A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Area of Science:

• Agricultural Science
• Agronomy
• Crop Modeling

Background:

• Traditional crop models assess nitrogen (N) impact post-harvest, limiting farmer’s ability for in-season adjustments.
• Hypothesis: Utilizing crop models for in-season forecasting can enhance current N management decisions.

Purpose of the Study:

• Evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at various growth stages.
• Compare the efficacy of analogous historical weather years versus a long-term dataset for forecast accuracy.
• Quantify the added value of crop models in predicting annual EONR and yields against benchmarks.

Main Methods:

• Employed the Agricultural Production Systems sIMulator (APSIM) model, calibrated with long-term experimental data from central Iowa.
• Integrated actual weather data up to specific crop stages with historical weather data for forecasting.
• Assessed predictions at four key phenological stages: planting, 6th leaf, 12th leaf, and silking.

Main Results:

• Corn yield predictions at planting time (R² = 0.77) closely matched observed yields at maturity (R² = 0.81).
• Economic optimum N rate (EONR) predictions were more accurate in continuous corn systems (RRMSE 25%) than in soybean-corn rotations (RRMSE 45%).
• The 35-year historical weather dataset improved forecast accuracy compared to selected analogous years (average 3% lower RRMSE).

Conclusions:

• The APSIM model, used as a forecasting tool, can enhance the year-to-year predictability of corn yields and optimal nitrogen application rates.
• While promising, further refinements in modeling and protocols are necessary for greater forecast accuracy, particularly for extreme weather events.
• In-season N management decisions can be improved by integrating crop model forecasts into farming practices.