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Understanding USDA Crop Forecasts

Author

Listed:
  • Vogel, Fred A.
  • Bange, Gerald A.

Abstract

Each month, the U.S. Department of Agriculture publishes statistics and related information about crop production in the United States and the world. Several USDA agencies are responsible for preparing these statistics. The methodology used by the National Agricultural Statistics Service to prepare the crop forecasts is described to give the user of these data a better understanding of the strengths and weaknesses. This is followed by a similar description of the world agriculture supply and demand estimates prepared by the World Agricultural Outlook Board.

Suggested Citation

  • Vogel, Fred A. & Bange, Gerald A., 1999. "Understanding USDA Crop Forecasts," USDA Miscellaneous 320799, United States Department of Agriculture.
    • Handle: RePEc:ags:usdami:320799
    DOI: 10.22004/ag.econ.320799
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    File URL: https://ageconsearch.umn.edu/record/320799/files/mp1554.pdf
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    Citations

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    Cited by:

    1. Kaus, Taylor T. & Mattos, Fabio L., 2017. "Too much information? How relevant are agricultural reports that provide similar information?," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258367, Agricultural and Applied Economics Association.
    2. Isengildina, Olga & Irwin, Scott H. & Good, Darrel L., 2013. "Do Big Crops Get Bigger and Small Crops Get Smaller? Further Evidence on Smoothing in U.S. Department of Agriculture Forecasts," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 45(1), pages 1-13, February.
    3. Tianyang Zhang & Ziran Li, 2022. "Can a rational expectation storage model explain the USDA ending grain stocks forecast errors?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(3), pages 313-337, March.
    4. Raghav Goyal & Michael K. Adjemian, 2023. "Information rigidities in USDA crop production forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(5), pages 1405-1425, October.
    5. Craig, Michael E., 2002. "Comparing 30 Meter Imagery from Landsat 5 and 7 for Crop Area Estimation," NASS Research Reports 234254, United States Department of Agriculture, National Agricultural Statistics Service.
    6. Goyal, Raghav & Adjemian, Michael K., 2021. "The 2019 government shutdown increased uncertainty in major agricultural commodity markets," Food Policy, Elsevier, vol. 102(C).
    7. Botto, Augusto C. & Isengildina, Olga & Irwin, Scott H. & Good, Darrel L., 2006. "Accuracy Trends and Sources of Forecast Errors in WASDE Balance Sheet Categories for Corn and Soybeans," 2006 Annual meeting, July 23-26, Long Beach, CA 21332, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. Goyal, Raghav & Adjemian, Michael K., 2022. "Information Rigidities in USDA Forecasts," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322339, Agricultural and Applied Economics Association.
    9. Arnade, Carlos Anthony & Hoffman, Linwood A., 2020. "The Impact of Public Information on Commodity Market Performance : The Response of Corn Futures to USDA Corn Production Forecasts," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304181, Agricultural and Applied Economics Association.
    10. Adjemian, Michael K. & Irwin, Scott H., 2020. "The market response to government crop news under different release regimes," Journal of Commodity Markets, Elsevier, vol. 19(C).
    11. GwanSeon Kim & Mehdi Nemati & Steven Buck & Nicholas Pates & Tyler Mark, 2020. "Recovering Forecast Distributions of Crop Composition: Method and Application to Kentucky Agriculture," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    12. Isengildina-Massa, Olga & Irwin, Scott H. & Good, Darrel L. & Gomez, Jennifer K., 2008. "The Impact of Situation and Outlook Information in Corn and Soybean Futures Markets: Evidence from WASDE Reports," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(1), pages 89-103, April.
    13. Olga Isengildina-Massa & Scott H. Irwin & Darrel L. Good & Jennifer K. Gomez, 2008. "Impact of WASDE reports on implied volatility in corn and soybean markets," Agribusiness, John Wiley & Sons, Ltd., vol. 24(4), pages 473-490.
    14. Sung No & Michael Salassi, 2009. "A Sequential Rationality Test of USDA Preliminary Price Estimates for Selected Program Crops: Rice, Soybeans, and Wheat," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(4), pages 470-482, November.
    15. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    16. repec:kap:iaecre:v:15:y:2009:i:4:p:470-482 is not listed on IDEAS
    17. repec:ags:unassr:234254 is not listed on IDEAS
    18. No, Sung Chul & Salassi, Michael E., 2008. "A Sequential Rationality and Efficiency Test of U.S. Department of Agriculture Program Crop Price Estimates: Rice, Wheat, and Soybeans," 2008 Annual Meeting, February 2-6, 2008, Dallas, Texas 6814, Southern Agricultural Economics Association.
    19. Isengildina-Massa, Olga & Irwin, Scott H. & Good, Darrel L., 2008. "Quantile Regression Methods of Estimating Confidence Intervals for WASDE Price Forecasts," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6409, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    21. Adjemian, Michael K. & Bruno, Valentina G. & Robe, Michel A., 2016. "Forward‐Looking USDA Price Forecasts," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235931, Agricultural and Applied Economics Association.
    22. MacDonald, Stephen, 2005. "A Comparison of USDA's Agricultural Export Forecasts with ARIMA-based Forecasts," MPRA Paper 70912, University Library of Munich, Germany.

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