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ISSN: 2157-7471
Journal of Plant Pathology & Microbiology
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Bacterial Structure of Agricultural Soils with High and Low Yields

Michelli de Souza dos Santos1*, Kavamura VN1, Reynaldo ÉF1, Souza DT1, da Silva EHFM2 and May A3

1Embrapa Environment, Laboratory of Environmental Microbiology, Jaguariúna-SP, Brazil

2Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba-SP, Brazil

3Embrapa Maize and Sorghum, Sete Lagoas-MG, Brazil

*Corresponding Author:
Michelli de Souza dos Santos
Pós-doctor Embrapa Environment Street SP 340
KM 127,5, S/N - Tanquinho Velho, Jaguariúna-SP, Brazil
Tel: 551933112704

Received Date: April 11, 2017; Accepted Date: April 20, 2017; Published Date: April 24, 2017

Citation:Dos Santos MD, Kavamura VN, Reynaldo EF, Souza DT, Da Silva EHFM, et al. (2017) Bacterial Structure of Agricultural Soils with High and Low Yields. J Plant Pathol Microbiol 8: 405. doi: 10.4172/2157-7471.1000405

Copyright: © 2017 Dos Santos MD, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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The purpose of this study was to evaluate the bacterial community structure at two agricultural fields in Brazil with a history of high and low productivity. 16S rRNA amplicon sequencing analyses revealed that low yield plots showed greater richness than the soils grown in high yield plots. The phylum Acidobacteria was more abundant in soil samples from PR site. The rhizosphere of soybean plants presented a similar bacterial community for both high and low yield plots. Soil samples from BA showed differences in the diversity between the plots with high and low productivity. The use of 16S rRNA amplicon sequencing allowed the assessment of differences between plots with different soybean yields. This might be useful in the future to harness plant microbiomes for increased crop productivity.


16S rRNA amplicons; Bacterial community; Productivity; Soybean


Soil microorganisms play important roles in ecosystems multifunctionality [1] and changes in land use, management practices and fertilisation regime can affect soil diversity [2-5]. Modifications in microbial diversity can be assessed with the use of next-generation sequencing technologies, such as the analysis of 16S rDNA amplicons data [6]. Decreased soil microbial diversity may be an important indicator of the loss of soil quality, revealing a balance among organisms and the functional domains in soils [7]. The mineralization of nutrients, it is approximately 90%, is related with activity of microorganisms, which leaves nutrients available in the soil solution [7]. The groups of microorganisms respond to environment live and interact with them, this interaction provides nutrients, from their metabolic pathways, to other organisms (many of them specific) and this indicates the type of activity that occurred in the studied area, as portrayed [8]. Thus, according to the authors, there is an increase in the diversity of soil bacteria when there is a larger plant variety, probably due to the different compositions of the exudates coming from the different plant species present in the system.

Soils with highest amount of available nutrients showed positive selection for the classes Alfaproteobacteria and Gammaproteobacteria, and, for soil with a lower amount of nutrients, the percentage of bacteria belonging to the Acidobacteria phylum has increased, which in turn, are considered bacteria with low potential of growth, however with great capacity to compete for substrates [9]. A study about the bacterial community of the forest and pasture soils verified that the bacteria belonging to the Fibrobacteres phylum were dominant in the soil under forest, whereas, in the soils of dominant pastures, the dominant bacteria belonged to the classes Betaproteobacteria and Alfaproteobacteria, belonging to the Proteobacteria phylum [10]. The authors verified in their researches that isolates of Pseudomonas sp from soil samples in lines of the crop. They kept in a no-tillage system, were grouped closer to the samples collected in forest area than from samples from conventional tillage system areas; this demonstrated that in the notillage the microbial community has a smaller effect, compared to the conventional tillage system [11].

In this way, microorganisms can be affected by environmental factors and management practices, interfering in their growth and metabolic pathways. The knowledge about the microbiota of soil can support to identify potential phytosanitary and yield problems. Probably, soils with high and low productivity present different structures and bacterial composition [12].

