www.elsevier.com/locate/still

Soil & Tillage Research 90 (2006) 16–28

Soil aggregation and bacterial community structure as

affected by tillage and cover cropping in the

Brazilian Cerrados

R.S. Peixoto a, H.L.C. Coutinho b, B. Madari b, P.L.O.A. Machado b,N.G. Rumjanek c, J.D. Van Elsas d, L. Seldin a, A.S. Rosado a,*

a Instituto de Microbiologia Professor Paulo de Goes, Universidade Federal do Rio de Janeiro/CCS,

Bloco I, Ilha do Fundao, CEP 21941-590 Rio de Janeiro, RJ, Brazilb Embrapa Solos, Rua Jardim Botanico 1024, Rio de Janeiro, RJ, CEP 22460-000, Brazil

c Embrapa Agrobiologia, Ant. Rod. Rio, Sao Paulo, Km 47 Seropedica, RJ, Brazild Department of Microbial Ecology, Groningen University, Kerklaan 30, 9750 RA Haren, The Netherlands

Received 21 September 2004; received in revised form 2 August 2005; accepted 2 August 2005

Abstract

Microbial-based indicators of soil quality are believed to be more dynamic than those based on physical and chemical

properties. Recent developments in molecular biology based techniques have led to rapid and reliable tools to characterize

microbial community structures. We determined the effects of conventional and no-tillage in cropping systems with and without

cover crops on bacterial community structure, total organic carbon (TOC) and soil aggregation. Tillage and rotation did not

affect TOC from bulk soil. However, TOC was greater in the largest aggregate size class (7.98–19 mm), and had greater mean-

weight diameter under no-tillage than under conventional tillage in the 0–5 cm soil layer. Soil bacterial community structure,

based on denaturing gradient gel electrophoresis of polymerase chain reaction amplified DNA (PCR/DGGE) using two different

genes as biomarkers, 16S rRNA and rpoB genes, indicated different populations in response to cultivation, tillage and depth, but

not due to cover cropping. Soil bacterial community structure and meanweight diameter of soil aggregates indicated alterations

in soil conditions due to tillage system.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Cerrados; rpoB; 16S rDNA; DGGE profiling; No-tillage; Soil structure

* Corresponding author. Fax: +55 21 25608344.

E-mail address: asrosado@micro.ufrj.br (A.S. Rosado).

0167-1987/$ – see front matter # 2005 Elsevier B.V. All rights reserved

doi:10.1016/j.still.2005.08.001

1. Introduction

Soil quality is a concept in constant development.

According to Doran (2002), high soil quality is a

requirement for the conservation of water resources, in

addition to being the basis for sustainable agricultural

.

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–28 17

production and also to improve soil ecosystem

functions.

The ‘‘Global Assessment of Soil Degradation’’

(GLASOD)-study reported human-induced soil degra-

dation processes resulting in soil erosion by water and

wind, and soil physical and chemical deterioration

(Oldeman, 1994). Soil degradation processes and the

degree to which yield losses occur in various soils are a

function of interacting factors, including soil physical,

chemical and biological properties. To get a handle on

such processes, researchers have set out to develop and

apply indicators of soil quality. Indicators should

ideally reflect ecosystem processes, be accessible to

many users and applicable to field conditions, and be

sensitive to variations in management and climate

(Kennedy and Smith, 1995).

The Cerrados region in central Brazil occupies 22%

of the country. It is characterized by high average

temperature (22–27 8C), rainfall (800–1600 mm) and

solar radiation (475–500 Cal/cm2/day) (Adamoli

et al., 1987). Technological development during the

last 30 years has overcome several natural impedi-

ments to productive agricultural systems in the

Cerrados, which is increasingly becoming the leading

grain producing area of Brazil.

In Brazil, there is a growing acceptance by farmers

to grow soybean (Glycine max), maize (Zea mays),

wheat (Triticum aestivum), barley (Hordeum vulgare),

sorghum (Sorghum vulgare), bean (Phaseolus vul-

garis) and cover crops (i.e. oats, sunn hemp

(Crotalaria juncea), millet (Pennisetum glaucum)

under no-tillage (NT) systems (Machado and Silva,

2001). This soil conservation management differs

from the conventional approach (ploughing followed

by light harrowings) as the seeds are inserted into the

soil through plant residues with no primary or

secondary tillage. This system is significantly bene-

ficial to the environment due to a remarkable increase

in soil water infiltration rate, and consequently in

diminution of soil water erosion and nutrient loss,

increase in soil organic matter content in the upper soil

layer, and greater economic stability of rural com-

munities (Machado and Silva, 2001; Sa et al., 2001).

