AGRICULTURE INVENTORY ELABORATION PART 2 SIMULATION

3B.1

AGRICULTUREINVENTORY

ELABORATION

PART 2SIMULATION

3B.2

Until September/2003, 70 NCs from NAI Parties were compiled and assessed by the UNFCCC-Secretariat

From the Compilation & Synthesis Report, the problems encountered by NAI Parties for the elaboration of the national inventory elaboration:

activity data 93 per cent emission factors 64 per cent methods 11 per cent

STATE-OF-ART OF NAI PARTIES

3B.3

INVENTORY ELABORATION

Previous activities:

Key source category determination

Sub-category importance determination

Methods to be applied per category (T1 for non-KS; T2/3 for KS)

Mass balance for shared items (crop residues, animal manure)

Single livestock characterization (basic linked to T1; enhaced linked to T2)

3B.4

INVENTORY ELABORATION.PREVIOUS ACTIVITIES

Preliminary key source determination

Two ways:

Using last/previous year GHG inventory data,and/or

Applying Tier 1 to all sectors for the year to be inventoried

3B.5

PRELIMINARY KEY SOURCE DETERMINATION.

STEPS

List of categories, according to IPCC disaggregation (excluding LUCF categories)

Decreasing ranking, according to their individual contribution to CO2-equiv. emissions

Estimating relative contribution of each category to the total national emissions

Calculating the cumulative contribution of the categories to the total national emissions,

Key sources should gather the upper 95% of GHG emissions

3B.6

PRELIMINARY KEY SOURCE DETERMINATION

CHILE, 1994 GHG-Inventory (Gg CO2-equivalent) (1)

SECTOR/sub-sector CO2 CH4 N2OTOTALS

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

- MANUFACTURING 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

- C MINING 195.3 195.3

- OIL AND NATURAL GAS 659.4 659.4

- OIL REFINING, FUEL STORAGE AND DISTRIBUTION 0.0

INDUSTRIAL PROCESSES 1870.0 44.1 248.0 2162.1

- CEMENT 1021.1 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

- FERROALLEYS 36.7 36.7

- PULP/ PAPER; FOODS/DRINKS; REFRIGERATION/OTHERS

0.0

SOLVENT USE 0.0 0.0 0.0 0.0

3B.7

PRELIMINARY KEY SOURCE DETERMINATION

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

- RICE CULTIVATION 134.4 134.4

- AGRICULTURAL SOILS: DIRECT EMISSIONS

4693.9 4693.9

- AGRICULTURAL SOILS: INDIRECT EMISSIONS

1495.9 1495.9

- AGRICULTURAL SOILS: PASTURE RANGE/PADDOCK

559.2 559.2

- AGRICULTURAL RESIDUE BURNING 52.0 607.5 659.5

WASTE: 0.0 1560.3 206.7 1767.0

- WASTEWATER TREATMENT: 3.2 3.2

- SOILD WASTE DISPOSAL LANDS 1557.1 1557.1

- INDUSTRIAL SOLID WASTE DISPOSAL 0.0

- UNTREATED WASTE WATER RUNOFF 206.7 206.7

- INDUSTRIAL LIQUID WASTES 202.9 202.9

TOTAL NATIONAL 38097.0 10142.8 9615.2 57854.9

1994 GHG-Inventory of Chile (Gg in CO2-equivalent) (Non-energy sectors)

3B.8

KEY SOURCES FOR THE 1994 GHG-Inventory of Chile

SECTOR/sub-sectorGg/yr CO2-

equiv.

ContributionSector

Ind. Cumul.

- 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

- Solid waste disposal lands 1557,1 2,7% 83,8% Waste

- 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 ferroalloys 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% Agric./Waste

- Industrial liquid residues 202,9 0,4% 99,4% Waste

- C mining 195,3 0,3% 99,7% Energy

- Rice cultivation 134,4 0,2% 99,9% Agriculture

- Sewage waters 3,2 0,0% 100,0% Energy

KSKS

NKSNKS

3B.9

Significance of animal species:

Example for CH4 emissions from Enteric Fermentation and Manure Management

Emissions estimated by Tier 1 To simplify: country with no division

into agroecological units

INVENTORY ELABORATION.SIGNIFICANCE OF SUBSOURCES

3B.10

Steps: Collection of animal species population If no national AD are available, the use of

FAOSTAT is appropriate Disaggregation between dairy and non-dairy

cattle, following expert’s judgment Filling in of IPCC software Table 4-1s1 with the

population data and default emission factors Estimation of individual contribution to the

total emissions of the source category

INVENTORY ELABORATION.SIGNIFICANCE OF SUBSOURCES

3B.11

Determination of Significant Sub-Source Categories

For significant species = enhanced characterization and Tier-2, if possible

Perform a rough estimation of CH4 emissions from enteric fermentation applying Tier-1

one way of screening species for their contribution to emissions

estimation has the only purpose of identifying categories requiring a Tier-2 estimation

use IPCC Software, sheet ‘4-1s1’: fill in animal population data, and collect default EF from Tables 4-3 and 4-4 of IPCC Guidelines Vol. 3 (also taken from the EFDB)

3B.12

Low Level of Data Availability

1 Disaggregation between dairy and non-dairy cattle, based on expert`s judgment

MODULE AGRICULTURE

SUBMODULEMETHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC ANIMALS AND MANURE MANAGEMENT

WORKSHEET 4-1

SHEET 1 of 2 METHANE EMISSIONS FROM ENTERIC FERMENTATION

COUNTRY ANYWHERE

YEAR 2003

STEP 1 STEP 2 STEP 3

A B C D E F

Animal SpeciesNº of

animals

EF for Enteric Ferment

ation

Emissions from Enteric

Fermentation

EF for Manure Manage

ment

Emissions due to Manure

Management

Total emissions from domestic

animals

(1000s)(kg/head/

year)(ton/year)

