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Long-Term Organic Nutrient ManagementFosters the Eubacterial CommunityDiversity in the Indian Semi-aridAlfisol as Revealed by LengthHeterogeneity–PCRDananjeyan Balachandar a , Melissa S. Doud b , Lisa Schneper c ,DeEtta Mills b & Kalai Mathee ca Department of Agricultural Microbiology , Tamil Nadu AgriculturalUniversity , Coimbatore , Indiab Department of Biological Sciences , College of Arts and Sciences,Florida International University , Miami , Florida , USAc Department of Molecular Microbiology and Infectious Diseases ,Herbert Wertheim College of Medicine, Florida InternationalUniversity , Miami , Florida , USAAccepted author version posted online: 23 Sep 2013.Publishedonline: 30 Dec 2013.

To cite this article: Dananjeyan Balachandar , Melissa S. Doud , Lisa Schneper , DeEtta Mills & KalaiMathee (2014) Long-Term Organic Nutrient Management Fosters the Eubacterial Community Diversityin the Indian Semi-arid Alfisol as Revealed by Length Heterogeneity–PCR, Communications in SoilScience and Plant Analysis, 45:2, 189-203, DOI: 10.1080/00103624.2013.841919

To link to this article: http://dx.doi.org/10.1080/00103624.2013.841919

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Communications in Soil Science and Plant Analysis, 45:189–203, 2014Copyright © Taylor & Francis Group, LLCISSN: 0010-3624 print / 1532-2416 onlineDOI: 10.1080/00103624.2013.841919

Long-Term Organic Nutrient Management Fostersthe Eubacterial Community Diversity in the Indian

Semi-arid Alfisol as Revealed by LengthHeterogeneity–PCR

DANANJEYAN BALACHANDAR,1 MELISSA S. DOUD,2

LISA SCHNEPER,3 DeETTA MILLS,2 AND KALAI MATHEE3

1Department of Agricultural Microbiology, Tamil Nadu Agricultural University,Coimbatore, India2Department of Biological Sciences, College of Arts and Sciences, FloridaInternational University, Miami, Florida, USA3Department of Molecular Microbiology and Infectious Diseases, HerbertWertheim College of Medicine, Florida International University, Miami, Florida,USA

Agricultural practices influence the community structure and functional diversity ofsoil microorganisms. In the present study, the impact of nutrient-management sys-tems on the changes in the biological properties of Indian semi-arid Alfisol wasassessed. The long-term organically managed (OGF) and inorganically fertilized (IGF)soils from century-old experimental plots were compared for eubacterial diversityusing amplicon length heterogeneity PCR (LH-PCR) targeting three hypervariabledomains (V1, V1_V2, and V3) of 16S rRNA gene. Of these domains, V1_V2 coulddiscriminate the bacterial communities between the soil types. The relative ratiosof amplicons differed between OGF and ICF soils, and eubacterial diversity wasdecreased substantially because of the inorganic chemical fertilizers, as compared toorganic amendments. The Bray–Curtis similarity index and diversity indices of ampli-cons were greater in OGF soil than in ICF soil. This polyphasic approach revealed thatthe diversity and functionality of the soil eubacterial community were encouraged bylong-term organic manures more than inorganic chemical fertilizers.

Keywords 16S rRNA gene, amplicon length heterogeneity, eubacterial diversity,organic manures, QPCR

Introduction

Soil microorganisms are considered as the most important biological factor that contributeto the soil fertility. They perform key roles in the organic-matter decomposition and min-eralization and serve as a source and sink for plant nutrients (Jenkinson and Ladd 1981).They exhibit a great diversity in community structure and metabolic activity, which are

Received 19 June 2012; accepted 28 December 2012.Address correspondence to Dananjeyan Balachandar, Department of Agricultural Microbiology,

Tamil Nadu Agricultural University, Coimbatore 641 003, India. E-mail: dbalu2000@yahoo.com

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influenced by their interactions with plants and animals (Giri et al. 2005). The soil micro-biome responds more quickly to any changes in soil composition and hence serves as themost sensitive indicator to any anthropogenic activity (Degens et al. 2001).

