Soil enzyme activities and physicochemical properties of typical woodlands in karst faulted basins
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摘要:目的土壤酶参与土壤中各种生物化学过程,与土壤理化性质密切相关。本文以喀斯特断陷盆地3种典型林地为研究对象,探究林地土壤酶活性与理化性质之间的关系,为该地区植被生态恢复工作提供参考依据。方法本研究以喀斯特断陷盆地云南松林、桉树林和天然次生林为研究对象,采用冗余分析方法,探讨不同林地土壤酶活性及其与理化性质之间的关系。结果(1)3种林地土壤pH介于5.47 ~ 6.03之间,10 ~ 20 cm和20 ~ 30 cm土层土壤密度,云南松林显著高于桉树林和次生林(P < 0.05),0 ~ 10 cm和10 ~ 20 cm土层全氮含量,桉树林显著高于云南松林和次生林(P < 0.05),0 ~ 10 cm土层速效磷含量,次生林显著高于云南松林和桉树林(P < 0.05),有机碳和铵态氮含量整体呈现次生林 > 云南松林 > 桉树林的规律。(2)3种林地0 ~ 10 cm土层酸性磷酸酶和脲酶活性为次生林 > 桉树林 > 云南松林,而10 ~ 20 cm土层呈现相反的规律。淀粉酶、纤维素酶和蔗糖酶活性在0 ~ 10 cm和10 ~ 20 cm土层均为次生林最高,云南松林次之,桉树林最低。此外,林地各土层间土壤酶活性具有显著性差异(P < 0.05),土壤酶活性呈现出随土层深度增加而逐渐降低的趋势。(3)冗余分析表明,有机碳、铵态氮、速效磷和pH均与蔗糖酶活性呈显著正相关关系,而全氮与蔗糖酶活性呈显著负相关关系。土壤密度与脲酶和酸性磷酸酶活性呈负相关关系。(4)蒙特卡洛检验表明土壤理化性质对土壤酶活性影响的重要性大小排序为:有机碳(41.4%) > 铵态氮(32.9%) > 速效磷(24.3%) > 土壤密度(12.6%) > 全氮(7.9%) > pH(5.5%)。结论综上分析表明,有机碳、铵态氮等是影响研究区内土壤酶活性变化的主要指标,在断陷盆地林地土壤肥力和酶活性恢复方面,次生林最佳,而云南松林的优势高于桉树林。Abstract:ObjectiveSoil enzymes are involved in all soil biochemical processes and are closely related to soil physicochemical properties. In this paper, three typical woodlands in karst faulted basins were studied, and the relationships between soil enzyme activities and physicochemical properties of forest lands were explored, which provided a reference for vegetation ecological restoration in this area.MethodIn this study, the Pinus yunnanensis, Eucalyptus maideni and natural secondary forest in the karst faulted basin were used as research objects. The relationship between soil enzyme activities and physicochemical properties was studied using the methods of redundancy analysis.Result(1) The soil pH values of the three forest lands ranged from 5.47 to 6.03, and the soil bulk densities of the 10−20 cm and 20−30 cm soil layers in the Pinus yunnanensis forest were significantly higher than those of the Eucalyptus maideni forest and the secondary forest (P < 0.05). The contents of total nitrogen (TN) in 0−10 cm and 10−20 cm layers of Eucalyptus maideni forest were significantly higher than those in Pinus yunnanensis forest and secondary forest (P < 0.05). The available phosphorus (AP) content in 0−10 cm soil layer of secondary forest was significantly higher than that in Pinus yunnanensis forest and Eucalyptus maideni forest (P < 0.05). The contents of soil orgenic carbon (SOC) and ammonium nitrogen (AN) showed the law of secondary forest > Pinus yunnanensis forest > Eucalyptus maideni forest. (2) The activity of acid phosphatase and urease in the 0−10 cm soil layer of three woodlands was in the order of secondary forest > Eucalyptus maideni forest > Pinus yunnanensis forest, while the 10−20 cm soil layer showed the opposite law. The highest activities of amylase, cellulase and invertase in 0−10 cm and 10−20 cm soil layers were obtained from secondary forest, followed by Pinus yunnanensis forest and the lowest in Eucalyptus maideni forest. In addition, the soil enzyme activities in the soil layers of the forests were significantly different (P < 0.05), and the soil enzyme activities showed a trend of decreasing with the increase of soil depth. (3) Through the redundancy analysis of soil physicochemical properties and enzyme activities in the three forest lands, the results showed that SOC, AN, AP and pH all had a significant positive correlation with invertase activity. However, TN was significantly negatively correlated with invertase activity. Soil bulk density was negatively correlated with urease and acid phosphatase activities. (4) the Monte Carlo test showed that the order of importance of soil physicochemical properties on soil enzyme activities was: SOC (41.4%) > AN (32.9%) > AP (24.3%) > soil bulk density (12.6%) > TN (7.9%) > pH (5.5%).ConclusionComprehensive analysis showed that SOC and AN were the main indicators affecting the changes of soil enzyme activity in the study area. The secondary forest is the best in the restoration of soil fertility and enzyme activity in karst faulted basins, while the advantage of Pinus yunnanensis forest is higher than that of Eucalyptus maideni forest.
