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Bacterial communities are important not only in the cycling of organic compounds but also in maintaining ecosystems. Specific bacterial groups can be affected as a result of changes in environmental conditions caused by human activities, such as agricultural practices. The aim of this study was to analyze the effects of different forms of tillage and residue management on soil bacterial communities by using phylogenetic and multivariate analyses. Treatments involving zero tillage (ZT) and conventional tillage (CT) with their respective combinations of residue management, i.e., removed residue (−R) and kept residue (+R), and maize/wheat rotation, were selected from a long-term field trial started in 1991. Analysis of bacterial diversity showed that soils under zero tillage and crop residue retention (ZT/+R) had the highest levels of diversity and richness. Multivariate analysis showed that beneficial bacterial groups such as fluorescent Pseudomonas spp. and Burkholderiales were favored by residue retention (ZT/+R and CT/+R) and negatively affected by residue removal (ZT/−R). Zero-tillage treatments (ZT/+R and ZT/−R) had a positive effect on the Rhizobiales group, with its main representatives related to Methylosinus spp. known as methane-oxidizing bacteria. It can be concluded that practices that include reduced tillage and crop residue retention can be adopted as safer agricultural practices to preserve and improve the diversity of soil bacterial communities.
Agricultural sustainability is linked to soil management and efficient use of natural and economic resources (25, 53). Sustainable handling of resources can be obtained by applying conservation agricultural practices, i.e., reduced tillage, crop residue retention, and crop rotation (26). Reduced tillage and crop residue retention have been proposed, as they facilitate water infiltration, reduce erosion, improve soil structure, increase soil organic matter and carbon content, and moderate soil temperatures (13, 16, 30, 33, 56). Compared with conventional tillage and crop residue removal, these practices can also decrease production costs by reducing the use of heavy machinery, fuels, water, and fertilizers (19, 23). The positive effect of these practices seems to be correlated with the improvement of soil structure and a higher availability of organic substrates for microorganisms (3, 30). Improved soil structure allows better soil aeration and diffusion of water and nutrients through the soil profile, while the retention of crop residues enhances microbial activity and the soil microbial biomass content (12, 28). These improvements in soil quality can also increase soil microbial diversity, thus protecting crops against pests and diseases through competition for soil nutrients (8).
Until now, most research has focused on microbial communities affected by agricultural practices, i.e., tillage and residue management, by using indicators such as plate counting and microbial biomass or by analyzing denaturing gradient gel bacterial banding patterns (21, 22, 37). Salles et al. (46) reported the use of canonical correspondence analysis on denaturing gradient gel electrophoresis banding pattern data to understand the effect of crop and land history on Burkholderia communities. However, few studies have applied phylogenetic and multivariate analyses to understand the effect of soil management practices, i.e., tillage and residue management, on microbial communities.
It is necessary to interpret the changes in microbial communities as a function of contextual environmental parameters to analyze the effect of anthropogenic activities on microbial communities (42). Once modifications in microbial communities are interpreted as a function of contextual environments, it becomes possible to determine the kind of organisms that dominate such environments and to establish whether specific practices could lead to changes in beneficial or nonbeneficial microorganisms for agro-ecosystems. Changes in microbial communities can then be related to food production, soil quality, and greenhouse gas emissions (19, 20, 36).
Govaerts et al. (19, 20, 21, 22) had previously characterized the soils used in this study. They showed that soils under zero tillage (ZT) and crop residue retention (+R) have better soil quality, crop yields, and catabolic diversity and a higher diversity of microflora groups than do soils under conventional tillage (CT) with or without crop residue retention (−R). The aim of this study was to complement the results of Govaerts et al. (19, 20, 21, 22) by using phylogenetic approaches and the additive main effect and multiplicative interactions (AMMI) model (18, 60) to analyze the effect of the above treatments on soil bacterial communities.
The study described here is part of long-term trial, which began in 1991. The research station is located near the former lake Texcoco, in the semiarid, subtropical highlands of central Mexico. The experimental design consisted of a randomized complete block with two replications, where treatments combined different wheat-maize rotations, tillage/planting methods, and residue management practices, but only four treatments were used in this research, which included the factor of disturbing soil (yes/no), referred to as conventional tillage (CT) and zero tillage (ZT), combined with the factor of residue management, i.e., removal (−R) or retention (+R) of crop residues. All four treatments were under rotation of maize and wheat. The sampling was done in the same season after the maize cropping cycle. Details of this experiment can be found in reference 22.
