Bio-physicalimpact analysis     ofclimate change with EPIC Presented by:       Christine Heumesser       University of Nat...
Bio-physical impact analysis of climate change with EPICCh. Heumesser1, E. Schmid1, F., Strauss1, R., Skalský2, J., Balkov...
1. EPIC - Environmental Policy Integrated Climate      1.1. Global EPIC database
EPIC - Environmental Policy IntegratedClimate                       Model Objectives: Assess the effect of soil erosion o...
EPIC - Environmental Policy IntegratedClimate Developed in the 1980s as “The Erosion Productivity Impact Calculator” to  ...
EPIC is part of a model familyWatershed Scale                     Field Scale:      APEX                              EPIC...
Major EPIC components   weather   hydrology (runoff, evapotranspiration, percolation)   erosion-sedimentation (wind and...
EPIC – Study Outline                     EPIC study outline: An EPIC study may involve several sites e.g. field, farm, an...
EPIC: Major input datasets1. Site Description/Topography     slope, elevation, size of field, latitude, etc.2. Weather    ...
EPIC - Homogenuous Response Units (HRU) Spatially explicit site-specific qualitiesConcept of HRUs allows to consistently...
EPIC - Homogenuous Response Units (HRU)Altitude, slope and soil classvalue assigned to 5’ spatialresolution pixel represen...
EPIC: major simulation output (txt files)Dry matter crop yield [t/ha]               Nitrogen (& P) Balances:Total biomass ...
EPIC OUTPUT DATA
Integrative Climate Change ImpactModelling                                                                                ...
1.1. Global EPIC database
Global EPIC – Land Cover      Global Land Cover (GLC2000; IFPRI, 2007)crop lands: 0.9 bil. ha            forest lands: 4.0...
Global EPIC – CROPS20 crops simulated on all GLC=> crop production possibility set in GLOBIOM    BARL       barley    CA...
Global EPIC – crop management 3 Crop Input Systems (rule-based) simulated on all GLC  => crop management possibility set ...
Mean Temperature change on croplandin 2050 in °C (Base 2000)                Tyndall Climate Change Data, A1fi Scenario
Mean Temperature change on cropland in2100 in °C (Base 2000)                Tyndall Climate Change Data, A1fi Scenario
Annual Precipitation Change on croplandin 2050 in mm (Base 2000)                Tyndall Climate Change Data, A1fi Scenario
Annual Precipitation Change on croplandin 2100 in mm (Base 2000)                 Tyndall Climate Change Data, A1fi Scenario
Corn Yields in t/ha (DM) on cropland,automatic fertilization and irrigation(AI management), (Base 2000)
Changes in Corn Yields on cropland in2050 in t/ha (DM), AI management system(Base 2000)
Changes in Corn Yields on croplandin 2100 in t/ha (DM), AI management system(Base 2000)
Changes in irrigation water on croplandin 2050 in mm, AI management system(Base 2000)
Changes in irrigation water on croplandin 2100 in mm, AI management system(Base 2000)
2. CLIMATE CHANGE AND IRRIGATEDAGRICULTRE IN SEMI-ARID CENTRAL EUROPE
Irrigated Agriculture inEurope In parts of Southern Europe: Agriculture accounts for up to 80 % of total  water use (most...
Climate Change in Europe A warming trend (+0.90 C for 1901-2005) throughout Europe has   been observed, which has been ac...
Climate Change in Europe Regional climate models project a larger warming in winter than   in summer in Northern Europe a...
Climate Change in Europe Mediterranean regions, Central Europe and Eastern Europe :  o Precipitation trends are projected...
Adaptation options in the Austrian Marchfeld      3.1. The region Marchfeld      3.2. Statistical climate data for Austria...
Case Study – the region Marchfeld Marchfeld is part of the                                    MarchfeldVienna Basin andin...
Statistical Climate Model for Austria Strauss, F., Formayer, H., Asamer, V., Schmid, E., 2010. Climate change data for Aus...
Statistical Climate Model for Austriao Observed increase in temperature until 2007 -> Increase in annual  average temperat...
Case studies:                                        For each year                For each year           Realizations of ...
Investment in Irrigation Systems under weatheruncertaintyTogether with S. Fuss (IIASA), J. Szolgayova (IIASA), Franziska S...
Investment in Irrigation Systems –Data and Methods Climate scenario with step-wise decreasing precipitation(-20% in 2040 ...
Investment in Irrigation Systems –Data and Methods Characteristics of the model:   o Weather/climate uncertainty for peri...
