Next Article in Journal
Selenium Alleviates Cadmium Toxicity in Pepper (Capsicum annuum L.) by Reducing Accumulation, Enhancing Stress Resistance, and Promoting Growth
Previous Article in Journal
Strategies to Improve γ-Aminobutyric Acid Biosynthesis in Rice via Optimal Conditions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 14
Issue 9
10.3390/plants14091292
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Response of Soil Microbial Diversity to Triple-Cropping System in Paddy Fields in Middle Reaches of Yangtze River

by
Haiying Tang
1,*,
Junlin Zhou
1,
Ning Liu
2,
Yao Huang
2,
Qin Liu
2,
Faizah Amer Altihani
3 and
Binjuan Yang
2,*
1
School of Agriculture and Biotechnology, Hunan University of Humanities, Science and Technology, Loudi 417000, China
2
Key Laboratory of Crop Physiology, Ecology and Genetic Breeding, Ecological Science Research Center, Jiangxi Agricultural University, Nanchang 330045, China
3
Department of Biology, College of Science, King Khalid University, Abha 61413, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Plants 2025, 14(9), 1292; https://doi.org/10.3390/plants14091292
Submission received: 7 January 2025 / Revised: 4 April 2025 / Accepted: 17 April 2025 / Published: 24 April 2025
(This article belongs to the Section Plant–Soil Interactions)
Browse Figures
Versions Notes

Abstract

:
To explore the characteristics of soil microbial community structure diversity for different planting patterns in paddy fields, and to screen out the planting patterns suitable for the promotion of double-cropping rice areas in the middle reaches of the Yangtze River, five typical planting patterns were set up in this study. The five patterns are Chinese milk vetch–early rice–late rice (CRR, CK), Chinese milk vetch–early rice–sweet potato || late soybean (CRI), rapeseed–early rice–late rice (RRR), rapeseed–early rice–sweet potato || late soybean (RRI) and potato–early rice–late rice (PRR). The variation characteristics of soil microbial community structure diversity and the correlation between soil environmental factors and soil microbial community structure diversity under the triple-cropping system in the double-cropping rice area of the middle reaches of the Yangtze River were studied by 16S rRNA high-throughput sequencing and real-time fluorescence quantitative polymerase chain reaction (PCR). The results showed that after two years of experiment, the pH values of each treatment increased, and the rapeseed–early rice–late rice (RRR) model performed better. The soil organic matter and total nitrogen content of the milk vetch–early rice–sweet potato || late soybean (CRI) model was the highest, which increased by 7.89~35.02% and 6.59~26.80% compared with other treatments. The content of soil available phosphorus and available potassium in the potato–early rice–late rice (PRR) model was higher than that in other treatments, which was increased by 29.48% and 126.49% compared with the control. The Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–sweet potato || late soybean (RRI) models were beneficial to increasing soil nitrate nitrogen and ammonium nitrogen content. Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–late rice (RRR) patterns were beneficial for improving the microbial diversity index. Proteobacteria, Chloroflexi, and Actinobacteria are the top three dominant phyla in terms of the relative abundance of soil bacteria, and the top three dominant fungi are Ascomycota, Basidiomycota, and Mucor. The Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–sweet potato || late soybean (RRI) patterns increased the relative abundance of soil Actinobacteria and Ascomycota. The contents of ammonium nitrogen, total organic carbon, nitrate nitrogen, and available phosphorus were the main environmental factors affecting soil microbial community structure. The findings can provide references for screening out the planting patterns suitable for the promotion of double-cropping rice areas in the middle reaches of the Yangtze River.

1. Introduction

Soil microorganisms are one of the important components of the soil ecosystem, which plays an important role in the formation of soil humus, the decomposition of organic matter, and nutrient cycling and transformation [1,2]. Soil microorganisms are not only the driving force of soil nutrient cycling and transformation but also affect soil structure, the soil environment, and crop growth and health [3,4]. They are the functional embodiment of soil and the index of environmental change and maintain soil health and fertility [5]. They are key drivers of nutrient cycling, decomposition of organic matter, and suppression of diseases [6]. Microbial diversity is vital for the sustainability of the agricultural system and the production of healthy crops. Complex soil environmental and agricultural practices affect the diversity and composition of microbes. Nevertheless, microbial diversity is seriously threatened due to rapid climate change, intensive agricultural practices, and land use change [7]. The loss in microbial diversity negatively affects nutrient cycling and disease resistance. which are essential for achieving better crop productivity [8].
Agricultural practices, including, tillage, the use of fertilizers, crop rotation and cover cropping, affect the diversity and composition of soil microbes [9,10,11,12]. Crop rotation is an effective practice which increases the number of soil bacteria and rhizosphere soil bacteria [13]. Studies have shown that there are more soil microorganisms in rotation cropping than that in continuous cropping [14]. Different rotation methods have different effects on microorganisms [15]. Zhang et al. [16] found that the long-term incorporation of green manure in winter could change the microbial community structure of paddy soil and improve microbial diversity and activity. Green manure incorporation could also increase microbial richness [17], increase the proportion of Bacillus and Proteus [18], and have a significant effect on the relative abundance of dominant phyla such as Proteobacteria, Acidobacteria, Gemmatimonadetes and Nitrobacteria [19]. Ding et al. [20] showed that the rice–turtle symbiotic system was beneficial in improving the relative abundance of the dominant bacterial genus, improving the diversity and activity of the bacterial community in paddy soil, stabilizing the soil microenvironment and improving soil fertility. Hu et al. [21] found that straw returning could increase the Chao1 richness index and Shannon diversity index of the rice rhizosphere bacterial community, which increased by 14.42% and 4.39%, respectively. There are few studies which have been conducted to determine the impact of different cropping patterns (CRR, CRI, RRR, RRI, and PRR) on the diversity and microbial community structure of paddy fields in the middle reaches of the Yangtze River under the triple-cropping system. Therefore, this study was conducted, using the methods of field experiments, 16S rRNA high-throughput sequencing and real-time fluorescence quantitative polymerase chain reaction (PCR).
Rice is a major cropping system of China and promoting the appropriate rice farming system, and improving the multiple-cropping index, are effective measures for alleviating the food crisis [22]. The middle reaches of the Yangtze River are an important area for grain production and it also an important area of double- and triple-rice production in China. At present, there are many problems in the middle reaches of the Yangtze River, such as single-cropping systems, long-term continuous cropping or multiple cropping, reductions in organic fertilizer input, and low-resource utilization rates, which lead to the decline of paddy soil quality [23] and restrict the development of farmland productivity [24]. Promoting the sustainable development of green ecology in its paddy fields by improving soil fertility and soil ecological environment quality is an important measure [25]. Related studies have shown that paddy–upland multiple cropping and winter-planting green manure can conserve water resources, improve water-use efficiency, increase soil nutrient content [26], improve soil structure, accelerate the mineralization and decomposition of soil nutrients, promote the transformation and flow of soil nutrients [27], reduce soil erosion, and reduce harmful gas emissions [28]. Among them, leguminous green manure has better nitrogen fixation capacity [29], which is conducive to reducing fertilizer input, improving the quality of cultivated land, and promoting agricultural green production [30]. Therefore, this study was conducted with the following aims: (1) to deeply analyze the changes in soil chemical properties and microbial community structure diversity; (2) analyze the correlation between soil environmental factors and soil microbial community structure diversity under the triple-cropping system of paddy fields in the middle reaches of the Yangtze River; (3) explore the high-yield and high-efficiency planting modes suitable for the middle reaches of the Yangtze River. This study can provide a reference for screening the planting patterns suitable for the promotion of double-cropping rice areas in the middle reaches of the Yangtze River.

2. Results

2.1. The Effects of Triple-Cropping System on Soil Chemical Properties in Paddy Fields in the Middle Reaches of the Yangtze River

The pH value of RRR (rapeseed–early rice–late rice) for early rice treatment in 2021 is the highest (Table 1). PRR (potato–early rice–late rice) treatment had the highest available P content and the highest available K content in winter cropping and early and late rice soil. The soil available K content in the early rice stage was significantly higher than that in other treatments by 28.36% to 52.08% (p < 0.05). Soil nitrate–nitrogen under CRI treatment reached the maximum at all stages, and was significantly higher during winter cropping and early rice stages than that of other treatments, by 47.56–132.76% and 90.76–649.89% (p < 0.05). In 2022, the organic matter content of early and late rice, soil nitrate nitrogen content of early and late rice, and soil ammonium–nitrogen content of late rice were the highest in CRI treatment. The soil ammonium–nitrogen content of late rice was significantly higher—1.26–1.94 times—than that of other treatments, except the RRI treatment (p < 0.05). The total soil nitrogen content of the RRI treatment was the highest in winter cropping and early rice, and was significantly higher than that of other treatments by 19.89% to 35.63% and 17.13% to 30.86% (p < 0.05), except for the RRR treatment. The soil available phosphorus content of winter cropping and early rice and the soil available potassium content of early and late rice reached the maximum under the PRR treatment.

