Random Forests for Global and Regional Crop Yield Predictions

By Jig Han Jeong, Jonathan P. Resop, N. Mueller, D. Fleisher, Kyungdahm Yun, E. Butler, Dennis Timlin, K. Shim, James Gerber, Vangimalla R. Reddy and 1 more. PLoS ONE, 2016

715 citations recorded by the source corpus.

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.

Subfields: Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics, Plant Science

View Paper: https://www.semanticscholar.org/paper/8d25708abc05c1a7f4c500212e4957396c7a49e3

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Citations in the corpus

A CNN-RNN Framework for Crop Yield Prediction (2019, methodology): “Jeong et al. (2016) applied random forest and multiple linear regression for yield prediction of wheat, maize, and potato.”
The effects of climate extremes on global agricultural yields (2019, methodology): “The Random Forest algorithm allows for an investigation of relationships between climate variables and yield anomalies by providing the relative variable importance and the functional relationship between each predictor and the target variable (Jeong et al 2016).”
Machine learning for large-scale crop yield forecasting (2020, methodology): “Previous studies (e.g. Shahhosseini et al. 2019; Cai et al. 2019; You et al. 2017; Jeong et al. 2016) have demonstrated that machine learning can play an important role in crop yield prediction and the same was confirmed by our results.”