The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Random Forests for Global and Regional Crop Yield Predictions
Open accessAbstract
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 predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.
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Cites
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Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
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Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
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Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116437108 · Cited by 7,692
Random forests for classification in ecology
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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Cited by
Weiskopf, S. R., Rubenstein, M. A., Crozier, L. G., Gaichas, S., Griffis, R., Halofsky, J. E., Hyde, K. J. W., Morelli, T. L., Morisette, J. T., Muñoz, R. C., et al. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137782 · Cited by 1,262
A CNN-RNN framework for crop yield prediction
Khaki, S., Wang, L., & Archontoulis, S. V. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01750 · Cited by 722
Coupling machine learning and crop modeling improves crop yield prediction in the US corn belt
Shahhosseini, M., Hu, G., Huber, I., & Archontoulis, S. V. (2021) · Scientific Reports · doi:10.1038/s41598-020-80820-1 · Cited by 452
Machine learning for large-scale crop yield forecasting
Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C., & Athanasiadis, I. N. (2021) · Agricultural Systems · doi:10.1016/j.agsy.2020.103016 · Cited by 357
High resolution wheat yield mapping using Sentinel-2
Hunt, M. L., Blackburn, G. A., Carrasco, L., Redhead, J. W., & Rowland, C. S. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111410 · Cited by 253
Metadata
- Authors
- Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., Timlin, D. J., Shim, K.-M., Gerber, J. S., Reddy, V. R., & Kim, S.-H.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-06-03
- Volume
- 11
- Issue
- 6
- Pages
- e0156571
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2416782259
- Cited by
- 893
- References
- 43
- FWCI
- 21.24
- Type
- journal article
- Language
- en
- Open access
- gold
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- Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., Timlin, D. J., Shim, K.-M., Gerber, J. S., Reddy, V. R., & Kim, S.-H. (2016). Random Forests for Global and Regional Crop Yield Predictions. PLOS ONE. https://doi.org/10.1371/journal.pone.0156571
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