95
0,95
56,69
EF ID= 43518
831,06
EF ID= 43485
88,00
0,55
0,55
46,38
457,08
EF ID= 43518
83,60
EF ID= 43485
0,45
373,98
37,62
EF ID= 43515
45,98
551,39
466,34
AD from
national statisitcs
Default values
taken from EFDB
3B.145
PRESCRIBED BURNING OF SAVANNAS
Emission estimates using default values
STEP 3
Savanna
category
I
J
K
L
Fraction
Oxidised of living
and dead biomass
Total Biomass
Oxidised
Carbon Fraction
of Living & Dead
Biomass
Total Carbon
Released
Default values
taken from EFDB
(Gg dm)
(Gg C)
Living: J = (G x I)
Dead: J = (H x I)
L = (J x K)
Living
0,94
53,29
0,4
21,32
Dead
0,94
43,60
0,45
19,62
Living
0,94
429,66
0,4
171,86
Dead
0,94
351,54
0,45
158,19
Living
0,94
35,36
0,4
14,15
Dead
0,94
43,22
0,45
19,45
North
Centre
South
Living
518,31
Dead
438,36
404,59
Totals
EF ID= 45949
Experts
CS values
taken from expert’s
judgement
3B.146
PRESCRIBED BURNING OF SAVANNAS
Emission estimates using default values
SUBMODULE
PRESCRIBED BURNING OF SAVANNAS
WORKSHEET
4-3
SHEET
COUNTRY
YEAR
3 OF 3
CHILE
2002
STEP 4
STEP 5
M
N
O
P
Q
R
NitrogenCarbon Ratio
Total
Nitrogen
Content
Emissions
Ratio
Emissions
Conversion
Ratio
Emissions
from
Savanna
Burning
0,0095
(Gg N)
(Gg C or Gg
N)
(Gg)
N = (L x M)
P = (L x O)
R = (P x Q)
0,005
2,02
16/12
CH4
2,70
0,06
24,29
28/12
CO
56,64
3,84
EF ID= 45998
Default values
taken from EFDB
P = (N x O)
R = (P x Q)
0,007
0,03
44/28
N2O
0,04
0,121
0,47
46/14
NOx
1,53
defaults
3B.147
PRESCRIBED BURNING OF SAVANNAS
Difference of estimates
PRESCRIBED BURNING OF SAVANNAS
Emissions
Emissions
Per cent
Gg gas
Gg gas
of
using
using
difference
CS values
Defaults
CH4
2,75
2,70
2%
CO
48,11
56,64
-15%
N2O
0,05
0,04
9%
NOx
1,94
1,53
27%
Gas emitted
3B.148
RICE
CULTIVATION
3B.149
RICE CULTIVATION




Anaerobic decomposition of organic material in
flooded rice fields produces CH4
The gas escapes to the atmosphere primarily by
transport through the rice plants
Amount emitted: function of rice species, harvests
nº/duration, soil type, tº, irrigation practices, and
fertilizer use
Three processes of CH4 release into the atmosphere:



Diffusion loss across the water surface (least important
process)
CH4 loss as bubbles (ebullition) (common and significant
mechanism, especially if soil texture is not clayey)
CH4 transport through rice plants (most important
phenomenon)
3B.150
RICE CULTIVATION
Methodological issues





1996 IPCC Guidelines outline one method, that uses annual harvested
areas and area-based seasonally integrated emission factors (Fc = EF
x A x 10-12)
In its most simple form, the method can be implemented using national
total area harvested and a single EF
High variability in growing conditions (water management practices,
organic fertilizer use, soil type) will significantly affect seasonal CH4
emissions
Method can be modified by disaggregating national total harvested
area into sub-units (e.g. areas under different water management
regimes or soil types), and multiplying the harvested area for each subunit by an specific EF
With this disaggregated approach, total annual emissions are equal to
the sum of emissions from each sub-unit of harvested area
3B.151
RICE CULTIVATION
Activity data






total harvested area excluding upland rice (national statistics or
international databases FAO (www.fao.org/ag/agp/agpc/doc) or IRRI
(www.irri.org/science/ricestat/pdfs)
harvested area differs from cultivated area according the number of
cropping within the year (multiple cropping)
regional units, recognising similarities in climatic conditions, water
management regimes, organic amendments, soil types, and others
(national statistics or mapping agencies or expert judgement)
harvested area per regional unit (national statistics or mapping
agencies)
cropping practices per regional unit (research agencies or expert
judgement)
amount/type of organic amendments applied per regional unit, to
allow the use of scaling factors (national statistics or international
databases or expert judgement)
3B.152
RICE CULTIVATION
Main features from decision-tree (1)


If no rice is produced, then reported as “NO”
If not key source:



If keysource:




and cropped area is homogeneous, then emissions can be estimated using total
harvested area (Box 1)
but cropped area in heterogeneous, then total harvested area muts be disaggregated into
homogeneous regional units applying default EF and scaling factors, if available
and the cropped area is homogeneous, then emissions must be estimated using total
harvested area and CS EFs (Box 2)
but cropped area variable, then the total harvested area must be divided into
homogeneous regional units and emissions estimated using CS EFs and scaling factors
for organic ammendements (if available) (Box 3)
The country is encouraged to produce seasonally-integrated EFs for each
regional unit (excluding organic ammendements) through a good practice
measurement programme
The EFs must include the multiple cropping effect
3B.153
RICE CULTIVATION
Numerical example

Assumptions:



Hypothetical country located in Asia
Key source condition
Total harvested area: 38,5 kha, disaggregated
into:

28,5 kha as irrigated and continously flooded

10,0 kha as irrigated, intermitently flooded and
single aereated
3B.154
RICE CULTIVATION
Regional units, from
national estatistics or
mapping agencies or MODULE
expert judgement SUBMODULE
WORKSHEET
SHEET
COUNTRY
YEAR
AD from national statistics
Water Management Regime
or international
databases
(FAO, IRRI)
EF: local research
or other country’s use
or from EFDB
AGRICULTURE
METHANE EMISSIONS FROM FLOODED RICE FIELDS
Scaling factor for water
management: local research or
other country’s use or EFDB
4-2
1 OF 1
FICTICIOUS LAND
(Agriculture, Rice Production,
2002
Intermitently Flooded, Single aeration)
A
B
C
D
E
Harvested Area
Scaling Factor
for Methane
Emissions
Correction
Factor for
Organic
Amendment
Seasonally Integrated
Emission Factor for
Continuously
Flooded Rice without
Organic Amendment
CH4 Emissions
(g/m2)
(Gg)
(m2 /1 000 000 000)
E = (A x B x C x D)
Irrigated
Continuously Flooded
Intermittently
Flooded
Single Aeration
0,285
1
2
20
11,40
0,1
0,5
2
20
2,00
Multiple Aeration
Rainfed
Deep
Water
0,00
Flood Prone
0,00
Drought Prone
0,00
Water Depth
50-100 cm
Water Depth > 100 cm
Totals
0,385
Enhancement factor for organic
ammendements: local research or
taken from the EFDB
(Agriculture, Rice Production)
0,00
0,00
13,40
3B.155