Chapter 8 Uncertainty Assessment for E Quantifying ...Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks 8-5 described with summary statistics, such

E

Authors:1JayBreidt,ColoradoStateUniversityStephenM.Ogle,ColoradoStateUniversityWendyPowers,MichiganStateUniversityCoeliHoover,USDAForestService

Contents:

8 UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks...................8‐3 ComponentsandInputstoanEntity‐ScaleMonteCarloUncertaintyAssessment............8‐48.1

8.1.1 ParameterUncertainty..................................................................................................8‐58.1.2 SamplingMethodUncertainty....................................................................................8‐68.1.3 LargeDatasetUncertainty............................................................................................8‐98.1.4 ModelUncertainty.........................................................................................................8‐16

ResearchGaps...............................................................................................................................................8‐208.2Appendix8‐A:ExampleOutputFilefromFVSSamplingUncertaintyBootstrappingApplicationFVSBoot(asprovidedinGreggandHummel,2002)................................................................................8‐21Appendix8‐B:UncertaintyTables....................................................................................................................8‐22Chapter8References.............................................................................................................................................8‐55

SuggestedChapterCitation:Breidt,F.J.,Ogle,S.M.,Powers,W.,Hover,C.,2014.Chapter8:UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

USDAisanequalopportunityproviderandemployer.

1AllauthorsofChapters3,4,5,and6providedstrategicinputintheparametersintheuncertaintychapter.

Chapter 8

Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks

Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks

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Acronyms,ChemicalFormulae,andUnitsCONUSSTATSGO ContinentalUnitedStatesSoilGeographicDatabaseDBH DiameteratbreastheightDNDC DeNitrification‐DeCompositionEPA U.S.EnvironmentalProtectionAgencyERS USDAEconomicResearchServiceFOFEM FirstOrderFireEffectsModelFVS ForestVegetationSimulatorGHG GreenhousegasIPCC IntergovernmentalPanelonClimateChangeNADP NationalAtmosphericDepositionProgramNARR NorthAmericanRegionalReanalysisNASS USDANationalAgriculturalStatisticsServiceNCEP NationalCentersforEnvironmentalPredictionNLCD NationalLandCoverDatabaseNOAA NationalOceanicandAtmosphericAdministrationNRCS USDANaturalResourcesConservationServiceNRI NationalResourceInventoryPDF ProbabilitydensityfunctionPRISM Parameter‐elevationRegressionsonIndependentSlopesModelSOC SoilorganiccarbonSSURGO SoilSurveyGeographicUSDA U.S.DepartmentofAgriculture


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8 UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks

Quantifyingtheuncertaintyofgreenhousegas(GHG)emissionsandreductionsfromagricultureandforestrypracticesisanimportantaspectofdecision‐makingforfarmers,ranchersandforestlandownersastheuncertaintyrangeforeachGHGestimatecommunicatesourlevelofconfidencethattheestimatereflectstheactualbalanceofGHGexchangebetweenthebiosphereandtheatmosphere.Inparticular,afarm,ranch,orforestlandownermaybemoreinclinedtoinvestinmanagementpracticesthatreducenetGHGemissionsiftheuncertaintyrangeforanestimateislow,meaningthathigherconfidenceintheestimatesexists.Thischapterpresentstheapproachforaccountingfortheuncertaintyintheestimatednetemissionsbasedonthemethodspresentedinthisreport.2AMonteCarloapproachwasselectedasthemethodforestimatingtheuncertaintyaroundtheoutputsfromthemethodologiesinthisreportasitiscurrentlythemostcomprehensive,soundmethodavailabletoassesstheuncertaintyattheentityscale.Limitationsanddatagapsexist;however,asnewdatabecomeavailablethemethodcanbeimprovedovertime.ImplementationofaMonteCarloanalysisiscomplicatedandrequirestheuseofastatisticaltooltoproduceaprobabilitydensityfunction(PDF)3aroundtheGHGemissionsestimate.4FromthePDF,theuncertaintyestimatecanbederivedandreported.

2TheIPCCGoodPracticeGuidance(IPCC,2000)recommendstwoapproaches—Tier1andTier2—fordevelopingquantitativeestimatesofuncertaintyforemissionsestimatesforsourcecategories.TheTier1methoduseserrorpropagationequations.Theseequationscombinetheuncertaintyassociatedwiththeactivitydataandtheuncertaintyassociatedwiththeemission(orother)factors.Thisapproachisappropriatewhereemissions(orremovals)areestimatedastheproductofactivitydataandanemissionfactororasthesumofindividualsub‐sourcecategoryvalues.TheTier2methodutilizestheMonteCarloStochasticSimulationtechnique.Usingthistechnique,anestimateofemission(orremoval)foraparticularsourcecategoryisgeneratedmanytimesviaanuncertaintymodel,resultinginanapproximatePDFfortheestimate.Wheresufficientandreliableuncertaintydatafortheinputvariablesareavailable,theTier2methodisthepreferredoption.3TheintegralofaPDFoveragivenintervalofvaluesistheprobabilityforarandomvariabletotakeonsomevalueintheinterval.Thatis,thePDFisafunctiongivingprobability“densities”anditsintegralgivesprobabilities.AnarrowerPDFforanestimateindicatessmallervariancearoundthecentral/mostlikelyvalue,i.e.,ahigherprobabilityofthevaluetobeclosertothecentral/mostlikelyvalue.Theuncertaintyforsuchanestimateislower.4GiventhecomplexityofMonteCarloanalysisandthenecessityforatool,theapproachpresentedhereisnotintendedfordevelopmentbyalandowner,ratheritisintendedforuseindevelopingatoolthatalandownerwouldusetoassessuncertaintyestimates.


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UncertaintyinGHGemissionsestimationarisesbecauseofunknownorincompletelyknownfactorsassociatedwith:

Parameters–Duetolimitationsassociatedwithavailableinputdata(e.g.,activitydataandemissionfactors).

Samplingmethods–Duetoeithermeasurementerrorsduringsamplecollectionorpotentialvariationsinvaluesobtainedfromsampling(i.e.,whenthechosensampleisnotfullyrepresentativeoftheentirepopulation).

Largedatasets–Duetomeasurementserrorsduringdatacollection,andvariationsindatasetvaluesforagivensetofconditions.

Models–Duetoapproximationerrorsandestimationerrors.Approximationerrorarisesbecausethemodelisasimplificationoftherealsystem,whileestimationerrorarisesbecausethetheoreticalmodelisfittedusinglimiteddata.

Concepts–Thisiscloselyrelatedtomodelapproximationerrorandoccursbecausetheconceptualscopedoesnotcapturetheactual/realscopethuscreatingabias.Foranentity,thisconceptualizationuncertaintymayberelativelysmall.

Theapproachtoaddressuncertaintydoesnotaddressconceptualuncertaintybecauseitisexpectedtobesmallanddifficulttoquantify.Thischapteraddressesparameteruncertainty,samplinguncertainty,largedatasetuncertainty,andmodelapproximationuncertainty.WheredataarecurrentlyunavailableorincompleteforestablishingPDF’sandestimatinguncertainty,theauthorsprovideexpertjudgmentand/oraqualitativedescriptionofuncertaintyintheinterestsofmakingtheGHGmanagementmethodsastransparentandcompleteaspossible.Inthefuture,newdatacanbeusedtorefineandimprovetheestimationofuncertainty.

Inthischapter,Section8.1includesthecomponentsandinputstoanentity‐scaleMonteCarlouncertaintyassessment,andSection8.2highlightsresearchgaps.

ComponentsandInputstoanEntity‐ScaleMonteCarloUncertainty8.1Assessment

ToconductaMonteCarlouncertaintyanalysisforeachoftheGHGquantificationmethodsandresultingnetGHGemissions,informationisrequiredabouttheuncertaintyassociatedwith:(1)theinputvariables(i.e.,parameters);(2)samplingmethodsusedtoobtaindata;(3)existinglargedatasetsusedasdatasources;and(4)externalmodelsused.Ideally,thisinformationwouldconsistofspecificPDFs(e.g.,normal,triangular,uniform,beta).Alternatively,theuncertaintymightbe

MonteCarloAnalysisforAssessingUncertainty IntheMonteCarlomethod,uncertaininputs(parametersandotherdata)anduncertainmodel

structurearedescribedviaPDFs.ByrandomlyselectingfromeachofthesePDFs,andrunningtheselectedinputsthroughtheselectedmodel,anuncertaintymodeloutputisobtained.CombiningthesemodeloutputsacrossmanyrandomselectionsleadstoanapproximatePDFdescribinguncertaintyinthemodeloutput,reflectingknownsourcesofuncertaintyintheinputsandmodelstructure.

AtoolisneededtorunaMonteCarloanalysistoassesstheuncertaintyformodeloutputs.FarmersandlandownersarenotexpectedtoperformaMonteCarloanalysisontheirown.

Acentralizeddatabaseisneededtostoreinformationontheknownuncertaintiesassociatedwiththeactivityandemissionfactordataforeachemissionssource.Thisreportpresentsreadilyavailabledatathatcanformtheinitialfoundationforsuchadatabase.


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describedwithsummarystatistics,suchaslowerandupperboundsforintervalswithspecifiedconfidence,minimum,maximum,mean,andstandarddeviation.ThissummaryinformationformsthebasisforconstructingapproximatePDFsfortheMonteCarlomethod.RepeatedselectionsaremadefromthesePDFs.TheseselectionsrepresenttherangeofpossibleoutcomesfromeachPDF.RandomsamplingfromthePDFswillensuresuchrepresentativeness.5ByrandomlyselectingfromeachofthesePDFsandrunningtheselectedinputsthroughthemodel,arangeofoutputsisobtained.CombiningthesemodeloutputsacrossmanyrandomselectionsleadstoanoutputPDFthatcanbeusedtodescribeuncertaintyintheestimate,accountingforknownsourcesofuncertaintyintheinputsandmodelstructure.

Thissectionpresentsreadilyavailableinformationoneachofthekeycomponentsofuncertainty.Insummary,althoughinformationonallthecomponentsaredescribedhere,theMonteCarlomethodforassessingnetGHGemissionsuncertaintyreliesmostheavilyonparameteruncertainty,forwhichthebestPDFdataandinformationareavailable.Othercomponentsofuncertaintyarediscussed,includinglimitationssuchascharacterizingtheuncertaintyassociatedwithothercomponents.Thesecomponentscanbereadilyimprovedorrefinedintheuncertaintyanalysisasadditionalinformationbecomesavailable.Overalluncertaintyistypicallygreaterthananyparticularuncertaintycomponent(e.g.,sampling,largedatasets,models)andcanbereadilyimprovedorrefinedasadditionalinformationbecomesavailable.Astheuncertaintyassociatedwiththeothercomponentsisaddressed,theuncertaintywillincrease(i.e.,addressingonlyparameteruncertaintysetsalowerboundforoveralluncertainty).Therefore,thequantificationofparameteruncertaintysetsalowerboundforoveralluncertainty.

8.1.1 ParameterUncertainty

ParameteruncertaintyistheprimarysourceofuncertaintyinthenetGHGestimates.ThissectionpresentsreadilyavailableinformationonparametersusedtoestimatenetGHGemissionsfromanimalproductionsystems,croplandsandgrazinglands,andforestryGHGestimationmethods.Foreachinputvariable,readilyavailableinformationwascollectedontheprobabilitydistribution;variance;standarddeviation;expectedmean,median,andmode;mostlikelyvalue;minimum;maximum;relativeuncertaintyabsolutevalues;confidenceinterval;anddatasources.Theinformationwascollectedprimarilyfrompublishedliterature,suchastheIntergovernmentalPanelonClimateChange(IPCC)Guidelines(2006),theU.S.NationalGHGInventoryReport(U.S.EPA,2012),andpeer‐reviewedjournals.Intheabsenceofpublisheddata,defaultfactorsareindicatedbasedonexpertjudgmentobtainedfromtheWorkingGroups.TheinformationobtainedtodateispresentedinAppendix8‐B.6,7,8

5AnalternativeapproachtoselectingfromthePDFsisLatinhypercubesampling(McKayetal.,1979;HeltonandDavis,2003).6Uncertaintyfortheforestrysectorismainlydrivenbymodelingandsamplinguncertainty;consequently,onlyafewparametershavebeenlistedinAppendix8‐B.7TheWetlandsChaptermethodssuggestuseoftheFVSandDNDCmodelsincombinationwiththelookuptablesfordominantshrubandgrasslandvegetationtypesfoundinChapter3,forestimatingbiomasscarbon,soilcarbon,N2O,andCH4emissionsandremovalsinwetlands.Descriptionsofthesemodelsandtheuncertaintyassociatedwiththelook‐uptablesareincludedintheUncertaintyAssessment(Chapter8).8AnuncertaintyassessmentwasnotcompletedfortheLand‐useChangeChaptermethods(i.e.,annualchangeincarbonstocksindeadwoodandlitterduetolandconversion,changeinsoilorganiccarbonstocksformineralsoils)astheyarebaseduponIPCC2006GuidanceandnoU.S.specificcustomizationsweremadetothesemethods.Uncertaintyassessmentsforeachland‐useandtransitionintooroutofaland‐usecategory


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Inordertomaketheuncertaintyestimationprocessfeasibleandconsistentattheentityscaleforusebyanentityorlandowner,atoolwillbeneededthatprovidesthefollowinguncertaintyinformationforinputvariables:

PDFsordistributions–Default

Emissionfactors–Default

Activitydata–Default,butcustomizable

Withdefaultuncertaintyinformationavailable,itisfeasibletoquantifyparameteruncertaintyviaPDFsandtocombinetheuncertaintyviaMonteCarlomethods.ThesePDFsareoftenrelativelycrude,relyingondefaultvaluesandconservativeexpertjudgment.OptionstoimprovethePDFs(i.e.,improveparameteruncertaintyquantification)areto:(1)developamethodtohelpelicitandrefinetheseuncertaintydistributionsatanentityscale;and(2)conductnewresearchtobetterunderstandthekeyparametersidentifiedinthisreportandtoquantifytheiruncertainties.9

TheuncertaintyassociatedwiththevariousinputstotheGHGestimationequationormodelsarecombinedtoestimateoveralluncertaintyattheentitylevelfor:(1)eachsourcecategoryemissionestimate;and(2)totalemissionestimatearrivedatbyaggregatingeachsourcecategory’sestimate.Althoughmostinputswithinacategoryandacrosscategoriesareindependent,certainvariablesmightbethesame,similar,orhighlyco‐related,andwillneedtobeaccountedforappropriatelyintheuncertaintyanalysis.

8.1.2 SamplingMethodUncertainty

Somesamplingmethods(i.e.,fieldmeasurements)willbeconductedtosupporttheestimationofemissionsusingtheGHGquantificationmethods.Forexample,fortheforestrysector,conductingfieldmeasurementsonsamplingplotsforlargeforestandonurbanforestsisusedtodetermineaggregateforestcharacteristics(e.g.,treecover).Additionally,somelargedatasetsandexternalmodelsthatthemethodsusealsoutilizedatathatwereobtainedfromavarietyofoutsidesamplingmethods.Forexample,forestinventorydatausedintheforestvegetationsimulator(FVS)modelandthe averagecarbonsequestrationratesusedinthei‐Treemodelusedataobtainedthroughsamplingmethods.Inaddition,thereareinstancesintheseexternalmodelsandlargedatasetswherethevariationinmeasurementsobtainedfromthesamplingmethodsisnottakenintoaccount,buttheycanimpactuncertainty.

