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  • Research article
  • Open Access

Environmental factors shaping the diversity of bacterial communities that promote rice production

†Contributed equally
BMC Microbiology201818:51

https://doi.org/10.1186/s12866-018-1174-z

©  The Author(s). 2018

  • Received: 29 June 2017
  • Accepted: 3 April 2018
  • Published:

Abstract

Background

Exploiting soil microorganisms in the rhizosphere of plants can significantly improve agricultural productivity; however, the mechanism by which microorganisms specifically affect agricultural productivity is poorly understood. To clarify this uncertainly, the rhizospheric microbial communities of super rice plants at various growth stages were analysed using 16S rRNA high-throughput gene sequencing; microbial communities were then related to soil properties and rice productivity.

Results

The rhizospheric bacterial communities were characterized by the phyla Proteobacteria, Acidobacteria, Chloroflexi, and Verrucomicrobia during all stages of rice growth. Rice production differed by approximately 30% between high- and low-yield sites that had uniform fertilization regimes and climatic conditions, suggesting the key role of microbial communities. Mantel tests showed a strong correlation between soil conditions and rhizospheric bacterial communities, and microorganisms had different effects on crop yield. Among the four growing periods, the rhizospheric bacterial communities present during the heading stage showed a more significant correlation (p <  0.05) with crop yield, suggesting their potential in regulating crop production. The biological properties (i.e., microbes) reflected the situation of agricultural land better than the physicochemical characterics (i.e., nutrient elements), which provides theoretical support for agronomic production. Molecular ecological network (MEN) analysis suggested that differences in productivity were caused by the interaction between the soil characteristics and the bacterial communities.

Conclusions

During the heading stage of rice cropping, the rhizospheric microbial community is vital for the resulting rice yield. According to network analysis, the cooperative relationship (i.e., positive interaction) between between microbes may contribute significantly to yield, and the biological properties (i.e., microbes) better reflected the real conditions of agricultural land than did the physicochemical characteristics (i.e., nutrient elements).

Keywords

  • Bacterial diversity
  • Bacterial community structure
  • Super hybrid rice
  • 16S rRNA pyrosequencing technology
  • Crop yield
  • Soil physicochemical properties

Background

Recent studies have suggested that modern agriculture will face substantial challenges over the coming decades [1], and the market demand for agricultural products will increase by at least 70% to 2050 [2]. Over the last few decades, improper agricultural production methods, e.g. the improper use of chemical fertilizers and pesticides [3], have triggered a series of environmental problems [4, 5]. Numerous studies have addressed sustainability issues, and one recommended approach is exploiting the soil microorganisms [2] to sustainably meet agricultural demands [6]. Soil microorganisms play important roles in agriculture, particularly in the nutrient supply and in the biocontrol of plant disease [7, 8]. Rhizospheric microorganisms exist within a narrow zone around the root of a plant and are found at densities of approximately 1011 cells per gram [9];additionally, these microorganisms, are considered as the plant’s second genome [10]. The rhizospheric microbial community structure is mainly mediated by root exudate (e.g., sugars, amino acids, siderophores, and enzymes) [11]. Therefore, the interaction between plant roots and rhizospheric microorganisms potentially influences ecosystem functioning by promoting the circulation of materials [12].

Complex biochemical processes occur between rhizospheric microorganisms and plants, and microorganisms enhance soil fertility [13–15], maintain below-ground ecological structure and are associated with plant health (e.g., diseases, pathogens and weed suppression) [16]. Microbial diversity is also an indicator of soil microorganisms in a region [17], which provide a vast amount of ecological information in terms of the soil. Although the relationships between the soil microbial diversity and the functioning and sustainability of agricultural ecosystems have not been fully elucidated [18, 19], it is accepted that microbial diversity plays an important role in agricultural production [20, 21]. Microbial diversity includes the range of microorganisms and their relative abundance in natural habitats [22]. In addition to microbial diversity, microbial community structure also responds to the basis of agroecosystem services in agricultural production [23]. Soil microbial communities drive globally important processes [24], including elemental cycles and energy flows. These microbial communities are involved in various processes that serve essential functions in agricultural production [25] by promoting crop absorption of nutrients and inhibiting harmful pathogens [7, 8, 26–28]. For example, soil microbes can promote plant growth through the degradation of manure fertilizers and form humus nutrients, which are then easily absorbed by plants. Other microorganisms may regulate soil pH, generating favourable conditions that permit functional microorganisms to work at full capacity and promote production [29]. This kind of microorganism often plays a crucial role in the agricultural ecosystem. Explaining the direct effects of soil microbial communities on crop-growth and yield is challenging because the ecosystem functions provides by most soil microorganisms are not well clear [30, 31]. Numerous studies have shown dramatically related results that indicate soil community structure is characterized by Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes, etc. Nevertheless, further consideration of the interactions between the microbial communities and the external environment has identified biological and abiotic factors related to crop yield. Some studies have associated the soil microbial properties with the transformation of edaphic nutrients, which have an essential role in plant growth that helps obtain high yields [10, 32]. However, the innate mechanism of how microorganisms specifically affect crop productivity is poorly understood, and our study is devoted to statistically explaining the links between them.

