施氮及添加硝化抑制剂对苜蓿草地N2O排放的影响

Effects of nitrogen application and nitrification inhibitor addition on N2O emissions in Medicago sativa L. grassland

  • 摘要: 为探究旱作紫花苜蓿(Medicago sativa L.)栽培草地氧化亚氮(N2O)排放对施氮水平及添加硝化抑制剂的响应特征,采用传统静态箱法研究了不同施氮水平0 kg(N)·hm-2(N0)、50 kg(N)·hm-2(N50)、100 kg(N)·hm-2(N100)和150 kg(N)·hm-2(N150)以及添加硝化抑制剂双氰胺(DCD)150 kg(N)·hm-2(N150+DCD)对陇东苜蓿草地N2O排放特征的影响。结果显示,监测期内N0、N50、N100和N150处理N2O平均排放速率分别为3.5 μg·m-2·h-1、4.1 μg·m-2·h-1、5.0 μg·m-2·h-1和6.1 μg·m-2·h-1,随着施氮梯度的增加,N2O排放速率呈增加趋势。添加硝化抑制剂DCD对N2O排放产生明显的抑制作用。与N150处理相比,N150+DCD处理下苜蓿草地N2O平均排放速率下降50.7%,N2O累计排放量显著降低61.6%(P < 0.05)。施氮对苜蓿产量没有显著影响,而N0、N50、N100和N150处理下单位苜蓿产量N2O排放量随氮肥梯度的增加而增加,各处理分别为6.5 mg·kg-1、7.8 mg·kg-1、11.3 mg·kg-1和12.5 mg·kg-1。N2O排放受土壤含水量影响深刻,生长季N2O排放通量与土壤水分呈显著正相关关系(P < 0.05),而与土壤温度无显著相关性(P>0.05)。综上,旱作紫花苜蓿栽培草地N2O排放通量随施氮水平的增加明显增加,在相同施氮水平下添加硝化抑制剂DCD能显著抑制N2O排放。相关研究结果对于该区域苜蓿草地合理施肥以及N2O减排具有一定的实践指导意义。

     

    Abstract: Nitrous oxide (N2O) is undoubtedly one of important greenhouse gases in the atmosphere, which can destroy the ozone layer and aggravate global warming. Agricultural activities, such as fertilizer application, crop straw returning, and biological nitrogen fixation, are the main sources of globally increasing N2O. Therefore, the study of N2O emission characteristics and its impact is of great significance for control and mitigation of environmental pollution. This study investigated the N2O release flux of alfalfa grassland as influenced by nitrogen application and nitrification inhibitor addition, using the static chamber method in Longdong District. The treatments included nitrogen applications of 0 (N0), 50 (N50), 100 (N100), and 150 (N150) kg(N)·hm-2; and nitrification inhibitor (dicyanogen, DCD) addition (N150+DCD). The static chambers were mounted for the estimation of N2O emissions from the enclosed alfalfa chambers for two hours daily, and the radiation, air temperature, soil temperature, and moisture were investigated simultaneously. The results showed that the average N2O emission rates were 3.5, 4.1, 5.0, and 6.1 μg·m-2·h-1 for N0, N50, N100, and N150 during the growing season, respectively. The N2O emission flux was significantly higher in N150 than that in other treatments (P < 0.05). Meanwhile, an increasing trend in the N2O emission rate was observed with the increasing nitrogen application gradient. Compared to the N150 treatment, the average N2O emission rate in the N150+DCD treatment decreased by 50.7%, and the cumulative N2O emissions significantly decreased by 61.6% (P < 0.05), indicating that the addition of a nitrification inhibitor had a significant inhibitory effect on the N2O emissions. Moreover, the addition of a soil nitrification inhibitor reduced the accumulation of NO3--N in the 0-40 cm soil layer and inhibited nitrification in the soil. The dry matter yield of alfalfa per cutting was not influenced by nitrogen application, as there were no significant differences between the N0 treatment and nitrogen application treatments (P>0.05). The N2O emissions per unit alfalfa yield were 6.5, 7.8, 11.3, and 12.5 mg·kg-1 for the N0, N50, N100, and N150 treatments, respectively. Therefore, the N2O emissions increased with the increasing nitrogen fertilizer application rates. It was also discovered that the N2O emissions were deeply affected by the soil moisture content. During the growing season, the N2O emission flux had a significant positive correlation with the soil moisture (P < 0.05), but no correlation with the soil temperature. Therefore, it could be concluded that nitrogen application can significantly stimulate N2O emissions in alfalfa grassland, which is the main reason for the highest N2O emissions being experienced during the alfalfa growing season. In addition, nitrogen application also had an impact on the N2O emissions per unit yield of alfalfa. The application of nitrogen together with a nitrification inhibitor can effectively reduce the N2O emissions caused by fertilization. While temperature may not influence N2O emissions, precipitation can stimulate N2O emissions during the growing season. These findings will help to provide a theoretical basis for greenhouse gas emission reduction and reduce the uncertainty concerning climate change prediction in the study area.

     

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