Introduction

In recent years, to meet various demands, natural forests have been converted to other land-use, such as agriculture, settlement and grassland. Changes in land-use may profoundly alter the microbial community in soil resulting in a loss of biodiversity, land degradation and nutrient exhaustion (Zhao et al., 2005; Costa et al., 2006; Upchurch et al., 2008; Ding et al., 2013). As we known, soil microorganisms are crucial for the function and stability of an ecosystem (Rogers and Tate Iii, 2001). This is because a high microbial diversity is critical for the sustainability of a soil ecosystem by altering immobilization of the essential nutrients and nutrient cycling (Li, 2012). Many studies have shown that the conversion of natural forest to other land-use types affected the soil microbial community composition and soil physicochemical properties (Grünzweig et al., 2003; Upchurch et al., 2008; Yu et al., 2011).

Maoershan National Forest Park is a natural forest area located in Shangzhi City in the southern part of Heilongjiang Province in Northeast China. It is comprised of  260 km² land area, 80% of which was previously natural forests. Since the early 1990s, some were utilized for agricultural landscape and settlement. Thus, these converted lands provide ideal conditions for study the response of soil microbial diversity in the land-use conversion of the natural forests to other land-use types under boreal climates. Black soil is a typical boreal soil type in Northeast China (Zeng et al., 1983). It is considered one of the most fertile soils in China as it possesses a high microbial diversity due to the rich nutrient contents. However, diverse land-use types have been developed to meet needs for crops, fiber and food, which may change the quality of black soil (Yu et al., 2011). To date, little is known about how soil microbial diversity responds to natural forest land conversion in the black soil in Northeast China.

Soil microbes, especially bacteria, can easily be influenced by land-use change, which can significantly influence diversity, composition and abundance of bacterial community in soil (Suleiman et al., 2013). In turn, these changes can cause an imbalance of the soil ecosystem (Valpassos et al., 2001; Aboim et al., 2008; Jesus et al., 2009). Therefore, it is important to understand soil ecosystems by studying the bacterial community structure and its relationship with the changes in land-use. In our previous study, changes in land-use type were shown to affect the changes in enzyme activities and the physical and chemical properties of soils (Cong et al., 2013). However, the impact of conversion of natural forest to other land-use patterns on soil bacterial community have not been yet examined in the black soil of Northeast China.

In this study, we examined differences in bacterial communities across a series of replicated land-use types in the Maoershan National Forest Park, China. We used denaturing gradient gel electrophoresis (DGGE) method based on 16S rRNA genes to obtain differences in the bacterial communities across those land-use types. The aim of this study is to investigate the influence of land-use change on the structure of the bacterial communities of black soil and, more specifically, to examine how shifts in the abundance and composition of soil bacterial communities correspond to changes in land-use type.

1 Materials and Methods

1.1 Sampling site description and soil sampling

The experimental site is located in Maoershan National Forest Park (45°10′-45°35′N, 127°20′-127°45′E), Shangzhi City, Heilongjiang Province, China. This site has a boreal climate with a mean annual rainfall of 723 mm, and an average annual temperature of 2.4°C (Pan et al., 2007). The soil located here is classified as representative black soil. The present study is a continuation of our previous work, in which the physical and chemical properties of soil samples have been described (Cong et al., 2013).

Our goal was to select plots representative of the land-use types in this region. Therefore, we chose four typical land uses to provide an overall picture of natural forest land-use changeJug. These included (i) three forest sites [(FL-1), (FL-2) and (FL-3)], (ii) two agricultural sites [(AL-4) and (AL-5)], (iii) three settlement sites [(SEL-6), (SEL-7) and (SEL-8)], (iv) and two slash sites [(SLL-9) and (SLL -10)]. The detailed descriptions of all sites are listed in the Table 1. The forest sites consist of pine (Pinus koraiensis) with varying amounts of Juglans, Syringa reticulate species. The agricultural plots are used to support populations with annual crop rotation between soybean and maize using conventional tillage practices. The settlement plots are dominated by barnyard grass. And the slash plots mainly consist of pine and spruce species.

