Background: The sowing date of spring maize in China's Loess Plateau is often restricted by the presowing temperature and soil water content (SWC). The effective measurement of the effects of presowing temperature and SWC on the sowing date is a major concern for agricultural production in this region. In this paper, we considered the average daily air temperature of ˃10 °C in the 7 days before sowing. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to simulate a spring maize yield under distinct combinations of SWC and sowing date for 51 years (1970-2020). Subsequently, through the cumulative probability distribution function, the contribution of presowing SWC to the spring maize yield was quantified, and the optimal sowing date of spring maize in each production location was determined.
Results: The results revealed that the daily average temperature of ˃10 °C for 7 days consecutively can be used effectively as the basis for the simulation of spring maize sowing date. The presowing SWC affected the spring maize yield but did not change the optimal sowing date. When the SWC of each study site is about 70% of the field capacity, Wenshui and Yuanping can properly delay sowing, and Lin county can sow early to obtain a higher yield.
Conclusion: This study provides an effective approach for optimizing presowing soil moisture management and the sowing date of spring maize in the semiarid regions of the Loess Plateau. © 2023 Society of Chemical Industry.
Keywords: DSSAT model; Loess Plateau; cumulative probability distribution; sowing date; spring maize.
© 2023 Society of Chemical Industry.