1. 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 STATE-OF-THE-ART AND FUTURE CHALLENGES IN AN EXPANDING GLOBAL MARKETPLACE November 17-20, 2008 Seattle, Washington, USA

2. EUFASOM Characteristics Partial Equilibrium, Bottom-Up Model Maximizes sum of consumer and producer surplus Constrained by resource endowments, technologies, policies Spatially explicit, discrete dynamic Integrates environmental effects Programmed in GAMS, Solved as LP

3. Food Timber Fiber Bioenergy Biomaterial Carbon Sinks Land use competition Nature Reserves Sealed Land

4. EUFASOM Structure Resources Land Use Technologies Processing Technologies ProductsMarkets Inputs Limits Supply Functions Limits Demand Functions, Trade Limits Environmental Impacts

5. Economic Surplus Maximization Market Equilibrium Forest InventoryLand Supply Water Supply Labor Supply National Inputs Import Supply Processing Demand Feed Demand Domestic Demand Export Demand CS PS

6. EUFASOM Modeling 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

7. Novel Features Biodiversity (Wetlands) Markov Chains (against curse of dimensionality)

8. Wetland Biodiversity Physical Wetland Potentials Species Conservation Targets Systematic Conservation Planning EUFASOM Reserve Locations Land Prices

9. Physical Wetland Potentials Physical Wetland Potentials Spatial Analysis of Wetlands

12. Peatland (Fens, Bogs) Wetforests Marshes, Reeds, Sedges Open Waters

13. Existing Wetlands Potential Wetlands Open Waters

14. Systematic Conservation Planning Systematic Conservation Planning Viable Population Analysis

15. 8 69VertebrateWetlandSpecies Biodiversity Scope

16. Mammals 1.Castor fiber Eurasian BeaverEuropäischer Biber 2.Galemys pyrenaicusPyrenean DesmanPyrenäen-Desman 3.Lutra lutraEuropean OtterFischotter 4.Microtus cabreraeCabrera's VoleCabreramaus 5.Microtus oec. arenicolaDutch Root VoleNiederländische Wühlmaus 6.Microtus oec. mehelyiPannonian Root VoleUngarische Wühlmaus 7.Mustela lutreolaEuropean MinkEuropäischer Nerz 8.Myotis capacciniiLong-fingered BatLangfußfledermaus 9.Myotis dasycnemePond BatTeichfledermaus Reptiles 1.Elaphe quatuorlineataFour-lined SnakeVierstreifennatter 2.Emys orbicularisEuropean Pond TortoiseEuropäische Sumpfschildkröte 3.Mauremys caspicaStripe Necked TerrapinKaspische Wasserschildkröte 4.Mauremys leprosaSpanish TerrapinSpanische Wasserschildkröte Amphibians 1.Alytes muletensis Mallorcan Midwife ToadBalearen-Geburtshelferkröte 2.Bombina bombina Fire-Bellied ToadRotbauchunke 3.Bombina variegata Yellow-Bellied ToadGelbbauchunke 4.Chioglossa lusitanica Golden-striped SalamanderGoldstreifensalamander 5.Discoglossus galganoi Iberian Painted Frog Iberian painted frog 6.Discoglossus montalentii Corsican Painted Frog Korsischer Scheibenzüngler 7.Discoglossus sardus Tyrrhenian Painted Frog Sardischer Scheibenzüngler 8.Pelobates f. insubricus Common Spadefoot Italienische Knoblauchkröte 9.Rana latastei Italian Agile Frog Italienischer Springfrosch 10.Salamandrina terdigitata Spectacled Salamander Brillensalamander 11.Triturus carnifex Italian Crested Newt Alpen-Kammolch 12.Triturus cristatus Great Crested NewtKammolch 13.Triturus dobrogicus Danube Crested Newt Donau-Kammolch 14.Triturus karelini Southern Crested Newt Balkankammmolch 15.Triturus montandoni Carpathian Newt Karpatenmolch Birds 1.Acrocephalus paludicolaAquatic WarblerSeggenrohrsänger 2.Alcedo atthisKingfisherEisvogel 3.Anser erythropusLesser White-fronted GooseZwerggans 4.Aquila chrysaetosGolden EagleSteinadler 5.Aquila clangaSpotted EagleSchelladler 6.Ardea purpurea purpureaPurple HeronPurpurreiher 7.Ardeola ralloidesSquacco HeronRallenreiher 8.Asio flammeusShort-eared OwlSumpfohreule 9.Aythya nyrocaFerruginous DuckMoorente 10.Botaurus stellaris stellarisBitternRohrdommel 11.Chlidonias hybridusWhiskered TernWeißbartseeschwalbe 12.Chlidonias nigerBlack TernTrauerseeschwalbe 13.Ciconia ciconiaWhite StorkWeißstorch 14.Ciconia nigraBlack StorkSchwarzstorch 15.Crex crexCorncrakeWachtelkönig 16.Fulica cristataCrested CootKammbläßhuhn 17.Gavia arctica Black-throated DiverPrachttaucher 18.Gelochelidon niloticaGull-billed TernLachseeschwalbe 19.Glareola pratincolaCollared PratincoleBrachschwalbe 20.Grus grusCraneKranich 21.Haliaeetus albicillaWhite-tailed EagleSeeadler 22.Hoplopterus spinosusSpur-winged PloverSpornkiebitz 23.Ixobrychus m. minutusLittle BitternZwergdommel 24.Marmaronetta angustrostrisMarbled TealMarmelente 25.Milvus migransBlack KiteSchwarzmilan 26.Nycticorax nycticoraxNight HeronNachtreiher 27.Oxyura leucocephalaWhite-headed DuckWeißkopf-Ruderente 28.Pandion haliaetusOspreyFischadler 29.Pelecanus crispusDalmatian PelicanKrauskopfpelikan 30.Pelecanus onocrotalusWhite PelicanRosapelikan 31.Phalacrocorax pygmaeusPygmy CormorantZwergscharbe 32.Philomachus pugnaxRuffKampfläufer 33.Platalea leucorodiaSpoonbillLöffler 34.Plegadis falcinellusGlossy IbisBraunsichler 35.Porphyrio porphyrioPurple GallinulePurpurhuhn 36.Porzana parva parvaLittle CrakeKleines Sumpfhuhn 37.Porzana porzanaSpotted CrakeTüpfelsumpfhuhn 38.Porzana pusillaBaillon´s CrakeZwergsumpfhuhn 39.Sterna albifronsLittle TernZwergseeschwalbe 40.Tadorna ferrugineaRuddy ShelduckRostgans 41.Tringa glareolaWood SandpiperBruchwasserläufer 1 4 3 2 15 14 11 6 12 13 10 2 1 7 8 9 5 4 3 9 2 1 3 4 5 6 7 8 1 26 9 16 8 7 6 5 4 3 2 17 13 12 11 10 21 20 23 15 14 19 27 25 24 18 28 34 22 33 32 29 35 30 31 41 40 38 36 39 37

