Abstract
Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.
Highlights
Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions
The proposed model achieved a less than 8% relative root mean square error (RRMSE) for both corn and soybean in all three states, outperforming all other machine learning models in the case study, and produced explainable insights
More comparison in terms of the relative root mean square errors (RMSE) (RRMSE), the relative squared error (RSE), the mean absolute error (MAE), the relative absolute error (RAE), and the coefficient of determination ( R2 ) of nine models are reported in Appendix 2
Summary
Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Machine learning models have been successfully used for crop yield prediction, including stepwise multiple linear regression[7], random forest[8], neural networks[9,10,11], convolutional neural networks[12], recurrent neural networks[13], weighted histograms r egression[14], interaction based m odel[15], and association rule mining and decision tree[16] Most of these studies were based on environmental and managerial variables only, due to lack of publicly available genotype data at the state or national scale. Due to the black-box nature of these models, prediction accuracy is sensitive to model structure and parameter calibration, and it can prove difficult to explain why predictions are accurate or inaccurate Crop models are another type of nonlinear models, including A PSIM20, DSSAT21,22, RZWQM23, and SWAP/ WOFOST24, which build upon the physiological understanding of plant and soil processes to develop biologically meaningful non-linear equations to predict crop yield and other phenotypes. Our robustness definition allowed the algorithm to strike a balance between prediction accuracy and generalizability
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