In each site, a central point was selected, and four sampling points at 20 m from the central point were chosen to five samples per site with three replicates. Then the 15 soil cores from same site was placed in a bag and mixed by shaking, and sifting air dried samples through 4 mm mesh sieve to provide a homogenous, representative sample. Thus, ten composited samples were obtained and stored at -80°C until DNA extraction.

1.2 DNA extraction and purification

The total DNA was extracted from 0.5 g soil sample by using the method of Li and Jin (2006) with three replicates. Replicate DNA extracts were pooled. DNA quality was checked using 1% (w/v) agarose gel electrophoresis. DNA quantity was determined using a MECASYS-OPTIZEN 3220 UV (Mecasys Co., Ltd.) spectrophotometer and was diluted to 50 ng/μL. Pooled DNA was stored at -20°C for PCR amplification.

1.3 16s rRNA amplification and DGGE

Pooled DNA was amplified by universal 16S rRNA gene primers (GC933-954f5’-GCACAAGCGGTGGAGCATGTGG-3’; 1369-1388r5’-GCCCGGGAACGTATTCACCG-3’) (Yu and Morrison, 2004). The amplification mixture contained 1 μL of each sample of pooled DNA, 5 μL of 10 × PCR buffer, 1 μL of 10 mM dNTPs, 6 μL of 25 mM MgCl₂, 1 μL of 10 μM GC933-954f primer, 1 μL of 10 μM 1 369~1 388r primer, and 1 U Taq DNA polymerase (Promega, Madison, Wisconsin, USA) in 20 μL volume. PCR amplification was conducted as follows: initial denaturation at 94°C for 5 min, then 10 cycles of 94°C for 30 s, 61°C for 30 s (-0.5°C at each cycle) and 72°C for 1 min, followed by 25 cycles of 94°C for 30 s, 56°C for 30 s, 72°C for 1 min.

Denaturing gradient gel electrophoresis (DGGE) was performed in a DCode universal mutation detection system (Bio-Rad Co., USA). 5 μL PCR product and 5 μL loading buffer was added into an 8% acrylamide gel with a chemical gradient (35-60% denaturant) and electrophoresed for 7 h at 150 V. After electrophoresis, the gel was removed and placed in an acetic acid solution (1%) for 15 min and then was washed in double-distilled water (ddH₂O). Next, it was stained with silver nitrate solution (0.2%) for 20 min then the stained gel was washed twice in ddH₂O and was placed in a developing solution (3% sodium carbonated and 0.06% formaldehyde). The development was stopped with 1% acetic acid. After rinsing with ddH₂O, the gel was dried and photographed. DGGE images were analyzed using Quantity One software (Bio-Rad, version 4.6.2, USA).

1.4 Sequencing analysis of DGGE bands

Forty-two bands were produced based presence or absence among all samples, and almost no variability in banding patterns was observed between replicates. They were marked and excised from the gel with a sharp razor blade, and eluted in 30 µL of sterile double distilled water at 100°C for 10 min and then centrifuged. The extracts were subjected to recover using the same set of corresponding primers under the same PCR conditions as for re-amplification. The DNA fragments were cloned into the plasmid pMD18-T vector (TaKaRa, Dalian, China) and subjected for sequencing analysis. The sequences of bands were analyzed for homology to nucleotide sequences by searching the National Center for Biotechnology Information (NCBI) website (http://www. ncbi.nlm.nih.gov/) using the BLAST search program.

1.5 Data analysis

To assess the bacterial communities, several parameters were calculated for each sample. Richness (R) was estimated by using the number of bands observed in a sample with the Shannon-Wiener diversity index (H') and equitability (E) was calculated using the following equations (Pielou, 1969; Rogers and Tate Iii, 2001; Costa et al., 2012):

H' = - ΣPi(lnPi)

E = H'/ln R

Where, Pi is the ratio of the peak value of a band and the sum of all peak values in all bands.

