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) [email protected]
Francesco RispoliPolicy and Technical Advisory Division (IFAD)
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
Retailing Index Insurance Retailing Index InsuranceRetailing Index InsuranceRetailing Index Insurance
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
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!