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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. - PowerPoint PPT Presentation
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Assessment of Agricultural Assessment of Agricultural Emission Abatement PotentialsEmission Abatement Potentials
1.1. Assess Local Management Potentials (= Assess Local Management Potentials (= Technical Potentials) with Data and Technical Potentials) with Data and Simulation Models (Simulation Models (EPICEPIC))
2.2. Determine Current Management Determine Current Management Distribution (Distribution (Need Good National Data!Need Good National Data!))
3.3. Assess Cost Functions (= Economic Assess Cost Functions (= Economic Potentials) with Potentials) with EUFASOMEUFASOM
1 1 Assessment of Technical Assessment of Technical
PotentialsPotentials
Erwin SchmidErwin Schmid
University of Natural Resources University of Natural Resources and Applied Life Sciences, Viennaand Applied Life Sciences, Vienna
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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
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Data for bio-physical modelling in EU25
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HRU delineation
Slope Class:1. 0-3%2. 3-6%3. 6-10%4. 10-15%5. …
Altitude:1. < 300 m2. 300-600 m3. 600-1100 m4. >1100 m
Texture:1. Coarse2. Medium3. Medium-fine4. Fine 5. Very fine
Stoniness:1. Low content2. Medium content3. High content
Soil Depth:1. shallow2. medium3. deep
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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-PELCOMNUTS2-level
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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
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conv. => mini. tillSOCconv. => redu. till
increase SOC0.18 t/ha/year
increase SOC0.11 t/ha/year
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conv. => redu. till conv. => mini. tillCrop
Yield
DM Crop Yield -0.13 t/ha, or
-3.6%
DM Crop Yield -0.30 t/ha, or
-7.9%
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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%)
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'indirect' N2O-N
emissions'direct' N2O-N
emissions
N2O-N 5.3 kg/ha/yr 511.9 Gg/yr
N2O-N 0.9 kg/ha/yr
91.7 Gg/yr
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conv. => mini. tillconv. => redu. 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
'direct'
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conv. => redu. till conv. => mini. till'indirect
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net-effect N2O-N -0.06 kg/ha/yr
-5.9 Gg/yr
net-effect N2O-N -0.08 kg/ha/yr
-8.0 Gg/yr
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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
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miscanthus poplar coppice
N2O-N 3.0 kg/ha/yr 293.9 Gg/yr
N2O-N 2.8 kg/ha/yr 275.2 Gg/yr
direct N2O
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miscanthus poplar coppiceindirect
N2O
N2O-N 0.4 kg/ha/yr
36.1 Gg/yr
N2O-N 0.8 kg/ha/yr
77.1 Gg/yr
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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
2 2 Assesment of Economic Assesment of Economic
PotentialsPotentials
The European Forest and The European Forest and Agricultural Sector Optimization Agricultural Sector Optimization
Model (EUFASOM)Model (EUFASOM)
Uwe A. SchneiderUwe A. SchneiderResearch Unit Sustainabilty and Global ChangeResearch Unit Sustainabilty and Global Change
Hamburg UniversityHamburg University
FoodTimberFiber
BioenergyBiomaterial
Carbon Sinks
Land use competition Nature
Reserves
SealedLand
EUFASOMEUFASOM
Partial Equilibrium Model Partial Equilibrium Model Maximizes sum of consumer and producer Maximizes sum of consumer and producer
surplussurplus Constrained by resource endowments, Constrained by resource endowments,
technologies, policiestechnologies, policies Spatially explicit, discrete dynamicSpatially explicit, discrete dynamic Integrates environmental effectsIntegrates environmental effects Programmed in GAMSProgrammed in GAMS
Model StructureModel Structure
Resources Land Use
Technologies
Processing Technologies
Products Markets
Inputs
Limits
Supply Functions
Limits
Demand Functions,Trade
Limits
Environmental Impacts
Processing
Markets
Feed mixing
Labor
Pasture
Other Inputs
Cropland
Water
Livestock production
Forestry, Nature,Crop
productionExport
Domestic demand