Hierarchical Cluster analysis based on the presence and absence of bands detected in all soil samples of the different land-use were performed using QuantityOne software (Bio-Rad, version 4.6.2, USA). A dendrogram according to 16S rRNA gene sequences was performed using MEGA4.0 software.

2 Results

2.1 Effect of different land-use on richness (R), Shannon-Wiener diversity index (H'), and equitability (E)

Species richness, Shannon-Wiener diversity index, and equitability for ten land sites were shown in the Table 2. A higher diversity and species richness was observed in Maoershan National Forest Park soils, varying from 47 in FL-1 to 68 in SLL-9. The equitability index varied from 0.69 to 0.79. And the Shannon-Wiener diversity index was comparable values in all soil samples, with the highest value in site SLL-9 (3.23) and lowest in FL-2 or SLL-10 (2.94).

2.2 Sequence analysis and clustering of 16s rRNA

In this study, the homology of forty-two sequences was 87%-99% with available data; most bands were affiliated to uncultured bacteria (Figure 1). Identified bacteria belonged to seven phyla (Figure 2). The most abundant sequences were from the phylum Proteobacteria including Alpha, Delta and Gamma classes (Figure 2). The DGGE bands including Band 3, Band 5, Band 8, Band 9, Band 14, Band 16, Band 17, Band 19, Band 20, Band 21, Band 22, Band 28, Band 37 and Band 39 were assigned to Proteobacteria, whereas the other DGGE bands had relatively highly homologous with Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes and Verrucomicrobia (Figure 1; Figure 2).

The hierarchical cluster analysis basing on the presence and absence of bands in samples from ten sites is depicted in the dendrogram of Figure 3. The samples tended to group in relation to the land-use type. The clustering analysis of DGGE band patterns identified two distinct clusters: Cluster I and Cluster II. Cluster I contained the soil samples from three forestry sites (FL-1, FL-2 and FL-3). The remaining soil samples were in Cluster II. Each cluster could be partitioned into sub-clusters which demonstrated the effect of different land-use on the bacterial communities. The results clearly demonstrated that DGGE profiles revealed marked differences in response to soil microbial communities under different land-use systems.

2.3 Effect of different land-use on bacterial community composition

At the phylum level, 7 major bacterial taxa present within most of the soils and, averaged across all of soil, the most abundant bacterial groups were the Proteobacteria, Acidobacteria and Verrucomicrobia (Figure 4). The relative abundances of the different taxa varied between and within land-use.

The absence, presence and abundance of bands were considered in estimating relative changes and abundance of the bacterial community. The results of these changes from all soil samples are demonstrated in Figure 4. Bacteroidetes were not detected in forest soil (FL-1, FL-2 and FL-3). Beta-Proteobacteria was unique to the settlement soil (site 6, 7 and 8), whereas Cyanobacteria and Verrucomicrobia were absent in agricultural soil (AL-4 and AL-5). Additionally, Acidobacteria and Proteobacteria (α, β, γ, δ classes) were dominant bacterial communities in all soil samples.

3 Discussion

In this study, the result of DGGE analysis showed that land-use conversion had a significant effect on the bacterial community composition in black soil of boreal region in China. Conversion of natural forests to other land-use caused a negative response in soil physicochemical characteristics in previous study (Jaiyeoba, 2003; Su et al., 2004), and accordingly, probably impacts the composition of bacterial community in the soil.

The main bacterial genera detected in this work were similar to the general isolation pattern found in soil ecosystems (Tangjang et al., 2009). Bacterial diversity and richness found in the soil plots reflected the equilibrium between production and decomposition inside this system, as soil plots (FL-3, AL-4, AL-5, SEL-8, SLL-9) present a higher abundance of plant species and a leaf litter layer over the soil, thereby leading to increased richness and abundance of bacteria (Table 2). Richness, defined by the Shannon-Wiener diversity index, was the highest for SLL-9 and evenness was the greatest for FL-1. Thus, different land-use had no clear effect on these parameters. However, Costa et al. (2012) found that plant species substitution could change the quantity and quality of organic matter under decomposition, as the plant residues would be different.