Ifforeststandsamplingisconductedatanentitylevelusingaformalprobabilitysamplingdesign,thenunbiasedestimatesofsamplingerrorvariancecanbecomputedviastandardtechniquesfromthefieldofsurveystatistics.Theexactformofthevarianceestimatedependsontheparticulardesignusedforthestandsampling.Thoughadditionaluncertaintiesarisefromtheactualmeasurementprotocolsusedinthefield,thesamplingerrorvarianceisamajorpartofthesamplinguncertainty.AcurrentlyfeasibleapproachtoincorporatinginformationonsamplingerrorvariancesintotheuncertaintyanalysisistomodelthesamplingerrorPDFasanormaldistributionwithzeroasitsmeanandtheestimatedsamplingerrorvarianceasitsvariance.10Thissection

arecontainedintheassociatedland‐usecategorychapterofthe2006IPCCGuidelines.http://www.ipcc‐nggip.iges.or.jp/public/2006gl/vol4.html9Atoolcouldprovideanoptiontousepre‐definedvaluessuchasthoseprovidedinAppendix8‐Boruser(i.e.,landowner)suppliedvaluestodefinePDFs.10Similarly,estimatesderivedfromexistingsurveysandtoolssuchasForestInventoryDataOnlineintheForestInventoryandAnalysisNationalProgramortheUSDANaturalResourcesInventory(NRI)SummaryReportshaveassociatedsamplingerrorvariancesfromwell‐establishedstatisticalprocedures.Inthesecases,


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providesthesamplingmethodsandtheirpotentialsourcesofuncertainty.However,giventhecomplexityinincorporatinginformationonuncertaintyforsamplingdataintheMonteCarlouncertaintyassessment,atoolwillbeneededtoquantifytheimpactofsamplinguncertaintyontheestimateofnetGHGemissions.11

8.1.2.1 ForestStandSamplingwithPlotsforUsewiththeForestVegetationSimulatorModel

TheFVSmodelisafamilyofforestgrowthsimulationmodelvariants.FVSestimatesforestcarbonstocksbasedonsampledataparameters(e.g.,thediameter,height,species,andcanopydensityoftreesfromrepresentativesampleplotsestablishedacrosstheforest).Forsamplingpurposes,anumberofplotsareestablishedwithinaforestthatcanserveasarepresentativesampleoftheentireforest.Asthevarianceinforesttypesincreases,thenumberofplotswillincrease.Thesizeandnumberofplotsshouldbedeterminedbasedonthevarianceincarbonstocksbetweenplots.Completeforestestimateshavesamplinguncertainty,butlargerandmorenumerousplotshelptocreateamorerepresentativesampleandlowertheuncertaintyassociatedwithcarbonstockestimatesproducedbyFVS.Bothpermanentandtemporaryplotscanbeusedinsampling;however,alargeruncertaintyisassociatedwithtemporaryplots.Notethattheuseofpermanentplotsisrecommendedinthisreport.Thistypeofsamplingmethodologyiscommonlyreferredtoasaforestinventory.

Onceplotshavebeendefined,alltreesaboveacertaindiameteratbreastheight(DBH)(commonly2.5cm.or1in.)aremeasuredandrecorded.12DBH,height,andavarietyofothermeasurementsarerecorded,butDBHaloneissufficientforusewithFVS.FVSusesDBHandavailableinformationtodevelopcarbondensityestimatesfortheentireforest.Ifprovided,FVSmodelsgrowthestimatesforfutureyearsbasedonaveragegrowthratesandvariablessuchasthinning.13SelectingplotstorepresententireforestsmeansFVSoutputsaresubjecttothesamplingmethoduncertainty.However,uncertaintyintheFVSoutputscanbelowered(i.e.,morerepresentativecarbonstockestimatescanbeobtained)bycollectingmoredetailedtreedatabeyondDBHaswellasensuringthatsampleplotsarelargeandnumerousenoughtocoverthevarietyoftreegrowthsettingsinaforest.

TheforestinventorydatarecommendedforusewithFVSinthisreportisbasedonthesamplingmethodsdescribedintheU.S.DepartmentofAgriculture(USDA)MeasurementGuidelinesfortheSequestrationofCarbon(Pearsonetal.,2007).Theseguidelinesalsodescribethepotentialuncertaintyassociatedwithsuchsamplingmethods.Accordingtothesemeasurementguidelines,areasonableestimateofthenetchangeincarbonstockswouldbewithin10percentofthetruevalueofthemeanatthe95percentconfidencelevelthatcanbeachievedbyhavingasufficientlylargesamplesize(Pearsonetal.,2007).Differentcarbonpoolsinaforestcanhavedifferentvariances;however,focusingonthestandinglivetreecomponentforforestryactivitiescancapturemostofthetotalvariance.

itmaybefeasibletousetheseestimatedvariancesfairlydirectly.Inothercases,itmightbenecessarytoconsidersmallareaestimationtechniquestodescribeuncertaintyaslarge‐scalesurveydataaredownscaledtoentitylevels.Describingthistypeofuncertaintywouldrequirebuildingstatisticalmodelsforcomplexsurveydataand,hence,isnotaddressedinthisreportasadditionalresearchisrequired.11Forexample,inaddressinguncertaintyingrowthofforestbiomass,algorithmstoaccountfornonlineargrowthpatternswillbeneeded.12Undercommonstandexams,eventreeslessthan2.5cmor1incanbemeasured,butthesetreesareoftenconsideredtobepartoftheunderstory(e.g.,byFIA).13Othervariables,suchasfertilization,onlyapplytoafewFVSvariants.


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TheforestinventorydatathatisusedformodelingthechangesinforeststandsislikelythelargestsourceofparameteruncertaintyfortheinputsandassumptionsusedintheFVSmodel;inaddition,therearemanycomponentsintheFVSmodelwherevariationinmeasurementsforthisdataisnottakenintoaccount.Forexample,thepotentialerrordistributionfrommonthtomonthincarbonstorageassociatedwithleavesandfoliageisnotaccountedforintheFVSmodel.

InaresearchpaperonobtainingsamplinguncertaintyinFVS,expertsnotethechallengeofreportingdistributionsofmodelinputsthatincludesamplinguncertaintyinFVSprojections(GreggandHummel,2002).Theystateintheirintroductionthat,“ithasn’tbeenpossibletocomputetheeffectsofsamplinguncertaintybecauseclassicalstatisticalmethodsarenotavailabletomakeinferencesaboutFVSprojections.Avarianceestimatorisnotavailablefortheresultsofsimulation.”

AsprovidedinAppendix8‐A,theFVSmodelprovidesquantitativeinformationontherangeandvariabilityofsamplingdata.ThisFVSapplication,calledFVSBoot,uses“bootstraps”todeterminefluctuationinestimateoutcomes(GreggandHummel,2002)(i.e.,allowsmodelertoempiricallyapproximatethesamplingdistributionofanystatistic/FVSattributeforwhichthemodelerwantstomakeinferences).

Bootstrapsampling14usingtheFVSBootprogramcanbeusedtoempiricallyapproximatethesamplingdistributionofstatisticsforwhichinferencesaretobedrawn.Newsamplesofstandconditionscanbegeneratedbysamplingtheoriginalplotswithreplacementtocreateabootstrapsample.Abootstrapmeancanbegeneratedfromthebootstrapsample.RepeatingthisprocessmultipletimeswillgenerateaMonteCarloapproximationofthedistributionofbootstrapmeans.Thestandarddeviationofthisapproximationwillbeanestimationofthetruestandarddeviationfortheentirepopulation.FVSBootdoesnotcoverallpotentialsourcesofvariationbutitcangiveameasureofimportantcomponentsofuncertaintyinFVSmodelprojections.WhiletheFVSBootprogramcanbeusedtodeterminethesamplinguncertaintyinFVS,itwasnotdevelopedoriginallytoproduceanoveralluncertaintyestimateforFVSoutputs.However,FVSBoothasbeenusedforsensitivityanalysisofsomeFVSoutputs(Hummeletal.,2013).AtoolwouldbeneededtofacilitatedevelopinganestimateoftheuncertaintybasedonacombinationoftheresultsfromtheFVSBootprogramandunderlyingequationsusedintheFVSmodel.

8.1.2.2 UrbanTreePopulationSamplingforUsewiththeFieldDataMethodUsingthei‐TreeEcoModel

Thei‐TreeEcomodelestimatesurbanforestcarbonstocksandgrossandnetannualcarbonsequestrationbasedonsampledataparameters(e.g.,thetreespecies,diameter,height,dieback,andcrownlightexposure)withacalculatedlevelofprecision.Asdesiredbythelandowner,alltreescanbemeasuredorarandomdistributionoffieldplotscanbemeasuredtoquantifytheurbantreepopulation.Largerandmorenumerousplotshelptocreateamorerepresentativesampleandlowertheuncertaintyassociatedwiththesampling.Thei‐TreeEcomodelusesthesampledataparametersandforest‐derivedallometricequationstoestimatecarbonvalues.Themodelalsoestimatesthestandarderroroftheestimatedcarbonvalue,whichisbasedonthesamplinguncertainty,ratherthantheerrorofestimationfromapplyingtheallometricequations.Estimationerrorisunknownandlikelylargerthanthereportedsamplingerror.AMonteCarlo

14Bootstrappingistheprocessofestimatingvariancebyrepeatedrandomsamplingwithreplacementofanexistingdataset.Forexample,todeterminetheprobabilitydistributionofaverageDBHforasampleof100trees,resamplesofthedatasetof100treescanbetakentoapproximatethevariance.


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analysistoolcouldusethestandarderroroftheestimatedcarbonvaluetoevaluatetheuncertaintyassociatedwithanentity’stotalnetGHGemissions.

8.1.2.3 SamplingandDAYCENTforEstimatingBiomassCarboninGrazingLandandAgroforestrySystems

SamplinguncertaintywillexistwhenestimatingbiomasscarbonusingthemethodprovidedinChapter3.Forexample,peakforageestimatesforgrazinglandscanbesampledusingthebiomassclippingmethod.15Thismethodisdestructivewiththeremovalofforagesamplesfromthefield.Thismethodhasbeenshowntoproduceestimateswithlowuncertainty(Lauenrothetal.,2006;Byrneetal.,2011).Non‐destructivemethodscanalsobeusedincludingthecomparativeyieldmethodforrangelands,16ortherobelpolemethodonrangelandsorpastures(Harmoneyetal.,1997;Vermeireetal.,2002).Thebiomassclippingmethodandcomparativeyieldmethodshavelessuncertaintythantherobelpoleorvisualobstructionmethod.Destructivesamplingmethods,however,aremoretimeandlaborintensive.UncertaintyassociatedwiththerobelpolemethodwasassessedintheBlackHillsofSouthDakotainastudybyUreskandBenzon(2007).Theauthorscompareddestructive(clipping)methodsforestimatingbiomassandtherobelpolemethod.Theyfoundtherewasalinearrelationshipbetweenthetwomethodsandthestandarderroroftherobelpoleestimateforasinglemeanwas373kgha‐1.Thestudyfurtherrecommendsthataminimumofthreetransectsbesampledformonitoringareaslessthan259hatobewithin20percentofthemeanand80percentconfident(UreskandBenzon,2007).InasimilarstudybyVermeireetal.,(2002),asinglevisualobstructionmodel(i.e.,robelpolemethod)effectivelyestimatedherbagestandingcropacrossrangetypesandproducedacoefficientofdeterminationof0.93.Anysamplingthatisdone,whetherdestructiveornon‐destructive,shouldoccuratlocationsthatarerepresentativeofthelandparcel.Ifsamplingtheforageisnotfeasible,defaultforageproductionvaluesareprovidedbytheUSDANaturalResourcesConservationService(USDA‐NRCS)inEcologicalSiteDescriptions.17Thesamplinguncertaintywilldependonthemethodusedtocollectthesampleandshouldbeprovidedbythefarmerorlandowner.

8.1.3 LargeDatasetUncertainty

InformationfromseverallargedatasetswillbeusedwiththeGHGquantificationmethodstoestimateemissionsfromtheanimalagriculture,croplandandgrazingland,andforestrysectors.Largedatasetscanbeconsideredanygroupingofdatapointsthatcoverawidetime‐seriesand/orlevelofreportedvariables.Thesedatasetsincludemultipledatalayers,GISdata,databases,andothersuchreportingcatalogues.

ThelargedatasetstobeusedincludetheSmithetal.(2006,alsoknownasGTR‐NE‐343),ForestInventoryandAnalysis,FVS,Daymet,andthedatasetfromthei‐Treemodel.Thesedatasetsprovidevaluesforestimationequationandmodelinputs.Theseinputsincluderegion‐andspecies‐specifictreegrowthrates,landandtreecover,inferredandobservedmeteorologicaldata,soiltypeanddistribution,ammoniacontent,andhistoricalclimatedatafortheNorthAmericancontinent.Thesedatawillbeusedtoinformthecarbondensitiesofsmallforestholdings,coverageofurbantrees,directandindirectN2Oemissions,soilpH,organicmattervalues,ambientairammoniaconcentrations,anddailyairtemperatureandvelocity.

15SeeSection15,“StandingBiomass”(USDANRCS,2011b).16SeeSection13,“DryWeightRank”(USDANRCS,2011b).17SeeUSDANRCS(2011a).


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Thissectionincludesadescriptionofthelargedatasetsusedforestimatingforestryandagroforestrysectorcarbonstocksandstockchanges,GHGemissionsandremovalsfromwetlands,soilcarbonstocks,andammoniaemissions.Thesectionalsoprovidesuncertaintyinformationobtainedfromthedatasetdevelopersandanapproachtoincorporatinguncertaintyassociatedwiththesedatasetsintotheoveralluncertaintyanalysis.

Manyofthelargedatasetsarecomplexandcovermultipleparameters.Insomeinstancesuncertaintyinformationisavailableforsomevariablesbutnotforothers,makingitdifficulttoassesstheuncertaintyoftheentiredataset.Table8‐1belowsummarizestheuncertaintydocumentationavailableforeachlargedatasetused.Themajorityofdatasetsdidnothavepublicallyavailabledocumentationcharacterizingtheassociateduncertainty.

Table8‐1:AvailabilityofUncertaintyInformationforLargeDatasets

DatasetName DatasetAbbreviation

AvailabilityofUncertaintyDocumentation

MethodsforCalculatingForestEcosystemandHarvestedCarbonwithStandardEstimatesforForestTypesoftheUnitedStates(Smithetal.,2006)

GTR‐NE‐343

Nopublishedquantificationofuncertainty.Standarderrorsavailableforcarbondensityforliveandstandingdeadtreesatthe50thand99thpercentileofvolume.

NationalLandCoverDatabase NLCDNopublishedquantitativeuncertaintyinformationfound.Authorsonlyprovideinformationoncontributingfactors.

DailySurfaceWeatherandClimatologicalSummaries

Daymet Nopublishedquantitativeuncertaintyinformationfound.

ContiguousUnitedStatesSoilGeographicDatabase

CONUSSTATSGO

Nopublishedquantitativeuncertaintyinformationfound.

SoilSurveyGeographicDatabase SSURGO Nopublishedquantitativeuncertaintyinformationfound.

AmmoniaMonitoringNetwork AMoNNopublishedquantitativeuncertaintyinformationfound.

Parameter‐ElevationRegressionsonIndependentSlopesModel PRISM


NorthAmericanRegionalReanalysis NARRRegional‐scaleaccuracyandbiasreportedbyMesinger(2006).

