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Plant and Soil

, Volume 424, Issue 1–2, pp 463–478 | Cite as

Diazotroph abundance and community composition in an acidic soil in response to aluminum-tolerant and aluminum-sensitive maize (Zea mays L.) cultivars under two nitrogen fertilizer forms

  • Chao Wang
  • Man Man Zheng
  • An Yong Hu
  • Chun Quan Zhu
  • Ren Fang Shen
Regular Article

Abstract

Aims

In acidic soil, plant aluminum (Al) tolerance and nitrogen (N) fertilizer form play the important roles in influencing plant growth. Diazotrophs as plant growth promoting rhizobacteria can contribute to plant available N, but the response to plant growth differences resulting from plant Al tolerance or N fertilizer form in acidic soil is poorly understood. Here, we investigated this response.

Methods

Three maize cultivars with different Al-tolerance levels were grown in an acidic soil that was added with nitrate (NO3-N) or ammonium (NH4+-N) fertilizers. After 42 days, maize biomasses and root morphologies as well as the physicochemical properties of bulk and rhizosphere soils were determined. Based on nifH gene, soil diazotroph abundance was determined by quantitative RT-PCR and community composition was assayed using high-throughput sequencing.

Results

For the same maize cultivar, application of NO3-N fertilizer relative to NH4+-N fertilizer increased maize biomass, root length, root surface area, and rhizosphere pH, but not significantly affect rhizosphere nifH copy number and community composition. The nifH copy numbers did not show significant differences among rhizosphere samples of three maize cultivars, but community composition was obviously different between Al-tolerant and Al-sensitive cultivars.

Conclusions

In acidic soil, maize cultivars depending on Al tolerance altered rhizosphere diazotrophic community composition, but this effect seemed to be not susceptible to N fertilizer forms.

Keywords

Acidic soil Maize cultivar Al tolerance Diazotroph Nitrogen form 

Introduction

Acidic soils cover about 30% of total land area and more than 50% of the potentially arable lands in the world (von Uexküll and Mutert 1995). Approximately 60% of the acidic soils are distributed in tropical and subtropical regions (Kochian et al. 2004), especially in developing countries (von Uexküll and Mutert 1995). For example, acidic soil occupies an area of ~ 21.8 billion ha in southern China, where the acidification is accelerating owing to the increased use of ammonium (NH4+-N)-based fertilizers, industrialization practices, and excessive soil utilization (Guo et al. 2010). Acidic soils decrease the production of staple food crops (maize, rice, peanuts, soybeans, etc.) (Kochian et al. 2004; von Uexküll and Mutert 1995) and negatively affect soil microbial processes (Pietri and Brookes 2008), threatening the development of sustainable agriculture. The limiting factors in acidic soils include the toxicities of aluminum (Al), manganese (Mn), and hydrogen (H), deficiencies of essential elements such as phosphorus (P), calcium (Ca), and magnesium (Mg) (Kochian et al. 2004), as well as water uptake imbalance in plants (Ma 2007), but Al toxicity is generally considered to be the major limiting factor (Kochian et al. 2004; Ma et al. 2014). At soil pH of 5.0 or less, Al from soil minerals become soluble in the soil solution, and micromolar Al concentration severely inhibits the growth and function of plants (Kochian et al. 2004) and soil microorganisms (Kunito et al. 2016).

In agricultural practices, lime is applied traditionally to reduce Al toxicity through increasing soil pH, but its application often leads to reacidification and the negative effect on soil structure (Doerge and Gardner 1985; Scott et al. 1997) and the ameliorated effect is limited in surface soil (Scott et al. 1997). Recently, the biological strategy has been suggested to ameliorate soil acidity (Tang et al. 2011). Nitrogen (N) is known as the main macronutrient for plant growth and absorbed more than other nutrients. NH4+-N and nitrate (NO3-N) are the most abundant N forms that are taken up by the roots, and the supply form to plants can alter overall cation-anion relationship, influencing alkali or acid production (Tang et al. 2011). When supplied by NH4+-N fertilizer, plants generally excrete net excess H+ and exacerbate acidification of rhizosphere. In contrast, NO3-N uptake by plant roots requires the release of excessive anions (such as OH and HCO3), which can increase rhizosphere pH (Masud et al. 2014). Although NO3-N application is limited in some areas due to the easy mobility in soil and pollutant for environment (Liu et al. 2005), the mobility can help ameliorate subsoil acidity (Tang et al. 2011). Ameliorating rhizosphere soil acidity using NO3-N fertilizer has attracted attention as a biological strategy for improving plant growth in acidic soil (Masud et al. 2014; Mehmood et al. 2017; Tang et al. 2011). The application of NO3-N fertilizer in acidic soil has been reported to decrease rhizosphere soil acidity and Al toxicity level and enhance plant growth (Masud et al. 2014; Mehmood et al. 2017).

Other alternative to reduce the deleterious effect of soil Al toxicity could be the cultivation of Al-tolerant crop cultivars (Ma et al. 2014), since the plant’s tolerance to Al plays a crucial role in regulating its growth in acidic soil (Kochian et al. 2004; Ma et al. 2014). A common mechanism of Al tolerance is secretions of organic acid anions (such as citrate, malate, and oxalate) and phenolic compounds from the roots (Kochian et al. 2004; Ma et al. 2014). For instance, Al-tolerant plant cultivars often exudate more organic acid anions from roots than Al-sensitive cultivars when exposed to Al stress (Piñeros et al. 2005). These secretions can form the complexes with Al ions in the rhizosphere and consequently decrease Al toxicity level. Additionally, nutrient uptake capacity of plants is positively correlated with the Al tolerance (Giannakoula et al. 2008; Mariano et al. 2015), because the typical symptoms of Al toxicity are the destruction of root structure and the inhibition of root elongation, subsequently inhibiting the uptake of water and nutrients (Ma et al. 2007, 2014). These differences in root exudates and soil nutrients induced by plant Al tolerance cause environmental differences between rhizosphere soils of Al-tolerant and Al-sensitive plant cultivars.

Plants often benefit from soil microbial populations, such as plant growth promoting rhizobacteria which play an important role in improving plant growth and health (Lugtenberg and Kamilova 2009). Plant associated N-fixing microorganisms (diazotrophs) as plant growth promoting rhizobacteria can contribute to plant available N for promoting plant growth (Pereira e Silva et al. 2013). It is estimated that the contribution to soil N from free-living diazotrophs can reach the level of 60 kg ha−1 year−1 in some agricultural and natural ecosystems (Smil 1999). An inoculation of diazotrophic bacteria can enhance crop production (Rodrigues et al. 2008; Fox et al. 2016), and even non-inoculated crops can obtain significant N from biological N fixation (Montañez et al. 2009; Appunu and Dhar 2006). Additionally, this important biological process can reduce the consumption of chemical fertilizers, further alleviating soil acidification and environmental pollution (Pereira e Silva et al. 2013).

Diazotrophs, as N2-fixers, have been widely studied in various ecosystems, and the N fixation rate is closely related to both diazotroph abundance and community structure (Hsu and Buckley 2009; Pereira e Silva et al. 2013). Soil factors including soil pH, carbon (C), N, C/N ratio, and available P are frequently reported to affect the diazotroph populations in different ecosystems (Mirza et al. 2014; Pereira e Silva et al. 2013; Wakelin et al. 2010). In the plant-soil system, due to the dependence on root exudates and rhizosphere nutrient availability, the abundance and community of rhizosphere microbes are significantly influenced by the physiological status of host plants (Hinsinger et al. 2009). The response of diazotrophs to rhizosphere environments varied with plant cultivars (Coelho et al. 2008) and adaptation of plants to stress and nutrient availability (Coelho et al. 2008; Rodríguez-Blanco et al. 2015). It is reported that N nutrient as a critical factor influenced the structure and abundance of rhizosphere diazotrophs (Coelho et al. 2008; Rodríguez-Blanco et al. 2015), while it was limited in the N fertilizer level. Little is known about the influence of N fertilizer forms on rhizosphere diazotrophs, especially in acidic soil. Furthermore, Li et al. (2012) reported that soybean cultivars differing in Al tolerance had different impacts on diazotroph abundances and community structures in acidic soil. Maize (Zea mays L.) is one of the most important crop worldwide for food, feed, and biofuel production, and it benefits from free-living diazotrophs (Hungria et al. 2010), which are different from leguminous plants, in which the root nodules through symbiosis are the main contributors to biological N (Li et al. 2016). Recent studies exist on the influence of maize cultivars on diazotroph populations in a neutral soil (Rodríguez-Blanco et al. 2015), but have not been undertaken in acidic soil, nor have they included the influence of maize’s Al tolerance.

