Potential use of historical reanalysis by agricultural re/insurance industry

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


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


Rainfall and yield variability

Australia


Potential impacts of climate change on crop production

Source: Climate change, impacts and adaptation. Canadian website.


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


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


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


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)


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


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


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


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


Product solutions for ag and ag insurance industries

Climate models / weather forecast

Queensland government, Department of Primary Industries


Product solutions for ag and ag insurance industries

Crop models


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


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


Special solutions for emerging markets

India

Source: presentation of the World Bank


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


Thanks for your attention!

Potential use of historical reanalysis by agricultural re/insurance industry

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