2009 kelly work example modelling guidance - agriculture sector

GAINS Agriculture GuideVersion 1 – A guide to the agricultural components of the GAINS model

Spring, 2009

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 [email protected]

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 [email protected] (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


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


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


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


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


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


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


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


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 – [email protected]

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

21 | P a g e

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

22 | P a g e

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.

23 | P a g e

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

24 | P a g e

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

25 | P a g e

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

26 | P a g e

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( = Iv

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 + (1q )q + (1 I = I lt

ltan ∗

∗ (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.

f I = 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).

27 | P a g e

pfarOM +

pfOM + 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

mlmmlmm

ccmc

ηηηη

(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.

28 | P a g e

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.

29 | P a g e

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

30 | P a g e

Agriculture: Livestock - other cattle-Other cattle - liquid (slurry) systems-No control-STORAGE

AGR_BEEF-OL-NOC-STORAGE

4.219 609.9 25 1 643.312

Sum for measure 4.219 12623.4 25 1 13315 Agriculture: Livestock - other cattle-Other cattle - solid systems-No control-APPLICATION

AGR_BEEF-OS-NOC-APPLICATION

1.641 2257.6 100 1 3704.21

Agriculture: Livestock - other cattle-Other cattle - solid systems-No control-GRAZING

AGR_BEEF-OS-NOC-GRAZING

1.641 625.4 100 1 1026.14

Agriculture: Livestock - other cattle-Other cattle - solid systems-No control-HOUSING

AGR_BEEF-OS-NOC-HOUSING

1.641 2482.8 100 1 4073.71

Agriculture: Livestock - other cattle-Other cattle - solid systems-No control-STORAGE

AGR_BEEF-OS-NOC-STORAGE

1.641 1253.2 100 1 2056.22

Sum for measure 1.641 6619 100 1 10860 Agriculture: Livestock - dairy cattle-Dairy cows - liquid (slurry) systems-Covered outdoor storage of manure; low efficiency-APPLICATION

AGR_COWS-DL-CS_low-APPLICATION

1.095 9725.28 75 1 7987.43

Agriculture: Livestock - dairy cattle-Dairy cows - liquid (slurry) systems-Covered outdoor storage of manure; low efficiency-GRAZING

AGR_COWS-DL-CS_low-GRAZING

1.095 3288.4 75 1 2700.78

Agriculture: Livestock - dairy cattle-Dairy cows - liquid (slurry) systems-Covered outdoor storage of manure; low efficiency-HOUSING

AGR_COWS-DL-CS_low-HOUSING

1.095 9088.5 75 1 7464.44

Agriculture: Livestock - dairy cattle-Dairy cows - liquid (slurry) systems-Covered outdoor storage of manure; low efficiency-STORAGE

AGR_COWS-DL-CS_low-STORAGE

1.095 448.98 75 1 368.75

Sum for measure 1.095 22551.16 75 1 18521 Agriculture: Livestock - dairy cattle-Dairy cows - liquid (slurry) systems-No control-

AGR_COWS-DL-NOC-APPLICATION

1.095 9654.8 25 1 2643.18

31 | P a g e

APPLICATION Agriculture: Livestock - dairy cattle-Dairy cows - liquid (slurry) systems-No control-GRAZING

AGR_COWS-DL-NOC-GRAZING

1.095 3288.4 25 1 900.261

Agriculture: Livestock - dairy cattle-Dairy cows - liquid (slurry) systems-No control-HOUSING

AGR_COWS-DL-NOC-HOUSING

1.095 9088.5 25 1 2488.15

Agriculture: Livestock - dairy cattle-Dairy cows - liquid (slurry) systems-No control-STORAGE

AGR_COWS-DL-NOC-STORAGE

1.095 748.3 25 1 204.861

Sum for measure 1.095 22780 25 1 6236.5 Agriculture: Livestock - dairy cattle-Dairy cows - solid systems-No control-APPLICATION

AGR_COWS-DS-NOC-APPLICATION

0.082 2980.8 100 1 245.692

Agriculture: Livestock - dairy cattle-Dairy cows - solid systems-No control-GRAZING

AGR_COWS-DS-NOC-GRAZING

0.082 3288.4 100 1 271.046

Agriculture: Livestock - dairy cattle-Dairy cows - solid systems-No control-HOUSING

AGR_COWS-DS-NOC-HOUSING

0.082 6170.5 100 1 508.603

Agriculture: Livestock - dairy cattle-Dairy cows - solid systems-No control-STORAGE

AGR_COWS-DS-NOC-STORAGE

0.082 7229.7 100 1 595.908

Sum for measure 0.082 19669.4 100 1 1621.2 Agriculture: Livestock - other animals (sheep, horses)-Horses-No control-APPLICATION

AGR_OTANI-HO-NOC-APPLICATION

0.069 2678.7 100 1 184.83

Agriculture: Livestock - other animals (sheep, horses)-Horses-No control-GRAZING

AGR_OTANI-HO-NOC-GRAZING

0.069 2421.9 100 1 167.111

Agriculture: Livestock - other animals (sheep, horses)-Horses-No control-HOUSING

AGR_OTANI-HO-NOC-HOUSING

0.069 3652.8 100 1 252.043

Agriculture: Livestock - other animals (sheep, horses)-Horses-No control-STORAGE

AGR_OTANI-HO-NOC-STORAGE

0.069 0 100 1 0

Sum for measure 0.069 8753.4 100 1 603.98

32 | P a g e

Agriculture: Livestock - other animals (sheep, horses)-Sheep and goats-No control-APPLICATION

