<p>Wheat is a major source of calories and protein for humans worldwide. Wheat is the most widely grown crop, with cultivation areas and production systems on every continent. The cultivated land area is vast because of its importance and adaptability to various environmental conditions. Global wheat production has not kept up with the growing population, provoking the need to develop new methods and techniques to increase genetic gains. The first research chapter of this Ph.D. dissertation involves performing genome-wide association studies (GWAS) to identify and examine transferability of marker-trait associations (MTAs) across environments. I evaluated yield and yield components traits among 270 soft red winter (SRW) wheat varieties. The population consists of experimental breeding lines adapted to the Midwestern and eastern United States and developed by public university breeding programs. Phenotypic data from a two-year field study and a 45K-SNP marker dataset were analyzed by FarmCPU model to identify MTAs for yield related traits. Grain yield was positively correlated with thousand kernel weight, biomass, and grain weight per spike while negatively correlated with days to heading and maturity. Sixty-one independent loci were identified for agronomic traits, including a region that with <i>–logP</i> of 16.35, which explained 18% of the variation in grain yield. Using 12 existing datasets from other states and seasons, in addition to my own data, I examined the transferability of significant MTAs for grain yield and days to heading across homogenous environments. For grain yield and days to heading, I only observed 6 out of 28 MTAs to hold up across homogenous environments. I concluded that not all marker-trait associations can be detected in other environments.</p><p>In the second research chapter of this Ph.D. dissertation, I dissected yield component traits under contrasting nitrogen environments by using field-based low-throughput phenotyping. I characterized grain yield formation and quality attributes in soft red winter wheat. Using a split-block design, I studied responses of 30 experimental lines, as sub-plot, to high nitrogen and low nitrogen environment, as main-plot, for two years. Differential N environments were imposed by the application, or lack thereof, of spring nitrogen application in a field, following a previous corn harvest. In this study, I measured agronomic traits, in-tissue nitrogen concentrations, nitrogen use efficiency, nitrogen harvest index and end-use quality traits on either all or subset of the germplasm. My data showed that biomass, number of spikes and total grain numbers per unit area were most sensitive to low nitrogen while kernel weight remained stable across environments. Significant genotype x N-environment interaction allowed me to select N-efficient germplasm, that can be used as founding parents for a potential breeding population specifically for low-N environments. I did this selection on the basis of superior agronomic traits and the presence of the desirable gluten quality alleles such as <i>Glu-A1b </i>(<i>2*</i>) and <i>Glu-D1d </i>(<i>5+10</i>).</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12245525 |
| Date | 04 May 2020 |
| Creators | Blake A Russell (8797199) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Trait_Identification_to_Improve_Yield_and_Nitrogen_Use_Efficiency_in_Wheat/12245525 |
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