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University of Illinois at Urbana-Champaign

Management, microbiology, and machine learning: A systems approach to evaluating the nitrogen cycling microbiome in agricultural soils

Abstract

dc:description

Soil microorganisms are integral to the regulation of Earth’s biogeochemical cycles. Within the nitrogen (N) cycle, the soil microbiome is responsible for a multitude of competing processes, some which lead to ecosystem N retention, and others to loss. Because they determine the fate of nitrogenous fertilizer inputs, these processes and the microbes that catalyze them are of special interest in agricultural settings. This dissertation explores several different aspects of the N cycling soil microbiome, ranging from community-level impacts of conservation agriculture to the effects of phylogenetic diversity on bioinformatics clustering methods. I first demonstrate that the combination of heavy N fertilizer inputs and minimal soil disturbance in no-till agricultural systems generate unique pH dynamics that differentially influence several key N cycling functional groups. Next, I illustrate the risk of introducing Type II statistical errors to our downstream data analysis when we fail to consider the breadth of diversity represented among the functional genes we assess. I then show that dissimilatory nitrate reduction to ammonium (DNRA), a process previously assumed unimportant in terrestrial soils, can occur under both canonical highly reducing conditions as well as oxic conditions within the same soil. I close by synthesizing the body of literature reporting DNRA under oxic conditions together with our understanding of the physiology and ecology of soil N cycling microbes to propose a function for DNRA as a nitrite toxicity mitigation strategy in oxic soils. This work emphasizes the importance of using a systems approach to adequately address the emergent properties within our study systems. I conclude that N cycling microbiome research requires careful consideration of the ecological and phylogenetic context in which a given study system is situated.

Degree

thesis:*
Name thesis:degree_name
Ph.D.
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Ecol, Evol, Conservation Biol
Grantor
University of Illinois at Urbana-Champaign
Year dc:date
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Egenriether, Sada Margaret
Contributors dc:contributor
  • Yang, Wendy
  • Kent, Angela
  • Yannarell, Anthony
  • Sanford, Robert
  • Zilles, Julie

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • Copyright 2021 Sada Egenriether
Language dc:language
en

Identifiers

dc:identifier.*
Handle dc:identifier
http://hdl.handle.net/2142/113110
OAI identifier oai:identifier
oai:www.ideals.illinois.edu:2142/113110

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Egenriether, Sada Margaret. Management, microbiology, and machine learning: A systems approach to evaluating the nitrogen cycling microbiome in agricultural soils. Dissertation thesis, University of Illinois at Urbana-Champaign, 2022. http://hdl.handle.net/2142/113110