Agricultural risk micro-insurance product for Mozambique
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Christian P. MORTGAT1, Pane STOJANOVSKI2, Auguste C. BOISSONNADE2, Alex BERNHARDT3 1Risk Management Solutions, Inc.; 2Asia Risk Centre, Inc.; 3Guy Carpenter & Company, LLC
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Agricultural risk micro-insurance product for Mozambique
- 1. 4th International Disaster and Risk Conference IDRC Davos 2012 Davos, Switzerland, 26-30 August, 2012 Developing Agricultural Risk Microinsurance Products for Mozambique Christian P. Mortgat, Stojanovski Pane, Auguste C. Boissonnade , Bernhardt Alex
- 2. Mozambique - Mosaic of Perils Drought Prone Districts Main River Basins Rov u ma Lur io Rio Lago de Cahora Bassa Zambeze Li m po po Incomati Tem be Population: 20.5 million Area: 800,000 km2 (15% >Texas) AGRO RISK DRIVERS: • Drought • Flood / Excess Rainfall Cyclone Prone Districts © 2012 2 Flood Prone Districts
- 3. Overview Background Data Modeling Products Next steps © 2012 3
- 4. Background – Capacity building effort – GIIF / IFC Grant 579027 – Partnership of RMS / Asia Risk Centre – Guy Carpenter Data Modeling Products Next steps © 2012 4
- 5. Maize Crop Production and Yield: 2008-09 © 2012 5
- 6. Cotton Crop Area And Production: 2009-2010 © 2012 6
- 7. Background Data Modeling Products Next steps © 2012 7
- 8. Data Requirements Meteorological: – Rainfall and other meteorological data: preferable 30 years of daily data (12 years acceptable) – Extreme (tail) events data: floods and droughts Data relevance (historical data relevance to current conditions); de-trending Yield data for weather indices that correlate with crop yield – Minimum 7 to 10 years of yield data for each crop – Yield data, estimated or sampled – Specific planted area location – often not available Crop /region specific vulnerability (impact on yields) Data quality and completeness – data cleaning, filling, enhancements © 2012 8
- 9. Meteorological Data - Reality – 113 stations distributed in 69 districts – Record duration from 10 to 50 years – 73 districts without stations (Weather stations only cover 1/3 of the territory and have many periods of incomplete data) © 2012 9
- 10. Other Sources – Reanalyzed Rainfall Data CRU (Climate Research Unit), University of East Anglia East Anglia – 1901 to 2002 – Daily rainfall at 0.5 degree grid (approximately 50 km) Santa Clara Rainfall Data Set (California) – 1950 to 1999 – Daily rainfall at 0.5 degree grid (approximately 50 km) Both sets have very similar estimates in Mozambique – The University of East Anglia was used (longer time period) © 2012 10
- 11. Meteorological Data – Weather Stations and Re- analyzed and Gridded Data Reanalyzed rainfall data © 2012 11
- 12. Remote Sensing Rainfall Estimates Tropical Rainfall Measuring Mission (TRMM) – National Oceanic and Atmospheric Administration (NOAA) and the Japanese Aerospace Exploration Agency, since 1998 – 3 hourly and monthly rainfall at 25 km grid resolution Water Requirement Satisfaction Index (WRSI) Precipitation Estimates – FEWS NET at the USGS and National Oceanic and Atmospheric Administration (NOAA), since 1995 – Supported by the University of Santa Barbara (California) – Dekadal rainfall at 10 km grid Both approaches lead to very similar results – WRSI was selected because a processing tool is supported on the internet © 2012 12
- 13. Background Data Modeling Products Next steps © 2012 13
- 14. Modeling Data Review Insurance Product Design – Cleaning, de-trending, enhancement – Derive a set of coverages and payout triggers Crop Vulnerability Development – Test model against historical data – Establish a correlation between a meteorological parameter and crop Long Term Risk Quantification yield or crop Mean Damage Ratio – Generate long term (500 years) (MDR) simulated rainfall – Quantify the uncertainty associated – Perform analysis to generate with the MDR standard quantities – Develop tools to calculate yield and – Exceedance curve losses – AAL – Reconstruct historical data in terms of Ground Up (GU) year losses, Average Understanding the Uncertainty Annual Loss (AAL) and Exceedance – Study yield variations relative to Probability (EP) Curves reference yield – Accommodate regional trend variations (North, Central, South) – Separate treatment for excess rainfall © 2012 (SPI) and drought (WRSI) 14
