Agricultural management effects on root-associated microbiome assemblage and implications for soil and plant health

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2021-12
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Lee, Conard
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Halverson, Larry
Howe, Adina
Whitham, Steven
Leandro, Leonor
Schmitz-Esser, Stephan
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Microbiology
Abstract
Microbes, plants, and biogeochemical processes all contribute to creating the living ecosystem we call soil. A better understanding of each and how they interact is crucial for understanding benefits associated with agricultural practices. This thesis explores questions about how plant selection and management influence root-associated microbiome assemblages, and how these interactions may contribute to soil and plant health. The premise is that management influences the soil microbial seedbank from which rhizosphere and subsequently endosphere colonists are selected, which is likely influenced by plant species and developmental stage. For these studies, we capitalized on the Marsden long-term cropping systems experimental site at Iowa State University. Cropping systems include a conventional 2-year corn-soybean rotation with recommended synthetic nitrogen fertilizer levels and two diversified systems a 3-year (corn-soybean-oat/red clover) and 4-yr (corn-soybean-oat/alfalfa-alfalfa) rotation fertilized with composted manure and synthetic N side dress as needed. We profiled soil, rhizosphere, and endosphere microbial communities via amplicon sequencing to explore how management influences soybean and maize root microbiome assembly. The first study was a rhizotron experiment where we explored how cropping system influenced root microbiome assembly at a maize developmental stage when it is poised for rapid N uptake, as well as nitrogen cycling dynamics, including measuring nitrification potential, gross rates of nitrification, and abundance of ammonia oxidizing-bacteria and -archaea. This study revealed that nitrification, ammonia-oxidizer composition, and microbial community composition differed significantly by cropping system. In a subsequent study of the same microbiome data, two linear and two non-linear machine learning approaches were explored for their ability to model nitrification and to identify nitrifier biomarkers predictive of nitrification in the soil, the rhizosphere, or a cropping system. Models were trained with nitrifier composition data and other metadata. Our results show that nitrification can be predicted from nitrifier microbiome composition data and the results can be as robust as traditional linear regression. This approach offers insight into the potential contribution of specific clades to nitrification and that inclusion of ammonia-oxidizer abundance did not substantively improve model performance. Lastly, we investigated the influence of management on the soybean root-associated microbial communities over plant development to determine relationships between community structure and Fusarium virguliforme (Fv; the causative agent of soybean sudden death syndrome) density in soil observed in the diversified system. Soybean root microbiomes differed between conventional and diversified systems, as well as between the 3- and 4-yr rotations. Several taxa experienced significant shifts in abundance, including greater abundance of Glomeromycota in the diversified system. Total Fv and fungal abundance measured by qPCR revealed that root-associated Fv density decreased with increasing length of rotation despite the soils harboring similar levels. We show a strong correlation between Fv abundance and microbial community composition that occurs in a cropping system-specific manner, suggestive of potential soil microbiome influences on Fv colonization of soybean roots. This work shows that increasing organic matter inputs (manure, different plant species rhizodeposits) exemplified in diversified cropping systems potentially increases microbial functional biodiversity within the microbial “seed bank”. As a consequence, rhizosphere microbial communities may be more resilient to perturbance, and perhaps to propagation of Fv in the root environment, and with nitrogen cycling dynamics in diversified cropping systems.
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