2.2. The Effects of Triple-Cropping System on Soil Microbial Community Diversity in Paddy Fields in the Middle Reaches of the Yangtze River

The soil bacterial Sobs index of PRR (potato–early rice–late rice) was significantly different from that of CRI (milk vetch–early rice–sweet potato || late soybean) and RRI (rapeseed–early rice–sweet potato || late soybean) (p < 0.05; Table 2). The Shannon index of soil bacteria for RRR was significantly higher than that for CRI and RRI—by 9.55% and 7.11%, respectively—and the Shannon index of soil fungi was significantly higher than that in CRI and RRI—by 122.73% and 145.00%, respectively (p < 0.05). The Simpson index of soil bacteria treated with CRI was significantly different from that of CK and RRR (p < 0.05), and the Simpson index of soil fungi treated with RRI was significantly higher than that of other treatments, by 25.37% and −37.70% (p < 0.05). The Coverage index of the soil bacteria and fungi in each treatment was not significantly different (p > 0.05). Therefore, the CRI, RRR and RRI treatments can improve the diversity of soil microbial communities.
The number in the figure represents the number of OTUs in the region (Figure 1). For the treatments CRR, CRI, RRR, RRI and PRR, the soil bacteria contains 4083, 3984, 4116, 4064 and 4241 OUTs, respectively, of which there are 71, 74, 55, 87 and 72 unique OUTs, respectively (Figure 1).
Their soil fungi contained 316, 268, 321, 268 and 319 OUTs, respectively, of which 14, 7, 15, 10 and 13 were unique OUTs.
The species composition ratio of different planting patterns at the classification level can reflect the changes in microbial community structure in paddy fields. The top five dominant genera in soil bacteria are Proteobacteria (14.98~25.84%), Chloroflexi (14.98~25.84%), Actinobacteriota (11.36~18.30%), Acidobacteriota (11.69~23.51%) and Firmicutes (3.37~8.04%) (Figure 2).
The relative abundance of Actinobacteria in the RRI treatment was the highest (23.51%), followed by the CRI treatment (22.4%: Figure 3). The relative abundance of Actinobacteria in the CRI and RRI treatments was significantly higher than that in other treatments (p < 0.05). The highest relative abundance of Firmicutes was the treatment CRI, which was 8.04%, followed by the treatment RRI. The relative abundance of Desulfobacterota and Nitrospirota in CK was the highest, at 3.80% and 4.28%, respectively.
Regarding the dominant species abundance of soil fungi at the phylum level, Ascomycota, Basidiomycota and Mucoromycota were the dominant groups. Ascomycota accounted for 77.15~91.28%, Basidiomycota accounted for 4.16~11.83%, and Mucoromycota accounted for 1.24~6.15% (Figure 4).
By analyzing the differences between groups of soil fungi at the phylum level (Figure 5), it can be found that the relative abundance of Ascomycota in each treatment was significantly different. The relative abundance of Ascomycota in CRI and RRI was the highest, which was 90.72% and 91.28%, respectively. The highest relative abundance of Mucor was for the RRR treatment, which was 6.15%.

2.3. Correlation Analysis Between Soil Environmental Factors and Soil Microbial Community Structure Diversity

The following shows the relative contribution of paddy soil environmental factors to soil bacteria at the phylum level. The results indicated that 67.44% of the variation in soil bacterial community was explained by the first ordination axis (Figure 6). Ammonium nitrogen, total organic carbon, nitrate nitrogen, available phosphorus and easily oxidized organic carbon were the main environmental factors affecting soil bacterial community structure. The CRI treatment was closely related to ammonium nitrogen, total organic carbon, easily oxidized organic carbon, available phosphorus and available potassium, and the treatment RRI was more closely related to nitrate nitrogen, total nitrogen, and organic matter.
The results indicated an 83.01% variation in the soil fungal community was explained by the first ordination axis (Figure 7). Nitrate nitrogen, ammonium nitrogen and total organic carbon were the main environmental factors affecting soil fungal community structure. Soil nitrate nitrogen, total organic carbon, ammonium nitrogen, available phosphorus, available potassium and readily oxidizable organic carbon were positively related to CRI and RRI. Soil microbial biomass carbon, soluble organic carbon and active organic carbon were more closely related to RRR.
As shown in the heat map of the correlation between soil environmental factors and microbial communities (Figure 8), organic matter in soil bacteria is significantly positively correlated with the relative abundance of Bdellovibrionota, and significantly negatively correlated with the relative abundance of Methylomirabilota and GAL15. Nitrate nitrogen, ammonium nitrogen and total organic carbon were significantly negatively correlated with the relative abundance of norank _ k _ norank, Nitrospirota and WS2. Ammonium nitrogen and total organic carbon were also significantly negatively correlated with the relative abundance of unclassified _ k _ norank _ d _ Bacteria, and significantly positively correlated with the relative abundance of Actinobacteriota. The readily oxidizable organic carbon was significantly positively correlated with the relative abundance of WPS-2 and Actinobacteria, and significantly negatively correlated with the relative abundance of TA06, WOR-1 and Spirochaetota.
As for fungi, pH was significantly positively correlated with the relative abundance of Zoopagomycota and SAR_k_norank. Available phosphorus was significantly negatively correlated with the relative abundance of Schizoplasmodiida, Blastocladiomycota, unclassified_k_Fungi and Mucoromycota. Nitrate nitrogen, ammonium nitrogen and total organic carbon were significantly positively correlated with the relative abundance of Ascomycota. However, ammonium nitrogen and total organic carbon were significantly negatively correlated with the relative abundance of Cladosporium and Aphelidea. The oxidizable organic carbon was significantly positively correlated with the relative abundance of the Nucleariidae and Fonticula groups, but significantly negatively correlated with the relative abundance of Cryptomycota, norank _k_ Cryptophyceae and Schizoplasmodiida.

3. Discussions

3.1. Effects of Triple-Cropping System on Soil Chemical Properties in Double-Cropping Rice Area of Middle Reaches of Yangtze River

Compared with the continuous cropping mode, paddy–upland multiple cropping can improve soil fertility. Ji et al. [31] showed that the total nitrogen and organic matter content of soil treated with milk vetch instead of chemical fertilizer increased significantly. Wan et al. [32] showed that the total nitrogen and organic matter content of soil applied with milk vetch green manure were higher than those of pure chemical fertilizer. The results of this study showed that after two years of experiments, the soil pH value of each treatment increased to a certain extent. Winter planting of green manure could increase soil pH value, alleviate soil acidification and improve soil conditions. At the 2021 early rice stage, the RRR treatment had a significantly higher pH (4.97 ± 0.09) compared to CRI (4.60 ± 0.11) and PRR (4.89 ± 0.07). This is because rapeseed cultivation promotes nitrifying bacteria activity, converting ammonium nitrogen (NH4+) into nitrate nitrogen (NO3−), which releases OH− and raises soil pH. In contrast, other cropping systems (e.g., Chinese milk vetch–early rice) may exacerbate acidification due to H+ secretion from leguminous nitrogen fixation. In this study, the soil organic matter content of the Chinese milk vetch–early rice–sweet potato || late soybean (CRI) model performed better than the control. This may be because green manure can increase the organic matter in the soil.
At the same time, multiple cropping can improve soil permeability, enhance soil permeability and improve fertilizer utilization. Wang et al. [33] showed that Chinese milk vetch can effectively improve the total nitrogen content of paddy soil. This study showed that after the late rice harvest in 2022, the soil organic matter and total nitrogen content of the Chinese milk vetch–early rice–sweet potato || late soybean (CRI) model were the highest, with an increase of 7.89~35.02% and 6.59~26.80% compared with other treatments. The likely reason is that the sufficient nutrients in the soil after winter crop incorporation promoted microbial proliferation, and microbial activity facilitates the decomposition and release of organic nutrients while increasing soil organic matter content. At the same time, soybeans can fix solid nitrogen in the air through rhizobia and increase soil total nitrogen content. Based on the two-year data, compared with the control, the soil available phosphorus and soil available potassium contents in the potato–early rice–late rice (PRR) model were higher than those in other treatments. This may be due to the slow release of soil nutrients under flooding conditions, and the soil colloid adsorbs a large amount of potassium and phosphorus. In addition, potato residues have a high carbon-to-nitrogen ratio (C/N), resulting in slow decomposition and the gradual release of mineral nutrients such as phosphorus and potassium. Root exudates (e.g., organic acids) activate insoluble phosphorus in the soil, increasing available phosphorus content. Liu et al. [34] showed that the green manure of Astragalus sinicus could increase the content of available nitrogen in soil. Zhang et al. [35] showed that paddy–upland rotation could increase nitrogen accumulation in paddy soil. The results of this study showed that CRI and RRI could significantly increase the content of nitrate nitrogen and ammonium nitrogen in soil. The amount of nitrogen fertilizer applied to dry crops was larger, and the application of nitrogen fertilizer promoted the increase in soil nitrogen content. Nitrogen loss is serious in the early rice season and winter fallow season in paddy fields [36], while soybean has a strong nitrogen fixation ability in dry crops, which can slow down nitrogen loss in soil.

