N-Time: An Automated Decision Support System for Sensor-Based Fertigation Management

Jackson S. Stansell, J. D. Luck, Tyler G. Smith, Hongfeng Yu, Daran R. Rudnick, Brian T. Krienke

Research output: Contribution to journalArticlepeer-review

Abstract

N-Time Fertigation Management System (N-Time) is a newly developed automated decision support system (DSS) that analyzes multispectral imagery and applies a nitrogen (N) recommendation algorithm to produce a binary action recommendation and accompanying prescription for an imminent fertigation application. Fertigation, the practice of applying fertilizer through an irrigation system, in irrigated grain maize production has traditionally been managed non- or semi-quantitatively using factors such as crop growth stage, grower intuition, or proportions of a total applied N goal to determine rate and timing of fertigation applications. Sensor-based fertigation management (SBFM), a management framework for fertigation of grain maize recently developed at the University of Nebraska–Lincoln, utilizes multispectral imagery to inform the number and timing of fertigation applications throughout the growing season. The core of the framework involves a frequent (i.e. weekly) monitoring and recommendation cycle for which data analysis, recommendation determination, and prescription generation. N-Time automates computational operations and facilitates workflow execution for these processes, reducing the time requirement per field from two hours down to 7.4 minutes for 12 cm/pixel resolution multispectral imagery (i.e. UAV captured) and 3.1 min for 3 m/pixel multispectral imagery (i.e. satellite captured). N-Time also demonstrated greater than 99% agreement with computational outputs produced using non-specialized software packages. N-Time was used to implement SBFM in research trials on productionscale maize fields in Nebraska during 2020 and 2021 and used in pilot full-field implementations on production-scale maize fields in Nebraska in 2021. Approximately 800 ha were under management between the two seasons. Results from on-farm research trials on approximately 320 ha in 2019 and 2020 demonstrated that SBFM produced higher N use efficiency than the grower’s best management in 94% of implementations and higher profitability than the grower’s best management in 59% of implementations.

Original languageEnglish (US)
Pages (from-to)259-264
Number of pages6
JournalVDI Berichte
Volume2395
DOIs
StatePublished - 2022

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint

Dive into the research topics of 'N-Time: An Automated Decision Support System for Sensor-Based Fertigation Management'. Together they form a unique fingerprint.

Cite this