Presentation is loading. Please wait.

Presentation is loading. Please wait.

Assessment of Agricultural Emission Abatement Potentials 1. Assess Local Management Potentials (= Technical Potentials) with Data and Simulation Models.

Similar presentations


Presentation on theme: "Assessment of Agricultural Emission Abatement Potentials 1. Assess Local Management Potentials (= Technical Potentials) with Data and Simulation Models."— Presentation transcript:

1 Assessment of Agricultural Emission Abatement Potentials 1. Assess Local Management Potentials (= Technical Potentials) with Data and Simulation Models (EPIC) 2. Determine Current Management Distribution (Need Good National Data!) 3. 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 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A 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 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A Data for bio-physical modelling in EU25

5 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A HRU delineation Slope Class: 1.0-3% 2.3-6% % % 5.… Altitude: 1.< 300 m m m 4.>1100 m Texture: 1.Coarse 2.Medium 3.Medium-fine 4.Fine 5.Very fine Stoniness: 1.Low content 2.Medium content 3.High content Soil Depth: 1.shallow 2.medium 3.deep

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

7 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A 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 lands Ø SOC 60 t/ha in topsoil

8 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A conv. => mini. till SOC conv. => redu. till increase SOC 0.18 t/ha/year increase SOC 0.11 t/ha/year

9 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A conv. => redu. tillconv. => mini. till Crop Yield DM Crop Yield t/ha, or -3.6% DM Crop Yield t/ha, or -7.9%

10 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A 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 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A 'indirect' N2O-N emissions'direct' N2O-N emissions N2O-N 5.3 kg/ha/yr Gg/yr N2O-N 0.9 kg/ha/yr 91.7 Gg/yr

12 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A conv. => mini. tillconv. => redu. till net-effect N2O-N kg/ha/yr Gg/yr net-effect N2O-N kg/ha/yr Gg/yr 'direct'

13 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A conv. => redu. tillconv. => mini. till 'indirect' net-effect N2O-N kg/ha/yr -5.9 Gg/yr net-effect N2O-N kg/ha/yr -8.0 Gg/yr

14 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A poplar coppicemiscanthus Ø 6.7 DM t/ha/yr Std: 1.5 t/ha/yr Ø 11.6 DM t/ha/yr Std: 4.0 t/ha/yr biomass

15 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A miscanthuspoplar coppice N2O-N 3.0 kg/ha/yr Gg/yr N2O-N 2.8 kg/ha/yr Gg/yr direct N2O

16 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A miscanthuspoplar coppice indirect N2O N2O-N 0.4 kg/ha/yr 36.1 Gg/yr N2O-N 0.8 kg/ha/yr 77.1 Gg/yr

17 Ϊ Ϊ Ϊ Ϊ Ϊ I N S E A 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 Food Timber Fiber Bioenergy Biomaterial Carbon Sinks Land use competition Nature Reserves Sealed Land

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

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

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

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

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

26 Agricultural Mitigation Potentials Carbon price (Euro/tce) Total Mitigation (mmtce) Technical Potential (EPIC) Economic Potential (EUFASOM)

27 EUFASOM More details

28 Important Equations Objective function (Total welfare equation) Objective function (Total welfare equation) Physical resource restrictions Physical resource restrictions Technical efficiency restrictions Technical efficiency restrictions Consumer preferences Consumer preferences Intertemporal Transition Restrictions Intertemporal Transition Restrictions Policy restrictions Policy restrictions

29 Ingredients of Equations Variables (endogenous) Variables (endogenous) Parameters (exogneous) Parameters (exogneous) Indexes (aggregate different cases of similar decisions [relationships] into one block variable [equation]) Indexes (aggregate different cases of similar decisions [relationships] into one block variable [equation]) Mathematical operators Mathematical operators

30 ParameterDescription  Technical coefficients (yields, requirements, emissions)  Objective function coefficients  Supply and demand functions  Supply and demand function elasticities  Discount rate, product depreciation, dead wood decomposition, state of nature probability  Resource endowments, (political) emission endowments Soil state transition probabilities  Land use change limits  Initial or previous land allocation  Alternative objective function parameters

