LAUSR

Abstract Determining how to optimize a scientific and efficient farmland nitrogen (N) fertilizer schedule by combining existing technology is currently a hot topic. Maize is one of the major crops in China, and enhancing the yield of maize is conducive to ensuring China's food security. In this study, the central region of Jilin Province was adopted as the research object, and a three-year (2014–2016) field experiment was performed. The data from 2014 were used to calibrate the DSSAT model, and the data from 2015 were used for validation. After calibration and validation, the DSSAT model and a genetic algorithm (GA) were used to optimize the N fertilizer schedule of maize under 20 years (1973–1992) of meteorological data for Changchun. The experimental data from 2016 were used to validate the results of the optimized N fertilizer schedule. As revealed from the results, the DSSAT model effectively simulated the growth and development of maize under drip irrigation and rain-fed methods in Changchun. The model was first calibrated based on the crop yield, phenological phases and soil moisture and N content data, and good agreement was achieved between the simulated and measured data in both the calibration and validation periods. In the calibration and validation periods, the normalized root mean square error (nRMSE) for grain yield was 1.45% and 1.61%, respectively. The total amount in the new N fertilizer schedule is 198 kg/ha, which is slightly higher than that in the traditional schedule (187.5 kg/ha), and the yield of maize in the proposed N fertilizer schedule was upregulated by 7–9% compared with the conventional N fertilization schedule in the experimental results for 2016. Through an analysis of economic benefits, drip irrigation is better than the rain-fed method, and the optimized N fertilization schedule will make the economic benefits more significant (8.4%–12.4% increase). Additionally, this method is easier to combine with remote sensing and weather forecasting, forming a real-time method of field management optimization schedule decision-making.