www.elsevier.com/locate/envint

Environment International 30 (2004) 19–30

Monitoring microbial biomass and respiration in different soils from the

Czech Republic—a summary of results

Jakub Hofman*, Ladislav Dusek, Jana Klanova, Jitka Bezchlebova, Ivan Holoubek

Faculty of Science, Research Centre for Environmental Chemistry and Ecotoxicology, Centre of Excellence, Masaryk University of Brno,

Kamenice 126/3, 62500 Brno, Czech Republic

Received 12 February 2003; accepted 29 April 2003

Abstract

The microbial biomass (Cbio), respiration (basal respiration (BR) and potential respiration (PR)), and derived indices for 520 independent

soil samples of 117 different soils from the Czech Republic were statistically analysed. The broad range of soil samples allowed the stepwise

breakdown of the database into six reasonable categories of soil: arable soils, loamy grassland soils, sandy grassland soils with weak organic

matter content, sandy grassland soils with moderate organic matter content, forest soils with moderate organic matter content, and forest

organic soils with rich organic matter content. Because soil microbiology lacks benchmarking values, the ranges of the microbial

characteristics for these categories were stated and are presented here. The separation into soil groups narrowed the ranges enough to be

useful for comparative purposes. The groups displayed significant differences in basal microbial parameters. The lowest microbial biomass

was found in arable soils and grassland sandy soils with weak organic matter content. The highest microbial biomass was shown by loamy

grassland soils and organic forest soils. Respiration displayed similar results to the microbial biomass. The derived indices revealed less

significant differences confirming their inner-standard nature. The relationships between the soil contamination and microbial parameters

were not explored because of the confounding effect of soil organic matter. However, it was not shown by the category of grassland sandy

soils with weak organic matter content suggesting they could be especially suitable for the biomonitoring of harmful effects of chemicals on

soil microorganisms.

D 2003 Elsevier Science Ltd. All rights reserved.

Keywords: Monitoring; Microbial biomass; Soil respiration; Soil contamination

1. Introduction importance for soil quality, which is well accepted (Tate,

If the monitoring programme is aimed at assessing

ecological risks, it should include some biological parame-

ters or bioindication methods (Suter et al., 2000). The

biological component has been more obscure in monitoring

because biological factors are often more difficult to inter-

pret and assess than physical or chemical indicators

(Kennedy and Papendick, 1995). Nevertheless, biological

monitoring can augment information about environmental

pollution and about the soil physical–chemical properties in

every soil survey. Properly chosen parameters allow both

the identification of current changes in the soil and the

prediction of future changes. For these purposes, the use of

microbial parameters seems to be very promising given their

0160-4120/$ - see front matter D 2003 Elsevier Science Ltd. All rights reserved.

doi:10.1016/S0160-4120(03)00142-9

* Corresponding author. Tel.: +420-5-4712-1407 (Mobile: +420-723-

539-571); fax: +420-5-4712-1431.

E-mail address: hofman@recetox.muni.cz (J. Hofman).

2000). Many studies have been published in which changes

in the soil microbial parameters gave early warning of

decreasing soil quality (e.g. Carter, 1986; Powlson et al.,

1987; Kandeler et al., 1999). Parameters describing the

amount, activities, and diversity of soil microorganisms

are also used as biological indicators of soil quality and

health (Doran and Parkin, 1996; Sparling, 1997; Stenberg,

1999). Microbial parameters integrate the chemical, physi-

cal and biological properties of soils since microbes repre-

sent a key part of the soil ecosystem (Oberholzer et al.,

1999). The monitoring of microbial parameters of soils has

mostly been driven by agricultural, ecological and basal soil

research in recent years (e.g. Dick, 1992; Wardle et al.,

1999). Less attention has been given to the use of microbial

characteristics in environmental monitoring or for the eval-

uation of contaminated sites (e.g. Chander and Brookes,

1993; Brookes, 1995; Giller et al., 1998; Kasyanova et al.,

1996; Kuperman and Carreiro, 1997).

Fig. 1. The soil monitoring projects realized by the RECETOX-TOCOEN

consortium in the Czech Republic from which soil data were used in this

study.

J. Hofman et al. / Environment International 30 (2004) 19–3020

Soil microorganisms are usually studied and monitored at

the process level and the biomass level. The process level

includes overall activities of the soil microorganisms, espe-

cially respiration (Sparling, 1997). At the biomass level, the

entire microbial community is evaluated as a single mass of

microbial matter without specification of its structure. Over-

all biomass can be expressed as carbon immobilized in the

microbial cells—Cbio (Vance et al., 1987; Anderson and

Domsch, 1978). In general, methods available for the

measurement of soil microbial parameters are cheap, simple,

quick, and well standardized (e.g. ISO 14240-1, 1997; ISO

14240-2, 1997; ISO/DIS 16072, 2002). The main disadvan-

tage of monitoring microbial parameters is the lack of

benchmarking values, because of the high variability of

the parameters and ambiguous relationships with many

environmental factors (Kennedy and Papendick, 1995;

Brookes, 1995; Dalal, 1998). Standardization of the values

is often a problem because each site contains its own

indigenous microflora and the ‘‘ideal’’ values for a good-

quality soil are not known. Only non-contaminated back-

ground soils of the same basic properties as the monitored

localities could be control sites, and these are usually not

available in routine monitoring (Giller et al., 1998). There is

a real problem with the interpretation of absolute values and

the biomonitoring of soil microorganisms is limited to a site-

specific approach. These drawbacks are also the main

argument of serious critics against the use of microbial

parameters in ecological risk assessment (Sheppard, 1999;

Kapustka, 1999). Hence, one of the key issues for soil

microbiology is to define characteristic thresholds and

reference values for different reasonable categories of soils

(Brookes, 1995; Stenberg, 1999; Oberholzer et al., 1999).

The objective of this study was to statistically evaluate

the microbial parameters aggregated from projects of soil

monitoring conducted by the RECETOX-TOCOEN consor-

tium in the years 1997–2002. Our aim was to state

representative values and ranges of the microbial parameters

for properly chosen soil categories and to explore relation-

ships with soil properties and soil contamination.

2. Materials and methods

From the original huge data set of the soils monitored by

the RECETOX-TOCOEN consortium in years 1997–2002,

only samples with measured microbial parameters were

chosen to obtain the final matrix for statistical evaluation.