In the process of production, we found a gap (more than 30%) in crop production between different super rice cultivation types with similar, fertilisation regimes and latitudinal and longitudinal positions. High-throughput sequencing methods served to collect rhizospheric microbial community information in soils. The relationship between soil physicochemical attributes and rhizospheric microorganisms at different stage (from pre-transplanting to the ripening stage) of the super hybrid rice “Y Liang You 900” was investigated to reveal the inborn mechanism of how microorganisms specifically affect crop productivity. We mainly explored the interaction of microbial communities. By comparing the differences between soil microorganisms in high- and low-yield areas during discrete periods, we explored the mechanism of how microorganisms specifically affect agricultural productivity based on soil characteristics. Our study concluded that (1) it is possible improve the average crop yield by controlling rational agricultural management in the heading stage, (2) the positive species interactions within communities may contribute to crop yield, and(3) the biological properties (i.e., microbes) were better than the physicochemical characteristics (i.e., nutrient elements) in terms of reflecting the actual situation of agricultural land.

Methods

Study sites and sampling

Soil samples were collected from Xupu (110°31′E, 27°23′N, Ha), Ningxiang (112°16′E, 28°08′N, Ly), Longhui-Zhaojiachong (110°56′E, 27°27′N, Hb) and Longhui-Niuxingzui (110°56′E, 27°29′N, Hc). The sites are paddy fields planted with “Y Liang You 900” super hybrid rice. Sampling was conducted on private land, and the land owner gave permission for our sampling activity. For all sites, the fertilization regime was the same and rice was harvested in October, 2014. The rice yield was calculated according to Chen et al. [33]. Rhizospheric soil samples were collected at four rice development stages: pre-transplanting stage (0 weeks), tillering stage (6 weeks), heading stage (14 weeks), and ripening stage (20 weeks). When sampling, three biological replicates were sampled from each site, resulting in a total of 48 samples (4 stages × 4 sites × 3 replicates = 48 samples, also see in Additional file 1: Table S2). Rhizosphere soils were sampled according to Smalla et al. [34]. After sampling, soil samples were separated into two parts, one part was air dried and stored at 4 °C until physiochemical analysis and the other part was frozen in liquid nitrogen and stored at − 80 °C until molecular analysis.

The yields of super hybrid rice “Y Liang You 900” from four sampling sites are presented in Additional file 1: Table S1. Based on the rice yield, the sites were referred to as ‘low-yield’ sites (Ly, 1.074 kg/m2) and ‘high-yield’ sites (Ha, 1.543 kg/m2; Hb, 1.517 kg/m2; and Hc, 1.589 kg/m2). The yield of ‘high-yield’ sites was approximately 30% higher than the yield of the ‘low-yield’ site.

Soil properties

Soil pH was measured using a pH metre and by dissolving 5 g of each soil sample in 25 mL of distilled water. The nitrogen contents, including total nitrogen (TN) and available nitrogen (AN), were determined by the Kjeldahl procedure [35]. Phosphorus (P) was determined photo metrically as orthophosphate with using a vanado-molybdate method [36], and potassium (K) was determined using inductively coupled plasma-atomic emission spectroscopy (ICP-AES) [37]. Additionally, soil organic matter (SOM) was analysed using the potassium dichromate method by titration with ammonium ferrous sulphate (i.e., Mohr’s salt solution) [38].

DNA extraction, PCR amplification and MiSeq sequencing

The soil samples were homogenized in liquid nitrogen and mixed completely, and 1 g of each soil sample was used for DNA extraction. Soil microbial genomic DNA was extracted using a soil microbial DNA extraction kit (MOBIO, San Diego, USA). The hyper-variable region (V4) of prokaryotic 16S rRNA [39] was amplified using the primer pair 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGTWTCTAAT-3′) [31]. PCR products were recovered and the quality and quantity of recovered PCR products was determined using a Nano-drop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Purified PCR products were subjected to the MiSeq platform (Illumina, San Diego, CA) with a 500-cycle kit (2 × 250 bp paired-ends) for sequencing. Data processing was conducted according to Tao et al. [40] and Yin et al. [41]. Specifically, reads were assigned to different samples according to the barcode sequence and primers were removed. The left and right reads were then merged with a minimum of 10 bp overlap and less than 5% mismatches using Flash [42]. Ambiguous bases (N) were removed from the merged sequences and chimers were removed by Uchim [43]. Finally, high-quality sequences were clustered using UCLUST [44] at 97% similarity level [45], and the OTU table was constructed after removing the false positive sequences (singletons). Taxa assignment was carried out by blasting the sequences to the RDP database [46] with 50% confidence. The rarefaction curve is shown in Additional file 1: Figure S1. To reduce the variations caused by different sequencing depth, sequencing depths was rarefied to16, 000 for all samples and the rarefied OTU table was used for all downstream analyses. All sequences were submitted to the NCBI database under the accession number SRP083104.

Statistical analysis

Soil microbial diversity indices, including the Shannon Wiener, Inverse Simpson, and Pielou evenness indices, were calculated using the R version 3.3.2 (https://www.r-project.org/) platform with the package vegan 2.4.2. [47]. Detrended correspondence analysis (DCA) for detecting the variation in microbial community composition among sites/stages was also performed using the vegan package. Permutational multivariate analysis of variance (Adonis) was used to test the effects of soil variables on crop yield. Standard and Mantel tests were carried out to identify the correlations between environmental factors and soil bacterial communities (based on Euclidean distance). The relationship between soil properties and microbial microorganisms in the heading stage was further analysed by the PLSPM model (partial least-squares path) using the plspm package [48]. Additional statistical analysis including one-way ANOVA and Pearson correlation analysis was also completed using R version 3.3.2, and a p value less than 0.05 was considered significant. A molecular ecological network (MEN) using an RMT-based network approach was built on the IEG website (http://ieg4.rccc.ou.edu/) to investigate the interaction between microbes [49–51] and the networks; finally, the result were visualized using Cytoscape 3.4.0.

