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Development Of A Data-Assimilation System To Forecast Agricultural Systems: A Case Study Of Constraining Soil Water And Soil Nitrogen Dynamics In The Apsim Model, Marissa S. Kivi, Bethany Blakely, Michael Masters, Carl J. Bernacchi, Fernando E. Miguez, Hamze Dokoohaki

United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: Faculty Publications

As we face today's large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through …