MAIZE NITROGEN MANAGEMENT USING REACTIVE SENSOR AND PROACTIVE MAIZE-N MODEL VIA FERTIGATION

Authors

Mohammed A. Naser, Al-Muthanna University & University of Nebraska-LincolnFollow

First Advisor

Richard B. Ferguson

Date of this Version

4-2021

Document Type

Dissertation

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Agronomy and Horticulture (Soil and Water Sciences), Under the Supervision of Professor Richard B. Ferguson. Lincoln, Nebraska: April, 2021

Copyright 2021 Mohammed Abdulridha Naser

Abstract

Applying a portion of total nitrogen (N) during the growing season has the potential to improve nitrogen use efficiency (NUE) by achieving greater synchrony between N supply and crop N demand, allowing for responsive adjustments to actual field conditions. Three studies from 2017-2019 evaluated using reactive sensor and proactive Maize-N model for determining in-season N requirements via fertigation in corn. The first study evaluated the integration of reactive sensor and proactive Maize-N model for determining the timing and rate of in-season N via fertigation. Overall, reactive and proactive fertigation treatments reduced total N applied by 35 to 65 kg N ha^-1 thus increasing NUE and profit compared to the University of Nebraska-Lincoln (UNL) algorithm and Holland and Schepers (H-S) algorithm treatments with no significant difference in yields. The second study evaluated Maize-N model for predicting economic optimum N rate (EONR), N uptake, and soil nitrate-N. Overall, Maize-N underestimated N rate recommendations by 47 kg N ha^-1 compared to the calculated actual EONR with no significant differences in yield. However, the Maize-N EONR reduced profit by 33.5 $ ha^-1. Maize-N underestimated N uptake by 24.7 kg N ha^-1. Additionally, Maize-N overestimated soil nitrate-N, but a calibrated model improved agreement between predicted and observed soil nitrate-N by 67.5%. The third study evaluated the performance of active and passive crop canopy sensors compared to the SPAD meter in terms of assessing in-season corn N status. Reasonable correlation at any growth stage did not always lead to the same fertigation decision, as the same decision for two sensors can be achieved if both sensor's SI values are greater than 0.95 thresholds or both less than or equal to 0.95. The overall fertigation decisions that matched between SPAD SI and active sensor SI across all growth stages and site years was 72%, and 37 to 48% between SPAD SI and passive sensor SI. Crop canopy sensor and Maize-N model integration will likely result in more accurate N rate and timing decisions.

Advisor: Richard B. Ferguson