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

Modelling for trade-offs analysis
at 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

Trade-offs in the land use sectors

Land sparing

Pollution

N2O emissions

Biodiversity Water use Food, feed, fiber, fuel

CO2 sink Soil degradation Farmers income

NATURAL LAND INTENSIFICATION MANAGED LAND

LAND

Havlík et al. Modelling for trade-offs analysis 2
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013

Outline
1. Model overview

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

3. Regional case study – Development scenarios

4. Conclusion


1. Model overview


GLOBIOM: Global Biosphere Management Model
Partial equilibrium model: Agriculture, Forestry, Bioenergy

DEMAND

SUPPLY


GLOBIOM

Spatial equilibrium model a la Takayama & Judge
Maximization of the social welfare (PS + CS)
Recursively dynamic (10 year periods)

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
1/ e
explicit: linearized non-linear functions p ˆ ˆ
p * (q / q)


Supply Chains
Wood products

Sawn wood
Natural Forests Pulp

Wood Processing Bioenergy

Managed Forests Bioethanol
Biodiesel
Methanol
Heat
Electricity
LAND USE CHANGE

Short Rotation Tree Bioenergy
Biogas
Plantations Processing
Crops
Corn
Wheat
Cropland Cassava
Potatoes
Rapeseed
etc…

Grassland Livestock Feeding
Livestock products
Beef
Lamb
Pork
Poultry
Other natural land Eggs
Milk


Main exogenous drivers:
Population
GDP
Technological change
Bio-energy demand (POLES team)
Diets (FAO, 2006)

Output: Production Q
- land use (change)
- water use
- GHG,
- other environment (nutrient cycle, biodiversity,…)
Consumption Q
Prices
Trade flows


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

HRU = Altitude & Slope & Soil

Altitude class, Slope class,
Soil Class
PX5

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;
Source: Skalský et al. (2008)

Spatial resolution
Simulation Units (SimU) = HRU & PX30 & Country zone
LC&LUstat
> 200 000 SimU

Country HRU*PX30

SimU delineation related
statistics on LC classes and
Cropland management systems

PX5
reference for geo-coded data on crop management;
input statistical data for LC/LU economic optimization;
Source: Skalský et al. (2008)


Crops - EPIC
Processes
• Weather
• Hydrology EPIC
• Erosion
Evaporation
• Carbon sequestration and
• Crop growth Rain, Snow, Transpiration
• Crop rotations Chemicals
• Fertilization
• Tillage
Subsurface
• Irrigation Flow
• Drainage Surface
Flow
• Pesticide
• Grazing
• Manure Below Root
Zone

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


Grasslands – CENTURY/EPIC

Source: EPIC model
(t/ha DM)


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


Livestock production systems distribution
Sere and Steinfeld (1996) classification updated by Robinson et al. (2011)


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


Production systems parameterization

Herrero, Havlík et al. forthcoming


Forests – G4M
Downscaling FAO country level information and forest growth
functions estimated from yield tables

Source: Kindermann et al. (2008)


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


Havlík et al. Modelling for trade-offs analysis

DO NOTHING scenario – Projected forest area

Tropical deforestation (2010-2050)


REDD policy scenario

Zero Net Deforestation and Forest
Degradation by 2020 (ZNDD)

Alternative futures scenarios
Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus


Scenario definition


Scenario definition

CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013 Kapos et al. (2008)

Results
Total land cover change (2010-2050)


Results
Agricultural commodity prices compared to DO NOTHING


Results
Agricultural input use compared to DO NOTHING


2b. Global case study:
Flexible livestock systems


2 reference scenarios

Systems Herds
REF0 Fixed Fixed
REF1 Flexible Flexible*
* in regions with specialized herds

30


LPS distribution for different animal types in 2030

31


Price changes 2000-2030

32


Annual average GHG emissions over 2020-2030

33


Mitigation scenarios

Scenario ALL AGR ANM ENT LUC DEF
Livestock
Enteric fermentation CH4 X X X X
Manure management CH4 X X X
Manure management N2O X X X
Manure grassland N2O X X X
Cropland
Crop fertilizer N2O X X
Rice CH4 X X
Land-use change
Deforestation CO2 X X X
Other LUC CO2 X X

34


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

35


2c. Global case study:
Land productivity growth
(Havlík et al, 2013; Valin et al, forthcoming)


Scenarios
• Alternative crop yield scenarios
– S0: No crop yield increase – B: Baseline - linear historical trend
– S: -50% yield improvement – C: + 100% in developing regions
• Fixed demand on B reference:
no rebound effect

• Fixed demand on B reference  no rebound effect


Results
Commodity price index 2030/2000


Results
Land cover change 2000-2030


Results
Average annual GHG emissions (2000-2030)


Results
Crop yield increase as a mitigation policy?

MACC_S0
GHG tax Productivity abatment levels
R&D investment cost
Marginal Abatement Cost Curve 120
with S0 crop yields
100

USD per tCO2-eq
versus 80

R&D investment necessary for S, B, C 60
- calculated as in Burney at al. (2010)
40

20
Crop yield growth can be a cost
0
efficient element of the mitigation S00 500 1000 1500 2000
S B C
MtCO2-eq
portfolio

What kind of intensification?
Productivity assumptions in developing countries

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

Management assumptions in developing countries

Pathway Crops Ruminants
Fertilizer Other input Non-feed cost
adjustment adjustment adjustment
Conventional Yes Yes Yes
Sust-Intens No Yes Yes
Free-Tech No No No

• Free demand  potential rebound effects

Food security x GHG: Trade-offs & Complementarities


3. Regional case study:
Development scenarios


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]

700.00

600.00

500.00

400.00 Industrious Ants
Herd of Zebra
300.00 Lone Leopards
Sleeping Lions
200.00

100.00

-
2010 2020 2030


Results

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


4. Conclusion


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


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


Key discussion points / challenges
• Global CGIAR agricultural systems classification/parameterization
database?

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


Thank you !

havlikpt@iiasa.ac.at
www.globiom.org

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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