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Article

Effects of Four-Year Oilseed Flax Rotations on the Soil Bacterial Community in a Semi-Arid Agroecosystem

by
Zhenyu Gou
1,
Yifan Wang
1,
Zhengjun Cui
2,
Bin Yan
1,*,
Yuhong Gao
1,
Bing Wu
3 and
Lizhuo Guo
1
1
Gansu Provincial Key Laboratory of Aridland Crop Science, College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China
2
College of Agronomy, Tarim University, Alar 843300, China
3
College of Life Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(4), 740; https://doi.org/10.3390/agronomy14040740
Submission received: 3 March 2024 / Revised: 31 March 2024 / Accepted: 1 April 2024 / Published: 2 April 2024
(This article belongs to the Section Soil and Plant Nutrition)
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Abstract

:
Crop rotation aims to improve the sustainability and production efficiency of agricultural ecosystems, especially as demands for food and energy continue to increase. However, the regulation of soil microbial communities using crop rotation with oilseed flax and its relationship with key soil physicochemical driving factors are still not clear. In order to investigate this matter, we carried out a field study lasting four years involving various crop rotation sequences including FWPF, FPFW, PFWF, FWFP, ContF, and ContF1. In addition to evaluating soil physicochemical parameters, we employed Illumina high-throughput sequencing technology to explore the structure and variety of soil microbial communities. The findings indicated a notable rise in pH value with the FPFW treatment in contrast to other treatments, along with significant increases in AP, MBC, MBN, and qSMBC compared to ContF. The number of OTUs in the FPFW, WFPF, FPFW, and PFWF treatments was significantly increased by 4.10–11.11% compared to ContF (p < 0.05). The presence of Actinobacteria and Acidobacteria was greatly impacted by the FPFW treatment, whereas the presence of Actinobacteria and Chloroflexi was notably influenced by the ContF treatment. The soil bacterial community was primarily influenced by TC, pH, and NO3−-N according to correlation analysis. Specifically, the FPFW therapy notably raised the soil pH level while lowering the TC level. Furthermore, the FPFW therapy led to a notable rise in the proportion of Acidobacteria and a significant decline in the proportion of Actinobacteria. These findings provide important theoretical support for using FPFW rotation to regulate soil microbial communities and solve the problems of continuous cropping.

1. Introduction

Agricultural operations have both a long-term and direct influence on soil properties and soil microbial composition. Microbial communities serve an important role in sustaining soil health and ecological processes [1,2]. Studies in the past have confirmed that soil bacteria not only play an essential role in organic matter transformation, nutrient cycling, energy flow, etc., but also have significant impacts on soil sustainability [3]. Soil bacterial communities, as essential components of the soil microbial community, affect biogeochemical cycles and biodiversity in ecosystems [4]. Some bacteria in the soil (such as rhizobia) can form symbiotic relationships with plant roots, helping plants absorb nutrients, promoting plant growth, and enhancing plant resistance to pathogens and adversity [5,6]. Some bacteria (such as actinomycetes) provide nitrogen sources for plants through nitrogen fixation, which has a positive effect on crop growth and development [7]. In addition, some bacteria also interact with other microorganisms in the plant–soil system, playing important regulatory roles in organic matter decomposition, the degradation of organic pollutants, and the maintenance of soil biodiversity [8,9]. Crop rotation, an essential agricultural strategy, has been intensively researched for its effect on soil bacterial communities. Wheat–corn rotation increased the diversity and relative abundance of actinomycetes and Alphaproteobacteria in the wheat rhizosphere microbial community [10]. Researchers discovered that the rotation methods of potato–oat and potato–forage–maize changed bacterial community composition, which helped to improve bacterial community structure [11]. Venter et al. also found that diversified planting had a positive impact on the abundance and diversity of soil bacterial communities [12]. Numerous studies have investigated the impact of rotation on major grain crops [10,11], including wheat, maize, and potatoes, but there have been few reports on the impact of rotation on the soil microbial community structure in field-grown oilseed flax.
Oilseed flax (Linum usitatissimum L.) is a specialty oil crop with a large market demand. However, limited by the resource endowment conditions of the main producing areas, the gap between supply and demand extends, resulting in an imbalance between supply and demand and higher economic added value [13,14]. This has led local farmers to engage in continuous sesame rotation for short-term economic benefits, leading to obstacles to continuous cropping and soil degradation [15]. Crop rotation is one possible solution to these problems. Zhang et al. found that different crop rotation systems constructed with oilseed flax, wheat, and potatoes can increase the content of NO3−-N and NH4+-N in the soil [16]. Chen et al. [17] found that continuous farming of oilseed flax had a considerable autotoxic impact. As a result of its rotation, soil enzyme activity within the 0–20 cm soil layer can be increased significantly, promote the decomposition of soil organic matter, and increase the conversion of carbon, nitrogen, and phosphorus nutrients, thereby creating a better soil microecological environment [18]. In oilseed flax farms, one study indicated that crop rotation greatly altered soil microbial community structure [19]. Research on the rotation of oilseed flax has mostly concentrated on the fertility of the soil, water utilization, and outputs during rotation and continuous cropping [15,16,17]. However, the soil microbial community structure is one of the main factors affecting continuous cropping, to which oilseed flax rotation has received very little attention. It is crucial to explore the specific effects of different crop rotation methods of oilseed flax on the soil bacterial community structure.
Here, we conducted a 4-year field trail in the Loess Plateau of western China to determine the relationship between soil bacterial community function and different oilseed flax rotation systems. The diversity of the population of bacterial was ascertained by 16S rRNA amplicon sequencing. We hypothesized that the composition and potential roles of the soil bacterial community would be manipulated by different oilseed flax rotation systems, aiming to clarify: (1) the outcomes of rotation systems on soil physicochemical properties; (2) the specific distribution of species in the soil microbial community in different rotation treatments; (3) the potential driving factors of changes in the soil microbial community under different rotation treatments.

