AGRICULTURE INVENTORY ELABORATION PART 2

1
AGRICULTUREINVENTORY ELABORATIONPART 2
2
Status of national communications from NAI Parties

• By September 2003, 70 national communications
  (NCs) from non-annex I (NAI) Parties had been
  compiled and assessed by the UNFCCC secretariat
• According to Compilation and Synthesis reports,
  the problems encountered by NAI Parties in
  elaborating their national inventories ranked
• activity data 93 per cent
• emission factors 64 per cent
• methods 11 per cent

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 34 years 12 NAI
  Parties for gt4 years
• 100 NAI Parties included CO2 99 included CH4
  and N2O 20 included HFCs, PFCs or SF6

4
Status of national communications from NAI Parties

• 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)

5
Status of national communications from NAI Parties

• 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)

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
INVENTORY ELABORATIONPrevious activities

• Preliminary key-source determination
• Two ways
• Using last years GHG inventory data
• Applying tier 1 methods for all the sectors for
  the year to be inventoried

8
DETERMINATION OF KEY SOURCES Steps

• Enumeration of source categories (SC)
• Ranking SC according to their emissions of CO2
  equivalent
• Estimating individual contributions of the SC to
  the total national emissions by dividing the
  specific contribution by total emissions and
  expresing the result in per cent
• Calculating the accumulative contribution of the
  SC
• Key sources, added together, should account for
  95 of GHG emissions

9
DETERMINATION OF KEY SOURCES
10
DETERMINATION OF KEY SOURCES
1994 GHG inventory of Chile (Gg CO2 equivalent)
(Non-energy sectors)
11
DETERMINATION OF KEY SOURCES
12
DETERMINATION OF KEY SOURCES Contribution per
sector
13
INVENTORY ELABORATIONMass balance

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

14
INVENTORY ELABORATION Mass balance

• According to experts judgement

15
INVENTORY ELABORATIONMass balance

• Factors to be applied to total wheat residues
• Total wheat residues
• total productionunit i (residue/production)
  factorunit i
• Total residues burned in
• Unit A total residuesunit A 0.50
• Unit B total residuesunit B 0.35
• Unit C total residuesunit C 0.20

16
INVENTORY ELABORATIONMass 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

3B.17
18
INVENTORY ELABORATION Mass balance

• Manure from non-dairy cattle, assigned to the
  different treatment systems

• Unit A total manure producedunit A x Fi
• If Fi is 0.90 Anaerobic lagoon
• If Fi is 0.10 direct grazing
• (Fi 0 for the rest of the treatment systems)

• Unit B total manure producedunit A x Fj
• If Fj is 0.75 Direct grazing
• If Fj is 0.10 Anaerobic lagoon
• If Fj is 0.20 Solid systems
• If Fj is 0.05 Other systems
• (Fj 0 for the rest of the treatment systems)

• Unit C total manure producedunit A x Fk
• If Fk is 0.35 Direct grazing
• If Fk is 0.35 Anaerobic lagoon
• If Fk is 0.20 Solid systems
• If Fk is 0.10 Other systems
• (Fk 0 for the rest of the treatment systems)

19
INVENTORY ELABORATIONSignificance of sub-sources

• Significance of animal species
• Example for CH4 linked to enteric fermentation
  and manure management
• CH4 emissions estimated by tier 1 method
• Country as a whole, without division into
  agroecological units

20
INVENTORY ELABORATION Significance of sub-sources

• 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

21
Significance of sub-sources
22
13
43 SIGN.
65 SIGN.
lt1
6
lt3
lt1
lt3
lt1
lt1
lt3
lt1
lt3
43 SIGN.
lt3
lt1
3B.21
22
INVENTORY ELABORATION

• Simulation for
• Enteric fermentation CH4 emissions
• Manure management CH4 and N2O emissions
• Agricultural soils N2O emissions
• Prescribed burning of savannas non-CO2 gas
  emissions
• Burning of crop residues non-CO2 gas emissions
• Rice cultivation CH4 emissions
• When possible, analysis of different scenarios
• Less accurate scenario No CS activity data
  (usual for non-collectable data factors,
  parameters)
• Medium accurate scenario No CS emission factors
  (very common fact)
• Most accurate scenario Availability of CS
  activity data and emission factors

