IJERD – International Journal of Environmental and Rural Development (2010) 1–1

Research article
erdComparison of Microbial Diversity of Paddy Soils in
Sustainable Organic Farming
CHUNCHARA THUITHAISONG
School of Environment, Resources and Development, Asian Institute of Technology,
Phatum Thani, Thailand
Email: Chunchara.Thuithaisong@ait.ac.th

PREEDA PARKPIAN AND OLEG V. SHIPIN
School of Environment, Resources and Development, Asian Institute of Technology,
Phatum Thani, Thailand

RAJENDRA P. SHRESTHA
School of Environment, Resources and Development, Asian Institute of Technology,
Phatum Thani, Thailand

KUNNIKA NAKLANG
Surin Rice Research Center, Bureau of Rice Research and Development,
Rice Department, Bangkok, Thailand
Received 1 January 2010

Accepted 5 March 2010

Abstract Agricultural management significantly influences soil microbiological properties,
such as microbial biomass carbon, microbial biomass nitrogen and respiration rate.
Besides soil parameters, microbial diversity is useful for monitoring changes in soil
quality to evaluate sustainable agriculture. This study aims to develop and use microbial
diversity in rice soil as an indicator of soil quality for sustainable organic rice farming. An
experiment was carried out using the existing rice fields (Kao Dawk Mali 105 variety) in
Surin Rice Research Center, Thailand. Four plots of rice received different management
practices for over 11 years, including (1) conventional farming (CF) with a normal rate ofchemical fertilizer applied, (2,3) two plots of organics: one with green manure (GM) andthe other with rice straw (RS) and (4) a control plot (CT) without external sources of plant
nutrients. Soil microbial communities were determined by cultural and molecular methods
such as total plate counts, community level physiological profiling (CLPP) with
BIOLOGTM Ecoplate, and PCR-amplified (16S rDNA) and analysed by denaturing
gradient gel electrophoresis (DGGE). Two-way ANOVA of results revealed that total
plate counts were significantly (P<0.05) affected by the four different management
practices. However, the GM plot (2.3x106 CFU g-1 of dry soil) and CF plot (1.8x106 CFU
g-1 of dry soil) were not significantly different (P>0.05). Likewise soil management
practices influenced the microbial diversity, both in functional and genetic diversity in therice plots studied. Further a narrow range of Shannon-Weaver diversity index (Hf') was
obtained with values between 2.77 for CT and 3.01 for GM. Statistical analysis (ANOVA)
performed using substrate richness (S) from potential substrate utilization patterns as the
input data, showed that the GM treatment increased the microbial diversity.
Keywords: Microbial diversity, organic farming, paddy soil, CLPP, PCR-DGGEINTRODUCTION
Rice production is commonly associated with conventional farming practices including the use ofchemical fertilizers and pesticides. Therefore, conventional farming practices, if not managed
properly, can have adverse environmental consequences (Schionning et al., 2004). To ensure
human survival it is necessary to maximize rice production while minimizing negative effects on© ISERD

