Extending the above- and belowground barley phenotype

Abstract

Plant breeding is crucial for agricultural productivity by improving crop performance and resilience to deficient growing conditions. Soil microorganisms, particularly those in the rhizosphere, play a pivotal role in nutrient cycling, pathogen suppression, and overall plant performance. The composition and dynamics of these microbial communities are influenced by both plant genotype and environmental factors including soil chemistry, climate, and water availability. While plant-associated microbiomes vary across environments, the contribution of environment and plant genetics in microbiome recruitment, particularly across geographic regions, is not well understood. This research encompasses a large-scale, multi-location, and multi-year study across seven field trials to assess plant performance and microbial community composition in the barley rhizosphere. A core focus is understanding how plant genotypes and environmental factors such as soil chemistry and growing location influence microbial assembly. Results indicated that rhizosphere microbial communities are shaped by both plant genetics and environmental conditions, with distinct bacterial and fungal community patterns emerging in different environments. Specifically, while bacterial diversity remained largely consistent across barley-growing regions, fungal communities exhibited greater variability and were more strongly influenced by location-year and climatic factors. A Genome-wide association study (GWAS) identified many quantitative trait loci (QTL) associated with microbial traits, with some loci co- localizing with agronomic traits. This suggests potential genetic mechanisms underlying the interaction between plants and their microbiomes. These findings highlight the potential for genetic selection to optimize rhizosphere microbiomes, potentially improving crop productivity and resilience in variable environments. In addition to field studies, this dissertation introduces a novel, open-source workflow for the analysis of unoccupied aerial system (UAS) imagery in agriculture. This workflow uses a semi-automated approach for plant classification, plot delineation, spectral index calculation, and data extraction using QGIS software. By testing the workflow on barley UAS data collected over multiple flights, we show that spectral indices such as the Visible Atmospheric Resistant Index (VARI) correlate strongly with ground truth data, demonstrating the workflow's efficacy in agricultural monitoring. These studies provide a comprehensive survey of barley microbial community composition, offering valuable insights into improving agricultural sustainability through plant-microbe optimization and precision agriculture.