(kg/head/year)

(ton/year) (Gg/year)

C = (A x B) E = (A x D) F =(C + E)/1000

Dairy cattle 1.000,0 57,0 57000,0 2,0 2000,0 59,00

Non-dairy cattle 5.000,0 49,0 245000,0 1,0 5000,0 250,00

Buffalo NO 55,0 5,0

Sheep 3.000,0 5,0 15000,0 0,16 480,0 15,48

Goats 50,0 5,0 250,0 0,17 8,5 0,26

Camels NO 46,0 1,9

Horses 10,0 18,0 180,0 1,6 16,0 0,20

Mules & Assess NO 10,0 0,9

Swine 1.500,0 1,5 2250,0 3,0 4500,0 6,00

Poultry 4.000,0 NE 0,018 72,0 0,07

Totals 318930,0 12076,50 331,01

3B.13

Determining significant animal species

MODULE AGRICULTURE

SUBMODULE METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCKENTERIC FERMENTATION AND MANURE MANAGEMENT

WORKSHEET 4-1

SHEET 1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC FERMENTATION AND MANURE MANAGEMENT

COUNTRY Hypothetical

YEAR 2003

STEP 1 STEP 2 STEP 3A B C D E F

Livestock Type Number of Animals

Emissions Factor for

Enteric Fermentation

Emissions from Enteric Fermentation

Emissions Factor for Manure

Management

Emissions from Manure

Management

Total Annual Emissions from

Domestic Livestock

(1000s) (kg/head/yr) (t/yr) (kg/head/yr) (t/yr) (Gg)C = (A x B) E = (A x D) F =(C + E)/1000

Dairy Cattle 1000 57 57,000.00 0.00 57.00

Non-dairy Cattle 5000 49 245,000.00 0.00 245.00

Buffalo 0 55 0.00 0.00 0.00

Sheep 3000 5 15,000.00 0.00 15.00

Goats 50 5 250.00 0.00 0.25

Camels 0 46 0.00 0.00 0.00

Horses 10 18 180.00 0.00 0.18

Mules & Asses 0 10 0.00 0.00 0.00

Swine 1500 1.5 2,250.00 0.00 2.25

Poultry 4000 0 0.00 0.00 0.00

Totals 319,680.00 0.00 319.68

>25%

Worksheet 4-1s1

Conclusion: Tier 2 method, supported by an enhanced characterization, for the non-dairy cattle

No other significant species

3B.14

Enhanced CharacterizationNon-Dairy Cattle

Enhanced characterization requires information additional to that provided by FAO Statistics. Consultation with local experts/industry is a valuable source

Assume that, using these sources, the inventory team determines that non-dairy cattle population is composed by:

Cows : 40% Steers : 40% Young growing animals : 20%

No information available to divide the animal population into climatic zones and production systems

Each of these homogenous groups of animals must have an estimate of feed intake and an EF to convert intake to CH4 emissions

Procedure is described in IPCC-GPG (pages 4.10-4.20)

3B.15


Parameter Symbol Cows Steer

Young Source

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

Feeding Situation Ca 0.28 0.23 0.25 Table 4-5 IPCC-GPG, 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 IPCC-GPG

Net Energy Maintenance (MJ/day)

NEm 30.0 31.5 19.0 Calculated using equation 4.1, IPCC-GPG

Net Energy Activity (MJ/day)

NEa 8.4 7.2 4.8 Calculated using equation 4.2a, IPCC-GPG

3B.16



Young Comments

Growth coefficient C - - 0.9 p.4.15, IPCC-GPG

Net Energy Growth (MJ/day)

NEg - - 4.0 Calculated using equation 4.3a, IPCC-GPG

Pregnancy coefficient

CP 0.1 - - Table 4.7, IPCC-GPG

Net Energy Pregnancy (MJ/day)

NEP 3.0 - - Calculated using equation 4.8, IPCC-GPG

Portion of GE that is available for maintenance

NEma/DE

0.49 0.49 0.49 Calculated using equation 4.9, IPCC-GPG

Portion of GE that is available for growth

NEga/DE 0.28 0.28 0.28 Calculated using equation 4.10, IPCC-GPG

Gross Energy Intake (MJ/day)

GE 139.3 130.4

117.7 Calculated using equation 4.11, IPCC-GPG

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.17

Tier-2 Estimation of CH4 emissions from Enteric Fermentation by Non-

Dairy Cattle

Enhanced characterization yielded CS-AD (average daily gross energy intake) per group of non-dairy cattle (cows, steers, growing animals)

These AD must be combined with specific EFs for animal group to obtain emission estimates

Determination of EFs requires selection of a suitable value for CH4 conversion rate (Ym)

In this example of country with no CS-data, a default value for Ym (MCF) can be obtained from the IPCC-GPG

3B.18

Tier-2 Estimation of CH4 emissionsEnteric Fermentation - Non-Dairy Cattle

Parameter Symbol

Cows Steer Young

Comments

Gross Energy Intake (MJ/day) (from the enhanced characterization)

GE 139.3

130.4 117.7 Calculated using equation 4.11, IPCC-GPG

CH4 conversion factor Ym 0.06 0.06 0.06 Table 4.8, IPCC-GPG, and EFDB

Emission Factor(kg CH4/head/yr)

EF 54.8 51.3 46.3 Calculated using equation 4.14, IPCC-GPG

Portion of group in total population (%)

- 40 40 20 Expert judgment, industry data

Population of group (thousand heads)

