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Article

Characteristics of Bacterial Community Structure in Yellow Paddy Soil After Long-Term Chemical Fertilisation, Organic Fertilisation, and Fertilisation Mode Conversion

by
Yehua Yang
1,2,
Xingcheng Huang
1,2,
Huaqing Zhu
1,2,
Yanling Liu
1,2,
Yarong Zhang
1,2,
Song Zhang
3,
Han Xiong
1,2,
Huan Yang
1,2 and
Yu Li
1,2,*
1
Institute of Soil and Fertilizer, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
2
Scientific Observing and Experimental Station of Arable Land Conservation and Agricultural Environment, Ministry of Agriculture, Guiyang 550006, China
3
Guiding Agricultural and Rural Bureau, Guiding 551300, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(3), 749; https://doi.org/10.3390/agronomy15030749
Submission received: 12 February 2025 / Revised: 10 March 2025 / Accepted: 18 March 2025 / Published: 20 March 2025
(This article belongs to the Section Farming Sustainability)
Browse Figures
Versions Notes

Abstract

:
This study aimed to compare bacterial community structure differences in yellow paddy soil under long-term chemical/organic fertilisation and fertiliser conversion to guide farmland fertilisation strategies in yellow loam areas. Treatments included (1) continuous application of chemical fertilisers for 27 years (CF-CF); (2) application of chemical fertiliser continuously for 24 years and then application of organic fertiliser for 3 years (CF-OF); (3) continuous application of organic fertiliser for 27 years (OF-OF); and (4) application of organic fertiliser continuously for 24 years and then application of chemical fertiliser for 3 years (OF-CF). The results show that long-term fertilisation alters genus-level bacterial taxa, while fertilisation mode changes significantly increase taxa quantities at both phylum and genus levels. Different fertilisation treatments affect the relative abundance of bacteria; the relative abundance of Firmicutes in OF-OF is significantly greater than that in CF-CF, while Gemmatimonadota and Patescibacteria show the opposite trend. Compared to CF-CF, CF-OF increases the relative abundance of Firmicutes and decreases that of Cyanobacteria, whereas OF-CF increases the relative abundance of Firmicutes compared to OF-OF. Notably, Patescibacteria is significantly enriched in CF-CF, while Cyanobacteria and Bacteroidota are significantly enriched in CF-OF, and Firmicutes and Myxomycophyta are significantly enriched in the OF-OF treatment. The bacterial community composition of CF-CF and CF-OF is similar, while the bacterial community composition of OF-OF and OF-CF is similar. In bacterial assembly processes, OF-CF improves the heterogeneous selection process and reduces the homogeneous dispersal process compared to OF-OF. The bacterial assembly process of OF-CF gradually becomes similar to that of CF-CF and CF-OF. Further analyses indicate that fertilisation influences the soil bacterial community composition by affecting total nitrogen, organic matter, available phosphorus, and pH. Overall, long-term different fertilisation predominates bacterial community distribution, while short-term changes in fertilisation mode have a smaller but significant effect on bacterial community distribution, influencing the quantity and relative abundance of bacterial taxa; the application of organic fertilisers is more beneficial for the even distribution of bacteria.