The soils used were monitored in the framework of the

‘‘Zlın’’, ‘‘Mountains’’, ‘‘Mokra’’, ‘‘Highways’’, ‘‘Kosetice’’,

and ‘‘Beroun’’ projects (Fig. 1). The region around the city

Zlın is regarded as a built up industrial area (Holoubek et al.,

2001). Project ‘‘Zlın’’ focused on the monitoring of fluvisols

in the Drevnice River tributary and the adjacent part of the

Morava flooding terraces. Thirty-one grassland soils were

sampled in nine sampling campaigns during 1997–2000

(Holoubek et al., 2000). Higher parts of the mountains on

the border are regarded as burdened by the long-air transport

of persistent pollutants (Holoubek et al., 1998). Monitoring

of the mountain soils consisted of five sampling campaigns

with six forest soils in the Krkonose and Beskydy Moun-

tains in 1998 and 2000. The periodic monitoring of soil

quality in the surroundings of the Mokra cement plant

involved the sampling and measuring of six grassland,

two arable, and four forest soils, repeated 17 times over

the years 1998–2002 (Holoubek et al., 2002a). Project

‘‘Highways’’ consisted of the one-shot monitoring of 12

arable, 10 forest, 3 grassland, and 9 anthropogenic soils

sampled in surroundings of the Czech highways D1 and

D11 in 1999 (Andel et al., 1999). The Kosetice observatory

was established as a regional station for the integrated

background monitoring network of the Global Environment

Monitoring System (GEMS) project of the United Nations

Environment Programme (UNEP) (Vana et al., 2001).

Microbial parameters were measured at three grassland

and six forest sites around the observatory in 2000–2002

(Hofman, 2001). One-shot monitoring of 6 arable, 11

grassland, 6 forest, and 2 anthropogenic soils in the valley

of the Beroun-Kraluv Dvur industrial area was carried out in

2001 (Holoubek et al., 2002b).

Soils at sampling plots were classified according to their

use (arable soil, grassland soil and forest soil), type (FAO

classification) and texture (loam, sandy loam, loamy sand

and sand). Soils were sampled at ca. 10� 10 m sampling

plots as 5–10 subsamples that were homogenized. Samples

were quickly transported to the laboratory in polyethylene

black bags and sieved through 2 mm mesh (with the

exception of the portion for determination of the physi-

cal–chemical properties). Samples for chemical analyses

were stored dried at laboratory temperature. Samples for

microbial analyses were manipulated and stored as appro-

priate to the biological nature of the samples (ISO 10381-6,

1993; Forster, 1995): the samples were freshly sieved (2

mm) and stored at 4 jC prior to analyses. Soil organic

carbon (Corg), total soil nitrogen (Ntot), pHKCl, cation

exchange capacity (CEC), humus quality parameters (HA/

J. Hofman et al. / Environment International 30 (2004) 19–30 21

FA, C/N, Q4/6), CaCO3 and particle size distribution were

assessed by the standard methods described, e.g. in Forster

(1995). Particles below 10 Am were described as clay. Total

heavy metal (As, Cd, Cr, Hg, Ni, Pb, Zn) content was

analysed, after mineralization with aqua regia, by atomic

absorption spectroscopy (AAS). Polyaromatic hydrocar-

bons—PAHs (16 compounds according to US EPA), poly-

chlorinated biphenyls—PCBs (congeners 28, 52, 101, 118,

138, 153, 180), and organo-chlorinated pesticides (HCH,

DDT, HCB) were analysed by GC-MS and GC-ECD after

dichloromethane extraction of samples spiked with recovery

standards and clean up. PCDDs/Fs were analysed with

HRMS and expressed as WHO toxic equivalent (TEQ)

according to van den Berg et al. (1998).

The microbial biomass (Cbio) in the soil samples was

measured with the chloroform fumigation–extraction meth-

od according to ISO 14240-2 (1997) and Vance et al.

(1987). Carbon in non-fumigated samples (i.e. control

samples without extractable organic matter from microbial

cells) was used as a measure of extractable extracellular

carbon (Cext). It can be used as expression of the organic

carbon actually available for microbial activities and growth

(Sikora and McCoy, 1990; Christensen and Christensen,

1991; Garcia and Hernandez, 1996). Basal respiration and

potential respiration were measured after pre-incubation of

soil samples (40% WHC in 22 jC for 7 days). Basal

respiration (BR) was measured after 24 h in closed jars

with moistened soil (60% WHC) without any addition of

substrate. Potential respiration (PR) was measured by sub-

strate-induced respiration (SIR) method according to ISO

14240-1 (1997). Respiration was measured as CO2 produc-

tion with GC. Well-known metabolic coefficient—qCO2

(i.e. biomass-specific basal respiration, i.e. BR/Cbio) was

calculated (Sparling, 1997). Metabolic coefficient derived

from potential respiration—PR-qCO2 (i.e. PR/Cbio)—with

similar interpretation as qCO2 was introduced and evaluat-

ed. PR/BR was derived to evaluate substrate availability

Table 1

Summary statistics of soil physical–chemical properties and contamination calcu

Valid

N

Mean Media

pHKCl 176 5.9 6.4

Ntot (%) 176 0.28 0.22

Corg (%) 266 4.3 2.6

CEC (mEq kg� 1) 173 255 214

Clay (%) 116 18.0 16.1

Cd (mg kg� 1) 290 0.31 0.23

Hg (mg kg� 1) 290 0.14 0.10

Pb (mg kg� 1) 290 30 22

Zn (mg kg� 1) 290 94 76

APAHs (16 US EPA) (Ag kg� 1) 300 1475 343

APCBs (7 congeners) (Ag kg� 1) 300 14.5 5.1

AHCHs (Ag kg� 1) 300 1.50 0.73

ADDT (Ag kg� 1) 300 234.4 12.9

HCB (Ag kg� 1) 300 36.8 1.0

WHO-TEQ (ng kg� 1) 46 2.71 1.60

according to Cheng et al. (1996) and Parkinson and Cole-

man (1991). Finally, we evaluated carbon pool coefficients:

biomass-specific available organic carbon—Cext/Cbio (Gar-

cia and Hernandez, 1996) and well-known microbial coef-

ficient—Cbio/Corg (Insam and Domsch, 1988).

Statistical analyses were carried out with the Statistica

6.0 program. Analysis of variance (ANOVA) was used as a

breakdown technique for the identification of significant

groupings of the soil samples used. Because most of the

microbial parameters and soil properties were not normally

distributed and the variances were not homogenous in some

cases, results of parametric ANOVA were validated with

non-parametric Kruskal–Wallis ANOVA. Post hoc compar-

isons of the soil groups were made with the parametric

Newman–Keuls test and verified with non-parametric Krus-

kal–Wallis and median tests. If the correlation was evalu-

ated, the results of Pearson parametric r coefficient were

verified with non-parametric Spearman rank correlation. In

all analyses, only results with p-levels of significance below

0.01 were considered as statistically significant.

