2. GAINS Agriculture Guide
Version 1 – A guide to the agricultural components of the GAINS model
Spring 2009
AP EnvEcon IMP Ireland Team
Dr Andrew Kelly Dr Luke Redmond Dr Fearghal King
IIASA Team
Dr Zbigniew Klimont Dr Wilfried Winiwarter
UCD IMP Ireland Team
Dr Amarendra Sahoo Dr Miao Fu
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Table of Contents
Acknowledgements............................................................................................................................2
Introduction.......................................................................................................................................3
Basic types of data and information..............................................................................................4
Submitting new data......................................................................................................................4
1. Animal numbers.........................................................................................................................5
Data Requirements I .................................................................................................................. 7
2. Fertilisers and area of land ........................................................................................................8
Data Requirements II............................................................................................................... 10
3. Abatement measures - Control Strategy ................................................................................. 12
The control strategy approach................................................................................................. 12
NH3 Abatement ........................................................................................................................ 15
CH4 Abatement......................................................................................................................... 16
N2O Abatement .........................................................................................................................17
NOX and PM Abatement ...........................................................................................................17
Data Requirements III ............................................................................................................. 18
4. Emission factors and relevant variables..................................................................................20
Data Requirements IV..............................................................................................................22
5. Cost data ...................................................................................................................................25
Cost calculation principles.......................................................................................................25
Data Requirements V...............................................................................................................27
Closing note .....................................................................................................................................28
Glossary............................................................................................................................................34
Appendix – Submission and Review of Data..................................................................................35
Reviewing data in the online system...........................................................................................36
Summary: Simplified request sheets for provision of new data.................................................38
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Acknowledgements
This piece has been compiled by AP EnvEcon as part of the IMP Ireland project that is co-
funded by the Environmental Protection Agency of Ireland. Key input to the work was provided
from the team at IIASA under the EC4MACS project that is funded by the EC’s Life programme.
As with many of the forthcoming pieces of work, documents will be released as versions that are
later updated to take account of new developments.
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Introduction
This document provides an overview of the principal data required for compiling a GAINS
model agricultural scenario. This document is designed to help inform and guide feedback from
experts in the agriculture sector to assist in the calibration of the relevant GAINS model
sections. The brief also provides a snapshot of some sample data and assumptions taken from
the GAINS ‘Ireland’ model. These data should be considered preliminary or default as most are
now updated in the live system. Whilst the focus of all examples is on Ireland, the brief is also
intended for broader use as a ‘case-study’ guidance document for other member states.
Specific national data can be viewed through the online model by registering at:
http://gains.iiasa.ac.at/
A basic guide to accessing model outputs can be found at the following web address – the guide
is for GAINS Asia, but the information are still relevant to any regional variation of the model:
http://gains.iiasa.ac.at/gains/download/GAINS-Asia-Tutorial-v2.pdf
In terms of content, this document outlines the categories and format of data that are utilised
within the GAINS Ireland model to estimate emissions from the Agriculture sector. The
pollutant emissions considered are NH3, N2O, CH4 and to a lesser extent PM and NOX. GAINS
not only models agriculture but also all other sectors, which are not detailed here. In
compilation of this report, the team have collaborated directly with IIASA to ensure an up-to-
date and relevant guide, however, over time changes in the model and processes will require
occasional revisions of this work to be developed.
The next development stages of the model, with respect to agriculture, will include a new
approach for considering nitrate leaching and the use of a Nitrogen flow (N-Flow) approach in
the estimation of primary agricultural emissions of nitrogen species from manure management.
The development of the model to include these new approaches will entail new model
parameters, and consequently, new data requests and a new version of this guide. However, the
principal data, especially activity related, will remain the same.
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For any queries or feedback in relation to the data and the modelling process in Ireland please
contact us directly at ImpIreland@APEnvEcon.com
Basic types of data and information
In this brief there are four principal grouped categories of data discussed that are required for
the GAINS model with respect to agriculture emissions – specifically:
1. Animal numbers
2. Fertiliser use and area of land
3. Abatement measures
4. Emission factors and other relevant variables
Each of these grouped categories are discussed in some detail in the sections that follow, with
further details in an appendix section, and as mentioned, yet further information available
through the online system. Each category section concludes with a subheading that attempts to
provide a summary of the data requirements for the modelling process.
As a first guideline it should be noted that data within the GAINS model are provided in five
year intervals – currently from 1990 to 2030. Thus values are required for parameters in
1990,1995,2000,2005,2010,2015,2020,2025,2030. Data submitted are therefore often a blend
of historical national data and more recent forecasts. As time advances the policy process will
require the relevant years to shift further outward towards new compliance periods. Thus the
process is an ongoing iterative exercise, and consistent and well structured data are extremely
important.
Submitting new data
This brief should provide an understanding of the types and structure of new data required. In
the appendix section a template for the provision of updated information and figures is
presented to assist with such a submission. However, through this format, or through direct
contact via ImpIreland@APEnvEcon.com (for Ireland only) – all submissions or comments will
be addressed to whatever extent possible.
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1. Animal numbers
Within the GAINS model, animal numbers and type of animal are the primary ‘activity’ driver in
the modelling process for agricultural emissions. In the same manner as the level of fuel use and
the type of fuel would be the main ‘activity’ driver for the transport sector.
At present GAINS requires input on a tier 1 level, although this may be changed over time to
account for more detailed information. For the moment however, animal numbers and types are
categorized as presented in Table 1. It is important to note that in respect of these numbers, the
focus is on live animals, and where significant seasonal differences occur, on the average
live animal numbers. An example of such a variation between live animal numbers and
average live animal numbers is presented in box 1.
Box 1: Example of the variation between live and average live animal numbers
The task the model performs in regard to these data is to determine an excretion rate, thus
ultimately an important element of this process is to focus on ensuring that an
appropriate average excretion rate is used that takes account of animal size and
Projected livestock data are often reported for two periods, June and December. Within
the GAINS model a single value for animal numbers is required. To consider sheep for
example, the variation in numbers in the two periods is quite pronounced due to the
presence or absence of lambs in the period.
June December Average number
Ewes 2056 1951 2003
Rams 529 419 474
Lambs 2120 0 1060
Total 4705 2370 3537
SHEEP (GAINS) Average number 3537
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their relative shares in the particular animal category. In other words, where a
significant proportion of the population are lambs and are only present for a part of the year, the
average number and excretion rate used for ‘Sheep’ should reflect this number of animals in the
average, and also account for the lower excretion rate of these lambs in the average excretion
used for the category ‘Sheep’. As the proportion of lambs should be reasonably consistent, this
checking of the average excretion rate does not need to be regularly assessed and adjusted.
There are also categories in the model for buffalos and camels. As there are only a few hundred
buffalo, and camels are largely irrelevant, these categories are ignored for Ireland at present.
These may be more relevant for other member states.
