Assessment of the N2O emission reduction potential in greenhouse vegetable fields based on the DNDC model
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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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Keywords:
- DNDC model /
- Greenhouse vegetable field /
- Emission reduction potential /
- N2O
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农业作为温室气体主要排放源之一, 贡献了全球60%的CH4和N2O排放[1]。近年来设施农业在世界范围被证明是一种极有效率的农业生产措施[2]。同时中国作为设施菜地第一生产大国, 截至2019年底生产面积已达4×106 hm2, 占蔬菜种植面积的19.1%[3]。设施菜地相比于粮食生产系统具有不受季节影响、集约化生产、复种指数高、施肥量大、灌溉频繁等特点[4-5]。研究表明, 典型塑料大棚菜地(每年两季)的年肥料输入量是当地小麦(Triticum aestivum L.)-玉米(Zea mays L.)轮作的6~14倍[6-7], 设施菜地生产中的灌溉总量比中国同一地区谷物作物的高出约2~7倍[8]。频繁且高强度的田间管理措施, 必然会导致农田N2O排放的增加[9]。设施菜地年N2O排放为29.8 kg(N)∙hm−2, 分别是水稻(Oryza sativa L.)、小麦-玉米等旱地作物和露地蔬菜的24.8倍、17.5倍、6.2倍[10-13]。可见, 设施菜地N2O排放问题已成为当前集约农区面临的重大农业和生态环境问题。已有研究表明采用合理的田间管理措施可以减少设施菜地29.1%~49.2% N2O排放[7]。因此, 如何保障蔬菜有效供给的前提下减少N2O排放, 不仅是当前设施农业可持续发展所面临的主要问题之一, 也是我国实现“双碳”目标的现实要求。
农田N2O排放是一个复杂的生物化学过程, 土壤理化性质(主要包括温湿度、土壤质地、有机质、pH、Eh等)、土壤生物、水肥管理及耕作措施等都会对农田N2O排放产生较大的影响[14]。尤其是设施菜地系统, 属于密闭或半密闭环境, 使之长期处于高温、高湿环境, 加之过高的化肥和灌溉水投入量, 相较于粮食等作物生产系统微生物活跃, 土壤氮素硝化反硝化能力强, 土壤N2O的排放量更大, 影响机制更加复杂[11], 因此难以对设施菜地系统N2O排放进行精准估算和减排潜力分析。目前农田N2O排放和减排潜力估算采用较为广泛的方法为IPCC默认方法, 即排放因子(EFs)。但由于排放因子代表所有土壤类型、气候条件和管理实践的平均值, 故该方法具有较大的不确定性[15-16]。同时还有较多的学者使用田间观测的方法对农田N2O减排潜力进行估算[17-19], 但由于试验中环境复杂多变和监测成本高等原因, 难以完全覆盖各种影响因子对N2O排放的影响。仅通过有限数量的田间原位试验和简单的排放系数定量研究土壤N2O减排潜力是远远不够的。所以, 近年来越来越多的学者采用试验与过程模型相结合的方式来研究农田N2O排放等问题[20]。DNDC模型作为能同时模拟CO2、CH4和N2O等主要温室气体排放的生物地球化学模型之一[21], 已经被证实可广泛应用于我国不同农田生态系统[22-25]。已有研究表明DNDC模型能够较好地模拟设施菜地N2O排放, N2O排放的试验数据与模拟数据的标准均方根误差(nRMSE)为17%, R2为0.87[26]。但目前DNDC模型在设施菜地的验证和应用仍然较少, 尤其缺乏评估多种不同情景模式的减排潜力的研究。本研究以京郊典型设施菜地为研究对象, 采用DNDC模型与田间观测相结合的方法, 设置一系列不同的情景模式, 利用校验后的DNDC模型定量评估各影响因子对设施菜地N2O排放的影响, 并与基线情景进行比较估算N2O减排潜力, 以期为农业固碳减排研究提供新方法, 为国家及地方制定农业“双碳”目标及行动方案提供参考。
1. 材料与方法
1.1 试验地点概况
试验地点位于北京市顺义区大孙各庄镇(116°28′E、40°00′N), 该地区属于暖温带半湿润大陆性季风气候区, 四季分明, 年平均气温11.5 ℃, 年平均降水量625 mm, 年相对湿度为50%。供试塑料大棚长为75 m, 宽为7 m, 是普通的半拱圆形设施大棚, 一侧为黏土墙, 棚面由无色透明塑料棚膜覆盖。在温室的上部和底端设有通风口, 用于控制温室内的温度和湿度。该试验地土壤类型为潮褐土, 有机质含量为14.31 g∙kg−1, pH为7.23, 全氮含量为1.17%, NO3−-N含量为195.47 mg(N)·kg−1, NH4+-N含量为2.15 mg(N)·kg−1。
1.2 试验设计
试验对象为黄瓜(Cucumis sativus L.)-番茄(Lycopersicon esculentum Miller)轮作系统, 试验共设置两个处理, 分别为农民习惯处理(FP)和滴灌施肥处理(FPD), 各处理3个重复, 各小区间由深1 m、长6 m的塑料布隔离带隔开, 每个试验小区面积为24 m2。供试黄瓜品种为‘中农12’, 2017年9月13日定植, 同年12月28日拉秧; 供试番茄品种为‘超杂32’, 定植时间为2018年3月16日, 同年7月16日拉秧。植物种植株距为45 cm, 行距为60 cm。种植期间灌溉施肥管理如表1所示。
表 1 设施黄瓜-番茄轮作周期的灌溉施肥管理表Table 1. Detailed irrigation and fertilization management for cucumber-tomato rotation in greenhouse种植季
Planting season项目
Project日期(月-日)
Date (month-day)施肥量
Fertilization rate [kg(N)∙hm−2]灌水量
