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

Uwe A. Schneider (Land Use Economics)

ContributorsChristine Schleupner (Wetland Geography)Christine Schleupner (Wetland Geography)

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

C Ivie Ramos (Bioenergy Options)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 CharacteristicsEUFASOM CharacteristicsEUFASOM Characteristics

P ti l E ilib i B tt U M d lPartial Equilibrium, Bottom-Up Model Maximizes sum of consumer and producer surplusConstrained by resource endowments, ytechnologies, policiesSpatially explicit, discrete dynamicSpatially explicit, discrete dynamicIntegrates environmental effectsP d i GAMS S l d LPProgrammed in GAMS, Solved as LP

Food BioenergyFoodTimber

gyBiomaterial

Fiber

Land use competition Nat reCarbon

Sinks

competition NatureReservesSinks

SealedLand

EUFASOM EUFASOM StructureStructureLimits

Limits

Resources Land Used k

TechnologiesProducts Markets

Inputs

Processing TechnologiesSupply

Demand Functions,

Environmental I t TechnologiesSupply

Functions,

TradeImpacts

Limits

Economic Surplus MaximizationEconomic Surplus MaximizationEconomic Surplus MaximizationEconomic Surplus Maximization

Forest InventoryLand Supply Processing Demand

Water Supply Domestic DemandCS

PS

Market EquilibriumLabor Supply Feed Demand

National Inputs Import Supply Export Demand

EUFASOM EUFASOM Climate Models

Modeling SystemModeling SystemFarm level &

Vi blSpatial Analysis T l

GIS Data Viable Population Analysis

Crop &

Toolsy

Engineering Equations

EUFASOM

Crop & Tree Simulation Models

Systematic Wetland

q

EUFASOMModels Wetland Conservation Planning

Other Economic Models

Novel FeaturesNovel FeaturesNovel FeaturesNovel Features

Biodiversity (Wetlands)

Markov Chains (against curse of

dimensionality)

Wetland BiodiversityWetland BiodiversityWetland BiodiversityWetland Biodiversity

Physical Wetland

SpeciesConservation

Potentials TargetsSystematic Conservation

PlanningPlanning

ReserveLand Reserve Locations

Land Prices

EUFASOM

Physical Wetland PotentialsPhysical Wetland Potentials

Spatial Analysis of Wetlands

Peatland (Fens, Bogs)

Marshes, Reeds, Sedges

Wetforests

a s es, eeds, Sedges

Open Waters

Wetforests

Existing WetlandsPotential WetlandsO W tOpen Waters

Systematic Conservation Systematic Conservation yyPlanningPlanning

Viable Population Analysis

BiodiversityBiodiversityScopeScope

69 69 VertebrateVertebrateVertebrate Vertebrate WetlandWetlandSpeciesSpeciesSpeciesSpecies

MammalsBirds2

54

1

4

116

8

76

32

1312

11

21

1514

18

1. Acrocephalus paludicola Aquatic Warbler Seggenrohrsänger2. Alcedo atthis Kingfisher Eisvogel3 Anser erythropus Lesser White fronted Goose Zwerggans

3

4

59

17

10

21

20

2319

25

24

22

3. 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 Marmelente

9

6 7

8

26 27

28

33

29

35

30

31

1. Castor fiber Eurasian Beaver Europäischer Biber2. Galemys pyrenaicus Pyrenean Desman Pyrenäen‐Desman3. Lutra lutra European Otter Fischotter4. Microtus cabrerae Cabreraʹs Vole Cabreramaus5. Microtus oec. arenicola Dutch Root Vole Niederländische Wühlmaus

6. Microtus oec. mehelyi Pannonian Root Vole Ungarische Wühlmaus7. Mustela lutreola European Mink Europäischer Nerz8. Myotis capaccinii Long‐fingered Bat Langfußfledermaus9. Myotis dasycneme Pond Bat Teichfledermaus

Reptiles

g25. 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

3432

41

40

38

36

39

37

Reptiles

Amphibians

1 Alytes muletensis MallorcanMidwife Toad Balearen Geburtshelferkröte

1

31514

6

13

21

5

4

3

40

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

1. Alytes muletensis Mallorcan Midwife Toad Balearen‐Geburtshelferkröte2. 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

4

21112

107

89

BiodiversityBiodiversity -- Spatial ResolutionSpatial ResolutionBiodiversity Biodiversity Spatial ResolutionSpatial Resolution

2016 cells 25 countries 6 biogeo-regions

SpeciesSpecies –– HabitatHabitat MappingMappingTAXON 1. Mires

2. Wet forests

3. Natural grasslands

4.1 Running waters

4.2 Standing waters

5. Further habitat

SpeciesSpecies Habitat Habitat MappingMappingg

Alcedo atthis x xAnser erythropus x x xAquila clanga / x / / /Aquila chrysaetos / /A dArdea purpurea purpurea x x xArdeola ralloides x xAsio flammeus / /Aythya nyroca x xBotaurus stellaris stellaris xChlidonias hybridus / xyChlidonias 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 xHoplopterus spinosus x x xIxobrychus minutus x x xMilvus migrans x x /Nycticorax nycticorax x x xOxyura leucocephala xPandion haliaetus / x /