No Irrigation            Sprinkler                Drip                                Soil 1    Soil 2    Soil 1      Soil...
Investment in Irrigation Systems –Results: Optimal Timing of Investment     Soil 1(higher quality)   Soil 2
Investment in Irrigation Systems – Policy Scenario 1 : Water Prices                    SPRINKLER                          ...
Investment in Irrigation Systems –  Policy Scenario 2 : Subsidies                               Soil 1 (higher quality)   ...
Investment in Irrigation Systems –  Policy Scenario 2 : Subsidies                                   Soil 2(lowerquality)  ...
Investment in Irrigation Systems – Conclusion Though more water efficient, drip irrigation seems too expensive for adopti...
CONCLUDING REMARKS
CONCLUDING REMARKS Climate change impact analyses require data and models (i.e.  biophysical and economic models) with su...
Thank you for your attention!christine.heumesser@boku.ac.aterwin.schmid@boku.ac.at
Bio-physical impact analysis of climate change with EPIC
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Bio-physical impact analysis of climate change with EPIC

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Bio-physical impact analysis of climate change with EPIC

Presented by Christine Heumesser at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis

June 6-7, 2011 • Dakar, Senegal

For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop

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Bio-physical impact analysis of climate change with EPIC

  1. 1. Bio-physicalimpact analysis ofclimate change with EPIC Presented by: Christine Heumesser University of Natural www.agrodep.org Resources and Life Sciences, Vienna AGRODEP Workshop on Analytical Tools for Climate Change Analysis June 6-7, 2011 • Dakar, Senegal Please check the latest version of this presentation on: http://agrodep.cgxchange.org/first-annual-workshop
  2. 2. Bio-physical impact analysis of climate change with EPICCh. Heumesser1, E. Schmid1, F., Strauss1, R., Skalský2, J., Balkovič2, et al. 1)University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development 2)Soil Science and Conservation Research Institute (SSCRI), Bratislava, Slovakia AGRODEP members’ meeting and workshop- June 6-8 2011- Dakar, Senegal
  3. 3. 1. EPIC - Environmental Policy Integrated Climate 1.1. Global EPIC database
  4. 4. EPIC - Environmental Policy IntegratedClimate Model Objectives: Assess the effect of soil erosion on productivity. Predict effects of management decisions on soil, water, nutrient and pesticide movements and their combined impact on soil loss, water quality and crop yields.
  5. 5. EPIC - Environmental Policy IntegratedClimate Developed in the 1980s as “The Erosion Productivity Impact Calculator” to assess the status of U.S. soil and water resources (Williams et al., 1984; Williams, 1990; Jones et al., 1991). EPIC compounds various components from CREAMS (Knisel, 1980), SWRRB (Williams et al., 1985), GLEAMS (Leonard et al., 1987), and has been continuously expanded and refined to allow simulation of many processes important in agricultural land management (Sharpley and Williams, 1990; Williams, 1995, 2000) => Environmental Policy Integrated Climate (Williams, 1995). A major carbon cycling routine was performed by Izaurralde et al. (2006). Current research efforts are focusing on model algorithm that address green house gases emissions (e.g. N2O, CH4).
  6. 6. EPIC is part of a model familyWatershed Scale Field Scale: APEX EPICAgricultural Policy Environmental Environmental Policy Impact eXtender Calculator SWAT Soil WaterAssessment Tool
  7. 7. Major EPIC components weather hydrology (runoff, evapotranspiration, percolation) erosion-sedimentation (wind and water) EPIC Model (Williams, 1995) Solar radiation nutrients (N, P, K) and carbon cycling (C) Wind salinity Plant Precipitation plant growth and competition growth soil temperature and moisture Erosion tillage & management & grazing Operations Runoff cost accounting Soil layers C, N, & P cycling Pesticide fate EPIC operates on a daily time step, and is capable of simulating hundreds of years if necessary. EPIC is programmed in FORTRAN
  8. 8. EPIC – Study Outline EPIC study outline: An EPIC study may involve several sites e.g. field, farm, and each site must have assigned soil, topography, weather station. Multiple runs may be defined for each site, with alternative weather, or field operations schedule data sets specified.
