Drought and uneven distribution of precipitation during stages of crop growth exert a severe reduction on crop yield. It is therefore necessary to evaluate the impact of drought on crop yields. In this study, data from a two-year (2016 and 2017) field experiment were used to calibrate and evaluate the parameters of the Decision Support System for the Agrotechnology Transfer (DSSAT) model. The evaluation model was then employed to analyze the impact of potential drought on the yield of summer maize (Zea mays L.) over different growth stages for 46 years (1970–2015). The simulated summer maize flowering and harvest date differed by three and one days of the observed in 2017. The d-index value and the normalized root-mean-square error (nRMSE) of the simulated and measured values were 0.90 and 3.72%, 0.95 and 10.21%, and 0.92 and 13.12%, for summer maize yield, soil water content, and leaf area index, respectively. This indicates that the parameters of the DSSAT model were extremely reliable and that the simulation results were better. The yield reduction of summer maize was concentrated within the range of 0–40% from 1970 to 2015, and the two-stage yield reduction was higher than the one-stage yield reduction. The highest probability of yield reduction occurs if drought occurs during jointing and heading stages. Irrigation is therefore recommended during jointing stage or heading stage. If local irrigation conditions permit, irrigation can be carried out both at the jointing and heading stages. This study provides a theoretical basis for drought resistance management and scientific irrigation of summer maize in the western Guanzhong plain.
HighlightsGood 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. Keywords: . Climatic year, DSSAT, Guanzhong Plain, Sowing date, Summer maize.
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