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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: f.rispoli@ifad.org