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  土壤学报  2018, Vol. 55 Issue (3): 543-556  DOI: 10.11766/trxb201710310320
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引用本文  

李光宇, 吴次芳. 土壤微生物研究在农田质量评价中的应用. 土壤学报, 2018, 55(3): 543-556.
LI Guangyu, WU Cifang. Application of Soil Microbial Studies to Farmland Quality Evaluation. Acta Pedologica Sinica, 2018, 55(3): 543-556.

基金项目

国家社会科学重大基金项目(14ZDA039)资助

通讯作者Corresponding author

吴次芳, E-mail:wucifang@zju.edu.cn

作者简介

李光宇(1989—),男,博士研究生,主要从事土壤微生物及农田管理研究。E-mail: sethlee0010@yahoo.com
土壤微生物研究在农田质量评价中的应用
李光宇 , 吴次芳     
浙江大学公共管理学院,杭州 310058
摘要:农田生产能力的可持续发展,是当前农田质量评价的主要目标之一。随着土壤微生物学研究的逐渐成熟,可考虑通过微生物角度对农田质量进行分析,以期从土壤微观机理解释农田发展的可持续性。基于文献汇总、统计学分析及对土壤微生物研究的认知,阐述了微生物群落如何反馈农田土壤质量,如对农田管理、作物产量以及土壤污染的反馈功能,并提出了可纳入农田质量评价系统的微生物学指标及相应的测定方法。根据统计学显著性检验方法,综合最小数据集法、土壤质量指数、内梅罗指数等农田土壤质量评价方法,提出农田质量评价的路径流程,以及土样采集与数据分析的具体方案。综上可知,利用土壤微生物指标构建的评价体系,能够更直观反映出农田整治改良项目对耕地造成的影响及收益状况。
关键词农田    可持续    质量评价系统    土壤微生物    

农田可持续发展与农田生态服务功能息息相关。农田质量建设应建立在平衡各生态功能的基础上,提高其生产服务能力,方可保证农田发展的可持续[1]。根据近年来对生态服务功能研究发现,生态系统的多功能性是维持可持续发展的关键因素,而土壤生物群落多样性的破坏将造成多功能性的丧失,进一步导致生态系统不可持续[2]。根据国土资源部2016年发布的《高标准农田建设评价规范》(GB/T33130-2016)所述,高标准农田建设旨在建设高产稳产、生态良好及抗灾能力强的高标准基本农田[3]。然而,其中的生态状况及抗病能力均难以用现有的农田质量指标系统进行直接衡量,但根据对农田土壤微生物研究发现,微生物与农田生态及抗病功能密切相关,故有学者认为可用微生物指标对农田土壤功能进行测定[4-5]。因此,土壤生物群落多样性以及功能特征可考虑纳入农田可持续评价的范围内,并且已有学者尝试地将微生物量作为指标加入农用地质量评价系统[6-7]。从近年来的土壤学研究可知,对于土壤微生物研究已经日趋成熟。尤其是分子生物学方面,DNA二代测序技术具有信息量丰富、成本低的特性,并在农业管理及森林土壤方面有了较为广泛的应用[8]。虽然诸多文献指出,微生物指标更易于解释农田质量变化,但如何将微生物指标应用于农田质量评价,相关研究依然较少。然而,直接将土壤微生物指标放入原有的农田质量评价系统是低效的,大规模的对每一块耕地都进行评价显然会造成评价成本失控、难以推广等问题。因此,本文旨在针对当前评价体系尚未成熟阶段,探索如何将土壤微生物指标有效应用于农田质量评价中。

