3b.1 agriculture inventory elaboration part 2. 3b.2 by september 2003, 70 national communications...

3B.1

AGRICULTUREINVENTORY

ELABORATION

PART 2

3B.2

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

Status of national communications from NAI Parties

3B.3


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.4

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.5

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.6

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.7

INVENTORY ELABORATIONPrevious 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.8

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.9

DETERMINATION OF KEY SOURCESCHILE, 1994 GHG inventory (Gg CO2 equivalent) (1)

SECTOR/subsector 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

- 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

- 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 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

- IRONALLEYS<<?iron alloys?>> 36.7 36.7

- PULP/ PAPER; FOODS/DRINKS; COOLING/OTHERS 0.0

SOLVENT USE 0.0 0.0 0.0 0.0

3B.10

DETERMINATION OF KEY SOURCES

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

- AGRICULTURAL RESIDUE BURNING 52.0 607.5 659.5

WASTE: 0.0 1560.3 206.7 1767.0

- SEWAGE WATER TREATMENT: 3.2 3.2

- URBAN SOIL WASTES 1557.1 1557.1

- INDUSTRIAL SOLID WASTES 0.0

- UNTREATED SEWAGE 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 CO2 equivalent) (Non-energy sectors)

DETERMINATION OF KEY SOURCESKEY SOURCES FOR THE 1994 GHG-Inventory of Chile

SECTOR/sub-sector Gg/yr CO2-equiv.Contribution

SectorIndividual Cumulative

- 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

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

3B.12

DETERMINATION OF KEY SOURCES Contribution per sector

Contribution of sectors toChile's GHG emissions

66.8%3.1%

26.7%

3.4% Energy

Industrial Processes

Agriculture

Waste

GHG Inventory of Chile for 1994

3B.13

INVENTORY ELABORATIONMass 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.14

INVENTORY ELABORATION Mass balance

According to experts’ judgement:

UNIT

END USE

ON-SITE OFF-SITE

TO FEED ANIMALS

INCORPORATED IN SOILS

MINERAL-IZED

BURNEDBURNED

(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

TO BE ACCOUN-

TED UNDER

AGRICULTURAL

SOILS

CROP RESIDUES BURNING

ENERY ENERGY

3B.15


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.16


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


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)

UnitClimatic

conditionsDirect

grazing

Under confinement

Anaero-bic

Liquid SolidDaily

spreadOthers

Unit A Dessert 0.10 No No No 0.90 No

Unit BMediterran-

ean0.75 0.10 No 0.10 0.05 No

Unit CCold and

humid0.35 0.35 No 0.20 0.10 No

3B.17

3B.18


Manure from non-dairy cattle, assigned to the different treatment systems: Unit A: total manure producedunit A x Fi

If Fi is 0.90 = Anaerobic lagoon If Fi is 0.10 = direct grazing (Fi= 0 for the rest of the treatment systems)

Unit B: total manure producedunit A x Fj 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.19

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

INVENTORY ELABORATIONSignificance of sub-sources

3B.20

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

INVENTORY ELABORATION Significance of sub-sources

Significance of sub-sources MODULE AGRICULTURE

SUBMODULE METHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK

ENTERIC FERMENTATION AND MANURE MANAGEMENT

WORKSHEET 4-1

SHEET 1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC

FERMENTATION AND MANURE MANAGEMENT

STEP 1 STEP 2 STEP 3

A 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 550 81 44.550 19 10.450 55,00

Non-dairy Cattle 2750 49 134.750 13 35.750 170,50

Buffalo 0 55 0 7 0 0,00

Sheep 2500 5 12.500 0,16 400 12,90

Goats 500 5 2.500 0,17 85 2,59

Camels 125 46 5.750 1,9 237,5 5,99

Horses 75 18 1.350 1,6 120 1,47

Mules & Asses 25 10 250 0,9 22,5 0,27

Swine 5030 1 5.030 7 35.210 40,24

Poultry 15000 NE NE 0,018 270 NE

Totals 206.680 82.545 288,96

22%

65% SIGN.

<3%

6%

13%

43% SIGN.

<1%

43% SIGN.

