Assessing Crop Insurance Risk Using An Agricultural Weather Index

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Assessing Crop Insurance Risk Using An Agricultural Weather Index

CAS Seminar on Reinsurance

June 6-7, 2005

S. Ming Lee

© 2005 AIR Worldwide Corporation CONFIDENTIAL

Challenges in Agricultural Risk Assessment

Every risk assessment starts with evaluation of historic data, i.e. crop yields, weather parameters, ….

Observed Corn Yields: Nemaha County

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Observed Corn Yield Distribution Nemaha County

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Yield


Challenges in Agricultural Risk Assessment…

However, direct use of historical crop yield distributions is inadequate for predicting future yields

Technological progress produces a trend in crop yield histories that must be removed in order to develop appropriate crop yield distributions

Weather variability produces significant crop yield variability that masks the technology trend, making its removal difficult

How to properly de-trend historical crop yield time series?


Typical Detrending Approach

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Weather Effect

Observed Yields

Technological Improvements


Trend in Corn Yield in Nemaha County, Nebraska Time Window: 1974-2001

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1974 1978 1982 1986 1990 1994 1998 2002

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1974 1978 1982 1986 1990 1994 1998 2002

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Low yields in 2002 due to a drought situation and lower yields in 2003 result in a less steep linear trend than for the time window 1974-2001



A shorter time window results in an almost horizontal slope

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1974 1978 1982 1986 1990 1994 1998 2002

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Summary of Yield Trends Computed for Different Time Windows, Corn Yield in Nemaha County, NE

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1974 1978 1982 1986 1990 1994 1998 2002

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1974 1978 1982 1986 1990 1994 1998 2002

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Proposed Weather-based Detrending Method

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Proposed De-Trending Method

Yield(t) = c0 + m*t + c1*AWI(t) +

c0, m and c1 ……… regression coefficients, m measures the

technology trend

t …………………… time (year)

AWI ………………. AIR Weather Index, weather indicator, measures weather effects on

yield

………………….. residual error

This equation is also called the AWI yield model


Crop Growth Depends on the Integrated Effect of Weather Over the Entire Growing Season

Weather data during a growing season should be partitioned into time periods corresponding to plant growth stages

Data need to be analyzed by… Crop

Corn Soybeans Wheat …

Location County Farm


Weather at Various Stages of Crop Development Determines Yield

Phenological stages of corn growth

Source: University of Illinois Extension


Introducing the AIR Agricultural Weather Index (AWI)

Effects of weather during different plant growth stages are indexed into a single AWI

AWI is a “score” for the overall quality of the growing season. Accounts for

Weather variables Accumulated precipitation; minimum, maximum and average

temperature Weather-derived parameters

Growing degree days, evapotranspiration Soil-related parameters

Plant-available water capacity, surface moisture, sub-surface moisture, runoff, crop moisture

Crop-specific parameters Water requirements, planting dates, crop phenological stages


AWI Computation - Overview

Temperature Precipitation Available Water Capacity (Soil)

+

Crop Specific DataSoil Moisture Levels

Run-Off

Degree Days

Etc.

AWI ModelTime Series of AWISurface Moisture %

Run Off [inches]

Evapotranspiration [inches]

Water Balance Model


Linear Detrending

Models based on just a linear trend Yield(t) = c0 + m*t +

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1974 1978 1982 1986 1990 1994 1998 2002

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LinearR2=.22

Corn Yield Time Series - Nemaha County, NE


Detrending Using a Single Weather Variable

Models based on one or two weather variables For example, June to August average temperature:

Yield(t) = c0 + m*t + c1*JJA(t) +

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JJA (R2=0.51)

R2=.51



AWI Yield Model Detrending

Yield model based on an agricultural weather index Yield(t) = c0 + m*t + c1*AWI(t) +


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R2=.77


County by County Model Comparison: Corn

Linear Trend

AWI Yield Model

Regression Coefficient

JJA Average Temperature


Estimating the Risk of Obtaining Yields Below a Defined Coverage Level

Yield Distributions

Linear

Log-linearAWI

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Yield (Bushels/Acre)Same coverage level, e.g 65% of mean value, for different distributions results in different probabilities (areas under curves)


… and Associated Risk (Exceedance Probabilities)


AWI Is a “Score” for the Overall Quality of the Growing Season

Corn – Le Sueur County, Minnesota

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Current Year

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Extending AWI Real-time Monitoring with Climate Forecasts

Corn – Le Sueur County, Minnesota

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In addition to historical and real time distributions, improved risk management comes from coupling AWI analysis with climate forecasts


Weather and Climate Modeling Resources at AIR

Multi-disciplinary team Climate scientists &

meteorologists Statisticians Software engineers Specialists in risk management

Computational horsepower 75-processor computer cluster

dedicated to data processing, analysis, and modeling

Additional database servers and computers for quality control and data analysis

Advanced numerical weather prediction (NWP) models


AIR Collects and Processes Over Ten Gigabytes of Weather Data Daily for Modeling and Analysis

NOAA Port

National Center for Environmental

Prediction

National Climatic Data

Center

• Weather observations

• Radar observations

• Severe weather reports

• Short-term climate data

• Long-term climate data• Numerical forecast

information


The Data Are Also Quality Controlled


High Quality Weather Data Provide a Solid Foundation For Agricultural Risk Analysis

Data quality control:

• Check for erroneous data

• Check for missing data

• Replace missing data where possible

Numerical weather

prediction

Statistical analysis & modeling

Climate data

archive

NOAA Port

National Center for Environmental

Prediction

National Climatic Data

Center

• Weather observations

• Radar observations

• Severe weather reports

• Short-term climate data

• Long-term climate data• Numerical forecast

information


Detailed Soil Data Supplement Weather Data

High resolution (~1 km) soil-specific Available Water Capacityinches

Source: STATSGO, USDA


Recap and Applications

The concept of AWI has been proven to explain most of the yield variability due to weather for corn and soybeans

AWI de-trended yield distributions reflect more accurately the weather risk related to growing corn and soybeans

Besides de-trending yield time series, the AWI Yield Model has further potential applications:

AWI can be used as a real time monitoring tool to assess current crop conditions

AWI can be used as an estimate of potential yield at harvest, which is available long before official NASS county yields are published

AWI can be utilized to objectively determine APH yields for individual farms and therefore can be included in a procedure to mitigate declining yields due to successive low yields

AWI de-trended yields can be utilized to build more accurate yield distributions for applications in risk assessment


Opportunities in Agricultural Risk Management

Producers (Farmers)

>200 m acres insured

Agribusinesses Commodities Markets

BrokersPrivate

Reinsurers

Risk Mgmt Agency

• Reinsures• Regulates • Subsidizes

Crop Insurers

$4 billion premium


… Crop Insurers

Optimizing policy allocations to Standard Reinsurance Agreement risk sharing funds

Better planning of reserve requirements and reinsurance needs


… Reinsurers

More informed underwriting decisions Better pricing decisions Better geographical diversification and portfolio management More effective hedging strategies using commodity futures contracts

Assessing Crop Insurance Risk Using An Agricultural Weather Index

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