1. 3B.1 AGRICULTURE INVENTORY ELABORATION PART 2

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 data93 per cent emission factors64 per cent methods11 per cent Status of national communications from NAI Parties

3. 3B.3 Status of national communications from NAI Parties NAI countries voluntarily submit their national GHG inventories and NCs By mid-2005, 117 NAI Parties had submitted their first national communication; 3 NAI Parties had submitted their second NC; 1 NAI Party did not include its national inventory Submitted inventories: 82 NAI Parties for 1 year (1994, mainly); 12 NAI Parties for 2 years (1990/94); 18 NAI Parties for 3–4 years; 12 NAI Parties for >4 years 100% NAI Parties included CO 2 ; 99% included CH 4 and N 2 O; 20% included HFCs, PFCs or SF 6

4. 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) Status of national communications from NAI Parties

5. 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) Status of national communications from NAI Parties

6. 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

7. 3B.7 INVENTORY ELABORATION Previous activities Preliminary key-source determination Two ways: Using last year’s GHG inventory data Applying tier 1 methods for all the sectors for the year to be inventoried

8. 3B.8 DETERMINATION OF KEY SOURCES Steps Enumeration of source categories (SC) Ranking SC according to their emissions of CO 2 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

9. 3B.9 DETERMINATION OF KEY SOURCES CHILE, 1994 GHG inventory (Gg CO 2 equivalent) (1) SECTOR/subsectorCO 2- CH 4 N2ON2O TOTALS Gg/year ENERGY36227.01575.2499.138301.3 - ENERGY INDUSTRIES9439.821.231.09492.0 - PROCESSING INDUSTRIES AND CONSTRUCTION9255.233.631.09319.8 - ROAD TRANSPORT12695.344.1310.013049.4 - RESIDENTIAL, COMMERCIAL, INSTITUTIONAL4049.6606.9124.04780.5 - AGRICULTURE, FORESTRY, FISHING787.114.73.1804.9 - C MINING > 195.3 - OIL AND NATURAL GAS 659.4 - OIL REFINING, FUEL STORAGE AND DISTRIBUTION 0.0 INDUSTRIAL PROCESSES1870.044.1248.02162.1 - CEMENT1021.1 - ASPHALT 0.0 - COPPER 0.0 - GLASS 0.0 - CHEMICAL PRODUCTS 44.1248.0292.1 - IRON AND STEEL812.2 - IRONALLEYS >36.7 - PULP/ PAPER; FOODS/DRINKS; COOLING/OTHERS 0.0 SOLVENT USE0.0

10. 3B.10 DETERMINATION OF KEY SOURCES AGRICULTURE:0.06760.38661.315421.6 - RICE CULTIVATION 134.4 - ENTERIC FERMENTATION 5564.8 - MANURE MANAGEMENT 1009.11304.82313.9 - AGRICULTURA SOILS: DIRECT EMISSIONS 4693.9 - AGRICULTURAL SOILS: INDIRECT EMISSIONS 1495.9 - AGRICULTURAL SOILS: PASTURE RANGE/PADDOCK 559.2 - AGRICULTURAL RESIDUE BURNING 52.0607.5659.5 WASTE:0.01560.3206.71767.0 - SEWAGE WATER TREATMENT: 3.2 - URBAN SOIL WASTES 1557.1 - INDUSTRIAL SOLID WASTES 0.0 - UNTREATED SEWAGE WATER RUNOFF 206.7 - INDUSTRIAL LIQUID WASTES 202.9 TOTAL NATIONAL38097.010142.89615.257854.9 1994 GHG inventory of Chile (Gg CO 2 equivalent) (Non-energy sectors)

11. DETERMINATION OF KEY SOURCES KEY SOURCES FOR THE 1994 GHG-Inventory of Chile SECTOR/sub-sectorGg/yr CO 2 -equiv. Contribution Sector IndividualCumulative - Road transport 13049,422,6% Energy - Energy industries 9492,016,4%39,0%Energy - Processing industries and construction 9319,816,1%55,1%Energy - Enteric fermentation 5564,89,6%64,7%Agriculture - Residential, commercial, institutional 4780,58,3%73,0%Energy - Agricultural soils, direct N 2 O 4693,98,1%81,1%Agriculture - Urban solid wastes 1557,12,7%83,8%Residues - Agricultural soils, indirect N 2 O 1495,92,6%86,3%Agriculture - Manure management-N 2 O 1304,82,3%88,6%Agriculture - Cement 1021,11,8%90,4%Energy - Manure management-CH 4 1009,11,7%92,1%Agriculture - Iron and allow 812,21,4%93,5%Industrial Processes - Agriculture, Forestry, Fishing 804,91,4%94,9%Energy - Agricultural residue burning659,51,1%96,0%Agriculture - Oil and natural gas659,41,1%97,2%Industrial Processes - Agricultural soils, pasture range and paddock559,21,0%98,1%Agriculture - Chemical products292,10,5%98,7%Industrial Processes - Waste water runoff206,70,4%99,0%Agriculture/Residues - Industrial liquid residues202,90,4%99,4%Residues - C mining195,30,3%99,7%Energy - Rice production134,40,2%99,9%Agriculture - Sewage water3,20,0%100,0%Energy

12. 3B.12 DETERMINATION OF KEY SOURCES Contribution per sector

13. 3B.13 INVENTORY ELABORATION Mass balance Mass balance for crop residues: To be done for each crop species Example: wheat production in a country with three agroecological units Characteristics of the agroecological units: A: Dessert climate, agriculture only under irrigation B: Mediterranean climate with well-marked four seasons; export agriculture under irrigation C: Rainy and rather cold climate with no dry season; no irrigation

14. 3B.14 INVENTORY ELABORATION Mass balance According to experts’ judgement: UNIT END USE ON-SITEOFF-SITE TO FEED ANIMALS INCORPORATED IN SOILS MINERAL- IZED BURNED BURNED (ENERGY) BIOGASBRIQUETSOTHERS A0.00 0.500.450.00 0.05 B0.10 0.050.350.200.100.05 C0.250.20 0.000.150.00 TO BE ACCOUN- TED UNDER AGRICULTURAL SOILS CROP RESIDUES BURNING ENERYENERGY

15. 3B.15 INVENTORY ELABORATION Mass balance Factors to be applied to total wheat residues: Total wheat residues = total production unit i × (residue/production) factor unit i Total residues burned in: Unit A = total residues unit A × 0.50 Unit B = total residues unit B × 0.35 Unit C = total residues unit C × 0.20

16. 3B.16 INVENTORY ELABORATION Mass balance Mass balance for animal manure Analysis at species level First diversion, confinement and direct grazing Second diversion, under confinement, according to the different manure treatment systems

17. INVENTORY ELABORATION Mass balance Example: non-dairy cattle population in the same country (same three agroecological units already described) First: disaggregation of the national population in agroecological unit populations Second: estimation of total manure produced per agroecological unit Non-dairy cattle (experts' judgement) Unit Climatic conditions Direct grazing Under confinement Anaero- bic LiquidSolid Daily spread Others Unit ADessert0.10No 0.90No Unit B Mediterran- ean 0.750.10No0.100.05No Unit C Cold and humid 0.35 No0.200.10No 3B.17

18. 3B.18 INVENTORY ELABORATION Mass balance Manure from non-dairy cattle, assigned to the different treatment systems: Unit A: total manure produced unit A x F i If F i is 0.90 = Anaerobic lagoon If F i is 0.10 = direct grazing (F i = 0 for the rest of the treatment systems) Unit B: total manure produced unit A x F j If F j is 0.75 = Direct grazing If F j is 0.10 = Anaerobic lagoon If F j is 0.20 = Solid systems If F j is 0.05 = Other systems (F j = 0 for the rest of the treatment systems) Unit C: total manure produced unit A x F k If F k is 0.35 = Direct grazing If F k is 0.35 = Anaerobic lagoon If F k is 0.20 = Solid systems If F k is 0.10 = Other systems (F k = 0 for the rest of the treatment systems)