Results

Soil properties

The pH varied from 4 to 7 (Additional file 1: Table S3), and the lowest pH was at the Ly site. The Ly site had a lower concentration of soil nutrients (N, P, and K) than the other sites; particularly, the SOM (soil organic matter) showed the largest difference between Ly and the high-yield sites. Permutational multivariate analysis of variance (Adonis) was conducted using three methods (Bray, Euclid and Horn), and analyses indicated that the rice yield was closely associated with the soil physiochemical properties (Additional file 1: Table S4). The DCA based on soil environmental factors (Fig. 1) showed the low-yield site had very different soil properties compared to the high-yield sites. Pearson correlation analysis showed various correlations between the rice yield and the soil properties (Additional file 1: Table S5). Among all soil properties, the available nitrogen (AN) and available potassium (AK) were significantly (Pearson correlation > 0.650; p <  0.05) and positively associated with rice yield. In addition, TN, TP, and SOM were also significantly correlated (p <  0.05) with crop yield in all stages of development. Furthermore, pH was substantially correlated with yield during the tillering stage.
Fig. 1
Fig. 1

Detrended correspondence analysis (DCA) of soil environmental factors with all samples from pre-transplanting stage to ripening stage

Bacterial diversity

The microbial community diversity indices, including Shannon and Simpson diversity and Pielou evenness were significantly higher in the high-yield sites than in the low-yield site, and generally, the diversity indices were the highest during the tillering stage. The diversity differed slightly between the pre-transplant and heading stages in the Ha and Hc sites; however, the bacterial diversity at the Ly site was similar during all stages of crop development. In addition, two-way ANOVA indicated that both stage and site had significant effects on microbial community diversity indices (Table 1). Pearson correlation showed the microbial community diversity during the pre-transplanting and heading stages was positively correlated (p <  0.05) with rice yield (Fig. 2). Adonis (permutational multivariate analysis of variance) also showed the microbial community during the pre-transplanting stage and the heading stage had a significant effect on crop yield (Additional file 1: Table S6), whereas the tillering and ripening stages did not show significant effects.
Table 1

Rhizospheric microbial community diversity at different developmental stages at four sites

 

Shannon diversity

Simpson

Pielou evenness

Ha_0p

7.01 ± 0.08abcde

0.9971 ± 0.0005ab

347.61 ± 60.66abcd

Ha_2p

7.28 ± 0.11a

0.9981 ± 0.0004a

553.05 ± 116.91a

Ha_3p

7.08 ± 0.03abcd

0.9976 ± 0.0005ab

421.17 ± 78.66abcd

Ha_4p

6.77 ± 0.05e

0.9948 ± 0.0026b

223.77 ± 95.43d

Hb_0p

7.05 ± 0.13abcde

0.9978 ± 0.0006ab

488.8 ± 131.09abc

Hb_2p

7.28 ± 0.06a

0.9983 ± 0.0002a

583.58 ± 62.07a

Hb_3p

7.19 ± 0.06ab

0.9982 ± 0.0001a

548.53 ± 21.6ab

Hb_4p

7.17 ± 0.01abc

0.9982 ± 0.0001a

551.81 ± 16.64ab

Hc_0p

7.03 ± 0.1abcde

0.9977 ± 0.0003ab

442.72 ± 61.61abcd

Hc_2p

7.07 ± 0.17abcde

0.9975 ± 0.0009ab

427.48 ± 139.1abcd

Hc_3p

7.05 ± 0.14abcde

0.9977 ± 0.0005ab

453.29 ± 85.08abcd

Hc_4p

6.87 ± 0.13cde

0.9969 ± 0.0005ab

328.54 ± 58.07bcd

Ly_0p

6.86 ± 0.12de

0.9962 ± 0.0012ab

277.73 ± 73.87bcd

Ly_2p

6.94 ± 0.1bcde

0.9971 ± 0.0007ab

363.47 ± 92.08abcd

Ly_3p

6.9 ± 0.11bcde

0.9957 ± 0.0021ab

264.65 ± 101.48bcd

Ly_4p

6.84 ± 0.04de

0.996 ± 0.001ab

257.85 ± 56.84 cd

Two-way ANOVA

Shannon

Simpson

Pielou evenness

Site effect

< 0.001

0.001

<  0.001

Stage effect

< 0.001

0.034

< 0.001

Cross effect

0.026

0.182

0.082

0p: pre-transplanting stage; 2p: tillering stage; 3p: heading stage; and 4p: ripening stage. The results are shown as the means and S.D. of three biological replicates. Values that do not share letters are different at the p < 0.05 level following Tukey’s t-test. Site, stage and cross effects were accessed by two-way ANOVA

Fig. 2
Fig. 2

Dcomparing the differences in microbial diversity indices was carried out by one-way analysis of variance (ANOVA) before the transplanting and heading stages. Error bars are based on the standard error and different lowercase letters indicate significant differences at the level of 0.05 as indicated by the ANOVA results. (a): Shannon Wiener index; (b): Inverse Simpson index; (c)Linear regression analysis of the relationship between crop yield and Shannon Wiener diversity index during pre-transplanting stage; and (d) Linear regression analysis of the relationship between crop yield and Shannon Wiener diversity index at the heading stage