<1%<1%<1%

<1%

<3%

<3%

<3%<3%

<1%

3B.21

3B.22

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.23

Enteric Fermentation

3B.24

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.25

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 sourcecategory is key source or not and on the relative

importance of the subcategory within the source category

3B.26

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

Low level of data availability

Species/category Number of animals (million)

Dairy cattle* 1.0

Non-dairy cattle 5.0

Buffalo 0

Sheep 3.0

Goat 0.05

Camels 0

Horses 0.01

Mules and asses 0

Swine 1.5

Poultry 4.0

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):

* Disaggregation between dairy and non-dairy cattle based on expert’s judgement.

3B.27

3B.28

Determination of significantsub-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))

Determining significant animal speciesMODULE 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 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.29

3B.30

Enhanced characterization ofnon-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).

Enhanced characterization of non-dairy cattle (1)

Parameter Symbol Cows Steers Young Comments

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 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

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

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 0.49 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

Gross energy intake (MJ/day)

GE 139.3 130.4 117.7 Calculated using equation 4.11, GPG2000

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

3B.33

Tier 2 estimation of CH4 emissions fromenteric fermentation by non-dairy cattle

Enhanced characterization yielded AD (average daily gross energy intake) for three types of non-dairy 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

Tier 2 estimation of CH4 emissions from enteric fermentation by non-dairy cattle


Gross energy intake (MJ/day) (from enhanced characterization)


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 Expert judgement, industry data

Population of group (thousand heads)

- 2 000 2 000 1 000

CH4 emissions

(Gg CH4/yr)

- 110 103 46

3B.34

3B.35


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.36

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)

Medium Level of Data Availability

Climate region

Production system

Population (thousand heads)

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 - 2 254 1 841 1 094

New total: 5,153,000 heads (against FAO: 5,000,000 heads).

3B.37

3B.38


Enhanced characterization yielded AD (average daily gross energy intake) for 18 classes of non-dairy 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 ‘Warm-Extensive Grazing’ and three for ‘Temperate-Intensive Grazing’)

Enhanced characterization, non-dairy cattleWarm Climate, Extensive Grazing (1)


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 GPG2000, and expert judgement

Females giving birth (%) - 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





Comments in green indicate improvements over previous example.

3B.39

Enhanced characterization, non-dairy cattleWarm Climate, Extensive Grazing (2)


Growth coefficient 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


Net energy pregnancy (MJ/day)


Portion of GE availablefor maintenance


Portion of GE available for growth




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)


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 GPG2000, and expert judgement

Females giving birth (%) - 81 - - Country-specific data


Maintenance coefficient Cfi 0.335 0.322 0.322 Table 4-4 GPG2000





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


Net Energy Pregnancy (MJ/day)


Portion of GE that is available for maintenance


Portion of GE that is available for growth.


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

3B.43


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)


Climate region

Production system

EF (kg CH4/head/yr)

Cows Steers Young


63.8 66.9 43.8

Intensive grazing 47.7 51.5 48.4

Feedlot 41.5 49.3 52.8


61.5 66.7 49.5


Feedlot 41.5 49.3 52.8

3B.44

3B.45


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

Medium level of data availabilityMODULE AGRICULTURE


WORKSHEET 4-1



YEAR 2003







Management


Management


Domestic Livestock


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.46

3B.47

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.48


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.49


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)

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

3B.51

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.52

Manure Management:CH4 Emissions

3B.53

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


Species/category Number of animals (million)

Dairy cattle* 1.0

Non-dairy cattle 5.0

Buffalo 0

Sheep 3.0

Goat 0.05

Camels 0

Horses 0.01

Mules and Asses 0

Swine 1.5

Poultry 4.0

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):

* Disaggregation between dairy and non-dairy cattle, based on expert`s judgement.

3B.54


MODULE AGRICULTURE


WORKSHEET 4-1



YEAR 2003







Management


Management


Domestic Livestock


Dairy Cattle 1000 57 57,000.00 1.6 1,600.00 58.60

Non-dairy Cattle 5153 57 293,721.00 1.6 8,244.80 301.97

Buffalo 0 55 0.00 1.6 0.00 0.00

Sheep 3000 5 15,000.00 0.196 588.00 15.59

Goats 50 5 250.00 0.2 10.00 0.26

Camels 0 46 0.00 2.32 0.00 0.00

Horses 10 18 180.00 1.96 19.60 0.20

Mules & Asses 0 10 0.00 1.08 0.00 0.00

Swine 1500 1.5 2,250.00 1.6 2,400.00 4.65

Poultry 4000 0 0.00 0.021 84.00 0.08

Totals 368,401.00 12,946.40 381.35

Worksheet 4-1s1

Significantspecies

3B.55

3B.56


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.57

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.58

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.59

Low level of data availability: CH4 emissions by non-dairy cattle, swine

Tier 2 method requires determination of three parameters to estimate EF: VS (kg): mass of volatile solids excreted Bo (m3/kg of VS): max. CH4 producing capacity; MCF: CH4 conversion factor