1 土壤微生物群落特征对农田质量的反馈 1.1 微生物对农田管理的反馈

该领域研究内容最为丰富,对土壤微生物指标的选取具有较大的参考价值。土壤微生物生物量碳(Microbial biomass carbon, MBC)可预测土壤有机碳总量的变化,MBC与有机碳含量测定相结合,可成为评价土壤碳源状况的有效指标微生物熵(microbial entropy, C mic:C org[9-10]。有学者利用氯仿熏蒸浸提法对MBC进行测定,对比牧草地与种植大麦的耕地在耕作或免耕中的MBC变化,种植大麦的耕地MBC最低,牧草地MBC最高,并且结果显示MBC对管理变化的响应较有机质更为敏感,并验证了微生物熵可作为评价有机质变化的有效指标[11]。在针对玉米地、草地、林地以及自然草地的研究中,研究人员指出呼吸熵可反映出农田出现的碳供给状况,呼吸熵的升高说明碳供给存在不足,免耕在提高微生物量的同时也增加了其对碳源的需求,但对比传统耕作,免耕有效提升了微生物丰度[12]。在之后总结前人研究发现,土壤代谢熵(metabolism entroph, qCO2)可以呈现出微生物对碳的利用状况,且与微生物熵呈现出反比关系,二者可相辅相成可以准确反映出土壤环境的变化[13]。当然有学者曾经提出,多数研究很难直接界定农业管理与微生物量间产生的关系如何,但也同时提到,主要问题在于技术,未来研究可能会更进一步阐明土壤微生物变化机理[14]。随着16SrRNA技术的逐渐成熟,结合基因库及PLFA进行研究,可以更为清晰地分析农田生态系统中肥料管理及植被变化对土壤微生物产生的影响[15]。研究人员利用Meta分析土壤与人造氮肥关系时,发现氮肥不利于微生物的生长,但也可在一定程度上抑制土壤CO2的排放[16]。针对不同农业管理方式下英国蔬菜农田进行研究发现,富碳大团聚体可直接为微生物生长提供养分,故土壤微生物对管理的响应要强于土壤动物[17]。相关学者研究结果证明土壤微生物较土壤物理化学指标更敏感,更适用于耕地质量的评价[18]。在对农田有机改良措施研究发现,土壤微生物会随着有机改良剂的加入而恢复,微生物的恢复会对氮循环、土壤质量及作物产量产生正向影响,牛粪效果远胜于猪粪和禽粪[19]。在地表植被方面,有研究认为轮作是否可以有效改善耕地质量,需要考虑植被类型,从微生物量上看,存在经济作物对微生物影响较小的情况,这说明部分经济作物的连作依然可以保证收益与生态的平衡[20]

1.2 微生物与作物产量的关系

土壤微生物指标是否可用于指示农田产能,在2000年前的研究较少。随着微生物研究技术的逐渐成熟,该类研究逐渐成为当前热点。近期多个研究提出土壤微生物多样性及群落组成影响着土壤生态多功能性,其中包括地上植被的生产能力[21-22]。此前有学者针对农作物产量与土壤中的MBC、有机碳、微生物熵以及代谢熵的关系进行研究,指出MBC与产量正相关,主要是源于外部碳输入的增加,更重要的是微生物熵与产量也呈现出正相关关系;研究还指出代谢熵与产量关系并不明朗,但土壤基础呼吸过高可能造成碳的流失,需要注意控制土壤碳平衡[23]。也有学者针对我国冬小麦和水稻农田系统进行研究,产量越好的情况下,作物植被根系释放更多渗滤液,有利于微生物量的增加[24]。在指示土壤肥力方面,有研究发现部分脂肪酸甲脂(FAMEs)以及基因末端限制性片段(T-RFs)可以作为指示土壤肥力的“关键性生态参数”[25]。近年来,有学者提出利用微生物指标来解释玉米与大豆的相对累积产率,以有机碳及可提取态磷含量为媒介,搭建了土壤微生物量、基础呼吸及酶活性与产率间的关系,并确定了微生物指标等级对应的相对产率等级[26]。伴随着丛枝菌根真菌(Arbuscular mycorrhizal fungi,AMF)研究的日趋成熟,越来越多文献显示该真菌与作物产量存在密切相关关系[27]。越来越多学者也开始利用AMF作为指标,指示土壤健康、植被生产能力及其他生态服务功能,甚至可根据其对土壤营养物质转化能力、作物的抗病能力以及促生长功能,研究设计更为优化的农田管理方式[28, 29]。自然生态系统中,土壤微生物多样性与植被生产能力往往关系密切,在针对澳洲大陆多个生态系统表层土研究发现,在表土层中生物多样性与土壤肥力、植被生产力有着显著正相关关系[30]。因此,土壤微生物多样性也可作为监控农田产能动态变化的重要指标。