Soil samples were taken from replicated plots at the end of the fallow period. Ten subsamples (0 to 15 cm) were collected at random from each plot with a 2-cm-diameter auger and mixed to yield one composite sample per treatment (7). Soil used for DNA extraction was stored immediately at −70°C to avoid modification of the composition of bacterial communities. Details of the techniques used to measure pH, electrolytic conductivity, total N and C, water-holding capacity, and soil microbial biomass (SMB) can be found in reference 36.
Total DNA was directly extracted from soil by using a hybrid technique based on methods described earlier (15, 55, 58). These techniques were modified to improve the purification of extracted DNA. Five grams of soil from the composite samples of each treatment was used for this procedure (43). Soil was added to a centrifuge tube containing 25 ml 0.15 M sodium pyrophosphate (24). The tube was shaken on a vortex for 1 min, left to settle for 10 min, and centrifuged at 7,700 × g for 10 min. The supernatant was decanted and discarded. This washing step was repeated three times. Five milliliters of 0.15 M phosphate buffer (0.15 M NaH2PO4, pH 8) was added to the tube containing the sediment, shaken on a vortex for 1 min, left to settle for 10 min, and centrifuged at 7,700 × g and 15°C for 10 min. The supernatant was decanted and discarded. This step was repeated once. The sediment was resuspended in 5 ml of lysis solution I (0.15 M NaCl, 0.1 M EDTA [pH 8.0], 10 mg of lysozyme ml−1), mixed, and incubated at 37°C for 1 h. Five milliliters of lysis solution II (0.1 M NaCl, 0.5 M Tris-HCl [pH 8.0], 12% sodium dodecyl sulfate) was added, and the solution was treated with two cycles of freezing at −70°C for 20 min and thawing at 65°C for 20 min. Two milliliters of 0.15 M aluminum ammonium sulfate [AlNH4(SO4)2] was added in the first thawing cycle (6). The sample was centrifuged at 7,700 × g and 15°C for 10 min. The supernatant was transferred to a clean tube and resuspended with 2.7 ml 5 M NaCl and 2.1 ml 10% Triton X-100 in 0.7 M NaCl. The tube was shaken and incubated at 65°C for 10 min. Twelve milliliters of chloroform-isoamyl alcohol (CH3Cl-C6H12O, 24:1) was added, mixed, and centrifuged at 3,000 × g for 20 min at 15°C. The solution was transferred to a clean tube, 12 ml 13% polyethylene glycol (MW, 8,000) dissolved in 1.6 M NaCl was added, and the mixture was incubated on ice overnight and centrifuged at 12,000 × g and 4°C for 30 min. The sediment and DNA extract were resuspended in 500 μl of sterile deionized water and transferred to a sterile microcentrifuge tube. One volume of absolute ethanol was added, and the mixture was incubated at 4°C for 30 min. The tube was mixed and centrifuged at 10, 000 × g and 4°C for 30 min. The sediment was washed with 1,000 μl of cool 70% ethanol and dried at 65°C. The sediment was resuspended with 300 μl of sterile deionized water and stored at −20°C.
The universal bacterial primers 46F (5′ GCC TAA CAC ATG CAA GTC 3′) and 1540R (5′ GGT TAC CTT GTT ACG ACT T 3′) were used to amplify highly variable regions V1 to V9 of 16S rRNA genes (14, 57). The PCR products were inserted into the pCR2.1 vector using the TOPO TA cloning kit (Invitrogen, Carlsbad, CA) and then transferred into Escherichia coli DH5α (Invitrogen, Carlsbad, CA). Sequencing reactions were performed by using Big Dye Terminator version 3.1 and an ABI Prism model 3100 DNA sequencer (Applied Biosystems). The specific conditions for these procedures can be found in reference 10.