Many studies have shown that this effect of reduced

and no-tillage systems is due to better structural

stability of soils (Gupta and Germida, 1988; Oyedele

et al., 1999), that can be measured and quantitatively

expressed by soil aggregation indices (Young et al.,

2001). Sa et al. (2001) reported a significant increase

in soil organic C (SOC) content in the upper 10 cm

layer in NT of a Brazilian soil compared with that

under natural vegetation and conventional tillage

(CT), because of high-crop residue input and lack of

soil disturbance.

Soil quality is strongly influenced by microbe-

mediated processes (Angers et al., 1993; Powlson

et al., 2001), and microbial-based indicators of soil

quality, such as microbial biomass, are believed to be

more dynamic than those based on physical and

chemical properties (Karlen et al., 1994). Therefore,

such indicators have the potential to serve as early

signals of soil degradation or soil improvement

(Salinas-Garcia et al., 2002). This is particularly

important for agricultural research in the Brazilian

Cerrados, where research is currently focused on

selecting more efficient crop rotation schemes to

enhance long-term soil C sequestration.

In recent years, different methods have been

developed for analysing the structure and diversity

of microbial communities. These are divided into

culture-dependent and culture-independent methods,

and have been used to study microbial communities in

agricultural soils from temperate climates (Van Elsas

et al., 1998). However, similar assessments under

tropical conditions have only recently been addressed

(Andrade et al., 2003), mainly by researchers in

southern Brazil and Argentina, where temperature and

rainfall characteristics are more subtropical.

The use of molecular markers, such as the 16S

rRNA gene, to detect, identify and fingerprint

microorganisms and microbial communities, has been

commonly applied to explore microbial diversity in

environmental samples (Muyzer and Smalla, 1998;

Van Elsas et al., 1998). Although 16S rRNA micro-

heterogeneity is typical for bacteria isolated from the

environment and of great concern in ecological and

taxonomic studies (Nubel et al., 1996), studies with

alternative molecular markers, such as the rpoB gene,

have not been performed extensively (Dahllof et al.,

2000). The gene encoding the RNA polymerase beta

subunit, rpoB, has been introduced in community

analyses of bacteria as an alternative to the 16S rRNA

gene (Peixoto et al., 2002), because it exists in a single

copy in all bacterial genomes studied so far and

contains conserved as well as variable regions.

Furthermore, rpoB has been shown to be more

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–2818

discriminative than the 16S rRNA gene (Mollet et al.,

1997; Dahllof et al., 2000).

Since the soil quality concept encompasses not

only productivity, but also environmental quality and

land use, the changes in soil with perturbations need to

be fully described to assist in the rebuilding or

maintenance of an ecosystem. Although ecosystem

functioning is governed largely by soil microbial

dynamics, microbial populations and their responses

to stresses have been traditionally studied at the

process level, in terms of total numbers of micro-

organisms, biomass, respiration rates, and enzyme

activities, with little attention being paid to responses

at the community level (Kennedy and Smith, 1995).

Our objective was to assess the effects of different

tillage systems on soil bacterial community structure,

using two molecular markers (16S rRNA and rpoB

genes), total organic carbon (TOC) and aggregate size

and stability properties, while evaluating their

performance as early indicators of soil quality.

2. Materials and methods

2.1. Experimental field

The soil studied was typical of the Cerrados

biome, a clayey Haplic Ferralsol (Latossolo Ver-

melho distrofico, Brazilian Soil Classification Sys-

tem; Typic Haplustox, US Soil Taxonomy). Samples

were taken from an area under secondary forest and

from an adjacent tillage experiment at Embrapa Rice

Table 1

Chemical and physical properties at different depths of an uncultivated and

region), under different tillage systems and crop rotations

Soil property Forest NT no cover

0–5 cm 5–10 cm 0–5 cm 5–10 cm

pH (H2O) 4.80 5.06 5.80 5.40

Al3+ (cmol kg�1) 1.17 0.63 n.d. 0.10

Ca2+ (cmol kg�1) 0.53 0.25 1.56 1.26

Mg2+ (cmol kg�1) nd nd 0.90 0.53

H+Al (cmol kg�1) 8.47 5.96 3.57 3.70

Organic carbon (g kg�1) 46.80 32.80 21.40 19.20

Sanda (g kg�1) 310 400

Silta (g kg�1) 110 100

Claya (g kg�1) 580 500

Soil density (Mg m�3) 0.78 0.87 1.26 1.27

a At 0–5 cm only; nd: not detected; NT: no-tillage; CT: conventional

and Beans, Santo Antonio de Goias, Brazil (168280S;

498170W; 823 m asl). The climate is Aw with a

yearly average temperature of 23 8C and total yearly

precipitation of 1473 mm. Approximately two-thirds

of this rain falls from October to April, whereas in

winter (June–September), soil water deficiency is

very common. Before establishing the experiment

in 1995, the soil was cultivated with upland rice

(Oryza sativa) between 1978 and 1981. Rice was

then replaced by Andropogum gayanus (ubiquitous

pasture grass) until 1989, when a 2-year maize/

common bean succession under disc–plough system

was introduced.