1.3 微生物对农田污染的反馈

该类研究一直是环境学的研究重点,以重金属污染为例,在农田中施加淤泥常作为提高土壤保水能力的重要措施,但是社会工业化使得淤泥中包含多种重金属污染物质,相关研究对比施用淤泥和肥料的耕地,长期施用淤泥将导致土壤微生物量降低,但研究同时发现微生物受到Zn与Cr这两种重金属的影响并不能确定[31]。一直以来,诸多学者均在试图将土壤微生物作为观测农田污染的指标,因受制于研究方法、自然波动变化以及土壤异质性的影响,很难准确采用微生物指标对污染状况进行评定,但也有学者同时指出随着分子生物学在土壤领域发展成熟,这种评价方式具有较大研究潜力[32]。之后越来越多研究显示,在农田重金属污染方面,微生物指标往往较土壤理化指标更具代表性[33]。研究人员通过研究不同微生物指标及生化指标发现,土壤中固氮细菌、代谢熵及反硝化作用受到金属Pb影响最为显著,其中固氮细菌最为敏感[34]。此外,依据电镀行业Zn对土壤微生物产生的毒性影响分析,pH变化对重金属状态影响较为明显,酸性条件下,Zn的毒性较强,但中性或碱性土壤,Zn的毒性被抵消,导致关于Zn对微生物功能影响存在争议[35]。相关研究发现,锌铜冶金厂周围的土壤微生物基础功能受损显著,然而高浓度的重金属污染将产生存在重金属抗性的菌种,但也将造成不适培养的菌种数量锐减,从而导致微生物碳氮比(Cmic:Nmic)升高,因此在已知存在重金属污染的部分情况下,可利用微生物生物量碳氮比作为评价是否存在重金属污染的重要指标据[36]。虽然土壤微生物量对重金属污染反应敏感度不足,特征菌种却受到重金属影响十分显著,具有代表性的是根瘤菌(Rhizobium),且具有巨大潜力成为重金属污染的指示物之一,未来土壤微生物也将成为生态决策的重要依据[37]。越来越多研究指出,微生物可以监控植物修复重金属的效果,微生物指标表现得更为敏感且高效[38]

2 农田质量的土壤微生物评价体系 2.1 微生物评价体系定位

根据现行的农田质量评价标准GB/T 33130-2016及农业部的《耕地质量等级》GB/T 33469-2016可知,农田质量评价体系已经日渐地完善,涉及范围越来越广,并明确了评价的对象与内容,提出从“数、质、效、管、影响”五个方面对耕地进行评价[3, 39]。但在耕地质量评价方面,多依据土壤基础理化性质进行评价,未能从土壤微生态角度进行探讨,存在深入研究的空间。其他对于农田质量的评价中,最常用的是以主成分分析及层次分析为基础发展而来的最小数据集法。以上几类评价方法均是基于规模性数据调查,并不是针对特定项目进行分析,这就造成测定成本较高、指标选取较粗略等问题。

在耕地的物理结构组成上,土壤和植被是最为主要的两大方面,毋庸置疑植被主要是指农作物。农作物的产量及质量可反映出植被的健康情况,而决定其产量和质量的,除地表影响因子外,土壤健康同样是决定性因素。国家统计局数据显示,农作物产量的提升过度依赖化肥农药,忽视了在保证产能稳定的情况下,同样要兼顾耕地生态效益。因此,在现有指标的基础上,可以加入微生物指标,以补充评价体系的不足,构建新的农田质量评价体系。在指标选取方面,笔者认为具体微生物指标需要按照评价区域实际情况进行选取,在尚无可参考背景数据的情况下,已有国际及行业衡量标准的指标可以优先考虑,比如微生物生物量碳氮、土壤呼吸及微生物多样性等,如表 1所示。但在微生物群落分析方面,因不同环境背景导致的差异性较显著,需设定靶标微生物类群进行针对性的分析评价。按照当前微生物学研究,有学者认为能够衡量土壤功能的指标有微生物生物量、多样性、土壤呼吸以及部分酶活性[21, 40]。其中土壤呼吸的测定已有现成标准,我国利用底物诱导土壤微生物呼吸,密闭气室分析土壤呼吸已经发展较为成熟,并建立了相应的国家标准,并可通过该法获得土壤微生物量,及计算出土壤呼吸熵状况[41]。在国际上,熏蒸浸提测定微生物量及磷脂脂肪酸谱图法测定微生物多样性已经比较成熟,可根据已有国际标准进行衡量分析[42, 43]。在土壤酶活性方面,可采取国际行业标准,利用微孔板对土壤酶活性进行测定[44]。在衡量土壤障碍因子方面,可采用微生物群落丰度进行衡量,近年提出的国际标准ISO 17601:2016可作为确定群落结构的标准方法也可用于确定微生物多样性[45]