Network analysis on relationships between key microbial communities

To explore the interactions between rhizospheric microbes, RMT-based molecular ecological networks (MEN) were constructed and analysed. All sites had similar RMT thresholds (0.88~ 0.90). However, the number of nodes and links was lower in the Ly site than in the other sites (Figs. 3 and 4, Additional file 1: Figures S2 and S3). There were more positive interactions between OTUs in the high-yield site (Ha, Hb and Hc) network than that in the low-yield site (Ly) network. Nodes with high degrees in the Ly networks were classified into Bacteroides (OTU_1367 and OTU_742) and Proteobacteria (OTU_807 and OTU_275), while in high-yield site networks, nodes with high degree were classified into Acidobacteria, Actinobacteria and Planctomycete. In addition, site Hb had the most complicated network (Additional file 1: Figure S3) with the most links and the maximal degree, presenting more intricate topological properties than the other sites.
Fig. 3
Fig. 3

Network constructed by thethe highest level (highly degree) of bacterial communities at site Hc. Each node signifies an OTU that could corresponds to a microbial population. Colours of the nodes indicate different major phyla. Blue and red lines represent positive and negative path coefficients, respectively

Fig. 4
Fig. 4

Network constructed by thethe highest level (highly degree) of bacterial communities in site Ly. Each node signifies an OTU that corresponds to a microbial population. Colours of the nodes indicate different major phyla. Blue and red lines represent positive and negative path coefficients, repectively

Microbial community composition and structure

All sequences were clustered into 14,332 operational taxonomic units (OTUs) and were assigned into 469 genera. The permutational multivariate analysis of variance (Adonis) from three methods (Bray, Euclid and Horn) at the OTUs (Additional file 1: Table S7) and the genus level (Additional file 1: Table S8) showed that the microbial community structure during the heading stage was closely associated with the yield. A venn diagram (Fig. 5c and Additional file 1: Figure S4) showed there were 87 OTUs shared by sites Ha, Hb, and Hc during the pre-transplanting stage, but this was not found at the Ly site. During the crop-growth period, 184 OTUs were shared by high-yield sites. Whereas the number of shared OTU taxa in the four sites reduced from 600 during the pre-transplanting stage to 370 during the crop-growth period. The number of unique OTUs in site Ly and the other sites increased when the stage changed from the pre-transplanting stage to the heading stage (Fig. 5d). An increase in unique OTUs in site Ly indicated that the difference between high- and low-yield sites increased. Detrended correspondence analysis (DCA) showed that the microbial community structure differed more substantially during three of the crop-growth stages than in the pre-transplanting stage (Fig. 5a and b; Additional file 1: Figure S4c and d). Furthermore, the community structure differences between the low- and high-yield sites were most obvious during the heading stage; therefore, we focused on the rhizospheric microbial community during the heading stage (Additional file 1: Table S9). Proteobacteria, Acidobacteria, and Chloroflexi were the most abundant phyla found in the rhizospheric microbial community during the heading stage (Additional file 1: Table S9a), and each accounted for more than 10% of the community. At the class level, Deltaproteobacteria was the most abundant class in all samples, and was followed by Anaerolineae, Betaproteobacteria, Alphaproteobacteria. (Additional file 1: Table S9b).
Fig. 5
Fig. 5

Analsis of compositions and structures of bacterial communities from four groups. (a) Detrended correspondence analysis (DCA) of 16S rRNA gene sequencing data at the genus level during the tillering stage; (b) Detrended correspondence analysis (DCA) of 16S rRNA gene sequencing data at the genus level during the heading stage; (c) Venn diagrams were calculated by R with the package gplots and based on OTU level during the heading stage. Figures in pictures represent the taxa number of OTUs with common ownership at different sites; (d) Variation trends in OTUs under different classifications from pre-transplanting to heading stages. ∩: Intersection of mathematical symbol; ∪:Union mathematical symbol; and S: Ha∩Hb∩Hc∩Ly (intersection of four sites)

Relationship between soil characteristics, microorganisms and crop yield

Mantel tests were carried out to determine the correlations between the soil physical and chemical properties and the soil microorganisms [52] (Table 2). The results indicated that soil conditions had a significant influence on the bacterial community structure (p <  0.05) during the pre-transplanting and heading stages. During the heading stage, there was a significantly negative correlation (Pearson correlation = − 0.647; p <  0.05, Additional file 1: Table S10) between the yield and Crenarchaeota (Additional file 1: Table S10). In addition, soil environmental factors, such as AK, had a significant negative correlation (Pearson correlation = − 0.666, p <  0.05; Additional file 1: Table S10) with Crenarchaeota, but a significant positive correlation with crop yield. Crop yield and AN were also significantly positively correlated (Pearson correlation = 0.668; p < 0.05; Additional file 1: Table S10). In addition, Proteobacteria and Bacteroidetes showed a significant positive correlation (Pearson correlation = 0.743–0.752; p < 0.01; Additional file 1: Table S10) with pH, whereas Chloroflexi exhibited a significant negative correlation with pH (Pearson correlation = − 0.621; p < 0.05, Additional file 1: Table S10). Overall, pH showed significant effects on constraining the bacterial community.
Table 2

Pearson correlation between microbial community diversity and rice yield

Pearson correlation

Yield

Before transplanting

Tillering stage

Heading stage

Ripening stage

Shannon.Wiener

0.614*

0.613*

0.650*

0.197

Simpson

0.579*

0.485

0.720**

0.286

Pielou

0.658*

0.477

0.594*

0.182

The stars indicate the significance level: *: P < 0.05, **: P < 0.01

Further analyses showed 17 phyla had significant effects on soil fertility and crop production. Some genera had significant impact on soil fertility as shown in Additional file 1: Table S11. The genera Geobacter, Syntrophorhabdus, Phaselicystis, Thiobacter, and Chondromyces (in addition to Cystobacter) of the phylum Proteobacteria, had negative impacts on crop yield. However, the abundance of the Proteobacteria was positively correlated with soil fertility. This also stressed that the structure and function of the bacterial community was diverse and complex at the phylum level. Armatimonadetes-gp2 and Armatimonadetes-gp5 in Armatimonadetes showed contradictory correlation with soil fertility and crop production. Gp17, Gp6, Gp24, and Gp25, which belong to Acidobacteria, were significantly corelated with production. Spartobacteria incertae sedis in Verrucomicrobia, and OD1-sedis in OD1 showed diverse effects on crop yield. Therefore, the overall effect could be directly observed at the phylum level, as well as on a more precise level when considering the individual effects of each bacterial community at the genus level.