19. 3B.19 Significance of animal species: Example for CH 4 linked to enteric fermentation and manure management CH 4 emissions estimated by tier 1 method Country as a whole, without division into agroecological units INVENTORY ELABORATION Significance of sub-sources

20. 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

21. Significance of sub-sources MODULEAGRICULTURE SUBMODULEMETHANE AND NITROUS OXIDE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC FERMENTATION AND MANURE MANAGEMENT WORKSHEET4-1 SHEET1 OF 2 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC FERMENTATION AND MANURE MANAGEMENT STEP 1 STEP 2STEP 3 ABCDEF Livestock TypeNumber 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 Cattle5508144.5501910.45055,00 Non-dairy Cattle275049134.7501335.750170,50 Buffalo0550700,00 Sheep2500512.5000,1640012,90 Goats50052.5000,17852,59 Camels125465.7501,9237,55,99 Horses75181.3501,61201,47 Mules & Asses25102500,922,50,27 Swine503015.030735.21040,24 Poultry15000NE 0,018270NE Totals 206.680 82.545288,96 22% 65% SIGN. <3% 6% 13% 43% SIGN. <1% 43% SIGN. <1% <3% <1% 3B.21

22. 3B.22 INVENTORY ELABORATION Simulation for: Enteric fermentation – CH 4 emissions Manure management – CH 4 and N 2 O emissions Agricultural soils – N 2 O emissions Prescribed burning of savannas – non-CO 2 gas emissions Burning of crop residues – non-CO 2 gas emissions Rice cultivation – CH 4 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

23. 3B.23 Enteric Fermentation

24. 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

25. 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 source category is key source or not and on the relative importance of the subcategory within the source category

26. 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 (AD 2 ) are still required along with default/regional EFs 3) High level of data availability good country-specific AD and EFs

27. Low level of data availability Species/categoryNumber 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. 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

28. 3B.28 Determination of significant sub-source categories Species contributing to 25% or more of emissions should have ‘enhanced’ characterization and tier 2 method should be applied Perform a rough estimation of CH 4 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))

29. Determining significant animal species >25% Worksheet 4-1s1 Conclusion: Tier 2 method, supported by an enhanced characterization, for the non-dairy cattle. No other significant species 3B.29

30. 3B.30 Enhanced characterization of non-dairy cattle population Enhanced characterization requires information additional to that provided by FAO statistics. Consultation with local experts or industry is valuable. Assume that (using the above information sources) the inventory team determines that the non-dairy cattle population is composed of: Cows – 40% Steers – 40% Young growing animals – 20% Each of these categories must have an estimate of feed intake and an EF to convert intake to CH 4 emissions. Procedure is described in IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (GPG2000) (pages 4.10–4.20).

31. Enhanced characterization of non-dairy cattle (1) ParameterSymbolCowsSteersYoungComments Weight (kg)W400450230Table A-2, IPCC-GL V3 Weight gain (kg/day)WG000.3Table A-2, IPCC-GL V3 Mature weight (kg)MW400450425Table A-2, IPCC-GL V3 Feeding situationCaCa 0.280.230.25Table 4-5 GPG2000, and expert’s judgment Females giving birth (%) -67--Table A-2, IPCC-GL V3 Feed digestibility (%)DE60 Table A-2, IPCC-GL V3 Maintenance coefficient Cf i 0.3350.322 Table 4-4 GPG2000 Net energy maintenance (MJ/day) NE m 30.031.519.0Calculated using equation 4.1, GPG2000 Net energy activity (MJ/day) NE a 8.47.24.8Calculated using equation 4.2a, GPG2000 3B.31

32. Enhanced characterization of non-dairy cattle (2) ParameterSymbolCowsSteersYoungComments Growth coefficientC--0.9p.4.15, GPG2000 Net energy growth (MJ/day) NE g --4.0Calculated using equation 4.3a, GPG2000 Pregnancy coefficientCPCP 0.1--Table 4.7, GPG2000 Net energy pregnancy (MJ/day) NE P 3.0--Calculated using equation 4.8, GPG2000 Portion of GE that is available for maintenance NE ma /DE 0.49 Calculated using equation 4.9, GPG2000 Portion of GE that is available for growth NE ga /DE 0.28 Calculated using equation 4.10, GPG2000 Gross energy intake (MJ/day) GE139.3130.4117.7Calculated 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

33. 3B.33 Tier 2 estimation of CH 4 emissions from enteric 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 (Y m ) In this example (country with no CS data) a default value for Y m can be obtained from GPG2000

34. Tier 2 estimation of CH 4 emissions from enteric fermentation by non-dairy cattle ParameterSymbolCowsSteersYoungComments Gross energy intake (MJ/day) (from enhanced characterization) GE139.3130.4117.7Calculated using equation 4.11, GPG2000 CH 4 conversion factor YmYm 0.06 Table 4.8, GPG2000, and EFDB Emission factor (kg CH 4 /head/yr) EF54.851.346.3Calculated using equation 4.14, GPG2000 Portion of group in total population (%) -40 20Expert judgement, industry data Population of group (thousand heads) -2 000 1 000 CH 4 emissions (Gg CH 4 /yr) -11010346 3B.34

35. 3B.35 Tier 2 estimation of CH 4 emissions from enteric fermentation by non-dairy cattle Tier 2 estimation for non-dairy cattle: 259 Gg CH 4 (against 245 Gg CH 4 for tier 1) Weighted EF: 52 kg CH 4 /head/yr (againts the default value of 49 kg CH 4 /head/yr) This value should be used in the worksheet to report emissions by non-dairy cattle

36. 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)

37. Medium Level of Data Availability Climate region Production system Population (thousand heads) CowsSteersYoung WarmExtensive grazing 1 473828610 Intensive grazing228414120 Feedlot409296 TemperateExtensive grazing 348201161 Intensive grazing15027575 Feedlot153132 Total-2 2541 8411 094 New total: 5,153,000 heads (against FAO: 5,000,000 heads). 3B.37

38. 3B.38 Tier 2 estimation of CH 4 emissions from enteric fermentation by non-dairy cattle 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’)

39. Enhanced characterization, non-dairy cattle Warm Climate, Extensive Grazing (1) ParameterSymbolCowsSteersYoungComments Weight (kg)W420380210Country-specific data Weight gain (kg/day)WG00.2 Country-specific data Mature weight (kg)MW420440430Country-specific data Feeding situationCaCa 0.33 Table 4-5 GPG2000, and expert judgement Females giving birth (%)-60--Country-specific data Feed digestibility (%)DE57 Country-specific data Maintenance coefficientCf i 0.3350.322 Table 4-4 GPG2000 Net energy maintenance (MJ/day) NE m 31.127.717.8Calculated using equation 4.1, GPG2000 Net energy activity (MJ/day) NE a 10.39.25.9Calculated using equation 4.2a, GPG2000 Comments in green indicate improvements over previous example. 3B.39

40. Enhanced characterization, non-dairy cattle Warm Climate, Extensive Grazing (2) ParameterSymbolCowsSteersYoungComments Growth coefficientC-1.00.9p.4.15, GPG2000 Net energy growth (MJ/day)NE g -3.42.4Calculated using equation 4.3a, GPG2000 Pregnancy coefficientCPCP 0.1--Table 4.7, GPG2000 Net energy pregnancy (MJ/day) NE P 3.1--Calculated using equation 4.8, GPG2000 Portion of GE available for maintenance NE ma /DE0.48 Calculated using equation 4.9, GPG2000 Portion of GE available for growth NE ga /DE0.26 Calculated using equation 4.10, GPG2000 Gross energy intake (MJ/day) GE162.2170.0111.2Calculated using equation 4.11, GPG2000 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