- 2,000 2,000 1,000

CH4 Emissions

(Gg CH4/yr

- 110 103 46 Weighed EF= 52

3B.19

Tier-2 Estimation of CH4 emissionsEnteric Fermentation by Non-Dairy

Cattle

Tier-2 estimation for non-dairy cattle: 259 Gg CH4 (245 Gg CH4 by Tier 1)

Weighed EF: 52 kg CH4/head/yr (49 kg CH4/head/yr, as

default value) This value should be used in the worksheet to

report emissions by non-dairy cattle Another chance: to modify worksheet to

recognize T2 and incorporate new Efs directly

3B.20

Medium Level of AD Availability

For AD1, the country has reliable statistics on livestock population

Applying the same procedure as above, the country determines that non-dairy cattle requires enhanced characterization

National statistics + expert judgment allow disaggregation of non-dairy cattle population into: 2 climate regions (some of previous example) 3 animal categories (cows, sterrs, young animals) 3 production systems It means 18 estimation units

3B.21

Medium Level of AD Availability

Climate Region

Production System

Population (1,000 hd)

Cows Steers

Young

Warm Extensive Grazing 1,473 828 610

Intensive Grazing 228 414 120

Feedlot 40 92 96

Temperate

Extensive Grazing 348 201 161

Intensive Grazing 150 275 75

Feedlot 15 31 32

Total 5,153 2,254 1,841 1,094

New Total: 5,153·103 heads (against FAO: 5,000·103 heads )

3B.22

Tier-2 Estimation of CH4 emissionsEnteric Fermentation - Non-Dairy

Cattle

Enhanced characterization yielded CS-AD (average daily GE intake) for 18 classes of animals

This AD must be combined with EFs for each animal class to obtain 18 emission estimates

Next slides will show detailed calculations to estimate GE intake only for 6 of the 18 classes (three types of animals for ‘Warm-Extensive Grazing’ and for ‘Temperate-Intensive Grazing’

3B.23

Enhanced characterization, Non-Dairy Cattle Warm Climate - Extensive

Grazing

Parameter Symbol

Cows

Steer

Young

Comments

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

Feeding Situation Ca 0.33 0.33 0.33 Table 4-5 IPCC-GPG, and expert judgment


- 60 - - Country-specific data

Feed Digestibility (%)

DE 57 57 57 Country-specific data


Cfi 0.335

0.322

0.322 Table 4-4 IPCC-GPG





Comments in green indicate improvements over previous example

3B.24

Enhanced characterization, Non-Dairy Cattle

Warm Climate - Extensive Grazing

Parameter Symbol

Cows Steer Young

Comments

Growth coefficient C - 1.0 0.9 p.4.15, IPCC-GPG


NEg - 3.4 2.4 Calculated using equation 4.3a, IPCC-GPG

Pregnancy coefficient CP 0.1 - - Table 4.7, IPCC-GPG




NEma/DE 0.48 0.48 0.48 Calculated using equation 4.9, IPCC-GPG

Portion of GE that is available for growth



GE 162.2 170.0 111.2 Calculated using equation 4.11, IPCC-GPGTo 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.25

Enhanced characterization, Non-Dairy Cattle Temperate Climate - Intensive

Grazing

Parameter Symbol

Cows

Steer

Young

Comments

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

Feeding Situation Ca 0.17 0.17 0.17 Table 4-5 IPCC-GPG, and expert judgment


- 81 - - Country-specific data

Feed Digestibility (%)

DE 72 72 72 Country-specific data


Cfi 0.335

0.322

0.322 Table 4-4 IPCC-GPG





Comments in green indicate improvements over previous example

3B.26

Enhanced characterization, Non-Dairy Cattle Temperate Climate, Intensive

Grazing


Young Comments

Growth coefficient C 0.8 1.0 0.9 p.4.15, IPCC-GPG


NEg 3.0 5.7 9.2 Calculated using equation 4.3a, IPCC-GPG

Pregnancy coefficient

CP 0.1 - - Table 4.7, IPCC-GPG




NEma/DE 0.53 0.53 0.53 Calculated using equation 4.9, IPCC-GPG

Portion of GE that is available for growth.



GE 120.1

123.9

121.5 Calculated using equation 4.11, IPCC-GPG

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.27

Medium Level of Data Availability

Estimated GE values are used for calculation of EF (using equation 4.14, IPCC-GPG).

Calculation of EF requires to select a value for methane conversion rate (Ym), this is, the fraction of energy in feed in take 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, IPCC-GPG).

18 estimates of EF were obtained (next slide)

3B.28


Climate Region

Production System

EF (kg CH4/head/yr)

Cows Steers Young

Warm Extensive Grazing

63.8 66.9max

43.8

Intensive Grazing

47.7 51.5 48.4

Feedlot 41.5min

49.3 52.8

Temperate

Extensive Grazing

61.5 66.7 49.5

Intensive Grazing

47.3 48.8 47.8

Feedlot 41.5min

49.3 52.8

Range from 41.5 to 66.9

3B.29


Weighed EF (Tier 2, CS-AD): 57 kg CH4/head/yr (range: 42-67 kg CH4/head/yr)

EF for Tier 2 (with default and aggregated AD): 52 kg CH4/head/yr

EF for Tier 1: 49 kg CH4/head/yr

Multiplication of EF with cattle population in each class yielded 18 estimates of annual emission of methane from enteric fermentation, with a total of 294 Gg CH4/year

