2009 Kelly work example Modelling guidance - Agriculture sector


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2009 Kelly work example Modelling guidance - Agriculture sector

  1. 1. GAINS Agriculture GuideVersion 1 – A guide to the agricultural components of the GAINS modelSpring, 2009
  2. 2. GAINS Agriculture GuideVersion 1 – A guide to the agricultural components of the GAINS modelSpring 2009AP EnvEcon IMP Ireland TeamDr Andrew Kelly Dr Luke Redmond Dr Fearghal KingIIASA TeamDr Zbigniew Klimont Dr Wilfried WiniwarterUCD IMP Ireland TeamDr Amarendra Sahoo Dr Miao Fu
  3. 3.   1 | P a g e   Table of ContentsAcknowledgements............................................................................................................................2Introduction.......................................................................................................................................3Basic types of data and information..............................................................................................4Submitting new data......................................................................................................................41. Animal numbers.........................................................................................................................5Data Requirements I .................................................................................................................. 72. Fertilisers and area of land ........................................................................................................8Data Requirements II............................................................................................................... 103. Abatement measures - Control Strategy ................................................................................. 12The control strategy approach................................................................................................. 12NH3 Abatement ........................................................................................................................ 15CH4 Abatement......................................................................................................................... 16N2O Abatement .........................................................................................................................17NOX and PM Abatement ...........................................................................................................17Data Requirements III ............................................................................................................. 184. Emission factors and relevant variables..................................................................................20Data Requirements IV..............................................................................................................225. Cost data ...................................................................................................................................25Cost calculation principles.......................................................................................................25Data Requirements V...............................................................................................................27Closing note .....................................................................................................................................28Glossary............................................................................................................................................34Appendix – Submission and Review of Data..................................................................................35Reviewing data in the online system...........................................................................................36Summary: Simplified request sheets for provision of new data.................................................38
  4. 4.   2 | P a g e   AcknowledgementsThis 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 providedfrom 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 arelater updated to take account of new developments.
  5. 5.   3 | P a g e   IntroductionThis document provides an overview of the principal data required for compiling a GAINSmodel agricultural scenario. This document is designed to help inform and guide feedback fromexperts in the agriculture sector to assist in the calibration of the relevant GAINS modelsections. The brief also provides a snapshot of some sample data and assumptions taken fromthe GAINS ‘Ireland’ model. These data should be considered preliminary or default as most arenow updated in the live system. Whilst the focus of all examples is on Ireland, the brief is alsointended 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 guideis 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.pdfIn terms of content, this document outlines the categories and format of data that are utilisedwithin the GAINS Ireland model to estimate emissions from the Agriculture sector. Thepollutant emissions considered are NH3, N2O, CH4 and to a lesser extent PM and NOX. GAINSnot only models agriculture but also all other sectors, which are not detailed here. Incompilation 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 requireoccasional revisions of this work to be developed.The next development stages of the model, with respect to agriculture, will include a newapproach for considering nitrate leaching and the use of a Nitrogen flow (N-Flow) approach inthe estimation of primary agricultural emissions of nitrogen species from manure management.The development of the model to include these new approaches will entail new modelparameters, and consequently, new data requests and a new version of this guide. However, theprincipal data, especially activity related, will remain the same.
  6. 6.   4 | P a g e   For any queries or feedback in relation to the data and the modelling process in Ireland pleasecontact us directly at ImpIreland@APEnvEcon.com Basic types of data and informationIn this brief there are four principal grouped categories of data discussed that are required forthe GAINS model with respect to agriculture emissions – specifically:1. Animal numbers2. Fertiliser use and area of land3. Abatement measures4. Emission factors and other relevant variablesEach of these grouped categories are discussed in some detail in the sections that follow, withfurther details in an appendix section, and as mentioned, yet further information availablethrough the online system. Each category section concludes with a subheading that attempts toprovide 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 fiveyear intervals – currently from 1990 to 2030. Thus values are required for parameters in1990,1995,2000,2005,2010,2015,2020,2025,2030. Data submitted are therefore often a blendof historical national data and more recent forecasts. As time advances the policy process willrequire the relevant years to shift further outward towards new compliance periods. Thus theprocess is an ongoing iterative exercise, and consistent and well structured data are extremelyimportant.Submitting new dataThis brief should provide an understanding of the types and structure of new data required. Inthe appendix section a template for the provision of updated information and figures ispresented to assist with such a submission. However, through this format, or through directcontact via ImpIreland@APEnvEcon.com (for Ireland only) – all submissions or comments willbe addressed to whatever extent possible.
  7. 7.   5 | P a g e   1. Animal numbersWithin the GAINS model, animal numbers and type of animal are the primary ‘activity’ driver inthe modelling process for agricultural emissions. In the same manner as the level of fuel use andthe 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 toaccount for more detailed information. For the moment however, animal numbers and types arecategorized as presented in Table 1. It is important to note that in respect of these numbers, thefocus is on live animals, and where significant seasonal differences occur, on the averagelive animal numbers. An example of such a variation between live animal numbers andaverage live animal numbers is presented in box 1.Box 1: Example of the variation between live and average live animal numbersThe task the model performs in regard to these data is to determine an excretion rate, thusultimately an important element of this process is to focus on ensuring that anappropriate average excretion rate is used that takes account of animal size andProjected livestock data are often reported for two periods, June and December. Withinthe GAINS model a single value for animal numbers is required. To consider sheep forexample, the variation in numbers in the two periods is quite pronounced due to thepresence or absence of lambs in the period.June December Average numberEwes 2056 1951 2003Rams 529 419 474Lambs 2120 0 1060Total 4705 2370 3537SHEEP (GAINS) Average number 3537
  8. 8.   6 | P a g e   their relative shares in the particular animal category. In other words, where asignificant proportion of the population are lambs and are only present for a part of the year, theaverage number and excretion rate used for ‘Sheep’ should reflect this number of animals in theaverage, and also account for the lower excretion rate of these lambs in the average excretionused for the category ‘Sheep’. As the proportion of lambs should be reasonably consistent, thischecking 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 hundredbuffalo, 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 GAINSMain Category Sub category GAINS CodeDairy Cattle Dairy Cows – Solid systems DSDairy Cows – Liquid (Slurry) systems DLOther (Beef) Cattle Other Cattle – Solid systems OSOther Cattle – Liquid (Slurry) systems OLPigs Pigs – Solid systems PSPigs – Liquid (Slurry) systems PLPoultry Laying Hens LHOther poultry OPSheep Sheep and goats SHHorses Horses HOFur Animals Fur animals (or other relevant productionanimal e.g. rabbits)FUIn terms of animal numbers, the model has these reported in 1000 head of animals e.g. 90.1represents 90,100 animals. Recently, the model allows displaying animal numbers in livestock
  9. 9.   7 | P a g e   units (LSU) in accordance with an FAO methodology. However, the inputting of data remains interms of live animal number for the aggregate categories described in Table 1 (input normally isin million heads). Pigs and cattle are subdivided into liquid and solid systems – referring to themanure management.Data Requirements IFor animal number data requirements, what is needed is the 1000 head of animals in Irelandunder 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 nationalFAPRI data, although values have not yet been adjusted to average live animal numbers. Insteadthe animal numbers for June have been used in all cases. For the period after 2020, the 2020figures are held as the scenario values for 2025 and 2030 in the absence of longer termforecasts.The approach taken to the FAPRI animal numbers data when adding it to GAINS Ireland hasbeen straightforward, with the following notes for specific categories:Sheep and HorsesNo modifications were required in relation to sheep and horse numbers. These are transferreddirectly from the national herd statistics into the model.PigsData for fatteners, sows and piglets are required by GAINS. Once again the key parameter is tospecify 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 comparableresults are obtained. For example, account for the number of sows, fatteners and piglets andcalculate the N excretion rate based on a weighted share of each category.PoultryFor Ireland ‘layers’ in the FAPRI data have been used for the LH category, with broilers andturkeys added to the OP category.