41. Enhanced characterization, Non-Dairy Cattle, Temperate Climate, Intensive Grazing (1) ParameterSymbolCowsSteersYoungComments Weight (kg)W405390240Country-specific data Weight gain (kg/day)WG0.150.330.65Country-specific data Mature weight (kg)MW445470452Country-specific data Feeding situationCaCa 0.17 Table 4-5 GPG2000, and expert judgement Females giving birth (%)-81--Country-specific data Feed digestibility (%)DE72 Country-specific data Maintenance coefficientCf i 0.3350.322 Table 4-4 GPG2000 Net energy maintenance (MJ/day) NE m 30.228.319.6Calculated using equation 4.1, GPG2000 Net energy activity (MJ/day) NE a 5.14.83.3Calculated using equation 4.2a, GPG2000 Comments in green indicate improvements over previous example. 3B.41

42. Enhanced characterization, Non-Dairy Cattle, Temperate Climate, Intensive Grazing (2) ParameterSymbolCowsSteerYoungComments Growth coefficientC0.81.00.9p.4.15, GPG2000 Net Energy Growth (MJ/day) NE g 3.05.79.2Calculated using equation 4.3a, GPG2000 Pregnancy coefficientCPCP 0.1--Table 4.7, GPG2000 Net Energy Pregnancy (MJ/day) NE P 3.0--Calculated using equation 4.8, GPG2000 Portion of GE that is available for maintenance NE ma /DE0.53 Calculated using equation 4.9, GPG2000 Portion of GE that is available for growth. NE ga /DE0.34 Calculated using equation 4.10, GPG2000 Gross Energy Intake (MJ/day) GE120.1123.9121.5Calculated using equation 4.11, GPG2000 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

43. 3B.43 Medium level of data availability Estimated GE values are used for calculation of EF (using equation 4.14, GPG2000) Calculation of EF required to select a value for methane conversion rate (Y m ), 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 (Y m =0.06, from Table 4.8, GPG2000) 18 estimates of EF were obtained (next slide)

44. Medium level of data availability Climate region Production system EF (kg CH 4 /head/yr) CowsSteersYoung WarmExtensive grazing 63.866.943.8 Intensive grazing47.751.548.4 Feedlot41.549.352.8 TemperateExtensive grazing 61.566.749.5 Intensive grazing47.348.847.8 Feedlot41.549.352.8 3B.44

45. 3B.45 Medium level of data availability Weighted EF (tier 2, country-specific AD): 57 kg CH 4 /head/yr (range: 42-67 kg CH 4 /head/yr) EF for tier 1: 49 kg CH 4 /head/yr EF for tier 2 (with default AD): 52 kg CH 4 /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 CH 4 /year Total for tier 1: 245 Gg CH 4 /year Total for tier 2 (with default AD): 259 Gg CH 4 /year

46. Medium level of data availability Worksheet 4-1s1 3B.46

47. 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)

48. 3B.48 Highest level of data availability EFs could be improved by: developing local capacities for measuring CH 4 emissions by cattle characterizing diverse feeds by their CH 4 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

49. 3B.49 Highest level of data availability Numerical example not developed here Few, if any, developing countries are currently in the position of having access to this level of information With high level of data availability, countries would be able to implement tier 3 methods (still not proposed by IPCC)

50. 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 CH 4 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

51. 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

52. 3B.52 Manure Management: CH 4 Emissions

53. 3B.53 Manure management – CH 4 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

54. Livestock characterization Species/categoryNumber 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, 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

55. Livestock characterization Worksheet 4-1s1 Significant species 3B.55

56. 3B.56 Livestock characterization The non-dairy cattle sub-source is the most significant, and deserves enhanced characterization and application of a tier 2 method for CH 4 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

57. 3B.57 Enhanced characterization of swine population (1) Estimation of CH 4 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

58. 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

59. 3B.59 Low level of data availability: CH 4 emissions by non-dairy cattle, swine Tier 2 method requires determination of three parameters to estimate EF: VS (kg): mass of volatile solids excreted B o (m 3 /kg of VS): max. CH 4 producing capacity; MCF: CH 4 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

60. Low level of data availability: CH 4 emissions from manure management for non-dairy cattle (default AD and EF) (1) ParameterSymbolCowsSteersYoungComments Gross energy intake (MJ/day) (from the enhanced characterization) GE139.3130.4117.7Calculated using equation 4.11, GPG2000 * Energy intensity of feed (MJ/kg) -18.45 IPCC default value Feed intake (kg dm/day) -7.557.076.38Calculated Feed digestibility (%)DE60 Table A-2, IPCC-GL V3 Ash content of manure (%) ASH888IPCC-GL V3, p. 4.23 Volatile solid excretion (kg dm/day) VS2.782.602.35Calculated using equation 4.16, GPG2000 Maximum CH 4 producing capacity of manure (m 3 CH 4 /kg VS) BoBo 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

61. Low level of data availability: CH 4 emissions from manure management for non-dairy cattle (default AD and EF) (2) ParameterSymbolCowsSteerYoungComments Methane conversion factor (%) MCF1.8 Table 4-8, p.4.25, IPCC-GL V3 (data for pasture/range/paddock system, weighted by climate region) Emission factor (kg CH 4 /head/yr) EF1.221.141.03Calculated using equation 4.17, GPG2000 Population (thousand heads) -2 000 1 000FAO database, local experts, industry CH 4 emissions (Gg CH 4 /yr) -2.452.291.03Total emissions: 5.8 Gg CH 4 /yr Total emissions estimated here are lower than those using Tier 1 (8.2 Gg CH 4 /yr). Weighted EF derived from this table is 1.2 kg CH 4 /head/yr, and this value should be used instead of the default (1.6 kg CH 4 /head/yr) in IPCC Software 3B.61

62. Low level of data availability: CH 4 emissions from manure management for Swine (default AD and EF) (1) Parameter SymbolWarm solid Warm liquid Temp. solid Temp. liquid Comments Gross energy intake (MJ/day) (from the enhanced characterization) GE13.0 Default value, Table B-2, p. 4.42, IPCC-GL V3 Energy intensity of feed (MJ/kg) -18.45 IPCC default value Feed intake (kg dm/day) -0.7 Calculated Feed digestibility (%)DE50 IPCC-GL V3, p. 4.23 Ash content of manure (%) ASH8888IPCC-GL V3, p. 4.23 Volatile solid excretion (kg dm/day) VS0.34 Calculated using equation 4.16, GPG2000 Max. CH 4 producing capacity of manure (m 3 CH 4 /kg VS) BoBo 0.29 Table B-2, p.4.42, IPCC-GL V3 3B.62

63. Low level of data availability: CH 4 emissions from manure management for Swine (default AD and EF) (2) Parameter SymbolWarm solid Warm liquid Temp solid Temp liquid Comments Methane conversion factor (%) MCF2651.535Table 4-8, p.4.25, IPCC-GL V3 * Emission factor (kg CH 4 /head/yr) EF0.515.60.48.4Calculated using equation 4.17, GPG2000 Population (thousand heads) -8109054060FAO Database, local experts, industry CH 4 emissions (Gg CH 4 /yr) -0.391.400.190.50Total emissions: 2.5 Gg CH 4 /yr Total emissions estimated were similar to those using tier 1 (2.4 Gg CH 4 /yr). Weighted EF derived from this table is 1.7 kg CH 4 /head/yr, and this value should be used instead of the default (1.6 kg CH 4 /head/yr) in IPCC Software, * Liquid/slurry was assumed to be the only system used. GPG2000 provides slightly different default values (Table 4.10), as well as a formula for accounting for recovery, flaring, and use of biogas. 3B.63

