Transcript Addressing the linkages between climate change and vulnerability to food insecurity Testing a methodology in Nicaragua Jeronim Capaldo – Agricultural Economics Division (ESA) Anna Ricoy.

Addressing the linkages between
climate change and vulnerability to
food insecurity
Testing a methodology in Nicaragua
Jeronim Capaldo – Agricultural Economics Division (ESA)
Anna Ricoy - Climate, Energy and Tenure Division (NRC)
Purpose, rationale and approach
• Purpose
To contribute to a comprehensive research approach that
bridges the gap between analysis of climate change (CC) impacts
on food security (FS) and policy-making
• Rationale
Downscale the broad and global CC agenda at the local level
Engage policy makers to better address the impact of CC on FS at
household level
• Approach
Focus on vulnerable groups
Address the access component of FS
Background:
Conceptual framework on CC and FS
Adaptive responses
Climate change
variables
CO2 fertilization
effects
Increase in global
temp.
Changes in Food
Systems Assets
Food production
assets
Infrastructure
Changes in
precipitation
Agriculturallybased livelihoods
Frequency of
extreme events
Non-farm
livelihoods assets
Greater weather
variability
Food preparation
assets
Changes in Food
Systems
Activities
Producing food
Food availability
Storing and
processing of
food
Food
accessibility
Distributing
food
Food system
stability
Food utilization
Consuming food
Migration
Source: Interdepartmental Group on Climate Change (IDWG) 2008
Changes in
Components of
Food Security
Changes in
consumption
patters
Background:
Conceptual framework on CC and FS
Adaptive responses
Climate change
variables
CO2 fertilization
effects
Increase in global
temp.
Changes in Food
Systems Assets
Food production
assets
Infrastructure
Changes in
precipitation
Agriculturallybased livelihoods
Frequency of
extreme events
Non-farm
livelihoods assets
Greater weather
variability
Food preparation
assets
Changes in Food
Systems
Activities
Producing food
Food availability
Storing and
processing of
food
Food
accessibility
Distributing
food
Food system
stability
Food utilization
Consuming food
Migration
Source: Interdepartmental Group on Climate Change (IDWG) 2008
Changes in
Components of
Food Security
Changes in
consumption
patters
Key analytical questions
• How does CC affect access to food at household level?
• How does household vulnerability to food insecurity
evolve as a result of CC?
• How will vulnerability be distributed as a result of CC?
• What policy instruments to increase the resilience of
vulnerable groups to deal with the impact of CC on FS?
• How to improve the design and targeting of policy
responses to address the impacts of CC on vulnerable
groups?
Methodological framework
Addressing the linkages between CC and vulnerability to food insecurity
Downscaling of
GCM using RCM
Highresolution CC
projections at
district level
Analysis of
vulnerability to
food insecurity
Detailed
profiling of
vulnerable
households
groups
Analysis of
implications at
policy level
Policy
recommendations
for the design and
implementation
of targeted policy
interventions
Methodological framework
Addressing the linkages between CC and vulnerability to food insecurity
Downscaling of
GCM using RCM
Highresolution CC
projections at
district level
Analysis of
vulnerability to
food insecurity
Detailed
profiling of
vulnerable
households
groups
Analysis of
implications at
policy level
Policy
recommendations
for the design and
implementation
of targeted policy
interventions
1 - Downscaling of CC scenarios
• Generation of high-resolution climate change projections using
RCMs (PRECIS, Hadley Center)
Change Temperature (Annual mean) –2080s
• Under ECHAM4, for A2 scenario
coordinates of the PRECIS grid
 CC scenarios to a 50x50km scale for the whole Nicaragua, at
“municipio” level
 Time series of estimated temperature and precipitation
projections to the 2030 horizon
Methodological framework
Addressing the linkages between CC and vulnerability to food insecurity
Downscaling of
GCM using RCM
Highresolution CC
projections at
district level
Analysis of
vulnerability to
food insecurity
Detailed
profiling of
vulnerable
households
groups
Analysis of
implications at
policy level
Policy
recommendations
for the design and
implementation
of targeted policy
interventions
2 - Analysis of vulnerability to food insecurity
• Quantitative analysis of the livelihood effect of CC:
- building on the notion of vulnerability to food insecurity
- using an analytical model developed by ESA based on
rural national household datasets
• CC enters the model through the impacts that temperature
and precipitation changes have on income (value of land
productivity) and food consumption (expenditure)
• Model allows characterizing vulnerability and identifying
variables associated with highest levels of vulnerability
 Profiling of vulnerable household groups
Methodological framework
Addressing the linkages between CC and vulnerability to food insecurity
Downscaling of
GCM using RCM
Highresolution CC
projections at
district level
Analysis of
vulnerability to
food insecurity
Detailed
profiling of
vulnerable
households
groups
Analysis of
implications at
policy level
Policy
recommendations
for the design and
implementation
of targeted policy
interventions
3 - Analysis of policy implications
Purpose: to provide recommendations for improvements in the
design and targeting of policy responses that address the impacts
of CC on household FS
Next steps, in-country:
What instruments should be promoted to increase households’ ability
to cope with the impacts of CC on FS and adapt to climate change?