The partial least squares path model (PLSPM, Fig. 6) showed an association between the yield and soil biological and abiotic factors, in general, at the heading stage. The bacterial diversity and the major bacterial communities were substantially related to the yield of super rice, but the structure of the bacterial communities was insignificant. The main soil factors affecting the yield of super rice were pH and SOM, which were also closely associated with the structure of soil bacterial communities. The goodness of fit to the model was 0.702 (> 0.350), which validates the result and provides a reference value for our study.
Fig. 6
Fig. 6

Partial least squares path modelling (PLSPM) of the association between the yield of super rice and soil biological and abiotic factors during the heading stage. Goodness-of-fit of the model is 0.702. Blue and orange arrows represent positive and negative path coefficients, respectively. *p < 0.05, **p < 0.01, **p < 0.01

Discussion

Relationship between rhizospheric microbial community and rice productivity

Crop productivity was mainly affected by the flow of energy and material in the soil ecosystem which is driven by soil organisms. Microorganisms located in the plant’s rhizosphere play pivotal roles in the soil geochemical cycle [53]. In the present study, the microbial community responded instantly to ecological changes during the pre-transplanting and heading stages. Generally, the bacterial diversity was higher in the high-yield sites than in the low-yield site, which suggested that high bacterial diversity potentially increases the yield of super rice. This is because high bacterial diversity maintained a relatively stable ecosystem in the rhizosphere, which allowed effective nutrient cycling [54]. The difference in the microbial communities between the high-yield sites and the low-yield site may be a result of different nutrient levels in the soils. This was further supported by the Mantel test, which showed that the soil physiochemical properties had significant effects on the soil microbial communities. Resident plants shaped and restructured rhizospheric microbial communities via root exudates [55], which provides nutrients for microbial communities and regulated microbial diversity [56, 57]. The ecosystem properties such as robustness and trophic interactions [58] were more stable in response to environmental fluctuations as diversity increased, for the richness of taxonomic diversity leads to the extension of niches and the utilization of resources. It is urgent to improve the stability and sustainability of farmland ecosystems [59], and meagre microbial diversity makes it difficult to resist the interference of detrimental factors. Additionally, a previous study by Laurent Philippot (2013) showed a loss in microbial diversity could affect nutrient cycling in soil [54], and microbial communities were closely related to material cycling.

Rhizospheric microbial community was crucial for rice production in the heading stage

The heading stage is a critical period for crop production; during this time, crops exhibit the most rapid growth and development of their entire life. It is a critical period for determining the amount of grains. During this stage, crops respond to external conditions [60]. Abundant nutrients, water, temperature, illumination, and other external conditions are required during this period, suggesting that this is an excellent time for farmers to increase production by means of topdressing. Soil fertility, a fundamental factor for plant growth, is mainly dependent on microbial transformations. Thus it is not surprising that the microbial community was most closely related to rice production during the heading stage. The association between the super rice with soil characteristics and the microbial microorganisms during the heading stage was further analysed using the PLSPM (partial least-squares path, Fig. 6). Bacterial communities such as Blastopirellula in the Planctomycete, are good indicators of soil fertility because the microorganisms respond to topdressing more rapidly than do the soil physicochemical characteristics [61], and microorganisms represent the true situation of the farmland; this is of great significance when establishing a stable and high-yield agricultural system. In the present study, some bacterial communities such as Chloroflexi, which do not produce oxygen during photosynthesis and have decreased nitrogen-fixation abilities, are abundant in soils; therefore, these bacteria are negatively correlated with the yield of super rice [62]. In contrast to detrimental bacteria, some bacterial communities, such as members of Acidobacteria, can degrade cellulose and adjust soil pH effectively [63, 64]; therefore, they are closely related to rice yield. However, the specific features of some Acidobacteria, e.g., GP6 and GP17, remain unclear.

Effects of microbial species interactions on rice productivity

Distinct sites had uniform fertilization regimes and similar climatic conditions, so the main difference in productivity was due to the rhizospheric microbial communities. In agricultural production, rhizospheric microorganisms are always associated with a considerable yield within a stable farmland ecosystem [65]. According to the molecular ecology networks, rhizospheric bacterial communities that played pivotal roles in the site Ly (Fig. 4), such as Bacteroidetes and Proteobacteria, are always classified as ‘copiotrophs’ (R-strategists), having a high growth rate in nutrient-rich conditions [66]. Owing to efficient metabolism in decomposing organic matter, the R-strategists should be chosen as the centre of communication among microbial communities, especially in artificially controlled nutrient-rich conditions. A farmland is a relatively complete ecosystem that keeps a dynamic balance between the input (contrived fertilizers) and output (plant growth) energetic processes; thus, due to rich resources leading to niche diversification and a reduction in the intensity of competition between co-occurring communities [57], the excellent decomposers and communicators with powerful metabolic rates, i.e., R-strategists, should present a positive role in the node of energy flows and material cycling. After all, co-occurrences may indicate a benign or mutually beneficial relationship. Conversely, the results showed that many R-strategists were negatively linked with their nearest neighbour in site Ly due to competition for resources, which implies a decline in microenvironment vitality. One possible explanation is that mutual exclusivity between bacterial communities caused by competition exists widely in the low-yield area, especially within similar niches. In addition, high-yield sites differed considerably from site Ly in terms of most members of Actinobacteria, Acidobacteria, and Planctomycetes. We infer that member of Acidobacteria and Actinobacteria had stronger adaptability and resilience, which enable their survival under stressful conditions [66], and regulated the surrounding environmental attributes through feedback mechanisms. The microbial community links showed more specific ecological significance than the physicochemical characteristics of farmland throughout the entire period of rice growth.