The sizes of the sequences obtained were 1,513 to 1,537 bp. All sequences were compared with reference sequences by BLAST search (2). The presence of chimeric sequences was checked with the RDP Chimera Check program. Multiple cycles of alignment based on phenetic procedures were performed with Clustal X version 1.7 to establish the best alignment conditions (54). The following settings were used: slow/accurate; gap-opening penalty, 15.00; gap extension penalty, 6.66; delay divergent sequences, 30%; DNA transition weight, 0.50; DNA weight matrix, IUB; negative matrix, off.
Phylogenetic relationships between sequences were determined by two methods, distance and maximum parsimony using PAUP* 4.0b10 software (52). The MODELTEST program (39) was used to select the best nucleotide substitution model. Distance matrixes were generated, and branch support was obtained with 100 bootstrap replicates.
For maximum-parsimony analyses, only the parsimonious informative characters were used, i.e., 820 bp for ZT/−R, 850 bp for ZT/+R, 867 bp for CT/−R, and 795 bp for CT/+R. Heuristic tree searches were done using a tree bisection reconnection model and a branch-swapping algorithm with 100 random stepwise replications. One hundred trees were saved for each pseudoreplicate. A rescaled consistency index, derived from trees obtained by unweighted analysis, was used to generate an a posteriori weighted data set. The same heuristic search conditions as used for the unweighted data were used to analyze the weighted data set. Branch support was obtained with 100 bootstrap replicates. Desulfurobacterium thermolithotrophum was used as the external group in every phylogenetic reconstruction.
Taxonomic assignment was obtained by using the Roselló-Mora and Amann prokaryote criteria (45). Phylogenetic groups were conformed at the order level for multivariate analysis.
The distance matrixes (generated with the PAUP* 4.0b10 software) were used to obtain the operational taxonomic units (OTUs) for each library. A 3% distance level between sequences was considered the cutoff among different OTUs. Rarefaction curve, richness estimator (abundance-based coverage estimator, bootstrap richness estimator, bias-corrected Chao1, and diversity indices [Shannon's H and Simpson's D]) were determined using DOTUR software version 1.51 (47) for each treatment.
LIBSHUFF analysis was used to compute the differences between the clone libraries and estimated homologous and heterologous coverage within and between libraries from different treatments (48).
The AMMI model (18, 60) was used to understand the responses of the 19 phylogenetic groups to different agricultural management practices represented by agricultural treatments ZT/+R, ZT/−R, CT/+R, and CT/+R. The AMMI model is usually used in plant breeding for studying genotype response patterns across different environmental conditions (11). In this case, the response variable was the relative proportion of the different phylogenetic groups in the four agricultural treatments and it can be represented as ij = μ + τi + δj + Σkt= 1λkαikγjk + ij, where μ is the grand mean over all genotypes and environments, τi is the additive effect of the ith genotype, δj is the additive effect of the jth environment, the constant λk is the singular value of the kth multiplicative component that is ordered λ1 ≥ λ2 ≥ … ≥ λj, and αik are elements of the kth left singular vector of the true interaction and represent the genotypic sensitivity to hypothetical environmental factors represented by the kth right singular vector with elements γjk. αik and γjk satisfy the orthonormalization constraints Σiαikαik′ = Σiγjkγjk′ = 0 for k ≠ k′ and Σiαik2= Σjγjk2= 1. This model was called the AMMI model by Zobel et al. (60) and Gauch (18). Gabriel (17) described the least-squares fit and explained how (i) the residual matrix of the genotype-environment interaction (GEI) term, Z = ij−i.−.j−.., is subjected to a singular-value decomposition after adjustment for the additive (linear) terms and (ii) the results can be interpreted in a graph called a biplot which reflects, in a reduced-dimensional graph, the most relevant information in the genotype-environment matrix.
The biplot is a graphical representation of the first two components of the AMMI model, that is, α1, α2, γ1, and γ2. While other multivariate analysis methods, such as a conventional principal-component analysis (PCA), are applied to the raw data, the AMMI model calculates the first two components by applying the additive analysis of variance to the raw data to determine the residuals of interaction (GEI term), which are then subjected to a PCA to represent, in a low dimension, the GEI effects of the different agricultural treatments on the phylogenetic groups (18).