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油用牡丹(Paeonia suffruticosa)是我国特有的木本油料树种,属于多年生小灌木[1-2],对其开发利用具有很高的经济效益、生态效益和社会效益[3-4]。现阶段,油用牡丹植株修剪和果实采摘机械化水平较低[5-6]。已有研究表明:根据油用牡丹植株的生长特点和农艺条件,采用切割方式切断茎秆能有效的进行果实采摘[7],但油用牡丹茎秆切割机理尚未明确。
目前,关于灌木切割的研究主要集中在力学参数与茎秆物理特性、微观组织、化学成分的关系上[8]。同时,刀具角度如滑切角[9]、斜切角[10-11]对茎秆切割有显著影响,并且茎秆本身的物理特性也会影响其切割性能[12-13]。研究表明:灌木微观结构属于典型的多孔胞元结构,这种微观组织成分和排列模式导致宏观动态力学性能与加载速率相关[14-16]。针对作物茎秆切割,许多研究基于Johnson-Cook模型[17-18]使用有限元方法模拟分析了其切割特性。如廖宜涛等[19-20]对芦竹(Arundo donax)切割的研究,郭茜[21]对藤茎类秸秆的切割特性研究,苏工兵等[22-23]对苎麻(Boehmeria nivea)茎秆切割研究。柳爱群等[24]基于准静态单轴拉伸和单轴扭转试验给出了材料参数的识别方法,季玉辉[25]提出了Johnson-Cook材料参数估计方法和估计程序。但茎秆切割研究中计算未考虑应变率效应,也未对本构方程参数的测定方法进行研究。
对处于果实成熟期的“紫斑”油用牡丹茎秆切割特性进行相关研究,提出以Johnson-Cook模型作为茎秆切割本构模型。通过电子万能试验机进行准静态拉伸试验和动态拉伸试验,得到油用牡丹茎秆材料的Johnson-Cook本构方程参数,基于ANSYS/LS-DYNA软件仿真计算油用牡丹茎秆的切割过程,并对其应变率效应和应变硬化效应进行分析。通过模拟和试验结果的对比验证了模型的可行性和正确性,为后续采摘机械研究提供依据。
1. 材料与方法
1.1 材 料
材料均选自北京市海淀区鹫峰国家森林公园牡丹园种植的“紫斑”油用牡丹,随机选取当年生长的新枝,取样时间为牡丹植株的果实成熟期。选择生长良好,无病虫害或机械损伤的茎秆,从果实果柄向下30 ~ 50 cm处剪下(保证与果实采摘时切割位置一致),茎秆经手工去除叶和侧枝,装入保鲜袋密封。并于当天在北京林业大学工学院实验室(26 ℃空调环境)进行茎秆力学试验。牡丹茎秆截面形状如图1,其形状近似为圆形,每次试验前用游标卡尺测量其截面尺寸,按照近似圆形进行面积计算。
由于茎秆材料的非均一性,茎秆进行拉伸试验时断裂位置较为随机,而应变测量需要保证断裂位置为茎秆两夹持端中央的有效标距内。因此,每个茎秆在试验前均预先进行中部去皮处理,如图2所示。由于茎秆表皮很薄,影响茎秆机械性能的厚壁组织和维管束组织主要分布在韧皮部和髓心,仅去除表皮对茎秆机械性能影响不大,预试验结果也表明茎秆去皮后拉伸无明显差异。选取的油用牡丹茎秆直径分为3级:细(直径2 ~ 3 mm)、中(直径3 ~ 4 mm)、粗(直径4 ~ 5 mm),试验材料平均分配到各个试验组,共进行12次单轴拉伸试验。经测定,试验中茎秆的平均含水率为56.7%。
1.2 准静态拉伸试验
为分析材料的应变效应,采用准静态拉伸试验可获得应变率为10−5 ~ 10−2 s−1时,油用牡丹茎秆的应力–应变曲线。准静态拉伸试验采用电子万能力学试验机(M4050 深圳市瑞格尔仪器有限公司,图3)。试样尺寸为标距10 mm,加载速率5 mm/min,应变率为8.4 × 10−3 s−1。
