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Optimization and Evaluation of Model-Based Irrigation and Nitrogen Decision Support Tools

Anthony A Amori, University of Nebraska - Lincoln


Process-based crop models are essential tools for assessing crop management options (irrigation and nitrogen management) or the impacts of climate change on agricultural production to ensure efficient management of resources, high crop yield, profitability, and environmental sustainability. However, the adoption of these models by farmers has been relatively low. Therefore, this research demonstrated the use of a simple crop simulation model and extended the understanding of the performance of the model for irrigation and nitrogen management to optimize yield and profits, while mitigating adverse environmental impact. The first main chapter (Chapter 2) focused on developing a multi-parameter optimization (MPO) procedure based on a stepwise kriging approach for the calibration of the Hybrid-maize model using different on field-measured soil moisture and grain yield datasets under two climatic conditions. While the regional MPO average of the soil moisture-related parameter combination moderately improved the simulation of soil water content dynamics, the regional MPO average of the grain yield-related parameter combination significantly improved the simulation of grain yield. The second main chapter (Chapter 3) evaluated different model-based irrigation strategies against farmer’s irrigation management practices for improved grain yield and irrigation water use productivity. The model-based irrigation scenarios significantly reduced the farmer’s irrigation amounts without affecting grain yield, indicating that the farmer had consistently over-applied irrigation than the crop water requirement. The third main chapter (chapter 4) compared total nitrogen rates recommended by Maize-N tool against farmer’s nitrogen management practices as guided by the University of Nebraska-Lincoln (UNL) recommendation tool in farmer’s maize fields across different years. The farmer applied rates showed over-estimation of N fertilization when compared with fertilizer recommended by the model. Overall, although both the Hybrid-maize and maize-N models demonstrated the potential to be included in farmers irrigation decision toolbox for irrigation and nitrogen management, extra caution should be taken when using the models, especially in water and nitrogen-limited conditions.

Subject Area

Agronomy|Water Resources Management|Agriculture

Recommended Citation

Amori, Anthony A, "Optimization and Evaluation of Model-Based Irrigation and Nitrogen Decision Support Tools" (2023). ETD collection for University of Nebraska - Lincoln. AAI30575496.