Analysis and modelling of land use change inrelation to food security and climate changePeter Verburg                     ...
Rationale                            Expansion of agricultural area                            Intensification of land use...
Rationale                             Expansion of agricultural area                             Intensification of land u...
Human influence on the environment   (Ellis et al., 2010)                                                            4
Human influence on the environment   (Ellis et al., 2010)                                                            5
Global scenarios of land cover change Macro-economic models (GTAP/IMPACT) and land allocation model (IMAGE, LandShift, CLU...
7
8
9
10
Landscapes are mosaicsComposition of landscapes isimportant (biodiversity, carbon,ecosystem services)Representation by dom...
Regional differences                                          350     Scenario                                          30...
Reference scenario (B1)13     (2000-2030)
Global and European biofuel14     Directives (2000-2030)
Spatial trade-offs  Global scale    Increased competitiveness of agriculture                    Marginal areas:         Pr...
Orchids Vs.   Bears              16
Land cover developmentsMain trends: Abandonment of marginal agricultural areas      decrease of agricultural area Urbaniza...
2000   2050              Ecosystem Service Assessment, CAS   18
Land cover developmentsMain trends: Abandonment of marginal agricultural areas      decrease of agricultural area Urbaniza...
Analyze effect of landuse change onecosystem services             Kienast et al., 2009                                20
Land cover developmentsMain trends: Abandonment of marginal agricultural areas      decrease of agricultural area Urbaniza...
Flood damage reduction                         The Netherlands                         Soil and CC alternative            ...
Changes in cultivation options                                 23
Land cover developmentsMain trends: Abandonment of marginal agricultural areas      decrease of agricultural area Urbaniza...
GTAP-CLUMondo model          GTAP-IMAGELand cover                                   2000                                  ...
Rationale                             Expansion of agricultural area                             Intensification of land u...
Challenges Data on land use intensity are limited Drivers of intensification largely unknown • Keys and McConnell, 2005 – ...
Challenges Data on land use intensity are limited Drivers of intensification largely unknown • Keys and McConnell, 2005 – ...
Intensity of agriculture in 160.000 LUCAS points          (N/ha)                              LUCAS 2003, 2006            ...
Temme and Verburg, 2011www.ivm.vu.nl/ag-intensity                             30
Challenges Data on land use intensity are limited Drivers of intensification largely unknown • Keys and McConnell, 2005 – ...
Drivers of agricultural intensity – Global scale                                      Crop specific yields,           Actu...
Explaining global distributions of yield gabFrontier production function                                            vi = n...
Explaining global distributions of yield gab • Determinants for the frontier yield:    – Temperature, PAR, precipitation, ...
Efficiency is an indicator of the management intensity                                                     35
Accessibility                                     LaborAccessibilityIrrigation        Market influence                  Ac...
Market influence                                              IrrigationIrrigation          AccessibilityMarket influence ...
Irrigation                            Irrigation                                       Labor                     Market st...
Challenges Data on land use intensity are limited Drivers of intensification largely unknown • Keys and McConnell, 2005 – ...
Global distribution of irrigation in farmlandLandscape systems:Land coverLand useLivestockPeopleEcosystem services        ...
Variables at grid cell levelVariable name   Description [unit]Irrigation      1 if irrigation,                0 if rainfed...
Variables at country levelVariable name           Description [unit]Water                   Natural total renewable water ...
Multilevel analysis   Binary logistic model:   • Only grid cell level – no multiple levels   Multi-level Model 1:   • incl...
Variable name                      Model 1                 Model 2              Binary logistic                           ...
Rationale                             Expansion of agricultural area                             Intensification of land u...
Import and land grabbingImport:                          Land Grab:  Macro-economic modelling:  • Partial equilibrium mode...
Conclusion Land cover and land use change are important drivers of food security Socio-economic and governance variables a...
The Global Land ProjectIHDP and IGBP funding                                               AIMES       Knowledge, Learning...
Global Land Project                      http://www.globallandproject.org                                                 ...
Thank you!Peter.Verburg@ivm.vu.nlInstitute for Environmental StudiesVU University Amsterdamhttp://www.ivm.vu.nl
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Peter Verburg — Analysis and modelling of land use change in relation to food security and climate change

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The Chinese Academy of Agricultural Sciences (CAAS) and the International Food Policy Research Institute (IFPRI) jointly hosted the International Conference on Climate Change and Food Security (ICCCFS) November 6-8, 2011 in Beijing, China. This conference provided a forum for leading international scientists and young researchers to present their latest research findings, exchange their research ideas, and share their experiences in the field of climate change and food security. The event included technical sessions, poster sessions, and social events. The conference results and recommendations were presented at the global climate talks in Durban, South Africa during an official side event on December 1.