  9. 9. EPIC: Major input datasets1. Site Description/Topography slope, elevation, size of field, latitude, etc.2. Weather monthly weather statistics (generator), daily weather records (obs. or simulated)3. Soil soil texture, pH, bulk density, etc.4. Land Use Management crops/crop rotations, planting & harvesting tillage operation (time & type), fertilization (time, type & amount), irrigation (time & amount), grazing, trees, etc.5. Constant Data Number of years and beginning year of simulation, weather gen. options, also includes information on production cost. Data file editor UTIL- Universal Text Integrated Language
  10. 10. EPIC - Homogenuous Response Units (HRU) Spatially explicit site-specific qualitiesConcept of HRUs allows to consistently integrate/aggregate bio-physicaleffects in economic land use models
  11. 11. EPIC - Homogenuous Response Units (HRU)Altitude, slope and soil classvalue assigned to 5’ spatialresolution pixel representsspatially most frequent classvalue (not average)HRU is spatially delineated as azone of global grid having sameclass of altitude, slope and soil
  12. 12. EPIC: major simulation output (txt files)Dry matter crop yield [t/ha] Nitrogen (& P) Balances:Total biomass yield [t/ha] Fertilization (FTN) [kg/ha],Water stress days [d/crop] Deposition (NPCP) [kg/ha],Nitrogen stress days [d/crop] Fixation (NFIX) [kg/ha]Carbon Balance: Nitrogen in Crop Yield (YLN) [kg/ha],Carbon in Crop Yield (YLC) [kg/ha], Air Volatilization (AVOL) [kg/ha],Carbon Respiration (RSPC) [kg/ha], Denitrification (DN) [kg/ha],Carbon in Sediment (YOC) [kg/ha], Organic Nitrogen in Sediment (YON) [kg/ha],Carbon in Percolation (CLCH) [kg/ha], Soluble Nitrogen in Runoff (QNO3) [kg/ha],Carbon in Runoff (CQV) [kg/ha], Soluble Nitrogen in Subsurface Flow (SSFN) [kg/ha],Topsoil Organic Carbon (OCPD) [t/ha] Soluble Nitrogen in Percolation (PRKN) [kg/ha],Water Balance: Nitrogen losses through Burnning (BURN) [kg/ha]Rainfall (PRCP) [mm], OthersIrrigation (IRGA) [mm], Sediment losses (MUST, USLE, RUSL) [t/ha]Potential EvapoTranspiration (PET) [mm], Gross Nitrogen Mineralization (GMN) [kg/ha]Actual EvapoTranspiration (ET) [mm], Net Nitrogen Mineralization (NMN) [kg/ha]Runoff (Q) [mm], Nitrification (NITR) [kg/ha]Subsurface flow (SSF) [mm], Denitrification (DNIT) [kg/ha]Percolation (PRK) [mm] etc.
  13. 13. EPIC OUTPUT DATA
  14. 14. Integrative Climate Change ImpactModelling Economic-, Thematical Bio-physical process Economic Land Use Energy & Databases modelling Models Ecosystems models Integration Climate Field, Farm, FAMOS[space] CGE Environment (BOKU) (Wegener Zentrum) Soil andTopography Region & PASMA PROMETEUS Austria EPIC (BOKU/WIFO) (WIFO) APEX Pixel & BeWhere MultiREG Land use & (TAES/JGCRI/BOKU) Countries (IIASA/BOKU) (Joanneum) management EU-Regions & ROW EUFASOM ERSEM (Uni. Hamburg, (Uni. Hamburg) IIASA, BOKU, u.a.)Economics &Administration & Statistics Global GLOBIOM POLES (IIASA, Uni. Hamburg, (JRC-IPTS) BOKU, u.a.) Scale
  15. 15. 1.1. Global EPIC database
  16. 16. Global EPIC – Land Cover Global Land Cover (GLC2000; IFPRI, 2007)crop lands: 0.9 bil. ha forest lands: 4.0 bil. haother agri. lands: 1.5 bil. ha wet lands: 0.2 bil. hagrass lands: 1.1 bil. ha nat. veg. lands: 2.5 bil. ha
  17. 17. Global EPIC – CROPS20 crops simulated on all GLC=> crop production possibility set in GLOBIOM  BARL barley  CASS cassava  POTA potatoes  CHKP chick peas  RAPE rape seeds  CORN corn  RICE rice  COTS cotton  RYE rye  COWP cow peas  SOYB soybeans  DRYB dry beans  SPOT sweet potatoes  GRSG grain sorghum  SUGC sugar cane  OATS oats  SUNF sunflower  PMIL millet  WWHT wheat  PNUT peanuts/groundnuts
