A one-day Strategic Foresight Conference took place at IFPRI Headquarters in Washington DC on November 7, 2014. Participants from leading global modeling groups, collaborating CGIAR centers and research programs, and other partners reviewed new long-term projections for global agriculture from IFPRI and other leading institutions, examined the potential impacts of climate change and other key challenges, and discussed the role of foresight work in identifying and supporting promising solutions. Topics included: Long-term outlook and challenges for food & agriculture Addressing the challenges Foresight in the CGIAR Webcast video of morning sessions available on Global Futures program website here: http://globalfutures.cgiar.org/2014/11/03/global-futures-strategic-foresight-conference/

1. Dominique van der Mensbrugghe
Center for Global Trade Analysis (GTAP)
Purdue University
Strategic Foresight Conference
IFPRI
Washington, DC, 7 November 2014
Insights from global model intercomparisons

2. Key policy relevant questions
• Long-term evolution of agricultural and food prices,
food security and nutrition
• Land expansion versus production intensification
• Impact of future climate change on prices, land use,
trade, undernourishment
• Potential role of biofuels

3. Long-term downward trend in real agricultural prices though-
out the 20th century
0
100
200
300
400
500
600
700
800
900
1000
1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Realpricesin2010$USpermetricton
Rice (Thai)
Wheat (US HWT)
Maize (US #2)
Source: World Bank pink sheet (http://go.worldbank.org/4ROCCIEQ50, accessed 7-Jan-2014) and own calculations
Note: 4-year leading moving average (last year available = 2013).

4. Yield improvements account for over 70 percent of
production growth
0
1
2
3
4
5
6
0
1,000
2,000
3,000
4,000
5,000
6,000
World East Asia South Asia Near East & N.
Africa
sub-Saharan
Africa
Latin America High-income
Percent
Kilogramperhectare
Average cereal yield, 1961 Average cereal yield, 2005 Annual growth, percent
Source: FAO.

5. Global land expansion for crops of around 250
million hectares
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0
200
400
600
800
1,000
1,200
World East Asia South Asia Near East & N.
Africa
sub-Saharan
Africa
Latin America High-income
Percent
Millionhectares
Crop land use, 1961 Crop land use, 2005 Growth (index 1961=1, right-axis)
Source: FAO.

6. What does the future portend for agricultural prices?
-80
-60
-40
-20
0
20
40
60
80
100
120
Wheat Maize Rice
Percentchange
1960-2010 Trend 2010-2050 w/o climate change 2010-2050 w/ climate change
Source: World Bank pink sheet and own calculations for historical series, Nelson et al. (2010) for future price scenarios.

7. Slowing population growth, however…
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
2010 2050
HIC ECA EAP LAC MNA SAA SSA
67
11
107
203
241
665
1,120
2,414
Population, SSP2, million
Note: 2010-2050 incremental change indicated in 2050 column. High-income (HIC), Europe & Central Asia (ECA), East Asia & Pacific (EAP),
Latin America & Caribbean (LAC), Middle East & North Africa (MNA), South Asia (SAA), Sub-Saharan Africa (SSA).
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
2010 2015 2020 2025 2030 2035 2040 2045 2050
Developing countries
SSP3
SSP3
SSP2
SSP2High-income countries
Population, SSP2 v. SSP3, million

8. Historical vs. projected yield growth, percent per annum
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
World Developing High-income World Developing High-income World Developing High-income
Wheat Rice Maize
1970/1990 1990/2010 2010/2030 2030/2050
Source: 1970/2010 FAOSTAT (accessed 22-Jul-2013), IFPRI’s IPRs and own calculations
Note: Slight differences in regional aggregations between history and projections. Maize yield projections equivalent to coarse grain
definition in GTAP.

9. Agricultural Model Intercomparison and
Improvement Project—AgMIP
• Wide range of model results
– Crop and economic models
• Confusing policy advice

10. AgMIP and global economic modelers
• 6 General equilibrium
– AIM (NIES, Japan), ENVISAGE (FAO, Italy), EPPA
(MIT, USA), FARM (USDA, USA), GTEM (ABARES,
Australia), MAGNET (LEI/Wageningen,
Netherlands),
• 4 Partial equilibrium
– GCAM (PNNL, USA), GLOBIOM (IIASA, Austria),
IMPACT (IFPRI), MAgPIE (PIK, Germany)

11. Scenario design
• Harmonization of key exogenous drivers
– Population and GDP (SSP2)
– Exogenous yield growth (IFPRI)
• 3 Optics
– Socio-economic (SSP2 vs. SSP3)
– Climate change (2 crop models x 2 climate models)
– Bio-energy

12. Still large differences in long-term price projections,
though sharp narrowing after comparison exercise
0.8
0.9
1.0
1.1
1.2
1.3
1.4
AIM ENVISAGE EPPA FARM GTEM MAGNET GCAM GLOBIOM IMPACT MAgPIE
Priceindex(2005**=1)
2030 orig.* 2050 orig.*
* original: relative to model-standard numéraire; rebased: relative to the price index for global GDP
Source: von Lampe et al (2014).