2. Materials and Methods

2.1. Site Description

The experiment began in 2019 at the Agricultural Sciences Institute in Dingxi City, Gansu Province, China (34°26′ N, 103°52′ E). The test location was situated in the semi-arid region of western China, at a height of 2060 m. The yearly mean temperature is 6.3 °C, with an average of 2453 h of sunshine and approximately 380 mm of rainfall per year. The soil at the experimental site is black loessial (Cumulic Haplustoll, USDA classification) [20], with a parent material of loess, sandy loam texture, and a high gravel content of 50–60%. The pH in soil depths ranging from 0 to 30 cm averages at 8.31, with organic matter at 17.51 g kg−1, total N at 1.05 g kg−1, total P at 0.81 g kg−1, and total K at 108.32 mg kg−1. The previous crop was oilseed flax.

2.2. Experimental Design

A completely random block design was used. The treatments included 6 crop rotations, each with a 4-year rotation cycle. The different crop rotation patterns were: (FWPF) oilseed flax-wheat-potato-oilseed flax, (FPFW) oilseed flax-potato-oilseed flax-wheat, (PFWF) potato-oilseed flax-wheat-oilseed flax, (FWFP) oilseed flax-wheat-oilseed flax-potato, and (ContF) continuous oilseed flax cropping, with (ConF1) continuous fallow as the control, and with 3 replicates for each treatment. All indicators before the establishment of the rotation system were determined at the same time (PreRt).
Each plot was a 3 m × 5 m rectangle. The oilseed flax variety was Dingya 22, planted at a density of 56 kg ha−1, with a sowing time between April 1st and 15th each year. The potato (Xindaping) was sown in early May each year at a density of 52,500 plants ha−1. The wheat (Ganchun 25) was sown in late March each year with a density of 3.75 million plants ha−1. The N and P application rates were the local best fertilization rates of oilseed flax: 112.5 kg N ha−1; 112.5 kg P2O5 ha−1; potato: 225 kg N ha−1; 150 kg P2O5 ha−1; wheat: 150 kg N ha−1; 112.5 kg P2O5 ha−1; and both N and P were applied as base fertilizers. The continuous fallow plot was only plowed and did not produce, and was given no fertilization. There was no irrigation or artificial weeding undertaken during the growth period.

2.3. Soil Sampling and Physicochemical Properties Analysis

Samples were collected before the establishment of the crop rotation system in 2019, and in autumn after the harvest in 2022. A 5 cm diameter hammer soil collector was utilized to acquire soil samples from the surface layer of 10 cm soil depth. Five soil samples were randomly collected from each plot and in sterile plastic bags, which were then brought to the laboratory on ice. Each sample was divided into two parts, with one part being processed through a 1 mm sieve (prewashed with ethanol) to remove large roots and rocks and kept at −80 °C for DNA extraction. The other part of the sample was processed through 1 mm and 2 mm sieves and stored at −4 °C for assessment of physical and chemical traits.
Total carbon (TC) was determined using the H2SO4-K2Cr2O7 heating method. A soil nitrate nitrogen (NO3−–N) extraction and ammonium nitrogen (NH4+–N) extraction, with 2.0 M KCl, were determined using a UV-1800 spectrophotometer (Mapada Instruments, Shanghai, China). Available phosphorus (AP), extracted with 0.5 M NaHCO3, was determined using the colorimetric method. The pH was measured using a pH meter (Mettler Toledo FE20, Shanghai, China) from a deionized soil suspension with a soil:water ratio of 1:4 (mass:volume). Measurement of the soil microbial biomass carbon (MBC) was via the fumigation extraction-capacity analysis method [21]. Determination of the soil microbial biomass nitrogen (MBN) utilized the extraction method from soil; the determination of MBN is consistent with the determination of soil MBC, and the extract was assessed using the Kjeldahl nitrogen method [21]. Soil microbial carbon quotient (qMBC) is an assessment of soil MBC [22].