Beginning in 1995, the following treatments were

estabilished: (1) no-tillage (no soil disturbance other

than sowing; NT) and (2) conventional tillage

(mouldboard plough followed by two light disc

harrowings; CT). Crop rotations included: (a) fal-

low/rice–fallow/soybean (no cover) and (b) sunhemp/

rice–pearl millet/soybean (cover). Rice and soybean

were summer cash crops while sunhemp and pearl

millet were winter cover crops. Tillage systems were

arranged in strips, and crop rotations were distributed

randomly, in triplicates, within each strip of a tillage

system. Tillage-rotation plots were 3.3 m � 40.0 m.

Experimental plots were irrigated during the dry

season, to avoid growth inhibition of winter crops. Soil

fertility and granulometric characteristics were ana-

lyzed (Table 1). Granulometric analysis indicated that

plots had sufficient edaphic homogeneity to support

comparative studies of soil quality indicators

(Table 1).

cultivated Haplic Ferrasol from Santo Antonio de Goias (Cerrados

NT cover CT no cover CT cover

0–5 cm 5–10 cm 0–5 cm 5–10 cm 0–5 cm 5–10 cm

5.73 5.60 6.10 5.90 6.10 6.16

0.07 0.23 0.30 0.10 0.30 0.10

1.26 0.90 2.76 2.53 2.50 2.43

0.90 0.55 1.06 1.03 1.16 0.96

4.10 4.36 3.33 3.56 3.10 3.30

19.40 16.90 24.20 22.40 25.10 22.40

390 370 360

110 150 150

500 480 490

1.33 1.27 1.13 1.27 1.30 1.33

tillage (n = 3).

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–28 19

2.2. Soil sampling for molecular analyses

Soils were sampled in June 1999 (dry season), and

in January 2000 (wet season), and consisted of a pool

of three sub-samples taken from each triplicate, from

depths of 0–5 and 5–10 cm, and kept at �20 8C prior

to the molecular analyses. These depths were chosen

so as to check for any possible effect of soil

temperature and humidity gradients, as well as contact

with or proximity to crop residues in the soil surface.

Therefore, a total of 30 bulk soil samples (five

treatments, three replicates, and two depths) per

season were analyzed for their molecular bacterial

community structure.

2.3. Soil sampling for aggregate stability and

organic C

Three replicate samples were collected from each

plot at field capacity at 0–5 and 5–10 cm depths in a pit

of 1 m2 dug to 0.4 m. In order to minimize

compression and to obtain a representative sample

for the aggregation state of the soil, samples were

taken using a trowel inserted into the soil at the lower

level of each sampling depth. Each soil sample was

first passed through a 19 mm sieve by gently breaking

apart the soil. Clods and aggregates larger than 19 mm

diameter were discarded. Soil samples were then air-

dried for 24 h in the shade to a water content of

15 � 2%. Very dry aggregates can lead to a false high

resistance to breakdown and result in apparent higher

stability indices (Castro-Filho et al., 2002). The air-

dried soil samples were placed in plastic bags in

cardboard boxes and were stored at ambient tempera-

ture (approximately 28 8C) until analysis at Embrapa

Soils in Rio de Janeiro.

2.4. Aggregation and organic C

Aggregate size classes were separated by wet

sieving, following a procedure based on Castro-Filho

et al. (2002) and Haynes (2000). Soil was wet-sieved

through a series of eight sieves (7.98, 4, 2, 1, 0.5, 0.25,

0.125 and 0.053 mm) (Salako et al., 1999). The line

between macro- and microaggregates is commonly

drawn at 0.25 mm (Oades and Waters, 1991).

A 30 g sub sample was moistened by capillarity for

5 min by placing it on a filter paper at the top 7.98 mm

sieve. The water volume was raised inside the water

tank to wet the filter paper and the soil. The filter paper

was then removed and wet sieving was conducted,

being replicated twice for each sample. Aggregate

separation was achieved by automatically moving the

sieve series up and down 3.5 cm with 30 repetitions

per min during a period of 15 min. After the 15 min

cycle, the stable aggregates from each sieve were

gently washed off the sieve into 100 ml glass beakers.

Aggregates were oven-dried (105 8C), weighed and

stored in plastic flasks at room temperature for TOC

analysis.