NaturalResourcesInventory NRI

Dataarecollectedusingatwo‐stagesamplingprocess.StatisticallyvaliduncertaintiesinmanagementpracticesarecomputableatMajorLandResourceAreasorStatelevel.

NationalAgriculturalStatisticsServiceAgriculturalCensus

NASS‐agriculturalcensus


NationalAgriculturalStatisticsService–CroplandDataLayer

NASS–CroplandDataLayer

NASSprovides accuracyinformationanderrormatrices(totalaccuracy,errorsofomissionandco‐mission),butnotonanannualbasisforcropsandStates.

EconomicResearchServiceCroppingPracticesSurvey

ERS‐CPS Nopublishedquantitativeuncertaintyinformationfound.

EconomicResourceServiceAgriculturalResourceManagementSurvey

ERS‐ARMSNopublishedquantitativeuncertaintyinformationfound.


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DatasetNameDataset

AbbreviationAvailabilityofUncertaintyDocumentation

NationalClimaticDataCenteroftheNationalOceanicandAtmosphericAdministration

NCDC(NOAA)

NCDCprovidesvaluesthatdescribetherangeoftheuncertainty,orsimply"range,"ofeachmonth’s,season's,oryear'sglobaltemperatureanomaly.Thesevaluesareprovidedasplus/minusvalues.

Modern‐EraRetrospectiveAnalysisforResearchandApplications

MERRA(NASA) Nopublishedquantitativeuncertaintyinformationfound.

NationalCentersforEnvironmentalPrediction NCEP(NOAA)


NationalAtmosphericDepositionProgram NADP

Regional‐scaleuncertaintywasassessedinDennisetal.(2011).

8.1.3.1 GTR‐NE‐343CarbonDensityValues

Estimatesofcarbonstocksandstockchangesfromthereport,“MethodsforCalculatingForestEcosystemandHarvestedCarbonwithStandardEstimatesforForestTypesoftheUnitedStates”(Smithetal.,2006)(USDAForestService,GeneralTechnicalReportNE‐343),arebasedonregionalaveragesandreflectthecurrentbestavailabledata.However,accordingtoGTR‐NE‐343,“quantitativeexpressionsofuncertaintyarenotavailableformostdatasummaries,coefficients,ormodelresultspresentedinthe[GTR‐NE‐343]tables.”GTR‐NE‐343lookuptablesincludesomeinformationabouttheconfidenceintervalsforliveandstandingdeadtreecarbondensitiesattwodifferentaveragevolumes(seeTable20ofGTR‐NE‐343),butitdoesnotprescribeamethodforapplyingthesesummaryuncertaintystatisticstostandlevelcarbonstockestimates.

Theuncertaintyassociatedwiththesereportedregionalaveragecarbonstockvaluesislikelyhigherasthesevaluesareappliedtosmaller‐scaleprojectsratherthanregions.Samplinguncertaintyassociatedwiththeregionalaverages,thatarebasedondatasummariesormodels,caninfluenceestimatesforspecificprojects.Theseprojectsaregenerallysmallsubsetsofaregion.Yet,variabilitywithinaregionforvaluesinadatasetwilllikelyhaveamuchgreaterinfluenceonuncertaintythantheactualsamplinguncertaintyassociatedwithcollectingregionalvalues(Smithetal.,2006).

OncetheuserfindsthetableinGTR‐NE‐343thatdescribestheforest’sspeciesmixandregion,theusercanusetheage(orvolume)oftheforeststand(whichisalsocollectedwithahighlevelofuncertainty)tofindoutthemetrictonsofcarbonperacredensityvalueforlivetreecarbon,downdeadwood,organicsoilcarbon,andothercategories.Theuncertaintyinformationisgivenas95percentconfidenceintervalsforthecarbondensityofliveandstandingdeadtrees,attwodifferentgrowingstockvolumes—the50thpercentileandthe99thpercentile.Theseconfidenceintervalsaregivenforeachforesttypeandregion.Tousethisinformationinanuncertaintyanalysisrequiresextrapolationtoothergrowingstockvolumes,whichrequiresmodelingtherelationshipbetweengrowingstockvolumeandvariationincarbondensity.Whilethesetablesaresimpleandeasytouse,theuncertaintyofresultsobtainedbyusingrepresentativeaveragevaluesmaybehighrelativetoothertechniquesthatusesite‐orproject‐specificdata.AdditionalresearchisneededtoincludethisuncertaintyintoaMonteCarloanalysisframework.

8.1.3.2 NationalLandCoverDatabaseMap

TheNationalLandCoverDatabase(NLCD)MapistheproductoftheMulti‐ResolutionLandCharacterizationpartnership,aconsortiumofFederalagenciesincludingtheU.S.GeologicalSurvey,EnvironmentalProtectionAgency(EPA),NationalOceanicandAtmosphericAdministration(NOAA),andtheUSDAForestServicethatarecontinuouslydevelopingdigitallandcoverdata.This


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associationhassuccessfullyprovidedlandcoverdataforthelower48States,Hawaii,Alaska,andPuertoRicofromdecadalLandsatsatelliteimageryandotherassociatedimagingdatasets.ThedatabaseprovidesLandsat‐based,30‐meterresolution,landcoveragecharacteristicsincludingthematicclass(e.g.,urban,agriculture,andforest),percentimpervioussurface,andpercenttreecanopycover.

Regardinguncertainty,theNLCDmapdocumentationindicates,“Unfortunately,thereisnoreadilyavailablereferencedatasetwithwhichtocomparetheinventorytogenerateaccuracystatistics.Referencedatahavetobespecificallygeneratedthroughmanualinterpretationofremotesensingdataforasampleoflocations,ashasbeendoneforaccuracyassessmentoflandcovermaps.Inlieuofsuchanapproach,whichisoutsidethescopeofthisstudy,thebestthatcanbedonecurrentlytodescribetheuncertaintyoftheinventorydataistoidentifytheknownconditionsthatcontributetoit”(NationalLandCoverDatabase,2008).

8.1.3.3 ContinentalUnitedStatesSoilGeographicDatabase

TheContinentalUnitedStatesSoilGeographicDatabase(CONUSSTATSGO)isadigitalgeneralsoilassociationmapthathasbeendevelopedbytheNationalCooperativeSoilSurveyanddistributedbytheUSDANRCS.Itconsistsofbroadbasedinventoryofsoilsandnon‐soilareasthatoccurinarepeatablepatternonthelandscapeandthatcanbecartographicallyshownatscaleandmapped.Noinformationisreadilyavailableontheuncertaintyassociatedwiththisdataset.

8.1.3.4 SoilSurveyGeographicDatabase

TheSoilSurveyGeographic(SSURGO)databasehasbeendevelopedbytheNationalGeospatialManagementCenter,formerlytheNationalCartographyandGeospatialCenter.TheSSURGOdatabasedepictsinformationaboutthekindsofsoilsanddistributionofsoilsonthelandscape.Thisdatasetisadigitalsoilsurveyandgenerallyisthemostdetailedlevelofsoilgeographicdataavailable.Uncertaintyinformationwasnotreadilyavailableforthisdatabasebeyondthedisclaimerthattheaccuracyofdatapoints‘metnationalmapaccuracystandards.’

8.1.3.5 AmmoniaMonitoringNetwork

TheAmmoniaMonitoringNetworkispartoftheNationalAtmosphericDepositionProgram(NADP),andwasoriginallyinitiatedbytheU.S.StateAgriculturalExperimentStations.Thedatasetprovidesconsistent,longtermrecordofammoniagasconcentrationsintheUnitedStates,drawingfrom50monitoringsitesacross37statesintotal.Uncertaintywasnotdirectlyaddressedinthedatasetmaterials,asidefromthedisclaimerthattheNADP’sCentralAnalyticalLaboratory(CAL)analyzes,qualityassures,andprovidestheanalyticaldatatotheNADP(2011).

8.1.3.6 Parameter‐elevationRegressionsonIndependentSlopesModel

TheParameter‐elevationRegressionsonIndependentSlopesModel(PRISM)isaclimatemappingsystemdevelopedbythePRISMClimateGroup.PRISMisaknowledge‐basedsystemthatusespointmeasurementsofprecipitation,temperature,andotherclimaticfactorstoproducecontinuous,digitalgridestimatesofmonthly,yearly,andevent‐basedclimaticparameters.Noinformationisreadilyavailableontheuncertaintyassociatedwiththisdataset.

8.1.3.7 DaymetWeatherDataset

DaymetisaweathermodeldevelopedbyOakRidgeNationalLaboratorythatprovidesinterpolationsextractedfromdailymeteorologicalobservationsontoagriddeddatasetwherenosuchobservationsarepresent.Daymetprovidesoutputparametersincludingtemperature,precipitation,humidity,solarradiation,andsnowwaterequivalent.TheDaymetdatasetisbasedon


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thespatialconvolutionofatruncatedGaussianweightingfilterwiththesetofstationlocations.Sensitivitytothetypicalheterogeneousdistributionofstationsincomplexterrainisaccomplishedwithaniterativestationdensityalgorithm.TheweatherdatasetsareproducedasoutputsfromtheDaymetmodelrun.ThisdatasetisusedasaninputforestimatingGHGemissionsfromcroplandsandgrazinglands,andammoniaemissionsfrommanuremanagement.Noinformationisreadilyavailableonuncertaintyassociatedwiththisdataset.

8.1.3.8 NorthAmericanRegionalReanalysisWeatherDataset

TheDAYCENTmodelsimulationsusetheNorthAmericanRegionalReanalysis(NARR)dataproductfordailytemperatureandprecipitation.TheNARRdatasetwaschosenbecauseitprovidesfull,gap‐filledcoveragefortheconterminousU.S.andismaintainedandupdatedregularly.AsdescribedbyMesinger(2006),“TheNationalCentersforEnvironmentalPrediction(NCEP)NorthAmericanRegionalReanalysis(NARR)isalong‐term,dynamicallyconsistent,high‐resolution,high‐frequency,atmosphericandlandsurfacehydrologydatasetfortheNorthAmericandomain.Itcoversthe25‐yearperiod1979–2003,andisbeingcontinuedinnear‐realtimeastheRegionalClimateDataAssimilationSystem,R‐CDAS.EssentialcomponentsofthesystemusedtogenerateNARRarethelateralboundariesfromandthedatausedfortheNCEP/DOEGlobalReanalysis,theNCEPEtaModelanditsDataAssimilationSystem,arecentversionoftheNOAAlandsurfacemodel,andtheuseofnumerousdatasetsadditionaltoorimprovedcomparedtothoseoftheGlobalReanalyses.Inparticular,NARRhassuccessfullyassimilatedhighqualityanddetailedprecipitationobservationsintotheatmosphericanalysis.Consequently,theforcingtothelandsurfacemodelcomponentofthesystemismoreaccuratethaninpreviousreanalyses,sothatNARRprovidesamuchimprovedanalysisoflandhydrologyandland‐atmosphereinteraction.”Noquantitativeinformationisreadilyavailableonuncertaintyassociatedwiththisdataset.

8.1.3.9 DAYCENTLandManagementDataSets

Dataonpastlanduseandmanagement(priortotheyear2000)arethebasisforrepresentativecroplandmanagementsystems,selectedbytheentitylandowner,thatareusedtoinitialize(“spinup”)theDAYCENTmodelforcomputingsoilorganiccarbonstockchanges.TheattributesofthemanagementsystemsarebasedprimarilyonthreelargedatasetsfortheUS:theNationalResourcesInventory(NRI),theNationalAgriculturalStatisticsService(NASS)croplandsurveys,andUSDAEconomicResearchServiceCroppingPracticesSurvey.TheuseofrepresentativecropmanagementsystemsfortheDAYCENTinitializationprocessintroducessomeuncertaintywhenappliedtoaspecificfarmorranchentity(whichhasauniquemanagementhistorythatmaybedifferentfromtheregionally‐basedrepresentativemanagementhistoriesspecifiedbyMajorLandResourceAreas.However,themajoruncertaintyforthemodelinitializationisdrivenbythetimingofmajorland‐coverchange(e.g.,conversionofgrasslandtocropland)whichcanbeuser‐specifiedfortheparticularentityandlandparcel.

NationalResourcesInventory.TheNRIisaninventoryoflandcoveranduse,soilerosion,primefarmland,wetlands,andothernaturalresourcecharacteristics.NRIwasdesignedasatooltoassessconditionsandtrendsforsoil,water,andrelatednaturalresourcesprimarilyonnon‐FederallandsoftheUnitedStates(NusserandGoebel,1997).TheNRIisastratifiedtwo‐stageareasampleofoverseveralhundredthousandpointsdistributedacrosstheUnitedStatesandCaribbean.Eachpointinthesurveyisassignedanareaweight(i.e.,expansionfactor)basedonotherknownareasandland‐useinformationsothateachpointhasastatisticallyassignedareathatitrepresents(NusserandGoebel,1997).Itshouldbenotedthatthereissomeuncertaintyassociatedwithscalingthepointdatatoaregionorthecountryusingtheexpansionfactors.Ingeneral,thoseuncertaintiesdeclineatlargerscales,suchasStatescomparedtosmallercountyunits,becauseofalargersamplesize.


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NationalAgriculturalStatisticsServiceCropSurveys.DatafromtheNASScountyagriculturalproductionsurveyswereusedtoconstructrepresentativecroprotationsfortheperiodpriorto(i.e.,before1979)thedatarecordintheNRI.NASSconductsthousandsofsurveyseachyearcoveringmanyfacetsofU.S.agriculture.Estimatesincludecropacreage,yield,production,irrigation,andlivestocknumbers.State‐levelcropestimatesareavailablefromasearlyas1866dependingontheStateandvariableofinterest.Somecounty‐levelcropdataisavailablefromasearlyas1915,withmostcropsavailableformostStatesbyabout1960.Dataaggregatedtothecountylevelaresubjecttoahighlevelofqualitycontrol,includingdatascreeningforoutliers,doublecheckingwithprimarydatacollectorsandcomparisonswithotheraggregatedatasetssuchasfromtheUSDAFarmServicesAgency.

USDAEconomicResearchService(ERS)CroppingPracticesSurvey.AncillarydataonhistoricalmanagementpracticesusedintheDAYCENTmodelinitializationincludenitrogenfertilizerrates(USDAERS,1997;2011).Meanfertilizerratessince1990wereestimatedforallmajorcrops,summarizedbyERSattheState‐level.IfaStatewasnotsurveyedforaparticularcroporiftherewerenotenoughdatatoproduceaStatelevelestimate,thendatawereaggregatedtoUSDAFarmProductionRegionsinordertoestimateameanandstandarddeviationforfertilizationrates(FarmProductionRegionsaregroupsofStateswithsimilaragriculturalcommodities).Crop‐specificregionalfertilizerratespriorto1990werebasedlargelyonextrapolationorinterpolationoffertilizerratesfromtheyearswithavailabledata.Forcropsinsomeagriculturalregions,littleornodatawereavailable,and,therefore,ageographicregionalmeanwasusedtosimulatenitrogenfertilizationrates(e.g.,nodataareavailablefortheStateofAlabamaduringthe1970sand1980sforcornfertilizationrates;therefore,meanvaluesfromthesoutheasternUnitedStateswereusedtosimulatefertilizationtocornfields).Nouncertaintydataareavailableforthisdataset.

