Economic and Social Development Department Food and Agriculture

Organization of the

United Nations

How climate change may affect global food demand and supply in the long-term?

Aikaterini Kavallari

Global Perspective Studies Team

Economic and Social Development Department

Challenges to ensure sustainable food security in the future

Economic and Social Development Department

131

48

15

68

30

6

0

20

40

60

80

100

120

140

0

200

400

600

800

1,000

1,200

1,400

sub-Saharan Africa South Asia East Asia Near East & N.Africa

Latin America Developedcountries

Incremental population growth 2005/07-2050, millions (left-axis)

Percent population growth 2005/07-2050 (right-axis)

Source: United Nations Population Division (2009).

An additional 2.5 billion persons—to 9.1 billion in 2050

Economic and Social Development Department

0

5

10

15

20

25

30

35

40

45

Developingcountries

sub-Saharan Africa South Asia East Asia Near East & N.Africa

Latin America

Average per capita incomes relative to developed countries 2006, percent

Average per capita incomes relative to developed countries 2050, percent

GDP per capita gaps converge only modestly

Source: Development Prospects Group, The World Bank.

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Agricultural production growth slows down

Source: FAO.

24

77

60

57

317

170

0 50 100 150 200 250 300 350

Developedcountries

Developingcountries

World

Percent change over period

1961/63-2005/07 2005/07-2050

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Potential impacts of climate change on global food demand and supply

- empirical results based on Agricultural Model Intercomparison and Improvement Project (AgMIP) Phase 1-

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The climate modeling chain in AgMIP: from biophysical to socioeconomic

General circulation

models (GCMS)

Global gridded

crop models

(GGCMs)

Global economic

models

DTemp DPrec

Dyield (Biophysical)

DArea DYield DCons DTrade

Climate Biophysical Economic

RCP’s Farm

practices CO2

Pop. GDP

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

Reference scenario: SSP2 (no climate change) Climate scenario: RCP 8.5

Economic and Social Development Department

-25

-20

-15

-10

-5

0

5

Wheat Rice Coarse grains Oil seeds Sugar CR5

IPSL/LPJ HADGEM2/LPJ IPSL/DSSAT HADGEM2/DSSAT

Source: Nelson et al. (2014).

Note: CR5: average of the five crops

Climate change impacts, percent change in exogenous yields relative to reference in 2050

Economic and Social Development Department

Perc

en

t ch

an

ge

-60

-40

-20

0

20

40

60

n

Mean

SD

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

Climate induced changes to global yields, land use, production, trade, consumption and prices relative to reference for CR5 in 2050

Source: Nelson et al. (2014). Notes: YEXO: exogenous yields,; YTOT: final yields; AREA: crop area; PROD: domestic production; TRSH: net imports relative to production; CONS: consumption; PRICE: average producer prices

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Conclusions

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Take away messages

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

• Food consumption is expected to drop implying that climate change may well exacerbate food security concerns

• Globally consumption responds less than supply because food demand is not so sensitive to price changes

• Still effects will be felt more in specific regions with already stressed natural resources

• Variability in trade and crop area responses is due to the varying assumptions about trade flexibility and ease of land conversion in the models -> both of which imply different degrees of adaptation to changes in agricultural markets

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Further reading

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

Proceedings of the National Academy of Sciences (PNAS) (2014): http://www.pnas.org/content/111/9/3274.abstract

• Nelson, Valin et al., “Climate change effects on agriculture: Economic responses to biophysical shocks”

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Annex

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Terminology

• SSPs: Shared Socioeconomic Pathways • RCPs: Representative Concentration Pathways • IPR: Intrinsic Productivity Rate • AgMIP: Agricultural Model Intercomparison Project

(http://www.agmip.org/) • LPJml: Lund-Potsdam-Jena managed Land Dynamic Global

Vegetation and Water Balance Model • DSSAT: Decision Support System for Agricultural Technology • HadGEM2: Hadley Centre Global Environment Model version 2 • IPSL: climate model of the Institute Pierre Simon Laplace

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Reference scenario details

• Based on the SSP2 narrative • Assumes a middle of the road growth of the economy with

intermediate socioeconomic challenges to climate change adaptation and mitigation

• Population and GDP growth path taken over from the SSP database, based in IIASA and OECD projections respectively https://secure.iiasa.ac.at/web-apps/ene/SspDb/dsd?Action=htmlpage&page=about

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Climate scenario details

• Radiative forcing of over 8.5 watts per square meter by the end of the century

• Excludes potentially positive effects of increasing CO2 concentration

• Crop models assume constant management practices (e.g. sowing dates)

• Crop models did not include effects of increased ozone concentration, increased weather variability and greater biotic stress