基于DNDC模型的设施菜地N2O减排潜力评估

Assessment of the N2O emission reduction potential in greenhouse vegetable fields based on the DNDC model

  • 摘要: 设施菜地因水肥投入高而导致大量N2O排放已成为当前研究热点。N2O作为主要温室气体之一, 探寻N2O减排潜力不仅可为设施菜地碳减排方案的制定提供一定参考, 还可为实现我国“双碳”目标提供科学依据。本研究以京郊典型设施黄瓜-番茄系统为研究对象, 通过田间试验与DNDC模型相结合的方法, 基于田间观测数据对模型进行校验, 然后以农民常规种植模式为基线情景, 改变田间管理措施(灌溉方式、施氮量、有机肥替代化肥等)和调控土壤理化性质土壤有机碳(SOC)、pH等为替代情景, 运用DNDC模型通过1250次模拟得到单一情景和多组合情景下N2O排放量, 并评估其减排潜力。结果表明, DNDC模型能够较好地模拟设施菜地土壤温湿度、蔬菜产量和N2O排放量。基线情景下N2O排放总量为12.18 kg(N)∙hm−2。单因子情景分析表明, 设施菜地N2O减排潜力变幅为12.23%~17.58%。敏感性指数显示N2O排放对土壤pH调控和化肥减施的响应比对其余单因子较为敏感, 其中相比于基线情景, 1.2倍土壤pH情景和减施30%化肥情景N2O排放量分别降低15.60%和14.86%。多组合因子情景表明, 与基线情景相比, 同时采用滴灌、减少30%的化肥施氮量和减施30%有机肥组合情景, 可降低31.69%的N2O排放。而相同组合在低SOC及高pH的土壤情景中N2O减排潜力可进一步降低, 达到55.58% 6.77 kg(N)∙hm−2。可见, DNDC模型可较好地模拟田间环境, 克服田间试验中有限的处理设置和较高的监测成本等局限性, 从而为设施菜地N2O排放定量评估和减排评价提供了一个较好的解决方案。DNDC对设施菜地N2O排放的单因子情景和组合情景的模拟结果表明, 结合土壤理化性质调控和水肥管理措施优化具有较大的N2O减排潜力。

     

    Abstract: The large amount of N2O emission associated with high water and fertilizer inputs in greenhouse vegetable fields has become a salient issue. As N2O is one of the major greenhouse gases, the research on reducing N2O emissions can provide not only a reference for the formulation of carbon reduction plans for greenhouse vegetable fields but also a scientific basis to realize China’s “dual carbon” target. In this study, the N2O emission of a typical greenhouse cucumber-tomato system in the Beijing suburbs was studied by using field monitoring and the DNDC model. The model was calibrated using field observations, and farmers’ conventional practices were set as the baseline scenario. The scenarios with changes in field management practices (e.g., irrigation method, N application rate, and replacement of chemical fertilizer by organic fertilizer) and regulation of soil physicochemical properties (soil organic carbon, pH, etc.) were set. N2O emissions were obtained from 1250 simulations of the DNDC model for single scenario and multiple combinations of scenarios, and their emission reduction potentials were evaluated. The results showed that the DNDC model can simulate the soil temperature, soil water-filled pore space, vegetable yield, and N2O emissions in greenhouse vegetable fields. The total N2O emissions in the baseline scenario were 12.18 kg(N)∙hm−2. The variation in the N2O reduction potential of greenhouse vegetable fields ranged from 12.23% to 17.58% under the single-factor scenario. The sensitivity index showed that N2O emissions were more sensitive to soil pH regulation and fertilizer reduction than to the other scenarios, with N2O emissions (10.28 kg(N)∙hm−2) reduced by 15.60% and 14.86% for the 1.2-unit-change-in-soil-pH scenario and the 30% fertilizer reduction scenario (10.38 kg(N)∙hm−2), respectively, compared to the baseline. The multiple combination scenarios showed that a reduction of 31.69% in N2O emissions from the baseline could be achieved with a combination of drip irrigation, 30% reduction in chemical N application, and 30% reduction in organic fertilizer. The N2O reduction potential further improved to 55.58% (6.77 kg(N)∙hm−2) for the same combination in the low soil organic carbon and high pH soil scenarios. Overall, the DNDC model can simulate the field environment and overcome the drawbacks of limited treatment settings and high monitoring costs in field experiments, providing a useful method to quantitatively assess and reduce N2O emissions in greenhouse vegetable fields. The combination of regulating soil physicochemical properties and optimizing water and fertilizer management can effectively reduce N2O emission in greenhouse vegetable fields.

     

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