<p>Improved phenotyping technologies and data analytic strategies have the potential to reduce the phenotyping bottleneck in breeding programs, increase the number of genotypes that can be evaluated, and improve genetic gain of maize. Ear photometry and remote sensing were evaluated in this dissertation for their integration into breeding programs to understand the development of grain yield in diverse germplasm and to better predict yield performance. Ear photometry was used in Chapter 2 to characterize the testcross performance of temperate and tropical inbred lines. The effect of heterosis among the temperate heterotic groups was more noticeable in the ear-related characteristics rather than kernel-size characteristics. Yield components were generally more heritable than grain yield per ear, so they were explored for their use in multi-trait genomic prediction for grain yield on a plot or ear basis in Chapter 3. Multi-trait genomic prediction of grain yield was improved where ear characteristics were known in the testing set compared with single-trait genomic prediction. Additionally, single-trait genomic prediction was more accurate in the temperate germplasm compared to the tropical germplasm. Thus, ear photometry is an efficient method to quickly assess yield components in maize and improve yield prediction in certain circumstances. In Chapter 4, the effect of row selection, plot size, and plot trimming on remote sensing trait repeatability and prediction accuracy of biomass yield in sorghum or grain yield in maize was evaluated. Decreased plot size and configuration has been suggested to increase the number of genotypes that can be evaluated per unit area. In this study, larger plot sizes were favorable for increasing repeatability and excluding outer rows improved predictive modelling. Plot trimming was never shown to be significantly different from non-trimmed plots in this study. Genomic prediction is another way to minimize experimental size and phenotypic data collection and was evaluated in Chapter 5. A reaction norm was used to model the trajectory of hybrid yield performance across a gradient of 86 environments. The heritability and prediction accuracy of grain yield were both improved in the higher-yielding environments compared to the lower-yielding environments. Single nucleotide polymorphisms with the highest magnitude of effects were selected in each environment. Twenty-one SNPs were selected indicating many SNPs were selected in multiple environments. Candidate genes in linkage disequilibrium with many of these SNPs were previously reported as stress adaptions. Genomic prediction and remote sensing were integrated for prediction of grain yield in Chapter 6. Heritability of remote sensing traits generally improved throughout the growing season. Prediction accuracy of BLUPs were improved through an integrated phenomic and genomic prediction model for all scenarios tested. In summary, ear photometry and remote sensing are technologies to evaluate large populations for unique plant trait characteristics that can be used in combination with genomic prediction to improve understanding of grain yield development and grain yield prediction.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22677628 |
| Date | 25 April 2023 |
| Creators | Seth A Tolley (7026389) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Integration_of_Genomics_and_Phenomics_for_Yield_Prediction_in_Temperate_and_Tropical_Maize/22677628 |
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