- 15. Remote Sensing Data - GeoWRSI Maize - WRSI Anomaly (compared to average) ‘04 / ‘05 Season Decad 3, August 2005 Maize WRSI – ’04 / ‘05 Season, Decad 3, August 2005 © 2012 http://hollywood.geog.ucsb.edu/wb/geowrsi.php 15
- 16. Maize Losses – Historical vs. Model 120,000,000 AAL Historical: 47.3M 100,000,000 AAL Model: 47.1M 80,000,000 Ground Up Losses (USD) 60,000,000 Historical Model 40,000,000 20,000,000 - 2001 2002 2003 2004 2005 2006 2007 2008 2009 © 2012 16
- 17. Maize Country Wise Probability of GU Loss 1.20 Historical vs. Model 1.00 HIST NORTH HIST CENTRAL Probability of Exceedance 0.80 HIST SOUTH 0.60 HIST COUNTRY MODEL NORTH 0.40 MODEL CENTRAL MODEL SOUTH 0.20 MODEL COUNTRY 0.00 - 50,000,000 100,000,000 150,000,000 200,000,000 Ground up loss (USD) © 2012 17
- 18. Background Data Modeling Products Next steps © 2012 18
- 19. Maize Micro Insurance – Barue District Pilot Barue district divided into 74 10km grids Each grid considered as an insured location Dekadal cumulative rainfall obtained from remote sensing at each grid (GeoWRSI) © 2012 19
- 20. Maize Micro Insurance – Barue District Pilot Crop failure under severe drought condition Number of farmers targeted: 750 Sum insured: approximately USD2,000.00 per location (grid) Triggering parameter: cumulative rainfall within a grid during three consecutive dekads within January and February Trigger: – Rainfall > or = to 27.5mm, no payout – Rainfall < 27.5mm, payout of total sum insured A location (grid) cannot trigger more than once during the coverage period Rate on line: approximately 15 percent © 2012 20
- 21. Cotton Micro Insurance – Pilot Project Package provider (agro inputs – seeds, fertilizers) has control over a whole district Insurance issued at the district level for each farmer buying the package from the provider Five districts selected for pilot project Rainfall aggregated at district level from GeoWRSI dekadal 10km grid Daily rainfall assumed constant during dekad as the dekadal rainfall divided by the number of days in the dekad Temperature obtained from NOAA estimates © 2012 21
- 22. Cotton Micro Insurance Structure © 2012 22
- 23. Moving Forward and Challenges Capacity building is not a single initial effort – Monitor launched program, identify deficiencies, improve / modify – Establish improved data collection networks, utilization of newly collected data, model and product updates – Institutional framework and support : countrywide key crop insurance schemers, premium subsidies, regulatory support © 2012 23
Editor's Notes
- Mozambique is located on the southeast coast of Africa. It is bound by Swaziland to the south, South Africa to the southwest, Zimbabwe to the west, Zambia and Malawi to the northwest, Tanzania to the north and the Indian Ocean to the east. The country is divided into two topographical regions by the Zambezi River. To the north of the Zambezi River, the narrow coastline moves inland to hills and low plateaus, and further west to rugged highlands. To the south of the Zambezi River, the lowlands are broader with the Mashonaland plateau and Lebomo mountains located in the deep south.The country covers an area of 799,380 sq km and has a 2,470 km shoreline with the Indian Ocean. This shoreline is about one-third of the seaboard of eastern Africa. Mozambique is divided into ten provinces and one capital city with provincial status. The provinces are subdivided into 129 districts. The districts are further divided in 405 Administrative Posts.It has a tropical climate with two seasons, a wet season from October to March and a dry season from April to September. Climatic conditions, however, vary depending on altitude. Rainfall is heavy along the coast and decreases in the north and south. Annual precipitation varies from 500 to 900 mm depending on the region with an average of 590 mm. Cyclones are also common during the wet season. (http://en.wikipedia.org/wiki/Mozambique)The geography of Mozambique is dominated by 10 main river systems that crisscross the country from west to east and drain into the Indian Ocean. The catchment areas of these rivers drain water from vast swathes of southern Africa. ---Mozambique with an area of 800,000 km^2 (15% larger than Texas) and a population of 20.5 million is divided into 10 provinces and 142 districts as shown in the maps below
- Maize and cotton production and yield dataThe maize production and yield in Mozambique are available from season 2000-2001 to season 2008-2009 (crop 2001 to 2009). The data are provided by a branch of “MinistériodaAgricultura (MINAG)”, Ministry of Agriculture.