31 VariableUnitTypeDescription CROP1E3 ha  0 Crop production PAST1E3 ha  0 Pasture LIVEmixed  0 Livestock raising FEEDmixed  0 Animal feeding TREE1E3 ha  0 Standing forests HARV1E3 ha  0 harvesting BIOM1E3 ha  0 Biomass crop plantations for bioenergy ECOL1E3 ha  0 Wetland ecosystem reserves LUCH1E3 ha  0 Land use changes RESRmixed  0 Factor and resource usage PROCmixed  0 Processing activities SUPP1E3 t  0 Supply DEMD1E3 t  0 Demand TRAD1E3 t  0 Trade EMITmixedFreeNet emissions STCKmixed  0 Environmental and product stocks WELF1E6 €FreeEconomic Surplus CMIX-  0 Crop Mix

32 IndexSymbolElements Time Periodst , , …, State of NaturekAlternative climate states Regionsr25 EU member states, 11 Non-EU international regions SpeciessAll individual and aggregate species categories Cropsc(s) Soft wheat, hard wheat, barley, oats, rye, rice, corn, soybeans, sugar beet, potatoes, rapeseed, sunflower, cotton, flax, hemp, pulse Treesf(s) Spruce, larch, douglas fir, fir, scottish pine, pinus pinaster, poplar, oak, beech, birch, maple, hornbeam, alnus, ash, chestnut, cedar, eucalyptus, ilex locust, 4 mixed forest types Perennialsb(s)Miscanthus, Switchgrass, Reed Canary Grass, Poplar,, Arundo, Cardoon, Eucalyptus Livestockl(s)Dairy, beef cattle, hogs, goats, sheep, poultry Wildlifew(s)43 Birds, 9 mammals, 16 amphibians, 4 reptiles Productsy17 crop, 8 forest industry, 5 bioenergy, 10 livestock Resources/InputsiSoil types, hired and family labor, gasoline, diesel, electricity, natural gas, water, nutrients Soil typesj(i)Sand, loam, clay, bog, fen, 7 slope, 4 soil depth classes Nutrientsn(i)Dry matter, protein, fat, fiber, metabolic energy, Lysine Technologiesm alternative tillage, irrigation, fertilization, thinning, animal housing and manure management choices Site qualityqAge and suitability differences Ecosystem statex(q)Existing, suitable, marginal Age cohortsa(q)0-5, 5-10, …, [years] Soil statevSoil organic classes StructuresuFADN classifications (European Commission 2008) Size classesz(u) = 100 all in ESU (European Commission 2008) Farm specialtyo(u) Field crops, horticulture, wine yards, permanent crops, dairy farms, grazing livestock, pigs and or poultry, mixed farms Altitude levelsh(u) 1100 meters Environmente16 Greenhouse gas accounts, wind and water erosion, 6 nutrient emissions, 5 wetland types PoliciespAlternative policies

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

34 Area underneath supply Market Equilibrium Demand Supply Price Quantity P* Q*

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

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

37 Consumer and Resource Surplus

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

39 Demand function Area underneath demand function Decreasing marginal revenues A constant elasticity demand function is uniquely defined by an observed price-quantity pair (p 0,q 0 ) and an estimated elasticity  (curvature) price sales Demand function q 00 p0p0 q0q0

40 Economic Surplus Maximization Implicit Supply and Demand Forest InventoryLand Supply Water Supply Labor Supply Animal Supply National Inputs Import Supply Processing Demand Feed Demand Domestic Demand Export Demand CS PS

41 Physical Resource Limits (r,t,i)

42 Forest Transistion Equations Standing forest area today + harvested area today <= forest area from previous period Equation indexed by k,r,t,j,v,f,u,a,m,p

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

44 Environmental Policy or

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

46 Commodity Equations (r,t,y) Demand  Supply

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


Download ppt "Assessment of Agricultural Emission Abatement Potentials 1. Assess Local Management Potentials (= Technical Potentials) with Data and Simulation Models."

Similar presentations


Ads by Google