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

Modelling for trade-offs analysisat regional and global scale

Petr Havlík + >30 collaborators in and outside IIASA

International Institute for Applied Systems Analysis (IIASA), Austria

International Livestock Research Institute (ILRI), Kenya

CGIAR Workshop: Analysis of Trade-offs in Agricultural Systems WUR Wageningen, February 19, 2013

Havlík et al. Modelling for trade-offs analysisCGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013

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LAND

NATURAL LAND INTENSIFICATION MANAGED LAND

Biodiversity

CO2 sink

Land sparing

Pollution

N2O emissions

Water use

Soil degradation

Food, feed, fiber, fuel

Farmers income

Trade-offs in the land use sectors


Outline

1. Model overview

2. Global case study – Sustainable intensification?a) Rigid system b) Flexible livestock systemsc) Land productivity

3. Regional case study – Development scenarios

4. Conclusion

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1. Model overview

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GLOBIOM: Global Biosphere Management Model

Partial equilibrium model: Agriculture, Forestry, Bioenergy

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DEMAND

SUPPLY


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 2

Demand functions explicit: linearized non-linear functions

GLOBIOM

Spatial equilibrium model a la Takayama & Judge

Maximization of the social welfare (PS + CS)

Recursively dynamic (10 year periods)

eqqpp /1)ˆ/(*ˆ

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Supply Chains

Natural Forests

Managed Forests

Short Rotation Tree Plantations

Cropland

Grassland

Other natural land

Bioenergy

Bioethanol Biodiesel MethanolHeatElectricityBiogas

Wood products

Sawn woodPulp

Livestock products

BeefLambPorkPoultryEggsMilk

CropsCornWheatCassavaPotatoesRapeseedetc…

LAN

D U

SE C

HA

NG

E

Wood Processing

Bioenergy Processing

Livestock Feeding

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Output: Production Q - land use (change)

- water use

- GHG,

- other environment (nutrient cycle, biodiversity,…)

Consumption Q

Prices

Trade flows

Main exogenous drivers:

Population

GDP

Technological change

Bio-energy demand (POLES team)

Diets (FAO, 2006)

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PX5

Altitude class, Slope class, Soil Class

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;

HRU = Altitude & Slope & Soil

Spatial resolutionHomogeneous response units (HRU) – clusters of 5 arcmin pixels

Source: Skalský et al. (2008)

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Simulation Units (SimU) = HRU & PX30 & Country zone

Source: Skalský et al. (2008)

Country HRU*PX30

PX5

SimU delineation relatedstatistics on LC classes and

Cropland management systems

reference for geo-coded data on crop management;

input statistical data for LC/LU economic optimization;

LC&LUstat> 200 000 SimU

Spatial resolution

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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, nitrogen leaching)

20 crops (>75% of harvested area)4 management systems: High input, Low input, Irrigated, Subsistence

Crops - EPIC

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Relative Difference in Means (2050/2100) in Wheat Yields[Data: Tyndall, Afi Scenario, simulation model: EPIC]

Crops - EPIC

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Source: EPIC model(t/ha DM)

Grasslands – CENTURY/EPIC


Gridded Livestock of the World – Robinson et al. (2011)

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Livestock


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Livestock production systems distribution

Sere and Steinfeld (1996) classification updated by Robinson et al. (2011)


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Livestock sector coverage

Livestock categories:

Bovines: Dairy & Other

Sheep & Goats: Dairy & Other

Poultry: Laying hens, Broilers, Mixed

Pigs

Production systems:

Ruminats

Grass based: Arid, Humid, Temperate/Highlands

Mixed crop-livestock: Arid, Humid, Temperate/Highlands

Monogastrics

Smallholders

Industrial


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Herrero, Havlík et al. forthcoming

Production systems parameterization


Downscaling FAO country level information and forest growth

functions estimated from yield tables

Source: Kindermann et al. (2008)

Forests – G4M

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2a. Global case study: Rigid system – Trade-offs at their best

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DO NOTHING scenario – Projected forest area

Tropical deforestation (2010-2050)


Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus

Alternative futures scenarios

Zero Net Deforestation and ForestDegradation by 2020 (ZNDD)

REDD policy scenario



Scenario definition



Kapos et al. (2008)

Scenario definition


Total land cover change (2010-2050)Results


Agricultural commodity prices compared to DO NOTHING

Results


Agricultural input use compared to DO NOTHING

Results


2b. Global case study: Flexible livestock systems

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30

Systems Herds

REF0 Fixed Fixed

REF1 Flexible Flexible*

* in regions with specialized herds

2 reference scenarios


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LPS distribution for different animal types in 2030


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Price changes 2000-2030


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Annual average GHG emissions over 2020-2030


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Scenario ALL AGR ANM ENT LUC DEFLivestockEnteric fermentation CH4 X X X X Manure management CH4 X X XManure management N2O X X XManure grassland N2O X X XCroplandCrop fertilizer N2O X XRice CH4 X XLand-use changeDeforestation CO2 X X XOther LUC CO2 X X

Mitigation scenarios


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Total abatement calorie cost (TACC) curves for different policy options by 2030


2c. Global case study: Land productivity growth

(Havlík et al, 2013; Valin et al, forthcoming)

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Scenarios

• Alternative crop yield scenarios– S0: No crop yield increase– S: -50% yield improvement

• Fixed demand on B reference: no rebound effect

– B: Baseline - linear historical trend– C: + 100% in developing regions

• Fixed demand on B reference no rebound effect


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Commodity price index 2030/2000

Results


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Land cover change 2000-2030

Results


ResultsAverage annual GHG emissions (2000-2030)


0 500 1000 1500 20000

20

40

60

80

100

120

GHG taxProductivity abatment levels

MtCO2-eq

US

D p

er

tCO

2-e

q

S0 S B C

R&D investment cost

Crop yield increase as a mitigation policy?

MACC_S0

Marginal Abatement Cost Curvewith S0 crop yields

versus

R&D investment necessary for S, B, C- calculated as in Burney at al. (2010)

Results

Crop yield growth can be a cost

efficient element of the mitigation

portfolio


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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

Pathway Crops RuminantsFertilizer Other input Non-feed cost

adjustment adjustment adjustmentConventional Yes Yes YesSust-Intens No Yes YesFree-Tech No No No

Management assumptions in developing countries

Productivity assumptions in developing countries

• Free demand potential rebound effects

What kind of intensification?


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Food security x GHG: Trade-offs & Complementarities


3. Regional case study: Development scenarios

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Four storylines for Eastern Africa


Storylines quantification

Main drivers:

– GDP

– Crop yields and management systems

– Livestock yield and production systems

– Producer cost

– Land use change limitations


GDP per capita in EAF [USD]

2010 2020 2030 -

100.00

200.00

300.00

400.00

500.00

600.00

700.00

Industrious AntsHerd of ZebraLone LeopardsSleeping Lions


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Calorie consumption in EAF [kcal/cap/day] GHG emissions in EAF in 2030 [MtCO2eq/y]

Results


4. Conclusion

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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


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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


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Key discussion points / challenges

• Global CGIAR agricultural systems classification/parameterization database?

• Linking between models to bridge the scales in trade-offs analysis?


Thank you !

[email protected]

mailto:[email protected]


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References

Havlík, 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 Use Change 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. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? 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/

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

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