Irrigation amount (mm)黄瓜季 Cucumber 施有机肥 Organic fertilizer application 09-13 700 — 施基肥 Basal dressing 09-13 710 41.7 第1次追肥 First dressing 10-12 140 32.1 第2次追肥 Second dressing 11-01 140 29.2 第3次追肥 Third dressing 11-20 140 29.2 第4次追肥 Fourth dressing 12-04 70 32.1 番茄季 Tomato 施有机肥 Organic fertilizer application 03-12 800 — 基肥 Basal dressing 03-16 950 29.2 第1次追肥 First dressing 04-15 150 29.2 第2次追肥 Second dressing 05-12 150 41.7 第3次追肥 Third dressing 05-27 150 41.7 第4次追肥 Fourth dressing 06-12 150 41.7 1.3 样品采集与测定
待黄瓜、番茄成熟, 记录每次收获的果实产量, 并计算总产量。模型中产量以果实总碳含量表示, 单位为kg(C)∙hm−2, 其换算方式如下:
$$ C=Y\times \left(1-M\right)\times R $$ (1) 式中: C为果实产量的含碳总量, 单位为kg(C)∙hm−2; Y为收获的果实产量, 单位为kg∙hm−2; M为果实含水率, 单位为%; R为干物质含量中所含碳的比例, DNDC模型使用手册中推荐值40%。
在整个作物生长周期中采用自动静态箱-气相色谱法测定N2O气体排放。各小区放置一个静态集气暗箱装置, 放置位置为各小区中间垄中间位置右侧, 底座一半位于垄上一半位于垄下。定植时使植株位于取样装置底座中央。每次取样时间为8:00—10:00, 取样频率约为每周2次, 灌溉和施肥后增加取样频率。取样装置由底座和不透明箱体两部分组成, 在蔬菜移栽前将底座埋入各试验小区, 根据设施蔬菜的株行距, 底座和顶箱体分别设计为长、宽、高分别为70 cm×80 cm×25 cm和70 cm×80 cm×60 cm的长方体, 从而最大限度保证取样气体的代表性。取样时, 将箱体置于底座的凹槽中, 并在底座的凹槽中注入水以确保密封性良好, 每个小区每次取5袋气体样品, 每间隔6 min取一袋, 并通过箱体上的温度传感器, 记录箱体内部和5 cm深度处土壤的温度。取样所得样品使用改进的Agilent 7890A气相色谱仪分析N2O浓度。N2O气体排放通量计算方法如下:
$$ F=\rho \times H \times \frac{{d}_{c}}{{d}_{t}} \times \frac{273}{273+T}\times \frac{P}{{P}_{0}} $$ (2) 式中: F为N2O的排放通量[mg(N2O-N)·m−2·h−1], 正值表示土壤向大气排放, 负值表示吸收; ρ为标准状态下气体的密度(g·L−1); H为采样箱气室高度(m); T为采样箱内气温(℃); dc/dt为采样箱内N2O气体浓度随时间变化的速率(μL·L−1∙h−1); P为采样时气压(Pa); P0为标准大气压(Pa), P0≈1。
1.4 DNDC模型介绍及验证方法
DNDC (反硝化-分解)模型是一个用来模拟农业生态系统中碳氮循环的过程模型。模型主要分为两部分: 一部分是通过土壤气候、作物生长和分解子模型将气候、土壤、植被和人类活动等生态驱动因子与温度、湿度、pH、底物浓度等土壤环境因子连接起来; 另一部分连接生物地球化学过程, 包括硝化、反硝化和发酵3个子模型。DNDC模型通过结合作物生长曲线、作物生理过程和环境因素(例如辐射、气温、土壤湿度和可利用氮素)对作物生长进行模拟。本研究使用R2、标准化的均方根误差(nRMSE)、相对误差(RE) 3个统计指标评价模型。R2值表征模型的结果解释观测值变化的能力, 其值越趋近于1则说明模型结果越好; nRMSE为表征模拟值与观测值偏差的指标, nRMSE值越趋近于0则说明模拟结果越好; RE表征模拟值和观测值的总差异, RE值越趋近于0说明模拟结果越好, −5%<RE<5%表示模拟效果很好, 5%<RE<10%则表示模拟效果合格。
$$ \mathrm{n}\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}\left(\text{%}\right)=\frac{100}{\overline {O}}\sqrt{\frac{{\displaystyle\sum\nolimits }_{i=1}^{n}{({P}_{i}-{O}_{i})}^{2}}{n}} $$ (3) $$ {R}^{2}={\left[\frac{\displaystyle\sum ({O}_{i}-\overline {O})({P}_{i}-\overline{P})}{\sqrt{\displaystyle\sum {({O}_{i}-\overline {O})}^{2}}{({P}_{i}-\overline{P})}^{2}}\right]}^{2} $$ (4) $$ \mathrm{RE}= \frac{1}n\sum\nolimits _{i=1}^{n}\frac{({O}_{i}-{P}_{i})}{{O}_{i}}\times 100\text{%} $$ (5) 式中: Pi为模拟值,
$ \overline{P} $ 为模拟值的平均值, Oi为观测值, n为观测值的个数,$ \overline{O} $ 为观测值的均值。DNDC模型检验的初始输入参数如表2所示。其中, 作物生理参数由田间试验或者模型默认值确定, 其余参数均由田间实测获得。DNDC预运行1年, 使用2017年观测数据用来初始化土壤气候条件以及土壤矿态氮含量。使用FP处理的观测数据校正DNDC的土壤生物地球化学参数, 包括最大生物量、作物生长期积温(在作物生长期间, 日平均气温的总累计量)、生产单位生物量所需的水量、N2O排放通量。使用FPD处理的观测数据验证模型。
表 2 温室黄瓜-番茄种植系统的DNDC模型输入参数汇总表Table 2. Summary table of input parameters of DNDC model of cucumber-tomato planting system in greenhouse参数 Parameter 黄瓜 Cucumber 番茄 Tomato 土壤表层有机碳 Surface soil organic carbon content [g(C)∙kg−1] 14.3 田间持水量 Field capacity (soil water-filled pore space, WFPS) 0.6 土壤质地 Soil texture 粉壤土 Silt loam 黏粒含量 Clay fraction 0.14 容重 Soil bulk density (g∙cm−3) 1.29 萎蔫点 Soil WFPS at wilting point 0.16 孔隙度 Porosity 0.65 目标产量 Max. biomass production [kg(C)∙hm−2] 560.00 1660.68 成熟时生物量分配比例
