Random Forests for Global and Regional Crop Yield Predictions

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of…

• NS
  National Science Foundation
• DA
  David and Lucile Packard Foundation
• UD
  U.S. Department of Agriculture

  Awards: 58-1265-1-074, 2016-67012-25208
• UO
  University of Washington

  Award: 58-1265-1-074
• SR
  Sight Research UK

  Award: NE/M021327/1
• RD
  Rural Development Administration

  Award: PJ01000707
• DF
  Directorate for Geosciences

  Award: 1521210
• NI
  National Institute of Food and Agriculture

  Awards: 2011-68004-30057, 2016-67012-25208
• AR
  Agricultural Research Service
• NE
  Natural Environment Research Council

  Award: NE/M021327/1