23
Enteric Fermentation
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
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
Enteric fermentation

• Inventory simulation for three scenarios
• 1) Low level of data availability
• no access to reliable statistics or other sources
  of AD, and cannot use Country Specific (CS) EFs
• 2) Medium level of data availability
• detailed statistics on livestock activity,
  although some Activity Data (AD2) are still
  required along with default/regional EFs
• 3) High level of data availability
• good country-specific AD and EFs

27
Low level of data availability
Animal population data from FAO database
ltwww.fao.orggt. 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 experts judgement.
3B.27
28
Determination of significantsub-source categories

• Species contributing to 25 or more of emissions
  should have enhanced characterization and tier
  2 method should be applied
• Perform a rough estimation of CH4 from enteric
  fermentation applying tier 1 method
• one way of screening species for their
  contribution to emissions
• estimation is to identify categories requiring
  application of tier 2 method
• use IPCC software, sheet 4-1s1 fill in animal
  population data, and collect default EF from
  Tables 4-3 and 4-4 of Revised 1996 IPCC
  Guidelines, Vol. 3 (also taken from the IPCC
  emission factor database (EFDB))

29
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.
3B.29
30
Enhanced characterization ofnon-dairy cattle
population

• Enhanced characterization requires information
  additional to that provided by FAO statistics.
  Consultation with local experts or industry is
  valuable.
• Assume that (using the above information sources)
  the inventory team determines that the non-dairy
  cattle population is composed of
• Cows 40
• Steers 40
• Young growing animals 20
• Each of these categories must have an estimate of
  feed intake and an EF to convert intake to CH4
  emissions. Procedure is described in IPCC Good
  Practice Guidance and Uncertainty Management in
  National Greenhouse Gas Inventories
  (GPG2000)(pages 4.104.20).

31
Enhanced characterization of non-dairy cattle (1)
3B.31
32
Enhanced characterization of non-dairy cattle (2)
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
Tier 2 estimation of CH4 emissions fromenteric
fermentation by non-dairy cattle

• Enhanced characterization yielded AD (average
  daily gross energy intake) for three types of
  non-dairy cattle
• These AD must be combined with emission factors
  for each animal group to obtain emission
  estimates
• Determination of EFs requires selection of a
  suitable value for methane conversion rate (Ym)
• In this example (country with no CS data) a
  default value for Ym can be obtained from GPG2000
  

34
Tier 2 estimation of CH4 emissions from enteric
fermentation by non-dairy cattle
3B.34
35
Tier 2 estimation of CH4 emissions from enteric
fermentation by non-dairy cattle

• Tier 2 estimation for non-dairy cattle
• 259 Gg CH4 (against 245 Gg CH4 for tier 1)
• Weighted EF
• 52 kg CH4/head/yr (againts the default value of
  49 kg CH4/head/yr)
• This value should be used in the worksheet to
  report emissions by non-dairy cattle

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
New total 5,153,000 heads (against FAO
5,000,000 heads).
3B.37
38
Tier 2 estimation of CH4 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 cattleWarm
Climate, Extensive Grazing (1)
Comments in green indicate improvements over
previous example.
3B.39
40
Enhanced characterization, non-dairy cattleWarm
Climate, Extensive Grazing (2)
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)
Comments in green indicate improvements over
previous example.
3B.41
42
Enhanced characterization, Non-Dairy Cattle,
Temperate Climate, Intensive Grazing (2)
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
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 (Ym), that is, the
  fraction of energy in feed intake that is
  converted to energy in methane
• In this example we assume the country uses a
  default value (Ym 0.06, from Table 4.8, GPG2000)
• 18 estimates of EF were obtained (next slide)

44
Medium level of data availability
3B.44
45
Medium level of data availability

• Weighted EF (tier 2, country-specific AD)57 kg
  CH4/head/yr (range 42-67 kg CH4/head/yr)
• EF for tier 1 49 kg CH4/head/yr
• EF for tier 2 (with default AD) 52 kg
  CH4/head/yr
• Multiplication of EF with cattle population in
  each class yielded 18 estimates of annual
  emissions of methane from enteric fermentation,
  with a total of 294 Gg CH4/year
• Total for tier 1 245 Gg CH4/year
• Total for tier 2 (with default AD) 259 Gg
  CH4/year