Biomass fraction at harvest果 Grain 0.65 0.36 叶 Leaf 0.15 0.22 茎 Stem 0.15 0.22 根 Root 0.05 0.20 成熟时C/N比值
C/N ratio at harvest果 Grain 12.00 26.00 叶 Leaf 11.97 26.00 茎 Stem 11.33 26.00 根 Root 25.00 45.00 生长积温 Thermal degree days for maturity (℃) 1000 1400 需水量 Water demand [g(water)∙g−1(DM)] 500 300 1.5 情景设置及敏感性分析
为合理评估设施菜地N2O减排潜力, 本研究在课题组已有研究基础上对现有文献进行汇总(表3), 从中选取对N2O排放影响较大并且可通过DNDC模型进行调控的灌溉方式、施氮量、有机肥替代化肥、pH、土壤有机碳含量(SOC)共5个主要影响因子进行情景设置[27-28]。各影响因子设计如表4所示。
表 3 设施菜地N2O减排措施Table 3. N2O emission reduction measures of greenhouse vegetable fields研究方法
Research method种植时间(年-月)
Period (year-month)减排措施
Emission reduction measure减排效果
Emission reduction
effect (%)参考文献
Reference黄瓜-芹菜(Apium graveolens L.)田间试验
Cucumber-celery field experiment2016-03—2016-07 滴灌、减施50%化肥
Drip irrigation, 50% chemical fertilizer reduction35.2~57.5 [13] 黄瓜-番茄田间试验
Cucumber-tomato field experiment2017-09—2017-12 滴灌、减施50%化肥
Drip irrigation, 50% chemical fertilizer reduction23.5~47.2 [11] 芹菜-番茄田间试验
Celery-tomato field experiment2009-10—2021-02 有机肥替代0%、25%、50%、75%、100%化肥
Organic fertilizer replacing 0%, 25%, 50%, 75%, 100%
chemical fertilizer66.3~85.1 [29] 番茄田间试验
Tomato field experiment2016-09—2016-12 有机肥替代0%、50%、100%化肥
Organic fertilizer replacing 0%, 50%, 100% chemical fertilizer45.1~33.2 [30] 室内培养
Indoor culture— 调整pH至5.5~7.5 Adjusting pH to 5.5−7.5 42.6~70.1 [31] 室内培养
Indoor culture— 调整pH至5.1~8.15 Adjusting pH to 5.1−8.15 18.8~42.3 [32] Meta分析
Meta-analysis— 土壤有机碳变化范围为6.27~24.1 g∙kg−1
The variation range of soil organic carbon was 6.27−
24.1 g∙kg−148.6% [33] 表 4 DNDC模型土壤基础性质及管理措施情景设置Table 4. Soil basic properties and management measures scenarios setting in DNDC model项目 Item 情景设置 Scenario 单位 Unit 值 Value 灌溉方式
Irrigation method滴灌、漫灌*
Flood irrigation, drip irrigation— — 有机肥替代化肥比例
Proportion of organic fertilizer
replacing chemical fertilizer在基准线上有机肥替代一定比例的化肥
Organic fertilizer replacing a certain proportion of chemical
fertilizer based on the baseline— 0%*, 25%, 50%, 75%, 100% 化肥施氮量
Chemical-N application rate在基准线上减少10%、20%、30%施氮量或提高10%
Reduce nitrogen application by 10%, 20%, 30% or increase it by 10% based on
the baselinekg(N)∙hm−2 1015, 1160, 1305, 1450*, 1595 有机肥施氮量
Organic-N application rate在基准线上减少10%、20%、30%施氮量或提高10%
Reduce nitrogen application by 10%, 20%, 30% or increase it by 10% based on
the baselinekg(N)∙hm−2 910, 1040, 1170, 1300, 1430 酸碱度
pH在基准线上提高或减少10%、20%土壤酸碱度
Increasing or decreasing 10%, 20% soil pH based on baseline— 5.78, 6.46, 7.23*, 7.90, 8.68 土壤有机碳含量
Soil organic carbon content在基准线上提高或减少20%、40%土壤有机质含量
Increasing or decreasing 20%, 40% soil organic matter content based on baselineg(C)∙kg−1 8.6, 11.4, 14.3*, 17.2, 20.0 *代表基线情景。* represents the baseline scenario. 根据模拟结果对输入参数的响应程度计算敏感性指数(S, sensitive index), 以敏感性指数评价模拟结果受不同情景输入参数的影响程度。
