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Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
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Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik

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  • -Producer prices (indicating regional integration)-Protection of forests, reduction in emissions-Changes between systems lead to higher productivity, also higher production/emission
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    • 1. Modelling for trade-offs analysisat regional and global scalePetr Havlík + >30 collaborators in and outside IIASAInternational Institute for Applied Systems Analysis (IIASA), AustriaInternational Livestock Research Institute (ILRI), Kenya CGIAR Workshop: Analysis of Trade-offs in Agricultural Systems WUR Wageningen, February 19, 2013
    • 2. Trade-offs in the land use sectors Land sparing Pollution N2O emissions Biodiversity Water use Food, feed, fiber, fuel CO2 sink Soil degradation Farmers income NATURAL LAND INTENSIFICATION MANAGED LAND LAND Havlík et al. Modelling for trade-offs analysis 2 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 3. Outline 1. Model overview 2. Global case study – Sustainable intensification? a) Rigid system b) Flexible livestock systems c) Land productivity 3. Regional case study – Development scenarios 4. ConclusionHavlík et al. Modelling for trade-offs analysis 3CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 4. 1. Model overviewHavlík et al. Modelling for trade-offs analysis 4CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 5. GLOBIOM: Global Biosphere Management ModelPartial equilibrium model: Agriculture, Forestry, Bioenergy DEMAND SUPPLY Havlík et al. Modelling for trade-offs analysis 5 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 6. GLOBIOMSpatial equilibrium model a la Takayama & JudgeMaximization of the social welfare (PS + CS)Recursively dynamic (10 year periods)Supply functions implicit – based on spatially explicit Leontief production functions: production system 1 (grass based)  productivity 1 + constant cost 1 production system 2 (mixed)  productivity 2 + constant cost 2Demand functions 1/ e explicit: linearized non-linear functions p ˆ ˆ p * (q / q) Havlík et al. Modelling for trade-offs analysis 6 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 7. Supply Chains Wood products Sawn wood Natural Forests Pulp Wood Processing Bioenergy Managed Forests Bioethanol Biodiesel Methanol Heat Electricity LAND USE CHANGE Short Rotation Tree Bioenergy Biogas Plantations Processing Crops Corn Wheat Cropland Cassava Potatoes Rapeseed etc… Grassland Livestock Feeding Livestock products Beef Lamb Pork Poultry Other natural land Eggs Milk Havlík et al. Modelling for trade-offs analysis 7 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 8. Main exogenous drivers: Population GDP Technological change Bio-energy demand (POLES team) Diets (FAO, 2006)Output: Production Q - land use (change) - water use - GHG, - other environment (nutrient cycle, biodiversity,…) Consumption Q Prices Trade flows Havlík et al. Modelling for trade-offs analysis 8 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 9. Spatial resolutionHomogeneous response units (HRU) – clusters of 5 arcmin pixels HRU = Altitude & Slope & Soil Altitude class, Slope class, Soil Class PX5 PX5 Altitude class (m): 0 – 300, 300 – 600, 600 – 1200, 1200 – 2500 and > 2500; Slope class (deg): 0 – 3, 3 – 6, 6 – 10, 10 – 15, 15 – 30, 30 – 50 and > 50; Soil texture class: coarse, medium, fine, stony and peat; Source: Skalský et al. (2008) Havlík et al. Modelling for trade-offs analysis 9 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 10. Spatial resolutionSimulation Units (SimU) = HRU & PX30 & Country zone LC&LUstat> 200 000 SimU Country HRU*PX30 SimU delineation related statistics on LC classes and Cropland management systems PX5 reference for geo-coded data on crop management; input statistical data for LC/LU economic optimization; Source: Skalský et al. (2008) Havlík et al. Modelling for trade-offs analysis 10 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 11. Crops - EPIC Processes • Weather • Hydrology EPIC • Erosion Evaporation • Carbon sequestration and • Crop growth Rain, Snow, Transpiration • Crop rotations Chemicals • Fertilization • Tillage Subsurface • Irrigation Flow • Drainage Surface Flow • Pesticide • Grazing • Manure Below Root ZoneMajor outputs: Crop yields, Environmental effects (e.g. soil carbon, nitrogen leaching)20 crops (>75% of harvested area)4 management systems: High input, Low input, Irrigated, Subsistence Havlík et al. Modelling for trade-offs analysis 11 