In the present work, we hypothesized that N fertilizer forms and maize cultivars grown in acidic soil would affect rhizosphere diazotrophic populations, because the rhizosphere environments may show significant differences not only for maize cultivars with different Al tolerance but also for the same cultivar supplied with different N fertilizer forms. Understanding the responses of rhizosphere diazotrophs to the biological remediation practices may contribute to improving strategies of plant N uptake in acidic soil. To address our hypothesis, we investigated the abundance and community structure of soil diazotrophic bacteria associated with maize cultivars having different Al tolerance levels that were cultivated in an acidic soil containing different N forms (NO3-N and NH4+-N). The drivers affecting the diazotrophic populations were also analyzed. The abundance and community structure of the diazotrophic bacteria were characterized by the nifH gene. Because the nifH gene encodes the iron protein subunit of nitrogenase reductase and is the most conserved gene in the nif operon (Roeselers et al. 2007), this functional gene has been widely used for studying diazotrophs in various environments (Pereira e Silva et al. 2011, 2013).

Materials and methods

Greenhouse pot experiment

The acidic soil (Quaternary red clay soil) used for the cultivation experiment was obtained from a pine forest at the Yingtan Red Soil Ecological Experiment Station (28° 14′ N, 117° 03′ E) in Jiangxi Province, China. The soil was air-dried and sieved to 2 mm before cultivation. The basic physicochemical properties were as follows: pH 4.60; organic matter, 6.03 g kg−1; alkali-hydrolyzable N, 23.40 mg kg−1; total N (TN), 0.64 g kg−1; available P (AP), 0.25 mg kg−1; and total phosphorus (P), 0.15 g kg−1, soil exchangeable Al (Al), 0.25 g kg−1. These basic soil physicochemical properties were determined as described below.

Plant was cultured in a plastic pot with dimensions of 135 mm (height) × 175 mm (open top) × 110 mm (flat bottom). Fertilizers were mixed into 2.5 kg of soil in each pot. Monopotassium phosphate (KH2PO4) and potassium sulfate (K2SO4) were applied at 33.2 mg K and 23.3 mg P kg−1 soil, for chemical K and P requirements. N fertilizer with NO3-N or NH4+-N used at the content of 88.9 mg N kg−1 soil was in the form of NaNO3 or (NH4)2SO4, respectively. Each treatment had three replicate pots. The mixed soil was pre-incubated for 7 days before sowing.

The three maize (Z. mays L.) cultivars were hybrid line “JingDan28” (JD) and “XianYu335” (XY) and inbred line “Mo17” (Mo). JD and XY as relatively Al-tolerant cultivars are locally grown cultivars (Bian et al. 2016). JD and XY are bred using different inbred lines, thus they have no genetic relationship. Mo is an Al-sensitive line (Piñeros et al. 2005) and the most commercial valuable inbred line. Root elongation inhibition also confirmed the tolerance ability of three maize cultivars to Al toxicity (Fig. S1). All maize seeds were provided by the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences. Maize was grown in a natural greenhouse of the Institute of Soil Science, Chinese Academy of Sciences. During the pot experiment, the temperature in the greenhouse was in the range of 18–32 °C with a relative humidity of 40–80%. On 23 April 2015, seeds were superficially disinfected with 10% sodium hypochlorite and then washed three times with purified water. Three seeds per pot were sown directly into the soil. No-plant pots were set as controls. The soil’s water content was maintained at 70% water-holding capacity.

Plants were harvested at jointing stage after 42 days of sowing. The roots of each plant were separated from the soil and shaken manually to remove the loosely attached soil, and the soil adhering to the roots was collected as rhizosphere soil. The rhizosphere soils obtained from three plants growing in the same pot were pooled as a rhizosphere soil sample; therefore, each treatment had three replicate rhizosphere samples due to the three replicate pots. Bulk soil samples were also collected from no-maize pot experiment. Each soil sample was thoroughly mixed and divided into two parts. One part was kept at 4 °C to analyze soil NH4+-N and NO3-N, dissolved organic N (DON), and dissolved organic C (DOC) and to extract soil DNA within 3 days; and other part was air-dried for measurements of soil pH, total C (TC), TN, soil organic C (SOC), AP, available K (AK), and soil exchangeable Al.

Root morphology and biomass

Maize shoots and roots were harvested simultaneously and washed three times with deionized water. Root morphology (total root length, root surface area, and root volume) was analyzed using a WinRhizo-LA1600 root scanner (Regent Instruments Inc., Quebec, Canada). The shoots and roots were dried at 105 °C for 20 min and then at 85 °C until a constant weight (Liu et al. 2015). The dry mass was determined using an analytical balance.

Soil property analysis

Soil pH was measured using a pH meter (Mettler Toledo FE20, Switzerland) in a soil solution (1:2.5 soil:water ratio). The NH4+-N and NO3-N were extracted with 2.0-M KCl (1:5 soil:solution ratio) and measured by a continuous flow analyzer (San++, Skalar, Holland). Alkali-hydrolyzable N was determined using the alkaline hydrolysis diffusion method (Lu 1999). Dissolved TN (DTN) and DOC were extracted with 0.5-M K2SO4 (1:5 soil:solution ratio). DOC in the extracts was analyzed using a TOC analyzer (Multi N/C 3000, Analytik, Jena, Germany), and DTN was determined using the Kjeldahl method (Lu 1999). DON was obtained from the difference between DTN and NH4+-N content. The soil’s water content was measured through oven-drying. Organic matter and SOC were analyzed using the dichromate oxidation method (Sims and Haby 1971). The TC and TN in the soils were measured on a Vario MAX CNS elemental analyzer (Elementar Instruments, Hanau, Germany). Total P was measured using the molybdenum blue method after HClO4-H2SO4 digestion (Lu 1999), and the AP was extracted with 0.03-M ammonium fluoride-hydrochloric acid and measured colourimetrically as described above. The AK was extracted with 1.0-M ammonium acetate and measured by flame photometry (FP640, Shanghai, China). Soil exchangeable Al was extracted with 1.0-M KCl (1:50 w/v) and determined by inductively-coupled plasma atomic emission spectrophotometry (ICP-AES, IRIS-Advantage, Thermo Elemental, MA, USA).

DNA extraction

Soil DNA was extracted from 0.5 g of soil samples using a Fast® DNA SPIN soil DNA isolation Kit (MP Biomedicals, CA, USA) and the DNA extraction was purified using a PowerClean®DNA Clean-up Kit (MoBio, CA, USA), following the manufacturer’s instructions. The DNA quality and concentration were analyzed with a NanoDrop ND-1000 (NanoDrop Technologies, Wilmington, USA). The purified DNA was stored at − 20 °C for further use.

nifH gene sequencing and bioinformatics analysis

To amplify the nifH fragments for high-throughput sequencing, the DNA was amplified using a nested PCR approach (Pereira e Silva et al. 2013). The primers PolF (5′-TGCGAYCCSAARGCBGACTC-3′) and PolR (5′-ATSGCCATCATYTCRCCGGA-3′) were used in the first amplification, and the primers RoeschF (5′-ACCCGCCTGATCCTGCACGCCAAGG-3′) and RoeschR (5′-ACGATGTAGATTTCCTGGGCCTTGTT-3′) were used in the second amplification (Pereira e Silva et al. 2013). In order to differentiate the sample in one sequencing run, a unique seven-base barcode sequence was added to the reverse primer in the second PCR round. The PCR program was that previously published by Pereira e Silva et al. (2013). PCR products were quantitated and combined in equimolar concentrations. A mixed DNA pool was used for sequencing on an Illumina MiSeq platform with 300-bp paired-end reads (Personal Biotechnology Co., Ltd., Shanghai, China). The sequencing data were submitted to NCBI Sequence Read Archive database under accession number SRP092274.

Pairs of reads from the raw data were merged using FLASH (version 1.2.7) (Magoč and Salzberg 2011). Then, sequences were processed using Mothur software (version 1.31.1) (Schloss et al. 2009). First, the merged sequences were quality-filtered to remove the low-quality sequences, barcodes, and primers. Second, the remaining sequences were translated into amino acid using the FunGene Pipeline of the Ribosomal Database Project (RDP) server (http://fungene.cme.msu.edu/FunGenePipeline/) (Mirza et al. 2014; Pereira e Silva et al. 2013). The sequences that did not match the nifH gene or contained in-frame stop codons in the middle were removed. Third, the nifH gene sequences were aligned (Gaby and Buckley 2014), and the failed reads and chimeras were also eliminated. The remaining sequences were clustered into operational taxonomic units (OTUs) with an identity cutoff of 90% (Pereira e Silva et al. 2013; Huang et al. 2016). Rarefaction curves of OTUs were calculated using Mothur. A representative sequence, which was the most abundant sequence in each OTU, was identified using the closest relative from a BLAST algorithm-based search within GenBank (http://blast.ncbi.nlm.nih.gov/Blast.cgi). In order to compare the difference of OTU numbers among samples, a randomly selected subset of 1960 sequences per sample was performed.