3. Results and discussion

The final matrix included 117 different localities from

various parts of the Czech Republic (Fig. 1). Finally, up to

520 independent soil samples were analysed, because sam-

pling was repeated over the years 1997–2002. Concerning

locality use, there were 326 grassland soil samples, 131

forest soil samples, and 51 arable soil samples. Eleven

anthropogenic soil samples were omitted from further anal-

ysis. The soil types were cambisols, fluvisols, or luvisols in

90% of the samples. Sandy and loamy–sandy soils (62%),

or loamy and sandy–loamy soils (30%) prevailed among

the soil texture classes. From Table 1, it is evident that the

samples covered a wide range of properties, and the soil set

is representative for the statistical evaluation of microbial

lated for the whole soil set

n Min Max 5th

percentile

95th

percentile

2.6 7.6 3.4 7.3

0.06 1.86 0.11 0.53

0.4 37.2 1.1 15.4

38 1122 82 586

3.9 49.0 5.7 36.3

0.09 1.98 0.12 0.64

0.01 0.89 0.04 0.34

7 316 14 75

12 902 45 169

14 22025 78 8807

0.9 345.8 1.5 61.1

0.03 64.66 0.18 3.72

0.2 6120.2 1.2 986.5

0.1 10295.1 0.2 10.2

0.30 16.35 0.51 12.76

Table 2

Summary statistics of microbial parameters and derived indices calculated for the overall soil set and correlation (Pearson r) of microbial characteristics with

selected soil properties (bold numbers mark p< 0.01)

Valid

N

Mean Median Min Max 5th

percentile

95th

percentile

pHKCl Ntot Corg Clay

Cbio (Ag C gd.w.� 1 ) 519 1155 848 22 8913 296 2537 � 0.29 0.89 0.66 � 0.02

BR (Ag CO2-C gd.w.� 1 h� 1) 489 1.43 1.12 0.07 16.44 0.26 3.62 � 0.07 0.35 0.48 � 0.14

PR (Ag CO2-C gd.w.� 1 h� 1) 501 9.21 7.44 0.26 59.16 2.16 21.13 0.02 0.26 0.42 � 0.21

Cext (Ag C gd.w.� 1 ) 519 136 100 0 1305 29 402 � 0.40 0.70 0.68 � 0.02

qCO2 (Ag C� mg Cbio� 1 h� 1) 488 1.49 1.25 0.13 6.22 0.35 3.71 0.02 � 0.08 0.11 � 0.17

PR/BR 489 7.8 6.9 0.9 49.9 2.9 16.1 0.08 � 0.21 � 0.20 0.11

PR/Cbio (Ag C� mg Cbio� 1 h� 1) 500 10.7 8.6 0.5 61.8 2.2 25.2 0.10 � 0.21 � 0.04 � 0.25

Cext/Cbio (Ag C� mg Cbio� 1 h� 1) 519 166 122 0 1981 23 421 � 0.11 � 0.11 � 0.00 � 0.10

Cbio/Corg (%) 266 3.6 2.8 0.0 27.4 0.9 9.3 0.35 � 0.12 � 0.32 0.15

J. Hofman et al. / Environment International 30 (2004) 19–3022

characteristics. Overall summary statistics of microbial

parameters (Table 2) displayed ranges (5th–95th percentile)

that are roughly comparable with data in the literature

(Wardle, 1992, 1998; Oberholzer et al., 1999; Leiros et

al., 2000). However, it is obvious that the range of the

values was too wide to provide any benchmarking values

for environmental studies. There was a significant correla-

tion of microbial biomass with Corg and Ntot (Table 2)

confirming results published in countless studies (e.g. War-

dle, 1992; Oberholzer et al., 1999; Garcia and Hernandez,

1996) that organic matter and soil nitrogen are the key soil

properties influencing microbial communities.

It is evident, that the soil data set should be divided into

reasonable categories to give suitable benchmarking ranges.

From the possible categorizations (according to soil type,

soil texture, or soil use), only soil use seemed to have

a straightforward discrimination effect on the microbial

parameters. Analysis of variance (ANOVA) revealed signif-

icant differences for all basal and derived microbial param-

Table 3a

Summary statistics of basal microbial parameters calculated for arable soils, gras

Valid

N

Mean Median Min Max

Microbial biomass—Cbio (lg C gd.w.� 1 )

Arable soilsA 52 440 421 22 867

Grassland soilsB 326 989 879 158 3069

Forest soilsC 130 1885 1078 300 8913

Basal respiration—BR (lg CO2-C gd.w.� 1 h� 1)

Arable soilsA 50 0.64 0.53 0.07 2.24

Grassland soilsB 301 1.25 1.09 0.16 5.15

Forest soilsC 127 2.26 1.77 0.18 16.44

Potential respiration—PR (lg CO2-C gd.w.� 1 h� 1)

Arable soilsA 52 6.04 5.15 1.02 14.70

Grassland soilsAB 307 8.40 7.27 0.26 26.92

Forest soilsB 131 12.61 9.49 1.54 59.16

Extractable extracellular organic matter—Cext (lg C gd.w.� 1 )

Arable soilsA 52 73 71 11 143

Grassland soilsAB 326 103 90 0 1305

Forest soilsB 130 247 195 51 857

The different letters by the soil category mark significant differences (Newman–K

The second part of the table displays correlation (Pearson r) of microbial charact

eters between forest, grassland and arable soils. Post hoc

comparisons showed that all basal parameters and most of

the indices displayed different values in each use category

(see Table 3a and Table 3b). These results could have been

anticipated, because soil management influences the soil

microorganisms and the soil microbial processes through

changes in the quantity and quality of plant residues and

through anthropogenic activities (Kandeler et al., 1999).

According to our results (Table 3a), microbial biomass,

respiration activities and also extractable extracellular or-

ganic matter were significantly lower in arable soils, mod-

erate in grassland soils and highest in forest soils. These

observations are consistent with the previously generally

accepted results (e.g. Smith and Paul, 1986; Wardle, 1992;

Santruckova, 1993; Wardle, 1998). The microbial coeffi-

cient Cbio/Corg which can be interpreted as substrate avail-

ability, was lowest in arable soils and forest soils. This

suggests that the immobilization of organic mater as micro-

bial biomass can be more difficult in arable and forest soils

sland soils, and forest soils separately

5th

percentile

95th

percentile

pHKCl Ntot Corg Clay

161 802 0.21 0.63 0.43 0.39

299 1992 0.17 0.63 0.12 � 0.24

421 7643 � 0.39 0.98 0.79 0.21

0.09 2.17 0.50 0.55 0.40 0.09

0.33 2.62 0.05 0.51 0.41 � 0.21

0.48 4.62 0.33 0.16 0.31 � 0.15

1.56 12.68 0.37 0.43 0.32 0.04

2.13 17.66 0.08 0.38 0.41 � 0.41

2.83 31.28 0.35 0.12 0.23 � 0.17

16 121 0.23 0.39 0.03 0.61

24 210 � 0.08 0.24 0.49 � 0.05

75 592 � 0.17 0.76 0.60 � 0.03

euls; p< 0.01) and outline increasing values of the parameter from A to B.

eristics with selected soil properties (bold numbers mark p< 0.01).