For low level of data: 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

Low level of data availability: CH4 emissions frommanure management for non-dairy cattle (default AD and EF) (1)



(from the enhanced characterization)

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

Feed digestibility (%) 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

*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 frommanure management for non-dairy cattle (default AD and EF) (2)

Parameter Symbol Cows Steer Young Comments

Methane conversion factor (%)

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)

Emission factor

(kg CH4/head/yr)



- 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

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 usedinstead of the default (1.6 kg CH4/head/yr) in IPCC Software

3B.61

Low level of data availability: CH4 emissions frommanure management for Swine (default AD and EF) (1)

Parameter Symbol Warm solid

Warm liquid

Temp.

solid

Temp.

liquidComments



GE 13.0 13.0 13.0 13.0 Default value, Table B-2, p. 4.42, IPCC-GL V3


- 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

Feed digestibility (%) DE 50 50 50 50 IPCC-GL V3, p. 4.23


ASH 8 8 8 8 IPCC-GL V3, p. 4.23


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

3B.62

Low level of data availability: CH4 emissions frommanure management for Swine (default AD and EF) (2)

Parameter Symbol Warm

solid

Warm

liquid

Temp

solid

Temp

liquidComments


MCF 2 65 1.5 35 Table 4-8, p.4.25, IPCC-GL V3 *

Emission factor

(kg CH4/head/yr)

EF 0.5 15.6 0.4 8.4 Calculated using equation 4.17, GPG2000


- 810 90 540 60 FAO Database, local experts, industry

CH4 emissions

(Gg CH4 /yr)

- 0.39 1.40 0.19 0.50 Total emissions:

2.5 Gg CH4 /yr

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 beused instead of the default (1.6 kg CH4/head/yr) in IPCC Software,

* Liquid/slurry was assumed to be the only system used. GPG2000 provides slightlydifferent default values (Table 4.10), as well as a formula for accounting for recovery,flaring, and use of biogas.

3B.63

Low level of data availability: resultsMODULE AGRICULTURE


WORKSHEET 4-1



YEAR 2003







Management


Management


Domestic Livestock


Dairy Cattle 1000 57 57,000.00 1.6 1,600.00 58.60

Non-dairy Cattle 5153 57 293,721.00 1.2 6,183.60 299.90

Buffalo 0 55 0.00 1.6 0.00 0.00

Sheep 3000 5 15,000.00 0.196 588.00 15.59

Goats 50 5 250.00 0.2 10.00 0.26

Camels 0 46 0.00 2.32 0.00 0.00

Horses 10 18 180.00 1.96 19.60 0.20

Mules & Asses 0 10 0.00 1.08 0.00 0.00

Swine 1500 1.5 2,250.00 1.7 2,550.00 4.80

Poultry 4000 0 0.00 0.021 84.00 0.08

Totals 368,401.00 11,035.20 379.44

3B.64

3B.65


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

Medium level of data availability (Swine)

Climate region

Manure management

system


Sows Boars Young

Warm Pasture/range/paddock

121 30 490

Liquid/slurry 8 3 40

Anaerobic lagoon 2 2 9

Temperate Pasture/range/paddock

130 36 555

Liquid/slurry 5 1 24

Anaerobic lagoon 8 1 40

Total - 274 73 1 158

New Total: 1,505,000 heads (FAO: 1,500,000)

3B.66

3B.67

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

Medium level of data availability: CH4 manure management, non-dairy cattle under ‘Warm, Intensive Grazing’ (CS-AD) (1)




GE 121.2 130.8 123.0 Country-specific values calculated using equation 4.11, GPG2000 *



Feed intake

(kg dm/day)

- 6.57 7.09 6.67 Calculated


Ash content of manure (%) ASH 8 8 8 IPCC-GL V3, p. 4.23



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.

