The European Forest and Agricultural The European Forest and Agricultural Sector Optimization ModelSector Optimization Model

Uwe A. Schneider (Land Use Economics)

ContributorsChristine Schleupner (Wetland Geography)

Kerstin Jantke (Wetland Biology)Erwin Schmid (Crop Simulation)

C. Ivie Ramos (Bioenergy Options)

FOREST SECTOR MODELING

STATE-OF-THE-ART AND FUTURE CHALLENGES IN AN EXPANDING GLOBAL MARKETPLACE

November 17-20, 2008 Seattle, Washington, USA

EUFASOM CharacteristicsEUFASOM Characteristics

Partial Equilibrium, Bottom-Up Model Maximizes sum of consumer and producer

surplusConstrained by resource endowments,

technologies, policiesSpatially explicit, discrete dynamic Integrates environmental effectsProgrammed in GAMS, Solved as LP

FoodTimberFiber

BioenergyBiomaterial

Carbon Sinks

Land use competition Nature

Reserves

SealedLand

EUFASOM EUFASOM StructureStructure

Resources Land Use

Technologies

Processing Technologies

Products Markets

Inputs

Limits

Supply Functions

Limits

Demand Functions,Trade

Limits

Environmental Impacts

Economic Surplus MaximizationEconomic Surplus Maximization

Mar

ket E

quili

briu

m

For

est I

nven

tory

Lan

d S

uppl

yW

ater

Sup

ply

Lab

or S

uppl

yN

atio

nal I

nput

s

Impo

rt S

uppl

y

Pro

cess

ing

Dem

and

Fee

d D

eman

dD

omes

tic D

eman

dE

xpor

t Dem

and

CS

P

S

EUFASOM EUFASOM Modeling SystemModeling System

EUFASOM

Crop & Tree Simulation Models

Spatial Analysis Tools

Farm level & GIS Data Viable

Population Analysis

Systematic Wetland Conservation Planning

Engineering Equations

Other Economic Models

Climate Models

Novel FeaturesNovel Features

Biodiversity (Wetlands)

Markov Chains (against curse of

dimensionality)

Wetland BiodiversityWetland Biodiversity

Physical Wetland

Potentials

SpeciesConservation

TargetsSystematic Conservation

Planning

EUFASOM

Reserve Locations

Land Prices

Physical Wetland PotentialsPhysical Wetland Potentials

Spatial Analysis of Wetlands

Peatland (Fens, Bogs)

Wetforests

Marshes, Reeds, Sedges

Open Waters

Existing WetlandsPotential WetlandsOpen Waters

Systematic Conservation Systematic Conservation PlanningPlanning

Viable Population Analysis

69 69 Vertebrate Vertebrate

WetlandWetlandSpeciesSpecies

BiodiversityBiodiversityScopeScope

Mammals

1. Castor fiber Eurasian BeaverEuropäischer Biber

2. Galemys pyrenaicus Pyrenean Desman Pyrenäen-Desman

3. Lutra lutra European Otter Fischotter4. Microtus cabrerae Cabrera's Vole Cabreramaus5. Microtus oec. arenicola Dutch Root Vole

Niederländische Wühlmaus6. Microtus oec. mehelyiPannonian Root Vole

Ungarische Wühlmaus7. Mustela lutreola European Mink Europäischer

Nerz8. Myotis capaccinii Long-fingered Bat

Langfußfledermaus9. Myotis dasycneme Pond Bat TeichfledermausReptiles

1. Elaphe quatuorlineata Four-lined Snake Vierstreifennatter2. Emys orbicularis European Pond Tortoise Europäische

Sumpfschildkröte3. Mauremys caspica Stripe Necked Terrapin Kaspische

Wasserschildkröte4. Mauremys leprosa Spanish Terrapin Spanische

Wasserschildkröte

Amphibians

1. Alytes muletensis Mallorcan Midwife Toad Balearen-Geburtshelferkröte

2. Bombina bombina Fire-Bellied Toad Rotbauchunke3. Bombina variegata Yellow-Bellied Toad Gelbbauchunke4. Chioglossa lusitanica Golden-striped Salamander Goldstreifensalamander5. Discoglossus galganoi Iberian Painted Frog Iberian painted frog6. Discoglossus montalentii Corsican Painted Frog Korsischer