In agriculture, several plant nutrient amendments are incorporated in soil for bettercrop yield that also influence the microbial diversity and activity. Organic manures andinorganic fertilizers increase the soil nutrient availability and in turn change the micro-bial community composition of soil (Crecchio et al. 2001). Several long-term studieshave shown increased microbial biomass due to addition of organic amendments such asmanures, straw, and sewage solids (Manna et al. 2005; Mandal et al. 2007; Liu et al. 2007).Organic fertilization enhances the buoyancy of soil ecosystem by improving the physicalstructure and stimulating the microbial activity beyond supplying the inorganic chemicalnutrients and thereby increases the soil sustainability (Saison et al. 2006). The long-termuse or overuse of inorganic fertilizers had relatively less positive effect on soil microbialand enzyme activities than organic fertilizers (Plaza et al. 2004). Hence, monitoring thebiological processes and microbial diversity of soil as influenced by nutrient managementis essential for sustaining the soil health for long-term productivity.

Molecular techniques, using stable genetic markers, provide relatively a new avenuefor studying the microbial community diversity in general as well as specific functionalgroups. The genetic marker 16S rRNA gene leads the phylogenetic analysis of eubacteria(Embley and Stackebrandt 1996). The community profile of any ecosystem is determinedeither by the sequence polymorphisms derived from 16S rRNA gene clonal libraries orby fingerprinting techniques such as denatured gradient gel electrophoresis (DGGE) (Sun,Deng, and Raun 2004); terminal restriction length polymorphism (T-RFLP) (Enwall et al.2007); single-strand conformational polymorphism (SSCP) (Smalla et al. 2007) and byanalyzing length heterogeneity–PCR (LH-PCR) of hypervariable regions of the 16S rRNAgene (Suzuki, Rappé, and Giovannoni 1998). LH-PCR uses the inherent length variationsof these hypervariable regions to produce a DNA fingerprint or profile from a metagenomicDNA sample (Suzuki, Rappé, and Giovannoni 1998). This fluorescent-based PCR methodis straightforward and, with optimization, highly reproducible (Mills et al. 2007; Doudet al. 2009, 2010). Approaches based on 16S rRNA gene sequences and variable regionlengths avoid the limitations of cultivability and have been used in the analyses of soileubacteriome (Moreno et al. 2006; Mills et al. 2006; Entry et al. 2008). In recent years,the quantitative PCR (qPCR) assay also has been emerged as a potential tool for studyingthe soil microbial communities quantitatively (Stubner 2002). Fierer et al. (2005) usedqPCR effectively to assess the abundance of major taxonomic groups of bacteria in soil.No attempt has been made so far to combine the LH-PCR and qPCR assays to assessthe community diversity and abundance of soil eubacteria as influenced by the nutrientamendments.

Monitoring the fertility of Indian soils is of vital importance to sustain the productiv-ity in order to meet the demand of food grains for an ever-increasing population. Previousstudies on Indian soils focused on the soil variables such as microbial biomass C, enzymesactivity (Mandal et al. 2007) and culturable bacterial counts (Vineela et al. 2008) to assessthe impact of long-term nutrient-management practices. However, no study so far has beenundertaken in the Indian soils to assess the impact of long-term nutrient managements onthe abundance and community diversity of eubacteria, which may be useful for assess-ing the soil fertility. This study focuses on the effect of conventional inorganic chemicalfarming (IGF) and organic farming (OGF) practices on the soil properties and eubacterialcommunity diversity by LH-PCR molecular profiling.

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Materials and Methods

Soil Sampling

The long-term permanent manurial experiment, being conducted in Alfisol (TypicHaplustalf) since 1909 at Tamil Nadu Agricultural University, Coimbatore, India (11◦ Nlatitude, 77◦ E longitude, and 426 m altitude), was selected for this study. The permanentmanurial experimental area is semi-arid subtropical climate with a mean annual precipita-tion of about 670 mm and mean annual maximum and minimum air temperatures of 34.2◦C and 20 ◦C, respectively. The soil is red sandy loam, arable, enriched with aluminum (Al)and iron (Fe) minerals, and low in humus. It has high contents of available calcium (Ca),magnesium (Mg), potassium (K), and sodium (Na) and is low in nitrogen (N) and phos-phorus (P). Maize followed by sunflower is the crop rotation adopted in the experimentalfield. Two long-term, nonreplicated nutrient-management plots (100 m2) were chosen forthis study. IGF refers to inorganic chemical fertilizer applied soil in which 60:20:10 kgha−1 of inorganic N, P2O5, and K2O were applied in the form of urea, superphosphate, andmuriate of potash, respectively, irrespective of crop. OGF refers to the organically man-aged soil that received composed cattle manure. The cattle manure was incorporated in soilon an N equivalent basis corresponding to IGF during the last plowing before sowing ofevery crop.

