Laila A. Puntel
Guillermo R. Balboa
Date of this Version
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
Under the supervision of Professor Laila A. Puntel and Guillermo R. Balboa
Lincoln, Nebraska, December 2023
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