On-Farm Evaluation of Remote Sensing-Based Strategy for Precision Nitrogen Management in Corn

Efficient in-season nitrogen (N) management in corn (Zea mays L.) is crucial for optimizing yield and reducing environmental losses. This dissertation evaluates the Remote Sensing and Calibration Strip-Based Precision Nitrogen Management (RS-CS-PNM) strategy, which uses a vegetation index (VI) from satellite imagery at V7–V8 to provide site-specific sidedress N recommendations. Chapter 1 introduces the challenges of in-season N management and the potential of remote sensing-based strategies to improve N use efficiency. Chapter 2 evaluates the RS-CS-PNM strategy across 18 on-farm trials in Indiana (2021–2023), comparing it to standard N practices. Results showed that the RS-CS-PNM strategy reduced total N applied in nearly half of the trials with minimal yield risk under favorable weather. Chapter 3 explores how timing and VI selection influence grain yield prediction and in-season optimum N rate estimation using the RS-CS-PNM strategy. Most satellite-derived VIs had a weak correlation with grain yield (R² = 0.02–0.31), leading to occasional misestimation of optimum N rates. Chapter 4 compares satellite with unmanned aerial vehicle (UAV) and chlorophyll meter platforms. While UAV and chlorophyll data showed stronger N responsiveness, their usability was constrained by labor, timing, and weather. Chapter 5 evaluates whether crop rotation affects in-season AONR estimation. No clear rotation effect was observed, likely due to low soil moisture conditions limiting plant response to N applied or already available soil N. Chapter 6 addresses early-season N assessment in commercial fields without calibration strips. Canopy cover fraction (CCF) from UAV imagery showed strong correlation with N uptake (R² up to 0.91), supporting its use in operationally efficient assessments. Chapter 7 summarizes key findings, acknowledges limitations, and outlines future research priorities. This work highlights the potential and challenges of scaling remote sensing–based N management strategies in real-world production systems.