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

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  • 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.

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Bibliographic Info

Paper provided by University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics in its series Marketing and Outlook Research Reports with number 37501.

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Date of creation: Feb 2008
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Handle: RePEc:ags:uiucmr:37501

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Keywords: Agricultural Finance; Financial Economics; Research Methods/ Statistical Methods;

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References

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  1. Kruse, John & Smith, Darnell, 1994. "Yield Estimation Throughout the Growing Season," Staff General Research Papers 768, Iowa State University, Department of Economics.
  2. Colino, Evelyn V. & Irwin, Scott H., 2007. "Outlook vs. Futures: Three Decades of Evidence in Hog and Cattle Markets," 2007 Conference, April 16-17, 2007, Chicago, Illinois 37577, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  3. 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.
  4. 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.
  5. 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.
  6. 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 254-59, April.
  7. Michael P. Clements & David F. Hendry, 2002. "Modelling methodology and forecast failure," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 319-344, 06.
  8. 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 TN 9896, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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Cited by:
  1. Chung, Wonho, 2013. "Reducing the Social Cost of Federal Crop Insurance: A Role for US Government Hedging with Weather Derivatives," Journal of Rural Development/Nongchon-Gyeongje, Korea Rural Economic Institute, vol. 36(2), August.
  2. Hennessy, David A., 2009. "Crop Yield Skewness and the Normal Distribution," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 34(1), April.
  3. Sheppard, Jessica, 2009. "Are Corn and Soybean Trend Yields Changing in Illinois?," SS-AAEA Journal of Agricultural Economics, Agricultural and Applied Economics Association.

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