AGRICULTURE INVENTORY ELABORATION PART 2 SIMULATION

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
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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 experts 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 experts judgment
13
Determining significant animal species
Worksheet 4-1s1
gt25
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 with local experts/industry is a
  valuable source
• Assume that, using these sources, the inventory
  team determines that non-dairy cattle population
  is composed by
• Cows 40
• Steers 40
• Young growing animals 20
• No information available to divide the animal
  population into climatic zones and production
  systems
• Each of these homogenous groups of animals must
  have an estimate of feed intake and an EF to
  convert intake to CH4 emissions
• Procedure is described in IPCC-GPG (pages
  4.10-4.20)

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
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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 5,153103 heads (against FAO
5,000103 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 Cattle Warm
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 Cattle
Temperate Climate - Intensive Grazing
Comments in green indicate improvements over
previous example
26
Enhanced characterization, Non-Dairy Cattle
Temperate 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
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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 of 294 Gg CH4/year
• Total for Tier 2 (with default and aggregated
  AD) 259 Gg CH4/year
• Total for Tier 1 245 Gg CH4/year

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)

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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)

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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
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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
1 Barley 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 FertilisersDetermination 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 (NOxNH3)-N/kg fertiliser-N
• It is determined that
• FSN 19,000 (1-0.1) 17,100 t fertiliser-N
  (barley)
• FSN 111,000 (1-0.1) 99,900 t fertiliser-N (all
  other crops)
• Total fertiliser-N 117,000 t fertiliser-N
• EF1 is 0.9 for barley (country-specific) and
  1.25 for the other crops (Table 4.17, IPCC-GPG)
• For the purpose of filling the IPCC Software
  sheet 4-5s1, a weighted EF1 is calculated as
  follows
• EF1 weighed average 17.1/117 (0.9) 99.9/117
  (1.25) 1.20
• From worksheet 4-5s1, the annual emission of
  N2O-N from use of synthetic fertilizer was
  estimated as 1.40 Gg N2O-N

