Mapping hotspots of climate change and food insecurity across the global tropics
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Transcript of Mapping hotspots of climate change and food insecurity across the global tropics
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