• ISSN 1008-505X
  • CN 11-3996/S
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Effects of warming on the decomposition rates of the straw of different crops in soils and modelling

  •   【Objectives】  This study aimed to investigate the effects of warming on the decomposition rates of different crop straws in soils.   【Methods】  A field embedding experiment was performed during the two growing seasons from December 2019 to October 2020. The decomposition percentages of the straw of maize, sweet potato and soybean during the summer crops growing season and the straw of winter wheat, garlic and rapeseed during the autumn crops growing season were measured. The coefficients of decomposition rates were simulated with a function including the initial C, N, lignin content and C/N ratio of different crop straws. The modelled decomposition coefficient k value was further used to predict the residual straw mass of different crops after the different embedding days. The predicted and observed residual straw mass was analyzed using a linear regression to evaluate the modelling efficiency.   【Results】  Warming significantly (P < 0.05) increased the decomposition percentage of the straw of maize, sweet potato and soybean during the early decomposition period, but had no significant (P > 0.05) effects on the decomposition percentage during other decomposition periods. Warming induced a significant (P < 0.05) increase in the decomposition percentage of the straw of rapeseed during the whole growing season. Warming had no significant (P > 0.05) effects on the decomposition percentage of the straw of garlic during the whole growing season. Warming had no significant (P > 0.05) effects on the coefficients of decomposition rates of the straw of each of the six crops. However, there were significant (P < 0.05) differences in the coefficients of decomposition rates of the straw among the six crops. The decomposition coefficient was highest for the garlic straw and lowest for the soybean straw under both control and warming treatments. The decomposition coefficient of the garlic straw was twice times higher than that of soybean straw. Multiple regression (k=−1.073C+7.315N+0.223C/N−0.004L+33.900) including the variables of the carbon content (C), nitrogen content (N), the ratio of carbon to nitrogen (C/N) and lignin content (L) could be used to model the variations in the coefficients of decomposition rates of the straw of the six crops under the different treatments. The model simulated 92.1% (R2 = 0.921, P < 0.001) of the variation in the decomposition rates of the straw. The slope for the linear regression function fitting the relationship between the modelled and the observed decomposition rates of the straw was very close the 1:1 line. The validation analysis based on the residual straw mass after the different embedding days and the modelled decomposition coefficients of straw showed that the modelled and observed values of residual straw mass of different crops fitted well (R2 = 0.922, P < 0.001), indicating that this model effectively simulated the straw decomposition dynamics.  【Conclusions】  Warming significantly increased the decomposition rates of the straw of maize, sweet potato and winter wheat compared with the control but did not impact the decomposition rates of the straw of soybean, garlic and rapeseed. The coefficients of decomposition rates of crop straws could be modelled by the C, N, lignin content and C/N of crop straws. This model could also well explain the remained straw mass of different crops after different embedding days.
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Effects of warming on the decomposition rates of the straw of different crops in soils and modelling