8.1.3.10 DNDCInputDatasets

TheDeNitrification‐DeComposition(DNDC)modelisproposedtoestimateGHGemissionsandremovalsfromwetlandssystems.DNDCisasoilbiochemistrymodelthatsimulatesthermodynamicandreactionkineticprocessesofcarbon,nitrogen,andwaterdrivenbytheplantandmicrobialactivitiesinecosystems(OlanderandHaugen‐Kozyra,2011).TheDNDCmodelreliesonspecificinputdatasetsthatcanbecategorizedintofivesources:(1)cropland/land‐usedata;(2)cropmanagementdata;(3)soilsdata;(4)weatherdata;and(5)atmosphericdepositiondata(Salasetal.,2012).Theseprimarysourcesofdataanduncertaintyassociatedwiththedatasetareprovidedbelow.

NationalAgriculturalStatisticsServiceCroplandDataLayerdataset.TheDNDCmodelusestheNASSCroplandDataLayerasasourceofcropland/land‐usedata.TheNASSCroplandDataLayerisanonlinegeospatialexploringtoolgeneratedfromsatelliteimageobservationsata30meterresolution.NASSprovidesaccuracyinformationanderrormatrices(totalaccuracy,errorsofomissionandco‐mission),butnotonanannualbasisforcropsandStates.NASSAgriculturalCensus.Thecensusisavailableeveryfiveyears,andusedatthecountyscale.ItprovidesinformationonU.S.farmsandranchesandistheonlysourceofuniform,comprehensiveagriculturaldataatthecountylevel.Farmersandranchersareaskedtorespondtothecensusbymailoronline.Informationincludingproductionexpenses,marketvalueofproducts,andoperationcharacteristicsareafewofthecategoriesofdata.Uncertaintyisnotassessedforthesedata.RemoteSensing.DNDCusesremotesensingtobuildregionaldatabasesoncroplandonaprojectandasneededbasis.TherangeofsensorsusedincludesRapidEye,Landsat,MODIS,andSAR(PALSAR,Radarsat,ENVISAT,etc.).Remotesensingisusedforestimatinghydroperiods(i.e.,wherethewatertableisatanygiventime).AsDNDCdoesnothaveagroundwatermodelingcomponent,


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remotesensingisusedtoidentifywhenwetlandsareflooded.Uncertaintyisnotassessedforthesedata.USDA,ERSAgriculturalResourceManagementSurvey(ARMS).ARMSdataareusedtopopulatethecropmanagementcomponentoftheDNDCmodule.USDAERSARMSprovidedataonthefinancialcondition,productionpractices,andresourceuseoffarmersatthefieldlevelwithintheUnitedStates.ARMSdataarereleasedand/orrevisedtwiceayear.Uncertaintyisnotassessedforthesedata.CONUSSTATSGO(Seedescriptionabove).Thesedataareusedtoassociatesoiltypesanduncertaintyofsoilsdatawithinthemodel.SSURGO(Seedescriptionabove).SSURGOdataareretrievedbyDNDCviaanautomatedretrievalscriptandextractfourkeysoilattributes:claycontent(texture),bulkdensity,organicmatter(soilorganiccarbon),andpH.NOAANationalClimaticDataCenter.DNDCusesstationdatafromtheNOAANationalClimaticDataCenter(NCDC)toinputtemperature,dewpoint,relativehumidity,precipitation,windspeedanddirection,visibility,andatmosphericpressure.Dataareprovidedatthesubhourly,hourly,daily,monthly,annual,andmultiyeartimescale.NCDCprovidesvaluesthatdescribetherangeoftheuncertainty,orsimply"range,"ofeachmonth,season,oryearglobaltemperatureanomaly.Thesevaluescanbeusedasplus/minusvalueswithinanoverallMonteCarloframework;however,atoolisneededtoutilizethisinformation.Daymet(Seedescriptionabove).TheseweatherdataareusedbyDNDCandhavebeenavailableformuchofNorthAmericafrom1980to2012.Uncertaintyinformationisnotavailableforthisdataset.NationalAeronauticsandSpaceAdministrationModern‐EraRetrospectiveAnalysisforResearchandApplications(MERRA).TheDNDCmodelreliesonMERRAsatellitedataasinputforthehydrologicalcycle.MERRAprovidesglobaldataonvariousaspectsofmoisturedistributionandvariability.Nearly30yearsofdataareavailableandhasundergoneanonlinebiascorrectionforsatelliteradianceobservations.Thiswasdonetocalibrateobservationsfromdifferentsatellites.UncertaintydataarenotavailableforMERRAoutput.NationalOceanicandAtmosphericAdministrationNationalCenterforEnvironmentalPrediction(NCEP).DNDCinputsNCEPnationalweather,water,andclimatedataintotheNCEPmodel.NCEPcreatesclimate,water,ocean,space,andenvironmentalhazardoutputs.UncertaintydataarenotavailableforNCEPoutput.NationalAtmosphericDepositionProgramNationalTrendsNetwork(NTN)Stations.DNDCrequirestotalnitrogendepositionandestimatesofaverageconcentration.DNDCreliesontheNADPNTNstationstoinputtotalnitrogendeposition(NO3andNH4)intothemodel.NADPNTNstationscollectprecipitationandchemistrysamplesawayfromurbanareaandpointsourcesofpollution.Thestation’sCentralAnalyticalLaboratoryreviewsdataforcompletenessandaccuracyandflagssamplesthatweremishandledorcompromised.SampledataarefurtherreviewedbytheNADPprogramofficetodoafinalchecktoresolvediscrepancies.Oncedataaremadeavailableonline,DNDCcalculatesmeannitrogendepositionforthesimulationtimeperiodandincorporatesthedataintotheprojectdatabase.NADPNTNstationdatadonothaveassociateduncertaintydataavailable,howeverregionaluncertaintywasanalyzedinapresentationbyDennisetal.(2011).


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8.1.3.11 ApproachforIncorporatingLargeDatasetUncertainty

AmongthelargedatasetstobeusedfortheGHGquantificationmethods,onlyGTR‐NE‐343hassomequantifieduncertaintyinformationforuseinaMonteCarloassessmentofnetGHGemissions.Becauseconfidenceintervalsforonlytwostockvolumesareavailable,onlyalinearrelationshipcanbemodeledwithGTR‐NE‐343information,andnodeparturesfromlinearitycanbeassessed.Furtheranalysisofcarbondensityatothergrowingstockvolumesrequirescomputationofadditionalconfidenceintervals.

Giventhelackofuncertaintyinformationformostoftherelevantlargedatasets,estimatingthissourceofuncertaintyisnotfeasible.Instead,relianceofthemethodsonthelargedatasetsisexplicitlyacknowledgedandreadilyavailableinformationonuncertaintyissummarizedasprovidedabove.

Somelarge“wall‐to‐wall”datasetsareformedviainterpolationofexistingdatafromafixedsetofmeasurementlocations.Forsuchdatasets,apotentialnear‐termnextstepmightbetoincorporateuncertaintybyimputingmeasurementsfromrandomly‐selectedmeasurementlocations.Thisrandomselectioncoulduseprobabilitiesinverselyproportionaltothedistancebetweenthemeasurementlocationsandtheentity.Ifmostlocationsarefarfromtheentity,thentheimputationsareincreasinglyuncertain.

Inthelongerterm,bothnewresearchandsynthesisofexistingresearchwillberequiredtoquantifylargedatasetuncertainty.Methodsfromgeostatistics,forexample,mightbeusedtodescribeanuncertainlargedatasetobtainedbyinterpolation.

8.1.4 ModelUncertainty

Inthecaseoftheexternalmodels,itishardtoappropriatelyaccountforapproximationerrorandoftenonlyonemodelexiststorepresentorestimateemissions(orremovals)fromaspecificactivityorprocess.Sincecomparablemodelsdonotexist,itisalmostimpossibletoestimatetheuncertaintyassociatedwithusingoneparticularmodelversusanother.Whilethisreportspecifiestheuseofseveralexternalmodels—DAYCENT,DNDC,FVS,i‐TreeCanopy,i‐TreeEco,FirstOrderFireEffectsModel(FOFEM)—giventheaboveconsiderations,limitedpublisheddatawasfoundonexternalmodeluncertaintyinherentwiththesemodels.

Thissectionincludesadescriptionoftheexternalmodelsusedforestimatingcarbonstocksandstockchangesfromthecroplandsandgrazinglands,wetlands,andforestrysectors,uncertaintyinformationobtainedfromthemodeldevelopers.Thesemodelshelpprovideaquantitativeandgeographicalviewintotheemissionsassociatedwithavarietyoffactorsfromagriculturalandforestrysystems.Forexample,giveninputssuchasarea,treediameter,treeheight,species,soiltype,andgeography,thesuiteofforestrymodelscanprovideemissionestimatesfromfiredisturbances,approximatechangesinforestcarbonstocks,orprovideurbanforestcarbonstockdata.Table8‐2belowsummarizestheuncertaintyinformationobtainedfromthemodeldevelopersforeachofthemodelsusedtoestimatenetGHGemissions.Giventhelackofquantitativeinformationonmodeluncertainty,thiscomponentofuncertaintywillnotbepartoftheMonteCarlouncertaintyassessment.

Table8‐2:UncertaintyInformationforProcess‐basedModels

ModelAvailabilityofUncertainty

DocumentationOccurrenceofUncertaintyBiases

DAYCENT Ogleetal.,2010BiasesbypracticearequantifiedinOgleetal.(2010).

DNDCa Inputuncertainty:Lietal.(2002) andZhangetal.(2009).Therehavebeen

AMonteCarloapproachorMostSensitiveFactoranalysiscanberunoncertaininputparameters


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ModelAvailabilityofUncertainty

DocumentationOccurrenceofUncertaintyBiases

nopapersfocusedonquantificationofDNDCmodelstructuraluncertainty.

(i.e.,soilmeasurements)toassessthevariabilityoftheparameters(includesexcerptsfromC_AGGwhitepaperbySalasetal.,2012).

ForestVegetationSimulator

Nopublishedquantificationofmodeluncertaintywasfound.

Existsbutnotquantifiableaccordingtoexperts.

i‐TreeCanopy(AerialDataMethod)

Nopublishedquantificationofmodeluncertaintyfound.

Modelbiasislikelylow,accordingtomodeldeveloper.

i‐TreeEco(FieldDataMethod)


Valuesarestandardized,biasisminimized.Unknownbiasfornationaldensityestimates.

FirstOrderFireEffectsModel


Regionalbiases(NorthRockyMountains,PacificNorthwestregions).

aDNDCdoesnotprovideuncertaintyparameterizationofoutputsatthesitelevel,however,theregionalmodelprovidesanoptionforassessinguncertaintyduetoinputuncertainty.

8.1.4.1 DAYCENTModel

TheDAYCENTmodelhasinherentuncertaintyassociatedwithpredictingsoilorganiccarbon(SOC)stockchanges(Ogleetal.,2010;U.S.EPA,2013).Theuncertaintyisassociatedwithimperfectsimulationoftheplantandsoilprocessesassociatedwiththealgorithmsandparameters.Toaddressthisuncertainty,thesimulatedmodelpredictionsofSOCstocksneedtobecomparedtomeasurements.Thecomparisonleveragesthescalabilityoftheprocess‐basedmodeltothewiderangeofconditionsthatexistinagriculturallands,whilehavinganunderlyingmeasurementbasistosupportthereporting(Conantetal.,2011).

ThedifferencesbetweenmeasurementsandsimulatedSOCstocksandstockchangeshavebeenanalyzedusinganempiricallybasedapproachinwhichastatisticalmodelwasdevelopedthatquantifiestheaccuracyandprecisioninthesimulatedpredictions(Ogleetal.,2007).Thelinearmixed‐effectmodelingapproachwasusedforthisanalysis,andvariousenvironmentalconditions(e.g.,climateandsoilcharacteristics)andmanagementpracticeswereevaluatedtodetermineifthemodelismoreaccurateorpreciseforparticularconditionsormanagementsystems.TheapproachreliedonmeasurementsofSOCstocksfromanetworkofsitesacrosstheU.S.agriculturallands.AnetworkiscurrentlybeingexpandedbytheUSDANRCSthatisexpectedtoprovideadditionalmeasurementssupportingtheentity‐scalemethodsforestimatingSOCstockchanges.ThisuncertaintyanalysiswillbeupdatedasnewmeasurementsbecomeavailablefromthenetworkandwillbeincorporatedintoaMonteCarloassessment.

8.1.4.2 DNDCModel

Structuraluncertaintyisrelatedtotheinherentuncertaintyofamodelthatremainsevenifnoneoftheinputdatahadanyvariability.Estimatingmodelstructuraluncertaintyrequirestheuseofindependentvalidationdata(i.e.,fieldmeasurementdatathatwerenotusedtodevelopthemodelalgorithms).Thisapproachrequiresnotonlyaccesstosufficientindependentfielddata,butalsothatthedataincludealltheinputdatathatDNDCrequires.AnumberofvalidationtestswithindependentfielddatahavebeenpublishedalthoughsummarystudiesarecurrentlynotavailabletoquantifyDNDCstructuraluncertainty.


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8.1.4.3 ForestVegetationSimulator

Aspreviouslydescribed,asourceofuncertaintyfortheFVSmodelissamplinguncertaintyassociatedwiththetreelist(themainuserinput).Theadditionaluncertaintyassociatedwiththemodeluncertaintyisdifficulttoquantify.

IntheFVSmodel,diametergrowthistheonlyvariablethatisconsideredstochastic.Forthediametergrowthmodule,arandomseedisusedforprojectionsofchangesinforeststandsratherthanusingthemeandiametervaluetoavoidunderestimatinggrowth.Thisprocessincreaseserrorpropagationbecausetheresultsofthediametergrowthmoduleareusedtomakefurtherestimatesinthemodel,e.g.,usinggrowthandyieldequations(i.e.,Jenkinsequations).However,thestochasticityofdiametergrowthisnotthemaindriverofmodeluncertainty.UncertaintyassociatedwiththeFVSmodeliscomplexbecauseitisderivedfrom20differentregionallyspecificmodelvariantsthatweredevelopedindependently.Eachmodelrunoranalysishastobecalibratedtoaccountforlocaltreevarietyandgrowthrates,introducinganotherlevelofcomplexity(VanDyck,2012).Additionally,errorsmaypropagatefromthebiasinregionalfactors,adjustingtolocalgeographies,climates,theuseoffielddata,andsamplinguncertainty.Giventheoverallcomplexityinherentinthemodel,FVSdoesnotincorporateuncertaintyintheoutputorpost‐analysisofmodelrunsandadditionalresearchisrequiredtoquantifymodeluncertainty.