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

• EFs could be improved by
• developing local capacities for measuring CH4
  emissions by cattle
• characterizing diverse feeds by their CH4
  conversion factors for different animal types
• development of local research to improve
  understanding of locally relevant factors
  affecting methane emissions
• adapting international information (scientific
  literature, EFDB, etc.) from areas with
  conditions similar to those of the country

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 CH4
• First results indicate that IPCC default EF used
  so far in preparation of inventories may be too
  high
• A similar project is being conducted in Brazil by
  EMBRAPA

3B.50
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 3050, 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
Manure ManagementCH4 Emissions
53
Manure management CH4

• We will continue with the assumptions relating to
  the same hypothetical country
• Again, tier 1 method will be applied to assess
  the significance of the different species for
  this source category
• with the purpose of identifying the need for
  enhanced characterization
• in practice, this should be done as a first step
  in inventory elaboration, considering that it is
  good practice to use the same characterization
  for all categories (it is presented here for
  training purposes only)
• Numerical examples for countries with different
  levels of data availability will be developed

54
Livestock characterization
From FAO database ltwww.fao.orggt, 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 experts judgement.
3B.54
55
Livestock characterization
Worksheet 4-1s1
Significant species
3B.55
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 CH4 from manure management
• Swine account for 20 of total emissions, and the
  country considers it appropriate to develop an
  enhanced characterization and apply a tier 2
  method for this species as well

57
Enhanced characterization of swine population (1)

• Estimation of CH4 emissions from manure
  management requires two types of activity data
• animal population
• manure management system usage
• Swine population GPG2000 recommends
  disaggregation into at least three categories
  (sows, boars and growing animals)
• However, neither IPCC-GL nor GPG2000 provides
  default EFs for these categories
• EFDB only provides EFs for European conditions
  (not suitable for our example in Latin America)
• Therefore, for the case of a country that lacks
  CS AD, we assume that the swine population is not
  classified into subcategories

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

• Tier 2 method requires determination of three
  parameters to estimate EF
• VS (kg) mass of volatile solids excreted
• Bo (m3/kg of VS) max. CH4 producing capacity
• MCF CH4 conversion factor
• For low level of data
• default AD derived from FAO database and expert
  judgement.
• default EF from IPCC-GL and GPG2000
• Examples for non-dairy cattle, swine in next
  slides

60
Low level of data availability CH4 emissions
frommanure management for non-dairy cattle
(default AD and EF) (1)
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 CH4 emissions
frommanure management for non-dairy cattle
(default AD and EF) (2)
Total emissions estimated here are lower than
those using Tier 1 (8.2 Gg CH4/yr). Weighted EF
derived from this table is 1.2 kg CH4/head/yr,
and this value should be used instead of the
default (1.6 kg CH4/head/yr) in IPCC Software
3B.61
62
Low level of data availability CH4 emissions
frommanure management for Swine (default AD and
EF) (1)
3B.62
63
Low level of data availability CH4 emissions
frommanure management for Swine (default AD and
EF) (2)
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.
Total emissions estimated were similar to those
using tier 1 (2.4 Gg CH4/yr). Weighted EF derived
from this table is 1.7 kg CH4/head/yr, and this
value should be used instead of the default (1.6
kg CH4/head/yr) in IPCC Software,
3B.63
64
Low level of data availability results
3B.64
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)
New Total 1,505,000 heads (FAO 1,500,000)
3B.66
67
Tier 2 estimation of CH4 from manure management
by non-dairy cattle, swine

• Next slides will show examples of detailed
  calculations for tier 2 method estimation of CH4
  emissions from manure management by
• Non-dairy cattle under Warm RegionExtensive
  Grazing system
• Swine under TemperateLiquid/Slurry system