Quantitative real-time PCR (qPCR) analysis

qPCR was performed on a LightCycler 480 Real-Time PCR System (Roche Diagnostics, Mannheim, Germany) using SYBR® premix Ex Taq™ II (Takara, Dalian, China), according to the manufacturer’s instructions. The purified DNA was diluted as a template with final content of 1–10 ng in a 25-μL reaction mixture. The primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACVSGGGTATCTAAT-3′) were used for total bacterial qPCR (Baldridge et al. 2015), and the primers PolF/PolR were applied to amplify the nifH gene (Huang et al. 2016). The cycling conditions were those of Huang et al. (2016). Each reaction was run in three replicates for each DNA sample.

To generate standard curves, the targeted genes of 16S rRNA or nifH were independently cloned into the pMD19-T vector (Takara, Dalian, China) and transformed into Escherichia coli DH5α competent cells. Plasmid DNA was extracted using a plasmid-extraction kit (Takara, Dalian, China). The DNA quality and concentration were analyzed with NanoDrop ND-1000. Standard curves for each gene were obtained by diluting the plasmid DNA, ranging from 108 to 102 copy numbers. The efficiency of the qPCR and the copy number were calculated as previously published (Mirza et al. 2014). The efficiencies obtained were between 98 and 105%, and the R2 values were greater than 0.98.

Data analysis

The data is displayed as the average of the three pot replicates with the standard deviation. The interaction influences of N fertilizer form and maize cultivar on maize biomass and root morphology, as well as the influences of N fertilizer form and soil site (bulk and rhizospheres of different maize cultivars) on soil properties, gene copy numbers, and OTU numbers, were analyzed by Scheirer–Ray–Hare test (non-parametric two-way analysis of variance (ANOVA)) using SPSS 22.0 (SPSS Inc., Chicago, IL, USA) (Dytham 2011). When the differences reached a significant level (p < 0.05), the differences under the same N fertilizer treatment were further analyzed using the non-parametric Kruskal–Wallis test followed by Mann–Whitney U test, and the comparison of the same indictor between different N form treatments was carried out using non-parametric Mann–Whitney U test (Dytham 2011). Significant differences in the relative abundances of the dominant genera levels of diazotroph among soil samples were tested by STAMP software (Parks et al. 2014). Pearson’s correlation coefficient was used to determine relationships between soil physicochemical properties and both the total gene abundance and relative abundances of taxonomic units (SPSS 22.0).

An OTU-based hierarchical cluster analysis with the unweighted pair group method of arithmetic averages (UPGMA) and a similarity (ANOSIM) analysis with Bray-Curtis distances were performed using the R software (Version 3.1.2, vegan packages). The correlations between soil variables and nifH gene community composition were analyzed using the Mantel test, and the soil variables that showed significant influences (p < 0.05) were selected for a canonical correspondence analysis (CCA) using R software.

Results

Maize growth and root morphology

In order to study the influence of maize growth differences on the rhizosphere diazotrophs, the growth responses of three maize cultivars to the N fertilizer form were firstly analyzed. Results of the two-way ANOVA following the Scheirer–Ray–Hare test showed that both the N fertilizer form and maize cultivar significantly influenced (p < 0.01) the plant biomass 42 days after sowing (Fig. 1). The shoot, root, and total biomass of each maize cultivar were higher in the NO3-N fertilizer than those in the NH4+-N fertilizer (Fig. 1), and the increase ratios were more substantial (> 103%) in Mo than in JD and XY (< 50%) (Table S1), suggesting that NO3-N fertilizer application in the acidic soil has a better improved effect on the growth of cultivar Mo. Under the same N fertilizer treatment, the biomasses of both JD and XY did not show significant differences, but they were markedly higher (p < 0.05) than that of Mo (Fig. 1), confirming that both JD and XY were relatively Al-tolerant cultivars and Mo was an Al-sensitive cultivar when grown in acidic soil.
Fig. 1

The shoot (a), root (b), and total biomasses (c) of three maize cultivars after 42 days of cultivation with an NH4+-N or NO3-N fertilizer. Values are the means ± SD of three pot replicates. All data were subjected to a two-way ANOVA using Scheirer–Ray–Hare test, N: N fertilizer form (NH4+-N and NO3-N); C: cultivars (JD, XY, and Mo). Different lowercase letters above the white columns indicate significant differences (p < 0.05, Kruskal–Wallis test) under NH4+-N fertilizer treatment; different capital letters above the gray columns indicate significant differences (p < 0.05, Kruskal–Wallis test) under NO3-N fertilizer treatment. The asterisk indicates a significant difference (p < 0.05, Mann-Whitney U test) between N fertilizer treatments

Both the N form and maize cultivar also significantly affected (p < 0.01) root morphology (Fig. 2). The total root lengths, root surface areas, and root volumes of the three maize cultivars were markedly higher in the NO3-N fertilizer than those in the NH4+-N fertilizer (Fig. 2), and the increases in the ratios were more substantial (> 95.0%) in Mo than in JD and XY (< 45.0%) (Table S1). Under the same N fertilizer treatment, the total root lengths, root surface areas, and root volumes of both JD and XY were higher (p < 0.05) than those of Mo (Fig. 2).
Fig. 2

Root length (a), surface area (b), and volume (c) of three maize cultivars after 42 days of cultivation with an NH4+-N or NO3-N fertilizer. Values are the means ± SD of three pot replicates. All data were subjected to a two-way ANOVA using Scheirer–Ray–Hare test, N: N fertilizer form (NH4+-N and NO3-N); C: cultivars (JD, XY and Mo). Different lowercase letters above the white columns indicate significant differences (p < 0.05, Kruskal–Wallis test) under NH4+-N fertilizer treatment; different capital letters above the gray columns indicate significant differences (p < 0.05, Kruskal–Wallis test) under NO3-N fertilizer treatment. The asterisk indicates a significant difference (p < 0.05, Mann-Whitney U test) between N fertilizer treatments

Soil characteristics

The physicochemical characteristics of both rhizosphere and bulk soils are shown in Table 1. A two-way ANOVA using the Scheirer–Ray–Hare test showed that plant cultivation significantly influenced (p < 0.01 or p < 0.05) all of the analyzed soil parameters, except for dissolved organic N (DON) and available phosphorus (AP), whereas the N fertilizer form only markedly affected (p < 0.01) soil pH, exchangeable aluminum (Al), ammonium (NH4+-N), nitrate (NO3-N), and available potassium (AK) (Table 1). The NO3-N fertilizer treatment significantly increased (p < 0.05) the rhizosphere pH levels of both XY and Mo. As an acid-releasing fertilizer, NH4+-N addition decreased the rhizosphere pH levels and increased the rhizosphere exchangeable Al contents for both JD and XY, whereas the decrease in Mo rhizosphere pH was not statistically different. For same maize cultivar, NO3-N fertilizer application significantly (p < 0.05) increased rhizosphere pH and decreased rhizosphere exchangeable Al contents, as compared with NH4+-N treatment. The rhizosphere soils showed lower total N (TN), NH4+-N, NO3N, and AK contents and a higher C/N ratio and dissolved organic C (DOC) content, although individual values did not reached statistical differences (p < 0.05). Although the SOC concentration was higher in the rhizosphere than that in the bulk soil, the increase in NO3-treated Mo reached a statistical difference (p < 0.05). In addition, TC content in the rhizosphere of NO3-treated Mo was also significantly higher (p < 0.05) than both JD and XY.
Table 1

Soil pH, exchangeable Al, and nutrient availability in the bulk and rhizosphere soils of three maize cultivars after 42 days of cultivation with an NH4+-N or NO3-N fertilizer

Soil properties

N fertilizer

Bulkab

Rhizosphere

Non-parametric two-way analysis of variance statisticc

JD

XY

Mo

pH

NH4+-N

4.62 ± 0.06a

4.52 ± 0.06b

4.51 ± 0.03b

4.57 ± 0.02ab

N: F = 267.41, p = 0.000; S: F = 14.55, p = 0.000

NO3-N

4.58 ± 0.06b

4.86 ± 0.03ab

4.89 ± 0.05a

4.91 ± 0.10a

N × S: F = 41.88, p = 0.000

Al (g kg−1)