Table 1: Animal categories in GAINS
Main Category Sub category GAINS Code
Dairy Cattle Dairy Cows – Solid systems DS
Dairy Cows – Liquid (Slurry) systems DL
Other (Beef) Cattle Other Cattle – Solid systems OS
Other Cattle – Liquid (Slurry) systems OL
Pigs Pigs – Solid systems PS
Pigs – Liquid (Slurry) systems PL
Poultry Laying Hens LH
Other poultry OP
Sheep Sheep and goats SH
Horses Horses HO
Fur Animals Fur animals (or other relevant production
animal e.g. rabbits)
FU
In terms of animal numbers, the model has these reported in 1000 head of animals e.g. 90.1
represents 90,100 animals. Recently, the model allows displaying animal numbers in livestock
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units (LSU) in accordance with an FAO methodology. However, the inputting of data remains in
terms of live animal number for the aggregate categories described in Table 1 (input normally is
in million heads). Pigs and cattle are subdivided into liquid and solid systems – referring to the
manure management.
Data Requirements I
For animal number data requirements, what is needed is the 1000 head of animals in Ireland
under each of the categories in Table 1, for the reporting years – 1990, 1995, 2000, 2005, 2010,
2015, 2020, 2025 and 2030. In Ireland these data have thus far been drawn from national
FAPRI data, although values have not yet been adjusted to average live animal numbers. Instead
the animal numbers for June have been used in all cases. For the period after 2020, the 2020
figures are held as the scenario values for 2025 and 2030 in the absence of longer term
forecasts.
The approach taken to the FAPRI animal numbers data when adding it to GAINS Ireland has
been straightforward, with the following notes for specific categories:
Sheep and Horses
No modifications were required in relation to sheep and horse numbers. These are transferred
directly from the national herd statistics into the model.
Pigs
Data for fatteners, sows and piglets are required by GAINS. Once again the key parameter is to
specify the number of animals in a manner consistent with the calculation of excretion rates.
Thus, the numbers and the N Excretion rate should be assessed to ensure that comparable
results are obtained. For example, account for the number of sows, fatteners and piglets and
calculate the N excretion rate based on a weighted share of each category.
Poultry
For Ireland ‘layers’ in the FAPRI data have been used for the LH category, with broilers and
turkeys added to the OP category.
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Cattle
Dairy cattle (cows) in the model refer to milk producing animals only. Thus all other animals e.g.
sucklers, are to be allocated to the ‘other cattle’ (beef) category. For Ireland, dairy cattle and
other cattle (beef) have been split according to the FAPRI distinction. With regard to the liquid
or slurry systems, the split for dairy cattle is assumed as 7% solid and 93% slurry. The split for
other (beef) cattle is 28% solid and 72% slurry.
Recent values in the model from 2000 out to 2020 for animal numbers are presented in Table 4.
All values can be updated with relative ease should improved information be available.
2. Fertilisers and area of land
Two further categories that are relevant to agricultural emissions in the model are mineral
fertiliser use and area of land. Principally these are related to NH3, N2O, NOx emissions and
nitrate leaching. The relevance and required data for these categories are discussed below.
Fertiliser
Fertiliser as an emission source is broken into two categories within the model – use and
production. Within Ireland the limited (if any since the closure of IFI) fertiliser production
means it is the use of fertiliser which is most relevant to emissions. Fertiliser is handled in
GAINS under the categories listed in Table 2.
Table 2: Mineral fertiliser use in GAINS
Main Category Sub category GAINS Code
Fertiliser use Fertilizer use - other N fertilizers (kt N) FCON OTHN
Fertilizer use – urea and ammonium
bicarbonate (kt N)
FCON UREA
Fertiliser production Nitrogen fertilizer production
(in N equivalents kt N)
FERTPRO
Ind. Process: Fertilizer production (all
compounds) (Mt)
PR FERT
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Thus, principally for Ireland, the model is interested in the use of urea and other fertilisers in
the Irish agricultural sector. The levels of use are recorded in thousand tonnes (kt) of N. Recent
values and forecasts are as presented in Table 4 under FCON OTHN and FCON UREA.
Land use sources
The model also takes account of land use and types and their relevance to emissions. This aspect
of calibration requires data in units of million hectares. Essentially describing how much land is
categorised under a given heading. Table 3 presents the land use and type categories that are
considered in the GAINS model which are relevant to Ireland.
Table 3: Land uses and types in GAINS
Main Category Sub category GAINS Code
Area of land type Million hectares of Forest FOREST
Million hectares of grassland and soils GRASSLAND
Million hectares of organic soils HISTOSOLS
Mass of nitrogen added Kt of N added to Forest land N INPUT FOREST
Kt of N added to grassland and soils N INPUT
GRASSLAND
Other relevant activity Million hectares of land that is ploughed, tilled
or harvested
AGR ARABLE
The model also identifies the area of arable agricultural land that is within subboreal or
temperate climates.
Open waste burning
Burning of agricultural residue in open fields can be a significant source of several pollutants. If
such practices occur in Ireland then the total amount of biomass burned (Mt) annually should
be estimated, reported and included within the ‘WASTE_AGR’ sector. Emissions of SO2, NOx,
NH3, NMVOC, CH4, CO, and Particulate Matter (PM) will be calculated in GAINS for this
activity.
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Other activities
Other activities such as the burning of fuel in greenhouses, or the use of fuels in agricultural
machinery are also captured within the model. However, these other activities, although linked
with agriculture, are captured under other sectors – specifically with these two examples, under
the residential/commercial and off-road transport sectors respectively.
Data Requirements II
Thus for this aspect of the model, the required data relate to approximate values for areas of
land, and the associated use of fertiliser on these areas. A sample of recent data for these
categories within the model – at the time of writing - are presented in Table 4 for assessment.