2.4. DNA Extraction, PCR Amplification, and Illumina Sequencing

The total genomic DNA was extracted from 0.5 g soil samples using the CTAB method [23]. The purity of the DNA samples was checked using 1% agarose gel electrophoresis, and a NanoDrop-2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) was used to quantify the concentration of DNA. PCR detection was performed using the method described by Zhou et al. [24], using the primers 8-F (5′-AGA GTT TGA TCI TGG CTC A-3′) and 556-R (5′-TGC CAG IAG CIG CGG TAA-3′) to amplify the V1–V3 region of the 16S rRNA gene of the microbial genomic DNA. The PCR mixture (25 μL) included 1× reaction buffer, each deoxynucleotide triphosphate at 10 mM, each primer at 10 pM, 0.7 U of Phusion Hot Start II High-Fidelity DNA Polymerase (FINNZYMES, Thermo Fisher Scientific, Wilmington, DE, USA), and 20 ng of microbial genomic DNA as the template. The PCR amplification procedure consisted of the initial denaturation at 95 °C for 10 min, followed by 25 cycles of denaturation at 95 °C for 45 s, annealing at 53 °C for 45 s, extension at 72 °C for 45 s, and a last extension at 72 °C for 10 min. All samples were subjected to three amplifications, mixed equally, and the DNA was purified using a DNA clean-up and concentration purification kit (recommended by Zymo Research, Irvine, CA, USA). In Shanghai, China, OE BioPharm Technology Co., Ltd., used an Illumina Miseq platform to undertake high-throughput sequencing.

2.5. Data Processing and Bioinformatics Analysis

We imported the original paired-end sequences into QIIME1 (version 2022.2) [25], used Fastq [26] to remove barcodes and primers, used vsearch to assemble paired-end sequences, and used Trimmomatic (version 0.35) to remove low-quality sequences. UCLUST (Edgar, 2010) was used to merge an OTU division based on 97% sequence similarity, and the sequence with the highest abundance in each OTU was selected as the representative sequence of the OTU. Subsequently, a matrix file containing the abundance of each OTU in each sample was constructed based on the number of sequences contained in each sample. For the representative sequences of each OTU, default parameters were used in the QIIME1 (version 2022.2) software. The bacteria were classified and annotated using the Silva database (https://www.arb-silva.de, accessed on 22 January 2023). Species alignment annotations were obtained using the RDP (version 2.13) classifier software, retaining annotation results with confidence intervals greater than 0.7. The “qiime diversity alpha” command line was used to calculate the α diversity index of the microbial community, including Observer OTU, Chao1, and Shannon. The “qiime diversity beta” calculates the distance between samples using the Weighted UniFrac and Unweighted UniFrac distance algorithms, and uses principal coordinate analysis (PCoA) analysis in the R (version 3.6.1) statistical software vegan package [27].

2.6. Statistical Analysis

An ANOVA was employed to analyze the significance of the differences in α diversity. A one-way analysis of similarities (ANOSIM) was used to compare the statistical differences between treatments in soil microbial community composition [28]. An ANOVA was conducted to analyze the differences in the abundance of high- and low-abundance species at the phylum level and the abundance of species at the genus level in the soil bacterial community. R statistical software (version 3.6.1) was used for statistical analysis of the soil physicochemical properties and biodiversity indices. For the analysis of soil physicochemical traits, a one-way ANOVA and Dunnett’s post hoc test were used to examine the differences between different rotation systems and single cropping. The relative abundance differences of microbial groups between different treatments were analyzed using a one-way analysis of variance (ANOVA) with a probability level of 0.05. To compare rotation systems, Tukey’s post hoc test was employed to determine statistical significance. The R program’s “Envfit” function was used to conduct PCA [29] ordination of environmental vectors. Canonical correspondence analysis (CCA) [30] was used in combination with the “Envfit” function to further elucidate the correlation between the microbial community structure and soil physicochemical factors. Microsoft Office Excel 2021 was used to process the data. Image rendering was completed using R (version 3.6.1) and Adobe Illustrator (version 25.0).

3. Results

3.1. Soil Physicochemical Properties

The different crop rotation modes had a considerable influence on the soil physicochemical parameters (p < 0.05) (Table 1). Specifically, the TC of the WFPF was significantly increased by 9.93% and 16.74% compared to the PFWF and FWFP treatments, respectively (p < 0.05). The NO3−-N ratio of the WFPF was 128.49%, 132.83%, and 115.93% higher than the FPFW, PFWF, and FWFP treatments, respectively. The NH4+-N ratio of the WFPF was 70.81%, 150.79%, and 280.72% higher than FPFW, PFWF, and FWFP treatments, respectively (p < 0.05). The AP of the PFWF and FPFW treatments was substantially higher than the other treatments (p < 0.05). In addition, the soil NO3−-N of the ContF decreased by 157.26% and 19.14% compared to the WFPF and FWFP treatments (p < 0.05), with no significant difference found compared to the FPFW and PFWF treatments. The NH4+-N of the WFPF was higher than the ContF1 and ContF treatments, with an increase of 82.66% and 159.02%, respectively (p < 0.05). The FWFP treatment had the highest MBC, resulting in the highest qSMBC, while the ContF treatment had the lowest, leading to the lowest qSMBC. The highest MBN was measured in the PFWF treatment. The WFPF contributed to the accumulation of TC, NH4+-N, and NO3−-N in the soil, while the PFWF and FPFW treatments significantly increased the AP value (p < 0.05). The pH value, MBC, MBN, and qSMBC values were significantly higher under the FPFW, FWFP, PFWF, and FWFP treatments compared to the other treatments (p < 0.05). NO3−-N consumption of the soil was accelerated by ContF. It is worth noting that the FPFW significantly increased the pH compared to the other treatments, and the AP, MBC, MBN, and qSMBC were significantly increased compared to the ContF (p < 0.05).

