Applied Microbiology and Biotechnology

, Volume 101, Issue 11, pp 4761–4773 | Cite as

Influence of straw incorporation with and without straw decomposer on soil bacterial community structure and function in a rice-wheat cropping system

  • Jun Zhao
  • Tian Ni
  • Weibing Xun
  • Xiaolei Huang
  • Qiwei Huang
  • Wei Ran
  • Biao Shen
  • Ruifu Zhang
  • Qirong Shen
Environmental biotechnology

Abstract

To study the influence of straw incorporation with and without straw decomposer on bacterial community structure and biological traits, a 3-year field experiments, including four treatments: control without fertilizer (CK), chemical fertilizer (NPK), chemical fertilizer plus 7500 kg ha−1 straw incorporation (NPKS), and chemical fertilizer plus 7500 kg ha−1 straw incorporation and 300 kg ha−1 straw decomposer (NPKSD), were performed in a rice-wheat cropping system in Changshu (CS) and Jintan (JT) city, respectively. Soil samples were taken right after wheat (June) and rice (October) harvest in both sites, respectively. The NPKS and NPKSD treatments consistently increased crop yields, cellulase activity, and bacterial abundance in both sampling times and sites. Moreover, the NPKS and NPKSD treatments altered soil bacterial community structure, particularly in the wheat harvest soils in both sites, separating from the CK and NPK treatments. In the rice harvest soils, both NPKS and NPKSD treatments had no considerable impacts on bacterial communities in CS, whereas the NPKSD treatment significantly shaped bacterial communities compared to the other treatments in JT. These practices also significantly shifted the bacterial composition of unique operational taxonomic units (OTUs) rather than shared OTUs. The relative abundances of copiotrophic bacteria (Proteobacteria, Betaproteobacteria, and Actinobacteria) were positively correlated with soil total N, available N, and available P. Taken together, these results indicate that application of straw incorporation with and without straw decomposer could particularly stimulate the copiotrophic bacteria, enhance the soil biological activity, and thus, contribute to the soil productivity and sustainability in agro-ecosystems.

Keywords

Straw incorporation Straw decomposer Rice-wheat cropping system Low-Middle Yangtze River plain Bacterial community structure 

Introduction

The rice-wheat production system is one of the long-standing cropping systems in China and inevitably produces plenty of crop residue by-product, reaching to 250 million t of rice and wheat straw annually (Liu et al. 2008). Open field burning of crop residues is a common post-harvest practice for disposal of these agricultural residues, which causes serious air pollution and leads to accelerating the soil organic matter losses (Cao et al. 2008; Ladha et al. 2004). Therefore, how to utilize such renewable natural resources effectively and eco-friendly becomes an urgent issue in China.

From a long-term perspective, straw incorporation is a valid practice to alleviate soil degradation, maintain soil fertility, and promote crop yields in intensive agricultural systems (Sun et al. 2015; Tirol-Padre et al. 2005; Wang et al. 2015b). However, straw returning in a short term always increases soil N immobilization and mineralization and thereby causing N deficiency and yield decline (Ladha et al. 2004). Moreover, residues may interface with crop planting and further hamper root penetration in paddy soils (Blanco-Canqui and Lal 2009). In addition, the straw decomposition rate is really low in the field conditions and has gradually restricted its expansion harness in agriculture (Han and He 2010).

It has been recognized that soil microorganisms play crucial roles in agro-ecosystems via participation in maintaining soil health, sustainability, and productivity. They are also important drivers involved in the process of degradation, transformation, and utilization of crop residues (Reddy et al. 2013). For instance, Qin et al. (2015) have reported that application of cellulose-decomposing bacteria (hereby defined as straw decomposer) could effectively hasten the straw decomposition process and thereby improve soil nutrient availability. Accordingly, addition of a straw decomposer seems a promising approach to offset the negative effects from straw incorporation in a short term. Recent research observed that chemical fertilizers plus a low or high amount of straw could significantly increase the bacterial abundance but revealed a similar community structure and diversity when compared to chemical fertilizers only (Sun et al. 2015). Furthermore, straw returning had distinct impacts on the soil bacterial community in the maturation of three different parent materials (Sun et al. 2016). In addition, Qin et al. (2015) demonstrated that maize residues with microbial decomposer had a synergistic effect on soil organic matter contents and plant growth through enhancing soil microbial functional diversity. However, there is still a research gap about how straw incorporation with straw decomposer impacts the soil bacterial community structure and function in a paddy-upland rotation.

To fulfil this, field experiments were performed in a rice-wheat cropping system in the Low-Middle Yangtze River (LMYR) plain to examine the effects of straw incorporation with and without straw decomposer on crop yields, soil physicochemical properties, soil enzymatic activity, microbial biomass, and bacterial community structure.

Materials and methods

Site descriptions

The field experiments were performed in Changshu (CS, 31° 18′ N, 120° 37′ E) and Jintan (JT, 31° 39′ N, 119° 28′ E) city, Jiangsu province, China. These two field sites were all located in the LMYR plain and had a northern subtropical monsoon climate, with similar average annual temperature (~15 °C) and precipitation (~1050 mm). But these two sites differed in soil texture, fertility, and the detailed information with respect to soil type, physicochemical properties, and bacterial community structure at the onset of the experiment that were described by Zhao et al. (2014b).

Experimental design and soil sampling

Four treatments, (1) control without fertilizer (CK); (2) chemical fertilizer (NPK) (conventional dosage for the LMYR plain); (3) chemical fertilizer with 7500 kg ha−1 straw incorporation (NPKS); (4) chemical fertilizer with 7500 kg ha−1 straw incorporation and 300 kg ha−1 straw decomposer (NPKSD), with four replicates in a randomized complete block design, were established in the wheat-growing season in October 2010 at both sites. The nitrogen (N), phosphorus (P), and potassium (K) fertilizer usages in the wheat and rice season were described as by Zhao et al. (2016) and broadcasted either as basal fertilizer or as basal and supplementary fertilizer before wheat sowing or rice transplanting. The straw decomposer used in this study was made by Jiangsu Xintiandi Biological Fertilizer Engineering Center Co., Ltd. by using cellulose-decomposing bacteria (Actinomycetes spp.) and fungi (Aspergillus spp.) with nutrient carrier. The wheat or rice residues (approximately 0.1 m in length) as well as the straw decomposer were evenly incorporated into the topsoil in the NPKS and NPKSD treatments before wheat sowing or rice transplanting. The wheat and rice grains from the entire plot were weighed after air drying.

Soil samples were taken after harvesting of wheat (June 2012) and rice (October 2012) in both sites, as described in detail previously (Zhao et al. 2014a, 2016). The collected soil samples were sieved (2 mm) and subsequently divided into three subsamples. One portion was stored at −80 °C for DNA extraction, another portion was stored at 4 °C for determining microbial biomass, and the reminder was air dried for measuring enzymatic activity and physicochemical properties. The soil physicochemical analyses of each sample were performed by Qiyang Red Soil Experimental Station of the Chinese Academy of Agricultural Sciences.

