基于天气的物流回归模型用于预测小麦头爆炸流行病
Monalisa De Cól1, Mauricio Coelho2, Emerson M Del Ponte1
1Departamento de Fitopatologia, Universidade Federal de Viçosa, Viçosa MG 36570-900, Brazil.
Plant disease
|March 29, 2024
概括
预测巴西的小麦头爆发疫情现在是可能的,有了新的经验模型. 该模型使用天气数据和小麦头号日期来预测疾病流行病,帮助作物管理.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 计算生物学 计算生物学
背景情况:
- 小麦头爆炸对巴西塞拉多地区的小麦产量构成重大威胁.
- 准确预测疾病流行病对于有效的作物管理和最大限度地减少产量损失至关重要.
研究的目的:
- 开发和验证经验模型,用于预测巴西的小麦头爆炸流行病.
- 为了确定主要的天气变量和预测疾病爆发的时间窗口.
主要方法:
- 在巴西多个地区 (2012-2020年) 收集了143个小麦头爆炸流行病的数据.
- 利用来自NASA POWER的每日天气数据和小麦出货日期 (WHD) 来创建36个潜在的预测器.
- 使用LASSO的后勤回归和模型开发的最佳子集选择,使用leave-one-out交叉验证 (LOOCV) 进行验证.
主要成果:
- 具有2-5个预测因素的模型实现了高性能:精度 (0.80-0.85),灵敏度 (0.80-0.91),特异性 (0.72-0.86) 和AUC (0.89-0.91).
- LOOCV准确度在0.76-0.81.81之间.
- 最终的模型结合了标注前的温度/湿度和标注后的降雨量,准确地预测了24年系列的爆发.
结论:
- 为适合热带和亚热带气候的小麦头爆发爆发开发了一个强大的预测模型.
- 该模型在预测历史流行病方面的准确性验证了其在疾病管理中的实际应用潜力.
- 这种工具可以显著帮助农民和研究人员减轻小麦头爆炸的影响.
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