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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
51
FIELD BURNING OF CROP RESIDUES
Worksheet 4-4, sheet 1
Priority order for collectable AD1 1. Values
collected from published statistics 2. If not
available, values can be derived from a) crop
area (in kha) b) crop yield (in ton ha-1) 3. From
FAO DB
Flowchart to be applied to each crop
A. Annual crop Production (Gg)
Priority order for non-collectable AD2 1. CS
values-research 2. CS values-expert judgment 3.
Values from countries with similar conditions 4.
Default values (search EFDB)
B. Residue/crop Ratio
C. Quantity of residues (Gg biomass)
52
FIELD BURNING OF CROP RESIDUES
Worksheet 4-4, sheet 1
Flowchart to be applied to each crop
Priority order for non-collectable AD 1. CS
values-research 2. CS values-expert judgment 3.
Values from countries with similar conditions 4.
IPCC default values (search EFDB)
C. Quantity of residue (Gg biomass) from previous
slide
D. Dry matter Fraction
E. Total quantity of dry residue (Gg dm)
53
FIELD BURNING OF CROP RESIDUES
Worksheet 4-4, sheet 1
Flowchart to be applied to each crop
E. Quantity of dry residue (Gg dm) from previous
slide
Priority order for non-collectable AD 1. CS
values-research 2. CS values-expert judgment 3.
Values from countries with similar conditions (No
default values)
F. Fraction burned in fields
For default values, search EFDB as combustion
efficiency
G. Fraction oxidised
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)
H. Total biomass burned (Gg dm burned)
54
4. OPEN THE SHEET 4-4s2 OF AGRICULTURE UNDER
SECTORS
55
FIELD BURNING OF CROP RESIDUES
Worksheet 4-4, sheet 2
Flowchart to be applied to each crop
H. Biomass burned (Gg dm burned) from previous
slide
Priority order for non-collectable AD 1. CS
values-research 2. CS values-expert judgment 3.
Values from countries with similar conditions 4.
Default values (search EFDB)
I. C fraction in residue
J. C released (Gg C)
K. N/C ratio
Total C and N released are obtained by addding
the values obtained per each individual crop
L. N released (Gg N)
56
5. OPEN THE SHEET 4-4s3 OF AGRICULTURE UNDER
SECTORS
Worksheet 4-4, sheet 3
Total emission estimates
57
6. GO TO THE OVERVIEW MODULE 7. OPEN THE
WORHSHEET 4-S2
Total emission estimates
58
FIELD BURNING OF CROP RESIDUES
Worksheet 4-4, sheet 3
Flowchart to be applied to aggregated figures
EFs If no CS values, use defaults (Table 4-16,
Reference Manual, 1996 Revised Guidelines)
P1 CH4 emited (Gg CH4)
Total C released (Gg C from all crops) from
previous slide
P2 CO emited (Gg CO)
C-N emitted (Gg C emitted as CH4 or CO Gg N
emitted as N2O or NOX)
M Non-CO2 emission rates (search EFDB)
O Conversion ratios
P3 N2O emited (Gg N2O)
P4 NOX emited (Gg NOX)
Total N released (Gg N from all crops) from
previous slide
59
FIELD BURNING OF CROP RESIDUES
Emission factors
60
FIELD BURNING OF CROP RESIDUESEmission estimates
using CS valuesWheat residues (1 of 3)
AD from national statistics
CS activity data, from research and monitoring
61
FIELD BURNING OF CROP RESIDUESEmission estimates
using CS valuesWheat residues (2 of 3)
CS activity data, from research and monitoring
62
FIELD BURNING OF CROP RESIDUESEmission estimates
using CS valuesWheat residues (3 of 3)
CS values for CH4/N2O D for CO/NOX
63
FIELD BURNING OF CROP RESIDUESEmission estimates
using default valuesWheat residues (1 of 3)
AD 1. from national statistics, or 2. from FAO
database (www.fao.org, then FAOSTAT- Agriculture
and Crops primary)
CS value, from monitoring or expert judgment
Activity data, taken from EFDB
64
FIELD BURNING OF CROP RESIDUESEmission estimates
using default valuesWheat residues (2 of 3)
Default activity data, from EFDB
65
FIELD BURNING OF CROP RESIDUESEmission estimates
using CS valuesWheat residues (3 of 3)
Default values, from EFDB
66
FIELD BURNING OF CROP RESIDUESDifferences in
emission estimatesIf CS or D values are used
67
Prescribed Burning of Savannas

• NAI GHG Inventory Training Workshop
• Agriculture Sector

68
PRESCRIBED BURNING OF SAVANNAS
Main issues derived from the Decision-tree

• If not occurring, then no emission estimates

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

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

• If key source, country-specific non-collectable
  activity
• data and emission factors must be preferred to
  be used
• (use of default values for key source is
  possible, if the country cannot
• provide the required AD or resources are
  jeopardised)

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

69
PRESCRIBED BURNING OF SAVANNAS

• Activity data required to estimate emissions

• collected by statistics agencies
• division of savannas into categories
• area per savanna category

• not collected by statistics agencies
• biomass density (kha) (column A in worksheets)
• dry matter fraction of biomass (ton DM/ha)
  (column B)
• fraction of biomass actually burned (column D)
• fraction of living biomass actually burned
  (column F)
• fraction oxidised of living and dead biomass
  (column I)
• C fraction of living and dead biomass (column K)
• Nitrogen/carbon ratio

• Emision factors C-N emission ratios as CH4, CO,
  N2O, NOX

• Other constants (conversion ratios)
• C to CH4 or CO (16/12 28/12, respectively)
• N to N2O or NOX (44/28 46/14, respectively)

