Modeling Corn-Soybean Rotation with Apsim in Western US Corn Belt.

Agricultural models can help integrate production factors and, in consequence, improve the understanding of complex biological systems. The Agricultural Production System Simulator (APSIM) is a modular modeling framework that was utilized for the mechanistic analysis of agricultural systems. To test the model a corn (Zea mays L.)-soybean (Glycine max L.) rotation was established in 2014 under both dryland and irrigated conditions at Scandia (KS, US). Both crops were present each year and two complete growing seasons, 2014 and 2015, were analyzed. Two treatments were simulated: Common Practices, CP (low seeding rate, wide-row spacing -76 cm, and no fertilizer application) and Ecological Intensification, EI (high seeding rate, narrow-row spacing-38 cm, balanced fertilization, fungicides and insecticide application). Seasonal leaf area index (LAI), biomass and nitrogen (N) content was determined at multiple growth stages for corn (V6, V13, R1, R3, R6) and soybean (V4, R1, R3, R5, R7). Model setup included rotations, variety characteristics, weather, and soil data. A total of six simulations were performed by combining crop rotation, water condition and treatment. Output variables were total biomass and by fraction, LAI, harvest index (HI) and total N content (aboveground fraction, excluding roots). Pearson's correlation coefficient, root mean square error (RMSE) and modeling efficiency (ME) were computed to test the model prediction accuracy. The ME for total biomass was 0.90 for corn and 0.64 for soybean. Total biomass for both corn and soybean was accurately predicted (R² 0.96 and 0.85 respectively, RMSE 221 and 225 g m^-2). Grain yield was more efficiently predicted for corn than for soybean (ME 0.81 vs 0.51, respectively). APSIM underestimated leaves fraction in all simulations (ME -2.37 and -1.31 for corn and soybean respectively). Correlation coefficient between simulated and observed data for HI was 0.66 (RMSE 0.09) for corn and 0.68 (RMSE 0.06) for soybean respectively.