Total for Tier 2 (with default and aggregated AD): 259 Gg CH4/year

Total for Tier 1: 245 Gg CH4/year

3B.30


MODULE AGRICULTURE

SUBMODULE METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCKENTERIC FERMENTATION AND MANURE MANAGEMENT

WORKSHEET 4-1

SHEET 1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC FERMENTATION AND MANURE MANAGEMENT


YEAR 2003

STEP 1 STEP 2 STEP 3A B C D E F

Livestock Type Number of Animals

Emissions Factor for

Enteric Fermentation

Emissions from Enteric Fermentation

Emissions Factor for Manure

Management

Emissions from Manure

Management

Total Annual Emissions from

Domestic Livestock

(1000s) (kg/head/yr) (t/yr) (kg/head/yr) (t/yr) (Gg)C = (A x B) E = (A x D) F =(C + E)/1000

Dairy Cattle 1000 57 57,000.00 0.00 57.00

Non-dairy Cattle 5153 57 293,721.00 0.00 293.72

Buffalo 0 55 0.00 0.00 0.00

Sheep 3000 5 15,000.00 0.00 15.00

Goats 50 5 250.00 0.00 0.25

Camels 0 46 0.00 0.00 0.00

Horses 10 18 180.00 0.00 0.18

Mules & Asses 0 10 0.00 0.00 0.00

Swine 1500 1.5 2,250.00 0.00 2.25

Poultry 4000 0 0.00 0.00 0.00

Totals 368,401.00 0.00 368.40

Worksheet 4-1s1

3B.31

Highest Level of Data Availability

Activity data could be improved by:

more accurate national statistics on livestock population

lowest uncertainties further disaggregation of cattle population (e.g., by

race or age, subdividing climate region by administrative units, soil type, forage quality, others)

implementation of geographically-explicit AD and cattle traceability systems

development of local research to obtain CS estimates of parameters used for livestock characterization (e.g., coefficients for maintenance, growth, activity or pregnancy)

3B.32


Emission factors could be improved by:

developing local capacities for measuring CH4 emissions by individuals

characterising diverse feeds used 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 conditions similar to those of the country

3B.33


Numerical example not developed here

Very few -if any- developing countries are in position of having this level of information

With high level of data availability, countries would be able to implement Tier-3 methods (CS methods)

3B.34

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, EF used in Tier-1 may have an uncertainty in the order of 30-50%, and default AD may have even higher values

Application of Tier-2 method with country-specific AD may substantially reduce uncertainty levels with respect to Tier-1 with default AD/EF

Priority should be given to improve the quality of AD estimates

3B.35

Direct N2O Emissions from Agricultural Soils

NAI GHG Inventory Training WorkshopAgriculture Sector

3B.36

AnthropogenicN inputs to soils

Mineral fertilizers

Histosols cultivation

N-fixing crops

Sewage sludges

Crop residues

Animal manuresFraction of …

(from the mass balance)

Other practicesdealing with soil N

3B.37

Assess individual contribution of different N sources to determineones (sub-categories) 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 a preliminary emission estimate

For the significant sub-categories, the best efforts should be invested to apply Tier 1b along with country-specific AD1, AD2 and emission factors

For non-significant sub-categories, Tier 1a along with country-specificAD1 and default AD2 and emission factors is acceptable

AGRICULTURAL SOILS

It is also acceptable to mix Tiers 1a and 1b for different N sources, which willdepend on the activity data availability

3B.38

Direct N2O – Agricultural Soils

Assumption of the same country

It will be assumed 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 on N2O from manure management

area devoted to N-fixing crops: FAO database

The country has no organic soils (histosols) and no sewage sludge application to soils

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 and N in crop residues applied to soils)

3B.39

Use of N-FertilizersFrom the FAO database:

Crop Area(1,000 ha)

Crop Yield(kg dm/ha)

Use of N Fertilizer (1000 t

N)

Wheat 824 1,545 n/a

Barley 1 356 (371) 1,488 (1400) 19.1

Maize 1,225 2,233 n/a

Rice 98 4,800 n/a

Soybeans 231 1,982 n/a

Potatoes 25 18,000 n/a

Total 2,779 -- 130

1 Barley data from industry sources, shown in parentheses

3B.40

Direct N2O – Agricultural Soils

From FAO database, only total country data for fertilizer use is available. Therefore, only Tier-1a method could be used unless further disaggregation can be done with the support of national sources

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 between FAO and the local industry

in this case, both sources reasonably matched for area and yield, and it can be assumed that 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 minus 19)

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

3B.41

Synthetic Fertilisers:Determination of FSN and EF1

FSN: annual amount of fertiliser N applied to soils, adjusted by amount of N that volatilises as NH3 and NOx

To adjust for volatilisation, use IPCC default value from Table 4-17, IPCC Guidelines, V2: 0.1 kg (NOx+NH3)-N/kg fertiliser-N

It is determined that: FSN= 19,000 (1-0.1) = 17,100 t fertiliser-N (barley) FSN= 111,000 (1-0.1) = 99,900 t fertiliser-N (all other

crops) Total fertiliser-N = 117,000 t fertiliser-N

EF1 is 0.9 % for barley (country-specific) and 1.25 % for the other crops (Table 4.17, IPCC-GPG)

For the purpose of filling the IPCC Software sheet 4-5s1, a weighted EF1 is calculated as follows:

EF1 = weighed 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.42

Emissions of N2O from Synthetic Fertilisers

MODULE AGRICULTURE

SUBMODULE AGRICULTURAL SOILS

WORKSHEET 4-5

SHEET 1 OF 5 DIRECT NITROUS OXIDE EMISSIONS FROM AGRICULTURAL FIELDS, EXCLUDING CULTIVATION OFHISTOSOLS


YEAR 2003

STEP 1 STEP 2A B C

Type of N input to soil Amount of N Factor for Direct Soil Input Direct Emissions Emissions

EF1

(kg N/yr) (kg N2O–N/kg N) (Gg N2O-N/yr)

C = (A x B)/1 000 000

Synthetic fertiliser (FSN) 117,000,000.00 0.012 1.40

Animal waste (FAW) 65,793,280.00 0.0125 0.82

N-fixing crops (FBN) 0.0125 0.00

Crop residue (FCR) 0.00 0.0125 0.00

Total 2.23

Combined EF(CS and defaultt)

3B.43

Indirect N2O Emissions from

Agricultural Soils


3B.44

Indirect N2O – Agricultural Soils

We will assume that the country only covers the following sources:

N2O(G): from volatilisation of applied synthetic fertiliser and animal manure N, and its subsequent deposition as NOx and NH4.

