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Microbial regulation of terrestrial nitrous oxide formation: understanding the biological pathways for prediction of emission rates
by
Hu, Hang-Wei
, He, Ji-Zheng
, Chen, Deli
in
Anthropogenic factors
/ Atmospheric models
/ Ecosystem
/ Ecosystem models
/ Ecosystems
/ Emissions
/ Environment
/ Environment models
/ Environmental factors
/ Fluxes
/ Greenhouse effect
/ Greenhouse gases
/ Guilds
/ Human influences
/ Isotopic enrichment
/ Microorganisms
/ Mitigation
/ Models, Theoretical
/ Nitrification
/ Nitrous oxide
/ Nitrous Oxide - metabolism
/ Soil - chemistry
/ Soil analysis
/ Soil Microbiology
/ Soils
/ Stable isotopes
/ Terrestrial ecosystems
2015
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Microbial regulation of terrestrial nitrous oxide formation: understanding the biological pathways for prediction of emission rates
by
Hu, Hang-Wei
, He, Ji-Zheng
, Chen, Deli
in
Anthropogenic factors
/ Atmospheric models
/ Ecosystem
/ Ecosystem models
/ Ecosystems
/ Emissions
/ Environment
/ Environment models
/ Environmental factors
/ Fluxes
/ Greenhouse effect
/ Greenhouse gases
/ Guilds
/ Human influences
/ Isotopic enrichment
/ Microorganisms
/ Mitigation
/ Models, Theoretical
/ Nitrification
/ Nitrous oxide
/ Nitrous Oxide - metabolism
/ Soil - chemistry
/ Soil analysis
/ Soil Microbiology
/ Soils
/ Stable isotopes
/ Terrestrial ecosystems
2015
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Do you wish to request the book?
Microbial regulation of terrestrial nitrous oxide formation: understanding the biological pathways for prediction of emission rates
by
Hu, Hang-Wei
, He, Ji-Zheng
, Chen, Deli
in
Anthropogenic factors
/ Atmospheric models
/ Ecosystem
/ Ecosystem models
/ Ecosystems
/ Emissions
/ Environment
/ Environment models
/ Environmental factors
/ Fluxes
/ Greenhouse effect
/ Greenhouse gases
/ Guilds
/ Human influences
/ Isotopic enrichment
/ Microorganisms
/ Mitigation
/ Models, Theoretical
/ Nitrification
/ Nitrous oxide
/ Nitrous Oxide - metabolism
/ Soil - chemistry
/ Soil analysis
/ Soil Microbiology
/ Soils
/ Stable isotopes
/ Terrestrial ecosystems
2015
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Microbial regulation of terrestrial nitrous oxide formation: understanding the biological pathways for prediction of emission rates
Journal Article
Microbial regulation of terrestrial nitrous oxide formation: understanding the biological pathways for prediction of emission rates
2015
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Overview
The continuous increase of the greenhouse gas nitrous oxide (N2O) in the atmosphere due to increasing anthropogenic nitrogen input in agriculture has become a global concern. In recent years, identification of the microbial assemblages responsible for soil N2O production has substantially advanced with the development of molecular technologies and the discoveries of novel functional guilds and new types of metabolism. However, few practical tools are available to effectively reduce in situ soil N2O flux. Combating the negative impacts of increasing N2O fluxes poses considerable challenges and will be ineffective without successfully incorporating microbially regulated N2O processes into ecosystem modeling and mitigation strategies. Here, we synthesize the latest knowledge of (i) the key microbial pathways regulating N2O production and consumption processes in terrestrial ecosystems and the critical environmental factors influencing their occurrence, and (ii) the relative contributions of major biological pathways to soil N2O emissions by analyzing available natural isotopic signatures of N2O and by using stable isotope enrichment and inhibition techniques. We argue that it is urgently necessary to incorporate microbial traits into biogeochemical ecosystem modeling in order to increase the estimation reliability of N2O emissions. We further propose a molecular methodology oriented framework from gene to ecosystem scales for more robust prediction and mitigation of future N2O emissions.
This review summarizes the major microbial pathways of soil N2O production, and key environmental factors modulating their relative contributions, and further proposes to use a combination of state-of-the-art approaches for better source partitioning and incorporation of microbial datasets to achieve better predictive ecosystem models.
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