11/9/2011

1

Global Science Challenges in the Bioeconomy

Trade-offs for the 4Fs

Michael Obersteiner

Leader

Ecosystems Service and Management ProgramInternational Institute for Applied Systems Analysis

National Science Congress X8-9th November, Jakarta

IIASA Science Plan

11/9/2011

2

IIASA’s niche in Global Change Science

• Too large to be solved by an individual country

• Too complex to be solved by one discipline

• Too distant to be solved by one generation

Too Large, Complex and Distant……

• Food Security

• Food as a geopolitical asset (food sovereignty)

• Mobilizing resources for the bio-based economy

• (Virtual) water and Climate change

• Provisioning of other ecosystem services

– Avoid deforestation

– Natural disaster management

– Nitrogen, Phosphorus

– Cultural Assets

11/9/2011

3

Integrated Global Data-Modeling System

• Production

• Consumption

• Food trade

• Agricultural prices

• Changes in Livestock and

Crop Production Systems

• Land use change

•Natural Capital

Geo-spatial scale

Socio-economic scale

• Weather/climate data

• Soil data

• Topographical data

• Land use and crop

management data (fertilization,

irrigation, etc.)

• Population

• GDP

• Prices

• Diets

• ,

GLOBIOM

Comparative

static/dynamic

impact analysis

EPIC Model

Erosion

C, N, & P cycling

Plant

growth

Precipitation

Soil

layers

Operations

Solar irradiance

Runoff

Wind

Representative EPIC modules

Pesticide fate

bio-physical

impacts

Biophysical Modelling

EPIC

Rain, Snow, Chemicals

Subsurface Flow

Surface Flow

Below Root Zone

Evaporation and

Transpiration

• Weather

• Hydrology

• Erosion

• Carbon sequestration

• Crop growth

• Crop rotations

• Fertilization

• Tillage

• Irrigation

• Drainage

• Pesticide

• Grazing

• Manure

Processes

Major outputs

� Crop yields, environmental effects (e.g. soil carbon)

� 20 crops (>75% of harvested area)

� 4 management systems: High input, Low input, Irrigated, Subsistence

11/9/2011

4

Global water use for food production

Irrigation depth (wheat)

11/9/2011

5

IrelandUK

DenmarkSweden

Netherlands

Belgium

Germany

France

Rwanda

Zimbabwe

Namibia

Zambia

Egypt

R2 = 0.8788

0

0.5

1

1.5

2

2.5

3

0 2000 4000 6000 8000 10000

Yield (kg ha-1)

Cro

p W

ate

r P

rod

ucti

vit

y (

kg

m-3

)

Asia

Europe

North America

South America

Africa

Oceania

Ireland

UK

DenmarkSweden

Eritrea

Chad

Sudan

Mali

R2 = 0.7859

0

0.5

1

1.5

2

2.5

3

0 2000 4000 6000 8000 10000 12000 14000

Yield (kg ha -1)

Cro

p W

ate

r P

rod

uc

tiv

ity (

kg

m-3

)

Asia

Europe

North America

South America

Africa

Oceania

Potential yield and yield gap at a global scale

Climate Change – Yield Impacts

Relative Difference in Means (2050/2100) in Wheat Yields

[Data: Tyndall, Afi Scenario, simulation model: EPIC]

11/9/2011

6

Relative Difference in Variances (2050/2100) in Wheat Yields

[Data: Tyndall, Afi Scenario, simulation model: EPIC]

Climate change – Yield Variance

12

G4M: Biophysical Modelling G4M: Biophysical Modelling -- ForestryForestry

Forest related parameters

Forest growth functions estimated from yield tables and NPP products

Major outputs:

Mean annual increment

Tree size

Sawn wood suitability

Harvesting cost

Management

Rotation times

Thinning

Regeneration

Species Change

11/9/2011

7

Forest Area Development A2rForest Area Development A2r

REDDREDD

Supply Chains

Natural ForestsNatural Forests

Managed ForestsManaged Forests

Short Rotation TreeShort Rotation Tree

PlantationsPlantations

CroplandCropland

GrasslandGrassland

Other natural landOther natural land

BioenergyBioenergy

Bioethanol

Biodiesel

Methanol

Heat

Electricity

Biogas

Wood productsWood products

Sawn wood

Pulp

Livestock productsLivestock products

Beef

Lamb

Pork

Poultry

Eggs

Milk

CropsCropsCorn

Wheat

Cassava

Potatoes

Rapeseed

etc0LAND USE CHANGE

LAND USE CHANGE

Wood Processing

BioenergyProcessing

Livestock Feeding

11/9/2011

8

Scenario building

Output: production Q � land use, water use, GHG, environment

consumption Q

trade flows

prices

Main exogenous drivers:

Population (IIASA projections)

Diets (FAO, 2006)

Bio-energy demand (POLES team, JRC Seville, WEO)

(GDP, technological change,…)

Urbanization

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1800 1850 1900 1950 2000 2050 2100 2150

Source: Grübler, 2007Source: Grübler, 2007

11/9/2011

9

Education

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1800 1850 1900 1950 2000 2050 2100 2150

Source: Lutz, 2007Source: Lutz, 2007

Democratization

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1800 1850 1900 1950 2000 2050 2100 2150

Source: Modelski, 2002Source: Modelski, 2002

11/9/2011

10

Food for a Week, Darfur Refugees, Chad© 2007 PETER MENZEL PHOTOGRAPHY© 2007 PETER MENZEL PHOTOGRAPHY

Food for a Week, Germany© 2005 PETER MENZEL PHOTOGRAPHY© 2005 PETER MENZEL PHOTOGRAPHY

11/9/2011

11

© 2005 PETER MENZEL PHOTOGRAPHY© 2005 PETER MENZEL PHOTOGRAPHY

The Natomo Family 6:30am March 27, 1993 Kouakourou, Mali

The Ukita Family4:30pm December 16, 1992 Tokyo, Japan

© 2005 PETER MENZEL PHOTOGRAPHY© 2005 PETER MENZEL PHOTOGRAPHY

11/9/2011

12

Technology & Trade & Food Security Scenarios

• BAU:

– SF0: not preventing to go hungry

– SF1: safety first, food security binding requirement

• IRR: Subsidized Irrigation projects

• TRD: Increased trade barriers - modeled as approximately a

doubling of the trade cost)

Consumption

11/9/2011

13

Average Price Index 2020 vs 2000

Fundamental Price Volatility Index in 2020

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

SF0 SF1 SF1/IRR SF1/TRD

11/9/2011

14

Environmental Impact: Deforestation

Corn Trade / Imports in 2020 in Million t

0

10

20

30

40

50

60

70

80

90

100

BAU IRR TRD

SF0

SF1

11/9/2011

15

Conclusions

• Desirable Food security constraint implies:

– Higher food prices and volatility

– Deforestation, N&P, water, GHGs….

• Adaptation Options to Mitigate Trade-offs

– Trade flexibility most effective and cheapest measure

– Yield-stabilizing management systems such as

irrigation is necessary for environmentally sustainable

food security

– Physical and Virtual Storage

Conclusion for Systems’ Science

• Integration of Agronomics with Economics&Finance

• Integration of Trade Policy

• Integration of Remotely Sensed Data for Crop Forecasting

• Integration of Environmental Policies

• Integration of Climate Science

• Integration with Energy system (Hydropower and Biofuels)

• Integration with Socio-economics and Education Systems

• Integration of…..

11/9/2011

16

Michael ObersteinerMichael Obersteiner

Ecosystems Services and Management ProgramEcosystems Services and Management Program

oberstei@iiasa.ac.atoberstei@iiasa.ac.at