AGR_OTANI-SH-NOC-APPLICATION

7.555 77 100 1 581.735

Agriculture: Livestock - other animals (sheep, horses)-Sheep and goats-No control-GRAZING

AGR_OTANI-SH-NOC-GRAZING

7.555 314 100 1 2372.27

Agriculture: Livestock - other animals (sheep, horses)-Sheep and goats-No control-HOUSING

AGR_OTANI-SH-NOC-HOUSING

7.555 162.7 100 1 1229.2

Agriculture: Livestock - other animals (sheep, horses)-Sheep and goats-No control-STORAGE

AGR_OTANI-SH-NOC-STORAGE

7.555 0 100 1 0

Sum for measure 7.555 553.7 100 1 4183.2 Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-Covered outdoor storage of manure; low efficiency-APPLICATION

AGR_PIG-PL-CS_low-APPLICATION

1.722 1028.212 87.1 1 1542.18

Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-Covered outdoor storage of manure; low efficiency-GRAZING

AGR_PIG-PL-CS_low-GRAZING

1.722 0 87.1 1 0

Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-Covered outdoor storage of manure; low efficiency-HOUSING

AGR_PIG-PL-CS_low-HOUSING

1.722 2919 87.1 1 4378.1

Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-Covered outdoor storage of manure; low efficiency-STORAGE

AGR_PIG-PL-CS_low-STORAGE

1.722 86.22 87.1 1 129.318

Sum for measure 1.722 4033.432 87.1 1 6049.6 Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-Low ammonia application; low efficiency-APPLICATION

AGR_PIG-PL-LNA_low-APPLICATION

1.722 613.98 1 1 10.573

Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-Low ammonia application; low efficiency-GRAZING

AGR_PIG-PL-LNA_low-GRAZING

1.722 0 1 1 0

Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-Low ammonia application;

AGR_PIG-PL-LNA_low-HOUSING

1.722 2919 1 1 50.265

33 | P a g e

low efficiency-HOUSING Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-Low ammonia application; low efficiency-STORAGE

AGR_PIG-PL-LNA_low-STORAGE

1.722 143.7 1 1 2.475

Sum for measure 1.722 3676.68 1 1 63.313 Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-No control-APPLICATION

AGR_PIG-PL-NOC-APPLICATION

1.722 1023.3 11.9 1 209.693

Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-No control-GRAZING

AGR_PIG-PL-NOC-GRAZING

1.722 0 11.9 1 0

Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-No control-HOUSING

AGR_PIG-PL-NOC-HOUSING

1.722 2919 11.9 1 598.156

Agriculture: Livestock - pigs-Pigs - liquid (slurry) systems-No control-STORAGE

AGR_PIG-PL-NOC-STORAGE

1.722 143.7 11.9 1 29.447

Sum for measure 1.722 4086 11.9 1 837.3 Agriculture: Livestock - poultry-Laying hens-No control-APPLICATION

AGR_POULT-LH-NOC-APPLICATION

1.57 130.1 100 1 204.257

Agriculture: Livestock - poultry-Laying hens-No control-GRAZING

AGR_POULT-LH-NOC-GRAZING

1.57 0 100 1 0

Agriculture: Livestock - poultry-Laying hens-No control-HOUSING

AGR_POULT-LH-NOC-HOUSING

1.57 180.5 100 1 283.385

Agriculture: Livestock - poultry-Laying hens-No control-STORAGE

AGR_POULT-LH-NOC-STORAGE

1.57 0.1 100 1 0.157

Sum for measure 1.57 310.7 100 1 487.8 Agriculture: Livestock - poultry-Other poultry-No control-APPLICATION

AGR_POULT-OP-NOC-APPLICATION

13.766 51 100 1 702.066

Agriculture: Livestock - poultry-Other poultry-No control-GRAZING

AGR_POULT-OP-NOC-GRAZING

13.766 0 100 1 0

Agriculture: Livestock - poultry-Other poultry-No control-HOUSING

AGR_POULT-OP-NOC-HOUSING

13.766 88.9 100 1 1223.8

Agriculture: Livestock - poultry-Other poultry-No control-STORAGE

AGR_POULT-OP-NOC-STORAGE

13.766 0.1 100 1 1.377

Sum for measure 13.766 140 100 1 1927.2

34 | P a g e

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

35 | P a g e

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 [email protected]. 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.

36 | P a g e

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

37 | P a g e

Figure A1: Screenshot of reviewing data in the online system

38 | P a g e

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

39 | P a g e

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

41 | P a g e

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)


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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


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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


Describe the pollutants affected by the

measure or technology and provide as

much quantitative data for their effect


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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


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.


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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


www.ImpIreland.ie

www.APEnvEcon.com

www.EPA.ie

The IMP Ireland project is funded by the Environmental Protection Agency of Ireland under the STRIVE programme 2007‐2013. Co‐funding is provided by AP EnvEcon. The project is led by AP EnvEcon.

2009 kelly work example modelling guidance - agriculture sector

Technology

2this request sheet

abated emission factor

thenitrogen excretion rate

unabated emission factor

low ammonia application

imp ireland project

principal data required

specific abatement measure