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AGRICULTURE INVENTORY ELABORATION PART 2 SIMULATION

AGRICULTURE INVENTORY ELABORATION PART 2 SIMULATION. STATE-OF-ART OF NAI PARTIES. Until September/2003, 70 NCs from NAI Parties were compiled and assessed by the UNFCCC-Secretariat

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AGRICULTURE INVENTORY ELABORATION PART 2 SIMULATION

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  1. AGRICULTUREINVENTORY ELABORATIONPART 2SIMULATION

  2. STATE-OF-ART OF NAI PARTIES • Until September/2003, 70 NCs from NAI Parties were compiled and assessed by the UNFCCC-Secretariat • From the Compilation & Synthesis Report, the problems encountered by NAI Parties for the elaboration of the national inventory elaboration: • activity data 93 per cent • emission factors 64 per cent • methods 11 per cent

  3. INVENTORY ELABORATION • Previous activities: • Key source category determination • Sub-category importance determination • Methods to be applied per category (T1 for non-KS; T2/3 for KS) • Mass balance for shared items (crop residues, animal manure) • Single livestock characterization (basic linked to T1; enhaced linked to T2)

  4. INVENTORY ELABORATION.PREVIOUS ACTIVITIES Preliminary key source determination • Two ways: • Using last/previous year GHG inventory data, and/or • Applying Tier 1 to all sectors for the year to be inventoried

  5. PRELIMINARY KEY SOURCE DETERMINATION.STEPS • List of categories, according to IPCC disaggregation (excluding LUCF categories) • Decreasing ranking, according to their individual contribution to CO2-equiv. emissions • Estimating relative contribution of each category to the total national emissions • Calculating the cumulative contribution of the categories to the total national emissions, • Key sources should gather the upper 95% of GHG emissions

  6. PRELIMINARY KEY SOURCE DETERMINATION

  7. PRELIMINARY KEY SOURCE DETERMINATION 1994 GHG-Inventory of Chile (Gg in CO2-equivalent) (Non-energy sectors)

  8. KS NKS

  9. INVENTORY ELABORATION.SIGNIFICANCE OF SUBSOURCES • Significance of animal species: • Example for CH4 emissions from Enteric Fermentation and Manure Management • Emissions estimated by Tier 1 • To simplify: country with no division into agroecological units

  10. INVENTORY ELABORATION.SIGNIFICANCE OF SUBSOURCES • Steps: • Collection of animal species population • If no national AD are available, the use of FAOSTAT is appropriate • Disaggregation between dairy and non-dairy cattle, following expert’s judgment • Filling in of IPCC software Table 4-1s1 with the population data and default emission factors • Estimation of individual contribution to the total emissions of the source category

  11. Determination of Significant Sub-Source Categories • For significant species = enhanced characterization and Tier-2, if possible • Perform a rough estimation of CH4 emissions from enteric fermentation applying Tier-1 • one way of screening species for their contribution to emissions • estimation has the only purpose of identifying categories requiring a Tier-2 estimation • use IPCC Software, sheet ‘4-1s1’: fill in animal population data, and collect default EF from Tables 4-3 and 4-4 of IPCC Guidelines Vol. 3 (also taken from the EFDB)

  12. Low Level of Data Availability 1 Disaggregation between dairy and non-dairy cattle, based on expert`s judgment

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

  14. Enhanced CharacterizationNon-Dairy Cattle • Enhanced characterization requires information additional to that provided by FAO Statistics. Consultation withlocal experts/industryis a valuable source • Assume that, using these sources, the inventory team determines that non-dairy cattle population is composed by: • Cows : 40% • Steers : 40% • Young growing animals : 20% • No information available to divide the animal population into climatic zones and production systems • Each of these homogenous groups of animalsmust have an estimate offeed intakeand anEFto convert intake to CH4 emissions • Procedure is described in IPCC-GPG (pages 4.10-4.20)

  15. Enhanced CharacterizationNon-Dairy Cattle

  16. Enhanced CharacterizationNon-Dairy Cattle 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

  17. Tier-2 Estimation of CH4 emissions from Enteric Fermentation by Non-Dairy Cattle • Enhanced characterization yielded CS-AD (average daily gross energy intake) per group of non-dairy cattle (cows, steers, growing animals) • These AD must be combined with specific EFs for animal group to obtain emission estimates • Determination of EFs requires selection of a suitable value for CH4 conversion rate (Ym) • In this example of country with no CS-data, a default value for Ym (MCF) can be obtained from the IPCC-GPG

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

  19. Tier-2 Estimation of CH4 emissionsEnteric Fermentation by Non-Dairy Cattle • Tier-2 estimation for non-dairy cattle: • 259 Gg CH4(245 Gg CH4 by Tier 1) • Weighed EF: • 52 kg CH4/head/yr(49 kg CH4/head/yr, as default value) • This value should be used in the worksheet to report emissions by non-dairy cattle • Another chance: to modify worksheet to recognize T2 and incorporate new Efs directly