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 12. Crops - EPIC Relative Difference in Means (2050/2100) in Wheat Yields [Data: Tyndall, Afi Scenario, simulation model: EPIC] Havlík et al. Modelling for trade-offs analysis 12 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 13. Grasslands – CENTURY/EPIC Source: EPIC model (t/ha DM) Havlík et al. Modelling for trade-offs analysis 13 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 14. LivestockGridded Livestock of the World – Robinson et al. (2011) Havlík et al. Modelling for trade-offs analysis 14 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 15. Livestock production systems distributionSere and Steinfeld (1996) classification updated by Robinson et al. (2011) Havlík et al. Modelling for trade-offs analysis 15 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 16. Livestock sector coverageLivestock categories: Bovines: Dairy & Other Sheep & Goats: Dairy & Other Poultry: Laying hens, Broilers, Mixed PigsProduction systems: Ruminats Grass based: Arid, Humid, Temperate/Highlands Mixed crop-livestock: Arid, Humid, Temperate/Highlands Monogastrics Smallholders Industrial Havlík et al. Modelling for trade-offs analysis 16 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 17. Production systems parameterization Herrero, Havlík et al. forthcoming Havlík et al. Modelling for trade-offs analysis 17 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 18. Forests – G4MDownscaling FAO country level information and forest growthfunctions estimated from yield tables Source: Kindermann et al. (2008) Havlík et al. Modelling for trade-offs analysis 18 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 19. 2a. Global case study:Rigid system – Trade-offs at their best Havlík et al. Modelling for trade-offs analysis 19 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 20. Havlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 21. DO NOTHING scenario – Projected forest area Tropical deforestation (2010-2050)Havlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 22. REDD policy scenario Zero Net Deforestation and Forest Degradation by 2020 (ZNDD)Alternative futures scenarios Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 23. Scenario definition Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 24. Scenario definition Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 25. Scenario definition Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013 Kapos et al. (2008)
    • 26. Results Total land cover change (2010-2050) Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 27. Results Agricultural commodity prices compared to DO NOTHING Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 28. Results Agricultural input use compared to DO NOTHING Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 29. 2b. Global case study: Flexible livestock systemsHavlík et al. Modelling for trade-offs analysis 29CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 30. 2 reference scenarios Systems Herds REF0 Fixed Fixed REF1 Flexible Flexible* * in regions with specialized herds 30Havlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 31. LPS distribution for different animal types in 2030 31Havlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 32. Price changes 2000-2030 32Havlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 33. Annual average GHG emissions over 2020-2030 33Havlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 34. Mitigation scenarios Scenario ALL AGR ANM ENT LUC DEF Livestock Enteric fermentation CH4 X X X X Manure management CH4 X X X Manure management N2O X X X Manure grassland N2O X X X Cropland Crop fertilizer N2O X X Rice CH4 X X Land-use change Deforestation CO2 X X X Other LUC CO2 X X 34Havlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 35. Total abatement calorie cost (TACC) curves for different policy options by 2030 35 Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 36. 2c. Global case study: Land productivity growth (Havlík et al, 2013; Valin et al, forthcoming)Havlík et al. Modelling for trade-offs analysis 36CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 37. Scenarios• Alternative crop yield scenarios – S0: No crop yield increase – B: Baseline - linear historical trend – S: -50% yield improvement – C: + 100% in developing regions• Fixed demand on B reference: no rebound effect• Fixed demand on B reference  no rebound effect Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 38. Results Commodity price index 2030/2000 Havlík et al. Modelling for trade-offs analysis 38 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 39. Results Land cover change 2000-2030 Havlík et al. Modelling for