17. 2016 cells25 countries6 biogeo-regions Biodiversity - Spatial Resolution

18. Species – Habitat Mapping

19. Mixed Integer Programming threshold 0 area population

20. Aquila Clanga Aquila Clanga Representation Maximum

21. Systematic Conservation 10 representations of each species (n Species =72) 151 cells selected (n Cells =2016)

22. 0 10 20 30 40 50 60 5 10 15 20 25 30 35 40 Area in million hectares Representation Minimum Mires (Peat lands) Wet Forest Wet Grass Water Course Water Bodies All Wetland

23. Million Euro per year 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)

24. Regional Location of Wetlands land area constant land costs increasing land costs Scandinavia Central Europe Western Europe Eastern Europe Southern Europe

25. Curses of Dimensionality Soil Carbon Dynamics

26. Soil Organic Carbon (tC/ha/20cm) 5 10 15 20 25 30 35 40 45 0 10 20 30 40 50 Time (years) Wheat-Lucerne 3/3 Wheat-Lucerne 6/3 No-till wheat-fallow Tilled wheat-fallow

27. Curse of Dimensionality? 20 species 5 management options per species 10 regions 5 soil types per region 5,000 land use alternatives

28. Curse of Dimensionality? 20 species 5 management options per species 10 regions 5 soil types per region 20 periods 5*E41 Trajectories

29. Soil Carbon Transition Probabilities SOC1SOC2SOC3SOC4SOC5SOC6SOC7SOC8 SOC10.810.19 SOC21 SOC30.090.91 SOC40.310.69 SOC50.5 SOC60.740.26 SOC71 SOC80.040.96 No-till wheat-Fallow

30. Markov Process Indexes: t = time, u = management, o,ố = soil carbon state

31. 5 10 15 20 25 30 35 40 45 0 10 20 30 40 50 Time (years) Wheat-Lucerne 3/3 Wheat-Lucerne 6/3 No-till wheat-fallow Tilled wheat-fallow Soil Organic Carbon (tC/ha/20cm)

32. 5 10 15 20 25 30 35 40 45 0 10 20 30 40 50 Time (years) Wheat-Lucerne 3/3 Wheat-Lucerne 6/3 No-till wheat-fallow Tilled wheat-fallow

33. Extensions? Markov chains are applicable to relatively independent environmental qualities (tree density, humus, salt, contamination) Method not suitable for complex environmental properties (climate)

34. Conclusions 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?

35. References 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