N2O(L): from leaching and runoff of applied fertiliser and animal manure

Indirect N2O emissions are estimated using Tier 1a method and IPCC default emission factors

Next slides show calculations as performed by IPCC Software

3B.45

Indirect N2O Emissions from Atmospheric Depositions

MODULE AGRICULTURE


WORKSHEET 4-5

SHEET 4 OF 5 INDIRECT NITROUS OXIDE EMISSIONS FROM ATMOSPHERIC DEPOSITION OF NH3 AND NOXCOUNTRY Hypothetical

YEAR 2003

STEP 6A B C D E F G H

Type of Synthetic Fraction of Amount of Total N Fraction of Total N Excretion Emission Factor Nitrous Oxide Deposition Fertiliser N Synthetic Synthetic N Excretion by Total Manure N by Livestock that EF4

Emissions

Applied to Fertiliser N Applied to Soil Livestock Excreted that Volatilizes Soil, NFERT

Applied that that Volatilizes NEX Volatilizes

Volatilizes FracGASMFracGASFS

(kg N/yr) (kg N/kg N) (kg N/kg N) (kg N/yr) (kg N/kg N) (kg N/kg N) (kg N2O–N/kg N) (Gg N2O–N/yr)

C = (A x B) F = (D x E) H = (C + F) x G /1 000 000

Total 130000000 0.1 13,000,000.00 249,240,080.00 0.2 49,848,016.00 0.01 0.63

From Table 4-17IPCC Guidelines V2

From Table 4.18IPCC-GPG Default value

3B.46

MODULE AGRICULTURE


WORKSHEET 4-5

SHEET 5 OF 5 INDIRECT NITROUS OXIDE EMISSIONS FROM LEACHING


YEAR 2003

STEP 7 STEP 8I J K L M N

Synthetic Fertiliser Livestock N Fraction of N That Emission Factor Nitrous Oxide Emissions Total Indirect Use NFERT Excretion NEX Leaches EF5

From Leaching Nitrous Oxide

FracLEACH Emissions

(kg N/yr) (kg N/yr) (kg N/kg N) (Gg N2O–N/yr) (Gg N2O/yr)

M = (I + J) x K x L/1 000 000 N = (H + M)[44/28]

130,000,000.00 249,240,080.00 0.3 0.025 2.84 5.46

Indirect N2O Emissions from Leaching & Runoff

From Table 4-17IPCC Guidelines V2

From Table 4.18IPCC-GPG

3B.47

Field Burning of Crop

Residues


3B.48

• If not occurring, then emission estimates are “NO”

• If occurring, then emissions must be are estimated using Worksheet 4-4 sheets 1-2-3 (IPCC software)

• If key source, then CS-values for non-collectable AD and emission factors must be preferred (default values for key source are possible if the country cannot provide the required AD or financial resources are jeopardised)

• If CS values are used, they must be reported in a transparent manner

• Only one method is available to estimate emissions from this source category

CROP RESIDUES BURNINGMain issues derived from the Decision-

Tree

3B.49

• Activity data required to estimate emissions:

• collected by statistics agencies: annual crop productions (alternative way = 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 oxidised• C fraction in dry matter• 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);

CROP RESIDUES BURNING

3B.50

MODULE AGRICULTURE

SUBMODU

LE FIELD BURNING OF AGRICULTURAL RESIDUES

WORKSHE

ET 4-4

SHEET 1 OF 3

COUNTRY

FICTICIOUS LAND

YEAR 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 Productio

n Crop Ratio Residue Fraction

Dry Residue

Burned in Oxidised Burned

crops) 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

. 0,00 0,00 0,00

1. OPEN THE IPCC SOFTWARE AND CHOOSE THE YEAR OF THE INVENTORY2. CLICK IN “SECTORS” IN THE MENU BAR, AND THEN CLICK IN AGRICULTURE3. OPEN SHEET 4-4s2

Main residue-producing crops:Cereals (wheat, barley, oat, rye, rice,maize, sorghum, sugar cane)Pulses (peas, bean, lentils)Potatoes, peanut, others

Identify theexisting residue-producing crops

3B.51

B. Residue/cropRatio

A. Annual cropProduction

(Gg)

C. Quantity ofresidues

(Gg biomass)

FIELD BURNING OF CROP RESIDUES

Worksheet 4-4, sheet 1

Flowchart to be applied to each crop Priority order forcollectable AD1:

1. Values collected frompublished statistics2. If not available,

values can bederived from:

a) crop area (in kha)b) crop yield(in ton ha-1)

3. From FAO DB

Priority order fornon-collectable AD2:1. CS values-research2. CS values-expert

judgment3. Values from countrieswith similar conditions

4. Default values(search EFDB)

3B.52



Flowchart to be applied to each crop

D. Dry matterFraction

E. Total quantity ofdry residue

(Gg dm)

C. Quantity ofresidue

(Gg biomass)from previous slide

Priority order fornon-collectable AD:1. CS values-research2. CS values-expert

judgment3. Values from countrieswith similar conditions4. IPCC default values

(search EFDB)

