A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Random Forests for Global and Regional Crop Yield Predictions
2016
PLoS ONE
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
doi:10.1371/journal.pone.0156571
pmid:27257967
pmcid:PMC4892571
fatcat:33kuo57jtjh3zlmgjxe6uipwau