Corn is one of American agriculture's greatest success stories, where we have witnessed incredible increases in yield potential over the last half-century. However, abiotic stress is still the primary limiting factor preventing plants from reaching their true yield potential. In addition, agriculture is not exempt from the deleterious effects of changing weather patterns and the altered climate our world will face as time progresses. Thus, increasing our understanding of how crops interact with their environment both above and below the soil will be crucial to increasing production on a global scale while maximizing profitability at a local level. Five studies were carried out to quantify the relationship between corn and multiple abiotic factors, including temperature, moisture, and nitrogen. In study one, Corn seed germination as a function of temperature was quantified, and the effects were compared between soybean and cotton, both major agronomic crops in Mississippi. Study two determined the effects of osmotic stress on corn seed germination, and commercial corn hybrids' variability was explored. In study three, functional relationships between temperature and early season growth and development were quantified, and the concept of a simple mathematical model for predicting growth as a function of temperature was explored. Study four exemplified the effects of increasing waterlogging durations from 0 to 14 days and determined critical limits for above and below-ground growth. Finally, in study five, growth, development, and physiology were determined as a function of nitrogen concentration. In addition, optimum concentrations to maximize vigor were estimated. Data generated from these studies exemplify how abiotic stress significantly affects corn during germination and early season growth and development. These datasets will be valuable foundations to build on as we explore how abiotic stress affects all growth stages of corn and other important agronomic crops. Functional relationships generated from these studies will be useful to update crop simulation models. Both simple and complex mathematical models have promising implications in emerging and developing precision and predictive agricultural technologies.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6533 |
| Date | 11 May 2022 |
| Creators | Walne, Charles Hunt |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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