  18. 18. Global EPIC – crop management 3 Crop Input Systems (rule-based) simulated on all GLC => crop management possibility set in GLOBIOM: o AN: automatic nitrogen fertilization – N-fertilization rates based on N- stress levels (e.g. N-stress free days in 90% of the vegetation period). The upper limit of N application is 200 kg/ha/a. o AI: automatic nitrogen fertilization and irrigation – N and irrigation rates are based on stress levels (e.g. N and water stress free days in 90% of the vegetation period). N and irrigation upper limits of 200 kg/ha/a and 300 mm/a. o SS: subsistence farming – no N fertilizations and irrigation. Global Climate Data o using Tyndall Climate Change Data, A1fi Scenario
  19. 19. Mean Temperature change on croplandin 2050 in °C (Base 2000) Tyndall Climate Change Data, A1fi Scenario
  20. 20. Mean Temperature change on cropland in2100 in °C (Base 2000) Tyndall Climate Change Data, A1fi Scenario
  21. 21. Annual Precipitation Change on croplandin 2050 in mm (Base 2000) Tyndall Climate Change Data, A1fi Scenario
  22. 22. Annual Precipitation Change on croplandin 2100 in mm (Base 2000) Tyndall Climate Change Data, A1fi Scenario
  23. 23. Corn Yields in t/ha (DM) on cropland,automatic fertilization and irrigation(AI management), (Base 2000)
  24. 24. Changes in Corn Yields on cropland in2050 in t/ha (DM), AI management system(Base 2000)
  25. 25. Changes in Corn Yields on croplandin 2100 in t/ha (DM), AI management system(Base 2000)
  26. 26. Changes in irrigation water on croplandin 2050 in mm, AI management system(Base 2000)
  27. 27. Changes in irrigation water on croplandin 2100 in mm, AI management system(Base 2000)
  28. 28. 2. CLIMATE CHANGE AND IRRIGATEDAGRICULTRE IN SEMI-ARID CENTRAL EUROPE
  29. 29. Irrigated Agriculture inEurope In parts of Southern Europe: Agriculture accounts for up to 80 % of total water use (mostly crop irrigation) In Northern Europe, agricultures contribution to total water use varies from almost zero to over 30 % (mostly livestock farming) (EEA 2009). Proportion of area equipped for irrigation in selected countries in Central Europe. Country Country area Arable land Area equipped for irrigation: Proportion of area actually irrigated (1000 ha) area total (1000ha) from area equipped for irrigation (1000 ha) [proportion of arable area] Austria 8387 1382 119 [~9%] 34% CzechRepublic 7887 3032 47 [~2%] 37% Hungary 9303 4592 153 [~3%] 49% Slovakia 4903 1377 180 [~13%] 25% Slovenia 2027 177 4 [~ 2%] 51% cp. Trnka et al. 2010; AQUASTAT and FAOSTAT statistical databases (FAO 2009). Assumed fraction of irrigation methods in Europe: Basin and Furrow: ~34%, Drip ~18%,Sprinkler ~48% (Sauer et al. 2010).
  30. 30. Climate Change in Europe A warming trend (+0.90 C for 1901-2005) throughout Europe has been observed, which has been accelerating in the last 30 years (Alcamo et al. 2007) Regarding observed changes in precipitation (1961–2006): Observed changes in annual precipitation 1961–2006 Source: The data come from two projects: ENSEMBLES (http://www.ensembles-eu.org) and ECA&D (http://eca.knmi.nl) EEA, 2009
  31. 31. Climate Change in Europe Regional climate models project a larger warming in winter than in summer in Northern Europe and the reverse in central and Southern Europe (cp. Christensen and Christensen 2007). Trends in precipitation and changes in seasonal precipitation are more variable spatially and temporally (IPCC 2007) For all scenarios mean annual precipitation increases in northern Europe and decreases further south (IPCC 2007).
  32. 32. Climate Change in Europe Mediterranean regions, Central Europe and Eastern Europe : o Precipitation trends are projected to be negative. o Precipitation sums will decline in the early growing season (April-June) (Trnka et al. 2010) o Major and unprecedented drought events are more likely to occur in the near future than at any time in the past 130 years (Brazdil et al. 2009a,b; Trnka et al. 2010) o A reduced groundwater recharge rate is predicted for Central and Eastern Europe (Eitzinger et al. 2003; in IPCC 2007) o For Central and Southern Europe, areas under water stress can increase from 19% in 2007 to 35% in 2070 (IPCC, 2007).