    Corresponding author: CHEN Shu-tao, chenstyf@aliyun.com
  • 1Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2. School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract:   【Objectives】  This study aimed to investigate the effects of warming on the decomposition rates of different crop straws in soils.   【Methods】  A field embedding experiment was performed during the two growing seasons from December 2019 to October 2020. The decomposition percentages of the straw of maize, sweet potato and soybean during the summer crops growing season and the straw of winter wheat, garlic and rapeseed during the autumn crops growing season were measured. The coefficients of decomposition rates were simulated with a function including the initial C, N, lignin content and C/N ratio of different crop straws. The modelled decomposition coefficient k value was further used to predict the residual straw mass of different crops after the different embedding days. The predicted and observed residual straw mass was analyzed using a linear regression to evaluate the modelling efficiency.   【Results】  Warming significantly (P < 0.05) increased the decomposition percentage of the straw of maize, sweet potato and soybean during the early decomposition period, but had no significant (P > 0.05) effects on the decomposition percentage during other decomposition periods. Warming induced a significant (P < 0.05) increase in the decomposition percentage of the straw of rapeseed during the whole growing season. Warming had no significant (P > 0.05) effects on the decomposition percentage of the straw of garlic during the whole growing season. Warming had no significant (P > 0.05) effects on the coefficients of decomposition rates of the straw of each of the six crops. However, there were significant (P < 0.05) differences in the coefficients of decomposition rates of the straw among the six crops. The decomposition coefficient was highest for the garlic straw and lowest for the soybean straw under both control and warming treatments. The decomposition coefficient of the garlic straw was twice times higher than that of soybean straw. Multiple regression (k=−1.073C+7.315N+0.223C/N−0.004L+33.900) including the variables of the carbon content (C), nitrogen content (N), the ratio of carbon to nitrogen (C/N) and lignin content (L) could be used to model the variations in the coefficients of decomposition rates of the straw of the six crops under the different treatments. The model simulated 92.1% (R2 = 0.921, P < 0.001) of the variation in the decomposition rates of the straw. The slope for the linear regression function fitting the relationship between the modelled and the observed decomposition rates of the straw was very close the 1:1 line. The validation analysis based on the residual straw mass after the different embedding days and the modelled decomposition coefficients of straw showed that the modelled and observed values of residual straw mass of different crops fitted well (R2 = 0.922, P < 0.001), indicating that this model effectively simulated the straw decomposition dynamics.  【Conclusions】  Warming significantly increased the decomposition rates of the straw of maize, sweet potato and winter wheat compared with the control but did not impact the decomposition rates of the straw of soybean, garlic and rapeseed. The coefficients of decomposition rates of crop straws could be modelled by the C, N, lignin content and C/N of crop straws. This model could also well explain the remained straw mass of different crops after different embedding days.

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  • 温室气体排放导致的全球气候变暖引起了国内外的广泛关注。CO2是全球变暖效应的最主要贡献者[1],大气中CO2浓度升高与全球碳平衡受到扰动有关[2]。全球变暖可能会引起一系列的生态系统变化[3],人们一般通过增温试验研究温度升高对生态系统的影响,并对未来生态系统可能产生的变化进行预估[4]

    植物残体分解过程是生态系统碳循环中的一个重要过程[5],这一过程影响土壤有机碳储存量和土壤碳排放量,而且会影响土壤条件[6-7]。以往已有关于在不同土壤生态系统中植物残体分解特性及环境影响因素的研究,这些研究主要通过野外填埋试验和室内培养试验进行[8-9]。已有研究发现,不同植物类型的分解速率存在很大差异[10-11],例如木本植物比草本植物分解地更慢[10]。有研究表明,在秸秆填埋后20 天内,大约70%的秸秆可被分解[12];Nygerg等[13]发现约70%~90%的秸秆在填埋后40 天内可被分解。有研究表明,植物残体氮含量和木质素含量分别对其分解过程起促进和抑制作用[14]。以往研究虽然表明温度对植物残体分解具有影响[15],但缺乏在野外条件下设置的增温试验来研究植物残体的分解特性,关于增温如何影响农田生态系统中作物秸秆分解率的研究非常鲜见,也特别缺乏对不同增温条件下不同作物秸秆分解速率系数的精确模拟研究[16],对增温和对照条件下不同类型作物秸秆分解速率系数的准确模拟有助于分析秸秆填埋后任一时间的剩余量和分解量,阐明不同类型秸秆在增温和对照条件下的阶段性分解规律,进行这样的研究对于阐明生态系统中植物残体分解对全球变暖的响应规律具有重要意义[17]

    本研究设置增温和对照田间试验,对两个生长季6种作物秸秆的分解率进行观测,并对其分解速率系数进行模拟,以期为评估农田生态系统秸秆分解过程在未来气候变化下的变异规律和制定合理的应对措施提供基础数据和理论支撑。

  • 1.   材料与方法

      1.1.   试验地点

    • 田间试验在南京信息工程大学农业气象试验站进行,试验地点为32°12'N, 118°42'E,海拔高度为18 m,年降水量为1062 mm,年平均气温为15.5 ℃。试验地土壤为黄棕壤,在本试验开始前采集0—20 cm土壤测定田间持水量为25.6%,pH(1∶2.5土水比)为6.50,容重为1.54 g/cm3,有机碳含量为7.40 g/kg,全氮含量为0.92 g/kg,有效磷(Olsen-P)含量为20.80 mg/kg。