1. Introduction

Fertilisation is an agricultural management measure to change soil fertility and increase productivity, while soil bacteria play a crucial role in promoting soil nutrient cycling [1,2,3]. Many studies have shown that the soil bacterial community composition is significantly affected by fertiliser application and that balanced application is more beneficial than unbalanced application [4]. Increased application of organic fertilisers is more beneficial than the application of chemical fertilisers alone [5], and long-term application is more advantageous than short-term application for enhancing soil bacterial diversity and improving community structure [6]. Therefore, investigating the role of fertiliser application on the structural composition and dynamics of the bacterial community enables an evaluation of the impact of fertilisers on soil ecosystems [7].
At present, chemical fertiliser is more widely applied because it has an appropriate nutrient ratio for crops and is easy to apply, and its other characteristics are favoured by farmers [8]. Moreover, rapid socio-economic development has driven rising demand for organic food, positioning organic agriculture as a key focus‌. The application of organic fertiliser alone is a growing trend [9]. Therefore, comparing the effects of chemical and organic fertilisers on soil bacteria is a current research hotspot. Bolo [10] reported that Chloroflexi and Acidobacteria were mostly dominant in treatments applied with organic inputs but were depressed under inorganic treatments. Wang [11] showed that long-term chemical fertilisation increased Acidobacteria, whereas organic fertilisation elevated Proteobacteria abundance. It can be seen that there are significant differences in the effects of single application of chemical fertilisers and organic fertilisers on soil bacterial community composition, which may be attributed to the regulatory effects of different fertilisation methods on soil physicochemical properties and nutrient supply [12,13,14]. Although the transformation of fertilisation patterns is common in agricultural production, current research still focuses on the impact of different fertilisation patterns on soil microbial community structure [15,16,17]. The dynamic response of soil bacterial communities to shifts between inorganic and organic fertilisation particularly regarding succession patterns, dominant species adaptation, and ecological function regulation post-transition remains understudied‌. Comparing these structural differences offers actionable guidance for sustainable farmland soil microbial resource management‌.
Soil bacterial communities are diverse and complex in terms of the types and quantities of bacteria and are influenced not only by fertilisation patterns but also by climate conditions [18], land use patterns [19], cultivation practises [20], and other factors. In agricultural management, it is necessary to study the impact of changes in fertilisation patterns on soil bacteria on the basis of local conditions. Yellow soil is a zonal soil formed under the humid bioclimatic conditions of subtropical regions and is the main cultivated soil in southwestern China. The yellow soil area of cultivated land in Guizhou Province amounts to 1.2326 million hectares, accounting for 35.49% of the total cultivated land area in the province and is the most widely distributed agricultural soil type in Guizhou. Therefore, in this study, long-term positioning experiments were performed at the Scientific Observing and Experimental Station of Arable Land Conservation and Agricultural Environment, Ministry of Agriculture, and Illumina MiSeq high-throughput sequencing technology was used to explore the effects of long-term inorganic fertilisation and organic fertilisation, as well as a change in fertilisation modes, on the bacterial community structure of yellow soil rice fields. The main factors influencing the bacterial community were identified, providing a theoretical basis for the interconversion between traditional agricultural practises and organic agricultural practises in yellow soil areas.

2. Materials and Methods

2.1. Experimental Design

In this study, pot experiments were carried out in the greenhouse of Guizhou Academy of Agricultural Sciences, Huaxi District, Guiyang City, Guizhou Province, China. The experiment was conducted under controlled rainfall conditions, with no obstructions around the greenhouse and temperatures similar to outdoors. The average temperature in 2022 was 15.7 °C, with a minimum temperature of −2.8 °C and a maximum temperature of 35.6 °C. The soil for the test was the soil from the long-term fertiliser positioning experimental field of the Guizhou Farmland Conservation and Scientific Observing and Experimental Station of Arable Land Conservation and Agricultural Environment, Ministry of Agriculture (106°39′52″ E, 26°29′49″ N), the soil type was Anthrosol formed on yellow soil parent material, and the parent material was residual deposits of Triassic limestone and sandy shale. The tested soil comes from a long-term fertilisation community that has been continuously applied with chemical fertiliser (CF) and organic fertilisers (OF) for 24 years. Soil collection took place in October 2019. Soil properties are shown in Table 1. Each test soil was treated with nitrogen, phosphorus, and potassium fertiliser or with organic fertiliser, with a total of four treatments: (1) continuous application of chemical fertilisers for 27 years (CF-CF); (2) application of chemical fertiliser continuously for 24 years and then application organic fertiliser for 3 years (CF-OF); (3) continuous application of organic fertiliser for 27 years (OF-OF); and (4) application of organic fertiliser continuously for 24 years and then application chemical fertiliser for 3 years (OF-CF). The amount of fertiliser applied was consistent with that used in the long-term positional fertiliser trial, and the fertiliser rates are shown in Table 2. The chemical fertilisers used were urea (N 46%), procalcium (P2O5 16%), and potassium chloride (K2O 60%). The organic fertilisers used were stable cattle manure (average total carbon (C): 10.4%, total nitrogen (N): 2.7 g∙kg−1, total phosphorus (P2O5): 1.3 g∙kg−1, and total potassium (K2O): 6 g∙kg−1). Each pot contained 25 kg of soil, and two rice plants were planted. The planting system consisted of one season of medium rice and phosphate fertiliser and potassic fertiliser or organic fertiliser was applied as the basal fertiliser according to the defined treatments before rice transplantation. Additionally, the chemical fertiliser groups received urea twice during the rice growing period. Starting from 2020, the experiment will continue for 3 years, and samples will be collected in 2022 to test relevant indicators.

2.2. Sampling

Rice was harvested on 28 September 2022, and soil samples were collected from the tillage layer (0–20 cm) after rice harvest. The repeated samples processed in the experiment were independently mixed and passed through a 2 mm sieve (produced by Youqin in Hangzhou, China) to remove impurities. A portion of each sample was stored at −80 °C for soil DNA extraction, and the remaining portion was air-dried for determination of soil nutrients.