3.2. Effects of Triple-Cropping System on Soil Microbial Community Structure Diversity in Double-Cropping Rice Paddy Field in Middle Reaches of Yangtze River

3.2.1. Soil Microbial α Diversity

Soil microorganisms can promote the decomposition and transformation of soil nutrients, form a good soil structure, and promote crop growth. Liu et al. [37] found that straw returning can provide exogenous organic materials for bacteria, promote the growth and reproduction of bacteria [38], and improve the diversity of soil microorganisms. Green manure incorporation can increase the number of bacteria and fungi in soil [39], because green manure incorporation can release a large amount of nutrients, improve the microbial characteristics of paddy soil, and affect the relative abundance of soil bacteria and fungi [40]. Jin et al. [41] found that straw returning could increase the diversity index of soil fungi in rice–oilseed rape rotation. The results of this experiment showed that paddy–upland multiple cropping had a certain effect on the diversity of soil bacteria and fungi.
The Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–sweet potato || late soybean (RRI) models could improve the Simpson index of soil bacteria and fungi. The rapeseed–early rice–late rice (RRR) model could improve the Sobs index and Shannon diversity index of soil bacteria and fungi. The bacterial Alpha diversity in the paddy field was higher than that in dry land [42], because the paddy field was flooded to form an anaerobic environment, and the anaerobic environment could enhance the stability between soil bacteria [43], thus improving bacterial diversity.

3.2.2. Soil Microbial Species Composition

Straw returning can increase the relative abundance of soil Proteobacteria and Chloroflexi [44]. Pu et al. [45] showed that the dominant genus groups in paddy soil were mainly Chloroflexi, Proteobacteria and Actinobacteria. Green manure turnover can increase the relative abundance of soil Proteobacteria and Actinobacteria. Lin et al. [46] found that the dominant groups of paddy soil in the early and late rice season were Proteobacteria, Nitrospira, Acidobacteria and Bacteroidetes. The results of this research showed that the dominant genera in soil bacteria were Proteobacteria (17.05~24.76%), Chloroflexi (14.98~25.84%) and Actinobacteria (11.69~23.51%). Proteobacteria are involved in soil nutrient cycling and can better ensure soil fertility [47]. Chloroflexi can ferment sugars and polysaccharides and promote the decomposition of organic matter in rice soil [47]. Actinobacteria can accelerate the decay of animal and plant remains in soil, which plays an important role in accelerating soil material circulation and energy flow and the construction of the soil environment [48]. The results of this experiment showed that the relative abundance of Actinobacteria in the Chinese milk vetch–early rice–sweet potato || late soybean (CRI) model and rapeseed–early rice–sweet potato | late soybean RRI) model was significantly higher than that of other treatments. The enrichment of Actinobacteria and Firmicutes in CRI and RRI treatments results from the combined effects of residue chemical composition, alternating flooded–upland habitats, and microbial functional strategies. Chinese milk vetch’s low C/N residues trigger a short-term proliferation of Firmicutes, whereas rapeseed’s high C/N residues and lignin content sustain the long-term dominance of Actinobacteria. These differences in microbial community structure reflect how distinct cropping patterns regulate soil carbon and nitrogen cycling pathways, providing a theoretical basis for optimizing paddy field management [49].
The return of Chinese milk vetch, rapeseed and green manure can provide a large amount of humus, and the organic carbon of paddy–upland multiple cropping treatment is higher than that of other treatments, which provides sufficient growth conditions for actinomycetes. The dominant fungal groups in this study were Ascomycota, Basidiomycota and Mucoromycota. Ascomycota accounted for 77.15~91.28%, Basidiomycota accounted for 4.16~11.83% and Mucoromycota accounted for 1.24~6.15%.
The relative abundance of Ascomycota in each treatment was significantly different. The relative abundance of Ascomycota in CRI and RRI was the highest, which was 90.72% and 91.28%, respectively. This is also consistent with Nie et al. [50], which stated that soil fungi are mainly composed of Ascomycota and Basidiomycota. Xu et al. [51] also showed that Ascomycota and Basidiomycota were the dominant phyla in soil fungi. In this experiment, the relative abundance of Ascomycota in CRI and RRI was the highest, which may be because water content, as a key factor, affected the structure of the soil fungal community [52,53].

3.3. Correlation Analysis Between Soil Environmental Factors and Soil Microorganisms

Soil microorganisms are closely related to soil fertility. The community structure of soil fungi and bacteria profoundly affects the physical and chemical properties of soil, and the physical and chemical properties of soil can affect soil microorganisms in turn. The results of this experiment showed that ammonium nitrogen, total organic carbon, nitrate nitrogen and available phosphorus were the main environmental factors affecting soil microbial community structure. Ammonium Nitrogen (NH4+-N) exhibited a highly significant positive correlation with Actinobacteria (r = 0.82, p < 0.01), as they utilize NH4+-N to synthesize extracellular enzymes for organic matter degradation. Total Organic Carbon (TOC) drove the growth of Chloroflexi (r = 0.75) and Proteobacteria (r = 0.68) by supplying energy substrates. Nitrate Nitrogen (NO3−-N) enhanced the activity of denitrifying bacteria (e.g., Proteobacteria). The high NO3−-N in CRI was positively correlated with Proteobacteria abundance. Available Phosphorus (AP) showed a negative correlation with Acidobacteriota (r = −0.61), as phosphorus limitation reduces their competitive advantage. The CRI treatment, characterized by high TOC, NH4+-N, and AP (Figure 6), supported the proliferation of Actinobacteria (22.4%) and Proteobacteria (24.76%), forming a microbial network centered on efficient carbon and nitrogen cycling. There was also a positive link between soil organic matter content and soil microbes, which aligns with earlier results where authors also found a positive association between soil organic matter content and soil microbes [53]. Yang et al. [54] found that the relative abundance of fungal species (Sarocladium) in paddy soil was significantly positively correlated with soil total organic carbon. Organic carbon and available nitrogen were the main environmental factors affecting soil fungal community structure [55,56]. Soil organic carbon and available nitrogen can provide the necessary carbon and nitrogen sources for the survival of soil microorganisms and promote the growth of microorganisms. In this experiment, soil ammonium nitrogen and total organic carbon were significantly negatively correlated with the relative abundance of Cladosporium, Aphelidea and unclassified _ k _ norank _ d _ Bacteria. Soil nitrate nitrogen, ammonium nitrogen and total organic carbon were significantly negatively correlated with the relative abundance of norank _ k _ norank, Nitrospirota and WS2, indicating that the growth of these bacteria did not have a high demand for soil carbon and nitrogen. On the other hand, it may also be because these bacteria are inefficient carbon and nitrogen utilization species [57]. Ammonium nitrogen and total organic carbon were significantly positively correlated with the relative abundance of Actinobacteriota. Nitrate nitrogen, ammonium nitrogen and total organic carbon were significantly positively correlated with the relative abundance of Ascomycota. In the multiple-cropping treatment, the return of dry crop straw to the field increases the input of the carbon source, which is conducive to the growth and reproduction of Actinomycetes.
Heatmap analysis reveals significant correlations between microbial taxa and environmental factors. Soil organic carbon (SOC) and available nitrogen (AN) provide essential carbon and nitrogen sources for microbial survival, thereby promoting microbial growth and activity. The relative abundances of Actinobacteriota and Ascomycota exhibit highly significant positive correlations with soil carbon and nitrogen levels (p < 0.01), demonstrating that these nutrients are essential for their growth and proliferation. The paddy–upland rotation system (CRI/RRI) significantly improved the evenness of bacterial and fungal diversity (as indicated by the Simpson index), while the conventional rice–rice rotation (RRR) was more conducive to species richness (reflected in Sobs/Shannon indices). Key findings demonstrate that green manure incorporation increased the relative abundance of Actinobacteriota by 23.51% compared to monoculture systems, with their saprophytic characteristics enabling efficient decomposition of green manure humus. Concurrently, the high organic carbon environment promoted Ascomycota dominance, reaching 91.28% of the fungal community. The carbon–nitrogen synergy analysis revealed that total organic carbon (TOC) and ammonium nitrogen (NH4+-N) significantly stimulated the proliferation of Actinobacteriota (+35%) and Ascomycota (+28%), while simultaneously suppressing Blastocladiomycota (−42%) and Nitrospirota (−38%). These results elucidate the differential regulatory mechanisms of microbial carbon and nitrogen utilization efficiency. These microbial community changes facilitate the establishment of an efficient nutrient cycling network through Proteobacteria-mediated polysaccharide decomposition and Chloroflexi-driven organic matter mineralization, ultimately enhancing the sustainability of soil fertility. The negative correlation between microbial communities and soil nutrients reflects the ecosystem’s response to high-input agricultural practices. Over the long term, these relationships may compromise soil self-sustaining capacity, leading to increased resource waste and environmental risks. By optimizing fertilization practices, improving soil health, and preserving microbial diversity, we can restore beneficial microbe–nutrient interactions to achieve sustainable soil utilization.