Thus, based on the results of the metagenomic analyzes, the present study had the objective of evaluating the microbiological variations between crops grown with different production histories in the Brazilian states (Paraná and Bahia).


Sampling areas and collection of soil samples

The sampling was carried out in two Brazilian regions (Paraná (PR) and Bahia (BA)) with all features of each site described in table x. Bulk soil samples consisted of soil without plant interference, collected after crop harvest from 0-20 cm depth. Three replicates of each plot were obtained, with each one corresponding to ten subsamples collected in zig-zag. Rhizosphere samples consisted of soil closely attached to roots of 20 plants at flowering stage.

All samples were placed in plastic bags and stored in a styrofoam box and immediately sent to the laboratory of Environmental Microbiology of Embrapa Environment. The soil and climatic characteristics of the places where the samples were collected are presented in Table 1.

Sampling sites Candói, Paraná (PR) São Desidério, Bahia (BA)A
Coordinates S-25° 31' 15,6', W-51° 47' 19.8'' S-13°15´01´´, W-46°13´18´´
Climate features Type Cfb
Rainy during winter and summer
  Rainy during winter and summer
Average anual temperature 16.9°C 24.7°C
Dry season June to August May to September
Rainy season September to February October to March
Monthly rainfall 150 mm -190 mm 100 mm -220 mm
Soil features Type LatossoloBurnoAlumínicoCâmbio LatossoloVermelhoAmareloDistrófico
Texture Clay Medium
Soil management   Crop rotation: soybean, oat, maize, wheat, barley Monoculture: soybean
Sampling Soil type Bulk soil and rhizosphere Bulk soil

Table 1: Frame with characteristics of sampling sites.

In addition to the samples collected in the agricultural fields, samples were collected from a native forest next to the fields for both areas.

Metagenomic DNA extraction

Metagenomic DNA extraction was performed for soil and rhizosphere samples using Power Soil DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA), according to the protocol provided by the manufacturer. In total, twenty-four soil samples were processed (Table 2). For Paraná area (PR), six bulk soil (BS) and six soybean rhizosphere (RZ) samples were collected for plots with low (Lp) and high (Hp) productivity (3 replicates each). For Bahia area (BA), six bulk soil (BS) samples were collected for plots with low (Lp) and high (Hp) productivity. Additionally, bulk soil samples were collected from a native forest adjacent to both areas (FBS).

Samples Productivity Soil type Area Barcodes
Hp.BS.PR1 High Bulk soil Paraná GATCT
Hp.BS.PR2 High Bulk soil Paraná ATCAG
Hp.BS.PR3 High Bulk soil Paraná ACACT
Hp.RZ.PR1 High Rhizosphere Paraná AGATG
Hp.RZ.PR2 High Rhizosphere Paraná CACTG
Hp.RZ.PR3 High Rhizosphere Paraná CAGAG
Lp.BS.PR1 Low Bulk soil Paraná AGCTA
Lp.BS.PR2 Low Bulk soil Paraná CACAC
Lp.BS.PR3 Low Bulk soil Paraná ACAGA
Lp.RZ.PR1 Low Rhizosphere Paraná CGCAG
Lp.RZ.PR2 Low Rhizosphere Paraná CTGTG
Lp.RZ.PR3 Low Rhizosphere Paraná GTGAG
FBS.PR1   Bulk soil Paraná TCATG
FBS.PR2   Bulk soil Paraná AGCAT
FBS.PR3   Bulk soil Paraná CAGCT
Hp.BS.BA1 High Bulk soil Bahia CATGT
Hp.BS.BA2 High Bulk soil Bahia CTGAT
Hp.BS.BA3 High Bulk soil Bahia CTGCA
Lp.BS.BA1 Low Bulk soil Bahia GATGA
Lp.BS.BA2 Low Bulk soil Bahia TACGC
Lp.BS.BA3 Low Bulk soil Bahia ACTGC
FBS.BA.1   Bulk soil Bahia GTCAC
FBS.BA.2   Bulk soil Bahia CGTAC
FBS.BA.3   Bulk soil Bahia TGCGT

Table 2: Description of soil samples used for metagenomic DNA extraction.

