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ANOVA revealed that the effects of tillage on soil water content, DOC, and soil bulk density were significant (P < 0.05), and soil water content and soil bulk density were higher in no-tillage with residue (NTS) than in moldboard plow with residue return (MPS) and moldboard plow without residue return (MPN) (P < 0.05); DOC was higher in NTS and MPS than in MPN (P < 0.05). The highest soil NH4+-N, NO3−-N, and DTN were found in NTS, although the effects were not significant (Table 1).
Table 1. Soil physicochemical properties under different tillage treatments (means ± SE)
Samples NH4+-N / (mg/kg) NO3−-N / (mg/kg) SWC / % DOC / (mg/kg) DTN / (mg/kg) SBD / (g/cm3) NTS 2.08±0.10 1.99±0.43 17.34±0.58a 75.22±3.22a 54.41±1.92 1.36±0.03a MPS 1.86±0.07 1.88±0.19 13.01±1.19b 73.19±1.59a 45.02±5.44 1.18±0.02b MPN 1.90±0.02 1.84±0.28 12.17±0.12b 56.54±6.93b 48.58±5.93 1.21±0.01b F values of ANOVA Tillage 2.81 0.06 9.93* 5.18* 0.98 22.15** Notes: NTS represents no-tillage with residue (maize straw) return; MPS represents moldboard plow with residue return (MPS); MPN represents moldboard plow without residue return; SWC and SBD are abbreviations of soil water content and soil bulk density, respectively; NO3−-N, soil nitrate (NO3−) contents; NH4+-N, soil ammonium (NH4+) contents; DOC, soil dissolved organic carbon; DTN, soil dissolved total nitrogen; different letters in each tillage treatment indicate significant differences * P < 0.05; **P < 0.01 -
All filtered reads were aligned against the NCBI-NR database. Soil bacterial community composition (Adonis, P = 0.008, R2 = 0.55) and archaeal composition (Adonis, P = 0.016, R2 = 0.62) differed significantly among tillage treatments, and PCoA plot showed that soil bacterial community composition (genus level) under NTS was obviously apart from MPN, and soil archaeal composition under NTS was apart from both MPN and MPS (Fig. 2). While soil fungal composition was not significantly different among tillage treatments (Adonis, P = 0.052).
Figure 2. Principal coordinate analysis (PCoA) plots based on Bray-Curtis distances between samples presenting bacterial, archaeal, and fungal communities (genus level) in soils of different tillage regimes encompassing NTS, MPS, and MPN. NTS represents no-tillage with residue (maize straw) return; MPS represents moldboard plow with residue return (MPS); MPN represents moldboard plow without residue return
Members of soil bacterial and archaeal communities were further analyzed in this study. Abundant phyla and genera with a relative abundance of over 1% were shown (Fig. 3, Table 2), among these bacterial phyla, Actinobacteria was the most abundant taxa with an abundance percentage of 36.20% trailed by Proteobacteria (30.27%), Acidobacteria (8.25%), Gemmatimonadetes (5.94%) and Chloroflexi (5.02%) (Fig. 3). For archaeal phyla, Thaumarchaeota (77.39%) and Euryarchaeota (15.50%) predominated in all treatments (Fig. 3). Several abundant bacterial and archaeal phyla were significantly affected by tillage practice (Fig. 3, Table 2), for example, soil bacteria phyla including Acidobacteria, Gemmatimonadetes, and Chloroflexi significantly differed between MPN and another two treatments (NTS and MPS), while did not change between NTS and MPS (Fig. 3, Table 2). Specifically, Chloroflexi was significantly less abundant under NTS and MPS than MPN, while Gemmatimonadetes and Acidobacteria were more abundant under NTS and MPS than MPN (Table 2). Soil archaeal phyla including unclassified Archaea and Candidatus Bathyarchaeota showed higher relative abundance under NTS than MPN (Fig. 3, Table 2).