3B.53

E. Quantity ofdry residue

(Gg dm)from previous slide

F. Fraction burnedin fields

H. Total biomassburned

(Gg dm burned)




G. Fractionoxidised



(No default values)

For default values,search EFDB as

combustion efficiency

To avoid doublecounting, a mass balance

of crop residue biomass mustbe internally performed:Fburned= Total biomass –(Fremoved from the field+

Featen by animals+Fother uses)

3B.54

4. OPEN THE SHEET 4-4s2 OF “AGRICULTURE” UNDER “SECTORS”

MODULE AGRICULTURE

SUBMODULE FIELD BURNING OF AGRICULTURAL RESIDUES

WORKSHEET 4-4

SHEET 2 OF 3

COUNTRY FICTICIOUS LAND

YEAR 2002

STEP 4 STEP 5

I J K L

Carbon Total Carbon Nitrogen- Total Nitrogen

Fraction of Released Carbon Ratio Released

Crops 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.55




H. Biomass burned(Gg dm burned)

from previous slideI. C fractionin residue

J. C released(Gg C)



4. Default values(search EFDB)

K. N/C ratio

L. N released(Gg N)

Total C and N releasedare obtained by

addding the valuesobtained per each

individual crop

3B.56


5. OPEN THE SHEET 4-4s3 OF “AGRICULTURE” UNDER “SECTORS”

MODULE AGRICULTURE


WORKSHEET 4-4

SHEET 3 OF 3


YEAR 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

Total emissionestimates

3B.57

6. GO TO THE “OVERVIEW” MODULE7. OPEN THE WORHSHEET 4-S2TABLE 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

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 0

E Prescribed Burning of Savannas 1 0 2 36

F Field Burning of Agricultural Residues (1) 44 1 33 921

1 Cereals

2 Pulse

3 Tuber and Root

4 Sugar Cane


G Other (please specify)

Total emissionestimates

3B.58



Flowchart to be applied to aggregated figures

Total C released(Gg C from all crops)from previous slide

Total N released(Gg N from all crops)from previous slide

MNon-CO2

emission rates(search EFDB)

OConversion

ratios

C-Nemitted

(Gg C emitted asCH4 or CO;

Gg N emitted asN2O or NOX)

P1CH4 emited(Gg CH4)

P2CO emited

(Gg CO)

P3N2O emited(Gg N2O)

P4NOX emited(Gg NOX)

EFs:If no CS values,

use defaults(Table 4-16, Reference Manual,

1996 Revised Guidelines)

3B.59


Emission factors

3B.60

FIELD BURNING OF CROP RESIDUESEmission estimates using CS values

Wheat residues (1 of 3)

MODULE AGRICULTURE

SUBMODU

LE FIELD BURNING OF AGRICULTURAL RESIDUES

WORKSHE

ET 4-4

SHEET 1 OF 3

COUNTRY

FICTICIOUS

YEAR 2002

STEP 1 STEP

2 STEP 3


(specify locally

Annual Residue

toQuantity of

Dry Matter

Quantity of

Fraction Fractio

n Total

Biomass

important Production Crop Ratio

ResidueFractio

nDry

ResidueBurned

in Oxidise

d Burned

crops) Fields

(Gg crop) (Gg biomass) (Gg dm) (Gg dm)

C = (A x B) E = (C x

D)

H = (E x F xG)

Wheat 18.350,50 1,50 27.525,8 0,9024.773,

20,12 0,96 2.735,0

AD fromnational statistics

CS activity data,from research and

monitoring

3B.61


Wheat residues (2 of 3) MODULE AGRICULTURE


WORKSHEET 4-4

SHEET 2 OF 3

COUNTRY FICTICIOUS

YEAR 2002

STEP 4 STEP 5

I J K L



Crops Residue

(Gg C) (Gg N)

J = (H x I) L = (J x K)

Wheat 0,45 1.230,7 0,0032 3,94

CS activity data,from research and

monitoring

3B.62


Wheat residues (3 of 3) MODULE AGRICULTURE


WORKSHEET 4-4

SHEET 3 OF 3

COUNTRY FICTICIOUS

YEAR 2002

STEP 6

M N O P

Gas Emission Ratio EmissionsConversion

RatioEmissions

(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/N2OD for CO/NOX

3B.63

FIELD BURNING OF CROP RESIDUESEmission estimates using default

valuesWheat residues (1 of 3)

MODULE AGRICULTURE

SUBMODULE

FIELD BURNING OF AGRICULTURAL RESIDUES

WORKSHEET

4-4

SHEET 1 OF 3

COUNTRY FICTICIOUS

YEAR 2002

STEP 1 STEP 2 STEP

3


(specify locally

Annual Residue

toQuantit

y of Dry

Matter Quantit

y of Fracti

on Fraction

Total Biomass

important

Production

Crop Ratio

Residue FractionDry

Residue

Burned in

Oxidised Burned

crops) Fields

(Gg crop)

(Gg biomass

)

(Gg dm)

(Gg dm)

EF ID= 43555

C = (A x B)

EF ID= 43636

E = (C x D)

EF ID= 45941

H = (E x F xG)

Wheat18.350,

51,30

23.855,7

0,8319.800

,20,12 0,94 2.140,4CS value,

from monitoring orexpert judgment

AD:1. from

national statistics, or2. from FAO database:

(www.fao.org, then “FAOSTAT-Agriculture” and “Crops primary”)

Activity data,taken from EFDB

3B.64

FIELD BURNING OF CROP RESIDUESEmission estimates using default

valuesWheat residues (2 of 3)