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Peter Verburg — Analysis and modelling of land use change in relation to food security and climate change

  1. 1. Analysis and modelling of land use change inrelation to food security and climate changePeter Verburg Beijing, 7-8 nov 2011
  2. 2. Rationale Expansion of agricultural area Intensification of land use Climate change All processes happen at same time systems depending land use, environmental, socio- Food/Feed/Fibre/Energy economic and governance conditions demand Import from other areas Change in consumption pattern 2
  3. 3. Rationale Expansion of agricultural area Intensification of land use systems Food/Feed/Fibre/Energy demand Import from other areas Change in consumption pattern 3
  4. 4. Human influence on the environment (Ellis et al., 2010) 4
  5. 5. Human influence on the environment (Ellis et al., 2010) 5
  6. 6. Global scenarios of land cover change Macro-economic models (GTAP/IMPACT) and land allocation model (IMAGE, LandShift, CLU-Mondo) Spatial resolution often 50x50 km One dominant land cover type per pixel Economic models assume ‘rational’ behaviour World region economic land demands are downscaled by simple rules: land suitability, distance to existing land cover types Variation in socio-economic and cultural factors disregarded 6
  7. 7. 7
  8. 8. 8
  9. 9. 9
  10. 10. 10
  11. 11. Landscapes are mosaicsComposition of landscapes isimportant (biodiversity, carbon,ecosystem services)Representation by dominantland cover types is incorrectat all (feasible) spatialresolutionsMosaics should berepresented explicitly 11
  12. 12. Regional differences 350 Scenario 300 250 Agricultural area Global models 200 150CLUE-Scanner European scale 100 models 50 0 Trade-off analysis -50 Africa Asia C&SAmer EU27 NAFTA World Reference Biofuel, w/o EU Biofuel, with EU Verburg et al., 2008 Annals of Regional Science Banse et al., 2010 Biomass and Bioenergy 12
  13. 13. Reference scenario (B1)13 (2000-2030)
  14. 14. Global and European biofuel14 Directives (2000-2030)
  15. 15. Spatial trade-offs Global scale Increased competitiveness of agriculture Marginal areas: Prime agricultural areas:Landscape scale Abandonment Intensification/scale enlargement 15
  16. 16. Orchids Vs. Bears 16
  17. 17. Land cover developmentsMain trends: Abandonment of marginal agricultural areas decrease of agricultural area Urbanization Loss of most productive agricultural lands Peri-urban development demand for ecosystem services besides food production: recreation etc. Expansion of agriculture in other regions Intensification of agricultural production on remaining area 17
  18. 18. 2000 2050 Ecosystem Service Assessment, CAS 18
  19. 19. Land cover developmentsMain trends: Abandonment of marginal agricultural areas decrease of agricultural area Urbanization Loss of most productive agricultural lands Peri-urban development demand for ecosystem services besides food production: recreation etc. Expansion of agriculture in other regions Intensification of agricultural production on remaining area 19
  20. 20. Analyze effect of landuse change onecosystem services Kienast et al., 2009 20
  21. 21. Land cover developmentsMain trends: Abandonment of marginal agricultural areas decrease of agricultural area Urbanization Loss of most productive agricultural lands Peri-urban development demand for ecosystem services besides food production: recreation etc. Adaptation to climate change flood risk and adaptation measures threaten most productive regions Expansion of agriculture in other regions Intensification of agricultural production on remaining area 21
  22. 22. Flood damage reduction The Netherlands Soil and CC alternative 22
  23. 23. Changes in cultivation options 23
  24. 24. Land cover developmentsMain trends: Abandonment of marginal agricultural areas decrease of agricultural area Urbanization Loss of most productive agricultural lands Peri-urban development demand for ecosystem services besides food production: recreation etc. Adaptation to climate change flood risk and adaptation measures threaten most productive regions Expansion of agriculture in other regions Intensification of agricultural production on remaining area 24
  25. 25. GTAP-CLUMondo model GTAP-IMAGELand cover 2000 2050 25
  26. 26. Rationale Expansion of agricultural area Intensification of land use systems Food/Feed/Fibre/Energy demand Import from other areas Change in consumption pattern 26
  27. 27. Challenges Data on land use intensity are limited Drivers of intensification largely unknown • Keys and McConnell, 2005 – meta-analysis of 91 case studies > Drivers are context specific > Drivers operate at different spatial/temporal scales/levels Role of governance unclear 27
  28. 28. Challenges Data on land use intensity are limited Drivers of intensification largely unknown • Keys and McConnell, 2005 – meta-analysis of 91 case studies > Drivers are context specific > Drivers operate at different spatial/temporal scales/levels Role of governance unclear 28
  29. 29. Intensity of agriculture in 160.000 LUCAS points (N/ha) LUCAS 2003, 2006 CAPRI 2000 29
  30. 30. Temme and Verburg, 2011www.ivm.vu.nl/ag-intensity 30
  31. 31. Challenges Data on land use intensity are limited Drivers of intensification largely unknown • Keys and McConnell, 2005 – meta-analysis of 91 case studies > Drivers are context specific > Drivers operate at different spatial/temporal scales/levels Role of governance unclear 31