Undisturbed topsoil (0–15 cm) was collected during the fallow period (September2007) to avoid the crop influence from each of the treatment plots. The nonreplicated plotswere divided into two equal halves and considered as sampling sites (sites 1 and 2), andfrom each site, six replicates were maintained throughout the analysis. The soil was pro-cessed for microbiological analysis on the same day of sampling to minimize the storageeffects. Stones and stubble were removed, and the soil was powdered, packed in water-tightplastic bags, and stored at –80 ◦C for physicochemical and metagenomic analyses.

Physicochemical and Microbial Analyses of Soil

The soil pH and electrical conductivity (EC) were estimated with a glass electrode usinga soil-to-water ratio of 1:1. Soil organic carbon (SOC) was estimated by dichromateoxidation, and available N, P, and K were determined in potassium chloride, Olsen, andneutral normal ammonium acetate extracts respectively (Sparks 1996). The soil availablemicronutrients [viz., copper (Cu), manganese (Mn), iron (Fe), and zinc (Zn)] were analyzedby atomic absorption spectroscopy after extraction with diethylenetriaminepentaacetic acid(DTPA) (Sparks 1996).

Chloroform fumigation–incubation method (Jenkinson and Ladd 1981) was usedto estimate the soil microbial biomass C. Number of culturable bacteria, fungi, anddiazotrophic bacteria was quantified using the dilution plating method as described byWeaver et al. (1994). Soil dehydrogenase (EC 1.1.1.1) was determined by monitoring therate of production of triphenyl formazon (TPF) (Klein, Loh, and Goulding 1971).

DNA Extraction and Quantification

Metagenomic DNA from 500 mg of each soil sample was extracted in triplicate usingthe Fast-DNA Spin Kit for Soil (Qbiogene, Irvine, Calif.), following the manufacturer’sspecifications. High-molecular-weight DNA was quantified using a Versafluor Fluorometer(BioRad, Hercules, Calif.), diluted to a 10 ng µL−1 working stock, and frozen at –20 ◦C.

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Table 1Primers used in LH-PCR

DomainAmplicon

length Primer sequence Reference

V1 100 bp P1F 5’-6FAM-GCGGCGTGCCTAATACATGC-3’P2R 5’-TTCCCCACGCGTTACTCACC-3’

Cocolin et al. (2001)

V1_V2 325 bp 27F 5’- 6FAM-AGAGTTTGATCMTGGCTCAG-3’355R 5’-GCTGCCTCCCGTAGGAGT-3’

Suzuki, Rappé, andGiovannoni (1998)

V3 200 bp 338F 5’-6FAM-ACTCCTACGGGAGGCAGCAG-3’518R 5’-ATTACCGCGGCTGCTGG-3’

Cocolin et al. (2001)

LH-PCR and Electropherogram

Three hypervariable domains within 16S rRNA gene were amplified for each sample usingpaired universal eubacterial primers (Table 1). The hypervariable domains amplified are (i)from the start of V1 to the end of V2 (V1_V2), (ii) V1 alone, and (iii) V3 alone (Doudet al. 2009). The V1_V2 region combination is used to assess the V2 region whose lengthis too short to be analyzed on its own. Even though the V1 region is being technicallyassayed twice, it is being done with different primer sets, which are likely to pick up dif-ferent microorganisms. Each domain can be considered a different marker as it provideswith different length amplicons. The V1_V2 region was amplified using the fluorescentlylabeled 27F-6FAM forward and the 355R reverse primers (Table 1). The first (V1) andthird (V3) hypervariable domains were multiplexed using the fluorescently labeled for-ward primers, P1F-6FAM and 338F-6FAM, and unlabeled reverse primers, P2R and 518R,respectively (Table 1).