Conclusion

The results of the present study demonstrated that the gaps in crop yield are strongly associated with variations in the soil microbial communities during the heading stage, which were due to the effects of soil characteristic before transplanting; thus, in addition to adjusting the microhabitat before transplanting, there is the potential to improve the average crop yield by controlling rational agricultural management during the heading stage. In addition, the positive species interactions within communities may contribute to yield, as seen through network analysis, and the biological properties (i.e., microbes) better reflect real-world farmland situations than do physicochemical characteristics (i.e., nutrient elements). However, we have essentially focused on the relationships among soil characteristics and the variations and interactions of microbial communities. Hypotheses can be formed based on correlation analyses and need to be further tested to determine whether they are applicable to other land types.

Notes

Abbreviations

Adonis: 

Permutational multivariate analysis of variance

AN: 

available nitrogen

DCA: 

Detrended correspondence analysis

K: 

potassium

MEN: 

Molecular ecological network

P: 

Phosphorus

PLSPM: 

Partial least-squares path

SOM: 

Soil organic matter

TN: 

Total nitrogen

Declarations

Acknowledgements

We are grateful to the data analysis platform of Institute for Environmental Genomics.

Funding

The study was supported by the National Nature Science Foundation of China (No. 31371565, No. 31501698 and No. 41573072). The funding body made substantial contributions to design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

The entire 16S rRNA gene sequences were deposited in the NCBI database, and the accession number is SRP083104. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

PL, QL and ZW: Design and conception of the experiment. ZL, DM, and HY: Data analysis and manuscript preparation. WC, JS and ZG: Execution of the experiments. YL, JZ and HL: Participated in discussions and supervised overall experimental and theoretical works. All authors have read and approved the manuscript.

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Hunan Hybrid Rice Research Center/State Key Laboratory of Hybrid Rice, Changsha, 410125, China
(2)
Hunan Institute of Microbiology, Changsha, 410009, China
(3)
College of Plant Protection, Hunan Agricultural University, Changsha, 410128, China
(4)
School of Minerals Processing and Bioengineering, Central South University, Changsha, 410083, China
(5)
Hunan Soil and Fertilizer Institute, Changsha, 410125, China
(6)
Hunan Institute of Agricultural Information and Engineering, Changsha, 410125, China
(7)
LongPing Graduate Institute, Hunan University, Changsha, 410125, China