What are the policies, institutions and multi-level governance
arrangements needed to support vulnerable households?
• Links to specific practices: synergies adaptation, mitigation,, FS
• Short + long-term policies addressing DRM/CCA measures tailored
to vulnerable groups
• Integration of the linkages between CC and household FS within all
the phases of the policy cycle
• Coherence between the local, national, regional level
Presentation of results of the analysis of
vulnerability to food insecurity
Addressing the linkages between CC and vulnerability to food insecurity
Downscaling of
GCM using RCM
Highresolution CC
projections at
district level
Analysis of
vulnerability to
food insecurity
Analysis of
implications at
policy level
Detailed profiling of
vulnerable
households groups
Policy
recommendations
for the design and
implementation of
targeted policy
interventions
Capaldo, P. Karfakis, M. Knowles, M. Smulders - ESA
Background on analysis of vulnerability to
food insecurity
• Improve targeting and design of interventions
• Initial steps
• Conceptual and methodological developments
• Country application
Concepts
• Definitions of vulnerability:
– Vulnerability to what?
– Current or future?
• Our view:
– A household’s probability to fall or stay below a foodsecurity threshold
Concepts
Analytical model
Households’ Demographic
characteristics
Households’ Assets
Climate Data
Data
Distribution of Land Productivity
Distribution of Consumption
Model
HH Food Security Threshold
Vulnerability
output
Vulnerability Threshold
Categorization of Households
Profiles
Targeting
Data sources
• Households:
– Rural Income-generating Activities dataset
(RIGA)
– 1831 Households surveyed in 2001
• Climate:
– Temperature and precipitation
– PRECIS ECHAM4, A2 scenario
– Downscaled data
Geographic distribution of vulnerability
mean food poverty rate
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
mean vulnerability
Improved targeting
Proportion of vulnerable households and average vulnerability (2001)
Not Vulnerable
Food secure
Food
insecure
Total
Vulnerable
Total
Average
vulnerability
Proportion
of
households
Average
vulnerability
Proportion
of
households
Average
vulnerability
70%
6%
5%
73%
75%
11%
7%
27%
18%
82%
25%
67%
77%
8%
23%
80%
100%
25%
Proportion
of
households
Improved targeting
Proportion of vulnerable households and average vulnerability (2001)
Not Vulnerable
Food secure
Food
insecure
Total
Vulnerable
Total
Average
vulnerability
Proportion
of
households
Average
vulnerability
Proportion
of
households
Average
vulnerability
70%
6%
5%
73%
75%
11%
7%
27%
18%
82%
25%
67%
77%
8%
23%
80%
100%
25%
Proportion
of
households
Improved targeting
Proportion of vulnerable households and average vulnerability (2001)
Not Vulnerable
Food secure
Food
insecure
Total
Vulnerable
Total
Average
vulnerability
Proportion
of
households
Average
vulnerability
Proportion
of
households
Average
vulnerability
70%
6%
5%
73%
75%
11%
7%
27%
18%
82%
25%
67%
77%
8%
23%
80%
100%
25%
Proportion
of
households
Profile of vulnerable households: gender
Proportion of vulnerable households and average vulnerability (2001), by
gender of head of household
Not Vulnerable
Vulnerable
Proportion of
Average
Proportion of
households vulnerability households
Total
Average
Proportion of
Average
vulnerability households vulnerability
Femaleheaded
household
s
9.87%
8%
3.01%
82%
12.88%
25%
Maleheaded
HH
67.20%
8%
19.92%
80%
87.12%
25%
Total
77.07%
8%
22.93%
80%
100%
25%
Profile of vulnerable households: assets and livelihoods
Class of
vulnerability
Education
(head)
unit
0-20%
20-50%
50-60%
60-70%
70-80%
80-90%
90-100%
Total
Years
2.51
1.89
0.72
0.64
1.56
0.77
0.94
2.06
adul. eq.