The PCR reaction was performed using 1X PCR Buffer, 2.5 mM MgCl2, 0.5 UAmpliTaq Gold LD DNA Polymerase (Applied Biosystems, Foster City, Calif.), 250 µMdNTPs (Promega, Madison, Wisc.), 0.5 µM forward and reverse primers, 1.0% bovineserum albumin fraction V (Fisher Scientific, Pittsburgh, Penn.), 20 ng of metagenomicDNA, and diethylpyrocarbonate-treated water to a final volume of 20 µL. For the mul-tiplex reactions, the water volume was adjusted to account for the additional primervolume. Amplification was performed using a MJ DNA Engine PTC 200 thermocycler(MJ Research, Waltham, Mass.) with the following parameters: initial denaturation at 95◦C for 10 min, 25 cycles of denaturation at 94 ◦C, annealing at 55 ◦C and extension at 72◦C (each for 1 min), and a final elongation at 72 ◦C for 10 min.

Fragment Analysis

Fragment analysis was performed by adding 0.5 µL of the PCR product to 9.5 µL of a96:1 concentration mixture of Hi-Di formamide and GeneScan 500 ROX size standard(Applied Biosystems). Samples were heated for 2 min and snap-cooled for 5 min beforerunning for 28 min on an ABI 310 Genetic Analyzer (Applied Biosystems). Capillaryelectrophoresis separation used the POP-4 polymer (Applied Biosystems), matrix DS-30_6FAM_HEX_NED_ROX, and filter D, and length separation was set to be recordedevery one base pair using bins. Output data were analyzed using GeneMapper ID, version

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3.7 software (Applied Biosystems). Analysis parameters were set to the local Southern sizecalling and the minimum noise threshold was set to 50 fluorescent units. Markers were cre-ated for the V1 and V3 and V1_V2 domains, ranging from 50–290 bp and 300–400 bp,respectively.

qPCR

The qPCR was performed to determine the total eubacterial 16S rRNA gene copies per gof soil using the nonfluorescent primers, 27F and 355R (Table 1) as described previously(Fierer et al. 2005). The PCR was performed in StepOne real-time PCR system (AppliedBiosystems) using plasmid containing V1_V2 hypervariable domains of 16S rRNA genefrom E. coli (JM109) as a standard. The diluted standards (106 to 1012 copies per reactionmixture) and soil DNA (2 µL) were added with 8 µL of master mix consisting of 5 µL ofABsolute QPCR SYBR Green mix (containing 3 mM MgCl2), 0.5 µM primers, and 0.1 µLbovine serum albumin (20 mg mL−1). Performance of quantitative PCR started at 95 ◦C for15 min to activate the chemically modified Thermo-Start DNA polymerase included in thekit. Afterward, 40 cycles were run that consisted of denaturation at 94 ◦C for 20 s, primerannealing at 55 ◦C for 20 s, extension at 72 ◦C for 15 s, and fluorescent data acquisition at80 ◦C for 15 s. Purity of the amplified fragments was checked by the observation of singlemelt curve at the end of qPCR run and the presence of single DNA band of expected sizeafter agarose gel electrophoresis (1.5%). Quantification of 16S rRNA gene copies presentin the soil samples was done by the software provided by ABI Systems (UK) and expressedas copies per g dry weight of the soil.

Statistical Analyses

Binning and normalization of LH-PCR data were performed to eliminate errors that areinherent to the collection and analysis software (Mills et al. 2003). Raw data for each soilsample and domain(s) were averaged and the relative ratios of peak heights (intensities)were calculated by dividing each individual peak height by the total intensity.

To show the reproducibility of LH-PCR data, each sample was run twice and the meanof two runs was used for further analysis. The mean relative ratios were used in all subse-quent analyses and for the multidimensional-scaling (MDS) plots. All data were importedinto MS Excel (Microsoft, Redmond, Wash.) and interleaved; ratios calculated using therelative abundance of the observed peaks, and then exported and analyzed using PRIMER5 statistical software (PRIMER E Ltd., Plymouth Marine Laboratory, Plymouth, UK).

The Bray–Curtis similarity index was used to discriminate the samples within sam-pling sites and between OGF and IGF (Rees et al. 2004). Similarity among the sampleswas visualized using nonmetric multidimensional scaling (MDS) cluster analysis using theBray–Curtis similarity coefficient (Hartmann et al. 2005). Analysis of variance (ANOVA)was used to compare the traditional diversity indices such as Shannon diversity (H),evenness (E), and richness (R) using SPSS software (SPSS Inc., Chicago, Ill.).

