[Xinhua] Scientists Improve Crop Yield Forecast by Machine Learning


 BEIJING, April 20 (Xinhua) -- Winter wheat yields in north China can be better predicted by a new hybrid forecast model powered by machine learning, according to a study by the Institute of Atmospheric Physics under the Chinese Academy of Sciences.
China is the world's largest wheat producer and consumer, as such, accurate yield forecasting is a top concern for researchers. The new hybrid model proposes an approach that combines machine learning and dynamical atmospheric prediction.
Developed by Chinese and American scientists, the new model was applied to north China over the subseasonal-to-seasonal period.
As an emerging statistical model, machine learning can better describe the non-linear relationship between input and forecast and has obvious advantages in yield forecast compared with a linear model.
The results of the study indicate that the hybrid model generally outperforms conventional models, with one metric that tells how far the prediction values are from the real values, decreasing by 30 percent to 55 percent compared with conventional models.
The results also showed that the new model achieved the best prediction result three or four months in advance of the harvest season.
The study demonstrates that the coupling of machine learning and dynamical atmospheric prediction is a useful tool for yield forecast, which could provide support to agricultural practitioners, policy-makers, and agricultural insurers.
The study was published in the journals Remote Sensing, Weather and Forecasting, and Atmospheric and Oceanic Science Letters
Cao, J., Wang, H., Li, J., Tian, Q., Niyogi, D. Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction. Remote Sensing. 2022, 14, 1707. https://doi.org/10.3390/rs14071707
Li, J., Bao, Q., Liu, Y., Wu, G., Wang, L., He, B., Wang, X., Yang, J., Wu, X. and Shen, Z. Dynamical seasonal prediction of tropical cyclone activity using the FGOALS-f2 ensemble prediction system. Weather and Forecasting, 2021, 36(5),1759-1778, https://doi.org/10.1175/WAF-D-20-0189.1

Li, S., Li, J., Yang, J., Bao, Q., Liu, Y. and Shen, Z. Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system. Atmospheric and Oceanic Science Letters, 2021, p.100116, https://doi.org/10.1016/j.aosl.2021.100116
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