Several aggregation indices were determined as

follows:

Mean-weight diameter (MWD) of aggregates

(Kemper and Rosenau, 1986):

MWD ¼Xn

i¼1

xiwi

where wi is the proportion of each aggregate size class

in relation to the total sample weight, where the

summation is carried out over all aggregate size

classes, including the one that passed the finest sieve

and xi is the mean diameter of each aggregate size

class (mm).

Mean geometric diameter (MGD) of aggregates

(Kemper and Rosenau, 1986):

MGD ¼ exp

�Pni¼1 wi log xiPn

i¼1 wi

�

where wi is the weight of aggregates (g) in a size class

with an average diameter xi.

Aggregate stability index (AS) of soils (Castro-

Filho et al., 2002):

AS ¼ weight of the dry sample� wp25� sand

weight of the dry sample� sand

� 100

where wp25 is the weight of aggregates<0.25 mm (g)

and sand is the weight of particles between 2.0 and

0.053 mm (g).

The TOC content of whole soil and aggregate size

classes was determined by dry combustion with a

Perkin-Elmer CHNS/O Analyzer 2400. The measured

C was considered organic since this soil did not

contain significant amounts of inorganic C. Coeffi-

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–2820

cient of variation of the method was 3%. The TOC

content of aggregate fractions was calculated by

multiplying the TOC concentration of the aggregate

size fractions (data not shown) against the propor-

tional soil mass in each corresponding aggregate

fraction (Madari et al., 2005).

2.5. Statistical analysis

Soil aggregation parameters and TOC were

analyzed using the SAS statistical package (SAS

Institute, 1990) for analysis of variance (F-test).

Significant differences in TOC, MWD, MGD, and AS

between tillage systems were determined using the

Tukey’s Studentized Range (HSD) test for each depth.

All results were based on three replications in the field.

The forest site was not part of the experimental design

and sampling was performed differently (soil profiles

50 m apart from one another in straight line). For this

reason the results obtained for the forest samples were

not included in the statistical analyses. Comparison of

means from the forest samples with samples of the

experiment was done by using standard deviation

(S.D.) values.

2.6. Molecular biology analyses

DNA was extracted from soil using the protocol of

Van Elsas et al. (1997), which included mechanical

lysis of cells, phenol and chloroform extractions, a

potassium acetate precipitation step and final purifica-

tion using the Wizard DNA clean-up Kit (Promega).

Soil DNA was analyzed by electrophoresis in 0.8%

(w/v) agarose gels in Tris-Borate-EDTA buffer as well

as in a spectrophotometer at 260 nm absorbance

(Beckman DU-600) to check its amount, purity and

molecular size. Final DNA obtained from soil samples

was not colored, of large molecular size (>10 kb) and

could be amplified by PCR using 16S rDNA and rpoB

primers. DNA extracts were amplified by polymerase

chain reaction (PCR) using 1 ml of the extract (5–

10 ng of DNA g soil�1) per 50 ml of reaction.

The rpoB and 16S rDNA primers used in PCR

were, respectively, rpoB 1698F and rpoB 2041R

(Dahllof et al., 2000), and 968F and 1401R (Nubel

et al., 1996). A GC clamp (Heuer and Smalla, 1997)

was added to the forward primers (F). All PCR

amplifications were performed using a thermal cycler

(Perkin-Elmer, Norwalk, USA) according to Peixoto

et al. (2002). The rpoB PCR mixtures (50 ml),

contained 5 ml of Taq buffer 10� (Invitrogen),

2.5 mM of each deoxynucleoside triphosphate,

25 pmol of each primer, 20 mg of bovine serum

albumin, 0.5 ml of formamide and 5 U Taq polymerase

(Invitrogen), all in sterile Milli-Q water and 1 ml of

template DNA. The PCR program was as follows:

denaturing step of 94 8C for 3 min, followed by 10

cycles of denaturing for 1 min at 94 8C, annealing for

1.5 min at 40 8C and extension for 2 min at 72 8Cfollowed by 25 cycles of denaturing for 1 min at

94 8C, annealing for 1.5 min at 50 8C and extension

for 2 min at 72 8C, and a final extension at 72 8C for

10 min.

PCR mixtures with 16S rDNA primers were

prepared with 1 ml of target DNA, 5 ml of Taq buffer

10� (Invitrogen), 2.5 mM of each deoxynucleoside

triphosphate, 25 pmol of each primer, 20 mg of bovine

serum albumin, 0.5 ml of formamide, 2.5 U of Taq

polymerase (Invitrogen) and sterile Milli-Q water to a

final volume of 50 ml. The PCR program was as

follows: denaturing step of 94 8C for 3 min, followed

by 30 cycles of denaturing for 1 min at 94 8C,

annealing for 1 min at 55 8C and extension for 2 min at

72 8C, followed by a final extension at 72 8C for

10 min. The amplification products were routinely

analyzed by electrophoresis in 1.4% agarose gels in

1� Tris-Borate-EDTA buffer (Sambrook et al., 1989).

