25.09.2014

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Agriculture, food security and climate change—the global context

Dominique van der Mensbrugghe

Center for Global Trade Analysis (GTAP)

Purdue University

Scaling in global regional and farm models

change the global context

Scaling in global, regional and farm models

Trade M workshop

Vienna, 24 September 2014

Key policy relevant questions

• Long-term evolution of agricultural and food prices, food security and nutrition

• Dual challenge—undernourishment and obesityg y

• Land expansion versus production intensification

• Impact of future climate change on prices, land use, trade, undernourishment

• Potential role of biofuels

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Long-term downward trend in real agricultural prices though-out the 20th century

700

800

900

1000

ic t

on

Rice (Thai)

100

200

300

400

500

600

700

Re

al p

rice

s in

20

10

$U

S p

er

me

tri

Wheat (US HWT)

Maize (US #2)

0

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

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).

Large quantity changes for major commodities

5

6

1.000

1.200

1961 2005 Growth (index 1961=1, right-axis)

1

2

3

4

200

400

600

800

Me

tric

to

ns

00

Meats Rice Wheat Coarse grains

Source: FAO.

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Yield improvements account for over 70 percent of production growth

5

6

5.000

6.000

Average cereal yield, 1961 Average cereal yield, 2005 Annual growth, percent

1

2

3

4

1.000

2.000

3.000

4.000

Kilo

gram

pe

r h

ect

are

00

World East Asia South Asia Near East & N. Africa

sub-Saharan Africa

Latin America High-income

Source: FAO.

Global land expansion for crops of around 250 million hectares

2,5

3,0

1.000

1.200

Crop land use, 1961 Crop land use, 2005 Growth (index 1961=1, right-axis)

0,5

1,0

1,5

2,0

200

400

600

800

Mill

ion

he

ctar

es

0,00

World East Asia South Asia Near East & N. Africa

sub-Saharan Africa

Latin America High-income

Source: FAO.

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Radical change in the future?

80

100

120

1960-2010 Trend 2010-2050 w/o climate change 2010-2050 w/ climate change

-60

-40

-20

0

20

40

60

Pe

rce

nt

chan

ge

-80

Wheat Maize Rice

Source: World Bank pink sheet and own calculations for historical series, Nelson et al. (2010) for future price scenarios.

Slowing population growth, however…

8.000

9.000

10.000

HIC ECA EAP LAC MNA SAA SSA

1,120

2,414

Population, SSP2, million

8.000

9.000

10.000

Developing countries

SSP3

SSP2

Population, SSP2 v. SSP3, million

2.000

3.000

4.000

5.000

6.000

7.000

8.000

107

203

241

665

2.000

3.000

4.000

5.000

6.000

7.000

SSP2High-income countries

0

1.000

2010 2050

67

11

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

2010 2015 2020 2025 2030 2035 2040 2045 2050

SSP3

g

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GDP per capita under SSP2 and SSP3, $2007

60.000

70.000

2010 2050—SSP2 2050—SSP3

1.3

1.1

10.000

20.000

30.000

40.000

50.000

0.83.6

2 0

4.8

3.25.1

2.8

1.82.1

1.3

2.5

1.2

2.0

0

World Developing East Asia & Pacific

South Asia Europe & Central Asia

Middle East & North Africa

Sub-Saharan Africa

Latin America & Caribbean

High-income

2.03.3

1.63.6

Note: Growth rates, percent per annum, on top of columns.

History vs. projected yield growth, percent per annum

3,5

4,0

4,5

1970/1990 1990/2010 2010/2030 2030/2050

0 0

0,5

1,0

1,5

2,0

2,5

3,0

0,0

World Developing High-income World Developing High-income World Developing High-income

Wheat Rice Maize

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.

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IFPRI vs. FAO AT projections

1 4

1,6

1,8

2,0

IFPRI 2010/2030 IFPRI 2030/2050 FAO AT 2050 2006/2030 FAO AT 2050 2030/2050

0,0

0,2

0,4

0,6

0,8

1,0

1,2

1,4

W ld D l i Hi h i W ld D l i Hi h i W ld D l i Hi h iWorld Developing High-income World Developing High-income World Developing High-income

Wheat Rice Maize

Source: IFPRI’s IPRs, Alexandratos and Bruinsma (2012) and own calculations

Note: Slight differences in regional aggregations between IFPRI and FAO projections. Maize yield projections equivalent to coarse grain definition in GTAP.