Table 3b

Summary statistics of derived microbial indices calculated for arable soils, grassland soils, and forest soils separately

Valid

N

Mean Median Min Max 5th

percentile

95th

percentile

pHKCl Ntot Corg Clay

Metabolic coefficient—qCO2 (lg C� mg Cbio� 1h� 1)

Arable soilsA 50 1.47 1.11 0.13 4.29 0.21 3.75 0.32 0.10 0.10 � 0.18

Grassland soilsA 301 1.41 1.20 0.18 6.22 0.50 3.28 � 0.20 0.09 0.24 � 0.01

Forest soilsA 126 1.75 1.46 0.17 4.59 0.29 3.99 0.49 � 0.32 � 0.13 � 0.30

Substrate availability index—PR/BR

Arable soilsC 50 13.2 10.7 3.9 49.9 4.3 25.9 � 0.18 � 0.18 � 0.09 0.24

Grassland soilsB 301 7.6 7.0 0.9 23.5 2.9 15.1 � 0.12 � 0.08 0.04 � 0.17

Forest soilsA 127 5.9 5.3 1.9 14.8 2.6 10.0 � 0.08 � 0.19 � 0.20 � 0.04

PR-metabolic coefficient—PR/Cbio (lg C� mg Cbio� 1h� 1)

Arable soilsB 52 15.5 12.9 4.6 61.8 4.9 44.2 0.10 � 0.19 � 0.32 � 0.33

Grassland soilsA 307 10.1 8.3 0.5 49.3 2.9 22.7 � 0.18 � 0.09 0.13 � 0.18

Forest soilsA 130 10.6 8.6 1.0 41.8 1.3 24.8 0.55 � 0.39 � 0.20 � 0.44

Biomass-specific extractable extracellular organic matter—Cext/Cbio (lg C� mg Cbio� 1 h� 1)

Arable soilsB 52 230 157 21 1981 46 541 � 0.14 � 0.31 � 0.51 � 0.25

Grassland soilsA 326 147 108 0 1906 18 366 � 0.32 � 0.11 0.14 0.05

Forest soilsB 130 193 144 30 741 67 462 0.33 � 0.36 � 0.18 � 0.24

Microbial coefficient—Cbio/Corg (%)

Arable soilsC 40 3.1 3.0 0.3 6.2 1.2 5.3 0.04 0.50 0.09 0.44

Grassland soilsB 137 4.8 3.6 0.6 27.4 1.0 11.3 0.29 0.02 � 0.34 0.14

Forest soilsA 78 1.9 1.7 0.0 4.9 0.7 3.4 0.17 � 0.12 � 0.40 0.17

The different letters by the soil category mark significant differences (Newman–Keuls; p< 0.01) and outline increasing values of the parameter from A to C.

The table displays the correlation (Pearson r) of microbial characteristics with selected soil properties (bold numbers mark p< 0.01).

J. Hofman et al. / Environment International 30 (2004) 19–30 23

than in grassland soils (Insam and Domsch, 1988; Bauhus et

al., 1998). The coefficient PR/BR as ‘‘substrate availability

index’’ (Parkinson and Coleman, 1991; Cheng et al., 1996)

showed a sharp distinction between three groups of the soils

descending from arable to grassland and to forest soils. The

ecophysiological coefficients qCO2 and PR-qCO2 revealed

Table 4a

Summary statistics of basal microbial parameters calculated for grassland soils di

Valid

N

Mean Median Min Ma

Microbial biomass—Cbio (lg C gd.w.� 1 )

G. loamy soilsC 31 1616 1398 928 306

G. sandy soils (weak Corg)A 45 678 621 158 165

G. sandy soils (moderate Corg)B 32 1123 1117 272 204

Basal respiration—BR (lg CO2-C gd.w.� 1 h� 1)

G. loamy soilsC 28 1.94 1.86 1.09

G. sandy soils (weak Corg)A 42 1.09 0.91 0.22

G. sandy soils (moderate Corg)B 30 1.51 1.35 0.31

Potential respiration—PR (lg CO2-C gd.w.� 1 h� 1)

G. loamy soilsB 28 11.52 10.76 5.16 1

G. sandy soils (weak Corg)A 45 8.20 8.15 1.23 1

G. sandy soils (moderate Corg)C 32 13.94 14.73 4.58 2

Extractable extracellular organic matter—Cext (lg C gd.w.� 1 )

G. loamy soilsA 31 82 76 14 19

G. sandy soils (weak Corg)A 45 103 92 10 53

G. sandy soils (moderate Corg)B 32 134 124 38 24

The different letters by the soil category mark significant differences (Newman–K

The second part of the table displays correlation (Pearson r) of microbial charact

small differences between different soil use categories,

confirming that these indices represent inner standardized

measures (Brookes, 1995). In evaluating main soil proper-

ties, the forest soils displayed significantly lower pH than

other categories and arable soils displayed higher clay

content. However, regarding the low correlation of pH or

vided into loamy soils and sandy soils with weak or moderate Corg content

x 5th

percentile

95th

percentile

pHKCl Ntot Corg Clay

9 1049 3014 � 0.27 0.64 0.39

6 282 1165 0.16 0.56 0.21 0.15

5 294 2040 0.50 0.55 0.31 � 0.29

3.43 1.15 3.28 0.01 � 0.03 0.50

3.13 0.38 2.48 � 0.12 0.53 0.42 0.13

3.10 0.64 3.03 0.28 0.28 � 0.06 � 0.22

8.28 5.21 17.87 � 0.27 � 0.37 � 0.31

9.25 3.09 12.46 0.07 0.14 0.21 � 0.03

2.31 5.26 20.81 0.51 0.22 0.21 � 0.56

7 15 151 0.52 0.34 0.50

4 24 210 � 0.31 0.27 0.20 0.21

4 64 239 0.38 0.33 � 0.12 0.13

euls; p< 0.01) and outline increasing values of the parameter from A to C.

eristics with selected soil properties (bold numbers mark p< 0.01).

