@article{Boussios:309616,
      recid = {309616},
      author = {Boussios, David and Skorbiansky, Sharon Raszap and  Maclachlan, Matthew},
      title = {Evaluating U.S. Department of Agriculture’s Long-Term  Forecasts for U.S. Harvested Area},
      address = {2021-02-10},
      number = {1962-2021-748},
      series = {ERR-285},
      pages = {28},
      month = {Feb},
      year = {2021},
      abstract = {This report examines the potential for statistical  forecast models to improve the performance of the U.S.  Department of Agriculture’s (USDA) long-term agricultural  baseline projections for the harvested area for U.S. corn,  soybeans, and wheat. After-the-fact analysis for years 1997  to 2017 reveals the baseline projections have,  historically, consistently overestimated the harvested area  of wheat and underestimated soybean area. The baseline  projections also tend to underestimate the corn area,  though to a lesser degree. Part of the difference between  the projections and realized values is likely attributable  to policy, program, weather, and other unforeseen changes  when USDA developed the projections. Still, the results of  quantitative forecast models show there may be substantial  potential for improvement on the existing methodology.  Forecasts generated using 3 econometric time-series models  did not improve performance relative to the current  baseline approach for nearer forecast horizons but improved  performance for projection horizon lengths of 8-10, 2-10,  and 4-10 years for harvested area of corn, soybeans, and  wheat, respectively, when using 1 of our statistical  measures. The forecasts generated using the econometric  models produce predictions with an average absolute  forecasting error 10 years out that is between 26 percent  to 60 percent smaller than those provided by baseline  projections. The results suggest that econometric models  offer the potential to improve the performance of  forecasting long-term trends in agricultural markets. As of  2020, USDA begun using statistical forecast models such as  these when developing its long-term agricultural  projections as complements to the existing process. USDA is  also in the process of testing these models for additional  commodities to improve the long-term projections for all  commodities.},
      url = {http://ageconsearch.umn.edu/record/309616},
      doi = {https://doi.org/10.22004/ag.econ.309616},
}