Optimization of Sowing Date, Irrigation, and Nitrogen Management of Summer Maize Using the DSSAT-CERES-Maize Model in the Guanzhong Plain, China

Highlights Good applicability of DSSAT was validated in simulating summer maize yield in the Guanzhong Plain, China. Optimal sowing dates of summer maize were obtained for different climatic years. The optimal irrigation and nitrogen management strategy conserved water and nitrogen. Abstract. Agricultural system models play an important role in simulating crop growth processes and water and fertilizer regulation in arid regions. To solve the current problems of optimizing the sowing date in different climatic years and the fertilizer application in low-precipitation conditions in the Guanzhong Plain, China, this study used two years (2016-2017) of experimental summer maize field data to calibrate and validate Decision Support System for Agro-technology Transfer (DSSAT) model parameters. The validated DSSAT model was then used to simulate and optimize sowing dates, irrigation, and fertilization of summer maize crops in the Guanzhong Plain. The relative root-mean-square error (nRMSE) between the measured and simulated values of summer maize crop yield was 8.57%, proving that the established DSSAT model and crop parameters were highly reliable. The nRMSE values for soil water content and nitrate-nitrogen were 7.86% and 8.72%, respectively, which indicated better simulation results. The optimal sowing date for summer maize in the Guanzhong Plain were mid- to late June, mid-June, and early to mid-June in wet, general, and dry years, respectively. The irrigation and nitrogen strategies for summer maize in the climatic years were as follows: 60 mm and 180 kg ha-1 in wet years, 60 mm and 180 kg ha-1 in general years, and 150 mm and 150 kg ha-1 in dry years. This study provides a scientific decision-making method for the production of summer maize to conserve water and fertilizer.