64. Low level of data availability: results 3B.64

65. 3B.65 Medium level of data availability Assume the country has good statistics on livestock population to develop an enhanced characterization with CS AD, but has to use default EFs Non-Dairy Cattle: Same 18 classes as for enteric fermentation Assume that 50% of manure from feedlot has liquid/slurry management system, and 50% anaerobic lagoons Swine: 18 classes are identified and quantified, based on combination of: Two climate regions Three manure management systems Three swine population categories

66. Medium level of data availability (Swine) Climate region Manure management system Population (thousand heads) SowsBoarsYoung WarmPasture/range/ paddock 12130490 Liquid/slurry8340 Anaerobic lagoon229 TemperatePasture/range/ paddock 13036555 Liquid/slurry5124 Anaerobic lagoon8140 Total-274731 158 New Total: 1,505,000 heads (FAO: 1,500,000) 3B.66

67. 3B.67 Tier 2 estimation of CH 4 from manure management by non-dairy cattle, swine Next slides will show examples of detailed calculations for tier 2 method estimation of CH 4 emissions from manure management by: Non-dairy cattle under ‘Warm Region–Extensive Grazing’ system Swine under ‘Temperate–Liquid/Slurry’ system

68. Medium level of data availability: CH 4 manure management, non-dairy cattle under ‘Warm, Intensive Grazing’ (CS-AD) (1) ParameterSymbolCowsSteersYoungComments Gross energy intake (MJ/day) (from the enhanced characterization) GE121.2130.8123.0Country-specific values calculated using equation 4.11, GPG2000 * Energy intensity of feed (MJ/kg) -18.45 IPCC default value Feed intake (kg dm/day) -6.577.096.67Calculated Feed digestibility (%)DE68 Country-specific data Ash content of manure (%)ASH888IPCC-GL V3, p. 4.23 Volatile solid excretion (kg dm/day) VS1.932.091.96Calculated using equation 4.16, GPG2000 Maximum CH 4 producing capacity of manure (m 3 CH 4 /kg VS) BoBo 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

69. Medium level of data availability: CH 4 manure management, non-dairy cattle under ‘Warm, Intensive Grazing’ (CS-AD) (2) ParameterSymbolCowsSteersYoungComments Methane conversion factor (%) MCF2.0 Table 4-8, p.4.25, IPCC-GL V3 Emission factor (kg CH 4 /head/yr) EF1.141.231.15Calculated using equation 4.17, GPG2000 Population (thousand heads) -228414120Country-specific data CH 4 emissions (Gg CH 4 /yr) -0.260.510.14 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 B o, 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

70. Medium level of data availability: CH 4 manure management, swine under ‘Warm, Liquid/Slurry’ (CS-AD) (1) ParameterSymbolSowsBoarsYoungComments Gross energy intake (MJ/day) (from the enhanced characterization) GE9.0 13.0Country-specific data (or from the enhanced characterization) Energy intensity of feed (MJ/kg) -18.45 IPCC default value Feed intake (kg dm/day) -0.49 0.70Calculated Feed digestibility (%)DE49 Country-specific data Ash content of manure (%) ASH444IPCC-GL V3, p. 4.23 Volatile solid excretion (kg dm/day) VS0.23 Calculated using equation 4.16, GPG2000 Maximum CH 4 producing capacity of manure (m 3 CH 4 /kg VS) BoBo 0.29 IPCC default values adjusted by local expert judgement 3B.70

71. Medium level of data availability: CH 4 manure management, swine under ‘Warm, Liquid/Slurry’ (CS-AD) (2) ParameterSymbolSowsBoarsYoungComments Methane conversion Factor (%) MCF72 Table 4-8, p.4.25, IPCC-GL V3 Emission factor (kg CH 4 /head/yr) EF11.7 16.9Calculated using equation 4.17, GPG2000 Population (thousand heads) -8340Country-specific data CH 4 emissions (Gg CH 4 /yr) -0.090.040.68 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 B o, even though the country has no locally developed studies, IPCC default was adjusted for local conditions following expert judgement. For other factors (ASH, MCF), IPCC default values were used. 3B.71

72. Medium level of data availability: EFs estimated by tier 2 for non-dairy cattle, with CS AD Climate region Production system EF (kg CH 4 /head/yr) CowsSteersYoung WarmExtensive grazing 1.71.81.2 Intensive grazing1.11.2 Feedlot28.834.236.6 TemperateExtensive grazing 1.21.30.9 Intensive grazing0.70.8 Feedlot23.227.629.6 Weighted EF: 3.2 kg CH 4 /head/yr Use this value in IPCC Software 3B.72

73. Medium level of data availability: swine, EF estimated by tier 2, with CS AD Climate region Manure management system EF (kg CH 4 /head/yr) SowsBoarsYoung WarmPasture/range/ paddock 0.3 0.5 Liquid/slurry11.7 16.8 Anaerobic lagoon14.3 21.5 TemperatePasture/range/ paddock 0.3 0.4 Liquid/slurry7.3 10.6 Anaerobic lagoon14.3 21.5 Weighted EF: 1.9 kg CH 4 /head/yr Use this value in IPCC Software 3B.73

74. Medium level of data availability: results Worksheet 4-1s1 Weighted EF 3B.74

75. 3B.75 Manure Management: N 2 O Emissions

76. 3B.76 Manure management – N 2 O 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 CH 4 from manure management (and also for enteric fermentation) One numerical example, developed here

77. 3B.77 Livestock characterization to estimate N 2 O 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 (N 2 O 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)

78. Livestock characterization to estimate N 2 O emissions from manure management LivestockClimateAWMS Population (1000s) Fraction of Total Pop.(%) Dairy cattleWarmLiquid/slurry606.0 Anaerobic lagoon606.0 TemperateLiquid/slurry404.0 Anaerobic lagoon404.0 Non-dairy cattle WarmLiquid/slurry1142.2 Anaerobic lagoon1142.2 TemperateLiquid/slurry390.8 Anaerobic lagoon390.8 SwineWarmLiquid/slurry513.4 Anaerobic lagoon130.9 TemperateLiquid/slurry302.0 Anaerobic lagoon493.3 PoultryAllWith bedding1 60040 Without bedding2 40060 In case the country does not have this information, IPCC-GL provides default AD for different animal waste management systems (AWMS) in different regions (Table 4-21 V3). 3B.78

79. 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

80. 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 CH 4 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)

81. Livestock characterization for estimating N 2 O 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) WarmL/SCows205.715500.0747 Steers466.815600.0755 Young487.315640.0759 ALCows205.715500.0747 Steers466.815600.0755 Young487.315640.0759 TempL/SCows75.716530.0750 Steers166.816630.0759 Young167.316680.0763 ALCows75.716530.0750 Steers166.816630.0759 Young167.316680.0763 * MMS = Manure management system L/S = Liquid/slurry AL = Anaerobic lagoon 3B.81

82. 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

83. N 2 O from manure management: use of IPCC software to estimate total N excretion (1) Estimated  IPCC Default  Data from livestock characterization 3B.83

84. N 2 O from manure management: use of IPCC software to estimate total N excretion (2) Calculated  IPCC Default  Data from livestock characterization 3B.84

85. N 2 O from manure management: use of IPCC software to estimate total N excretion (3) IPCC Default  Data from livestock characterization 3B.85

86. N 2 O from manure management: use of IPCC software to estimate total N excretion (4) IPCC Default  Data from livestock characterization 3B.86