70

• OPEN THE IPCC SOFTWARE AND CHOOSE THE YEAR OF THE
  INVENTORY
• GO TO THE MENU BAR AND CLICK IN SECTORS AND
  THEN IN AGRICULTURE
• OPEN THE SHEET 4-3s1
• FILL IN WITH THE DATA

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
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 judgment 4. Data provided by
third countries with similar features 5. IPCC
defaults (Table 4-14, Reference Manual,
1996 Revised Guidelines)
71
PRESCRIBED BURNING OF SAVANNAS
Flow chart to estimate non-CO2 emissions
To be applied to each savanna category
B Biomass density (ton dm/ha)
D F actually burned
C Total biomass exposed to burning (Gg dm)
Ideally, CS values based on measurements. If not,
CS values based on expert judgment. If not,
default values (search EFDB)
A Area burned (k ha)
E Biomass actually Burned (Gg dm)
F F of living biomass burned
G Living biomass actually burned (Gg dm)
H Dead biomass actually burned (Gg dm)
72
5. GO SHEET 4-3s2 IN SECTORS/AGRICULTURE OF THE
IPCC SOFTWARE 6. FILL IT WITH THE DATA
73
PRESCRIBED BURNING OF SAVANNAS
Flow chart to estimate non-CO2 emissions
Applicable per each savanna category
If no CS values, defaults in EFDB, as combustion
efficiency
I1 Fraction of living biomass oxidised (Gg dm)
N Total N released (Gg N)
G Living biomass actually burned (Gg dm) from
previous slide
M N/C ratio
K1 C fraction of living biomass
J1 Oxidised living biomass (Gg dm)
L Total C released (Gg C)
H Dead biomass actually burned (Gg dm) from
previous slide
L1 C released from living biomass (Gg C)
J2 Oxidised dead biomass (Gg dm)
I2 Fraction of dead biomass oxidised (Gg dm)
L2 C released from dead biomass (Gg C)
K2 C fraction of dead biomass
74
7. GO TO SHEET 4.3s3 IN SECTORS/AGRICULTURE 8.
FILL IT GO THE DATA
TOTAL EMISSION ESTIMATES
75
9. GO TO OVERVIEW MODULE 8. OPEN THE WORKSHEET
4S2
Total emission estimates From Savanna Burning
76
PRESCRIBED BURNING OF SAVANNAS
Applicable to aggregated figures
If no CS EFs, defaults in EFDB
O N2O NOx emission rates
R N2O emitted (Gg N2O)
P N2O-N released (Gg N)
N Total N released (Gg N) from previous slide
R NOx emitted (Gg NOX)
P NOx-N released (Gg N)
Q N2O NOx conversion rates
O CH4 CO emission rates
R CH4 emitted (Gg CH4)
L Total C released (Gg C) from previous slide
P CH4-C released (Gg C)
R CO emitted (Gg CO)
P CO-C released (Gg C)
Q CH4 CO conversion rates
77
PRESCRIBED BURNING OF SAVANNASExamples of
default emission factors
78
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

79
PRESCRIBED BURNING OF SAVANNASEmission estimates
using CS values
CS values (field measurements, experts judgment)
AD from national statistics (census, surveys,
mapping)
80
PRESCRIBED BURNING OF SAVANNASEmission estimates
using CS values
CS values (field measurements, lab analysis,
experts judgment)
81
PRESCRIBED BURNING OF SAVANNASEmission estimates
using CS values
CS values for CH4 N2O D values for CO NOx
82
PRESCRIBED BURNING OF SAVANNASEmission estimates
using default values
AD from national statisitcs
Default values taken from EFDB
83
PRESCRIBED BURNING OF SAVANNASEmission estimates
using default values
Default values taken from EFDB
CS values taken from experts judgment
84
PRESCRIBED BURNING OF SAVANNASEmission estimates
using default values
Default values taken from EFDB
85
PRESCRIBED BURNING OF SAVANNASDifference of
estimates
86
RICE CULTIVATION