NH4+-N

0.25 ± 0.02b

0.37 ± 0.03a

0.35 ± 0.06a

0.33 ± 0.031ab

N: F = 72.31, p = 0.000; S: F = 3.59, p = 0.022

NO3-N

0.25 ± 0.03a

0.22 ± 0.04a

0.22 ± 0.04a

0.24 ± 0.04a

N × S: F = 10.16, p = 0.000

TC (g kg−1)

NH4+-N

4.79 ± 0.16ab

4.97 ± 0.17a

4.70 ± 0.11b

4.84 ± 0.16ab

N: F = 0.00, p = 1.000; S: F = 8.55, p = 0.000

NO3-N

4.79 ± 0.12ab

4.72 ± 0.06b

4.74 ± 0.07b

5.06 ± 0.08a

N × S: F = 8.86, p = 0.000

TN (g kg−1)

NH4+-N

0.73 ± 0.03a

0.63 ± 0.01b

0.60 ± 0.01b

0.67 ± 0.07ab

N: F = 0.84, p = 0.366; S: F = 32.92, p = 0.000

NO3-N

0.72 ± 0.01a

0.61 ± 0.02b

0.62 ± 0.03b

0.67 ± 0.04ab

N × S: F = 1.04, p = 0.387

C/N

NH4+-N

6.53 ± 0.14b

7.85 ± 0.26a

7.80 ± 0.08a

7.23 ± 0.58ab

N: F = 0.75, p = 0.392; S: F = 36.90, p = 0.000

NO3-N

6.69 ± 0.26b

7.78 ± 0.27a

7.66 ± 0.33a

7.60 ± 0.34ab

N × S: F = 1.67, p = 0.189

SOC (g kg−1)

NH4+-N

3.58 ± 0.16a

3.80 ± 0.17a

4.00 ± 0.50a

4.08 ± 0.61a

N: F = 0.54, p = 0.466; S: F = 9.72, p = 0.000

NO3-N

3.38 ± 0.16b

3.77 ± 0.11ab

3.79 ± 0.30ab

4.25 ± 0.40a

N × S: F = 0.96, p = 0.422

DOC (g kg−1)

NH4+-N

0.11 ± 0.01b

0.14 ± 0.01a

0.14 ± 0.00a

0.15 ± 0.01a

N: F = 0.13, p = 0.719; S: F = 9.00, p = 0.000

NO3-N

0.12 ± 0.00b

0.13 ± 0.01ab

0.13 ± 0.01ab

0.14 ± 0.01a

N × S: F = 2.25, p = 0.097

NH4+-N (mg kg−1)

NH4+-N

107.68 ± 14.50a

44.96 ± 3.92b

42.24 ± 11.35b

97.75 ± 11.01ab

N: F = 836.75, p = 0.000; S: F = 69.53, p = 0.000

NO3-N

11.36 ± 0.70a

4.45 ± 0.53b

2.83 ± 0.31b

8.77 ± 2.28a

N × S: F = 44.49, p = 0.000

NO3-N (mg kg−1)

NH4+-N

3.66 ± 0.92a

2.23 ± 0.15b

2.70 ± 0.34ab

3.10 ± 0.43a

N: F = 1435.15, p = 0.000; S: F = 212.26, p = 0.000

NO3-N

103.42 ± 3.20a

35.72 ± 2.32b

26.06 ± 6.44b

35.27 ± 10.94b

N × S: F = 201.07, p = 0.000

DON (mg kg−1)

NH4+-N

12.73 ± 1.12a

17.81 ± 6.36a

15.07 ± 5.75a

15.77 ± 3.48a

N: F = 2.51, p = 0.121; S: F = 1.81, p = 0.161

NO3-N

11.95 ± 2.33a

14.16 ± 2.20a

13.66 ± 1.17a

14.34 ± 2.38a

N × S: F = 0.30, p = 0.826

AK (mg kg−1)

NH4+-N

146.87 ± 7.37a

33.19 ± 9.23c

35.61 ± 8.92c

83.55 ± 11.45b

N: F = 27.45, p = 0.000; S: F = 526.77, p = 0.000

NO3-N

154.71 ± 10.19a

29.58 ± 4.55b

28.37 ± 6.42b

35.01 ± 4.08b

N × S: F = 25.04, p = 0.000

AP (mg kg−1)

NH4+-N

0.65 ± 0.31a

0.84 ± 0.15a

0.68 ± 0.10a

0.87 ± 0.14a

N: F = 2.50, p = 0.122; S: F = 0.93, p = 0.437

NO3-N

0.71 ± 0.21a

0.59 ± 0.10a

0.70 ± 0.13a

0.71 ± 0.17a

N × S: F = 1.99, p = 0.132

aValues are the means ± SD of three pot replicates

bValues within each row followed by different letters indicate significant difference (p < 0.05, Kruskal–Wallis test) under the same N form treatment. The values in bold type for bulk or same cultivar samples indicate significant difference (p < 0.05, Mann–Whitney U test) between the NH4+-N and NO3-N fertilizer treatments

cResults of two-way ANOVA using Scheirer–Ray–Hare test, N: N fertilizer form (NH4+-N and NO3-N); S: soil sites (bulk and rhizospheres of JD, XY, and Mo)

Copy numbers of both nifH and 16S rRNA genes and their correlations with soil variables

Copy numbers of both nifH and 16S rRNA genes in soil samples were quantified by qPCR (Fig. 3). Plant cultivation, but not N fertilizer form or crop cultivars, markedly influenced (p < 0.01) the copy numbers of both nifH and 16S rRNA genes and their ratios. The nifH gene copy numbers were obviously higher (p < 0.05) in the rhizosphere than those in bulk soils, but there were no significant differences between rhizosphere soils (Fig. 3). Although values of total 16S rRNA gene copies in rhizosphere samples increased, as compared with in bulk soils, only those of NH4+-treated JD and Mo and NO3-treated Mo reached statistical differences (p < 0.05). Similarly, the increase in the ratio of nifH to 16S rRNA in rhizosphere samples of both JD and Mo did not reach statistical differences, but the ratio of XY did.
Fig. 3

Copy numbers of both nifH (a) and 16S rRNA genes (b) and the ratio of nifH to 16S rRNA gene copies (c) in the bulk and rhizosphere soils of three maize cultivars after 42 days of cultivation with an NH4+-N or NO3-N fertilizer. Values are the means ± SD of three pot replicates. All data were subjected to a two-way ANOVA using Scheirer–Ray–Hare test, N: N fertilizer form (NH4+-N and NO3-N); S: soil sites (bulk and rhizospheres of JD, XY, and Mo). Different lowercase letters above the white columns indicate significant differences (p < 0.05, Kruskal–Wallis test) under NH4+-N fertilizer treatment; different capital letters above the gray columns indicate significant differences (p < 0.05, Kruskal–Wallis test) under NO3-N fertilizer treatment. The asterisk indicates a significant difference (p < 0.05, Mann–Whitney U test) between N fertilizer treatments

For all soil samples including bulk and rhizosphere soils (Table 2), the copy numbers of both nifH and 16S rRNA genes had significantly positive (p < 0.05) correlations with soil DOC and the C/N ratio and negative (p < 0.05) correlations with TN and AK. In addition, the nifH gene copy number was also positively (p < 0.05) correlated with SOC and significantly negatively correlated with NO3-N. In the bulk samples, the 16S rRNA gene copy number was significantly negatively correlated with DON. If only the rhizosphere samples was analyzed, the copy number of nifH gene or 16S rRNA gene did not show significant correlation with each soil variable (Table 2).
Table 2

Pearson’s correlation coefficients between soil variables and both the nifH and 16S rRNA gene copy numbers