Previous reports indicated that agricultural management actions such as fertilization, irrigation and herbicide could affect the soil bacterial community structure (Calderon et al., 2001; Shannon, 2001). Therefore, tillage is an important factor driving shifts in the soil microbial community structure. In the present study, it was shown that conversion of forest soil into agricultural land influenced the abundance of four bacterial phyla, e.g., Cyanobacteria, δ-Proteobacteria, Gemmatimonadetes and Bacteroidetes. Cyanobacteria have been found not detected after the conversion from natural forests to agricultural land. Generally, Cyanobacteria can affect the soil fertility and increase the contents of organic matter and nitrogen. These results suggested that deliberate management of soils have a negative impact on the bacterial community structure (Pankratova, 2006). However, δ-Proteobacteria and Gemmatimonadetes had significantly higher abundances in agricultural land compared to forest soils. In the study of Jangid et al. (2008), it was revealed that β-, δ-and γ-Proteobacteria were predominant in ungrazed and cropped soils, whereas the numbers of α-Proteobacteria decreased in the cropped soils. Furthermore, there was a minor difference between the agricultural soil from AL-4 (soybean) and AL-5 (maize), which may be correlated to their crop species. Some studies suggested plant species to be one of major factors influencing the microbial community structure (Calderon et al. 2001; Deangelis et al. 2009; Mendes et al. 2011; Underwood et al. 2009). However, Kielak et al. (2008) reported that plant species have only a minor influence on the soil bacterial community composition. Kuramae et al. (2012) also found that soil physicochemical factors, such as C: N, phosphate, and pH, were the main factors explaining the variation in bacterial communities, as opposed to the independent impact of vegetation type and land-use practices, but they also showed that exceptions were the pine forest and natural grassland plots.

In the present study, bacterial diversity in the settlement soil was higher than that of the other soils (Table 2). Three settlement soil samples, except for SEL-6, were distinguished by the presence of seven phyla. Particularly, β-Proteobacteria was only present in the settlement soil. In addition, δ-Proteobacteria was shown to be a more abundant bacteria compared to the other soils. Some possible explanations for these results are as follows. First, the abundance of bacteria may be due to the release of rubbish or overuse of chemicals in residential environments. Second, the settlement land has a greater human or animal activity than forest land; thus, residential soils are frequently exposed to contaminations and disturbances. Third, higher diversity in plant species may create an increased bacterial diversity. Moreover, previous studies found that there were more disturbances in residential soils (Mendes et al., 2011; Munir and Xagoraraki, 2011; Liu et al., 2012).

During the last decades, some of the forest land in Maoershan National Forest Park has been converted for wood production (Li, 2004). Generally, two soil management methods were used after a wood harvest. In one method, fire is used after a harvest; in the other method, fire is not used. In this study, there was little change in both the cutting-blank cutover land and the burnt land compared to forest soils. However, Bacteroidetes had not been observed in the soil of the slash land, indicating a lower impact of burnt management on microbial community composition. This finding was in contrast with the study by Rachid et al. (2013), who found that green management (no fire) had little influence on the bacterial community of soils. In contrast to burnt land, the phylum Bacteroidetes was found in the cutting-blank cutover land. This variation can be explained by the different abundance of litterfall in the two soils. Chave et al. (2010) showed that litterfall was the main pathway for nutrient and energy return to the soil. In our study, litterfall in the cutting-blank cutover land was higher than that of the burnt land (data not show). Moreover, removal of nutrients is common after using fire treatment (Yang, 1992). Therefore, the soil nutrient status and litterfall of cutting-blank cutover land may result to similar bacterial communities with that of forest soils.

