Satellite innovations for scaling-up index insurance

Transcription

1 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. Satellite innovations for scaling-up index insurance 10 th International Microinsurance Conference November 2014 Mexico City, Mexico Francesco Rispoli Senior Technical Specialist, Inclusive Rural Financial Services IFAD - International Fund for Agricultural Development

2 1. Background 2. The project: IFAD-WFP remote sensing for index insurance 3. Preliminary findings and considerations 4. Next steps

3 IFAD-WFP WRMF Weather Risk Management Facility IFAD-WFP partnership on index insurance since 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

4 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

5 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

6 1. Background 2. The project: IFAD-WFP remote sensing for index insurance 3. Preliminary findings 4. Next steps

7 Researching for new solutions Remote sensing for index insurance IFAD-WFP WRMF project, financed by AFD, from 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

8 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 Pluviométrie (mm) Crop-Cut Yield (Kg/Ha) Pluviométries Dept. Diourbel - 26 postes (AMMA DMN-CERAAS-CIRAD) Gossas Department: Groundnut Yields per Crop Cutting, 2004/05 Series No of crop cuts

9 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

10 Remote sensing methodologies tested RSSP Type of product/approach Remote sensing data used VITO Vegetation indices (NDVI and fapar) SPOT-VGT NDVI / fapar (1*1km) TAMSAT rainfall estimates (8*8km) SoS based on RFEs FewsNet Actual evapotranspiration MODIS based ET (1*1km) (USGS) 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 sarmap Radar-based estimation of soil moisture SoS based on Soil Water Index Radar crop maps and SoS indicators ERS (50*50km) resolution and METOP ASCAT (50*50 and 25*25 km) CosmoSkyMed data (3*3m)

11 Testing: Senegal sites Central Senegal 4 sites in: Diourbel, Nioro, Koussanar, (Kaffrine) 20 km * 20 km test sites Groundnut; Millet; Maize

12 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

13 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

14 1. Background 2. The project: IFAD-WFP remote sensing for index insurance 3. Preliminary findings and considerations 4. Next steps

15 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 Have expanded temporal testing Improvements in design and calibration of indices More yield data for validating and interpreting results

16 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 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

17 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 enduser needs and technical capacity for design and calibration of indices Improvement of indices based on: Further support from project and local experts Incorporation of additional data

18 4. Crop maps and masks can increase 2013 findings performance of indices 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 Developing maps to discriminate between crops Test combination of maps and other methodologies

19 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 Further work on demonstrating the zoning capacity of the methodologies developed

20 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

21 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