Evaluation of Sweet Corn Yield and Nitrogen Leaching with CERES-Maize Considering Input Parameter Uncertainties

A study was conducted to evaluate the ability of the CERES-Maize model in the Decision Support System for Agrotechnology Transfer (DSSAT) to simulate sweet corn (Zea mays L. var. saccharata) yield and nitrogen leaching in Florida, considering input parameter uncertainties. In this type of biological system modeling, uncertainties in predictions with respect to input parameter uncertainty are often not reported. Thus, the result of model verification could be misleading if there are large uncertainties in field observations, since single model prediction values cannot comprehensively represent heterogeneous field conditions. Instead, comparisons between the distributions of model simulations and field observations were recommended in this study. A two-factor split-plot field experiment was conducted with three nitrogen fertilizer levels (185, 247, and 309 kg N ha-1) and two irrigation levels (I1 and I2; I2 = 1.5 × I1, where I1 is the irrigation demand calculated based on a daily soil water balance). Yield response to different nitrogen fertilizer and irrigation management levels was evaluated, and the cumulative nitrogen leaching was estimated for each of the treatments based on a nitrogen balance. Next, the field experiment treatments were simulated with the calibrated CERES-Maize model using parameter sets generated from parameter distributions derived with the generalized likelihood uncertainty estimation (GLUE) method in a previous study. Simulated dry matter yields and cumulative nitrogen leaching were compared to field-measured or estimated values. Measured total and marketable yields were not affected by irrigation level. Estimated nitrogen leaching increased significantly with higher levels of irrigation and nitrogen fertilizer application. The calibrated CERES-Maize model accurately predicted the phenology dates, with an error of 0 and 1 day for anthesis and maturity dates, respectively. The prediction uncertainties (due to uncertain input parameter values), as measured by the standard deviation (SD) in predicted anthesis and maturity dates, were only 1 and 2 days after planting, respectively. The model also accurately predicted the changes in dry matter yield caused by different nitrogen and irrigation levels, with a relative absolute error (RAE) less than 12% for all but one treatment. Due to the uncertainties in soil and genetic parameters, the prediction SD of simulated dry yields ranged from 655 kg ha-1 at I1 to 960 kg ha-1 at I2, while the observation SD ranged from 220 to 463 kg ha-1 for measured dry yields. The uncertainties in simulated dry yield were higher than the uncertainties of measured values due to relatively high variations in estimated genetic coefficients. The model performance could be improved further if the variations in estimated genetic coefficients could be reduced. The difference between the simulated and estimated nitrogen leaching amounts was significant and complex, ranging from -31 to 43 kg N ha-1 with an average absolute difference of 15.3%. This discrepancy was probably due to both the errors in estimation of potential nitrogen leaching in the field experiment using a mass balance approach and the inaccuracy of model predictions. Nevertheless, the increase in nitrogen leaching resulting from higher nitrogen fertilizer levels was correctly predicted. The uncertainties in simulated N leaching covered more than 67% of the uncertainties of estimated leaching for all but one treatment, indicating that estimated soil parameters via the GLUE method were able to represent the heterogeneity of field soil. In general, the CERES-Maize model is able to simulate sweet corn production under different management conditions sufficiently to allow exploration of tradeoffs between crop yield and nitrogen leaching for sweet corn production in Florida.