Denaturing gradient gel electrophoresis (DGGE)

was carried out using a DCode system (BioRad

laboratories) at 75 V and 60 8C for 16 h in 0.5X TAE

buffer (20 mM Tris-acetate (pH 7.4), 10 mM sodium

acetate, 0.5 mM disodium EDTA). The 6% (w/v)

polyacrylamide gels were made with a denaturing

gradient ranging from 45 to 70% according to the

manufacturer’s protocols (BioRad Dcode) and used

with PCR products with both primer sets. After

electrophoresis, gels were stained for 40 min with

SYBR green I nucleic acid gel stain (1:10 000

dilution; Molecular Probes, The Netherlands). Stained

gels were photographed on a UV transillumination

table with a Kodak digital science camera (model

DC120).

Digitized images of DGGE gels were analyzed by

Image Quant to generate a densitometric profile.

Bands were considered when the peak height relative

to total peak height exceeded 1% according to

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–28 21

Table 2

Effect of tillage on aggregation indices of the Haplic Ferralsol

Treatment Sampling depth (cm)

AS (%) MWD (mm) MGD (mm)

0–5 5–10 0–5 5–10 0–5 5–10

Forest 88 (4) 94 (6) 5.63 (1.09) 8.46 (2.99) 1.12 (0.03) 1.20 (0.10)

NT 88 a 81 a 6.60 a 3.50 a 1.14 a 1.05 a

CT 83 a 83 a 4.26 b 4.15 a 1.03 a 1.07 a

Values in parentheses are standard deviation (n = 3). Comparison of the means was done by Tukey’s Studentized Range (HSD) test. Values

followed by the same letters within the grouping of columns are not significantly different by the F-test of variance ( p � 0.1). AS: aggregate

stability index; MWD: mean-weight diameter; MGD: mean geometric diameter; NT: no-tillage; CT: conventional tillage.

Iwamoto et al. (2000). Calculation of similarities was

based on the Pearson (product–moment) correlation

coefficient and resulted in a distance matrix. Pearson

correlation is an objective coefficient that does not

suffer from typical peak/shoulder mismatches as often

found when band-matching coefficients are applied

and is recommended for DGGE profiles (Boon et al.,

2002). The clustering algorithm of Ward was used to

calculate dendrograms of each DGGE gel using the

software package Statistica.

3. Results

3.1. Chemical and physical properties

Soil had good aggregation properties at both

depths, irrespective of management (Table 2). About

Table 3

Effect of tillage on distribution of aggregate size classes (values indicate

Treatment Aggregate size classes (mm)

19–7.98 7.98–4 4–2 2–1 1–0.5 0.5–0.2

0–5 cm

Forest 338 (95) 100 (36) 70 (27) 61 (22) 131 (27) 128 (25

NT 434 a* 60 a 53 a 63 a 99 b 115 a

CT 251 b* 67 a 61 a 76 a 135 a 137 a

5–10 cm

Forest 560 (231) 98 (15) 52 (10) 40 (22) 68 (55) 79 (86

NT 193 a 67 a 62 a 89 a 134 a 155 a

CT 245 a 65 a 54 a 80 a 126 a 144 a

Values in parentheses are standard deviation (n = 3). Comparison of the m

followed by the same letters within the grouping of columns are not signifi

p � 0.1). NT: no-tillage; CT: conventional tillage.* p � 0.05.

90% of the soil mass was incorporated in aggregates

and mainly in the largest aggregate size class

(Table 3), indicating strong macroaggregation.

Due to the relatively short duration of the

experiment (5 years at the time of sampling), tillage

system did not show significant effects (Table 2) on

either AS or MGD. The MWD, however, was higher

under NT than under CT in the 0–5 cm soil layer

( p � 0.1). This effect was due primarily to an increase

in largest aggregates under NT than CT (Table 3),

although a significantly lower fraction of 1–0.5 mm

aggregates under NT than CT occurred.

