Graduate Studies


First Advisor

Dr. Joe Luck

Date of this Version

Spring 5-5-2023

Document Type



Cross, T.A. (2023). Assessment of Sensor-Based Fertigation Management Performance in Irrigated Maize Production. [Master's thesis, University of Nebraska - Lincoln]. DigitalCommons@University of Nebraska - Lincoln.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Mechanized Systems Management, Under the Supervision of Professor Joe D. Luck. Lincoln, Nebraska: April, 2023

Copyright © 2023 Taylor A. Cross


Digital tools such as nitrogen (N) models and sensor-based decision support systems help suggest N rates and total N requirements for corn crop production have been shown to improve N use efficiency (NUE). Most recently a decision support tool, N-Time™, was developed to help inform the timing in-season of N applications through the fertigation process utilizing a sensor-based fertigation management approach (SBFM). While this tool was tested at 12 central-eastern Nebraska sites from 2019 to 2020, the first objective was to conduct additional testing and development of treatment variations to assess updated SBFM logic based on previous field trial performance results. In addition, the preliminary version of N-Time™ utilized remote-sensed imagery from unmanned aerial vehicles (UAVs) however a reliable source of commercially available imagery from UAVs has difficult to acquire and could hamper broad adoption. Thus, second objective was to test different imagery sources (i.e., satellite-based imagery) to understand if such data could also perform as well in the N-Time™ platform and provide acceptable data for broad acres. Finally, while preliminary field performance results showed high success for improvement of NUE and profitability, one site struggled with respect to these two-performance metrics over a three-year period which sparked the need for further analysis. The last objective was to determine if the indicator-block approach to assessing N stress was being negatively impacted in some way in this field resulting in lack of N applications in certain sectors. This project aims to address these three primary issues to further improve understanding of the SBFM approach embedded within the N-time™ platform, its performance in terms of NUE and profitability, and facilitate adoption of this approach to reduce negative impacts on the environment. With 100% sites showing more efficient performance for NUE and high performing treatment variations averaging $40/ac increase in profitability, SBFM treatment development continued to improve performance metrics throughout 2021 and 2022 research trials.

Advisor: Dr. Joe D. Luck