the european forest and agricultural sector optimization model
DESCRIPTION
The European Forest and Agricultural Sector Optimization Model. Uwe A. Schneider (Land Use Economics) Contributors Christine Schleupner (Wetland Geography) Kerstin Jantke (Wetland Biology) Erwin Schmid (Crop Simulation) C. Ivie Ramos (Bioenergy Options). FOREST SECTOR MODELING - PowerPoint PPT PresentationTRANSCRIPT
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 MarketsInputs
Limits
Supply Functions
Limits
Demand Functions,Trade
Limits
Environmental Impacts
Economic Surplus MaximizationEconomic Surplus Maximization
Mar
ket E
quili
briu
m
Fore
st In
vent
ory
Land
Sup
ply
Wat
er S
uppl
yLa
bor S
uppl
yN
atio
nal I
nput
s
Impo
rt Su
pply
Proc
essi
ng D
eman
dFe
ed D
eman
dD
omes
tic D
eman
dEx
port
Dem
and
CS
PS
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
Amphibians1. 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
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
21
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
mill
ion
hect
ares
Representation Minimum
Mires (Peat lands)Wet ForestWet GrassWater CourseWater Bodies
All Wetland
Mill
ion
Euro
per
yea
r
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
land
are
a
constant land costs
increasing land costs
Scandinav
ia
Centra
l Euro
pe
West
ern Euro
pe
Eastern
Europe
Southern
Europe
Curses of DimensionalityCurses of Dimensionality
Soil Carbon Dynamics
Soil
Org
anic
Car
bon
(tC/h
a/20
cm)
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 SOC8SOC1 0.81 0.19SOC2 1SOC3 0.09 0.91SOC4 0.31 0.69SOC5 0.5 0.5SOC6 0.74 0.26SOC7 1SOC8 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-fallowSoil
Org
anic
Car
bon
(tC/h
a/20
cm)
Soil
Org
anic
Car
bon
(tC/h
a/20
cm)
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)
ConclusionsConclusionsToday’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