    • 1.2.   试验设计

    • 于2019年11月—2020年10月冬小麦−大豆轮作年进行田间试验,夏熟作物为冬小麦,秋熟作物为大豆。试验地采用区组设计,设置对照和增温处理,每个处理4个重复,共8个小区,每个小区面积为2.5 m×2.5 m,在每个增温处理小区设置4根250 W红外辐射加热管,红外辐射加热管可同时改变显热和潜热,可较准确模拟实际增温状态[18]。加热管均匀分布于整个小区,昼夜对试验区加热。每根加热管上方覆盖一个不锈钢罩,以防止雨雪淋湿加热管造成短路。在对照处理仅设置不锈钢罩以模拟遮光效应。2019—2020冬小麦−大豆轮作年对照和增温处理的土壤5 cm深度年平均温度分别为20.43℃±0.05℃和22.58℃±0.04℃。增温和对照处理的施肥量及施肥时间相同。小麦基施复合肥(N-P2O5-K2O=7.7–7.7–7.7 g/m2),追施尿素24.7 g/m2,夏大豆基施复合肥同小麦。

      在2019—2020夏熟作物生长季填埋玉米、红薯、大豆秸秆,在2020秋熟作物生长季填埋冬小麦、大蒜、油菜籽秸秆。秸秆碳含量、氮含量、碳氮比、木质素含量见表1。秸秆预先在70℃烘箱中烘干,再剪成2 cm长小段,在每个48 µm孔径的尼龙网袋(尺寸为10 cm×15 cm)中装入10 g秸秆。于每个生长季播种后第2天(即2019年11月4日和2020年6月1日)在每个小区埋入每种作物的5个网袋在种植作物条件下秸秆的分解状态可与实际农业管理措施条件下基本一致。网袋填埋深度为10 cm,网袋在两根灯管间均匀排列,横向间隔约15 cm。在2019—2020夏熟作物生长季,于填埋后的20、50、163、180、197天将网袋从各个小区中取出;在2020年秋熟作物生长季,于网袋填埋后的20、50、80、110、141天将网袋从各个小区中取出。由于每个处理设了4个重复,故网袋数为2×6×5×4个。

      秸秆类型 Straw typeC (%)N (%)C/N木质素 (%) Lignin
      玉米 Maize40.74±0.351.42±0.0928.96±1.944.55±0.30
      红薯 Sweet potato41.05±0.272.05±0.1220.49±2.038.75±2.08
      大豆 Soybean45.31±0.212.00±0.1623.42±2.5413.26±3.67
      冬小麦 Winter wheat39.03±0.321.41±0.1228.08±2.338.07±0.85
      大蒜 Garlic40.37±0.092.14±0.0118.83±0.0713.62±1.51
      油菜籽 Rapeseed39.08±0.390.84±0.0846.79±4.9116.49±5.30

      Table 1.  The carbon, nitrogen and lignin content and C/N ratio of the test crop straws

    • 1.3.   秸秆分解率及土壤温度、湿度测定

    • 将填埋到田间对照和增温处理土壤中的尼龙网袋取出,由于秸秆装在300目的尼龙网袋中,因而秸秆上粘附的土壤颗粒非常少。把网袋放在塑料盆中,在非常短的时间内用水浸泡几下网袋中的秸秆并迅速冲洗掉秸秆上附着的少量泥土,以减少清洗秸秆过程中的损失量。清洗后的秸秆在105℃下杀青1 h,然后于70 ℃烘干至恒重,在天平上称量秸秆质量。采用公式(1)计算秸秆分解率。

      式中,DR、M1、M2分别代表分解率、填埋前秸秆质量、剩余秸秆质量。在每次取出网袋秸秆时测定5 cm深度土壤温度和含水量。

    • 1.4.   数据分析及秸秆分解速率系数的模拟

    • 采用方差分析研究不同填埋天数对照和增温处理中秸秆分解率的差异,采用配对T检验分析整个生长季对照和增温处理中秸秆分解率的差异。秸秆分解速率符合一级动力学模型,即公式(2)。

