Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach

Area of Science:

• Agricultural Science
• Environmental Science
• Agronomy

Background:

• Soil nitrogen availability exhibits significant spatial and temporal variability.
• Over-application of nitrogen fertilizer in maize production leads to environmental nitrogen losses.
• Static nitrogen recommendation tools are common but may not account for site-specific conditions.

Purpose of the Study:

• Compare the efficacy of a static tool (Corn N Calculator) and a dynamic tool (Adapt-N) for predicting the economically optimum nitrogen rate (EONR) in maize production.
• Evaluate the impact of dynamic versus static nitrogen recommendations on farmer profitability and environmental nitrogen losses.
• Assess the accuracy of nitrogen recommendation tools using field trial data.

Main Methods:

• Conducted 14 N-rate trials in New York from 2011-2015.
• Compared the Corn N Calculator (CNC) with grower-estimated and default yields against the dynamic Adapt-N tool.
• Utilized field data, soil, crop, management information, and real-time weather data for Adapt-N simulations.

Main Results:

• Adapt-N significantly improved EONR prediction (RMSE = 34 kg ha) compared to CNC.
• The dynamic Adapt-N tool increased farmer profits by accounting for weather and site-specific conditions.
• Dynamic nitrogen management led to reduced application rates and lower simulated environmental nitrogen losses.

Conclusions:

• Dynamic decision tools like Adapt-N offer superior nitrogen management strategies for maize production.
• Implementing dynamic tools can enhance farm profitability while mitigating environmental nitrogen pollution.
• Site-specific and weather-adaptive nitrogen recommendations are crucial for sustainable agriculture.