• NAI GHG Inventory Training Workshop
• Agriculture Sector

87
RICE CULTIVATION

• Anaerobic decomposition of organic material in
  flooded rice fields produces CH4
• The gas escapes to the atmosphere primarily by
  transport through the rice plants
• Amount emitted function of rice species,
  harvests nº/duration, soil type, tº, irrigation
  practices, and fertiliser use
• Three processes of CH4 release into the
  atmosphere
• Diffusion loss across the water surface (least
  important process)
• CH4 loss as bubbles (ebullition) (common and
  significant mechanism, especially if soil texture
  is not clayey)
• CH4 transport through rice plants (most important
  phenomenon)

88
RICE CULTIVATIONMethodological issues

• 1996 IPCC Guidelines outline one method, that
  uses annual harvested areas and area-based
  seasonally integrated emission factors (Fc EF x
  A x 10-12)
• In its most simple form, the method can be
  implemented using national total area harvested
  and a single EF
• High variability in growing conditions (water
  management practices, organic fertiliser use,
  soil type) will significantly affect seasonal CH4
  emissions
• Method can be modified by disaggregating national
  total harvested area into sub-units (e.g. areas
  under different water management regimes or soil
  types), and multiplying the harvested area for
  each sub-unit by an specific EF
• With this disaggregated approach, total annual
  emissions are equal to the sum of emissions from
  each sub-unit of harvested area

89
RICE CULTIVATIONActivity data

• total harvested area excluding upland rice
  (national statistics or international databases
  FAO (www.fao.org/ag/agp/agpc/doc) or IRRI
  (www.irri.org/science/ricestat/pdfs)
• harvested area differs from cultivated area
  according the number of cropping within the year
  (multiple cropping)
• regional units, recognising similarities in
  climatic conditions, water management regimes,
  organic amendments, soil types, and others
  (national statistics or mapping agencies or
  expert judgment)
• harvested area per regional unit (national
  statistics or mapping agencies)
• cropping practices per regional unit (research
  agencies or expert judgment)
• amount/type of organic amendments applied per
  regional unit, to allow the use of scaling
  factors (national statistics or international
  databases or expert judgment)

90
RICE CULTIVATIONMain features from decision-tree

• If no rice is produced, then reported as NO
• If not key source
• and cropped area is homogeneous, then emissions
  can be estimated using total harvested area (Box
  1)
• but cropped area in heterogeneous, then total
  harvested area muts be disaggregated into
  homogeneous regional units applying default EF
  and scaling factors, if available
• If keysource
• and the cropped area is homogeneous, then
  emissions must be estimated using total harvested
  area and CS EFs (Box 2)
• but cropped area variable, then the total
  harvested area must be divided into homogeneous
  regional units and emissions estimated using CS
  EFs and scaling factors for organic ammendements
  (if available) (Box 3)
• The country is encouraged to produce
  seasonally-integrated EFs for each regional unit
  (excluding organic ammendements) through a good
  practice measurement programme
• The EFs must include the multiple cropping effect

91
RICE CULTIVATIONNumerical example

• Assumptions
• Hypothetical country located in Asia
• Key source condition
• Total harvested area 38,5 kha, disaggregated
  into
• 28,5 kha as irrigated and continously flooded
• 10,0 kha as irrigated, intermitently flooded and
  single aireated

92
RICE CULTIVATION
EF local research or other countrys use or from
EFDB
Regional units, from national estatistics
or mapping agencies or expert judgment
Scaling factor for water management local
research or other countrys use or
EFDB (Agriculture, Rice Production, Intermitently
Flooded, Single aeration)
AD from national statistics or international
databases (FAO, IRRI)
Enhancement factor for organic ammendements
local research or taken from the
EFDB (Agriculture, Rice Production)
93

• THANK YOU
• SERGIO GONZALEZ
• sgonzale_at_inia.cl