* 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)



MCF 2.0 2.0 2.0 Table 4-8, p.4.25, IPCC-GL V3

Emission factor

(kg CH4/head/yr)



- 228 414 120 Country-specific data

CH4 emissions

(Gg CH4 /yr)

- 0.26 0.51 0.14

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 Comments



GE 9.0 9.0 13.0 Country-specific data (or from the enhanced characterization)



Feed intake

(kg dm/day)

- 0.49 0.49 0.70 Calculated



ASH 4 4 4 IPCC-GL V3, p. 4.23



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

3B.70

Medium level of data availability: CH4 manure management, swine under ‘Warm, Liquid/Slurry’ (CS-AD) (2)

Parameter Symbol Sows Boars Young Comments

Methane conversion Factor (%)

MCF 72 72 72 Table 4-8, p.4.25, IPCC-GL V3

Emission factor

(kg CH4/head/yr)



- 8 3 40 Country-specific data

CH4 emissions

(Gg CH4 /yr)

- 0.09 0.04 0.68

In this case, the country has its own estimation for feed/gross energy intake, feeddigestibility, 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 wasadjusted 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

Production system

EF (kg CH4/head/yr)

Cows Steers Young


1.7 1.8 1.2


Feedlot 28.8 34.2 36.6


1.2 1.3 0.9


Feedlot 23.2 27.6 29.6

Weighted EF: 3.2 kg CH4/head/yrUse this value in IPCC Software

3B.72

Medium level of data availability: swine, EF estimated by tier 2, with CS AD

Climate region

Manure management

system

EF (kg CH4/head/yr)

Sows Boars Young

Warm 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

Temperate 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/yrUse this value in IPCC Software

3B.73

Medium level of data availability: results

MODULE AGRICULTURE


WORKSHEET 4-1



YEAR 2003







Management


Management


Domestic Livestock


Dairy Cattle 1000 57 57,000.00 1.6 1,600.00 58.60

Non-dairy Cattle 5153 57 293,721.00 3.2 16,489.60 310.21

Buffalo 0 55 0.00 1.6 0.00 0.00

Sheep 3000 5 15,000.00 0.196 588.00 15.59

Goats 50 5 250.00 0.2 10.00 0.26

Camels 0 46 0.00 2.32 0.00 0.00

Horses 10 18 180.00 1.96 19.60 0.20

Mules & Asses 0 10 0.00 1.08 0.00 0.00

Swine 1505 1.5 2,257.50 1.9 2,859.50 5.12

Poultry 4000 0 0.00 0.021 84.00 0.08

Totals 368,408.50 21,650.70 390.06

Worksheet 4-1s1

Weighted EF

3B.74

3B.75

Manure Management:N2O Emissions

3B.76

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.77

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)

Livestock characterization to estimate N2O emissions from manure management

Livestock Climate AWMSPopulation

(1000s)Fraction of

Total Pop.(%)Dairy cattle Warm Liquid/slurry 60 6.0

Anaerobic lagoon 60 6.0

Temperate Liquid/slurry 40 4.0


Non-dairy cattle

Warm Liquid/slurry 114 2.2




Swine Warm Liquid/slurry 51 3.4




Poultry All With bedding 1 600 40

Without bedding 2 400 60

In case the country does not have this information, IPCC-GL provides defaultAD for different animal waste management systems (AWMS) in different regions(Table 4-21 V3).

3B.78

3B.79

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.80

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)

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)

N retention

N excretion (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

AL 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

Temp L/S 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 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

* MMS = Manure management system L/S = Liquid/slurry AL = Anaerobic lagoon

3B.81

3B.82

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

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 ANAEROBIC LAGOONS

SHEET NITROGEN EXCRETION FOR ANIMAL WASTE MANAGEMENT SYSTEM


YEAR 2003

A B C DLivestock Type Number of Animals Nitrogen Excretion

NexFraction of Manure

Nitrogen per AWMS (%/100)

Nitrogen Excretion per AWMS, Nex

(# of animals) (kg//head/(yr) (fraction) (kg/N/yr)D = (A x B x C)

Non-dairy Cattle 5153000 56 0.03 8,657,040.00

Dairy Cattle 1000000 70 0.1 7,000,000.00

Poultry 4000000 0 0.00

Sheep 3000000 0 0.00

Swine 1500000 16 0.042 1,008,000.00

Others 0.00

TOTAL 16,665,040.00

Estimated

IPCC Default

IPCC Default

Data from livestock characterization

3B.83

MODULE AGRICULTURE



SPECIFY AWMS LIQUID SYSTEMS



YEAR 2003





(1000s) (kg//head/(yr) (fraction) (kg/N/yr)

D = (A x B x C)