2.3. Soil Bacteria Assay

The DNA for the soil samples was extracted according to the instructions of the E.Z.N.A.® soil DNA kit (Omega Biotek, Norcross, GA, USA), the quality of the extracted genomic DNA was checked via agarose gel electrophoresis with 1% agarose, and the quality of the extracted genomic DNA was determined via the NanoDrop2000 (Thermo Scientific, Waltham, MA, USA). The DNA concentration and purity were determined. The V3-V4 region of soil bacterial 16S rRNA was amplified by the primers 338F (5′-ACTCCTACGGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGGTWTCTAAT-3′), and the PCR products were recovered on a 2% agarose gel. The PCR products were purified via the PCR Clean-Up Kit (Shanghai, China) and quantified via a Qubit 4.0 (Thermo Fisher Scientific, Waltham, MA, USA). The purified PCR products were used for library construction via the NEXTFLEX Rapid DNA-Seq Kit and sequenced via the Illumina Nextseq2000 platform by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

2.4. Determination of Soil Chemical Indicators

Soil pH was determined at a water/soil ratio of 2.5:1 via a potentiometric method, the total organic carbon (OM) content in the soil was determined via the potassium dichromate method, the total nitrogen (TN) content was determined via the semitrace Kjeldahl method, the available phosphorus (AP) content was determined via UV‒visible spectrophotometry, and the available potassium (AK) content was determined via flame photometry [21].

2.5. Statistical Analysis

High throughput sequencing data analysis will be conducted on 23 October 2022. The double-ended raw sequenced sequences were quality controlled via fastp software (https://github.com/OpenGene/fastp, version 0.19.6, accessed date 23 October 2022), and FLASH software (http://www.cbcb.umd.edu/software/flash, version 1.2.11, accessed date 23 October 2022) was used for splicing. The quality-controlled spliced sequences were clustered, and chimaeras were eliminated via UPARSE v7.1 software (http://drive5.com/uparse/, accessed date 23 October 2022) on the basis of 97% similarity for operational taxonomic unit (OTU) clustering. The OTU species taxonomy was annotated via the RDP classifier (https://bio.tools/rdp, version 2.11, accessed date 23 October 2022) on the basis of the Silva 16S rRNA gene database (v138), with a confidence threshold of 70%, and the community composition of each sample was determined at different taxonomic classification levels.
Statistical analyses were performed via SPSS 20.0 software, and the significance of differences was tested via one-way ANOVA calculations (Duncan’s test; p < 0.05 was considered to indicate a significant difference). Redundancy analysis (RDA) was applied via Canoco 5.0 software, other analyses were performed via the Meggie Bio online analysis system (https://www.majorbio.com/web/www/index, accessed date 22 November 2022). Bacterial alpha diversity indices were calculated with Mothur v1.30.1. Principal coordinate analysis (PCoA) was conducted based on Bray–Curtis dissimilarity using Vegan v2.5-3 package. Graphs were constructed via Excel 2010.

3. Results

3.1. Effects of Different Fertilisation Treatments on Bacterial Alpha Diversity Indices

The different fertiliser treatments did not significantly affect the soil bacterial Chao1 index but significantly affected the Shannon, Simpson, and Coverage indices (Figure 1). The Chao and Shannon indices of the CF-OF treatments were greater than those of the other fertiliser treatments and were approximately 2.42%, 2.27%, and 2.27% greater than those of the CF-CF, OF-OF, and OF-CF treatments, respectively. The Simpson index of the OF-CF treatment was the highest and was significantly greater than that of the CF-OF treatment (approximately 44.44% greater) and compared with those of the CF-CF and OF-OF treatments, the difference was not significant. The trends in the Coverage index and Shannon index of each treatment were opposite to those of the other treatments. The Coverage index of the CF-OF treatment was approximately 1.24–1.64% lower than that of the other fertiliser treatments, and the differences among the other treatments were not significant.