Results

Physicochemical and Biological Properties

The OGF samples recorded an increased soil pH and electrical conductivity than IGF soil(Table 2). The amount of organic carbon, available N and K, and micronutrients such asMn, Fe, and Zn were significantly greater in OGF soil as compared to IGF (Table 2).

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Table 2Physicochemical characterization of soil samples

Inorganic chemical farming (IGF) Organic farming (OGF)

Property Site 1 Site 2 Site 1 Site 2

pH 8.6 (±0.02)a 8.5 (±0.06)a 8.8 (±0.11)b 8.8 (±0.03)bEC (dS/m) 0.06 (±0.003)a 0.06 (±0.003)a 0.12 (±0.006)b 0.12 (±0.005)bOrganic carbon (g/kg) 3.4 (±0.11)a 3.4 (±0.10)a 9.4 (±0.13)b 9.4 (±0.30)bAvailable N (mg/kg) 81 (±6.4)a 79 (±5.1)a 118 (±2.1)b 119 (±2.6)bAvailable P (mg/kg) 14.7 (±1.1)a 15.2 (±0.9)a 15.2 (±0.5)a 14.9 (±0.8)aAvailable K (mg/kg) 92 (±0.7)a 94 (±2.6)a 144 (±1.4)b 144 (±1.3)bAvailable Cu (µg/kg) 1.62 (±0.52)a 1.71 (±0.44)a 2.32 (±0.71)a 2.11 (±0.55)aAvailable Mn (µg/kg) 15.2 (±2.71)a 14.8 (±3.11)a 22.4 (±4.12)b 27.30 (±5.10)bAvailable Fe (µg/kg) 4.6 (±1.2)a 4.4 (±1.2)a 6.8 (±1.5)b 6.2 (±2.8)bAvailable Zn (µg/kg) 0.8 (±0.02)a 0.8 (±0.02)a 1.4 (±0.5)b 1.0 (±0.7)b

Notes. Values are mean (±SE) (n = 6), and values followed by the same letter in each row are notsignificantly different from each other as determined by Duncan’s multiple-range test (P < 0.05).

Table 3Microbial parameters of soil as affected by long-term organic manure and chemical

fertilizer applications

Soil sample

Soil microbialbiomass C(µg/g soil)

Total culturablebacteria

(×108cfu /g soil)

Total culturablefungi

(×103cfu /g soil)Total diazotrophs(×103cfu /g soil)

IGF Site 1 124 (±3.4)a 24 (±2.51)a 18 (±1.15)a 12 (±1.15)aSite 2 128 (±2.8)a 22.3 (±0.88)a 18 (±1.52)a 11.67 (±0.33)a

OGF Site 1 249 (±14.1)b 248 (±13.07)b 240 (±13.42)b 245 (±7.64)bSite 2 251 (±13.2)b 246 (±10.26)b 248 (±3.18)c 247 (±6.17)b

Notes. Values are mean (±SE) (n = 6) and values followed by the same letter in each column arenot significantly different from each other as determined by Duncan’s multiple-range test (P < 0.05).

However, the available P and Cu levels were not significantly different between OGF andIGF. Nearly two-fold greater microbial biomass C was recorded in OGF soil as compared toIGF (Table 3). In addition, plot with organic manures recorded a 10-fold greater culturablebacterial, diazotrophic bacterial, and fungal counts than plots receiving chemical fertilizers(Table 3). The SOC contents showed positive correlation with microbial biomass C andmicrobial populations. Likewise, the soil dehydrogenase activity compared between OGFand IGF soils also revealed that OGF soil had nearly 35% more activity than IGF soil inboth the sampling sites (Figure 1).