  32. 32. Drivers of agricultural intensity – Global scale Crop specific yields, Actual yield 5 arc-min [Monfreda et al., 2008] Frontier yield/ Stochastic frontier yield gap production function Reasons for Inefficiency factors / inefficiency Multiple Regressions Neumann et al., 2010 Agricultural Systems 32
  33. 33. Explaining global distributions of yield gabFrontier production function vi = noise ui = inefficiency xi = actual productivity ¤i = frontier productivity Neumann et al., 2010 Agricultural Systems 33
  34. 34. Explaining global distributions of yield gab • Determinants for the frontier yield: – Temperature, PAR, precipitation, soil fertility constraints • Determinants for deviation from the frontier yield (=inefficiency effects): – Irrigation, market accessibility, market influence, agricultural population, slope Neumann et al., 2010 Agricultural Systems 34
  35. 35. Efficiency is an indicator of the management intensity 35
  36. 36. Accessibility LaborAccessibilityIrrigation Market influence Accessibility Slope Irrigation Neumann et al., 2010 Agricultural Systems 36
  37. 37. Market influence IrrigationIrrigation AccessibilityMarket influence Market influence Market influence Accessibility Neumann et al., 2010 Agricultural Systems 37
  38. 38. Irrigation Irrigation Labor Market strength Accessibility LaborNeumann et al., 2010 Agricultural Systems 38
  39. 39. Challenges Data on land use intensity are limited Drivers of intensification largely unknown • Keys and McConnell, 2005 – meta-analysis of 91 case studies > Drivers are context specific > Drivers operate at different spatial/temporal scales/levels Role of governance unclear 39
  40. 40. Global distribution of irrigation in farmlandLandscape systems:Land coverLand useLivestockPeopleEcosystem services Irrigated farmland Rainfed farmland Portmann et al., 2010 40
  41. 41. Variables at grid cell levelVariable name Description [unit]Irrigation 1 if irrigation, 0 if rainfedSlope Slope [%]Discharge River discharge [mm/yr]Humidity Humidity, calculated as precipitation [mm] / potential evapotranspiration (PET) [mm/yr] [index]Evap Evaporation [mm/yr]ET Evapotranspiration [mm/yr]Access Travel time to markets [hours]Population Population density [persons/km2] 41
  42. 42. Variables at country levelVariable name Description [unit]Water Natural total renewable water resources [m3/yr/ha]Political stability Likelihood that the government will be destabilized [index]Control of corruption Control of corruption (the extent to which public power is exercised for private gain) [index]Government Quality of public and civil service and the degree of itseffectiveness independence from political pressures [index]GDP Gross Domestic Product per capita [US$]Democracy Level of institutionalized democracy [index]Autocracy Level of autocracy [index] 42
  43. 43. Multilevel analysis Binary logistic model: • Only grid cell level – no multiple levels Multi-level Model 1: • includes all independent biophysical grid cell variables (slope, discharge, humidity, evaporation and ET) • Includes country level Multi-level Model 2: • includes in addition to these variables the socio-economic grid cell variables (access and population) • includes country level variables (water, government performance and government type) 43
  44. 44. Variable name Model 1 Model 2 Binary logistic regression Unstand. T-ratio Unstand. T-ratio Unstand. Wald coeff. coeff. coeff. testGrid cell level (level one)Fixed effectsIntercept -0.566** -3.2 -0.570** -3.2 0.542*** 119.3Ln(slope) -0.018 -0.3 0.009 0.2 0.136*** 248.7Ln(discharge) 0.150*** 5.3 0.133** 5.3 0.078*** 94.6Humidity -1.211*** -5.4 -1.039** -2.6 -0.347*** 88.6Evap 0.002 1.7 0.001 0.6 0.003*** 221.0ET <-0.001 -0.1 -0.0011 -1.7 -0.002*** 470.8Ln(access) -0.319*** -4.3 -0.382*** 467.9Ln(population) 0.278** 3.4 0.241*** 1467.8Country level (level two)Ln(water) -0.006 <-0.1Government_performance 0.409* 2.2Government_type -0.434** -2.7Variance 0.558 0.557Model fit (ROC) 0.806 0.812 0.724 44
  45. 45. Rationale Expansion of agricultural area Intensification of land use systems Food/Feed/Fibre/Energy demand Import from other areas Change in consumption pattern 45
  46. 46. Import and land grabbingImport: Land Grab: Macro-economic modelling: • Partial equilibrium models • General equilibrium models Land supply/demand determines land price Land supply mostly only constrained by agro- ecological suitability 46
  47. 47. Conclusion Land cover and land use change are important drivers of food security Socio-economic and governance variables are important and deserve more attention in global scale assessments Current studies focus too much attention on biophysical component of climate change Local patterns of adaptation need to be accounted for in global assessments Knowledge available in the Land Science community may help analysis of food security and climate change 47
  48. 48. The Global Land ProjectIHDP and IGBP funding AIMES Knowledge, Learning and iHOPE Integrated History Societal Change (KLSC) (in preparation) of people on Earth (led by AIMES). Co-sponsored by PAGES and IHDP 48
  49. 49. Global Land Project http://www.globallandproject.org 49
  50. 50. Thank you!Peter.Verburg@ivm.vu.nlInstitute for Environmental StudiesVU University Amsterdamhttp://www.ivm.vu.nl

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