68
Medium level of data availability CH4 manure
management, non-dairy cattle under Warm,
Intensive Grazing (CS-AD) (1)
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 CH4 manure
management, non-dairy cattle under Warm,
Intensive Grazing (CS-AD) (2)
In this case, the country has its own estimation
for feed/gross energy intake, feed digestibility,
and animal population for each of the different
classes of non-dairy cattle. For Bo, even though
the country has no locally developed studies,
IPCC default was adjusted for local conditions
following expert judgement. For other factors
(ASH, MCF), IPCC default values were used.
3B.69
70
Medium level of data availability CH4 manure
management, swine under Warm, Liquid/Slurry
(CS-AD) (1)
3B.70
71
Medium level of data availability CH4 manure
management, swine under Warm, Liquid/Slurry
(CS-AD) (2)
In this case, the country has its own estimation
for feed/gross energy intake, feed digestibility,
and animal population for each of the different
classes of non-dairy cattle. For Bo, even though
the country has no locally developed studies,
IPCC default was adjusted for local conditions
following expert judgement. For other factors
(ASH, MCF), IPCC default values were used.
3B.71
72
Medium level of data availability EFs estimated
by tier 2 for non-dairy cattle, with CS AD
Weighted EF 3.2 kg CH4/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
Weighted EF 1.9 kg CH4/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
Manure ManagementN2O Emissions
76
Manure management N2O

• Only tier 1 provided for this source. Steps
• characterization of livestock population
• determination of average N excretion rate for
  each defined livestock category
• determination of fraction of N excretion that is
  managed in each MMS identified
• determination of an EF for each MMS
• multiplication of total N excretion by EF, and
  summation of all estimates
• We will continue with the assumption of a
  hypothetical country in Latin America, with same
  animal characterization used for CH4 from manure
  management (and also for enteric fermentation)
• One numerical example, developed here

77
Livestock characterization to estimate N2O
emissions from manure management

• Assume that only a small fraction of the manure
  produced in the country undergoes some form of
  management
• Dairy and non-dairy cattle mostly grazing, with
  urine/faeces deposited directly on soil (N2O
  emissions accounted under Agricultural Soils)
• Cattle in feedlots assumed to have liquid/slurry
  (50) and anaerobic lagoon (50) management
  systems
• Swine a small fraction as liquid/sslurry or
  anaerobic lagoons (Table 4.22 IPCC-GL V3)
• Poultry all manure managed (60 with / 40
  without bedding) (Table 4.13 GPG2000)

78
Livestock characterization to estimate N2O
emissions from manure management
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
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
Determination of country-specific average N
excretion per head for non-dairy cattle

• Assume that the country has information about
  crude protein content of feed for the different
  classes identified
• Crude protein data are combined with feed intake
  data (from the same livestock characterization
  used for estimating CH4 emissions) to obtain N
  intake
• Assume that the country uses IPCC default value
  for N retention in body and products (0.07 for
  non-dairy cattle, GPG2000, Table 4.15)

81
Livestock characterization for estimating N2O
emissions from manure management
MMS Manure management system L/S
Liquid/slurry AL Anaerobic lagoon
3B.81
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
N2O from manure management use of IPCC software
to estimate total N excretion (1)
Estimated ?
IPCC Default ?
IPCC Default ?
Data from livestock characterization
3B.83
84
N2O from manure management use of IPCC software
to estimate total N excretion (2)
Calculated ?
IPCC Default ?
IPCC Default ?
Data from livestock characterization
3B.84
85
N2O from manure management use of IPCC software
to estimate total N excretion (3)
IPCC Default ?
Data from livestock characterization
3B.85
86
N2O 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 N2O from
manure management
IPCC Default ?
IPCC Default ?
IPCC Default ?
IPCC Default ?
IPCC defaults obtained from Table 4-22, IPCC-GL
V3, and Tables 4.12 and 4.13, GPG2000.
Note cells corresponding to poultry were
manually altered to accommodate these new
categories from GPG2000, not included in IPCC-GL.
3B.87
88
Direct N2O Emissions from Agricultural Soils
89
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
3B.89
90
AGRICULTURAL SOILS
Assess individual contribution of different N
sources to determine ones (subcategories) which
are significant for the source category (25 or
more of source category N2O emissions)
For this, apply Tier 1a method and default values
to get an economic emission estimate
For the significant subcategories, the best
efforts should be invested to apply Tier 1b along
with country-specific AD1 and AD2 (parameters)
and country-specific emission factors
For non-significant subcategories, Tier 1a, along
with country-specific AD1, default AD2
(parameters) and default 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
3B.90
91
Direct N2O Agricultural soils

