SCOR Group results at September 30, 2005November 3, 2005
Potential use of historical reanalysis by agricultural
re/insurance industry
Olena Sosenko Australia
April, 2009
2SCOR Group results at September 30, 2005November 3, 2005
Content
1. Global agriculture
2. Agriculture and climate change
3. Interest of re/insurance industry in climate/weather data
4. What kind of data is required and how it is used
5. Data period, data consumers
6. Product solutions for ag and ag insurance industries
7. Special solutions for emerging markets
3SCOR Group results at September 30, 2005November 3, 2005
Global agriculture
1.7 billion more mouth to feed by 2030
Food is driver for revolutions and wars
70% of the world water is used for agriculture
90% of risks are weather/climate related
Production variability, reducing arable land, drought, desertification, flooding, soil salinity and erosion
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Rainfall and yield variability
Australia
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Potential impacts of climate change on crop production
Source: Climate change, impacts and adaptation. Canadian website.
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Climate change points leading to insurance opportunities
Increasing of yield variability and production risk
Increasing of water stress problems: drought and flooding
Increasing of agri risk management importance
Increasing of demand for new products/covers
Innovative types of insurance relating to climate and climate change
Index insurance products (weather index, yield index)
NDVI (satellite) based insurance
7SCOR Group results at September 30, 2005November 3, 2005
Interest of re/insurance industry in weather/climate data
Property and casualty insurance
- Flood
- Hurricanes
- Storms
- Tornadoes
- Heat waves
- Wind
- Fire
- Rain, snow, ice, sleet
Agricultural insurance
- Hail
- Rain
- Drought (rain + temperature)
- Frost
-Temperature anomalies (too cold, too hot)
- Fire
- Wind
8SCOR Group results at September 30, 2005November 3, 2005
What kind of data the ag insurance industry requires
Digital
Annual/ seasonal/ per month/ per event
Long term records only at the stage of insurance program development
Might need the same data yearly for product implementation
Easy accessible, reasonable price
High spatial resolution (per administrative unit, agroecological zone)
If modelled (or in case of forecast) – high preciseness
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How ag re/insurance industry uses the data
Price the pure cost of risk (frequency x severity) = net rate. Burning analysis
Create catastrophic models (return period of cat events)
Define the covered trigger (index products)
Confirm loss occurrence, trigger hit
Calculate the indemnity (index product)
10SCOR Group results at September 30, 2005November 3, 2005
Choice of data periodSouth NSW
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
1950-2007 1965-2007 1980-2007 1990-2007
Lo
ss c
ost Coolamon
Murray
Wagga Wagga
Northern NSW
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
1950-2007 1965-2007 1980-2007 1990-2007
Lo
ss c
ost Coolah
Moree Plains
Quirindi
11SCOR Group results at September 30, 2005November 3, 2005
Who are the data consumers in case of ag risk management
Re/insurance companies
Underwriting agencies
Loss adjustment companies
Consulting companies
Government working groups
Ag growers
Marketing companies (AWB)
World Bank, UN: agro risk management projects
12SCOR Group results at September 30, 2005November 3, 2005
Example of weather/climate indices used for ag insurance
Indices Purpose of the use
Temperature below +20C and its duration
Frost damage of fruits, grape
Hail size and frequency Hail damage of broadacre, cotton, fruits
Rainfall amount during vegetative season+ temperature
Lack of rain, excessive rain for broadacre. Index based insurance. Drought
Soil temperature below freezing threshold
Winter frost for broadacre in Northern hemisphere
Accumulative sunshine hours. Fruit, vine grape, corn maturation.
Wind speed Forestry, plantation damages
Snow amount Forestry, plantation damages
13SCOR Group results at September 30, 2005November 3, 2005
Some demanded data enhancements or innovations for re/insurance needs
Risk mapping (hail, storm pathways)
Modelling and prediction of winter risk scenarios in Northern hemisphere
Drought modelling and prediction
Climate extreme trends
Improvement of data spatial homogeneity
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Product solutions for ag and ag insurance industries
Climate models / weather forecast
Queensland government, Department of Primary Industries
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Product solutions for ag and ag insurance industries
Crop models
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Application for re/insurance industry
Climate models Crop models
Management of weather related re/insurance portfolio
Climate/weather database
Cat modelling
Yield statistic simulation
Insurance product development and implementation
Translation of climate risks into understandable for farmers language
Risk probability and accumulation monitoring
17SCOR Group results at September 30, 2005November 3, 2005
Use of crop models for insurance programs
Country, program leader
Principles of crop model
Insurance program
Mexico, AGROASEMEX
Crop-Soil-Climate Interface model:•Physiological age of the crop•Gross accumulation of CO2
•Distribution of dry matter
Catastrophic Crop Insurance – drought events
Ethiopia, the World Bank
FAO yield forecasting model
Index type of drought cover
Australia, Underwriting agency
Regional Commodity Forecasting System:•Daily water routine between plants, soil and atmosphere
Water stress insurance for Broadcare crops
18SCOR Group results at September 30, 2005November 3, 2005
Special solutions for emerging markets
India
Source: presentation of the World Bank
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Special solutions for emerging markets
India
Rainfall Index insurance product: since 2003, ICICI Lombard + the World Bank
Crop yield-based scheme in frame of National Agricultural Insurance Scheme implemented by Agricultural Insurance Company of India
Mongolia: Index-based livestock insurance, protection from dzud.
Peru: ENSO-based flood insurance for ag income related institutions
Mexico
Used weather derivative to reinsure the crop insurance program
Rainfall insurance contracts in conjunction with water rights
20SCOR Group results at September 30, 2005November 3, 2005
Thanks for your attention!
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