| Intro |
The application of machine learning in crop yield prediction has gained considerable traction, yet
uncertainties persist regarding the impact of the yield trends on these predictions and the
differences between the detrending methods. In our study, we utilized extreme gradient boosting
(XGBoost) to scrutinize the effects of no trend processing (NTP), input year as a feature (IYF), input
average yield as a feature (IAYF), input linear yield as a feature (ILYF), and the global detrending
method (GDT) on the yield prediction of maize and soybean in the Midwestern United States.
Based on our findings, compared with that of NTP, the incorporation of the yield trend as
a predictor in XGBoost significantly improved the accuracy and reduced the uncertainty of the
yield prediction. Notably, GDT emerged as a standout performer, significantly reducing the average
yield prediction error by 0.091 t/ha for soybean and 0.158 t/ha for maize with respect to NTP, and
concurrently improving the determination coefficient (R2) by 20.6% and 19.6% for soybean and
maize, respectively. Compared with IYF, IAYF, and ILYF, GDT showed substantial improvements
ranging from 3.8% to 12.7% in R2 for soybean and 3.6% to 12.7% for maize. The SHapley Additive
ExPlanations (SHAP) framework showed that the enhanced vegetation index (EVI), particularly
during the soybean podding and maize dough formation stages, played a crucial role in understanding
the variations in interannual yield variability. These findings confirmed the importance of
GDT in crop yield prediction via machine learning and could be used to facilitate future advancements
in machine learning applications for yield forecasting. |
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