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The Value of Improved ENSO Prediction to U.S. Agriculture

Abstract

The economic value of long-range weather prediction is measured by the increase in social welfare arising from the use of the prediction in economic decisionmaking. This paper describes a study of the economic value of ENSO prediction to U.S. agriculture. The interdisciplinary study involved the analysis of data and models from meteorology, plant science, and economics under a framework based on Bayesian decision analysis. The estimated annual value of perfect ENSO prediction to U.S. agriculture is $323 million.

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References

  • Adams, R. M., Bryant, K. J., McCarl, B. A., Legler, D. M., O'Brien, J. J., Solow, A. R., and Weiher, R.: 1995, ‘Value of Improved Long-Range Weather Information’, Contemp. Econ. Policy 13, 10–19.

    Google Scholar 

  • Bryant, K. J., Benson, V. W., Kiniry, J. R., Williams, J. R., and Lacewell, R. D.: 1992, ‘Simulating Corn Yield Response to Irrigation Timings: Validation of the EPIC Model’, J. Prod. Agric. 5, 237–242.

    Google Scholar 

  • Chang, C. C. and McCarl, B. A.: 1992, The Agricultural Sector Model, Texas A&M University, Department of Agricultural Economics Paper, p. 62.

  • Fajardo, D., McCarl, B., and Thompson, R.: 1981, ‘A Multicommodity Analysis of Trade Policy: The Case of Nicaraguan Agriculture’, Amer. J. Agric. Econ. 34, 23–31.

    Google Scholar 

  • Kiladis, G. N. and Diaz, H. F.: 1989, ‘Global Climate Anomalies Associated with the Extremes of the Southern Oscillation’, J. Clim. 2, 1069–1090.

    Google Scholar 

  • Kite-Powell, H. L. and Solow, A. R.: 1994, ‘A Bayesian Approach to Estimating Benefits of Improved Forecasts’, Meteorol. Appl. 1, 351–354.

    Google Scholar 

  • Kiniry, J. R., Major, D. J., Izaurralde, R. C., Williams, J. R., Gassmann, P. W., Morrison, M., Bergentine, R., and Zentner, R. P.: 1995, ‘EPIC Model Parameters for Cereal, Oilseed, and Forage Crops in the Northern Great Plains Region’, Can. J. Plant Sci. 75, 679–688.

    Google Scholar 

  • Lambert, D., McCarl, B., He, Q., Kaylen, M., Rosenthal, W., Chang, C., and Nayda, W.: 1995, ‘Uncertain Yields in Sectoral Welfare Analysis: An Application to Global Warming’, J. Agric. Appl. Econ. 27, 423–436.

    Google Scholar 

  • Latif, M., Barnett, T. P., Cane, M. A., Flugel, M., Graham, N. E., von Storch, H., Xu, J.-S., and Zebiak, S. E.: 1994, ‘A Review of ENSO Prediction Studies’, Clim. Dyn. 9, 167–180.

    Google Scholar 

  • O'Brien, J. J.: 1993, Report of the Workshop on the Economic Impact of ENSO Forecasts on the American, Australian, and Asian Continents, Florida State University, Tallahassee, FL, p. 86.

    Google Scholar 

  • Ropelewski, C. F. and Halpert, M. S.: 1986, ‘North American Precipitation and Temperature Patterns Associated with the El Niño/Southern Oscillation (ENSO)’, Mon. Wea. Rev. 114, 2352–2362.

    Google Scholar 

  • Sittel, M.: 1994a, ‘Marginal Probabilities of the Extremes of ENSO Events for Temperature and Precipitation in the Southeastern U.S.’, Florida State University, FL, COAPS Report 94–1, p. 155.

    Google Scholar 

  • Sittel, M.: 1994b, ‘Differences in Means of ENSO Extremes for Maximum Temperature and Precipitation in the U.S.’, Florida State University, FL, COAPS Report 94–2, p. 76.

    Google Scholar 

  • Steiner, J. L., Williams, J. R., and Jones, O. R.: 1987, ‘Evaluation of EPIC Using a Dryland Wheat-Sorghum-Fallow Crop Rotation’, Agron. J. 79, 732–738.

    Google Scholar 

  • Williams, J. R., Jones, C. A., Kiniry, J. R., and Spanel, D. A.: 1989, ‘The EPIC Crop Growth Model’, Trans. Amer. Soc. Agric. Eng. March, 497–511.

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Solow, A.R., Adams, R.F., Bryant, K.J. et al. The Value of Improved ENSO Prediction to U.S. Agriculture. Climatic Change 39, 47–60 (1998). https://doi.org/10.1023/A:1005342500057

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  • DOI: https://doi.org/10.1023/A:1005342500057

Keywords

  • Social Welfare
  • Decision Analysis
  • Weather Prediction
  • Economic Decisionmaking
  • Plant Science