  10. 10.   8 | P a g e   CattleDairy 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 andother cattle (beef) have been split according to the FAPRI distinction. With regard to the liquidor slurry systems, the split for dairy cattle is assumed as 7% solid and 93% slurry. The split forother (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 landTwo further categories that are relevant to agricultural emissions in the model are mineralfertiliser use and area of land. Principally these are related to NH3, N2O, NOx emissions andnitrate leaching. The relevance and required data for these categories are discussed below.FertiliserFertiliser as an emission source is broken into two categories within the model – use andproduction. Within Ireland the limited (if any since the closure of IFI) fertiliser productionmeans it is the use of fertiliser which is most relevant to emissions. Fertiliser is handled inGAINS under the categories listed in Table 2.Table 2: Mineral fertiliser use in GAINSMain Category Sub category GAINS CodeFertiliser use Fertilizer use - other N fertilizers (kt N) FCON OTHNFertilizer use – urea and ammoniumbicarbonate (kt N)FCON UREAFertiliser production Nitrogen fertilizer production(in N equivalents kt N)FERTPROInd. Process: Fertilizer production (allcompounds) (Mt)PR FERT
  11. 11.   9 | P a g e   Thus, principally for Ireland, the model is interested in the use of urea and other fertilisers inthe Irish agricultural sector. The levels of use are recorded in thousand tonnes (kt) of N. Recentvalues and forecasts are as presented in Table 4 under FCON OTHN and FCON UREA.Land use sourcesThe model also takes account of land use and types and their relevance to emissions. This aspectof calibration requires data in units of million hectares. Essentially describing how much land iscategorised under a given heading. Table 3 presents the land use and type categories that areconsidered in the GAINS model which are relevant to Ireland.Table 3: Land uses and types in GAINSMain Category Sub category GAINS CodeArea of land type Million hectares of Forest FORESTMillion hectares of grassland and soils GRASSLANDMillion hectares of organic soils HISTOSOLSMass of nitrogen added Kt of N added to Forest land N INPUT FORESTKt of N added to grassland and soils N INPUTGRASSLANDOther relevant activity Million hectares of land that is ploughed, tilledor harvestedAGR ARABLEThe model also identifies the area of arable agricultural land that is within subboreal ortemperate climates.Open waste burningBurning of agricultural residue in open fields can be a significant source of several pollutants. Ifsuch practices occur in Ireland then the total amount of biomass burned (Mt) annually shouldbe 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 thisactivity.
  12. 12.   10 | P a g e   Other activitiesOther activities such as the burning of fuel in greenhouses, or the use of fuels in agriculturalmachinery are also captured within the model. However, these other activities, although linkedwith agriculture, are captured under other sectors – specifically with these two examples, underthe residential/commercial and off-road transport sectors respectively. Data Requirements IIThus for this aspect of the model, the required data relate to approximate values for areas ofland, and the associated use of fertiliser on these areas. A sample of recent data for thesecategories within the model – at the time of writing - are presented in Table 4 for assessment.
  13. 13.   11 | P a g e   Table 4: Summary of sample agricultural data (rounded up) in the GAINS scenarioActivity Sector Unit 2000 2005 2010 2015 2020DS AGR_COWS M animals 0.082 0.078 0.078 0.08 0.09DL AGR_COWS M animals 1.095 1.03 1.03 1.06 1.20OS AGR_BEEF M animals 1.64 1.64 1.60 1.61 1.65OL AGR_BEEF M animals 4.22 4.23 4.10 4.14 4.25PS AGR_PIG M animals 0 0 0 0 0PL AGR_PIG M animals 1.72 1.69 1.80 1.50 1.33LH AGR_POULT M animals 1.57 1.95 1.56 1.50 1.43OP AGR_POULT M animals 13.77 14.14 13.14 12.61 12.07SH AGR_OTANI M animals 7.56 6.39 5.43 5.33 4.68HO AGR_OTANI M animals 0.07 0.08 0.08 0.08 0.08FU AGR_OTANI M animals 0 0 0 0 0NOF FCON_UREA kt N 57.61 37.34 33.6 35.04 37.38NOF FCON_OTHN kt N 349.99 314.83 302.39 315.32 336.45NOF PR_FERT Mt 0.956 0 0 0 0NOF FERTPRO kt N 248 0 0 0 0NOF IO_NH3_EMISS kt NH3 0 0 0 0 0NOF WT_NH3_EMISS kt NH3 0 0 0 0 0NOF OTH_NH3_EMISS kt NH3 0.57 0.56 0.57 0.57 0.57FIRE_AREA GRASSLAND M ha 0 0 0 0 0RICE_AREA AGR_ARABLE M ha 0 0 0 0 0FIRE_AREA FOREST M ha 0 0 0 0 0AREA FOREST M ha 0.28 0.28 0.28 0.28 0.28AREA GRASSLAND M ha 8.48 8.48 8.48 8.48 8.48N_INPUT FOREST kt N 0 0 0 0 0N_INPUT GRASSLAND kt N 0 0 0 0 0AREA AGR_ARABLE_SUBB M ha 0 0 0 0 0AREA AGR_ARABLE_TEMP M ha 0 0 0 0 0N_INPUT AGR_ARABLE_SUBB kt N 0 0 0 0 0N_INPUT AGR_ARABLE_TEMP kt N 0 0 0 0 0AREA HISTOSOLS M ha 0 0 0 0 0NOF AGR_ARABLE M ha 1.1 1.1 1.1 1.1 1.1NOF WASTE_AGR Mt 0 0 0 0 0
  14. 14.   12 | P a g e   3. Abatement measures - Control StrategyThis 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 emissionfactors related to abatement efficiency are defined, and secondly the degree to which a givenmeasure or package of measures is applied in a given scenario is defined through the ‘controlstrategy’ file.Therefore, on the one hand you have information that identifies how effective a specific measureis at reducing emissions from a given source, and on the other you have information defininghow much of a given pollution source is covered by each specific abatement measure.The control strategy approachThus far, this brief has identified the animal numbers and other ‘activity’ variables that can beloosely described as ‘sources’ in the process of agricultural emission estimation. In this sectionthe potential abatement options that can be applied to these sources to reduce agriculturalemissions are discussed. Packages of abatement measures within the GAINS model are referredto as control strategies. These control strategies are a vital component of the final emissionestimations as they determine what actions have been taken to reduce emissions from a givensource.The approach in the model is to define for a given activity or source, the proportion of thatactivity which is ‘managed’ by a specific abatement measure. For example, if there are 100,000dairy cattle and 50% of them in 2005 have their manure managed via low efficiency lowammonia application, then the control strategy value for this particular measure should be set at50% for 2005. The remaining 50% in 2005 is uncontrolled unless otherwise defined, meaningthat 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 orbase emissions for 100,000 cattle was 10kt of NH3, then the simplified model function is aspresented in Box 1 where a 50% ‘low efficiency low ammonia application’ control is defined.