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the environment (e.g. soil quality and microbial diversity). Alternative farming systems such asorganic farming are required. Organic farming avoids or minimizes the use of synthetic fertilizers,
pesticides and antibiotics. It has a positive effect on the build up of soil organic matter (SOM),
mainly because of high organic matter inputs to the soil (green manure or rice straw), which isbeneficial for soil microbial biomass and activity. Soil micro-organisms such as bacteria, fungi,
protozoa, and nematode, play an important role in maintaining soil quality in agricultural
management systems. Sustaining soil fertility is governed largely by the decomposition activity ofthe microorganisms. Agricultural management practices have been reported to influence soil
microbial community structures (Sun et al., 2004). Microbial communities in soil mediate key
processes that control ecosystem carbon (C) and nitrogen (N) cycling as well as other beneficial
plant nutrients. The essence of sustainable agriculture is the maintenance of viable, diverse
populations and functioning microbial communities in the soil. Microbial diversity relates toenvironmental impact and soil functioning, both of which seem inadequate for use as indicators(Anderson, 2003). Therefore, analyses of soil microbial diversity and community structures are
essential when monitoring environmental influences on soil quality. Bacteria occurring in the soil
are excellent indicators of soil health because they are involved in many soil processes and they
respond quickly to changes in the soil ecosystem (Winding, 2004). Bacterial diversity is critical toecosystem functioning because of the diversity of processes for which bacteria are responsible,
including decomposition and nutrient cycling (Kennedy, 1999).
Recently a number of different methods and approaches have been used to investigate themicrobial community. Previously investigation of microbial populations was determined by total
plate count, which underestimates the actual soil microbial population. This is because less than
0.1% of agricultural soil micro-organisms are culturable. The BIOLOGTM EcoPlate assay issensitive to changes in the short term from management practices. To investigate microbial
diversity in the soil more data are required for PCR-amplified (16S rDNA) and analysed by
DGGE to study genetic diversity of bacterial in the paddy soil. To accomplish this, a careful
and thorough investigation of factors affecting bacterial diversity is required. A full appreciation of
the genetic diversity of soil bacteria has not yet been accomplished compared to functional
diversity. The aim of this paper is to investigate microbial diversity to serve as a biologicalindicator which can be further developed for use in an organic rice field.
METHODOLOGY
Study area, experimental design and soil sampling procedures
The study area was used to grow rice under organic and conventional farming at the Surin Rice
Research Center, located in Korat Plateau, Muang District, Surin Province, Thailand (latitude 14o
55'N and longitude 103o 25'E). The studied soil belongs to the Roi Et series, which is widelydistributed and used for rice production in northeastern Thailand. This soil has properties of aninfertile acid loam and sandy loam to a depth of 10 cm, which is the active rhizosphere for rice
plants and good microbiological activity. The experimental design consisted of four plots, eachwith an area of 0.16 ha. All four plots included (1) CF, using a chemical fertilizer (16-16-8, NP2O5-K2O), (2, 3) two organic treatments; one received Sesbania rostrata as GM, and the othertreatment/plot with RS, and (4) control plot (CT), which has no external source of plant nutrients.
Rice (Oriza sativa, KDML 105 variety) was grown on all plots under rainfed lowland conditions.
Representative top soil samples were taken from the plots which have been cultivated with rice for
eleven years. After sampling, all samples were kept at 4oC for total plate count and BIOLOGTM
EcoPlate analysis, and -20oC for PCR-DGGE analysis. Each individual treatment was sampledthree times in 2007; before growing rice in May, when maximum tillering occurred, andimmediately after rice harvest. A topsoil layer (0-10 cm) was collected using a soil core device
with diameter of 2.5 cm. Based on random sampling, six points were chosen from each plot and© ISERD

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when the sampling was completed, topsoil from the six points was thoroughly mixed into one
sample.
Total plate count analysis
The effect of agricultural management practice on the total number of culturable bacteria wasdetermined by the plate count technique (pour plate) of colony-forming units (CFU). Five grams offresh soil were prepared in 45 ml sterile ¼ Ringer solution (Merk, Germany), and homogenized ina centrifuge tube (50 ml) using vortex for 3 min. at maximum speed. Then the samples were
centrifuged for 5 min at 129 x g (or 1,074 rpm) (Calbrix et al., 2007) and the supernatant liquids
were serially diluted (10-fold steps) with sterile ¼ Ringer solution and 0.1 ml aliquots of eachdilution were pipetted into a sterile Petri plate in triplicate, then melted tryptic soy agar (TSA) was
poured in and mixed with the sample. All plates were incubated at 37oC for 48 hr.
Community levels physiological profile (CLPP) analysis
CLPP was undertaken using the BiologTM Ecoplate and each plate containing 96 microtiter wells
was divided into three replicated sets. Thirty-two wells in each of the sets contained different
substrates and a blank control well contained water. Soil extracts were prepared with 5 g of freshsoil in 45 ml sterile ¼ Ringer solution (Merk, Germany), and homogenized in a centrifuge tube (50ml) using vortex for 3 min. at maximum speed. Then the samples were centrifuged for 5 min. at
129 x g (or 1,074 rpm) (Calbrix et al., 2007) and the supernatant liquid was diluted (10-3 dilution)with sterile ¼ Ringer solution and inoculated onto BIOLOGTM EcoPlate system (Biolog Inc., CA,
USA). The aliquots of 130 μl from a 10-3 dilution were inoculated into the microplates. The plates
were incubated at 37oC in dark. Color development in each well was examined optical density(OD) at 405 nm every 12 h for 7 d (Zak et al., 1994) using a microplate reader (DNM-9602G,
Beijing Prolong New Technology Co., Ltd).
Polymerase chain reaction-denature gradient gel electrophoresis (PCR-DGGE) analysis
For the analysis, variations of diversity of microbial genes in paddy soil were studied using directly
extracting total DNA, and amplifying 16S rDNA by PCR-DGGE. Community DNA was directly
isolated from the paddy soil samples without duplicate using the FastDNA® SPIN Kit for Soil andthe FastPrep® Instrument as described by the manufactures (MP Biomedicals, Solon, Ohio, USA).
Extracted DNA was then purified by Sephadex G-50 Mini Column® according to the manufacture(Geneaid, Taiwan) to remove humic acid and fulvic acid contamination. Total bacterial
communities were analysed with specific primer sets; 357f-GC and 518r (Kurisu et al., 2002;
Muzer et al., 1993). Extracted DNA was amplified by PCR amplification using these specificprimer sets (357f-GC and 518r) for 35 cycles in a 24 µl reaction volume. PCRs were performed
using a thermal cycler (BioRad Laboratories, lnc). PCR mixtures were amplified as follow:
preincubated at 94°C for 9 min, 35 cycles of denaturing (30 sec at 94°C), annealing (30 sec at53°C), and extension (30 sec at 72°C) and a final elongation was at 72°C for 10 min (Kurisu et al.,2002). Analysis of PCR products by DGGE was performed according to Muzer et al. (1993) withmodifications using a DCodeTM Universal Mutation Detection System (Bio-Rad Laboratories,
USA). A 20 µl volume of amplified products was loaded onto 8% (wt/vol) polyacrylamide gel in
1x TAE buffer reservoir (7 L). The 8% (wt/vol) polyacrylamide gel (bisacrylaminde gel stock
solution, 37.55:1 (Bio-Rad Laboratories, lnc) were prepare with denaturing gradients of urea andformamide ranging from 20 to 70%. The electrophoresis was run at 60°C, first for 300 min at aconstant voltage of 130 V. After electrophoresis, the gel was incubated for 20 min in 15 mL ofMilli-Q water containing two drops of ethidium bromide and photographed with a Polaroid camera.
The gels were analyed using Quantity One software (Bio-Rad Laboratories lnc, USA).