3.2. Effects of Different Fertiliser Treatments on the Number of Bacterial Species

The results of Venn diagram analysis at the bacterial phylum level revealed that the numbers of bacterial taxa in the CF-CF, CF-OF, OF-OF, and OF-CF treatments were 49, 54, 49, and 52, respectively (Figure 2A). The CF-OF treatment increased the number of bacterial taxa by five compared with the CF-CF treatment, and the OF-CF treatment increased the number of bacterial taxa by three compared with the OF-OF treatment. The number of bacterial taxa common to the different fertilisation treatments was 46, with 0, 3, 1, and 1 taxa being unique to the CF-CF, CF-OF, OF-OF, and OF-CF treatments, respectively. At the genus level, the number of bacterial taxa was 976, 1006, 882, and 955 for the CF-CF, CF-OF, OF-OF, and OF-CF treatments, respectively (Figure 2B). The CF-OF treatment increased the number of bacterial taxa by 30 compared with the CF-CF treatment, and the OF-CF treatment increased the number of bacterial taxa by 73 compared with the OF-OF treatment. The number of bacterial taxa common to the different fertiliser treatments was 687, with 45, 66, 29, and 47 taxa unique to the CF-CF, CF-OF, OF-OF, and OF-CF treatments, respectively.
The different types of fertilisation patterns significantly affected the relative abundance of bacteria at the phylum level (Figure 3A). There were 11 groups of bacteria with relative abundances greater than 1%, accounting for 94.89% to 95.63% of the total bacterial population. The dominant bacterial phylum (relative abundance > 10%) was composed mainly of Chloroflexi (22.65~25.66%), Actinobacteria (19.45~22.88%), Acidobacteria (15.00~19.02%), and Proteobacteria (12.99~15.72%), accounting for 74.58~78.66% of the bacterial population. Firmicutes (4.83~8.92%), Desulfobacterota (2.99~4.47%), Myxomycophytota (2.06~2.97%), Bacteroidota (1.55~2.56%), Gemmatimonadota (1.40~2.12%), Cyanobacteria (0.30~2.73%), and Patescibacteria (0.29~1.54%) together account for 16.30~21.04% of the bacterial population. Among them, there was no significant difference in the relative abundance of Actinobacteria and Desulfobacteria in the CF-CF and OF-OF treatments, whereas the relative abundance of Actinobacteria in the OF-CF treatment was significantly greater than that in the CF-OF treatment, and the relative abundance of Desulfobacteria showed the opposite trend. The relative abundance of Acidobacteriota in the OF-CF treatment was significantly greater than that in the CF-CF treatment, whereas the difference between OF-OF and CF-OF was not significant. The relative abundance of Firmicutes in the OF-OF treatment was significantly greater than that in the other fertilisation treatments, with the order of relative abundance being OF-OF > OF-CF > CF-OF > CF-CF. The relative abundance of Myxomycophagyta in the OF-CF and OF-OF treatments was significantly greater than that in the CF-OF treatment, but the difference was not significant compared with that in the CF-CF treatment. The relative abundance of Gemmatimonadota in the CF-CF treatment was significantly greater than that in the OF-OF and OF-CF treatments, whereas there was no significant difference between the OF-OF and CF-OF treatments. The Cyanobacteria levels in the CF-OF treatment were significantly greater than those in the other fertilisation treatments, whereas the differences in the other treatments were not significant. The relative abundance of Patescibacteria in the CF-CF treatment was significantly greater than that in the OF-OF treatment, whereas the difference between the OF-CF and CF-OF treatments was not significant; there were no significant differences in the other phyla.
At the genus level, there were 30 groups of bacteria with relative abundances greater than 1%, accounting for 49.44% to 55.04% of the bacterial population (Figure 3B). Among them, 20 bacteria presented significant differences in relative abundance (p < 0.05), and 12 presented extremely significant differences (p < 0.05). The relative abundances of norank_f_ norank_o RBG-13-54-9 and norank_f_ Gemmatimonadaceae in the CF-CF and CF-OF groups were significantly greater than those in the OF-OF and OF-CF groups, but unclassified_f_ Xanthomonaceae and Mycobacterium presented the opposite trend. Compared with those in the CF-CF treatment, the relative abundances of norank_f67-14 and Bryobacter significantly decreased in the CF-OF treatment, whereas the relative abundances of norank_f_onorank_cKD4-96 and norank_f_onorank_cC0119 significantly increased. There were no significant differences in the other bacterial genera. Compared with those in the OF-OF treatment, the relative abundances of norank_f67-14, norank_f_Methyloglycellaceae, Desulfobaca, and Bacillus significantly decreased in the OF-CF treatment, whereas the relative abundances of Intrasporangium and Candidatus_Solibacter significantly increased.

