CLIMATE CHANGE: AGRICULTURAL AND ECONOMIC IMPACTS

Intersessional Meeting of the Intergovernmental Group on Tea

Rome, 5-6 May 2014

Dominique van der Mensbrugghe

Agricultural Development Economics (ESA)

Food and Agriculture Organization of the United Nations

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

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 2

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

Real

pri

ces

in 2

010

$US

per

met

ric

ton

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).

http://go.worldbank.org/4ROCCIEQ50

Slowing population growth, however…

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 3

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

SSP2 High-income countries

Population, SSP2 v. SSP3, million

GDP per capita under SSP2 and SSP3, $2007

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 4

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

World Developing East Asia & Pacific

South Asia Europe & Central Asia

Middle East & North Africa

Sub-Saharan Africa

Latin America & Caribbean

High-income

2010 2050—SSP2 2050—SSP3

0.8 3.6

2.0

4.8

3.2 5.1

3.3

2.8

1.8 2.1 1.3

1.6

2.5

1.2

1.3

1.1

2.0

3.6

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

History vs. projected yield growth, percent per annum

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 5

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.

0.5

1.0

1.5

2.0

2.5Pr

ice

inde

x in

205

0 (2

005=

1)

AGR WHT RIC CGR CR5

0.5

1.0

1.5

2.0

2.5

Variation of world prices across commodities in 2050

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 6

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.

The climate modeling chain: from biophysical to socioeconomic

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 7

General circulation

models (GCMS)

Global gridded crop

models (GGCMs)

Global economic

models

∆Temp ∆Prec

∆Yield (Biophysical)

∆Area ∆Yield ∆Cons ∆Trade

Climate Biophysical Economic

RCP’s Farm

practices CO2

Pop. GDP

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

An extreme climate scenario?

• RCP 8.5 was selected – Currently on path consistent with 8.5 w/m2

– Excludes potentially positive effects of increasing CO2 concentration

– And crop models assume constant management practices (e.g. sowing dates)

• Is this the worst case? Crop models ignore: – Tropospheric ozone (spatially differentiated) – Pests, weeds and diseases – Extreme events

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 8

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

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 9

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

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

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 10

-25

-20

-15

-10

-5

0

5 Wheat Rice Coarse grains Oil seeds Sugar CR5

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 to yields, land use, production, trade, consumption and prices in 2050

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 11

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

Perc

ent c

hang

e

-60

-40

-20

0

20

40

60

n

MeanSD

2891

-0.17(0.131)

2891

-0.11(0.166)

2891

0.11(0.249)

2891

-0.02(0.25)

2891

-0.01(0.264)

2891

-0.03(0.063)

2891

0.2(0.242)

YEXO YTOT AREA PROD TRSH CONS PRICE

Take away messages

• Long-term price trends depend on population and income growth and evolution of yields.

• Climate impacts will negatively affect prices, with many of the increases ranging from 5-25%.

• Analysis is complicated by significant uncertainty—climate, impacts of climate changes and future economic structure.

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 12

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”

Dominique van der Mensbrugghe ESA/FAO

Rome, 5-6 May 2014 13

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”

CLIMATE CHANGE: �AGRICULTURAL AND ECONOMIC IMPACTS��Intersessional Meeting of the Intergovernmental Group on Tea�Rome, 5-6 May 2014�Long-term downward trend in real agricultural prices though-out the 20th centurySlowing population growth, however…GDP per capita under SSP2 and SSP3, $2007History vs. projected yield growth, percent per annumVariation of world prices across commodities in 2050The climate modeling chain: �from biophysical to socioeconomicAn extreme climate scenario?Four potential yield outcomes for maize in 2045 under RCP 8.5†Simulated impacts for the four climate scenarios: global average for major crops in 2050 wrt referenceClimate induced changes to yields, land use, production, trade, consumption and prices in 2050Take away messagesFurther reading