A systems-level yield gap assessment of maize-soybean rotation under high- and low-management inputs in the Western US Corn Belt using APSIM

Quantifying yield gaps (potential minus actual yield) and identifying management practices to close those gaps is critical for sustaining high-yielding production systems. The objectives of this study were to: 1) calibrate and validate the APSIM maize and soybean models using local field experimental data and 2) use the calibrated model to estimate and explain yield gaps in the long term as a function of management (high- vs low-input) and weather conditions (wet-warm, wet-cold, dry-warm and dry-cold years) in the western US Corn Belt. The model was calibrated and validated using in-season crop growth data from six maize-soybean rotations obtained in 2014 and 2015 in Kansas, US. Experimental data included two management systems: 1) Common Practices (CP, low-input), wide row spacing, lower seeding rate, and lack of nutrient applications (except N in maize), and 2) Intensified Practices (IP, high-input), narrow rows, high seeding rate, and balanced nutrition. Results indicated that APSIM simulated in-season crop above ground mass and nitrogen (N) dynamics as well yields with a modeling efficiency of 0.75 to 0.92 and a relative root mean square error of 18 to 31%. The simulated maize yield gap across all years was 4.2 and 2.5 Mg ha(-1) for low- and high-input, respectively. Similarly, the soybean yield gap was 2.5 and 0.8 Mg ha(-1). Simulation results indicated that the high-input management system had greater yield stability across all weather years. In warm-dry years, yield gaps were larger for both crops and water scenarios. Irrigation reduced yield variation in maize more than in soybean, relative to the rainfed scenario. Besides irrigation, model analysis indicated that N fertilization for maize and narrow rows for soybean were the main factors contributing to yield gains. This study provides a systems level yield gap assessment of maize and soybean cropping system in Western US Corn Belt that can initiate dialogue (both experimental and modeling activities) on finding and applying best management systems to close current yield gaps.