Ranking the magnitude of crop and farming system e¡ects on soil
microbial biomass and genetic structure of bacterial communities
Martin Hartmann1, Andreas Fliessbach2, Hans-Rudolf Oberholzer3 & Franco Widmer1
1
Molecular Ecology, Agroscope FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Zurich, Switzerland; 2Research
Institute of Organic Agriculture, Frick, Switzerland; and 3Soil Fertility and Soil Protection, Agroscope FAL Reckenholz, Swiss Federal Research Station for
Agroecology and Agriculture, Zurich, Switzerland
Correspondence: Franco Widmer, Molecular
Ecology, Agroscope FAL Reckenholz, Swiss
Federal Research Station for Agroecology and
Agriculture, Reckenholzstrasse 191, 8046
Zurich, Switzerland. Tel.: 141 (0) 44 377 73
76; fax 141 (0) 44 377 72 01;
e-mail: [email protected]
Received 19 September 2005; revised 27
January 2006; accepted 6 February 2006.
First published online 13 April 2006.
DOI:10.1111/j.1574-6941.2006.00132.x
Editor: Jim Prosser
Keywords
agricultural farming systems; crop effect; soil
bacterial community structures; terminal
restriction fragment length polymorphism;
ribosomal intergenic spacer analysis; statistical
ranking.
Abstract
Biological soil characteristics such as microbial biomass, community structures,
activities, and functions may provide important information on environmental
and anthropogenic influences on agricultural soils. Diagnostic tools and detailed
statistical approaches need to be developed for a reliable evaluation of these
parameters, in order to allow classification and quantification of the magnitude of
such effects. The DOK long-term agricultural field experiment was initiated in
1978 in Switzerland for the evaluation of organic and conventional farming
practices. It includes three representative Swiss farming systems with biodynamic,
bio-organic and conventional fertilization and plant protection schemes along
with minerally fertilized and unfertilized controls. Effects on microbial soil
characteristics induced by the long-term management at two different stages in
the crop rotation, i.e. winter wheat after potato or corn, were investigated by
analyzing soil bacterial community structures using analysis of PCR-amplified
rRNA genes by terminal restriction fragment length polymorphism and ribosomal
intergenic spacer analysis. Application of farmyard manure consistently revealed
the strongest influence on bacterial community structures and biomass contents.
Effects of management and plant protection regimes occurred on an intermediate
level, while the two stages in the crop rotation had a marginal influence that was
not significant.
Introduction
Sustainable land use for the preservation and improvement
of soil fertility is important for agriculturally managed
ecosystems (Tilman, 1999; Doran & Zeiss, 2000). Factors
influencing soil characteristics have mostly been determined
based on chemical or physical parameters (Gerhardt, 1997;
Eck & Stewart, 1998; Castillo & Joergensen, 2001; Izquierdo
et al., 2003). However, transformation processes and nutrient cycles mediated by soil biota also influence soil quality,
and therefore, biological soil characteristics such as microbial biomass, community structure, activity and function
may also reflect the influences of different environmental or
anthropogenic factors (Kennedy & Smith, 1995). Soil microbiota play an important role in nutrient turnover (Dighton,
1995; Kennedy, 1999). Therefore, it has been suggested that
it is essential to analyze the effects of different factors on the
bacterial community structures in agricultural soils (Kennedy & Smith, 1995; Pankhurst et al., 1996; Kennedy & Gewin,
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c
1997; Kennedy, 1999). Because cultivation-dependent techniques access only a small proportion of the microbial
community (Colwell & Grimes, 2000), molecular tools may
allow for a more detailed assessment of changes in microbial
community structures in soil, and thus may be of assistance
for the determination and monitoring of soil quality (Hill
et al., 2000; Kirk et al., 2004). PCR-based techniques such as
denaturing gradient gel electrophoresis (DGGE), single
strand conformation polymorphism (SSCP), terminal restriction fragment length polymorphism (T-RFLP), and
ribosomal intergenic spacer analysis (RISA) have been
frequently applied in analyses of soil microbial community
structures (for a review see Kirk et al., 2004).
Microbial diversity in agricultural soils was found to be
low when compared to pristine soils (Torsvik et al., 2002),
but the specific factors responsible for these differences are
not well understood. Many studies have shown that organic
farming, which avoids the use of synthetic fertilizers and
pesticides, may lead to increased soil biodiversity and
FEMS Microbiol Ecol 57 (2006) 378–388
379
Ranking the magnitude of crop and farming system effects
biological activity in soils when compared to conventional
farming (Mäder, 1995; Bossio et al., 1998; Carpenter-Boggs
et al., 2000). Biodynamic farming, as a specific form of
organic farming (Steiner, 1993), has also been reported to
sustain better soil quality than conventional farming practices (Reganold et al., 1993). In addition to different farming
systems, factors such as plant species (Grayston et al., 1998;
Smalla et al., 2001; Marschner et al., 2004), soil type (Girvan
et al., 2003) and tillage (Lupwayi et al., 1998) may also
influence biological soil characteristics.