8.1.4.4 i‐TreeModel

i‐Tree(formerlytheUrbanForestEffectsmodel)isanurbanforestryanalysismodeldevelopedbyDavidJ.Nowak(USDAFS),DanielE.Crane(NRS),andPatrickMcHale(SUNYCollegeofEnvironmentalScienceandForestry).Thei‐Treemodelhelpsquantifythestructureofcommunitytreesandtheenvironmentalservicesthattheyprovide.Itprovidessixanalyticaltoolsincluding:

i‐TreeEco:Providesafullpictureoftheentireurbanforest(usedintheFieldDataMethod)

i‐TreeStreets:Quantifiesbenefitsfromamunicipalitiesstreetleveltrees

i‐TreeHydro:Modelstheeffectsoftreesonwatershedstreamflowandwaterquality

i‐TreeVue:UsesNLCDsatelliteimagerytoassesstreecanopy

i‐TreeDesign:Assessesmultipletreesatparcellevel

i‐TreeCanopy:Providesaquantifiableestimateoftreecoverandotherlandcovertypes(usingintheAerialDataMethod)

i‐TreeEcoandi‐TreeCanopyarerecommendedinthisreportforusebyanentitytoestimatethechangeincarbonstocksintheirurbanforests.

i‐TreeEcoUncertaintyInformation:Thei‐TreeEcomodelproducesuncertaintyestimatesbasedonsamplingerror,butitdoesnotcalculateamodelestimationerror.Accordingtoi‐Treedevelopers,estimationerrorisbasedontheuncertaintyinherentinthebiomassconversionequationsandemissionfactors.Thedevelopersalsonotethatmodelbiasislikelylowgiventhattheinputassumesagivenrandomsampleoftrees,andtreespeciesequationsareselectedbasedonstandheight.Ifaparticularspeciesequationisnotavailablethemodelusestheaverageofavailableequationsfromtheclosestgenera(Nowak,2012).AMonteCarloanalysistoolcouldusethestandarderroroftheestimatedcarbonvaluetoevaluatetheuncertaintyassociatedwithanentity’stotalnetGHGemissions.

i‐TreeCanopyUncertaintyInformation:Thei‐TreeCanopymodelproducesastatisticalestimateofthestandarderrorofthepercenttreecoverestimatebasedontheratioofsamplepoints


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classifiedastreestototalsamplepoints.Ini‐TreeCanopytheuserimportsashapefile,samplespoints,andclassifiesthemaseithertreesornon‐trees.Ananalysisofthetreepointtototalpointratioisusedtoestimatethestandarderrorassociatedwiththepercenttreecoverestimate,asdescribedinthei‐TreeCanopytechnicalnotes,18andshowninEquation8‐1below.

Table8‐3showsestimatesofthestandarderrorasrelatedtotheratiooftreepointstototalsamplepoints(pvalue),wherethetotalnumberofsampledpoints(N)equals1,000.

Basedonthestandarderrorformula,standarderrorisgreatestwhenpequals0.5,andisleastwhenpisverysmallorverylarge(seeTable8‐3).AMonteCarloanalysistoolcouldusethestandarderroroftheestimatedpercenttreecovervaluetoevaluatetheuncertaintyassociatedwithanentity’stotalnetGHGemissions.

8.1.4.5 FirstOrderFireEffectsModel

FOFEMisacomputationalmodelforpredictingtreemortality,fuelconsumption,smokeproduction,andsoilheatingcausedbyeitherprescribedfireorwildfire.FOFEMwasdevelopedbytheIntermountainFireSciencesLaboratoryinMissoula,MT,oftheUSDAForestService.FirstorderfireeffectsarethosecharacterizedwiththedirectimmediateconsequencesofafireincludingGHGemissionestimates.FOFEMisdividedintofournationalregions:PacificWest,InteriorWest,NorthEast,andSouthEast.Themodelincludesseveralforestcovertypestoprovideanadditionallevelofdetailresolution.Thequantitativeoutputcanbeusedinassessmentsafterfiredamage,inanalyzingprescribedfireimpacts,andmodelingvulnerabilitiesinregionalforestgroups.

FOFEMhasaregionalbiasgiventhattheempiricalrelationshipsandassumptionsarebasedonforestedsystemsintheNorthRockyMountainsandthePacificNorthwest.However,theseuncertaintiesarenotquantifiedoradjustedforuseindifferentregions.Forinstance,SoutheastfiresburnwellathumiditylevelsthatwouldnotsupportthemintheWest.Thisphenomenonisnotaccountedforinthemodelandthereisnouncertaintyquantificationaroundtheoutput.Therearealsomaterialdifferencessuchaslitterbulkdensitythatinfluencesconsumptionandemissionwhichcanvaryconsiderablyregiontoregion(Lutes,2012).

18I‐TreeCanopyTechnicalNotes:http://www.itreetools.org/canopy/resources/iTree_Canopy_Methodology.pdf

Table8‐3:EstimatesofStandardError(SE)(N=1,000)ofPercentTreeCoverfromi‐TreeCanopywithVaryingpValues

p SE0.01 0.00310.1 0.00950.3 0.01450.5 0.01580.7 0.01450.9 0.00950.99 0.0031

Equation8‐1:EstimatingStandardErrorofPercentTreeCoverfromi‐TreeCanopy

/ (e.g., 0.33 0.67/1000=0.0149)

Where:

N=Totalnumberofsampledpoints(e.g.,1,000)

n=Totalnumberofpointsclassifiedasatree(e.g.,330)

p=n/N(e.g.,330/1,000=0.33)

q=1− p(e.g.,1− 0.33=0.67)


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8.1.4.6 ApproachforIncorporatingModelUncertainty

Giventhelackofuncertaintyinformationformostoftherelevantexternalmodels,itisnotcurrentlyfeasiblefortheGHGquantificationmethodstoquantifythissourceofuncertainty.Instead,relianceofthemethodsonthemodelswillbeexplicitlyacknowledged.Thepotentialimpactsofuncertainmodelsontheaccuracyandprecisionoftheresultingestimatesisdescribedqualitativelyintheprevioussections.

Itmaybepossibleintheneartermtoelicitexpertjudgmentsonthelevelofmodeluncertaintyattheentitylevel.ModelsusedintheGHGquantificationmethodsaretypicallyconstructedatscalesnosmallerthantheentitylevel.Itisexpectedthatthemodeluncertaintyattheentitylevelwouldbenosmallerthanthemodeluncertaintyatthemodel’sscale,andpossiblylargerduetoadditionalerrorfromdownscalingtotheentitylevel.

Inthelongerterm,moreresearchisneededtoevaluatemodelpredictionswithindependentdata,notusedinthedevelopmentofthemodel.Thedifferencesbetweenmodelpredictionsandindependentdataarethebestpossiblesourceofinformationregardingmodeluncertainty.

ResearchGaps8.2

ThereadilyavailableinformationonparameteruncertaintyisprovidedinthetablesinAppendix8‐B.Asindicated,muchoftheinformationtocharacterizetheuncertaintyisnotavailableandthedatathatareprovidedaremostlydefaultvaluesfromtheliteratureandassumedprobabilitydensityfunctions.ToconductaMonteCarloanalysisforuncertaintyestimation,itisimportanttoobtainprobabilitydensityfunctionsorsummarystatisticsforalluncertainvariables.SignificantresearchisneededtoobtainnewdataandtosynthesizeexistingandnewdatainordertotrulyassessuncertaintyassociatedwitharangeoffactorscausinguncertaintyintheGHGestimatesdevelopedusingtherecommendedmethodsdescribedinthisreport.Inparticular,moreresearchisneededtoassessparameter,sampling,largedatasets,andmodeluncertainties.


8-21

Appendix8‐A:ExampleOutputFilefromFVSSamplingUncertaintyBootstrappingApplicationFVSBoot(asprovidedinGreggandHummel,2002)

ThefollowingtableillustratesstandarddeviationsurroundingthesamplingerroroftheBasalAreaoutputs.FVSBootcanbeconfiguredtodeterminestandarddeviationofthesamplingerrorforanyFVSoutput.

Table8‐A‐1:ExampleOutputFilefromFVSSamplingUncertaintyBootstrappingApplicationFVSBoot(asprovidedinGreggandHummel,2002)

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Appendix8‐B:UncertaintyTables

Thissectionpresentsreadilyavailabledataontheuncertaintyassociatedwithactivityandemissionfactordata.Table8‐B‐1liststhedata

elem

entsthatareprovidedinthesubsequenttablesforeachagriculturesystem.Inparticular,readilyavailableuncertaintyinform

ationis

providedinthefollowingtables:

Table8‐B‐2:CroplandUncertaintyTem

plate

Table8‐B‐3:AnimalPopulationUncertaintyTem

plate

Table8‐B‐4:EntericFermentationandHousingUncertaintyTem

plate

Table8‐B‐5:M

anureManagem

entUncertaintyTable

Table8‐B‐6:ForestryUncertaintyTable

Table8‐B‐1:DataElem

entsProvided

ColumnLabel

Description

DataElem

entNam

eThenam

eofthevariable

Abbreviation/Symbol

Theshorthandrepresentationusedinthereport

EmissionType

Emissionsestimatesthatdependonthedataelement(CH

4,N2O,NH3,CO

2)DataInputUnit

Unitassociatedwiththedataelement

InputSource

Entityentry,defaultentry,m

odeloutput,orfrom

adatabase

Statistic

Availablestatisticfortheparameter

TypeofStatistic

Mean,median,ormode

ProbabilityDistributionType

Theprobabilitydistributionfunctionofthedataelem

ent(normal,lognormal,uniform,triangular,beta)

RelativeUncertainty

Rangeofvaluesaroundthemostlikelyvalue,expressedasapercentofthemostlikelyvalue

ConfidenceLevel

Theprobabilitythattheconfidencerangecapturesthetruevalueofthedataelem

entgivenadistributionof

samples.

EffectiveLowerLimit

Minimum

valuefordataelement(excludingoutliers)

EffectiveUpperLimit

Maximum

valuefordataelement(excludingoutliers)

DataSource

Referenceforinformationrelatedtothedataelementandassociateduncertainty

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Table8‐B‐2:CroplandUncertaintyTem

plate

CroplandSub‐Source

Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic


RelativeuncertaintyLow(%)

RelativeuncertaintyHigh(%)

ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Croplands–MultipleSub‐

sources

Area

A

CH4,N2O,

CO2

Hectares

Entity

Entry


sources

CropYield

YCH

4,N2O,

CO2

Metrictonsdry

mattercrop

yield/year

Entity

Entry


sources

Meatyieldper

parcelofland

Meat

CO2

kgcarcass

yield

Entity

Entry


sources

Milkproduction

perparcelofland

MilkProd.

CO2

kgfluidmilk

yield

Entity

Entry

BiomassCarbonStock

Changes

Meanannual

woodybiomass

(t=currentyear's

stocks)

Wt

CO2

Metrictons

CO2‐eqyear‐1

Model

Output

DAYCEN

T

model

simulations

andgrow

th

functions

foragro‐

forestry

BiomassCarbonStock

Changes

Meanannual

woodybiomass(t‐

1=Previousyear's

stocks)

Wt‐1

CO2

Metrictons

CO2‐eqyear‐1

Model

Output

DAYCEN

T

model

simulations

andgrow

th

functions

foragro‐

forestry

BiomassCarbonStock

Changes

Meanannual

herbaceous

biom

ass

(t=currentyear's

stocks)

Ht

CO2

Metrictons

CO2‐eqyear‐1

Entity

Entry

BiomassCarbonStock

Changes

Meanannual

herbaceous

biom

ass(t‐

1=Previousyear's

stocks)

Ht‐1

CO2

Metrictons

CO2‐eqyear‐1

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

BiomassCarbonStock

Changes

ForageYield

Forageyield

forgrazing

lands

CO2

Metrictonsdry

matterper

hectare

Entity

Entry

BiomassCarbonStock

Changes

Num

beroftrees

byageofdiameter

classforeach

agroforestry

practice

Num

berof

Trees

CO2

Num

ber

Entity

Entry

BiomassCarbonStock

Changes

Diameteratbreast

heightfora

subsam

pleoftrees

DBH

CO2

Meters

Entity

Entry

BiomassCarbonStock

Changes

RoottoShoot

Ratio

R:S

CO2

Ratio

Default

Entry

Westetal.

(2010)

BiomassCarbonStock

Changes

Drymatter

contentof

harvestedcrop

biom

assorforage

DM

CO2

Dimensionless

Entity

Entry

BiomassCarbonStock

Changes

HarvestIndex

HI

CO2

Fraction

Default

Entry

Westetal.

(2010)

BiomassCarbonStock

Changes

Cropharvestor

forageyield,

correctedfor

moisturecontent

Y dm

CO2

Metrictons

biom

assha

‐1

Entity

Entry

BiomassCarbonStock

Changes

Approximate

fractionof

calendaryear

representingthe

grow

ingseason

Y f

CO2

Fraction

Entity

Entry

BiomassCarbonStock

Changes

Carbonfractionof

aboveground

biom

ass

CCO

2Fraction

Default

Entry

0.45

Normal

11.0

11.0

IPCC(1997)

CO2fromLiming

Annualapplication

oflime

M

CO2

Metrictons/

year

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

CO2fromLiming

MetrictonsCO2

emissionsper

metrictonsoflime

EF

CO2

Metrictons

C/metrictons

lime

Default

Entry

‐0.04

46.0

46.0

Westand

McBride

(2005)

CO2fromUreaFertilizer

Application

Annualamountof

ureafertilization

M

CO2

Metrictons

urea/year

Entity

Entry

CO2fromUreaFertilizer

Application

ProportionofCin

urea

EF

CO2

Metrictons

C/metrictons

urea

Default

Entry

0.2

deKleinet

al.(2006)

DirectN

2OEmissions

Areaoforganic

soils(histosols)

drainedona

parcelofland(ha)

Aos

N2O

ha

Entity

Entry

DirectN

2OEmissions

Prior‐yearcrop

type

N2O

MetrictonsN

year

‐1 ha‐1

Entity

Entry

DirectN

2OEmissions

Emissionrate

modeledat0level

ofNinput(Nt=0)

ER0

N2O

Metrictons

N2O‐Nha‐1 year‐

1

Model

Output

DirectN

2OEmissions

Emissionfactor

forthetypical

fertilizationrate

EFtypical

N2O

Metrictons

N2O‐Nmetric

tons

‐1N

Model

Output

DirectN

2OEmissions

TypicalNfertilizer

rate

Ntf

N2O

MetrictonsN

ha‐1year‐1

Database

DirectN

2OEmissions

Emissionratefor

thetypicalcase

modeled

ERtypical

N2O

MetrictonsN

ha‐1year‐1

Model

Output

DirectN

2OEmissions

ActualNfertilizer

rate,including

syntheticand

organic

Nf

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DirectN

2OEmissions

BaseEFscalarfor

ΔNf>zeroand

non‐grassland

crops

S EF

N2O

(metrictons

N2O‐N(metric

tonsN)‐2 )ha

year

Default

Entry

0.0274

Appendix3‐A

DirectN

2OEmissions

BaseEFscalarfor

ΔN=>zeroand

grassland

S EF

N2O

(metrictons

N2O‐N(metric

tonsN)‐2 )ha

year

Default

Entry

0.117

Appendix3‐A

DirectN

2OEmissions

BaseEFscalarfor

ΔNf<zero

S EF

N2O

(metrictons

N2O‐N(metric

tonsN)‐2 )ha

year

Default

Entry

0

Appendix3‐A

DirectN

2OEmissions

Drymatter

contentof

harvestedbiom

ass

DM

N2O

Entity

Entry

DirectN

2OEmissions

Residue:yield

ratios

N2O

Ratio

Entity

Entry

DirectN

2OEmissions

Amountofresidue

harvested,burned

orgrazed

Rr

N2O

Entity

Entry

DirectN

2OEmissions

Fractionoflive

biom

assremoved

bygrazing

F r

N2O

Entity

Entry

DirectN

2OEmissions

Nmineralization

from

manure

Nman

N2O

Entity

Entryand

Model

Output

DirectN

2OEmissions

Nmineralization

from

com

post

Ncomp

N2O

Entity

Entryand

Model

Output

DirectN

2OEmissions

Totaldrymatter

yieldofcrop

N2O

Metrictonsdry

matteryear

‐1

Entity

Entry

DirectN

2OEmissions

Stockingratesand

methods

N2O

Head/acre

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DirectN

2OEmissions

Scalingfactorfor

slow

‐release

fertilizers,0where

noeffect

S sr

N2O

Dimensionless

Default

Entry

‐0.21

Normal

‐0.3

‐0.12Meta‐analysis

DirectN

2OEmissions

Scalingfactor

nitrification

inhibitors–sem

i‐arid/aridclimate

S inh

N2O

Dimensionless

Default

Entry

‐0.38

Normal

‐0.51


DirectN

2OEmissions

Scalingfactor

nitrification

inhibitors–mesic

climate

S inh

N2O

Dimensionless

Default

Entry

‐0.4

Normal

‐0.52


DirectN

2OEmissions

Scalingfactorfor

notill,sem

i‐arid/aridclimate,

<10years

followingno‐till

adoption

S till

N2O

Dimensionless

Default

Entry

0.38

0.04

0.72

vanKesselet

al.(2012);Six

etal.(2004)