Enzyme activities and microbial biomass analysis

The activities of three soil enzymes were assayed using air-dried soil samples according to Alef and Nannipieri (1995). Soil cellulase (EC 3.2.1.4) activity was determined using carboxymethyl cellulose as a substrate and glucose as a product after incubation at 37 °C for 72 h. Urease (EC 3.5.1.5) activity was measured by the released ammonium equivalent using the phenol sodium hypochlorite colorimetric method. Alkaline phosphatase (EC 3.1.3.1) activity was evaluated using disodium phenyl phosphate as a substrate and phenol as a product after incubation at 37 °C for 24 h. Soil enzymatic activities were expressed as micrograms of products per gram of dry weight soil mass per day. Microbial biomass carbon (MBC) and nitrogen (MBN) were determined by the chloroform fumigation-K2SO4 extraction method (Brookes et al. 1985; Vance et al. 1987).

Soil genomic DNA extraction and quantification of 16S rRNA gene abundance

For each sample, total genomic DNA was successively isolated in triplicate from 0.5 g fresh soil using the PowerSoil® DNA Isolation Kit (Mo Bio Laboratories, Inc., Carlsbad, CA, USA). The triplicate extracts of each sample were pooled, and the quality and quantity of the DNA were determined with a NanoDrop ND-2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). DNA samples were subsequently diluted to 10 ng μl−1 and stored at −20 °C for further use.

Absolute quantitative PCR was performed in biological triplicates, and each involved three technical replicates with two negative controls on an ABI 7500 cycler (Applied Biosystems, Weiterstadt, Germany) to enumerate the abundance of the bacterial 16S ribosomal RNA (rRNA) gene using the primer set 347F: 5″-GGAGGCAGCAGTRRGGAAT-3″ (Kim et al. 2008) and Bact531R: 5″-CTNYGTMTTACCGCGGCTGC-3″ (Nossa et al. 2010). The 20 μl reaction mixture contained 10 μl of the Premix Ex Taq™ (2×) (Takara Bio Inc., Kyoto, Japan), 0.4 μl of each primer (10 μM), 0.4 μl of ROX Reference Dye II (50×), 2 μl of template DNA, and 6.8 μl of ddH2O. Thermal conditions were set as follows: 30 s at 95 °C for initial denaturation; 40 cycles of 5 s at 95 °C, 34 s at 60 °C. A standard curve with an amplification efficiency of 102.3% and an R2 value of 0.9999 was obtained using serial dilutions of plasmid DNA containing a fragment of the 16S rRNA gene from Bacillus subtilis 168. After the real-time PCR assay, the specificity of the amplification was verified by melting curve analysis and agarose gels electrophoresis. Copy numbers were log10 transformed to normalize the values prior to statistical analysis.

Barcoded pyrosequencing of 16S rRNA gene

To characterize the bacterial composition and community diversity under different treatments, three biological replicates of each treatment were randomly selected for pyrosequencing. Bacterial 16S rRNA gene amplification and barcoded pyrosequencing were performed as previously described by Zhao et al. (2014a). Briefly, triplicate PCR products of each sample were pooled, purified using the PCR Purification Kit (Axygen BioScience, Union City, CA, USA), and quantified using a TBS-380 Mini-Fluorometer (Turner BioSystems, Sunnyvale, CA, USA) after PCR amplification. Equimolar concentrations of the 48 purified amplicons (3 biological replicates × 4 treatments × 2 sampling sites × 2 sampling time points) were pooled together and subjected to unidirectional pyrosequencing at Majorbio Bio-pharm Technology Co., Ltd., on a 454 GS FLX+ instrument (Roche, Basel, Switzerland).

Nucleotide sequence accession numbers

The pyrosequencing-generated data are available in the NCBI Sequence Read Archive (SRA) database under accession number SRP061278.

Analyses of 454 pyrosequencing data

The pyrosequencing data were processed and analyzed using the mothur software package, version 1.32.0 (Schloss et al. 2009), following a previously established procedure (Zhao et al. 2014a, 2014b). Briefly, sequences with a minimum flow length of 450 flows were de-noised by using the PyroNoise algorithm (Quince et al. 2011) and further quality controlled with the default settings. The quality-filtered sequences were then barcode sorted, and the unique sequences were aligned against the SILVA bacterial database (Pruesse et al. 2007). After screening, filtering, and pre-clustering processes, the retained sequences were chimera checked (Edgar et al. 2011) and such were removed from further analysis. To equalize for sampling effort, all samples were then rarefied to 3572 sequences. Operational taxonomic units (OTUs) were defined at 97% nucleotide similarity, resulting in the set of usable OTUs defined as rarefied OTU table. Taxonomy was assigned to bacterial OTUs by using Ribosomal Database Project (RDP) naive Bayesian rRNA Classifier (Wang et al. 2007) for each representative sequence against the RDP reference database with a confidence threshold of 0.80. Phylogenetic tree of all representative sequences was generated by the Clearcut program (Sheneman et al. 2006).

The relative abundance of a given phylogenetic group was set as the number of sequences affiliated with that group divided by the total number of sequences per sample. Bacterial alpha-diversity (observed OTU richness, Sobs; the abundance-based coverage estimator, ACE; Chao1 estimator, Shannon diversity, and Pielou’s evenness) was calculated based either on the rarefied OTU table or both the rarefied OTU table and the phylogenetic tree (Faith’s phylogenetic diversity, Faith’s PD).

Statistical analyses

All statistical analyses were performed using PASW Statistics 18 (SPSS Inc., Chicago, USA) and R (version 3.1.1) (R Development Core Team 2012). Data were tested for normality (Shapiro-Wilk normality test) and homogeneity of variance and were logarithm or square root transformed when necessary to meet the criteria for a normal distribution. In all tests, a P value of <0.05 was considered statistically significant. The significant difference among treatments at each sampling time and site was tested using one-way ANOVA followed by Tukey’s post hoc test.

The rare species in the community data were down-weighted by applying the Hellinger transformation (Legendre and Gallagher 2001). The Bray-Curtis and Euclidean distances were used to construct a community dissimilarity matrix and an environmental dissimilarity matrix, respectively. Redundancy analysis (RDA) was performed to visualize the relationships between bacterial communities and environmental variables. Monte Carlo permutation test was further used to calculate the correlation between bacterial communities and environmental variables. Pearson’s correlation coefficient between abundant phyla (proteobacterial classes) and environmental variables was also calculated.

Results

Crop yields

The yields of winter wheat and summer rice in the different treatments in 2012 are summarized in Table 1. In general, both wheat and rice yields were significantly (P < 0.05) higher in the fertilized treatments than unfertilized control, whereas there were no significant differences among the fertilized treatments, except for the wheat yield in 2012 in Jintan.
Table 1

Wheat and rice yields (t ha−1) under different treatments

Treatment

Jintan

 

Changshu

Winter wheat

Summer rice

 

Winter wheat

Summer rice

CK

2.15 ± 0.15c

5.43 ± 0.60b

 

2.99 ± 0.60b

6.64 ± 0.31b

NPK

5.15 ± 0.33b

9.18 ± 0.47a

 

4.93 ± 0.56a

9.83 ± 0.80a

NPKS

5.71 ± 0.13a

10.25 ± 0.88a

 

5.35 ± 0.13a

10.85 ± 0.83a

NPKSD

5.75 ± 0.26a

9.96 ± 0.67a

 

4.97 ± 0.81a

9.91 ± 0.54a

Values (means ± SD, n = 4) within the same column followed by different letters are significantly different at P < 0.05 according to Tukey’s post hoc test

CK control without fertilizer, NPK chemical fertilizer (conventional dosage), NPKS chemical fertilizer with straw incorporation, NPKSD chemical fertilizer with straw incorporation and straw decomposer