The results of the analysis of interactions between bacterial (phylogenetic) groups and agricultural treatments (environments) are represented as a series of vectors in a two-dimensional space drawn from the origin (axis 1 = 0, axis 2 = 0) to the endpoints determined by their scores, i.e., contribution to data variation. The agricultural treatments located toward the center of the biplot (axis 1 = 0, axis 2 = 0) do not discriminate the bacterial groups, which means that the groups have the same response in terms of GEI in those agricultural treatments, whereas agricultural treatments far away from the center (axis 1 = 0, axis 2 = 0) discriminate the phylogenetic groups differently. In the same way, bacterial groups whose presence showed a negligible response to soil management were located toward the graph center (axis 1 = 0, axis 2 = 0). Conversely, bacterial groups located far from the center (axis 1 = 0, axis 2 = 0) had a noticeable response to agricultural practices.
Bacterial groups located in the same direction of a particular agricultural practice were positively affected (increased their proportion) by the corresponding practice, whereas those bacterial groups located in the opposite direction of the vector representing the agricultural practice were negatively affected by the conditions prevailing in that environment. An angle of less than 90° or greater than 270° between a phylogenetic group vector and an agricultural treatment vector indicated that the phylogenetic group had a positive response to the treatment, i.e., an increase in its relative proportion within the population. Conversely, a negative response is indicated if the angle between the vectors is between 90° and 270°.
The 16S rRNA gene sequences obtained in this study have been deposited in the GenBank database under accession numbers EU440544 to EU440729, FJ889182 to FJ889353, EU665003 to EU665174, and EU449555 to EU449738.
Initial characterization of the soil at the experimental site showed characteristics similar to those normally found in an agricultural soil of the central highlands of Mexico (Table (Table1).1). The SMB, organic C, and total N were greater in soils with retained residues (ZT/+R, CT/+R) than in soils where residues were removed (ZT/−R, CT/−R).
One hundred seventy-two clones per treatment were used for phylogenetic reconstructions using both the distance and maximum-parsimony methods. Since similar tree topologies were obtained with both methods, only results from the maximum-parsimony analysis will be discussed here.
The results of the phylogenetic analyses (see Fig. S1 to S4 in the supplemental material) show the phylogenetic relationship between the sequences amplified from each soil treatment and 16S rRNA gene GenBank sequences. While we were able to assign numerous sequences at the species level (see Fig. S1 to S4 in the supplemental material), phylogenetic groups were constructed only at the order level to reduce the number of bacterial groups for statistical analysis. The number, description, and relative proportions of groups by treatment are shown in Table Table2.2. The most abundant groups were Acidobacteriales, with relative proportions of 0.43 to 0.54; Sphingomonadales, with relative proportions of 0.14 to 0.18; Myxococcales, with relative proportions of 0.058 to 0.814; and Xanthomonadales, with relative proportions of 0.035 to 0.098. The bacterial groups found in all treatments with lower relative proportions were Actinomycetales, with relative proportions of 0.011 to 0.040; Burkholderiales, with relative proportions of 0.011 to 0.029; Gemmatimonadales, with relative proportions of 0.005 to 0.058; and Oceanospirillales, with relative proportions of 0.005 to 0.017. The Pseudomonadales group, of which the main representatives were related to fluorescent Pseudomonas spp., was found in higher proportions in soils with retained residue and zero or conventional tillage (ZT/+R and CT/+R). Conversely, the highest proportions of Acidobacteriales and Oceanospirillales group members were found in those soils with removed residues and zero or conventional tillage (ZT/−R and CT/−R).
LIBSHUFF analysis of the sequence libraries generated for each treatment indicated that the differences in their bacterial composition were due to the distinct makeup of the communities (P < 0.001). Rarefaction analysis showed that the treatments presented different degrees of diversity (Fig. (Fig.1).1). A decline in the rate of OTUs from rarefaction curves was observed only at 10 and 20% cutoffs (data not shown), indicating that at the family and order levels, the major bacterial groups were detected. Analysis with the DOTUR software also showed that ZT/+R soils had greater richness and diversity than CT/+R soils and those with removed residues (ZT/−R and CT/−R) (Table (Table33).