References

  1. Berg G. Plant–microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture. Appl Microbiol Biot. 2009;84(1):11–8.View ArticleGoogle Scholar
  2. Barea J. Future challenges and perspectives for applying microbial biotechnology in sustainable agriculture based on a better understanding of plant-microbiome interactions. J Soil Sci Plant Nut. 2015;15(2):261–82.Google Scholar
  3. Larkin RP. Soil health paradigms and implications for disease management. Annu Rev Phytopathol. 2015;53:199–221.View ArticlePubMedGoogle Scholar
  4. Gunnell D, Eddleston M, Phillips MR, Konradsen F. The global distribution of fatal pesticide self-poisoning: systematic review. BMC Public Health. 2007;7(1):1.View ArticleGoogle Scholar
  5. Leach A, Mumford J. Pesticide environmental accounting: a method for assessing the external costs of individual pesticide applications. Environ Pollut. 2008;151(1):139–47.View ArticlePubMedGoogle Scholar
  6. Altieri MA. Linking ecologists and traditional farmers in the search for sustainable agriculture. Front Ecol Environ. 2004;2(1):35–42.View ArticleGoogle Scholar
  7. Lugtenberg B. Principles of plant-microbe interactions: microbes for sustainable agriculture: Springer; 2014.Google Scholar
  8. Raaijmakers JM, Lugtenberg BJ. Perspectives for rhizosphere research. Molecular Microbial Ecology Of The Rhizosphere. 2013;1 & 2:1227–32.View ArticleGoogle Scholar
  9. Egamberdieva D, Kamilova F, Validov S, Gafurova L, Kucharova Z, Lugtenberg B. High incidence of plant growth-stimulating bacteria associated with the rhizosphere of wheat grown on salinated soil in Uzbekistan. Environ Microbiol. 2008;10(1):1–9.PubMedGoogle Scholar
  10. Berendsen RL, Pieterse CM, Bakker PA. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17(8):478–86.View ArticlePubMedGoogle Scholar
  11. Uren NC. Types, amounts, and possible functions of compounds released into the rhizosphere by soil-grown plants. The rhizosphere Biochemistry and organic substances at the soil-plant interface Marcel Dekker, New York. 2007:1–21.Google Scholar
  12. Singh BK, Millard P, Whiteley AS, Murrell JC. Unravelling rhizosphere–microbial interactions: opportunities and limitations. Trends Microbiol. 2004;12(8):386–93.View ArticlePubMedGoogle Scholar
  13. Kennedy A, Smith K. Soil microbial diversity and the sustainability of agricultural soils. Plant Soil. 1995;170(1):75–86.View ArticleGoogle Scholar
  14. Kennedy A. Bacterial diversity in agroecosystems. Agric Ecosyst Environ. 1999;74(1):65–76.View ArticleGoogle Scholar
  15. Parkinson D, Coleman DC. Microbial communities, activity and biomass. Agric Ecosyst Environ. 1991;34(1–4):3–33.View ArticleGoogle Scholar
  16. Smith RG, Gross KL, Robertson GP. Effects of crop diversity on agroecosystem function: crop yield response. Ecosystems. 2008;11(3):355–66.View ArticleGoogle Scholar
  17. Gans J, Wolinsky M, Dunbar J. Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science. 2005;309(5739):1387–90.View ArticlePubMedGoogle Scholar
  18. Giller K, Beare M, Lavelle P, Izac A-M, Swift M. Agricultural intensification, soil biodiversity and agroecosystem function. Appl Soil Ecol. 1997;6(1):3–16.View ArticleGoogle Scholar
  19. Pankhurst C, Ophel-Keller K, Doube B, Gupta V. Biodiversity of soil microbial communities in agricultural systems. Biodivers Conserv. 1996;5(2):197–209.View ArticleGoogle Scholar
  20. Fragoso C, Brown G, Patrón J, Blanchart E, Lavelle P, Pashanasi B, Senapati B, Kumar T. Agricultural intensification, soil biodiversity and agroecosystem function in the tropics: the role of earthworms. Appl Soil Ecol. 1997;6(1):17–35.View ArticleGoogle Scholar
  21. Swift M, Anderson J. Biodiversity and ecosystem function in agricultural systems. In: Biodiversity and ecosystem function.: Springer; 1994. p. 15–41.Google Scholar
  22. Øvreås L, Torsvik V. Microbial diversity and community structure in two different agricultural soil communities. MICROBIAL ECOL. 1998;36(3–4):303–15.Google Scholar
  23. Stephen A, Karp DS, DeClerck F, Kremen C, Naeem S: Functional traits in agriculture: agrobiodiversity and ecosystem services. Trends in Ecology & Evolution 2015, 30(9):531.Google Scholar
  24. Fuhrman JA. Microbial community structure and its functional implications. Nature. 2009;459(7244):193–9.View ArticlePubMedGoogle Scholar
  25. Lupwayi N, Rice W, Clayton G. Soil microbial diversity and community structure under wheat as influenced by tillage and crop rotation. Soil Biol Biochem. 1998;30(13):1733–41.View ArticleGoogle Scholar
  26. Whipps JM. Microbial interactions and biocontrol in the rhizosphere. J Exp Bot. 2001;52(suppl 1):487–511.View ArticlePubMedGoogle Scholar
  27. Thakore Y. The biopesticide market for global agricultural use. Ind Biotechnol. 2006;2(3):194–208.View ArticleGoogle Scholar
  28. Ehlers R-U. Einsatz der Biotechnologie im biologischen Pflanzenschutz. Schriftenreihe der Deutschen Phytomedizinischen Gesellschaft eV. 2006;8:17–31.Google Scholar
  29. Chaparro JM, Sheflin AM, Manter DK, Vivanco JM. Manipulating the soil microbiome to increase soil health and plant fertility. Biology & Fertility of Soils. 2012;48(5):489–99.View ArticleGoogle Scholar
  30. Edwards J, Johnson C, Santos-Medellín C, Lurie E, Podishetty NK, Bhatnagar S, Eisen JA, Sundaresan V. Structure, variation, and assembly of the root-associated microbiomes of rice. P Natl Acad Sci. 2015;112(8):E911–20.View ArticleGoogle Scholar
  31. Peiffer JA, Spor A, Koren O, Jin Z, Tringe SG, Dangl JL, Buckler ES, Ley RE. Diversity and heritability of the maize rhizosphere microbiome under field conditions. P Natl Acad Sci. 2013;110(16):6548–53.View ArticleGoogle Scholar
  32. Bardgett RD, van der Putten WH. Belowground biodiversity and ecosystem functioning. Nature. 2014;515(7528):505–11.View ArticlePubMedGoogle Scholar
  33. Chen S, Zheng X, Wang D, Chen L, Xu C, Zhang X. Effect of long-term Paddy-upland yearly rotations on Rice (Oryza sativa) yield, soil properties, and Bacteria community diversity. ScientificWorldJournal. 2012;2012(3):279641.PubMedPubMed CentralGoogle Scholar