4. Materials and Methods

4.1. Experimental Site

The experiment was carried out in the rice experimental field of Jiangxi Agricultural University Science and Technology Park from 2020 to 2022. The area belongs to a subtropical humid monsoon climate. The average annual total solar radiation is 4.79 * 1013 J hm−2, the average annual sunshine hours are 1532.9 h, the average annual temperature is 19.4 °C, and the average annual precipitation is 2051.1 mm. Before the experiment, the pH value of 0~15 cm soil was 5.28, the organic matter was 28.48 g kg−1, the total nitrogen was 1.99 g kg−1, the available phosphorus was 28.99 mg kg−1, and the available potassium was 19.07 mg kg−1.

4.2. Test Materials and Field Experiment Design

This experiment was a field experiment with a randomized block design. A total of 5 treatments were designed, which were Chinese milk vetch–early rice–late rice (CRR), Chinese milk vetch–early rice–sweet potato || late soybean (CRI), rapeseed–early rice–late rice (RRR), rapeseed–early rice–sweet potato || late soybean (RRI) and potato–early rice–late rice (PRR) (Table 3). Each treatment had three replicates and a total of 15 plots. The area of each plot was 33 m2 (11 m × 3 m), and the soil fertility of each plot was the same before the experiment.
Chinese milk vetch and rapeseed were sown, and potato was cut and planted. The sowing rate of Chinese milk vetch was 37.5 kg/hm2, the sowing rate of rapeseed was 15 kg/hm2, and the planting density of potatoes was 73,000 plants/hm2. Fifteen days before rice transplanting, the straws of milk vetch, rapeseed and potato were all turned back to the field. The sweet potato and soybean were furrowed and ridged, with a ridge width of 1.2 m and a ridge height of 0.35 m. Each ridge was planted with 4 rows of soybean and 1 row of sweet potato. Sweet potato was sowed in strips. The row spacing between sweet potato was 0.3 m, the plant spacing was 0.25 m, and the planting density was 18,182 plants/hm2. With hole sowing, soybean row spacing was 0.2 m. Plant spacing was 0.2 m, and there was a planting density of 145,455 plants/hm2. The row spacing of rice is 0.2 m, and the plant spacing is 0.2 m. The specific field management is shown in Table 4.

4.3. Determination of Soil Chemical Properties

Soil samples from the 0–20 cm plow layer were collected at each point with a ‘5-point sampling method’ at the maturity stage of winter crops and after harvest of early and late rice, respectively. Specifically, the intersection point of the field diagonals was designated as the central sampling location. Four equidistant sampling points were established along the diagonals radiating from this center. The soil samples of each plot were mixed evenly. In total, 15 samples were obtained for each stage. One part was naturally air-dried to determine the content of soil pH, soil organic matter, total nitrogen, available phosphorus, available potassium [58,59] and total organic carbon, and the other part was sealed in a 4 °C (<72 h) refrigerator to determine active organic carbon, dissolved organic carbon, particulate organic carbon and microbial biomass carbon [60,61,62].

4.4. Determination of Soil Microbial Community Structure Diversity

After the late rice was harvested, the 0~20 cm topsoil was randomly taken from each plot by the ‘five-point sampling method’. After mixing, it was immediately frozen in liquid nitrogen and stored in a refrigerator at −80 °C. The sample was commissioned by Meiji Biomedical Technology Co., Ltd. for high-throughput sequencing. The main steps are as follows: First, the microbial DNA in the soil sample was extracted using the DNeasy® PowerSoil® Pro Kit from QIAGEN, Germantown, MD, USA, and then the DNA concentration and purity were determined by an ultra-micro spectrophotometer (Beijing Persee General Instrument Co., Ltd., Beijing, China), and DNA integrity was detected by 1% agarose gel electrophoresis. Primer 338F: ACTCCTACGGGGAGGCAGCAG and primer 806R: GGACTACHVGGGTWTCTAAT were used to amplify the 16S rRNA of microorganisms [63]. Primer SSU0817: FTTAGCATGGAATAATRRAATAGGA and primer 1196R: TCTGGACCTGGTGAGTTTCC were used to amplify the 18sDNA of microorganisms. The products were detected by 2% agarose gel electrophoresis. The PCR products were identified, purified and quantified, and the Miseq library was constructed. Sequencing was performed using the Illumina Miseq PE300 platform (commissioned by Shanghai, Meiji Biotechnology Co., Ltd., Shanghai, China).

4.5. Data Processing

FLASH v1.2.7 software was used to splice the number of double-ended reads of soil bacteria obtained by high-throughput sequencing, and then Qiimev1.9.1 software was used to process the final valid tag results. With Up-arsev7.0.1001 software, the final effective data results, with a 97% similarity, were aggregated to obtain an operational classification unit (OTU). The α-diversity index, including the Chao1 index, Shannon index, and species number were calculated by Mothur polymerization results for the OTU. Chao1 is suitable for assessing changes in total species richness, while Shannon index captures overall differences in community structure. The combined use of multiple indices avoids the limitations of relying on a single metric, thereby providing a more comprehensive understanding of the ecological significance of microbial communities.
The data were processed by Microsoft Excel 2019, and the data statistics and variance analysis were performed by SPSS 22.0 system software. One-way ANOVA and the Duncan method were used for variance analysis and multiple comparisons. LSD was used to compare the difference in sample averages. Origin 8.0 and R Studio (4.2.0) were used for making figures. The Pearson correlation coefficient and redundancy analysis (RDA) were performed to determine the correlation between soil properties and soil microbial population composition. The analysis of soil microbial community structure was carried out by using the cloud platform of Shanghai Meiji Biological Company (Shanghai, China).

5. Conclusions

In conclusion, milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–sweet potato || late soybean (RRI) models were beneficial in increasing soil nitrate nitrogen and ammonium nitrogen content. On the other hand, Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–late rice (RRR) patterns were beneficial in improving the microbial diversity index. Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–sweet potato || late soybean (RRI) patterns increased the relative abundance of Actinobacteria and Ascomycota in soil. Soil ammonium nitrogen, total organic carbon, nitrate nitrogen and available phosphorus were the main environmental factors affecting soil microbial community structure in different cropping patterns. Nevertheless, in the future, the response of a microbial community to dynamic changes of carbon and nitrogen in different planting patterns may be further improved, and the microbial community structure related to nitrogen cycle should be studied. Further, the role of different bacterial and fungal communities in soil organic carbon accumulation in different cropping patterns must also be studied. Additionally, studies on functional metagenomic linking with soil microbes, root metabolites and soil inputs are also needed, and can better our understanding of the impact of different cropping patterns on soil bacterial and fungal communities.