Non-dairy Cattle 5153000 56 0.03 8,657,040.00

Dairy Cattle 1000000 70 0.1 7,000,000.00

Poultry 4000000 0 0.00

Sheep 3000000 0 0.00

Swine 1500000 16 0.054 1,296,000.00

Others 0.00

TOTAL 16,953,040.00


Calculated

IPCC Default

IPCC Default


3B.84

MODULE AGRICULTURE



SPECIFY AWMS OTHER (POULTRY MANURE WITH BEDDING)



YEAR 2003





(1000s) (kg//head/(yr) (fraction) (kg/N/yr)

D = (A x B x C)

Non-dairy Cattle 0.00

Dairy Cattle 0.00

Poultry 4000000 0.6 0.6 1,440,000.00

Sheep 0.00

Swine 0.00

Others 0.00

TOTAL 1,440,000.00


IPCC Default


3B.85

MODULE AGRICULTURE



SPECIFY AWMS OTHER (POULTRY MANURE WITHOUT BEDDING)



YEAR 2003





(1000s) (kg//head/(yr) (fraction) (kg/N/yr)D = (A x B x C)

Non-dairy Cattle 0.00

Dairy Cattle 0.00

Poultry 4000000 0.6 0.4 960,000.00

Sheep 0.00

Swine 0.00

Others 0.00

TOTAL 960,000.00


IPCC Default


3B.86

MODULE AGRICULTURE


WORKSHEET 4-1

SHEET 2 OF 2 NITROUS OXIDE EMISSIONS FROM ANIMAL PRODUCTION EMISSIONS FROM ANIMAL WASTE MANAGEMENT SYSTEMS (AWMS)


YEAR 2003

STEP 4A B C

Animal Waste Nitrogen Excretion Emission Factor For Total Annual Emissions Management System Nex(AWMS) AWMS of N2O (AWMS) EF3

(kg N/yr) (kg N2O–N/kg N) (Gg)

C=(AxB)[44/28] / 1 000 000

Anaerobic lagoons 16,665,040.00 0.001 0.03

Liquid systems 16,953,040.00 0.001 0.03

Daily spread 960,000.00

Poultry manure with bedding 1,440,000.00 0.02 0.05

Pasture range and paddock 0.00

Poultry manure w/o bedding 960,000.00 0.005 0.01

Total 36,978,080.00 Total 0.11

Use of IPCC software for estimating N2O from manure management

IPCC Default

IPCC Default

IPCC defaults obtained from Table 4-22, IPCC-GL V3, and Tables 4.12 and 4.13, GPG2000.

IPCC Default

IPCC Default

Note: cells corresponding to poultry were manually altered to accommodatethese new categories from GPG2000, not included in IPCC-GL.

3B.87

3B.88

Direct N2O Emissions from Agricultural Soils

AnthropogenicN inputs to soils

Mineral fertilizers

Histosols cultivation

N-fixing crops

Sewage sludges

Crop residues

Animal manures

Fraction of …(from the mass balance)

Other practicesdealing with soil N

3B.89

Assess individual contribution of different N sources to determineones (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-specificAD1, default AD2 (parameters) and default 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.90

3B.91

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)

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

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.92

3B.93

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.94

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: 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 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:

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

Emissions of N2O from synthetic fertilizers

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 default)

3B.95

3B.96

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

Emissions of N2O from animal manure (1)

Country’s estimate

From Table 4-17IPCC Guidelines V2

MODULE AGRICULTURE


WORKSHEET 4-5A (SUPPLEMENTAL)

SHEET 1 OF 1 MANURE NITROGEN USED


YEAR 2003

A B C D E FTotal Nitrogen Fraction of Nitrogen Fraction of Nitrogen Fraction of Nitrogen Sum Manure Nitrogen Used

Excretion Burned for Fuel Excreted During Excreted Emitted as (corrected for NOX and Grazing NOX and NH3 NH3 emissions), FAW

(kg N/yr) (fraction) (fraction) (fraction) (fraction) (kg N/yr)

F = 1 - (B + C + D) F = (A x E)

249,240,080.00 0 0.7 0.2 0.10 24,924,008.00

Data from livestockcharacterization

3B.97

MODULE AGRICULTURE


WORKSHEET 4-5



YEAR 2003

STEP 1 STEP 2A B C


EF1


C = (A x B)/1 000 000


Animal waste (FAW) 24,924,008.00 0.0125 0.31

N-fixing crops (FBN) 0.0125 0.00

Crop residue (FCR) 0.0125 0.00

Total 1.72

Emissions of N2O from animal manure (2)