Scheibenzüngler7. Discoglossus sardus Tyrrhenian Painted Frog Sardischer

Scheibenzüngler8. Pelobates f. insubricus Common Spadefoot Italienische

Knoblauchkröte9. Rana latastei Italian Agile Frog Italienischer

Springfrosch10. Salamandrina terdigitata Spectacled Salamander Brillensalamander11. Triturus carnifex Italian Crested Newt Alpen-Kammolch12. Triturus cristatus Great Crested Newt Kammolch13. Triturus dobrogicus Danube Crested Newt Donau-Kammolch14. Triturus karelini Southern Crested Newt Balkankammmolch15. Triturus montandoni Carpathian Newt Karpatenmolch

Birds

1. Acrocephalus paludicola Aquatic Warbler Seggenrohrsänger2. Alcedo atthis Kingfisher Eisvogel3. Anser erythropus Lesser White-fronted Goose Zwerggans4. Aquila chrysaetos Golden Eagle Steinadler5. Aquila clanga Spotted Eagle Schelladler6. Ardea purpurea purpurea Purple Heron Purpurreiher7. Ardeola ralloides Squacco Heron Rallenreiher8. Asio flammeus Short-eared Owl Sumpfohreule9. Aythya nyroca Ferruginous Duck Moorente10. Botaurus stellaris stellaris Bittern Rohrdommel11. Chlidonias hybridus Whiskered Tern

Weißbartseeschwalbe12. Chlidonias niger Black Tern Trauerseeschwalbe13. Ciconia ciconia White Stork Weißstorch14. Ciconia nigra Black Stork Schwarzstorch15. Crex crex Corncrake Wachtelkönig16. Fulica cristata Crested Coot Kammbläßhuhn17. Gavia arctica Black-throated Diver Prachttaucher18. Gelochelidon nilotica Gull-billed Tern Lachseeschwalbe19. Glareola pratincola Collared Pratincole Brachschwalbe20. Grus grus Crane Kranich21. Haliaeetus albicilla White-tailed Eagle Seeadler22. Hoplopterus spinosus Spur-winged Plover Spornkiebitz23. Ixobrychus m. minutus Little Bittern Zwergdommel24. Marmaronetta angustrostris Marbled Teal Marmelente25. Milvus migrans Black Kite Schwarzmilan26. Nycticorax nycticorax Night Heron Nachtreiher27. Oxyura leucocephala White-headed Duck Weißkopf-

Ruderente28. Pandion haliaetus Osprey Fischadler29. Pelecanus crispus Dalmatian Pelican Krauskopfpelikan30. Pelecanus onocrotalus White Pelican Rosapelikan31. Phalacrocorax pygmaeus Pygmy Cormorant Zwergscharbe32. Philomachus pugnax Ruff Kampfläufer33. Platalea leucorodia Spoonbill Löffler34. Plegadis falcinellus Glossy Ibis Braunsichler35. Porphyrio porphyrio Purple Gallinule Purpurhuhn36. Porzana parva parva Little Crake Kleines

Sumpfhuhn37. Porzana porzana Spotted Crake Tüpfelsumpfhuhn38. Porzana pusilla Baillon´s Crake Zwergsumpfhuhn39. Sterna albifrons Little Tern Zwergseeschwalbe40. Tadorna ferruginea Ruddy Shelduck Rostgans41. Tringa glareola Wood Sandpiper Bruchwasserläufer

1

4

3

2

1514

11

6

12

13

10

2

1

7

89

5

4

3

9

2

1

3

4

5

6 7

8

1

26

9

16

8

76

54

32

17

1312

11

10

21

20

23

1514

19

27

25

24

18

28

34

22

33

32

29

35

30

31

41

40

38

36

39

37

2016 cells 25 countries 6 biogeo-regions

Biodiversity - Spatial ResolutionBiodiversity - Spatial Resolution

TAXON 1. Mires2. Wet forests

3. Natural grasslands

4.1 Running waters

4.2 Standing waters

5. Further habitat

Alcedo atthis x xAnser erythropus x x xAquila clanga / x / / /Aquila chrysaetos / /Ardea purpurea purpurea x x xArdeola ralloides x xAsio flammeus / /Aythya nyroca x xBotaurus stellaris stellaris xChlidonias hybridus / xChlidonias niger x xCiconia ciconia x x xCiconia nigra x x x /Crex crex / x /Fulica cristata x xGavia arctica xGelochelidon nilotica x x /Glareola pranticola x xGrus grus / / / / /Haliaeetus albicilla x x xHoplopterus spinosus x x xIxobrychus minutus minutus

x x xMilvus migrans x x /Nycticorax nycticorax x x xOxyura leucocephala xPandion haliaetus / x /