The dendrogram constructed using the DEEG band profiles of soil bacterial communities from the ten plots revealed differences in the microbial community structure in relation to differing land-use (Figure 3). These results (two clusters) revealed that bacterial communities can change, mainly according to the type of vegetation. Forest system is composed of diverse plant species, while the conventional management of soils keeps them covered by a single annual or semi-perennial culture (Costa et al., 2012). Garbeva et al., (2004) affirmed that the type of vegetation cover is a factor determining the soil microbial community structure, as plants are the greatest providers of specific types of carbon and energy sources.

4 Conclusions

A molecular approach was used in this study, namely DGGE, to assess the bacterial community structure of ten different land-uses in the Northeast China, which might affect the productivity of soil ecosystem. The results showed that land-use change have significant impacts on soil microbial communities, which were likely to respond to these changes. Together these results suggested that the utility of sequence-based approaches to analyze bacterial communities provides detailed information on individual communities and permit the robust assessment of the biogeographical patterns exhibited by soil microbial communities.

Authors’ contributions

Mu Peng and Syed Sadaqat Shah wrote this paper, Qiuyu Wang analyzed the experiment data, Fanjuan Meng and designed this experiment.

References

Aboim M.C., Coutinho, H.L., Peixoto R.S., Barbosa J.C., and Rosado A.S., 2008, Soil bacterial community structure and soil quality in a slash-and-burn cultivation system in Southeastern Brazil, Applied soil ecology, 38(2):100-108

https://doi.org/10.1016/j.apsoil.2007.09.004

Calderon F.J., Jackson L.E., Scow K.M., and Rolston., D.E., 2001, Short-term dynamics of nitrogen, microbial activity, and phospholipid fatty acids after tillage, Soil Science Society of America Journal, 65(1):118-126

https://doi.org/10.2136/sssaj2001.651118x

Chave J., Navarrete D., Almeida S., Alvarez E., Aragão L.E., Bonal D., Châtelet P., Silva-Espejo J., Goret J.Y., and Von H.P., 2010, Regional and seasonal patterns of litterfall in tropical South America, Biogeosciences 7(1):43-55

https://doi.org/10.5194/bg-7-43-2010

Cong L., Zhengfeng S., Yu C., and Shao P.Y., 2013, Effects on Physicochemical Properties and Enzymatic Activities of Soil by Different Land Utilization Patterns in Forest Region, Forest Engineering, 3:006

Costa P.M.O., Souza-Motta C.M., and Malosso E., 2012, Diversity of filamentous fungi in different systems of land use, Agroforestry systems, 85(1):195-203

https://doi.org/10.1007/s10457-011-9446-8

Costa R., Götz M., Mrotzek N., Lottmann J., Berg G., and Smalla., K. 2006, Effects of site and plant species on rhizosphere community structure as revealed by molecular analysis of microbial guilds, FEMS Microbiology Ecology 56(2):236-249

https://doi.org/10.1111/j.1574-6941.2005.00026.x

PMid:16629753

Deangelis K.M., Brodie E.L., DeSantis T.Z., Andersen G.L., Lindow S.E., and Firestone M.K., 2009, Selective progressive response of soil microbial community to wild oat roots, The ISME journal, 3(2):168-178

https://doi.org/10.1038/ismej.2008.103
PMid:19005498

Ding G.C., Piceno Y.M., Heuer H., Weinert N., Dohrmann A.B., Carrillo A., Andersen G.L., Castellanos T., Tebbe C.C., and Smalla K., 2013, Changes of soil bacterial diversity as a consequence of agricultural land use in a semi-arid ecosystem, PloS one, 8(3):e59497

https://doi.org/10.1371/journal.pone.0059497
PMid:23527207 PMCid:PMC3603937

Garbeva P., Van Veen J.A., and Van Elsas J.D. 2004, Microbial diversity in soil: selection of microbial populations by plant and soil type and implications for disease suppressiveness, Annu Rev Phytopathol 42:243-270