When investigating system-specific differences in agricultural soils it is important to consider changes that become
apparent only after a long period of time (Temple et al.,
1994; Poulton, 1996). In a long-term experiment at
Rothamsted, UK, started in 1848, it has been demonstrated
that effects on yield and ecological parameters can become
apparent only after 40 years (Powlson & Johnston, 1994).
However, at that time, tools for determining specific biological soil characteristics such as community structures were
not available. The DOK long-term field experiment in
Switzerland was established in 1978 and enables the investigation of effects of fertilization and plant protection strategies in agricultural systems on soil characteristics and crop
yield (Mäder et al., 2002). The DOK experiment includes
biodynamic (BIODYN), bio-organic (BIOORG) and (FYM)
based conventional farm yard manure (CONFYM) farming
systems along with mineral (CONMIN) and unfertilized
(NOFERT) controls and a 7-year crop rotation that is
running temporally shifted in three parallels. The effects of
farming systems and crops in a defined rotation were
thoroughly investigated assessing chemical, physical and
biological soil characteristics as well as yield and energy
balance (Alföldi et al., 1995; Mäder et al., 2000, 2002). Mean
crop yields were 20% lower in the organic systems, i.e.
BIODYN and BIOORG, when compared to the conventional CONFYM system, but reduced input of NPK fertilizer
(34–51%), pesticides (97%), and energy (20–56%) revealed
ecological advantages of organic farming (Mäder et al.,
2002). In addition, increases in microbial activity, soil
aggregate stability, microbial and earthworm biomass, root
colonization by mycorrhiza, and number of arthropods
represent some characteristics reported for the organic
farming systems when compared to CONFYM (Mäder
et al., 2002).
In this study, microbial soil characteristics were determined for a detailed assessment and ranking of effects of the
different factors in the DOK system. Soil microbial biomass
was determined in order to allow for the comparison of data
to previous studies, and to validate the quality of soil DNA
extraction. Extracted soil DNA was used to resolve bacterial
community structures with two independent PCR-based
ribosomal RNA gene operon profiling techniques, i.e. TRFLP and RISA. Both techniques are compatible with semiFEMS Microbiol Ecol 57 (2006) 378–388
automated application and detailed statistical analyses,
allowing one to test the robustness of the results obtained
(Hartmann et al., 2005). In addition, T-RFLP and RISA
target different genetic regions and therefore differ in their
phylogenetic resolution, which has been reported to be
approximately at the genus level for T-RFLP and at the
species level for RISA (Fisher & Triplett, 1999; Dunbar et al.,
2001).
Materials and methods
Experimental system and soil sampling
Analyses were performed on soil samples from the DOK
long-term agricultural field experiment in Switzerland,
which was started in 1978. The experiment is situated
300 m above sea level in a topographically leveled area on
alluvial loess (haplic luvisol) that has been agriculturally
cultivated for decades (Mäder et al., 2000). The mean
precipitation in the area is 785 mm year1 and the annual
mean temperature is 9.5 1C. For a detailed description of the
experimental concept and characteristics, including data on
energy efficiency, system productivity and different physical,
chemical, and biological soil characteristics see previous
publications (Alföldi et al., 1995; Mäder et al., 2000, 2002).
Five different treatment types, i.e. BIODYN, BIOORG,
CONFYM, CONMIN, and NOFERT (Table 1), were
sampled from the split-split block field design with four
replicates. Plots of 5 20 m have been arranged on an area
of 1.4 ha. BIOORG has been maintained according to
standard organic farming practice in Switzerland (Eidg.Volkswirtschaftsdepartement, 1997), while for BIODYN
guidelines for biodynamic farming have been applied
(Kirchmann, 1994). The conventional system CONFYM
has been maintained according to Swiss standard recommendations (Eidg.Volkswirtschaftsdepartement, 1998). The
same 7-year crop rotation has been used in all systems, i.e.
potato, winter wheat 1, soy bean, corn and winter wheat 2,
followed by 2 years of grass clover, and has been temporally
shifted in three parallels. Therefore, the effects of different
combinations of preceding and actual crop could be investigated. Soil preparation was identical for all systems.
Bulk soils were sampled in March 2003 before the first
fertilizer application after winter. Samples of all four field
replicates from all five treatments and from two different
positions in the crop rotation, i.e. winter wheat 1 after
potato (P-WW1) and winter wheat 2 after corn (C-WW2),
were obtained. From each plot, 16 soil cores each with a
diameter of 2.5 cm were taken to a depth of 20 cm, pooled
and transported to the laboratory. Plant debris was removed
and soils were sieved and immediately subjected to microbial biomass determination and DNA extraction.