Figure Figure22 shows the relationship between bacterial groups and management practices according to AMMI analysis. The ZT/+R, ZT/−R, and CT/+R treatments had the most important effect on phylogenetic groups, while a discriminable effect was revealed by CT/−R, as shown by its lower vector magnitude. The phylogenetic groups that showed a noticeable response to the soil treatment were Acidobacteriales, Actinomycetales, Burkholderiales, Caulobacteriales, Gemmatimonadales, Pseudomonadales, Rhizobiales, Rhodospirillales, Sphingomonadales, and Xanthomonadales. For example, treatment ZT/+R had a positive effect on the presence of Pseudomonadales, Xanthomonadales, Rhizobiales, Gemmatimonadales, Sphingomonadales, and Rhodospirillales and a negative effect on the presence of Acidobacteriales and Actinomycetales (Fig. (Fig.2).2). Similarly, treatment CT/+R had a positive effect on the presence of Burkholderiales, Caulobacteriales, Pseudomonadales, Sphingomonadales, and Acidobacteriales and a negative effect on the presence of Rhizobiales, Gemmatimonadales, and Rhodospirillales (Fig. (Fig.2).2). Treatments with zero tillage and residue removal (ZT/−R) had a positive effect on the presence of Rhodospirillales, Gemmatimonadales, Rhizobiales, Actinomycetales, and Acidobacteriales and reduced the abundance of Xanthomonadales, Pseudomonadales, Sphingomonadales, Burkholderiales, and Caulobacteriales. Treatment CT/−R had a negative effect on the presence of Acidobacteriales and Actinomycetales. Other phylogenetic groups, i.e., those that were grouped near the center of the graphs, such as Bacillales, Caldilineales, Chromatiales, Clostridiales, Legionellales, Myxococcales, Oceanospirillales, Rubrobacteriales, and unclassified Gammaproteobacteria, were less affected by management practices. These analyses support the conclusions of previous studies that agricultural management practices can affect the bacterial composition of soil.
The effects of agricultural practices on the environment and the quality of soil are globally evident. For example, changes in soil nutrient cycling lead to losses of inorganic N, resulting in the contamination of aquifers and rivers and reduced yields. At the same time, a deterioration of soil structure results in soil compaction and in a decrease in organic matter and soil microbial biomass. Therefore, conservation agriculture has been proposed as a sustainable alternative to conventional agriculture practices.
Conservation agricultural practices, such as crop rotation, reduced tillage, and retention of crop residue, are believed to improve soil quality while maintaining or increasing food production (19, 20, 30). At the microbial level, changes in soil quality due to the use of different management practices are expected not only to affect total soil microbial biomass but also to alter the amount of populations in the microbial community, as revealed by the richness and diversity analyses of this study (Table (Table3).3). Changes in microbial populations could have important effects on nutrient cycling and on other processes directly related to crop sustainability and greenhouse gas emissions, such as nitrification, denitrification, and CH4 oxidation (32, 49).
The soils analyzed in this study had been previously investigated by Govaerts et al. (19, 20, 21, 22). They showed the effect of tillage and residue management on characteristics such as physical and chemical soil characteristics, crop yields, soil structure, catabolic activity, and microflora groups. We were able to confirm some of these results, as shown in Tables Tables11 and and22 and Fig. Fig.22 (also see Fig. S1 to S4 in the supplemental material). We have used phylogenetic and AMMI analyses to investigate the composition of bacterial communities in agricultural soils from the central highlands of Mexico by analyzing treatments such as zero and conventional tillage (ZT/CT), kept and removed residue (+R/−R), and maize and wheat rotation.
AMMI analysis showed that the presence of the Pseudomonadales group, mostly represented by the fluorescent Pseudomonas spp. (see Fig. S2 to S4 in the supplemental material), was mainly favored by retained residues (CT/+R and ZT/+R) (Fig. (Fig.2).2). It is known that members of this group, i.e., Pseudomonas fluorescens and P. putida, participate in several beneficial soil processes, including the suppression of plant root pathogens (35) and the reduction of soilborne diseases in natural suppressive soils (41). In addition, Pseudomonadales are capable of rapid growth as a result of fresh organic material inputs, and their presence in an ecosystem might indicate better soil fertility (38). This is consistent with our results, where the removal of crop residue with zero tillage, i.e., ZT/−R, negatively affected this group since the Pseudomonadales are located on the opposite side of the vector for ZT/−R.