- We do not address cotton in the rest of this presentationThe cotton production and yield are available for two provinces (Cabo del Gado and Mampula) for approximately 15 years. The data are provided by “Instituto de Algodao de Mozambique”, (IAM), Mozambique Cotton Institute. These two provinces produce between 60 and 70 percent of the total country production. Maps of the areas of productions are presented below.
- The rainfall data is available for 113 stations with daily data spanning between 10 and 50 years. The rainfall gauges are located within 69 districts leaving 73 districts without stations.Given the limited geographical coverage of the rainfall stations, additional sources of data are needed
- Re-analyzed rainfall dataRe-analyzed rainfall data are generated from actual rainfall gauge readings after cleaning and de-trending. Using interpolation and smoothing techniques, a uniform grid of daily rainfall data and, at limes, other meteorological parameters is generated over the region of interest. The advantage of this information is that it covers the region uniformly and usually for a long period of time (50 years or more). The main limitations are that those data are not being updated on a regular basis and that they contain a fair amount of assumptions in their development. Two such data bases were studied and compared in this project:The Climate Research Unit (CRU) of the University of East Anglia, East Anglia that covers the period from 1901 to 2002 with daily rainfall generated at 0.5 degree level of resolution.The Santa Clara Rainfall Data of the University of Santa Clara, California that covers the period from 1950 to 1999 with daily rainfall generated at 0.5 degree level of resolution.Both sets provide data with similar trends, although randomness is present between them. The CRU data was selected for long term risk estimation because it covered a longer period of time.
- Two such data bases were studied and compared in this project:The data from the Tropical Rainfall Measuring Mission (TRMM) operated by the National Oceanic and Atmospheric Administration (NOAA) and the Japanese Aerospace Exploration Agency since 1998. They provide 3 hourly and monthly rainfall at 25 km grid resolution.The Water Requirement Satisfaction Index (WRSI) Precipitation Estimates operated by FEWS NET at the USGS and the National Oceanic and Atmospheric Administration (NOAA), since 1995. This database is supported by the University of Santa Barbara, California and provides dekadal rainfall at 10 km grid resolution.Both databases provide generally consistent estimates although uncertainties are present between the data sets. The WRSI was selected for use for the risk estimation because of its ease of use and internet access.
- GEO WRSI Is a geo-spatial, stand-alone application for estimation of WRSI (Water Requirements Satisfaction Index ) Is implemented by the USGS for the FEWSNET Activity. Runs a crop-specific water balance model for a selected region in the world, using raster data inputs. Produces a range of outputs which can either be used qualitatively to help assess and monitor crop conditions during the crop growing season, or can be regressed with yields to produce yield estimation models and yield estimates. WRSI OUTPUTSWRSI MapWRSI Anomaly MapStart of Season MapTotal Actual Evapotranspiration at different stages of crop growthTotal Water Deficit at different stages of crop growthTotal Water Requirement at different stages of crop growthTotal Surplus Water at different stages of crop growthMaximum Water Deficit experienced in any one dekadMaximum Surplus Water experienced in any one dekadInputs - DefaultDekadal Precipitation and Potential Evapo Transpiration (PET)Crop Parameters Start of Season (Computed)Water Holding CapacityInputs – ModifiedLength Of Growing Period (LGP) – 16 dekads
- Reconstruction of historical ground up losses and exceedance probability curves for maize cotton presented in the following slides.Maize Historical vs. Model Losses