在不同生长阶段使用 LASSO,弹性网和逐步多重线性回归技术的天气变量估计玉米产量
Ananta Vashisth1, K S Aravind2
1Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India. ananta.iari@gmail.com.
Scientific reports
|January 6, 2026
概括
准确的玉米产量预测对农业至关重要. 这项研究发现,弹性网模型优于使用天气数据估计不同生长阶段的玉米产量.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 统计建模 统计建模
背景情况:
- 准确的玉米产量估计对于粮食安全和农业规划至关重要.
- 传统方法往往缺乏精度,需要先进的统计方法.
- 了解天气参数对作物发展的影响是关键.
研究的目的:
- 评估四种统计建模方法,以估计玉米在不同生长阶段的产量.
- 根据天气数据确定最准确的模型来预测玉米产量.
- 确定影响玉米产量的最有影响力的天气参数.
主要方法:
- 使用了最小绝对收缩和选择运算符 (LASSO),弹性网,步进多重线性回归 (SMLR) 和PCA-SMLR.
- 使用历史玉米产量数据 (1984-2021) 和每日天气参数开发模型.
- 验证了2020年和2021年的Kharif季节在植物,开花和谷物填充阶段的模型.
主要成果:
- 弹性网模型展示了最低的根平均平方误差 (RMSE) 和正常化的RMSE (nRMSE),表明了优异的性能.
- 估计和观察到的收益率之间的百分比偏差在增长阶段从4.8-29.1%不等.
- 温度和明亮的阳光时间被确定为玉米产量的最重要的预测因素.
结论:
- 弹性网模型是不同生长阶段玉米产量估计最可靠的模型.
- 拉索和SMLR也提供了有价值的玉米产量预测.
- 天气参数,特别是温度和阳光,是玉米产量的关键驱动因素.
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