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380
M. Hartmann et al.
Table 1. Agricultural management regimes in the DOK long-term field experiment
Treatment
Fertilization
Farm yard manure
(FYM)
Mineral fertilizer
Plant protection
Weed control
Disease control
Insect control
Special treatments
Unfertilized
(NOFERT)
Biodynamic
(BIODYN)
Bio-organic
(BIOORG)
Conventional
(CONFYM)
Mineral
(CONMIN)
–
FYM (BIOORG)
and slurry
–
FYM (CONFYM)
and slurry
Mineral (N,P,K)
–
–
FYM (BIODYN)
and slurry
–
Mineral (N,P,K)
Mechanical
Mechanical
Mechanical
Rock powder
Plant extracts
bio-control
Biodynamic
preparationsw
Rock powder
Plant extracts
bio-control
Biodynamic
preparations
Rock powder
Plant extracts
bio-control
Cuz
Mechanical and
herbicides
Chemical
Chemical
Mechanical and
herbicides
Chemical
Chemical
Plant growth
regulators
Plant growth
regulators
Farm yard manure (FYM) application of 1.4 livestock units ha1 year1 (2000 kg organic carbon ha1 year1) was performed with aerobically
composted FYM(BIODYN) (C/N = 8), slightly aerobically rotted FYM(BIOORG) (C/N = 11); anaerobically rotted FYM(CONFYM) (C/N = 12). For details on the
experiment refer to previous publications Mäder et al. (2002, 2000).
w
Preparations 500 (horn manure) and 501 (horn silica) were applied (Steiner, 1993).
z
CuSO4 was used for plant protection in BIOORG potato until 1991.
Soil microbial biomass
Genetic profiling of soil bacterial populations
To obtain a validated determination of soil microbial
biomass, two techniques were applied. Chloroform fumigation extraction (CFE) was performed according to Vance
et al. (1987). Total soluble organic carbon of 20 g (dry
weight equivalent) chloroform fumigated and control soil
was extracted with 80 mL of 0.5 M K2SO4 and analyzed for
extracted carbon by infrared spectrometry (DimaTOC,
Dimatec, Essen, Germany). Soil microbial biomass CmicCFE (mg kg1 soil) was determined using the conversion
factor 0.45 (Martens, 1995).
Substrate-induced respiration (SIR) was performed according to Anderson & Domsch (1978). To 50 g (dry weight
equivalent) of pre-equilibrated soil samples, 150 mg glucose
was added, mixed, and the initial CO2 production response
was measured with an infrared gas analyzer (IRGA) according to Heinemeyer et al. (1989). Soil microbial biomass
Cmic-SIR (mg kg1 soil) was calculated from the initial
respiration rates using the conversion factor 30 (Kaiser
et al., 1992).
T-RFLP and RISA were performed as described by Hartmann et al. (2005). PCR for T-RFLP analysis was performed
with bacteria-specific small subunit ribosomal RNA (SSU
rRNA) gene primers 27F (5 0 -AGAGTTTGATCMTGGCTCAG-3 0 , FAM-labeled) and 1378R (5 0 -CGGTGTGTACAAGGCCCGGGAACG-3 0 ) (Heuer et al., 1997) on 10 ng
soil DNA, corresponding to an average of 200 mg dry weight
soil, in a total volume of 50 mL. PCR products were digested
with MspI (Promega, Madison, WI). Restriction products
were purified with Microcon YM-30 filter columns (Millipore, Billerica, MA). The T-RF product sizes and quantities
were analyzed on an ABI Prism 3100 Genetic Analyzer
(Applied Biosystems, Foster City, CA) equipped with 36 cm
capillaries filled with POP-4. Peak calling was performed
using Genotyper v3.7 NT (Applied Biosystems) and
raw data were standardized using z-transformation
(Excel, Microsoft, Redmond, WA). PCR for RISA was
performed with bacteria-specific primers bRISAfor
(5 0 -TGCGGCTGGATCCCCTCCTT-3 0 , HEX-labeled) and
bRISArev (5 0 -CCGGGTTTCCCCATTCGG-3 0 ) (Normand
et al., 1996) on 30 ng DNA, corresponding to 600 mg dry
weight soil, in a total volume of 25 mL. The PCR products
were purified with Microcon YM-100 filter columns
(Millipore). Analysis of RISA products was as described for
T-RFLP.
Extraction and quantification of DNA from soil
Nucleic acids were extracted from 0.5 g fresh soil according
to the protocol of Bürgmann et al. (2001) using a FastPrep
bead beater (FP 120, Savant Instruments Inc., Holbrook,
NY). Extracted DNA was quantified with PicoGreens
(Molecular Probes, Eugene, OR) on a luminescence spectrometer (Perkin Elmer, LS 30, Wellesley, MA) (Hartmann
et al., 2005). In addition to the four field replicates, a pooled
DNA sample of the four corresponding replicates was
prepared.