  33. 33. Adaptation options in the Austrian Marchfeld 3.1. The region Marchfeld 3.2. Statistical climate data for Austria (EPIC input) 3.3. Investment in Irrigation Systems
  34. 34. Case Study – the region Marchfeld Marchfeld is part of the MarchfeldVienna Basin andinfluenced by a semi-aridclimate Arable area: 65,000 ha. Area supporting irrigation: 60,000 ha of which 30% are regularly irrigated (sprinkler irrigation). Cereals, root crops and vegetables comprise the main agricultural products of the region. 312 soil types can be differentiated in Marchfeld (Anonymous 1972). 1975-2007: the average annual precipitation sum was 531 mm Vegetation period from April – September the average monthly precipitation sum was only 331 mm
  35. 35. Statistical Climate Model for Austria Strauss, F., Formayer, H., Asamer, V., Schmid, E., 2010. Climate change data for Austria and the period 2008-2040 with one day and km² resolution (No. Discussion Paper DP-48-2010). Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Austria.  Statistical climate model for Austria based on in situ weather observations from 1975- 2007 (Central Institute for Meteorology and Geodynamics)  Based on linear regression and bootstrapping methods.Precipitation Class Temperature [ C] Class[mm]100 bis <500 500 <0 0>500 bis <600 600 >0 bis <2.5 1>600 bis <700 700 >2.5 bis <4.5 3>700 bis <800 800 >4.5 bis <5.5 5>800 bis <900 900 >5.5 bis <6.5 6>900 bis <1000 1000 >6.5 bis <7.5 7>1000 bis <1250 1250 >7.5 bis <8.5 8>1250 bis <1500 1500 >8.5 bis <9.5 9>1500 2000 >9.5 bis <10.5 10 Statistical Climate Model Databse for Austria (average over 1961-1990 (Strauss et al. 2010; Auer et al., 2000) : StartClim (Schöner et al., 2003)
  36. 36. Statistical Climate Model for Austriao Observed increase in temperature until 2007 -> Increase in annual average temperature until year 2040.o No trend in precipitation in period 1975-2007-> Assumption: distribution of precipitation similar to past 30 years. To generate a climate spectrum: Stochastic weather scenarios for the period 2008-2040 by bootstrapping of temperature residuals, observed data for solar radiation, precipitation, relative humidity, wind: drawn from observations of historical period Various precipitation scenarios for sensitivity analysis o increasing/decreasing annual precipitation sums; o unchanged annual precipitation sums with seasonal redistribution
  37. 37. Case studies: For each year For each year Realizations of daily values: Crop yields for all •Temperature management •Precipitation options Statistical •Radiation InvestmentClimate Model •Relative EPIC model humidity •Wind Profits •Site specific information
  38. 38. Investment in Irrigation Systems under weatheruncertaintyTogether with S. Fuss (IIASA), J. Szolgayova (IIASA), Franziska Strauss (BOKU), Erwin Schmid (BOKU)Leading questions: Aim to model an farner’s decision to invest in a sprinkler or drip irrigation system under precipitation uncertainty. How is the decision to invest affected by: o Various soil types? o Policy instruments o Water prices o Subsidies
  39. 39. Investment in Irrigation Systems –Data and Methods Climate scenario with step-wise decreasing precipitation(-20% in 2040 compared to base period 1975-2007) EPIC o Carrots, Sugar Beet, Potato, Corn, Winter Wheat, o Conventional tillage o Drip and Sprinkler irrigation o Automatically determined nitrogen fertilizer and irrigation amount. o 2 soil types Profit calculation: Producer’s Information, Statistic Austria, Average commodity prices 2005-2008