Author Contributions

Conceptualization, H.T. and B.Y.; writing—original draft preparation, H.T. and B.Y.; writing—review and editing, J.Z., N.L., Y.H., Q.L. and F.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported and funded by the National Natural Science Foundation of China (32160528); Modern agricultural industry system in the Jiangxi Province-Paddy Field Integrated Planting and Breeding Industry Technology System (JXARS-12); 2024 Hunan Provincial Department of Education Key Research Project (24A0648); 2024 Ministry of Education Supply-Demand Matching Employment Education Project (2024011153277). 2024 Hunan Provincial College Student Innovation Training Program Project: (234242204); 2024 Hunan Provincial Teaching Reform Research Project for Undergraduate Universities (202401001457); 2024 Hunan Education Science 14th Five-Year Plan Project (ND247979); 2023 Project of Hunan Province Social Science Achievements Appraisal Committee: Study on the Sustainable Development Strategy of Grain Production in the Middle Reaches of the Yangtze River during the 14th Five-Year Plan period (XSP2023GLC107).

Data Availability Statement

Data is contained within the article.

Acknowledgments

The authors are thankful to Muhammad Aamer for his suggestions that improved the quality of our work. The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for supporting this work through small group research under grant number RGP 1/269/45.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wang, X.; Chi, Y.; Song, S. Important soil microbiota’s effects on plants and soils: A comprehensive 30-year systematic literature review. Front. Microbiol. 2024, 15, 1347745. [Google Scholar] [CrossRef] [PubMed]
  2. Nabi, M. Role of microorganisms in plant nutrition and soil health. In Sustainable Plant Nutrition; Academic Press: Cambridge, MA, USA, 2023; pp. 263–282. [Google Scholar]
  3. Wei, X.; Xie, B.; Wan, C.; Song, R.; Zhong, W.; Xin, S.; Song, K. Enhancing soil health and plant growth through microbial fertilizers: Mechanisms, benefits, and sustainable agricultural practices. Agronomy 2024, 14, 609. [Google Scholar] [CrossRef]
  4. Vincze, É.B.; Becze, A.; Laslo, É.; Mara, G. Beneficial Soil Microbiomes and Their Potential Role in Plant Growth and Soil Fertility. Agriculture 2024, 14, 152. [Google Scholar] [CrossRef]
  5. Islam, W.; Noman, A.; Naveed, H.; Huang, Z.; Chen, H.Y.H. Role of environmental factors in shaping the soil microbiome. Environ. Sci. Pollut. Res. 2020, 27, 41225–41247. [Google Scholar] [CrossRef]
  6. Khaziev, F.K. Soil and biodiversity. Russ. J. Ecol. 2011, 42, 199–204. [Google Scholar] [CrossRef]
  7. Li, Y.M.; Lin, Q.Y.; Wang, S.P.; Li, X.Z.; Liu, W.T.; Luo, C.Y.; Zhang, Z.H.; Zhu, X.X.; Jiang, L.L.; Li, X.N. Soil bacterial community responses to warming and grazing in a Tibetan alpine meadow. FEMS Microbiol. Ecol. 2016, 92, fiv152. [Google Scholar] [CrossRef] [PubMed]
  8. Zhao, P.P.; Huang, Y.T.; Liu, B.Y.; Chen, J.Y.; Lei, Z.Y.; Zhang, Y.H.; Cheng, B.H.; Zhou, T.; Peng, S.L. Effects of daytime and nighttime warming on soil microbial diversity. Geoderma 2024, 447, 116909. [Google Scholar] [CrossRef]
  9. Mo, Y.; Bier, R.; Li, X.; Daniels, M.; Smith, A.; Yu, L.; Kan, J. Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning. Communi. Biol. 2024, 7, 1349. [Google Scholar] [CrossRef]
  10. Suman, J.; Rakshit, A.; Ogireddy, S.D.; Singh, S.; Gupta, C.; Chandrakala, J. Microbiome as a key player in sustainable agriculture and human health. Front. Soil Sci. 2022, 2, 821589. [Google Scholar] [CrossRef]
  11. Khan, M.F.; Hof, C.; Niemcova, P.; Murphy, C.D. Biotransformation of fluorinated drugs and xenobiotics by the model fungus Cunninghamella elegans. Methods Enzymol. 2024, 696, 251–285. [Google Scholar]
  12. Khan, M.F.; Chowdhary, S.; Koksch, B.; Murphy, C.D. Biodegradation of amphipathic fluorinated peptides reveals a new bacterial defluorinating activity and a new source of natural organofluorine compounds. Environ. Sci. Technol. 2023, 57, 9762–9772. [Google Scholar] [CrossRef]
  13. Feng, C.; Yi, Z.; Qian, W.; Liu, H.; Jiang, X. Rotations improve the diversity of rhizosphere soil bacterial communities, enzyme activities and tomato yield. PLoS ONE 2023, 18, e0270944. [Google Scholar] [CrossRef]
  14. Wang, Y.; Ji, H.; Wang, R.; Guo, S.; Gao, C. Impact of root diversity upon coupling between soil C and N accumulation and bacterial community dynamics and activity: Result of a 30 year rotation experiment. Geoderma 2017, 292, 87–95. [Google Scholar] [CrossRef]
  15. Tang, J.W.; Zeng, B.J.; Liang, Y.G.; Li, C.; Chen, J.H. Research advances in effects of different patterns on soil microbial communities. Agric. Res. Appl. 2014, 5, 65–69. [Google Scholar]
  16. Zhang, J.; Cao, W.; Xu, C.; Liu, J. Effects of incorporation of milk vetch (Astragalus sinicus) on microbial populations and enzyme activities of paddy soil in Jiangxi. Soil Fertil. Sci. China 2012, 49, 19–25. [Google Scholar]
  17. Zhang, X.; Zhang, R.; Gao, J.; Wang, X.; Fan, F.; Ma, X.; Yin, H.; Zhang, C.; Feng, K.; Deng, Y. Thirty-one years of rice-rice-green manure rotations shape the rhizosphere microbial community and enrich beneficial bacteria. Soil Biol. Biochem. 2017, 104, 208–217. [Google Scholar] [CrossRef]
  18. Bottner, P.; Austrui, F.; Cortez, J.; Billes, G.; Couteaux, M. Decomposition of 14C-and 15N-labelled plant material, under controlled conditions, in coniferous forest soils from a north–south climatic sequence in Western Europe. Soil Biol. Biochem. 1998, 30, 597–610. [Google Scholar] [CrossRef]
  19. Huang, M.; Tian, A.; Chen, J.; Cao, F.; Chen, Y.; Liu, L. Soil bacterial communities in three rice-based cropping systems differing in productivity. Sci. Rep. 2020, 10, 9867. [Google Scholar] [CrossRef] [PubMed]
  20. Ding, J.; Meng, X.; Wang, R.; Chen, L.; Zhou, J.; Chen, C.; Huang, H. Effects of long-term application of rice-turtle co-culture on soil bacterial community structure and diversity. J. South. Agric. 2021, 52, 1860–1868. [Google Scholar]
  21. Hu, R.; Zheng, L.; Liu, H.; Huang, J. Effects of straw returning on microbial diversity in rice rhizosphere and occurrence of rice sheath blight. J. Plant Prot. 2020, 47, 1261–1269. [Google Scholar]
  22. Xie, Y.; Chen, X.; Hu, Z.; Chen, S.; Zhang, H.; Ling, H.; Shen, S. Effects of short-time conservation tillage managements on greenhouse gases emissions from soybean-winter wheat rotation system. Huan Jing Ke Xue 2016, 37, 1499–1506. [Google Scholar]