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Table 4: Summary of sample agricultural data (rounded up) in the GAINS scenario
Activity Sector Unit 2000 2005 2010 2015 2020
DS AGR_COWS M animals 0.082 0.078 0.078 0.08 0.09
DL AGR_COWS M animals 1.095 1.03 1.03 1.06 1.20
OS AGR_BEEF M animals 1.64 1.64 1.60 1.61 1.65
OL AGR_BEEF M animals 4.22 4.23 4.10 4.14 4.25
PS AGR_PIG M animals 0 0 0 0 0
PL AGR_PIG M animals 1.72 1.69 1.80 1.50 1.33
LH AGR_POULT M animals 1.57 1.95 1.56 1.50 1.43
OP AGR_POULT M animals 13.77 14.14 13.14 12.61 12.07
SH AGR_OTANI M animals 7.56 6.39 5.43 5.33 4.68
HO AGR_OTANI M animals 0.07 0.08 0.08 0.08 0.08
FU AGR_OTANI M animals 0 0 0 0 0
NOF FCON_UREA kt N 57.61 37.34 33.6 35.04 37.38
NOF FCON_OTHN kt N 349.99 314.83 302.39 315.32 336.45
NOF PR_FERT Mt 0.956 0 0 0 0
NOF FERTPRO kt N 248 0 0 0 0
NOF IO_NH3_EMISS kt NH3 0 0 0 0 0
NOF WT_NH3_EMISS kt NH3 0 0 0 0 0
NOF OTH_NH3_EMISS kt NH3 0.57 0.56 0.57 0.57 0.57
FIRE_AREA GRASSLAND M ha 0 0 0 0 0
RICE_AREA AGR_ARABLE M ha 0 0 0 0 0
FIRE_AREA FOREST M ha 0 0 0 0 0
AREA FOREST M ha 0.28 0.28 0.28 0.28 0.28
AREA GRASSLAND M ha 8.48 8.48 8.48 8.48 8.48
N_INPUT FOREST kt N 0 0 0 0 0
N_INPUT GRASSLAND kt N 0 0 0 0 0
AREA AGR_ARABLE_SUBB M ha 0 0 0 0 0
AREA AGR_ARABLE_TEMP M ha 0 0 0 0 0
N_INPUT AGR_ARABLE_SUBB kt N 0 0 0 0 0
N_INPUT AGR_ARABLE_TEMP kt N 0 0 0 0 0
AREA HISTOSOLS M ha 0 0 0 0 0
NOF AGR_ARABLE M ha 1.1 1.1 1.1 1.1 1.1
NOF WASTE_AGR Mt 0 0 0 0 0
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3. Abatement measures - Control Strategy
This section covers abatement measures in the model that relate to emissions from agriculture.
In the model, abatement measures are described in two ways. Firstly, the costs and emission
factors related to abatement efficiency are defined, and secondly the degree to which a given
measure or package of measures is applied in a given scenario is defined through the ‘control
strategy’ file.
Therefore, on the one hand you have information that identifies how effective a specific measure
is at reducing emissions from a given source, and on the other you have information defining
how much of a given pollution source is covered by each specific abatement measure.
The control strategy approach
Thus far, this brief has identified the animal numbers and other ‘activity’ variables that can be
loosely described as ‘sources’ in the process of agricultural emission estimation. In this section
the potential abatement options that can be applied to these sources to reduce agricultural
emissions are discussed. Packages of abatement measures within the GAINS model are referred
to as control strategies. These control strategies are a vital component of the final emission
estimations as they determine what actions have been taken to reduce emissions from a given
source.
The approach in the model is to define for a given activity or source, the proportion of that
activity which is ‘managed’ by a specific abatement measure. For example, if there are 100,000
dairy cattle and 50% of them in 2005 have their manure managed via low efficiency low
ammonia application, then the control strategy value for this particular measure should be set at
50% for 2005. The remaining 50% in 2005 is uncontrolled unless otherwise defined, meaning
that the ‘unabated’ or base emission factor for the source is used for this proportion of the cattle.
In practice then, if the measure discussed above reduced emissions by 25%, and the unabated or
base emissions for 100,000 cattle was 10kt of NH3, then the simplified model function is as
presented in Box 1 where a 50% ‘low efficiency low ammonia application’ control is defined.
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The controls considered with respect to agriculture, generally relate to animal storage/housing,
ammonia application, low nitrogen animal feed, urea substitution, manure burning and
biofiltration systems. The list of measures can be extended and developed over time, and where
a specific national measure is not represented for a given pollutant, it may be possible in time to
incorporate this. A forum for contributing national information on measures is currently
planned under the IMP Ireland project. Details will be provided as this initiative develops.
Box 1 Control strategies in the modelling process – Simplified example
Thus the details of abatement measures and the assumption of how they will be structured over
relevant activities are critical to the emission estimation and forecasting of the GAINS Ireland
modelling work. Generally it is research work to obtain the necessary information for what
measures were in place historically. However, a significant challenge in calibrating the model is
to establish plausible control strategy packages for future years for the member states. This
raises a related task – which is to define the applicability of a given measure in the future.
Applicability of a given pollutant control abatement measure
One of the further aspects of the model is the applicability limits for certain technologies. In
other words, where the control strategy defines what measures are already implemented or
planned, the applicability parameter defines what the maximum implementation rates are for a
given measure. Within the modelling framework applicability is an important concept for the
optimisation mode. In this mode, the model will look at not just what is planned to be done in
terms of emission abatement, but what else could be done to reduce emissions further and what
1. Number of dairy cattle is 100,000
2. Emissions for 100,000 dairy cattle are 10kt of NH3
3. The low efficiency ‘low ammonia application’ technique reduces emissions by 25%
4. 50% of the dairy cattle are covered by this abatement measure
5. 50% of the dairy have no abatement measure in place
6. Emissions are 5kt for the 50% of the cattle without any abatement measure
7. Emissions are 5kt less 25%, therefore 3.75kt, for the 50% of the dairy cattle with the
abatement measure in place over them
8. Total emissions are therefore 8.75kt for this defined source
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will be the associated cost. As such where there are specific national considerations or
restrictions on, say urea substitution for fertiliser use, the applicability file should reflect this. If
the applicability of a measure is set to zero, the model will not identify this measure as a
potential option – in other words it rules it out as a possibility to reduce emissions in that
specific member state for a discussed sector/animal category using this measure. Generally such
assertions need to be supported by national evidence and research to justify the limitation of
abatement options that may be considered for a country.
The details of the optimisation process are not discussed in this document, but in essence, the
model considers the efficiency of abatement measures, their associated cost, and the
applicability when determining what package of regional measures will deliver on a specific
emission reduction/effect based target.
The next subheadings look at the principal agriculture related abatement measures identified for
each of the pollutants covered by the model. This is not to say these are the only sources of
emissions, rather these are the sources of emissions covered by a specific abatement technology
or process. The principal abatement measures relating to agriculture for Ireland – as defined
within the model at the time of writing - can be summarised as presented in Table 5. In many
cases the measures refer to specific stages of the animal cycle – application, grazing, housing
and storage, with varied emissions associated with each stage.
Table 5: Definitions of principal control strategy categories defined in the sample scenario for
Ireland
Technology Definition
BAN Ban on agricultural burning
CAGEUI/II… Emission standards for construction and agricultural machinery
CS_low Covered outdoor storage of manure, low efficiency
LNA_low Low ammonia application with mean efficiency
SA Animal house adaptation
LNA_low Low ammonia application with mean efficiency
SA Animal house adaptation
LNF_SA Combination of low nitrogen feed and animal house adaptation
PM_INC Burning of poultry manure
LNF_CS Combination of low efficiency outdoor manure storage and low nitrogen feed
LNF_SA_LNA Combination of LNF & SA with mean efficiency low ammonia application
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NH3 Abatement
Table 6A presents a sample of abatement options for NH3 in Ireland under a scenario within the
model. This identifies which type of animal is covered by which proportion of a given NH3
abatement measures. Table 6B present further categories of NH3 emission abatement options
that are not defined within this sample scenario for Ireland. In many cases combinations of
measures are possible such as ‘BF_CS_LNA’.