表 1 农田土壤质量评价 Table 1 Farmland soil quality evaluation

在农田土壤基础质量测定方面,有机质往往是根据有机碳含量进行衡量的,有机碳与土壤微生物量存在正相关关系,不应忽略的是土壤氮含量是农作物及土壤微生物的直接氮源,故也应依据与微生物相关情况对土壤氮指标进行完善,比如硝态氮、铵态氮与土壤固氮能力关系紧密,而且在国际上常作为确定土壤氮源的重要指标,近年来国内也开始出台了一系列相应国家标准及行业标准[59, 60, 73],并在测定水平上已经逐渐成熟。根据相关文献对我国部分地区的土壤研究,土壤中存在各类形态氮,其中可矿化氮与微生物关系最为密切,因此,氮矿化能力可作为衡量土壤氮转化能力的重要指标[74]。氮矿化能力可用净氮矿化率来衡量,该指标可在生物培养的基础上用国标的方法测定铵态氮和硝态氮获得,也可参照国际标准ISO 14238:2012,但考虑到该指标与土壤其他形式氮存在重复性,故可作为备选指标[75]。此外,有效磷、速效钾等应当加入土壤养分测定范围内,然而当前指定的国家标准中GB/T 30600-2014并未明确说明土壤养分的具体内容[76]

2.2 微生物评价方法构建

根据对当前农田质量评价方法汇总可知,评价指标的选取方法主要为最小数据集法(Minimum Data Set, MDS)及总数据集法(Total Data Set, TDS)。总数据集顾名思义为不进行指标筛选,利用所有指标进行评价的方法。在确定适用的评价指标之后,需选取合理的评价方法进行评价,农田质量评价常用的方法为土壤质量指数法(Soil Quality Index, SQI),其理念应用于当前各类评价体系中,例如综合指数评价、耕地质量等级评价标准等[77-79]。其他环境学常见的评价方法,如内梅罗指数(Nemoro Quality Index, NQI)、支持向量机(Support Vector Machine, SVM)以及判别分析的方法也可用于土壤质量评价中(如表 2)。在伊朗地区针对两种指标选取方法进行比对发现,当MDS与SQI结合时,可以得到与TSD类似的结果,故最小数据集法是减少工作量的有效方法,但值得注意的是,MDS与NQI结合与TDS的结果是不一致的[80]。随着数据挖掘技术的不断普及,机器学习中常见的分类方法,也开始在各类质量评价体系中得到应用,最具代表性的方法为SVM。SVM是利用已有的分等数据作为训练数据,将测定的数据进行分类,最终确定农田质量,该方法建立在大量采样数据的基础上[81]。此外,判别分析也可作为分类方法,以呈现出不同管理方式对农田土壤质量的影响差异,可用于分析管理效益[82]。对于以上多种评价方法综合可知,评价方法一般有几个特点:1)基于稳定农田状态进行评价;2)因研究对象状态稳定,故较少考虑自然因素动态影响状况,无需设立对照组;3)需要一定数据基础,或者旨在定位区域性土壤特征,如大量的布点及土样采集工作;4)理化性质分析为主导,少有文献将酶活性或微生物指标加入评价体系中;5)数据分析较复杂,更适用于研究分析,很难用以衡量实际农田整治项目产生的效益。

表 2 常用的土壤质量的评价方法 Table 2 The common method of soil quality assessment

然而,在缺乏土壤微生物数据库的前提下,直接利用现有的评价方法,确立微生物评价方案是不易实现。由于以往的土壤调查数据,不能从微生物学角度对耕地质量进行范围界定,故评价指标难以结合实际。考虑到生态学对照实验设计方法,可以以实地数据为主,利用统计学方法进行分析,并与项目的初始目的进行对照分析,获得真实可靠的工程效益信息,将评价分析框架在图 1中构建。在分析流程中,可增加对照区域,该区域的选取应以自然条件稳定、管理措施不变为基本准则,尽可能选取毗邻项目区却受影响较小的农田,如图 2A所示。对照农田可考虑选择多块区域,土壤背景性质尽可能相似。当项目区面积较大时,可根据项目区的地理区位选择对照区,以去除自然条件差异造成的影响,如图 2B所示。施工之前,在同一项目区划定三至四个采样区,每个采样区混合采样,并测定土壤数据,并保存数据。

图 1 农田改良项目的质量评价流程 Fig. 1 Flowchart for quality evaluation of farmland improvement project

