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Weather, Technology, and Corn and Soybean Yields in the U.S. Corn Belt

Author

Listed:
  • Tannura, Michael A.
  • Irwin, Scott H.
  • Good, Darrel L.

Abstract

The purpose of this study was to investigate the relationship between weather, technology, and corn and soybean yields in the U.S. Corn Belt. Corn and soybean yields, monthly temperature, and monthly precipitation observations were collected over 1960 through 2006 for Illinois, Indiana, and Iowa. Multiple regression models were developed based on specifications found in studies by Thompson (1962 1963 1969 1970 1985 1986 1988). Estimated models explained at least 94% and 89% of the variation in corn and soybean yields for each state, respectively. Analysis of the regression results showed that corn yields were particularly affected by technology, the magnitude of precipitation during June and July, and the magnitude of temperatures during July and August. The effect of temperatures during May and June appeared to be minimal. Soybean yields were most affected by technology and the magnitude of precipitation during June through August (and especially during August). Structural change tests were performed on each model to test for changes in any of the regression model parameters. Some breakpoints were identified, but were difficult to explain since the results were not consistent across states and crops. Additional tests for structural change were directed specifically at the trend variable in corn models. The tests did not indicate a notable change in the technology trend for corn since the mid-1990s. Corn and soybean yield forecasts from the regression models on June 1 and July 1 were no more accurate then trend yield forecasts. Regression model forecasts for corn improved on August 1, while model forecasts for soybeans improved by September 1. U.S. Department of Agriculture (USDA) corn and soybean forecasts were always more accurate than those from the regression models. Nonetheless, encompassing tests showed that the accuracy of USDA yield forecasts could be significantly improved by the information contained in regression model forecasts. Across states and forecast months, combining regression model forecasts with USDA forecasts improved accuracy an average of 10% for corn and 6% for soybeans. In sum, this research provided strong evidence that precipitation, temperature, and a linear time trend to represent technological improvement explained all but a small portion of the variation in corn and soybean yields in the U.S. Corn Belt. An especially important finding was that relatively benign weather for the development of corn since the mid-1990s should not be discounted as an explanation for seemingly “high” yields. The potential impact of this finding on the agricultural sector is noteworthy. Trend yield forecasts based on perceptions of a rapid increase in technology may eventually lead to poor forecasts. Unfavorable weather in the future may lead to unexpectedly low corn yields that leave producers, market participants, and policymakers wondering how such low yields could have occurred despite technological improvements.

Suggested Citation

  • Tannura, Michael A. & Irwin, Scott H. & Good, Darrel L., 2008. "Weather, Technology, and Corn and Soybean Yields in the U.S. Corn Belt," Marketing and Outlook Research Reports 37501, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    • Handle: RePEc:ags:uiucmr:37501
    DOI: 10.22004/ag.econ.37501
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    References listed on IDEAS

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    1. Wolfram Schlenker & Michael J. Roberts, 2006. "Nonlinear Effects of Weather on Corn Yields," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(3), pages 391-398.
    2. James W. Mjelde & Harvey S.J. Hill & John F. Griffiths, 1998. "A Review of Current Evidence on Climate Forecasts and Their Economic Effects in Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(5), pages 1089-1095.
    3. Michael P. Clements & David F. Hendry, 2002. "Modelling methodology and forecast failure," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 319-344, June.
    4. John Kruse & Darnell B. Smith, 1994. "Yield Estimation Throughout the Growing Season," Center for Agricultural and Rural Development (CARD) Publications 94-tr29, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    5. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    6. Kruse, John & Smith, Darnell, 1994. "Yield Estimation Throughout the Growing Season," Staff General Research Papers Archive 768, Iowa State University, Department of Economics.
    7. Lynch, Alee L. & Holt, Matthew T. & Gray, Allan W., 2007. "Modeling Technical Change in Midwest Corn Yields, 1895-2005: A Time Varying-Regression Approach," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon 9896, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. Davis, Floyd E. & Harrell, George D., 1942. "Relation of Weather and Its Distribution to Corn Yields," Technical Bulletins 169143, United States Department of Agriculture, Economic Research Service.
    9. Lawrence H. Shaw, 1964. "The Effect of Weather on Agricultural Output: A Look at Methodology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 46(1), pages 218-230.
    10. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    11. Good, Darrel L. & Irwin, Scott H., 2005. "Understanding USDA Corn and Soybean Production Forecasts: Methods, Performance and Market Impacts over 1970 - 2004," AgMAS Project Research Reports 14785, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    12. Good, Darrel L. & Irwin, Scott H., 2006. "Understanding USDA Corn and Soybean Production Forecasts: Methods, Performance and Market Impacts over 1970 - 2005," AgMAS Project Research Reports 37514, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    13. Evelyn V. Colino & Scott H. Irwin, 2010. "Outlook vs. Futures: Three Decades of Evidence in Hog and Cattle Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 1-15.
    14. John Kruse & Darnell B. Smith, 1994. "Yield Estimation Throughout the Growing Season," Food and Agricultural Policy Research Institute (FAPRI) Publications (archive only) 94-tr29, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    15. Robert K. Kaufmann & Seth E. Snell, 1997. "A Biophysical Model of Corn Yield: Integrating Climatic and Social Determinants," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 178-190.
    16. Hill, Harvey S.J. & Mjelde, James W., 2002. "Challenges and Opportunities Provided by Seasonal Climate Forecasts: A Literature Review," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 34(3), pages 603-632, December.
    17. Teigen, Lloyd D. & Thomas, Milton, Jr., 1995. "Weather and Yield, 1950-94: Relationships, Distributions, and Data," Staff Reports 278796, United States Department of Agriculture, Economic Research Service.
    18. Hill, Harvey S.J. & Mjelde, James W., 2002. "Challenges And Opportunities Provided By Seasonal Climate Forecasts: A Literature Review," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 34(3), pages 1-30, December.
    19. John P. Doll, 1967. "An Analytical Technique for Estimating Weather Indexes from Meteorological Measurements," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 49(1_Part_I), pages 79-88.
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