LH-PCR Fingerprinting Analysis of the Soil Eubacteria

The variable domains V1, V1_V2, and V3 of 16S rRNA gene were amplified frommetagenomic DNA, and the length heterogeneity was compared between the two soils.Among the three domains resolved by LH-PCR, V3 could not be detected in the sam-ples due to very low proportions as compared to V1 in the multiplex PCR mixture. The

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Figure 1. Dehydrogenase activity of soils as affected by long-term organic manure and chemicalfertilizer applications. Values are mean (±SE) (n = 6) and values followed by the same letter in eachpanel are not significantly different from each other as determined by Duncan’s multiple-range test(P < 0.05).

mean number of LH-PCR fragments and their relative ratio to total amplicons obtainedfrom V1_V2 are presented in Figure 2. The LH profile of the V1_V2 domain amplified15 and 12 fragments between 300 to 400 bp long in OGF (Figures 2A, 2B) and IGF soils(Figures 2C, 2D), respectively. The eight fragments (viz., 331, 340, 344, 346, 351, 354, and356 bp) were common between OGF and IGF soils, whereas 300, 310, 317, 328, 337, 348,and 368 bp fragments were detected in OGF soils only. Four fragments with sizes of 326,

Figure 2. Relative ratio of V1_V2 amplicons from two different long-term nutrient-managed soils:OGF soil site I (A) and site II (B); IGF soil site I (C) and site II (D). Error bar represents mean ± SE(n = 6).

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Figure 3. Relative ratio of V1 amplicon from two different long-term nutrient-managed soils: OGFsoil site I (A) and site II (B); IGF soil site I (C) and site II (D). Error bar represents mean ± SE(n = 6).

329, 332, and 350 bp were found only in IGF soils. In both the soil types, the most abun-dant fragment was 315 bp (34% of the total fragments). Other than the 315-bp fragment,the relative ratios of amplicons were uniformly abundant in OGF soils, which was not incase of IGF soils (Figures 2A–2D).

The mean LH-PCR fragment numbers and their relative ratio to total ampliconsobtained from V1 domain of 16S rRNA gene are presented in Figure 3. The V1 domainLH-PCR amplified a length range from 62 to 92 bp (Figures 3A–3D) with 12 peaks foreach management. Among these peaks, 71 and 82 bp were dominant in both soil samples.The abundance of amplicons 84, 86, and 89 bp were significantly greater in OGF soils,whereas the 71-bp amplicon dominated in IGF soils (Figures 3A–3D).

Eubacterial Diversity and Similarity between OGF and IGF Soils

Shannon diversity indices [viz., richness (S), diversity (H), and evenness (E)] were cal-culated using overall mean number of fragments and abundance of each fragment ofsoil (Table 4). Among the two domains used to discriminate the soils, V1_V2 per-formed better than V1 alone, as it recorded more diversity and richness than V1.Among the soil types, OGF recorded significantly greater species richness, diversityindex, and evenness than IGF. The samples collected within the soil type and threeDNA extractions of the same sample did not show significant difference in the diversityindices.

To determine the similarity and differences between two different soil types, the nor-malized V1 and V1_V2 bacterial profiles were compared using the Bray–Curtis similarityindex, which uses peak length and its relative abundance. The similarities between the sitesare plotted on nonmetric MDS plot (Figure 4). The nonmetric MDS analysis of the aver-aged relative peak areas tightly clustered the OGF away from IGF soils. The LH profilesfrom both sites in OGF soils clustered together and were separated from the profiles of theIGF soils only with V1_V2.

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Table 4Richness, diversity, and evenness mean values for V1 domain and V1_V2 domains of 16S

rRNA genes from OGF and IGF soils

Sample Richnessa (S) Diversityb (H) Evennessc (E)

V1 domainOGF Site 1 8 (±0.2)a 1.57 (±0.12)a 0.75 (±0.01)a

Site 2 13 (±0.4)c 1.90 (±0.13)b 0.74 (±0.01)aIGF Site 1 8 (±0.3)a 1.64 (±0.11)a 0.79 (±0.02)a

Site 2 10 ( ± 1.2)b 1.75 (±0.21)a,b 0.76 (±0.01)a

V1_V2 domainsOGF Site 1 12 (±0.6)c 2.10 (±0.05)c 0.85 (±0.02)b

Site 2 13 (±0.5)c 2.15 (±0.04)c 0.84 (±0.01)bIGF Site 1 11 (±0.5)b 1.91 (±0.05)b 0.76 (±0.02)a

Site 2 11 (±0.2)b 1.96 (±0.06)b 0.75 (±0.02)a

Notes. Values are mean (±SE) (n = 6). Within each domain set, indices followed by the sameletter are not significantly different from each other as determined by Duncan’s multiple-range test(P < 0.05).

aRichness (S) = Number of dominant peaks in each sample.bDiversity (H) = –pi ln (pi), where pi is relative ratio of individual peak height.cEvenness (E) = H / Hmax, where Hmax = ln(s).

