利用 DSSAT CROPGRO 建模方法优化 Uttarakhand Tarai 地区的大豆种植

Naveen Kumar Bind, Amit Bijalwan, Chinmaya Kumar Sahu, Ravi Kiran
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摘要

大豆(Glycine max)是全球重要的油籽作物,但在印度,尽管有高产品种,其平均谷物产量仍然相对较低。本研究旨在利用农业技术转让决策支持系统(DSSAT)的 CROPGRO 模型,探讨不同播种日期对作物生长和产量的影响,从而优化印度北阿坎德邦塔赖地区的大豆种植。该试验于 2022 年和 2023 年在北阿坎德邦的潘特纳加进行,采用了三次重复的分块设计。针对不同的播种日期对模型进行了校准和验证,并比较了模拟值和观测值之间的关键参数,如出苗天数、生理成熟天数、谷物产量、收获指数和叶面积指数。在验证过程中,谷物产量的 RMSE 和 R2 分别为 48.44 和 0.90,生理成熟度分别为 1.10 和 0.99,收获指数分别为 0.042 和 0.99,叶面积指数分别为 1.14 和 0.97。结果表明,调整播种期会对大豆的生长和产量产生显著影响,最佳播种期可获得更高的产量和更好的作物表现。具体而言,7 月 22 日播种的大豆产量最高,而 8 月 21 日播种的大豆产量最低。事实证明,DSSAT CROPGRO 模型是在不同环境条件下模拟大豆生长和预测作物结果的宝贵工具。
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Optimizing Soybean Cultivation in Uttarakhand's Tarai Region Using the DSSAT CROPGRO Modeling Approach
Soybean (Glycine max) is a vital oilseed crop globally, but in India, its average grain yields remain relatively low despite the presence of high-yielding varieties. This study aimed to optimize soybean cultivation in the Tarai region of Uttarakhand, India, by exploring the impact of different sowing dates on crop growth and yield using the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO model. The experiment was conducted in 2022 and 2023 at Pantnagar, Uttarakhand, using a split-plot design with three replications. The model was calibrated and validated for different sowing dates, and key parameters such as emergence days, physiological maturity days, grain yield, harvest index, and leaf area index were compared between simulated and observed values. During validation RMSE and R2 was 48.44 and 0.90 for grain yield, 1.10 and 0.99 for physiological maturity, 0.042 and 0.99 for harvest index and 1.14 and 0.97 for LAI respectively. The results showed that adjusting sowing dates can significantly affect soybean growth and yield, with optimal sowing times resulting in higher yields and better crop performance. Specifically, sowing on July 22nd resulted in the highest grain yield, while sowing on August 21st led to the lowest. The DSSAT CROPGRO model proved to be a valuable tool for simulating soybean growth and predicting crop outcomes under varying environmental conditions.
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