Table 6A: Control strategies (as percentages) assumed at present for NH3 from agriculture
(filtered list) in the Irish sample scenario
Activity Sector Technology 2000 2005 2010 2015 2020
DL AGR_COWS CS_low 75 75 77 80 90
DL AGR_COWS LNA_low 0 0 1 2 4
LH AGR_POULT SA 0 0 15 15 15
LH AGR_POULT LNF_SA 0 5 14.5 14 13
LH AGR_POULT LNF_SA_LNA 0 0 0.5 1 2
OL AGR_BEEF CS_low 75 77 78 80 80
OL AGR_BEEF LNA_low 0 0 1 2 4
OP AGR_POULT SA 0 0 35 0 0
OP AGR_POULT LNF_SA 0 5 26 35 15
OP AGR_POULT PM_INC 0 1 4 30 50
PL AGR_PIG CS_low 87.1 60 26.25 26.25 26.25
PL AGR_PIG LNA_low 1 0 0 0 0
PL AGR_PIG LNF_CS 0 10 23 23 23
PL AGR_PIG LNF_SA 0 10 18 17.5 17
PL AGR_PIG LNF_SA_LNA 0 1.5 2 2.5 3
Table 6B: Further categories of control strategies not yet assumed as planned for NH3 in the
Irish sample scenario
Technology Definition
BF Biofiltration – can be combined with CS and/or LNA
STRIP Stripping
SUB_U Urea substitution
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CH4 Abatement
The agricultural sector is the most significant source for CH4, however, no specific CH4
abatement technologies are as of yet defined for Ireland within the model sample scenario.
Table 7: Further categories of control strategies not yet assumed as planned for CH4 in Ireland
from the sample scenario
Technology Definition
AUTONOM Autonomous productivity increase in milk/beef production per animal
CONCENTR Replacement of roughage for more concentrate in animal feed
FARM_AD
Farm-scale anaerobic digestion (applicable to large farms, i.e. >100 dairy cows, >200
beef cattle, or > 1000 pigs)
HOUS_AD Single household scale anearobic digestion plant for household energy needs
COMM_AD Community scale anaerobic digestion plant (HOUS_AD < COMM_AD < FARM_AD)
INCRFEED Increased feed intake
NSCDIET Change to more non-structural carbohydrates (NSC) in concentrate feed
PROPPREC Propionate precursors
SA Stable adaptation
BAN Ban on agricultural waste burning
ORG_BIO Biogasification
ORG_CAP Capping of landfill
ORG_COMP Large-scale composting
ORG_FLA1 Gas recovery with flaring when landfill already capped
ORG_FLA2 Combined capping and gas recovery with flaring when landfill uncapped
ORG_INC Incineration of organic waste
ORG_USE1 Gas recovery with gas utilization when landfill already capped
ORG_USE2 Combined capping and gas recovery with utilization when landfill uncapped
PAP_CAP Capping of landfill
PAP_FLA1 Gas recovery with flaring when landfill already capped
PAP_FLA2 Combined capping and gas recovery with flaring when landfill uncapped
PAP_INC Incineration of paper waste
PAP_REC Paper recycling
PAP_USE1 Gas recovery with gas utilization when landfill already capped
PAP_USE2 Combined capping and gas recovery with utilization when landfill uncapped
GAS_USE Gas recovery and utilization from wastewater
INT_SYS Integrated sewage system
Table 7 presents a list of the categories of CH4 abatement related to the agriculture and waste
sector that could be defined.
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N2O Abatement
With regard to N2O, the multi-pollutant analysis performed by the model considers the role of
technologies in reducing specific pollutants, but also accounts for the potential of causing a
corresponding increase in emissions of another pollutant. For example, in the context of N2O the
‘deep injection’ of nitrogen is determined by the sum of low nitrogen application from the
ammonia module. However, whilst this practice reduces ammonia emissions, it will increase
N2O emissions and is accounted for in this manner as below in Table 8A. The values represent
small percentage fractions of increase in N2O and are calculated to be consistent with the
ammonia module
Table 8A: The role of N input deep injection on N2O emissions in Irish sample scenario
Activity Sector Technology 2000 2005 2010 2015 2020
Land AGR_ARABLE_TEMP N_Input Deep Inject 0.01 0.01 0.04 0.07 0.12
Land GRASSLAND N_Input Deep Inject 0.01 0.02 0.21 0.40 0.76
It should be noted that the control strategies listed in table 8b are not specific defined
technologies, rather they are approaches that can be employed to reduce the level of N
application. In this manner they can influence the level of N2O emissions.
Table 8B: Further categories of control strategies for N2O not contained within the Irish sample
scenario
Technology Definition
FERT_RED Fertilizer reduction
FERTTIME Fertilizer timing
NITR_INH Nitrification inhibitors
PRECFARM Precision farming
FALLOW Stop agricultural use (of histosols)
NOX and PM Abatement
Table 9a presents a list of the NOX and PM abatement measures defined in a sample scenario for
Ireland within the model. The emission controls in this case relate exclusively to the emission
standard associated with the agricultural or construction related machinery. Clearly, these
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categories of emissions and controls could be accounted for within the transport sector, but they
are presented here to note how these agriculture related activities are captured.
Table 9A: Control strategies (as percentages) assumed at present for PM2.5 and NOX from
agriculture (filtered list) in the Irish sample scenario
Activity Sector Technology 2000 2005 2010 2015 2020
Vehicles TRA_OT_AGR_MD TRA_OT_AGR-MD-
CAGEUI
1 10 10 8 7
Vehicles TRA_OT_AGR_MD TRA_OT_AGR-MD-
CAGEUII
0 10 10 9 8
Vehicles TRA_OT_AGR_MD TRA_OT_AGR-MD-
CAGEUIII
0 0 22 21 20
Vehicles TRA_OT_AGR_MD TRA_OT_AGR-MD-
CAGEUIV
0 0 0 22 45
Table 9B: Further categories of control strategies not yet assumed as planned for PM2.5 and
NOX in the sample scenario for Ireland
Technology Definition
BAN Ban on agricultural burning
Table 9b presents a list of the further agricultural abatement measure related to NOX and PM
that currently exist within the model as an option.
Data Requirements III
Tables 6 through 9 present data from a sample control strategy currently identified out to 2020
in relation to emissions from the agriculture sector. The paired tables (B tables) also include the
other potential categories of technologies that could be engaged or defined within the model.
The requirement here is to identify if the approximate share of these measures seems
appropriate for the Irish context, and to identify any missing measures. Control strategies must
be defined for at least 2000 to 2020 inclusive.