Soil under forest vegetation had higher TOC

concentration in macroaggregates at both depths than

under agricultural management (Table 4). Concentra-

tion of TOC in aggregate fractions was mostly a

function of aggregate mass, since TOC per aggregate

was similar (data not shown). Soil under NT

g kg�1 soil) of the Haplic Ferralsol

Macroaggregates

(mm)

Microaggregates

(mm)

5 0.25–0.125 0.125–0.053 19–0.25 0.25–0.05

) 63 (22) 21 (6) 828 (37) 85 (28)

54 a 21 a 823 a 74 a

76 a 32 a 728 a 108 a

) 34 (34) 11 (9) 899 (62) 44 (43)

87 a 31 a 700 a 118 a

78 a 32 a 715 a 110 a

eans was done by Tukey’s Studentized Range (HSD) test. Values

cantly different by the F-test in the analysis of variance (no asterisk:

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–2822

Table 4

Effect of tillage on total organic C (g TOC kg�1 whole-soil) in aggregate size classes of the Haplic Ferralsol

Treatment Aggregate size classes (mm) Macroaggregates

(mm)

Microaggregates

(mm)

19–7.98 7.98–4 4–2 2–1 1–0.5 0.5–0.25 0.25–0.125 0.125–0.053 19–0.25 0.25–0.05

0–5 cm

Forest 8.7 (1.6) 3.0 (1.1) 2.5 (1.1) 1.9 (0.9) 3.7 (1.4) 3.2 (1.0) 1.5 (0.2) 0.6 (0.1) 23.0 (3.9) 2.1 (0.3)

NT 8.0 a 1.2 a 1.1 a 1.2 a 1.5 b* 1.7 a 0.8 a 0.3 a 14.6 a 1.1 a

CT 4.9 b 1.4 a 1.2 a 1.5 a 2.4 a* 2.2 a 1.1 a 0.5 a 13.7 a 1.6 a

5–10 cm

Forest 14.3 (6.6) 2.5 (0.3) 1.5 (0.4) 1.1 (0.5) 1.5 (1.2) 1.6 (1.7) 0.6 (0.6) 0.2 (0.1) 22.4 (3.0) 0.8 (0.7)

NT 3.2 a 1.1 a 1.1 a 1.4 a 1.9 a 2.1 b 1.2 a 0.4 a 10.7 b 1.7 a

CT 5.3 a 1.4 a 1.1 a 1.6 a 2.3 a 2.5 a 1.2 a 0.5 a 14.2 a 1.7 a

Values in parentheses are standard deviation (n = 3). Comparison of the means was done by Tukey’s Studentized Range (HSD) test. Values

followed by the same letters within the grouping of columns are not significantly different by the F-test in the analysis of variance (no asterisk:

p � 0.1). NT: no-tillage; CT: conventional tillage.* p � 0.05.

Fig. 1. DGGE banding pattern of 16S rDNA PCR amplification of soil samples (A, 0–5 cm depth, B, 5–10 cm depth). L, marker (from top to

bottom Staphylococcus aureus MB, Bacillus subtillis IS 75, Escherichia coli HB101); TW: tillage with winter cover crop; T: tillage without

winter cover crop; NTW: no-tillage with winter cover crop; NT: no-tillage without winter cover crop; F: native forest; (1) first sampling; (2)

second sampling.

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–28 23

Fig. 2. DGGE banding pattern of rpoB PCR amplification of soil samples (A, 0–5 cm depth, B, 5–10 cm depth). L, marker (from top to bottom

Staphylococcus aureus MB, Bacillus subtillis IS 75, Escherichia coli HB101); TW: tillage with winter cover crop; T: tillage without winter cover

crop; NTW: no-tillage with winter cover crop; NT: no-tillage without winter cover crop; F: native forest; (1) first sampling; (2) second sampling.

Fig. 3. Inferred similarity of dominant bacterial community structure using 16S rDNA DGGE profiles after cluster analysis with Ward and

Pearson correlation coefficient. TW: tillage with winter cover crop; T: tillage without winter cover crop; NTW: no-tillage with winter cover crop;

NT: no-tillage without winter cover crop; F: native forest; (1) first sampling; (2) second sampling; 0–5 and 5–10 cm depth.

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–2824

Fig. 4. Inferred similarity of dominant bacterial community structure using rpoB-DGGE profiles after cluster analysis with Ward and Pearson

correlation coefficient. TW: tillage with winter cover crop; T: tillage without winter cover crop; NTW: no-tillage with winter cover crop; NT: no-

tillage without winter cover crop; F: native forest; (1) first sampling; (2) second sampling; 0–5 and 5–10 cm depth.

accumulated greater TOC in the largest aggregate size

class in the 0–5 cm layer compared to CT (Table 4). In

some smaller aggregate sizes, CT accumulated greater

TOC than NT. These results show the importance of

the formation of large macroaggregates for TOC

accumulation in soil.

Soil aggregation was not affected by rotation

sequence. Thus, the data presented in Tables 2–4 are

averaged across rotation sequence.