Figure 4. Nonmetric multidimensional scaling analysis of V1 alone (A) and V1_V2 (B) domains;data based on Bray–Curtis similarity coefficient. � represents OGF soils and represents IGF soils.

Abundance of Eubacteria in OGF and IGF Soils Assessed by qPCR

The abundance of total eubacterial population as quantified by qPCR targetingV1_V2 domains of 16S rRNA gene using metagenomic DNA as template also discrim-inated the OGF soils with IGF soils (Figure 5). As compared with culturable microbialcounts, nearly 10-fold increased abundance of 16S rRNA gene copies was found in OGFsoil than IGF soil in both the sampling sites.

Discussion

Soil fertility management practices aim to ensure adequate nutrients availability in soil forthe plant growth to increase the yields. However, these practices have to be monitored veryclosely for maintaining the fertility and long-term sustainability of soil. Soils of semi-arid

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Figure 5. Abundance of eubacteria as estimated by qPCR in soils as affected by long-term organicmanure and chemical fertilizer applications. Values are mean (±SE) (n = 6) and values followedby the same letter in each panel are not significantly different from each other as determined byDuncan’s multiple-range test (P < 0.05).

tropics are generally low in organic matter and are highly degraded and as such maintainingthe soil organic carbon is difficult in view of rapid mineralization of added nutrient amend-ments due to high temperature and bright sunshine. Regular addition of organic manureis the only way to increase the soil organic carbon (Chand 2010). Moreover, the presentfarming systems in India in general rely more on inorganic fertilizers as a result of high-fertilizer-responsive cultivars and hybrids. Hence, we compared the long-term effects oftwo nutritional management practices, conventional chemical farming (IGF) and organicfarming (OGF), on the soil quality as determined by physical, chemical, and biologicalvariables.

Organic Farming Increases the Soil Carbon and Macro- and Micronutrients

With the exception of soil available P and copper (Cu), we found differences inphysicochemical properties between OGF and IGF soils (Table 1). Significant increasein SOC buildup of OGF soil than IGF soil in the present investigation is in agreementwith other studies (Crecchio et al. 2001; Chu et al. 2007). Application of organics (OGF)improves soil structure and aggregation, thereby facilitating SOC buildup, whereas it isinsignificant with IGF (Mikha and Rice 1998; Maeder et al. 2002).

Nitrogen is often a key limiting factor for soil organisms and addition of organic andinorganic N can change the soil properties (Sarathchandra et al. 2001). In the present study,N concentration was significantly greater in OGF compared to IGF (Table 1). Similarly,there was a significant increase in K and micronutrients due to long-term application oforganic manures than chemical fertilizers, which are in accordance with the previous obser-vations made in the Indian soils (Rudrappa et al. 2006; Rasool, Kukal, and Hira 2008; Sahaet al. 2008). The available P levels of both IGF and OGF soils were statistically on par witheach other, which is likely due to fixation of applied P either through organic or inorganicamendments (Saha et al. 2008).

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Organic Farming Favors the Soil Microbial Biomass, Population, and Activity

The microbiological parameters (include microbial biomass, culturable bacteria, fungi, anddiazotrophic bacteria) distinguished the OGF system from IGF. On continuous receipt ofthe organic amendments as a sole nutritional input, OGF soil exhibited greater culturablebacterial, fungal, and diazotrophic populations than IGF soils. A similar trend was alreadyobserved in several Indian soils also (Vineela et al. 2008). To minimize the cultivationbias of the eubacterial population assessed by classical plate count method (Kirk et al.2004), the qPCR targeting eubacterial community gene (16S rRNA gene) was also assessedfor these two soils. The qPCR approach is unique among the methods of communityanalysis in that it allows for a relatively rapid yet quantitative assessment of the abun-dance of specific phylogenetic groups of microorganisms in soil (Fierer et al. 2005).In the present study, qPCR assay also confirmed that the abundance of eubacteria wassignificantly greater in organically managed soil than the inorganic chemical fertilizedsoil.