Agricultural Model Intercomparison and Improvement Project—AgMIP

• Wide range of model results

– Crop and economic models

• Confusing policy advice

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AgMIP and global economic models

• 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),GCAM (PNNL, USA), GLOBIOM (IIASA, Austria), IMPACT (IFPRI), MAgPIE (PIK, Germany)

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

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Still large differences in long-term price projections, though sharp narrowing after comparison exercise

1,3

1,4

2030 orig.* 2050 orig.*

0,9

1,0

1,1

1,2

Pri

ce in

dex

(2

00

5**

= 1

)

0,8

AIM ENVISAGE EPPA FARM GTEM MAGNET GCAM GLOBIOM IMPACT MAgPIE* original: relative to model-standard numéraire; rebased: relative to the price index for global GDP** trended 2005, i.e. hypothetical in the absence of short-term shocks

Source: von Lampe et al (2014).

2.0

2.5

0 (2

005=

1)

2.0

2.5

Variation of world prices across commodities in 2050

0.5

1.0

1.5

Pri

ce in

dex

in 2

05

0.5

1.0

1.5

AGR WHT RIC CGR CR5

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.

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Cereal production—all above AT 2050 scenario

3.000

3.500

4.000

ton

s

MAGNET

IMPACT

1.000

1.500

2.000

2.500

Ce

real

pro

du

ctio

n, m

illio

n m

etr

ic t

AT 2050

500

1961 1971 1981 1991 2001 2011 2021 2031 2041

Source: 1961/2005 FAOSTAT (accessed 20-Feb-2014) and model simulations for 2005/2050.

Cropland projections vary significantly across models

1.800

1.900

2.000MAGNET

AIM

ENVISAGEMAgPIE

GCAM

1.200

1.300

1.400

1.500

1.600

1.700

Cro

pla

nd

, mill

ion

he

ctar

e

GCAMGLOBIOMIMPACTEPPA

GTEM

FARM

1.000

1.100

1961 1971 1981 1991 2001 2011 2021 2031 2041

Source: 1961/2005 FAOSTAT (accessed 20-Feb-2014) and model simulations for 2005/2050.

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The climate modeling chain: from biophysical to socioeconomic

Climate Biophysical Economic

General circulation

models (GCMS)

General circulation

models (GCMS)

Global gridded crop

models (GGCMs)

Global gridded crop

models (GGCMs)

Global economic

models

Global economic

models

TempPrecTempPrec

Yield(Biophysical)

Yield(Biophysical)

AreaYieldConsTrade

AreaYieldConsTrade

Farm Farm PP

RCP’sRCP’s practicesCO2

practicesCO2

Pop.GDPPop.GDP

Source: Nelson et al., PNAS (2013).

Four potential yield outcomes for maize in 2045 under RCP 8.5†

Source: Müller and Robertson (2014).† Excludes CO2 effects.

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Simulated impacts for the four climate scenarios: global average for major crops in 2050 wrt reference

0

5

Wheat Rice Coarse grains Oil seeds Sugar CR5

-20

-15

-10

-5

-25

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).

Climate induced changes in world average producer prices for five main crops (CR5) relative to reference in 2050

60%

70%

80%

enar

io, 2

05

0

IPSL & LPJ HadGEM & LPJ IPSL & DSSAT HadGEM & DSSAT

10%

20%

30%

40%

50%

Pri

ce c

han

ge r

elat

ive

to r

efer

ence

sce

0%

AIM ENVISAGE EPPA FARM GTEM MAGNET GCAM GLOBIOM IMPACT MAgPIE

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.

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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 significant pockets of poverty (SSA and South Asia) and 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)

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

Special issue of Agricultural Economics (2014):http://onlinelibrary.wiley.com/doi/10.1111/agec.2014.45.issue-1/issuetoc

Alexandratos, N. & J. Bruinsma (2012), “World Agriculture Towards 2030/2050: The 2012 Revision,”, FAO, Rome. http://www.fao.org/docrep/016/ap106e/ap106e.pdf

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

Special issue

Lotze Campen, von Lampe, Kyle et al., Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison”

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”