Table 4b

Summary statistics of derived microbial indices calculated for grassland soils divided into loamy soils and sandy soils with weak or moderate Corg content

Valid

N

Mean Median Min Max 5th

percentile

95th

percentile

pHKCl Ntot Corg Clay

Metabolic coefficient—qCO2 (lg C� mg Cbio� 1 h� 1)

G. loamy soilsA 28 1.29 1.36 0.55 2.53 0.59 2.48 0.13 � 0.44 0.21

G. sandy soils (weak Corg)A 42 1.69 1.34 0.27 3.77 0.53 3.67 � 0.14 0.20 0.21 � 0.06

G. sandy soils (moderate Corg)A 30 1.71 1.47 0.18 6.22 0.31 4.06 � 0.39 � 0.03 � 0.13 0.26

Substrate availability index—PR/BR

G. loamy soilsA 28 6.2 6.2 2.6 10.8 2.9 10.0 � 0.30 � 0.30 � 0.75

G. sandy soils (weak Corg)B 42 8.9 8.7 1.8 17.3 3.9 15.9 � 0.02 � 0.59 � 0.23 � 0.13

G. sandy soils (moderate Corg)B 30 10.4 9.6 4.3 20.5 4.7 20.4 0.02 0.08 0.26 � 0.09

PR-metabolic coefficient—PR/Cbio (lg C� mg Cbio� 1 h� 1)

G. loamy soilsA 28 7.7 7.1 1.7 16.3 2.2 14.1 0.10 � 0.64 � 0.40

G. sandy soils (weak Corg)B 45 14.6 11.4 1.5 49.3 4.9 35.0 0.02 � 0.23 � 0.04 � 0.20

G. sandy soils (moderate Corg)B 32 14.5 13.5 2.9 34.7 3.7 30.6 � 0.32 � 0.23 � 0.21 0.10

Biomass-specific extractable extracellular organic matter—Cext/Cbio (lg C� mg Cbio� 1 h� 1)

G. loamy soilsA 31 59 51 5 179 7 131 0.43 � 0.04 0.04

G. sandy soils (weak Corg)B 45 189 155 13 735 18 563 � 0.41 0.02 0.06 0.01

G. sandy soils (moderate Corg)B 32 149 119 32 664 42 459 � 0.49 � 0.10 � 0.25 0.46

Microbial coefficient—Cbio/Corg (%)

G. loamy soilsB 8 9.0 8.3 5.5 15.5 5.5 15.5 � 0.59 0.13 � 0.30

G. sandy soils (weak Corg)A 45 3.3 3.2 0.6 7.5 1.5 6.4 0.33 0.29 � 0.23 0.31

G. sandy soils (moderate Corg)A 32 2.9 2.9 0.6 5.5 0.7 4.7 0.46 0.26 � 0.05 � 0.19

The different letters by the soil category mark significant differences (Newman–Keuls; p< 0.01) and outline increasing values of the parameter from A to C.

The table displays the correlation (Pearson r) of microbial characteristics with selected soil properties (bold numbers mark p< 0.01).

J. Hofman et al. / Environment International 30 (2004) 19–3024

clay with microbial parameters (Table 2), the reason for

differences in the microbial characteristics was probably due

to the different organic matter content. Both Ntot and Corg

displayed significant increase from arable soils to grassland

soils and to forest soils. However, the organic matter content

was not the only factor that explained the differences in the

Table 5

Summary statistics of basal microbial parameters and microbial coefficient calcul

Valid

N

Mean Median Min Max

Microbial biomass—Cbio (lg C gd.w.� 1)

F. soils (moderate Corg) 38 901 834 357 1458

F. soils (rich Corg) 39 1828 1383 421 6437

Basal respiration—BR (lg CO2-C gd.w.� 1 h� 1)

F. soils (moderate Corg) 36 1.80 1.54 0.42 4.62

F. soils (rich Corg) 38 3.43 2.74 0.18 16.44

Potential respiration—PR (lg CO2-C gd.w.� 1 h� 1)

F. soils (moderate Corg) 38 11.28 8.80 2.59 26.70

F. soils (rich Corg) 40 20.66 18.04 1.54 59.16

Extractable extracellular organic matter—Cext (lg C gd.w.� 1)

F. soils (moderate Corg) 38 174 153 61 592

F. soils (rich Corg) 39 249 213 51 578

Microbial coefficient—Cbio/Corg (%)

F. soils (moderate Corg) 38 2.2 2.2 1.0 4.9

F. soils (rich Corg) 40 1.5 1.5 0.0 4.8

All differences were significant (Newman–Keuls; p< 0.01). The second part of th

selected soil properties (bold numbers mark p< 0.01).

microbial parameters, because Cbio pooled with Corg also

revealed significant differences (Table 3b).

Arable soils showed the lowest values for both parame-

ters of microbial quantity and activity (Table 3a). The reason

is probably due to the significantly lower amount of total

organic matter in the soils as the consequence of cropping

ated for forest soils divided according to moderate or rich Corg content

5th

percentile

95th

percentile

pHKCl Ntot Corg Clay

361 1446 0.30 0.80 0.38 0.30

671 5493 � 0.52 0.98 0.75 0.06

0.45 4.31 0.51 0.24 0.55 � 0.15

0.23 7.50 0.33 � 0.04 0.11 � 0.25

2.88 23.91 0.54 0.25 0.57 � 0.32

4.37 43.56 0.52 � 0.24 � 0.07 � 0.28

68 363 0.65 0.16 0.23 � 0.10

73 535 � 0.45 0.77 0.66 � 0.09

1.1 4.1 � 0.01 0.19 � 0.42 0.25

0.5 2.8 0.23 0.08 � 0.25 0.12

e table displays the correlation (Pearson r) of microbial characteristics with

J. Hofman et al. / Environment International 30 (2004) 19–30 25

management (Wardle, 1998; Smith and Paul, 1986). Arable

soil cultivation and management can also act as stress for

the microbial communities due to disturbances of the soil

profile. The available fraction of organic matter (Cext) was

significantly lower than in the forest soils. The microbial

coefficient Cbio/Corg indicated relatively high microbial

biomass in proportion to low organic matter content. The

relationships of the microbial parameters to organic carbon

and total nitrogen in the soils were weak (r = 0.63 and

r = 0.43, respectively). Analysis of variance between differ-

ent texture categories showed no significant trends for any

microbial parameter and there is no need to break down the

arable soils group.