https://doi.org/10.1146/annurev.phyto.42.012604.135455
PMid:15283667

Grünzweig J.M., Sparrow S.D., and Chapin F.S., 2003, Impact of forest conversion to agriculture on carbon and nitrogen mineralization in subarctic Alaska, Biogeochemistry, 64(2):271-296

https://doi.org/10.1023/A:1024976713243

Jaiyeoba I. 2003, Changes in soil properties due to continuous cultivation in Nigerian semiarid Savannah, Soil and Tillage Research, 70(1):91-98

https://doi.org/10.1016/S0167-1987(02)00138-1

Jangid K., Williams M.A., Franzluebbers A.J., Sanderlin J.S., Reeves J.H., Jenkins M.B., Endale D.M., Coleman D.C., and Whitman W.B., 2008, Relative impacts of land-use, management intensity and fertilization upon soil microbial community structure in agricultural systems, Soil Biology and Biochemistry, 40(11):2843-2853

https://doi.org/10.1016/j.soilbio.2008.07.030

Jesus E.C., Marsh T.L., Tiedje J.M., and Fatima M.S., 2009, Changes in land use alter the structure of bacterial communities in Western Amazon soils, The ISME journal, 3(9):1004-1011

https://doi.org/10.1038/ismej.2009.47
PMid:19440233

Kuramae E.E., Yergeau E., Wong L.C., Pijl A.S., Veen J.A. and Kowalchuk G.A. 2012, Soil characteristics more strongly influence soil bacterial communities than land‐use type, FEMS Microbiology Ecology, 79(1):12-24

https://doi.org/10.1111/j.1574-6941.2011.01192.x
PMid:22066695

Li H.L., Shu M.J., Rong Q.G., and Richare D.B., 2012, Differential effects of legume species on the recovery of soil microbial communities and carbon and nitrogen contents, in abandoned fields of the Loess Plateau, China, Environmental Management, 50:1193-1203

https://doi.org/10.1007/s00267-012-9958-7
PMid:23064665

Li S.J., Sui Y.Z., Li Y.W., Sun Z.H., and Wang F.Y., 2004, Landscape pattern aim its diversities for the Maoer shan Forest Farm in Heilongjiang Province, Jouranl of Northeast Forest University, 32:1-15

Liu D., Chai T., Xia X., Gao Y., Cai Y., Li X., Miao Z., Sun L., Hao H., and Roesler U., 2012, Formation and transmission of Staphylococcus aureus (including MRSA) aerosols carrying antibiotic-resistant genes in a poultry farming environment, Science of the Total Environment, 426:139-145

https://doi.org/10.1016/j.scitotenv.2012.03.060
PMid:22542226

Mendes R., Kruijt M., Bruijn I., Dekkers E., Van Der Voort M., Schneider J.H., Piceno Y.M., DeSantis T.Z., Andersen G.L., and Bakker P. A., 2011, Deciphering the rhizosphere microbiome for disease-suppressive bacteria, Science, 332(6033):1097-1100

https://doi.org/10.1126/science.1203980
PMid:21551032

Munir M., and Xagoraraki I., 2011, Levels of antibiotic resistance genes in manure, biosolids, and fertilized soil, Journal of environmental quality, 40(1):248-255

https://doi.org/10.2134/jeq2010.0209
PMid:21488514

Pankratova E., 2006, Functioning of cyanobacteria in soil ecosystems, Eurasian Soil Science, 39(1):118-127

https://doi.org/10.1134/S1064229306130199

Pan J.F., Guo Q.X., G., and Wang H.R., 2007, Application of mountain microclimate simulation model to climate simulation to maershan region, Journal of northeast forestry university, 35:51-54

Pielou E.C., 1969, An introduction to mathematical ecology, An introduction to mathematical ecology