Default activity data,from EFDB

MODULE AGRICULTURE


WORKSHEET 4-4

SHEET 2 OF 3

COUNTRY FICTICIOUS

YEAR 2002

STEP 4 STEP 5

I J K L



Crops Residue

(Gg C) (Gg N)

J = (H x I) L = (J x K)

Wheat 0,48 1.027,4 0,012 12,33

EF ID= 43716 EF ID= 43796

3B.65


Wheat residues (3 of 3)

Default values,from EFDB

MODULE AGRICULTURE


WORKSHEET 4-4

SHEET 3 OF 3

COUNTRY FICTICIOUS

YEAR 2002

STEP 6

M N O P

Emission

RatioEmissions 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

3B.66

FIELD BURNING OF CROP RESIDUESDifferences in emission estimates

If CS or D values are used

Emissions Emissions Per cent

Gas emitted

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%

3B.67

Prescribed Burning of Savannas


3B.68

PRESCRIBED BURNING OF SAVANNASMain 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)

• 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

• Only one methods is available to estimate emissions from this source category

3B.69

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.70

1. OPEN THE IPCC SOFTWARE AND CHOOSE THE YEAR OF THE INVENTORY

2. GO TO THE MENU BAR AND CLICK IN “SECTORS” AND THEN IN “AGRICULTURE”

3. OPEN THE SHEET 4-3s14. FILL IN WITH THE DATA MODULE AGRICULTURE

SUBMODULE PRESCRIBED BURNING OF SAVANNAS

WORKSHEET 4-3

SHEET 1 OF 3


YEAR 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) (Gg dm) (Gg dm) (Gg dm)

C = (A x B) E = (C x D) G = (E x F) H = (E - G)

15,5 7 108,50 0,85 92,23 0,45 41,50

50,72

0,00 0,00 0,00

0,00

Sources for AD on categories of savannas andarea covered by category:

1. National statistics2. National mapping systems

Sources for AD on biomass density:1. National statistics

2. National vegetation surveys and mapping3. National expert judgment

4. Data provided by third countries with similar features5. IPCC defaults (Table 4-14, Reference Manual, 1996

Revised Guidelines)

The first 3 steps isto determine:

1. the categories ofsavannas existing per

ecological unit2. the area burned

per category3. the biomass density

per category

3B.71

PRESCRIBED BURNING OF SAVANNASFlow chart to estimate non-CO2 emissionsTo be applied to each savanna category

BBiomass density

(ton dm/ha)

AArea burned

(k ha)

CTotal biomass

exposed to burning(Gg dm)

EBiomass actually

Burned(Gg dm)

FF of living

biomass burnedG

Living biomassactually burned

(Gg dm)

DF actually burned

HDead biomass

actually burned(Gg dm)

Ideally, CS valuesbased on measurements.If not, CS values based

on expert judgment.If not, default values

(search EFDB)

3B.72

5. GO SHEET 4-3s2 IN “SECTORS/AGRICULTURE” OF THE IPCC SOFTWARE6. FILL IT WITH THE DATA

MODULE AGRICULTURE


WORKSHEET 4-3

SHEET 2 OF 3


YEAR 2002

STEP 3

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 0,45 16,81

Dead 0,95 48,19 5 240,94

Living 0,00 0,00

Dead 0,00 0,00

3B.73


GLiving biomass

actually burned (Gg dm)from previous slide

HDead biomass

actually burned (Gg dm)from previous slide

Flow chart to estimate non-CO2 emissionsApplicable per each savanna category

I1Fraction of livingbiomass oxidised

(Gg dm)

I2Fraction of deadbiomass oxidised

(Gg dm)

J1Oxidised living

biomass(Gg dm)

J2Oxidised dead

biomass(Gg dm)

K1C fraction of

living biomass

K2C fraction of

dead biomass

L2C released fromdead biomass

(Gg C)

L1C released fromliving biomass

(Gg C)

LTotal C released

(Gg C)

MN/C ratio

NTotal N released

(Gg N)

If no CS values,defaults in EFDB, as

combustion efficiency

3B.74

7. GO TO SHEET 4.3s3 IN “SECTORS/AGRICULTURE”8. FILL IT GO THE DATA

MODULE

AGRICULTURE


WORKSHEET 4-3

SHEET 3 OF 3


YEAR 2002

STEP 4 STEP 5

L M N O P Q R

Total Carbon

Released

Nitrogen- Carbon

Ratio

Total Nitrogen Content

Emissions

Ratio

Emissions Conversion

Ratio

Emissions from Savanna Burning

(Gg C) (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

257,75 0,015 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

TOTAL EMISSIONESTIMATES

3B.75

9. GO TO “OVERVIEW” MODULE8. OPEN THE WORKSHEET 4S2TABLE 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

13 Other (please specify) 0

C Rice Cultivation 0

1 Irrigated 0

2 Rainfed 0

3 Deep Water 0


D Agricultural Soils 0

E Prescribed Burning of Savannas 1 0 2 36

F Field Burning of Agricultural Residues (1) 44 1 33 921

1 Cereals

2 Pulse

3 Tuber and Root

4 Sugar Cane


G Other (please specify)

Total emission estimatesFrom Savanna Burning

3B.76


LTotal C released

(Gg C)from previous slide

NTotal N released

(Gg N)from previous slide

ON2O & NOx

emission rates

OCH4 & CO

emission rates

PN2O-N released

(Gg N)

PCH4-C released

(Gg C)

PNOx-N released

(Gg N)

PCO-C released

(Gg C)

QN2O & NOx

conversion rates

QCH4 & CO

conversion rates

R N2O emitted

(Gg N2O)

RNOx emitted

(Gg NOX)