$$ S=\frac{\left({O}_{2}-{O}_{1}\right)/{O}_{12}}{\left({I}_{2}-{I}_{1}\right)/{I}_{12}} $$ (6) 式中: I2、I1和I12分别为输入参数的最大值、最小值和平均值, O2、O1和O12为对应模拟输出结果的最大值、最小值和平均值。S值越大说明模拟结果受输入参数的影响程度越大。在本研究中, S值越大说明设施菜地的N2O排放受输入参数影响程度越大。
1.6 数据处理
N2O排放总量由作物生长期内逐日排放量累加得到。本文使用DNDC版本为9.5, 使用Excel 2016 进行数据计算, 使用SPSS进行统计分析, 使用Origin 2021进行制图。
2. 结果与分析
2.1 DNDC模型的校验
2.1.1 设施菜地系统作物产量的模拟及验证
DNDC模型对作物产量的模拟结果如图1所示, FP模式中黄瓜产量的观测值为491.16 kg(C)·hm−2, 番茄产量的观测值为2144.57 kg(C)·hm−2; 黄瓜产量的模拟值为512.83 kg(C)∙hm−2, 番茄产量的模拟值为2157.37 kg(C)∙hm−2。试验数据与模型数据的产量nRMSE为1.35%, RE为−2.50%。FPD模式中黄瓜产量的观测值为529.27 kg(C)·hm−2, 番茄产量的观测值为2184.16 kg(C)∙hm−2; 黄瓜产量的模拟值为512.83 kg(C)·hm−2, 番茄产量的模拟值为2147.10 kg(C)∙hm−2。试验数据与模型数据的产量nRMSE为2.11%, RE为2.40%。故DNDC模型对产量的模拟结果处于优秀水平, 说明DNDC模型可以模拟不同管理措施对蔬菜产量的不同影响。
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图 1 DNDC模型对不同灌溉施肥方式下设施菜地系统作物产量的模拟结果FP: 农民习惯处理; FPD: 滴灌施肥处理。FP: farmer’s conventional treatment; FPD: drip fertigation treatmentFigure 1. Simulation results of crops yield of greenhouse vegetable system under different irrigation and fertilization treatments by DNDC model2.1.2 对设施菜地土壤温度、土壤孔隙含水率的模拟与验证
DNDC模型对土壤5 cm深度处温度的模拟如图2所示, FP处理与模拟值的nRMSE为25.32%, R2为0.956; FPD处理与模拟值的nRMSE为26.08%, R2为0.959。表明土壤温度实测值与模拟值之间模拟结果良好。从图中可以看出黄瓜季10月底开始, 土壤温度的实测值高于模拟值, 原因是为防止冬季大棚温度过低会在大棚顶部覆盖棉被。同时由图可知在番茄季4月下旬开始, 土壤温度实测值低于模拟值, 原因是为防止大棚气温过高会在中午开启大棚通风口。模型可以较为准确地模拟设施菜地土壤温度的变化, 但是模型无法模拟冬季覆盖棉被及夏季打开通风口等更加具体的耕作措施。
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图 2 DNDC模型对不同灌溉施肥方式下设施菜地土壤5 cm温度的模拟结果FP: 农民习惯处理; FPD: 滴灌施肥处理。Figure 2. Simulation results of 5 cm soil temperature of greenhouse vegetable system under different irrigation and fertilization treatments by DNDC modelFP: farmer’s conventional treatment; FPD: drip fertigation treatment.DNDC模型对0~20 cm土壤孔隙含水率的模拟如图3所示, FP处理观测值与模拟值的nRMSE为15.89%, R2为0.964; FPD处理观测值与模拟值的nRMSE为18.56%, R2为0.944。模型模拟值与观测值较为一致, 说明DNDC模型能够较好地模拟土壤孔隙含水率的变化。
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图 3 DNDC模型对不同灌溉施肥方式下设施菜地土壤0~20 cm孔隙含水率的模拟结果FP: 农民习惯处理; FPD: 滴灌施肥处理。Figure 3. Simulation results of 0−20 cm soil water-filled pore space (WFPS) of greenhouse vegetable system under different irrigation and fertilization treatments by DNDC modelFP: farmer’s conventional treatment; FPD: drip fertigation treatment.2.1.3 设施菜地N2O通量的模拟与验证
N2O模拟值及其与观测值之间的相关性如图4所示。由于在未定植前无法安装静态集气箱, 故无法捕捉到每个季度由翻耕施加基肥导致的第一个排放峰值。N2O通量模拟值与FP处理的观测值R2为0.918, 与FPD处理的观测值的R2为0.824。表明模拟数据与观测数据之间具有较高的相关性。FP处理观测值与模拟值的nRMSE为25.56%, FPD处理观测值与模拟值的nRMSE为37.24%。在图中可以看出DNDC模型很好地模拟了N2O的排放趋势, 模拟值与观测值之间基本吻合。故DNDC模型具有较好地模拟设施菜地N2O排放的能力。
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图 4 DNDC模型对不同灌溉施肥方式下设施菜地N2O排放的模拟结果FP: 农民习惯处理; FPD: 滴灌施肥处理。Figure 4. Simulation results of N2O emission of greenhouse vegetable system under different irrigation and fertilization treatments by DNDC modelFP: farmer’s conventional treatment; FPD: drip fertigation treatment.2.2 不同情景设置下N2O减排潜力评估
2.2.1 单因子情景下减排潜力评估