Thus the approach should be to consider pollution abatement measures in place and planned
within Ireland and to reconcile these with the available definitions within the GAINS model. In
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the Irish context, where a specific and important measure is not defined within the model, this
should be discussed with the modelling team – ImpIreland@APEnvEcon.com
Ultimately when considering the balance of control strategies in the model it is also important
to take account of how a given control strategy influences the ‘abated’ emission factor and to
consider this with regard to best available national research on agricultural emissions.
Furthermore, related to control strategies, it is possible within the model to restrict the potential
of a given abatement measure where it is either unfeasible or impractical and some justification
can be provided to support this. Such restrictions are also part of the data requirement for this
aspect of the model.
The handling of emission factors is discussed in the following section. Factors for individual
sector and measure combinations should be examined through the online model.
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4. Emission factors and relevant variables
Thus far this brief has considered the activities identified in the model for agriculture that give
rise to emissions, and the measures which can reduce the emissions from these sources. In this
section the emission factors, and other variables relevant to emission estimation are considered.
The emission factors are presented in two forms in the model – the unabated and the abated
emission factors. The unabated emission factors, as briefly described in Box 1, refer to the
emissions that would arise from a source if no abatement measures are in place. The abated
emission factors, also briefly described in Box 1, are the emissions that occur from the same
source, but where a specific abatement measure has been applied.
Previously in section 4, the current control strategies for a sample Irish scenario were presented
for specific sources of agricultural pollution. However, control strategy data do not represent all
sources of emissions, as there can be sources which have no control in place (generally signalled
by the NOC abbreviation in GAINS). Thus, there are many additional emission sources to be
considered in emission calculation which are not related to any control strategy. These are
simply activities that give rise to emissions, where no abatement measure is in place. The
emissions from such uncontrolled sources are a simple function of the level of activity by the
unabated emission factor for that activity. For example if keeping 100,000 cattle is assumed to
generate 10kt of methane, then the unabated emission factor for methane from 100,000 cattle is
defined as 10kt.
For emission calculation from a source where an abatement measure is in place, the emission
calculation process still uses the unabated emission factor, but accounts for the influence of the
abatement technology through what is known as the ‘removal efficiency’ of the given technology
or measure.
Thus, to use the notional example above for methane emissions from 100,000 cattle, if a special
feed were to reduce methane emissions by 75%, the removal efficiency would be 75%. Thus
where this technology is in place the emissions would be:
1. Unabated emission factor: 10kt per 100,000 cattle
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2. Removal Efficiency: 75%
3. Abated emission factor: 2.5kt per 100,000 cattle
There are also additional parameters relevant to the agricultural emissions which can also be
calibrated within the model. These parameters are briefly listed below:
1. Housing periods (days housed)
In the model there are two relevant parameters here – DAYS and TIME_GH. First of all DAYS
refers to the number of full days in a given year that a given animal spends in housing – thus a
value of 180 indicates that the animals in question spend 180 days of the year in housing.
TIME_GH is specific to dairy cows (DL, DS) and is a percentage figure that indicates the
proportion of time that dairy cows spend in housing during the grazing period – e.g. the time
when the animals are brought into housing for milking.
These two parameters are used in splitting total annual N-excretion rate into N-excreted in
animal house and during grazing (see also below).
2. N Excretion rates
N excretion rates are of obvious significance to agricultural emissions. Two rates are sought in
the GAINS model here for all animals – N_EXCR_H and N_EXCR_G – the former refers to the
nitrogen excretion rate of animals during the housed periods, whereas the latter refers to the
nitrogen excretion rate of the animals during their grazing periods. The data are recorded in
units of total kg/N per year. These are totalled within the model to given the N_EXCR or total
nitrogen excretion rate for the year.
3. N Volatilisation rates
The nitrogen volatilisation rates are defined within the model for the different emission stages.
The four stages are encompassed in the four volatilisation parameters – VOL_H, VOL_S,
VOL_A and VOL_G. Where H, S, A and G refer to Housing, outside storage, application of
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manure and grazing. They are expressed as a percentage of N available at a given stage in
manure that will be lost as NH3.
4. Milk yield
The GAINS model requires for dairy cows information about the milk yield over time. These
data are used for a multitude of purposes. On one hand it can be used to calculate N-excretion
rates in case there is no native data but it also considers the relationship between emission
factors for ammonia and methane and animal productivity, i.e., an increase in milk yield is
correlated with an increase in emission factors in the absence of specific countermeasures.
GAINS can make use of an estimate of such a relationship provided by national experts or can
use the default relationship developed in GAINS based on the data from several countries. This
approach however would ignore specific local circumstances that may cause a variation.
Data Requirements IV
The requirement here is to evaluate whether the identified emission factors in the GAINS model
are comparable to national values for estimated emissions for a given activity (e.g. dairy cattle)
and a given measure (e.g. low ammonia application with low efficiency) at a given stage (e.g.
housing, grazing). Clearly, if the assumed technologies are incorrect then this inconsistency
should be addressed first before assessing the individual emission factors.
As the measures are somewhat aggregate, it may also be necessary to aggregate comparable
national emission factors to compare against them. This approach will ideally involve
consultations between the IMP team, specific national experts, and IIASA.
Tables 10 and 11 , present some of the key parameters and values assumed within the model at
present for the sample scenario. The values in these tables are base emission factors /
parameters relating to N and CH4 – the model also takes account of agricultural NOx and PM
emissions – however, these are primarily associated with agricultural machinery and are
captured under the ‘other transport’ subsector. Agricultural burning can also be defined within
the model to account for these associated emissions.
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Table 10: Days, Housing and base N Volatilisation rates
AGR_ABB DAYS N_EXCR_H
Kg N/yr
N_EXCR_G
Kg N/yr
N_EXCR Tot
Kg N/yr
TIME_GH
%
VOL_H
% N
VOL_S
% N
VOL_A
% N
VOL_G
% N
DL 133 41.72 52.279 94 12.5 17.94 1.8 23.65 5.18
DS 133 41.72 52.280 94 12.5 12.18 16.25 8 5.18
OL 143 26.97 41.88 68.85 0 11.33 2.1 27 1.23
OS 143 26.97 41.88 68.85 0 7.58 4.14 7.78 1.23
PL 365 12.44 0 12.436 0 19.33 1.18 8.5 3
PS 365 12.44 0 12.436 0 19.33 1.18 8.5 3
LH 365 0.84 0 0.84 0 17.7 0.01 15.5 0
OP 365 0.51 0 0.51 0 14.4 0.01 9.65 0
SH 64 1.40 6.60 8 0 9.55 0 5 3.92
HO 183 25.07 24.93 50 0 12 0 10 8
FU 365 4.1 0 4.1 0 12 0 25 0
BS 0 0 0 0 0 0 0 0 0
CM 0 0 0 0 0 0 0 0 0
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Table 11: CH4 emission factors associated with the activities causing CH4 emissions
Activity and Sector Implied kt of CH4 emissions
per unit of activity
AGR_BEEF-OL-[M animals] 7.389
AGR_BEEF-OL_F-[M animals] 60.167
AGR_BEEF-OS-[M animals] 60.315
AGR_COWS-DL-[M animals] 21.107
AGR_COWS-DL_F-[M animals] 84.429
AGR_COWS-DS-[M animals] 83.028
AGR_OTANI-HO-[M animals] 18
AGR_OTANI-SH-[M animals] 6
AGR_PIG-PL-[M animals] 12.904
AGR_POULT-LH-[M animals] 0.117
AGR_POULT-OP-[M animals] 0.117
TRA_OT_AGR-MD-[PJ] 0.004
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5. Cost data
Thus far this report has considered the sources of agricultural emissions, the emission factors
associated with sources and the pollution abatement potential of measures. A further important
aspect of the model is the cost associated with measures identified in the control strategies. Cost
data are important as they assist the model in identifying cost-effective abatement solutions to a
given environmental objective or ‘problem’. Thus, just because a specific measure may be very
effective at reducing emissions from a source, if the cost is too high, it may not be the most
efficient use of available resources.
