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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
Beijing, 7-8 nov 2011
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Rationale
Food/Feed/Fibre/Energydemand
Expansion of agricultural area
Intensification of land use systems
Import from other areas
Change in consumption pattern
All processes happen at same time depending land use, environmental, socio-
economic and governance conditions Clim
ate
chan
ge
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Rationale
Food/Feed/Fibre/Energydemand
Expansion of agricultural area
Intensification of land use systems
Import from other areas
Change in consumption pattern
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Human influence on the environment (Ellis et al., 2010)
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Human influence on the environment (Ellis et al., 2010)
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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
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Landscapes are mosaics
Composition of landscapes is important (biodiversity, carbon, ecosystem services)
Representation by dominant land cover types is incorrect at all (feasible) spatial resolutions
Mosaics should be represented explicitly
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CLUE-Scanner
-50
0
50
100
150
200
250
300
350
Africa Asia C&SAmer EU27 NAFTA World
Reference Biofuel, w/o EU Biofuel, with EU
Regional differences
Verburg et al., 2008 Annals of Regional ScienceBanse et al., 2010 Biomass and Bioenergy
Scenario
Global models
European scale models
Trade-off analysis
Agr
icul
tura
l are
a
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Ref
eren
ce s
cena
rio (B
1)
(200
0-20
30)
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Glo
bal a
nd E
urop
ean
biof
uel
Dire
ctiv
es (2
000-
2030
)
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Spatial trade-offsIncreased competitiveness of agriculture
Prime agricultural areas:Intensification/scale enlargement
Marginal areas:Abandonment
Global scale
Landscape scale
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Orchids Vs. Bears
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Land cover developments
Main 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
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Land cover developments
Main 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
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Analyze effect of land use change on ecosystem services
Kienast et al., 2009
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Land cover developments
Main 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
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Flood damage reduction
The Netherlands
Soil and CC alternative
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Changes in cultivation options
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Land cover developments
Main 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
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Land coverGTAP-IMAGEGTAP-CLUMondo model
2000
2050
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Rationale
Food/Feed/Fibre/Energydemand
Expansion of agricultural area
Intensification of land use systems
Import from other areas
Change in consumption pattern
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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
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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
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Intensity of agriculture in 160.000 LUCAS points
(N/ha)
LUCAS 2003, 2006
CAPRI 2000
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Temme and Verburg, 2011www.ivm.vu.nl/ag-intensity
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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
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Drivers of agricultural intensity – Global scale
Reasons for inefficiency
Frontier yield/yield gap
Actual yieldCrop specific yields,
5 arc-min [Monfreda et al., 2008]
Stochastic frontier production function
Inefficiency factors /Multiple Regressions
Neumann et al., 2010 Agricultural Systems
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Frontier production function
vi = noiseui = inefficiencyxi = actual productivity¤i = frontier productivity
Explaining global distributions of yield gab
Neumann et al., 2010 Agricultural Systems
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• 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
Explaining global distributions of yield gab
Neumann et al., 2010 Agricultural Systems
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Efficiency is an indicator of the management intensity
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Accessibility Irrigation Slope
Irrigation
Accessibility Labor
Market influenceAccessibility
Neumann et al., 2010 Agricultural Systems
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IrrigationMarket influence
Market influenceAccessibility
Accessibility Market influence
Market influenceIrrigation
Neumann et al., 2010 Agricultural Systems
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Irrigation Labor
Market strengthAccessibilityLabor
Irrigation
Neumann et al., 2010 Agricultural Systems
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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
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Landscape systems:Land coverLand useLivestockPeople
Ecosystem services
Portmann et al., 2010
Global distribution of irrigation in farmland
Irrigated farmlandRainfed farmland
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Variables at grid cell level
Variable name Description [unit]
Irrigation 1 if irrigation, 0 if rainfed
Slope 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]
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Variables at country level
Variable 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 effectiveness
Quality of public and civil service and the degree of its independence frompolitical pressures [index]
GDP Gross Domestic Product per capita [US$]
Democracy Level of institutionalized democracy [index]
Autocracy Level of autocracy [index]
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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)
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Variable name Model 1 Model 2 Binary logistic regression
Unstand. coeff.
T-ratio Unstand. coeff.
T-ratio Unstand. coeff.
Wald test
Grid 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*** 88.6
Evap 0.002 1.7 0.001 0.6 0.003*** 221.0ET <-0.001 -0.1 -0.0011 -1.7 -0.002*** 470.8
Ln(access) -0.319*** -4.3 -0.382*** 467.9
Ln(population) 0.278** 3.4 0.241*** 1467.8
Country level (level two)Ln(water) -0.006 <-0.1
Government_performance 0.409* 2.2
Government_type -0.434** -2.7
Variance 0.558 0.557
Model fit (ROC) 0.806 0.812 0.724
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Rationale
Food/Feed/Fibre/Energydemand
Expansion of agricultural area
Intensification of land use systems
Import from other areas
Change in consumption pattern
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Import and land grabbing
Land Grab:Import:
Macro-economic modelling:• Partial equilibrium models• General equilibrium models
Land supply/demand determines land price
Land supply mostly only constrained by agro-ecological suitability
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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
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IHDP and IGBP funding
AIMES
iHOPE Integrated History of people on Earth(led by AIMES). Co-sponsored by PAGES and IHDP
Knowledge, Learning and Societal Change (KLSC)(in preparation)
The Global Land Project
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Global Land Project
http://www.globallandproject.org
