Transcript Livelihood, Early Assessment, Protection LEAP – Ethiopia

Livelihood, Early Assessment,
Protection
LEAP – Ethiopia
LEAP Training 21 to 23 April 2010, Addis Ababa
LEAP objective and purpose
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Based on 2006 Ethiopia Drought Index project
experience
GFDRR Project that links drought and flood
monitoring and early warning with the GoE
managed risk management framework
Software platform to calculate weather based
indices:
• Monitoring of drought and other risks
• Guide to disbursements for a PSNP scale-up
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Integral part of the Ethiopia Risk Management
Framework
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Ex-ante risk management tool
Ethiopia Risk Management
Framework II. Develop budgeted
I. IMPROVE INDEX:
LEAP
contingency plans
Early Warning
System
Contingency
Planning
with reliable baseline for appropriate and
and trigger points
timely response
Ex-Ante
Financing
IV. Establish timely
of contingency
emergency financing plans
through use of
contingency financing
Capacity
Building
III. Build planning and
for effective plan
implementation
implementation
capacity at regional
level
LEAP products
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WRSI: Water Requirement Satisfaction
Index and 19 related parameters (PET,
water excess etc.)
Yield reduction estimates
Livelihood parameters (number of
beneficiaries, cost for livelihood protection
at Woreda level)
Moisture index (rainfall/evapotranspiration
comparison)
WRSI Index = intermediate
product
Crop
LPCI
and
Weather
Information
Number of
Beneficiaries
Crop baskets
Water Balance
Calculation
Crop Yields
Rangeland Yields
WRSI
Index
Prices of Food
Shocks in
Livelihood
Studies
WHAT DO WE WANT FROM LIVELIHOOD
PROTECTION COST INDEX?
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Represent cost of intervening early to protect
vulnerable livelihoods
Signal amount of financial resources needed for
regions to protect vulnerable livelihoods before
harvest
Trusted by GoE and donors to trigger timely
resources
Provide early warning of livelihood stress levels
Crop and pasture monitoring tool
Easily customizable according to new purposes
Open source and available for free
Developing the LPCI
Target group: vulnerable population
 80 to 90% of Rural Livelihoods
directly or indirectly depend on
rainfed agriculture
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Weather data
Crop + soil coefficients
Water
Requirement
Satisfaction
Index
(WRSI)
Number of
Beneficiaries
for Livelihood Protection
for Crops + Rangelands
Yield reduction in %
“hot spot” monitoring
Costs
(Livelihood
Protection
Cost Index LPCI)
CONTINGENCY PLANNING
LPCI triggers
Contingent
Financing
Livelihood Analysis
•Risk Profiling
activates
Contingency
Plans
implement
e.g. SNNPR
•Drought Risk Assessment
•Drought Impact Assessment
•Drought Scenarios
 Types of intervention needed
 Timing of intervention
Appropriate
and timely
response
protects
 Target population
 Costs
•Implementing partners
Livelihoods
Current LEAP phase
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Capacity building – LEAP training and
study tours
Development of flood and pastoralist
indices
Verification of LEAP drought index,
stakeholder support
Complete hand over to GoE
LEAP is being developed by
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WFP Rome, initiator, insurance based risk financing (Niels
Balzer, Ulrich Hess – Vanessa Cardamone, Giuditta De
Simone)
MoARD intents to make LEAP corner stone of food
security warning system (Mattewos Hunde)
Local Consultants (Addisu and Girma; crop and needs
data)
NMA Ethiopia, provides data and feedback.
CSA and MoARD provide data
Several government bodies provide feedback
International Consultants (Sandro Calmanti and Peter
Hoefsloot)
WFP/VAM Addis, overall guidance, data delivery and
feedback from the field (Elliot Vhurumuku – Teshome
Erkineh and Dr Muktar Reshid)
World Bank finances through GFDRR Grant (Will
Wiseman – Wout Soer)
Data providers
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National Met Agency (rainfall and et0)
University of Reading (rainfall)
NOAA (rainfall estimates)
LEWS project (Livestock Early
Warning System): Forage Maps
MoARD (crop and production
data)
CSA (crop and needs data)
USGS (NDVI)
EUMETSAT Germany : Meteosat NDVI
Almost all provide data for free -> enhances
sustainability
What has been done so far
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First steps to LEAP three years ago
Now version 2.20
Software will be updated regularly (next year
floods and rangeland)
100 page manual and 40-page tutorial
included
LEAP website : http://vam.wfp.org/leap
Training has been provided in 6 workshops to
over 75 Ethiopian professionals.
Users: WFP, World Bank consultants, MoARD,
NMA, and YOU!
Recent LEAP developments
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NMA provides real-time rainfall through Dr.
Muktar Reshid
ITHACA (Fabio Giulio Tonolo , Franca
Disabato): methodology based on MODIS to
detect floods
ENEA (Sandro Calmanti) works on rangeland
production estimates linked to needs
Servers in Addis will be installed at DRMFSS
and NMA (Mattewos Hunde and Teshome
Erkineh)
Peter Hoefsloot integrates all above in LEAP
Continuously running LEAP activities
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Providing new data to the LEAP
community through internet
download and mail:
• rainfall estimates (also through mail)
• NMA data integration (ET0 and rainfall)
• NDVI (vegetation greenness)
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Monthly LEAP bulletins in cropping
season
Technical support through mail
LEAP bulletins
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Monthly in Belg
and Meher
cropping seasons
Maps
Highlights
Still to do
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Emphasis on making LEAP
operational
Introduce improved needs and
beneficiary algorithms into LEAP
(based on work by Sandro Calmanti)
Training trainers and software
developers
Ensure continuous records - some
datasets have gaps
LEAP is free!
Download from LEAP page
 http://vam.wfp.org/leap (program
+ data = 76MB)
If you need data by mail rather than internet update, please send
an e-mail to
[email protected]
Drought Index Verification
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Correlation against historical
production/yield and beneficiary
data
• FAO/WFP natl cereal crop production,
1999-2003  87%
• MoARD natl cereal crop yields, 1995-2003
 75%
• WFP beneficiary numbers, 1994-2004 
81%
• DPPA % of drought affected pop  80%
Drought Index Verification II
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Field verification
• Purpose: (1) Verify accuracy of drought index for rigor
in capturing actual performance of dominant staple
crops on ground; (2) Generate baseline date for further
validation of index
• 13 woredas associated with 10 selected weather stations
visited
• Minor differences emanated from identified gaps in
model but comparison against modelled estimates
reveal that index fairly captured conditions in most
woredas
• Recommendation: Improved agro-met data inputs;
further improvement and pilot testing
A water balance model drives the
index
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Index based on just rainfall too crude.
Water balance model: bookkeeping on crop water
use