试验中,使用CCD相机记录茎秆拉伸至断裂的变形过程,采用视频引伸计[26-28]测量拉伸应变。这是基于机器视觉的一种应变测量方法,其基本原理是利用标定好的相机追踪被测对象上的标记点或纹理特征,通过计算其位移来确定试件的变形量。其基本工作原理如图4所示。
1.3 动态拉伸试验
材料的处理和装置与准静态拉伸试验相同。试验在常温中进行,按照加载速率的不同将茎秆材料分为4组,每组同样分配3个等级直径的茎秆并进行12次试验。4组加载速率分别为25、50、100和200 mm/min,对应应变率分别为4.20 × 10−2、8.40 × 10−2、1.68 × 10−1和3.36 × 10−1 s−1。
2. 结果与分析
2.1 准静态力学性能
油用牡丹茎秆在室温和准静态拉伸条件下的真实应力–应变曲线如图5所示。从图5可以看出:油用牡丹茎秆在准静态拉伸过程中,流动应力随应变增加迅速升高,当应力达到一定值后(A点),茎秆进入稳定塑性流动状态,应变强化率(Δσ/Δε)基本不变,随着应变的继续增大,茎秆流动应力近似直线增加(BC段),呈现显著的应变硬化效应。
2.2 茎秆的应变率效应
图6为油用牡丹茎秆在常温下不同应变率时的真实应力–应变曲线。图中黑色实线为准静态拉伸(应变率为8.40 × 10−3 s−1)结果,其余曲线为不同应变率下动态拉伸结果。由图6可知:茎秆在动态拉伸条件下的曲线明显高于准静态拉伸,在应变相同时,茎秆拉伸应变率越高,应力值越大。当茎秆应变率由8.40 × 10−3 s−1(准静态)增大为3.36 × 10−1 s−1,拉伸应变ε = 12%时,茎秆流动应力由7.78 MPa 增大至10.58 MPa,增加约36%,表明茎秆存在应变率强化效应。而且随着应变率升高,产生相同应变需要更大的应力,即茎秆产生相同的塑性变形需要更大的力,导致茎秆的塑性变形功(材料发生塑性变形所消耗的功W,W =
∫ε0σdε )[29-30]增加。3. 建立本构模型
Johnson-Cook模型是一个能反映应变率强化效应的理想刚塑性强化模型[17-18],其表达式如式(1)所示:
σy=(A+Bεn)(1+Cln˙ε˙ε0)[1−(T−TrTm−Tr)m] (1) 式中:
σy 表示材料塑性变形时的流动应力(MPa);ε 为等效塑性应变(%);˙ε 为试验应变率(s−1);˙ε0 为准静态参考应变率(s−1),取˙ε0 为8.40 × 10−3 s−1;T为试验温度(℃);Tr 为室温(℃);Tm 为材料熔点(℃);A、B、n、C、m为材料参数,其中,A、B和n为应变硬化参数,A为材料屈服强度(MPa),B和n分别为材料应变硬化的硬化模量(MPa)和硬化指数,C表示材料应变率系数,m为材料温升软化指数。在式(1)中,流动应力
σy 的计算包括3部分:第一个括号表达的是室温下,准静态加载时材料的本构关系,体现了材料的应变硬化现象;第二个括号表达的是应变率强化效应的影响;第三个括号表示材料的温升软化效应[31-32]。一方面,由式(1)可知Johnson-Cook模型是针对材料塑性变形中应力与应变关系的本构模型,虽然茎秆材料和金属材料材性差异明显,但从破坏形式上来说茎秆材料破坏过程也要经历塑性变形阶段直至材料断裂,因此本构模型需要能够描述茎秆材料塑性变形中应力–应变关系,这一点Johnson-Cook模型能够满足;另一方面,茎秆准静态拉伸和动态拉伸试验的结果表明茎秆材料呈现显著的应变硬化和应变率效应,这符合式(1)所描述的材料塑性变形的流动应力主要影响因素。因此,Johnson-Cook本构模型可以作为茎秆切割本构模型。同时,由于茎秆剪切过程不会释放大量热量使温度急剧上升,试验温度约等于室温(