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Statistical analyses
The consistency of the microbial biomass determined with
CFE and SIR as well as DNA content was evaluated with
Pearson Product-Moment Correlation Coefficients (r). The
FEMS Microbiol Ecol 57 (2006) 378–388
381
Ranking the magnitude of crop and farming system effects
two-sided t-test was used to determine treatment specific
differences. Explorative statistical analyses of T-RFLP and
RISA data were performed with Ward cluster analysis based
on Euclidean distances with Statistica version 6.1 (StatSoft
Inc., Tulsa, OK). For cluster analysis, the mean of replicates
and the corresponding pool samples were used in order to
reduce dendrogram complexity. The distance matrices used
for dendrogram construction were compared using the
Mantel test (Mantel, 1967) with NTSYS-pc 2.1 (Rohlf, 2000).
The main factors explaining differences among genetic
profiles as well as differences observed in Ward dendrograms
were quantified by applying Monte Carlo permutation tests
using CANOCO for Windows 4.5 (Microcomputer Power, Ithaca,
NY) according to ter Braak and Smilauer (2002), followed by
Bonferroni correction (Bland & Altman, 1995). Significance
values were assigned to nodes in dendrograms. Partitioning of
variance based on the different treatment factors (Borcard
et al., 1992) and ordination of samples relative to treatment
factors were performed using redundancy analysis (RDA) with
CANOCO (Hartmann et al., 2005).
The percentage of fragments with significantly different
abundance in each farming system and/or in each position
in the crop rotation was determined by one way analysis of
variance (ANOVA) with Statistica (StatSoft Inc). Potential
indicator fragment categories affected by a specific treatment factor, without interaction with other factors, were
identified using two-factorial ANOVA.
Results
Biomass and DNA content
Average soil DNA content was highest in BIODYN at both
stages in the crop rotation, i.e. winter wheat after potato (PWW1) and winter wheat after corn (C-WW2) (Fig. 1). The
lowest DNA contents were detected in NOFERT. Differences
were only significant (P o 0.05) between BIODYN and the
three systems NOFERT, CONMIN and CONFYM at the PWW1 crop rotation stage. The soil microbial carbon (Cmic)
biomass determined with chloroform fumigation extraction
(CFE) revealed highest average values in BIODYN plots and
lowest average values in CONMIN plots for both stages in
the crop rotation. Cmic biomass in FYM-treated systems, i.e.
BIODYN, BIOORG and CONFYM, was significantly
(P 4 0.05) higher than in NOFERT and CONMIN, except
for CONFYM at the C-WW2 crop rotation stage. In addition, significant (P o 0.05) differences were also observed
among plots receiving FYM, i.e. between BIODYN/
BIOORG and CONFYM at the P-WW1 crop rotation stage.
The soil Cmic biomass determined with SIR revealed highest
average values in BIODYN plots and lowest values in
NOFERT plots in both stages in the crop rotation. The
FYM-treated plots, i.e. BIODYN, BIOORG and CONFYM,
FEMS Microbiol Ecol 57 (2006) 378–388
Fig. 1. Microbial biomass parameters determined in soils of the DOK longterm field experiment for two stages in the crop rotation, i.e. winter wheat 1
after potato (P-WW1; open bars) and winter wheat 2 after corn (C-WW2;
hatched bars). Microbial biomass was determined with soil DNA extraction
(a), chloroform fumigation extraction (CFE; b) and substrate induced respiration (SIR; c). Data are presented as mean values and standard deviations of
four independent field replicates. Systems marked with different lower case
(P-WW1) or upper case (C-WW2) letters are significantly different (t-test;
P o 0.05). BIODYN: biodynamic; BIOORG: bio-organic; CONFYM: conventional; NOFERT: unfertilized; CONMIN: minerally fertilized (see Table 1)
contained significantly (P o 0.05) higher amounts of Cmic
compared to NOFERT and CONMIN plots. Significantly
(P o 0.05) higher biomass was also found in BIODYN as
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382
M. Hartmann et al.
compared to CONFYM in the C-WW2 crop rotation stage.
The correlation between soil DNA contents and biomass was
r = 0.69 (P o 0.001) for SIR and r = 0.64 (P o 0.001) for
CFE. The correlation between CFE- and SIR-biomass was
r = 0.84 (P o 0.001).
Soil bacterial community structures
In bacterial genetic community profiles, determined with
T-RFLP and RISA targeting the rRNA gene operon, 79 T-RFs
with sizes of 61 to 488 relative migration units (rmu) and 73
RISA fragments of 288 to 998 rmu were unambiguously
scored across all samples. A cluster analysis of pools and of
mathematical averages of replicates of both profiling data
sets revealed identical dendrogram topologies with distinct
groups for each farming and control system (Fig. 2).
Arithmetic averages of the four replicates clustered consistently with the corresponding pools of DNA extracts. The
correlation of distance matrices used for dendrogram construction revealed coefficients between 0.87 and 0.93
(P o 0.001). Systems receiving FYM were differentiated at
the highest branching level (P o 0.001) from the control
systems with both methods and at both stages in the crop
rotation (Fig. 2; branches I vs. II). Among soils receiving
FYM, BIODYN was significantly (P o 0.01) separated on
the second branching level from systems BIOORG and
CONFYM (Fig. 2; branches IIa vs. IIb). BIOORG and
CONFYM formed distinct groups at the lowest branching
node (Fig. 2; branches IIb1 vs. IIb2), but differences were
only significant (P o 0.05) for RISA profiles at the P-WW1
crop rotation position. CONMIN separated consistently
from NOFERT (Fig. 2; branches Ia vs. Ib), but differences
were statistically significant only for T-RFLP profiles of the
P-WW1 crop rotation stage.