Soil physicochemical characteristics

The selected soil edaphic properties of all samples are outlined in Table 2. Overall, the soil pH and available K (AK) contents differed significantly (P < 0.05) among treatments in the wheat harvest soils in both CS and JT, while the soil available P (AP) concentrations were remarkably (P < 0.05) different in the wheat harvest soils of CS and rice harvest soils of JT, respectively. No significant differences were observed for the soil organic matter (OM), total N (TN), total P (TP), and total K (TK) among treatments. Compared to the CK treatment, fertilization increased the soil nutrient contents especially the available nutrients, whereas there were no significant differences for soil properties among NPK, NPKS, and NPKSD treatments. MANOVA analysis revealed that the soil pH, OM, TN, and TP contents were remarkably (P < 0.05) affected by sampling times and sites, whereas soil available nutrient (available N, AN; available P, AP) contents were notably (P < 0.001) affected by treatment and sampling site (Table 2).
Table 2

Physicochemical characteristics of soils under different treatments from different sampling times and sites

Treatmenta

pH (H2O)

Organic matter (g kg−1)

Total N (g kg−1)

Total P (g kg−1)

Total K (g kg−1)

Available N (mg kg−1)

Available P (mg kg−1)

Available K (mg kg−1)

CS wheat harvest

 CK

7.55 ± 0.05b

17.6 ± 1.6a

1.78 ± 0.02a

1.40 ± 0.06a

18.4 ± 1.5a

119.8 ± 9.4b

15.9 ± 3.3b

104. 3 ± 7.6b

 NPK

7.66 ± 0.03a

17.9 ± 1.0a

1.90 ± 0.07a

1.45 ± 0.02a

20.1 ± 3.3a

158.2 ± 11.2a

23.9 ± 1.5a

136.3 ± 18.2ab

 NPKS

7.72 ± 0.03a

18.7 ± 2.2a

1.91 ± 0.10a

1.45 ± 0.05a

20.2 ± 0.2a

156.1 ± 9.5a

22.8 ± 2.0a

144.0 ± 11.4a

 NPKSD

7.73 ± 0.04a

19.6 ± 1.9a

1.91 ± 0.05a

1.45 ± 0.06a

21.0 ± 0.2a

155.6 ± 9.8a

23.8 ± 1.0a

147.0 ± 14.7a

CS rice harvest

 CK

7.60 ± 0.28a

20.1 ± 4.9a

2.07 ± 0.29a

0.88 ± 0.11a

18.4 ± 4.6a

112.2 ± 7.6b

15.2 ± 4.1a

66.7 ± 12.0a

 NPK

7.80 ± 0.02a

22.1 ± 2.0a

2.03 ± 0.29a

0.95 ± 0.07a

18.6 ± 4.1a

158.1 ± 13.4a

20.8 ± 8.2a

70.8 ± 8.8a

 NPKS

7.68 ± 0.19a

20.4 ± 1.0a

2.17 ± 0.11a

0.91 ± 0.06a

19.8 ± 4.3a

159.1 ± 12.3a

20.1 ± 0.9a

80.0 ± 9.5a

 NPKSD

7.66 ± 0.13a

20.3 ± 3.4a

2.10 ± 0.34a

0.93 ± 0.13a

19.0 ± 3.8a

162.7 ± 10.8a

21.2 ± 5.2a

84.7 ± 4.9a

JT wheat harvest

 CK

6.92 ± 0.02ab

22.8 ± 2.2a

1.36 ± 0.04a

1.05 ± 0.05a

15.2 ± 1.5a

118.0 ± 11.8a

9.7 ± 3.5a

74.7 ± 6.0b

 NPK

6.86 ± 0.04b

23.6 ± 2.6a

1.47 ± 0.09a

1.09 ± 0.12a

15.3 ± 0.9a

136.2 ± 10.4a

12.6 ± 5.2a

83.5 ± 5.8ab

 NPKS

7.11 ± 0.04a

23.3 ± 1.0a

1.55 ± 0.07a

1.07 ± 0.07a

15.4 ± 1.7a

134.2 ± 5.8a

14.7 ± 4.3a

100.3 ± 16.2a

 NPKSD

7.02 ± 0.15ab

23.3 ± 1.4a

1.50 ± 0.09a

1.11 ± 0.07a

15.0 ± 0.3a

137.5 ± 6.3a

14.7 ± 6.8a

98.5 ± 3.9ab

JT rice harvest

 CK

7.11 ± 0.16a

27.0 ± 3.4a

1.50 ± 0.12a

0.61 ± 0.02a

15.5 ± 1.6a

129.3 ± 7.1a

6.4 ± 0.2b

49.7 ± 9.1a

 NPK

7.07 ± 0.11a

29.1 ± 3.7a

1.57 ± 0.10a

0.66 ± 0.04a

15.2 ± 1.6a

135.5 ± 15.9a

14.9 ± 4.0ab

45.5 ± 2.3a

 NPKS

7.15 ± 0.10a

31.2 ± 1.4a

1.69 ± 0.09a

0.71 ± 0.03a

15.4 ± 3.0a

140.9 ± 7.4a

15.6 ± 3.3a

61.0 ± 12.5a

 NPKSD

7.11 ± 0.16a

31.5 ± 2.6a

1.73 ± 0.10a

0.71 ± 0.06a

16.2 ± 2.7a

145.1 ± 14.7a

13.8 ± 4.3ab

58.7 ± 13.6a

Analysis of variance

 Treatment (T)

0.12

0.32

0.08

0.15

0.80

<0.001

0.001

<0.001

 Sampling time (ST)

0.04

<0.001

<0.001

<0.001

0.70

0.27

0.27

<0.001

 Sampling site (SS)

<0.001

<0.001

<0.001

<0.001

<0.001

<0.001

<0.001

<0.001

 T × ST

0.25

0.86

0.87

0.87

0.98

0.82

0.96

0.07

 T × SS

0.20

0.80

0.83

0.83

0.90

0.005

0.91

0.29

 ST × SS

0.10

0.007

0.47

0.47

0.40

0.36

0.41

0.001

 T × ST × SS

0.95

0.34

0.86

0.86

0.90

0.66

0.68

0.85

Values (mean ± SD, n = 3) within the same column followed by different letters indicate significantly different at P < 0.05 according to the Turkey’s post hoc test. P values in italics are statistically significant

aTreatment as described in Table 1

Enzymatic activity and microbial biomass

The soil cellulase, urease, and alkaline phosphatase activities were considerably (P < 0.05) different among the treatments in both sampling times and sites except for the alkaline phosphatase activity in the rice harvest soils of CS (Table 3). The cellulase activity was consistently higher in the NPKS and NPKSD treatments than the CK and NPK treatments in both sampling times and sites, being highest in the NPKSD treatment. The highest urease activity was observed in the NPKS treatment in the wheat harvest soils and the NPKSD treatment in the rice harvest soils in CS, while the trend was opposite in the soils of JT. The highest alkaline phosphatase activity was in the NPKSD treatment in both the wheat and the rice harvest soils in JT, whereas the alkaline phosphatase activity was highest in the NPKS treatment in the wheat harvest soils and the NPK treatment in the rice harvest soils in CS. Treatment, sampling time, sampling site, and most of the interactions between or among them had a significant (P < 0.001) effect on soil cellulase and urease activity. Alkaline phosphatase activity was remarkably (P < 0.05) affected by treatment, sampling site, and all the interaction terms between or among treatment, sampling time, and sampling site (Table 3).
Table 3