DirectN

2OEmissions

Scalingfactorfor

notill,sem

i‐arid/aridclimate,

≥10years

followingno‐till

adoption

S till

N2O

Dimensionless

Default

Entry

‐0.33

‐0.5

‐0.16

vanKesselet

al.(2012);Six

etal.(2004)

DirectN

2OEmissions

Scalingfactorfor

notill,mesic/w

et

climate,<10years

followingno‐till

adoption

S till

N2O

Dimensionless

Default

Entry

‐0.015

‐0.16

0.16

vanKesselet

al.(2012);Six

etal.(2004)

DirectN

2OEmissions

Scalingfactorfor

notill,mesic/w

et

climate,≥10years

followingno‐till

adoption

S till

N2O

Dimensionless

Default

Entry

‐0.09

‐0.19

0.01

vanKesselet

al.(2012);Six

etal.(2004)

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DirectN

2OEmissions

Ninslow‐release

Nfertilizerapplied

totheparcelof

land

Nsr

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

DirectN

2OEmissions

Ninmanure

amendm

ents(and

sewagesludge)

addedtothe

parcel

Nman

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

DirectN

2OEmissions

Nexcretedby

cattle,poultryand

swinedirectlyon

theparcelofland

(metrictonsN

year‐1ha‐1)

Nprp

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

DirectandIndirectN

2O

Emissions

Ninsynthetic

fertilizerapplied

toaparcelofland

Nsfert

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

DirectN

2OEmissions

Nfrom

achangein

soilorganicmatter

mineralizationdue

toLUCortillage

changeappliedto

aparcelofland

Nmin

N2O

MetrictonsN

year

‐1

Entity

Entry

DAYCEN

T

model

derived

DirectN

2OEmissions

Nfractionof

aboveground

biom

assforthe

croporforage

Na

N2O

Dimensionless

Entity

Entry

DirectN

2OEmissions

Nfractionof

belowground

biom

assforthe

croporforage

Nb

N2O

Dimensionless

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

IndirectN

2OEmissions

Emissionratefor

croppedhistosols,

EROS

N2O

Metrictons

N2O‐Nha‐1

year

‐1

Default

Entry

0.008

Uniform

0.002

0.02 4

IPCC(2006)

IndirectN

2OEmissions

Nfertilizerapplied

oforganicorigin

includingmanure,

sewagesludge,

compostandother

organic

amendm

ents

F ON

N2O

MetrictonsN

year

‐1

Entity

Entry

IndirectN

2OEmissions

FractionofNSN

thatvolatizesas

NH3andNOx

FRSN

N2O

kgNkg‐1 N

sfert

Default

Entry

0.1

Uniform

0.03

0.3

IPCC(2006)

IndirectN

2OEmissions

Fractionor

proportionofF

ON

thatvolatizesas

NH3andNOx

FRON

N2O

kgNkg‐1 N

ON

Default

Entry

0.2

Uniform

0.05

0.5

IPCC(2006)

IndirectN

2OEmissions

Emissionfactor

forvolatilizedNor

proportionofN

volatilizedasNH3

andNOxthatis

transformedto

N2Oinreceiving

ecosystem

EFvol

N2O

kgN

2O‐N

kg‐1N

Default

Entry

0.01

Uniform

0.002

0.05

IPCC(2006)

IndirectN

2OEmissions

Fractionor

proportionofNt

thatleachesor

runsoff

FRLeach

N2O

kgNkg‐1 N

Default

Entry

0.3

Uniform

0.1

0.8

IPCC(2006)

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

IndirectN

2OEmissions

Emissionfactor

forleachedand

runoffNor

proportionof

leachedandrunoff

Nthatis

transformedto

N2Oinreceiving

ecosystem

EFLeach

N2O

kgN

2O‐N

kg‐1N

Default

Entry

0.0075

Uniform

0.0005

0.02 5

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Cultivationperiod

forriceunderi,j,

andkconditions

t ijk

CH4

Days

Entity

Entry

Methanefrom

WetlandRice

Cultivation

Annualharvested

areaofricefori,j,

andkconditions

Aijk

CH4

Hectares/year

Entity

Entry

Methanefrom

WetlandRice

Cultivation

Applicationrateof

organic

amendm

ent(s)

ROAi

CH4

Metrictons/

hectare

Entity

Entry

Methanefrom

WetlandRice

Cultivation

Baselineem

ission

factorfor

continuously

floodedfields

withoutorganic

amendm

ents

EFc

CH4

kgCH4/ha/

day

Default

Entry

1.3

Uniform

0.8

2.2

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

duringthe

cultivationperiod

–ScalingFactor

SFwfor

continuously

flooded

CH4

ScalingFactor

from

IPCC

Default

Entry

1

Uniform

0.79

1.26

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

duringthe

cultivationperiod

–ScalingFactor

SFwforsingle

aeration

CH4

ScalingFactor

from

IPCC

Default

Entry

0.6

Uniform

0.46

0.8

IPCC(2006)

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Methanefrom

WetlandRice

Cultivation

Waterregime

duringthe

cultivationperiod

–ScalingFactor

SFwfor

multiple

aerations

CH4

ScalingFactor

from

IPCC

Default

Entry

0.52

Uniform

0.41

0.66

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

beforethe

cultivationperiod

–Scalingfactor

SFpfornon‐

floodedpre‐

season<180

days

CH4

ScalingFactor

from

IPCC

Default

Entry

1

Uniform

0.88

1.14

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

beforethe

cultivationperiod

–Scalingfactor

SFpfornon‐

floodedpre‐

season>180

days

CH4

ScalingFactor

from

IPCC

Default

Entry

0.68

Uniform

0.58

0.8

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

beforethe

cultivationperiod

–Scalingfactor

SFpforflooded

pre‐season>

30days

CH4

ScalingFactor

from

IPCC

Default

Entry

1.9

Uniform

1.65

2.18

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Organic

amendm

entused

–scalingfactor

SFo

CH4

ScalingFactor

from

IPCC

Default

Entry

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAifor

straw

incorporation

lessthan30

daysbefore

cultivation

CH4

Conversion

factorfrom

IPCC

Default

Entry

1

Uniform

0.97

1.04

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAifor

straw

incorporation

morethan30

daysbefore

cultivation

CH4

Conversion

factorfrom

IPCC

Default

Entry

0.29

Uniform

0.2

0.4

IPCC(2006)

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAifor

compost

CH4

Conversion

factorfrom

IPCC

Default

Entry

0.05

Uniform

0.01

0.08

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAiforfarm

yardmanure

CH4

Conversion

factorfrom

IPCC

Default

Entry

0.14

Uniform

0.07

0.2

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAifor

greenmanure

CH4

Conversion

factorfrom

IPCC

Default

Entry

0.5

Uniform

0.3

0.6

IPCC(2006)

MethaneUptakebySoils

PotentialCH4

Oxidationinsoils

PCH4for

grassland

CH4

kgCH4ha‐1

year

‐1

Default

Entry

3.2

Normal

0

6.9

DelGrossoet

al.(2000)


PotentialCH4

Oxidationinsoils

PCH4for

coniferous

forest

CH4

kgCH4ha‐1

year

‐1

Default

Entry

2.8

Normal

0.1

5.5

DelGrossoet

al.(2000)


PotentialCH4

Oxidationinsoils

PCH4for

deciduous

forest

CH4

kgCH4ha‐1

year

‐1

Default

Entry

11.8

Normal

1.9

21.6

DelGrossoet

al.(2000)


CH4oxidation

attenuationfactor:

croplandincluding

set‐aside(CRP)

grassland,grazing

land,andfertilized

orrecently

harvestedforests

AF

CH4

N/A

Default

Entry

0.30

Normal

0.07

1Sm

ithetal.

(2000)


CH4oxidation

attenuationfactor:

naturalvegetation,

0‐100yearsafter

abandonm

entof

agricultural

productionor

timberharvest

AF

CH4

N/A

Default

Entry

0.3+(0.007

×years

since

abandonm

ent)

Normal

0.07+

(0.007

×years

since

abando

nment)

1Sm

ithetal.

(2000)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-33

8-33


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


CH4oxidation

attenuation

factor:>100years

post‐managem

ent

orneverusedfor

agricultural

managem

entor

timberharvest

AF

CH4

N/A

Default

Entry

1

Normal

0.07

1Sm

ithetal.

(2000)

N2Ofrom

WetlandRice

TotalNinputs

from

all

agronomic

sources:mineral

fertilizer,organic

amendm

ents,

residues,and

additional

mineralization

from

LUCortillage

change(metric

tonsNyear‐1 )

Nt

N2O

MetrictonsN

year

‐1

Entity

Entry

N2Ofrom

WetlandRice

FertilizerN

managem

ent

N2O

Rate

Entity

Entry

N2Ofrom

WetlandRice

Organicfertilizer

N

N2O

%N

Entity

Entry

N2Ofrom

WetlandRice

CropresidueN

N2O

%N

Entity

Entry

N2Ofrom

WetlandRice

Emissionfactoror

proportionofN

ttransformedto

N2O

EF

N2O

kgN

2O‐N

(kgN)‐1

Default

Entry

0.0022

Normal

0.2%

0.2%

Akiyamaet

al.(2005)

N2Ofrom

WetlandRice

Scalingfactorto

accountfor

drainageeffects

SFDfor

continuously

flooded

system

s

N2O

Dimensionless

Default

Entry

0

Akiyamaet

al.(2005)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-34


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

N2Ofrom

WetlandRice

Scalingfactorto

accountfor

drainageeffects

SFDforaerated

system

sN2O

Dimensionless

Default

Entry

0.59

Normal

0.4%

0.4%

Akiyamaet

al.(2005)

NonCO2Emissions

BiomassBurn

BorealForest(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.34

Normal102%

102%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Wildfire

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.4

Normal340%

340%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Crow

nfire

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.43

Normal104%

104%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Surfacefire

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.15

Normal

96%

96%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Postloggingslash

burn

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.33

Normal130%

130%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tem

perateForest

(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.45

Normal

51%

51%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Postloggingslash

burn

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.62

Normal264%

264%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Shrublands(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.72

Normal147%

147%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Callunahealth

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.71

Normal121%

121%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Fynbos

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.61

Normal195%

195%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Savanna

woodlands(early

dryseasonburns)

(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.4

Normal

93%

93%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Savanna

woodlands

(mid/latedry

seasonburns)(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.74

Normal

99%

99%

IPCC(2006)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-35

8-35


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

NonCO2Emissions

BiomassBurn

Savannawoodland

(mid/late)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.72

Normal270%

270%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tropicalsavanna

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.73

Normal598%

598%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Othersavanna

woodlands

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.68

Normal931%

931%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Savanna

grasslands(early

dryseasonburns)

(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.74

Normal183%

183%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tropical/sub‐

tropicalgrassland

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.74

Normal270%

270%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tropical/sub‐

tropicalgrassland

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.92

Normal151%

151%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tropicalpasture

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.35

Normal427%

427%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Savanna

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.86

Normal

85%

85%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Emissionfactor

EFfor

grassland

CH4

gGHG/kg

burned

biom

ass

Default

Entry

2.3

Mean

Normal

8.0%

8.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Emissionfactor

EFforcrop

residue

CH4

gGHG/kg

burned

biom

ass

Default

Entry

2.7

Mean

Normal50.0%

50.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Emissionfactor

EFfor

grassland

N2O

gGHG/kg

burned

biom

ass

Default

Entry

0.21

Mean

Normal93.0%

93.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Emissionfactor

EFforcrop

residue

N2O

gGHG/kg

burned

biom

ass

Default

Entry

0.07

Mean

Normal50.0%

50.0%

IPCC(2006)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-36


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cfor

shrublands

CH4/N2O

%Burned

Default

Entry

0.72

Mean

Normal68.0%

68.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cfor

grasslands

withearly

seasonburns

CH4/N2O

%Burned

Default

Entry

0.74

Mean

Normal50.0%

50.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cfor

grasslands

withmidto

lateseason

burns

CH4/N2O

%Burned

Default

Entry

0.77

Mean

Normal66.0%

66.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cforsm

all

grains

CH4/N2O

%Burned

Default

Entry

0.9

Mean

Normal50.0%

50.0%

Expert

Assessm

ent

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cforlarge

grainandother

cropresidues

CH4/N2O

%Burned

Default

Entry

0.8

Mean

Normal50.0%

50.0%

Expert

Assessm

ent

NonCO2Emissions

BiomassBurn

Moisturecontent

ofresiduesand

forage

CH

4/N2O

%moisture

Default

Entry

NonCO2Emissions

BiomassBurn

Residuetoyield

ratioofcrop

R:Y

CH4/N2O

Metrictons

residue/

metrictonsdry

matteryield

Default

Entry

SOCChangeMineralSoils

SoilorganicC

stockattheendof

theyear

SOC t

CO2

MetrictonsC

ha‐1

Model

Output

DAYCEN

T

Model

derived

Ogleetal.

(2007);EPA

(2013)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-37

8-37


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


SoilorganicC

stockatthe

beginningofthe

year

SOC t‐1

CO2

MetrictonsC

ha‐1

Model

Output

DAYCEN

T

Model

derived

Ogleetal.

(2007);EPA

(2013)


Cropselectionand

RotationSequence

CO

2Managem

ent

ListDeveloped

byExperts

Entity

Entry


Irrigation

applicationrate

CO

2Gallonsper

minute

Entity

Entry


MineralFertilizer

applicationRate

CO

2lbs/squarefoot

Entity

Entry


LimeAmendm

ent

applicationRate

CO

2lbs/squarefoot

Entity

Entry


Organic

Amendm

ent

applicationRate

CO

2lbs/squarefoot

Entity

Entry


Num

berofpasses

ineachoperation

CO

2Num

ber

Entity

Entry


Depthofdrainage

CO

2Meters

Entity

Entry


Lengthoffield

CO

2Meters

Entity

Entry


HistoricalW

eather

Patterns

CO

2PRISM

WeatherData

Model

Output


Physicaland

Chem

ical

PropertiesofSoil

CO

2NRCSSURRGO

database

Model

Output

SOCChangeMineralSoils–

GrazingLand

AnimalSizeused

forgrazing

CO

2lbs

Entity

Entry

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-38


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


GrazingLand

StockingRate

CO

2Head/acre

Entity

Entry


GrazingLand

Irrigation

applicationrate

CO

2Gallonsper

minute

Entity

Entry


GrazingLand

MineralFertilizer

applicationRate

CO

2lbs/squarefoot

Entity

Entry


GrazingLand

Depthofdrainage

CO

2Meters

Entity

Entry

SOCChangeOrganicSoils

Emissionfactor

EFforcropland

incool

temperate

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

11

Mean

Normal

45.0

45.0

Ogleetal.

