期刊
NUTRIENT CYCLING IN AGROECOSYSTEMS
卷 122, 期 2, 页码 173-189出版社
SPRINGER
DOI: 10.1007/s10705-022-10197-2
关键词
Climate change; CH4 flux; Paddy soil; Rice yield; Carbon cycle
类别
资金
- National Natural Science Foundation of China [42071023, 41775152]
Elevated CO2 concentrations do not have a significant impact on CH4 emissions in paddy fields, but they do increase the CH4 emission/yield ratio linearly. There is a significant linear correlation between CH4 flux and rice shoot biomass. Soil urease activity, dissolved organic carbon, ammonium content, invertase activity, dissolved organic carbon, and nitrate content can partially explain the variation in CH4 emissions.
There are significant differences in future atmospheric CO2 concentration under different carbon emission scenarios. Most studies have investigated the effects of elevated CO2 concentrations on CH4 emissions from paddy fields under a fixed CO2 concentration. However, atmospheric CO2 concentration are gradually increasing. Therefore, the relationship between elevated CO2 concentrations and CH4 emissions in paddy fields is still poorly understood. To investigate the effects of elevated CO2 concentrations on CH4 emissions and their mechanisms in paddy fields, a field experiment was conducted using open-top chambers during the 2018-2019 rice (Oryza sativa L.) growing seasons. The experimental treatments included three CO2 concentration levels: ambient CO2 concentration (C), low elevated CO2 concentration (C-1, 120 mu mol mol(-1) above C in 2018; C-2, 160 mu mol mol(-1) above C in 2019), and high elevated CO2 concentration (C-3, 200 mu mol mol(-1) above C). CH4 fluxes were measured using a transparent static chamber-laser greenhouse gas analyzer. The results showed that elevated CO2 concentrations had no significant effect on CH4 emissions in paddy fields, but they significantly increased the CH4 emission/yield ratio, which increased linearly with increasing CO2 concentration. In addition, there was a significant linear correlation between CH4 flux and the shoot biomass of rice. Stepwise regression analysis showed that the linear model based on soil urease activity and dissolved organic carbon and ammonium content explained 76% of the variation in CH4 emissions during the 2018 rice-growing season. Meanwhile, the linear model based on soil invertase activity and dissolved organic carbon and nitrate content explained 73% of the variation in CH4 emissions during the 2019 rice-growing season. Moreover, the linear model based on rice shoot biomass and soil dissolved organic carbon content explained 88% of the variation in CH4 emissions during two rice-growing seasons. Overall, the shoot biomass of rice, the activities of invertase and urease, and the unstable C and N substrate content in soil effectively controlled CH4 emissions in a japonica rice paddy field.
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