      式中:M为剩余秸秆质量;M0为秸秆初始质量;t为秸秆填埋后时间;k为秸秆分解速率系数。

      基于公式(2)和秸秆填埋后不同天数的剩余秸秆质量,可计算得到2019—2020夏熟作物生长季和2020秋熟作物生长季共6种作物秸秆在对照和增温处理下的分解速率系数k值。参考Parton等[19]的方法,采用多元回归建立基于秸秆碳含量、氮含量、碳氮比、木质素含量的增温和对照条件下不同类型秸秆分解速率系数k值的模拟模型。采用分解速率系数k模拟值与观测值的回归图评估对k值的模拟效果。利用模型模拟得到的k值进一步模拟增温和对照处理下不同作物在填埋后不同天数的剩余秸秆质量,将剩余秸秆质量的模拟值与观测值进行回归分析以评估模型模拟效果。

    2.   结果与分析

      2.1.   不同处理下的土壤温度及含水量

    • 在2019—2020夏熟作物生长季和2020秋熟作物生长季,在秸秆从土壤中取出的每个日期,增温处理下土壤温度始终高于对照(图1)。在2019—2020轮作年对照和增温处理的土壤温度变化范围分别为10.42℃~28.71℃和13.55℃~29.95℃。虽然不同日期下的增温幅度略有差异,但增温处理温度始终高于对照,受气温变化幅度的影响相对较小。在2019—2020轮作年,对照和增温处理的土壤含水量变化范围分别为10.98%~35.24%和9.05%~31.48%,在2019—2020夏熟作物、秋熟作物轮作年增温造成土壤含水量极显著(P < 0.001)下降。

      Figure 1.  Soil temperature and moisture at different days after straw embedding during summer crop (a) and autumn crop growing seasons

    • 2.2.   增温和对照处理不同作物秸秆的分解率变化

    • 图2所示,在夏熟作物生长季,在秸秆填埋后20 天,对照和增温处理玉米秸秆分解率存在显著(P < 0.05)差异,对照和增温处理红薯秸秆分解率存在边缘显著(P = 0.062)差异,填埋后20 天后对照和增温处理玉米及红薯秸秆分解率均无显著(P > 0.05)差异;而对照和增温处理大豆秸秆分解率在整个生长季无显著(P > 0.05)差异。由此可见,在夏熟作物生长季,增温对玉米和红薯秸秆分解率的促进作用主要体现在填埋的前20天。

      Figure 2.  The decomposition percentages of straws under control and warming treatments at different days after straw embedding

      在秋熟作物生长季,在秸秆填埋后50天对照和增温处理冬小麦秸秆分解率存在显著(P < 0.031)差异,在其他时间分解率均无显著(P > 0.05)差异。对照和增温处理大蒜秸秆分解率在整个生长季均无显著(P > 0.05)差异。在秸秆填埋后141天对照和增温处理油菜籽秸秆分解率存在边缘显著(P = 0.065)差异,配对T检验表明,在整个生长季增温处理油菜籽秸秆分解率显著(P < 0.05)高于对照。可见,在秋熟作物生长季,秸秆在整个填埋阶段的差异性相对较小。

      大蒜秸秆在填埋初期的分解率即达到很高的数值,例如填埋后20 天对照和增温处理大蒜秸秆分解率分别为(76.90±2.06)%和(77.25±0.56)%。经过初期分解后,对照和增温处理大蒜秸秆在整个生长季的分解率变化不大。与大蒜秸秆不同,其他作物秸秆在整个生长季的分解率表现为逐渐增加趋势,特别是红薯秸秆在整个生长季分解率增幅较大,这表明红薯秸秆物质释放过程相对于其他秸秆具有相对更大的梯度。

    • 2.3.   秸秆分解速率系数的模拟

    • 根据公式(2),对增温和对照条件下6种作物秸秆在填埋后不同天数的质量进行非线性拟合可得到不同作物秸秆分解速率系数k值(表2)。配对T检验结果表明,增温对秸秆分解速率系数影响不显著(P > 0.05),但不同作物秸秆分解速率系数存在显著差异,无论是对照还是增温条件下,大蒜秸秆的分解速率系数均最高,大豆秸秆的最低,大蒜秸秆分解速率系数是大豆秸秆的3倍,表明大蒜秸秆分解较快,红薯、冬小麦、油菜籽3种作物秸秆分解速率系数之间无显著差异(P > 0.05)。

