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REU Student Project Abstract

Grace Nystrom Headshot
Grace Hystrom
Faculty Mentor(s): Dr. Steve Moose
Monitoring Nitrogen-Responsive Phenotypes in Corn with the TerraSentia Rover
An ample supply of nitrogen is essential for promoting the greatest genetic yield potential in maize. Nitrogen stimulates chlorophyll production which promotes photosynthesis; insufficient chlorophyll results in less yield. Nitrogen availability also affects soil nutrition, fertilizer content, isotope assembly and many other environmental components. It is important to study how corn responds to nitrogen. Adequate nitrogen influences yield and the environment around us. It is difficult to measure how nitrogen affects corn; typical fields have variability in soil N supply. Only measuring the grain is half the story. Visual effects of N deficiency in the leaves/vegetative stage occur too late to remedy. A more yellow-color indicates insufficient nitrogen and a less yellow-color indicates sufficient nitrogen. Previous research explains that two plants of similar phenotypes can have very different biomasses suggesting differences in nitrogen availability. Large amounts of biomass imply that the plant has received adequate amounts of nitrogen unlike small amounts of biomass. A comparison of previous data shows a correlation between nitrogen-responsive phenotypes in maize. Using the TerraSentia rover as a field resource promotes efficiency and sustainability. The rover is able to decrease physical labor while conserving oil and gas. Automated imaging offers new opportunities to measure nitrogen-responsive phenotypes; drones work well above the canopy, but the rover works well inside the canopy. The rover uses a series of algorithms to scan the inside of plant rows with accuracy and intuitiveness. Prior evaluation of ear and plant heights of the entire plant for one growth season detected noticeable differences of nitrogen treatment for maize traits. The rover is able to collect concise height measurements while we are able to collect precise measurements; the error gap is diminutive. We use data from last year’s growth season of ear and plant heights and compare it to this year’s ear and plant height data. This year we will evaluate N-responsiveness of plant height and ear height again, plus additional traits collected by the rover: stand count, stem width, leaf area-index (LAI). We can also try to develop new algorithms for other traits. Utilizing the rover has proven to be consistent when analyzing field data. Measurements of plants with the rover occur weekly to follow nitrogen-responsive growth. If the rover is able to show consistency throughout data sets then our experiment will be proven successful.