Mapping hotspots of climate change and food insecurity across

the global tropics

Polly Ericksen, Philip Thornton, An Notenbaert, Laura Cramer, Mario Herrero

15 March 2011

CCAFS-to-CRP7 transition

1. Three initial target regions (East Africa, West Africa, Indo-Gangetic Plain) five by 2012

Possible regions: Southern Africa, West Asia-North Africa, Central Africa, Central America, upland South America, lowland South America, South Asia outside the IGP, South-East Asia, East Asia, Pacific, coastal zones, small island states, …

2. Vulnerability mapping work + selection criteria + list of potential target regions as inputs to a process of selection

3. Weighting exercise for each candidate region for different stakeholder groups:

Contact points and global partnersCRP7 management teamCRP7 steering committee

4. Final decision by November

Vulnerability of food security to climate change

Vulnerable people/systems ~ those that stand a high change to be negatively affected by a (series of) events

“Where are the areas that are most likely to experience more food insecurity due to climate change?”

3 components• Exposure• Sensitivity• Coping capacity

Construction of “vulnerability domains”

Food security

.....exists when all people, at all times, have physical and economic access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for an active and healthy life.

(World Food Summit 1996)

FOOD UTILISATION

Components of Food Security& Key Elements

FOOD ACCESS

• Affordability• Allocation• Preference

• Nutritional Value• Social Value• Food Safety

FOOD AVAILABILITY

• Production• Distribution• Exchange

Vulnerability to climate changeClimate Change

Change in type, frequency & magnitude of climate

events

FOOD SYSTEMRESILIENCE / VULNERABILITY

SOCIETAL CHANGE

Change in institutions, resource accessibility,

economic conditions, etc.

Capacity to cope

with &/or

recover from CC

Exposureto CC

GECAFS 2005

Vulnerability analysis

Exposure of populations to the impacts of climate change

(hi, lo)

Sensitivity of food

systems to these

impacts(hi, lo)

Coping capacity of

populations to address these impacts (hi, lo)

x x

Agricultural land areas from 35 S to 45 N (Ramankutty et al., 2008) plus LGP>60 days⁰ ⁰

Multi-model global averages of surface warming (relative to 1980-99) for the SRES scenarios

Region Jun-Aug Dec-Jan

Sahara Small decrease(5-20%)

Inconsistent

West Africa Inconsistent Inconsistent

East Africa Small increase (5-20%) Inconsistent

Southern Africa Inconsistent Large decrease (>20%)

GCM consistency in regional precipitation projections for 2090-2099 (SRES A1B). IPCC, 2007

What to do for impact / exposure analysis?

• Use ensembles of “equally-likely” combinations of climate model + emissions scenario mean response and s.e. of response

• Downscale spatially, from 2° lat-long grids to a more useful resolution (e.g. 9-km grids)

• Downscale temporally from long-term climatology to characteristic daily weather data

Use MarkSim as a GCM downscaler: difference interpolation + stochastic downscaling + weather typing

Generate exposure indicators based on daily data

Select climate model

Select emissions scenario

Select the centre year of the time slice and number of years of data wanted

Select location (the ILRI cafeteria in Nairobi)

Exposure: several thresholds

1 Length of growing period (LGP) declines by >5%

2 Flip from LGP > 120 days in the 2000s to LGP < 120 in the 2050s

3 Flip from Reliable Crop Growing Days per year > 90 days in the 2000s to RCGDs < 90 in the 2050s

4 Flip from an average annual temp < 8°C in the 2000s to Tav > 8°C in the 2050s

5 Flip from an average annual maximum daily temp < 30°C in the 2000s to Tmax > 30°C in the 2050s

6 As above, but for the 150 days from the start of the primary growing season

7 Rainfall per rainday decreases by >10% to the 2050s

8 Rainfall per rainday increases by >10% to the 2050s

9 Areas in which current annual rainfall CV is >21%

Areas that flip from > 90 Reliable Crop Growing Days (RCGD) per year in the 2000s to < 90 RCGD by the 2050s

Cropping becomes very risky in areas with RCGD < 90

Reliable Crop Growth Days, calculated over n seasons per year as n

RCGD = Σ season length j * (1 – failure rate j ) j=1

Exposure 3

Areas where maximum temperature during the primary growing season is currently < 30 °C but will flip to > 30 °C by the 2050s