The presence of Rhodospirillales and Burkholderiales was also favored by ZT/+R and CT/+R, respectively (Fig. (Fig.2).2). Organisms identified in this analysis, such as Azospirillum spp., Burkholderia cepacia, and Herbaspirillum seropedicae, are agriculturally and environmentally important due to their atmospheric nitrogen fixation, biological plant pathogen control, and plant growth stimulation capabilities (4, 46, 50).
Another bacterial group whose presence was favored by zero tillage with residue retention (ZT/+R) and negatively affected by zero tillage with residue removal (ZT/−R) was that of the Xanthomonadales. This group includes bacteria frequently associated with plant roots and well-known plant pathogens, e.g., Xanthomonas campestris and Lysobacter enzymogenes (1, 51). It is interesting that the treatments that favor the presence of Xanthomonadales have a similar effect not only on members of the Pseudomonadales but also on the Burkholderiales, known to counteract plant pathogens and promote plant growth as mentioned before (Fig. (Fig.2).2). These observations suggest that a balance between those bacterial groups can contribute to adequate soil quality and improve crop yield (19, 20, 21).
The Sphingomonadales and Acidobacteriales groups were also sensitive to agricultural practices, i.e., tillage and residue management. Residue management affected the Sphingomonadales group, as treatments with residue retention (CT/+R) had a positive effect on this group, while treatments with residue removal (ZT/−R) a negative effect. Organisms related to the genus Kaistobacter mainly represented the Sphingomonadales group, about whose possible functional role in soil ecosystems no information is available. Acidobacteriales have been found in a high proportion in many soils (29, 40). In our study, the Acidobacteriales group was the most abundant group, being negatively affected by ZT/+R and CT/−R treatments and positively affected by CT/+R and ZT/−R treatments. Since little information is available about their functional role in soil processes, further studies are necessary in order to fully understand the function and importance of these groups for soil quality.
The Rhizobiales group was positively affected by zero tillage, i.e., ZT/+R and ZT/−R treatments. This might have important environmental implications, as most representatives of the former were related to Methylosinus spp., reported as type II methanotrophs capable of methane oxidation (31, 34). The fact that zero tillage with residue retention is known to improve soil structure and, as a consequence, aeration and nutrient transfer through the soil profile, could explain the observed increase in the members of the Methylosinus group. These treatments are expected to facilitate methane oxidation, making the mitigation of certain greenhouse gas emissions a possible new advantage of conservation agricultural practices, i.e., reduced tillage and residue retention (27, 59).
Myxococcales and Oceanospirillales were some of the groups less affected by agricultural practices. Myxococcales are well known as specialists in the degradation of biomacromolecules, as some members of this group are able to decompose cellulose (44). Within the Oceanospirillales group, the main representatives were related to Halomonas spp. These organisms are capable of dissimilatory nitrate reduction and, like other less abundant groups, may have pivotal roles in soil ecology, as suggested by their abilities to process important soil nutrients (5, 9).
Our results show that the greatest values for C and N content, SMB, and microbial diversity were found in ZT/+R soils. Therefore, it appears safe to conclude that zero tillage, when combined with the right residue management strategy and crop sequence, can increase microbial richness, strengthen the presence of beneficial bacterial groups in soil, and contribute to the balance between beneficial bacteria and plant pathogens, thus contributing to the sustainability of agricultural practices. This analysis also indicated that higher rates of methane oxidation could be expected from soils with ZT/+R, since this treatment favored the abundance of methane-oxidizing bacteria from the Rhizobiales group. Analysis of methane dynamics is currently being done to confirm our current results.
This research was funded by the Department of Biotechnology and Bioengineering (Cinvestav, Mexico). J.A.C.-N., F.N.R.-O., and L.P.-Z. received grant-aided support from the Consejo Nacional de Ciencia y Tecnología (CONACyT, Mexico).
We thank the International Maize and Wheat Improvement Center (CIMMYT, Int.) for soil samples.
Published ahead of print on 9 April 2010.
†Supplemental material for this article may be found at http://aem.asm.org/.