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, ….

© 2005 AIR Worldwide Corporation CONFIDENTIAL 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?

© 2005 AIR Worldwide Corporation CONFIDENTIAL Typical Detrending Approach Weather Effect Observed Yields Technological Improvements

© 2005 AIR Worldwide Corporation CONFIDENTIAL Trend in Corn Yield in Nemaha County, Nebraska Time Window:

© 2005 AIR Worldwide Corporation CONFIDENTIAL Trend in Corn Yield in Nemaha County, Nebraska Time Window:  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

© 2005 AIR Worldwide Corporation CONFIDENTIAL Trend in Corn Yield in Nemaha County, Nebraska Time Window:  A shorter time window results in an almost horizontal slope

© 2005 AIR Worldwide Corporation CONFIDENTIAL Summary of Yield Trends Computed for Different Time Windows, Corn Yield in Nemaha County, NE

© 2005 AIR Worldwide Corporation CONFIDENTIAL Proposed Weather-based Detrending Method Weather Effect Observed Yields Technological Improvements

© 2005 AIR Worldwide Corporation CONFIDENTIAL Proposed De-Trending Method Yield(t) = c 0 + m*t + c 1 *AWI(t) +  c 0, m and c 1 ………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

© 2005 AIR Worldwide Corporation CONFIDENTIAL 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

© 2005 AIR Worldwide Corporation CONFIDENTIAL Weather at Various Stages of Crop Development Determines Yield Phenological stages of corn growth Source: University of Illinois Extension

© 2005 AIR Worldwide Corporation CONFIDENTIAL 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

© 2005 AIR Worldwide Corporation CONFIDENTIAL AWI Computation - Overview TemperaturePrecipitationAvailable Water Capacity (Soil) + Crop Specific Data Soil Moisture Levels Run-Off Degree Days Etc. AWI Model Time Series of AWI Surface Moisture % Run Off [inches] Evapotranspiration [inches] Water Balance Model

© 2005 AIR Worldwide Corporation CONFIDENTIAL Linear Detrending  Models based on just a linear trend  Yield(t) = c 0 + m*t + 

© 2005 AIR Worldwide Corporation CONFIDENTIAL Detrending Using a Single Weather Variable  Models based on one or two weather variables  For example, June to August average temperature: Yield(t) = c 0 + m*t + c 1 *JJA(t) + 

© 2005 AIR Worldwide Corporation CONFIDENTIAL AWI Yield Model Detrending  Yield model based on an agricultural weather index  Yield(t) = c 0 + m*t + c 1 *AWI(t) + 

© 2005 AIR Worldwide Corporation CONFIDENTIAL County by County Model Comparison: Corn Linear Trend AWI Yield Model Regression Coefficient JJA Average Temperature

© 2005 AIR Worldwide Corporation CONFIDENTIAL Estimating the Risk of Obtaining Yields Below a Defined Coverage Level Yield Distributions Linear Log-linearAWI Frequency Yield (Bushels/Acre)Same coverage level, e.g 65% of mean value, for different distributions results in different probabilities (areas under curves)

© 2005 AIR Worldwide Corporation CONFIDENTIAL … and Associated Risk (Exceedance Probabilities)

© 2005 AIR Worldwide Corporation CONFIDENTIAL AWI Is a “Score” for the Overall Quality of the Growing Season

© 2005 AIR Worldwide Corporation CONFIDENTIAL Extending AWI Real-time Monitoring with Climate Forecasts In addition to historical and real time distributions, improved risk management comes from coupling AWI analysis with climate forecasts

© 2005 AIR Worldwide Corporation CONFIDENTIAL 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

© 2005 AIR Worldwide Corporation CONFIDENTIAL 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

© 2005 AIR Worldwide Corporation CONFIDENTIAL The Data Are Also Quality Controlled

© 2005 AIR Worldwide Corporation CONFIDENTIAL 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

© 2005 AIR Worldwide Corporation CONFIDENTIAL Detailed Soil Data Supplement Weather Data High resolution (~1 km) soil-specific Available Water Capacity inches Source: STATSGO, USDA

© 2005 AIR Worldwide Corporation CONFIDENTIAL 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

© 2005 AIR Worldwide Corporation CONFIDENTIAL Opportunities in Agricultural Risk Management Producers (Farmers) >200 m acres insured Agribusinesses Commodities Markets Brokers Private Reinsurers Risk Mgmt Agency Reinsures Regulates Subsidizes Crop Insurers $4 billion premium

© 2005 AIR Worldwide Corporation CONFIDENTIAL … Crop Insurers  Optimizing policy allocations to Standard Reinsurance Agreement risk sharing funds  Better planning of reserve requirements and reinsurance needs

© 2005 AIR Worldwide Corporation CONFIDENTIAL … Reinsurers  More informed underwriting decisions  Better pricing decisions  Better geographical diversification and portfolio management  More effective hedging strategies using commodity futures contracts