3.2. The Impact of Different Crop Rotation Methods on Soil Bacterial Communities

A total of 2,088,073 raw 16S rRNA sequences were obtained from 21 different experimental sites (dataset 1). A total of 20,109 different operational taxonomic units (OTU) with 97% similarity were discovered (dataset 3).
According to the analysis of soil bacterial diversity using the Observe OTU, Chao1, and Shannon indices (Table 2), the results of the Observe OTU index indicated that the number of OTUs in the FPFW was the highest, and was significantly different from the number found in other treatments (except FWFP) (p < 0.05). In addition, the number of OTUs in the WFPF, FPFW, and PFWF treatments was significantly increased by 4.10–11.11% compared to the ContF treatment (p < 0.05). The ContF had a greater Chao 1 compared to PreRt and ContF1 by 17.27% and 7.39%, respectively (p < 0.05). In the oilseed flax rotation system, compared to ContF, the Chao 1 of the FPFW and FWFP treatments significantly decreased by 4.50% and 5.11%, respectively (p < 0.05). The Shannon index showed no significant change between the ContF and ContF1 treatments, but PreRt has a considerably lower Shannon index compared to ContF and ContF1 (p < 0.05). The Chao 1 and Shannon index of ContF1, ContF, and the flax rotations (WFPF, FPFW, PFWF, and FWFP) were significantly higher than those of PreRt; different flax rotation methods significantly affect the structure of soil bacterial communities (p < 0.05). The FPFW and FWFP treatments significantly decreased soil bacterial community richness (p < 0.05), with a nonsignificant effect on diversity.
Based on PCoA analysis of the Unweight UniFrac and Weight UniFrac, the soil bacterial communities under different rotation modes showed certain differences. Among them, WFPF and PreRt were clearly different from the other treatments on PC1, and FPFW was clearly different from the other treatments on PC2 (Figure 1A). Based on the PCoA analysis of Weight UniFrac, FPFW was clearly different from ContF on PC1, and PreRt was clearly different from the other treatments on PC2 (Figure 1B). This indicated that WFPF, PreRt, and FPFW, as well as ContF and FPFW, led to substantial differences in bacterial structure.
Different crop rotation methods significantly affected the abundance of Actinobacteria, Acidobacteria, Bacteroidetes, and Planctomycetes (p < 0.05) (Figure 2). Among the high-abundance species (>1.0%), the abundance of Actinobacteria decreased significantly by 66.50% and 47.50% in the FPFW compared to the ContF and ContF1 treatments, respectively (p < 0.05). Actinobacteria and Chloroflexi were significantly more abundant in the ContF Compared with PreRt (p < 0.05), but insignificantly different from ContF1 (Figure 2A). Compared to the PreRt and ContF1 treatments, the abundance of Acidobacteria and Bacteroidetes was lowest in the ContF. Compared to ContF, ContF1, and PreRt treatments, Acidobacteria abundance rose by 75.76%, 43.70%, and 34.73% in the FPFW, respectively (p < 0.05). In comparison to the PreRt, the abundance of Nitrospirae was higher in the PFWF, FPFW, and WFPF treatments, but not significantly different from the ContF1. The abundance of Chloroflexi in the FWFP, PFWF, FPFW, and WFPF treatments was significantly higher than in the PreRt (p < 0.05). PFWF showed substantial differences with ContF1, but the other three crop rotation strategies did not (p < 0.05). The abundance of Bacteroidetes was significantly lower in the FWFP, PFWF, and WFPF treatments than in the PreRt (p < 0.05). In the low-abundance species (<1.0%) (Figure 2B), the abundance of BRC1 in FWFP and PFWF treatments was lower than that in ContF and PreRt treatments (p < 0.05), but insignificant from ContF1. FPFW and WFPF treatments exhibited considerably greater abundances of BRC1 (p < 0.05). Armatimonadetes were considerably greater in FWFP and ContF1 than in the other treatments (p < 0.05). This indicated that the FPFW had a significant effect on the abundance of Actinobacteria and Acidobacteria, while the ContF had a significant effect on the abundance of Actinobacteria and Chloroflexi (p < 0.05).
We determined the effect of different crop rotation methods on the soil bacterial community at the genus level (Table 3). The results showed that the abundance of different genera under the ContF was higher than those under other treatments, and there were dramatic differences to the other treatments, except for Devosia (p < 0.05). The effect of ContF1 on the abundance of different genera was similar to that of ContF, with only nonsignificant differences in the impact on Devosia and Rubrobacter. Compared to PreRt, the WFP, FPFWF, PFWF, and FWFP treatments dramatically reduced Kaistobacter relative abundance (p < 0.05). Among the four oilseed flax rotations (WFP, FPFWF, PFWF, FWFP), the FWFP significantly reduced the abundance of Flavisolibacter and Rubellimicrobium (p < 0.05), the PFWF and FWFP treatments significantly reduced the abundance of Adhaeribacter (p < 0.05), and the PFWF significantly increased the abundance of Rubrobacter (p < 0.05), with insignificance between the other treatments.
In order to further study the relationship between different crop rotation methods and soil bacterial communities, PCA analysis was conducted on the soil bacterial community (Figure 3). The results showed that the first principal component explained 75.21% of the variation, and the second principal component explained 20.15% of the variation, suggesting a clearly separated trend in the soil sample bacterial communities under different crop rotation methods. FPFW and FWFP had a positive PC2 value (mainly loaded by Gemmatimonadetes, Acidobacteria, and Planctomycetes), while WFPF and PFWF had negative PC2 values (mainly loaded by Actinobacteria). ContF had a positive PC1 value (mainly loaded by Actinobacteria), and ContF1 was not correlated. Significant differences in the UniFrac distances of different crop rotation systems were found (p < 0.05) (Figure 2).