基于DNDC模型对不同单因子情景下N2O排放总量模拟如图5。采用滴灌措施使N2O排放总量减少12.64%。N2O排放总量与化肥施氮量呈现较明显的正相关, 以化肥施氮量1450 kg(N)∙hm−2为基线, 当施氮量减少30%时, N2O排放总量相对于基线中N2O排放总量12.18 kg(N)∙hm−2减少14.86%。随有机肥替代化肥比例升高, N2O排放总量呈现先降低后升高的趋势, 并且有机肥替代化肥比例为75%时N2O排放总量最小, 相较基准线降低12.23%。N2O排放总量对pH的变化响应较为复杂, 当pH相较基线7.23提高20%时N2O排放总量最低, 较基准线降低了15.60%。N2O排放总量随SOC的升高而升高, 当SOC为设置基准线的60%时, N2O排放总量比基准线减少17.57%。
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图 5 DNDC模型对不同情景下设施菜地N2O排放总量模拟结果*代表基线。横坐标为设置的各组情景。SOC为土壤有机碳。FP: 农民习惯处理; FPD: 滴灌施肥处理。Figure 5. Simulation results of total N2O emissions of greenhouse vegetable system under different scenarios by DNDC model* represents the baseline scenario. The X-axis is scenario settings. SOC is soil organic carbon. FP: farmer’s conventional treatment; FPD: drip fertigation treatment.由DNDC的模拟结果计算设施菜地不同情景设置下输入参数的N2O排放累积敏感性指数, 结果如表5所示。该结果表明, 对N2O排放总量结果影响最大的影响因子为pH, 其次为施氮量。而有机肥替代化肥比例与土壤有机碳含量影响较小, 其中有机肥替代化肥比例和土壤pH的变化与N2O排放总量呈负相关。
表 5 基于DNDC模型的设施菜地系统N2O排放总量敏感性指数Table 5. Sensitive indexes of total N2O emissions of greenhouse vegetable system based on DNDC model输入参数 Parameter 情景设置 Scenario setting 敏感性指数 Sensitive index 灌溉方式 Irrigation method 滴灌、漫灌 Flood irrigation, drip irrigation — 有机肥替代化肥比例
Proportion of organic fertilizer replacing chemical fertilizer0~100% 0.466 化肥施氮量 Nitrogen application rate 1015~1595 kg(N)∙hm−2 0.580 pH 5.74~8.62 0.867 土壤有机碳含量 Soil organic carbon content 8.6~20.0 g(C)∙kg−1 0.428 2.2.2 组合情景下N2O减排潜力评估
本研究对灌溉方式、化肥施氮量及有机肥施氮量等3种管理措施共50种情景进行模拟。所有情景中产量为2650.40~2670.73 kg(C)·hm−2, 变化幅度仅为基准线的−0.68%~0.07%, 变化较小。仅考虑耕作措施组合情景的N2O排放结果如图6所示, N2O排放量的变化范围为8.32~13.20 kg(N)·hm−2。其中滴灌+0.7倍化肥施氮量+0.7倍有机肥施氮量情景为仅通过改变耕作措施能达到N2O排放总量最小的情景。该情景的N2O排放总量为8.32 kg(N)·hm−2, 比基准线情景减少31.69%。此外，模拟得到N2O排放随施氮量增加而增加。同时考虑耕作措施和土壤环境变化组合情景的N2O排放模拟结果如图7、图8所示, pH对N2O的影响较小与单因子情景相同, pH为5.74时情景的N2O排放高于其他pH情景。同样与单因子情景中相同SOC越高则N2O排放越高。在所有情景中N2O排放总量范围为5.41~16.63 kg(N)·hm−2, 其中滴灌+0.6倍SOC+1.2倍pH+0.7倍化肥施氮量+0.7倍有机肥施氮量情景为所有情景中N2O排放总量最小的情景。该情景排放量为5.41 kg(N)·hm−2, 相较基线情景N2O排放总量减少55.58%。故本研究表明选择适宜的耕作措施可以减少设施菜地N2O排放3.86 kg(N)·hm−2, 在此基础上结合土壤理化性质调控可进一步减少N2O排放至6.77 kg(N)·hm−2。
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图 6 DNDC 模型对不同管理措施组合情景下设施菜地N2O排放模拟F和D分别代表漫灌和滴灌。*代表基线。Figure 6. N2O emission simulation of greenhouse vegetable system under different management measures combination scenarios by DNDC modelF and D represent flood irrigation and drip irrigation. * represents the baseline scenario.![]()
图 7 DNDC 模型对漫灌条件下不同组合情景的设施菜地N2O排放模拟结果SOC为土壤有机碳; *代表基线, pH基线值为7.23, SOC baseline值为14.3 g(C)∙kg−1。Figure 7. N2O emission simulation results of greenhouse vegetable system by DNDC model under different combination scenarios under flooding irrigationSOC is soil organic carbon. * represents the baseline scenario. The baseline vales of pH and SOC are 7.23 and 14.3 g(C)∙kg−1, respectively.![]()
图 8 DNDC 模型对滴灌条件下不同组合情景的设施菜地N2O排放模拟结果SOC为土壤有机碳; *代表基线, pH基线值为7.23, SOC baseline值为14.3 g(C)∙kg−1。Figure 8. N2O emission simulation results of greenhouse vegetable system by DNDC model under different combination scenarios under drip irrigationSOC is soil organic carbon. * represents the baseline scenario. The baseline vales of pH and SOC are 7.23 and 14.3 g(C)∙kg−1, respectively.3. 讨论
