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Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli
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Accounting Greenhouse Gas Emissions for Irish Agriculture: Known/Unknown - M.I. Khalli

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  • 1. Climate Change Research Programme EPA Greenhouse Gas Modelling Workshop held on 24 November 2010 in the Gresham Hotel, Dublin
  • 2. Reporting of total anthropogenic emissions &removals of GHG to EU & UNFCCC Six sectors: 1. Energy 2. Industrial processes 3. Solvents and other products 4. Agriculture 5. Land Use, Land Use Change and Forestry 6. Waste
  • 3. Methodological Development Tier 1: Simple approach, relies on default emission factor (EF) drawn from previous studies and even somewhat on Activity Data (AD) Tier 2: Complex approach, requires detailed country-specific data derived from enhanced characterisation- disaggregated. Tier 3: Models (ecosystem/process-based), taking into account the country-specific measured data as well as soil and environmental conditions. Moving from Tier 1 to Tier 2 and 3 depending on robust data available under Irish conditions
  • 4. Carbon and Nitrogen Accounting • Tier 2 approach over Tier 1 would provide better estimates, depending on the variability of soil organic carbon (SOC) and N dynamics. • Tier 3 approach reflects robust emission accounting and identify mitigation options but needs to include more variables regulating GHG emissions. • Tier 3 also provide a flexible and a robust way to assess how different scenarios and measures for land use management (LUM) and change (LUC) can affect soil C and N dynamics. • Even the best models require measurement-based validation at field scale and therefore benchmark sites are required. • Combining modelling and geostatistical techniques may be a better option to assess and project soil C and N stocks/emissions.
  • 5. Preliminary Concepts: SOIL CARBONMONITORING, ACCOUNITNG & REPORTING Step 6: Develop SOC Map (Arc- GIS) and update/improve data Step 1: Data acquisition (National Database & Others Step 5: Total C stock by • Identify locations, relevant) integration, meta-analysis missing land parcels & land transition factors & new soil C data. • Predict coefficients of change for major land use categories. • Update LULUCF. • Prioritise research gaps. Step 2: Data Compilation (Depth Distribution: LU, Soil Step 4: Develop 3D SOC type, Climate, etc.) model by substitution of empirical models Step 3: Synthesize/develop empirical models using pedotransfer functions
  • 6. Step 1: Data Acquisition CORINE Land Cover (LC), National Soil Database (NSDB), Kiely et al. (2009), Land Parcel Information System (LPIS), Soil Maps and Others• 1 km Buffer on Irish National Grid: SOC under a LC contains a Great Soil Group (GSG) >50% area
  • 7. Number of Sites/Land Cover and Great SoilGroup (GSG) represented Grassland Rough Arable Others Gleys 83 10 7 Podzols 15 3 NA Brown Podzolics 50 1 12  Soil depth: 0-10 cm 111 SOC (confidence 75%), no Bulk Grey Br Podzolics for 9 16 density Brown Earth 66 NA 5  Some anomalies in representing5major soil group Lithosols 3 NA  Specific LU absent 4 Rendzinas 2 NA Peats 18 21 6 (?) Regosol/Sand 0 0 0 Total 350 51 46 581 NA = Not available
  • 8. Number of sites and GSG represented Kiely et al. (2009) database Grassland Arable Rough Forest Peat 29 (7) 12 (4) 10 (4) 9 (5) 11 (3)  Soil depth: 0-50 cm, no matching SOC with bulk density (BD)  Representation of all GSGs under a LC is not available  Specific LU information, as of NSDB, are absent  SOC contents are highly variable with NSDB.
  • 9. Step 2: Data Compilation(Depth Distribution: LU/LC, Soil type, Climate, etc.) • In addition to 50 cm depth, SOC for arable and grassland measured at 100 cm depth are also included. • Non-linear relationship between soil depth, SOC and bulk density (BD) are adopted. • Empirical equations are developed to estimate SOC and BD (to calculate soil mass) down to 100 cm except Rendzinas to 50 cm.
