Graduate Studies
First Advisor
Laila A. Puntel
Second Advisor
Guillermo R. Balboa
Date of this Version
12-2023
Citation
A thesis presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Master of Science
Major: Agronomy
Under the supervision of Professor Laila A. Puntel and Guillermo R. Balboa
Lincoln, Nebraska, December 2023
Abstract
Adoption of dynamic model-based nitrogen (N) management tools are a potential solution for increased profit and N use efficiency (NUE) in corn production. Most previous studies evaluating these tools used small plot research which does not accurately represent large scale performance and inhibits adoption. Two dynamic model-based N management tools, which were commercially available in 2021 and 2022 (Adapt-N and Granular), were tested at fifteen on-farm research locations in Nebraska. The objectives of this study were to (i) evaluate the benefits of dynamic model-based N management in Nebraska by benchmarking performance with the Grower’s traditional N management, and (ii) evaluate the site-specific performance of model-based N management tools across different soil textures. N recommendations from grower and model-based N management were replicated throughout each site in a randomized field length strip trial design. N rate blocks consisting of 4-6 N rates were embedded in the trial design to calculate site-specific Economically Optimum Nitrogen Rates (EONR) after harvest. N rate recommendations were applied and data was collected using commercial applicators and harvesters. Site-specific calculations of observed EONR were made to determine whether N recommendations were able to maximize profitability over grower N management. Adapt-N significantly improved NUE over Grower practice by 7% testing sites, with no difference in yields or profits. Granular significantly decreased NUE by 11% compared to the Grower management with no difference in yield or profit. In multi-rate block trials, Adapt-N decreased the N rate at 3 of 4 soil textures and improved NUE in two of four soil textures compared to Grower treatment. Granular increased profits at 3 of 6 textures and decreased NUE at 3 of 6 textures, increased N at 4 of 6 textures compared to grower treatment. Both crop-model based N tools provided more accurate recommendations than the UNL static calculator (Adapt-N RMSE=30, Granular RMSE=43 vs RMSE=69). The Adapt-N tool out-performed grower’s traditional management (RMSE=40). These results indicate good potential for data-driven dynamic model-based N management to increase the sustainability of corn production in Nebraska relative to Grower practice and the static university N calculator, but performance varies considerably among models.
Advisors: Laila A. Puntel and Guillermo R. Balboa
Comments
Copyright 2023, Seth Norquest