Cost is therefore a vital element of optimisation as cost-effectiveness underpins much of the
process. Cost is however, a complicated aspect of the model. In this section a somewhat
technical description of how costs for measures are determined is presented.
Cost calculation principles
Agricultural cost calculation for GAINS aims at estimation of unit costs which represent the
annual increase in costs that a typical operator or farmer will bear as a result of introducing a
new technique or measure. Therefore the calculation shows additional costs compared with the
normal practice. Only direct costs and savings associated with the technique are considered and
all figures are net of taxes. Depending on the actual measure the cost calculation will include
investments and operating costs or only the latter component.
Investments cover the expenditure accumulated until the start-up of an abatement technology.
These costs include - depending on the actual technique - delivery of the installation,
construction, civil works, ducting, engineering and consulting, license fees, land requirement
and capital. In GAINS, investment functions have been developed where these cost components
are aggregated into one function (eq.1) and they consider the average, sector- and region-
specific, size of the installations. The form of the function is described by its coefficients cif and
civ. This equation might include additional parameters like flue gas volume (for stationary
combustion sources) as well as a retrofitting factor. Although the original investment costs
might be expressed in different units, i.e., per unit of capacity, energy use, animal place, volume
of manure stored, etc., they are converted in GAINS into €/MWth or €/animal place. For
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agriculture, the coefficients of this function have been estimated drawing on the information
available from international and national sources, e.g., UNECE (2007) and Webb et al. (2006).
)
s
ci
+ci(=I
v
f
(eq.1)
Investments are annualized (eq.2) over the technical lifetime of the technology lt by using the
real interest rate q (as %/100). In the EU and UNECE work an interest rate of 4% was used.
1-)q+(1
q)q+(1
I=I lt
lt
an ∗
∗ (eq.2)
Further we consider the annual fixed expenditures (eq.3) that cover the costs of repairs,
maintenance and administrative overhead. These cost items are not related to the actual use of
the installation and are estimated assuming percentage f of the total investments. The value of f
will vary depending on the type of equipment, e.g., 1-2% for buildings up to about 5% for
machinery like tractors or manure spreaders.
fI=OM
fix
∗ (eq. 3)
Finally, the variable operating costs (eq.4) are related to the actual operation of the installation
and take into account, i.e., additional labour demand, increased or decreased energy demand,
additional feed costs, waste disposal, contractor costs, but also savings of fertilizers. These cost are
calculated as the sum of the specific demand (saving with negative sign) λx and its (country-
specific) price cx.
c=OM
xxvar
λ∑ (eq.4)
The unit costs are calculated considering (if necessary) the number of animal production cycles
per year ar and the utilization factor pf of the capacity (eq.5).
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pf
arOM
+
pf
OM+I=ca
fixfixan
•
(eq.5)
These unit costs are used along with the reduction efficiency of the measure to derive marginal
costs (eq.6) that relate the extra costs for an additional measure to the extra abatement of that
measure (compared to the abatement of the less effective option). GAINS uses the concept of
marginal costs for ranking the available abatement options, according to their cost effectiveness,
into the so-called “national cost curves”. If, for a given emission source (category), a number of
control options M are available, the marginal costs mcm for control option m are calculated as
1
11
−
−−
−
−
=
mlml
mlmmlm
m
cc
mc
ηη
ηη
(eq.6)
where
cm unit costs for option m and
ηlm pollutant l removal efficiency of option m
Data Requirements V
The requirement for cost data is broadly to consider the cost of implementing and maintaining a
specific control strategy. These data should be checked against the values within GAINS as
determined by the described methodology above. Where significant differences occur an effort
should be made to value the costs using the above methodology and submit the results to the
modelling process. Where only partial information is available, this may also be presented to the
team for consideration and revision of values within the model.
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Closing note
There are further pieces of information required in the GAINS modelling process, however, what
is contained within this brief represents the principal data required to more accurately represent
the agricultural sector in the model.
In all cases it should be remembered that data can be changed and updated as necessary, thus
the objective should always be to provide ‘best available data’. Forecasting will always entail
degrees of uncertainty.
As a closing note, Table 13, presents a full emission profile for NH3 from agriculture from a year
2000 sample model scenario for Ireland. This shows the sector and activity, the level of activity
associated, the measure in place, the effectiveness and the ultimate emissions. Total emissions
are 121kt of NH3.
As can be seen, for a given source e.g. the same 4.219 ‘other cattle’, values are presented for the
portion of the activity covered by the measure (e.g. CS Low) and not covered by any measure
(e.g. NOC – No control). The 4,219 is not cumulative, but the approach to proportions of activity
covered by a technology require the value to be reported under each heading. Furthermore, it
can be seen that measures are applied to different stages of the animal cycle – e.g. Application,
grazing, housing and storage. Table 13 is presented to give an idea of how all the various
information is assembled within the model framework.
To facilitate input to this ongoing process, the appendix provides a guide to reviewing data in
the online model. Some provisional scenarios are not publicly viewable and thus for
consideration of the latest data a request to the national team involved should be made. The
second part of the appendix contains some adapted and simplified data submission sheets for
stakeholders looking to provide updated information for the model.
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Table 13: Summary of total animal numbers, measures, emission factors after abatement and emissions of NH3 for a sample GAINS
scenario
Sector-Animal-Technology-Stage Abbr.