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Data analysis and statistical analysis
Plate count data were normalized by log transformation and significant differences between groupmeans were determined by analysis of variance (ANOVA) and calculation of the minimum
significant difference by the Least-Significant Different (LSD) test at P = 0.05. The net optical
diversity (OD) at 405 nm for each substrate well for BIOLOGTM EcoPlate was calculated bysubtracting the control well OD from the substrate well OD. If this subtraction gave a negative
number, 0 was used in the subsequent analyses. Microbial activity in each microplate wasexpressed as average well-color development (AWCD). The AWCD was determined by thefollowing equation; AWCD = ∑ ODi/31, where ODi is the optical density value from each of the 31
wells by water blank substraction (Garland and Mills, 1991). The absorbance values at 72 h wereused to calculate diversity indices. Shannon-Weaver index (Hf') was calculated as follows; Hf' = -∑pi (ln pi), where pi is the ratio of the activity on each substrate (ODi) to the sum of activities on all
substrates (∑ ODi). Substrate richness (Sf) is the number of different substrates that were used by
the bacterial community, i.e. equivalent to species richness in the soil, and was calculated bycounting all positive OD reading (Zak et al., 1994). The AWCD, Sf, and Hf' were analyzed by
ANOVA and comparisons of means by the LSD test at P = 0.05. The genetic diversity indices were
calculated from DGGE profiles. Scanned gels analyzed the intensity of individual bands (peakheight of bands) in all lanes with Quantity One software package. Genetic diversity was evaluatedby calculating Shannon-Weaver index (Hg') as follows; Hg' = -∑ (ni/N)ln(ni/N), where ni is theintensity of band i as judged by its peak height, i is the number of bands in DGGE gel profile and N
is the sum of all peak heights in a given DGGE profile.
RESULTS AND DISCUSSION
Total plate countLower numbers of culturable heterotrophic bacteria (CFU) were isolated from the CT because thisplot had low SOM (5.85±3.32 g kg-1) and also low in biological activity such as basal respiration(3.80±1.95 mg CO2-C kg-1 d-1), potentially mineralizable nitrogen (8.71±3.55 mg NH4-N kg-1,
microbial biomass carbon and nitrogen (0.331±0.355 g kg-1 and 0.186±0.158 g kg-1) (Thuithaisong
et al., 2010). Two-way ANOVA of the results revealed that total plate counts were significantly(P<0.05) affected by the four different management practices. However, a GM plot (2.29x106 CFU
g-1 of dry soil) and CF plot (1.80x106 CFU g-1 of dry soil) were not significantly different (P>0.05)(Table 1). This implied that without plant nutrients provide to the soil, growth of culturable bacteria
was restricted. Both GM and CF at the rate applied are equally effective in supporting a better
growth and perhaps survival of the cultivable bacteria, because they both contain an array oforganic compounds such as carbohydrates, fatty acids and peptide that are substrates for growth ofculturable bacteria.
Community level physiological profiles
The overall color development in the BIOLOGTM Ecoplates was calculated as the mean of all 31
wells. The temporal changes obtained from AWCD were different between soil samples, with theCT plot being the lowest, suggesting a lower level of microbial community in this plot. The numberof carbon sources that gave positive reactions for each soil treatments again highlighted the lower
values for CT compared to the other plots (Table 1). The GM plot had a high AWCD value and ahigh number of carbon sources. All indices of bacterial diversity (functional diversity) in GM plotincreased the microbial diversity, especially the Shannon-Weaver index (Hf') and substrate richness(Sf) as indicated by their highest values at 3.01 and 14.78 respectively.