Import
Model StructureModel StructureForest
Inventory
Spatial ResolutionSpatial Resolution
Soil textureSoil texture Stone contentStone content Altitude levelsAltitude levels SlopesSlopes Soil stateSoil state
Political regionsPolitical regions Ownership Ownership
(forests)(forests) Farm typesFarm types Farm sizeFarm size
Many crop and tree Many crop and tree speciesspecies
Tillage, planting Tillage, planting irrigation, fertilization irrigation, fertilization harvest regimeharvest regime
DynamicsDynamics
5 (to 20) year time steps5 (to 20) year time steps State of forests (and soil organic matter)State of forests (and soil organic matter) Technical progressTechnical progress Demand & industry growthDemand & industry growth Resource and global changeResource and global change Policy scenariosPolicy scenarios
Agricultural Mitigation PotentialsAgricultural Mitigation Potentials
0
50
100
150
200
250
300
350
400
450
500
0 100 200 300 400 500 600 700 800
Car
bon
pric
e (E
uro/
tce)
Total Mitigation (mmtce)
TechnicalPotential (EPIC)
EconomicPotential(EUFASOM)
EUFASOMEUFASOM
More detailsMore details
Important EquationsImportant Equations
Objective function (Total welfare equation)Objective function (Total welfare equation)
Physical resource restrictionsPhysical resource restrictions
Technical efficiency restrictionsTechnical efficiency restrictions
Consumer preferencesConsumer preferences
Intertemporal Transition RestrictionsIntertemporal Transition Restrictions
Policy restrictionsPolicy restrictions
Ingredients of EquationsIngredients of Equations
Variables (endogenous)Variables (endogenous)
Parameters (exogneous)Parameters (exogneous)
Indexes (aggregate different cases of Indexes (aggregate different cases of similar decisions [relationships] into one similar decisions [relationships] into one block variable [equation])block variable [equation])
Mathematical operatorsMathematical operators
Parameter Description
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
Variable Unit Type DescriptionCROP 1E3 ha 0 Crop productionPAST 1E3 ha 0 Pasture LIVE mixed 0 Livestock raisingFEED mixed 0 Animal feeding TREE 1E3 ha 0 Standing forestsHARV 1E3 ha 0 harvestingBIOM 1E3 ha 0 Biomass crop plantations for bioenergy ECOL 1E3 ha 0 Wetland ecosystem reservesLUCH 1E3 ha 0 Land use changesRESR mixed 0 Factor and resource usagePROC mixed 0 Processing activitiesSUPP 1E3 t 0 SupplyDEMD 1E3 t 0 DemandTRAD 1E3 t 0 TradeEMIT mixed Free Net emissionsSTCK mixed 0 Environmental and product stocksWELF 1E6 € Free Economic SurplusCMIX - 0 Crop Mix
Index Symbol ElementsTime Periods t 2005-2010, 2010-2015, …, 2145-2150State of Nature k Alternative climate statesRegions r 25 EU member states, 11 Non-EU international regions Species s All individual and aggregate species categories
Crops c(s)Soft wheat, hard wheat, barley, oats, rye, rice, corn, soybeans, sugar beet, potatoes, rapeseed, sunflower, cotton, flax, hemp, pulse
Trees f(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
Perennials b(s) Miscanthus, Switchgrass, Reed Canary Grass, Poplar, , Arundo, Cardoon, Eucalyptus Livestock l(s) Dairy, beef cattle, hogs, goats, sheep, poultry Wildlife w(s) 43 Birds, 9 mammals, 16 amphibians, 4 reptilesProducts y 17 crop, 8 forest industry, 5 bioenergy, 10 livestockResources/Inputs i Soil types, hired and family labor, gasoline, diesel, electricity, natural gas, water, nutrients Soil types j(i) Sand, loam, clay, bog, fen, 7 slope, 4 soil depth classes Nutrients n(i) Dry matter, protein, fat, fiber, metabolic energy, Lysine
Technologies malternative tillage, irrigation, fertilization, thinning, animal housing and manure management choices
Site quality q Age and suitability differences Ecosystem state x(q) Existing, suitable, marginal Age cohorts a(q) 0-5, 5-10, …, 295-300 [years]Soil state v Soil organic classesStructures u FADN classifications (European Commission 2008) Size classes z(u) < 4, 4 - < 8, 8 - < 16, 16 - < 40, 40- < 100, >= 100 all in ESU (European Commission 2008)
Farm specialty o(u)Field crops, horticulture, wine yards, permanent crops, dairy farms, grazing livestock, pigs and or poultry, mixed farms
Altitude levels h(u) < 300, 300 – 600, 600 – 1100, > 1100 meters
Environment e 16 Greenhouse gas accounts, wind and water erosion, 6 nutrient emissions, 5 wetland types
Policies p Alternative policies
Objective Function
Maximize+ Area underneath demand curves- Area underneath supply curves- Costs± Subsidies / Taxes from policies
The maximum equilibrates markets!