Biological characteristics of soils under different treatments from different sampling times and sites

Treatment§

Cellulase activity (mg g−1 day−1)

Urease activity (mg g−1 day−1)

Alkaline phosphatase activity (mg g−1 day−1)

Microbial biomass C (mg kg−1)

Microbial biomass N (mg kg−1)

CS wheat harvest

 CK

0.289 ± 0.023b

3.81 ± 0.26b

56.3 ± 0.8bc

389.3 ± 54.8a

37.6 ± 2.1b

 NPK

0.370 ± 0.030b

4.38 ± 0.20ab

47.2 ± 2.9c

541.2 ± 45.3a

51.3 ± 4.6ab

 NPKS

0.779 ± 0.014a

4.75 ± 0.26a

67.0 ± 1.3a

534.8 ± 43.2a

53.0 ± 3.8a

 NPKSD

0.803 ± 0.006a

4.33 ± 0.16ab

65.3 ± 4.1ab

557.2 ± 63.9a

51.9 ± 2.9a

CS rice harvest

 CK

0.338 ± 0.022c

4.39 ± 0.25b

55.7 ± 3.2a

258.5 ± 35.7a

23.4 ± 2.1a

 NPK

0.453 ± 0.025c

4.33 ± 0.01b

69.2 ± 11.5a

306.1 ± 41.7a

35.3 ± 5.4a

 NPKS

0.849 ± 0.058b

5.53 ± 0.21a

66.4 ± 6.0a

353.1 ± 28.7a

35.9 ± 6.3a

 NPKSD

1.139 ± 0.061a

5.90 ± 0.07a

63.7 ± 4.4a

323.9 ± 38.7a

33.5 ± 4.6a

JT wheat harvest

 CK

0.327 ± 0.030c

6.15 ± 0.11b

9.3 ± 0.4b

358.7 ± 93.3a

21.4 ± 6.8b

 NPK

0.603 ± 0.077b

6.69 ± 0.27b

10.6 ± 0.7ab

460.6 ± 38.8a

28.6 ± 2.4ab

 NPKS

0.796 ± 0.033a

6.92 ± 0.47ab

11.1 ± 0.5a

471.0 ± 96.2a

29.1 ± 3.7ab

 NPKSD

0.882 ± 0.004a

7.52 ± 0.23a

12.0 ± 0. a

456.8 ± 42.7a

33.2 ± 4.4a

JT rice harvest

 CK

0.510 ± 0.082c

2.89 ± 0.08b

6.8 ± 0.3ab

262.8 ± 62.2a

19.2 ± 6.9b

 NPK

0.640 ± 0.062bc

3.10 ± 0.12ab

5.7 ± 0.4b

314.9 ± 49.5a

28.1 ± 2.9ab

 NPKS

0.926 ± 0.062ab

3.17 ± 0.12a

6.6 ± 0.3ab

325.0 ± 94.0a

31.3 ± 2.3a

 NPKSD

1.195 ± 0.108a

3.13 ± 0.04ab

7.6 ± 0.8a

330.8 ± 74.2a

29.8 ± 5.1a

Analysis of variance

 Treatment (T)

<0.001

<0.001

0.001

0.003

<0.001

 Sampling time (ST)

<0.001

<0.001

0.73

<0.001

<0.001

 Sampling site (SS)

<0.001

0.001

<0.001

0.09

<0.001

 T × ST

0.03

0.09

0.001

0.56

0.83

 T × SS

0.001

0.019

0.008

0.93

0.58

 ST × SS

0.42

<0.001

<0.001

0.11

<0.001

 T × ST × SS

0.32

<0.001

<0.001

0.89

0.83

Values (means ± SD, n = 3) within the same column followed by different letters indicate significantly different at P < 0.05 according to Tukey’s post hoc test. P values in italics are statistically significant

aTreatment as described in Table 1

Generally, soil MBN contents differed considerably (P < 0.05) among treatments in both sampling times and sites, with the exception of rice harvest soils of CS, whereas no significant differences were observed for the soil MBC contents among treatments in both CS and JT. Compared to the CK treatment, fertilized treatments increased the soil MBC and MBN, while there were no significant differences among the fertilized treatments (Table 3). Both treatment and sampling time had a significant (P < 0.05) effect on soil MBC and MBN, while the soil MBN was also notably (P < 0.001) affected by sampling site and the interaction term between sampling time and sampling site (Table 3).

Bacterial abundance

Overall, significant (P < 0.05) differences were observed for the bacterial abundance among treatments in both sampling times and sites (Fig. 1). Moreover, the bacterial abundance was higher in the NPKS and NPKSD treatments compared with the CK and NPK treatments in both sampling times and sites even though the bacterial abundance of the NPKS treatment was not significantly (P < 0.05) higher than the NPK (wheat harvest soil in CS) or CK (rice harvest soil in CS) treatments. The highest bacterial abundance was observed in the NPKSD treatment in both sampling times and sites.
Fig. 1

Bacterial abundance under different fertilizer regimes in different sampling times and sites was quantified using real-time PCR. Error bars indicate the standard deviations of the means from 12 replicates (three replicates per plot). Different letters at each sampling time and site are significantly different at P < 0.05 according to the Turkey’s post hoc test. The treatment abbreviations are defined in Table 1

Bacterial α-diversity and community composition

A total of 279,560 high-quality 16S rRNA gene sequences were obtained from 48 soil samples in this study (sequences ranging from 3572 to 10,943). After equalizing sampling effort, 171,456 sequences were retained and clustered into 19,265 OTUs at 97% sequence similarity, with 1226–1528 OTUs per sample. The bacterial richness, diversity, and evenness in the individual samples under different treatments of both sampling times and sites were calculated based on 3572 sequences. Normally, treatment had no significant influences on soil bacterial α-diversity indices in both sampling times and sites with the exception of Pielou’s evenness in the wheat harvest soils of JT, being highest in the NPKSD treatment (Supplementary Table S1). Interestingly, bacterial richness (Sobs, ACE, and Chao1) and Faith’s PD were significantly (P < 0.001) affected by sampling time, while Shannon diversity varied markedly (P < 0.001) between sampling sites (Supplementary Table S1). Spearman correlation analysis revealed that soil OM were positively and significantly correlated with bacterial α-diversity indices except for the Faith’s PD (Supplementary Table S2), indicating that the accumulation of soil OM contents could help restore soil bacterial diversity.

Proteobacteria (30.0–38.4%), Acidobacteria (8.8–18.1%), Chloroflexi (8.0–15.2%), Bacteroidetes (3.5–9.6%), and Actinobacteria (1.4–3.7%) were the five most abundant phyla across all samples, accounting for 65.4–70.6% of the total bacterial sequences from each of the soils (Supplementary Fig. S1). In CS, the relative abundances of phyla Acidobacteria and Bacteroidetes varied significantly (P < 0.05) among different treatments in the wheat harvest soils, while there were no significant differences of abundant phyla among different treatments in the rice harvest soils. In JT, the relative abundances of Proteobacteria and Acidobacteria changed remarkably (P < 0.05) among different treatments in the wheat harvest soils while the relative abundances of Actinobacteria shifted significantly (P < 0.05) in the rice harvest soils.

