Joe D. Luck
Date of this Version
Stansell, J.S. (2021). Development and Automation of a Sensor-Based Fertigation Management Framework for Improved Nitrogen Use Efficiency and Profitability in Irrigated Row Crop Production Systems. [Master's thesis, University of Nebraska - Lincoln]. DigitalCommons@University of Nebraska - Lincoln.
Responsive nitrogen (N) management techniques for in-season N management offer the ability for measuring real-time crop N status resulting from interactions between crop genetics, environmental conditions, and management practices. Research has demonstrated that responsive N management techniques may be implemented for determining fertigation timing in corn production. A proposed sensor-based fertigation management (SBFM) framework that enabled predictive capacity within a responsive N management framework was conceptualized and implemented in 10 on-farm research trials conducted in Nebraska during 2019 and 2020. SBFM resulted in higher NUE than grower management in 94% of implementations and higher profit in 59% of implementations. At its core, SBFM involved weekly completion of six processes during the growing season: 1) image collection, 2) image processing, 3) image analysis, 4) fertigation decision determination, 5) fertigation prescription generation, and 6) fertigation application execution. In the absence of specialized software, processes 2-5 were executed using multiple software packages and manually guided computational processes. Manual completion of these complex processes was prohibitively time intensive and error prone, limiting the scalability potential of SBFM for commercial crop production. N-Time Fertigation Management System (N-Time) automated execution of weekly SBFM processes from image import through fertigation prescription export in a file format compatible with commercially available rate controllers. N-Time eliminated the need for other software packages to complete weekly processes and minimized manual input requirements. Comparative analysis demonstrated excellent agreement between manual execution and N-Time output, with less than 0.1% average difference in computed sufficiency index and 99.2% agreement on fertigation decisions. Modeling of N-Time execution speed indicated that N-Time could execute SBFM weekly process workflow for a typical quarter section in under 7.5 minutes using high-resolution UAV imagery and in approximately 3 minutes using moderate-resolution satellite imagery. Accurate and fast automated execution of SBFM weekly processes provided significant potential for adoption of SBFM in irrigated commercial crop production.
Advisor: Joe D. Luck