3.3. Soil Bacterial Diversity and Its Correlation with Soil Physicochemical Properties

The correlation analysis showed that the abundance of the soil bacterial communities was notably influenced by the soil’s physicochemical properties (p < 0.05) (Table 4). Among the high-abundance species, the abundance was most strongly correlated with TC and pH, followed by AP and NO3−-N, while among the low-abundance species, the abundance was most strongly correlated with TC and AP, followed by pH and NO3−-N. NH4+-N did not show significant correlation with bacterial community abundance. Verrucomicrobia had significant negative correlations with TC and AP, and significant positive correlation with pH (p < 0.05). Additionally, we also found that most bacterial communities were only influenced by one or two soil physicochemical factors, and the abundance of these bacterial communities was basically consistent with the correlation with the soil physicochemical factors. Gemmatimonadetes and Armatimonadetes were significantly negatively correlated with TC and AP, Proteobacteria and BRC1 were significantly positively correlated with pH, and Nitrospirae was significantly negatively correlated with pH (p < 0.05). TC was significantly positively correlated with Actinobacteria and significantly negatively correlated with Acidobacteria (p < 0.05). Conversely, Actinobacteria showed a negative correlation with pH, while Acidobacteria showed a positive correlation (p > 0.05).
CCA analysis was performed to investigate the relationship between soil physicochemical properties and soil bacterial community in various crop rotation modes, aiming to further explore the key factors influencing the soil bacterial community (Figure 4). The findings indicated that the explanatory variances of the CCA1 axis and CCA2 axis were 46.62% and 20.18%, respectively, resulting in a total explanatory variance of 66.80%. This indicated that the CCA1 axis and CCA2 axis can better reflect the relationship between the bacterial community and soil physicochemical factors. The differential distribution of bacterial communities was mainly constrained to the CCA1 axis and CCA2 axis. For the CCA1 axis, the relatively important factors were the pH, TC, and NO3−-N; for the CCA2 axis, the relatively important factors were the AP, TC, and NH4+-N. It is worth noting that pH and AP were positively correlated with Acidobacteria and FPFW on CCA1, and Actinobacteria and ContF were negatively correlated. On CCA1, TC was negatively correlated with Acidobacteria and FPFW, and positively correlated with Actinobacteria and ContF.

4. Discussion

4.1. Effects of Different Rotation Methods on the Soil Physicochemical Properties

Soil physicochemical characteristics play a crucial role the alterations of the soil bacterial composition in farm lands [31]. Rotation has a significant impact on the changes in the soil physicochemical properties. Previous studies have indicated that rotation systems have a greater impact on soil TN, AP, and organic matter content compared to continuous cropping [32]. Continuous cropping reduces the absorption of atmospheric nitrogen by plants, leading to a decrease in nitrogen fixation, while more ammonia is produced within plant cells. Accumulated nitrate and ammonia may become the main sources of these substances [33]. Continuous maize cropping significantly reduces the nitrification potential of soil aggregates, while maize–peanut rotation has the opposite effect, significantly increasing the nitrification potential of soil aggregates and promoting a more even distribution of nitrifying microorganisms among different particle sizes. The NH4+ content and pH are considered the primary factors affecting the nitrification potential of soil aggregates and the changes in nitrifying microbial communities [34]. In our research results, compared to continuous cropping, the soil NO3−-N content was significantly reduced by 19.14–157.26%. In contrast, rotation increased the accumulation of TC and NH4+-N in the soil. This indicated that continuous cropping may lead to a faster loss of nitrogen or a slower rate of nitrogen fixation, leading to a decline in soil NO3−-N content, or that continuous cropping may suppress the nitrification process in the soil, causing a decrease in the nitrate nitrogen content. Xia et al. also reached this conclusion in their study [34].
The pH value is an important parameter for soil physical and chemical properties. Research results from Mayer et al. showed that long-term continuous cropping and crop rotation had little effect on soil pH changes, but played an important role in controlling soil nutrients such as TC [35,36]. Through studying four different oilseed flax rotation methods, we found that only the WFPF treatment showed no significant difference compared to ContF and ContF1; the other three treatments significantly increased the soil pH value. A possible reason for this difference is that different crop rotation methods may lead to changes in the soil microbial community, and microbes play a key role in regulating and maintaining the soil pH. Certain microbes can secrete enzymes and metabolites to regulate soil pH, and changes in the microbial community under different rotation methods may lead to differences in the soil pH.
McDaniel et al. discovered that rotation considerably enhanced soil microbial biomass C (by 20.7%) and N (by 26.1%), and regardless of crop type or management measures, rotation maintained the soil fertility and productivity stability by increasing soil carbon, nitrogen, and microbial biomass [37]. Liu et al.’s study also showed that rotation increased soil MBC and MBN by 43.15% and 84.45%, respectively [38]. However, there have also been conflicting research results. In contrast with continuous maize cultivation, a two-year maize–black ryegrass rotation decreased C and N acquisition enzyme activity and soil microbial concentrations of C and N [39]. In our study, different rotation methods significantly increased soil MBC, but the impact on soil MBN varied greatly depending on the rotation method. Rotation methods can change nutrient cycling, organic matter decomposition, and microbial activity in the soil, thereby affecting the MBC and MBN content in the soil. A possible reason for this difference is that PFWF is conducive to increasing nitrogen accumulation in the soil, or that changing the rate of microbial nitrogen cycling promotes more nitrogen being fixed in the soil by microbes. The WFPF and FPFW treatments significantly reduced soil MBN, possibly because rotation inhibited microbial nitrogen cycling, reducing the microbial nitrogen content in the soil.