  15. 15.   13 | P a g e   The controls considered with respect to agriculture, generally relate to animal storage/housing,ammonia application, low nitrogen animal feed, urea substitution, manure burning andbiofiltration systems. The list of measures can be extended and developed over time, and wherea specific national measure is not represented for a given pollutant, it may be possible in time toincorporate this. A forum for contributing national information on measures is currentlyplanned under the IMP Ireland project. Details will be provided as this initiative develops.Box 1 Control strategies in the modelling process – Simplified exampleThus the details of abatement measures and the assumption of how they will be structured overrelevant activities are critical to the emission estimation and forecasting of the GAINS Irelandmodelling work. Generally it is research work to obtain the necessary information for whatmeasures were in place historically. However, a significant challenge in calibrating the model isto establish plausible control strategy packages for future years for the member states. Thisraises a related task – which is to define the applicability of a given measure in the future.Applicability of a given pollutant control abatement measureOne of the further aspects of the model is the applicability limits for certain technologies. Inother words, where the control strategy defines what measures are already implemented orplanned, the applicability parameter defines what the maximum implementation rates are for agiven measure. Within the modelling framework applicability is an important concept for theoptimisation mode. In this mode, the model will look at not just what is planned to be done interms of emission abatement, but what else could be done to reduce emissions further and what1. Number of dairy cattle is 100,0002. Emissions for 100,000 dairy cattle are 10kt of NH33. The low efficiency ‘low ammonia application’ technique reduces emissions by 25%4. 50% of the dairy cattle are covered by this abatement measure5. 50% of the dairy have no abatement measure in place6. Emissions are 5kt for the 50% of the cattle without any abatement measure7. Emissions are 5kt less 25%, therefore 3.75kt, for the 50% of the dairy cattle with theabatement measure in place over them8. Total emissions are therefore 8.75kt for this defined source
  16. 16.   14 | P a g e   will be the associated cost. As such where there are specific national considerations orrestrictions on, say urea substitution for fertiliser use, the applicability file should reflect this. Ifthe applicability of a measure is set to zero, the model will not identify this measure as apotential option – in other words it rules it out as a possibility to reduce emissions in thatspecific member state for a discussed sector/animal category using this measure. Generally suchassertions need to be supported by national evidence and research to justify the limitation ofabatement options that may be considered for a country.The details of the optimisation process are not discussed in this document, but in essence, themodel considers the efficiency of abatement measures, their associated cost, and theapplicability when determining what package of regional measures will deliver on a specificemission reduction/effect based target.The next subheadings look at the principal agriculture related abatement measures identified foreach of the pollutants covered by the model. This is not to say these are the only sources ofemissions, rather these are the sources of emissions covered by a specific abatement technologyor process. The principal abatement measures relating to agriculture for Ireland – as definedwithin the model at the time of writing - can be summarised as presented in Table 5. In manycases the measures refer to specific stages of the animal cycle – application, grazing, housingand storage, with varied emissions associated with each stage.Table 5: Definitions of principal control strategy categories defined in the sample scenario forIrelandTechnology DefinitionBAN Ban on agricultural burningCAGEUI/II… Emission standards for construction and agricultural machineryCS_low Covered outdoor storage of manure, low efficiencyLNA_low Low ammonia application with mean efficiencySA Animal house adaptationLNA_low Low ammonia application with mean efficiencySA Animal house adaptationLNF_SA Combination of low nitrogen feed and animal house adaptationPM_INC Burning of poultry manureLNF_CS Combination of low efficiency outdoor manure storage and low nitrogen feedLNF_SA_LNA Combination of LNF & SA with mean efficiency low ammonia application
  17. 17.   15 | P a g e   NH3 AbatementTable 6A presents a sample of abatement options for NH3 in Ireland under a scenario within themodel. This identifies which type of animal is covered by which proportion of a given NH3abatement measures. Table 6B present further categories of NH3 emission abatement optionsthat are not defined within this sample scenario for Ireland. In many cases combinations ofmeasures 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 scenarioActivity Sector Technology 2000 2005 2010 2015 2020DL AGR_COWS CS_low 75 75 77 80 90DL AGR_COWS LNA_low 0 0 1 2 4LH AGR_POULT SA 0 0 15 15 15LH AGR_POULT LNF_SA 0 5 14.5 14 13LH AGR_POULT LNF_SA_LNA 0 0 0.5 1 2OL AGR_BEEF CS_low 75 77 78 80 80OL AGR_BEEF LNA_low 0 0 1 2 4OP AGR_POULT SA 0 0 35 0 0OP AGR_POULT LNF_SA 0 5 26 35 15OP AGR_POULT PM_INC 0 1 4 30 50PL AGR_PIG CS_low 87.1 60 26.25 26.25 26.25PL AGR_PIG LNA_low 1 0 0 0 0PL AGR_PIG LNF_CS 0 10 23 23 23PL AGR_PIG LNF_SA 0 10 18 17.5 17PL AGR_PIG LNF_SA_LNA 0 1.5 2 2.5 3Table 6B: Further categories of control strategies not yet assumed as planned for NH3 in theIrish sample scenarioTechnology DefinitionBF Biofiltration – can be combined with CS and/or LNASTRIP StrippingSUB_U Urea substitution
  18. 18.   16 | P a g e   CH4 AbatementThe agricultural sector is the most significant source for CH4, however, no specific CH4abatement 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 Irelandfrom the sample scenarioTechnology DefinitionAUTONOM Autonomous productivity increase in milk/beef production per animalCONCENTR Replacement of roughage for more concentrate in animal feedFARM_ADFarm-scale anaerobic digestion (applicable to large farms, i.e. >100 dairy cows, >200beef cattle, or > 1000 pigs)HOUS_AD Single household scale anearobic digestion plant for household energy needsCOMM_AD Community scale anaerobic digestion plant (HOUS_AD < COMM_AD < FARM_AD)INCRFEED Increased feed intakeNSCDIET Change to more non-structural carbohydrates (NSC) in concentrate feedPROPPREC Propionate precursorsSA Stable adaptationBAN Ban on agricultural waste burningORG_BIO BiogasificationORG_CAP Capping of landfillORG_COMP Large-scale compostingORG_FLA1 Gas recovery with flaring when landfill already cappedORG_FLA2 Combined capping and gas recovery with flaring when landfill uncappedORG_INC Incineration of organic wasteORG_USE1 Gas recovery with gas utilization when landfill already cappedORG_USE2 Combined capping and gas recovery with utilization when landfill uncappedPAP_CAP Capping of landfillPAP_FLA1 Gas recovery with flaring when landfill already cappedPAP_FLA2 Combined capping and gas recovery with flaring when landfill uncappedPAP_INC Incineration of paper wastePAP_REC Paper recyclingPAP_USE1 Gas recovery with gas utilization when landfill already cappedPAP_USE2 Combined capping and gas recovery with utilization when landfill uncappedGAS_USE Gas recovery and utilization from wastewaterINT_SYS Integrated sewage systemTable 7 presents a list of the categories of CH4 abatement related to the agriculture and wastesector that could be defined.