  • 10. Step 3: Synthesize/develop empirical models using pedotransfer functions  Data for SOC in the NSDB are up to10 cm depth and that original data are taken to calculate its stocks as: SOC (Z 10cm) = SOCz10  SOC for depths (Z) >10 cm are calculated using empirical models developed from the measured/interpolated SOC ratio functions with depth as: SOC (Z >10cm) = a e(b*z)*SOCz10  Due to lack of BD information in the NSDB, empirical models are also developed from measured/interpolated data to calculate it, as: BD (Z=10-100 cm) = a e(b*SOCz)
  • 11. SOC distribution ratio with soil depth: GrasslandGreat Soil LC Specific LCS (All)Group Soil Type Specific (STS, Mean) (LCS, Mean)Gleys 1.3397*e(-0.034*z)*SOCz10; (R2 = 0.998) 1.3620 1.3071 * e(-0.035*z) *e(-0.034*z)Podzols 1.4432*e(-0.040*z)*SOCz10; (R2 = 0.953) *SOCz10 *SOCz10Brown Podzolics 1.4275*e(-0.035*z)*SOCz10; (R2 = 0.999) (R2 = 0.999) (R2 = 0.894)Grey B. Podzols 1.2800*e(-0.034*z)*SOCz10; (R2 = 0.995)Brown Earth 1.4356*e(-0.034*z)*SOCz10; (R2 = 0.999)Lithosols a 1.0611*e(-0.057*z)*SOCz10; (R2 = 0.974)Rendzinas b 1.9042*e(-0.040*z)*SOCz10; (R2 = 0.968)Peats c 0.9206*e(-0.037*z)*SOCz10; (R2 = 0.918)Sand d 0.8167*e(-0.019*z)*SOCz10; (R2 = 0.890)a= df rough; b= df IFS 12, 22 &31, rep BE & peat mineral; c= df from both grass * peat; d= Original
  • 12. BD from pedotransfer function (SOC): GrasslandGreat Soil STS (Mean) LCS (Mean) LCS (All)GroupGleys 1.4725*e(-0.085*SOCz); (R2 = 0.998) 1.3582 1.3949 *e(-0.074*SOCz); *e(-0.084*SOCz);Podzols 1.7859*e(-0.104*SOCz); (R2 = 0.918) (R2 = 0.990) (R2 = 0.643)Brown Podzolics 1.1509*e(-0.044*SOCz); (R2 = 0.964)Grey Br. Podzols 1.4306*e(-0.089*SOCz); (R2 = 0.998)Brown Earth 1.2400*e(-0.047*SOCz); (R2 = 0.988)Lithosols a 0.8593*e(-0.033*SOCz); (R2 = 0.908)Rendzinas b 1.1730*e(-0.050*SOCz); (R2 = 0.936)Peats c 1.1078*e(-0.003*SOCz); (R2 = 0.830)Sand d 1.1858*e(-0.0025*SOCz); (R2 = 0.956)a= df rough; b= df IFS 12, 22 &31, rep BE & peat mineral; c= df from both grass * peat; d= Original
  • 13. Rough and Arable SOC distribution ratio with soil depth BD from pedotransfer function (SOC)
  • 14. Step 4/5: Depth distribution of SOC stocks for each GSG STS equations better represent SOC stocks with depth for a particular soil.LCS would provide similar estimate ofSOC stocks in a LC but either over- orunder-estimate for a soil type
  • 15. Depth distribution of SOC stocks for major LC± peat Large variability in SOC stocks under a LC can be reduced by separating peats from other soil types STS could best estimate of SOC density. For 0-30 cm: Grass = 1 Rough = 1.57 (+67 t) Arable = 0.74 (-30 t) Representative samplings for peats could better estimate SOC under a LC.
  • 16. LU areas covering IS and GSG derivedfrom overlaying LPIS, GSM and ISM ISM/GSM LPIS Map
  • 17. OC stocks (STS) in Indicative soils (IS) & GSG SOC stocks are calculated using the equations developed but covering soils of ISM and GSM • Giving higher level of disaggregation for SOC across soil depth
  • 18. OC stocks (STS) in IS & GSG Showing higher SOC stocks than grassland in all soil types and depths
  • 19. SC stocks (STS) in IS & GSG Demonstrating lower SOC stocks than grassland and rough. Peats under arable are misplacement/anomalies
  • 20. Disaggregated total SOC stocks (STS)under grassland (LPIS 2004) Calculation: LC LU GSG ISM Area (ha) Pasture = 4,328,569 Rough = 3,185 Hay = 81 Silage = 1,173 Total = 4,333,009 Disaggregation of grassland using LPIS is non-realistic due to identification problems of LU by farmers but CSO
  • 21. Disaggregated total SOC stocks (STS)under arable crops (LPIS 2004) CSO reported area = 424,000 ha: This underestimation is related to areas misplaced /identification error in the LPIS but exist, requiring re-synthesis
  • 22. National SOC stocks: Other stocks derivedfrom Eaton et al. (2008)
  • 23. Conclusions and further studies  The empirical approaches provide robust estimate of SOC stocks for the development of Tier 2 through 3 and thereby for LUC.  It can further be improved through elimination of following anomalies: * Missing/misplaced LU area in the LPIS * Missing SOC data for soil types under various LU * Inclusion of LUM and Input categories in the LPIS, advantageous  Update/improve data for LPIS and refine SOC & develop Maps (Step 5 & 6).  Accounting N2O emission for Irish agriculture using same data sources.  LULUCF: Land transition factors (LU, LUM & Input) through Meta-analysis, leading to Tier 2 development.  Develop/validate models for GHG accounting through geo-regression using LU, soil & environmental variables .  Identify research gaps
  • 24. Acknowledgements  Christoph Müller and Tom Bolger, UCD  Phillip O’Brien and Frank McGovern, EPA  Ger Kiely, UCC  Gary Lanigan and Karl Richards, Teagasc  Researchers from UCD, TCD, UL, UCC... ….

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