  20. Medium Level of AD Availability • For AD1, the country has reliable statistics on livestock population • Applying the same procedure as above, the country determines that non-dairy cattle requires enhanced characterization • National statistics + expert judgment allow disaggregation of non-dairy cattle population into: • 2 climate regions (some of previous example) • 3 animal categories (cows, sterrs, young animals) • 3 production systems • It means 18 estimation units

  21. Medium Level of AD Availability New Total: 5,153·103 heads (against FAO: 5,000·103 heads)

  22. Tier-2 Estimation of CH4 emissionsEnteric Fermentation - Non-Dairy Cattle • Enhanced characterization yielded CS-AD (average daily GE intake) for 18 classes of animals • This AD must be combined with EFs for each animal class to obtain 18 emission estimates • Next slides will show detailed calculations to estimate GE intake only for 6 of the 18 classes (three types of animals for ‘Warm-Extensive Grazing’ and for ‘Temperate-Intensive Grazing’

  23. Enhanced characterization, Non-Dairy CattleWarm Climate -Extensive Grazing Comments in green indicate improvements over previous example

  24. Enhanced characterization, Non-Dairy CattleWarm Climate -Extensive Grazing 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

  25. Enhanced characterization, Non-Dairy CattleTemperate Climate -Intensive Grazing Comments in green indicate improvements over previous example

  26. Enhanced characterization, Non-Dairy CattleTemperate Climate, Intensive Grazing 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

  27. Medium Level of Data Availability • Estimated GE values are used for calculation of EF (using equation 4.14, IPCC-GPG). • Calculation of EF requires to select a value for methane conversion rate (Ym), this is, the fraction of energy in feed in take that is converted to energy in methane. • In this example we assume the country uses a default value (Ym =0.06, from Table 4.8, IPCC-GPG). • 18 estimates of EF were obtained (next slide)

  28. Medium Level of Data Availability Range from 41.5 to 66.9

  29. Medium Level of Data Availability • Weighed EF (Tier 2, CS-AD): 57 kg CH4/head/yr (range: 42-67 kg CH4/head/yr) • EF for Tier 2 (with default and aggregated AD):52 kg CH4/head/yr • EF for Tier 1:49 kg CH4/head/yr • Multiplication of EF with cattle population in each class yielded 18 estimates of annual emission of methane from enteric fermentation, with a total of294 Gg CH4/year • Total for Tier 2 (with default and aggregated AD):259 Gg CH4/year • Total for Tier 1:245 Gg CH4/year

  30. Medium Level of Data Availability Worksheet 4-1s1

  31. Highest Level of Data Availability • Activity data could be improved by: • more accurate national statistics on livestock population • lowest uncertainties • further disaggregation of cattle population (e.g., by race or age, subdividing climate region by administrative units, soil type, forage quality, others) • implementation of geographically-explicit AD and cattle traceability systems • development of local research to obtain CS estimates of parameters used for livestock characterization (e.g., coefficients for maintenance, growth, activity or pregnancy)

  32. Highest Level of Data Availability • Emission factors could be improved by: • developing local capacities for measuring CH4 emissions by individuals • characterising diverse feeds used by their CH4 conversion factors for different animal types • development of local research to improve understanding of locally-relevant factors affecting methane emissions • adapting international information (scientific literature, EFDB, etc.) from conditions similar to those of the country

  33. Highest Level of Data Availability • Numerical example not developed here • Very few -if any- developing countries are in position of having this level of information • With high level of data availability, countries would be able to implement Tier-3 methods (CS methods)

  34. Estimation of Uncertainties • It is good practice to estimate and report uncertainties of emission estimates, which implies estimating uncertainties of AD and EF • According to IPCC, EF used in Tier-1 may have an uncertainty in the order of 30-50%, and default AD may have even higher values • Application of Tier-2 method with country-specific AD may substantially reduce uncertainty levels with respect to Tier-1 with default AD/EF • Priority should be given to improve the quality of AD estimates

  35. Direct N2O Emissions from Agricultural Soils NAI GHG Inventory Training Workshop Agriculture Sector

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

  37. AGRICULTURAL SOILS Assess individual contribution of different N sources to determine ones (sub-categories) which are significant for the source category (25% or more of source category N2O emissions) For this, apply Tier 1a method and default values, to get a preliminary emission estimate For the significant sub-categories, the best efforts should be invested to apply Tier 1b along with country-specific AD1, AD2 and emission factors For non-significant sub-categories, Tier 1a along with country-specific AD1 and default AD2 and emission factors is acceptable It is also acceptable to mix Tiers 1a and 1b for different N sources, which will depend on the activity data availability