Microbial biomass is a small but very dynamic component of soil organic matter thatfluctuates with weather, crop, input, and season (Garcia and Rice 1994). As previouslyreported, the soil microbial biomass in the present study was also significantly greater insoil amended with organic amendments than synthetic fertilizers (Tu, Ristaino, and Hu2006; Liu et al. 2007) (Table 3). The microbial biomass flux between these two systems,OGF and IGF, can be in part due to enhanced nutrient availability for succession of diver-sified microorganisms in the former and short-term burst of microbial activity in the latter.This argument is further supported by significantly greater microbial populations in OGFthan IGF soils. Soil dehydrogenase activity reveals the total oxidative activity of microor-ganisms, which in turn reflects the viable microbial communities of soil (Nannipieri,Grego, and Ceccanti 1990). The dehydrogenase activity and microbial biomass were pro-portional to the addition of number and amount of nutrients (Ebhin Masto et al. 2006)and reported to increase due to organic amendments (Mandal et al. 2007). In the presentinvestigation a similar trend was observed. The observation that dehydrogenase activitywas less in mineral fertilized soil compared to organic soils is consistent with the studiesof Marinari et al. (2000) and Kautz, Wirth, and Ellmer (2004).

LH-PCR Analysis Discriminates Eubacterial Communities of Soil Influenced by OGFand IGF

In the present investigation, the culture-independent LH-PCR profiling was used todiscriminate the eubacterial communities between OGF and IGF soils. Using DGGE,it has been shown that organic manure application increased the soil bacterial diver-sity (Marschner, Kandeler, and Marschner 2003; Sun, Deng, and Raun 2004; Liu et al.2007), denitrifying bacterial communities (Enwall, Philippot, and Hallin 2005) andammonia-oxidizing bacterial communities (Chu et al. 2007) more than inorganic chemi-cal fertilization. Likewise, T-RFLP fingerprinting of 16S rRNA gene of soils influenced byorganic and inorganic amendments was clearly discriminative (Enwall et al. 2007). As LH-PCR data are phylogenetically relevant in that natural length variation within ampliconsequences, they can be directly associated with specific taxonomic sequences archivedin databases using common sequence alignment and analysis tool (Suzuki, Rappé, andGiovannoni 1998; Mills et al. 2007). In the present study, the LH-PCR profile clearly dis-criminated the OGF soils with IGF soils and also indicated that the organic amendmentsfavored more eubacterial community diversity than the chemical fertilization.

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In the present study, we also showed that V1_V2 domain of hypervariable regionof 16S rRNA gene was a better target to analyze the community rather than V1 andV3 domains alone. Not only does this domain discriminate two soil types but also itreveals that OGF soil had greater eubacterial community diversity than IGF (Figure 2 andTable 4).

Conclusion

To discriminate the eubacterial community of Indian semi-arid Alfisol influenced bylong-term addition of organic manures and chemical fertilizers, for the first time, theculture-independent metagenomic approach was adopted. The LH profiling of hyper-variable domains of ribosomal gene from the soil metagenomic DNA was assessed for thediversity of eubacterial community along with classical culturable technique. The nutri-tional and microbiological properties of soil have been more significantly increased due tolong-term organic manure application than chemical fertilizers. The eubacterial abundanceand diversity were greater in the soil amended with long-term organic manures than chem-ical fertilization. We also confirmed that the hypervariable region V1_V2 of 16S rRNAgene is a potential probe to resolve the community profiling of soil influenced by variousanthropogenic and natural means. The fragment length and abundance of each ampliconvaried between two nutrient-management systems. It is concluded that long-term additionof organic amendments for semi-arid tropics is vital to maintain the microbial diversity andto sustain the soil fertility.

Acknowledgment

We thank S. Chellamuthu, Professor of Soil Science, Tamil Nadu Agricultural University,Coimbatore, India, for his guidance and valuable suggestions for soil sampling from thelong-term experimental plots.

Funding

The financial support given by Department of Biotechnology, New Delhi, India, through anassociateship for specialized training of young scientists in niche areas of biotechnologyand by the Indian Council on Agricultural Research, New Delhi, through the All-IndiaNetwork Project on Soil Biodiversity and Biofertilizers are gratefully acknowledged. M. S.D. is funded by MBRS Research Initiative for Scientific Enhancement (RISE) ProgramNIH\NIGMS R25 GM061347.

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