Grassland soils represented a moderate ecosystem be-

tween forest and arable soils from the point of view of soil

microorganisms (Table 3a). The values of microbial biomass

could reach nearly 2000 Ag g� 1 and also both respiration

rates were significantly higher than in soils burdened with

Fig. 2. The results of selected physical–chemical soil properties for soil groups:

weak organic matter content (GSSW), sandy grassland soils with moderate organi

(FSM), and forest organic soils with rich organic matter content (FSR). Boxes dis

percentiles. Numbers in parentheses display valid N in group. Different letters di

agricultural management. A key feature of grasslands is

their high turnover of shoot and root biomass, and conse-

quently, a large pool of organic matter at the soil surface

supporting a uniquely large and active soil microbial com-

munity (Bardgett and Cook, 1998). Because of the very

different types of the grassland ecosystems and broad

spectrum of the possible environmental conditions, the

ranges for the soil microbial parameters were much wider

than for arable soils (Table 3a). Thus, grassland soils were

separated according to soil texture and organic matter

content. Significantly higher values of basal microbial

parameters were revealed by loamy soils when compared

with lighter texture classes (sandy and loamy sandy) and

these two groups should be evaluated separately. Derived

microbial indices were significantly different (with the

exception of qCO2) in these categories as well. Both

metabolic indices displayed higher values in fine-textured

soils than in coarse-textured soils. This could be due to

arable soils (AS), loamy grassland soils (GSL), sandy grassland soils with

c matter content (GSSM), forest soils with moderate organic matter content

play lower and upper quartiles and median. Whiskers display 5th and 95th

splay significantly (Newman–Keuls; p< 0.01) different results.

J. Hofman et al. / Environment International 30 (2004) 19–3026

greater physical protection of organic matter in former ones

(Hassink, 1995). Grassland loams revealed no significant

differences in microbial parameters between groups with

different organic matter content but grassland sandy soils

should be divided according to organic carbon content

because all microbial parameters distinguished between

soils with poor ( < 1.2%), weak (1.2–2.9%), and moderate

(2.9–5.8%) content of Corg. The statistics for the three most

important groups of the grassland soils are summarized in

Table 4a and Table 4b.

Forest soils revealed the highest values of microbial

biomass and respiration. This observation was stronger

because we included high-mountain humus soils in the data

set. These soils disposed of as much as 30% Corg and

showed Cbio up to 7000 Ag g� 1, basal respiration up to 5

Ag CO2-C g� 1 h� 1, and potential respiration up to 30 Ag

Fig. 3. The results of selected contaminant contents for soil groups: arable soils (A

matter content (GSSW), sandy grassland soils with moderate organic matter cont

forest organic soils with rich organic matter content (FSR). Boxes display lower a

Numbers in parentheses display valid N in group. Different letters display signif

logarithmic scale of X-axis.

CO2-C g� 1 h� 1. These results correspond with data known

for the organic soils of both deciduous and coniferous

forests (Leiros et al., 2000; Bauhus et al., 1998). The strong

relationship with soil organic matter content was observed:

the correlation of microbial biomass with Ntot and Corg was

0.98 and 0.79, respectively. The group of forest soils was

not homogenous and they should be separated into rich

organic forest soils with Corg above 5.8% and other forest

soils. Rich organic forest soils displayed significantly higher

values for all basal microbial parameters and a significantly

lower microbial coefficient. When the organic matter con-

tent was moderate, forest soils were comparable with sandy

grassland soils. The summary statistics of organic rich and

normal forest soils are displayed in Table 5 (qCO2, PR/BR,

PR-qCO2 and Cext/Cbio were omitted because their values

were not significantly different). In both groups, there were

S), loamy grassland soils (GSL), sandy grassland soils with weak organic

ent (GSSM), forest soils with moderate organic matter content (FSM), and

nd upper quartiles and median. Whiskers display 5th and 95th percentiles.

icantly (Newman–Keuls; p< 0.01) different results. PAHs and PCBs show

J. Hofman et al. / Environment International 30 (2004) 19–30 27

strong correlations of microbial biomass with total nitrogen.

In organic rich soils there was also a weaker correlation with

organic carbon in the soils, while on the contrary microbial

respiration correlated more strongly with Corg. Forest soils

from high-mountain ecosystems were very different from

other soils. The results of Cbio/Corg and PR/BR could imply

that there is lower substrate availability in forest soils

corresponding with their high C/N (Vance and Chapin,

2001) or low pH (Bauhus et al., 1998).

To measure the relationships between soil microorgan-

isms and contamination is very difficult, because the

microorganisms could also be stressed or stimulated in

uncontaminated soils due natural soil properties, e.g.

temperature, moisture, organic matter content, pH, etc.

(Domsch et al., 1983; Wardle, 1992). In our study, the

values established as background levels for the Czech

Republic (Czech Ministry of Environment, 1996) were

used as a criterion of soil contamination. These levels

are: Cd—0.5 mg kg� 1; Hg—0.4 mg kg� 1; Pb—80 mg

kg� 1; Zn—150 mg kg� 1; APAHs (16 US EPA)—1000 Agkg� 1; APCBs (seven congeners)—20 Ag kg� 1; AHCHs—50 Ag kg� 1; ADDT—50 Ag kg� 1; HCB—50 Ag kg� 1;

WHO-TEQ—1 ng kg� 1. Contamination data showed that

some samples were seriously contaminated with some

pollutants (Table 1). Soils with at least one of the named

pollutants above these levels were included in the sub-

group of contaminated soils in our study. A comparison of

Fig. 4. The results of basal microbial parameters for soil groups: arable soils (AS), l

content (GSSW), sandy grassland soils with moderate organic matter content (GS

organic soils with rich organic matter content (FSR). Boxes display lower and uppe

in parentheses display valid N in group. Different letters display significantly (N

microbial parameters between sub-groups of contaminated

and non-contaminated samples was done in the framework

of each soil category. The comparison showed that there

was a significantly higher microbial biomass content in

contaminated arable soils, forest soils as well as grassland

loamy soils. This observation should not be misinterpreted

as a positive relationship between soil contamination and

microbial biomass. The reason for the elevated microbial

biomass is the significantly higher organic matter content

in soils that also support higher contamination (Posthuma

et al., 1998; van Straalen and Denneman, 1989; Kuperman

and Carreiro, 1997). It is the main example where envi-

ronmental factors confound with environmental pollution

making the ecotoxicological interpretation impossible

(Giller et al., 1998). When contamination of grassland

sands was compared, contaminated soils displayed signif-

icantly lower basal and potential respiration and higher

content of total and extractable organic matter (Corg and

Cext). These observations were much more evident in the

grassland sandy soils with weak organic matter content.

Such results need to be very carefully interpreted. How-

ever, they could imply that selecting the reasonable cate-

gory of soils could omit confounding factors in the

microflora–contamination relationship. These soils could

be suitable not only for the laboratory testing of chemical

effects on soil microflora but also for the biomonitoring of

the harmful effects of chemicals on real ecosystems.

oamy grassland soils (GSL), sandy grassland soils with weak organic matter

SM), forest soils with moderate organic matter content (FSM), and forest

r quartiles and median. Whiskers display 5th and 95th percentiles. Numbers

ewman–Keuls; p< 0.01) different results.