Rachid C.T., Santos A.L., Piccolo M.C., Balieiro F.C., Coutinho H.L., Peixoto R.S., Tiedje J.M., and Rosado A.S., 2013, Effect of Sugarcane Burning or Green Harvest Methods on the Brazilian Cerrado Soil Bacterial Community Structure, PloS one, 8(3):e59342

https://doi.org/10.1371/journal.pone.0059342
PMid:23533619 PMCid:PMC3606482

Rogers B., and Tate Iii R., 2001, Temporal analysis of the soil microbial community along a toposequence in Pineland soils, Soil Biology and Biochemistry, 33(10):1389-1401

https://doi.org/10.1016/S0038-0717(01)00044-X

Shannon C. E., 2001, A mathematical theory of communication, ACM SIGMOBILE Mobile Computing and Communications Review, 5(1):3-55

https://doi.org/10.1145/584091.584093

Su Y.Z., Zhao H.L., Zhang T.H., and Zhao X.Y., 2004, Soil properties following cultivation and non-grazing of a semi-arid sandy grassland in northern China, Soil and Tillage Research, 75(1):27-36

https://doi.org/10.1016/S0167-1987(03)00157-0

Suleiman A.K.A., Manoeli L., Boldo J.T., Pereira M.G., and Roesch L.F.W. 2013, Shifts in soil bacterial community after eight years of land-use change, Systematic and applied microbiology, 36(2):137-144

https://doi.org/10.1016/j.syapm.2012.10.007
PMid:23337029

Tangjang S., Arunachalam K., Arunachalam A., and Shukla A.K., 2009, Microbial Population Dynamics of Soil Under Traditional Agroforestry Systems in Northeast India, Research Journal of Soil Biology, 1(1):1-7

Underwood E.C., Viers J.H., Klausmeyer K.R., Cox R.L., and Shaw M.R., 2009, Threats and biodiversity in the mediterranean biome, Diversity and Distributions, 15(2):188-197

https://doi.org/10.1111/j.1472-4642.2008.00518.x

Upchurch R., Chiu C.Y., Everett K., Dyszynski G., Coleman D. C., and Whitman W.B., 2008, Differences in the composition and diversity of bacterial communities from agricultural and forest soils, Soil Biology and Biochemistry, 40(6):1294-1305

https://doi.org/10.1016/j.soilbio.2007.06.027

Valpassos M.A.R., Cavalcante E.G.S., Cassiolato A.M.R. and Alves M.C., 2001, Effects of soil management systems on soil microbial activity, bulk density and chemical properties, Pesquisa Agropecuária Brasileira, 36(12):1539-1545

https://doi.org/10.1590/S0100-204X2001001200011

Yang Y.S., Li Z.W., and Yang L.Z., 1992, Effect of forest fire on nitrogen cycling in forest ecosystems, Journal of Fujian Colleage of Forest, 12:105-111

Yu Z., and Morrison M., 2004, Comparisons of different hypervariable regions of rrs genes for use in fingerprinting of microbial communities by PCR-denaturing gradient gel electrophoresis, Applied and environmental microbiology, 70(8):4800-4806

https://doi.org/10.1128/AEM.70.8.4800-4806.2004
PMid:15294817 PMCid:PMC492348

Yu Z., Wang G., Jin J., Liu J., and Liu X., 2011, Soil microbial communities are affected more by land use than seasonal variation in restored grassland and cultivated Mollisols in Northeast China, European Journal of Soil Biology, 47(6):357-363

https://doi.org/10.1016/j.ejsobi.2011.09.001

Zhao W.Z., Xiao H.L., Liu Z.M., and Li J., 2005, Soil degradation and restoration as affected by land use change in the semiarid Bashang area, northern China, Catena, 59(2):173-186

https://doi.org/10.1016/j.catena.2004.06.004

Zeng Z.S., Sun S.M., and Qiao Q., 1983, Situation and Regulation of the Agroecosystem of the Black Soil Region in China, Soil Science, 135(1):54-58

https://doi.org/10.1097/00010694-198301000-00011