Agronomy and Horticulture Department

 

First Advisor

George L. Graef

Date of this Version

Spring 2-2020

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 (Plant Breeding and Genetics), Under the Supervision of Professor George L. Graef. Lincoln, Nebraska: February 2020

Copyright 2020 Shawn Jenkins

Abstract

The rising demand for soybean [Glycine Max (L.) Merrill] taken in consideration with current climatic trends accentuates the importance of improving soybean seed yield response per unit water (WP). To further our understanding of the quantitative WP trait, a multi-omic approach was implemented for improved trait identification and predictive modeling opportunities. Through the evaluation of two recombinant inbred line populations jointly totaling 439 lines subjected to contrasting irrigation treatments, informative agronomic, phenomic, and genomic associations were identified. Across both populations, relationships were identified between lodging at maturity (r = -0.58, H = 0.86), canopy to air temperature differential at the V5 growth stage (r = -0.31, H = 0.39), the SR680 spectral index collected at the R5 growth stage, (r = 0.62, H = 0.39), and a quantitative trait loci at approximately 30 centimorgans on chromosome 19 (r = 0.27) to WP. Through the integration of significant agronomic, phenomic, and genomic traits, predictive models of WP were developed across environments on an entry mean basis (r = 0.72, RMSE = 0.67 kg ha-1 mm-1) and on a per plot basis (r = 0.95, RMSE = 0.39 kg ha-1 mm-1) using machine learning algorithms. Our results highlight the value of integrating multiple dataset types to study and model quantitative traits. Through the application of our findings, soybean breeders can potentially deploy multi-omic selection models in early generation screening stages to increase the rate of genetic gain in relation to soybean WP.

Advisor: George L. Graef

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