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Assessment of Agricultural Emission Abatement Potentials

Assessment of Agricultural Emission Abatement Potentials. Assess Local Management Potentials (= Technical Potentials) with Data and Simulation Models ( EPIC ) Determine Current Management Distribution ( Need Good National Data! ) Assess Cost Functions (= Economic Potentials) with EUFASOM.

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Assessment of Agricultural Emission Abatement Potentials

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  1. Assessment of Agricultural Emission Abatement Potentials • Assess Local Management Potentials (= Technical Potentials) with Data and Simulation Models (EPIC) • Determine Current Management Distribution (Need Good National Data!) • Assess Cost Functions (= Economic Potentials) with EUFASOM

  2. 1 Assessment of Technical Potentials Erwin Schmid University of Natural Resources and Applied Life Sciences, Vienna

  3. Problem Statement and Research Objective Bio-physical Impacts of land use management are usually discontinuous outcomes of stochastic natural processes (erosion, leaching, etc.) under certain local conditions (weather, soil, topography, management, etc.). Concept of Homogeneous Response Units (HRU) + bio-physical process model EPIC Tool providing spatially and temporally explicit bio-physical impact vectors: Comparative Dynamic Impact Analysis Consistent Linkage with Economic Land use Optimisation Models

  4. Data for bio-physical modelling in EU25

  5. HRU delineation • Slope Class: • 0-3% • 3-6% • 6-10% • 10-15% • … • Altitude: • < 300 m • 300-600 m • 600-1100 m • >1100 m • Texture: • Coarse • Medium • Medium-fine • Fine • Very fine • Soil Depth: • shallow • medium • deep • Stoniness: • Low content • Medium content • High content

  6. CORINE-PELCOM PTF (Hyprese, pH, BD ...) Weather,Crop Rotation, and Crop Management NUTS2-level Data Processing daily time steps EPIC Simulations bio-physical Impacts EPIC INPUT DATABASE for soil and topographic parameters

  7. Scenario Analysis I) Alternative Crop Residue Systems: 1) conventional tillage ~5% of crop residues after crop planting 2) reduced tillage ~15% of crop residues after crop planting 3) minimum tillage ~40% of crop residues after crop planting II) Biomass Production Systems: 4) miscanthus 5) poplar coppice 9555 HRUs arable landsØ SOC 60 t/ha in topsoil

  8. conv. => redu. till SOC conv. => mini. till increase SOC0.18 t/ha/year increase SOC0.11 t/ha/year

  9. conv. => redu. till Crop Yield conv. => mini. till DM Crop Yield-0.30 t/ha, or -7.9% DM Crop Yield-0.13 t/ha, or -3.6%

  10. N2O-N emissions • IPCC default values for direct and indirect N2O-N emissions • We base it on • nitrification (0.54%), and • de-nitrification (11%).Khalil, Mary, and Renault (2004) in Soil Biology & Biochemistry. => 'direct' N2O-N emissions • 'indirect'N2O-N emissions we use N in leaching (2.5%), run-off (2.5%), volatiliziation (1%)

  11. 'direct' N2O-N emissions 'indirect' N2O-N emissions N2O-N5.3 kg/ha/yr511.9 Gg/yr N2O-N0.9 kg/ha/yr91.7 Gg/yr

  12. conv. => redu. till 'direct' conv. => mini. till net-effect N2O-N-0.12 kg/ha/yr-12.5 Gg/yr net-effect N2O-N-0.38 kg/ha/yr-37.1 Gg/yr

  13. conv. => redu. till 'indirect' conv. => mini. till net-effect N2O-N-0.08 kg/ha/yr-8.0 Gg/yr net-effect N2O-N-0.06 kg/ha/yr-5.9 Gg/yr

  14. miscanthus biomass poplar coppice Ø 11.6 DM t/ha/yr Std: 4.0 t/ha/yr Ø 6.7 DM t/ha/yr Std: 1.5 t/ha/yr

  15. miscanthus direct N2O poplar coppice N2O-N3.0 kg/ha/yr293.9 Gg/yr N2O-N2.8 kg/ha/yr275.2 Gg/yr

  16. miscanthus indirect N2O poplar coppice N2O-N0.4 kg/ha/yr36.1 Gg/yr N2O-N0.8 kg/ha/yr77.1 Gg/yr