87. Use of IPCC software for estimating N 2 O from manure management IPCC Default  IPCC defaults obtained from Table 4-22, IPCC-GL V3, and Tables 4.12 and 4.13, GPG2000. IPCC Default  Note: cells corresponding to poultry were manually altered to accommodate these new categories from GPG2000, not included in IPCC-GL. 3B.87

88. 3B.88 Direct N 2 O Emissions from Agricultural Soils

89. Anthropogenic N inputs to soils Mineral fertilizers Histosols cultivation N-fixing crops Sewage sludges Crop residues Animal manures Fraction of … (from the mass balance) Other practices dealing with soil N 3B.89

90. Assess individual contribution of different N sources to determine ones (subcategories) which are significant for the source category (25% or more of source category N 2 O emissions) For this, apply Tier 1a method and default values to get an economic emission estimate For the significant subcategories, the best efforts should be invested to apply Tier 1b along with country-specific AD1 and AD2 (parameters) and country-specific emission factors For non-significant subcategories, Tier 1a, along with country-specific AD1, default AD2 (parameters) and default emission factors, is acceptable AGRICULTURAL SOILS It is also acceptable to mix Tiers 1a and 1b for different N sources, which will depend on the activity data availability 3B.90

91. 3B.91 Direct N 2 O – 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 EF 1 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 N 2 O from manure management) area devoted to N-fixing crops (FAO database) The country has no organic soils (histosols) Direct N 2 O 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)

92. Use of N fertilizers From the FAO database: CropArea (1000 ha) Crop yield (kg/ha) Use of N fertilizer (1000 t N) Wheat8241 545n/a Barley 1 356 (371)1 488 (1400)19.1 Maize1 2252 233n/a Rice984 800n/a Soybeans2311 982n/a Potatoes2518 000n/a Total2 779--130 1 Barley data from industry sources, shown in parentheses. 3B.92

93. 3B.93 Direct N 2 O – 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, EF 1 was estimated to be 0.9% for fertilizer applied to barley crops in the country Since there are no organic soils in the country, EF 2 is not needed Emissions from grazing livestock are included here. Note that the GPG2000 includes this source under manure management

94. 3B.94 Synthetic fertilizers: determination of F SN and EF 1 F SN : annual amount of fertilizer N applied to soils, adjusted by amount of N that volatilizes as NH 3 and NO x To adjust for volatilization, use IPCC default value from Table 4-17, IPCC-GL, V2: 0.1 kg (NO x +NH 3 )-N/kg fertilizer-N It is determined that: F SN = 19,000 (1-0.1) = 17,100 t fertilizer-N (barley) F SN = 111,000 (1-0.1) = 99,900 t fertilizer-N (all other crops) Total fertilizer-N = 117,000 t fertilizer-N EF 1 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 EF 1 is calculated as follows: EF 1 = weighted average = 17.1/117 (0.9) + 99.9/117 (1.25) = 1.20% From worksheet 4-5s1, the annual emission of N 2 O-N from use of synthetic fertilizer was estimated as 1.40 Gg N 2 O-N

95. Emissions of N 2 O from synthetic fertilizers Combined EF (CS and default) 3B.95

96. 3B.96 Manure applied to soils: determination of F AM F AM : annual amount of manure N applied to soils, adjusted by amount of N that volatilizes as NH 3 and NO x 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 (NO x +NH 3 )-N/kg animal manure N It is determined that: F AM = 24,924 t animal manure N applied to soils Next two slides illustrate the use of IPCC software to estimate F AM (named as F AW in IPCC-GL) and estimation of an annual emission of N 2 O-N from application of animal manure to soil of 0.31 Gg N 2 O-N

97. Emissions of N 2 O from animal manure (1) Country’s estimate From Table 4-17 IPCC Guidelines V2 Data from livestock characterization 3B.97

98. Emissions of N 2 O from animal manure (2) IPCC default 3B.98

99. 3B.99 N-fixing crops: determination of F BN F BN : 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 (Frac NCRBF ) 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: F BN = 27,748 t fixed-N This value is introduced in IPCC software worksheet 4-4s1 to estimate an annual emission of N 2 O-N from N-fixing crops of 0.35 Gg N 2 O-N

100. Emissions of N 2 O from N-fixing crops IPCC default Estimated activity data 3B.100

101. 3B.101 Crop residues: determination of F CR F CR : 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: F CR = 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 N 2 O-N from N in crop residues of 0.47 Gg N 2 O-N IPCC Software worksheet was designed for Tier-1a method, and use of Tier 1b requires manually altering sheet 4-5s1, cell C23

102. Crop residues: determination of F CR CropCrop (1000 t) (1) Res/Crop (2) Frac DM (2) Frac NCR (2) Frac BURN (3) Frac FUEL (3) Frac FOD (3) Eq. 4.29 GPG (t N 2 0-N) Wheat1,2731.30.850.00280.200.12,757 Barley1481.20.850.00430.200.1456 Maize2,7351.00.780.008100.2 10,369 Rice4701.40.900.00670003,971 Soybean4582.10.850.02300018,797 Potatoes4500.40.800.0110001,584 Total--- 37,934 (1)Source: FAO statistics (2)Source: Table 4.16, GPG2000 (except Frac DM for potatoes, which was estimated by experts) (3)Source: Country-specific data F CR 3B.102

103. N 2 O emissions from N in crop residues Total direct N 2 O emissions (excluding pasture, range and paddock): 2.54 Gg N 2 O-N/yr IPCC default 3B.103

104. N excretion from pasture/range/paddock Default values 3B.104

105. N 2 O emissions from pasture/range/paddock From Table 4-8 IPCC Guidelines V2 3B.105

106. 3B.106 Indirect N 2 O Emissions from Agricultural Soils

107. 3B.107 Indirect N 2 O – 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: N 2 O (G) : from volatilization of applied synthetic fertilizer and animal manure N, and its subsequent deposition as NO x and NH 4 N 2 O (L) : from leaching and runoff of applied fertilizer and animal manure Indirect N 2 O emissions are estimated using Tier 1a method and IPCC default emission factors The next slides show calculations as performed by IPCC Software

108. Indirect N 2 O emissions from atmospheric depositions From Table 4-17 IPCC Guidelines V2 From Table 4.18 GPG2000 Default value 3B.108

109. Indirect N 2 O emissions from leaching and runoff From Table 4-17 IPCC Guidelines V2 From Table 4.18 GPG2000 3B.109

110. 3B.110 Field Burning of Crop Residues

111. 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 residues Main issues derived from the decision tree

112. 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 CH 4, CO, N 2 O, NO X Other constants (conversion ratios): C to CH 4 or CO (16/12; 28/12, respectively) N to N 2 O or NO X (44/28; 46/14, respectively) Burning of crop residues

113. 3B.113 MODULEAGRICULTURE SUBMODUL E FIELD BURNING OF AGRICULTURAL RESIDUES WORKSHE ET 4-4 SHEET1 OF 3 COUNTRY FICTICIOUS LAND YEAR2002 STEP 1STEP 2STEP 3 CropsABCDEFGH (specify locallyAnnualResidue toQuantity ofDry MatterQuantity ofFraction Total Biomass importantProduction Crop RatioResidueFractionDry ResidueBurned inOxidised Burned crops) Fields (Gg crop) (Gg biomass) (Gg dm) C = (A x B) E = (C x D) H = (E x F xG) 0,00 Wheat157501,320.475,000,8517.403,750,750,911.747,53 Maize520015.200,000,52.600,000,50,91.170,00 Rice10501,41.470,000,851.249,500,850,9955,87. 0,00 1. OPEN THE IPCC SOFTWARE AND CHOOSE THE YEAR OF THE INVENTORY 2. CLICK 0N “ SECTORS ” IN THE MENU BAR, AND THEN CLICK ON AGRICULTURE 3. OPEN SHEET 4-4s2 Main residue-producing crops: Cereals (wheat, barley, oats, rye, rice, maize, sorghum) Sugarcane Pulses (peas, beans, lentils) Potatoes, peanut, others Identify the existing residue- producing crops