J. Hofman et al. / Environment International 30 (2004) 19–3028

4. Conclusions

In our study, we proved that it was essential to break the

soil data sets into reasonable categories, driven in particular

by environmental factors, with soil use in first place. When

the breakdown analysis is summarized, six significantly

different groups of soils were found from the point of view

of microbial parameters: arable soils, loamy grassland soils,

sandy grassland soils with weak organic matter content,

sandy grassland soils with moderate organic matter content,

forest soils with moderate organic matter content, and forest

organic soils with rich organic matter content. These cate-

gories showed narrower ranges for microbial characteristics

for possible comparative purposes. Figs. 2–5 display the

comparison of main soil properties, contamination, and

microbial characteristics for these categories. The discrim-

Fig. 5. The results of derived microbial coefficients for soil groups: arable soils (A

matter content (GSSW), sandy grassland soils with moderate organic matter cont

forest organic soils with rich organic matter content (FSR). Boxes display lower a

Numbers in parentheses display valid N in group. Different letters display signifi

ination of the soils according to microbial characteristics

(Figs. 4 and 5) corresponded very strongly with the organic

matter content in the soils (Fig. 2). Clay content, cation

exchange capacity or pH seemed to play a minor role,

although in the forest soils, pH could be one of the reasons

for worse substrate availability. The promotion of contam-

inant accumulation in soils by organic matter content is

evident in all soils, especially in high mountain organic soils

(Fig. 3). The lowest microbial biomass was found in arable

soils and grassland sandy soils with weak Corg content. The

highest microbial biomass was shown by loamy grassland

soils and organic forest soils. Respiration activities dis-

played similar results with the microbial biomass. Derived

microbial indices were shown to be useful because they

revealed more interesting information. The differences in

basal microbial parameters were often more significant than

S), loamy grassland soils (GSL), sandy grassland soils with weak organic

ent (GSSM), forest soils with moderate organic matter content (FSM), and

nd upper quartiles and median. Whiskers display 5th and 95th percentiles.

cantly (Newman–Keuls; p< 0.01) different results.

J. Hofman et al. / Environment International 30 (2004) 19–30 29

the differences in the derived indices, confirming the inner-

standard nature of derived indices (Brookes, 1995).

Acknowledgements

Support for this study was received from Czech Ministry

of Environment project (VaV 340/1/00) and Czech Grant

Agency project (GACR 526/98/1147).

References

Andel P, Hofman J, Adamec V, Cupr P, Holoubek I, Dusek L, et al.

Zhodnocenı ekologickeho rizika provozu dalnice D1 a dalnice D11.

TOCOEN Report No. 153. Brno: TOCOEN, s.r.o.; 1999.

Anderson JPE, Domsch KH. A physiological method for the quantitative

measurement of microbial biomass in soil. Soil Biol Biochem 1978;10:

215–21.

Bardgett RD, Cook R. Functional aspects of soil animal diversity in agri-

cultural grasslands. Appl Soil Ecol 1998;10:263–76.

Bauhus J, Pare D, Cote L. Effects of tree species, stand age and soil type on

soil microbial biomass and its activity in a southern boreal forest. Soil

Biol Biochem 1998;30:1077–89.

Brookes PC. The use of microbial parameters in monitoring soil pollution

by heavy metals. Biol Fertil Soils 1995;19:269–79.

Carter MR. Microbial biomass as an index for tillage-induced changes in

soil biological properties. Soil Tillage Res 1986;7:29–40.

Chander K, Brookes PC. Residual effect of zinc, copper and nickel in

sewage-sludge on microbial biomass in a sandy loam. Soil Biol Bio-

chem 1993;25:1231–9.

Cheng WX, Zhang QL, Coleman DC, Caroroll CR, Hoffmman CA. Is

available carbon limiting microbial respiration in the rhizosphere? Soil

Biol Biochem 1996;28:1283–8.

Christensen S, Christensen BT. Organic matter available for denitrification

in different soil fractions—effect of freeze thaw cycles and straw dis-

posal. J Soil Sci 1991;42:637–47.

Czech Ministry of Environment. Metodicky pokyn odboru pro ekologicke

skody MZP—Kriteria znecistenı zemin a podzemnı vody. Vestnık MZP

3/1996. Praha: MZP CR; 1996.

Dalal RC. Soil microbial biomass—what do the numbers really mean? Aust

J Exp Agric 1998;38:649–65.

Dick RP. A review—long-term effects of agricultural systems on soil bio-

chemical and microbial parameters. Agric Ecosyst Environ 1992;40:

25–36.

Domsch KH, Jagnow G, Anderson T-H. An ecological concept for the

assessment of side-effects of agrochemicals on soil microorganisms.

Residue Rev 1983;86:65–105.

Doran JW, Parkin TB. Quantitative indicators of soil quality: a minimum

data set. In: Doran JW, Jones AJ, editors. Methods for assessing soil

quality. SSSA Special Publication Number 49. Madison: Soil Science

Society of America; 1996. p. 25–38.

Forster JC. Soil sampling, handling, storage and analysis. In: Alef K,

Nannipieri P, editors. Methods in soil microbiology and biochemistry.

London: Academic Press; 1995. p. 49–123. ISBN 0-12-513840-7.

Garcia C, Hernandez T. Organic matter in bare soils of the Mediterranean

region with a semiarid climate. Arid Soil Res Rehabil 1996;10:31–41.

Giller KE, Witter E, McGrath SP. Toxicity of heavy metals to microorgan-

isms and microbial processes in agricultural soils: a review. Soil Biol

Biochem 1998;30:1389–414.

Hassink J. Density fractions of soil macroorganic matter and microbial

biomass as predictors of C and N mineralization. Soil Biol Biochem

1995;27:1099–108.

Hofman J. Biological potential of the soils from Kosetice observatory—

September 2000. In: Vana M, editor. Quality of the natural environ-

ment in the Czech Republic at the regional level. Results of the

Kosetice Observatory. Praha: CHMU; 2001. p. 175–84. ISBN 80-

85813-88-2.

Holoubek I, Trıska J, Cudlın P, Caslavsky J, SchrammK-W, Kettrup A, et al.

Project TOCOEN. The fate of selected organic pollutants in the environ-

ment: Part XXXI. The occurrence of POPs in high mountain ecosystems

of the Czech Republic. Toxicol Environ Chem 1998;66:17–25.

Holoubek I, Hofman J, Dolezal L, Dusek L. Shrnutı experimentalnıch

vysledku z prıpadove studie Zlın provadene v ramci projektu VaV

340/1/96: Vliv povodnı v roce 1997. TOCOEN Report No. 180. Brno:

TOCOEN, s.r.o.; 2000.

Holoubek I, Hofman J, Klanova J, Green N, Kohoutek J, Cupr P, et al.