  19. 19.   17 | P a g e   N2O AbatementWith regard to N2O, the multi-pollutant analysis performed by the model considers the role oftechnologies in reducing specific pollutants, but also accounts for the potential of causing acorresponding 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 theammonia module. However, whilst this practice reduces ammonia emissions, it will increaseN2O emissions and is accounted for in this manner as below in Table 8A. The values representsmall percentage fractions of increase in N2O and are calculated to be consistent with theammonia moduleTable 8A: The role of N input deep injection on N2O emissions in Irish sample scenarioActivity Sector Technology 2000 2005 2010 2015 2020Land AGR_ARABLE_TEMP N_Input Deep Inject 0.01 0.01 0.04 0.07 0.12Land GRASSLAND N_Input Deep Inject 0.01 0.02 0.21 0.40 0.76It should be noted that the control strategies listed in table 8b are not specific definedtechnologies, rather they are approaches that can be employed to reduce the level of Napplication. 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 samplescenarioTechnology DefinitionFERT_RED Fertilizer reductionFERTTIME Fertilizer timingNITR_INH Nitrification inhibitorsPRECFARM Precision farmingFALLOW Stop agricultural use (of histosols)NOX and PM AbatementTable 9a presents a list of the NOX and PM abatement measures defined in a sample scenario forIreland within the model. The emission controls in this case relate exclusively to the emissionstandard associated with the agricultural or construction related machinery. Clearly, these
  20. 20.   18 | P a g e   categories of emissions and controls could be accounted for within the transport sector, but theyare 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 fromagriculture (filtered list) in the Irish sample scenarioActivity Sector Technology 2000 2005 2010 2015 2020Vehicles TRA_OT_AGR_MD TRA_OT_AGR-MD-CAGEUI1 10 10 8 7Vehicles TRA_OT_AGR_MD TRA_OT_AGR-MD-CAGEUII0 10 10 9 8Vehicles TRA_OT_AGR_MD TRA_OT_AGR-MD-CAGEUIII0 0 22 21 20Vehicles TRA_OT_AGR_MD TRA_OT_AGR-MD-CAGEUIV0 0 0 22 45Table 9B: Further categories of control strategies not yet assumed as planned for PM2.5 andNOX in the sample scenario for IrelandTechnology DefinitionBAN Ban on agricultural burningTable 9b presents a list of the further agricultural abatement measure related to NOX and PMthat currently exist within the model as an option.Data Requirements IIITables 6 through 9 present data from a sample control strategy currently identified out to 2020in relation to emissions from the agriculture sector. The paired tables (B tables) also include theother 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 seemsappropriate for the Irish context, and to identify any missing measures. Control strategies mustbe defined for at least 2000 to 2020 inclusive.Thus the approach should be to consider pollution abatement measures in place and plannedwithin Ireland and to reconcile these with the available definitions within the GAINS model. In
  21. 21.   19 | P a g e   the Irish context, where a specific and important measure is not defined within the model, thisshould be discussed with the modelling team – ImpIreland@APEnvEcon.comUltimately when considering the balance of control strategies in the model it is also importantto take account of how a given control strategy influences the ‘abated’ emission factor and toconsider 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 potentialof a given abatement measure where it is either unfeasible or impractical and some justificationcan be provided to support this. Such restrictions are also part of the data requirement for thisaspect of the model.The handling of emission factors is discussed in the following section. Factors for individualsector and measure combinations should be examined through the online model.
  22. 22.   20 | P a g e   4. Emission factors and relevant variablesThus far this brief has considered the activities identified in the model for agriculture that giverise to emissions, and the measures which can reduce the emissions from these sources. In thissection 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 abatedemission factors. The unabated emission factors, as briefly described in Box 1, refer to theemissions that would arise from a source if no abatement measures are in place. The abatedemission factors, also briefly described in Box 1, are the emissions that occur from the samesource, but where a specific abatement measure has been applied.Previously in section 4, the current control strategies for a sample Irish scenario were presentedfor specific sources of agricultural pollution. However, control strategy data do not represent allsources of emissions, as there can be sources which have no control in place (generally signalledby the NOC abbreviation in GAINS). Thus, there are many additional emission sources to beconsidered in emission calculation which are not related to any control strategy. These aresimply activities that give rise to emissions, where no abatement measure is in place. Theemissions from such uncontrolled sources are a simple function of the level of activity by theunabated emission factor for that activity. For example if keeping 100,000 cattle is assumed togenerate 10kt of methane, then the unabated emission factor for methane from 100,000 cattle isdefined as 10kt.For emission calculation from a source where an abatement measure is in place, the emissioncalculation process still uses the unabated emission factor, but accounts for the influence of theabatement technology through what is known as the ‘removal efficiency’ of the given technologyor measure.Thus, to use the notional example above for methane emissions from 100,000 cattle, if a specialfeed were to reduce methane emissions by 75%, the removal efficiency would be 75%. Thuswhere this technology is in place the emissions would be:1. Unabated emission factor: 10kt per 100,000 cattle
  23. 23.   21 | P a g e   2. Removal Efficiency: 75%3. Abated emission factor: 2.5kt per 100,000 cattleThere are also additional parameters relevant to the agricultural emissions which can also becalibrated 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 DAYSrefers to the number of full days in a given year that a given animal spends in housing – thus avalue 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 theproportion of time that dairy cows spend in housing during the grazing period – e.g. the timewhen the animals are brought into housing for milking.These two parameters are used in splitting total annual N-excretion rate into N-excreted inanimal house and during grazing (see also below).2. N Excretion ratesN excretion rates are of obvious significance to agricultural emissions. Two rates are sought inthe GAINS model here for all animals – N_EXCR_H and N_EXCR_G – the former refers to thenitrogen excretion rate of animals during the housed periods, whereas the latter refers to thenitrogen excretion rate of the animals during their grazing periods. The data are recorded inunits of total kg/N per year. These are totalled within the model to given the N_EXCR or totalnitrogen excretion rate for the year.3. N Volatilisation ratesThe 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
  24. 24.   22 | P a g e   manure and grazing. They are expressed as a percentage of N available at a given stage inmanure that will be lost as NH3.4. Milk yieldThe GAINS model requires for dairy cows information about the milk yield over time. Thesedata are used for a multitude of purposes. On one hand it can be used to calculate N-excretionrates in case there is no native data but it also considers the relationship between emissionfactors for ammonia and methane and animal productivity, i.e., an increase in milk yield iscorrelated 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 canuse the default relationship developed in GAINS based on the data from several countries. Thisapproach however would ignore specific local circumstances that may cause a variation.Data Requirements IVThe requirement here is to evaluate whether the identified emission factors in the GAINS modelare 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 inconsistencyshould be addressed first before assessing the individual emission factors.As the measures are somewhat aggregate, it may also be necessary to aggregate comparablenational emission factors to compare against them. This approach will ideally involveconsultations 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 atpresent 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 PMemissions – however, these are primarily associated with agricultural machinery and arecaptured under the ‘other transport’ subsector. Agricultural burning can also be defined withinthe model to account for these associated emissions.