Notably, the relative abundances of proteobacteria classes responded differently to different treatments at distinct sampling times and sites (Supplementary Fig. S2). Interestingly, the relative abundances of Betaproteobacteria and Gammaproteobacteria were consistently higher in the NPKS and NPKSD treatments than CK and NPK treatments even though some of the differences were not significant (Supplementary Fig. S2B, D).

To explore the overall influences of fertilization practices on the unique and shared OTUs in CS and JT, respectively, data from two sampling times were combined and the Venn diagrams were generated (Fig. 2). In CS, the number of OTUs unique to CK, NPK, NPKS, and NPKSD treatment was 61, 58, 76, and 76, respectively, accounting for 32.8% of the total observed OTUs (827), while 292 OTUs were shared among all treatments and accounted for 35.3% of the total observed OTUs (Fig. 2a). Similar to the results of CS, the percentage of unique and shared OTUs in JT accounted for 32.6 and 36.4% of the total observed OTUs (450), respectively (Fig. 2b). The majority (56.4–75.4%) of the unique OTUs in all treatments in CS were Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, Gammaproteobacteria, unclassified Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, and Chloroflexi (Fig. 3a). The relative abundances of all phyla (proteobacterial classes) were significantly (P < 0.05) different among treatments except for the Gammaproteobacteria, unclassified Proteobacteria, and Acidobacteria. Compared to the CK and NPK treatments, the relative abundances of Betaproteobacteria, Actinobacteria, and Bacteroidetes were higher in the NPKS treatment while the NPKSD treatment had higher abundance of Alphaproteobacteria, Betaproteobacteria, Bacteroidetes, and Chloroflexi. Interestingly, the phylum Nitrospirae with low abundance was only found in the NPKSD treatment, indicating that the soil nitrification might have been enhanced in this treatment in CS. Likewise, the dominant phyla (classes) in the unique OTUs of JT were similar to CS, but the pattern in each treatment was not consistent with CS (Fig. 3b). The relative abundances of Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, Gammaproteobacteria, Actinobacteria, Chloroflexi, Gemmatimonadetes, Chlorobi, Verrucomicrobia, and unclassified bacteria shifted remarkably (P < 0.05) in the unique OTUs among treatments (Fig. 3b). The relative abundance of Betaproteobacteria was higher in the NPKS treatment while the NPKSD treatment had a higher abundance of Betaproteobacteria and Deltaproteobacteria in comparison to CK and NPK treatments. In addition, the majority (79.0–81.3%) of the shared OTUs in CS was classified into Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, Gammaproteobacteria, unclassified Proteobacteria, Acidobacteria, Bacteroidetes, and Chloroflexi, but only the relative abundance of Bacteroidetes varied markedly (P < 0.05) among treatments (Fig. 4a). Similarly, the abundant taxa in the shared OTUs of JT were consistent in different treatments and even in line with CS (Fig. 4). Collectively, different fertilization practices had discriminatory capacities to shift the soil microbiomes (unique OTUs) and a core microbiome (shared OTUs) sustained throughout not only different treatments but also different sampling sites (Figs. 3 and 4).
Fig. 2

Venn diagram of the number of shared and unique OTUs in the different fertilizer treatments in CS (a) and JT (b). Only the OTUs present in four biological replicates of each treatment were retained. The treatment abbreviations are defined in Table 1

Fig. 3

Relative abundance of bacterial phyla (proteobacterial classes) in the unique OTUs of each treatment in CS (a) and JT (b). Error bars indicate the standard deviations of the means of six replicates. The asterisk above the taxa indicates significant differences at P < 0.05 among different treatments according to Tukey’s post hoc test. The treatment abbreviations are defined in Table 1

Fig. 4

Relative abundance of bacterial phyla (proteobacterial classes) in the shared OTUs of each treatment in CS (a) and JT (b). Error bars indicate the standard deviations of the means of six replicates. The asterisk above the taxa indicates significant differences at P < 0.05 among different treatments according to Tukey’s post hoc test. The treatment abbreviations are defined in Table 1

Relationships between bacterial community structure and soil physicochemical variables

The RDA ordination plots showed the relationships between the soil bacterial community and the soil physicochemical characteristics (Fig. 5 and Supplementary Fig. S3). Overall, the bacterial communities of each sampling time and site rather than treatment were grouped together, with the separation from the sampling sites in the first axis and sampling times in the second axis (Supplementary Fig. S3). Monte Carlo permutation test showed significant and positive correlations between soil physicochemical traits (r = 1.95, P < 0.001) and bacterial communities.
Fig. 5

Redundancy analysis of soil bacterial communities and soil characteristics for individual samples from wheat harvest of CS (a), rice harvest of CS (b), wheat harvest of JT (c), and rice harvest of JT (d), respectively. The treatment abbreviations are defined in Table 1

The NPKS and NPKSD treatments exhibited different effects on soil bacterial community in different sampling times and sites (Fig. 5). In the wheat harvest soils, the bacterial communities of NPKS and NPKSD treatments were similar, but they clearly separated from the CK and NPK treatments, with the CK and NPK treatment being separated from each other (Fig. 5a, c). In the rice harvest soils, there were no considerable impacts on soil bacterial communities in CS. However, the bacterial communities of NPKSD treatments in JT were separated from CK, NPK, and NPKS treatments, with the bacterial communities of NPK and NPKS treatments grouping together. In addition, all the fertilized treatments were associated with higher soil properties. The effect of individual soil variables on bacterial community is shown by the direction and the length of the vectors. In CS, soil pH, AN, AP, and AK were significant (P < 0.05) correlated with soil bacterial communities in the wheat harvest soils while soil TP and TK showed significant (P < 0.05) correlations with soil bacterial communities in the rice harvest soils. In JT, soil pH, TN, and AK had a significant (P < 0.05) correlation with bacterial communities in the wheat harvest soils while only soil TN exhibited a markedly (P < 0.05) correlation with bacterial communities in the rice harvest soils. Statistical analyses further revealed that the treatment was a significant factor in driving the formation of bacterial communities with the exception of the rice harvest soils in CS (Supplementary Table S3).

Pearson’s correlation coefficient was used to evaluate the relationships between the relative abundance of abundant phyla (proteobacterial classes) and the soil attributes (Table 4). All of the abundant phyla (classes) were positively or negatively correlated with soil pH (P < 0.05). The relative abundances of Proteobacteria and Betaproteobacteria were significantly and positively correlated with soil pH, TN, TP, TK, AN, AP, and AK contents (P < 0.01), while they were negatively correlated with soil OM contents (P < 0.01). The relative abundance of Alphaproteobacteria had a positive correlation with soil pH and TN contents (P < 0.01). Acidobacteria showed a negative correlation with soil pH, TN, TK, AN, and AP (P < 0.05), whereas Actinobacteria exhibited an opposite pattern, being positively correlated with soil pH, TN, AN, and AP (P < 0.05). Bacteroidetes had a negative correlation with soil pH, TN, and TK (P < 0.05) while Verrucomicrobia showed positive correlation with soil OM and negative correlation with soil pH, TP, TK, and AK (P < 0.01). In addition, Nitrospirae was positively correlated with soil TP and AK (P < 0.01) and negatively correlated with soil pH and TN (P < 0.01).
Table 4

Pearson’s correlation coefficients between abundant phyla (average relative abundance >1%) and soil properties