Farming systems and controls significantly (P o 0.001;
Table 2) influenced the soil bacterial community structures.
However, the major effect was attributed to the application
of FYM, i.e. NOFERT/CONMIN vs. BIODYN/BIOORG/
CONFYM (P o 0.001; Table 2). Highly significant differences (P = 0.002) were also observed related to the organic
fertilization scheme, i.e. BIODYN, BIOORG and CONFYM.
The influence of the stage in the crop rotation on the genetic
profile data was marginally insignificant (T-RFLP P = 0.055;
RISA P = 0.054).
Ordination of data by constrained redundancy analysis
also revealed a strong effect of FYM application on the
bacterial community profiles (Fig. 3). Samples from NOFERT and CONMIN separated from BIODYN, BIOORG,
and CONFYM on the first ordination axis (RDA 1),
explaining 25.3% (T-RFLP) or 18.1% (RISA) of the variance. NOFERT vs. CONMIN and BIODYN vs. BIOORG
and CONFYM separated on the second ordination axis
(RDA 2) explaining 6.3% (T-RFLP) or 6.8% (RISA) of the
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Fig. 2. Cluster analysis based on Euclidean distances of bacterial terminal restriction fragment length polymorphism (T-RFLP) and ribosomal
intergenic spacer analysis (RISA) data for two stages in the crop rotation
of the DOK long-term field experiment, i.e. winter wheat 1 after potato
(P-WW1) and winter wheat 2 after corn (C-WW2). Asterisks at nodes
indicate significant branchings as determined with Monte Carlo permutation testing performed on the four independent replicates
(P o 0.001; P o 0.01; P o 0.05). Labels on specific branches refer
to information specified in the text. Euclidean distance matrix correlation
was determined with Mantel test statistics and are indicated for
comparison of the two stages in the crop rotation (P-WW1 and
C-WW2) within the same profiling methods (horizontal arrows) and for
comparison of different profiling methods within the same crop rotation
(vertical arrows). BIODYN: biodynamic; BIOORG: bio-organic; CONFYM:
conventional; NOFERT: unfertilized; CONMIN: minerally fertilized (see
Table 1); (m): arithmetic mean of all four field replicates; (p): pooled DNA
sample of all four field replicates.
variance. The stage in the crop rotation had minor influence
on the distribution of the genetic profiling data.
Partitioning of total variance in the data sets based on the
redundancy analysis on all canonical axes revealed that
39.6% (T-RFLP) or 34.7% (RISA) of the variance was
explained by the treatment factors, i.e. the farming system
and crop rotation (Table 3). The influence of farming
systems and controls was approximately eight times
higher than that of the stage in the crop rotation. FYM
application accounted for 30.9% and 26.1% of the variance,
respectively. BIODYN, NOFERT and CONMIN contributed
more strongly to the total variance than BIOORG and
CONFYM.
T-RFs and RISA fragments, which significantly discriminated the different genetic profiles, were identified using
FEMS Microbiol Ecol 57 (2006) 378–388
383
Ranking the magnitude of crop and farming system effects
Table 2. Significance of effects of agricultural management factors in
the DOK experiment on bacterial community structures as determined
with Monte Carlo permutation testing of terminal restriction fragment
length polymorphism (T-RFLP) and ribosomal intergenic spacer analysis
(RISA) data
Treatment
T-RFLP
RISA
Farming system and controls
0.001
0.001
0.002
0.055
0.001
0.001
0.002
0.054
FYM applicationw
Organic fertilization schemez
Stage in the crop rotation‰
BIODYN, BIOORG, CONFYM, NOFERT, and CONMIN (see Table 1).
w
BIODYN, BIOORG, and CONFYM vs. CONMIN and NOFERT.
BIODYN, BIOORG, and CONFYM.
‰
Different stages in the crop rotation, i.e. winter wheat after potato
(P-WW1) and winter wheat after corn (C-WW2).
z
(Table 4). This analysis revealed that 81% of the
fragments were significantly (P o 0.05) influenced by the
treatment factors. Quantitative differences in abundances
were found for 44% (T-RFLP) and 60% (RISA), while
qualitative differences were detected for 37% (T-RFLP) and
21% (RISA) of the fragments. Seventy-six percent or 78% of
all fragments revealed significantly altered abundance
among all treatments, i.e. BIODYN, BIOORG, CONFYM,
CONMIN and NOFERT, while 25% or 28% altered between
the two stages in the crop rotation. Most of the fragments
with significantly different abundances among the farming
systems were attributed to FYM application. Using pairwise
comparisons of the treatments, 37–60% of all fragments
were significantly different in abundance between the plots
that did receive FYM and plots that did not receive FYM,
whereas 11 to 53% altered within the three farming systems
and 15 or 22% between the controls. Forty-six percent or
52% of all fragments significantly altered only based on the
farming system without revealing a farming system crop
rotation position interaction and were designated potential
farming system indicators. Three percent or 4% of the
fragments were identified as potential indicators for the
stages in the crop rotation. Thirty eight percent and 49% of
the fragments were identified as potential FYM application
indicators. Percentages of altering fragments between
T-RFLP and RISA as determined with ANOVA (Table 4)
showed high correlation of r = 0.94 (P o 0.001).