RCH4 emitted

(Gg CH4)

RCO emitted

(Gg CO)

If no CS EFs,defaults in EFDB

Applicable to aggregated figures

3B.77

PRESCRIBED BURNING OF SAVANNASExamples of default emission factors

3B.78


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.79


Emission estimates using CS values

STEP 1 STEP 2

A B C D E F G H

Savanna category

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)

North15,5 7,00 108,50 0,85 92,23 0,55 50,72

41,50

Centre

145,8 5,00 729,00 0,95 692,55 0,50 346,28

346,28

South22,0 4,00 88,00 1,00 88,00 0,45 39,60

48,40

Totals

436,60

436,18

AD from national statistics(census, surveys, mapping)

CS values(field measurements, expert’s

judgment)

3B.80



STEP 3

I J K L

Savanna category

Biomass type

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)

NorthLiving 0,9 37,35 0,4 14,94

Dead 0,95 48,19 0,45 21,68

CentreLiving 0,9 324,77 0,4 129,91

Dead 0,95 280,48 0,45 126,22

SouthLiving 0,9 41,38 0,4 16,55

Dead 0,95 35,74 0,45 16,08

TotalsLiving 403,50 325,39

Dead 364,41

CS values(field measurements, lab

analysis, expert’s judgment)

3B.81




WORKSHEET 4-3

SHEET 3 OF 3

COUNTRY CHILE

YEAR 2002

STEP 4 STEP 5

M N O P Q R

Nitrogen-

Carbon Ratio

Total Nitrogen

Content

Emissions Ratio

Emissions

Conversion

Ratio

Emissions from

Savanna Burning

(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

0,0142 4,88 P = (N x

O)

R = (P x Q)

0,006 0,03 44/28 N2O 0,05

0,121 0,59 46/14 NOx 1,94

CS values for CH4 & N2OD values for CO & NOx

3B.82


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 0,95 103,08 0,55 56,69

EF ID= 43475

EF ID= 43485

EF ID= 43518

46,38

145,80 6,00 874,80 0,95 831,06 0,55 457,08

EF ID= 43445

EF ID= 43485

EF ID= 43518

373,98

22,00 4,00 88,00 0,95 83,60 0,45 37,62

EF ID= 43480

EF ID= 43485

EF ID= 43515

45,98

551,39

466,34

Default valuestaken from EFDB

AD fromnational statisitcs

3B.83

PRESCRIBED BURNING OF SAVANNASEmission estimates using default

values STEP 3

I J K L

Savanna category

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)

NorthLiving 0,94 53,29 0,4 21,32

Dead 0,94 43,60 0,45 19,62

CentreLiving 0,94 429,66 0,4 171,86

Dead 0,94 351,54 0,45 158,19

SouthLiving 0,94 35,36 0,4 14,15

Dead 0,94 43,22 0,45 19,45

TotalsLiving 518,31 404,59

Dead 438,36

EF ID= 45949 Experts


CS valuestaken from expert’s

judgment

3B.84

PRESCRIBED BURNING OF SAVANNASEmission estimates using default

values


WORKSHEET 4-3

SHEET 3 OF 3

COUNTRY CHILE

YEAR 2002

STEP 4 STEP 5

M N O P Q R

Nitrogen-

Carbon Ratio

Total Nitrogen Content

Emissions

Ratio

Emissions Conversion

Ratio

Emissions from Savanna Burning

(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

0,0095 3,84 P = (N x

O)

R = (P x Q)

EF ID= 45998

0,007 0,03 44/28 N2O 0,04

0,121 0,47 46/14 NOx 1,53

defaults


3B.85


Difference of estimates


Emissions Emissions Per cent

Gas emitted

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%

3B.86

RICE CULTIVATION


3B.87

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 fertiliser 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.88

RICE CULTIVATIONMethodological

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 fertiliser 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 sub-unit 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.89

RICE CULTIVATIONActivity 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 judgment)

harvested area per regional unit (national statistics or mapping agencies)

cropping practices per regional unit (research agencies or expert judgment)

amount/type of organic amendments applied per regional unit, to allow the use of scaling factors (national statistics or international databases or expert judgment)

3B.90

RICE CULTIVATIONMain features from decision-

tree If no rice is produced, then reported as “NO” If not key source:

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

If keysource: 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.91

RICE CULTIVATIONNumerical 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 aireated

3B.92

RICE CULTIVATION

MODULE AGRICULTURE

SUBMODULE METHANE EMISSIONS FROM FLOODED RICE FIELDS

WORKSHEET 4-2

SHEET 1 OF 1


YEAR 2002

A B C D E

Water Management Regime 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

(m2 /1 000 000 000) (g/m2) (Gg)

E = (A x B x C x D)

Irrigated

Continuously Flooded

0,285 1 2 20 11,40

Intermittently Flooded

Single Aeration

0,1 0,5 2 20 2,00

Multiple Aeration

0,00

Rainfed Flood Prone 0,00

Drought Prone 0,00

Deep Water

Water Depth 50-100 cm

0,00

Water Depth > 100 cm

0,00

Totals 0,385 13,40

AD from national statisticsor international databases

(FAO, IRRI)

Scaling factor for watermanagement: local research or

other country’s use or EFDB(Agriculture, Rice Production,

Intermitently Flooded, Single aeration)

Enhancement factor for organicammendements: local research or

taken from the EFDB(Agriculture, Rice Production)

EF: local researchor other country’s use

or from EFDBRegional units, fromnational estatistics ormapping agencies or

expert judgment

3B.93

THANK YOU

SERGIO [email protected]

Documents

AGRICULTURE INVENTORY ELABORATION PART 2 SIMULATION