  25. 25.   23 | P a g e   Table 10: Days, Housing and base N Volatilisation ratesAGR_ABB DAYS N_EXCR_HKg N/yrN_EXCR_GKg N/yrN_EXCR TotKg N/yrTIME_GH%VOL_H% NVOL_S% NVOL_A% NVOL_G% NDL 133 41.72 52.279 94 12.5 17.94 1.8 23.65 5.18DS 133 41.72 52.280 94 12.5 12.18 16.25 8 5.18OL 143 26.97 41.88 68.85 0 11.33 2.1 27 1.23OS 143 26.97 41.88 68.85 0 7.58 4.14 7.78 1.23PL 365 12.44 0 12.436 0 19.33 1.18 8.5 3PS 365 12.44 0 12.436 0 19.33 1.18 8.5 3LH 365 0.84 0 0.84 0 17.7 0.01 15.5 0OP 365 0.51 0 0.51 0 14.4 0.01 9.65 0SH 64 1.40 6.60 8 0 9.55 0 5 3.92HO 183 25.07 24.93 50 0 12 0 10 8FU 365 4.1 0 4.1 0 12 0 25 0BS 0 0 0 0 0 0 0 0 0CM 0 0 0 0 0 0 0 0 0
  26. 26.   24 | P a g e   Table 11: CH4 emission factors associated with the activities causing CH4 emissionsActivity and Sector Implied kt of CH4 emissionsper unit of activityAGR_BEEF-OL-[M animals] 7.389AGR_BEEF-OL_F-[M animals] 60.167AGR_BEEF-OS-[M animals] 60.315AGR_COWS-DL-[M animals] 21.107AGR_COWS-DL_F-[M animals] 84.429AGR_COWS-DS-[M animals] 83.028AGR_OTANI-HO-[M animals] 18AGR_OTANI-SH-[M animals] 6AGR_PIG-PL-[M animals] 12.904AGR_POULT-LH-[M animals] 0.117AGR_POULT-OP-[M animals] 0.117TRA_OT_AGR-MD-[PJ] 0.004
  27. 27.   25 | P a g e   5. Cost dataThus far this report has considered the sources of agricultural emissions, the emission factorsassociated with sources and the pollution abatement potential of measures. A further importantaspect of the model is the cost associated with measures identified in the control strategies. Costdata are important as they assist the model in identifying cost-effective abatement solutions to agiven environmental objective or ‘problem’. Thus, just because a specific measure may be veryeffective at reducing emissions from a source, if the cost is too high, it may not be the mostefficient use of available resources.Cost is therefore a vital element of optimisation as cost-effectiveness underpins much of theprocess. Cost is however, a complicated aspect of the model. In this section a somewhattechnical description of how costs for measures are determined is presented.Cost calculation principlesAgricultural cost calculation for GAINS aims at estimation of unit costs which represent theannual increase in costs that a typical operator or farmer will bear as a result of introducing anew technique or measure. Therefore the calculation shows additional costs compared with thenormal practice. Only direct costs and savings associated with the technique are considered andall figures are net of taxes. Depending on the actual measure the cost calculation will includeinvestments 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 requirementand capital. In GAINS, investment functions have been developed where these cost componentsare 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 andciv. This equation might include additional parameters like flue gas volume (for stationarycombustion sources) as well as a retrofitting factor. Although the original investment costsmight be expressed in different units, i.e., per unit of capacity, energy use, animal place, volumeof manure stored, etc., they are converted in GAINS into €/MWth or €/animal place. For
  28. 28.   26 | P a g e   agriculture, the coefficients of this function have been estimated drawing on the informationavailable from international and national sources, e.g., UNECE (2007) and Webb et al. (2006).)sci+ci(=Ivf(eq.1)Investments are annualized (eq.2) over the technical lifetime of the technology lt by using thereal interest rate q (as %/100). In the EU and UNECE work an interest rate of 4% was used.1-)q+(1q)q+(1I=I ltltan ∗∗ (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 ofthe installation and are estimated assuming percentage f of the total investments. The value of fwill vary depending on the type of equipment, e.g., 1-2% for buildings up to about 5% formachinery like tractors or manure spreaders.fI=OMfix∗ (eq. 3)Finally, the variable operating costs (eq.4) are related to the actual operation of the installationand 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 arecalculated as the sum of the specific demand (saving with negative sign) λx and its (country-specific) price cx.c=OMxxvarλ∑ (eq.4)The unit costs are calculated considering (if necessary) the number of animal production cyclesper year ar and the utilization factor pf of the capacity (eq.5).
  29. 29.   27 | P a g e   pfarOM+pfOM+I=cafixfixan•(eq.5)These unit costs are used along with the reduction efficiency of the measure to derive marginalcosts (eq.6) that relate the extra costs for an additional measure to the extra abatement of thatmeasure (compared to the abatement of the less effective option). GAINS uses the concept ofmarginal 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 ofcontrol options M are available, the marginal costs mcm for control option m are calculated as111−−−−−=mlmlmlmmlmmccmcηηηη(eq.6)wherecm unit costs for option m andηlm pollutant l removal efficiency of option mData Requirements VThe requirement for cost data is broadly to consider the cost of implementing and maintaining aspecific control strategy. These data should be checked against the values within GAINS asdetermined by the described methodology above. Where significant differences occur an effortshould be made to value the costs using the above methodology and submit the results to themodelling process. Where only partial information is available, this may also be presented to theteam for consideration and revision of values within the model.