  23. Wang, S.; Yang, W.; Yang, B.; Wang, L.; Huang, G. Effects of different winter plantings on soil aggregate structure, organic carbon and total nitrogen in double rice field. Acta Agric. Univ. 2018, 40, 1–9. [Google Scholar]
  24. Yuan, J.; Yang, B.; Hu, Q.; Tang, H.; Li, S.; Huang, G. Effects of paddy field cropping patterns on soil organic carbon and carbon pool management index in the middle reaches of the Yangtze River. Chin. J. Eco-Agric. 2021, 29, 1205–1214. [Google Scholar]
  25. Wang, Z.; Miu, J.; Liu, Y.; Tang, H.; Zhang, P.; Zhong, C.; Huang, G.; Zhao, Q. Effect of various crops rotations on soil quality in double cropping rice field in the middle reaches of the Yangtze River. Chin. J. Eco-Agric. 2020, 28, 1703–1714. [Google Scholar]
  26. Yang, B.; Huang, G.; Chen, H.; Yan, L. Optimum combination of winter green manure plowed and nitrogen application levels for high nitrogen uptake and utilization in rice. J. Plant Nutr. Fertil. 2016, 22, 1187–1195. [Google Scholar]
  27. Pan, F.; Lu, J.; Liu, W.; Geng, M.; Li, X.; Cao, W. Study on characteristics of decomposing and nutrients releasing of three kinds of green manure crops. J. Plant Nutr. Fertil. 2011, 17, 216–223. [Google Scholar]
  28. Hou, X.; Jia, Z.; Han, Q.; Sun, H.; Wang, W.; Nie, J.; Yang, B. Effects of different rotational tillage patterns on soil structure, infiltration and water storage characteristics in dryland. Trans. Chin. Soc. Agric. Mach. 2012, 28, 85–94. [Google Scholar]
  29. Yang, B.; Huang, G. Circulating agricultural model of planting Astragalus sinensis in winter in double-cropping paddy field. Jiangsu Agric. Sci. 2018, 46, 51–56. [Google Scholar]
  30. Lin, Z.H.; Chen, T.B.; Zhou, L.X. Characteristics of the application of chemical fertilizers and their rational. Resour. Sci. 1998, 20, 29–34. [Google Scholar]
  31. Ji, W.Y.; Si, L.L.; Wang, J.H. Effect of Astragalus sinicus application instead of chemical fertilizer on soil nutrients and rice yield. J. Zhejiang Agr. Sci. 2022, 63, 29–31. [Google Scholar]
  32. Wan, S.; Zhu, H.; Tang, S.; Xi, G.; Wang, Y. Effects of Astragalus sinicus manure and fertilizer combined application on biological properties of soil in Anhui double cropping rice areas along the Yangtze River. J. Plant Nutr. Fertil. 2015, 21, 387–395. [Google Scholar]
  33. Wang, L.M.; Huang, D.F.; He, C.M. Impacts of the Chinese milk vetch (Astragalus sinicus L.) residue incorporation on soil physicochemical, microbial properties and rice yields in yellow-mud paddy field. Acta Ecol. Sin. 2023, 11, 1–16. [Google Scholar]
  34. Liu, C.; Zhang, C.; Li, B.; Lyu, Y.; Nie, L.; Zhang, L. Effects of Astragalus sinicus combined with chemical fertilizer on nitrogen absorption and utilization of rice and nitrogen distribution and residue of Astragalus sinicus in rice-soil system. J. Appl. Ecol. 2021, 32, 1791–1798. [Google Scholar]
  35. Zhang, S.; Lu, J.; Cong, R.; Ren, T.; Li, X.; Liao, S.; Zhang, Y.; Guo, S.; Zhou, M.; Huang, Y. Effect of rapeseed rotation on the yield of next-stubble crops. Sci. Agric. Sin. 2020, 53, 2852–2858. [Google Scholar]
  36. Yang, T.; Wang, Y.-Q.; Wu, H.-L.; Yu, Y.; Chen, J.-R.; Chen, X.-F.; Qin, W.-J.; Liu, J.; Xu, C.-X. Analysis of seasonal distribution characteristics and pollution risk of nitrogen and phosphorus losses in double-cropping rice fields in the Poyang Lake Plain. J. Agric. Environ. Sci. 2023, 42, 852–860. [Google Scholar]
  37. Liu, Z.; Rong, Q.; Zhou, W.; Liang, G. Effects of inorganic and organic amendment on soil chemical properties, enzyme activities, microbial community and soil quality in yellow clayey soil. PLoS ONE 2017, 12, e0172767. [Google Scholar] [CrossRef]
  38. Lin, Y.; Ye, G.; Kuzyakov, Y.; Liu, D.; Fan, J.; Ding, W. Long-term manure application increases soil organic matter and aggregation, and alters microbial community structure and keystone taxa. Soil Biol. Biochem. 2019, 134, 187–196. [Google Scholar] [CrossRef]
  39. Liming, Z.; Xiaohua, D.; Miliang, Z.; Feng, T.; Jiongping, Z.; Zhimin, J.; Panfeng, J.; Minghua, Z. Effects of different green manures on microbial biomass and enzyme activities of tobacco-planting soil. Chin. Tob. Sci. 2016, 37, 13–18. [Google Scholar]
  40. Yang, Z.; XU, M.; Zheng, S.; Jun, N.; Gao, J.; Liao, Y.; Jian, X. Effects of long-term winter planted green manure on physical properties of reddish paddy soil under a double-rice cropping system. J. Integr. Agric. 2012, 11, 655–664. [Google Scholar] [CrossRef]
  41. Jin, Y.; Li, X.; Cai, Y.; Hu, H.; Liu, Y.; Fu, S.; Zhang, B. Effects of straw returning with chemical fertilizer on soil enzyme activities and microbial community structure in rice-rape rotation. Environ. Sci. 2021, 42, 3985–3996. [Google Scholar]
  42. Liu, W.; Zhang, J.; Qiu, C.; Bao, Y.; Feng, Y.; Lin, X. Study on community assembly processes under paddy-upland rotation. Soils 2020, 52, 710–717. [Google Scholar]
  43. Wang, X.T.; Chen, R.R.; Jin, Z.W.; Feng, Y.Z.; Yao, T.Y.; Lin, X.G. Comparative study on rhizosphere effects and bacterial communities in the rhizospheres of rice and wheat. Acta Pedol. Sin. 2019, 56, 443–453. [Google Scholar]
  44. Wang, Q.; Jiang, X.; Guan, D.; Wei, D.; Zhao, B.; Ma, M.; Chen, S.; Li, L.; Cao, F.; Li, J. Long-term fertilization changes bacterial diversity and bacterial communities in the maize rhizosphere of Chinese Mollisols. Appl. Soil Ecol. 2018, 125, 88–96. [Google Scholar] [CrossRef]
  45. Pu, J.; Li, Z.; Zhong, J.; Jin, Z.; Tang, H.; Wei, C.; Dong, W.; He, T.; Li, Q. Effect of the combination of green manure with nitrogen fertilizer on microbial community in karst paddy soil. Acta Microbiol. Sin. 2022, 62, 2417–2432. [Google Scholar]
  46. Lin, X.; Shi, H.; Wu, L.; Cheng, Y.; Cai, S.; Huang, S.; He, S.; Huang, Q.; Zhang, K. Effects of cultivation methods on soil microbial community structure and diversity in Red Paddy. Ecol. Environ. 2020, 29, 2206. [Google Scholar]
  47. Zhong, J.; Tang, H.-Q.; Li, Z.; Dong, W.; Wei, C.; Li, Q.; He, T. Effects of combining green manure with chemical fertilizer on the bacterial community structure in karst paddy soil. J. Plant Nutr. Fertil. 2021, 27, 1746–1756. [Google Scholar]
  48. Wang, G.; Liu, J.; Yu, Z.; Wang, X.; Jin, J.; Liu, X. Research progress of Acidobacteria ecology in soils. Biotechnol. Bull. 2016, 32, 14. [Google Scholar]
  49. Ouchari, L.; Boukeskasse, A.; Bouizgarne, B.; Ouhdouch, Y. Antimicrobial potential of actinomycetes isolated from the unexplored hot Merzouga desert and their taxonomic diversity. Biol. Open 2019, 8, 035410. [Google Scholar] [CrossRef]
  50. Nie, S.; Wang, Y.; Lei, X.; Zhao, L.; Lin, R.; Wang, F.; Xing, S. Responses of fungal community structure and functional group to fertilization in yellow clayey soil. Chin. J. Appl. Ecol. 2018, 29, 2721–2729. [Google Scholar]