Figure 3. Compositions of the soil bacterial and archaeal communities at phylum levels under NTS, MPS, and MPN treatments. Groups accounting for < 1% are integrated into others. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
Table 2. The results of the post-hoc LSD test on the abundant soil microbial taxa (phylum and genus level) that were significantly affected by tillage regimes
Taxonomy Name NTS vs MPS NTS vs MPN MPS vs MPN Mean Difference P Mean Difference P Mean Difference P Bacteria (Phylum) Acidobacteria 0.0063 0.050 0.0129** 0.003 0.0065* 0.046 Gemmatimonadetes –0.0028 0.505 0.0098* 0.045 0.0126* 0.018 Chloroflexi –0.0045 0.054 –0.0109*** 0.001 –0.0063* 0.015 Fungi (Phylum) Basidiomycota –0.0695* 0.014 –0.0510* 0.044 0.0185 0.393 Unclassified Fungi –0.0180 0.450 –0.0721* 0.018 –0.0541 0.052 Archaea (Phylum) Unclassified Archaea 0.0017 0.070 0.0029** 0.009 0.0012 0.165 Candidatus Bathyarchaeota 0.0045 0.065 0.0064* 0.019 0.0019 0.165 Bacteria (Genus) Bradyrhizobium 0.0075*** 0.001 0.0120*** 0.000 0.0045** 0.009 Gemmatirosa –0.0020 0.425 0.0066* 0.030 0.0086** 0.010 Sphingomonas –0.0093* 0.035 –0.0106* 0.021 –0.0014 0.707 Unclassified Chloroflexi –0.0031 0.063 –0.0020** 0.003 –0.0036* 0.037 Candidatus Solibacter 0.0021* 0.050 0.0043** 0.002 0.0023* 0.034 Reyranella 0.0045** 0.002 0.0056*** 0.001 0.0011 0.257 Unclassified Geodermatophilaceae –0.0039** 0.005 –0.0050** 0.002 –0.0010 0.303 Rubrobacter –0.0054 0.086 –0.0113** 0.005 –0.0060 0.061 Fungi (Genus) Rhizopus –0.0021 0.410 –0.0077* 0.018 –0.0056 0.058 Pseudogymnoascus –0.0098* 0.013 –0.0041 0.194 0.0057 0.087 Fonsecaea –0.0018 0.466 –0.0111** 0.003 –0.0093** 0.008 Spizellomyces –0.0076** 0.003 –0.0021 0.224 0.0054* 0.013 Rhodotorula –0.0133** 0.013 –0.0040 0.338 0.0093 0.051 Archaea (Genus) Nitrososphaera –0.0640* 0.042 –0.0784* 0.020 –0.0144 0.583 Candidatus Nitrosocosmicus –0.0044 0.287 –0.0129* 0.014 –0.0085 0.066 Unclassified Archaea 0.0017 0.070 0.0029** 0.009 0.0012 0.165 Unclassified Candidatus Bathyarchaeota 0.0045 0.065 0.0064* 0.019 0.0019 0.387 Notes: * P < 0.05; ** P < 0.01; *** P < 0.001 For the bacterial genera (Table 2), a total of 21 abundant genera accounted for about 42.70% of all the reads (data not shown). Bradyrhizobium (Rhizobiales) and Candidatus Solibacter significantly differed among three tillage treatments with a relative abundance of NTS >MPS >MPN. Reyranella exhibited significantly higher relative abundance under NTS than MPS and MPN (Table 2), while Sphingomonas and unclassified Geodermatophilaceae exhibited significantly lower relative abundance under NTS than MPS and MPN (Table 2). For the archaeal genera, unclassified Candidatus Bathyarchaeota showed higher relative abundance under NTS than MPN. Candidatus Nitrosocosmicus showed significantly lower relative abundance under NTS than MPN, and Nitrososphaera showed significantly lower relative abundance under NTS than MPS and MPN (Table 2).
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A total of 407 level-3 KEGG pathways were obtained across all sampling sites, and KEGG pathways with a relative abundance > 1% were analyzed (Fig. 4). The dominant pathways were carbon metabolism, biosynthesis of amino acids, ABC transporters, pyrimidine metabolism, and quorum sensing with 5.1%, 4.5%, 3.0%, 2.9%, and 2.7% of the total annotated genes, respectively. Compared with MPN, ABC transporters and quorum sensing were more abundant in soils under NTS, and alanine, aspartate, and glutamate metabolism, pyrimidine metabolism, and carbon metabolism were less abundant in soils under NTS (Fig. 4); starch and sucrose metabolism was more abundant in soils under MPS; citrate cycle, pyrimidine metabolism, alanine, aspartate, and glutamate metabolism and purine metabolism were less abundant in soils under MPS (Fig. 4). There were no significantly different pathways between MPS and NTS (Fig. 4). Furtherly, at KO (KEGG Orthology) level, there was statistically divergence (Adonis, P = 0.019, R2 = 0.40) among different tillage regimes, and PCoA plot showed that MPN was apart from NTS and MPS (Fig. 5), while NTS and MPS were relatively closer. The top 50 abundant KO functional categories were further analyzed to show the different categories among tillage regimes (Fig. 6a). Specifically, compared with MPN, the four KO functional categories (K01997, K01998, K01999, and K02051) were more abundant in soils under NTS (Fig. 6a) representing ABC transporters and quorum sensing pathways. A total of six KO functional categories (including K03657, K01992, K03086, K02027, K00384, and K03798) were less abundant in soils under NTS representing selenocompound metabolism, nucleotide excision repair, mismatch repair, flagellar assembly, and ABC transporters pathways. The two KO functional categories (K03088 and K06147) representing transcription and ABC transporters were overrepresented in soils under MPS compared to MPN; a total of seven KO functional categories including amino acid and nucleotide metabolism (K01955, K00548, K00525), fatty acid degradation (K00626), carbon and carbohydrate metabolism (K00626 and K01681) and others (K03086 and K02355) were underrepresented in soils under MPS than MPN (Fig. 6a).