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

92
Use of N fertilizers
From the FAO database
1 Barley data from industry sources, shown in
parentheses.
3B.92
93
Direct N2O Agricultural soils

• From FAO database, only total country data for
  fertilizer use are available. Therefore, only
  Tier 1a method could be used
• Data from barley industry/research can be used to
  apply Tier 1b method
• to ensure consistency, it is recommended to
  compare crop area and crop yield data from FAO
  with data from local industry
• in this case, the two sources reasonably matched
  in terms of area and yield, and it can be assumed
  that the industry estimation of N fertilizer
  usage is compatible with the FAO N fertilizer
  data
• from previous table, it can be derived that
  19,000 t N fertilizer were applied to barley
  crops, and 111,000 t N fertilizer to the rest
  (130,000 minus 19,000)
• from local research, EF1 was estimated to be 0.9
  for fertilizer applied to barley crops in the
  country
• Since there are no organic soils in the country,
  EF2 is not needed
• Emissions from grazing livestock are included
  here. Note that the GPG2000 includes this source
  under manure management

94
Synthetic fertilizersdetermination of FSN and
EF1

• FSN annual amount of fertilizer N applied to
  soils, adjusted by amount of N that volatilizes
  as NH3 and NOx
• To adjust for volatilization, use IPCC default
  value from Table 4-17, IPCC-GL, V2 0.1 kg
  (NOxNH3)-N/kg fertilizer-N
• It is determined that
• FSN 19,000 (1-0.1) 17,100 t fertilizer-N
  (barley)
• FSN 111,000 (1-0.1) 99,900 t fertilizer-N
  (all other crops)
• Total fertilizer-N 117,000 t fertilizer-N
• EF1 is 0.9 for barley (country specific) and
  1.25 for the other crops (Table 4.17, GPG2000)
• For the purpose of filling the IPCC software
  sheet 4-5s1, a weighted EF1 is calculated as
  follows
• EF1 weighted average 17.1/117 (0.9)
  99.9/117 (1.25) 1.20
• From worksheet 4-5s1, the annual emission of
  N2O-N from use of synthetic fertilizer was
  estimated as 1.40 Gg N2O-N

95
Emissions of N2O from synthetic fertilizers
Combined EF (CS and default)
3B.95
96
Manure applied to soilsdetermination of FAM

• FAM annual amount of manure N applied to soils,
  adjusted by amount of N that volatilizes as NH3
  and NOx
• To calculate amount of manure N applied to soils,
  use total amount of manure produced (using
  livestock characterization previously applied to
  other sources) and subtract the amounts used for
  fuel, feed and construction (here assumed to be
  zero) and those deposited on soils by grazing
  livestock (whose emissions are reported
  separately as direct emissions)
• To adjust for volatilization, use IPCC default
  value from Table 4-17, IPCC-GL, V2 0.2 kg
  (NOxNH3)-N/kg animal manure N
• It is determined that
• FAM 24,924 t animal manure N applied to soils
• Next two slides illustrate the use of IPCC
  software to estimate FAM (named as FAW in
  IPCC-GL) and estimation of an annual emission of
  N2O-N from application of animal manure to soil
  of 0.31 Gg N2O-N

97
Emissions of N2O from animal manure (1)
Countrys estimate
From Table 4-17 IPCC Guidelines V2
Data from livestock characterization
3B.97
98
Emissions of N2O from animal manure (2)
IPCC default
3B.98
99
N-fixing cropsdetermination of FBN