ANOVA
Discussion
Data on physical, chemical and biological soil characteristics
of the present and previous studies from the DOK field
Table 3. Percentage of variance in terminal restriction fragment length
polymorphism (T-RFLP) and ribosomal intergenic spacer analysis (RISA)
data sets explained by treatment factors of the DOK experiment as
determined with redundancy analysis
Fig. 3. Constrained ordination determined by redundancy analysis of soil
bacterial terminal restriction fragment length polymorphism (T-RFLP) and
ribosomal intergenic spacer analysis (RISA) profiles for two stages in the
crop rotation of the DOK long-term field experiment, i.e. winter wheat 1
after potato (P-WW1) and winter wheat 2 after corn (C-WW2). Data
points are based on T-RFs (a) or RISA fragments (b) of each of the four field
replicates and the corresponding pool (indicated by asterisks). First (RDA 1)
and second (RDA 2) ordination axes are displayed with explained variance in
parentheses. Overall correlations between analyzed factors (farming system
and stage in the crop rotation) and dependent variables (T-RFs and RISA
fragments) on the first two ordination axes were indicated by the corresponding r value. Vector directions indicate maximum variation due to the
corresponding factor, while vector lengths indicate strength of the correlation. P-WW1: winter wheat 1 after potato (closed symbols); C-WW2:
winter wheat 2 after corn (open symbols). BIODYN: biodynamic (^/B);
BIOORG: bio-organic ( / ); CONFYM: conventional (m/n); NOFERT:
unfertilized ( / ); CONMIN: minerally fertilized (’/&) (see Table 1).
FEMS Microbiol Ecol 57 (2006) 378–388
All measured factors
Farming systems and controlsw
FYM applicationz
Stage in the crop rotation‰
NOFERT
CONMIN
BIODYN
BIOORG
CONFYM
T-RFLP
RISA
39.6
35.7
30.9
3.9
14.1
8.9
12.8
4.4
4.4
34.7
30.6
26.1
4.1
9.0
8.2
11.2
5.3
4.5
Influence of all treatment factors investigated, i.e. stage in the crop
rotation (P-WW1 and C-WW2) and farming or control systems (BIODYN,
BIOORG, CONFYM, NOFERT, and CONMIN; see Table 1).
w
BIODYN, BIOORG, CONFYM, CONMIN, and NOFERT.
z
BIODYN, BIOORG, and CONFYM.
‰
P-WW1 and C-WW2.
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384
M. Hartmann et al.
Table 4. Number and percentage of terminal restriction fragments (TRF) and ribosomal intergenic spacer analysis (RISA) fragments significantly (P o 0.05) influenced by treatment factors of the DOK experiment
as determined with ANOVA
Total number detected
Fragments differing in at least one factorw
Quantitative differencesw
Qualitative differencesz
Fragments differing between:‰
Stage in the crop rotation
All farming systems and controls
FYM and no FYM application
NOFERT and CONMIN
NOFERT and BIODYN
NOFERT and BIOORG
NOFERT and CONFYM
CONMIN and BIODYN
CONMIN and BIOORG
CONMIN and CONFYM
BIODYN and BIOORG
BIODYN and CONFYM
BIOORG and CONFYM
Potential farming and control system indicatorz
Potential FYM application indicatorz
Potential preceding crop indicatorz
T-RFLP
N (%)
RISA
N (%)
79 (100)
64 (81)
35 (44)
29 (37)
73 (100)
59 (81)
44 (60)
15 (21)
22 (28)
60 (76)
49 (62)
17 (22)
46 (58)
42 (53)
42 (53)
47 (60)
40 (51)
39 (49)
23 (29)
35 (44)
9 (11)
36 (46)
30 (38)
3 (4)
18 (25)
57 (78)
44 (60)
11 (15)
41 (56)
33 (45)
28 (38)
35 (48)
27 (37)
27 (37)
26 (36)
39 (53)
16 (22)
38 (52)
36 (49)
2 (3)
Percentages are indicated in parentheses.
w
Significantly (P o 0.05) different intensities in at least one treatment.
Present or absent T-RF or RISA fragment in at least one treatment.