 

pH

OM

TN

TP

TK

AN

AP

AK

Proteobacteria

0.74**

0.46**

0.53**

0.47**

0.50**

0.49**

0.52**

0.49**

Alphaproteobacteria

0.51**

−0.19

0.52**

−0.10

0.04

0.30*

0.21

−0.05

Betaproteobacteria

0.61**

0.35*

0.32*

0.50**

0.52**

0.35*

0.46**

0.55**

Gammaproteobacteria

0.50**

0.29*

0.44**

0.10

−0.16

0.06

−0.02

0.11

Deltaproteobacteria

0.38**

0.46**

0.25

0.37**

0.33*

0.15

0.15

0.23

Acidobacteria

0.68**

0.25

0.77**

0.15

0.39**

0.36*

0.38**

0.10

Actinobacteria

0.52**

−0.25

0.66**

0.02

0.16

0.31*

0.40**

−0.02

Bacteroidetes

0.65**

0.13

0.70**

0.22

0.29*

−0.25

−0.25

0.20

Chloroflexi

0.30*

−0.03

0.44**

0.40**

0.19

0.09

0.08

0.31*

Verrucomicrobia

0.44**

0.73**

−0.21

0.70**

0.38**

−0.01

−0.27

0.70**

Gemmatiomnadetes

0.73**

0.74**

0.55**

0.50**

0.46**

−0.20

0.45**

0.43**

Nitrospirae

0.48**

−0.01

0.55**

0.38**

−0.23

−0.14

−0.15

0.38**

Values in italics are indicate statistically significant

*P < 0.05; **P < 0.01

Discussion

Rice-wheat rotation is the most important agricultural system for staple food production worldwide, and crop straw incorporation is a feasible practice for improving soil quality and productivity (Liu et al. 2014; Wang et al. 2015a, c). In this study, both the NPKS and the NPKSD treatments resulted in a slight increase in crop yield as compared with the NPK treatment, inconsistent with a previous study finding that both low and high straw returning treatments significantly improved the crop yield through a 30-year field trial (Sun et al. 2015). There is no doubt that long-term straw incorporation is conductive to improve soil fertility by increasing the soil OM and total N remarkably (Nakamura et al. 2003; Sun et al. 2016). However, short-term straw returning always has limited impact on soil nutrient status, which might be the reason for the mild yield increase after only a 3-year straw incorporation in this study. Nonetheless, crop straw incorporation had favorable effects on soil fertility and productivity, and these effects became gradually positive over the time.

Soil enzymatic activity is widely used as a biological indicator of soil function and represents the driver of soil nutrient cycling (Dick et al. 1997; Nannipieri et al. 2003). The selected cellulase, urease, and alkaline phosphatase are involved in the process of soil C, N, and P transformation, respectively (Dick 1994). Unsurprisingly, the soil cellulase activity was considerably higher in the NPKS and NPKSD treatments than the CK and NPK treatments, which was coherent with the findings reported by Han and He (2010) and Kautz et al. (2004), indicating that the soil cellulase activity was heavily induced by straw returning and the application of straw decomposer could definitely accelerate the decomposition rate and increase the cellulase activity in turn. The soil urease activity was increased slightly in the NPKS and NPKSD treatments, which was in line with the shifts of soil MBN contents, albeit some of these increases were not significant. This result was in discordance with the report that straw additions significantly decreased urease activities in a 110-day soil incubation experiment (Wu et al. 2013), suggesting that the induced N immobilization and mineralization might be the possible reason for this observation in such a short time (Ladha et al. 2004). However, the long-term residue additions remarkably enhanced soil urease activity; yet, these effects were not as strong as organic manure amendments (Dick et al. 1988), which was also similar for soil MBN contents and bacterial abundance (Sun et al. 2015; Zhao et al. 2014a). These findings might be attributable to that organic fertilizer that always contained vast quantities of readily utilizable nutrient sources as compared to crop residues, which could support the growth of soil microorganisms and thus increase the soil microbial biomass and bacterial abundance shortly (Kandeler et al. 1999).

Maintaining high levels of microbial diversity in soil is of vital importance for sustainable agriculture (Kennedy and Smith 1995; Pimentel et al. 1992), which is becoming a key issue in developing sustainable productivity systems (Bhat 2013; Zhao et al. 2014b). In addition, loss of microbial diversity is the general consequence of long-term chemical fertilization, while crop residues returning could mitigate the negative effects from chemical fertilization on bacterial diversity (Ramirez et al. 2010; Sun et al. 2016). Sun et al. (2015) also observed that bacterial diversity and richness by straw returning showed a considerable decrease as compared with the CK treatment, whereas there was no significant effect between the straw returning and the NPK treatment through a 30-year field experiment. However, the bacterial richness and diversity of the NPKS and NPKSD treatments showed no significant differences as compared with the CK and NPK treatments in this study, which might be due to the short-term straw incorporation over only 3 years. Interestingly, bacterial richness and Faith’s PD displayed temporal patterns whereas the Shannon diversity showed spatial patterns in the current study. CS and JT were all located in the LMYR plain with similar climate, especially the annual temperature and precipitation. Thus, we reasoned that the bacterial richness and Faith’s PD were mainly driven by the changes of soil temperature from wheat harvest to rice harvest, while the soil type, especially the soil nutrient status, led to the shifts of Shannon diversity, which was in accordance with the finding of Kennedy et al. (2005) and Wang et al. (2016).

Our previous study revealed that the bacterial community composition was remarkably different at the phylum (class) level among CK, NPK, NPKM, and NPKMOI treatments in the wheat harvest soils (Zhao et al. 2016), while only the Alphaproteobacteria, Betaproteobacteria, Acidobacteria, and Bacteroidetes were significantly different among treatments in CS and the Proteobacteria, Gammaproteobacteria, and Acidobacteria shifted markedly among treatments in JT (Supplementary Figs. S1 and S2), thus indicating that the driving forces from straw incorporation with and without straw decomposer were weaker than those by the organic fertilizer. Moreover, the anaerobic condition during the rice cultivation is regarded as strong driving force and has the capacity to counteract the effects of fertilizer regimes by normalizing and regulating the soil bacterial community composition based on their oxygen requirements, resulting in stable bacterial communities in paddy filed soils (Kikuchi et al. 2007). This is in line with the present study. Furthermore, we observed that the bacterial taxa belonging to the unique OTUs were changed remarkably among the CK, NPK, NPKS, and NPKSD treatments both in CS and JT (Fig. 3). Particularly, the relative abundance of Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, Actinobacteria, and Bacteroidetes were higher either in the NPKS or NPKSD treatments in CS or JT, indicating that soil functions, such as soil nitrification and organic matter transformation, might be enhanced under these treatments (Baker et al. 2013; Schrijver and Mot 1999), which also corresponded to the results of soil enzymatic activities in the present study. Additionally, the abundance of bacterial taxa in the shared OTUs was steady across the CK, NPK, NPKS, and NPKSD treatments and even in CS and JT (Fig. 4), which suggested that a core microbiome might occur in the LMYR plain in spite of treatment, sampling time, and sampling site.