Yield of many crops is considerably reduced at higher temperatures Boote et al. (1998)

Exposure 6

Using current rainfall variability as a proxy for climate variability

Areas with current annual rainfall CV > 21% (the modal CV for cropped areas in the tropics, excluding irrigated areas)

Rainfall CV (%, x-axis), cropping extent (y-axis)

Exposure 9

Mapping the number of these 9 potential climate threats that apply in each pixel

For the positive temperature flip (from < 8 °C to > 8 °C), we reduced the number of threats by one

Expanded crop suitability? Andes, parts of Central and highland South Asia, Southern China

Multiple Exposures

Regional maps for E Africa, W Africa, IGPExposures 5, 6

Availability: crop production

Also mapped beans, rice, wheat, sorghum, millet and cassava.

You, L., S.Crespo, Z. Guo, J. Koo, W. Ojo, K. Sebastian, M.T. Tenorio, S. Wood, U. Wood-Sichra. Spatial Production Allocation Model (SPAM) 2000 Version 3 Release 2. http://MapSPAM.info.

Availability: Food Production Index

Average 2003-2007. FAO Statistics Division, FAOSTAT.

Access: population with less than $2 per day

http://geonetwork.csi.cgiar.org/geonetwork/srv/en/main.home

Access: staple food prices

Jan-

05

Jul-0

5

Jan-

06

Jul-0

6

Jan-

07

Jul-0

7

Jan-

08

Jul-0

8

Jan-

09

Jul-0

90

100200300400500600700800900

Price volatility in Kampala

Maize

Beans

Rice

Pri

ces

US

D/M

T

Jan-

05

Aug-0

5

Mar

-06

Oct-0

6

May

-07

Dec-0

7

Jul-0

8

Feb-0

9

Sep-0

90

100

200

300

400

500

600

700

Price volatility Nairobi

Maize prices

Bean prices

US

D /

MT

http://www.fao.org/giews/pricetool/)

Utilization: wasting prevalence

World Development Indicators Database

Resource pressure: arable land per capita

FAO STAT

Vulnerability of food security to climate change

Vulnerable people/systems ~ those that stand a high change to be negatively affected by a (series of) events

“Where are the areas that are most likely to experience more food insecurity due to climate change?”

3 components• Exposure• Sensitivity• Coping capacity

Construction of “vulnerability domains”

Exposure• The thresholds

Sensitivity

• Areas with more dependence on crop agriculture are assumed to be more sensitive to a change in climate.

Coping capacity

• We considered that chronic food insecurity could be a proxy for coping capacity, as inability to tackle chronic food insecurity indicates a number of institutional, economic and political problems.

Combination in “domains”

• 3 components * 2 classes 8 domains

Domain Exposure Sensitivity Coping capacity

HHL High High Low

HHH High

HLL Low Low

HLH High

LHL Low High Low

LHH High

LLL Low Low

LLH High

Combination in “domains”

• 3 components * 2 classes 8 domains

Domain Exposure Sensitivity Coping capacity

HHL High High Low

HHH High

HLL Low Low

HLH High

LHL Low High Low

LHH High

LLL Low Low

LLH High

LGP change > 5%

LGP flips to < 120 days

RCGD flip to < 90 days

Max. daily temp flip to > 30 deg C

Growing season Temp flip to >30 degC

Rain per rain day decrease > 10%

Rain per rain day increase > 10%

CV rainfall > 21%

Conclusions

• Climate hotspot indications:– Cropping thresholds (growing period reduced)– Temperature extremes (max and min)

increasing– Increased dryness, increased rain intensity?

• Food security hotspots:– Stagnant PI– Poverty– Undernourished population

Conclusions

• Domains– High exposure, high sensitivity, low capacity– But also watch HHH because other capacity

indicators– HLL: increase in cropping?– Variation in “low exposure” category– Populations included vary

Next steps

• Try with other coping capacity indicators– E.g. with better household level data

• Reduce the number of domains– Ideas?

• Map Drivers of food insecurity not Outcomes

• Modeling scenarios of food security to 2050

International Livestock Research InstituteBetter lives through livestock

Animal agriculture to reduce poverty, hunger and environmental degradation in developing countries

ILRI  www.ilri.org