‰
Differences regarding stage in the crop rotation (P-WW1 and C-WW2)
and farming or control systems (BIODYN, BIOORG, CONFYM, NOFERT,
and CONMIN; see Table 1).
z
Significant (P o 0.05) differences without interaction of farming
system and crop rotation.
z
experiment revealed reproducible and representative differences induced by different agricultural management and
control systems (Alföldi et al., 1995; Mäder et al., 2002;
Widmer et al., in press). Biomass and DNA contents were
correlating with previous biomass determinations in the
DOK field experiment (Mäder et al., 2002; Widmer et al., in
press) and confirmed the high comparability of these indices
as reported before (Marstorp et al., 2000; Bundt et al., 2001;
Blagodatskaya et al., 2003; Hofman & Dusek, 2003; Hartmann et al., 2005; Widmer et al., in press). The extraction of
high quality DNA is essential for performing unbiased
genetic profiling, and correlating DNA content with established biomass estimates represents one means to validate
the quality and representativity of extracted DNA. The
relatively large variation among the replicates of DNA
measurements, which reduced the statistical discrimination
power, may be mainly explained by extraction from fresh
soil as well as by the small sample sizes of 0.5 g soil. However,
the highly reproducible genetic profiles among replicate
samples and the profiling techniques revealed a high repre2006 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
sentativity of these DNA extracts, which represents a prerequisite for the detailed analysis of effects due to
agricultural management factors (Widmer et al., in press)
or other influences (Hartmann et al., 2005).
Statistical analysis of data from genetic profiling revealed
(i) identical dendrogram topologies and highly similar
distance matrices (Fig. 2), (ii) similar significant differences
by permutation testing (Table 2, Fig. 2), (iii) similar sample
distribution by redundancy analysis (Fig. 3), and (iv) a high
correlation of changes detected by ANOVA (Table 4). In
addition, the genetic profiles of pooled samples, representing the experimental averages of replicates, clustered consistently with the corresponding arithmetic averages of the
four field replicates (Fig. 2). Altogether, the high consistency
provided strong support that T-RFLP and RISA are reliable
tools for studying effects on soil bacterial community
structures (Hartmann et al., 2005).
Agriculturally managed soils tend to show decreased
microbial diversity when compared to pristine soils (Torsvik
et al., 2002). However, few direct comparisons reported the
extent to which factors such as fertilization and crop species
induce differences in microbial community structures (Kennedy et al., 2004; Marschner et al., 2004; Widmer et al., in
press). Cluster analysis and hierarchical ranking of treatment
effects on soil bacterial community structures with discriminative statistics represented a helpful approach to classify the
extents of different environmental or agricultural impacts
(Hartmann et al., 2005). In addition, fragments indicating
treatment-specific effects such as the farming system or cropspecies in the genetic profiles can be statistically identified
(Table 4; Hartmann et al., 2005). In the future, this may allow
for the phylogenetic identification of organisms associated
with specific treatments and the development of treatmentspecific indicator diagnostics, which are prerequisites for
environmental monitoring (Widmer et al., in press).
The genetic profiles of bacterial communities in the soils
of the DOK field revealed major differences between soils
receiving FYM, i.e. BIODYN, BIOORG and CONFYM, and
soils receiving no (NOFERT) or mineral fertilization (CONMIN). Cluster analysis and permutation testing (Fig. 2,
Table 2), canonical ordination (Fig. 3) and ANOVA (Table 4)
revealed that the effects on microbial communities induced
by FYM application were the most pronounced. Most of the
detected ribotypes, i.e. 62% of T-RFs and 60% of RISA
fragments, indicated highly significant differences between
FYM and non-FYM treated plots. This may be explained by
the addition of the readily available organic matter and
nutrients contained in FYM, by the introduction of bacterial
populations via FYM application, or by secondary effects of
the FYM application such as altered soil characteristics or
plant growth (Bossio et al., 1998). Strong effects on microbial communities induced by addition of organic matter via
FYM application were found with DNA-based approaches
FEMS Microbiol Ecol 57 (2006) 378–388
385
Ranking the magnitude of crop and farming system effects
(Parham et al., 2003; Sun et al., 2004; Widmer et al., in
press), by analyzing fatty acid profiles (Carpenter-Boggs
et al., 2000; Peacock et al., 2001) or using community level
substrate utilization (CLSU) analysis (Widmer et al., in
press). In contrast, in a field experiment started in 1984,
Suzuki et al. (2005) suggested that effects on microbial
communities, as determined by T-RFLP and fatty acid
methyl ester (FAME) profiles, may be dominated by mineral
fertilizer application due to changes in chemical soil parameters such as soil pH or exchangeable calcium. In the
present study, mineral fertilization induced consistent differences in microbial communities when compared to
unfertilized plots (Fig. 2, Table 4), but these differences were
clearly smaller when compared to FYM-related effects. This
finding is supported by the results of other studies comparing FYM and minerally fertilized systems (Carpenter-Boggs
et al., 2000; Parham et al., 2003; Sun et al., 2004; Widmer
et al., in press). The causes for these different findings are
not known, but different conditions regarding systems, soil
types or climate may be important. Furthermore, comparisons among different studies may be difficult, because
terms such as ‘biodynamic’ or ‘conventional’ farming in
current agriculture are subject to different definitions regarding fertilization, plant protection and soil cultivation.