  38. Direct N2O – Agricultural Soils • Assumption of the same country • It will be assumed that the country has the following AD: • usage of synthetic N fertilizers: FAO database • usage of synthetic N fertilizers for barley crop: Industry source • estimate of EF1 for N applied to barley crops: local research, which due to improved practices in this crop (e.g., fractioning of N applications), is lower than the IPCC default EF • N excretion from different animal categories under pasture/range/paddock AWMS: data from previous example on N2O from manure management • area devoted to N-fixing crops: FAO database • The country has no organic soils (histosols) and no sewage sludge application to soils • Direct N2O emissions are estimated using a combination of Tier 1a (for most of the sources) and Tier 1b (for use of N fertilizers in barley and N in crop residues applied to soils)

  39. Use of N-Fertilizers From the FAO database: 1Barley data from industry sources, shown in parentheses

  40. Direct N2O – Agricultural Soils • From FAO database, only total country data for fertilizer use is available. Therefore, only Tier-1a method could be used unless further disaggregation can be done with the support of national sources • Data from barley industry/research can be used to apply Tier-1b method: • to ensure consistency, it is recommended to compare crop area and crop yield data between FAO and the local industry • in this case, both sources reasonably matched for area and yield, and it can be assumed that industry estimation of N fertilizer usage is compatible with the FAO N fertilizer data • from previous table, it can be derived that 19,000 t N fertilizer were applied to barley crops, and 111,000 t N fertilizer to the rest (130 minus 19) • from local research, EF1 was estimated to be 0.9% for fertilizer applied to barley crops in the country • Since there are no organic soils in the country, EF2 is not needed

  41. Synthetic Fertilisers:Determination of FSN and EF1 • FSN: annual amount of fertiliser N applied to soils, adjusted by amount of N that volatilises as NH3 and NOx • To adjust for volatilisation, use IPCC default value from Table 4-17, IPCC Guidelines, V2:0.1 kg (NOx+NH3)-N/kg fertiliser-N • It is determined that: • FSN= 19,000 (1-0.1) = 17,100 t fertiliser-N (barley) • FSN= 111,000 (1-0.1) = 99,900 t fertiliser-N (all other crops) • Total fertiliser-N= 117,000 t fertiliser-N • EF1 is0.9 % for barley(country-specific) and1.25 % for the other crops(Table 4.17, IPCC-GPG) • For the purpose of filling the IPCC Software sheet 4-5s1, a weighted EF1 is calculated as follows: • EF1= weighed average= 17.1/117 (0.9) + 99.9/117 (1.25)=1.20 % • From worksheet 4-5s1, the annual emission of N2O-N from use of synthetic fertilizer was estimated as 1.40 Gg N2O-N

  42. Emissions of N2O from Synthetic Fertilisers Combined EF (CS and defaultt)

  43. Indirect N2O Emissions from Agricultural Soils NAI GHG Inventory Training Workshop Agriculture Sector

  44. Indirect N2O – Agricultural Soils • We will assume that the country only covers the following sources: • N2O(G): from volatilisation of applied synthetic fertiliser and animal manure N, and its subsequent deposition as NOx and NH4. • N2O(L): from leaching and runoff of applied fertiliser and animal manure • Indirect N2O emissions are estimated using Tier 1a method and IPCC default emission factors • Next slides show calculations as performed by IPCC Software

  45. Indirect N2O Emissions from Atmospheric Depositions From Table 4.18 IPCC-GPG Default value From Table 4-17 IPCC Guidelines V2

  46. Indirect N2O Emissions from Leaching & Runoff From Table 4-17 IPCC Guidelines V2 From Table 4.18 IPCC-GPG

  47. Field Burning of Crop Residues NAI GHG Inventory Training Workshop Agriculture Sector

  48. CROP RESIDUES BURNINGMain issues derived from the Decision-Tree • If not occurring, then emission estimates are “NO” • If occurring, then emissions must be are estimated • using Worksheet 4-4 sheets 1-2-3 (IPCC software) • Only one method is available to estimate emissions • from this source category • If key source, then CS-values for non-collectable AD and emission factors must be preferred (default values for key source are possible if the country cannot provide the required AD or financial resources are jeopardised) • If CS values are used, they must be reported in a • transparent manner

  49. CROP RESIDUES BURNING • Activity data required to estimate emissions: • collected by statistics agencies: annual crop productions (alternative way = FAO database) • not collected by statistics agencies: • residue to crop ratio • dry matter fraction of biomass • fraction of crop residues burned in field • fraction of crop residues oxidised • C fraction in dry matter • Nitrogen/Carbon ratio • Emision factors: C-N emission ratios as CH4, CO, N2O, NOX • Other constants (conversion ratios): • C to CH4 or CO (16/12; 28/12, respectively) • N to N2O or NOX (44/28; 46/14, respectively);

  50. 1. OPEN THE IPCC SOFTWARE AND CHOOSE THE YEAR OF THE INVENTORY 2. CLICK IN “SECTORS” IN THE MENU BAR, AND THEN CLICK IN AGRICULTURE 3. OPEN SHEET 4-4s2 • Main residue-producing crops: • Cereals (wheat, barley, oat, rye, rice, • maize, sorghum, sugar cane) • Pulses (peas, bean, lentils) • Potatoes, peanut, others Identify the existing residue- producing crops

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