Sequence processing and data analysis

Raw sequences were manipulated using Galaxy software (https:// After processing, 2,387,087 sequences were analyzed using the QIIME (Quantitative Insights into Microbial Ecology) software version 1.8.1 [13]. To identify Operational Taxonomic Units (OTUs) with 97% similarity, UCLUST tool [14] was used. A representative sequence of each OTU was aligned against Greengenes database, using the core set of the NAST algorithm [14]. Chimeric sequences were removed by the UCHIME method [15]. The taxonomic classification was performed using Greengenes, reference database of sequences, via UCLUST [16]. Sequences are available on MG-RAST. Diversity indexes based on the OTU table were calculated and PCoA plots were generated using PAST software [17]. In addition, SIMPER (Similarity Percentage) test was performed to weigh the contribution of each phylum in the similarity/dissimilarity among the samples [18].


Soil chemical analysis of soil samples

The soil analysis, performed indicated in the 0-20 cm sample (Table 3).

Samples pH Al(cmolc/dm3) V (%) OM(dag/kg) Ca(cmolc/dm3)
Hp.BS.PR 6.23 0.02 47.75 7.07 5.42
Hp.RZ.PR 6.57 0.01 67.68 8.87 7.15
Lp.BS.PR 6.00 0.02 43.11 4.27 3.67
Lp.RZ.PR 6.53 0.00 62.49 5.27 3.20
FBS.PR 5.40 1.56 10.13 13.5 1.7
Hp.BS.BA 5.87 0.00 42.96 1.51 1.33
LP.BS.BA 5.63 0.02 34.11 1.12 0.77
FBS.BA 5.27 0.19 3.91 1.74 0.01
Samples Mg(cmolc/dm3) K (mg/dm3) P(mg/dm3) C(%)
Hp.BS.PR 1.53 181.70 13.62 4.11
Hp.RZ.PR 2.5 267.47 16.93 5.16
Lp.BS.PR 0.95 131.30 7.92 2.48
Lp.RZ.PR 1.68 203.97 11.69 3.07
FBS.PR 0.45 137.4 10.48 7.85
Hp.BS.BA 0.43 43.89 12.87 4.85
LP.BS.BA 0.29 31.44 8.62 3.14
FBS.BA 0.07 9.36 0.0 9.1

Table 3: Mean (n=4) of the chemical analyzes of bulk soil and soybean rhizosphere samples collected in Paraná (PR) and Bahia (BA) with high (Hp) and low productivity (Lp) plots. As the control, a native forest bulk soil (FBS) was used for both areas (PR and BA).

Variation in OTU richness and bacterial composition in soil samples collected in Paraná

Bulk soil samples collected from low productivity plots displayed a 20% higher richness than both bulk soil samples collected from high productivity plots and bulk soil samples from the forest. For soybean rhizosphere samples, the difference in richness between high and low productivity plots was less pronounced (Figure 1).


Figure 1: Number of OTUs obtained for bulk soil and soybean rhizosphere samples for high and low productivity plots in Paraná region.

Sequences were classified into forty-two phyla but only nine phyla (AD3, Acidobacteria, Actinobacteria, Chloroflexi, Gemmatimonadetes, Planctomycetes, Proteobacteria, WS3 and Verrucomicrobia) had a relative frequency greater than 1% (Figure 2). It is possible to note that, in general, there are differences in the frequency of some edges.