114. 3B.114 B. Residue/crop ratio A. Annual crop production (Gg) C. Quantity of residues (Gg biomass) Field burning of crop residues Worksheet 4-4, sheet 1 Flowchart to be applied to each crop Priority order for collectable AD 1 : 1. Values collected from published statistics 2. If not available, values can be derived from: a) crop area (in kha) b) crop yield (in tonne/ha) 3. From FAO DB Priority order for non-collectable AD 2 : 1. CS values - research 2. CS values - expert judgement 3. Values from countries with similar conditions 4. Default values (search EFDB)

115. 3B.115 D. Dry matter Fraction E. Total quantity of dry residue (Gg dm) C. Quantity of residue (Gg biomass) from previous slide Priority order for non-collectable AD: 1. CS values - research 2. CS values - expert judgement 3. Values from countries with similar conditions 4. IPCC default values (search EFDB) Field burning of crop residues Worksheet 4-4, sheet 1 Flowchart to be applied to each crop

116. 3B.116 E. Quantity of dry residue (Gg dm) from previous slide F. Fraction burned in fields H. Total biomass burned (Gg dm burned) G. Fraction oxidized Priority order for non-collectable AD: 1. CS values - research 2. CS values - expert judgement 3. Values from countries with similar conditions (no default values) For default values, search EFDB as combustion efficiency To avoid double counting, a mass balance of crop residue biomass must be internally performed: Fburned= Total biomass – (Fremoved from the field+ Featen by animals+ Fother uses) Field burning of crop residues Worksheet 4-4, sheet 1 Flowchart to be applied to each crop

117. 4. OPEN SHEET 4-4s2 OF “ AGRICULTURE ” UNDER “ SECTORS ” MODULEAGRICULTURE SUBMODULEFIELD BURNING OF AGRICULTURAL RESIDUES WORKSHEET4-4 SHEET2 OF 3 COUNTRYFICTICIOUS LAND YEAR2002 STEP 4 STEP 5 IJKL CarbonTotal CarbonNitrogen-Total Nitrogen Fraction ofReleasedCarbon RatioReleased Crops Residue (Gg C) (Gg N) J = (H x I) L = (J x K) 0,00 Wheat0,485.638,820,01267,67 Maize0,47549,900,0211,00 Rice0,41391,910,0145,49. 0,00 3B.117

118. 3B.118 H. Biomass burned (Gg dm burned) from previous slide I. C fraction in residue J. C released (Gg C) Priority order for non-collectable AD: 1. CS values - research 2. CS values - expert judgement 3. Values from countries with similar conditions 4. Default values (search EFDB) K. N/C ratio L. N released (Gg N) Total C and N released are obtained by addding the values obtained per each individual crop Field burning of crop residues Worksheet 4-4, sheet 2 Flowchart to be applied to each crop

119. Worksheet 4-4, sheet 3 5. OPEN SHEET 4-4s3 OF “ AGRICULTURE ” UNDER “ SECTORS ” MODULEAGRICULTURE SUBMODULEFIELD BURNING OF AGRICULTURAL RESIDUES WORKSHEET4-4 SHEET3 OF 3 COUNTRYFICTICIOUS LAND YEAR2002 STEP 6 MNOP Emission RatioEmissionsConversion RatioEmissions from Field Burning of Agricultural Residues (Gg C or Gg N) (Gg) N = (J x M) P = (N x O) CH 4 0,00532,90 16/1243,87 CO0,06394,84 28/12921,29 N = (L x M) P = (N x O) N2ON2O0,0070,59 44/280,93 NO x 0,12110,18 46/1433,46 Total emission estimates 3B.119

120. 6. GO TO THE “ OVERVIEW ” MODULE 7. OPEN THE WORHSHEET 4-S2 TABLE 4 SECTORAL REPORT FOR AGRICULTURE (Sheet 2 of 2) SECTORAL REPORT FOR NATIONAL GREENHOUSE GAS INVENTORIES (Gg) GREENHOUSE GAS SOURCE AND SINK CATEGORIES CH 4 N2ON2O NO x CONMVOC 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 Cultivation0 1 Irrigated0 2 Rainfed0 3 Deep Water0 4 Other (please specify) D Agricultural Soils 0 E Prescribed Burning of Savannas10236 F Field Burning of Agricultural Residues (1) 44133921 1 Cereals 2 Pulse 3 Tuber and Root 4 Sugar Cane 5 Other (please specify) G Other (please specify) Total emission estimates 3B.120

121. Total C released (Gg C from all crops) from previous slide Total N released (Gg N from all crops) from previous slide M Non-CO 2 emission rates (search EFDB) O Conversion ratios C-N emitted (Gg C emitted as CH 4 or CO; Gg N emitted as N 2 O or NO X ) P CH 4 emitted (Gg CH4) P CO emitted (Gg CO) P N 2 O emitted (Gg N2O) P NO X emitted (Gg NOX) EFs: If no CS values, use defaults (Table 4-16, Reference Manual, Rev. 1996 IPCC Guidelines) Field burning of crop residues Worksheet 4-4, sheet 3 Flowchart to be applied to aggregated figures 3B.121

122. Field burning of crop residues Emission factors 3B.122

123. Field burning of crop residues Emission estimates using country-specific values Wheat residues (1 of 3) MODULEAGRICULTURE SUBMODUL E FIELD BURNING OF AGRICULTURAL RESIDUES WORKSHEE T 4-4 SHEET1 OF 3 COUNTRY FICTICIOUS YEAR2002 STEP 1 STEP 2 STEP 3 CropsABCDEFGH (specify locally AnnualResidue toQuantity of Dry Matter Quantity ofFraction Total Biomass importantProduction Crop RatioResidueFraction Dry Residue Burned inOxidised Burned crops) Fields (Gg crop) (Gg biomass) (Gg dm) C = (A x B) E = (C x D) H = (E x F xG) Wheat18.350,501,5027.525,80,9024.773,20,120,962.735,0 AD from national statistics CS activity data, from research and monitoring 3B.123

124. 3B.124 Field burning of crop residues Emission estimates using country-specific values Wheat residues (2 of 3) MODULEAGRICULTURE SUBMODULEFIELD BURNING OF AGRICULTURAL RESIDUES WORKSHEET4-4 SHEET2 OF 3 COUNTRYFICTICIOUS YEAR2002 STEP 4 STEP 5 IJKL CarbonTotal CarbonNitrogen-Total Nitrogen Fraction ofReleasedCarbon RatioReleased Crops Residue (Gg C) (Gg N) J = (H x I) L = (J x K) Wheat0,451.230,70,00323,94 CS activity data, from research and monitoring

125. 3B.125 Field burning of crop residues Emission estimates using country-specific values Wheat residues (3 of 3) MODULEAGRICULTURE SUBMODULEFIELD BURNING OF AGRICULTURAL RESIDUES WORKSHEET4-4 SHEET3 OF 3 COUNTRYFICTICIOUS YEAR2002 STEP 6 MNOP GasEmission RatioEmissionsConversion RatioEmissions (Gg C or Gg N) (Gg) N = (J x M) P = (N x O) CH 4 0,003113,83 16/125,10 CO0,0673,84 28/12172,30 N = (L x M) P = (N x O) N2ON2O0,0180,07 44/280,11 NO x 0,1210,48 46/141,57 CS values for CH 4 /N 2 O D for CO/NO X