3.3. Effects of Different Fertiliser Applications on Differences in Bacterial Species

Discriminant analysis of multilevel species differences via LEfSe (LDA threshold of 3.5) revealed significant differences in bacterial species across multiple levels in the fertilisation treatments (Figure 4). At the phylum to genus level, there were a total of five differentiated bacterial groups across fertiliser treatments, with Patescibacteria significantly enriched in the CF-CF treatment, Cyanobacteria and Bacteroidota significantly enriched in the CF-OF treatment, and Firmicutes and Myxomycophyta significantly enriched in the OF-OF treatment. Firmicutes and Myxomycophyta were significantly enriched in the OF-OF treatment.
The community composition of the different treatments was analysed for differences via principal component analysis (Figure 5). The results revealed that at the phylum level, the bacterial community compositions of CF-CF and CF-OF were more similar to those of OF-OF and OF-CF, with the first and second principal component axes explaining 32.91% and 13.72% of the variation in the structural composition of the bacterial communities, respectively, totalling 46.63% (Figure 5A). At the genus level, the effect of changing the fertilisation pattern on bacterial beta diversity was consistent with that at the phylum level, with the difference that the communities in OF-OF and OF-CF were relatively unique at the phylum level and that the community in OF-CF gradually became closer to those in CF-CF and CF-OF (Figure 5B). The first and second principal component axes explained 21.50% and 14.95% of the variation in community structural composition at the bacterial genus level, respectively, totalling 36.45%.

3.4. Effects of Different Fertiliser Applications on Bacterial Assembly Processes

On the basis of the OTU level, the contributions of different ecological processes to deterministic and stochastic processes in microbial community assembly were assessed via βNTI analyses (Figure 6). The assembly processes in the CF-CF and CF-OF treatments mainly consisted of the deterministic processes of heterogeneous selection (HeS) and drift and other ecological processes (Drift (and others)), which accounted for 75.00% and 25.00%, respectively. Community assembly in the OF-OF and OF-CF treatments was mainly driven by the deterministic process of heterogeneous selection (HeS), the stochastic process of homogeneous dispersal (HD), and drift and other ecological processes (Drift (and others)). The three processes accounted for 37.50%, 37.50%, and 25.00% in the OF-OF treatment and 62.50%, 12.50%, and 25.00% in the OF-OF treatment, respectively. Compared with OF-OF, the OF-CF treatment increased the contribution of deterministic processes and reduced the contribution of stochastic processes.

3.5. Environmental Factor Correlation Analysis

Redundancy analysis was used to resolve the effects of soil environmental factors on the bacterial community structure at the phylum level, which revealed that the first axis explained 52.56% and the second axis explained 4.63%, totalling 57.19% (Figure 7). The bacterial community structure significantly differed among the different fertilisation treatments, with similar bacterial community compositions in the CF-CF and CF-OF treatments and similar bacterial community compositions in the OF-OF and OF-CF treatments. The results also revealed that soil total nitrogen (TN) (p = 0.002), organic matter (OM) (p = 0.004), effective phosphorus (AP) (p = 0.012), and pH (p = 0.034) were the key factors influencing the soil bacterial community composition. The projected point distances in the bacterial community structure at the facultative level from soil TN, OM, AP, and pH were shorter for the OF-OF and OF-CF treatments than for the CF-CF and CF-OF treatments, indicating that the horizontal community structure of bacteria at the phylum-level in the OF-OF and OF-CF treatments was less affected by soil TN, OM, AP, and pH than the CF-CF and CF-OF treatments were (Figure 7A). Soil TN, OM, AP, and pH were significantly, strongly, and positively correlated with Actinobacteriota, Acidobacteria, and Firmicutes and Myxomycophyta and were significantly, strongly and negatively correlated with the other phyla (Figure 7B).

4. Discussion

4.1. Fertilisation Pattern Affects Soil Bacterial Alpha Diversity

The soil microbial alpha diversity index is an indicator used to assess community diversity, and the alpha diversity index provides insight into the stability of soil microbial ecosystems [22,23]. In this study, the difference in bacterial alpha diversity index between CF-CF treatment and OF-OF treatment was not significant (Figure 1), indicating that long-term application of chemical fertilisers or organic fertilisers is beneficial for building a stable soil microbial system, which is similar to the results of Zhang [24]. Other research results have shown that long-term application of chemical fertilisers significantly reduces soil bacterial diversity, while long-term application of organic fertilisers is beneficial for improving or stabilising soil bacterial diversity. The differences in these results may be due to differences in fertiliser types, cultivation methods, and experimental time [25,26]. However, in this study, changing the fertilisation method can affect the soil bacterial alpha diversity index. Compared with CF-CF, the Shannon index of CF-OF treatment is significantly increased, while the Coverage index is significantly reduced. Applying organic fertiliser in the short term can significantly improve soil bacterial diversity. However, changing the fertilisation method can affect the soil bacterial alpha diversity index. Compared with that of the CF-CF treatment, the Shannon index of the CF-OF treatment significantly increased, whereas the coverage index significantly decreased. Compared with the OF-OF treatment, the short-term application of organic fertiliser significantly improved soil bacterial diversity; however, there was no significant difference in the alpha diversity index of the OF-CF treatment, and the short-term application of chemical fertilisers did not change soil bacterial diversity. The reason may be that organic fertilisers carry certain microorganisms unlike chemical fertilisers, and short-term application of organic fertiliser increases the number of soil bacterial species and thus reduces bacterial representation [27,28].