Figure 4. Statistical Analysis of Metagenomic Profiles (STAMP) showing the differentially abundant metabolic pathways among the three tillage regimes. Welch’s two-sided t-test for each different two tillage treatments with one filter (P < 0.05) to generate extended error bar figures. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
Figure 5. Principal coordinate analysis (PCoA) based on Bray-Curtis distances between samples presenting microbial function composition at KO level in soils of different tillage regimes encompassing NTS, MPS, and MPN. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
Figure 6. (a) Statistical Analysis of Metagenomic Profiles (STAMP) showing the differentially abundant KO functional categories between tillage regimes. Welch’s two-sided t-test for each different pair of tillage treatments with one filter (P < 0.05) to generate extended error bar figures. (b) Correlation heatmap of different KO functional categories between groups shown in (a) and soil environmental factors. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return. SWC, soil water content; SBD, soil bulk density; NH4+-N, soil ammonium (NH4+) contents; NO3−-N, soil nitrate (NO3−) contents; DOC, soil dissolved organic carbon; DTN, soil dissolved total nitrogen
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Regression analysis revealed that changes in microbial functional composition (KO level) were highly in line with changes in bacterial composition (Fig. 7), and functional contribution analysis showed that Solirubrobacter, Sphingomonas, and Bradyrhizobium contributed most to those different KO functional categories, notably, Bradyrhizobium contributed most strongly to K01997, K01998, and K01999 (Fig. 8).
Figure 7. Regression analysis of functional composition (KO level) and bacterial, archaeal composition (genus level) based on the Bray-Curtis dissimilarity values for the abundance data. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
Figure 8. Contribution analysis of the top 20 abundant genera to the different KO functional categories among three tillage treatments. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
We further established the relationships between soil taxonomic, functional compositions and several soil environmental factors (DOC, DTN, NO3−-N, NH4+-N, soil water content, and soil bulk density), and db-RDA analysis revealed that environmental factors explained 47.90% and 70.70% of the variability of the bacterial and archaeal composition, respectively. Among them, soil water content (R2 = 0.790, P = 0.013), DOC (R2 = 0.669, P = 0.026), and soil bulk density (R2 = 0.669, P = 0.026) were the main environmental factors that contributed to the soil bacterial composition differences (genus level), whereas DOC (R2 = 0.669, P = 0.02) was the key factor that influenced the archaeal community structure. Additionally, soil bulk density (R2 = 0.817, P = 0.011) and DOC (R2 = 0.658, P = 0.007) were the main influential factors that contributed to differences in the soil microbial function (Fig. 9).
Figure 9. Distance-based redundancy analysis (db-RDA) between the bacterial (a), archaeal communities (b), KO functional categories (c), and soil environmental factors. Environmental variables which significantly affected bacterial, archaeal communities, and functional compositions are marked with * (P < 0.05) based on the results of envfit analysis. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return. SWC, soil water content; SBD, soil bulk density NH4+-N, soil ammonium (NH4+) contents; NO3−-N, soil nitrate (NO3−) contents; DOC, soil dissolved organic carbon; DTN, soil dissolved total nitrogen
More abundant KO functional genes in NTS soil involved in ABC transporters and quorum sensing such as K01997, K01998, and K01999 were significantly positively correlated with dissolved organic carbon (DOC) (Fig. 6b). Besides, K00384 was significantly negatively correlated with soil DOC (Fig. 6b).