      秸秆类型 Straw type对照 Control增温 Warming
      玉米 Maize7.5±0.3 c7.3±0.3 c
      红薯 Sweet potato9.0±0.7 b9.0±0.7 ab
      大豆 Soybean5.3±0.6d4.8±0.5 d
      冬小麦 Winter wheat8.8±0.3 bc7.8±0.5 bc
      大蒜 Garlic11.0±0.6 a10.5±0.6 a
      油菜籽 Rapeseed8.3±0.3 bc8.8±0.5 bc
      注(Note):数据后不同字母表示不同作物间存在显著差异 (P < 0.05) Values followed by different letters indicate significant differences between the different straw types (P < 0.05).

      Table 2.  The decomposition coefficient (k) of crop straws under control and warming treatments

      由于夏熟作物和秋熟作物生长季的温度存在差异,故应采用温度敏感系数(其取值一般设定为2[20])对秸秆分解速率系数进行矫正。以作物秸秆碳含量(C)、氮含量(N)、碳氮比(C/N)、木质素含量(L)为因变量,采用多元线性回归,得到分解速率系数的模拟方程:k=−1.073C+7.315N+0.223C/N−0.004L+33.900 (R2 = 0.921),模拟值和实测值进行的拟合结果表明两者间具有极显著线性回归关系(R2 = 0.921,P < 0.001)。对不同变量相关显著性统计分析(表3)表明,其中木质素含量的效应不显著,碳氮含量及碳氮比这3个变量可解释分解速率系数变异的92.1%。

      变量
      Variables
      参数
      Parameters
      标准误差
      Standard errors
      标准化参数
      Standardized coefficient
      C−1.0730.102−1.283
      N7.3152.5361.923
      C/N0.2230.1271.175
      L−0.0040.083−0.010

      Table 3.  The statistics for the model simulating the variations in the coefficient of decomposition rates of straw

      利用模拟得到的秸秆分解速率系数k值对本研究填埋试验中测定的不同填埋天数后的剩余秸秆质量进行验证分析(图3),可见增温和对照处理下作物剩余秸秆质量的模拟值和测定值均具有非常好的一致性(R2 = 0.950,P < 0.001),表明本研究中的秸秆分解速率系数k值模拟方程能有效模拟增温和对照处理下各种作物秸秆在不同填埋天数后的剩余质量(图4)。

      Figure 3.  The relationship between the modelled and observed coefficient of decomposition rates of straw

      Figure 4.  Relationship between modelled and measured residual straw mass

    3.   讨论

      3.1.   增温对秸秆分解率的影响

    • 温度影响作物的分解过程[14-15],我们的研究科学假定增温可能会在整个生长季各个填埋阶段促进作物秸秆分解。然而,从在本研究结果看,夏熟作物生长季,增温仅在填埋后的20天左右提高了玉米、红薯的分解率,对其他作物秸秆在整个生长季的总体分解率未产生显著影响,增温仅提高了油菜籽秸秆在全生长季的分解率。这样的试验结果与进行试验前的科学假定存在差异。一方面,大多数作物秸秆的分解率对增温的响应过程不是持续的,增温仅促进了易分解成分的降解,而对难分解成分没有作用,因而,增温对秸秆的总体分解率的促进作用不明显。另一方面,本研究设定的增温幅度为年平均增加2.15℃,这一数值与IPCC预测的本世纪末全球变暖限定值(1.5℃~2.0℃)[1](IPCC2013)基本一致。在这样一个相对较小的增温幅度下,大部分作物秸秆的分解过程受其影响可能相对较小。