Area
underneath supply
Market Equilibrium
Demand
Supply
Price
Quantity
P*
Q*
Market Equilibrium
Demand
Supply
Price
Quantity
P*
Q*
ProducerSurplus
ConsumerSurplus
At the intersection of supply and demand function
(equilibrium), the sum of consumer and producer
surplus is maximized
k,t
TREEk,t k,t k,r, j,v,f ,u,a ,m,p k,r,T, j,v,f ,u,a ,m,p
k,t k,r, j,v,f ,u,a ,m,p
k,t
CS
Max WELF RS TREE
C
Basic Objective Function
Terminal value of standing forests
Discount factor xState of nature probability
Consumer surplusResource surplusCosts of production and trade
k,r,t ,yk,r,y
k,t k,t k,r,t ,yk,r,y
k,r,t ,ik,r,i
DEMD d
CS RS SUPP d
RESR d
DEMDr,t,y
SUPPr,t,y
RESRr,t,i
Consumer and Resource Surplus
Economic Principles
• Rationality ("wanting more rather than less of a good or service")
• Law of diminishing marginal returns • Law of increasing marginal cost
Demand function
Area underneath demand function
0 0, p ,qDEMDr,t,y
•Decreasing marginal revenues•A constant elasticity demand function is uniquely defined by an observed price-quantity pair (p0,q0) and an estimated elasticity (curvature)
price
sales
Demand function
q00
p0
q0
q(p) p
p q
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
Physical Resource
Limits(r,t,i)
CROPk,r,t , j,v,c,u,q,m,p,i k,r,t , j,v,c,u,q,m,p
k, j,v,c,u,q,m,p
PASTk,r,t , j,v,s,u,q,m,p,i k,r,t , j,v,s,u,q,m,p
k, j,v,s,u,q,m,p
BIOMk,r,t , j,v,b,u,q,m,p,i k,r,t , j,v,b,u,q,m,p
k, j,v,b,u,q,m,p
CROP
PAST
BIOM
HARVk,r,t , j,v,f ,u,a ,m,p,i k,r,t , j,v,f ,u,a ,m,p
k, j,v,f ,u,a ,m,p
TREEk,r,t , j,v,f ,u,a ,m,p,i k,r,t , j,v,f ,u,a,m,p
k, j,v,f ,u,a ,m,p
ECOLk,r,t , j,v,s,u,x,m,p,i k,r,t , j,v,s,u,x,m,p
k, j,v,s,u,x,m,
HARV
TREE
ECOL
r,t ,i
p
LIVEk,r,t ,l,u,m,p,i k,r,t ,l,u,m,p
k,l,u,m,p
PROCk,r,t ,m,i k,r,t ,m
k,m
FEEDk,r,t ,l,m,i k,r,t ,l,m
k,l,m
LIVE
PROC
FEED
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
k,r,t 1, j,v,f ,u,a 1,m,p t 1 a 1k,r,t , j,v,f ,u,a ,m,p a 1
