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机器学习与生态系统组合模型的结合大大改善了从美国农田流出的氧化 (N₂O) 流量的预测

Prateek Sharma¹, Bruno Basso^1,2,3, Aditya Manuraj¹

¹Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824.

Proceedings of the National Academy of Sciences of the United States of America

|March 4, 2026

概括

一个整体建模系统 (EMS) 改善了农业排放的氧化 (N2O) 排放的预测. 这种方法准确地预测N2O流动,识别土壤碳和等关键驱动因素,以便制定更好的缓解策略.

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:

氧化 (N2O) 是一种强大的温室气体,其大气度不断增加.
在农业中低效的合成肥使用有助于N2O排放.
预测土壤N2O流量是很困难的,因为复杂的生态化学过程和高变性.

研究的目的:

开发和验证一个集成建模系统 (EMS),以更好地预测美国农田的N2O流量.
提高对影响农业土壤N2O排放的关键驱动因素的理解.
评估EMS对区域N2O库存和减缓战略的潜力.

主要方法:

将一组生态系统模型与一组机器学习 (ML) 模型结合起来,以创建EMS.
培训和验证EMS在美国中西部多个不同地点,作物和管理实践的约12,000个N2O室测量.
分析环境和土壤变量对N2O流量预测的贡献.

主要成果:

电子气体系统准确地预测了每日N2O流量,在培训和测试场所实现R2 = 0.84.
确定了六个N2O排放的主要驱动因素:土壤有机碳 (SOC), (NH4+),酸盐 (NO3-),充满水的孔隙空间,温度和地面生物质.
高的N2O排放发生在含有足够SOC和矿物N的潮湿,温暖的土壤中,而低SOC的土壤显示出最小的流量.

结论:

与独立模型相比,EMS显著提高了N2O流量预测的准确性.
了解土壤条件 (SOC,矿物N) 和环境因素的相互作用对于N2O排放动态至关重要.
电子气体系统在预测新地点的N2O流量方面具有很强的潜力,有助于制定有针对性的农业减排战略.

关键词:

在这里,我们可以看到AIAIAI.组合建模组合建模组合建模组合建模机器学习是机器学习.多模组合的多模组合.氧化排放的排放量是多少

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