Exploring management strategies to improve yields and reduce reactive nitrogen emissions in a summer maize‐winter wheat cropping system under long‐term climate variability

Abstract Achieving high stable crop yields and minimal environmental damage is crucial to enhance the sustainability of agriculture in China. Process‐based models are indispensable tools to develop agronomy management practices to achieve sustainable agriculture by simulating crop production and emissions of reactive nitrogen (N), particularly in complex climate scenarios. In this study, a long‐term field experiment with an intensive summer maize‐winter wheat rotation system in north‐central China was simulated using the DeNitrification‐DeComposition (DNDC) model. The DNDC model validation and calibration was done by using two‐year monitoring data of crop yields and nitrous oxide emission fluxes and ammonia volatilization. Moreover, the optimal management practices to promote crop production and reduce the reactive N loss under 22 years of climate variability were explored using the calibrated DNDC model in this region. The results showed that the DNDC model effectively simulated wheat and maize yields, N uptake, ammonia volatilization, and nitrous oxide emissions. Sensitivity analyses demonstrated that the agronomic management practices (N rates and ratio of base to topdressing, planting time, and tillage depth) substantially affected crop yields and reactive N losses under long‐term climate variability. Compared with current farming practices, optimal Nutrient Expert (NE) management achieved an increase in high yields and environmental pollution radiation by altering the rate of N application and ratio of base to topdressing. Moreover, the optimal management strategies developed by the DNDC model, such as adjusting the planting date and tillage depth, further increased the average grain yield by 2.9% and reduced the average reactive N losses by 10.5% compared with the NE management implemented in the annual rotation cropping with a 22‐year simulation. This study suggests that the modeling method facilitates the development of most effective agronomic management practices to promote crop production and alleviate the negative impact on environment.