Biological mitigation of soil nitrous oxide emissions by plant metabolites

一氧化二氮 土壤水分 代谢物 环境化学 硝化作用 化学 初级代谢物 土壤pH值 自行车 孵化 氮气 反硝化 农学 环境科学 生物 生物化学 土壤科学 有机化学 历史 考古
作者
Yufang Lu,Fangjia Wang,J. Min,Herbert J. Kronzucker,Hua Yao,Haoming Yu,Feng Zhou,Weiming Shi
出处
期刊:Global Change Biology [Wiley]
卷期号:30 (5): e17333-e17333 被引量:25
标识
DOI:10.1111/gcb.17333
摘要

Plant metabolites significantly affect soil nitrogen (N) cycling, but their influence on nitrous oxide (N2O) emissions has not been quantitatively analyzed on a global scale. We conduct a comprehensive meta-analysis of 173 observations from 42 articles to evaluate global patterns of and principal factors controlling N2O emissions in the presence of root exudates and extracts. Overall, plant metabolites promoted soil N2O emissions by about 10%. However, the effects of plant metabolites on N2O emissions from soils varied with experimental conditions and properties of both metabolites and soils. Primary metabolites, such as sugars, amino acids, and organic acids, strongly stimulated soil N2O emissions, by an average of 79%, while secondary metabolites, such as phenolics, terpenoids, and flavonoids, often characterized as both biological nitrification inhibitors (BNIs) and biological denitrification inhibitors (BDIs), reduced soil N2O emissions by an average of 41%. The emission mitigation effects of BNIs/BDIs were closely associated with soil texture and pH, increasing with increasing soil clay content and soil pH on acidic and neutral soils, and with decreasing soil pH on alkaline soils. We furthermore present soil incubation experiments that show that three secondary metabolite types act as BNIs to reduce N2O emissions by 32%-45%, while three primary metabolite classes possess a stimulatory effect of 56%-63%, confirming the results of the meta-analysis. Our results highlight the potential role and application range of specific secondary metabolites in biomitigation of global N2O emissions and provide new biological parameters for N2O emission models that should help improve the accuracy of model predictions.
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