There is a general concept that copiotrophs or r-strategists are thrived under conditions when/where resources are abundant, and oligotrophs or K-strategists are relatively more abundant under resource-limited conditions (Eilers et al. 2010; Pianka 1970). Proteobacteria, Betaproteobacteria, Actinobacteria, and Bacteroidetes are considered as r-strategists while Acidobacteria and Verrucomicrobia are known as K-strategists (Fierer et al. 2007; Ramirez et al. 2012). In this study, the relative abundances of r-strategists such as Proteobacteria, Betaproteobacteria, and Actinobacteria were significantly and positively correlated with soil nutrient contents while the relative abundances of K-strategists such as Acidobacteria and Verrucomicrobia had a negative relationship with soil nutrient availability. Thus, we speculated that the NPKS and NPKSD treatments could especially stimulate the relative abundance of copiotrophic bacteria by increasing the available nutrient contents, which was consistent with the finding of Xun et al. (2016), demonstrating that the organic fertilizer facilitated the growth of copiotrophic taxa, which might help to improve soil productivity and sustainability in intensive agricultural systems.

The RDA ordination plots showed that the NPKS and NPKSD treatment had different effects on the bacterial community structure in different sampling times and sites (Fig. 5). Interestingly, Sun et al. (2016) observed that the NPKS treatment had a discriminatory influence on the bacterial community in different parent materials, being different from both the CK and the NPK treatments in purple sandy shale and quaternary red clay soil. While in the granite soil, the bacterial community of NPKS treatment was similar to the NPK treatment and separated with the CK treatment; observation in this study coincided with the report that both the bacterial communities of the NPK, NPK + LS, and NPK + HS treatments were grouped together and separated from the CK treatment (Sun et al. 2015). In this study, the bacterial communities of the NPKS and NPKSD treatments appeared similar and well separated from the CK and NPK treatments in the wheat harvest soils of both CS and JT. However, these clear separation was weakened in the rice harvest soil. These findings indicate that the NPKS and NPKSD treatments remarkably shaped the soil bacterial communities and the periodic flooding practice in rice cultivation offset the impacts from straw incorporation. In addition, we also found that sampling site, sampling time, and treatment were all significant factors in shaping the bacterial community structures, with the sampling site variability overwhelming the sampling time and then the treatment variability in the current study (Supplementary Fig. S3, Supplementary Table S3), which is in accordance with the results of Zhao et al. (2014a), suggesting that the short-term treatment had less effect on the bacterial community than seasonal shifts.

In general, the NPKS and NPKSD treatments can improve crop yields, soil available nutrient contents, enzymatic activities, and bacterial abundance. These practices have significant effects on the bacterial composition of unique OTUs rather than shared OTUs. Moreover, they are able to shape the bacterial community structures, especially in the wheat harvest soils, and stimulate the copiotrophic bacteria gradually.

Notes

Acknowledgements

This research was financially supported by the National Key Basic Research Program of China (2015CB150502), the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions, the 111 Project (B12009), the National Infrastructure of Microbial Resources (NIRM), and the Research Innovation Program for College Graduates of Jiangsu Province (CXZZ13_0301). We also would like to thank the anonymous referees for their constructive comments, which significantly improved the manuscript.

Compliance with ethical standards

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

253_2017_8170_MOESM1_ESM.pdf (623 kb)
ESM 1(PDF 623 kb)