3.2. Bacterial community profiles

Soil DNA of sufficient purity for reproducible PCR

amplification was obtained, yielding approximately

5–20 mg g�1 soil, with an average size of about 20 kb

for all soil samples. There were no significant

variations in DNA yields obtained from different soil

samples. Soil extracts were colorless, indicating low

humic acid contamination.

Cluster analysis of the DGGE profiles obtained

from the three replicates of each treatment resulted in

an average similarity value of 95% (data not shown).

This indicated a large degree of homogeneity among

replicates.

Complex community fingerprints were observed

with both marker genes, consisting of a few dominant

bands against a background of numerous fainter

bands. DGGE profiles from the DNA amplified by 16S

ribosomal RNA gene based PCR are shown in Fig. 1.

Only a few differences between samples from the

different management systems were observed. In

addition, there were only slight differences between

the bacterial community structures in the 0–5 and 5–

10 cm depths.

Fig. 2 shows the bands obtained with the rpoB

primers. Differences between samples were more

clearly visualized than when using 16S ribosomal

RNA (Fig.2). The DGGE patterns generated by both

systems were further clustered according to the Ward

algorithm and the Pearson coefficient. The dendro-

grams obtained are shown in Figs. 3 and 4.

In general, the results provided by the two different

sets of primers were in agreement with each other

(Figs. 3 and 4). Two main clusters were observed, one

made up of CT samples and the other from NT and

forest samples. This second cluster was further split

into two groups, one from NT and the other from

forest.

In addition, the dendrogram derived from the 16S

rDNA DGGE profiles analysis revealed a minor effect

of the different soil depths on the overall bacterial

community structure (Fig. 3). Different rotation

schemes did not produce any observable effect on

soil bacterial diversity (Figs. 3 and 4).

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–28 25

4. Discussion

It is well known that soil structural properties change

when management practices are modified (Salako et al.,

1999; Castro-Filho et al., 2002), and that the soil surface

is a vital interface in agroecosystems (Franzluebbers,

2002). Our study showed that NT, in comparison to CT,

tended to create soil aggregation properties more

similar to the soil under forest vegetation, particularly in

the 0–5 cm layer. This effect of NT, which was

expressed in the surface soil layer, is characteristic in

the sense that NT generally has a stratifying effect on

soil properties, compared to CT. This is such an

important characteristic that Franzluebbers (2002)

suggested a stratification ratio as an indicator of soil

quality. Surface soil under NT promoted the formation

of larger aggregates compared with CT, and through

this effect, accumulated more TOC in the largest

aggregate size class. As no difference was found in the

TOC content of the bulk soil under tillage systems, it

can be concluded that soil structural properties were

better early indicators of alterations in soil conditions

induced by tillage than TOC of bulk soil.

Macroaggregates are thought to provide more

favorable conditions for microbial activity than micro-

aggregates (Gupta and Germida, 1988). Several reports

have shown the importance of microorganisms in the

dynamics of aggregation, especially fungi and actino-

mycetes and also the role of microbial by-products such

as polysaccharides, for aggregate stabilization (Gupta

and Germida, 1988). Therefore, it has been suggested

that biotic soil quality factors may be inversely related

to tillage intensity (Angers et al., 1993).

Soil quality is obviously a concept in constant

development, and it is foreseeable that this will remain

so for some time to come. Since soil quality is strongly

influenced by microbe-mediated processes, and func-

tion can be related to diversity, it is likely that

microbial community structure will have the potential

to serve as an early indication of soil degradation or

soil improvement. Analysis of tillage treatments offers

possibilities for exploring the relationships among soil

organic matter, organic matter turnover and C fluxes,

since NT practices generally promote accumulation of

organic matter more so than CT (Salinas-Garcia et al.,

2002). Thus, analysis of microbial communities could

provide data to elucidate the links between soil biotic

and abiotic factors.

Molecular techniques represent a significant step

forward in studies of soil organisms, as they allow the

assessment of the non-culturable microorganisms.

PCR–DGGE profiling generally reveals the most

dominant microbial populations (Muyzer et al., 1993;

Muyzer and Smalla, 1998). While biases can occur

during PCR amplification of 16S rRNA gene

sequences (Muyzer and Smalla, 1998), it is still

unclear as to the extent.

The search for improved biomarkers is important

given that no single biomarker has so far been shown

to be without limitations. It has been shown previously

that the rpoB gene can be used as a molecular marker

for PCR and DGGE with soil DNA (Peixoto et al.,

2002). The results using rpoB as a target for PCR

amplification confirmed those found on the basis of

the 16S rDNA marker. Single copy genes, such as

rpoB, have the potential to be better biomarkers than

heterogeneous multiple-copy genes, such as 16S

rRNA (Dahllof et al., 2000), since species would be

represented by only one band on rpoB-DGGE profiles

rather than potentially many bands on 16S rDNA-

based DGGE profiles. Therefore, one may suggest that

the rpoB gene could be a more sensitive marker.

