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