(2003)


Emissionfactor

EFforcropland

inwarm

temperate

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

14

Mean

Normal

35.0

35.0

Ogleetal.

(2003)


EmissionfactorEFforcropland

insubtropical

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

14

Mean

Normal

46.0

46.0

Ogleetal.

(2003)


Emissionfactor

EFforgrazing

landincool

temperate

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

2.8

Mean

Normal

45.0

45.0

Ogleetal.

(2003)


Emissionfactor

EFforgrazing

landinwarm

temperate

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

3.5

Mean

Normal

35.0

35.0

Ogleetal.

(2003)


Emissionfactor

EFforgrazing

landin

subtropical

regions

CO2

MetrictonsC

ha‐1year‐1

Default

Entry

3.5

Mean

Normal

46.0

46.0

Ogleetal.

(2003)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-39

8-39

Table8‐B‐3:AnimalPopulationUncertaintyTem

plate

AnimalPopulationData

Elem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic


RelativeUncertaintyLow(%)

RelativeUncertaintyHigh(%)

ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

NumberofAnimals

Beefreplacementheifers

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Dairyreplacementheifers

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Maturebeefcow

sN

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Steers(>500lbs)

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Bulls

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Stockers(All)

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Cattleonfeed

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Dairycow

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Cattle

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Americanbison

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

SheepNOF

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Feedlotsheep

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Goats

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Horses

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Mules/burros/asses

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-40

Table8‐B‐4:EntericFerm

entationandHousingUncertaintyTem

plate

DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DailyMilkProduction

Milk

CH4

kgmilk/animal/day

EntityEntry

3%

5%

Expert

Assessm

ent

Daysinmilk

DIM

CH4

Days

EntityEntry

Drymatterintake

DMI

CH4

kg/animal/day

EntityEntry

DailyWorkDonebyAnimal

Work

CH4

Hours/day

EntityEntry

Averagelivebodyweight–lactating

beefcow

sBW

CH4

kg

EntityEntry

BeefCow

MatureWeight

MW

CH4

lbs

EntityEntry

SteerDailyWeightGainto24months

WG

CH4

lbs/day

EntityEntry

BeefSteerMatureWeight

MW

CH4

lbs

EntityEntry

BeefHeiferMatureWeight

MW

CH4

lbs

EntityEntry

Netenergyrequiredbytheanimalfor

maintenance

NE m

CH4

MJday

‐1

EntityEntry

MilkFatContent

Fat

CH4

Percent

EntityEntry

StarchContentofDiet(DairyCow

s)

Starch

CH5

kg/animal/day

EntityEntry

AcidDetergentFiberContentofDiet

ADF

CH4

kg/head/day

EntityEntry

DE–EachFeedType

DE

CH4

Percentofgrossenergy

EntityEntry

NeutralDetergentFiberinDiet(Dairy

Cows)

NDF

CH4

Percent

EntityEntry

CrudeProteininDiet

CP

CH4

Percent

EntityEntry

AcidDetergentFiberContentofDiet

(DairyCow

s)

ADF

CH4

Percent

EntityEntry

NeutralDetergentFiberinDiet

NDF

CH4

Percent

EntityEntry

SupplementalFat(feedlot)

S.Fat

CH4

Percent

EntityEntry

3%

Mean

24

Expert

Assessm

ent

DietaryForage%

CH

4Percent

EntityEntry

TotalDigestibleNutrients(DairyCow

s)

TDN

CH4

kg

EntityEntry

YmFeedlot–AllRegions

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmBeefCattleNotonFeed(stocker)

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

Ym

BeefCattleNotonFeed(allforaging

animalsexceptdairy)

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-41

8-41

DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

YmDairyRepl.Heif.–California

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–West

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

Ym

DairyRepl.Heif.–NorthernGreat

Plains

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–Southcentral

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–Northeast

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–Midwest

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–Southeast

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

Maximum

dailyemissionsfordairy

cows

E max

CH4

MJ/head

DefaultEntry

45.98

Millsetal.

(2003)

Averagelivebodyweightforlactating

cows

BW

N2O/NH3

kg

EntityEntry

TypicalAmmoniaLossesfrom

Dairy

HousingFacilities–Opendirtlots(cool,

humidregion)

NH3loss

N2O 3

PercentofN

ex

DefaultEntry

15%

30%

Koelshand

Stow

ell

(2005)


Dairy

HousingFacilities–Opendirtlots(hot,

aridregion)

NH3loss

N2O

PercentofN

ex

DefaultEntry

30%

45%

Koelshand

Stow

ell

(2005)


Dairy

HousingFacilities–Roofedfacility

(flushedorscraped)

Roofedfacility(dailyscrapeandhaul)

NH3loss

N2O

PercentofN

ex

DefaultEntry

5%

15%

Koelshand

Stow

ell

(2005)


Dairy


(shallowpitunderfloor)

NH3loss

N2O

PercentofN

ex

DefaultEntry

10%

20%

Koelshand

Stow

ell

(2005)


Dairy


(beddedpack)

NH3loss

N2O

PercentofN

ex

DefaultEntry

20%

40%

Koelshand

Stow

ell

(2005)


Dairy


(deeppitunderfloor,includesstorage

loss)

NH3loss

N2O

PercentofN

ex

DefaultEntry

30%

40%

Koelshand

Stow

ell

(2005)


Beef

HousingFacilities–Opendirtlots(cool,

humidregion)

NH3loss

N2O

PercentofN

ex

DefaultEntry

30%

45%

Koelshand

Stow

ell

(2005)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-42

DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


Beef

HousingFacilities–Opendirtlots(hot,

aridregion)

NH3loss

N2O

PercentofN

ex

DefaultEntry

40%

60%

Koelshand

Stow

ell

(2005)


Beef


(beddedpack)

NH3loss

N2O

PercentofN

ex

DefaultEntry

20%

40%

Koelshand

Stow

ell

(2005)


Beef



loss)

NH3loss

N2O

PercentofN

ex

DefaultEntry

30%

40%

Koelshand

Stow

ell

(2005)

N2OEmissionFactorformanurein

housing(drylotsandpitstorage)

EFN2O

N2O

kgN

2O‐N/kgN

DefaultEntry

IPCC(2006)

NitrogenExcretionfrom

BeefCattle—

Daysonfeedforanindividualration

DOF x

N2O,NH3

Days

EntityEntry


BeefCattle—

Livebodyweightatfinishoffeeding

period

BW

FN

2O,NH3

kg

EntityEntry


BeefCattle—

Livebodyweightatthestartoffeeding

period

BW

IN

2O,NH3

kg

EntityEntry


BeefCattle—

Standardreferenceweightforexpected

finalbodyfat

SRW

N2O,NH3

kg

EntityEntry


BeefCattle—

Concentrationofcrudeproteinoftotal

ration

C CP‐x

N2O,NH3

gcrudeprotein/gdry

feed

EntityEntry

MonthlyBeefFeedlotNH3Emissions—

Dietarycrudeprotein

CP

NH3

Percentofdrymatter

EntityEntry

Averagemonthlytemperature

T

N2O,NH3

DegreesKelvin

EntityEntry


Grow‐Finish

Pigs–Averagedailyfeedintakeover

finishingperiod

ADFI

G

N2O,NH3

g/day

EntityEntry


Grow‐Finish

Pigs–Concentrationofcrudeproteinof

total(wet)ration

C CP

N2O,NH3

Percent

EntityEntry

C

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er 8

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8-43

DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


Grow‐Finish

Pigs–Daysonfeedtofinishanimal

(grow‐finishphase)

DOF G

N2O,NH3

Days

EntityEntry


Grow‐Finish

Pigs–Final(market)bodyweight

BW

FN2O,NH3

kg

EntityEntry


Grow‐Finish

Pigs–Averagedressingpercent(yield)

atfinalw

eight

DP F

N2O,NH3

Percent

EntityEntry


Grow‐Finish

Pigs–Initialbodyweight

BW

IN2O,NH3

kg

EntityEntry


Grow‐Finish

Pigs–Averagefat‐freeleanpercentage

atfinalw

eight

FFLP

FN2O,NH3

Percent

EntityEntry


WeaningPigs–

Averagedailyfeedintakeoverfinishing

period

ADFI

G

N2O,NH3

g/day

EntityEntry


WeaningPigs–


(wet)ration

C CP

N2O,NH3

Percent

EntityEntry


WeaningPigs–

Daysonfeedtofinishanimal(nursery

phase)

DOF N

N2O,NH3

Days

EntityEntry


WeaningPigs–

Averagefat‐freeleangainfrom

20to

120kg

FFLPG

N2O,NH3

g/day

EntityEntry


WeaningPigs–

Finalbodyweightinnurseryphase

BW

F‐N

N2O,NH3

kg

EntityEntry


WeaningPigs–

Initialbodyweightinnurseryphase

BW

I‐N

N2O,NH3

kg

EntityEntry


GestatingSow

s–Averagedailyfeedintakeduring

gestation

ADFI

SN2O,NH3

g/day

EntityEntry


GestatingSow

s–Concentrationofcrudeprotein

C CP

N2O,NH3

Percent

EntityEntry


GestatingSow

s–Gestationperiodlength

GL

N2O,NH3

Days

EntityEntry


GestatingSow

s–Gestationleantissuegain

GLTG

N2O,NH3

Kg

EntityEntry

Cha

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tain

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Gas

Sou

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8-44

DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


GestatingSow

s–Num

berofpigsinlitter

LITTERN2O,NH3

Head

EntityEntry


Lactating

Sows—

Averagedailyfeedintakeduring

lactation

ADFI

LACT

N2O,NH3

g/day

EntityEntry


Lactating

Sows—

Concentrationofcrudeprotein

C CP

N2O,NH3

Percent

EntityEntry


Lactating

Sows—

Lactationlength(daysto

weaning)

LL

N2O,NH3

Days

EntityEntry


Lactating

Sows—

Lactationleantissuegain

LLTG

N2O,NH3

Kg

EntityEntry


Lactating

Sows—

Litterweightatw

eaning

L WEAN

N2O,NH3

Kg

EntityEntry


Lactating

Sows—

Litterweightatbirth

LWBIRTHN2O,NH3

kg

EntityEntry


Swine


(flushedorscraped)Roofedfacility

(dailyscrapeandhaul)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

5%

15%

Koelshand

Stow

ell

(2005)


Swine


(shallowpitunderfloor)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

10%

20%

Koelshand

Stow

ell

(2005)


Swine


(beddedpack)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

20%

40%

Koelshand

Stow

ell

(2005)


Swine



loss)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

30%

40%

Koelshand

Stow

ell

(2005)


Broilers,

Turkeys,andDucks—Feedintakeper

phase

FIx

N2O,NH3

gfeed/finishedanimal

EntityEntry


Broilers,

Turkeys,andDucks—Concentrationof

crudeproteinoftotalrationineach

phase

C CP‐X

N2O,NH3

gcrudeprotein/g(wet)

feed

EntityEntry

C

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er 8

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Ass

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8-45

8-45

DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


Broilers,

Turkeys,andDucks—Retentionfactor

fornitrogen

NRF

N2O,NH3

Fraction

EntityEntry


LayingHens—

Feedintake

FI

N2O,NH3

gfeed/finishedanimal

EntityEntry


LayingHens—


ration

C CP

N2O,NH3

gcrudeprotein/g(wet)

feed

EntityEntry


LayingHens—

Eggweight

Egg w

tN2O,NH3

gEntityEntry


LayingHens—

Fractionofeggsproducedeachday

Egg p

ro

N2O,NH3

Eggs/hen/day

EntityEntry


Poultry

Housing–Roofedfacility(litter)(Meat

Producingbirds)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

25%

50%

Koelshand

Stow

ell

(2005)


Poultry

Housing–Roofedfacility(stacked

manureunderfloor‐,includesstorage

loss)(Egg‐producingbirds)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

25%

50%

Koelshand

Stow

ell

(2005)

MethaneEmissionsfrom

Goats–

Emissionfactorforgoats

EFG

CH4

kgCH4/head/day

DefaultEntry

0.0137

Mean

IPCC(2006)

MethaneEmissionsfrom

Bison–

Emissionfactorforbison

EFAB

CH4

kgCH4/head/day

DefaultEntry

Cha

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tain

ty A

sses

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8-46

Table8‐B‐5:M

anureManagem

entUncertaintyTem

plate

DataElem

entNam

e

DataElementAbbreviation/Symbol

EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

TotalDryManure–BeefFinishingCattle

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

2.4

Mean

‐20

20

ASABE(2005)

TotalDryManure–BeefCow

(confinem

ent)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

6.6

Mean

‐20

20

ASABE(2005)

TotalDryManure–BeefGrowingcalf

(confinem

ent)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

2.7

Mean

‐20

20

ASABE(2005)

TotalDryManure–DairyLactatingcow

CH4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

8.9

Mean

‐20

20

8.7

11.3

ASABE(2005)

TotalDryManure–DairyDrycow

CH4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

4.9

Mean

‐20

20

8.8

11.2

ASABE(2005)

TotalDryManure–DairyHeifer

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

3.7

Mean

‐20

20

ASABE(2005)

TotalDryManure–DairyVeal118kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.12

Mean

‐20

20

ASABE(2005)

TotalDryManure–HorseSedentary500

kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

3.8

Mean

‐20

20

ASABE(2005)

TotalDryManure–HorseIntense

exercise500kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

3.9

Mean

‐20

20

ASABE(2005)

TotalDryManure–PoultryBroiler

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.03

Mean

‐20

20

ASABE(2005)

TotalDryManure–PoultryTurkey(male)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.07

Mean

‐20

20

ASABE(2005)

TotalDryManure–PoultryTurkey

(fem

ales)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.04

Mean

‐20

20

ASABE(2005)

TotalDryManure–PoultryDuck

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.04

Mean

‐20

20

ASABE(2005)

TotalDryManure–Layer

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.02

Mean

‐20

20

ASABE(2005)

TotalDryManure–Sw

ineNurserypig

(12.5kg)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.13

Mean

‐20

20

ASABE(2005)

TotalDryManure–Sw

ineGrowfinish(70

kg)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.47

Mean

‐20

20

ASABE(2005)

TotalDryManure–Sw

inegestatingsow

200kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.5

Mean

‐20

20

ASABE(2005)

C

hapt

er 8

: Unc

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inty

Ass

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8-47

8-47

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

TotalDryManure–Sw

ineLactatingsow

192kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

1.2

Mean

‐20

20

ASABE(2005)

TotalDryManure–Sw

ineBoar200kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.38

Mean

‐20

20

ASABE(2005)

Volatilesolids–BeefFinishingcattle

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.81

Mean

‐25

25

ASABE(2005)

Volatilesolids–BeefCow(confinem

ent)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.89

Mean

‐25

25

ASABE(2005)

Volatilesolids–BeefGrowingcalf

(confinem

ent)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.85

Mean

‐25

25

ASABE(2005)

Volatilesolids–DairyLactatingcow

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.84

Mean

‐25

25

ASABE(2005)

Volatilesolids–DairyDrycow

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.85

Mean

‐25

25

ASABE(2005)

Volatilesolids–DairyHeifer

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.86

Mean

‐25

25

ASABE(2005)

Volatilesolids–DairyVeal118kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

Mean

‐25

25

ASABE(2005)