Figure 2: Bacterial phyla with abundance higher than 1% and PCoA plots obtained for soil samples collected in the state of Paraná during the development of the soybean crop. A - Relative frequency of phyla from bulk soil samples collected from high and low productivity plots and forest. B - Principal Coordinates Analysis (PCoA) plot showing the structure of bacterial communities from bulk soil samples collected from high and low productivity plots and forest. . C - Relative frequency of phyla from soybean rhizosphere samples collected from high and low productivity plots. D - Principal Coordinates Analysis (PCoA) plot showing the structure of bacterial communities from soybean rhizosphere samples collected from high and low productivity plots.

PCoA plot clearly show that bacterial communities from bulk soil samples are different, with the first two axes corresponding to more than 69% of the variation (Figure 2B). The first axis explains 54.57% of the variation, thus forest soil samples are very different from bull soil samples collected from agricultural field. Besides, more than 14% explain the difference between bacterial communities obtained from high and low productivity plots. For soybean rhizosphere samples, the first axis itself explains the difference of bacterial communities between high and low productivity plots (Figure 2D).

These differences can be better observed through SIMPER test, which compares the relative frequencies of the phyla found in the samples. There is a dissimilarity of 64.11% for bulk soil samples collected from high and low productivity plots, with Acidobacteria corresponding to 9.35% of the total difference) and Proteobacteria to 1.02%. Soybean rhizosphere samples showed a lower dissimilarity than bulk soil samples (49.41%), with Acidobacteria being responsible for 1.41% of the total difference and Proteobacteria to 1.12%.

Variation in OTU richness in soil samples collected in the state of Bahia

The microbiota of the soil samples collected in the field with a high productivity historic presented a 50% lower richness than the microbial communities found in the samples in the low productivity plot. The richness of the OTUs of the native forest soil sample was similar to that of the low yield field samples (Figure 3).


Figure 3: Alpha diversity of the soil samples collected in soybean farms in Paraná State.

Microbial composition of soil samples collected in Bahia State

The Bacteria domain sequences found in the soil samples collected from Bahia State, in fields cultivated with soybean and the native forest of the place were classified in forty-two phyla, and only eight phyla had a relative frequency greater than 1% for Most of the samples were AD03, Acidobacteria, Actinobacteria, Chloroflexi, Firmicutes, Gemmatimonadetes, Proteobacteria, Verrucomicrobia and WPS-2 (Figure 4). As in the other samples evaluated, there was also a difference in the frequency of some phyla.


Figure 4: Relative Frequency and PCoA of the phyla of soil samples collected in the soybean farms in Bahia State. A-relative frequency of the edges of soil samples. B - Dissimilarity of OTUs found in soil samples by the Principal Coordinates Analysis (PCoA) method.

The analysis of PCoA showed that a separation of the samples took place, due to the historic of productivity of the stands studied in Bahia State and the native forest collected, so that the samples were separated by approximately 54%. These differences can be better analyzed by performing the SIMPER test (Similarity Percentage), which compares the relative frequencies of the obtained phyla for the samples. Thus, in general, when comparing the samples by productivity historic (high and low), there was dissimilarity among samples of 86.46%. The main phyla that contributed to this differentiation were: Acidobacteria (9.03%), Proteobacteria (6.12%), Actinobacteria (6%) and Chlorofexi (1.80%).


In this study, the differences among the structure of bacterial communities from bulk and soybean rhizosphere samples were evaluated, as well as bulk soil from a native forest next to the agricultural field. It was observed that bulk soil and rhizosphere of soybean plants are different niches, hosting distinct microbial communities. The results show that there are significant differences between bacterial communities from bulk soil and soybean rhizosphere based on high and low productivity for both areas. Bulk soil samples showed a greater differentiation between the plots with a history of high and low productivity, whereas for rhizosphere samples this difference is less pronounced. This behavior can be as expected due to the close association of plant roots and microbes via the production of molecules known as exudates, which are beneficial to microbial life [19]. Soil microorganisms are attracted to the roots of plants through a wellknown mechanism, which involves cross-signaling between roots and microbes [20]. However, a certain selection might occur. Thus, plants or improved genotypes of cultivated plants have the ability to act on the microbial community in their rhizosphere, due to the distinction in excited signaling, especially in stress situations, such as the physical change of the soil, this is capable of harming the development of the Plant [21].