3.1 DNDC模型模拟的不确定性评价
DNDC模型开发时间早、使用范围广、扩展内容丰富, 是目前国际上最成功的模拟陆地生物地球化学循环的模型之一[34]。并且在过去20多年中, 世界各地的研究人员对DNDC进行了修改和调整, 以适应特定的目的和情况[35]。DNDC目前已经被很多的研究证明能够较好地模拟农田产量与N2O的排放[36-37]。本研究中DNDC模型对蔬菜产量模拟效果较好, FP和FPD处理产量模拟值与实测值的nRMSE仅为1.35%和2.1%。但在本研究中土壤5 cm深度温度模拟结果与实测结果仍然存在差异。首先模型无法模拟由农业设施改进而产生的影响。如在拟合土壤温度时所产生的误差, 是由于模型无法模拟冬季覆盖棉被保温以及夏季打开通风口等措施而产生的。同时由于覆盖棉被措施影响光照导致冬季土壤含水量(WFPS)的实测值高于模拟值, 打开通风口也使设施菜地空气流动增加导致夏季时WFPS的模拟值高于实测值。另一方面在本研究中实测的N2O通量略低于DNDC模型模拟结果[38-41]。其他研究人员的研究中也显示此问题, 在一些灌溉事件之后, DNDC高估了N2O通量, 大部分的研究人员将此归结于模型高估了土壤厌氧条件的持续时间或反硝化速率对土壤厌氧条件变化的敏感性[25,39]。目前中国设施农业在快速发展中, 设施内的设备与技术也在不断进步[40]。但DNDC模型中对覆膜技术的参数输入仍然较简单, 仅有大棚温室、地膜覆盖两种模式, 对于两种措施的描述也仅有铺设次数及时间。故随着设施农业技术及设备的不断更新换代, DNDC模型用于设施菜地可能需要设计更加详细的参数。同时随着制备肥料技术逐步发展, DNDC模型中化肥施用输入参数也逐渐不能全面描述新型化肥对农业生态系统碳氮循环过程产生的影响。虽然总体来说本研究中DNDC能较为准确地模拟设施菜地各项指标的变化, 但是DNDC模型仍然需要结合更多的研究校准内部参数, 并且根据农业技术发展增加新的描述参数。
3.2 各影响因子对设施菜地N2O排放的影响
滴灌措施对N2O排放的影响仍存在争议, 大部分研究表明使用滴灌措施可以有效地减少N2O的排放[5,13,42]。滴灌措施的土壤含水量较低, 因此滴灌会降低产生N2O的细菌的活性, 从而减少N2O的产生与排放[43]。另一方面有研究表明滴灌的氮肥供应可以更好地综合供水和作物需求, 从而减少氮素在环境中的损失[44], 与本研究中滴灌措施减少N2O的排放结果一致。在本研究基线土壤条件情景下, 随着有机肥替代化肥比例增加, 设施菜地N2O的排放会先降低再升高。这与奚雅静等[30]的研究结果相似, 这是由于适量施用有机肥使易被利用的矿物质氮替换为有机氮, 减少了氮供应, 降低了N2O排放[45]。但同时有机肥会提供更多的可溶性有机碳作为碳基质[46], 因此也会加速反硝化过程, 将更多的土壤有效氮转化为N2O, 使反硝化菌的活性增强, 刺激N2O排放[47-48]。所以当有机肥替代化肥比例达100%时, N2O排放总量又高于基线情景。本研究中的化肥施氮量对N2O的排放影响较大, 施氮量和有机质含量主要是通过影响土壤中参与硝化反应与反硝化反应的底物浓度来影响土壤N2O排放[49], 因此本研究中N2O排放与化肥施氮量和SOC呈正相关。pH对N2O的排放产生影响的机理较为复杂, 近年来的研究发现在较高土壤含水率时, 反硝化作用是土壤产生N2O的主要来源[27], 有研究表明酸性土壤中反硝化真菌群落数量更多[50], 因此酸性土壤中N2O排放较高。另一方面过低pH会降低N2O还原酶的活性, 从而降低微生物对有机质和矿物质氮源的利用率[51-52]。
3.3 设施菜地N2O减排潜力分析
吴震等[7]使用Meta分析方法对菜地N2O排放进行分析, 研究结果表明菜地减施氮肥可降低49.4%N2O排放、有机肥替代可降低26.6% N2O排放、优化灌溉可降低34.3% N2O排放, 与本研究结果相近。谢海宽等[13]于北京房山区通过试验测得减少施氮量可以减少6.46 kg(N)·hm−2的N2O排放, 与本研究减少施氮量可以减少4.49 kg(N)·hm−2的N2O排放存在一些差异。此误差可能是由于该试验采样频率限制, 而采用内插法计算N2O排放总量存在误差导致。而Zhang等[26]的研究中番茄减氮施肥的减排潜力为39.98 kg(N)·hm−2。在实际生产中农户为保证设施菜地产量多采取较高的水肥投入[53], 大量的水肥投入必然会导致更高的N2O的排放。同时也有研究指出随种植年限的增长N2O的排放也会增加[54], 本研究也对3种管理措施组合成的50组情景进行10年的模拟, 所有情景的N2O排放总量均逐年增加。其中滴灌+0.7倍化肥施氮量+0.7倍有机肥施氮量情景10年累计N2O排放总量仍为所有情景中最少, 相较基线情景10年累计N2O排放总量减少39.16%, 高于1年模拟结果得出的减排潜力。在实际土壤环境中较难单一控制某一影响因素, 如SOC与土壤孔隙度以及土壤容重等指标呈较强的相关性[55]。因此田间土壤的基础性质很难进行单一的改良, 并且改良土壤基础性质成本较高。在田间种植中通过调整土壤基础理化性质以减少N2O排放量的做法可行性较差。
4. 结论
1)模型评估表明, DNDC模型对设施菜地蔬菜产量、土壤温度、土壤孔隙含水率和N2O排放模拟结果较好, DNDC模型可以用来对设施菜地N2O排放进行模拟评价。
2)设施菜地N2O排放主要影响因素为pH, 其次为化肥施氮量, 有机肥替代化肥比例与SOC则影响最小。施氮量与SOC的增加会增加N2O排放。而有机肥替代化肥比例增加会使N2O排放先减少再增加。而土壤pH变化对N2O排放的影响较为复杂, 但调整pH会显著影响N2O排放。此外由滴灌代替漫灌也可降低N2O排放。
3)在仅考虑管理措施情景中, 使用滴灌并减少30%有机肥施氮量和30%化肥施氮量为 N2O排放最小的情景。该情景较基线情景可减少31.69%的N2O排放。在综合考虑土壤SOC与pH变化下, 滴灌并且减少30%化肥施氮量及30%有机肥施氮量, 同时选择比基线SOC低40%的土壤, 并提高20% pH的情景为全情景中N2O排放最小的情景, 该情景相较基线情景可以减少55.58%的N2O排放。故设施菜地N2O减排潜力可达6.77 kg(N)∙hm−2。
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图 1 DNDC模型对不同灌溉施肥方式下设施菜地系统作物产量的模拟结果