4.2. Different Crop Rotation Methods and Their Effects on Soil Bacterial Communities

Soil bacterial communities play a very important role in decomposing organic matter, soil nutrient transformation, soil resistance, biological control, and maintenance of soil ecosystem stability [40,41,42,43]. Different crop rotation methods have a significant impact on soil bacterial communities. Dias et al. believe that the impact of crop rotation on the structure of bacterial communities is related to the duration of the rotation cycle [44], while others believe that, compared with continuous cropping, soils with higher crop diversity due to crop rotation produce higher microbial richness and diversity [12]. Liu’s research revealed that crop rotation enhanced the Shannon index by 7.68% when compared to continuous cropping, and was unaffected by rotation order. Their research also found that impact on the abundance index Chao1 of bacteria was not as significant as the effect of the Shannon index of bacteria [38]. We obtained the opposite conclusion; the rotation treatment significantly increased the abundance of bacteria, but the bacterial diversity was no different from that found under continuous cropping. Peralta et al. found that the diversity of soil bacterial communities decreased along with the variety of crop rotations [45], while Li et al. found that continuous cropping could significantly change the structure of the soil microbial community, resulting in higher bacterial diversity and abundance [33]. Accordingly, soil bacterial communities are influenced by various factors, and different environmental conditions and treatments have led to different conclusions.
Different crop rotation strategies profoundly affected the abundance of Actinobacteria, Acidobacteria, and Bacteroidetes in soil bacterial communities (p < 0.05) (Figure 2). Studies have found that actinomycetes are sensitive to physical disturbances in the soil, and their abundance in uncultivated soil is higher than in cultivated soil [46], while Souza et al.’s study of wheat rotation systems showed that rotation increased the abundance of Actinobacteria [47]. Our research found that, compared with continuous cropping, FPFW significantly reduced the abundance of Actinobacteria. This indicated that the survival and reproduction of actinomycetes are closely related to the soil environment. Uncultivated soil often maintains a more natural state, while cultivated soil is more disturbed by human cultivation, fertilization, and other factors, in which Actinomycete abundance is reduced as a result of disrupting the irreparable actinomycete mycelium. The abundance of Actinobacteria under the ContF did not decrease, which may have been related to the cultivation method of the oilseed flax. It is possible that the shallow cultivation depth of the ContF treatment did not damage the mycelium of actinomycetes. A high content and significant taxonomic diversity of Acidobacteria make them one of the leading microbial groups in soil [48]. An important component of the C, N, and S cycles in the soil is Acidobacteria, one of the key bacterial groups involved in the decomposition of organic matter [49,50]. In our research, FPFW significantly increased the abundance of Acidobacteria, which matches the findings of Soman et al. [47].
Physicochemical qualities of soil have a direct impact on the structure and function of the bacterial population, whereas bacterial activity can influence soil physicochemical parameters. In the soil ecosystem, this feedback is crucial [2]. The analyses of this study showed that the abundance of soil bacterial communities was significantly correlated with TC, pH, AP, and NO3−-N, but not significantly correlated with NH4+-N. According to De et al., soil pH, C, and N are involved in the construction of the soil bacterial community [51]. In addition, some studies have suggested that pH may be the only factor that affects the bacterial community [52]. Wu et al.’s research indicated that bacterial diversity is closely related to soil pH, as they observed higher bacterial diversity in neutral soil samples and lower bacterial diversity in acidic soil samples. The study also identified soil pH as a key factor influencing the composition of the bacterial community [53]. Li’s research found that AP and AK also significantly affect the soil bacterial community [54], whereas Wang et al.’s study showed that phosphorus does not have a significant effect of the soil bacterial community [55]. Cao et al.’s research found that nitrogen mainly affects the soil microbial community through changes in NO3−-N and available nitrogen [56].

