A quantitative assessment of soybean yield and nitrogen economy

Abstract

Improving the nitrogen (N) economy in soybeans (Glycine max L.) production is essential for developing more efficient, profitable, and environmentally sustainable agricultural systems. While soybeans can meet much of their N demand through biological N₂ fixation, uncertainty remains around when and where fertilizer inputs are needed and how environmental conditions influence N uptake, seed yield, and quality. This dissertation combines field data, crop modeling, and machine learning to provide data-driven insights into soybean N dynamics across diverse U.S. environments. Chapter 1 provides a summary and presentation of the background and objectives of this dissertation. Chapter 2 uses a standardized protocol to evaluate soybean yield responses to N and sulfur (S) fertilization across 26 field trials located across the Midwest region of the United States (US). Results showed that N fertilization rarely increased yield and often introduced uncertainty. In contrast, S fertilization improved N uptake, yield, and N status in some environments. An apparent N dilution curve was developed to help identify in-season plant N limitations, improve nutrient diagnosis, and provide more precise recommendations. Chapter 3 explores the effect of weather and soil factors that influence seed yield, N₂ fixation, and their uncertainty using data from 35 sites. Precipitation, vapor pressure deficit, and soil texture emerged as key factors explaining variability in both yield and N₂ fixation. Small additions of S helped improve N uptake and yield stability in environments with limited organic matter or water availability. Chapter 4 focuses on characterizing seasonal patterns of N₂ fixation using plant samples collected at reproductive stages and analyzed through Bayesian modeling. Peak N₂ fixation typically occurred between full pod and seed fill stages but varied widely depending on water availability and atmospheric conditions. These results highlight the limitations of relying on single-timepoint measurements and the value of time-series data for modeling complex biological processes. Together, the findings of this dissertation support more targeted, data-informed recommendations for improving yield and N use efficiency while delivering practical tools to help farmers, advisors, and researchers advance soybean productivity and long-term sustainability.