Volatilesolids–HorseSedentary500kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.79

Mean

‐25

25

ASABE(2005)

Volatilesolids–HorseIntenseexercise

500kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.79

Mean

‐25

25

ASABE(2005)

Volatilesolids–PoultryBroiler

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.73

Mean

‐25

25

ASABE(2005)

Volatilesolids–PoultryTurkey(male)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.8

Mean

‐25

25

ASABE(2005)

Volatilesolids–PoultryTurkey(fem

ales)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.79

Mean

‐25

25

ASABE(2005)

Volatilesolids–PoultryDuck

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.58

Mean

‐25

25

ASABE(2005)

Volatilesolids–Layer

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.73

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

ineNurserypig(12.5

kg)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.83

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

ineGrowfinish(70kg)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.8

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

inegestatingsow

200

kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.9

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

ineLactatingsow

192

kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.83

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

ineBoar200kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.89

Mean

‐25

25

ASABE(2005)

Storagetemperature

T

CH4

Kelvin

EntityEntry

Manuretemperature

Tmanure

NH3

Kelvin

EntityEntry

Ambientairvelocity

Va

NH3

m/s

DefaultEntry

Height

hN2O

m

EntityEntry

Width

W

NH3

m

EntityEntry

Radius

rNH3

m

EntityEntry

pH

pH

NH3

‐EntityEntry

7.5

6.5

8.5

Expert

Assessm

ent

Cha

pter

8: U

ncer

tain

ty A

sses

smen

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Qua

ntify

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Gre

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Gas

Sou

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and

Sin

ks

8-48

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Totalnitrogenatagivenday–beef

finishingcattle

N2O

kgN/kgdrymanure

EntityEntry

0.07

Mean

ASABE(2005)

Totalnitrogenatagivenday–beefcow

(confinem

ent)

N2O

kgN/kgdrymanure

EntityEntry

0.03

Mean

ASABE(2005)

Totalnitrogenatagivenday–beef

grow

ingcalf(confinem

ent)

N2O

kgN/kgdrymanure

EntityEntry

0.05

Mean

ASABE(2005)

Totalnitrogenatagivenday–dairy

lactatingcow

N2O

kgN/kgdrymanure

EntityEntry

0.05

Mean

ASABE(2005)

Totalnitrogenatagivenday–dairydry

cow

N2O

kgN/kgdrymanure

EntityEntry

0.05

Mean

ASABE(2005)

Totalnitrogenatagivenday–dairyheifer

N2O

kgN/kgdrymanure

EntityEntry

0.03

Mean

ASABE(2005)

Totalnitrogenatagivenday–dairyveal

118kg

N2O

kgN/kgdrymanure

EntityEntry

0.13

Mean

ASABE(2005)

Totalnitrogenatagivenday–Horse

Sedentary500kg

N2O

kgN/kgdrymanure

EntityEntry

0.02

Mean

ASABE(2005)

Totalnitrogenatagivenday–Horse

IntenseExercise

N2O

kgN/kgdrymanure

EntityEntry

0.04

Mean

ASABE(2005)

Totalnitrogenatagivenday–poultry,

broiler

N2O

kgN/kgdrymanure

EntityEntry

0.04

Mean

ASABE(2005)


turkey(male)

N2O

kgN/kgdrymanure

EntityEntry

0.06

Mean

ASABE(2005)


turkey(females)

N2O

kgN/kgdrymanure

EntityEntry

0.06

Mean

ASABE(2005)


duck

N2O

kgN/kgdrymanure

EntityEntry

0.04

Mean

ASABE(2005)

Totalnitrogenatagivenday–layer

N2O

kgN/kgdrymanure

EntityEntry

0.07

Mean

ASABE(2005)

Totalnitrogenatagivenday–swine

nurserypig(12.5kg)

N2O

kgN/kgdrymanure

EntityEntry

0.09

Mean

ASABE(2005)

Totalnitrogenatagivenday–swinegrow

finish(70kg)

N2O

kgN/kgdrymanure

EntityEntry

0.08

Mean

ASABE(2005)


gestatingsow200kg

N2O

kgN/kgdrymanure

EntityEntry

0.06

Mean

ASABE(2005)


lactatingsow192kg

N2O

kgN/kgdrymanure

EntityEntry

0.07

Mean

ASABE(2005)

Totalnitrogenatagivenday–swineboar

200kg

N2O

kgN/kgdrymanure

EntityEntry

0.07

Mean

ASABE(2005)

Totalammonianitrogeninthemanure–

beefearthenlot

TAN

NH3

kgNH3/m

3 EntityEntry

0.1

Mean

00.02

ASABE(2005)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

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as S

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es a

nd S

inks

8-49

8-49

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource


poultry,leghornpullets

TAN

NH3

kgNH3/m

3 EntityEntry

0.85

Mean

0.66

1.04

ASABE(2005)


poultry,leghornhen

TAN

NH3

kgNH3/m

3 EntityEntry

0.88

Mean

0.54

1.22

ASABE(2005)


poultry,broiler

TAN

NH3

kgNH3/m

3 EntityEntry

0.75

Mean

ASABE(2005)

Ammoniaconcentrationintheliquid–

dairylagooneffluent

NH3

NH3

kgNH3/m

3 Calculated

0.08

Mean

ASABE(2005)


dairyslurry(liquid)

NH3

NH3

kgNH3/m

3 Calculated

0.14

Mean

ASABE(2005)


SwineFinisher‐Slurrywet‐dryfeeders

NH3

NH3

kgNH3/m

3 Calculated

0.5

Mean

ASABE(2005)


SwineSlurrystorage‐dryfeeders

NH3

NH3

kgNH3/m

3 Calculated

0.34

Mean

0.19

0.49

ASABE(2005)


Swineflushbuilding

NH3

NH3

kgNH3/m

3 Calculated

0.14

Mean

ASABE(2005)


Swineagitatedsolidsandwater

NH3

NH3

kgNH3/m

3 Calculated

0.05

Mean

ASABE(2005)


SwineLagoonsurfacewater

NH3

NH3

kgNH3/m

3 Calculated

0.04

Mean

ASABE(2005)


SwineLagoonsludge

NH3

NH3

kgNH3/m

3 Calculated

0.07

Mean

ASABE(2005)

MethaneConversionFactor(MCF)a–Dairy

Cow

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)

MethaneConversionFactor

a –Cattle

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Buffalo

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Market

Swine

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a–Breeding

Swine

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)

MethaneConversionFactora–Layer(Dry)

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a–Broiler

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a–Turkey

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Duck

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Sheep

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Goat

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Horse

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Mule/Ass

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-50

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource


a –Buffalo

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Invessel

manurecomposting

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a–Staticpile

manurecomposting

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Intensive

windrow

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Passive

windrow

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)

Maximum

MethaneProducingCapacities–

BeefReplacementHeifers

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐20

20

U.S.EPA

(2011)

Maximum


DairyReplacementHeifers

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.17

‐20

20

U.S.EPA

(2011)

Maximum


MatureBeefCow

sBo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐20

20

U.S.EPA

(2011)

Maximum


Steers(>500lbs)

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐20

20

U.S.EPA

(2011)

Maximum


Stockers(All)

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.17

‐20

20

U.S.EPA

(2011)

Maximum


CattleonFeed

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐20

20

U.S.EPA

(2011)

Maximum


DairyCow

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.24

‐20

20

U.S.EPA

(2011)

Maximum


Cattle

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.19

‐20

20

U.S.EPA

(2011)

Maximum


Buffalo

b Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.1

IPCC(2006)

Maximum


MarketSwine

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.48

‐30

30

IPCC(2006)

Maximum


BreedingSw

ine

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.48

‐30

30

IPCC(2006)

Maximum


Layer(dry)

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.39

‐30

30

IPCC(2006)

Maximum


Layer(wet)

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.39

‐30

30

IPCC(2006)

Maximum


Broiler

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.36

‐30

30

IPCC(2006)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-51

8-51

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Maximum


Turkey

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.36

‐30

30

IPCC(2006)

Maximum


Duck

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.36

‐30

30

IPCC(2006)

Maximum


Sheep

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.19

‐20

20

IPCC(2006)

Maximum


Feedlotsheep

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.36

‐20

20

IPCC(2006)

Maximum


Goat

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.17

‐30

30

IPCC(2006)

Maximum


Horse

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.3

‐30

30

IPCC(2006)

Maximum


Mule/Ass

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐30

30

IPCC(2006)

EmissionfactorforthefractionofCH4

producedthatleaksfrom

theanaerobic

digester–Digesterswithsteelorlined

concreteorfiberglassdigesterswithagas

holdingsystem

(eggshapeddigesters)and

monolithicconstruction

EFCH

4,

leakage

CH4

%

DefaultEntry

2.8

CDM(2012)



theanaerobic

digester–UASBtypedigesterswith

floatinggasholdersandnoexternalw

ater

seal

EFCH

4,

leakage

CH4

%

DefaultEntry

5

CDM(2012)



theanaerobic

digester–Digesterswithunlined

concrete/ferrocement/brickmasonry

archedtypegasholdingsection;

monolithicfixeddomedigesters

EFCH

4,

leakage

CH4

%

DefaultEntry

10

CDM(2012)



theanaerobic

digester–Otherdigesterconfigurations

EFCH

4,

leakage

CH4

%

DefaultEntry

10

CDM(2012)

Tem

porarystorageofliquid/slurry

manure–N

2Oemissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.005

‐50

100

U.S.EPA

(2011)

Long‐termstorageofsolidmanure–N

2O

emissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.002

‐50

100

U.S.EPA

(2011)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-52

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Long‐termstorageofslurrymanure–N2O

emissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.005

‐50

100

U.S.EPA

(2011)

CattleandSwineDeepBedding(Active

Mix)‐N

2Oemissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.07

IPCC(2006)

CattleandSwineDeepBedding(NoMix)‐

N2Oemissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.01

IPCC(2006)

PitStorageBelow

AnimalConfinem

ents‐

N2Oemissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.002

IPCC(2006)

Naturalaerationaerobiclagoons–N2O

conversionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.01

‐50

100

IPCC(2006)

Forcedaerationaerobiclagoons–N2O

conversionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.005

‐50

100

IPCC(2006)

N2Oemissionfactorforliquidstorage–

uncoveredliquidmanurewithacrustc

EFN20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.8

‐50

100

IPCC(2006)


uncoveredliquidmanurewithoutacrustc

EFN20

N2O

kgN

2O‐N/kgN

DefaultEntry

0

‐50

100

IPCC(2006)


coveredliquidmanurec

EFN20

N2O

kgN

2O‐N/kgN

DefaultEntry

0

‐50

100

IPCC(2006)

Composting–Ammoniaemission(loss)

relativetototalnitrogeninmanure

EFNH3

NH3

kgNH3‐N/kgN

DefaultEntry

0.05

Hellebrand

andKalk

(2000)

ManureManagem

ent–MultipleSources–

collectionefficiency,coveredstorage(with

orwithoutcrust)

ηCH

4percentage

DefaultEntry

1

Sommeretal.

(2004)

ManureManagem


collectionefficiency,uncoveredstorage

withcrustformation

ηCH

4percentage

DefaultEntry

0

Sommeretal.

(2004)

ManureManagem


collectionefficiency,uncoveredstorage

withoutcrustform

ation

ηCH

4percentage

DefaultEntry

‐0.40

Sommeretal.

(2004)

ManureManagem


Ratecorrectingfactors(b

1)

b 1

CH4

dimensionless

DefaultEntry

1

Sommeretal.

(2004)

ManureManagem


Ratecorrectingfactors(b

2)

b 2

CH4

dimensionless

DefaultEntry

0.01

Sommeretal.

(2004)

ManureManagem


Arrheniusparam

eter,cattle

A

CH4

gCH

4/kgVS/hr

DefaultEntry

43.33

Sommeretal.

(2004)

ManureManagem


Arrheniusparam

eter,swine

A

CH4

gCH

4/kgVS/hr

DefaultEntry

43.21

Sommeretal.

(2004)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-53

8-53

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Potentialm

ethaneyieldofthemanure

cattle

E CH4,pot‐

CH4

kgCH4/kgVS

DefaultEntry

0.48

Sommeretal.

(2004)

Potentialm

ethaneyieldofthemanure‐

swine

E CH4,pot

CH4

kgCH4/kgVS

DefaultEntry

0.5

Sommeretal.

(2004)

ManureManagem


Kinem

aticviscosityofaire

vNH3

m2 /s

DefaultEntry

White(1999)

ManureManagem


MassdiffusivityofNH3e

D

NH3

m2 /s

DefaultEntry

Watson

(1966)and

Baker(1969)

Tem

porarystackandlong‐termstockpile–

Resistancetomasstransferthroughthe

manuree

Rs

NH3

s/m

DefaultEntry

Rotzetal.

(2011)

Tem


Resistancetomasstransferthroughthe

covere

Rc

NH3

s/m

DefaultEntry

Rotzetal.

(2011)

Tem


Ratiodegradablevolatilesolidstototal

volatilesolids‐cattleliquidmanure

VS d/VS T

CH4

Unitless

DefaultEntry

0.46

Mølleretal.

(2004)

Tem


Ratiodegradablevolatilesolidstototal

volatilesolids‐swineliquidmanure

VS d/VS T

CH4

Unitless

DefaultEntry

0.89

Mølleretal.

(2004)

Tem


RatioNon‐degradablevolatilesolidsto

totalvolatilesolids‐cattleliquidmanure

VS n

d/VS

T

CH4

Unitless

DefaultEntry

0.54

Mølleretal.

(2004)

Tem


Rationon‐degradablevolatilesolidsto

totalvolatilesolids–swineliquidmanure

VS n

d/VS

T

CH4

Unitless

DefaultEntry

0.11

Mølleretal.

(2004)

Solid‐liquidseparation–Efficiencyof

mechanicalsolid‐liquidseparation

e

CH4,N2O,

NH3

Percent

EntityEntry

Fordand

Flem

ing

(2002)

a Thevaluesformethaneconversionfactor(MCF)varydependingonthetemperatureandthemanuremanagem

entsystem.IPCC(2006)providesestimateduncertainty

rangesfortheseMCFs.

b TherearenodataforNorthAmericaregion;thedatafrom

WesternEuropeareusedtocalculatetheestimation.Thereisnoreporteduncertaintyforthisadaptedvalue.

c IPCC(2006)reportslargeuncertaintieswithdefaultN

2Oemissionfactors.TheN

2OEFvaluesvarydependingontheanimalspeciesandtemperatureofthemanure

managem

entsystem.

d ValuesforN2Oconversionfactorsareavailablefordairycow,cattle,swine,andotheranimalsandcanbefoundinthechapter.

e Defaultvaluesareavailableinthechapter.

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-54

Table8‐B‐6:ForestryUncertaintyTable

ForestrySub‐

SourceCategory

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

UrbanForestry–

AerialDataMethod

TreeCover

Percent

CO

2%

Model

Output

i‐TreeCanopy

UrbanForestry–

AerialDataMethod

UrbanArea

CO

2m

2 Entity

Entry

UrbanForestry–

AerialDataMethod

Average

Annual

Carbon

Sequestration

CO

2kg

C/m

2 /yearDefault

Entry

2.8

mean

14.814.8

Now

aketal.(2013)

UrbanForestry–

AerialDataMethod

Average

Carbon

Storage

CO

2kgC/m

2 Default

Entry

7.69

mean

Now

aketal.(2013)


8-55

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