Date of Award
Doctor of Philosophy
Asheesh K. Singh
Rapid characterization of physiological traits driving yield are becoming desirable aides to breeding programs to increase the rate of genetic gain. Each chapter in this dissertation investigates areas related to high-throughput phenotyping and physiological traits driving soybean yield. Chapter two seeks to understand the response of diverse soybean germplasm to seeding rate. An evaluation of final plot seed yield, seed protein percentage, seed oil percentage, seed weight, height, maturity, and plant lodging revealed a significant genotype x seeding rate interaction only for lodging, suggesting current soybean germplasm and soybean of wide genetic ancestry respond similarly to seeding rate. Our second objective was to identify physiological traits at multiple growth stages predicting yield response under contrasting levels of seeding rate. Adaptive elastic net models characterized diverging traits between seeding rates and determined chlorophyll traits as the leading predictors across seeding rates. Chapter three quantifies biomass partitioning strategies and residue quality determined through carbon:nitrogen (C:N) ratios in the same diverse panel of SoyNAM genotypes in Chapter 2. Above-ground plant components were dissected at three reproductive stages and revealed significant differences in biomass partitioning by R4. Significant genetic variation in C:N residue quality was found with no apparent negative relationship to final grain yield. Optimal biomass partitioning strategies for yield and improved residue C:N ratios for whole-system nitrogen sustainability can be targeted for yield improvement. Lastly, chapter four includes a QTL mapping study of vegetative indices used for yield prediction in Chapter 1 in four SoyNAM RIL populations derived from five of the 32 parent NAM genotypes evaluated in Chapters 1 and 2. Five QTL were detected for grain yield and vegetative indices NDVI, NMDI, NWIB, PSRI, and VREI2 measured at R5, spanning chromosomes 1, 3, 10 and 18. These QTL can serve as aides to MAS in soybean breeding and inform future studies aimed at dissecting the physiology of soybean grain yield. The overall research provides insights on soybean biomass partitioning and evidence of the presence of genetic variation in residue traits; physiological traits to predict yield in diverse germplasm and row-density management systems; and genomic regions mapped to spectral wavelengths related to soybean seed yield.
Race Heith Higgins
Higgins, Race Heith, "Identification and QTL mapping of physiological drivers of soybean yield under contrasting management systems using high-throughput phenotyping" (2018). Graduate Theses and Dissertations. 16723.