While full and real comparisons between soil

populations may probably only be achieved through

very extensive cloning and sequencing of all

components, utilization of PCR–DGGE profiling

has proven to be a powerful tool in assessing

community structure differences in soil. Identification

of bands through DNA sequencing was beyond the

scope of this study, which focused on the impact of

management on bacterial community structure.

Further studies are required to determine if both

primer sets are amplifying the same or different

dominant bacterial populations. Moreover, a compar-

ison between rpoB genes with 16S rRNA genes from

environmental DNA is not fully possible since a

widely accessible database for rpoB sequences is not

yet available. In our laboratory, we are sequencing

rpoB genes in an attempt to improve the database from

this promising molecular marker.

Analysis of binary data from DGGE banding

patterns is a powerful and relatively easy way of

comparing microbial communities from different

samples (Boon et al., 2002; Clegg et al., 2003).

However, we consider that there is probably a limit in

the resolution for the determination of richness

R.S. Peixoto et al. / Soil & Tillage Research 90 (2006) 16–2826

through DGGE profiling, since co-migration of bands

through the gel can underestimate the number of

species. While it is unrealistic to assume that absolute

values for the number of species within a community

can be obtained through PCR–DGGE profiling, it may

be a suitable approach in comparative analyses of the

dominant members of communities. It is unclear to

what extent the PCR–DGGE banding patterns reflect

the true community structure distinguished by

management, but it is likely that the differences

between tillage treatments are real because the

potential for PCR bias is common to all samples.

In our study, cluster analysis of both 16S rDNA and

rpoB profiles revealed a similar trend, with the bacterial

community structure being defined primarily by tillage

treatment. The analyses suggest that soil under NT

contained a bacterial community structure more similar

to that under forest. This was expected since tillage (and

N fertilization) constitutes a key component of

agricultural ‘‘disturbance’’ (Robertson, 1997). Thus,

the differences found in bacterial community structure

most likely resulted from different long-term soil

management. Since changes in tillage are often

accompanied by changes in other agronomical factors,

such as herbicide applications, the comparison was in

reality between two divergent management systems,

characterized by the main (but not sole) factor of tillage

type. It has been suggested before that soil structural

property, i.e. the level of soil aggregation, has a higher

impact on microbial diversity and community structure

than factors like bulk soil pH and type and input of

organic compounds (Van Elsas et al., 2002).

Burns et al. (1999) compared the population

structure of autotrophic ammonia oxidizers between

tilled and native forest sites, by using molecular and

conventional techniques. They reported that total C,

microbial-biomass C, and direct microscopic counts

were significantly higher under native forest than in

tilled soils. They also observed a lowered genetic

diversity of autotrophic ammonia oxidizer 16S rDNA

sequences in tilled than native forest soils, which may

have been due to repeated disturbance by plowing that

reduced the number of niches to carry diversified

ecotypes.

Research initiatives would greatly benefit from soil

quality indicators sensitive enough to enable early

prediction of changes in soil C due to management.

Removal of native vegetation in the tropics, and

conversion to crops and pastures have, in most cases,

led to degradation of both soil and water resources,

even though high grain yields are being met (Machado

and Silva, 2001). Therefore, soil quality indicators

measured in tropical land still covered by native

vegetation could serve as benchmarks for the

development of management to achieve a sustainable

agriculture. We hypothesize that soil structural

improvement under NT systems creates the environ-

mental conditions needed for re-establishment of

native microbial genotypes repressed by soil degrada-

tion from conventional agricultural systems.

In our work, it was possible to assess changes in the

dominant bacterial community that occurred with

management. However, in order to fully understand

these changes, more research is needed to link

microbial community structure and function. Screen-

ing for functional genes (e.g., those from N or C cycle)

will be valuable to provide clues on environmental

functioning. Once background baseline data are

established, changes in ecosystem functioning can

be detected and management systems developed to

protect these systems from irreversible ruin (Kennedy

and Smith, 1995).

We believe it is crucial to relate information

derived from soil bacterial community structure

studies to soil aggregation and organic C. In future

studies, we intend to link the two aspects of soil

quality, by assessing microbial diversity directly in

different soil aggregate size classes.

Acknowledgments

This work was supported by grants from Embrapa,

the Brazilian National Research Council (CNPq) and

FAPERJ. We are grateful to Dr. Cleber Moraes

Guimaraes, from Embrapa Rice and Beans, for setting

up and managing the field experiment, as well as for

the soil sampling infra-structure.

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