Puntel, L. A., Sawyer, J. E., Barker, D. W., Dietzel, R., Poffenbarger, H., Castellano, M. J., et al. (2016). Modeling long-term corn yield response to nitrogen rate and crop rotation. Front. Plant Sci. 7, 1630. doi:10.3389/fpls.2016.01630.
Quantify model prediction accuracy before and after calibration, and report calibration steps
Compare crop model-based techniques in estimating optimal N rate for corn
Utilize the calibrated model to explain factors causing year to year variability in yield and optimal N
We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn [Zea mays L.] and soybean [Glycine max L.] yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. We evaluate APSIM the model performance of a “blind phase” (uncalibrated model) where management and cultivar information were used, and a “calibrated phase” (calibrated model) where crop yield and soil organic carbon (SOC) data were provided into the model.
- The model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model.
- The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration).
- For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N/ha).
- For accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring (April to June) precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation.