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Article

Introducing Legumes into Wheat–Maize Rotation Complicates Soil Microbial Co-Occurrence Network and Reduces Soil Allelochemicals in Succeeding Wheat Season

by
Yaqian Yan
1,2,†,
Haiyang Jin
2,†,
Fei Zheng
2,
Xiwen Yang
1,
Hang Song
2,
Jiarui Wang
2,
Baoting Fang
2,
Hongjian Cheng
2,
Xiangdong Li
2,* and
Dexian He
1,*
1
College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
2
Wheat Research Institute, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2025, 15(12), 1307; https://doi.org/10.3390/agriculture15121307
Submission received: 7 May 2025 / Revised: 12 June 2025 / Accepted: 16 June 2025 / Published: 18 June 2025
(This article belongs to the Section Agricultural Soils)
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Abstract

:
Increasing species richness through rotation is considered a promising measure to enhance agroecosystem functions and services. However, the legacy effects of introducing legumes into a wheat–maize rotation in the North China Plain on soil microecology, especially the soil metabolome, in the succeeding wheat season have not been elucidated. This study established three cropping systems: (1) a continuous winter wheat–summer maize rotation (M), (2) a winter wheat–summer peanut (summer maize) rotation (PM), and (3) a winter wheat–summer soybean (summer maize) rotation (SM). The soil physicochemical properties, microbial communities, and metabolomes were analyzed at the stage of the succeeding wheat crop. Introducing peanuts or soybeans into a wheat–maize rotation significantly reduced the soil bacterial abundance and increased the soil fungal Shannon index. This rotation adjustment had a substantial impact on the structure and taxa composition of the soil microbial community. Crop diversification increased the number of total edges, the average degree, and the average number of neighbors in the soil microbial co-occurrence network. Different crop rotations significantly affected the soil metabolic profiles in the positive and negative ion modes. Crop diversification significantly reduced the abundance of coumarin and coumaric acid in the soils. In conclusion, introducing peanuts or soybeans into a wheat–maize rotation could increase the soil fungal community diversity, change the soil microbial community structure and taxa composition, increase the complexity of the soil microbial ecological network, and reduce the abundance of soil allelochemicals. Our study demonstrated the continuity of the impact of crop rotation on soil ecology, and revealed the ecological advantages of crop diversification from the perspective of soil microbiology and metabolomics.

1. Introduction

In recent decades, in pursuit of simplified planting equipment and operations, agricultural producers around the world have increasingly tended to specialize in the production of a single crop or two crops, and rarely three or more crops [1,2]. The North China Plain primarily features a continuous rotation system of winter wheat followed by summer maize in its double-cropping practice. In contrast, although rotations of winter wheat with summer soybeans or summer peanuts are well-suited to the regional climate, they represent only a minor portion of the cultivated land. The large-scale and continuous loss of crop diversity has had adverse effects on crop production and the environment, such as reduced soil quality and health [3,4], decreased fertilizer use efficiency [2], increased greenhouse gas emissions [5,6], and decreased yield potential and stability [7,8].
The advantages of diversified crop rotation over continuous monotonous crop planting have been widely reported. Increasing crop diversity in rotations has the potential to restore positive aboveground–belowground interactions [9], bring positive impacts to soil microorganisms and soil health [10], and enhance soil ecosystem functions [3]. Effective crop diversity could optimize nutrient and water utilization [11,12], and increase the stability of food production under climate change [13,14]. Furthermore, some research data suggest that diversified crop sequences could enhance natural biological control and disease management, and minimize the demand for synthetic pesticides (including fungicides, insecticides, and herbicides) in agricultural production [15,16]. In particular, introducing grain legumes into cereal crop systems could reduce chemical fertilizer applications while maintaining crop yields [17,18,19,20]. Therefore, increasing crop diversity through rotation, especially including legumes, should be considered as a necessary foundation for sustainable agriculture in the future.
The soil microbial community is crucial for ecosystem function as well as plant growth and health [21,22]; soil microbes are key participants in the biogeochemical cycling processes of ecosystems [21] and important regulators of plant productivity [23]. Crop planting provides carbon sources for soil microorganisms through root exudates and organic residues. Carbon sources with diverse biochemical properties recruit specific microbial taxa for reproduction and growth [24,25]. Earlier research showed that rotating crops notably altered the diversity, structure, and interactions within soil bacterial and fungal communities [26]; reduced the abundance and pathogenicity of potential plant pathogens; and increased the abundance of potential plant growth-promoting microorganisms [27,28,29]. Compared with low-diversity crop rotation, a more diverse crop rotation increased the soil microbial biomass and activity [9,30,31] and increased the soil microbial community richness and diversity [32]. In addition, some studies have shown that the response of the soil microbial community to crop rotations varies by crop types. Liu et al. [33] showed that the soil microbial diversity of a soybean rotation with wheat and maize was significantly lower than that of long-term continuous soybean cropping. Zhou et al. [34] revealed that a two-year rotation increased the soil bacterial diversity, but decreased the soil fungal diversity and abundance. The selection of different crop-planting types is influenced by regional characteristics, due to the various light, temperature, water, and nutrient resources in different regions. A winter wheat rotation with summer maize, peanuts, or soybeans is suitable for planting in the North China Plain. Introducing summer peanuts or soybeans into the dominant rotation could increase the crop diversity. However, little is known about the effects of this on the soil microbial community in the succeeding winter wheat season.
Soil metabolites, originating from plant root exudates and the soil microbial metabolism, play a pivotal role in regulating plant growth [35]. Untargeted metabolomics allows for a high-throughput qualitative and quantitative analysis of low-molecular-weight metabolites within samples, and the implementation of this approach can enhance our comprehension of soil biochemical functions [36]. In recent years, soil metabolomics technology has emerged as a promising approach for evaluating the soil quality in agricultural and environmental studies [35,37,38]. However, the impact of crop rotation on the soil metabolome has been poorly documented. Through the quantitative analysis of soil polyphenols, Fan et al. [39] revealed the regulatory mechanism of crop rotation on the soil nutrient availability and microbiota, demonstrating that the alternating rotation of tea seedlings and strawberries greatly reduced soil polyphenols, stimulated the proliferation of nutrient-cycling-related microbiota, and created a more balanced and healthy microbial community. Zhang et al. [40] revealed the beneficial effects of a ginseng–celandine rotation on the soil quality through untargeted metabolomics, screened 94 soil metabolites with significant differences, and observed a strong correlation between soil metabolites and microbial communities. The investigation into the ecological sustainability of different crop rotation systems, with a focus on the soil metabolome, needs to be intensified considerably.
In the present study, the legacy effects of introducing grain legumes into crop rotations on the soil microbial community and metabolome were evaluated in the context of a 6-year field experiment. The objectives were (i) to determine whether the soil microbial community diversity, composition, and co-occurrence network pattern still showed significant responses to different crop rotations in the succeeding wheat season, (ii) to identify which soil metabolites significantly varied in response to different crop rotations, and (iii) to evaluate the sustainability of the cropping system in the North China Plain from the perspective of soil microbiology and metabolomics. We hypothesized that the soil microbial community and metabolome would be different among the rotation regimes, owing to the disparate organic inputs.

2. Materials and Methods

2.1. Experimental Site and Design

The field experimental site was located at the Henan Modern Agricultural Research and Development Station (35°00′ N, 113°41′ E), Xinxiang City, Henan Province, China. The region is situated in the southern part of the North China Plain, where a continuous winter wheat–summer maize rotation is the dominant cropping system. The yearly mean temperature and precipitation during the study period are shown in Figure 1. The soil is a typical fluvo-aquic soil (classified as aquic inceptisol according to the U.S. Soil Taxonomy [41] with a sandy loam texture, developed from alluvial sediments of the Yellow River. The soil on which the experiment was located had a content of 4.29 g·kg−1 organic carbon (SOC), a content of 0.31 g·kg−1 total nitrogen, a content of 0.87 g·kg−1 total phosphorus, a content of 19.65 g·kg−1 total potassium, and a pH value of 8.52.
The experimental treatments started in June 2015, and before that, a continuous winter wheat–summer maize rotation was carried out in the field. The treatments included three winter wheat-based rotations: a continuous winter wheat–summer maize rotation (M), a winter wheat–summer peanut (summer maize) rotation (PM), and a winter wheat–summer soybean (summer maize) rotation (SM). The cropping sequences of the different treatments are shown in Table 1. The winter wheat, summer corn, summer peanut, and summer soybean varieties were, respectively, Zhengmai 1860, Zhengdan 958, Yuhua 22, and Zheng 1307. The fertilization doses for each crop are shown in Table S2. The study was conducted using a randomized block design, with three repetitions for each treatment. Each plot measured 6 m × 5 m and was isolated from the others by concrete walls that extended 2 m into the ground. This setup was intended to prevent the exchange of water and nutrients between adjacent plots. Winter wheat was sown in the middle of October and harvested in early June of the following year. Summer maize, peanuts, or soybeans were sown subsequently and harvested in September. In addition to the crop rotations, the field managements (irrigation and fertilization) in each plot were consistent.

2.2. Soil Sampling

Bulk soil samples were collected with a sampling depth of 0–20 cm on 1 June 2021 after the wheat harvest. Five soil cores (3 cm diameter) were collected randomly from each plot and mixed to form the soil sample for the plot. The soil samples were transported to the laboratory on ice and sieved through 2 mm to remove plant residues and stones. Each sample was divided into two subsamples. One subsample was air-dried for a soil physical–chemical analysis, and the other subsample was stored at −80 °C for the extraction of the soil microbial genomic DNA and metabolites.

2.3. Soil Physical–Chemical Analysis

The soil physical–chemical characteristics at the maturity stage of wheat following various crop rotations were assessed using the techniques outlined by Lu [42]. The pH level of the soil was determined using a compound electrode (PHS-3E, INESA, Shanghai, China) with a soil-to-water ratio set at 1:2.5. The soil organic C was analyzed through the dichromate oxidation method. The total nitrogen was measured by the Kjeldahl method. The ammonium N (NH4+-N) and nitrate N (NO3−-N) were extracted with a 1 mol·L−1 KCl solution (1:5, w/v) and determined by a Futura continuous flow analyzer (AMS Alliance, Frépillon, France). The total phosphorus in the soil was digested using H2SO4-HClO4, and the available phosphorus was extracted using 0.5 mol·L−1 NaHCO3. The digestion and extraction solutions were detected using the molybdenum antimony colorimetric method. The total potassium in the soil was dissolved using HF-HClO4, and the available potassium was extracted using 1 mol·L−1 CH3COONH4 and determined using a flame photometer [42,43].

2.4. Soil DNA Extraction and Quantitative PCR Analysis

Microbial genomic DNA was isolated from a 500 mg soil sample utilizing a FastDNA Spin Kit for Soil and a FastPrep Instrument (MP Biomedicals, Solon, OH, USA). The purity and concentration of the DNA were measured with a NanoDrop 2000 (Thermo Scientific, Waltham, MA, USA), while the integrity of the DNA was assessed through 1% agarose gel electrophoresis.
Quantitative PCR of the 16S rRNA gene and the ITS sequence was performed using the primers 515F/907R [44] and ITS3F/ITS4R [45,46], respectively. The reagents and instruments in the qPCR reaction were ChamQ SYBR Color qPCR Master Mix (Cat. No. Q411-02, Vazyme Biotech, Nanjing, China) and an Applied Biosystems 7300 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The plasmid containing the target gene was serially diluted to create a standard curve. According to preliminary experiments, the qPCR annealing temperatures of the 16S rRNA gene and the ITS sequence were set to 60 °C and 58 °C, respectively. Each sample was analyzed in triplicate.

2.5. Amplicon High-Throughput Sequencing

The response of the soil microbial community to different crop rotations was investigated by amplicon sequencing the 16S rRNA gene and the ITS sequence. The 16S rRNA gene and the ITS sequence of the soil microbial genomic DNA were PCR-amplified using the universal primers 515F/907R [44] and ITS3F/ITS4R [45,46], respectively. The reagents and instruments in the PCR reaction were TransStart FastPfu DNA Polymerase (Cat. No. AP221-02, TransGen Biotech, Beijing, China) and a GeneAmp PCR System 9700 (Applied Biosystems, Foster City, CA, USA). According to preliminary experiments, the annealing temperature was set to 55 °C, and the reaction cycles of the 16S rRNA gene and the ITS sequence were set to 27 and 35, respectively. Each sample was amplified in triplicate, and then the products of the same sample were pooled and detected by 2% agarose gel electrophoresis. The target DNA fragments were purified using the AxyPrep DNA Gel Extraction Kit (Cat. No. AP-GX-250, Axygen, Union City, CA, USA). The purified amplicons were quantified by the PicoGreen fluorescent dye method using a QuantiFluor-ST Handheld Fluorometer (Promega, Fitchburg, WI, USA), and pooled according to the corresponding volume ratio. The PE300 high-throughput sequencing of the mixture was performed on an Illumina Miseq platform (Illumina, San Diego, CA, USA).
A bioinformatics analysis of the high-throughput sequencing data was performed using the Majorbio Cloud Platform (https://cloud.majorbio.com/, access on 18 April 2023) online [47]. The FLASH (v1.2.7) [48] was used to splice the paired-end reads (overlap of ≥10 bp). fastp (v0.19.6) [49] was used to perform quality control and data-filtering features, and the sample data were distinguished according to different barcodes. USEARCH [50] was used to perform operational taxonomic unit (OTU) clustering for non-repeated sequences (removing single sequences) with 97% similarity (UPARSE, v11) [50,51] and chimera removal (UCHIME) [52]. The OTU taxonomy assignment based on the SILVA (v138) [53,54] and UNITE (v8) [55] datasets was performed using RDP Classifier [56]. The valid data were randomly resampled according to the minimum sequence number of sample sequences. The subsequent analyses of the microbial community were performed with the resampled valid data.

2.6. Soil Metabolite Extraction and Untargeted Analysis

Soil metabolites were extracted from 1000 mg of frozen soil from each plot with a 1 mL methanol solution (methanol/water = 4:1, v/v) containing 0.02 mg·mL−1 of L-2-chlorophenylalanine as the internal standard. After grinding, the metabolites were extracted by low-temperature ultrasound. After being kept at −20 °C for 30 min, the mixture was centrifuged to collect the supernatant. This was subsequently evaporated using nitrogen gas and reconstituted in 120 μL of an acetonitrile solution (acetonitrile/water = 1:1, v/v). The supernatant was further processed via low-temperature ultrasonic extraction followed by centrifugation. Finally, it was subjected to high-performance liquid chromatography coupled with tandem mass spectrometry (LC-MS) for the determination of the metabolite composition and concentration. The chromatographic conditions were as follows: Mobile phase A consisted of 95% water and 5% acetonitrile (with 0.1% formic acid added), while mobile phase B comprised 47.5% acetonitrile, 47.5% isopropanol, and 5% water (also containing 0.1% formic acid). The elution flow rate was set at 0.40 mL·min−1, the sample injection volume was 10 μL, and the column was maintained at a temperature of 40 °C. The sample components were ionized by electrospray, and the mass spectrum signals were collected by the positive and negative ion scanning modes. The equal volume mixture of all the samples was used as a quality control (QC) sample, and one QC sample was added between every 10 samples. The raw data of the high-performance liquid chromatography tandem mass spectrometry were aligned and identified using Progenesis QI. The data were normalized according to the following procedure: removing the missing value by 80%, filling the missing value with the minimum, normalizing the sum, and deleting the variables with RSD > 30% of the QC sample. The soil metabolites were classified by matching the mass spectrum information with the METLIN metabolic database (https://metlin.scripps.edu, access on 3 May 2023) [57].

2.7. Statistical Analysis

An alpha diversity analysis of the soil microbial community was performed using mothur (v1.30.2) [58,59]. Furthermore, the community coverage (Good’s coverage) [60] was used to evaluate whether the sequencing was sufficient. The beta diversity of the soil microbial community was analyzed by a non-metric multidimensional scaling (NMDS) analysis based on the Bray Curtis distance matrix. The effect of different treatments on the microbial community structure was evaluated by a permutational multivariate analysis of variance (PERMANOVA) and an analysis of similarities (ANOSIM). The correlation between the soil microbial community structure and the physical–chemical properties was determined by the Mantel test. A soil microbial co-occurrence network analysis among the genera with a relative abundance of >0.1% was performed with the Pearson correlation and Euclidean distance using the CoNet plugin [61] in Cytoscape (v3.3.0) [62]. Gephi (v0.9.2) was used to visualize the network. Topological parameter calculations were performed by Network Analyzer [63].
A partial least squares discriminant analysis (PLS-DA) was used to compare the metabolic profiles under different treatments. Differential soil metabolites among the different treatments were screened based on the VIP value of the PLS-DA analysis (VIP > 1) and the p-value of Student’s t-test (p < 0.05). Correlations between the soil microbial community and the metabolite data were determined through a Procrustes analysis.
The physical–chemical properties, microbial abundance, community alpha diversity indices, dominant taxa relative abundance, and metabolite abundance among the different treatments were compared using a one-way ANOVA followed by the LSD test for multiple comparisons at p < 0.05.

3. Results

3.1. Soil Physical–Chemical Properties

Compared with the continuous winter wheat–summer maize rotation, the winter wheat–summer peanut (summer maize) rotation significantly decreased the soil organic carbon, total nitrogen, and mineral nitrogen (p < 0.05), while there was no significant change in those properties in the winter wheat–summer soybean (summer maize) rotation (p > 0.05) (Table 2). Introducing peanuts or soybeans into a wheat–maize rotation significantly increased the soil total phosphorus (p < 0.05) and decreased the soil available potassium (p < 0.05). The winter wheat–summer peanut (summer maize) rotation significantly increased the soil available phosphorus (p < 0.05). There was no significant difference in the soil pH or total potassium among the different crop rotations.

3.2. Abundance and Alpha Diversity of Soil Microbial Community

The internal transcribed spacer (ITS) sequence is a region of the ribosomal RNA gene in eukaryotes that is hypervariable, and therefore useful for species identification and phylogenetic analyses. The abundances of soil bacteria and fungi were measured using quantitative PCR, targeting the 16S rRNA gene for bacteria and the ITS sequence for fungi. The results showed that crop diversification significantly decreased the 16S rRNA gene copy number upon the maturity of the succeeding wheat (p < 0.05), while there was no significant difference in the soil fungal abundance among the different rotations (p > 0.05) (Table 3).
After the Illumina MiSeq sequencing data were quality controlled, filtered, and randomly resampled according to the minimum sequence number, 31,560 sequences of the 16S rRNA gene and 59,661 sequences of the ITS region were obtained from each sample. Rarefaction curves based on Good’s coverage showed that, as the read number increased, the sequencing coverage of each sample tended towards a saturation plateau (Figure S1), indicating that the randomly resampled data could cover most microbial taxa in the samples and were sufficient for a subsequent analysis of the soil microbial community.
The results of the soil microbial alpha diversity showed that the different crop rotations had no significant impact on the soil bacterial richness (Chao1 index) or diversity (Shannon index) upon the maturity of the succeeding wheat (p > 0.05) (Table 3). Introducing peanuts or soybeans into the wheat–maize rotation significantly increased the soil fungal diversity (Shannon index) (p < 0.05), while it had no significant impact on the soil fungal richness (Chao1 index) (p > 0.05).

3.3. Beta Diversity and Composition of Soil Microbial Community

To evaluate the beta diversity of the soil microbial community, a non-metric multidimensional scaling (NMDS) analysis was employed. The results showed that the soil microbial communities after different crop rotations were obviously separated (Figure 2), indicating that there were differences in the structure of the soil bacterial (stress = 0.047) and fungal (stress = 0.075) communities among the different treatments. This result was confirmed by a permutational multivariate analysis of variance (PERMANOVA) and an analysis of similarities (ANOSIM). The different crop rotations significantly affected the structure of the soil bacterial community (PERMANOVA: r2 = 0.347, p = 0.004; ANOSIM: r = 0.918, p = 0.004) and fungal community (PERMANOVA: r2 = 0.4398, p = 0.004; ANOSIM: r = 0.712, p = 0.004) upon the maturity of the succeeding wheat. A Mantel test analysis demonstrated that the soil bacterial community structure was significantly correlated with the soil organic carbon and total nitrogen, while the soil fungal community structure was significantly correlated with the pH value, total phosphorus, available phosphorus, and available potassium (Table S3).
After the classification annotation, a total of 5719 bacterial OTUs were obtained, among which the three crop rotation treatments shared 3229 bacterial OTUs, and 450, 400, and 386 bacterial OTUs were unique to the M, PM, and SM treatments, respectively (Figure 3). Similarly, a total of 1380 fungal OTUs were obtained, among which the three crop rotation treatments shared 592 fungal OTUs, and 128, 130, and 178 fungal OTUs were unique to the M, PM, and SM treatments, respectively. The results indicated that the compositions of the soil microbial community upon the maturity of the succeeding wheat after the different crop rotations had both similarities and differences (Figure 3). Due to the preferable classification annotation at the family level, families with an average relative abundance greater than 0.1% were selected for a differential taxa analysis (Table S4). In the soil bacterial community upon the maturity of the succeeding wheat after the different crop rotations, compared with the continuous winter wheat–summer maize rotation, the winter wheat–summer peanut (summer maize) rotation significantly increased the relative abundances of Micrococcaceae, Iamiaceae, and Pseudonocardiaceae (p < 0.05), and significantly decreased the relative abundances of Microscillaceae, Streptosporangiaceae, Longimicrobiaceae, and Sutterellaceae (p < 0.05). The winter wheat–summer soybean (summer maize) rotation significantly increased the relative abundances of Microscillaceae, Comamonadaceae, Beijerinckiaceae, Saprospiraceae, and Sutterellaceae (p < 0.05), and significantly decreased the relative abundances of Dongiaceae and Streptosporangiaceae (p < 0.05). In the soil fungal community upon the maturity of the succeeding wheat after the different crop rotations, the winter wheat–summer peanut (summer maize) and winter wheat–summer soybean (summer maize) rotations were characterized by higher relative abundances of Plectosphaerellaceae, Nectriaceae, Clavicipitaceae, and Bionectriaceae (p < 0.05) (Table S4).

3.4. Co-Occurrence Network of Soil Microbial Community

A diagram and the topological parameters of the soil microbial co-occurrence network upon the maturity of the succeeding wheat after the different crop rotations are shown in Figure S2 and Table 4, respectively. The microbial ecological networks of the soil in the M treatment consisted of 254 nodes and 1679 edges, while that of the PM treatment included 256 nodes and 1873 edges, and that of the SM treatment comprised 264 nodes and 1834 edges. Compared with the continuous winter wheat–summer maize rotation, the number of copresence edges increased by 8.82% and the mutual exclusion edges increased by 14.35% in the winter wheat–summer peanut (summer maize) rotation, while the corresponding increases were 7.88% and 10.62% in the winter wheat–summer soybean (summer maize) rotation. Additionally, the average degree values in these two treatments were observed to increase by more than 5% compared to those in the continuous winter wheat–summer maize rotation. The results indicated that introducing summer peanuts or soybeans to a continuous winter wheat–summer maize rotation increased the complexity of the soil microbial co-occurrence network upon the maturity of the succeeding wheat.

3.5. Soil Metabolites

Through an untargeted metabonomic analysis, a total of 474 metabolites were identified in all the soil samples. A partial least squares discriminant analysis (PLS-DA) was applied to observe the variance within the soil metabolic profiles after the different crop rotations. The R2Y values in the positive and negative ion modes were 0.951 and 0.947, respectively, and the Q2 values in the positive and negative ion modes were 0.604 and 0.552, respectively, indicating the excellent reliability and predictive capability of the PLS-DA model. The PLS-DA plot showed that the soil metabolic profiles in the positive and negative ion modes were obviously separated among different crop rotations (Figure 4), indicating that different crop rotations altered the soil metabolic profiles upon the maturity of the succeeding wheat.
As shown in Figure S3, there were differential soil metabolites (VIP > 1, p < 0.05) upon the maturity of the succeeding wheat after different crop rotations. A total of 78 identifiable metabolites were screened in the comparison of the continuous winter wheat–summer maize rotation and the winter wheat–summer peanut (summer maize) rotation. A total of 65 identifiable metabolites were screened in the comparison of the continuous winter wheat–summer maize rotation and the winter wheat–summer soybean (summer maize) rotation. Due to the lack of research on the related functions of soil metabolites, the further impact of most differential metabolites on soil ecological functions is still unclear. It is noteworthy that introducing peanuts or soybeans into a wheat–maize rotation significantly decreased the abundance of the allelochemicals coumarin and coumaric acid in the soils upon the maturity of the succeeding wheat (p < 0.01) (Figure 5). The abundance of coumaric acid in the winter wheat–summer peanut (summer maize) rotation was significantly lower than that in the winter wheat–summer soybean (summer maize) rotation (p < 0.01).

4. Discussion

The soil pH value is a short-term changing soil property that is easily influenced by crop residue decomposition [64], rhizosphere processes [65], and fertilizer management [66]. However, in this study, there were no significant differences in the soil pH value among the different crop rotation treatments, which was consistent with the results of some previous research [65,67]. This study investigated the legacy effects of different crop rotations in the same crop-growing season, and the almost consistent pH value may be attributed to the same crop cultivation (winter wheat) lasting for more than 7 months [67,68]. The responses of the soil mineral composition could have resulted from the nutrient transformation and absorption of different crop rotations [69,70]. In this study, introducing peanuts or soybeans into a wheat–maize rotation in the North China Plain increased the soil total phosphorus, while the values of organic carbon and total nitrogen decreased. Legume rotation could inhibit phosphorus migration, activate fixed phosphorus, improve the distance of phosphorus movement, and finally, improve the total phosphorus content in the soil [71]. Meanwhile, leguminous plants consume a different rhizosphere phosphorus pool than cereal plants and have a stronger soil p utilization capacity; therefore, legume residues contain more p and have lower C/P ratios than cereals, thereby affecting the soil P pool [72,73]. The biomass of corn straw is greater than that of soybean/peanut straw. Therefore, when maize, soybean, and peanut straw are returned to the field in full, the input of the soybean/peanut straw to the soil C is less, resulting in a low soil SOC content. When planting crops in summer, compared with corn, soybeans/peanuts have less N input. Meanwhile, research has shown that, when exogenous nitrogen is added, the nitrogen fixation effect of leguminous crops is inhibited [74], and compared to other crops, the increase in atmospheric nitrogen deposition also inhibits the total nitrogen fixation of leguminous crops [75].
Previous studies have shown that different crop rotations significantly affect the soil microbial activity and carbon metabolism characteristics at the time of the soybean harvest in the second year, indicating that crop rotation has legacy effects on the soil microecology [31]. Samaddar et al. [76] revealed that adding alfalfa to a crop rotation significantly changed the microbial community structure and taxa composition of tomato rhizosphere soil, while no significant changes were observed in the microbial alpha diversity. The research by Ma et al. [77] showed that there are significant differences in the Shannon index of fungi under soybean–wheat and cotton–wheat rotations, and the microbial community structure is significantly altered. In Liu’s study, compared with a winter wheat monoculture, a pea–winter wheat–winter wheat–millet rotation and a corn–wheat–wheat–millet rotation significantly increased the Chao1 index of wheat soil bacteria, but had no significant effect on fungi [69]. In this study, the responses of the soil bacterial and fungal communities to crop rotations were different upon the maturity of the succeeding wheat (Table 3). Introducing peanuts or soybeans into a wheat–maize rotation significantly increased the diversity of the soil fungal community, while there was no significant difference in the soil bacterial diversity among the different crop rotations. Due to differences in light, heat, water, and nutrient utilization, the amount and type of crop residues in different cropping systems vary greatly, and organic substances with different biochemical properties would stimulate specific decomposers [78]. Moreover, the C:N ratios of fungal mycelia are higher than those of bacterial cells [79]. Bacteria and fungi often show different substrate preferences. Generally, fungi are considered to be the main decomposer of lignocellulose, while bacteria prefer readily available carbon sources [80]. Therefore, it could be inferred that the different responses of the soil bacterial and fungal communities to crop rotation may be attributed to different substrate preferences.
A co-occurrence network analysis is helpful to understand the interactions between soil microorganisms [81]. Previous studies have shown that, compared with the continuous cropping of soybeans, a maize–soybean rotation increased the number of nodes and edges of the microbial ecological network in the bulk and rhizosphere soil during the soybean growth period, forming a more complex soil microbial ecological network [82,83]. Increasing the planting frequency of pulse crops in cereal-based cropping systems increased the number of edges and the connectedness of the soil ecological network during the wheat growth period, and enhanced the interaction between soil microorganisms [84]. Introducing peanuts or soybeans into a wheat–maize rotation increased the total number of edges, the average degree, and the average number of neighbors in the present study; that is, crop diversification increased the complexity of the soil microbial ecological network (Table 4). This is consistent with the results of Lu et al. [85] in the North China Plain, which showed that a wheat–soybean rotation increased the intercorrelation and complexity of the soil microbial ecological networks compared with a wheat–maize rotation. Previous studies have pointed out that the higher the complexity of the ecological networks, the stronger their ability to cope with environmental stress [86]. Therefore, the increased complexity of the soil microbial ecological network caused by diversified crop rotation may be an explanatory perspective for the increased stability of food production under climate change.
The changes in the soil metabolites were the result of the combined action of plant root exudates and microbial metabolic processes. Although it is challenging to distinguish metabolites from microorganisms and plants, a soil metabolome analysis could comprehensively reflect the metabolic processes of soil ecosystems [87]. In this study, there was significant consistency between the characteristics of the soil microbial community structure and the trends of soil metabolic profiles among different treatments (Figure S4), which is consistent with previous results [38,88]. A soil metabolomics analysis facilitates a comprehensive understanding of changes in soil microbial communities. The results of the comparative analysis in this study showed that introducing peanuts or soybeans into a wheat–maize rotation significantly down-regulated the abundance of coumarin and coumaric acid in the soil (Figure 5). Coumarin has inhibitory effects on the seed germination of durum wheat [89]. A low concentration of coumarin promotes the root growth of wheat, while a high concentration of coumarin inhibits root and shoot growth [90]. At the same time, coumarin has antimicrobial activity [91]. Root secretion of coumarin may potentially modulate alterations in microbial communities [92,93,94] and significantly affect the structure and taxa composition of the rhizosphere soil microbial community [95,96,97]. The sensitivity of fungal communities to coumarin is higher than that of bacterial communities, and there was a negative correlation between the fungal mycelium and the exogenous addition of coumarin [90]. A plant available phosphorus and iron deficiency could induce the root secretion of coumarin [93,98,99]. In this study, the decrease in coumarin abundance in the soil of the winter wheat–summer peanut (summer maize) rotation and the winter wheat–summer soybean (summer maize) rotation may be related to the higher soil available phosphorus (Table 2). Coumaric acid is autotoxic to the growth of cucumber plants, and the exogenous addition of coumaric acid alters the structure and taxa composition of rhizosphere bacterial and fungal communities [100,101,102]. Coumaric acid inhibits cucumber plant growth by inducing negative interactions between the plants and microorganisms [102,103]. Studies have shown that the abundance of coumaric acid in soil metabolites increases significantly with an increase in the number of long-term continuous cropping years of alfalfa [104]. The accumulation of coumaric acid in the soil would inhibit the seed germination and plant growth of alfalfa, promote the growth of soil pathogenic fungi, and aggravate the occurrence of alfalfa root rot [104]. Therefore, the reduction in the coumarin and coumaric acid abundance in the soil by introducing legumes into a wheat–maize rotation in this study may be beneficial to soil ecological function and crop health.
The grain yield of wheat after different crop rotations (Table S5) [105] showed that, compared with the winter wheat–summer maize rotation, the winter wheat–summer soybean (summer maize) rotation significantly increased the 1000-grain weight and yield of succeeding wheat, while the winter wheat–summer peanut (summer maize) rotation had no significant effect on the grain yield of the succeeding wheat. The results of this study indicate that the soil nutrients and ecological functions varied after different crop rotation systems. Therefore, on the basis of the adjustment and optimization of the planting structure, it is of great practical significance to investigate the supporting technologies for nutrient management after different crop rotations, with the aim of further improving the efficiency of crop nutrient utilization.

5. Conclusions

Different crop rotations have significant legacy effects on the soil physical–chemical properties, microbial community, and metabolome during the succeeding cropping period. Introducing peanuts or soybeans into a wheat–maize rotation in the North China Plain could increase the soil total phosphorus, decrease the soil available potassium, decrease the soil bacterial abundance, increase the soil fungal diversity, and alter the soil microbial structure and composition upon the maturity of the succeeding wheat. Diversified rotations would complicate the soil microbial co-occurrence network, enhance its ability to cope with environmental pressures, and reduce soil allelochemicals in the succeeding crop season. Introducing peanuts or soybeans into a wheat–maize rotation in the North China Plain has advantages in terms of microbiological characteristics and metabolic profiles.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15121307/s1, Figure S1: Rarefaction curves based Good’s coverage of soil microbial communities; Figure S2: Soil microbial co-occurrence network after different crop rotations; Figure S3: Volcano plot of differential soil metabolites after different crop rotations; Figure S4: Procrustes analysis of soil microbial community structure and metabolic profile after different crop rotations; Table S1: Soil chemical properties; Table S2: Information about fertilization; Table S3: Mantel test correlations between soil bacterial community structure and soil properties; Table S4: Relative abundance (%) of dominant differential families in soil microbial community after different crop rotations; Table S5: Grain yield and its components of succeeding wheat in different crop rotations.