5.34
6.79
6.74
7.73
7.63
8.72
8.36
6.15
Female head
Bin.
0.13
0.13
0.08
0.10
0.17
0.13
0.15
0.13
Access to
safe water
Bin.
0.59
0.48
0.51
0.37
0.36
0.57
0.31
0.53
Distance to
major road
Km
54.45
60.41
23.54
57.55
37.88
54.93
56.90
54.04
0.39
0.19
0.23
0.20
0.09
0.05
0.06
0.30
HH Size
# Bikes
Land
operated
Acres
8.47
7.05
7.24
6.27
3.40
6.41
4.64
7.57
Land owned
Acres
10.88
8.68
8.09
7.61
2.64
5.29
6.02
9.44
1.27
0.64
0.47
0.70
0.55
0.87
0.73
1.05
# draft anim.
HH received
Loan
Bin.
0.09
0.12
0.02
0.05
0.00
0.05
0.01
0.08
Gov’t prog.
Bin.
1.56
1.35
1.18
1.24
0.96
1.86
0.99
1.45
Fertil. Chem.
Bin.
0.45
0.33
0.26
0.25
0.31
0.31
0.16
0.38
Fertil. Org.
Bin.
0.08
0.03
0.04
0.02
0.00
0.09
0.02
0.06
Pesticide
Bin.
0.53
0.44
0.45
0.42
0.40
0.47
0.30
0.48
%
0.04
0.05
0.07
0.06
0.07
0.06
0.05
0.05
Temperature
Vulnerability and Crops
Figure 11: Vulnerability and Crops in Chinandega
Did not grow crop
Grew crop
60%
52%
50%
40%
28%
30%
24%
20%
10%
22%
17%
10%
3%
4%
0%
mais
beans
mango
lemon
Profile of vulnerable households: assets and
livelihoods
•
•
•
•
•
•
•
•
•
•
•
education of head < 3 years
highest education in the hh < 6 years
household size > 5 members
agriculture oriented > 50% share of income
low use of fertilizers and pesticides in the area
livestock in TLU < 4 units
no irrigation
no credit access
distance to road > 60 km
distance to health facility > 6 km
distance to school > 1.5 km
Policy Simulations: Current Climate
Figure 16 - Simulation: Current Climate and Policies
30%
25%
vulnerability
20%
15%
10%
5%
0%
actual
min 2 yr educ
min 5 yr educ
fertil. (chem.)
fertil. (organic.)
pesticides
Policy Simulations: Higher Temperatures
Figure 17 - Simulation: Global Warming
60%
50%
vulnerability
40%
30%
20%
10%
0%
actual
5% C increase in temp.
10% increase in temp.
Policy Simulations: Higher Temp.+
Responses
Figure 18 - Simulation: Global Warming and Policies
45%
40%
35%
vulnerability
30%
25%
20%
15%
10%
5%
0%
no interventions
min 2 yr educ
fertil. & pest.
all measures
Conclusions on the analysis of vulnerability
to food insecurity
• Model contributes to improved program design and
preparedness planning by:
– Making distinction between transitory and chronically
food insecure households
– Estimating impact of shocks (e.g. climate) on household
vulnerability and number of affected households
– Profiling the vulnerable
Lessons learned
How can the assessment be improved?
• matching data to geographical locations with
GIS
• biophysical impacts on crop production
• Estimation of vulnerability with climate data
requires non-linear models
• Estimation of probability
Moving forward
• Nicaragua is a pilot. Lessons learned will serve to
improve the methodology
• Replication envisaged in different institutional and
policy contexts
• Ultimate goal is to develop a robust research
framework on the impacts of CC on household FS
and related policy-level implications
Thank you!
Anna Ricoy
[email protected]
Jeronim Capaldo
[email protected]