4.2. Fertilisation Affects the Number of Taxa and Relative Abundance of Soil Bacteria

Soil bacteria play an important role in soil nutrient cycling, particularly the more relatively abundant flora, which play a central role in nutrient cycling [29]. The number of bacterial taxa was 94 greater in the CF-CF treatment than in the OF-OF treatment (Figure 2), which suggests that long-term application of chemical fertilisers alone is more likely to increase the number of bacterial taxa in the soil than the application of organic fertilisers alone. Compared to CF-CF, CF-OF treatments showed increases of 5 phylum-level and 30 genus-level bacterial taxa, while OF-CF treatments exhibited 3 phylum-level and 73 genus-level increases relative to OF-OF (Figure 2), demonstrating that short-term fertilisation adjustments effectively enhance soil bacterial diversity‌. The reason for this may be that the long-term application of chemical or organic fertiliser alone leads to an imbalance in nutrient levels, which results in a decrease in bacterial diversity, whereas short-term inputs of different fertilisers increase the diversity of nutrients and promote the growth and reproduction of certain bacterial taxa [30,31].
The dominant microbial composition of each treatment group is basically the same at the phylum and genus levels, with only differences in relative abundance (Figure 3). In terms of long-term fertilisation, the relative abundance of Firmicutes was significantly greater in the OF-OF treatment than in the CF-CF treatment, whereas the abundances of Gemmatimonadota and Patescibacteria were significantly lower than those in the CF-CF treatment. The reason for this may be that Firmicutes are enteric flora, and the organic fertiliser used in this study was cow dung organic fertiliser, which led to an increase in the relative abundance of soil Firmicutes after application [32]. Moreover, Firmicutes prefer a moist environment, and long-term application of organic fertilisers promotes the growth of Firmicutes by increasing the soil moisture content [33,34], as evidenced by the fact that Firmicutes were enriched in the OF-OF treatment (Figure 4). In contrast, Gemmatimonadota prefers dry soil conditions [35]; therefore, the relative abundance of Gemmatimonadota was highest in the CF-CF treatment, and the relative abundance of Gemmatimonadota in the other treatments was in the order of CF-OF, OF-CF, and OF-OF treatments. No culturable bacteria are found in Patescibacteria, and their roles and functions are not known. The relatively high relative abundance and significant enrichment of Patescibacteria in the CF-CF treatment in this study are presumed to be related to nutrient limitation due to long-term fertiliser application [36]. However, the relative abundance does not represent the absolute abundance of bacteria; for example, Cyanobacteria had the highest relative abundance in the CF-CF treatment but was enriched in the CF-OF treatment. Therefore, combining the relative abundance and enrichment of bacteria to understand the role of fertiliser application is more reasonable. Overall, both the timing of fertiliser application and the mode of application affected the relative abundance and enrichment of bacteria.