5. Conclusions

In this study, different rotations of oilseed flax significantly altered the soil physical and chemical parameters and increased the number of OTUs in the soil bacterial community compared to ContF and ContF1. Different rotation methods significantly reduced the richness of the soil bacterial communities, with no significant impact on diversity. Different rotation methods had a drastic impact on the relative abundance of Actinobacteria, Acidobacteria, and Bacteroidetes. TC, pH, and NO3−-N were the main driving factors affecting the soil bacterial community. The FPFW treatment significantly increased the soil pH and decreased the TC value. Furthermore, the FPFW treatment increased the abundance of Acidobacteria while reducing the abundance of Actinobacteria. This was opposite to the effects found in ContF. These findings provide an important theoretical basis for using FPFW rotation to regulate soil microbial communities and address the obstacles to continuous cropping.

Author Contributions

Writing—original draft preparation, Z.G.; conceptualization, methodology, B.Y. and L.G.; validation, B.Y. and Z.C.; resources, Y.W. and Z.C.; writing—review and editing, L.G. and Y.G.; supervision, Y.G.; project administration, B.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Research Program Sponsored by the State Key Laboratory of Aridland Crop Science of China (GSCS-2022-07), the National Natural Science Foundation of China (32260551), the National Natural Science Foundation of China (31760363), the China Agriculture Research System of MOF and MARA (CARS-14-1-16), the Gansu Education Science and Technology Innovation Industry Support program (2021CYZC-38).