• FBN amount of N fixed by N-fixing crops
  cultivated annually (in our case, soybeans)
• To calculate amount of N fixed, we assume that
  there are no crop-specific values for
  grain/biomass ratio or for moisture content of
  biomass therefore, default data are used
• Grain production is estimated from FAO statistics
  (457,842 t/yr)
• N content of biomass (FracNCRBF) is obtained from
  Table 4.16 (GPG2000) 0.023 kg N/kg dry biomass
• Residue/crop product ratio is 21, and dry matter
  fraction is 0.85 (from same table as above)
• It is determined (by using equation 4.26,
  GPG2000) that
• FBN 27,748 t fixed-N
• This value is introduced in IPCC software
  worksheet 4-4s1 to estimate an annual emission of
  N2O-N from N-fixing crops of 0.35 Gg N2O-N

100
Emissions of N2O from N-fixing crops
Estimated activity data
IPCC default
3B.100
101
Crop residuesdetermination of FCR

• FCR amount of N in crop residues returned to
  soil annually
• It is estimated by adjusting the total amount of
  crop residue N produced to account for the
  fraction that is burned in the field and for the
  fraction that is removed from the field
• We assume that the country has enough data to
  apply Tier 1b method (equation 4.29 in GPG2000)
• It is determined that
• FCR 37,934 t N in crop residues that are
  returned to soils
• This value is introduced in sheet 4-5s1 of the
  IPCC software to estimate an annual emission of
  N2O-N from N in crop residues of 0.47 Gg N2O-N
• IPCC Software worksheet was designed for Tier-1a
  method, and use of Tier 1b requires manually
  altering sheet 4-5s1, cell C23

102
Crop residues determination of FCR

• Source FAO statistics
• Source Table 4.16, GPG2000 (except FracDM for
  potatoes, which was estimated by experts)
• Source Country-specific data

FCR
3B.102
103
N2O emissions from N in crop residues
IPCC default
Total direct N2O emissions (excluding pasture,
range and paddock) 2.54 Gg N2O-N/yr
3B.103
104
N excretion from pasture/range/paddock
Default values
3B.104
105
N2O emissions from pasture/range/paddock
From Table 4-8 IPCC Guidelines V2
3B.105
106
Indirect N2O Emissions from Agricultural Soils
107
Indirect N2O Agricultural soils

• We will continue with the assumption of a
  hypothetical country in Latin America
• We will assume that the country only covers the
  following sources
• N2O(G) from volatilization of applied synthetic
  fertilizer and animal manure N, and its
  subsequent deposition as NOx and NH4
• N2O(L) from leaching and runoff of applied
  fertilizer and animal manure
• Indirect N2O emissions are estimated using Tier
  1a method and IPCC default emission factors
• The next slides show calculations as performed by
  IPCC Software

108
Indirect N2O emissions from atmospheric
depositions
From Table 4.18 GPG2000
Default value
From Table 4-17 IPCC Guidelines V2
3B.108
109
Indirect N2O emissions from leaching and runoff
From Table 4-17 IPCC Guidelines V2
From Table 4.18 GPG2000
3B.109
110
Field Burning of Crop Residues
111
Burning of crop residuesMain issues derived from
the decision tree

• 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

112
Burning of crop residues

• Activity data required to estimate emissions

• collected by statistics agencies annual crop
  production (alternate way is FAO database)

• not collected by statistics agencies
• residue to crop ratio
• dry matter fraction of biomass
• fraction of crop residues burned in field
• fraction of crop residues oxidized
• C fraction in dry matter
• Nitrogen/carbon ratio

