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Abstract
Soybean is the subject of considerable breeding efforts because of its versatile uses. Major challenges for breeders include accurately phenotyping thousands of genotypes to inform breeding decisions and developing cultivars with competitive performances across variable environmental conditions. The focus of this research is to develop and deploy remote sensing methodologies to support soybean breeding and identify and characterize genetic variability associated with improved drought tolerance. In Chapter 2, Matti, an open-source high-throughput phenotyping (HTP) application was developed that estimates maturity as lines mature in the field. Moderate to high correlations were found between observed and estimated maturity for determinate and indeterminate breeding lines in early generation and yield trial stages of the breeding pipeline. The timing of estimates generally aligned with the true maturity distributions indicating that Matti was providing real-time maturity information. In Chapter 3, a QTL analysis was performed on a RIL population from the ultra-slow canopy wilting (CW) accession PI 603535 and fast wilting cultivar Benning using CW scores and remote sensing traits as phenotypes. Seven CW QTL were identified in the combined analysis, with those accounting for the highest phenotypic variance typically colocalizing with remote sensing QTL. QTL were not consistently identified across environments highlighting the genetic complexity of drought tolerance. However, slow CW lines developed can serve as valuable breeding stocks for future breeding efforts and genetic studies. In Chapter 4, 16 near-isogenic lines (NILs) were developed by backcrossing genomic regions associated with improved drought tolerance traits from PI 416937 into elite backgrounds. A drought shelter induced severe drought conditions for comparison with an adjacent rainfed field. Two NILs with Chr 4 and 12 introgressions exhibited the largest decrease in CW over their recurrent parents. The multispectral index GNDVI exhibited the strongest correlation with CW score, indicating utility as HTP proxy trait in drought evaluations. Chapter 5 describes SHP Buddy, an open-source QGIS plugin that provides an intuitive method for quickly generating accurate shapefiles for common breeding experiment layouts. Reliable shapefiles improve record keeping of aerial imagery and the quality of high-throughput phenotyping data extracted.