Residue Return Effects Outweigh Tillage Effects on Soil Microbial Communities and Functional Genes in Black Soil Region of Northeast China
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Abstract: Conservation tillage as an effective alternative to mitigate soil degradation has attracted worldwide attention, but the influences of conservation tillage on soil microbial community and especially function remain unclear. Shotgun metagenomics sequencing was performed to examine the taxonomic and functional community variations of black soils under three tillage regimes, namely no-tillage with residue (maize straw) return (NTS), moldboard plow with residue return (MPS), and moldboard plow without residue return (MPN) in Northeast China. The results revealed: 1) Soil bacterial and archaeal communities differed significantly under different tillage regimes in contrast to soil fungal community. 2) The overlay of less tillage and residues return under NTS led to unique soil microbial community composition and functional composition. Specifically, in contrast to other treatments, NTS increased the relative abundances of some taxa such as Bradyrhizobium, Candidatus Solibacter, and Reyranella, along with the relative abundances of some taxa such as Sphingomonas, Unclassified Chloroflexi and Nitrososphaera decreased; NTS had a unique advantage of increasing the relative abundances of genes involved in ‘ATP-binding cassette (ABC) transporters’ and ‘quorum sensing (QS)’ pathways, while MPN favored the genes involved in ‘flagellar assembly’ pathway and some metabolic pathways such as ‘carbon’ and ‘glyoxylate and dicarboxylate’ and ‘selenocompound’ metabolisms. 3) Significantly different soil bacterial phyla (Acidobacteria, Gemmatimonadetes, and Chloroflexi) and metabolic pathways existed between MPN and another two treatments (NTS and MPS), while did not exist between NTS and MPS. 4) Dissolved organic carbon (DOC) and soil bulk density were significantly affected (P < 0.05) by tillage and accounted for the variance both in microbial (bacterial) community structure and functional composition. These results indicated that a change in tillage regime from conventional to conservation tillage results in a shift of microbial community and functional genes, and we inferred that residue return played a more prominent role than less tillage in functional shifts in the microbial community of black soils.
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Figure 2. Principal coordinate analysis (PCoA) plots based on Bray-Curtis distances between samples presenting bacterial, archaeal, and fungal communities (genus level) in soils of different tillage regimes encompassing NTS, MPS, and MPN. NTS represents no-tillage with residue (maize straw) return; MPS represents moldboard plow with residue return (MPS); MPN represents moldboard plow without residue return
Figure 3. Compositions of the soil bacterial and archaeal communities at phylum levels under NTS, MPS, and MPN treatments. Groups accounting for < 1% are integrated into others. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
Figure 4. Statistical Analysis of Metagenomic Profiles (STAMP) showing the differentially abundant metabolic pathways among the three tillage regimes. Welch’s two-sided t-test for each different two tillage treatments with one filter (P < 0.05) to generate extended error bar figures. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
Figure 5. Principal coordinate analysis (PCoA) based on Bray-Curtis distances between samples presenting microbial function composition at KO level in soils of different tillage regimes encompassing NTS, MPS, and MPN. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
Figure 6. (a) Statistical Analysis of Metagenomic Profiles (STAMP) showing the differentially abundant KO functional categories between tillage regimes. Welch’s two-sided t-test for each different pair of tillage treatments with one filter (P < 0.05) to generate extended error bar figures. (b) Correlation heatmap of different KO functional categories between groups shown in (a) and soil environmental factors. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return. SWC, soil water content; SBD, soil bulk density; NH4+-N, soil ammonium (NH4+) contents; NO3−-N, soil nitrate (NO3−) contents; DOC, soil dissolved organic carbon; DTN, soil dissolved total nitrogen
