Show simple item record

dc.contributor.advisorRooney, William L
dc.creatorCrozier, Daniel Shaw
dc.date.accessioned2021-02-02T16:21:33Z
dc.date.available2022-08-01T06:51:44Z
dc.date.created2020-08
dc.date.issued2020-06-08
dc.date.submittedAugust 2020
dc.identifier.urihttps://hdl.handle.net/1969.1/192239
dc.description.abstractThe genetic yield potential in grain sorghum [Sorghum bicolor (L.) Moench] hybrids has increased at a slower rate than other cereal crops. Advances in new technology provide opportunities for breeders to enhance selection accuracy and throughput efficiency of new germplasm to bolster rates of genetic gain. In this thesis, Genotyping-By-Sequencing was used to analyze the structure of heterotic groups in sorghum and access the relationship between the genetic similarity of parental lines and heterosis. Three distinct groups of germplasm in the Texas A&M sorghum breeding program were found through K-means clustering that closely aligned with functional classification as B-lines, R-lines, and forage lines. Forage lines exhibited the greatest range of genetic diversity followed by R-lines, then B-lines. Significant heterosis was observed for grain yield, plant height, days to flower, and panicle exsertion; yet, estimates of genetic similarity were not a good predictor of heterosis or hybrid performance amongst elite Texas A&M sorghum inbred lines. However, some parental inbred performance measurements may be predictive of hybrid performance. Additionally, in this thesis, a phenotyping pipeline was developed utilizing CT imaging to quantify three-dimensional structural characteristics from grain sorghum caryopses which can then be related to end-use quality. It was possible to accurately classify 19 sorghum genotypes based on CT-derived estimates of embryo volume, endosperm hardness, endosperm texture, endosperm volume, pericarp volume, and kernel volume.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectGenotype-By-Sequencingen
dc.subjectHeterosisen
dc.subjectPredictionen
dc.subjectX-ray Computed Tomographyen
dc.subjectGrain Qualityen
dc.subjectMachine Learningen
dc.titleNew Tools for Developing Improved Sorghum Hybridsen
dc.typeThesisen
thesis.degree.departmentSoil and Crop Sciencesen
thesis.degree.disciplinePlant Breedingen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberKlein, Robert R
dc.contributor.committeeMemberRiera-Lizarazu, Oscar
dc.type.materialtexten
dc.date.updated2021-02-02T16:21:33Z
local.embargo.terms2022-08-01
local.etdauthor.orcid0000-0002-3101-235X


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record