 

pH

Al

TC

TN

C/N

SOC

DOC

NH4+-N

NO3-N

DON

AK

AP

All soil samples including bulk and rhizosphere soils

nifH gene copy number

0.162

0.197

0.138

− 0.573**

0.651**

0.434*

0.737**

− 0.147

− 0.473*

0.387

− 0.705**

0.101

16S rRNA gene copy number

0.079

0.159

0.183

− 0.469*

0.557**

0.271

0.432*

− 0.051

− 0.329

0.375

− 0.522**

− 0.041

The rhizosphere soil samples

nifH gene copy number

− 0.151

0.156

0.030

0.073

− 0.065

0.039

0.350

0.206

− 0.364

0.158

0.245

− 0.075

16S rRNA gene copy number

− 0.129

0.117

0.164

− 0.024

0.138

− 0.106

− 0.058

0.245

− 0.216

0.288

0.222

− 0.116

The bulk soil samples

nifH gene copy number

0.419

0.154

0.220

− 0.131

0.274

− 0.420

− 0.021

− 0.043

0.053

0.417

0.414

0.305

16S rRNA gene copy number

− 0.122

− 0.376

− 0.334

− 0.086

− 0.165

0.287

− 0.164

− 0.119

0.245

− 0.972**

− 0.240

− 0.420

*Significant at p < 0.05

**Significant at p < 0.01

nifH gene presence in the community

The read lengths of each sequences ranged from 285 to 342 bp, with an average of 308 bp. After quality control and the screening of amino acid functions, 67,501 high-quality nifH gene sequences were obtained from 24 soil samples (range 1961–4388), and the corresponding OTU numbers per sample varied between 33 and 64 (defined at a 90% similarity cutoff). Rarefaction analysis suggested that there was a large variation in the OTU numbers among samples (Fig. S2), but the further analysis showed that the N fertilizer form or maize cultivation did not significantly affected OTU numbers at the same sequencing depth (1960 sequences per sample) (Fig. S3). Based on OTU sequences, diazotroph species were identified using BLAST. Except for unclassified diazotroph species that contained less than 3% of the total nifH gene sequences in each sample, all of the diazotroph strains were from phyla Proteobacteria. The dominant genera across all of the soil samples were Bradyrhizobium, Rhodopseudomonas, Methylocella, Azorhizobium, Xanthobacter, Methylosinus, Azospirillum, Azonexus, Azohydromonas, and Burkholderia (> 1%), and they accounted for more 95% of the total nifH gene sequences in each sample (Fig. 4). Bradyrhizobium was the most common genus present in the samples, ranging from 33.0–55.4% of the total diazotrophs. The relative abundances of the genera Bradyrhizobium, Rhodopseudomonas, Methylocella, Azorhizobium, Azospirillum, and Azonexus were significantly different among the samples (Table S2). Regardless of the N fertilizer form, the rhizosphere soils of XY had the highest relative abundances of Bradyrhizobium and Rhodopseudomonas. The genus Methylocella in bulk soils occupied 45.6–46.2% of the total diazotroph sequences, but there was a significantly decreased relative abundance in rhizosphere soils (6.8–15.2%). The relative abundances of both Azorhizobium and Azonexus in the rhizosphere soils of JD were significantly higher than in other samples. The genus Azospirillum occupied rather high proportions (37.5–52.7%) in the rhizosphere soils of Mo, which were significantly higher than in other samples (2.0–5.2%).
Fig. 4

Relative abundances of the ten most abundant diazotrophic genera (> 1%) in the soil samples. Error bars represent the standard deviations of three replicates. The statistical differences for each genus across all of the samples are shown in Table S2

A hierarchical cluster analysis showed that diazotroph community composition of bulk samples was clearly separated from those of the rhizosphere soils, whereas the samples from the same cultivar’s rhizosphere or bulk soils were not separated obviously by the form of N fertilizers (Fig. 5). Additionally, the communities from the rhizosphere soils of both JD and XY were grouped together, separated from those of Mo. This was further confirmed by ANOSIM calculations (Table S3), in which significant differences (p < 0.01) in the communities were observed between bulk and rhizosphere samples, but significant differences were not found between the two N fertilizer treatments. The rhizosphere samples of three maize cultivars were different from each other, but the difference between Mo and JD or XY was greater distinct than that between JD and XY (Table S3). These results indicated that the factor influencing diazotroph communities is the maize cultivar, not the form of the N fertilizer.
Fig. 5

Hierarchical cluster analysis of the diazotrophic communities from the bulk soil and rhizospheres of three maize cultivars under an NH4+-N or NO3-N fertilizer treatment. Black indicates the samples which were subjected to the NH4+-N fertilizer treatment, and red indicates the samples which were subjected to the NO3-N fertilizer treatment

Relationships between the nifH gene community structure and soil variables

The Mantel test showed that multiple soil variables were clearly related (p < 0.01 or p < 0.05) to the nifH gene community structure, and the correlation coefficients were exhibited in the order: AK > NO3-N > C/N > TN > DOC > SOC > TC (Table S4). The CCA analysis revealed that there was an explanation of 39.38% of these selected variables in the overall diazotrophic community structure. The first two constrained axes could explain 23.86% of the total variation, with 14.24% for first axis and 9.62% for the second axis (Fig. 6). The bulk samples were closely associated with the higher contents of AK, NO3N, and TN and were separated from those of rhizosphere soils along the first axis. The communities of Mo rhizosphere samples were positively related to TC, DOC, and SOC, while the C/N mainly contributed to the rhizosphere samples of JD and XY. These two groups of rhizosphere samples were separated along the second axis.
Fig. 6

Canonical correspondence analysis (CCA) of the diazotroph community structure and soil variables across all of the soil samples. The direction of the corresponding arrow indicates the steepest increase in the variation, and the length indicates the relative importance in explaining the variation. Black indicates the samples which were subjected to the NH4+-N fertilizer treatment, and red indicates the samples which were subjected to the NO3-N fertilizer treatment

The relative abundances of diazotroph genera from all soil samples exhibited different correlation patterns with soil variables (Table 3). The genera Bradyrhizobium and Azorhizobium were positively correlated with the C/N and negatively correlated with TN, and Bradyrhizobium was also negatively correlated with NH4+-N and AK. Both Rhodopseudomonas and Xanthobacter were negatively correlated with TC. Methylocella had a positive correlation with TN, NO3-N, and AK and a negatively correlation with C/N, SOC, DOC, and DON. The relative abundances of Methylosinus and Azospirillum were positively correlated with DOC, while Methylosinus was also positively correlated with SOC. Azohydromonas was positively correlated with SOC, while Burkholderia was negatively correlated with TN. Focusing only on the rhizosphere soils, the correlation patterns changed in part (Table S5). Bradyrhizobium was only negatively correlated with NH4+-N and AK. Methylocella would be negatively correlated with TC. Azospirillum had a positive correlation with TN, NH4+-N, and AK and a negatively correlation with C/N.
Table 3

Pearson’s correlation coefficient between soil variables and the relative abundances of the dominant diazotrophic genera for all soil samples

 

pH

Al

TC

TN

C/N

SOC

DOC

NH4+-N

NO3-N

DON

AK

AP

Bradyrhizobium

0.182

0.124

− 0.113

− 0.577**

0.566**

0.141

0.219

− 0.492*

− 0.116

0.320

− 0.580**

− 0.209

Rhodopseudomonas

0.288

− 0.246

− 0.466*

− 0.382

0.214

− 0.012

− 0.057

− 0.230

− 0.184

− 0.125

− 0.363

− 0.111

Methylocella

− 0.262

− 0.133

− 0.256

0.693**

− 0.821**

− 0.587**

− 0.748**

0.231

0.458*

− 0.410*

0.874**

− 0.091

Azorhizobium

0.045

0.212

− 0.124

− 0.481*

0.469*

− 0.145

0.081

− 0.269

− 0.095

0.186

− 0.386

− 0.144

Xanthobacter

− 0.151

0.025

− 0.424*

− 0.137

− 0.023

− 0.401

− 0.319

− 0.196

0.248

− 0.134

0.098

− 0.181

Methylosinus

0.306

− 0.262

− 0.121

− 0.184

0.128

0.390

0.418*

− 0.263

− 0.075

0.311

− 0.371

− 0.158

Azospirillum

0.112

− 0.132

0.388

0.125

0.016

0.447*

0.433*

0.271

− 0.160

0.109

− 0.062

0.292

Azonexus

− 0.062

0.355

0.256

− 0.247

0.352

− 0.019

0.169

− 0.178

− 0.121

0.250

− 0.346

0.010

Azohydromonas

− 0.116

0.144

− 0.052

− 0.256

0.243

0.421*

0.145

− 0.095

− 0.164

− 0.307

− 0.278

0.095

Burkholderia

− 0.210

0.238

− 0.120

− 0.420*

0.400

0.139

0.109

− 0.071

− 0.275

− 0.217

− 0.325

− 0.109

*Significant at p < 0.05

**Significant at p < 0.01

Discussion

Compared with the NH4+-N fertilizer, the NO3-N fertilizer application significantly increased the maize biomass and root development in acidic soil (Figs. 1 and 2), which was in agreement with other studies (Masud et al. 2014; Mehmood et al. 2017; Tang et al. 2011). In this trial, soil physicochemical properties influenced by two N fertilizer forms included soil pH, exchangeable Al, NH4+-N, and NO3-N contents (Table 1). It has been reported that maize biomass was higher with NO3-N nutrition than NH4+-N nutrition in the presence of equimolar concentrations of the single N form (Cramer and Lewis 1993; Jackson and Volk 1995). Furthermore, the ameliorated Al toxicity level in the NO3-added rhizosphere soil could also contribute to the improved maize growth, because of the sensitivity of maize species to Al stress (Ma et al. 2014). Likewise, Mehmood et al. (2017) reported that under NO3-N fertilizer treatment, the decreased rhizosphere exchangeable Al content improved maize growth, and there was a significant negative correlation between rhizosphere exchangeable Al level and maize biomass. As the apparent stress symptom of Al phytotoxicity, root growth inhibition was effectively alleviated (Fig. 2), further indicating the role of NO3-N fertilizer in alleviating rhizosphere Al toxicity. It can be suggested that applying NO3-N fertilizer in acidic soil should be a potential strategy to reduce Al toxicity and improve plant growth.