Beside the changes in the community structures, the number of microorganisms also increased in FYM-related systems, as indicated by higher microbial biomass, i.e. CFECmic, SIR-Cmic and DNA content (Fig. 1). An increase in
biomass parameters through the stimulation of microbial
growth by providing organic substrates was previously
observed in the DOK field experiment (Mäder et al., 2002;
Widmer et al., in press) and in other systems (Goyal et al.,
1993; Bossio et al., 1998; Carpenter-Boggs et al., 2000;
Peacock et al., 2001; Parham et al., 2003).
Previous results from CLSU analyses in the DOK field
experiment revealed smaller differences of bacterial profiles
between BIODYN and BIOORG than between any other
analyzed system (Mäder et al., 2002). In contrast, T-RFLP
and RISA profiles in the present study revealed smallest
differences between BIOORG and CONFYM, whereas BIODYN was significantly different from these systems (Fig. 2,
Table 4). Differences between organic and conventional
farming systems have been reported in an other long-term
system, managed for 8 to 9 years before sampling (Bossio
et al., 1998; Lundquist et al., 1999). Differences between bioorganically and biodynamically treated soils have not been
observed in a short-term experiment with a duration of one
growing season (Carpenter-Boggs et al., 2000). This suggested that the differences between the systems may develop
over a long period of time and may not be detectable in
short-term experiments. Long-term factors such as increased soil pH, humus content and organic carbon, in
combination with short-term factors such as different
FEMS Microbiol Ecol 57 (2006) 378–388
amounts of N, P, K and Mg, may have lead to the development of distinct microbial communities in BIODYN (Alflödi et al., 1995). The special character of the biodynamic
system was also observed in the microbial biomass parameters, which revealed the highest average amounts in
BIODYN, followed by BIOORG, and the lowest amounts in
CONFYM (Fig. 1). The lower content of microbial biomass
in the conventional system was in agreement with former
studies (Bossio et al., 1998; Castillo & Joergensen, 2001;
Mäder et al., 2002). Therefore, more detailed investigations
of factors and affected soil microorganisms in the BIODYN
soils of the DOK field experiment are required.
The influence of crops on soil microbial communities has
been reported to be complex and to depend on crop type as
well as on specific soil characteristics, sampling time and
sample type (Grayston et al., 1998; Smalla et al., 2001;
Marschner et al., 2004). Widmer et al. (in press) have
reported significant effects of grass-clover and winter wheat
on soil microbial community structures by using T-RFLP
analysis in the DOK field experiment. These crop effects
were smaller than the effects of FYM application, but
stronger than those of BIODYN, BIOORG and CONFYM.
Other studies have come to various conclusions by finding
either dominant effects of fertilizer when compared to crops
(Kennedy et al., 2004), or opposite results (Bardgett et al.,
1999). In the present study, the effects of the different stages
in the crop rotation, i.e. winter wheat following potato and
winter wheat following corn, which simulates a preceding
crop effect on the soil microbial community structures, were
smaller than any farming system related effects and marginally insignificant (Fig. 2). Although the effects of preceding
crops on the bacterial community structures have been
reported before (Lupwayi et al., 1998), they appeared to be
smaller when compared to fertilization and plant protection. This was also confirmed by the dominant influence of
the farming systems on data variance when compared to the
preceding crop (Table 3). The crop effects described by
Widmer et al. (in press) ranked between the effects of FYM
application and those of different FYM types and thus were
clearly more pronounced than the effects of preceding crops
reported in the present study.
Conclusions
The stability and consistency of the experimental system and
the methodological approach demonstrated the importance
of well designed long-term field experiments and robust
monitoring techniques. The application of T-RFLP and
RISA in combination with statistical analysis allowed to
hierarchically rank the effects of defined agricultural management factors on soil bacterial community structures.
Data from the present as well as a previous study by Widmer
et al. (in press) revealed that the application of farm yard
2006 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
386
manure to the soils had the most significant influence on
bacterial community structures and biomass parameters.
The influence of the crops occurred on the second hierarchical level followed by effects driven by biodynamic, bioorganic and conventional management systems. The preceding crops in the crop rotation had only a minor influence
on community structures and soil biomass. The identification of treatment-associated taxa detected by statistical tools
may be an important subsequent step in order to provide
powerful indicators and diagnostic tools.
Acknowledgements
The DOK field experiment is a long-term project funded by
the Swiss Federal Office for Agriculture (BLW). The continuous high quality management of the field experiment by
the field teams of Agroscope FAL Reckenholz and FiBL, as
well as support given by farmers, is greatly acknowledged.
We wish to acknowledge Roland Kölliker for providing
important assistance in statistical analysis and for helpful
comments on this manuscript. We are grateful to Manuel
Pesaro and David Dubois for critical discussions and comments on this manuscript. The project was supported by
funding from the Swiss National Science Foundation (SNF).
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