SpeciesSpecies – Habitat – Habitat MappingMapping

Mixed Integer ProgrammingMixed Integer Programming

threshold0 area

population

Aquila Aquila ClangaClanga

Representation

Maximum

Systematic ConservationSystematic Conservation

10 representations of each species

(nSpecies=72)

151 cells selected

(nCells=2016)

0

10

20

30

40

50

60

5 10 15 20 25 30 35 40

Are

a in

mil

lion

hec

tare

s

Representation Minimum

Mires (Peat lands)

Wet Forest

Wet GrassWater CourseWater Bodies

All Wetland

Mil

lion

Eur

o pe

r ye

ar

0

2000

4000

6000

8000

10000

12000

14000

16000

0 5 10 15 20 25 30 35 40 45 50

Representation Minimum

Area Minimization (Endogenous Land Prices)

Area Minimization (Exogenous Land Prices)

Cost Minimization (Endogenous Land Prices)

Cost Minimization (Exogenous Land Prices)

Regional Location of WetlandsRegional Location of Wetlands

lan

d a

rea

constant land costs

increasing land costs

Scandin

avia

Centra

l Euro

pe

Wes

tern E

urope

Easter

n Euro

pe

South

ern E

urope

Curses of DimensionalityCurses of Dimensionality

Soil Carbon Dynamics

Soi

l Org

anic

Car

bon

(tC

/ha/

20cm

)

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50Time (years)

Wheat-Lucerne 3/3

Wheat-Lucerne 6/3

No-till wheat-fallow

Tilled wheat-fallow

Curse of Dimensionality?Curse of Dimensionality?

20 species5 management options per species10 regions 5 soil types per region

5,000 land use alternatives

Curse of Dimensionality?Curse of Dimensionality?

20 species5 management options per species10 regions 5 soil types per region20 periods

5*E41 Trajectories

Soil Carbon Transition ProbabilitiesSoil Carbon Transition Probabilities

SOC1 SOC2 SOC3 SOC4 SOC5 SOC6 SOC7 SOC8

SOC1 0.81 0.19

SOC2 1

SOC3 0.09 0.91

SOC4 0.31 0.69

SOC5 0.5 0.5

SOC6 0.74 0.26

SOC7 1

SOC8 0.04 0.96

No-till wheat-Fallow

Markov ProcessMarkov Process

,o

,o

t ,u,

t ,u tu

o u,o,o t 1,u,ou u,o

X

X

X

L

Indexes: t = time, u = management, o,ố = soil carbon state

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50Time (years)

Wheat-Lucerne 3/3

Wheat-Lucerne 6/3

No-till wheat-fallow

Tilled wheat-fallowSoi

l Org

anic

Car

bon

(tC

/ha/

20cm

)

Soi

l Org

anic

Car

bon

(tC

/ha/

20cm

)

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50Time (years)

Wheat-Lucerne 3/3

Wheat-Lucerne 6/3

No-till wheat-fallow

Tilled wheat-fallow

Extensions?Extensions?

Markov chains are applicable to relatively independent environmental qualities (tree density, humus, salt, contamination)

Method not suitable for complex environmental properties (climate)

ConclusionsConclusions

Today’s solution – tomorrow’s problem?EUFASOM aims at integrated

assessments of food, climate, biodiversity, and water issues from land use

Computing power and model integration offer new opportunities – what about validation?

ReferencesReferences Schneider, U.A. “Soil organic carbon changes in dynamic land use decision

models” Agriculture, Ecosystems and Environment 119 (2007) 359–367 Cowie, A., U.A. Schneider and L. Montanarella (2007). Potential synergies

between existing multilateral environmental agreements in the implementation of Land Use, Land Use Change and Forestry activities. Environmental Science & Policy 10(4):335-352

Schneider U.A., J. Balkovic, S. De Cara, O. Franklin, S. Fritz, P. Havlik, I. Huck, K. Jantke, A.M.I. Kallio, F. Kraxner, A. Moiseyev, M. Obersteiner, C.I. Ramos, C. Schleupner, E. Schmid, D. Schwab, R. Skalsky (2008), “The European Forest and Agricultural Sector Optimization Model – EUFASOM”, FNU-156, Hamburg University and Centre for Marine and Atmospheric Science, Hamburg.

Schleupner, C. Estimation of Spatial Wetland Distribution Potentials in Europe. FNU-135. 2007. Hamburg, Hamburg University and Centre for Marine and Atmospheric Science.

www.fnu.zmaw.de