4.3. Fertilisation Affects Soil Bacterial Beta Diversity and Assembly Processes

The results of principal component analysis revealed that the bacterial community composition was more similar in the CF-CF and CF-OF treatments than in the OF-OF and OF-CF treatments at both the phylum and genus levels (Figure 5). The distribution range of CF-CF and OF-CF treatments is more dispersed compared to CF-OF and OF-OF treatments, which indirectly indicates that the application of chemical fertilisers is more conducive to soil bacterial variation, while the application of organic fertilisers is more conducive to soil bacterial aggregation. At the genus level, the structural compositions of the bacterial communities in the OF-OF and OF-CF treatments gradually became unique, unlike those in the CF-CF and CF-OF treatments, and those in the OF-CF treatment gradually moved closer to those in the CF-CF and CF-OF treatments. It is possible that the bacterial communities in the OF-CF treatment would become similar to those in the CF-CF and CF-OF treatments with increasing years of fertiliser application. These findings indicate that the short-term application of chemical fertilisers can rapidly change the soil bacterial community composition but that the short-term application of organic fertilisers does not. This finding is similar to the results of Zhang [37]. This was also demonstrated by βNTI analysis (Figure 6). βNTI analyses are generally used to assess the contributions of different ecological processes to deterministic and stochastic processes in microbial community assembly [38]. In this study, the assembly processes of CF-CF and OF-OF treatments were different, and the OF-OF treatment increased the stochastic process of homogeneous dispersal compared with CF-CF treatment, suggesting that long-term application of organic fertilisers is more conducive to the uniform distribution of soil bacteria and avoids local over-aggregation, thus maintaining ecosystem stability and functionality. In addition, the assembly process was relatively consistent for the CF-CF and CF-OF treatments but not for the OF-OF and OF-CF treatments. Compared with the OF-OF treatment, the OF-CF treatment increased heterogeneous selection and decreased homogeneous dispersal processes, again demonstrating that fertiliser application increases deterministic processes, decreases stochastic processes in soil bacteria, and disrupts soil bacterial homogeneity [39]. The main reason for this is that the addition of organic matter leads to a balanced and diverse nutrient profile in the soil environment, which reduces interactions between microbial groups associated with obtaining and utilising resources for rapid development and reproduction, thus increasing the homogeneity of the bacterial community [40,41]. Compared with fertiliser application alone, the use of organic‒inorganic blends [42] promotes complexity in soil microbial networks, suggesting that fertiliser diversity is somewhat more conducive to improving microbial diversity and promoting soil ecosystem stability and health.

4.4. Changes in the Soil Environment Are Important in Influencing the Composition of Soil Bacterial Communities

Previous studies have generally demonstrated that the structural composition of microbial communities is influenced by the soil environment [43,44,45]. Research has shown that long-term fertilisation has a significantly higher impact on the distribution of soil bacterial communities than changes in short-term fertilisation patterns. Furthermore, as TN, OM, AP, and pH increase, the bacterial composition of OF-OF and OF-CF treatments shows a clear separation from that of CF-CF and CF-OF treatments (Figure 7). This finding is similar to the results of most previous studies, except that the environmental factors did not affect the distribution of bacterial communities to the same extent, and the differences that arose were due to the different physicochemical properties of the soils at the different trial sites in response to fertiliser application [46,47]. In this study, only the effects of pH, OM, TN, AP, and AK on the distribution of bacterial communities were determined, and the degree of explanation was 57.19%, which indicates that the distribution of bacterial communities was also greatly influenced by other environmental factors in addition to the abovementioned ones. In future studies, researchers should consider the addition of other soil nutrients, such as total nutrients [48], trace elements and intermediate elements [49], physical traits [50], and other environmental factors, to systematically assess and clarify the effects of soil environmental factors on soil bacteria.

5. Conclusions

The long-term application of different fertilisers affected soil bacterial structure by changing soil chemical properties, but the effect on bacterial diversity was not significant; changes in fertiliser application patterns affected soil bacterial diversity, relative abundance, and species composition to a certain extent but had less of an effect on the bacterial community structure.

Author Contributions

Y.Y.: methodology, formal analysis, writing—original draft. X.H.: methodology, writing—review and editing, supervision. H.Z.: software, formal analysis, data curation. Y.L. (Yanling Liu): supervision, funding acquisition. Y.Z.: software, formal analysis, data curation. S.Z.: software, formal analysis, data curation. H.X.: software, formal analysis, data curation. H.Y.: software, formal analysis, data curation. Y.L. (Yu Li): writing—review and editing, supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the Natural Science Foundation Project of Guizhou province (ZK [2024]ZD082) by Y.L. (Yanling Liu); the National Key R&D Program (2024YFD1900105-04) by Y.L. (Yu Li); the Youth Science and Technology Fund of Guizhou Academy of Agricultural Sciences ([2024]18) by H.X.; the Open Competition Mechanism to Select the Best Candidates of Guizhou Academy of Agricultural Sciences (JBGS (2024)06) by Y.L. (Yu Li); the Germplasm Resource Project Special of Guizhou Academy of Agricultural Sciences ([2023]12) by Y.L. (Yu Li); and the Science and Technology Innovation Project Special of Guizhou Academy of Agricultural Sciences ([2023]13) by Y.L. (Yanling Liu).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no competing interests.

