Abstract
To be able to compare many agricultural models, a general framework for model comparison when field data may limit direct comparison of models is proposed, developed, and also demonstrated. The framework first calibrates the benchmark model against the field data, and next it calibrates the test model against the data generated by the calibrated benchmark model. The framework is validated for the modeling of the soil nutrient nitrogen (N), a critical component in the overall agriculture system modeling effort. The nitrogen dynamics and related carbon (C) dynamics, as captured in advanced agricultural modeling such as RZWQM, are highly complex, involving numerous states (pools) and parameters. Calibrating many parameters requires more time and data to avoid underfitting. The execution time of a complex model is higher as well. A study of tradeoff among modeling complexities vs. speed-up, and the corresponding impact on modeling accuracy, is desirable. This paper surveys soil nitrogen models and lists those by their complexity in terms of the number of parameters, and C-N pools. This paper also examines a lean soil N and C dynamics model and compares it with an advanced model, RZWQM. Since nitrate and ammonia are not directly measured in this study, we first calibrate RZWQM using the available data from an experimental field in Greeley, CO, and next use the daily nitrate and ammonia data generated from RZWQM as ground truth, against which the lean model’s N dynamics parameters are calibrated. In both cases, the crop growth was removed to zero out the plant uptake, to compare only the soil N-dynamics. The comparison results showed good accuracy with a coefficient of determination (R2) match of 0.99 and 0.62 for nitrate and ammonia, respectively, while affording significant speed-up in simulation time. The lean model is also hosted in MyGeoHub cyberinfrastructure for universal online access.
Highlights
Mathematical models of agriculture systems have been developed since the 1950s [1,2,3] for on-field decision management support and prediction
Since soil nitrate and ammonia data are not available, we first calibrate RZWQM using the available data from an experimental field in Greeley, CO, and use the daily nitrate and ammonia data generated from RZWQM as ground truth, against which the lean model’s N dynamics parameters are calibrated
Due to unavailability of high-frequency agriculture field nitrogen data, we first calibrate RZWQM using the available data from an experimental field in Greeley, CO, and use the daily nitrate and ammonia data generated from RZWQM as ground truth, against which the lean model’s N dynamics parameters are calibrated
Summary
Mathematical models of agriculture systems have been developed since the 1950s [1,2,3] for on-field decision management support and prediction. These models receive as inputs, weather, agriculture management, and model parameters from a user and predict various agriculture variables as a function of time (common timescale is per day) and depth (models are typically one-dimensional). Weather inputs are daily temperature, rainfall, radiation, humidity, etc. Management inputs could be tillage time and type, sowing, irrigation, and fertilizer application time and quantity, and harvest day. Before using an agriculture model, its unknown parameters need to be estimated or calibrated
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