126. Field burning of crop residues Emission estimates using default values Wheat residues (1 of 3) MODULEAGRICULTURE SUBMODULEFIELD BURNING OF AGRICULTURAL RESIDUES WORKSHEET4-4 SHEET1 OF 3 COUNTRY FICTICIOUS YEAR2002 STEP 1 STEP 2 STEP 3 CropsABCDEFGH (specify locally AnnualResidue to Quantity of Dry Matter Quantity of Fraction Total Biomass importantProduction Crop RatioResidueFraction Dry Residue Burned in Oxidised Burned crops) Fields (Gg crop) (Gg biomass) (Gg dm) EF ID= 43555 C = (A x B) EF ID= 43636 E = (C x D) EF ID= 45941 H = (E x F xG) Wheat18.350,51,3023.855,70,8319.800,20,120,942.140,4 CS value, from monitoring or expert judgement AD: 1. from national statistics, or 2. from FAO database: (www.fao.org, then “ FAOSTAT-www.fao.org Agriculture ” and “ Crops primary ” ) Activity data, taken from EFDB 3B.126

127. 3B.127 Field burning of crop residues Emission estimates using default values Wheat residues (2 of 3) Default activity data, from EFDB MODULEAGRICULTURE SUBMODULEFIELD BURNING OF AGRICULTURAL RESIDUES WORKSHEET4-4 SHEET2 OF 3 COUNTRYFICTICIOUS YEAR2002 STEP 4 STEP 5 IJKL CarbonTotal CarbonNitrogen-Total Nitrogen Fraction ofReleasedCarbon RatioReleased Crops Residue (Gg C) (Gg N) J = (H x I) L = (J x K) Wheat0,481.027,40,01212,33 EF ID= 43716EF ID= 43796

128. 3B.128 Field burning of crop residues Emission estimates using CS values Wheat residues (3 of 3) Default values, from EFDB MODULEAGRICULTURE SUBMODULEFIELD BURNING OF AGRICULTURAL RESIDUES WORKSHEET4-4 SHEET3 OF 3 COUNTRYFICTICIOUS YEAR2002 STEP 6 MNOP Emission RatioEmissionsConversion RatioEmissions (Gg C or Gg N) (Gg) N = (J x M) P = (N x O) CH 4 0,0055,14 16/126,85 CO0,0661,64 28/12143,83 N = (L x M) P = (N x O) N2ON2O0,0070,09 44/280,14 NO x 0,1211,49 46/144,90 EF ID= 43583, 43548, 43543, 43549

129. 3B.129 Field burning of crop residues Differences in emission estimates if country-specific or default values are used Emissions Per cent Gas emittedGg gas of using difference CS valuesDefaults CH 4 5,106,85-25% CO172,30143,8320% N2ON2O0,110,14-18% NO x 1,574,90-68%

130. 3B.130 Prescribed Burning of Savannas

131. 3B.131 PRESCRIBED BURNING OF SAVANNAS Main issues derived from the Decision-tree If not occurring, then no emission estimates If occurring, then emissions must be are estimated using Worksheet 4-3, sheets 1-2-3 (IPCC software) 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

132. 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 CH 4, CO, N 2 O, NO X Other constants (conversion ratios): C to CH4 or CO (16/12; 28/12, respectively) N to N2O or NOX (44/28; 46/14, respectively)

133. 3B.133 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 4-3s1 4.FILL IN WITH THE DATA MODULEAGRICULTURE SUBMODULEPRESCRIBED BURNING OF SAVANNAS WORKSHEET4-3 SHEET1 OF 3 COUNTRYFICTICIOUS LAND YEAR2002 STEP 1STEP 2 ABCDEFGH 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) C = (A x B) E = (C x D) G = (E x F)H = (E - G) 15,57108,500,8592,230,4541,50 50,72 0,00 Sources for AD on categories of savannas and area covered by category: 1. National statistics 2. National mapping systems Sources for AD on biomass density: 1. National statistics 2. National vegetation surveys and mapping 3. National expert judgement 4. Data provided by third countries with similar features 5. IPCC defaults (Table 4-14, Reference Manual, 1996 Revised Guidelines) The first 3 steps is to determine: 1. the categories of savannas existing per ecological unit 2. the area burned per category 3. the biomass density per category

134. 3B.134 PRESCRIBED BURNING OF SAVANNAS Flow chart to estimate non-CO 2 emissions To be applied to each savanna category B Biomass density (ton dm/ha) A Area burned (k ha) C Total biomass exposed to burning (Gg dm) E Biomass actually Burned (Gg dm) F F of living biomass burned G Living biomass actually burned (Gg dm) D F actually burned H Dead biomass actually burned (Gg dm) Ideally, CS values based on measurements. If not, CS values based on expert judgement. If not, default values (search EFDB)

135. 3B.135 5. GO SHEET 4-3s2 IN “SECTORS/AGRICULTURE” OF THE IPCC SOFTWARE 6. FILL IT WITH THE DATA MODULEAGRICULTURE SUBMODULEPRESCRIBED BURNING OF SAVANNAS WORKSHEET4-3 SHEET2 OF 3 COUNTRYFICTICIOUS LAND YEAR2002 STEP 3 IJKL 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) Living0,937,350,4516,81 Dead0,9548,195240,94 Living 0,00 Dead 0,00

136. 3B.136 PRESCRIBED BURNING OF SAVANNAS G Living biomass actually burned (Gg dm) from previous slide H Dead biomass actually burned (Gg dm) from previous slide Flow chart to estimate non-CO 2 emissions Applicable per each savanna category I1 Fraction of living biomass oxidised (Gg dm) I2 Fraction of dead biomass oxidised (Gg dm) J1 Oxidised living biomass (Gg dm) J2 Oxidised dead biomass (Gg dm) K1 C fraction of living biomass K2 C fraction of dead biomass L2 C released from dead biomass (Gg C) L1 C released from living biomass (Gg C) L Total C released (Gg C) M N/C ratio N Total N released (Gg N) If no CS values, defaults in EFDB, as combustion efficiency

137. 3B.137 7. GO TO SHEET 4.3s3 IN “SECTORS/AGRICULTURE” 8. FILL IT GO THE DATA MODULE AGRICULTUR E SUBMODULEPRESCRIBED BURNING OF SAVANNAS WORKSHEET4-3 SHEET3 OF 3 COUNTRYFICTICIOUS LAND YEAR2002 STEP 4STEP 5 LM N OPQR Total Carbon Released Nitrogen- Carbon Ratio Total Nitrogen Content Emissions Ratio EmissionsConversion 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) 0,0041,0316/12 CH 4 1,37 0,0615,4628/12 CO36,08 257,750,0153,87 P = (N x O) R = (P x Q) 0,0070,0344/28 N 2 O0,04 0,1210,4746/14 NO x 1,54 TOTAL EMISSION ESTIMATES

138. 3B.138 9. GO TO “OVERVIEW” MODULE 8. OPEN THE WORKSHEET 4S2 TABLE 4 SECTORAL REPORT FOR AGRICULTURE (Sheet 2 of 2) SECTORAL REPORT FOR NATIONAL GREENHOUSE GAS INVENTORIES (Gg) GREENHOUSE GAS SOURCE AND SINK CATEGORIES CH 4 N2ON2O NO x CONMVOC 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 Cultivation0 1 Irrigated0 2 Rainfed0 3 Deep Water0 4 Other (please specify) D Agricultural Soils 0 E Prescribed Burning of Savannas10236 F Field Burning of Agricultural Residues (1) 44133921 1 Cereals 2 Pulse 3 Tuber and Root 4 Sugar Cane 5 Other (please specify) G Other (please specify) Total emission estimates From Savanna Burning