IPCC default

3B.98

3B.99

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

MODULE AGRICULTURE


WORKSHEET 4-5



YEAR 2003

STEP 1 STEP 2A B C


EF1


C = (A x B)/1 000 000


Animal waste (FAW) 24,924,008.00 0.0125 0.31

N-fixing crops (FBN) 27748000 0.0125 0.35

Crop residue (FCR) 0.0125 0.00

Total 2.06

Emissions of N2O from N-fixing crops

IPCC defaultEstimatedactivity data

3B.100

3B.101

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

Crop residues: determination of FCR

Crop Crop (1000 t)

(1)

Res/Crop

(2)

FracDM

(2)

FracNCR

(2)

FracBURN

(3)

FracFUEL

(3)

FracFOD

(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

Total --- --- --- --- --- --- --- 37,934

(1) Source: FAO statistics(2) Source: Table 4.16, GPG2000 (except FracDM for potatoes, which was estimated by experts)(3) Source: Country-specific data FCR

3B.102

MODULE AGRICULTURE


WORKSHEET 4-5



YEAR 2003

STEP 1 STEP 2A B C


EF1


C = (A x B)/1 000 000


Animal waste (FAW) 24,924,008.00 0.0125 0.31

N-fixing crops (FBN) 27748000 0.0125 0.35

Crop residue (FCR) 37,934,124.00 0.0125 0.47

Total 2.54

N2O emissions from N in crop residues

Total direct N2O emissions (excluding pasture, range and paddock): 2.54 Gg N2O-N/yr

IPCC default

3B.103

MODULE AGRICULTURE



SPECIFY AWMS PASTURE RANGE AND PADDOCK



YEAR 2003





(1000s) (kg//head/(yr) (fraction) (kg/N/yr)D = (A x B x C)

Non-dairy Cattle 5153000 40 0.95 195,814,000.00

Dairy Cattle 1000000 70 0.2 14,000,000.00

Poultry 4000000 0.00

Sheep 3000000 0.00

Swine 1500000 16 0.102 2,448,000.00

Others 0.00

TOTAL 212,262,000.00

N excretion from pasture/range/paddock

Default values3B.104

N2O emissions from pasture/range/paddock

MODULE AGRICULTURE


WORKSHEET 4-5

SHEET 3 OF 5 NITROUS OXIDE SOIL EMISSIONS FROM GRAZING ANIMALS - PASTURE RANGE AND PADDOCK


YEAR 2003

STEP 5A B C

Animal Waste Nitrogen Excretion Emission Factor for Emissions Of N2O from

Management System Nex(AWMS) AWMS Grazing Animals

(AWMS) EF3 (kg N/yr) (kg N2O–N/kg N) (Gg)

C = (A x B)[44/28]/1 000 000

Pasture range & paddock 212,262,000.00 0.02 6.67


3B.105

3B.106

Indirect N2O Emissions from Agricultural Soils

3B.107

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

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 4.18GPG2000

Default value

3B.108

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 and runoff


From Table 4.18GPG2000

3B.109

3B.110

Field Burning of Crop Residues

3B.111

• 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 non-

collectable 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

Burning of crop residuesMain issues derived from the decision tree

3B.112

• 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)

Burning of crop residues

3B.113

MODULE AGRICULTURE

SUBMODUL

E FIELD BURNING OF AGRICULTURAL RESIDUES

WORKSHEE

T 4-4

SHEET 1 OF 3

COUNTRY FICTICIOUS LAND

YEAR 2002


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) (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 0N “SECTORS” IN THE MENU BAR, AND THEN CLICK ON AGRICULTURE3. OPEN SHEET 4-4s2

Main residue-producing crops:Cereals (wheat, barley, oats, rye, rice,maize, sorghum)SugarcanePulses (peas, beans, lentils)Potatoes, peanut, others

Identify theexisting residue-producing crops

3B.114

B. Residue/cropratio

A. Annual cropproduction

(Gg)

C. Quantity ofresidues

(Gg biomass)

Field burning of crop residuesWorksheet 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 tonne/ha)3. From FAO DB

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

judgement3. Values from countrieswith similar conditions

4. Default values(search EFDB)

3B.115

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

judgement3. Values from countrieswith similar conditions4. IPCC default values

(search EFDB)