T≈Tr ),因而忽略温度的影响,方程(1)可简化为:σy=(A+Bεn)(1+Cln˙ε˙ε0) (2) 根据式(2),通过油用牡丹茎秆拉伸试验,可以拟合得到模型中的各参数,从而建立能反映油用牡丹茎秆切割性能的Johnson-Cook本构模型。
本研究进行了准静态拉伸试验,此时
˙ε=˙ε0 ,流动应力σy=(A+Bεn) ,将其两边取对数后得到:ln(σy−A)=nlnε+lnB (3) 这在以
ln(σy−A) 为纵坐标,以lnε 横坐标的对数坐标中表示为斜率n、截距lnB的一条直线,通过试验数据的拟合,即可得到B、n的对应值。A表示材料的屈服强度,可以直接由准静态试验的应力–应变曲线读取。拟合得到茎秆的应变硬化参数:A = 4.75 MPa,B = 3.404 MPa,n = 0.147。为得到茎秆应变率系数C,令K = 4.75 + 3.404
ε0.147 ,取试验中茎秆以不同应变率拉伸时的极限强度σi,则Johnson-Cook本构方程可简化为:σi=K(1+Cln˙ε˙ε0) (4) 令
Y=σiK−1 ,X=ln˙ε˙ε0 ,式(4)可转换成Y = CX。根据动态拉伸试验结果,采用最小二乘法拟合得到应变率系数C = 0.103。建立油用牡丹茎秆Johnson-Cook本构方程为
σy=(4.75+3.404ε0.147)(1+0.103ln˙ε) (5) 通过准静态拉伸试验和动态加载试验,得到油用牡丹茎秆材料的本构模型参数(表1)。
表 1 油用牡丹茎秆本构模型参数Table 1. Constitutive model parameters of oil tree peony stem参数
Parameter屈服强度
Yield strength (A)/MPa应变硬化模量
Strain hardening modulus (B)/MPa应变硬化指数
Strain hardening index (n)应变率系数
Strain rate coefficient (C)值 Value 4.75 3.404 0.147 0.103 按公式(5)中对应材料参数进行拟合,图7是计算结果和试验结果的对比图。图中实线为试验结果,虚线为计算结果。由图7可以看到由Johnson-Cook模型拟合得到的本构曲线与试验结果吻合较好,这说明拟合得到的各材料参数是正确的,Johnson-Cook模型能有效地表达油用牡丹茎秆在不同应变率下的塑性本构关系,能预测不同应变率下茎秆塑性流动应力。
4. 数值仿真分析
4.1 茎秆剪切试验
试验采用自制的夹具与刀具,在电子万能试验机上进行(图8a)。试验时,按2 cm间距标记剪切点,测量并计算剪切点处截面积,装夹好试件后进行试验,如图8b所示。
4.2 茎秆剪切有限元仿真
采用式(5) 油用牡丹茎秆本构方程,基于ANSYS/LS-DYNA建立了有限元模型如图9a所示,计算得到油用牡丹茎秆剪切过程中不同时刻的应力场,分别如图9b和9c所示。模拟结果表明刀具与茎秆的接触面产生了应力集中,存在明显的局部变形,模拟结果与茎秆切割的实际受力和变形情况一致。
4.3 分析与讨论
为了验证茎秆本构模型的正确性和模型参数的准确性,本文将模拟得到的结果与试验结果进行对比分析。
图10是茎秆最大切割力的仿真结果与试验结果的对比图,为了进一步检验两者的相关性,使用SPSS软件进行配对t检验,结果如表2 ~ 4所示。