Data Availability Statement

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

Acknowledgments

Authors are thankful to the editors and anonymous reviewers for their valuable feedback and suggestions on improving the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 14 00740 g001
Figure 1. PCoA analysis of soil bacterial communities under different crop rotation modes. (A) Unweighted UniFrac PCoA; (B) weighted UniFrac PCoA.
Figure 1. PCoA analysis of soil bacterial communities under different crop rotation modes. (A) Unweighted UniFrac PCoA; (B) weighted UniFrac PCoA.
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Figure 2. The abundance of soil bacterial communities under different crop rotation modes. (A) High-abundance species (>1.0%); (B) groups with a proportion of <1.0%. * Groups with significant differences (p < 0.05). Different lowercase letters indicate significant differences in results between treatments, with a significance level of p < 0.05.
Figure 2. The abundance of soil bacterial communities under different crop rotation modes. (A) High-abundance species (>1.0%); (B) groups with a proportion of <1.0%. * Groups with significant differences (p < 0.05). Different lowercase letters indicate significant differences in results between treatments, with a significance level of p < 0.05.
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Figure 3. PCA analysis of the soil bacterial community under different crop rotation modes.
Figure 3. PCA analysis of the soil bacterial community under different crop rotation modes.
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Figure 4. CCA analysis of the soil bacterial community and soil environmental factors.
Figure 4. CCA analysis of the soil bacterial community and soil environmental factors.
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Table 1. Soil physicochemical properties under different crop rotation methods.
Table 1. Soil physicochemical properties under different crop rotation methods.
TreatmentsTC
g kg−1		NO3−-N
mg kg−1		NH4+-N
mg kg−1		AP
mg kg−1		pHMBC
g kg−1		MBN
g kg−1		qSMBC
PreRt9.53 bcndnd12.33 d8.12 dndndnd
ConF19.42 bc12.52 bc3.46 bc32.76 c8.13 d493.26 c10.59 c52.38 b
ContF10.61 a11.91 c2.44 d41.45 b8.12 d368.68 d72.59 b34.74 c
WFPF10.74 a30.64 a 6.32 a46.09 b8.12 d454.40 c18.15 c42.30 c
FPFW10.12 ab13.41 bc3.70 b54.78 a8.23 a540.91 b15.56 c53.43 b
PFWF9.77 bc13.16 bc 2.52 cd59.70 a8.21 b552.67 b101.11 a56.55 b
FWFP9.20 c14.19 b1.66 d9.44 d8.16 c868.42 a73.11 b94.37 a
Distinct letters within a column indicate notable variations in treatment outcomes at a significance level of p < 0.05. “nd” indicates missing data.
Table 2. Soil bacterial communities under different crop rotation methods (α Diversity).
Table 2. Soil bacterial communities under different crop rotation methods (α Diversity).
TreatmentsObserved OTUChao1 Shannon
PreRt3240 ± 456 e967 ± 2 c8.06 ± 0.17 b
ContF14114 ± 51 c1056 ± 10 b8.34 ± 0.02 a
ContF4077 ± 443 d1134 ± 34 a8.39 ± 0.05 a
WFPF4147 ± 267 c1140 ± 1 a8.37 ± 0.04 a
FPFW4530 ± 12 a1083 ± 0.70 b8.44 ± 0.01 a
PFWF4244 ± 520 b 1145 ± 11 a8.35 ± 0.02 a
FWFP4525 ± 255 a1076 ± 12 b8.47 ± 0.03 a
Distinct letters within a column indicate notable variations in treatment outcomes at a significance level of p < 0.05.
Table 3. Impact of different crop rotation methods on the soil bacterial community at the genus level.
Table 3. Impact of different crop rotation methods on the soil bacterial community at the genus level.
GroupPreRtContF1ContFWFPFFPFWPFWFFWFPFp
Arthrobacter0.033 B0.131 A0.182 A0.058 B0.027 B0.043 B0.033 B12.31<0.0001
Kaistobacter0.054 C0.111 B0.194 A0.023 D0.028 D0.019 D0.015 D9.52<0.0001
Skermanella0.032 C0.059 A0.050 AB0.016 CD0.009 D0.017 CD0.023 CD4.850.007
Modestobacter0.009 C0.040 A0.044 A0.016 C0.011 C0.020 B0.012 C4.580.009
Methylobacterium0.016 B0.033 A0.045 A0.009 B0.008 B0.011 B0.012 B11.92<0.0001
Rhodoplanes0.008 B0.031 A0.039 A0.008 B0.008 B0.011 B0.011 B9.99<0.0001
Nitrosovibrio0.003 C0.039 B0.057 A0.009 C0.007 C0.011 C0.006 C7.060.001
Lentzea0.004 B0.028 A0.029 A0.009 B0.006 B0.009 B0.007 B6.020.002
Steroidobacter0.007 B0.028 A0.037 A0.008 B0.008 B0.008 B0.011 B9.6<0.0001
Flavisolibacter0.008 B0.026 A0.027 A0.008 B0.008 B0.006 B0.003 C4.990.006
Kribbella0.002 C0.021 A0.020 A0.008 B0.006 B0.006 B0.007 B6.370.002
Bradyrhizobium0.005 B0.020 A0.025 A0.005 B0.004 B0.006 B0.008 B8.420.001
Mycobacterium0.003 B0.022 A0.022 A0.007 B0.004 B0.006 B0.007 B10.31<0.0001
Opitutus0.003 B0.014 A0.018 A0.004 B0.007 B0.004 B0.006 B9.19<0.0001
Adhaeribacter0.005 B0.014 A0.015 A0.005 B0.005 B0.003 C0.003 C4.810.007
Devosia0.008 A0.009 A0.010 A0.005 B0.005 B0.005 B0.004 B6.820.002
Rubrobacter0.004 BC0.005 BC0.010 A0.004 C0.003 C0.006 B0.004 C10.06<0.0001
Bacillus0.006 B0.008 A0.009 A0.003 C0.004 C0.004 C0.004 C5.490.004
Rubellimicrobium0.002 C0.004 B0.007 A0.003 C0.002 C0.003 C0.001 D5.80.003
Distinct uppercase letters within the same row signify notable variances at the 0.01 significance level.
Table 4. Correlation between the abundance of soil bacterial communities at the phylum level and the soil.
Table 4. Correlation between the abundance of soil bacterial communities at the phylum level and the soil.
TaxonTCNO3−-NNH4+-NAPpH
Bacterial relative abundance (>1%)
Nitrospirae−0.33−0.070.340.38−0.63 **
Firmicutes0.50 *−0.28−0.35−0.02−0.63 **
Verrucomicrobia−0.73 **0.21−0.22−0.47 *0.71 **
Chloroflexi0.410.16−0.120.300.26
Planctomycetes−0.36−0.11−0.25−0.120.32
Bacteroidetes−0.32−0.030.390.230.19
Gemmatimonadetes−0.71 **−0.28−0.22−0.49 *0.17
Acidobacteria−0.62 **−0.16−0.100.100.24
Actinobacteria0.67 **0.220.140.08−0.06
Proteobacteria0.250.130.030.150.57 *
Bacterial relative abundance (<1%)
GAL15−0.51 *−0.150.29−0.140.04
Crenarchaeota0.300.53 *0.410.27−0.13
TM6−0.140.000.04−0.150.29
BRC10.160.220.380.300.46 *
[Thermi]0.60 *0.320.260.060.00
Tenericutes0.140.220.200.47 *0.20
WS3−0.23−0.11−0.38−0.16−0.38
Elusimicrobia−0.220.330.380.000.17
WS2−0.17−0.23−0.030.48 *−0.09
TM7−0.22−0.25−0.150.14−0.04
Fibrobacteres−0.20−0.20−0.130.13−0.08
Chlorobi−0.290.000.380.360.03
Armatimonadetes−0.45 *−0.24−0.27−0.69 **−0.13
FBP−0.40−0.030.04−0.34−0.12
Cyanobacteria−0.61 **−0.04−0.15−0.080.03
Note: * and ** indicate significant differences at p < 0.05 and p < 0.01, respectively.
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Gou, Z.; Wang, Y.; Cui, Z.; Yan, B.; Gao, Y.; Wu, B.; Guo, L. Effects of Four-Year Oilseed Flax Rotations on the Soil Bacterial Community in a Semi-Arid Agroecosystem. Agronomy 2024, 14, 740. https://doi.org/10.3390/agronomy14040740

AMA Style

Gou Z, Wang Y, Cui Z, Yan B, Gao Y, Wu B, Guo L. Effects of Four-Year Oilseed Flax Rotations on the Soil Bacterial Community in a Semi-Arid Agroecosystem. Agronomy. 2024; 14(4):740. https://doi.org/10.3390/agronomy14040740

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Gou, Zhenyu, Yifan Wang, Zhengjun Cui, Bin Yan, Yuhong Gao, Bing Wu, and Lizhuo Guo. 2024. "Effects of Four-Year Oilseed Flax Rotations on the Soil Bacterial Community in a Semi-Arid Agroecosystem" Agronomy 14, no. 4: 740. https://doi.org/10.3390/agronomy14040740

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