Bacterial structure of samples collected in Paraná

Soil samples from the high productivity plots showed a higher relative frequency of Acidobacteria phylum, compared to the soil samples collected in the low productivity plots. It is known that species of this phylum are capable of reducing nitrates and nitrites and may also form a biofilm, which can improve soil structure. Besides, they can produce compounds that catalyze various proteins and use of soil carbon [22]. However, the phylum Acidobacteria is still little understood, although its abundance in the studied samples may suggest their importance in nutrient cycling, since the nutrients available in the soil for the plants and /or other organisms are one of the attributes that most interfere with soil quality [23]. Thus, the decrease of Acidobacteria in the low productivity plots may be directly related to the productivity of the crop in these areas. This phylum also displayed a higher frequency in forest samples, resembling to the soil of the field of high productivity. In general, the frequencies of native forest phyla resembled that of the high productivity field, rather than the low productivity, as well as edaphic factors. The native forest bulk soil collected in Paraná State is characterized as Atlantic Forest soil, known to have one of the largest biodiversity on the planet, able to maintain its vegetation in full equilibrium, being considered, therefore, a hotspot [24].

Proteobacteria and Actinobacteria phyla appeared more frequently in samples of low productivity plots. In this way, soils with high nitrogen and carbon content usually present a higher occurrence of Proteobacteria, whereas, in soils with lower levels of nutrients, Acidbacteria appear more frequently [9]. It is observed that soils with higher nutrient contents are those resulting from the high productivity fields or the native forest. Thus, the bacterial composition of soil samples collected within plots with different yields can act as soil quality bioindicators, through the evaluation of the frequency of existing phyla.

Bacterial structure of samples collected in Bahia

Soil samples from high yielding plots showed a greater relative frequency of Proteobacteria and Acidobacteria. The phylum Proteobacteria is the largest and most distinguished group of bacteria known, being very diverse morphologically and metabolically. Their representatives are easily found in cultivated soils, being highly important in the nitrogen and sulfur cycles [10]. These two phyla are the most abundant in soil samples, with the phylum Proteobacteria being more commonly found in nutrient-rich soils. This might explain their higher frequency in soil samples with a high productivity history. The class β-Proteobacteria congregates copiotrophic microorganisms, being more frequently verified in soils with greater carbon content, that is, greater amount of organic matter [25].

In soil samples from low-productivity plots, a higher frequency of the Actinobacterium phylum occurs. The phylum is related to Grampositive bacteria, generally known as decomposers of organic material (cellulose, lignin and chitin), producing a mass of proteins that serves to nourish other organisms [26]. The phylum Actinobacterium is composed of microbes able to produce antimicrobial compounds. However, production of these substances in excess may eventually impair the development of plants or microorganisms beneficial to the development of the crop [27].

Variation in OTU richness in soil samples collected in Paraná and Bahia State

All the soil samples presented a greater richness of OTUs in the samples collected in the areas with low productivity historic, showing a possible imbalance in these environments. This might help explain the productivity differences in these plots. Soil samples from fields with low yield history have a higher number of species; however, changes in soils may impair sustainability, causing anomalies in plant groups, and also changes in bacterial communities [28].

Soil richness of the native forest resembled, in the samples of Paraná State, the soil of the field of high productivity for Paraná state. The opposite was observed for Bahia, where the native forest soil resembled the soil samples from low productivity plots. The high productivity and native forest soils of the Paraná State are chemically similar whereas in Bahia State, the native forest soil is poor in nutrients, closer to the soil of the low productivity fields than to the high productivity soil. Thus, it is known that chemical modifications in soil can interfere in bacterial communities, such as, for example, pH and soil phosphorus content [29].


There is fluctuation of microbial communities in the soil in different growing environments. The diversity and richness of bacterial communities can be used as bioindicators of soil quality.


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