References

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Agronomy 15 00749 g001
Figure 1. Bacterial chao index (A), Shannon index (B), Simpson index, (C) and Coverage index (D) for different fertilisation treatments. Note: Different lowercase letters indicate significant differences at the p < 0.05 level.
Figure 1. Bacterial chao index (A), Shannon index (B), Simpson index, (C) and Coverage index (D) for different fertilisation treatments. Note: Different lowercase letters indicate significant differences at the p < 0.05 level.
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Figure 2. Number of taxa at phylum (A) and genus (B) level of bacteria in different fertilisation treatments.
Figure 2. Number of taxa at phylum (A) and genus (B) level of bacteria in different fertilisation treatments.
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Figure 3. Column stacks for different treatments based on phylum (A) and genus (B) level. Note: * represents significant at the p < 0.05 level, ** represents significant at the p < 0.01 level.
Figure 3. Column stacks for different treatments based on phylum (A) and genus (B) level. Note: * represents significant at the p < 0.05 level, ** represents significant at the p < 0.01 level.
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Figure 4. Bacterial LEFSe multilevel species hierarchical tree (A) and LDA discrimination results. (B) Effects of different fertiliser treatments on bacterial beta diversity.
Figure 4. Bacterial LEFSe multilevel species hierarchical tree (A) and LDA discrimination results. (B) Effects of different fertiliser treatments on bacterial beta diversity.
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Figure 5. Principal component analysis of different treatments based on the level of phylum (A) and genus (B).
Figure 5. Principal component analysis of different treatments based on the level of phylum (A) and genus (B).
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Figure 6. Relative importance of different ecological processes based on OTU level. Note: Deterministic processes—Homogeneous selection (HoS), heterogeneous selection (HeS), also known as variable selection—Stochastic processes—Differential limitation (DL), Homogeneous dispersion (HD), Drift (and others) (DR)—and other ecological processes, where the length of the column represents the relative importance of the ecological process.
Figure 6. Relative importance of different ecological processes based on OTU level. Note: Deterministic processes—Homogeneous selection (HoS), heterogeneous selection (HeS), also known as variable selection—Stochastic processes—Differential limitation (DL), Homogeneous dispersion (HD), Drift (and others) (DR)—and other ecological processes, where the length of the column represents the relative importance of the ecological process.
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Figure 7. Redundancy analysis based on phylum level. Note: (A,B) Environmental factors in relation to treatments and species, respectively.
Figure 7. Redundancy analysis based on phylum level. Note: (A,B) Environmental factors in relation to treatments and species, respectively.
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Table 1. Chemical properties of the tested soil.
Table 1. Chemical properties of the tested soil.
Trial Soil pHOM (g∙kg−1)TN (g∙kg−1)AP (mg∙kg−1)AK (mg∙kg−1)
CF6.7448.171.9015.43264.33
OF6.4257.242.7616.83335.00
Table 2. Test treatment and annual fertilisation amount.
Table 2. Test treatment and annual fertilisation amount.
Treatment CodeOrganic Fertiliser (g∙pot−1)Total Amount (g∙pot−1)
NP2O5K2O
CF-CF0.001.600.800.80
CF-OF592.591.600.773.56
OF-OF592.591.600.773.56
OF-CF0.001.600.800.80
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Yang, Y.; Huang, X.; Zhu, H.; Liu, Y.; Zhang, Y.; Zhang, S.; Xiong, H.; Yang, H.; Li, Y. Characteristics of Bacterial Community Structure in Yellow Paddy Soil After Long-Term Chemical Fertilisation, Organic Fertilisation, and Fertilisation Mode Conversion. Agronomy 2025, 15, 749. https://doi.org/10.3390/agronomy15030749

AMA Style

Yang Y, Huang X, Zhu H, Liu Y, Zhang Y, Zhang S, Xiong H, Yang H, Li Y. Characteristics of Bacterial Community Structure in Yellow Paddy Soil After Long-Term Chemical Fertilisation, Organic Fertilisation, and Fertilisation Mode Conversion. Agronomy. 2025; 15(3):749. https://doi.org/10.3390/agronomy15030749

Chicago/Turabian Style

Yang, Yehua, Xingcheng Huang, Huaqing Zhu, Yanling Liu, Yarong Zhang, Song Zhang, Han Xiong, Huan Yang, and Yu Li. 2025. "Characteristics of Bacterial Community Structure in Yellow Paddy Soil After Long-Term Chemical Fertilisation, Organic Fertilisation, and Fertilisation Mode Conversion" Agronomy 15, no. 3: 749. https://doi.org/10.3390/agronomy15030749

APA Style

Yang, Y., Huang, X., Zhu, H., Liu, Y., Zhang, Y., Zhang, S., Xiong, H., Yang, H., & Li, Y. (2025). Characteristics of Bacterial Community Structure in Yellow Paddy Soil After Long-Term Chemical Fertilisation, Organic Fertilisation, and Fertilisation Mode Conversion. Agronomy, 15(3), 749. https://doi.org/10.3390/agronomy15030749

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