k,r,t 1, j,v,f ,u,a ,m,p t 1 a Ak,r,t , j,v,f ,u,a ,m,p a 1
r, j,v,f ,u,a ,m,p t 1
TREETREE
TREEHARV
INIT
Emission(Environmental
Impact) Accounting
Equation(k,r,t,e)
CROPk,r,t,j,v,c,u,q,m,p,e k,r,t , j,v,c,u,q,m,p
j,v,c,u,q,m,p
PASTk,r,t,j,v,c,u,q,m,p,e k,r,t , j,v,c,u,q,m,p
j,v,c,u,q,m,p
BIOMk,r,t,j,v,b,u,q,m,p,e k,r,t , j,v,b,u,q,m,p
j,v,b,u
k,r,t ,e
CROP
PAST
BIOM
EMIT
,q,m,p
TREEk,r,t,j,v,f,u,a,m,p,e k,r,t , j,v,f ,u,a ,m,p
j,v,f ,u,a ,m,p
ECOLk,r,t,j,v,s,u,x,m,p,e k,r,t , j,v,s,u,x,m,p
j,v,s,u,x,m,p
LIVEk,r,t,s,u,m,p,e k,r,t,s,u,m,p
s,u,m,p
k,r,t ,s,u, , ,
TREE
ECOL
LIVE
LUCHe k,r,t ,s,u, ,
s,u, ,
PROCk,r,t ,m,e k,r,t ,m
m
FEEDk,r,t ,l,m,e k,r,t ,l,m
m,l
STCKk,r,t ,d,e k,r,t ,d k,r,t 1,d
d
LUCH
PROC
FEED
STCK STCK
Environmental Policy
k,r,t ,e r,t ,eEMIT
k r,t ,e k,r,t ,ek,r,t ,e
WELF ( ) EMIT
or
PROCr,t ,m,y k,r,t ,m
m
PROC 0
Industrial Processing (k,r,t,y)
• Processing activities can be bounded (capacity limits) or enforced (e.g. when FASOM is linked to other models)
CROPr,t , j,v,c,u,q,m,p,y r,t , j,v,c,u,q,m,p
j,v,c,u,q,m,p
PASTr,t , j,v,s,u,q,m,p,y r,t , j,v,
PROCr,t ,m,y r,t ,m
m
FEEDr,t ,l,m,y r,t ,l,m
m
r,r ,t ,yr
r,t ,y
CROP
PAST
PROC
FEED
TRAD
DEMD
s,u,q,m,pj,v,s,u,q,m,p
BIOMr,t , j,v,b,u,q,m,p,y r,t , j,v,b,u,q,m,p
j,v,b,u,q,m,p
HARVr,t , j,v,f ,u,a ,m,p,y r,t , j,v,f ,u,a ,m,p
j,v,f ,u,a ,m,p
TREEr,t , j,v,f ,u,a ,m,p,y r,t , j,v,f ,u,a ,m,p
j,v,f ,u,
BIOM
HARV
TREE
a,m,p
ECOLr,t , j,v,s,u,x,m,p,y r,t , j,v,s,u,x,m,p
j,v,s,u,x,m,p
LIVEr,t ,l,u,m,p,y r,t ,l,u,m,p
l,u,m,p
r,r,t ,yr
r,t ,y
ECOL
LIVE
TRAD
SUPP
Commodity Equations
(r,t,y)
Demand Supply
Duality restrictions (k,r,t,u)
• Prevent extreme specialization• Incorporate difficult to observe data• Calibrate model based on duality theory• May include „flexibility contraints“
CMIXr,t , j,v,c,u,q,m,p r,t ,c,u r,t ,t ,u
k, j,v,c,q,m,p t
CROP CMIX
Past periods
Observed crop mixes
Crop Mix VariableNo crop (c) index!
Crop Area Variable