Assessing Crop Insurance Risk Using An Agricultural Weather Index

1. Assessing Crop Insurance Risk Using An Agricultural Weather Index CAS Seminar on Reinsurance June 6-7, 2005 S. Ming Lee www.air-worldwide.com

2. Challenges in Agricultural Risk Assessment • Every risk assessment starts with evaluation of historic data, i.e. crop yields, weather parameters, ….

3. 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?

4. Typical Detrending Approach Technological Improvements Weather Effect Observed Yields

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

6. Trend in Corn Yield in Nemaha County, Nebraska Time Window: 1974-2003 • 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

7. Trend in Corn Yield in Nemaha County, Nebraska Time Window: 1982-2003 • A shorter time window results in an almost horizontal slope

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

9. Proposed Weather-based Detrending Method Technological Improvements Weather Effect Observed Yields

10. Proposed De-Trending Method Yield(t) = c0 + m*t + c1*AWI(t) +e 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 e ………………….. residual error This equation is also called the AWI yield model

11. 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

12. Weather at Various Stages of Crop Development Determines Yield Phenological stages of corn growth Source: University of Illinois Extension

13. 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

14. AWI Computation - Overview Run Off [inches] Surface Moisture % Evapotranspiration [inches] Time Series of AWI AWI Model Crop Specific Data Soil Moisture Levels Run-Off Degree Days Etc. Temperature Precipitation Available Water Capacity (Soil) Water Balance Model +

15. Linear Detrending • Models based on just a linear trend • Yield(t) = c0 + m*t + e

16. 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) + e

17. AWI Yield Model Detrending • Yield model based on an agricultural weather index • Yield(t) = c0 + m*t + c1*AWI(t) +e

18. County by County Model Comparison: Corn Linear Trend JJA Average Temperature AWI Yield Model Regression Coefficient

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

20. … and Associated Risk (Exceedance Probabilities)

21. AWI Is a “Score” for the Overall Quality of the Growing Season 2002 1992 1993 1988

22. 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

23. 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

24. AIR Collects and Processes Over Ten Gigabytes of Weather Data Daily for Modeling and Analysis • Weather observations • Radar observations • Severe weather reports • Short-term climate data • Long-term climate data • Numerical forecast information NOAA Port National Center for Environmental Prediction National Climatic Data Center

25. The Data Are Also Quality Controlled

26. 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 • Weather observations • Radar observations • Severe weather reports • Short-term climate data • Long-term climate data • Numerical forecast information NOAA Port National Center for Environmental Prediction National Climatic Data Center Numerical weather prediction Climate data archive Statistical analysis & modeling

27. Detailed Soil Data Supplement Weather Data High resolution (~1 km) soil-specific Available Water Capacity inches Source: STATSGO, USDA

28. 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

29. Opportunities in Agricultural Risk Management Brokers Private Reinsurers Crop Insurers $4 billion premium Risk Mgmt Agency • Reinsures • Regulates • Subsidizes Commodities Markets Agribusinesses Producers (Farmers) >200 m acres insured

30. … Crop Insurers • Optimizing policy allocations to Standard Reinsurance Agreement risk sharing funds • Better planning of reserve requirements and reinsurance needs

31. … Reinsurers • More informed underwriting decisions • Better pricing decisions • Better geographical diversification and portfolio management • More effective hedging strategies using commodity futures contracts