Simulating within-field spatial and temporal corn yield response to nitrogen with APSIM model

Abstract

Context

Process-based crop growth models can explain soil and crop dynamics that influence the optimal N rate for crop production. Currently, there is a lack of understanding regarding the accuracy of process-based models for site-specific zones within fields, as well as the key factors that need to be considered when calibrating these models for zone-specific economic optimum N rate (EONR).

Objective

We calibrated the Agricultural Production Systems sIMulator (APSIM) model in contrasting zones within fields, quantified the model performance, and used the calibrated model to develop long-term corn yield response to N to assess the temporal variability between zones and sites to assist decision making.

Methods

We conducted four N rate experiments (2 fields × 2 zones within a field) over two years in southeast Nebraska. Experimental data were used to calibrate and test the APSIM model. APSIM simulated corn yield response to N for each zone and site was obtained by running numerous iterations of the calibrated model at different N rates. Observed and simulated corn yield response to N rate were analyzed with statistical models to estimate the EONR.

Results and conclusions

The APSIM model predicted corn yield over 11 historical years with a relative root mean square error (RRMSE) of 12% and yield at EONR in the N studies with RRMSE of 8.8%. The simulated EONR was lower than the observed EONR across sites, years, and zones with greater error than yield. The simulated yield increase with N fertilization was under-estimated in fine textured soils and over-estimated in medium textured soils. Long-term corn yield response to N showed that temporal variation in simulated EONR was greater than spatial variation. Long-term EONR and yield at EONR increased with increasing rainfall, while yield at zero N was greatest in normal years. Temporal variation was driven primarily by year-to-year variation in N loss (CV of 67% ± 9.5). Soil texture, hydrological properties, water table, and tile drainage were key variables for accurate site-specific model calibration. Improvements in simulating site-specific EONR may be realized by including in-situ or remotely sensed data for better estimation of N dynamics. We concluded that APSIM can provide valuable insights into systems dynamics in this region, but it can’t provide precise N-rate estimates. Our study contributes to understanding of the within-field variability using simulation modeling.