估计由有机和无机肥料输入产生的土壤N2O排放,采用基于Tier-2回归的元分析方法,对美国农田进行分析
Yushu Xia1, Hoyoung Kwon2, Michelle Wander1
1Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Campaign, 1102 S. Goodwin Ave, Urbana, IL 61801, USA.
The Science of the total environment
|March 27, 2024
概括
该研究开发了一种用于估计美国农业二氧化 (N2O) 排放的二级方法,揭示了关键的管理和环境驱动因素. 结果为更准确的温室气体 (GHG) 报告和政策制定提供了信息.
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
- 气候科学 气候科学
背景情况:
- 准确的温室气体 (GHG) 排放估计对于美国农业报告和政策制定至关重要.
- 目前用于估计美国农业土壤中的氧化 (N2O) 的方法包括Tier-1排放因子 (EFs) 和Tier-3基于过程的模型.
- 一级方法是不精确的,而三级方法是计算密集且昂贵的,因此需要中间方法.
研究的目的:
- 开发和验证一个基于Tier-2回归的方法,用于估计来自美国农业土壤的直接N2O排放.
- 通过使用全面的元数据库,确定影响N2O排放的关键环境和管理因素.
- 为区域N2O排放估计提供更准确和更具成本效益的方法.
主要方法:
- 从139项研究中编制了1883个现场N2O观测的元数据库.
- 基于回归的分析,特别是通用线性模型 (GLMs),被用于使用环境和管理共变量来建模N2O排放 (N2O_m).
- 该研究分析了测量期,耕作,肥料类型,土壤质地,作物类型,水资源管理和土壤秩序对N2O排放的影响.
主要成果:
- 月度土壤N2O排放量在生长季节 (0.38公斤N/ha) 高于休期 (0.15公斤N/ha).
- 根据耕作 (传统 > 无耕作),肥料类型 (液体 > 固体) 和土壤质地 (细 > 粗) 观察到N2O排放的显著差异.
- GLMs确定了耕作,水资源管理,N输入类型和速率,土壤特性和海拔作为N2O排放的关键共变量.
结论:
- 开发的Tier-2方法为现有的N2O估计方法提供了一个可行的替代方案,平衡准确性和成本.
- 测量期,农业管理实践和土壤特性显著影响N2O排放,需要在估计模型中考虑它们.
- 提高N2O排放和共变量的数据质量和可用性对于提高区域规模估计的准确性和为有效的气候政策提供信息至关重要.
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