  34. Smalla K, Wieland G, Buchner A, Zock A, Parzy J, Kaiser S, Roskot N, Heuer H, Berg G. Bulk and rhizosphere soil bacterial communities studied by denaturing gradient gel electrophoresis: plant-dependent enrichment and seasonal shifts revealed. Appl Environ Microb. 2001;67(10):4742–51.View ArticleGoogle Scholar
  35. Page AL: Methods of soil analysis. Part 2. Chemical and microbiological properties: American Society of Agronomy, soil science society of America; 1982.Google Scholar
  36. Naumann K, Bassler R. Chemische Untersuchung von Futtermitteln: Neumann-Neudamm; 1976.Google Scholar
  37. Zhao F, McGrath S, Crosland A. Comparison of three wet digestion methods for the determination of plant Sulphur by inductively coupled plasma atomic emission spectroscopy (ICP-AES). Commun Soil Sci Plan. 1994;25(3–4):407–18.View ArticleGoogle Scholar
  38. Jankauskas B, Slepetiene A, Jankauskiene G, Fullen MA, Booth CA. A comparative study of analytical methodologies to determine the soil organic matter content of Lithuanian Eutric Albeluvisols. Geoderma. 2006;136(3):763–73.View ArticleGoogle Scholar
  39. Baker M, Stein JC, Wurgler J. When does the market matter? Stock prices and the investment of equity-dependent firms. In: National Bureau of Economic Research; 2002.Google Scholar
  40. Tao J, Liu X, Liang Y, Niu J, Xiao Y, Gu Y, Ma L, Meng D, Zhang Y, Huang W. Maize growth responses to soil microbes and soil properties after fertilization with different green manures. Appl Microbiol Biot. 2016;101(3):1–11.Google Scholar
  41. Yin H, Niu J, Ren Y, Cong J, Zhang X, Fan F, Xiao Y, Zhang X, Deng J, Xie M. An integrated insight into the response of sedimentary microbial communities to heavy metal contamination. Sci Rep-Uk. 2015;5(1):14266.View ArticleGoogle Scholar
  42. Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957–63.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Edgar RC, Haas BJ, Clemente JC, Christopher Q, Rob K. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194–200.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1.View ArticlePubMedGoogle Scholar
  45. Melendrez MC, Lange RK, Cohan FM, Ward DM. Influence of molecular resolution on sequence-based discovery of ecological diversity among Synechococcus populations in an alkaline siliceous hot spring microbial mat. Appl Environ Microb. 2011;77(4):1359–67.View ArticleGoogle Scholar
  46. Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM: Ribosomal Database Project: data and tools for high throughput rRNA analysis. NUCLEIC ACIDS RES 2013:gkt1244.Google Scholar
  47. Team RC: R: a language and environment for statistical Computing 2013.Google Scholar
  48. Sanchez G, Trinchera L: plspm: Partial Least Squares data analysis methods. R package version 02–2. URL: http://CRANR-project.org/package=plspm 2012.
  49. Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X. Functional molecular ecological networks. MBio. 2010;1(4):1592–601.Google Scholar
  50. Ye D, Jiang YH, Yang Y, He Z, Feng L, Zhou J. Molecular ecological network analyses. Bmc Bioinformatics. 2012;13(1):113.View ArticleGoogle Scholar
  51. Wang H, Wei Z, Mei L, Gu J, Yin S, Faust K, Raes J, Deng Y, Wang Y, Shen Q. Combined use of network inference tools identifies ecologically meaningful bacterial associations in a paddy soil. Soil Biol Biochem. 2017;105:227–35.View ArticleGoogle Scholar
  52. Zhang Y, Zhao Z, Dai M, Jiao N, Herndl GJ. Drivers shaping the diversity and biogeography of total and active bacterial communities in the South China Sea. Mol Ecol. 2014;23(9):2260–74.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Bakker MG, Manter DK, Sheflin AM, Weir TL, Vivanco JM. Harnessing the rhizosphere microbiome through plant breeding and agricultural management. Plant Soil. 2012;360(1–2):1–13.View ArticleGoogle Scholar
  54. Philippot L, Spor A, Hénault C, Bru D, Bizouard F, Jones CM, Sarr A, Maron PA. Loss in microbial diversity affects nitrogen cycling in soil. Isme J. 2013;7(8):1609.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Berg G, Smalla K. Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol Ecol. 2009;68(1):1–13.View ArticlePubMedGoogle Scholar
  56. An M, Zhou X, Wu F, Ma Y, Yang P. Rhizosphere soil microorganism populations and community structures of different watermelon cultivars with differing resistance to fusarium oxysporum f. Sp. niveum. Can J Microbiol. 2011;57(5):355–65.View ArticlePubMedGoogle Scholar
  57. Cornell HV, Lawton JH. Species interactions, local and regional processes, and limits to the richness of ecological communities: a theoretical perspective. J Anim Ecol. 1992;61(1):1–12.View ArticleGoogle Scholar
  58. Thébault E, Loreau M: Trophic interactions and the relationship between species diversity and ecosystem stability. Am Nat 2005, 166(Volume 166, Number 4):E95–114.Google Scholar
  59. Creer S, Sinniger F. Cosmopolitanism of microbial eukaryotes in the global deep seas. Mol Ecol. 2012;21(5):1033–5.View ArticlePubMedGoogle Scholar
  60. Ryu C, Suguri M, Umeda M. Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing. Fuel & Energy Abstracts. 2011;122(3):214–24.Google Scholar
  61. García-Orenes F, Morugán-Coronado A, Zornoza R, Scow K. Changes in soil microbial community structure influenced by agricultural management practices in a Mediterranean agro-ecosystem. PLoS One. 2013;8(11):e80522.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Niu J, Rang Z, Zhang C, Chen W, Tian F, Yin H, Dai L. The succession pattern of soil microbial communities and its relationship with tobacco bacterial wilt. BMC Microbiol. 2016;16(1):233.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microb. 2009;75(15):5111–20.View ArticleGoogle Scholar
  64. Bruce T, Martinez IB, Neto OM, Vicente ACP, Kruger RH, Thompson FL. Bacterial community diversity in the Brazilian Atlantic forest soils. MICROBIAL ECOL. 2010;60(4):840–9.View ArticleGoogle Scholar
  65. Brussaard L, De Ruiter PC, Brown GG. Soil biodiversity for agricultural sustainability. Agric Ecosyst Environ. 2007;121(3):233–44.View ArticleGoogle Scholar
  66. Trivedi P, Anderson IC, Singh BK. Microbial modulators of soil carbon storage: integrating genomic and metabolic knowledge for global prediction. Trends Microbiol. 2013;21(12):641–51.View ArticlePubMedGoogle Scholar

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