表 2 峰值切割力仿真结果与试验结果的成对样本相关系数Table 2. Correlation coefficient between simulation results and test results of cutting force样本数量 Sample number 相关系数 Correlation coefficient P值 P value 10 0.937 < 0.000 1 表 4 切割能量仿真结果与试验结果的配对t检验Table 4. Paired t test between simulation results and test results of cutting energy成对差分 Paired difference t值
t value自由度
dfP值
P value均值
Mean标准差
SD均值的标准差
SD of mean差分的95%置信区间 95% confidence interval of difference 下限 Lower limit 上限 Upper limit 2.975 3.535 1.543 −1.691 4.201 −1.625 9 0.086 从图10可以看出:茎秆的峰值切割力随茎秆的直径增大而增大,仿真结果与试验结果较一致。而且表2和表3的配对t检验结果表明:两者的相关系数达到了0.937,且P值小于0.5,说明两组数据显著相关;同时,t检验结果的P值为0.912,大于0.05,说明置信区间为95%的情况下,两组样本没有显著性差异。表4说明切割能量和剪切强度的仿真结果与实际结果也没有显著性差异。
表 3 峰值切割力仿真结果与试验结果的配对t检验Table 3. Paired t test between simulation results and test results of cutting force成对差分 Paired difference t值
t value自由度
dfP值
P value均值
Mean标准差
SD均值的标准差
SD of mean差分的95%置信区间 95% confidence interval of difference 下限 Lower limit 上限 Upper limit 0.158 4.409 1.394 −2.996 3.312 0.113 9 0.912 通过分析表明:茎秆剪切数值仿真结果和试验结果是一致的,两者无显著差异。本文改进的Johnson-Cook模型可以作为茎秆切割本构模型,提出的模型参数测定方法是准确的。
5. 结 论
本研究提出以Johnson-Cook方程作为油用牡丹茎秆切割本构方程,通过准静态拉伸试验和动态拉伸试验确定了茎秆材料参数,并进行了茎秆切割试验研究和数值模拟,得到以下结论:
(1)油用牡丹茎秆切割过程存在明显的应变率效应,塑性变形过程中茎秆流动应力随应变率增大而增大,塑性变形功也随之增加。
(2)对于油用牡丹茎秆,可以通过准静态拉伸试验和动态拉伸试验的方式测定Johnson-Cook模型的静态和动态材料参数。
(3)采用改进的Johnson-Cook模型模拟茎秆切割过程,仿真结果与试验结果一致。表明该模型可以较好地预测茎秆材料的切割过程及其性能。
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图 2 土壤酶活性与理化性质的冗余度分析
Suc. 蔗糖酶;Cel. 纤维素酶;Ure. 脲酶;Acp. 酸性磷酸酶;Amy. 淀粉酶;BD. 土壤密度;SOC. 有机碳;AN. 铵态氮;TN. 全氮;AP. 速效磷。下同。Suc, sucrase;Cel, cellulase;Ure, urease;Acp, acid phosphatase;Amy, amylase;BD, soil bulk density;SOC, soil organic carbon;AN, ammonium nitrogen;TN, total nitrogen;AP, available phosphorus. The same below.