  30. 30.   28 | P a g e   Closing noteThere are further pieces of information required in the GAINS modelling process, however, whatis contained within this brief represents the principal data required to more accurately representthe agricultural sector in the model.In all cases it should be remembered that data can be changed and updated as necessary, thusthe objective should always be to provide ‘best available data’. Forecasting will always entaildegrees of uncertainty.As a closing note, Table 13, presents a full emission profile for NH3 from agriculture from a year2000 sample model scenario for Ireland. This shows the sector and activity, the level of activityassociated, the measure in place, the effectiveness and the ultimate emissions. Total emissionsare 121kt of NH3.As can be seen, for a given source e.g. the same 4.219 ‘other cattle’, values are presented for theportion 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 activitycovered by a technology require the value to be reported under each heading. Furthermore, itcan 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 variousinformation is assembled within the model framework. To facilitate input to this ongoing process, the appendix provides a guide to reviewing data inthe online model. Some provisional scenarios are not publicly viewable and thus forconsideration of the latest data a request to the national team involved should be made. Thesecond part of the appendix contains some adapted and simplified data submission sheets forstakeholders looking to provide updated information for the model.
  31. 31.   29 | P a g e   Table 13: Summary of total animal numbers, measures, emission factors after abatement and emissions of NH3 for a sample GAINSscenarioSector-Animal-Technology-Stage Abbr.SectoralactivityAbatedemissionfactorCapacitiescontrolledMilk yieldcoefficient Emissions[Units]tNH3/Unit % ratio t NH3Agriculture: Livestock - other cattle-Othercattle - liquid (slurry) systems-Coveredoutdoor storage of manure; low efficiency-APPLICATIONAGR_BEEF-OL-CS_low-APPLICATION4.219 7743.022 75 1 24501.6Agriculture: Livestock - other cattle-Othercattle - liquid (slurry) systems-Coveredoutdoor storage of manure; low efficiency-GRAZINGAGR_BEEF-OL-CS_low-GRAZING4.219 625.4 75 1 1978.98Agriculture: Livestock - other cattle-Othercattle - liquid (slurry) systems-Coveredoutdoor storage of manure; low efficiency-HOUSINGAGR_BEEF-OL-CS_low-HOUSING4.219 3711.1 75 1 11743.2Agriculture: Livestock - other cattle-Othercattle - liquid (slurry) systems-Coveredoutdoor storage of manure; low efficiency-STORAGEAGR_BEEF-OL-CS_low-STORAGE4.219 365.94 75 1 1157.96Sum for measure 4.219 12445.462 75 1 39382Agriculture: Livestock - other cattle-Othercattle - liquid (slurry) systems-No control-APPLICATIONAGR_BEEF-OL-NOC-APPLICATION4.219 7677 25 1 8097.56Agriculture: Livestock - other cattle-Othercattle - liquid (slurry) systems-No control-GRAZINGAGR_BEEF-OL-NOC-GRAZING4.219 625.4 25 1 659.661Agriculture: Livestock - other cattle-Othercattle - liquid (slurry) systems-No control-HOUSINGAGR_BEEF-OL-NOC-HOUSING4.219 3711.1 25 1 3914.4
  32. 32.   30 | P a g e   Agriculture: Livestock - other cattle-Othercattle - liquid (slurry) systems-No control-STORAGEAGR_BEEF-OL-NOC-STORAGE4.219 609.9 25 1 643.312Sum for measure 4.219 12623.4 25 1 13315Agriculture: Livestock - other cattle-Othercattle - solid systems-No control-APPLICATIONAGR_BEEF-OS-NOC-APPLICATION1.641 2257.6 100 1 3704.21Agriculture: Livestock - other cattle-Othercattle - solid systems-No control-GRAZINGAGR_BEEF-OS-NOC-GRAZING1.641 625.4 100 1 1026.14Agriculture: Livestock - other cattle-Othercattle - solid systems-No control-HOUSINGAGR_BEEF-OS-NOC-HOUSING1.641 2482.8 100 1 4073.71Agriculture: Livestock - other cattle-Othercattle - solid systems-No control-STORAGEAGR_BEEF-OS-NOC-STORAGE1.641 1253.2 100 1 2056.22Sum for measure 1.641 6619 100 1 10860Agriculture: Livestock - dairy cattle-Dairycows - liquid (slurry) systems-Coveredoutdoor storage of manure; low efficiency-APPLICATIONAGR_COWS-DL-CS_low-APPLICATION1.095 9725.28 75 1 7987.43Agriculture: Livestock - dairy cattle-Dairycows - liquid (slurry) systems-Coveredoutdoor storage of manure; low efficiency-GRAZINGAGR_COWS-DL-CS_low-GRAZING1.095 3288.4 75 1 2700.78Agriculture: Livestock - dairy cattle-Dairycows - liquid (slurry) systems-Coveredoutdoor storage of manure; low efficiency-HOUSINGAGR_COWS-DL-CS_low-HOUSING1.095 9088.5 75 1 7464.44Agriculture: Livestock - dairy cattle-Dairycows - liquid (slurry) systems-Coveredoutdoor storage of manure; low efficiency-STORAGEAGR_COWS-DL-CS_low-STORAGE1.095 448.98 75 1 368.75Sum for measure 1.095 22551.16 75 1 18521Agriculture: Livestock - dairy cattle-Dairycows - liquid (slurry) systems-No control-AGR_COWS-DL-NOC-APPLICATION1.095 9654.8 25 1 2643.18
  33. 33.   31 | P a g e   APPLICATIONAgriculture: Livestock - dairy cattle-Dairycows - liquid (slurry) systems-No control-GRAZINGAGR_COWS-DL-NOC-GRAZING1.095 3288.4 25 1 900.261Agriculture: Livestock - dairy cattle-Dairycows - liquid (slurry) systems-No control-HOUSINGAGR_COWS-DL-NOC-HOUSING1.095 9088.5 25 1 2488.15Agriculture: Livestock - dairy cattle-Dairycows - liquid (slurry) systems-No control-STORAGEAGR_COWS-DL-NOC-STORAGE1.095 748.3 25 1 204.861Sum for measure 1.095 22780 25 1 6236.5Agriculture: Livestock - dairy cattle-Dairycows - solid systems-No control-APPLICATIONAGR_COWS-DS-NOC-APPLICATION0.082 2980.8 100 1 245.692Agriculture: Livestock - dairy cattle-Dairycows - solid systems-No control-GRAZINGAGR_COWS-DS-NOC-GRAZING0.082 3288.4 100 1 271.046Agriculture: Livestock - dairy cattle-Dairycows - solid systems-No control-HOUSINGAGR_COWS-DS-NOC-HOUSING0.082 6170.5 100 1 508.603Agriculture: Livestock - dairy cattle-Dairycows - solid systems-No control-STORAGEAGR_COWS-DS-NOC-STORAGE0.082 7229.7 100 1 595.908Sum for measure 0.082 19669.4 100 1 1621.2Agriculture: Livestock - other animals(sheep, horses)-Horses-No control-APPLICATIONAGR_OTANI-HO-NOC-APPLICATION0.069 2678.7 100 1 184.83Agriculture: Livestock - other animals(sheep, horses)-Horses-No control-GRAZINGAGR_OTANI-HO-NOC-GRAZING0.069 2421.9 100 1 167.111Agriculture: Livestock - other animals(sheep, horses)-Horses-No control-HOUSINGAGR_OTANI-HO-NOC-HOUSING0.069 3652.8 100 1 252.043Agriculture: Livestock - other animals(sheep, horses)-Horses-No control-STORAGEAGR_OTANI-HO-NOC-STORAGE0.069 0 100 1 0Sum for measure 0.069 8753.4 100 1 603.98