Studie stavu kontaminace zivotnıho prostredı regionu Zlın-2001. TO-

COEN Report No. 216. Brno: TOCOEN, s.r.o.; 2001.

Holoubek I, Hofman J, Cupr P, Klanova J, Skarek M, Dolinova J, et al.

Monitoring stavu zivotnıho prostredı v okolı cementarny Mokra, CMC

a.s., 2001. Zavıreena zprava. TOCOEN Report No. 217. Brno: TO-

COEN, s.r.o.; 2002a.

Holoubek I, Hofman J, Cupr P, Kohoutek J, Andel P, Sanka M, et al. Studie

zatızenı zivotnıho prostredı v berounske kotline, 2001. Zaverecna zpra-

va. TOCOEN Report No. 218. Brno: TOCOEN, s.r.o.; 2002b.

Insam H, Domsch KH. Relationship between soil organic carbon and mi-

crobial biomass on consequences of reclamation sites. Microb Ecol

1988;15:177–88.

ISO 10381-6. Soil quality—Sampling: Part 6. Guidance on the collection,

handling and storage of soil for the assessment of aerobic microbial

processes in the laboratory. International Organization for Standardiza-

tion. Geneve, Switzerland; 1993.

ISO 14240-1. Soil quality—Determination of soil microbial biomass: Part

1. Substrate-induced respiration method. International Organization for

Standardization. Geneve, Switzerland; 1997.

ISO 14240-2. Soil quality—Determination of soil microbial biomass: Part

2. Fumigation–extraction method. International Organization for Stand-

ardization. Geneve, Switzerland; 1997.

ISO/DIS 16072. Soil quality—Laboratory methods for determination of

microbial soil respiration. International Organization for Standardiza-

tion. Geneve, Switzerland; 2002.

Kandeler E, Tscherko D, Spiegel H. Long-term monitoring of microbial

biomass, N mineralisation and enzyme activities of a Chernozem under

different tillage management. Biol Fertil Soils 1999;28:343–51.

Kapustka LA. Microbial endpoints: the rationale for their exclusion as

ecological assessment endpoints. Hum Ecol Risk Assess 1999;5:691–6.

Kasyanova YV, Ananyeva ND, Blagodatskaya YV, Orlinskiy DB. Ecolo-

go-microbiological soil monitoring in the vicinity of a chemical plant.

Eurasian Soil Sci 1996;28:133–43.

Kennedy AC, Papendick RI. Microbial characteristics of soil quality. J Soil

Water Conserv 1995;50:243–8.

Kuperman RG, Carreiro MM. Soil heavy metal concentrations, microbial

biomass and enzyme activities in a contaminated grassland ecosystem.

Soil Biol Biochem 1997;29:179–90.

Leiros MC, Trasar-Cepeda C, Seoane S, Gil-Sotres F. Biochemical proper-

ties of acid soils under climax vegetation (Atlantic oakwood) in an area

of the European temperate–humid zone (Galicia, NW Spain): general

parameters. Soil Biol Biochem 2000;32:733–45.

Oberholzer HR, Rek J, Weisskopf P, Walther U. Evaluation of soil quality

by means of microbiological parameters related to the characteristics of

individual arable sites. Agribiol Res 1999;52:113–25.

Parkinson D, Coleman DC. Methods for assessing soil microbial popula-

tions, activity and biomass. Microbial communities, activity and bio-

mass. Agric Ecosyst Environ 1991;34:3–33.

Posthuma L, van Gestel CAM, Smit CE, Bakker DJ, Vonk JW. Validation

of toxicity data and risk limits for soils: Final report. Report No.

607505. Bilthoven, RIVM; 1998.

Powlson DS, Brookes PC, Christensen BT. Measurement of soil microbial

biomass provides an early indication of changes in total soil organic

matter due to straw incorporation. Soil Biol Biochem 1987;19:159–64.

J. Hofman et al. / Environment International 30 (2004) 19–3030

Santruckova H. Microbial biomass as a measure of soil biological-activity.

Rostl Vyroba 1993;39:779–88.

Sheppard SC. Soil microbial bioassays: quick and relevant but are they

useful? Hum Ecol Risk Assess 1999;5:697–705.

Sikora LJ, McCoy JL. Attempts to determine available carbon in soils. Biol

Fertil Soils 1990;9:19–24.

Smith JL, Paul EA. The role of soil type and vegetation on microbial

biomass and activity. Terr Ecosyst Proc 1986;IV:460–6.

Sparling GP. Soil microbial biomass, activity and nutrient cycling as in-

dicators of soil health. In: Pankhurst CE, Doube BM, Gupta VVSR,

editors. Biological indicators of soil health. Wallingford: CAB Interna-

tional; 1997. p. 97–120. ISBN 0851991580.

Stenberg B. Monitoring soil quality of arable land: microbiological indica-

tors. Acta Agric Scand, B Soil Plant Sci 1999;49:1–24.

Suter II GW, Efroymson RA, Sample BE, Jones DS. Ecological risk assess-

ment for contaminated sites. Boca Raton: Lewis Publishers; 2000. ISBN

1-56670-525-8, 437 p.

Tate RL. Soil microbiology. 2nd ed. New York: Wiley; 2000. ISBN

0471317918, 508 p.

van den Berg M, Birnbaum L, Bosveld ATC, Brunstrom B, Cook P, Feeley

M, et al. Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs

for humans and wildlife. Environ Health Perspect 1998;106: 775–92.

van Straalen NM, Denneman CAJ. Ecotoxicological evaluation of soil

quality criteria. Ecotoxicol Environ Saf 1989;18:241–51.

Vana M, Holoubek I, Pacl A, Pekarek J, Smrckova V, Machalek P, et al.

Quality of the natural environment in the Czech Republic at the regional

level. Results of the Kosetice Observatory. Praha: CHMU; 2001. ISBN

80-85813-88-2.

Vance ED, Chapin III FS. Substrate limitations to microbial activity in taiga

forest floors. Soil Biol Biochem 2001;33:173–88.

Vance ED, Brookes PC, Jenkinson DS. An extraction method for measur-

ing soil microbial biomass C. Soil Biol Biochem 1987;19:703–7.

Wardle DA. A comparative assessment of factors which influence microbial

biomass carbon and nitrogen levels in soil. Biol Rev 1992;67: 321–58.

Wardle DA. Controls of temporal variability of the soil microbial biomass:

a global-scale synthesis. Soil Biol Biochem 1998;30:1627–37.

Wardle DA, Yeates GW, Nicholson KS, Bonner KI, Watson RN. Response

of soil microbial biomass dynamics, activity and plant litter decompo-

sition to agricultural intensification over a seven-year period. Soil Biol

Biochem 1999;31:1707–20.