Livelihood Early Assessment Protection LEAP Ethiopia LEAP Training

Livelihood, Early Assessment, Protection LEAP – Ethiopia LEAP Training 21 to 23 April 2010, Addis Ababa

LEAP objective and purpose n n n Based on 2006 Ethiopia Drought Index project experience GFDRR Project that links drought and flood monitoring and early warning with the Go. E 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 n Integral part of the Ethiopia Risk Management Framework n 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 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 capacity at regional level

LEAP products n n 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? n n n n 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 Go. E 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 n 80 to 90% of Rural Livelihoods directly or indirectly depend on rainfed agriculture n 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 Ø Types of intervention needed Ø Timing of intervention Appropriate and timely response protects Ø Target population Ø Costs • Implementing partners Livelihoods

Current LEAP phase n n Capacity building – LEAP training and study tours Development of flood and pastoralist indices Verification of LEAP drought index, stakeholder support Complete hand over to Go. E

LEAP is being developed by n n n n n WFP Rome, initiator, insurance based risk financing (Niels Balzer, Ulrich Hess – Vanessa Cardamone, Giuditta De Simone) Mo. ARD 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 Mo. ARD 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 n n n n n National Met Agency (rainfall and et 0) University of Reading (rainfall) NOAA (rainfall estimates) LEWS project (Livestock Early Warning System): Forage Maps Mo. ARD (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 n n n n 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, Mo. ARD, NMA, and YOU!

Recent LEAP developments n n n 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 n Providing new data to the LEAP community through internet download and mail: • rainfall estimates (also through mail) • NMA data integration (ET 0 and rainfall) • NDVI (vegetation greenness) n n Monthly LEAP bulletins in cropping season Technical support through mail

LEAP bulletins n n n Monthly in Belg and Meher cropping seasons Maps Highlights

Still to do n n 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 n http: //vam. wfp. org/leap (program + data = 76 MB) If you need data by mail rather than internet update, please send an e-mail to peter. hoefsloot@planet. nl

Drought Index Verification n Correlation against historical production/yield and beneficiary data • FAO/WFP natl cereal crop production, 1999 -2003 87% • Mo. ARD natl cereal crop yields, 1995 -2003 75% • WFP beneficiary numbers, 1994 -2004 81% • DPPA % of drought affected pop 80%

Drought Index Verification II n 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 n n Index based on just rainfall too crude. Water balance model: bookkeeping on crop water use