  51. Xu, H.F.; Wang, Z.W.; Sheng, R. Effects of farming patterns on abundance and community structure of fungi in paddy soils. J. Huazhong Agric. 2022, 41, 35–41. [Google Scholar]
  52. Zhang, L.; Shao, J.; Lin, Y.; Kuang, X.; Zhang, H.; Qing, C.; Ma, L.; Yao, B. Influence of microbial diversity and activity of soil on the rice-rice-rape rotation. Ecol. Environ. Sci. 2017, 26, 204–210. [Google Scholar]
  53. Li, Y.; Sheng, H.; Yin, Z.; Xiao, H.; Xue, Y.; Zhou, P. Response of microbial community in purple mud of double-cropped rice fields to 5-year continuous application of organic amendment and liming. Chin. J. Soil Sci. 2022, 53, 482–491. [Google Scholar]
  54. Yang, X.; Huang, X.; Wang, C.; Wang, X.; Yin, X.; Lin, S.; Wang, W. Comparison of fungal community structure and diversity in typical paddy fields. China Environ. Sci. 2020, 40, 4549–4556. [Google Scholar]
  55. Zhao, C.M.; Wang, W.B.; Zhang, Y.F. Characterization of soil microbial community structure among different parent materials in rubber plantation. J. South. Agric. 2021, 52, 1869–1876. [Google Scholar]
  56. Wang, Y.Y.; Wang, Z.F.; Huang, R.; Lv, S.; Gao, M. Characterization of soil microbial community structure as affected by vegetation in Jinyun Mountain. Acta Pedol. Sin. 2019, 56, 1210–1220. [Google Scholar]
  57. Qingyuan, L.; Chunbo, H. AMF helps in carbon sequestration in soil. Mar. Geol. Front. 2016, 32, 41–46. [Google Scholar]
  58. Bao, S.D. Soil Agricultural Chemical Analysis; China Agricultural Press: Beijing, China, 2000; pp. 265–267. [Google Scholar]
  59. Olsen, S.; Sommers, L.; Page, A. Methods of soil analysis: Part 2 chemical and microbiological properties of phosphorus. In ASA Monograph 9; Wiley: New York, NY, USA, 1982; pp. 403–430. [Google Scholar]
  60. Helmke, P.A.; Sparks, D.L. Lithium, sodium, potassium, rubidium, and cesium. In Methods of Soil Analysis: Part 3 Book Series No. 5. Soil Science Society of America; Madison, Ed.; Wiley: New York, NY, USA, 1996; pp. 551–573. [Google Scholar]
  61. Cambardella, C.A.; Elliott, E.T. Particulate soil organic matter changes across a grassland cultivation sequence. Soil Sci. Soc. Am. J. 2007, 56, 777–783. [Google Scholar] [CrossRef]
  62. Bowles, T.; Acosta-martinez, M.; Calderon, V.; Louise, F. Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an inten sively-managed agricultural landscape. Soil Biol. Biochem. 2014, 68, 252–262. [Google Scholar] [CrossRef]
  63. Wear, E.K.; Wilbanks, E.G.; Nelson, C.E.; Carlson, C.A. Primer selection impacts specific population abundances but not community dynamics in a monthly time-series 16S rRNA gene amplicon analysis of coastal marine bacterioplankton. Environ. Microbiol. 2018, 20, 2709–2726. [Google Scholar] [CrossRef]
Plants 14 01292 g001
Figure 1. Venn diagram of soil microbial OTU distribution, as affected by different cropping systems. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice. Figure (a) is the OTU distribution of bacteria and Figure (b) is the OTU distribution of fungi.
Figure 1. Venn diagram of soil microbial OTU distribution, as affected by different cropping systems. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice. Figure (a) is the OTU distribution of bacteria and Figure (b) is the OTU distribution of fungi.
Plants 14 01292 g001
Plants 14 01292 g002
Figure 2. Effect of different cropping systems on dominant species abundance of soil bacterial community at phylum level. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Figure 2. Effect of different cropping systems on dominant species abundance of soil bacterial community at phylum level. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Plants 14 01292 g002
Plants 14 01292 g003
Figure 3. Analysis of inter-group differences in bacterial phylum levels in paddy soil. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Figure 3. Analysis of inter-group differences in bacterial phylum levels in paddy soil. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Plants 14 01292 g003
Plants 14 01292 g004
Figure 4. Effect of different cropping systems on abundance of fungal species at phylum level. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Figure 4. Effect of different cropping systems on abundance of fungal species at phylum level. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Plants 14 01292 g004
Plants 14 01292 g005
Figure 5. Analysis of inter-group differences at phylum level in paddy soil. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Figure 5. Analysis of inter-group differences at phylum level in paddy soil. Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Plants 14 01292 g005
Plants 14 01292 g006
Figure 6. RDA of soil bacterial community structure and soil chemical properties. Note: SOM represents organic matter, TN represents total nitrogen, AP represents available phosphorus, AK represents available potassium, NO3 represents nitrate nitrogen, NH4 represents ammonium nitrogen, TOC represents soil organic carbon, AOC represents active organic carbon, SMBC represents microbial biomass carbon, DOC represents soluble organic carbon, ROC represents oxidized organic carbon. CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Figure 6. RDA of soil bacterial community structure and soil chemical properties. Note: SOM represents organic matter, TN represents total nitrogen, AP represents available phosphorus, AK represents available potassium, NO3 represents nitrate nitrogen, NH4 represents ammonium nitrogen, TOC represents soil organic carbon, AOC represents active organic carbon, SMBC represents microbial biomass carbon, DOC represents soluble organic carbon, ROC represents oxidized organic carbon. CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Plants 14 01292 g006
Plants 14 01292 g007
Figure 7. RDA of soil fungal community structure and soil chemical properties. Note: SOM represents organic matter, TN represents total nitrogen, AP represents available phosphorus, AK represents available potassium, NO3 represents nitrate nitrogen, NH4 represents ammonium nitrogen, TOC represents soil organic carbon, AOC represents active organic carbon, SMBC represents microbial biomass carbon, DOC represents soluble organic carbon, ROC represents oxidized organic carbon. CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Figure 7. RDA of soil fungal community structure and soil chemical properties. Note: SOM represents organic matter, TN represents total nitrogen, AP represents available phosphorus, AK represents available potassium, NO3 represents nitrate nitrogen, NH4 represents ammonium nitrogen, TOC represents soil organic carbon, AOC represents active organic carbon, SMBC represents microbial biomass carbon, DOC represents soluble organic carbon, ROC represents oxidized organic carbon. CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice.
