机器学习揭示了来自各种长期作物系统的土壤二氧化排放的动态控制
Jashanjeet Kaur Dhaliwal1, Dinesh Panday1,2, G Philip Robertson3,4
1Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, Tennessee, USA.
Journal of environmental quality
|October 9, 2024
概括
长期的农业管理影响土壤的二氧化 (N2O) 排放. 机器学习模型揭示了控制不同作物系统N2O流量的不同因素,突出了土壤和气候变量的重要性.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 土壤科学 土壤科学
背景情况:
- 在密集作物系统中,土壤的二氧化 (N2O) 排放量是高度可变的,这使得对控制因素的理解变得复杂.
- 长期的生态和农业生态系统研究为分析复杂的环境过程提供了宝贵的数据.
研究的目的:
- 在各种长期作物系统中调查土壤N2O排放的驱动因素.
- 利用机器学习来预测N2O流量,并确定关键控制变量和值.
主要方法:
- 利用了17年的N2O流量数据,来自四个不同的玉米-大豆-冬季小麦轮换 (传统,不耕种,减少投入,有机).
- 采用随机森林机器学习模型,利用环境和土壤生物地球化学变量预测每日N2O流量.
- 为每个系统单独训练模型,评估可预测性和识别影响因素.
主要成果:
- 机器学习模型解释了29%-42%的每日N2O流量变化,在玉米阶段的准确性更高.
- 对N2O排放的控制因素因管理系统而异.
- 传统系统受氨和温度的影响;无耕作系统受气候变量影响;低输入/有机系统受酸盐,降水和温度的影响.
结论:
- 长期数据和机器学习可以根据管理,环境和生物地球化学驱动因素预测N2O排放.
- 不同的农业管理实践导致不同的值反应影响N2O排放.
- 在低投入和有机系统中的覆盖作物影响土壤的可用性和N2O排放.
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