13. 0.5
1.0
1.5
2.0
2.5Priceindexin2050(2005=1)
AGR WHT RIC CGR CR5
0.5
1.0
1.5
2.0
2.5
Variation of world prices across commodities in 2050
Note: All agriculture (AGR), wheat (WHT), rice (RIC), coarse grains (CGR), index for wheat, rice, coarse grains, oil seeds and sugar (CR5).
Source: AgMIP global economic runs, February 2013 and own calculations.

14. Cereal production—all above AT 2050 scenario
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
1961 1971 1981 1991 2001 2011 2021 2031 2041
Cerealproduction,millionmetrictons
MAGNET
IMPACT
AT 2050
Source: 1961/2005 FAOSTAT (accessed 20-Feb-2014) and model simulations for 2005/2050.

15. Cropland projections vary significantly across models
1,000
1,100
1,200
1,300
1,400
1,500
1,600
1,700
1,800
1,900
2,000
1961 1971 1981 1991 2001 2011 2021 2031 2041
Cropland,millionhectare
MAGNET
AIM
ENVISAGE
MAgPIE
GCAM
GLOBIOM
IMPACT
EPPA
GTEM
FARM
Source: 1961/2005 FAOSTAT (accessed 20-Feb-2014) and model simulations for 2005/2050.

16. The climate modeling chain:
from biophysical to socioeconomic
General
circulation
models
(GCMS)
Global
gridded crop
models
(GGCMs)
Global
economic
models
DTemp
DPrec
DYieldx
(Biophysical)
DArea
DYield
DCons
DTrade
Climate Biophysical Economic
RCP’s
Farm
practices
CO2
Pop.
GDP
Source: Nelson et al., PNAS (2013).

17. Simulated yield impacts for the four climate
scenarios: global average in 2050 wrt reference
-25
-20
-15
-10
-5
0
5
Wheat Rice Coarse grains Oil seeds Sugar CR5
Percentchangeinyieldin2050wrtreference
IPSL/LPJ HADGEM2/LPJ IPSL/DSSAT HADGEM2/DSSAT
Source: Shocks from IFPRI as interpreted for use in the ENVISAGE model, Nelson, van der Mensbrugghe et al. (2014).

18. Climate induced changes in world average producer prices
for five main crops (CR5) relative to reference in 2050
0%
10%
20%
30%
40%
50%
60%
70%
80%
AIM ENVISAGE EPPA FARM GTEM MAGNET GCAM GLOBIOM IMPACT MAgPIE
Pricechangerelativetoreferencescenario,2050
IPSL & LPJ HadGEM & LPJ IPSL & DSSAT HadGEM & DSSAT
Source: von Lampe et al. (2014), based on model results as of February 15, 2013.
Note: All changes relative to the reference scenario for the same year.

19. Take away messages
• Fifty years of substantial progress, but
– Significant pockets of poverty and under-
nourishment
– Areas of unsustainable farm practices
• In many aspects, next 50 years appear less daunting
– Declining population growth and reaching food saturation thresholds,
– Albeit with continued large pockets of poverty and continued concerns with
sustainability—soils, water, etc.
• However, new issues emerge:
– Climate change
– Bio-energy
• Quantitative analysis in the future will require more cooperation
– Model comparison and validation
– Model integration (climate, crop and economic)

20. Further reading
• von Lampe, Willenbockel et al., “Why do global long-term scenarios for agriculture
differ? An overview of the AgMIP Global Economic Model Intercomparison”
• Robinson, van Meijl, Willenbockel et al., “Comparing supply-side specifications in
models of global agriculture and the food system”
• Valin, Sands, van der Mensbrugghe et al., “The future of food demand:
understanding differences in global economic models”
• Schmitz, van Meijl et al., “Land-use change trajectories up to 2050: insights from a
global agro-economic model comparison”
• Müller and Robertson, “Projecting future crop productivity for global economic
modeling”
• Nelson, van der Mensbrugghe et al., “Agriculture and climate change in global
scenarios: why don’t the models agree”
• Lotze-Campen, von Lampe, Kyle et al., “Impacts of increased bioenergy demand on
global food markets: an AgMIP economic model intercomparison”
Special issue
Special issue of Agricultural Economics (2014):
http://onlinelibrary.wiley.com/doi/10.1111/agec.2014.45.issue-1/issuetoc
Proceedings of the National Academy of Sciences (PNAS) (2013):
http://www.pnas.org/content/early/2013/12/12/1222465110.full.pdf+html
• Nelson et al., “Climate change effects on agriculture: Economic responses to
biophysical shocks”
Alexandratos, N. & J. Bruinsma (2012), “World Agriculture Towards 2030/2050: The 2012
Revision,”, FAO, Rome. http://www.fao.org/docrep/016/ap106e/ap106e.pdf