根据时空空间深度学习,检测和归因玉米的极端产量损失.
Renhai Zhong1,2, Yue Zhu1, Xuhui Wang3
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Fundamental research
|June 27, 2024
概括
深度学习准确地估计了玉米产量变化,并确定了极端高温作为产量损失的主要驱动因素,这对全球粮食安全至关重要.
科学领域:
- 农业科学 农业科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 准确的作物产量估计和了解气候压力影响对于全球粮食安全至关重要.
- 深度学习对产量预测有前途,但其归因气候极端影响的能力尚不清楚.
研究的目的:
- 开发一个深度学习框架,用于估计玉米产量变化.
- 将产量损失归因于美国玉米带的极端气候事件.
- 为了确定受气候压力影响的关键作物生长阶段.
主要方法:
- 开发了一个基于深度神经网络的多任务学习框架.
- 将模型应用于美国玉米带 (2006-2018) 的县级玉米产量数据.
- 进行了归因分析,以确定热量,蒸汽压力赤字和降水的影响.
主要成果:
- 该模型准确地预测了收益率变化 (R2 = 0.81) 和极端的2012年异常 (R2 = 0.79).
- 极端热应激是产量损失的主要原因 (72.5%),其次是蒸汽压力赤字 (17.6%) 和降水 (10.8%).
- 2012年,丝化阶段被确定为对气候压力的产量反应最关键的阶段.
结论:
- 一个新的时空深度学习框架可以评估和归因作物产量对气候变化的反应.
- 这种方法对了解和减轻气候变化对农业的影响有价值.
- 研究结果支持加强在气候变化中确保粮食安全的战略.
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