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Polymerase chain reaction-denaturing gradient gel electrophoresis
Bacterial community structure determined by DGGE banding patterns are showed in Fig.1.
Numerous distinct DGGE bands, resulting from differences between the 16S rDNA gene sequencesof different bacterial species, were obvious. Each band represents at least one unique ribotype, as
reflected by the relative band intensity on a DGGE gel. The higher the intensity, the more dominantis the bacterial ribotype corresponding to that band. The DGGE patterns from the paddy soils ofdifferent agricultural management practices (CT, RS, GM and CF) had distinctly different featuresof bacterial community, suggesting that different soil environmental conditions influence bacterial
community structure. Based on the DGGE image, the expression level of 16S rDNA genesequences was strongly expressed in samples collected in May but they were weak in October andDecember. SOM, which is beneficial for soil microbial growth, was highest in the samples
collected in May (those soils were under aerobic condition). Therefore the intensity of DGGE
bands in May was expressed strongly due to the aerobic bacteria. In contrast bacterial populations
from rice soils of the four plots decreased in all the samples collected in October. Because such
samples were obtained under anaerobic condition the intensity of DGGE bands has lowest. The
degradation of SOM by aerobic bacteria was strongly inhibited because oxygen levels werereduced by flooding. However, the intensity of DGGE bands in soil samples collected in December
was higher than in October because the soils were again under aerobic conditions. The ShannonWeaver Index (Hg') was quite similar between RS and GM plots and their Hg' was higher than from
CF and CT soil treatments.
May

MarkerHaeIII
Base pairs

Oct

Dec

CT RS GM CF CT RS GM CF CT RS

GM CF

310_
271-234-118-194--

72--

Fig. 1 DGGE images of PCR-amplified products from different agricultural practices (CT,RS, GM and CF) during different sampling time (May, October, and December)Table 1 Effect of different farming management practices on soil microbial communities

CT

Total plate count(CFU g-1 of dry
soil)
8.42±5.49x104 a*

0.19±0.10RS

3.23±4.04x105 b

GM

6c

Plot

2.29±1.61x10

No. of C
sourcesutilized
6.0±3.87 a

2.77±0.15

0.28±0.16 b

9.7±5.95 b

c

c

AWCD

0.46±0.30a13.9±7.93

Functional diversity(Hf')

Genetically diversity

(Sf)

(Hg')

(Sg)

a

1.62±0.32a

6.0±2.00 a

2.83±0.15 a

9.0±1.73 b

2.08±0.23 a

9.0±3.00 a

b

c

a

8.3±3.05 a

3.01±0.82a5.3±2.96

14.8±4.12

1.96±0.43CF
1.80±1.33x106 c
0.30±0.17 d
8.8±4.57 b
2.86±0.12 a
10.0±3.04 b
1.89±0.38 a
8.3±2.52 a* Values are expressed as means and standard deviations followed the different regular letters (a, b, c) in row are
significantly different (P<0.05) by Least Significant Different (LSD). AWCD=average well color development, Hf'=Shannon-Weaver diversity index of functional diversity, Hg' =Shannon-Weaver diversity index of genetic diversity Sf=substrate richness, and Sg=species richness.
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This indicates that the influence of management practice on soil microbial communities did
occur. Based on the finding organic farming (RS, GM) seemed to have an edge over theconventional management (CF). However, the statistical analysis (ANOVA) performed for the
Shannon-Weaver index (Hg') and species richness (Sg) from DGGE profiles data showed nosignificant differences between the four plots (CT, RS, GM and CF) (Table 1).
CONCLUSION
Based upon the results obtained from this study soil management practices have influenced
microbial diversity, both in functional and genetic diversity, in the rice plots studied. Further anarrow range of Shannon-Weaver diversity index (Hf') was obtained with the values between 2.77
for CT to 3.01 for GM. In the statistical analysis performed using substrate richness (Sf) frompotential substrate utilization patterns data, the GM treatment increased microbial diversity. Even
within the constraints of organic farming practices, it is possible for farmers to adopt and continue
this application to improve soil bio-physiochemical properties, provided that the rate and source oforganic materials used is site specific.
ACKNOWLEGEMENT
The research was funded by the Biodiversity Research and Training Program, established by the
National Center for Genetic Engineering and Biotechnology under the National Science andTechnology Development Agency and Thailand Research Fund.
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