Enhancing precision nitrogen management in corn through integration of soil properties and remote sensing: A mechanistic approach

Sustainable intensification of agriculture requires optimizing nitrogen (N) use efficiency while maintaining crop productivity. However, the complex interactions between soil properties, N management, and crop response limit our ability to develop precise management strategies. This study aimed to (1) quantify how soil properties mediate the relationship between N inputs (fertilizer N) and crop spectral responses, and (2) develop soil-specific N response functions using remote sensing indicators. Multi-source data including soil properties, N treatments (0, regular, and luxury rates), and Sentinel-2 derived spectral indices were collected from a corn field in Ontario, Canada. Structural equation modeling and RF analysis were employed to develop soil-specific N response functions and evaluate prediction accuracy across three scenarios of increasing data integration. Cation exchange capacity (CEC) and OM strongly modulated N response patterns, with GNDVI showing the strongest correlation with yield ( r = 0.80) in high-CEC zones. Incorporating soil properties improved yield prediction accuracy by 16% compared to using remote sensing alone, while adding N management data further reduced prediction error by 21%. The luxury N rate increased yields by 4.8% compared to regular rates, but the response varied significantly with soil properties. Soil properties fundamentally modulate crop response to N inputs (fertilizer N), challenging the conventional approach of uniform N recommendations. This study provides a framework for developing soil-specific N management strategies that can enhance both productivity and environmental sustainability in spatially variable fields. • Soil properties, particularly CEC, modulate spectral expression of crop nitrogen status in remote sensing data. • Integration of soil properties with remote sensing improves yield prediction accuracy by 16%. • Machine learning reveals soil-specific nitrogen response patterns for precision management. • Framework enables targeted nitrogen recommendations based on soil-spectral relationships.