In spite of improving rhizosphere environment, NO3-N fertilizer application relative to NH4+-N fertilizer did not significantly alter soil diazotroph populations (Figs. 3, 5, and 6 and Table S3). There is a highly negative correlation between soil pH and exchangeable Al content in acidic soil (Mokolobate and Haynes 2002). Although these two soil variables have been frequently reported to affect soil microbial processes in many ecosystems (Kunito et al. 2016; Nelson and Mele 2006; Pereira e Silva et al. 2013), they had little impacts on both rhizosphere diazotroph abundance (Table 2) and community compositions (Fig. 6 and Table S4) in the current experiment. It is likely that the fluctuations in the maize rhizosphere pH and exchangeable Al content caused by the two N fertilizers were so narrow that they were not enough to cause the change. Similarly, our previous study found that the low changes in soil pH and exchangeable Al content in acidic soil did not lead to variations in the dominant bacterial community (Wang et al. 2013). Zhao et al. (2016) suggested that the limited pH range did not explain the relationship between soil pH and bacterial populations. In addition, high NH4+-N and NO3-N concentrations have also been reported to cause the negative effects on diazotrophic bacteria (Pereira e Silva et al. 2013; Lindsay et al. 2010), but these two nitrogenous nutrients did not lead to the obviously different influences on the abundance of the diazotroph community in the current acid soil. A possible explanation is that, for the overall community structure, the greater influence of plants on diazotroph populations concealed the effects of N fertilizer forms, which were well illustrated in Figs. 5 and 6. Although the higher biomass may lead to the higher amount of root exudates, they would be distributed over the greater rhizosphere region under NO3-N fertilizer treatment (Fig. 2). As a result, the higher C concentration was not observed in the NO3-added cultivar rhizosphere (Table 1), which may limit the increase in bacterial abundance. Accordingly, although NO3-N fertilizer instead of NH4+-N fertilizer could improve plant growth in acidic soil, such effect was not reflected in rhizosphere diazotrophs.

Plant Al tolerance has been known to be important for plant growth in acidic soil (Kochian et al. 2004; Ma et al. 2014), as Al-tolerant maize cultivars obtained higher biomasses than Al-sensitive cultivar (Fig. 1). First, maize cultivation significantly influenced the diazotroph populations (Figs. 3, 5, and 6), which were in line with other studies (Chaudhary et al. 2015; Rodríguez-Blanco et al. 2015). It may be mainly attributed to plant root exudates, which are recognized as key determinants for rhizobacterial proliferation (Hinsinger et al. 2009). This supported our correlation analysis, in which the diazotroph abundance was positively correlated with C availability (DOC, SOC, and C/N) (Table 2). However, a statistical difference in the diazotrophic abundance levels was not seen among the rhizosphere samples of Al-tolerant and Al-sensitive cultivars (Fig. 4), which did not support our hypothesis that Al-tolerant maize cultivars may have higher diazotrophic abundance levels in their rhizospheres than the Al-sensitive cultivar, because of more organic acid anions secreted by plant roots (Piñeros et al. 2005). This can be explained by a lack of significant differences in both SOC and DOC concentrations (Table 1) and the uncorrelation between diazotrophic abundance and C availability (Table 2) across all of the rhizosphere samples. This was consistent with our previous study (Wang et al. 2013), in which wheat Al tolerances did not cause significant difference between the total bacterial abundance. In contrast, Li et al. (2012) reported that a higher abundance of nifH gene was detected in the rhizosphere of an Al-sensitive soybean cultivar when compared with an Al-tolerant cultivar at the seedling stage and explained that the increase in organic acid anions secreted by the Al-tolerant cultivar might participate in the chelation of phytotoxic Al, leaving little to be utilized by the rhizosphere’s microorganisms. Another explanation may be that the high level of organic acid secretion in Al-tolerant cultivars can only be utilized by a small number of diazotrophic strains, and this number was not great enough to cause significant changes in the total diazotrophic abundance.

However, the response patterns of the diazotroph community structure to maize cultivars was distinguished from their total abundance levels because there was a separation in the rhizosphere’s diazotrophic communities (Fig. 5 and Table S3). Although rhizosphere diazotrophic communities of three maize cultivars were different from each other, the greater difference appeared between Al-tolerant and Al-sensitive cultivars (Fig. 5 and Table S3), suggesting that maize trait with Al tolerance may be an important cause of altering diazotroph community. A similar result was obtained with soybean in the study of Li et al. (2012). The root exudates are also considered as main drivers of the microbial community composition in plant–microorganism interactions (Li et al. 2016; Hinsinger et al. 2009), leading plants to select specific microbial populations in their rhizosphere (Rodríguez-Blanco et al. 2015). Thus, the amounts of root exudates may be responsible for the total microbial abundance, while the composition of the root exudates is related to community composition. Burgmann et al. (2005) reported that different C source composition in root exudates induced different levels of growth and activation of diazotrophic strains. Correspondingly, the plant Al tolerance is closely associated with the different compositions of root exudates, with higher levels of Al-related chelates existing in Al-tolerant cultivars (Kochian et al. 2004). Alternatively, some secondary metabolites in root exudates can act as signal molecules that exhibit great stimulatory or inhibitory effects on the microbial community composition (Berg and Smalla 2009; Li et al. 2016). For instance, the secretion of isoflavones in soybean roots specifically attracts the diazotroph genus Bradyrhizobium (Berg and Smalla 2009), and flavonoids secreted by maize roots can significantly increase the expression of genes that mediate N fixation (Li et al. 2016). The flavonoid’s exudation from roots is a part of the plant’s Al-tolerance strategy (Barceló and Poschenrieder 2002). In addition, the increased ratio of nifH to 16S rRNA gene copies in rhizosphere samples (Fig. 3) could imply that some metabolites from the root exudates specifically stimulate the growth of diazotrophic bacteria. Thus, the secretions in response to Al toxicity should be the important factor stimulating rhizosphere diazotrophic species.

Another factor that cannot be ignored is soil nutrient availability. In the present study, DOC and SOC showed strong influences on the diazotroph community structure (Fig. 6 and Table S4) but were only positively related to the relative abundances of three genera, Methylosinus, Azospirillum, and Azohydromonas, whereas changes in the relative abundances of four genera, Bradyrhizobium, Methylocella, Azorhizobium, and Burkholderia, were positively or negatively associated with TN, NH4+-N, and AK (Table 3). Furthermore, the lower contents of TN, NH4+-N, and AK were observed in the rhizospheres of Al-tolerant cultivars with greater growth rates, and these soil nutrients had the closely correlation with Bradyrhizobium, Azorhizobium, and Azospirillum in the rhizosphere samples (Table S5), suggesting that they contributed to the shift in the rhizosphere diazotrophic communities induced by plant Al tolerance. Therefore, the influence of plant Al tolerance on acidic soil diazotrophs is a complex process. Further research should clarify the contributions of root exudates and soil nutrients to the rhizosphere’s microbial population. These results also suggested that the use of diazotrophic community should be a better parameter than total abundance to analyze the response of soil diazotrophs to different maize cultivars in acidic soil.

