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| Spatiotemporal pattern of cropland nitrous oxide emissions: Driving factors and global assessment |
| Received:February 01, 2020 |
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| KeyWord:nitrous oxide;agricultural ecosystem;zonality;driving factor;model simulation |
| Author Name | Affiliation | | ZHOU Feng | College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Laboratory for Earth Surface Processes, Peking University, Beijing 100871, China | | CUI Xiao-qing | College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Laboratory for Earth Surface Processes, Peking University, Beijing 100871, China | | SHANG Zi-yin | Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen AB24 3UU, UK | | WANG Qi-hui | College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Laboratory for Earth Surface Processes, Peking University, Beijing 100871, China |
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| Abstract: |
| The understanding of the spatiotemporal pattern of global cropland nitrous oxide emissions is critical foundation for designing mitigation strategies. In the past four decades, there were many studies that had focused on the underlying mechanisms and global assessment of global cropland nitrous oxide emissions. Here, the authors summarized previous studies related the scaling effects of climate, soil conditions, and agricultural management practices on the spatiotemporal pattern of global cropland nitrous oxide emissions. The authors introduced the research progress on multi-model ensembles(emission factor, land surface models, atmospheric inversion, and flux upscaling) and their application for global and regional assessment. Finally, the authors identified the research challenges on inter-regional observations, manipulative experiments related to extreme climate events, regional predictability of land surface models, and model-data assimilation. Addressing these challenges will enhance our mechanistic understanding of global nitrogen cycle and provide scientific supports for development of climate-smart agriculture. |
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