Figure 2. Redundancy analysis of soil enzyme activities and physicochemical properties
表 1 林地基本概况
Table 1 Basic profile of forest lands
林地
Forest land地理坐标
Geographical coordinates坡度
Slope
degree/(°)坡向
Slope
aspect海拔
Altitude/m植被盖度
Vegetation
coverage/%植株密度/(株·hm− 2)
Plant density/
(tree·ha− 1)冠幅
Crown diameter/
(m × m)云南松林
Pinus yunnanensis forest102°46′41″E,23°40′30″N 15 西偏南
West by south1 560 80 3 838 3.32 × 3.26 桉树林
Eucalyptus maideni forest102°57′11″E,23°42′34″N 11 北偏东
North by east1 511 65 2 619 2.25 × 2.33 次生林
Secondary forest102°55′03″E,23°44′06″N 10 西偏北
West by north1 416 75 6 756 1.84 × 1.79 表 2 3种林地不同土层深度理化性质
Table 2 Physicochemical properties of different soil depths in three forest lands
林地
Forest land土层深度
Soil depth/cm土壤密度
Soil bulk density/
(g·cm− 3)pH 有机碳
Organic carbon/
(g·kg− 1)铵态氮
Ammonium
nitrogen/
(mg·kg− 1)全氮
Total nitrogen/
(g·kg− 1)速效磷
Available phosphorus/
(mg·kg− 1)云南松林
Pinus yunnanensis forest0 ~ 10 1.07 ± 0.05Aa 5.99 ± 0.09Aa 32.50 ± 5.41Aa 5.74 ± 1.72Aa 5.02 ± 0.07Aa 1.01 ± 0.17Aa 10 ~ 20 1.25 ± 0.02Ab 5.98 ± 0.07Aa 25.05 ± 1.84Aab 3.32 ± 0.05Aa 4.91 ± 0.16Aa 1.00 ± 0.30Aa 20 ~ 30 1.28 ± 0.05Ab 5.99 ± 0.05Aa 15.22 ± 1.32Ab 2.66 ± 0.34Aa 3.93 ± 0.08Aa 0.67 ± 0.25Aa 桉树林
Eucalyptus maideni forest0 ~ 10 1.02 ± 0.02Aa 5.47 ± 0.16Ba 42.07 ± 4.34Ba 4.44 ± 1.25Aa 17.15 ± 0.21Ba 1.13 ± 0.05Aa 10 ~ 20 1.14 ± 0.06Bb 5.83 ± 0.08Aa 21.86 ± 2.05Ab 3.57 ± 0.35Aa 10.93 ± 0.30Bb 0.97 ± 0.10Aa 20 ~ 30 1.18 ± 0.04Bb 5.64 ± 0.26Ba 10.94 ± 1.91Ab 2.62 ± 0.26Aa 4.58 ± 0.05Ac 1.63 ± 0.90Ba 次生林
Secondary
forest0 ~ 10 1.09 ± 0.09Aa 5.95 ± 0.09Aa 46.33 ± 7.55Ba 24.35 ± 3.96Ba 6.44 ± 0.12Aa 2.33 ± 0.18Ba 10 ~ 20 1.13 ± 0.07Ba 5.88 ± 0.13Aa 35.04 ± 3.63Ba 14.69 ± 1.88Bb 4.25 ± 0.09Aa 1.88 ± 0.50Aa 20 ~ 30 1.18 ± 0.09Ba 6.03 ± 0.04Aa 17.05 ± 3.16Ab 10.85 ± 1.83Bb 5.30 ± 0.04Aa 1.76 ± 0.31Ba 注:数据为均值 ± 标准差,n = 3。同列同一树种不同小写字母表示均值间差异显著(P < 0.05),同列同一土层不同大写字母表示均值间差异显著(P < 0.05),下同。Notes: the data in the table are mean ± standard deviation, n = 3.
Difference between the mean values of the same tree species in different lowercase letters indicates a significant difference (P < 0.05), and the difference between the mean values in the same soil layer in the same column indicates a significant difference (P < 0.05). The same below.表 3 土壤酶活性与理化性质RDA排序的特征值及累计解释量
Table 3 Eigenvalues and cumulative variances of RDA ordination of soil enzyme activities and physicochemical properties
排序轴 Sorting axis 第Ⅰ轴AxisⅠ 第Ⅱ轴AxisⅡ 第Ⅲ轴
Axis Ⅲ第Ⅳ轴Axis Ⅳ 土壤酶活性特征解释量 Soil enzyme activity eigenvalues 0.657 0.153 0.032 0.008 土壤酶活性特征与理化性质相关 Soil enzyme activity and physicochemical properties correlations 0.961 0.855 0.711 0.841 土壤酶活性特征累计解释量 Cumulative variances of soil enzyme activity/% 65.7 81.0 84.2 85.0 土壤酶活性特征−理化性质关系累计解释量 Cumulative variances of correlations/% 77.3 95.2 99.0 99.9 典范特征值 Sum of all canonical eigenvalues 0.851 总特征值 Sum of all eigenvalues 1.000 -
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