  34. 34.   32 | P a g e   Agriculture: Livestock - other animals(sheep, horses)-Sheep and goats-Nocontrol-APPLICATIONAGR_OTANI-SH-NOC-APPLICATION7.555 77 100 1 581.735Agriculture: Livestock - other animals(sheep, horses)-Sheep and goats-Nocontrol-GRAZINGAGR_OTANI-SH-NOC-GRAZING7.555 314 100 1 2372.27Agriculture: Livestock - other animals(sheep, horses)-Sheep and goats-Nocontrol-HOUSINGAGR_OTANI-SH-NOC-HOUSING7.555 162.7 100 1 1229.2Agriculture: Livestock - other animals(sheep, horses)-Sheep and goats-Nocontrol-STORAGEAGR_OTANI-SH-NOC-STORAGE7.555 0 100 1 0Sum for measure 7.555 553.7 100 1 4183.2Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-Covered outdoor storageof manure; low efficiency-APPLICATIONAGR_PIG-PL-CS_low-APPLICATION1.722 1028.212 87.1 1 1542.18Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-Covered outdoor storageof manure; low efficiency-GRAZINGAGR_PIG-PL-CS_low-GRAZING1.722 0 87.1 1 0Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-Covered outdoor storageof manure; low efficiency-HOUSINGAGR_PIG-PL-CS_low-HOUSING1.722 2919 87.1 1 4378.1Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-Covered outdoor storageof manure; low efficiency-STORAGEAGR_PIG-PL-CS_low-STORAGE1.722 86.22 87.1 1 129.318Sum for measure 1.722 4033.432 87.1 1 6049.6Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-Low ammonia application;low efficiency-APPLICATIONAGR_PIG-PL-LNA_low-APPLICATION1.722 613.98 1 1 10.573Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-Low ammonia application;low efficiency-GRAZINGAGR_PIG-PL-LNA_low-GRAZING1.722 0 1 1 0Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-Low ammonia application;AGR_PIG-PL-LNA_low-HOUSING1.722 2919 1 1 50.265
  35. 35.   33 | P a g e   low efficiency-HOUSINGAgriculture: Livestock - pigs-Pigs - liquid(slurry) systems-Low ammonia application;low efficiency-STORAGEAGR_PIG-PL-LNA_low-STORAGE1.722 143.7 1 1 2.475Sum for measure 1.722 3676.68 1 1 63.313Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-No control-APPLICATIONAGR_PIG-PL-NOC-APPLICATION1.722 1023.3 11.9 1 209.693Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-No control-GRAZINGAGR_PIG-PL-NOC-GRAZING1.722 0 11.9 1 0Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-No control-HOUSINGAGR_PIG-PL-NOC-HOUSING1.722 2919 11.9 1 598.156Agriculture: Livestock - pigs-Pigs - liquid(slurry) systems-No control-STORAGEAGR_PIG-PL-NOC-STORAGE1.722 143.7 11.9 1 29.447Sum for measure 1.722 4086 11.9 1 837.3Agriculture: Livestock - poultry-Layinghens-No control-APPLICATIONAGR_POULT-LH-NOC-APPLICATION1.57 130.1 100 1 204.257Agriculture: Livestock - poultry-Layinghens-No control-GRAZINGAGR_POULT-LH-NOC-GRAZING1.57 0 100 1 0Agriculture: Livestock - poultry-Layinghens-No control-HOUSINGAGR_POULT-LH-NOC-HOUSING1.57 180.5 100 1 283.385Agriculture: Livestock - poultry-Layinghens-No control-STORAGEAGR_POULT-LH-NOC-STORAGE1.57 0.1 100 1 0.157Sum for measure 1.57 310.7 100 1 487.8Agriculture: Livestock - poultry-Otherpoultry-No control-APPLICATIONAGR_POULT-OP-NOC-APPLICATION13.766 51 100 1 702.066Agriculture: Livestock - poultry-Otherpoultry-No control-GRAZINGAGR_POULT-OP-NOC-GRAZING13.766 0 100 1 0Agriculture: Livestock - poultry-Otherpoultry-No control-HOUSINGAGR_POULT-OP-NOC-HOUSING13.766 88.9 100 1 1223.8Agriculture: Livestock - poultry-Otherpoultry-No control-STORAGEAGR_POULT-OP-NOC-STORAGE13.766 0.1 100 1 1.377Sum for measure 13.766 140 100 1 1927.2
  36. 36.   34 | P a g e   Glossary Related Organisations, Abbreviations and AcronymsAQ Air QualityCH4 MethaneCLE… A prefix for a scenario based on ‘Current Legislation’CLRTAPDOAFConvention on Long-Range Transboundary Air PollutionDepartment of Agriculture and FoodDOEHLG Department of Environment Heritage and Local GovernmentEPA Environmental Protection AgencyGAINS Greenhouse Gas and Air Pollution Interactions and SynergiesGHG Greenhouse GasesIAM Integrated Assessment ModellingIIASA International Institute for Applied Systems AnalysisKt Kilo tonMTFR Maximum technical feasible reductionMRR Maximum reductions in RAINSN2O Nitrous OxideNEC/D National Emissions Ceiling/s DirectiveNECPI National Emissions Ceilings Policy and Instruments groupNH3 AmmoniaNTM Non technical measuresNOx Nitrogen OxidePj PetajouleRAINS Regional Air Pollution Information and SimulationSRM Source-Receptor matricesTFIAM Task Force on Integrated Assessment ModellingTFEIP Task Force on Emission Inventory ProjectionsTM Technical measures
  37. 37.   35 | P a g e   Appendix – Submission and Review of DataThis appendix has two elements. Firstly, as it is not practical to include 50 pages of potentialtechnology, measure and animal combinations, a brief guide to beginning to assess data throughthe online system is presented. This should allow users to begin to investigate parameters andwill enable them to suggest changes or amendments to modelled parameters. In Ireland queriesor submissions of in relation to the Agricultural sector in the GAINS model can be processed byemailing ImpIreland@APEnvEcon.com. Some provisional scenarios will not be accessiblethrough the online model.Secondly, template format for data provision is described to allow users to contribute data andhelp with the refining of model parameters. The format is simplified to aid with datasubmission. However, it is likely that some submissions made in this format will requirebilateral 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 arerevised as information improves. However, it will always remain the case that specific studies ornational experts may be able to provide additional and detailed information for one aspect of themodel and thereby aid the development. Thus the purpose of this appendix is to supportindividuals in making all manner of contributions whether basic parameters or developmentalsuggestions. 
