Optimization of sowing date for spring maize in China's loess plateau based on presowing temperature and soil water content.

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.

Evaluating the accuracy of ARMA and multi-index methods for predicting winter wheat maturity date.

BACKGROUND: Accurate and timely prediction of regional winter wheat maturity date can provide essential information to improve the management of agriculture and avoid declines in the yield and quality of crops. In this paper, we propose the use of an autoregressive moving-average model to predict vegetation indices on 1, 9, and 17 May each year, and applied them to the methods of evaluating crop maturity based on vegetation indices. Growing degree days and a widely applied local empirical method were selected to explore and compare the feasibility of several methods. We analyzed winter wheat harvested from the Guanzhong Plain during 2003-2013 and used leave-one-out cross-validation to compare and verify the performance of the maturity prediction methods. RESULTS: The results demonstrated that (i) the vegetation index methods and growing degree days methods predicted maturity with higher accuracy than did the widely applied local empirical method, and (ii) the two-step filtering method based on future meteorological data from The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble exhibited the highest prediction accuracy on 1 May and had the lowest error fluctuation range on 17 May. CONCLUSION: These results provide new insights for predicting regional crop maturity, deploying agricultural harvesting equipment in various regions, and avoiding decreases in crop yields caused by adverse weather. © 2021 Society of Chemical Industry.