Sectoral
activity
Abated
emission
factor
Capacities
controlled
Milk yield
coefficient Emissions
[Units]
t
NH3/Unit % ratio t NH3
Agriculture: Livestock - other cattle-Other
cattle - liquid (slurry) systems-Covered
outdoor storage of manure; low efficiency-
APPLICATION
AGR_BEEF-OL-
CS_low-APPLICATION
4.219 7743.022 75 1 24501.6
Agriculture: Livestock - other cattle-Other
cattle - liquid (slurry) systems-Covered
outdoor storage of manure; low efficiency-
GRAZING
AGR_BEEF-OL-
CS_low-GRAZING
4.219 625.4 75 1 1978.98
Agriculture: Livestock - other cattle-Other
cattle - liquid (slurry) systems-Covered
outdoor storage of manure; low efficiency-
HOUSING
AGR_BEEF-OL-
CS_low-HOUSING
4.219 3711.1 75 1 11743.2
Agriculture: Livestock - other cattle-Other
cattle - liquid (slurry) systems-Covered
outdoor storage of manure; low efficiency-
STORAGE
AGR_BEEF-OL-
CS_low-STORAGE
4.219 365.94 75 1 1157.96
Sum for measure 4.219 12445.462 75 1 39382
Agriculture: Livestock - other cattle-Other
cattle - liquid (slurry) systems-No control-
APPLICATION
AGR_BEEF-OL-NOC-
APPLICATION
4.219 7677 25 1 8097.56
Agriculture: Livestock - other cattle-Other
cattle - liquid (slurry) systems-No control-
GRAZING
AGR_BEEF-OL-NOC-
GRAZING
4.219 625.4 25 1 659.661
Agriculture: Livestock - other cattle-Other
cattle - liquid (slurry) systems-No control-
HOUSING
AGR_BEEF-OL-NOC-
HOUSING
4.219 3711.1 25 1 3914.4
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Glossary
Related Organisations, Abbreviations and Acronyms
AQ Air Quality
CH4 Methane
CLE… A prefix for a scenario based on ‘Current Legislation’
CLRTAP
DOAF
Convention on Long-Range Transboundary Air Pollution
Department of Agriculture and Food
DOEHLG Department of Environment Heritage and Local Government
EPA Environmental Protection Agency
GAINS Greenhouse Gas and Air Pollution Interactions and Synergies
GHG Greenhouse Gases
IAM Integrated Assessment Modelling
IIASA International Institute for Applied Systems Analysis
Kt Kilo ton
MTFR Maximum technical feasible reduction
MRR Maximum reductions in RAINS
N2O Nitrous Oxide
NEC/D National Emissions Ceiling/s Directive
NECPI National Emissions Ceilings Policy and Instruments group
NH3 Ammonia
NTM Non technical measures
NOx Nitrogen Oxide
Pj Petajoule
RAINS Regional Air Pollution Information and Simulation
SRM Source-Receptor matrices
TFIAM Task Force on Integrated Assessment Modelling
TFEIP Task Force on Emission Inventory Projections
TM Technical measures
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Appendix – Submission and Review of Data
This appendix has two elements. Firstly, as it is not practical to include 50 pages of potential
technology, measure and animal combinations, a brief guide to beginning to assess data through
the online system is presented. This should allow users to begin to investigate parameters and
will enable them to suggest changes or amendments to modelled parameters. In Ireland queries
or submissions of in relation to the Agricultural sector in the GAINS model can be processed by
emailing ImpIreland@APEnvEcon.com. Some provisional scenarios will not be accessible
through the online model.
Secondly, template format for data provision is described to allow users to contribute data and
help with the refining of model parameters. The format is simplified to aid with data
submission. However, it is likely that some submissions made in this format will require
bilateral discussions to amend data into an appropriate format for use in the model.
Ultimately, there is ongoing work in this area and aspects of the model and its parameters are
revised as information improves. However, it will always remain the case that specific studies or
national experts may be able to provide additional and detailed information for one aspect of the
model and thereby aid the development. Thus the purpose of this appendix is to support
individuals in making all manner of contributions whether basic parameters or developmental
suggestions.
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Reviewing data in the online system
A more interactive approach to reviewing and suggesting new data can be taken by visiting
http://gains.iiasa.ac.at/gains/EU/index.login?logout=1 and registering to view the model. Once
logged in, there are many ways to present and analyse the data within the model. The following
step by step process is a reasonable starting point.
1. Click on the ‘emissions’ tab at the top
2. Click on the ‘emissions’ tab at the top
3. Select the pollutant of interest from the drop down menu on the left
4. Select the output format from the menu table on the left. For example – under the
‘Detailed Results by:’ heading select Control Option
5. Then select the Scenario, year and region on the right hand side of the page and click
‘Show data table’
6. This will then generate a table of information
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Figure A1: Screenshot of reviewing data in the online system
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Summary: Simplified request sheets for provision of new data
Animal Numbers Submission
• Template for presenting animal numbers
• Summary of categories
• Reiterate N Excretion consideration
• Reiterate seasonal variation consideration
Submission for fertiliser use or land use data
• Submit data on the application of fertilisers and the basic land uses
Submission for milk yield, N2O and manure parameters
• Submit data on average milk yields and some N2O related parameters
• Submit data on manure parameters relating to housing, storage, application and grazing
Technology or process emission factor submission
• Specify pollutant
• Specify technology description
• Provide notes and references where possible
• Provide emission factor used nationally
Technology or process coverage
• Define the technology or process and the coverage it has nationally
Feasibility of measures submission
• Identify measures that cannot be applied
• Describe why they cannot be applied
• Reference study
Sectoral or subsectoral emission estimates
• Present estimates of emissions for the sector or subsector
• Reference study
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Animal Number submission 1 of 2
This request sheet is to provide information on the number of live animals at a number of five year intervals from 2000
• Remember the seasonal variation for lambs
• Live animals or average live animal numbers (not: production figures)
• Focus on appropriate N-Excretion
• Number presented in million head of animals
Some notes for submission
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Animal Number submission 2 of 2
Activity Sector Unit 2000 2005 2010 2015 2020 2025 2030
DL AGR_COWS M animals
DS AGR_COWS M animals
OL AGR_BEEF M animals
OS AGR_BEEF M animals
PL AGR_PIG M animals
PS AGR_PIG M animals
LH AGR_POULT M animals
OP AGR_POULT M animals
SH AGR_OTANI M animals
HO AGR_OTANI M animals
FU AGR_OTANI M animals
BS AGR_OTANI M animals
CM AGR_OTANI M animals
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Submission for fertiliser use and land use data 1 of 2
This request sheet is to provide information on the fertiliser use and land uses at a number of five year intervals from 2000
• The request sheet collects relevant statistical information to estimate soil nitrogen budgets and related fluxes to the
atmosphere
• Fertilizer production is to be given both for total mass (PR_FERT, in Mt) as well as for nutrient content (FERTPRO, kt N) to
account for production-related emissions. Agricultural use should be reported separately for compounds experiencing high
ammonia loss (urea and ammonium bicarbonate, FCON_UREA) and for all other fertilizers (FCON_OTHN) according to the
amount of nutrient.
• Other relevant inputs of nitrogen to soil comprise of atmospheric deposition (ATM_DEPO) and crop residue nitrogen
(CROP_RESID). Nitrogen inputs to ecosystems (AGR_ARABLE, GRASSLAND, FOREST) are calculated in the system and
need not be entered
• Different types of rice-growing area (in flooded vs dry “upland” areas, to be presented in million ha) allow to estimate
methane emissions; “histosol” denotes a type of carbon-rich soil linked with high N2O emissions when used for agriculture.
