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通过本地优化的N管理,中国的田中CH₄缓解和提高收益的巨大潜力

Haoyu Qian¹, Zhengqi Yuan¹, Xiangcheng Zhu²

¹Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China, Nanjing Agricultural University, Nanjing, China.

Global change biology

|March 16, 2026

概括

在大米田中优化 (N) 管理可以显著减少甲 (CH4) 排放量16%-21%. 这些做法还将大米产量提高7%,为粮食安全和气候变化减缓带来双重利益.

科学领域:

农业科学农业科学
环境科学环境科学
气候科学气候科学

背景情况:

(N) 管理对于粮食安全和减轻温室气体 (GHG) 排放至关重要.
现场特定的管理对大米田中甲 (CH4) 排放的影响不太清楚,并且不在当前的IPCC指南中.
米种植是CH4排放的主要来源,受N管理的影响.

研究的目的:

量化优化,特定地点的管理对田CH4排放的影响.
评估这些做法在同时提高产量和减少排放方面的潜力.
根据不同的管理制度,为估计CH4排放的改进方法提供信息.

主要方法:

对N管理实践的多区域实地实验的元分析.
研究土壤N的可用性和有机物分解对甲基生成的影响.
机器学习模型与调查数据相结合,以估计区域影响.

主要成果:

局部优化的管理 (延迟应用,减少输入,深度放置) 将CH4排放量减少16%至21%.
这些做法通过减少土壤N的可用性和有机物分解来限制甲基生成的基质来抑制CH4.
在中国,优化能战略可以使CH4排放量减少16%,大米产量增加7%.

结论:

优化,特定于地点的N管理为农业生产率和气候变化缓解提供了显著的副效益.
这些发现凸显了将优化管理纳入气候政策和排放清单的必要性.
在多样化的管理下,提高CH4排放量的量化对于有效的气候战略至关重要.

关键词:

机器学习模型机器学习模型这是一个元分析.来自大米田的甲排放.多区域实验多区域实验管理管理大米的产量大米的产量.

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