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Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning.

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China faces significant soybean import reliance. A new high-resolution dataset, ChinaSoyYield1km, aids agricultural policy by detailing soybean yield variations from 2001-2020.

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Area of Science:

  • Agricultural Science
  • Geospatial Data Science
  • Environmental Science

Background:

  • China's substantial soybean demand necessitates imports due to insufficient domestic production.
  • Optimizing domestic planting structures and agricultural policies requires accurate, high-resolution yield data.

Purpose of the Study:

  • To develop a high-resolution (1-km) annual soybean yield dataset for China (2001-2020), named ChinaSoyYield1km.
  • To provide a data-driven tool for informing agricultural policy and optimizing soybean cultivation strategies.

Main Methods:

  • Utilized ensemble learning algorithms and spatial decomposition techniques.
  • Integrated multi-source data including climate variables, remote sensing imagery, soil properties, agricultural management, and official yield records.
  • Achieved a 1-km spatial resolution for the annual soybean yield dataset.

Main Results:

  • The ChinaSoyYield1km dataset captures over 50% of county-scale yield variability.
  • Demonstrated superior accuracy compared to existing datasets, with Root Mean Square Error (RMSE) reductions of 0.18–0.60 t/ha.
  • Provides a nuanced understanding of factors influencing soybean yield.

Conclusions:

  • The ChinaSoyYield1km dataset is a valuable resource for enhancing agricultural studies and policy-making.
  • Facilitates improved planning and decision-making in soybean cultivation for the scientific community and government.
  • Contributes to addressing China's soybean supply-demand imbalance through data-informed strategies.