通过将遥感数据与DSSAT模型的数据相连接,优化冬季小麦的顶装饰
Yu Zhao1,2, Zeyang Wen2, Chao Wang1
1College of Agriculture, Shanxi Agricultural University, Key Laboratory of Sustainable Dryland Agriculture of Shanxi Province, Taiyuan, China.
Frontiers in plant science
|December 8, 2025
概括
本研究介绍了冬季小麦中 (N) 施肥的数据同化策略,优化N应用以降低成本和环境影响,同时保持高产量.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 过度的化肥导致环境污染,水和土壤质量下降,成本增加,农业可持续性下降.
- 优化 (N) 应用对于可持续农业和经济可行性至关重要.
研究的目的:
- 开发和评估一个两步数据同化策略,为冬季小麦提供精确的顶配饰建议.
- 通过优化N肥料来平衡作物产量和生产成本.
主要方法:
- 建立了一个数据同化系统,通过使用粒子群优化来最大限度地减少遥感衍生和作物模型模拟的地面干生物质 (AGB) 之间的差异.
- 使用DSSAT模型构建目标产量和N经济回报曲线,以优化N受精建议.
主要成果:
- 开发了一个强大的AGB监测模型,具有高的确定系数 (R2=0.94,R2=0.82).
- 对AGB和收益率的数据同化稳定性很高,具有较低的正常化根平均平方误差 (nRMSE).
- 发现基于数据同化的N受精建议,与高产水平应用相比,N受精应用减少了6.69%-34.08%.
结论:
- 开发的数据同化策略有效地优化了冬季小麦的N化肥.
- 这种方法平衡了作物生产率和经济回报,有助于农业的可持续性.
- 该战略提供了一个可行的解决方案,以减轻与过度施肥相关的环境污染.
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