Figure 7. Regression analysis of functional composition (KO level) and bacterial, archaeal composition (genus level) based on the Bray-Curtis dissimilarity values for the abundance data. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return
Figure 9. Distance-based redundancy analysis (db-RDA) between the bacterial (a), archaeal communities (b), KO functional categories (c), and soil environmental factors. Environmental variables which significantly affected bacterial, archaeal communities, and functional compositions are marked with * (P < 0.05) based on the results of envfit analysis. NTS, no-tillage with residue (maize straw) return; MPS, moldboard plow with residue return; MPN, moldboard plow without residue return. SWC, soil water content; SBD, soil bulk density NH4+-N, soil ammonium (NH4+) contents; NO3−-N, soil nitrate (NO3−) contents; DOC, soil dissolved organic carbon; DTN, soil dissolved total nitrogen
Table 1. Soil physicochemical properties under different tillage treatments (means ± SE)
Samples NH4+-N / (mg/kg) NO3−-N / (mg/kg) SWC / % DOC / (mg/kg) DTN / (mg/kg) SBD / (g/cm3) NTS 2.08±0.10 1.99±0.43 17.34±0.58a 75.22±3.22a 54.41±1.92 1.36±0.03a MPS 1.86±0.07 1.88±0.19 13.01±1.19b 73.19±1.59a 45.02±5.44 1.18±0.02b MPN 1.90±0.02 1.84±0.28 12.17±0.12b 56.54±6.93b 48.58±5.93 1.21±0.01b F values of ANOVA Tillage 2.81 0.06 9.93* 5.18* 0.98 22.15** Notes: NTS represents no-tillage with residue (maize straw) return; MPS represents moldboard plow with residue return (MPS); MPN represents moldboard plow without residue return; SWC and SBD are abbreviations of soil water content and soil bulk density, respectively; NO3−-N, soil nitrate (NO3−) contents; NH4+-N, soil ammonium (NH4+) contents; DOC, soil dissolved organic carbon; DTN, soil dissolved total nitrogen; different letters in each tillage treatment indicate significant differences * P < 0.05; **P < 0.01 Table 2. The results of the post-hoc LSD test on the abundant soil microbial taxa (phylum and genus level) that were significantly affected by tillage regimes
Taxonomy Name NTS vs MPS NTS vs MPN MPS vs MPN Mean Difference P Mean Difference P Mean Difference P Bacteria (Phylum) Acidobacteria 0.0063 0.050 0.0129** 0.003 0.0065* 0.046 Gemmatimonadetes –0.0028 0.505 0.0098* 0.045 0.0126* 0.018 Chloroflexi –0.0045 0.054 –0.0109*** 0.001 –0.0063* 0.015 Fungi (Phylum) Basidiomycota –0.0695* 0.014 –0.0510* 0.044 0.0185 0.393 Unclassified Fungi –0.0180 0.450 –0.0721* 0.018 –0.0541 0.052 Archaea (Phylum) Unclassified Archaea 0.0017 0.070 0.0029** 0.009 0.0012 0.165 Candidatus Bathyarchaeota 0.0045 0.065 0.0064* 0.019 0.0019 0.165 Bacteria (Genus) Bradyrhizobium 0.0075*** 0.001 0.0120*** 0.000 0.0045** 0.009 Gemmatirosa –0.0020 0.425 0.0066* 0.030 0.0086** 0.010 Sphingomonas –0.0093* 0.035 –0.0106* 0.021 –0.0014 0.707 Unclassified Chloroflexi –0.0031 0.063 –0.0020** 0.003 –0.0036* 0.037 Candidatus Solibacter 0.0021* 0.050 0.0043** 0.002 0.0023* 0.034 Reyranella 0.0045** 0.002 0.0056*** 0.001 0.0011 0.257 Unclassified Geodermatophilaceae –0.0039** 0.005 –0.0050** 0.002 –0.0010 0.303 Rubrobacter –0.0054 0.086 –0.0113** 0.005 –0.0060 0.061 Fungi (Genus) Rhizopus –0.0021 0.410 –0.0077* 0.018 –0.0056 0.058 Pseudogymnoascus –0.0098* 0.013 –0.0041 0.194 0.0057 0.087 Fonsecaea –0.0018 0.466 –0.0111** 0.003 –0.0093** 0.008 Spizellomyces –0.0076** 0.003 –0.0021 0.224 0.0054* 0.013 Rhodotorula –0.0133** 0.013 –0.0040 0.338 0.0093 0.051 Archaea (Genus) Nitrososphaera –0.0640* 0.042 –0.0784* 0.020 –0.0144 0.583 Candidatus Nitrosocosmicus –0.0044 0.287 –0.0129* 0.014 –0.0085 0.066 Unclassified Archaea 0.0017 0.070 0.0029** 0.009 0.0012 0.165 Unclassified Candidatus Bathyarchaeota 0.0045 0.065 0.0064* 0.019 0.0019 0.387 Notes: * P < 0.05; ** P < 0.01; *** P < 0.001 -