  38. 38.   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 visitinghttp://gains.iiasa.ac.at/gains/EU/index.login?logout=1 and registering to view the model. Oncelogged in, there are many ways to present and analyse the data within the model. The followingstep by step process is a reasonable starting point.1. Click on the ‘emissions’ tab at the top2. Click on the ‘emissions’ tab at the top3. Select the pollutant of interest from the drop down menu on the left4. Select the output format from the menu table on the left. For example – under the‘Detailed Results by:’ heading select Control Option5. 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
  39. 39.   37 | P a g e   Figure A1: Screenshot of reviewing data in the online system
  40. 40.   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 considerationSubmission for fertiliser use or land use data• Submit data on the application of fertilisers and the basic land usesSubmission 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 grazingTechnology or process emission factor submission• Specify pollutant• Specify technology description• Provide notes and references where possible• Provide emission factor used nationallyTechnology or process coverage• Define the technology or process and the coverage it has nationallyFeasibility of measures submission• Identify measures that cannot be applied• Describe why they cannot be applied• Reference studySectoral or subsectoral emission estimates• Present estimates of emissions for the sector or subsector• Reference study
  41. 41.   39 | P a g e   Animal Number submission 1 of 2This 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 animalsSome notes for submission
  42. 42.   40 | P a g e   Animal Number submission 2 of 2Activity Sector Unit 2000 2005 2010 2015 2020 2025 2030DL AGR_COWS M animalsDS AGR_COWS M animalsOL AGR_BEEF M animalsOS AGR_BEEF M animalsPL AGR_PIG M animalsPS AGR_PIG M animalsLH AGR_POULT M animalsOP AGR_POULT M animalsSH AGR_OTANI M animalsHO AGR_OTANI M animalsFU AGR_OTANI M animalsBS AGR_OTANI M animalsCM AGR_OTANI M animals
  43. 43.   41 | P a g e   Submission for fertiliser use and land use data 1 of 2This 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 theatmosphere• Fertilizer production is to be given both for total mass (PR_FERT, in Mt) as well as for nutrient content (FERTPRO, kt N) toaccount for production-related emissions. Agricultural use should be reported separately for compounds experiencing highammonia loss (urea and ammonium bicarbonate, FCON_UREA) and for all other fertilizers (FCON_OTHN) according to theamount 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 andneed not be entered• Different types of rice-growing area (in flooded vs dry “upland” areas, to be presented in million ha) allow to estimatemethane 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
  45. 45.   43 | P a g e   Submission for milk yield, N2O and manure parametersThis 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 increasedmetabolism (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 aresourced independently – see explanations below.• The table below provides an explanation for manure parametersParameter Unit ExplanationDAYS 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_HkgN/yearNitrogen excretion rate - during housing periodN_EXCR_GkgN/yearNitrogen excretion rate - during grazing periodN_EXCRkgN/yearNitrogen excretion rateVOL_H % NNitrogen volatilization (as NH3-N) from housing (expressed as % of N available in manure at a given stage, here refers to thevalue 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 % NNitrogen volatilization (as NH3-N) during grazing (expressed as % of N available in manure at a given stage, here refers toN_EXCR_G)SMG fraction Share of manure applied to grasslandSome notes for submission
  46. 46.   44 | P a g e   Milk yieldsActivity Sector Unit 2000 2005 2010 2015 2020 2025 2030NOF AGR_COWS_MILK kg milk/animal 0 0 0 0 0 0 0N2O parametersParameter Value Unit ExplanationFRAC_GRASS 0.26 fraction Fraction of mineral fertilizer applied to grasslandCLIMFROST 0.15 fraction Part of country exposed to frequent frost-thaw-cyclesN_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 areaManure parametersAGR_ABB DAYS N_EXCR_H N_EXCR_G N_EXCR TIME_GH VOL_H VOL_S VOL_A VOL_G SMGDL 133 41.721 52.279 94.000 12.500 17.940 1.800 23.650 5.180 0.000DS 133 41.721 52.279 94.000 12.500 12.180 16.250 8.000 5.180 0.000OL 143 26.974 41.876 68.850 0.000 11.330 2.100 27.000 1.230 0.000OS 143 26.974 41.876 68.850 0.000 7.580 4.140 7.780 1.230 0.000LH 365 0.840 0.000 0.840 0.000 17.700 0.010 15.500 0.000 0.000PL 365 12.436 0.000 12.436 0.000 19.330 1.180 8.500 3.000 0.000PS 365 12.436 0.000 12.436 0.000 19.330 1.180 8.500 3.000 0.000OP 365 0.509 0.000 0.509 0.000 14.400 0.010 9.650 0.000 0.000SH 64 1.403 6.597 8.000 0.000 9.550 0.000 5.000 3.920 0.000HO 183 25.068 24.932 50.000 0.000 12.000 0.000 10.000 8.000 0.000FU 365 4.100 0.000 4.100 0.000 12.000 0.000 25.000 0.000 0.000BS 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000CM 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
  47. 47.   45 | P a g e   Submission for technology or process emission factorsThis 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 efficiencyMeasure / Technology Description Emission informationName of measure or technology Describe what is involvedDescribe the pollutants affected by themeasure or technology and provide asmuch quantitative data for their effectNotes for submission
  48. 48.   46 | P a g e   Submission for technology or process coverage within a countryThis 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 particularabatement measure or technology.• Provide references to any relevant studies or reports.Measure / Technology Description Emission informationName of measure or technology Describe what is involvedDescribe the pollutants affected by themeasure or technology and provide asmuch quantitative data for their effectNotes for submission
  49. 49.   47 | P a g e   Submission for technology or process feasibilityThis request sheet is to provide information as to why a technology or process cannot/should not be employed in a countryProvide references to any related studies or reportsMeasure / Technology Description Reason for N/AName of measure or technology Describe what is involvedIdentify why the measure cannot orshould not be considered as anabatement option within the model fora given country. Or where therestriction is only partial, identify themaximum extent to which the measurecan be adapted.Notes for submission
  50. 50.   48 | P a g e   Submission for sectoral or subsectoral emission estimatesThis 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 EmissionsNotes for submission
  51. 51. 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.