      除了大蒜秸秆在填埋后20天的分解率达到70%以上,其余作物秸秆的分解率在填埋后20天略高于40%,在分解末期达到或接近80%,这表明不同作物秸秆中难分解成分具有类似的难降解属性[21],并且较低的增温幅度促进其降解的效果有限,其降解还依赖土壤中微生物的长期作用[22]。由于本研究中的秸秆填埋试验设定为一个生长季的时间尺度,而秸秆完全分解的时间长度会比一个生长季更长,本研究尚未能从秸秆全分解阶段尺度上对秸秆分解过程进行观测和模拟,今后有必要设置更长时间尺度(1年)的增温条件下的秸秆分解试验,并设定高于本研究中增温幅度的不同增温梯度,以研究增温影响秸秆分解的长期效应。

      在实际土壤环境中,秸秆矿化过程中形成的微细植物残体片段、中间产物及腐殖质等物质可能随水分运动进入了土壤,与土壤粘粒结合成有机无机复合体而被保存下来,这部分碳并未转化成CO2而进入大气[23-24],本研究中的分解速率可视为质量损失率,因此,网袋法可能是一种相对粗糙的研究方法。此外,温度对秸秆分解率的影响存在阶段性,特别是对易分解性成分含量较高的“初期阶段”影响显著,随易分解成分的减少而影响强度减弱。今后有必要在网袋填埋法的基础上结合其他方法(如同位素示踪法)对秸秆分解过程中以CO2形式的排放量和土壤固持量进行更深入地研究。

    • 3.2.   秸秆分解速率系数的模拟

    • 不同作物秸秆由于碳氮含量等自身属性的差异,会造成秸秆分解时呈现出不同的分解速率,本研究表明,虽然增温未显著影响秸秆分解速率系数,但不同作物秸秆分解速率系数存在差异,秸秆碳含量、氮含量、碳氮比、木质素含量对于分解速率系数具有很好的解释性。以往的研究表明植物残体的分解速率与氮含量和木质素含量有关[25]。一般而言,氮含量与植物蛋白质含量有关[26-27],氮含量高的植物其易分解组分含量也高,分解速率相对更快,而木质素为植物中的难分解组分,木质素分解速率相对较慢[5,28]。黄耀等[29]的室内培养试验表明,植物残体分解速率系数可用基于植物残体氮含量和木质素含量的线性方程模拟。利用培养试验的结果,生物地球化学循环模型CENTURY模型将植物残体的分解特性表达为木质素含量与氮含量比值的方程[30-31]。本研究的田间填埋试验结果表明,不同处理下不同作物秸秆分解速率系数可由碳含量、氮含量、碳氮比、木质素含量解释,本试验结果与以往研究具有一致性,但同时表明秸秆碳含量和碳氮比对秸秆分解速率系数也具有显著(P > 0.05)影响,在模拟方程中加入碳含量和碳氮比2个因子后对秸秆分解系数的解释性达到99.7%,这表明基于一个生长季尺度的秸秆填埋试验与室内培养试验结果存在差异,以往的室内培养试验主要关注几十天内的秸秆短期分解过程,代表易分解组分的氮含量和难分解组分的木质素起控制作用[32],而在生长季尺度上,秸秆分解相对较充分,前期分解易分解组分,后期分解难分解组分,不仅代表前期易分解组分的氮含量和代表后期难分解组分的木质素含量起控制作用,而且秸秆碳含量和碳氮比也是重要决定因素[33-34],秸秆分解速率系数与秸秆碳含量和木质素含量均表现为负相关关系。另外,本研究中秸秆分解速率系数模拟方程的标准化回归参数(表4)表明,碳含量在回归方程中的效应(−4.3471)相对最大,而木质素含量的效应(−0.7641)相对最小,由此说明,秸秆碳含量可能是控制秸秆分解速率的最关键因子,这在建立植物残体动态分解模型时应予以充分考虑,将本研究结果推广,在生物地球化学循环模型的植物残体分解模块中加入碳含量和碳氮比[12, 35],将有助于提高模型模拟效果。

    4.   结论
    • 1)增温仅影响部分秸秆的初期分解率,除油菜籽秸秆外,增温对玉米、红薯、大豆、冬小麦、大蒜秸秆生长季平均分解率均无显著影响。

      2)基于秸秆碳含量、氮含量、碳氮比、木质素含量的模拟方程可准确预测秸秆的分解速率及其在土壤中的长期分解量。

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