Optimizing nitrogen topdressing for winter wheat by coupling remote sensing data with the DSSAT model.

Area of Science:

• Agricultural Science
• Environmental Science
• Data Science

Background:

• Excessive nitrogen fertilization leads to environmental pollution, reduced water and soil quality, increased costs, and decreased agricultural sustainability.
• Optimizing nitrogen (N) application is crucial for sustainable agriculture and economic viability.

Purpose of the Study:

• To develop and evaluate a two-step data assimilation strategy for precise nitrogen topdressing recommendations for winter wheat.
• To balance crop yield and production costs through optimized N fertilization.

Main Methods:

• Established a data assimilation system by minimizing discrepancies between remote sensing-derived and crop model-simulated aboveground dry biomass (AGB) using particle swarm optimization.
• Constructed target yields and N economic return curves using the DSSAT model for optimized N fertilization recommendations.

Main Results:

• Developed a robust AGB monitoring model with high determination coefficients (R²=0.94, R²=0.82).
• Demonstrated high data assimilation stability for AGB and yield, with low normalized root mean square errors (nRMSE).
• Found that data assimilation-based N fertilization recommendations reduced application by 6.69%-34.08% compared to high-yield level applications.

Conclusions:

• The developed data assimilation strategy effectively optimizes N fertilization for winter wheat.
• This approach balances crop productivity and economic returns, contributing to agricultural sustainability.
• The strategy offers a viable solution to mitigate environmental pollution associated with excessive fertilization.