Weather Risk Management Facility Agricultural Risk Transfer Mechanisms and Needs for Weather and Climate Services

WMO EAG-FRT I13- 14 December

Geneva

Richard ChoulartonClimate Change and Disaster Risk Reduction Coordination

Office (WFP) Richard.Choularton@wfp.org

Francesco RispoliPolicy and Technical Advisory Division (IFAD)

f.rispoli@ifad.org

Weather Risk Management Facility

• Joint initiative of IFAD and WFP

• Launched in 2008 with the support of the Bill and Melinda Gates Foundation

• Objective: Promote agricultural development and more effective disaster risk management by increasing the access of vulnerable smallholders to a wide range of risk management tools

WRMF activities

• 36 weather index insurance programs evaluated and reviewed

• A comprehensive analysis of opportunities, challenges and good practice with WII

• Five appraisal missions (Kenya, India, Mali, Ethiopia, China)

• Two pilots (Ethiopia and China)• R4 initiative together with OXFAM• Testing remote sensing in Mali

IFAD-WFP findings on WII

• WII is technically feasible but not applicable to all situations

• WII is one in a set of risk management options to be considered

• A risk assessment is always needed to determine what kind of risk management tools should be applied and if WII has a role

Retailing Index Insurance

• Retailing WII as a stand-alone insurance product is challenging

• Easier to meet famers demand when it is linked to credit, to inputs, to value chains, etc.

• Farmers need a strong and clear value proposition to consider investing in insurance

• Technical assistance per se is not sufficient for market development

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Weather data and infrastructure

• Improve the infrastructure and quality of weather data

• Remote sensing technologies and innovative delivery methodologies

• The future potential of WII largely depends on how the industry will be able to expand the technology frontier

WRMF – WII Ethiopia Pilot Lessons and Challenges

• Client education – Awareness and understanding of the product creates trust

• Timeliness of pay-outs• Combination with credit and

inputs • Weather data quality

WRMF – WII China Pilot Lessons and Challenges

• Despite solid infrastructure and good data, data access was one of the main challenge

• New weather stations are needed for scaling up

• Existence of highly subsidized MPCI• Lack of awareness of the product and

little trust in insurance companies

Data Requirements for Weather Index Insurance

• Availability of historical weather and yield data (approx. 30 years)

• Sufficient quality standard of data and access

• Functioning meteorological service

• Reliable weather station network

• Weather station reasonably close to potential customers

• Weather station secure from tampering

Challenges and Opportunities: MaliMajor technical challenges Possible responses

Ground-based weather data

Insufficient number of observations Not enough weather stations Costly maintenance Not enough local staff Lack of real-time transmission of

data

Adapted satellite observation for micro-level and centralized data collection

Weather infrastructure capacity support

Fully-automated weather stations linked to a clear system of data management, analysis and dissemination

Spatial variability North/South climate variations East/West weather variations Localized variability

Appropriate delimitation of target area

Historical modelling Development of localized indexes

based on remote sensing technology

Researching new solutions: Mali

Key expected results

•Understand of potential performance of satellite based indexes at micro-level•Develop specific remote sensing methodologies for micro-level index insurance application

Researching new solutions: MaliSatellite data benefits:•Difficult to tamper with•Available across large areas of the globe •Available in real-time via the internet•Relatively low-cost•Becoming more readily available•Currently used by reinsurers to supplement weather station data Satellite data weaknesses:•Difficult to achieve or access high resolution, good quality, informative data sets at micro-level•Limited time series of data •Regulatory challenges•Buyers willingness to purchase an insurance products based on satellite data

Addressing the needs of the most vulnerable – Integrating risk transfer,

risk reduction, and safety nets.

Early Warning System

with reliable baseline and trigger points

Contingency Planning

for appropriate and timely response

Contingent Financing

of contingency plans

Capacity Building

for effective plan implementation

Ethiopia Risk Management Framework

Ethiopia’s LEAP Early Warning System and Index

R4 Rural Resilience Initiative

http://resources.oxfamamerica.org/pages/view.php?ref=26002&search=harita&offset=0&order_by=relevance&sort=DESC&archive=0&k=

R4 Operational Model

Governments

DonorsFunding

Safety Net Program

Poorest Households

(Safety Net Participants) (Pay Labor)

Cash / Food

IFW Prem

iums

FundingIFW Voucher

Safety Net HARITA

Drought index triggered

Financial Institutions

Less Poor Households(Not Safety Net

Participants)(Pay Cash)Payouts

Payouts

Cash premiums

Cash premiums

IFW = Insurance-for-Work

Climate and Met Data Issues

• Use of ground and satalite datasets to reconstruct histroical data set for index design

• Scaling up quickly makes infrastrucutre costs high, in comparison to pilots

• Swtiched to satalitte-based index, developed through community design process, community by communtiy

• Major research effort to validate index

Thank you very much!