图 2 农田项目采样区示意图 Fig. 2 Samplingsites in the treatment plots of the field experiment

项目完成后,在农田处于稳定状态后进行原区域采样(按照肥料有效周期,一般为3~6个月后),获得数据后,计算指标的变化指数,第i块样区的第j个指标变化指数公式如下:

$ \Delta {\alpha _{{\rm{ij}}}}{\rm{ = }}\frac{{{\alpha _{{\rm{ij1}}}}{\rm{ - }}{\alpha _{{\rm{ij2}}}}}}{{{\alpha _{{\rm{ij1}}}}}} $

第j块对照区第j个指标变化指数为:

$ \Delta {c_{{\rm{ij}}}}{\rm{ = }}\frac{{{c_{{\rm{ij1}}}}{\rm{ - }}{c_{{\rm{ij2}}}}}}{{{c_{{\rm{ij1}}}}}} $

αij1cij1分别为农田整治项目施工前,采样区和对照区的指标测定值;αij2cij2为施工后的测定值。设定显著性水平为0.05,进行独立样本t检验,公式如下:

$ t = \frac{{\overline {\Delta {\alpha _{\rm{j}}}} - \overline {\Delta {c_{\rm{j}}}} }}{{\sqrt {\frac{{({n_\alpha } - 1)s_\alpha ^2 + ({n_c} - 1)s_c^2}}{{{n_\alpha } + {n_c} - 2}}\left( {\frac{1}{{{n_\alpha }}} + \frac{1}{{{n_c}}}} \right)} }} $

其中,${\overline {\Delta {\alpha _{\rm{j}}}} }$${\overline {\Delta {c_{\rm{j}}}} }$分别代表针对第i个变量∆αj的平均值和∆cj的平均值,nαnc分别为采样区和对照区的数量,Sα2Sc2分别为项目采样区指标变化指数的方差和对照区变化指数的方差。项目区范围较大造成自然条件差异时,在不同位置确定对照区和采样区,如图 2B,再分别采样按照上述算法分析,以保证项目区域内农田质量得到提升。当项目区土壤不均质,或者多种项目类型时,如图 3,可先划分不同类型采样区,再确定内部的采样区,再进行采样及数据分析,但与上面统计检验不同的是,需用多重比较分析整治措施与对照组数据间的显著性(方差齐性选择Dunnett’s双尾检验控制对照组分析,方差不齐可选用Dunnett’s T3检验)。

图 3 多类型采样示意图 Fig. 3 Multi-types sampling area

上述是以增长指数为依据的算法,在选取采样区或对照区存在偏差的情况下,部分指标的变化指数可能会受到第一次采样数据αij1cij1的影响。因此,对于变化指数未通过检测的指标,可进行第二次统计检验,用变化差值直接衡量变化,不再除以第一次采样数据,最终以两次变化显著的指标为准。评价初级结果为定性结果,可绘制项目效益表格。根据表格中产生的效益,和指标要求进行对比,如不能符合要求需采取补救工程措施,完成后进行重复上述测定。该分析方法易于操作,并采用了较为宽松的结果讨论方式。

该方法更适用于定量指标的测定,需要结合土壤理化性质分析结论。对于测序获得的群落相对丰度数据,不建议采用该法,可考虑采取主坐标轴分析与群落分类分析相结合的方式对微生物群落进行分析[83-84]。值得注意的是,微生物指标并不是万能药,在近年来的研究中,部分文献指出,微生物多样性与粮食产量不能呈现出绝对正相关的关系,其关系受到作物类型、微生物群落等因素影响更为明显[85-86]。此外,在部分微生物指标的测定技术上仍有不足,测定方法存在一定争议,作为评价指标需结合土壤理化性质,慎重使用[87]

3 结论与展望

综上可知,对于农田土壤的质量评价,有必要从微生物角度进行分析,且微生物参与的农田质量评价具有以下特点:

1)评价指标代表性更强,并可通过研究其相关关系得到更多信息。有学者提到土壤管理确实会对土壤有机质产生显著影响,但土地利用影响在有机质上的反馈并不显著,并指出利用溶解性有机碳、氮及磷作为指标更为敏感[88]。传统评价体系中缺少对氮磷指标的重视,尤其是对土壤氮源的重视是远远不够的。土壤氮含量测定的加入可丰富土壤指标,例如,土壤有机碳和全氮比例可反映出土壤氮源是否利于土壤微生物生长[89],过高说明土壤氮源不足,过低则说明含氮量过高产生氮流失造成农田污染风险加大,并可能造成土壤微生物量的降低[90]。微生物生物量碳及微生物生物量氮的测定,不仅仅能够反映出土壤微生物量的多少,还可以根据二者的比例获知土壤真菌占比情况[91]