trade-offs analysis 39 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 40. Results Average annual GHG emissions (2000-2030) Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 41. Results Crop yield increase as a mitigation policy? MACC_S0 GHG tax Productivity abatment levels R&D investment costMarginal Abatement Cost Curve 120 with S0 crop yields 100 USD per tCO2-eqversus 80R&D investment necessary for S, B, C 60 - calculated as in Burney at al. (2010) 40 20Crop yield growth can be a cost 0efficient element of the mitigation S00 500 1000 1500 2000 S B C MtCO2-eqportfolio Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 42. What kind of intensification? Productivity assumptions in developing countries Scenario Crops Ruminants TREND FAO historic trend 1980-2010 Bouwman et al. (2005) trend SLOW 50% TREND growth rate 50% TREND growth rate CONV Closing 50% EPIC yield gap Closing 50% efficiency gap CONV-C Closing 50% EPIC yield gap TREND CONV-L TREND Closing 50% efficiency gap Management assumptions in developing countries Pathway Crops Ruminants Fertilizer Other input Non-feed cost adjustment adjustment adjustment Conventional Yes Yes Yes Sust-Intens No Yes Yes Free-Tech No No No• Free demand  potential rebound effects Havlík et al. Modelling for trade-offs analysis 42 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 43. Food security x GHG: Trade-offs & ComplementaritiesHavlík et al. Modelling for trade-offs analysis 43CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 44. 3. Regional case study: Development scenariosHavlík et al. Modelling for trade-offs analysis 44CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 45. Four storylines for Eastern Africa Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 46. Storylines quantificationMain drivers: – GDP – Crop yields and management systems – Livestock yield and production systems – Producer cost – Land use change limitationsHavlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 47. GDP per capita in EAF [USD]700.00600.00500.00400.00 Industrious Ants Herd of Zebra300.00 Lone Leopards Sleeping Lions200.00100.00 - 2010 2020 2030 Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 48. ResultsCalorie consumption in EAF [kcal/cap/day] GHG emissions in EAF in 2030 [MtCO2eq/y] Havlík et al. Modelling for trade-offs analysis 48 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 49. 4. ConclusionHavlík et al. Modelling for trade-offs analysis 49CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 50. Strengths• Bio-economic model (“Integrated assessment”) - consistent coverage of economic and environmental parameters• Land use model – solid relationship between production and land• Bottom-up representation with detailed management systems description• Multiscale approach – 10x10km – Region – World• Global coverage – regional trade-offs (leakage)• Multisectorial representation – trade-offs between agriculture and forestry Havlík et al. Modelling for trade-offs analysis 50 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 51. Weaknesses• Partial equilibrium model – no income feedbacks, no other sectors• Single representative consumer at the region level – poor food security proxy• Water resources – economic versus physical irrigation water availability Havlík et al. Modelling for trade-offs analysis 51 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 52. Key discussion points / challenges• Global CGIAR agricultural systems classification/parameterization database?• Linking between models to bridge the scales in trade-offs analysis? Havlík et al. Modelling for trade-offs analysis 52 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 53. Thank you !havlikpt@iiasa.ac.atwww.globiom.org Havlík et al. Modelling for trade-offs analysis CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
    • 54. ReferencesHavlík, P., Valin, H., Mosnier, A., Obersteiner, M., Baker, J. S., Herrero, M., Rufino, M. C. &Schmid, E. (2013). Crop Productivity and the Global Livestock Sector: Implications for Land UseChange and Greenhouse Gas Emissions. American Journal of Agricultural Economics 95(2), 442—448.Valin, H., Havlík, P., Mosnier, A., Herrero, M., Schmid E. and Obersteiner M. Agriculturalproductivity and greenhouse gas emissions: trade-offs or synergies between mitigation and foodsecurity? Environmental Research Letters, under review.World Wildlife Fund (WWF) 2011. Living Forests Report. Chapter 1.http://wwf.panda.org/what_we_do/how_we_work/conservation/forests/publications/living_forests_report/ Havlík et al. Modelling for trade-offs analysis 54 CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013

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