[1] Antoun H, Beauchamp C J, Goussard N et al., 1998. Potential of Rhizobium and Bradyrhizobium species as plant growth promoting rhizobacteria on non-legumes: effect on radishes (Raphanus sativus L.). Plant and Soil, 204(1): 57–67. doi: 10.1023/A:1004326910584 [2] Babin D, Deubel A, Jacquiod S et al., 2019. Impact of long-term agricultural management practices on soil prokaryotic communities. Soil Biology and Biochemistry, 129: 17–28. doi: 10.1016/j.soilbio.2018.11.002 [3] Bu R Y, Ren T, Lei M J et al., 2020. Tillage and straw-returning practices effect on soil dissolved organic matter, aggregate fraction and bacteria community under rice-rice-rapeseed rotation system. Agriculture, Ecosystems & Environment, 287: 106681. doi: 10.1016/j.agee.2019.106681 [4] Buchfink B, Xie C, Huson D H, 2015. Fast and sensitive protein alignment using DIAMOND. Nature Methods, 12(1): 59–60. doi: 10.1038/nmeth.3176 [5] Chen X L, Henriksen T M, Svensson K et al., 2020. Long-term effects of agricultural production systems on structure and function of the soil microbial community. Applied Soil Ecology, 147: 103387. doi: 10.1016/j.apsoil.2019.103387 [6] Chevrot R, Rosen R, Haudecoeur E et al., 2006. GABA controls the level of quorum-sensing signal in Agrobacterium tumefaciens. Proceedings of the National Academy of Sciences of the United States of America, 103(19): 7460–7464. doi: 10.1073/pnas.0600313103 [7] Degrune F, Dufrêne M, Colinet G et al., 2015. A novel sub-phylum method discriminates better the impact of crop management on soil microbial community. Agronomy for Sustainable Development, 35(3): 1157–1166. doi: 10.1007/s13593-015-0291-4 [8] Degrune F, Theodorakopoulos N, Dufrêne M et al., 2016. No favorable effect of reduced tillage on microbial community diversity in a silty loam soil (Belgium). Agriculture, Ecosystems & Environment, 224: 12–21. doi: 10.1016/j.agee.2016.03.017 [9] Dhaliwal S S, Naresh R K, Gupta R K et al., 2020. Effect of tillage and straw return on carbon footprints, soil organic carbon fractions and soil microbial community in different textured soils under rice-wheat rotation: a review. Reviews in Environmental Science and Bio/Technology, 19(1): 103–115. doi: 10.1007/s11157-019-09520-1 [10] Dong W Y, Liu E K, Yan C R et al., 2017. Impact of no tillage vs. conventional tillage on the soil bacterial community structure in a winter wheat cropping succession in northern China. European Journal of Soil Biology, 80: 35–42. doi: 10.1016/j.ejsobi.2017.03.001 [11] Hao J Q, Lin Y, Ren G X et al., 2021. Comprehensive benefit evaluation of conservation tillage based on BP neural network in the Loess Plateau. Soil and Tillage Research, 205: 104784. doi: 10.1016/j.still.2020.104784 [12] Hao M M, Hu H Y, Liu Z et al., 2019. Shifts in microbial community and carbon sequestration in farmland soil under long-term conservation tillage and straw returning. Applied Soil Ecology, 136: 43–54. doi: 10.1016/j.apsoil.2018.12.016 [13] Harper J K, Roth G W, Garalejić B et al., 2018. Programs to promote adoption of conservation tillage: a Serbian case study. Land Use Policy, 78: 295–302. doi: 10.1016/j.landusepol.2018.06.028 [14] Kanokratana P, Uengwetwanit T, Rattanachomsri U et al., 2011. Insights into the phylogeny and metabolic potential of a primary tropical peat swamp forest microbial community by metagenomic analysis. Microbial Ecology, 61(3): 518–528. doi: 10.1007/s00248-010-9766-7 [15] Kravchenko Y S; Zhang X Y; Liu X B et al., 2011. Mollisols properties and changes in Ukraine and China. Chinese Geographical Science, 21(3): 257–266. doi: 10.1007/s11769-011-0467-z [16] Lang J, Faure D, 2014. Functions and regulation of quorum-sensing in Agrobacterium tumefaciens. Frontiers in Plant Science, 5: 14. doi: 10.3389/fpls.2014.00014 [17] Legrand F, Picot A, Cobo-Díaz J F et al., 2018. Effect of tillage and static abiotic soil properties on microbial diversity. Applied Soil Ecology, 132: 135–145. doi: 10.1016/j.apsoil.2018.08.016 [18] Li D H, Liu C M, Luo R B et al., 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics, 31(10): 1674–1676. doi: 10.1093/bioinformatics/btv033 [19] Li M, He P, Guo X L et al., 2021. Fifteen-year no tillage of a Mollisol with residue retention indirectly affects topsoil bacterial community by altering soil properties. Soil and Tillage Research, 205: 104804. doi: 10.1016/j.still.2020.104804 [20] Li R, Li Y, Kristiansen K et al., 2008. SOAP: short oligonucleotide alignment program. Bioinformatics, 24(5): 713–714. doi: 10.1093/bioinformatics/btn025 [21] Li W, Godzik A, 2006. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 22(13): 1658–1659. doi: 10.1093/bioinformatics/btl158 [22] Li Y, Li Z, Cui S et al., 2019. Residue retention and minimum tillage improve physical environment of the soil in croplands: a global meta-analysis. Soil and Tillage Research, 194: 104292. doi: 10.1016/j.still.2019.06.009 [23] Li Y, Zhang Q P, Cai Y J et al., 2020a. Minimum tillage and residue retention increase soil microbial population size and diversity: implications for conservation tillage. Science of the Total Environment, 716: 137164. doi: 10.1016/j.scitotenv.2020.137164 [24] Li Y Z, Song D P, Liang S H et al., 2020b. Effect of no-tillage on soil bacterial and fungal community diversity: a meta-analysis. Soil and Tillage Research, 204: 104721. doi: 10.1016/j.still.2020.104721 [25] Lin B, Lu Y H, 2015. Bacterial and archaeal guilds associated with electrogenesis and methanogenesis in paddy field soil. Geoderma, 259–260: 362–369. doi: 10.1016/j.geoderma.2015.03.001 [26] Liu F T, Kou D, Chen Y L et al., 2021b. Altered microbial structure and function after thermokarst formation. Global Change Biology, 27(4): 823–835. doi: 10.1111/gcb.15438 [27] Liu Y X, Qin Y, Chen T et al., 2021a. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein & Cell, 12(5): 315–330. doi: 10.1007/s13238-020-00724-8 [28] Miura T, Niswati A, Swibawa I G et al., 2016. Shifts in the composition and potential functions of soil microbial communities responding to a no-tillage practice and bagasse mulching on a sugarcane plantation. Biology and Fertility of Soils, 52(3): 307–322. doi: 10.1007/s00374-015-1077-1 [29] Moran M A, 2009. Metatranscriptomics: eavesdropping on complex microbial communities. Microbe, 4(7): 329–335. doi: 10.1128/microbe.4.329.1 [30] Navarro-Noya Y E, Gómez-Acata S, Montoya-Ciriaco N et al., 2013. Relative impacts of tillage, residue management and crop-rotation on soil bacterial communities in a semi-arid agroecosystem. Soil Biology & Biochemistry, 65: 86–95. doi: 10.1016/j.soilbio.2013.05.009 [31] Nelkner J, Henke C, Lin T W et al., 2019. Effect of long-term farming practices on agricultural soil microbiome members represented by metagenomically assembled genomes (MAGs) and their predicted plant-beneficial genes. Genes, 10(6): 424. doi: 10.3390/genes10060424 [32] Noguchi H, Park J, Takagi T, 2006. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Research, 34(19): 5623–5630. doi: 10.1093/nar/gkl723 [33] O’Donnell A G, Seasman M, Macrae A et al., 2001. Plants and fertilisers as drivers of change in microbial community structure and function in soils. Plant and Soil, 232(1−2): 135–145. doi: 10.1023/A:1010394221729 [34] Oksanen J, Blanchet F G, Kindt R et al., 2013. Vegan: Community Ecology Package. R package version 2.0–10. https://CRAN.R-project.org/package=vegan. [35] Pankhurst C, Kirkby C, Hawke B et al., 2002. Impact of a change in tillage and crop residue management practice on soil chemical and microbiological properties in a cereal-producing red duplex soil in NSW, Australia. Biology and Fertility of Soils, 35(3): 189–196. doi: 10.1007/s00374-002-0459-3 [36] Parks D H, Beiko R G, 2010. Identifying biologically relevant differences between metagenomic communities. Bioinformatics, 26(6): 715–721. doi: 10.1093/bioinformatics/btq041 [37] Rincon-Florez V A, Carvalhais L C, Dang Y P et al., 2020. Significant effects on soil microbial communities were not detected after strategic tillage following 44 years of conventional or no-tillage management. Pedobiologia, 80: 150640. doi: 10.1016/j.pedobi.2020.150640 [38] Romero-Salas E A, Navarro-Noya Y E, Luna-Guido M et al., 2021. Changes in the bacterial community structure in soil under conventional and conservation practices throughout a complete maize (Zea mays L. ) crop cycle. Applied Soil Ecology, 157: 103733. doi: 10.1016/j.apsoil.2020.103733 [39] Schmidt R, Mitchell J, Scow K, 2019. Cover cropping and no-till increase diversity and symbiotroph: saprotroph ratios of soil fungal communities. Soil Biology & Biochemistry, 129: 99–109. doi: 10.1016/j.soilbio.2018.11.010 [40] Schneijderberg M, Schmitz L, Cheng X et al., 2018. A genetically and functionally diverse group of non-diazotrophic Bradyrhizobium spp. colonizes the root endophytic compartment of Arabidopsis thaliana. BMC Plant Biology, 18: 61. doi: 10.1186/s12870-018-1272-y [41] Sekaran U, Sagar K L, De Oliveira Denardin L G et al., 2020. Responses of soil biochemical properties and microbial community structure to short and long-term no-till systems. European Journal of