Phenomic and Genomic Approaches to Understand Photoperiod Associated Flowering, Plant Height and Yield in Southern Maize (Zea Mays L.)

Abstract

Tropical maize germplasm holds a wealth of diversity that could be used for crop improvement. Phenomic and genomic tools can help characterize phenotypes associated with both crop improvement as demonstrated here. Phenomics and genomics were used in this dissertation to characterize maize for crop improvement. Chapter I identified 7 loci, including three novel loci, that were linked to photoperiod-associated flowering in a novel recombinant inbred line (RIL) population derived from Tx773 and three temperate adapted lines (LH195, LH82 and PB80) grown in Texas, Wisconsin and Iowa in over three years. Chapter II showed that allelic effect sizes of economically valuable loci are both dynamic in temporal growth, resulting in characterizations of phenotypic variability overlooked traditional laborious phenotyping methods. Chapter III demonstrated how unoccupied aerial systems (UAS)-based phenotyping can reveal novel and dynamic relationships between time-specific associated loci with complex traits. These relationships were previously impractical to evaluate but doing so demonstrated many candidate genes putatively involve in the regulation of plant architecture even in early stages of maize growth and development. Chapter IV is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the benefit of phenomic selection approaches in estimating breeding values before harvest. Chapter V showed that (i) it is possible to predict complex traits using high throughput phenomic data between different managements and years, and that (ii) temporal phenotype data can reveal time-dependent association between RILs and abiotic stresses, to select resilient plants. Chapter VI showed that (i) complex traits can be predicted using the high throughput phenomic data between different managements and years, and (ii) temporal phenotype data can reveal time-dependent association between RILs and abiotic stress, which can help to select resilient plants. Chapter VII showed that when weather data was combined with temporal phenomic data, prediction abilities increased and were found to be more effective in yield prediction when tested and untested environments were less similar. Overall, temporal phenomic and weather data could moderately predict grain yield under the most challenging predictive breeding scenario of untested genotypes in untested environments.