  17. Conclusions Tool -HRU concept and EPIC- addressing land use and management specific bio-physical impacts spatially and temporally explicit! a change in Crop Residue Systems increases SOC by 0.1 and 0.2 t/ha/yr (c.p.) reduces direct N2O-N emissions at EU25 level by 2.4% and 7.2% reduces indirect N2O-N emissions at EU25 level by 6.4% and 8.7% but with +/- effects locally reduces crop yield output by 4% and 8% (c.p.) other side effects (increased pesticide use, fertilizer, etc.) evaluate environmental impacts of biomass production systems

  18. 2 Assesment of Economic Potentials

  19. The European Forest and Agricultural Sector Optimization Model (EUFASOM) Uwe A. Schneider Research Unit Sustainabilty and Global Change Hamburg University

  20. Bioenergy Biomaterial Food Timber Fiber Land use competition Nature Reserves Carbon Sinks Sealed Land

  21. EUFASOM • Partial Equilibrium Model • Maximizes sum of consumer and producer surplus • Constrained by resource endowments, technologies, policies • Spatially explicit, discrete dynamic • Integrates environmental effects • Programmed in GAMS

  22. Model Structure Limits Limits Resources Land Use Technologies Products Markets Inputs Demand Functions, Trade Processing Technologies Environmental Impacts Supply Functions Limits

  23. Model Structure Forest Inventory Cropland Domestic demand Markets Water Forestry, Nature, Crop production Export Labor Processing Import Other Inputs Livestock production Feed mixing Pasture

  24. Spatial Resolution • Political regions • Ownership (forests) • Farm types • Farm size • Soil texture • Stone content • Altitude levels • Slopes • Soil state • Many crop and tree species • Tillage, planting irrigation, fertilization harvest regime

  25. Dynamics • 5 (to 20) year time steps • State of forests (and soil organic matter) • Technical progress • Demand & industry growth • Resource and global change • Policy scenarios

  26. Agricultural Mitigation Potentials 500 450 400 Technical Potential (EPIC) 350 Economic Potential (EUFASOM) 300 Carbon price (Euro/tce) 250 200 150 100 50 0 0 100 200 300 400 500 600 700 800 Total Mitigation (mmtce)

  27. EUFASOM More details

  28. Important Equations • Objectivefunction (Total welfareequation) • Physicalresourcerestrictions • Technical efficiencyrestrictions • Consumer preferences • Intertemporal Transition Restrictions • Policyrestrictions

  29. IngredientsofEquations • Variables (endogenous) • Parameters (exogneous) • Indexes (aggregate different casesofsimilardecisions [relationships] intoone block variable [equation]) • Mathematicaloperators

  30. Objective Function Maximize + Area underneath demand curves - Area underneath supply curves - Costs ± Subsidies / Taxes from policies The maximum equilibrates markets!

  31. Market Equilibrium Price Area underneathdemand curve Supply P* Area underneathsupply Demand Q* Quantity

  32. Market Equilibrium At the intersection of supply and demand function (equilibrium), the sum of consumer and producer surplus is maximized Price Supply Consumer Surplus P* Producer Surplus Demand Q* Quantity

  33. Basic Objective Function Terminal value of standing forests Discount factor x State of nature probability Consumer surplus Resource surplus Costs of production and trade

  34. Consumer and Resource Surplus

  35. Economic Principles • Rationality ("wanting more rather than less of a good or service") • Law of diminishing marginal returns • Law of increasing marginal cost

  36. Demand function price • Decreasing marginal revenues • A constantelasticitydemandfunctionisuniquelydefined byan observedprice-quantity pair (p0,q0) and an estimatedelasticity  (curvature) Area underneath demand function Demand function p0 sales q00 q0

  37. Land Supply Forest Inventory Processing Demand Water Supply CS Domestic Demand PS Labor Supply Implicit Supply and Demand Feed Demand Animal Supply National Inputs Export Demand Import Supply Economic Surplus Maximization

  38. Physical Resource Limits(r,t,i)

  39. Forest Transistion Equations • Standing forest area today + harvested area today <= forest area from previous period • Equation indexed byk,r,t,j,v,f,u,a,m,p

  40. Emission(Environmental Impact) Accounting Equation(k,r,t,e)

  41. Environmental Policy or

  42. Industrial Processing (k,r,t,y) • Processing activities can be bounded (capacity limits) or enforced (e.g. when FASOM is linked to other models)

  43. Commodity Equations (r,t,y) Demand  Supply

  44. Duality restrictions (k,r,t,u) Observed crop mixes • Prevent extreme specialization • Incorporate difficult to observe data • Calibrate model based on duality theory • May include „flexibility contraints“ Crop Mix Variable No crop (c) index! Crop Area Variable Past periods

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