Flowchart to be applied to each crop

3B.116

E. Quantity ofdry residue

(Gg dm)from previous slide

F. Fraction burnedin fields

H. Total biomassburned

(Gg dm burned)

G. Fractionoxidized



(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)



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

MODULE AGRICULTURE

SUBMODULE FIELD BURNING OF AGRICULTURAL RESIDUES

WORKSHEET 4-4

SHEET 2 OF 3


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.117

3B.118

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



Worksheet 4-4, sheet 3

5. OPEN 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.119

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.120

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)

PCH4 emitted

(Gg CH4)

PCO emitted

(Gg CO)

PN2O emitted

(Gg N2O)

PNOX emitted

(Gg NOX)

EFs:If no CS values,

use defaults(Table 4-16, Reference Manual,

Rev. 1996 IPCC Guidelines)


Flowchart to be applied to aggregated figures

3B.121

Field burning of crop residues

Emission factors

3B.122

Field burning of crop residuesEmission estimates using country-specific values

Wheat residues (1 of 3)

MODULE AGRICULTURE

SUBMODUL

E FIELD BURNING OF AGRICULTURAL RESIDUES

WORKSHEE

T 4-4

SHEET 1 OF 3

COUNTRY FICTICIOUS

YEAR 2002



(specify locally

Annual Residue to Quantity of Dry

Matter Quantity of Fraction Fraction

Total Biomass

important Production Crop Ratio Residue FractionDry

ResidueBurned 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)

Wheat 18.350,50 1,50 27.525,8 0,90 24.773,2 0,12 0,96 2.735,0

AD fromnational statistics

CS activity data,from research and

monitoring

3B.123

3B.124

Field burning of crop residues Emission estimates using country-specific values


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.125

Field burning of crop residues Emission estimates using country-specific values


MODULE AGRICULTURE


WORKSHEET 4-4

SHEET 3 OF 3

COUNTRY FICTICIOUS

YEAR 2002

STEP 6

M N O P

Gas 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/N2OD for CO/NOX

Field burning of crop residues Emission estimates using default values

Wheat residues (1 of 3) MODULE AGRICULTURE


WORKSHEET 4-4

SHEET 1 OF 3

COUNTRY FICTICIOUS

YEAR 2002



(specify locally

Annual Residue toQuantity

of Dry Matter

Quantity of

Fraction Fraction Total

Biomass

important Production Crop Ratio Residue FractionDry

ResidueBurned

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)

Wheat 18.350,5 1,30 23.855,7 0,83 19.800,2 0,12 0,94 2.140,4

CS value,from monitoring orexpert judgement

AD:1. from

national statistics, or2. from FAO database:

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

Activity data,taken from EFDB

3B.126

http://www.fao.org/

3B.127

Field burning of crop residuesEmission estimates using default values


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.128

Field burning of crop residuesEmission estimates using CS values


Default values,from EFDB

MODULE AGRICULTURE


WORKSHEET 4-4

SHEET 3 OF 3

COUNTRY FICTICIOUS

YEAR 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

3B.129

Field burning of crop residues Differences in emission estimates

if country-specific or default 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.130

Prescribed Burning of Savannas

3B.131

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.132

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.133

1. OPEN THE IPCC SOFTWARE AND CHOOSE THE YEAR OF THE INVENTORY2. 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 judgement

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.134

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 basedon expert judgement.If not, default values

(search EFDB)

3B.135

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.136


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.137

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.138

9. GO TO “OVERVIEW” MODULE8. 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

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.139


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.140

PRESCRIBED BURNING OF SAVANNASExamples of default emission factors

3B.141


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.142

PRESCRIBED BURNING OF SAVANNASEmission 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



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

41,50

Centre145,8 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

judgement)

3B.143


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 judgement)

3B.144



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.145

PRESCRIBED BURNING OF SAVANNASEmission 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



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.146


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

Default valuestaken from EFDB

CS valuestaken from expert’s

judgement

3B.147



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

defaultsDefault values

taken from EFDB

3B.148

PRESCRIBED BURNING OF SAVANNASDifference 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.149

RICE CULTIVATION

3B.150

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.151

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 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 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.152

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 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.153

RICE CULTIVATIONMain features from decision-tree (1)

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.154

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 aereated

3B.155

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 judgement

3b.1 agriculture inventory elaboration part 2. 3b.2 by september 2003, 70 national communications...

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