Area of ecosystems also should be presented in M ha; the split into “temperate” and “subboreal” arable areas (AGR_ARABLE)
is performed inside the sysem, data need not be presented.
• Regarding accidental fires (FIRE_MASS – GRASSLAND and FOREST, resp.) as well as agricultural waste combustion
(WASTE_AGR), the mass of burnt biomass should be presented (in million metric tons, Mt)
Some notes for submission
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Submission for fertiliser use and land use data 2 of 2
Activity Sector Unit 2000 2005 2010 2015 2020 2025 2030
NOF PR_FERT Mt
NOF FERTPRO kt N
NOF FCON_UREA kt N
NOF FCON_OTHN kt N
NOF IO_NH3_EMISS kt NH3
NOF WT_NH3_EMISS kt NH3
NOF OTH_NH3_EMISS kt NH3
FIRE_MASS GRASSLAND
Mt
biomass
FIRE_MASS FOREST
Mt
biomass
NOF WASTE_AGR Mt
NOF AGR_ARABLE M ha
RICE_AREA AGR_ARABLE M ha
AREA RICE_FLOOD M ha
AREA RICE_INTER M ha
AREA RICE_UPLAND M ha
AREA AGR_ARABLE_SUBB M ha
AREA AGR_ARABLE_TEMP M ha
AREA GRASSLAND M ha
AREA FOREST M ha
AREA HISTOSOLS M ha
N_INPUT AGR_ARABLE_SUBB kt N
N_INPUT AGR_ARABLE_TEMP kt N
N_INPUT GRASSLAND kt N
N_INPUT FOREST kt N
N_INPUT ATM_DEPO kt N
N_INPUT CROP_RESID kt N
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Submission for milk yield, N2O and manure parameters
This request sheet is to provide information on the milk yields, N2O variables and other parameters
• Milk yield is the average mass of milk (kg) per animal produced by the dairy herd in a given year. It is used to scale increased
metabolism (most of all, nitrogen excretion) due to productivity increases.
• Under N2O parameters, the principal value to adjust is the fraction of mineral fertiliser applied to grassland. Other values are
sourced independently – see explanations below.
• The table below provides an explanation for manure parameters
Parameter Unit Explanation
DAYS days Time animals spent in housing (full days)
TIME_GH % Percentage of time dairy cows spent in housing during grazing period, e.g., coming in for milking, etc.
N_EXCR_H
kg
N/year
Nitrogen excretion rate - during housing period
N_EXCR_G
kg
N/year
Nitrogen excretion rate - during grazing period
N_EXCR
kg
N/year
Nitrogen excretion rate
VOL_H % N
Nitrogen volatilization (as NH3-N) from housing (expressed as % of N available in manure at a given stage, here refers to the
value of N_EXCR_H )
VOL_S % N Nitrogen volatilization (as NH3-N) from outside storage (expressed as % of N available in manure at a given stage )
VOL_A % N Nitrogen volatilization (as NH3-N) from manure application (expressed as % of N available in manure at a given stage )
VOL_G % N
Nitrogen volatilization (as NH3-N) during grazing (expressed as % of N available in manure at a given stage, here refers to
N_EXCR_G)
SMG fraction Share of manure applied to grassland
Some notes for submission
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Milk yields
Activity Sector Unit 2000 2005 2010 2015 2020 2025 2030
NOF AGR_COWS_MILK kg milk/animal 0 0 0 0 0 0 0
N2O parameters
Parameter Value Unit Explanation
FRAC_GRASS 0.26 fraction Fraction of mineral fertilizer applied to grassland
CLIMFROST 0.15 fraction Part of country exposed to frequent frost-thaw-cycles
N_EXR_MILK 14.15 kg/t additional N-excretion in kg per ton of milk excessive production (above 3000 kg per animal)
AREA_TOT 41.496 Mha total land area
Manure parameters
AGR_ABB DAYS N_EXCR_H N_EXCR_G N_EXCR TIME_GH VOL_H VOL_S VOL_A VOL_G SMG
DL 133 41.721 52.279 94.000 12.500 17.940 1.800 23.650 5.180 0.000
DS 133 41.721 52.279 94.000 12.500 12.180 16.250 8.000 5.180 0.000
OL 143 26.974 41.876 68.850 0.000 11.330 2.100 27.000 1.230 0.000
OS 143 26.974 41.876 68.850 0.000 7.580 4.140 7.780 1.230 0.000
LH 365 0.840 0.000 0.840 0.000 17.700 0.010 15.500 0.000 0.000
PL 365 12.436 0.000 12.436 0.000 19.330 1.180 8.500 3.000 0.000
PS 365 12.436 0.000 12.436 0.000 19.330 1.180 8.500 3.000 0.000
OP 365 0.509 0.000 0.509 0.000 14.400 0.010 9.650 0.000 0.000
SH 64 1.403 6.597 8.000 0.000 9.550 0.000 5.000 3.920 0.000
HO 183 25.068 24.932 50.000 0.000 12.000 0.000 10.000 8.000 0.000
FU 365 4.100 0.000 4.100 0.000 12.000 0.000 25.000 0.000 0.000
BS 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
CM 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
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Submission for technology or process emission factors
This request sheet is to provide information on the emission factors relating to processes or abatement technologies
• Identify the pollutants influenced
• Provide references where available
• Provide as much detail as possible on the emission abatement achieved
• Note any other specific issues with application or efficiency
Measure / Technology Description Emission information
Name of measure or technology Describe what is involved
Describe the pollutants affected by the
measure or technology and provide as
much quantitative data for their effect
Notes for submission
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Submission for technology or process coverage within a country
This request sheet is to provide information on the extent to which a given technology or process is employed in a country
• The objective is to provide information that identifies the proportion of a given activity that is covered by a particular
abatement measure or technology.
• Provide references to any relevant studies or reports.
Measure / Technology Description Emission information
Name of measure or technology Describe what is involved
Describe the pollutants affected by the
measure or technology and provide as
much quantitative data for their effect
Notes for submission
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Submission for technology or process feasibility
This request sheet is to provide information as to why a technology or process cannot/should not be employed in a country
Provide references to any related studies or reports
Measure / Technology Description Reason for N/A
Name of measure or technology Describe what is involved
Identify why the measure cannot or
should not be considered as an
abatement option within the model for
a given country. Or where the
restriction is only partial, identify the
maximum extent to which the measure
can be adapted.
Notes for submission
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Submission for sectoral or subsectoral emission estimates
This request sheet is to provide emission estimates for a given aspect or subsector of the agricultural sector.
• Provide references to any related studies or reports.
Sector / Subsector Pollutant Year Emissions
Notes for submission