Author Contributions

Conceptualization, Y.Y. and H.J.; methodology, Y.Y. and H.J.; software, F.Z. and X.Y.; validation, H.S., J.W. and B.F.; formal analysis, X.Y.; investigation, H.S.; resources, J.W.; data curation, H.C.; writing—original draft preparation, Y.Y. and H.J.; writing—review and editing, X.L. and D.H.; visualization, H.S.; supervision, F.Z. and D.H.; project administration, X.L. and D.H.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Key Research and Development Project of Henan Province (251111110900); the Special Fund for Henan Agriculture Research System (HARS-22-01-G5); the Young Elite Scientists Sponsorship Program by the Henan Association for Science and Technology (2025HYTP073); the Outstanding Youth Science and Technology Fund of the Henan Academy of Agricultural Sciences (2024YQ08); and the Science and Technology Innovation Team Program of the Henan Academy of Agricultural Sciences (2023TD07).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The datasets analyzed during the current study are available in the NCBI SRA repository, https://www.ncbi.nlm.nih.gov/sra/PRJNA1002612, accessed on 3 May 2023.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Yearly mean temperature and precipitation.
Figure 1. Yearly mean temperature and precipitation.
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Figure 2. Non-metric multidimensional scaling (NMDS) analysis of soil microbial community after different crop rotations. M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation.
Figure 2. Non-metric multidimensional scaling (NMDS) analysis of soil microbial community after different crop rotations. M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation.
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Figure 3. OTU distribution Venn diagram of soil microbial communities after different crop rotations. M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation.
Figure 3. OTU distribution Venn diagram of soil microbial communities after different crop rotations. M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation.
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Figure 4. Partial least squares discriminant analysis (PLS-DA) of soil metabolic profiles after different crop rotations. M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation.
Figure 4. Partial least squares discriminant analysis (PLS-DA) of soil metabolic profiles after different crop rotations. M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation.
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Figure 5. Abundance of coumarin and coumaric acid in soil after different crop rotations. M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation. ** indicates p < 0.01, *** indicates p < 0.001.
Figure 5. Abundance of coumarin and coumaric acid in soil after different crop rotations. M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation. ** indicates p < 0.01, *** indicates p < 0.001.
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Table 1. Cropping sequences of different rotations.
Table 1. Cropping sequences of different rotations.
2015–20162016–20172017–20182018–20192019–20202020–2021
MMaize–WheatMaize–WheatMaize–WheatMaize–WheatMaize–WheatMaize–Wheat
PMPeanut–WheatMaize–WheatPeanut–WheatMaize–WheatPeanut–WheatPeanut–Wheat
SMSoybean–WheatMaize–WheatSoybean–WheatMaize–WheatSoybean–WheatSoybean–Wheat
Table 2. Soil physical–chemical properties upon maturity of succeeding wheat after different crop rotations.
Table 2. Soil physical–chemical properties upon maturity of succeeding wheat after different crop rotations.
pHSOC (g kg−1)TN (g kg−1)TP (g kg−1)TK (g kg−1)Nmin (mg kg−1)AP (mg kg−1)AK (mg kg−1)
M8.54 ± 0.02 a5.77 ± 0.25 a0.375 ± 0.005 a0.780 ± 0.017 b18.69 ± 0.10 a69.72 ± 2.50 ab8.91 ± 1.06 b165.67 ± 7.55 a
PM8.51 ± 0.01 a4.82 ± 0.28 b0.342 ± 0.016 b0.818 ± 0.013 a18.70 ± 0.29 a67.66 ± 3.17 b11.36 ± 0.77 a116.67 ± 10.35 b
SM8.51 ± 0.02 a5.32 ± 0.12 a0.357 ± 0.007 ab0.839 ± 0.012 a18.82 ± 0.16 a73.02 ± 2.15 a10.68 ± 0.81 ab119.35 ± 2.95 b
M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation. SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; Nmin: mineral nitrogen; AP: available phosphorus; AK: available potassium. Values are means ± standard deviations (n = 3). Different letters after values in each column indicate significant differences (p < 0.05).
Table 3. Abundance and alpha diversity of soil microbial community after different crop rotations.
Table 3. Abundance and alpha diversity of soil microbial community after different crop rotations.
Bacterial CommunityFungal Community
16S rRNA Gene Copy Number (109 Copies g−1 Dry Soil)Chao 1 IndexShannon IndexITS Sequence Copy Number (107 Copies g−1 Dry Soil)Chao 1 IndexShannon Index
M7.58 ± 0.97 a4386 ± 108 a6.79 ± 0.04 a7.06 ± 1.22 a655 ± 74 a3.55 ± 0.24 b
PM4.71 ± 0.91 b4345 ± 140 a6.75 ± 0.05 a5.66 ± 0.36 a700 ± 64 a4.28 ± 0.24 a
SM4.40 ± 1.27 b4309 ± 61 a6.76 ± 0.03 a5.24 ± 1.18 a721 ± 19 a4.38 ± 0.12 a
M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation. Values are means ± standard deviations (n = 3). Different letters after values in each column indicate significant differences (p < 0.05).
Table 4. Topological parameters of soil microbial co-occurrence network after different crop rotations.
Table 4. Topological parameters of soil microbial co-occurrence network after different crop rotations.
Topological ParameterMPMSM
Number of nodes254256264
Number of edges167918731834
Number of copresence edges850925917
Number of mutual exclusion edges829948917
Average degree13.22014.13613.894
Average number of neighbors15.52716.20015.694
M: winter wheat–summer maize rotation; PM: winter wheat–summer peanut (summer maize) rotation; SM: winter wheat–summer soybean (summer maize) rotation. Soil microbial co-occurrence network analysis among genera with relative abundance of >0.1% was performed with Pearson correlation and Euclidean distance using CoNet plugin in Cytoscape.
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MDPI and ACS Style

Yan, Y.; Jin, H.; Zheng, F.; Yang, X.; Song, H.; Wang, J.; Fang, B.; Cheng, H.; Li, X.; He, D. Introducing Legumes into Wheat–Maize Rotation Complicates Soil Microbial Co-Occurrence Network and Reduces Soil Allelochemicals in Succeeding Wheat Season. Agriculture 2025, 15, 1307. https://doi.org/10.3390/agriculture15121307

AMA Style

Yan Y, Jin H, Zheng F, Yang X, Song H, Wang J, Fang B, Cheng H, Li X, He D. Introducing Legumes into Wheat–Maize Rotation Complicates Soil Microbial Co-Occurrence Network and Reduces Soil Allelochemicals in Succeeding Wheat Season. Agriculture. 2025; 15(12):1307. https://doi.org/10.3390/agriculture15121307

Chicago/Turabian Style

Yan, Yaqian, Haiyang Jin, Fei Zheng, Xiwen Yang, Hang Song, Jiarui Wang, Baoting Fang, Hongjian Cheng, Xiangdong Li, and Dexian He. 2025. "Introducing Legumes into Wheat–Maize Rotation Complicates Soil Microbial Co-Occurrence Network and Reduces Soil Allelochemicals in Succeeding Wheat Season" Agriculture 15, no. 12: 1307. https://doi.org/10.3390/agriculture15121307

APA Style

Yan, Y., Jin, H., Zheng, F., Yang, X., Song, H., Wang, J., Fang, B., Cheng, H., Li, X., & He, D. (2025). Introducing Legumes into Wheat–Maize Rotation Complicates Soil Microbial Co-Occurrence Network and Reduces Soil Allelochemicals in Succeeding Wheat Season. Agriculture, 15(12), 1307. https://doi.org/10.3390/agriculture15121307

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