FP: 农民习惯处理; FPD: 滴灌施肥处理。FP: farmer’s conventional treatment; FPD: drip fertigation treatment
Figure 1. Simulation results of crops yield of greenhouse vegetable system under different irrigation and fertilization treatments by DNDC model
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图 2 DNDC模型对不同灌溉施肥方式下设施菜地土壤5 cm温度的模拟结果
FP: 农民习惯处理; FPD: 滴灌施肥处理。
Figure 2. Simulation results of 5 cm soil temperature of greenhouse vegetable system under different irrigation and fertilization treatments by DNDC model
FP: farmer’s conventional treatment; FPD: drip fertigation treatment.
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图 3 DNDC模型对不同灌溉施肥方式下设施菜地土壤0~20 cm孔隙含水率的模拟结果
FP: 农民习惯处理; FPD: 滴灌施肥处理。
Figure 3. Simulation results of 0−20 cm soil water-filled pore space (WFPS) of greenhouse vegetable system under different irrigation and fertilization treatments by DNDC model
FP: farmer’s conventional treatment; FPD: drip fertigation treatment.
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图 4 DNDC模型对不同灌溉施肥方式下设施菜地N2O排放的模拟结果
FP: 农民习惯处理; FPD: 滴灌施肥处理。
Figure 4. Simulation results of N2O emission of greenhouse vegetable system under different irrigation and fertilization treatments by DNDC model
FP: farmer’s conventional treatment; FPD: drip fertigation treatment.
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图 5 DNDC模型对不同情景下设施菜地N2O排放总量模拟结果
*代表基线。横坐标为设置的各组情景。SOC为土壤有机碳。FP: 农民习惯处理; FPD: 滴灌施肥处理。
Figure 5. Simulation results of total N2O emissions of greenhouse vegetable system under different scenarios by DNDC model
* represents the baseline scenario. The X-axis is scenario settings. SOC is soil organic carbon. FP: farmer’s conventional treatment; FPD: drip fertigation treatment.
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图 6 DNDC 模型对不同管理措施组合情景下设施菜地N2O排放模拟
F和D分别代表漫灌和滴灌。*代表基线。
Figure 6. N2O emission simulation of greenhouse vegetable system under different management measures combination scenarios by DNDC model
F and D represent flood irrigation and drip irrigation. * represents the baseline scenario.
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图 7 DNDC 模型对漫灌条件下不同组合情景的设施菜地N2O排放模拟结果
SOC为土壤有机碳; *代表基线, pH基线值为7.23, SOC baseline值为14.3 g(C)∙kg−1。
Figure 7. N2O emission simulation results of greenhouse vegetable system by DNDC model under different combination scenarios under flooding irrigation
SOC is soil organic carbon. * represents the baseline scenario. The baseline vales of pH and SOC are 7.23 and 14.3 g(C)∙kg−1, respectively.
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图 8 DNDC 模型对滴灌条件下不同组合情景的设施菜地N2O排放模拟结果
SOC为土壤有机碳; *代表基线, pH基线值为7.23, SOC baseline值为14.3 g(C)∙kg−1。
Figure 8. N2O emission simulation results of greenhouse vegetable system by DNDC model under different combination scenarios under drip irrigation
SOC is soil organic carbon. * represents the baseline scenario. The baseline vales of pH and SOC are 7.23 and 14.3 g(C)∙kg−1, respectively.
