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Satellite innovations for scaling-up index insurance
10th International Microinsurance Conference
11-13 November 2014 Mexico City, Mexico
Francesco Rispoli Senior Technical Specialist, Inclusive Rural Financial Services IFAD - International Fund for Agricultural Development
1. Background 2. The project: IFAD-WFP remote sensing for index insurance 3. Preliminary findings and considerations 4. Next steps
IFAD-WFP WRMF Weather Risk Management Facility IFAD-WFP partnership on index insurance
since 2008. Index insurance as one tool with potential
to: Reduce small holder vulnerability Improve incomes and productivity in
agriculture Unlock access to credit Enhance food security
Data challenges Weather data: Lack of stations in sparsely populated areas or close enough
to the insured area(s) Not all stations provide the right quality of data Long time-series of quality data is rarely available New stations? Issue of volume needed to cover population
and heterogeneous areas plus long-term maintenance
Yield data: Good quality, sufficient time series at disaggregated level
frequently unavailable
The importance of good data
Lack of sufficient, quality data = impossible design or unreliable product Unreliable products: Farmers not adequately compensated for losses
(basis risk)
Loss of trust in insurance sector
Impact on demand
1. Background 2. The project: IFAD-WFP remote sensing for index insurance 3. Preliminary findings 4. Next steps
Researching for new solutions Remote sensing for index insurance IFAD-WFP WRMF project, financed by AFD, from 2012 – 2014 now 2016
Evaluate feasibility of satellite-based technology for index insurance to
benefit smallholder farmers at village level
Develop, test, validate, evaluate opportunities and constraints of indices created by different remote sensing methodologies
Aims to contribute to: Finding a sustainable approach to index insurance for smallholders Developing indices which can accurately depict yield loss at village
level due to weather and other perils
Disseminate results across the industry, feed into IFAD and WFP programmes
Remote sensing applications Technical challenges Small farm size Mixed cropping High rainfall and
yield variability within a small area
Cloud cover during critical growth periods
Spatial resolution Vs historical data
Pluviométries 2007 - Dept. Diourbel - 26 postes(AMMA DMN-CERAAS-CIRAD)
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Remote sensing applications Operational constraints to overcome
Sustainability of operation at local level Cost of raw image data Technology capacity and cost for processing Transfer of technical capacity and
transparency of methodology for derived data
Acceptance of stakeholders and farmers
Remote sensing methodologies tested
RSSP Type of product/approach Remote sensing data used VITO Vegetation indices (NDVI and
fAPAR) SoS based on RFEs
SPOT-VGT NDVI / fAPAR (1*1km) TAMSAT rainfall estimates (8*8km)
FewsNet (USGS)
Actual evapotranspiration MODIS based ET (1*1km)
EARS Relative evapotranspiration MSG based relative ET (3*3km) ITC Vegetation indices (NDVI) SPOT-VGT NDVI (1*1km) IRI Rainfall Estimates NOAA based RFE2 ARC (10*10km) Geoville Radar-based estimation of soil
moisture SoS based on Soil Water Index
ERS (50*50km) resolution and METOP ASCAT (50*50 and 25*25 km)
sarmap Radar crop maps and SoS indicators
CosmoSkyMed data (3*3m)
Testing: Senegal sites • Central Senegal
• 4 sites in: Diourbel,
Nioro, Koussanar, (Kaffrine)
• 20 km * 20 km test sites
• Groundnut; Millet; Maize
Ground data monitoring
Responsible: local research institutions – ISRA and CERAAS
4 villages per 20km x 20km area
30 fields per crop type, 3 crop types (maize, groundnut, millet) Monitoring of varieties, sowing dates, crop development, causes of
losses, end of season yield measurements Survey of farming practices, identification of adverse years and
causes of losses Installation of rain gauges at field level
Validation and Evaluation
Validation of performance compared with historical data and 2013 ground data (VITO)
Evaluation (technical and operational) • Responsible: Evaluation Committee (Independent,
multi-sectoral) • Evaluation criteria includes Technical performance &
accuracy; availability & use of data; cost & sustainability; ownership and transparency
1. Background 2. The project: IFAD-WFP remote sensing for index insurance 3. Preliminary findings and considerations 4. Next steps
1. Performance of the indices
2013 findings
• Performance of the methodologies developed is overall encouraging
• Ground monitoring showed high spatial variation of yields within same area, and between plots
2014 +
• Have expanded temporal testing
• Improvements in design and calibration
of indices
• More yield data for validating and
interpreting results
2. Different methodologies are more suited for different operational contexts (crops and areas)
2013 findings
Some methodologies perform better for certain crop-area combinations
Overall better performance with millet and groundnut, varied performance with maize
2014 +
Have expanded temporal testing to further analyse strengths and weakness of different approaches in different conditions
Future potential: expand spatial in other areas and environments e.g. Kenya
3. Index design and calibration is key 2013 findings
Design and calibration activities as important as capability of methodology
Design and calibration of indices significantly influence performance of the remote sensing methodologies
Remote sensing providers must have or develop good understanding of end-user needs and technical capacity for design and calibration of indices
2014+
Improvement of indices based on:
Further support from project and local experts
Incorporation of additional data
4. Crop maps and masks can increase performance of indices
2013 findings
2 RSSPs developed maps and masks to identify land use and discriminating between crops
Promising results, especially based on radar technology
Segmentation of areas based on maps and masks could increase performance of methodologies, especially those based on vegetation indices and on evapotranspiration
2014+
Developing maps to discriminate between crops
Test combination of maps and other methodologies
5. Remote sensing for identifying unit areas of insurance (UAI)
2013 findings: Remote sensing applications can be useful for the definition
of Unit Areas of Insurance (UAI) of index insurance programs
Critical to scaling-up: finding optimal size to limit ‘spatial basis risk’ but not raise administrative costs and burden
RS can provide a spatial zoning tool to segment geographical areas by risk profile and, therefore, identify UAIs of appropriate size.
2014 + Further work on demonstrating the zoning capacity of the
methodologies developed
Next steps
Refinement of products and testing in 2014 and 2015
Validate the performance against the 2014 crop season
Expand spatial testing in other areas and environments e.g. Kenya 2015
More complete evaluation in 2016
Dissemination of findings in 2016 and beyond
Das Bild kann nicht angezeigt werden. Dieser Computer verfügt möglicherweise über zu wenig Arbeitsspeicher, um das Bild zu öffnen, oder das Bild ist beschädigt. Starten Sie den Computer neu, und öffnen Sie dann erneut die Datei. Wenn weiterhin das rote x angezeigt wird, müssen Sie das Bild möglicherweise löschen und dann erneut einfügen.
Francesco Rispoli: [email protected]