Diazotrophs are found in more than 30 genera that are distributed among the phyla Proteobacteria, Cyanobacteria, and Firmicutes (Rösch et al. 2002). All of the diazotrophs detected in the present acidic soil were from the phylum Proteobacteria. Because different plant species favor distinct beneficial diazotrophic species (Mutch and Young 2004), it is important to select suitable diazotrophic strains to be used as plant inoculants. The relative abundances of genera Bradyrhizobium and Rhodopseudomonas significantly increased in the rhizosphere of XY, indicating that XY was preferential for these two genera. The strains related to genus Bradyrhizobium are found to have high survival capacity in acidic soil (Appunu and Dhar 2006; Li et al. 2012), in which they contribute to N fixation under both symbiotic and non-symbiotic conditions (Pereira e Silva et al. 2013) and even perform plant growth-promoting rhizobacterial functions (Coelho et al. 2008). Inoculants of Rhodopseudomonas strains promote plant growth under low fertilizer inputs (Wong et al. 2014). Members of the Azorhizobium and Azonexus genera showed high relative abundances in the rhizosphere of JD, possibly because of their symbiotic relationship with plants (Garg and Geetanjali 2007). The predominated diazotrophic strains in the rhizosphere of Mo were from the genus Azospirillum, which has the potential to improve plant growth (Rodrigues et al. 2008). Li et al. (2012) also found Azospirillum-specific responses to Al-sensitive soybean cultivars. These diazotrophic species identified in each maize rhizosphere could provide a better understanding of the changes in plant-diazotroph interactions in acidic soil.

Conclusions

The present pot experiment showed that although both N fertilizer form and maize Al tolerance play the important roles in influencing maize growth in acidic soil, maize Al tolerance rather than N fertilizer form significantly affects rhizosphere diazotroph composition but not their overall abundance. The change of soil diazotroph community was closely related to the C and nutrient availability, which suggested that the different impact of maize cultivars on diazotroph community should be driven by the different root exudate components and nutrient uptake. Future work should be needed to investigate which root exudate components specifically stimulate soil diazotrophic species. Considering the sensitivity of soil diazotrophs to plants, the diazotrophic strain applied as a plant inoculant would depend on the plant cultivar.

Notes

Funding information

This research was funded by the National Key Basic Research Program of China (No. 2014CB441002), the “Strategic Priority Research Program” of the Chinese Academy of Sciences (No. XDB15030000), the Natural Science Foundation of China (No. 41501328), and the Frontier Field Project of the Chinese Academy of Sciences (No. ISSASIP1638).

Supplementary material

11104_2017_3550_MOESM1_ESM.docx (348 kb)
ESM 1 (DOCX 348 kb)

