Dynamic interactions of nitrogen fertilizer and straw application on greenhouse gas emissions and sequestration of soil carbon and nitrogen: A 13-year field study

2022 ◽  
Vol 325 ◽  
pp. 107753
Author(s):  
Qiong Huang ◽  
Guangbin Zhang ◽  
Jing Ma ◽  
Kaifu Song ◽  
Xiaoli Zhu ◽  
...  
2020 ◽  
Vol 12 (4) ◽  
pp. 2365-2380
Author(s):  
Xavier Morel ◽  
Birger Hansen ◽  
Christine Delire ◽  
Per Ambus ◽  
Mikhail Mastepanov ◽  
...  

Abstract. Arctic and boreal peatlands play a major role in the global carbon (C) cycle. They are particularly efficient at sequestering carbon because their high water content limits decomposition rates to levels below their net primary productivity. Their future in a climate-change context is quite uncertain in terms of carbon emissions and carbon sequestration. Nuuk fen is a well-instrumented Greenlandic fen with monitoring of soil physical variables and greenhouse gas fluxes (CH4 and CO2) and is of particular interest for testing and validating land-surface models. But knowledge of soil carbon stocks and profiles is missing. This is a crucial shortcoming for a complete evaluation of models, as soil carbon is one of the primary drivers of CH4 and CO2 soil emissions. To address this issue, we measured, for the first time, soil carbon and nitrogen density, profiles and stocks in the Nuuk peatland (64∘07′51′′ N, 51∘23′10′′ W), colocated with the greenhouse gas measurements. Measurements were made along two transects, 60 and 90 m long and with a horizontal resolution of 5 m and a vertical resolution of 5 to 10 cm, using a 4 cm diameter gouge auger. A total of 135 soil samples were analyzed. Soil carbon density varied between 6.2 and 160.2 kg C m−3 with a mean value of 50.2 kg C m−3. Mean soil nitrogen density was 2.37 kg N m−3. Mean soil carbon and nitrogen stocks are 36.3 kg C m−2 and 1.7 kg N m−2. These new data are in the range of those encountered in other arctic peatlands. This new dataset, one of very few in Greenland, can contribute to further development of joint modeling of greenhouse gas emissions and soil carbon and nitrogen in land-surface models. The dataset is open-access and available at https://doi.org/10.1594/PANGAEA.909899 (Morel et al., 2019b).


PLoS ONE ◽  
2013 ◽  
Vol 8 (8) ◽  
pp. e72019 ◽  
Author(s):  
Benjamin D. Duval ◽  
Kristina J. Anderson-Teixeira ◽  
Sarah C. Davis ◽  
Cindy Keogh ◽  
Stephen P. Long ◽  
...  

2012 ◽  
Vol 63 (3) ◽  
pp. 269 ◽  
Author(s):  
J. A. Baldock ◽  
I. Wheeler ◽  
N. McKenzie ◽  
A. McBrateny

Organic carbon and nitrogen found in soils are subject to a range of biological processes capable of generating or consuming greenhouse gases (CO2, N2O and CH4). In response to the strong impact that agricultural management can have on the amount of organic carbon and nitrogen stored in soil and their rates of biological cycling, soils have the potential to reduce or enhance concentrations of greenhouse gases in the atmosphere. Concern also exists over the potential positive feedback that a changing climate may have on rates of greenhouse gas emission from soil. Climate projections for most of the agricultural regions of Australia suggest a warmer and drier future with greater extremes relative to current climate. Since emissions of greenhouse gases from soil derive from biological processes that are sensitive to soil temperature and water content, climate change may impact significantly on future emissions. In this paper, the potential effects of climate change and options for adaptation and mitigations will be considered, followed by an assessment of future research requirements. The paper concludes by suggesting that the diversity of climate, soil types, and agricultural practices in place across Australia will make it difficult to define generic scenarios for greenhouse gas emissions. Development of a robust modelling capability will be required to construct regional and national emission assessments and to define the potential outcomes of on-farm management decisions and policy decisions. This model development will require comprehensive field datasets to calibrate the models and validate model outputs. Additionally, improved spatial layers of model input variables collected on a regular basis will be required to optimise accounting at regional to national scales.


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