  40. 40. Investment in Irrigation Systems –Data and Methods Characteristics of the model: o Weather/climate uncertainty for period 2009-2040 (i.e. 300 precipitation scenarios) o Agents choose the optimal investment strategy and time to maximize expected sum of profits in planning period 2009-2040. o Model is performed for each crop and soil type separately. Dynamic programming approach under weather uncertainty. o Stochastic optimal control problem on a finite horizon with a discrete stochastic component. o The optimal actions are derived recursively by dynamic programming using the Bellman equation
  41. 41. No Irrigation Sprinkler Drip Soil 1 Soil 2 Soil 1 Soil 2 Soil1 Soil 2 Mean Mean Mean Mean Mean MeanCORN Yields t/ha/a 6.2 4.6 7.9 7.5 7.9 7.5CARROTS 5.4 3.5 5.5 5.2 5.5 5.3POTATO 7.0 5.3 7.1 6.6 7.1 6.7S.BEETS 7.8 6.2 10.1 9.8 10.3 10.0W.WHEAT1 4.7 3.0 4.8 4.7 4.8 4.7W.WHEAT2 4.9 3.0 5.1 4.9 5.1 5.0CORN Irrig.mm/ha/a 127 254 113 229CARROTS 39 148 34 132POTATO 53 163 47 144S.BEETS 162 280 143 262W.WHEAT1 35 141 32 126W.WHEAT2 36 142 32 126CORN Profit €/ha/a 130 -82 10 -115 -249 -319CARROTS 8323 4715.4 8352 7592 7910 7358POTATO 2349 1234 2113 1750 1816 1532S.BEETS 48 -215 60 -57 -166 -229W.WHEAT1 460 107 204 120 -100 -132W.WHEAT2 516 127 280 186 -22 -65
  42. 42. Investment in Irrigation Systems –Results: Optimal Timing of Investment Soil 1(higher quality) Soil 2
  43. 43. Investment in Irrigation Systems – Policy Scenario 1 : Water Prices SPRINKLER Sugar Winter Winter IRRIGATION Corn Carrot Potatoes beets wheat 1 wheat 2 No policy - 2024 - 2024 - - Soil 1 0.20 € - 2024 - - - - (higher quality) 0.50 € - 2024 - - - - 1€ - 2024 - - - - 2€ - 2028 - - - - SPRINKLER Sugar Winter Winter Soil 2 IRRIGATION Corn Carrot Potatoes beets wheat 1 wheat 2(lower quality) No policy - 2009 2009 2009 2023 2015 0.20 € - 2009 2009 2009 - - 0.50 € - 2009 2009 2023 - - 1€ - 2009 2009 - - - 2€ - 2009 2009 - - -
  44. 44. Investment in Irrigation Systems – Policy Scenario 2 : Subsidies Soil 1 (higher quality) Corn Carrots Potatoes Sugar beets Winter w. 1 Winter w. 2 Drip Spri. Drip Spri. Drip Spri. Drip Spri. Drip Spri. Drip Spri. Nopolicy - - - 2024 - - - 2024 - - - - 10% - - - 2024 - - - 2024 - - - - 30% - - - 2024 - - - 2024 - - - - 50% - - - 2024 - - - 2024 - - - - 60% - - - 2024 - - - 2024 - - - - 70% - - 2020 - - - - 2024 - - - - 80% - - 2011 - - - 2024 - - - - - 90% - - 2009 - - - 2009 - - - - -
  45. 45. Investment in Irrigation Systems – Policy Scenario 2 : Subsidies Soil 2(lowerquality) Corn Carrots Potatoes Sugar beets Winter w. 1 Winter w. 2 Drip Spri. Drip Spri. Drip Spri. Drip Spri. Drip Spri. Drip Spri. Nopolicy - - - 2009 - 2009 - 2009 - 2023 - 2015 10% - - - 2009 - 2009 - 2009 - 2023 - 2015 30% - - - 2009 - 2009 - 2009 - 2023 - 2015 50% - - - 2009 - 2009 - 2009 - 2023 - 2015 60% - - 2009 - - 2009 - 2009 - 2023 - 2015 70% - - 2009 - - 2009 2009 - - 2023 - 2015 80% - - 2009 - - 2009 2009 - 2022 - - 2015 201 90% 9 - 2009 - 2009 - 2009 - 2015 - 2009 -
  46. 46. Investment in Irrigation Systems – Conclusion Though more water efficient, drip irrigation seems too expensive for adoption, regardless whether crops are cultivated on soil 1 or 2. Water prices do not enforce adoption of drip irrigation but rather drive out all irrigation systems. Subsidies on drip irrigation systems seem effective to support the adoption of drip irrigation. However, to ensure full adoption of drip, subsidies of ~ 70-90% of capital costs are needed, regardless of soil type.
  47. 47. CONCLUDING REMARKS
  48. 48. CONCLUDING REMARKS Climate change impact analyses require data and models (i.e. biophysical and economic models) with sufficient reliability, detail and resolution. Adaptation options need to be locally/regionally as well as empirically assessed/evaluated => stakeholder participation Empirical model analysis yield powerful complementary information about adaptation options, impacts and externalities over space and time.
  49. 49. Thank you for your attention!christine.heumesser@boku.ac.aterwin.schmid@boku.ac.at

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