Agronomy and Horticulture, Department of

 

Date of this Version

12-4-2015

Document Type

Thesis

Comments

A Thesis Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Agronomy, Under the Supervision of Professors Aaron Lorenz and Timothy Arkebauer. Lincoln, Nebraska: December, 2015

Copyright (c) 2015 Jonathan Paul Luetchens

Abstract

Rapid introduction of cheap and precise genotyping technology has created a void between genotypes and phenotypes in maize breeding. While detailed genetic information is easily accessible, the data are lacking robust phenotypes to be used in mapping studies like genome-wide association. As a result, high-throughput phenotyping tools are necessary to rigorously characterize specific traits. In this study, agronomic traits and an active spectrometer system were used to monitor 36 era hybrids – popular commercial maize hybrids grown from 1936 to 2012 – to discover how various traits have changed over time. In conjunction with increased grain yield of 76 kg/ha per year, modern hybrids displayed a decreased anthesis silking interval, as well as decreased stalk lodging, root lodging, plant height, ear height, and early vegetative biomass, and increased staygreen. In addition, modern hybrids displayed increased leaf chlorophyll and water contents. The 760/730 vegetation index, designed to study plant health and nitrogen uptake using the red edge region of the electromagnetic spectrum, correlated strongly to total leaf chlorophyll content (R2 = 0.64) and also displayed higher values in modern hybrids at numerous points throughout the growing season. By understanding these morphological and physiological trends of maize hybrids over time, breeders can continue to select for traits that are known to enhance yield. Moreover, this research shows that high-throughput phenotyping tools that estimate chlorophyll content can be implemented into a breeding program because the technology can detect superior cultivars.

Advisors: Aaron Lorenz and Timothy Arkebauer

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