Egbuikwem et al. (2025) Optimizing in-season nitrogen management through satellite-guided fertigation in field-scale maize production

Identification

• Journal: Computers and Electronics in Agriculture
• Year: 2025
• Date: 2025-12-05
• Authors: Precious N. Egbuikwem, Derek M. Heeren, Guillermo R. Balboa, Laila A. Puntel, Yeyin Shi, Daran R. Rudnick, Joe D. Luck, Eric Wilkening, Kuan Zhang
• DOI: 10.1016/j.compag.2025.111275

Research Groups

• Department of Biological Systems Engineering, University of Nebraska-Lincoln, United States
• Department of Agronomy and Horticulture, University of Nebraska-Lincoln, United States
• Department of Biological and Agricultural Engineering, Kansas State University, United States
• Agrifac USA, Seward, Nebraska, United States
• Department of Electrical and Computer Engineering, University of Nebraska–Lincoln, United States
• Syngenta Group Basel, Basel, Switzerland

Short Summary

This study developed and evaluated a practical framework using PlanetScope satellite imagery and the Holland–Schepers sensor algorithm to guide in-season, variable-rate fertigation in maize, demonstrating significant improvements in nitrogen use efficiency and comparable yields with reduced nitrogen input.

Objective

• To develop and evaluate a practical, remote sensing-based decision-support framework for in-season, site-specific nitrogen management in commercial maize production using PlanetScope satellite imagery to guide variable-rate fertigation.

Study Configuration

• Spatial Scale: Field-scale maize production (commercial fields).
• Temporal Scale: 2023 and 2024 growing seasons.

Methodology and Data

• Models used: Holland–Schepers sensor algorithm (contextualized with PlanetScope imagery), biologically informed rule-based stress-classification framework (using Normalized Difference Red Edge (NDRE) and soil water depletion (SWD)).
• Data sources: PlanetScope (PS) satellite imagery (NDRE), UAV benchmarks (MicaSense Altum and RedEdge-3 sensors for NDRE), soil water depletion (SWD) measurements, retrospective yield and management data.

Main Results

• In 2024, PlanetScope-guided fertigation achieved yields statistically comparable to the Full-N treatment while reducing total nitrogen input by 23 %.
• Significant improvements in nitrogen use efficiency (NUE) were observed, with the Fertigation treatment outperforming Full-N by 12 % in agronomic efficiency (AE) and 26 % in partial factor productivity of N (PFPN).
• Satellite-derived Sufficiency Index (SI) values (from NDRE) were strongly correlated with UAV benchmarks (ρ = 0.82–0.95).
• PlanetScope consistently overestimated NDRE relative to UAV data, particularly under nitrogen-deficient conditions, underscoring the need for local calibration and bias correction.
• A biologically informed, rule-based stress-classification framework was developed to differentiate nitrogen stress from water stress using NDRE and soil water depletion (SWD).
• Full-Yield plots achieved approximately 12,000 kg/ha at NDRE = 0.78 and SWD = 64 mm.
• The resulting NDRE–SWD–yield patterns highlight the feasibility of disentangling stress types under commercial field conditions.

Contributions

• Development of a practical, scalable, and data-driven framework for satellite-guided variable-rate fertigation using PlanetScope imagery, addressing a major barrier to broad adoption of in-season site-specific nitrogen management.
• Demonstrated significant improvements in nitrogen use efficiency and reduced nitrogen input while maintaining comparable yields in commercial maize production.
• Introduction of a biologically informed, rule-based stress-classification framework to differentiate nitrogen and water stress using NDRE and soil water depletion.
• Provides actionable guidance for implementing satellite-guided fertigation at commercial scale and a reference for future research and extension programs.

Funding

Not explicitly stated in the provided text.

Citation

@article{Egbuikwem2025Optimizing,
  author = {Egbuikwem, Precious N. and Heeren, Derek M. and Balboa, Guillermo R. and Puntel, Laila A. and Shi, Yeyin and Rudnick, Daran R. and Luck, Joe D. and Wilkening, Eric and Zhang, Kuan},
  title = {Optimizing in-season nitrogen management through satellite-guided fertigation in field-scale maize production},
  journal = {Computers and Electronics in Agriculture},
  year = {2025},
  doi = {10.1016/j.compag.2025.111275},
  url = {https://doi.org/10.1016/j.compag.2025.111275}
}