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

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

113
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
Field burning of crop residues Worksheet 4-4,
sheet 1 Flowchart to be applied to each crop
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 tonne/ha) 3. From
FAO DB
A. Annual crop production (Gg)
Priority order for non-collectable AD2 1. CS
values - research 2. CS values -
expert judgement 3. Values from countries with
similar conditions 4. Default values (search EFDB)
B. Residue/crop ratio
C. Quantity of residues (Gg biomass)
115
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 judgement 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)
116
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 judgement 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 oxidized
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)
117
4. OPEN SHEET 4-4s2 OF AGRICULTURE UNDER
SECTORS
3B.117
118
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 judgement 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)
119
5. OPEN SHEET 4-4s3 OF AGRICULTURE UNDER
SECTORS
Worksheet 4-4, sheet 3
Total emission estimates
3B.119
120
6. GO TO THE OVERVIEW MODULE 7. OPEN THE
WORHSHEET 4-S2
Total emission estimates
3B.120
121
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, Rev. 1996 IPCC Guidelines)
P CH4 emitted (Gg CH4)
Total C released (Gg C from all crops) from
previous slide
P CO emitted (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
P N2O emitted (Gg N2O)
P NOX emitted (Gg NOX)
Total N released (Gg N from all crops) from
previous slide
3B.121
122
Field burning of crop residues
Emission factors
3B.122
123
Field burning of crop residuesEmission estimates
using country-specific valuesWheat residues (1
of 3)
AD from national statistics
CS activity data, from research and monitoring
3B.123
124
Field burning of crop residues Emission
estimates using country-specific valuesWheat
residues (2 of 3)
CS activity data, from research and monitoring
125
Field burning of crop residues Emission
estimates using country-specific valuesWheat
residues (3 of 3)
CS values for CH4/N2O D for CO/NOX
126
Field burning of crop residues Emission
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 judgement
Activity data, taken from EFDB
3B.126
127
Field burning of crop residuesEmission estimates
using default valuesWheat residues (2 of 3)
Default activity data, from EFDB
128
Field burning of crop residuesEmission estimates
using CS valuesWheat residues (3 of 3)
Default values, from EFDB
129
Field burning of crop residues Differences in
emission estimatesif country-specific or default
values are used
130
Prescribed Burning of Savannas
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)

• 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

132
PRESCRIBED BURNING OF SAVANNAS

• Activity data required to estimate emissions

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

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

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

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

133

• 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 judgement 4. Data provided by
third countries with similar features 5. IPCC
defaults (Table 4-14, Reference Manual,
1996 Revised Guidelines)
134
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 judgement. 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)
135
5. GO SHEET 4-3s2 IN SECTORS/AGRICULTURE OF THE
IPCC SOFTWARE 6. FILL IT WITH THE DATA
136
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
137
7. GO TO SHEET 4.3s3 IN SECTORS/AGRICULTURE 8.
FILL IT GO THE DATA
TOTAL EMISSION ESTIMATES
138
9. GO TO OVERVIEW MODULE 8. OPEN THE WORKSHEET
4S2
Total emission estimates From Savanna Burning
139
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
140
PRESCRIBED BURNING OF SAVANNASExamples of
default emission factors
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
PRESCRIBED BURNING OF SAVANNASEmission estimates
using CS values
CS values (field measurements, experts judgement)
AD from national statistics (census, surveys,
mapping)
143
PRESCRIBED BURNING OF SAVANNASEmission estimates
using CS values
CS values (field measurements, lab analysis,
experts judgement)
144
PRESCRIBED BURNING OF SAVANNASEmission estimates
using CS values
CS values for CH4 N2O D values for CO NOx
145
PRESCRIBED BURNING OF SAVANNASEmission estimates
using default values
AD from national statisitcs
Default values taken from EFDB
146
PRESCRIBED BURNING OF SAVANNASEmission estimates
using default values
Default values taken from EFDB
CS values taken from experts judgement
147
PRESCRIBED BURNING OF SAVANNASEmission estimates
using default values
Default values taken from EFDB
148
PRESCRIBED BURNING OF SAVANNASDifference of
estimates
149
RICE CULTIVATION
150
RICE CULTIVATION

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

151
RICE CULTIVATIONMethodological issues

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

152
RICE CULTIVATIONActivity data

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

153
RICE CULTIVATIONMain features from decision-tree
(1)

• If no rice is produced, then reported as NO
• If not key source
• and cropped area is homogeneous, then emissions
  can be estimated using total harvested area (Box
  1)
• but cropped area in heterogeneous, then total
  harvested area muts be disaggregated into
  homogeneous regional units applying default EF
  and scaling factors, if available
• If keysource
• and the cropped area is homogeneous, then
  emissions must be estimated using total harvested
  area and CS EFs (Box 2)
• but cropped area variable, then the total
  harvested area must be divided into homogeneous
  regional units and emiss