表 1 设施黄瓜-番茄轮作周期的灌溉施肥管理表
Table 1 Detailed irrigation and fertilization management for cucumber-tomato rotation in greenhouse
种植季
Planting season项目
Project日期(月-日)
Date (month-day)施肥量
Fertilization rate [kg(N)∙hm−2]灌水量
Irrigation amount (mm)黄瓜季 Cucumber 施有机肥 Organic fertilizer application 09-13 700 — 施基肥 Basal dressing 09-13 710 41.7 第1次追肥 First dressing 10-12 140 32.1 第2次追肥 Second dressing 11-01 140 29.2 第3次追肥 Third dressing 11-20 140 29.2 第4次追肥 Fourth dressing 12-04 70 32.1 番茄季 Tomato 施有机肥 Organic fertilizer application 03-12 800 — 基肥 Basal dressing 03-16 950 29.2 第1次追肥 First dressing 04-15 150 29.2 第2次追肥 Second dressing 05-12 150 41.7 第3次追肥 Third dressing 05-27 150 41.7 第4次追肥 Fourth dressing 06-12 150 41.7 
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表 2 温室黄瓜-番茄种植系统的DNDC模型输入参数汇总表
Table 2 Summary table of input parameters of DNDC model of cucumber-tomato planting system in greenhouse
参数 Parameter 黄瓜 Cucumber 番茄 Tomato 土壤表层有机碳 Surface soil organic carbon content [g(C)∙kg−1] 14.3 田间持水量 Field capacity (soil water-filled pore space, WFPS) 0.6 土壤质地 Soil texture 粉壤土 Silt loam 黏粒含量 Clay fraction 0.14 容重 Soil bulk density (g∙cm−3) 1.29 萎蔫点 Soil WFPS at wilting point 0.16 孔隙度 Porosity 0.65 目标产量 Max. biomass production [kg(C)∙hm−2] 560.00 1660.68 成熟时生物量分配比例
Biomass fraction at harvest果 Grain 0.65 0.36 叶 Leaf 0.15 0.22 茎 Stem 0.15 0.22 根 Root 0.05 0.20 成熟时C/N比值
C/N ratio at harvest果 Grain 12.00 26.00 叶 Leaf 11.97 26.00 茎 Stem 11.33 26.00 根 Root 25.00 45.00 生长积温 Thermal degree days for maturity (℃) 1000 1400 需水量 Water demand [g(water)∙g−1(DM)] 500 300
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表 3 设施菜地N2O减排措施
Table 3 N2O emission reduction measures of greenhouse vegetable fields
研究方法
Research method种植时间(年-月)
Period (year-month)减排措施
Emission reduction measure减排效果
Emission reduction
effect (%)参考文献
Reference黄瓜-芹菜(Apium graveolens L.)田间试验
Cucumber-celery field experiment2016-03—2016-07 滴灌、减施50%化肥
Drip irrigation, 50% chemical fertilizer reduction35.2~57.5 [13] 黄瓜-番茄田间试验
Cucumber-tomato field experiment2017-09—2017-12 滴灌、减施50%化肥
Drip irrigation, 50% chemical fertilizer reduction23.5~47.2 [11] 芹菜-番茄田间试验
Celery-tomato field experiment2009-10—2021-02 有机肥替代0%、25%、50%、75%、100%化肥
Organic fertilizer replacing 0%, 25%, 50%, 75%, 100%
chemical fertilizer66.3~85.1 [29] 番茄田间试验
Tomato field experiment2016-09—2016-12 有机肥替代0%、50%、100%化肥
Organic fertilizer replacing 0%, 50%, 100% chemical fertilizer45.1~33.2 [30] 室内培养
Indoor culture— 调整pH至5.5~7.5 Adjusting pH to 5.5−7.5 42.6~70.1 [31] 室内培养
Indoor culture— 调整pH至5.1~8.15 Adjusting pH to 5.1−8.15 18.8~42.3 [32] Meta分析
Meta-analysis— 土壤有机碳变化范围为6.27~24.1 g∙kg−1
The variation range of soil organic carbon was 6.27−
24.1 g∙kg−148.6% [33]
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表 4 DNDC模型土壤基础性质及管理措施情景设置
Table 4 Soil basic properties and management measures scenarios setting in DNDC model
项目 Item 情景设置 Scenario 单位 Unit 值 Value 灌溉方式
Irrigation method滴灌、漫灌*
Flood irrigation, drip irrigation— — 有机肥替代化肥比例
Proportion of organic fertilizer
replacing chemical fertilizer在基准线上有机肥替代一定比例的化肥
Organic fertilizer replacing a certain proportion of chemical
fertilizer based on the baseline— 0%*, 25%, 50%, 75%, 100% 化肥施氮量
Chemical-N application rate在基准线上减少10%、20%、30%施氮量或提高10%
Reduce nitrogen application by 10%, 20%, 30% or increase it by 10% based on
the baselinekg(N)∙hm−2 1015, 1160, 1305, 1450*, 1595 有机肥施氮量
Organic-N application rate在基准线上减少10%、20%、30%施氮量或提高10%
Reduce nitrogen application by 10%, 20%, 30% or increase it by 10% based on
the baselinekg(N)∙hm−2 910, 1040, 1170, 1300, 1430 酸碱度
pH在基准线上提高或减少10%、20%土壤酸碱度
Increasing or decreasing 10%, 20% soil pH based on baseline— 5.78, 6.46, 7.23*, 7.90, 8.68 土壤有机碳含量
Soil organic carbon content在基准线上提高或减少20%、40%土壤有机质含量
Increasing or decreasing 20%, 40% soil organic matter content based on baselineg(C)∙kg−1 8.6, 11.4, 14.3*, 17.2, 20.0 *代表基线情景。* represents the baseline scenario.
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表 5 基于DNDC模型的设施菜地系统N2O排放总量敏感性指数
Table 5 Sensitive indexes of total N2O emissions of greenhouse vegetable system based on DNDC model
输入参数 Parameter 情景设置 Scenario setting 敏感性指数 Sensitive index 灌溉方式 Irrigation method 滴灌、漫灌 Flood irrigation, drip irrigation — 有机肥替代化肥比例
Proportion of organic fertilizer replacing chemical fertilizer0~100% 0.466 化肥施氮量 Nitrogen application rate 1015~1595 kg(N)∙hm−2 0.580 pH 5.74~8.62 0.867 土壤有机碳含量 Soil organic carbon content 8.6~20.0 g(C)∙kg−1 0.428
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