References

  1. Alef K, Nannipieri P (1995) Methods in applied soil microbiology and biochemistry. Academic Press, LondonGoogle Scholar
  2. Baker BJ, Sheik CS, Taylor CA, Jain S, Bhasi A, Cavalcoli JD, Dick GJ (2013) Community transcriptomic assembly reveals microbes that contribute to deep-sea carbon and nitrogen cycling. ISME J 7(10):1962–1973CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bhat AK (2013) Preserving microbial diversity of soil ecosystem: a key to sustainable productivity. Int J Curr Microbiol App Sci 2(8):85–101Google Scholar
  4. Blanco-Canqui H, Lal R (2009) Crop residue removal impacts on soil productivity and environmental quality. Crit Rev Plant Sci 28(3):139–163CrossRefGoogle Scholar
  5. Brookes PC, Landman A, Pruden G, Jenkinson DS (1985) Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol Biochem 17(6):837–842CrossRefGoogle Scholar
  6. Cao G, Zhang X, Wang Y, Zheng F (2008) Estimation of emissions from field burning of crop straw in China. Chinese Sci Bull 53(5):784–790CrossRefGoogle Scholar
  7. Dick RP (1994) Soil enzyme activities as indicators of soil quality. In: Doran JW, Coleman DC, Bezdicek DF, Stewart BA (eds) Defining soil quality for a sustainable environment. SSSA Special Publication 35, Soil Sci Soc Am. Madison, USA, pp 107–124Google Scholar
  8. Dick RP, Pankhurst C, Doube BM, Gupta V (1997) Soil enzyme activities as integrative indicators of soil health. In: Pankhurst C, Doube BM, Gupta V (eds) Biological indicators of soil health. CAB International, Wallingford, pp 121–156Google Scholar
  9. Dick RP, Rasmussen PE, Kerle EA (1988) Influence of long-term residue management on soil enzyme activities in relation to soil chemical properties of a wheat-fallow system. Biol Fert Soils 6(2):159–164CrossRefGoogle Scholar
  10. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16):2194–2200CrossRefPubMedPubMedCentralGoogle Scholar
  11. Eilers KG, Lauber CL, Knight R, Fierer N (2010) Shifts in bacterial community structure associated with inputs of low molecular weight carbon compounds to soil. Soil Biol Biochem 42(6):896–903CrossRefGoogle Scholar
  12. Fierer N, Bradford MA, Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88(6):1354–1364CrossRefPubMedGoogle Scholar
  13. Han W, He M (2010) The application of exogenous cellulase to improve soil fertility and plant growth due to acceleration of straw decomposition. Bioresource Technol 101(10):3724–3731CrossRefGoogle Scholar
  14. Kandeler E, Stemmer M, Klimanek E-M (1999) Response of soil microbial biomass, urease and xylanase within particle size fractions to long-term soil management. Soil Biol Biochem 31(2):261–273CrossRefGoogle Scholar
  15. Kautz T, Wirth S, Ellmer F (2004) Microbial activity in a sandy arable soil is governed by the fertilization regime. Eur J Soil Biol 40(2):87–94CrossRefGoogle Scholar
  16. Kennedy AC, Smith KL (1995) Soil microbial diversity and the sustainability of agricultural soils. Plant Soil 170(1):75–86CrossRefGoogle Scholar
  17. Kennedy NM, Gleeson DE, Connolly J, Clipson NJW (2005) Seasonal and management influences on bacterial community structure in an upland grassland soil. FEMS Microbiol Ecol 53(3):329–337CrossRefPubMedGoogle Scholar
  18. Kikuchi H, Watanabe T, Jia Z, Kimura M, Asakawa S (2007) Molecular analyses reveal stability of bacterial communities in bulk soil of a Japanese paddy field: estimation by denaturing gradient gel electrophoresis of 16S rRNA genes amplified from DNA accompanied with RNA. Soil Sci Plant Nutr 53(4):448–458CrossRefGoogle Scholar
  19. Kim B-S, Kim BK, Lee J-H, Kim M, Lim YW, Chun J (2008) Rapid phylogenetic dissection of prokaryotic community structure in tidal flat using pyrosequencing. J Microbiol 46(4):357–363CrossRefPubMedGoogle Scholar
  20. Ladha JK, Khind CS, Gupta RK, Meelu OP, Pasuquin E (2004) Long-term effects of organic inputs on yield and soil fertility in the rice-wheat rotation. Soil Sci Soc Am J 68(3):845–853Google Scholar
  21. Legendre P, Gallagher ED (2001) Ecologically meaningful transformations for ordination of species data. Oecologia 129(2):271–280CrossRefGoogle Scholar
  22. Liu H, Jiang GM, Zhuang HY, Wang KJ (2008) Distribution, utilization structure and potential of biomass resources in rural China: with special references of crop residues. Renew Sust Energ Rev 12(5):1402–1418CrossRefGoogle Scholar
  23. Liu S, Huang D, Chen A, Wei W, Brookes PC, Li Y, Wu J (2014) Differential responses of crop yields and soil organic carbon stock to fertilization and rice straw incorporation in three cropping systems in the subtropics. Agr Ecosyst Enviro 184:51–58CrossRefGoogle Scholar
  24. Nakamura A, Tun CC, Asakawa S, Kimura M (2003) Microbial community responsible for the decomposition of rice straw in a paddy field: estimation by phospholipid fatty acid analysis. Biol Fert Soils 38(5):288–295CrossRefGoogle Scholar
  25. Nannipieri P, Ascher J, Ceccherini M, Landi L, Pietramellara G, Renella G (2003) Microbial diversity and soil functions. Eur J Soil Sci 54(4):655–670CrossRefGoogle Scholar
  26. Nossa CW, Oberdorf WE, Yang L, Aas JA, Paster BJ, DeSantis TZ, Brodie EL, Malamud D, Poles MA, Pei Z (2010) Design of 16S rRNA gene primers for 454 pyrosequencing of the human foregut microbiome. World J Gastroentero 16(33):4135CrossRefGoogle Scholar
  27. Pianka ER (1970) On r-and K-selection. Am Nat 104(940):592–597CrossRefGoogle Scholar
  28. Pimentel D, Stachow U, Takacs DA, Brubaker HW, Dumas AR, Meaney JJ, O'Neil JAS, Onsi DE, Corzilius DB (1992) Conserving biological diversity in agricultural/forestry systems. Bioscience:354–362Google Scholar
  29. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glöckner FO (2007) SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 35(21):7188–7196CrossRefPubMedPubMedCentralGoogle Scholar
  30. Qin S, Jiao K, Lyu D, Shi L, Liu L (2015) Effects of maize residue and cellulose-decomposing bacteria inocula on soil microbial community, functional diversity, organic fractions, and growth of Malus hupehensis Rehd. Arch Agron Soil Sci 61(2):173–184Google Scholar
  31. Quince C, Lanzen A, Davenport RJ, Turnbaugh PJ (2011) Removing noise from pyrosequenced amplicons. BMC Bioinformatics 12(1):38CrossRefPubMedPubMedCentralGoogle Scholar
  32. R Development Core Team (2012) R: a language and environment for statistical computing, vol 2012. R Foundation for Statistical Computing, ViennaGoogle Scholar
  33. Ramirez KS, Craine JM, Fierer N (2012) Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob Chang Biol 18(6):1918–1927CrossRefGoogle Scholar
  34. Ramirez KS, Lauber CL, Knight R, Bradford MA, Fierer N (2010) Consistent effects of nitrogen fertilization on soil bacterial communities in contrasting systems. Ecology 91(12):3463–3470CrossRefPubMedGoogle Scholar
  35. Reddy AP, Simmons CW, D’haeseleer P, Khudyakov J, Burd H, Hadi M, Simmons BA, Singer SW, Thelen MP, Vander Gheynst JS (2013) Discovery of microorganisms and enzymes involved in high-solids decomposition of rice straw using metagenomic analyses. PLoS One 8(10):e77985CrossRefPubMedPubMedCentralGoogle Scholar
  36. 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(23):7537–7541CrossRefPubMedPubMedCentralGoogle Scholar
  37. Schrijver AD, Mot RD (1999) Degradation of pesticides by actinomycetes. Crit Rev Microbiol 25(2):85–119CrossRefPubMedGoogle Scholar
  38. Sheneman L, Evans J, Foster JA (2006) Clearcut: a fast implementation of relaxed neighbor joining. Bioinformatics 22(22):2823–2824CrossRefPubMedGoogle Scholar
  39. Sun L, Xun W, Huang T, Zhang G, Gao J, Ran W, Li D, Shen Q, Zhang R (2016) Alteration of the soil bacterial community during parent material maturation driven by different fertilization treatments. Soil Biol Biochem 96:207–215CrossRefGoogle Scholar
  40. Sun R, Zhang X-X, Guo X, Wang D, Chu H (2015) Bacterial diversity in soils subjected to long-term chemical fertilization can be more stably maintained with the addition of livestock manure than wheat straw. Soil Biol Biochem 88:9–18CrossRefGoogle Scholar
  41. Tirol-Padre A, Tsuchiya K, Inubushi K, Ladha JK (2005) Enhancing soil quality through residue management in a rice-wheat system in Fukuoka, Japan. Soil Sci Plant Nutr 51(6):849–860CrossRefGoogle Scholar
  42. Vance ED, Brookes PC, Jenkinson DS (1987) An extraction method for measuring soil microbial biomass C. Soil Biol Biochem 19(6):703–707CrossRefGoogle Scholar
  43. Wang J, Wang X, Xu M, Feng G, Zhang W, Lu CA (2015a) Crop yield and soil organic matter after long-term straw return to soil in China. Nutr Cycl Agroecosys 102(3):371–381CrossRefGoogle Scholar
  44. Wang J, Xue C, Song Y, Wang L, Huang Q, Shen Q (2016) Wheat and rice growth stages and fertilization regimes alter soil bacterial community structure, but not diversity. Front Microbiol 7:1207PubMedPubMedCentralGoogle Scholar
  45. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16):5261–5267CrossRefPubMedPubMedCentralGoogle Scholar
  46. Wang W, Lai DYF, Wang C, Pan T, Zeng C (2015b) Effects of rice straw incorporation on active soil organic carbon pools in a subtropical paddy field. Soil Till Res 152:8–16CrossRefGoogle Scholar
  47. Wu F, Jia Z, Wang S, Chang SX, Startsev A (2013) Contrasting effects of wheat straw and its biochar on greenhouse gas emissions and enzyme activities in a Chernozemic soil. Biol Fert Soils 49(5):555–565CrossRefGoogle Scholar
  48. Xun W, Zhao J, Xue C, Zhang G, Ran W, Wang B, Shen Q, Zhang R (2016) Significant alteration of soil bacterial communities and organic carbon decomposition by different long-term fertilization management conditions of extremely low-productivity arable soil in South China. Environ Microbiol 18(6):1907–1917CrossRefPubMedGoogle Scholar
  49. Zhao J, Ni T, Li J, Lu Q, Fang Z, Huang Q, Zhang R, Li R, Shen B, Shen Q (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
  50. Zhao J, Ni T, Li Y, Xiong W, Ran W, Shen B, Shen Q, Zhang R (2014a) Responses of bacterial communities in arable soils in a rice-wheat cropping system to different fertilizer regimes and sampling times. PLoS One 9(1):e85301CrossRefPubMedPubMedCentralGoogle Scholar
  51. Zhao J, Zhang R, Xue C, Xun W, Sun L, Xu Y, Shen Q (2014b) Pyrosequencing reveals contrasting soil bacterial diversity and community structure of two main winter wheat cropping systems in China. Microb Ecol 67(2):443–453CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Jiangsu Key Lab for Organic Waste UtilizationNanjing Agricultural UniversityNanjingChina
  2. 2.Key Laboratory of Microbial Resources Collection and Preservation, Ministry of AgricultureInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural SciencesBeijingChina

Personalised recommendations