Soil Science, 71(6): 1018–1033. doi: 10.1111/ejss.12924 [42] Smith A P, Marín-Spiotta E, Balser T, 2015. Successional and seasonal variations in soil and litter microbial community structure and function during tropical post agricultural forest regeneration: a multiyear study. Global Change Biology, 21(9): 3532–3547. doi: 10.1111/gcb.12947 [43] Somenahally A, Dupont J I, Brady J et al., 2018. Microbial communities in soil profile are more responsive to legacy effects of wheat-cover crop rotations than tillage systems. Soil Biology and Biochemistry, 123: 126–135. doi: 10.1016/j.soilbio.2018.04.025 [44] Souza R C, Cantão M E, Vasconcelos A T R et al., 2013. Soil metagenomics reveals differences under conventional and no-tillage with crop rotation or succession. Applied Soil Ecology, 72: 49–61. doi: 10.1016/j.apsoil.2013.05.021 [45] Souza R C, Hungria M, Cantão M E et al., 2015. Metagenomic analysis reveals microbial functional redundancies and specificities in a soil under different tillage and crop-management regimes. Applied Soil Ecology, 86: 106–112. doi: 10.1016/j.apsoil.2014.10.010 [46] Sun R B, Li W Y, Dong W X et al., 2018. Tillage changes vertical distribution of soil bacterial and fungal communities. Frontiers in Microbiology, 9: 699. doi: 10.3389/fmicb.2018.00699 [47] Wang H, Wang S L, Wang R et al., 2019. Direct and indirect linkages between soil aggregates and soil bacterial communities under tillage methods. Geoderma, 354: 113879. doi: 10.1016/j.geoderma.2019.113879 [48] Wang H H, Li X, Li X et al., 2020a. Long-term no-tillage and different residue amounts alter soil microbial community composition and increase the risk of maize root rot in northeast China. Soil & Tillage Research, 196: 104452. doi: 10.1016/j.still.2019.104452 [49] Wang H H, Guo Q C, Li X et al., 2020b. Effects of long-term no-tillage with different straw mulching frequencies on soil microbial community and the abundances of two soil-borne pathogens. Applied Soil Ecology, 148: 103488. doi: 10.1016/j.apsoil.2019.103488 [50] Wang Q, Liang A Z, Chen X W et al., 2021. The impact of cropping system, tillage and season on shaping soil fungal community in a long-term field trial. European Journal of Soil Biology, 102: 103253. doi: 10.1016/j.ejsobi.2020.103253 [51] Wang Y, Li C Y, Tu C et al., 2017. Long-term no-tillage and organic input management enhanced the diversity and stability of soil microbial community. Science of the Total Environment, 609: 341–347. doi: 10.1016/j.scitotenv.2017.07.053 [52] Xing W L, Cheng X R, Xiong J et al., 2020. Variations in soil biological properties in poplar plantations along coastal reclamation stages. Applied Soil Ecology, 154: 103649. doi: 10.1016/j.apsoil.2020.103649 [53] Yan S S, Song J M, Fan J S et al., 2020. Changes in soil organic carbon fractions and microbial community under rice straw return in Northeast China. Global Ecology and Conservation, 22: e00962. doi: 10.1016/j.gecco.2020.e00962 [54] Yang H S, Meng Y, Feng J X et al., 2020. Direct and indirect effects of long-term ditch-buried straw return on soil bacterial community in a rice-wheat rotation system. Land Degradation & Development, 31(7): 851–867. doi: 10.1002/ldr.3481 [55] Yu C, Li Y, Mo R L et al., 2020. Effects of long-term straw retention on soil microorganisms under a rice-wheat cropping system. Archives of Microbiology, 202(7): 1915–1927. doi: 10.1007/s00203-020-01899-8 [56] Zhang, S X, Liu, P, Zhang, S Q et al., 2022. Contribution of rhizodeposit associated microbial groups to SOC varies with maize growth stages. Geoderma, 422: 115947. doi: 10.1016/j.geoderma.2022.115947 [57] Zhang X P, Gao G B, Wu Z Z et al., 2019b. Agroforestry alters the rhizosphere soil bacterial and fungal communities of moso bamboo plantations in subtropical China. Applied Soil Ecology, 143: 192–200. doi: 10.1016/j.apsoil.2019.07.019 [58] Zhang Y, Li X J, Gregorich E G et al., 2019a. Evaluating storage and pool size of soil organic carbon in degraded soils: tillage effects when crop residue is returned. Soil & Tillage Research, 192: 215–221. doi: 10.1016/j.still.2019.05.013 [59] Zhao P Z, Li S, Wang E H et al., 2018. Tillage erosion and its effect on spatial variations of soil organic carbon in the black soil region of China. Soil & Tillage Research, 178: 72–81. doi: 10.1016/j.still.2017.12.022