139. 3B.139 PRESCRIBED BURNING OF SAVANNAS L Total C released (Gg C) from previous slide N Total N released (Gg N) from previous slide O N2O & NOx emission rates O CH4 & CO emission rates P N2O-N released (Gg N) P CH4-C released (Gg C) P NOx-N released (Gg N) P CO-C released (Gg C) Q N2O & NOx conversion rates Q CH4 & CO conversion rates R N2O emitted (Gg N2O) R NOx emitted (Gg NOX) R CH4 emitted (Gg CH4) R CO emitted (Gg CO) If no CS EFs, defaults in EFDB Applicable to aggregated figures

140. 3B.140 PRESCRIBED BURNING OF SAVANNAS Examples of default emission factors

141. 3B.141 PRESCRIBED BURNING OF SAVANNAS Example based in a ficticious country having three ecological regions: north, centre, south Northern zone: shortest drought period Southern zone: longest drought period Central zone: intermediate situation Two scenarios: use of country-specific values for the majority of the ADs and EFs use of default values for all the ADs and EFs

142. 3B.142 PRESCRIBED BURNING OF SAVANNAS Emission estimates using CS values STEP 1STEP 2 ABCDEFGH Savann a categor y 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) C = (A x B) E = (C x D) G = (E x F)H = (E - G) North 15,57,00108,500,8592,230,5550,72 41,50 Centre 145,85,00729,000,95692,550,50346,28 South 22,04,0088,001,0088,000,4539,60 48,40 Totals 436,60 436,18 AD from national statistics (census, surveys, mapping) CS values (field measurements, expert ’ s judgement)

143. 3B.143 PRESCRIBED BURNING OF SAVANNAS Emission estimates using CS values STEP 3 IJKL 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) North Living0,937,350,414,94 Dead0,9548,190,4521,68 Centre Living0,9324,770,4129,91 Dead0,95280,480,45126,22 South Living0,941,380,416,55 Dead0,9535,740,4516,08 Totals Living 403,50 325,39 Dead 364,41 CS values (field measurements, lab analysis, expert ’ s judgement)

144. 3B.144 PRESCRIBED BURNING OF SAVANNAS Emission estimates using CS values SUBMODULEPRESCRIBED BURNING OF SAVANNAS WORKSHEET4-3 SHEET3 OF 3 COUNTRYCHILE YEAR2002 STEP 4STEP 5 M N OPQR Nitrogen- Carbon Ratio Total Nitrogen Content Emissions Ratio EmissionsConversi on 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) 0,0062,0616/12 CH 4 2,75 0,0620,6228/12 CO48,11 0,01424,88 P = (N x O) R = (P x Q) 0,0060,0344/28 N 2 O0,05 0,1210,5946/14 NO x 1,94 CS values for CH4 & N2O D values for CO & NOx

145. 3B.145 PRESCRIBED BURNING OF SAVANNAS Emission estimates using default values STEP 1STEP 2 ABCDEFGH 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) C = (A x B) E = (C x D) G = (E x F)H = (E - G) 15,507,00108,500,95103,080,5556,69 EF ID= 43475 EF ID= 43485 EF ID= 43518 46,38 145,806,00874,800,95831,060,55457,08 EF ID= 43445 EF ID= 43485 EF ID= 43518 373,98 22,004,0088,000,9583,600,4537,62 EF ID= 43480 EF ID= 43485 EF ID= 43515 45,98 551,39 466,34 Default values taken from EFDB AD from national statisitcs

146. 3B.146 PRESCRIBED BURNING OF SAVANNAS Emission estimates using default values STEP 3 IJKL 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) North Living0,9453,290,421,32 Dead0,9443,600,4519,62 Centre Living0,94429,660,4171,86 Dead0,94351,540,45158,19 South Living0,9435,360,414,15 Dead0,9443,220,4519,45 Totals Living 518,31 404,59 Dead 438,36 EF ID= 45949Experts Default values taken from EFDB CS values taken from expert ’ s judgement

147. 3B.147 PRESCRIBED BURNING OF SAVANNAS Emission estimates using default values SUBMODULEPRESCRIBED BURNING OF SAVANNAS WORKSHEET4-3 SHEET3 OF 3 COUNTRYCHILE YEAR2002 STEP 4STEP 5 M N OPQR Nitrogen- Carbon Ratio Total Nitrogen Content Emissions Ratio EmissionsConversion 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) 0,0052,0216/12 CH 4 2,70 0,0624,2928/12 CO56,64 0,00953,84 P = (N x O) R = (P x Q) EF ID= 45998 0,0070,0344/28 N 2 O0,04 0,1210,4746/14 NO x 1,53 defaults Default values taken from EFDB

148. 3B.148 PRESCRIBED BURNING OF SAVANNAS Difference of estimates PRESCRIBED BURNING OF SAVANNAS Emissions Per cent Gas emittedGg gas of using difference CS valuesDefaults CH 4 2,752,702% CO48,1156,64-15% N2ON2O0,050,049% NO x 1,941,5327%

149. 3B.149 RICE CULTIVATION

150. 3B.150 RICE CULTIVATION Anaerobic decomposition of organic material in flooded rice fields produces CH 4 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 CH 4 release into the atmosphere: Diffusion loss across the water surface (least important process) CH 4 loss as bubbles (ebullition) (common and significant mechanism, especially if soil texture is not clayey) CH4 transport through rice plants (most important phenomenon)

151. 3B.151 RICE CULTIVATION Methodological issues 1996 IPCC Guidelines outline one method, that uses annual harvested areas and area-based seasonally integrated emission factors (Fc = EF x A x 10 -12 ) In its most simple form, the method can be implemented using national total area harvested and a single EF High variability in growing conditions (water management practices, organic fertilizer use, soil type) will significantly affect seasonal CH 4 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

152. 3B.152 RICE CULTIVATION Activity data total harvested area excluding upland rice (national statistics or international databases FAO (www.fao.org/ag/agp/agpc/doc) or IRRI (www.irri.org/science/ricestat/pdfs)www.fao.org/ag/agp/agpc/docwww.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)

153. 3B.153 RICE CULTIVATION Main 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

154. 3B.154 RICE CULTIVATION Numerical example Assumptions: Hypothetical country located in Asia Key source condition Total harvested area: 38,5 kha, disaggregated into: 28,5 kha as irrigated and continously flooded 10,0 kha as irrigated, intermitently flooded and single aereated

155. 3B.155 RICE CULTIVATION MODULEAGRICULTURE SUBMODULEMETHANE EMISSIONS FROM FLOODED RICE FIELDS WORKSHEET4-2 SHEET1 OF 1 COUNTRYFICTICIOUS LAND YEAR2002 ABCDE Water Management RegimeHarvested AreaScaling Factor for Methane Emissions Correction Factor for Organic Amendment Seasonally Integrated Emission Factor for Continuously Flooded Rice without Organic Amendment CH 4 Emissions (m 2 /1 000 000 000) (g/m 2 )(Gg) E = (A x B x C x D) IrrigatedContinuously Flooded 0,285122011,40 Intermittently Flooded Single Aeration0,10,52202,00 Multiple Aeration 0,00 RainfedFlood 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 statistics or international databases (FAO, IRRI) Scaling factor for water management: local research or other country ’ s use or EFDB ( Agriculture, Rice Production, Intermitently Flooded, Single aeration ) Enhancement factor for organic ammendements: local research or taken from the EFDB (Agriculture, Rice Production) EF: local research or other country ’ s use or from EFDB Regional units, from national estatistics or mapping agencies or expert judgement