References

  1. Appunu C, Dhar B (2006) Symbiotic effectiveness of acid-tolerant Bradyrhizobium strains with soybean in low pH soil. Afr J Biotechnol 5:842–845Google Scholar
  2. Baldridge MT, Nice TJ, McCune BT, Yokoyama CC, Kambal A, Wheadon M, Diamond MS, Ivanova Y, Artyomov M, Virgin HW (2015) Commensal microbes and interferon-lambda determine persistence of enteric murine norovirus infection. Science 347:266–269CrossRefPubMedGoogle Scholar
  3. Barceló J, Poschenrieder C (2002) Fast root growth responses, root exudates, and internal detoxification as clues to the mechanisms of aluminium toxicity and resistance: a review. Environ Exp Bot 48:75–92CrossRefGoogle Scholar
  4. Berg G, Smalla K (2009) Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol Ecol 68:1–13CrossRefPubMedGoogle Scholar
  5. Bian DH, Jia GP, Cai LJ, Ma ZY, Eneji AE, Cui YH (2016) Effects of tillage practices on root characteristics and root lodging resistance of maize. Field Crop Res 185:89–96CrossRefGoogle Scholar
  6. Burgmann H, Meier S, Bunge M, Widmer F, Zeyer J (2005) Effects of model root exudates on structure and activity of a soil diazotroph community. Environ Microbiol 7:1711–1724CrossRefPubMedGoogle Scholar
  7. Chaudhary DR, Gautam RK, Yousuf B, Mishra A, Jha B (2015) Nutrients, microbial community structure and functional gene abundance of rhizosphere and bulk soils of halophytes. Appl Soil Ecol 91:16–26CrossRefGoogle Scholar
  8. Coelho MRR, Vos MD, Carneiro NP, Marriel IE, Paiva E, Seldin L (2008) Diversity of nifH gene pools in the rhizosphere of two cultivars of sorghum (Sorghum bicolor) treated with contrasting levels of nitrogen fertilizer. FEMS Microbiol Lett 279:15–22CrossRefPubMedGoogle Scholar
  9. Cramer MD, Lewis OAM (1993) The influence of nitrate and ammonium nutrition on the growth of wheat (Triticum aestivum) and maize (Zea mays) plants. Ann Bot 72:359–365CrossRefGoogle Scholar
  10. Doerge TA, Gardner EH (1985) Reacidification of two lime amended soils in western Oregon. Soil Sci Soc Am J 49:680–685CrossRefGoogle Scholar
  11. Dytham C (2011) Choosing and using statistics: a biologist’s guide. Wiley-Blackwell, OxfordGoogle Scholar
  12. Fox AR, Soto G, Valverde C, Russo D, Laqares A Jr, Zorreguieta Á, Alleva K, Pascuan C, Frare R, Mercado-Blanco J, Dixon R, Ayub ND (2016) Major cereal crops benefit from biological nitrogen fixation when inoculated with the nitrogen-fixing bacterium Pseudomonas protegens Pf-5 X940. Environ Microbiol 18:3522–3534CrossRefPubMedGoogle Scholar
  13. Gaby JC, Buckley DH (2014) A comprehensive aligned nifH gene database: a multipurpose tool for studies of nitrogen-fixing bacteria. Database (Oxford) 2014:bau001CrossRefGoogle Scholar
  14. Garg N, Geetanjali (2007) Symbiotic nitrogen fixation in legume nodules: process and signaling. A review. Agron Sustain Dev 27:59–68CrossRefGoogle Scholar
  15. Giannakoula A, Moustakas M, Mylona P, Papadakis I, Yupsanis T (2008) Aluminum tolerance in maize is correlated with increased levels of mineral nutrients, carbohydrates and proline, and decreased levels of lipid peroxidation and Al accumulation. J Plant Physiol 165:385–396CrossRefPubMedGoogle Scholar
  16. Guo JH, Liu XJ, Zhang Y, Shen JL, Han WX, Zhang WF, Christie P, Goulding KWT, Vitousek PM, Zhang FS (2010) Significant acidification in major Chinese croplands. Science 327:1008–1010CrossRefPubMedGoogle Scholar
  17. Hinsinger P, Bengough AG, Vetterlein D, Young IM (2009) Rhizosphere: biophysics, biogeochemistry and ecological relevance. Plant Soil 321:117–152CrossRefGoogle Scholar
  18. Hsu SF, Buckley DH (2009) Evidence for the functional significance of diazotroph community structure in soil. Isme J 3:124–136CrossRefPubMedGoogle Scholar
  19. Huang J, Xu X, Wang M, Nie M, Qiu S, Wang Q, Quan Z, Xiao M, Li B (2016) Responses of soil nitrogen fixation to Spartina alterniflora invasion and nitrogen addition in a Chinese salt marsh. Sci Rep 6:20384CrossRefPubMedPubMedCentralGoogle Scholar
  20. Hungria M, Campo RI, Souza EM, Pedrosa FO (2010) Inoculation with selected strains of Azospirillum brasilense and A. lipoferumim-proves yields of maize and wheat in Brazil. Plant Soil 331:413–425CrossRefGoogle Scholar
  21. Jackson WA, Volk RJ (1995) Attributes of the nitrogen uptake systems of maize (Zea mays L.): maximal suppression by exposure to both nitrate and ammonium. New Phytol 130:327–335CrossRefGoogle Scholar
  22. Kochian LV, Hoekenga OA, Piñeros MA (2004) How do crop plants tolerate acid soils? Mechanisms of aluminum tolerance and phosphorous efficiency. Annu Rev Plant Biol 55:459–493CrossRefPubMedGoogle Scholar
  23. Kunito T, Isomura I, Sumi H, Park HD, Toda H, Otsuka S, Nagaoka K, Saeki K, Senoo K (2016) Aluminum and acidity suppress microbial activity and biomass in acidic forest soils. Soil Biol Biochem 97:23–30CrossRefGoogle Scholar
  24. Li B, Li YY, Wu HM, Zhang FF, Li CJ, Li XX, Lambers H, Li L (2016) Root exudates drive interspecific facilitation by enhancing nodulation and N2 fixation. P Natl Acad Sci USA 113:6496–6501CrossRefGoogle Scholar
  25. Li Y, Yang T, Zhang P, Zou A, Peng X, Wang L, Yang R, Qi J, Yang Y (2012) Differential responses of the diazotrophic community to aluminum-tolerant and aluminum-sensitive soybean genotypes in acidic soil. Eur J Soil Biol 53:76–85CrossRefGoogle Scholar
  26. Lindsay EA, Colloff MJ, Gibb NL, Wakelin SA (2010) The abundance of microbial functional genes in grassy woodlands is influenced more by soil nutrient enrichment than by recent weed invasion or livestock exclusion. Appl Environ Microbiol 76:5547–5555CrossRefPubMedPubMedCentralGoogle Scholar
  27. Liu GD, Wu WL, Zhang J (2005) Regional differentiation of non-point source pollution of agriculture-derived nitrate nitrogen in groundwater in northern China. Agric Ecosyst Environ 107:211–220CrossRefGoogle Scholar
  28. Liu T, Zhu LS, Wang JH, Wang J, Zhang J, Sun X, Zhang C (2015) Biochemical toxicity and DNA damage of imidazolium-based ionic liquid with different anions in soil on Vicia faba seedlings. Sci Rep 5:18444CrossRefPubMedPubMedCentralGoogle Scholar
  29. Lu RK (1999) Soil and agricultural chemical analysis methods. Chinese Agriculture and Sciences Press, BeijingGoogle Scholar
  30. Lugtenberg B, Kamilova F (2009) Plant-growth-promoting rhizobacteria. Annu Rev Microbiol 63:541–556CrossRefPubMedGoogle Scholar
  31. Ma JF (2007) Syndrome of aluminum toxicity and diversity of aluminum resistance in higher plants. Int Rev Cytol 264:225–252CrossRefPubMedGoogle Scholar
  32. Ma JF, Chen ZC, Shen RF (2014) Molecular mechanisms of Al tolerance in gramineous plants. Plant Soil 381:1–12CrossRefGoogle Scholar
  33. Magoč T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:2957–2963CrossRefPubMedPubMedCentralGoogle Scholar
  34. Mariano ED, Pinheiro AS, Garcia EE, Keltjens WG, Jorge RA, Menossi M (2015) Differential aluminium-impaired nutrient uptake along the root axis of two maize genotypes contrasting in resistance to aluminium. Plant Soil 388:323–335CrossRefGoogle Scholar
  35. Masud MM, Guo D, Li JY, Xu RK (2014) Hydroxyl release by maize (Zea mays L.) roots under acidic conditions due to nitrate absorption and its potential to ameliorate an acidic Ultisol. J Soils Sediments 14:845–853CrossRefGoogle Scholar
  36. Mehmood K, Li JY, Jiang J, Masud MM, Xu RK (2017) Effect of low energy-consuming biochars in combination with nitrate fertilizer on soil acidity amelioration and maize growth. J Soils Sediments 17:790–799CrossRefGoogle Scholar
  37. Mirza BS, Potisap C, Nüsslein K, Bohannan BJ, Rodrigues JL (2014) Response of free-living nitrogen-fixing microorganisms to land use change in the Amazon rainforest. Appl Environ Microbiol 80:281–288CrossRefPubMedPubMedCentralGoogle Scholar
  38. Mokolobate MS, Haynes RJ (2002) Increases in pH and soluble salts influence the effect that additions of organic residues have on concentrations of exchangeable and soil solution aluminium. Eur J Soil Sci 53:481–489CrossRefGoogle Scholar
  39. Montañez A, Abreu C, Gill PR, Hardarson G, Sicardi M (2009) Biological nitrogen fixation in maize (Zea mays L.) by 15N isotope-dilution and identification of associated culturable diazotrophs. Biol Fertil Soils 45:253–263CrossRefGoogle Scholar
  40. Mutch LA, Young JP (2004) Diversity and specificity of Rhizobium leguminosarum biovar viciae on wild and cultivated legumes. Mol Ecol 13:2435–2444CrossRefPubMedGoogle Scholar
  41. Nelson DR, Mele PM (2006) The impact of crop residue amendments and lime on microbial community structure and nitrogen-fixing bacteria in the wheat rhizosphere. Aust J Soil Res 44:319–329CrossRefGoogle Scholar
  42. Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30:3123–3124CrossRefPubMedPubMedCentralGoogle Scholar
  43. Pereira e Silva MC, Semenov AV, van Elsas JD, Salles JF (2011) Seasonal variations in the diversity and abundance of diazotrophic communities across soils. FEMS Microbiol Ecol 77:57–68CrossRefPubMedGoogle Scholar
  44. Pereira e Silva MC, Schloter-Hai B, Schloter M, van Elsas JD, Salles JF (2013) Temporal dynamics of abundance and composition of nitrogen-fixing communities across agricultural soils. PLoS One 8:e74500CrossRefPubMedPubMedCentralGoogle Scholar
  45. Pietri JCA, Brookes PC (2008) Relationships between soil pH and microbial properties in a UK arable soil. Soil Biol Biochem 40:1856–1861CrossRefGoogle Scholar
  46. Piñeros MA, Shaff JE, Manslank HS, Alves VM, Kochian LV (2005) Aluminum resistance in maize cannot be solely explained by root organic acid exudation. A comparative physiological study. Plant Physiol 137:231–241CrossRefPubMedPubMedCentralGoogle Scholar
  47. Rodríguez-Blanco A, Sicardi M, Frioni L (2015) Plant genotype and nitrogen fertilization effects on abundance and diversity of diazotrophic bacteria associated with maize (Zea mays L.) Biol Fertil Soils 51:391–402CrossRefGoogle Scholar
  48. Rodrigues EP, Rodrigues LS, de Oliveira ALM, Baldani VLD, Teixeira KRD, Urquiaga S, Reis VM (2008) Azospirillum amazonense inoculation: effects on growth, yield and N2 fixation of rice (Oryza sativa L.) Plant Soil 302:249–261CrossRefGoogle Scholar
  49. Roeselers G, Stal LJ, van Loosdrecht MCM, Muyzer G (2007) Development of a PCR for the detection and identification of cyanobacterial nifD genes. J Microbiol Methods 70:550–556CrossRefPubMedGoogle Scholar
  50. Rösch C, Mergel A, Bothe H (2002) Biodiversity of denitrifying and dinitrogen-fixing bacteria in an acid forest soil. Appl Environ Microbiol 68:3818–3829CrossRefPubMedPubMedCentralGoogle Scholar
  51. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541CrossRefPubMedPubMedCentralGoogle Scholar
  52. Scott BJ, Conyers MK, Poile GJ, Cullis BR (1997) Subsurface acidity and liming affect yield of cereals. Aust J Agric Res 48:843–854CrossRefGoogle Scholar
  53. Sims JR, Haby VA (1971) Simplified colorimetric determination of soil organic matter. Soil Sci 112:137–141CrossRefGoogle Scholar
  54. Smil V (1999) Nitrogen in crop production: an account of global flows. Glob Biogeochem Cycles 13:647–662CrossRefGoogle Scholar
  55. Tang CX, Conyers MK, Nuruzzaman M, Poile GJ, Liu DL (2011) Biological amelioration of subsoil acidity through managing nitrate uptake by wheat crops. Plant Soil 338:383–397CrossRefGoogle Scholar
  56. von Uexküll HR, Mutert E (1995) Global extent, development and economic impact of acid soils. Plant Soil 171:1–15CrossRefGoogle Scholar
  57. Wakelin SA, Gupta VVSR, Forrester ST (2010) Regional and local factors affecting diversity, abundance and activity of free-living, N2-fixing bacteria in Australian agricultural soils. Pedobiologia 53:391–399CrossRefGoogle Scholar
  58. Wang C, Zhao XQ, Chen RF, Chu HY, Shen RF (2013) Aluminum tolerance of wheat does not induce changes in dominant bacterial community composition or abundance in an acidic soil. Plant Soil 367:275–284CrossRefGoogle Scholar
  59. Wong WT, Tseng CH, Hsu SH, Lur HS, Mo CW, Huang CN, Hsu SC, Lee KT, Liu CT (2014) Promoting effects of a single Rhodopseudomonas palustris inoculant on plant growth by Brassica rapa chinensis under low fertilizer input. Microbes Environ 29:303–313CrossRefPubMedPubMedCentralGoogle Scholar
  60. Zhao J, Ni T, Li J, Lu Q, Fang ZY, Huang QW, Zhang RF, Li R, Shen B, Shen QR (2016) Effects of organic-inorganic compound fertilizer with reduced chemical fertilizer application on crop yields, soil biological activity and bacterial community structure in a rice-wheat cropping system. Appl Soil Ecol 99:1–12CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil ScienceChinese Academy of SciencesNanjingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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