Machine learning-enhanced crop modeling for improved nitrogen optimization in corn production

Overview

This research-driven solution addresses one of the most persistent challenges in corn production: determining the optimal rate of nitrogen fertilizer application. The current Economic Optimal Nitrogen Rate (EONR) algorithm is highly site- and year-specific, leading to inconsistent economic and environmental outcomes. By integrating field data with crop simulation models (CSM) and machine learning (ML), this approach generates a vast library of "what-if" agronomic scenarios that capture the complex interactions between cultivar genetics, weather, management, and soil properties (GEMS). The result is a more robust, data-informed nitrogen recommendation framework that balances profitability with reduced environmental impact, particularly nitrogen leaching.

Technical specifications

Key features:

• Integration of crop simulation models (CSM) with machine learning algorithms to refine nitrogen rate recommendations
• Generation of thousands of in-silico agronomic scenarios capturing GEMS variability across space and time
• Spatial analysis identifying subsoil constraints as a primary driver of reduced nitrogen use efficiency
• Adaptive nitrogen recommendations: standard application rates when soil moisture conditions are favorable, and up to 20% reduction in nitrogen under typical conditions to optimize economic and environmental tradeoffs
• On-farm validation framework linking simulation outputs to real-world field experiments
• Scalable methodology applicable across different soil types, weather conditions, and management practices

How it works:

The system uses digital soil maps to select representative field points where crop models simulate yield, net return, and nitrogen leaching under varying weather scenarios and management practices. These simulations produce extensive training data for machine learning algorithms, which then refine the EONR algorithm to deliver more accurate, site-specific nitrogen recommendations.

Technology readiness level

The approach has been validated through a field experiment involving 28 spatial points where crop simulations quantified GEMS impacts on net return and nitrogen leaching. Initial results have identified subsoil constraints as the key factor limiting nitrogen use efficiency. The next phase involves deploying the ML-enhanced EONR algorithm in on-farm maize experiments to generate additional validation data. The research team is actively seeking partnership support for ML development and integration with crop model outputs to enable broader on-farm testing and eventual deployment.