2)更倾向于针对具体农田整治项目的研究,在尚未进行规模采样的基础上,可以更准确反映出农田中存在的问题。在当前土壤微生物数据未形成数据库的情况下,对照评价分析可规避缺少多年数据积累的问题。该采样评价方式源于对生态学实验设计的改进,随机布点采样可提现其科学合理性[92]。统计学检验的方式相对更加保守,可保证农田质量变化的真实性。

3)土壤的微生物群落结构与土壤理化性质结合,可定位对农田影响最为显著的环境因素。根据当地自然禀赋特征,可改善管理措施,提高土壤质量,以达到提高农田质量的要求。土壤微生物与作物及根际微生物可产生复杂的交互作用,分析阐明其中机理,可以寻求农田管理的最优模式,当然,土壤微生物的复杂程度也是当前研究的巨大挑战[93]。有学者指出,充分利用微生物与作物的相互作用对抗不利环境,是未来的发展方向[94-95]

农田质量评价从广度上看已经逐渐完善,但从可持续发展角度看仍有可深入的空间。文章以土壤微生物学为视角,通过对现有土壤微生物学研究的汇总,提出了新的农田质量评价体系及分析流程,总结了土壤微生物评价所具备的优势及特点。农田的可持续发展与土壤微生物息息相关,本文为推进土壤微生物在农田质量评价中的应用研究提供了理论依据,未来研究可偏重于评价体系量化方向,更为精确地评价农田质量水平。

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Application of Soil Microbial Studies to Farmland Quality Evaluation
LI Guangyu , WU Cifang     
School of Public Affairs, Zhejiang University, Hangzhou 310058, China
Abstract: Soil microbial flora is usually used as a biomarker of sustainability of farmland. With the research on soil microbiology gradually maturing, it becomes advisable to evaluate farmland quality from the angle of soil microbiology and to analyze whether or not the tract of farmland under study may have a bright future of sustainable development from the aspect of soil microcosmic mechanisms. However, the farmland quality evaluation systems, currently available, have not yet illustrated how to evaluate farmland quality from the angle of soil microbiota. Based on reviews of the literature concerning farmland quality and soil microbiology, this paper elaborated how to use soil microbiota as feedback of farmland quality, especially when related to farmland management, food production and soil pollution. In line with the existing international and domestic standards of the industry, the paper marked out microbial biomass, soil basal respiration, hydrolase activity and microbial diversity as, microbiological indices for the farmland quality evaluation systems. In using the common methods to evaluate soil quality, the objects studied are always kept in a stable state. Moreover, the evaluations using these methods seldom involves any soil microbial indices. They usually need the support of basic data or are more suitable for analysis of large-scale soil properties and positioning. In addition, most of the evaluation methods are more suitable for scientific research, but not so for application to farmland improvement projects. Based on the designing of ecological comparative experiments and the method of Duncan test, a new flowchart was plotted out for farmland quality environment. By comparing control with treatment in the experiment, variance rate and variation value could be obtained for analysis of effect of the treatment. Post hoc validation tests, were performed to analyze significance of difference between treatments and control. In the end, an effect-evaluation table was established, reflecting effects of treatments on farmland quality. This study indicates that the farmland quality evaluation system encompassing soil microbial indices is more sensitive and comprehensive than the traditional ones, and the combination of soil microbial properties and soil physico-chemical properties makes the environment more reliable. It is worth to note that the new system is more suitable for quantitative indices, and needs to analyze results of the evaluation by taking into account changes in soil physical and chemical properties. For the relative abundances of soil microbial communities obtained via sequencing, this method still has certain points worth pondering over. It is advisable to adopt the method of community classification in analyzing soil microbial communities. Soil microbial properties readily reveals mechanism of changes in farmland. Even though in the course of using soil microbiota to evaluate farmland quality, there are still a lot technical and analytical difficulties to overcome, it cannot be ignored that soil microorganisms play an irreplaceable role in the evaluation of farmland quality and will become an important analytical tool for defining quality of farmland in future.
Key words: Farmland    Sustainability    Quality assessment system    Soil microbiota