Mi d I t P iMi d I t P iMixed Integer ProgrammingMixed Integer Programming

population

threshold0 areathreshold0 area

Aquila Aquila ClangaClangaClangaClanga

R t tiRepresentationMaximum

Systematic ConservationSystematic ConservationSystematic ConservationSystematic Conservation

10 representations of each speciesof each species

(nSpecies=72)

151 cells selected(nCells=2016)( Cells )

60

50

es

Mires (Peat lands)W t F t

All Wetland40

n he

ctar

e Wet ForestWet GrassWater Course

20

30

n m

illio

n Water CourseWater Bodies

10

20

Are

a in

0

10

5 10 15 20 25 30 35 40Representation Minimum

16000Area Minimization (Endogenous Land Prices)

r 12000

14000Area Minimization (Endogenous Land Prices)Area Minimization (Exogenous Land Prices)Cost Minimization (Endogenous Land Prices)

per y

ear

10000

12000 Cost Minimization (Exogenous Land Prices)

on E

uro

6000

8000

Mill

io

4000

6000

0

2000

00 5 10 15 20 25 30 35 40 45 50

Representation Minimum

Regional Location of WetlandsRegional Location of WetlandsRegional Location of WetlandsRegional Location of Wetlands

constant land costs

a

constant land costs

increasing land costs

lan

d a

reag

Curses of DimensionalityCurses of Dimensionalityyy

Soil Carbon Dynamics

4520

cm)

35

40

Wheat-Lucerne 3/3

n (tC

/ha/

2

30Wheat-Lucerne 6/3

c C

arbo

n

20

25

il O

rgan

i

15 No-till wheat-fallow

Soi

5

10 Tilled wheat-fallow

0 10 20 30 40 50Time (years)

Curse of Dimensionality?Curse of Dimensionality?Curse of Dimensionality?Curse of Dimensionality?

20 species5 management options per species5 a age e op o s pe spec es10 regions 5 soil types per region5 soil types per region

5,000 land use alternatives5,000 land use alternatives

Curse of Dimensionality?Curse of Dimensionality?Curse of Dimensionality?Curse of Dimensionality?

20 i20 species5 management options per species10 regions 5 soil types per region5 soil types per region20 periods

5*E41 Trajectories

Soil Carbon Transition ProbabilitiesSoil Carbon Transition ProbabilitiesSoil Carbon Transition ProbabilitiesSoil Carbon Transition Probabilities

SOC1 SOC2 SOC3 SOC4 SOC5 SOC6 SOC7 SOC8SOC1 SOC2 SOC3 SOC4 SOC5 SOC6 SOC7 SOC8SOC1 0.81 0.19SOC2 1SOC2 1SOC3 0.09 0.91SOC4 0.31 0.69SOC4 0.31 0.69SOC5 0.5 0.5SOC6 0.74 0.26SOC7 1SOC8 0.04 0.96

No-till wheat-Fallow

Markov ProcessMarkov ProcessMarkov ProcessMarkov Process

,ot ,u tX L≤∑

( ),o

,o,u

u

X X

∑

∑ ∑( )t ,u,o u,o,o t 1,u,ou u,o

X X −= ρ ⋅∑ ∑ % %%,

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

45

35

40

Wheat-Lucerne 3/320cm

)

30

35

Wheat Lucerne 6/3n (tC

/ha/

2

20

25Wheat-Lucerne 6/3

c C

arbo

n

15 No-till wheat-fallow

il O

rgan

i

5

10 Tilled wheat-fallowSoi

0 10 20 30 40 50Time (years)

4520

cm)

35

40

Wheat-Lucerne 3/3

n (tC

/ha/

2

30

35 Wheat Lucerne 3/3

Wheat-Lucerne 6/3

c C

arbo

n

20

25Wheat Lucerne 6/3

l Org

anic

15

20

No-till wheat-fallow

Soil

5

10 Tilled wheat-fallow

0 10 20 30 40 50Time (years)

Extensions?Extensions?Extensions?Extensions?

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

Method not suitable for complex environmental properties (climate)environmental properties (climate)

ConclusionsConclusionsConclusionsConclusions

Today’s solution – tomorrow’s problem?Today s solution tomorrow s problem?EUFASOM aims at integrated assessments of food climate biodiversityassessments of food, climate, biodiversity, and water issues from land useC ti d d l i t tiComputing power and model integration offer new opportunities – what about

lid ti ?validation?

ReferencesReferencesReferencesReferencesSchneider, U.A. “Soil organic carbon changes in dynamic land use decision models” Agriculture Ecosystems and Environment 119 (2007) 359 367models Agriculture, Ecosystems and Environment 119 (2007) 359–367Cowie, A., U.A. Schneider and L. Montanarella (2007). Potential synergiesbetween existing multilateral environmental agreements in the implementation ofLand Use, Land Use Change and Forestry activities. Environmental Science &Policy 10(4):335-352Policy 10(4):335-352Schneider 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 HamburgAgricultural 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 ScienceAtmospheric Science.

www.fnu.zmaw.de