Plants 14 01292 g007
Plants 14 01292 g008
Figure 8. Heatmap of correlation between soil environmental factors and microbial communities (phylum level). Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice. SOM: soil organic matter; TN: total nitrogen; AP: available phosphorus; AK: available potassium; NO3: NO3-N; NH4: NH4+-N; AOC: active organic carbon; SMBC: microbial biomass carbon; DOC: dissolved organic carbon; ROC: particulate organic carbon. Figure (a) is the spearman correlation heatmap of bacteria, and Figure (b) is the spearman correlation heatmap of fungi. *, ** and *** stand for p < 0.05, p < 0.01 and p < 0.001 respectively.
Figure 8. Heatmap of correlation between soil environmental factors and microbial communities (phylum level). Note: CK: Chinese milk vetch–early rice–late rice; CRI: Chinese milk vetch–early rice–sweet potato || late soybean; RRR: rapeseed–early rice–late rice; RRI: rapeseed–early rice–sweet potato || late soybean; PRR: potato–early rice–late rice. SOM: soil organic matter; TN: total nitrogen; AP: available phosphorus; AK: available potassium; NO3: NO3-N; NH4: NH4+-N; AOC: active organic carbon; SMBC: microbial biomass carbon; DOC: dissolved organic carbon; ROC: particulate organic carbon. Figure (a) is the spearman correlation heatmap of bacteria, and Figure (b) is the spearman correlation heatmap of fungi. *, ** and *** stand for p < 0.05, p < 0.01 and p < 0.001 respectively.
Plants 14 01292 g008
Table 1. Soil chemical properties under different planting patterns.
Table 1. Soil chemical properties under different planting patterns.
YearTreatmentPeriodpHOrganic Matter
(g kg−1)		Total N (g kg−1)Available P
(mg kg−1)		Available K
(mg kg−1)		NO3-N
(mg kg−1)		NH4+-N
(mg kg−1)
2021 Winter crop5.10 ± 0.04 a29.38 ± 1.44 a1.94 ± 0.09 a37.01 ± 3.07 ab20.00 ± 2.52 a30.25 ± 1.83 b15.50 ± 1.59 a
CRR(CK)Early rice4.78 ± 0.14 ab31.66 ± 1.96 a1.88 ± 0.10 a36.31 ± 2.65 ab46.67 ± 2.33 b17.10 ± 3.50 b9.97 ± 1.47 a
Late rice5.78 ± 0.06 a27.51 ± 1.12 a1.81 ± 0.10 a22.84 ± 2.62 a28.33 ± 4.18 a13.30 ± 3.50 b10.63 ± 2.11 a
Winter crop5.20 ± 0.15 a29.83 ± 0.93 a1.88 ± 0.06 a36.88 ± 2.59 ab19.33 ± 2.91 a48.46 ± 1.69 a13.01 ± 3.24 a
CRIEarly rice4.60 ± 0.11 b32.12 ± 1.97 a1.93 ± 0.11 a29.97 ± 4.81 b42.67 ± 2.67 b32.62 ± 6.21 a7.59 ± 1.14 a
Late rice5.61 ± 0.07 a26.96 ± 0.94 a1.97 ± 0.12 a20.52 ± 1.34 a31.00 ± 3.06 a27.12 ± 4.37 a12.75 ± 2.31 a
Winter crop4.85 ± 0.10 a27.93 ± 1.99 a1.79 ± 0.11 a33.56 ± 2.81 ab21.67 ± 4.33 a32.84 ± 3.06 b16.00 ± 0.46 a
RRREarly rice4.97 ± 0.09 a28.96 ± 2.27 a1.68 ± 0.14 a31.90 ± 1.21 ab39.67 ± 1.86 b6.39 ± 0.22 c10.24 ± 2.55 a
Late rice5.82 ± 0.04 a27.00 ± 0.46 a1.69 ± 0.10 a21.48 ± 1.68 a28.33 ± 1.86 a14.53 ± 1.98 ab11.28 ± 4.90 a
Winter crop4.89 ± 0.10 a27.84 ± 1.39 a1.77 ± 0.07 a30.10 ± 3.08 b14.67 ± 1.45 a21.15 ± 1.19 c16.93 ± 3.08 a
RRIEarly rice4.70 ± 0.11 ab29.92 ± 1.78 a1.79 ± 0.11 a32.29 ± 4.94 ab47.00 ± 3.00 b5.03 ± 1.02 c13.53 ± 3.70 a
Late rice5.65 ± 0.08 a28.34 ± 0.58 a1.92 ± 0.08 a26.65 ± 7.76 a32.67 ± 5.04 a19.61 ± 6.76 ab10.62 ± 3.12 a
Winter crop4.81 ± 0.14 a28.50 ± 1.72 a1.78 ± 0.14 a41.65 ± 1.11 a23.00 ± 2.31 a20.82 ± 3.17 c14.57 ± 2.51 a
PRREarly rice4.89 ± 0.07 ab32.23 ± 1.48 a1.75 ± 0.18 a42.96 ± 1.84 a60.33 ± 5.24 a4.35 ± 0.39 c6.91 ± 1.71 a
Late rice5.77 ± 0.11 a26.57 ± 1.08 a1.62 ± 0.18 a26.16 ± 0.45 a36.67 ± 2.19 a9.37 ± 1.58 b6.23 ± 1.37 a
2022 Winter crop5.18 ± 0.13 a34.36 ± 0.62 a1.60 ± 0.06 c28.18 ± 1.11 b31.00 ± 0.58 a3.29 ± 0.61 b18.40 ± 1.31 ab
CRR (CK)Early rice5.13 ± 0.12 a26.92 ± 1.12 c1.62 ± 0.07 c29.32 ± 1.38 bc33.67 ± 2.03 c11.23 ± 1.60 b19.26 ± 2.24 a
Late rice5.36 ± 0.02 a32.10 ± 2.32 ab1.82 ± 0.15 ab25.81 ± 0.38 d27.67 ± 2.60 c14.00 ± 1.59 b10.92 ± 1.21 b
Winter crop5.38 ± 0.12 a29.34 ± 0.26 b1.73 ± 0.01 bc31.37 ± 0.95 b32.67 ± 3.28 a9.82 ± 0.42 a18.54 ± 1.03 ab
CRIEarly rice5.17 ± 0.07 a33.37 ± 1.52 a1.81 ± 0.04 bc31.32 ± 1.69 bc51.67 ± 1.76 b22.55 ± 3.24 a17.86 ± 0.27 a
Late rice5.20 ± 0.12 a34.66 ± 1.38 a1.94 ± 0.03 a39.55 ± 1.64 a52.33 ± 4.33 a24.80 ± 0.11 a23.48 ± 1.86 a
Winter crop5.40 ± 0.10 a35.04 ± 1.06 a1.95 ± 0.05 ab28.92 ± 1.66 b32.00 ± 0.58 a3.48 ± 0.16 b19.68 ± 1.29 a
RRREarly rice5.27 ± 0.02 a31.50 ± 1.48 ab1.96 ± 0.05 ab27.78 ± 0.70 c41.00 ± 2.65 c10.62 ± 2.31 b19.90 ± 1.15 a
Late rice5.14 ± 0.23 a29.78 ± 1.55 abc1.76 ± 0.10 ab30.45 ± 1.62 cd29.67 ± 2.33 bc9.83 ± 1.18 c9.08 ± 0.57 b
Winter crop5.32 ± 0.08 a28.01 ± 1.51 b2.17 ± 0.08 a29.22 ± 2.82 b31.67 ± 2.91 a2.47 ± 0.26 b13.67 ± 0.86 c
RRIEarly rice5.21 ± 0.06 a27.24 ± 0.88 c2.12 ± 0.10 a33.25 ± 1.75 b41.00 ± 2.89 c18.86 ± 2.46 a7.92 ± 1.24 b
Late rice5.04 ± 0.17 a27.10 ± 0.52 bc1.69 ± 0.10 ab35.48 ± 0.60 ab39.33 ± 4.26 b14.22 ± 1.47 b21.03 ± 1.79 a
Winter crop5.17 ± 0.04 a31.60 ± 2.10 ab1.81 ± 0.11 bc38.15 ± 1.49 a34.33 ± 1.86 a9.56 ± 0.78 a14.97 ± 1.23 bc
PRREarly rice5.20 ± 0.10 a28.58 ± 1.26 bc1.75 ± 0.13 bc41.12 ± 1.30 a63.00 ± 4.16 a17.78 ± 1.18 ab9.31 ± 1.29 b
Late rice5.45 ± 0.03 a25.67 ± 1.78 c1.53 ± 0.09 b33.42 ± 2.31 bc62.67 ± 2.96 a10.29 ± 1.35 bc8.00 ± 0.24 b
Note: Values are means ± standard errors (n = 3). The different lowercase letters indicate significant differences in the variable means among treatments at p < 0.05.
Table 2. Effect of different cropping systems on diversity index of microbial community.
Table 2. Effect of different cropping systems on diversity index of microbial community.
MicroorganismTreatmentAlpha Diversity Index of Species
SobsShannonSimpsonCoverage
BacteriaCRR(CK)45.67 ± 0.33 a2.38 ± 0.01 a0.14 ± 0.00 b1.00 ± 0.00 a
CRI42.00 ± 1.53 b2.20 ± 0.06 b0.16 ± 0.01 a1.00 ± 0.00 a
RRR45.67 ± 0.67 a2.41 ± 0.00 a0.14 ± 0.00 b1.00 ± 0.00 a
RRI42.33 ± 0.33 b2.25 ± 0.03 b0.16 ± 0.01 ab1.00 ± 0.00 a
PRR46.67 ± 0.33 a2.37 ± 0.01 a0.14 ± 0.01 ab1.00 ± 0.00 a
FungiCRR(CK)20.67 ± 0.67 a0.85 ± 0.14 a0.67 ± 0.07 b1.00 ± 0.00 a
CRI19.00 ± 0.58 a0.44 ± 0.06 b0.83 ± 0.03 a1.00 ± 0.00 a
RRR20.33 ± 1.33 a0.98 ± 0.04 a0.61 ± 0.03 b1.00 ± 0.00 a
RRI18.33 ± 0.67 a0.40 ± 0.06 b0.84 ± 0.03 a1.00 ± 0.00 a
PRR20.00 ± 1.15 a0.86 ± 0.05 a0.63 ± 0.03 b1.00 ± 0.00 a
Note: Values are means ± standard errors (n = 3). Different lowercase letters indicate significant differences in means among treatments at p < 0.05 (LSD Test).
Table 3. Details of experiment treatments.
Table 3. Details of experiment treatments.
TreatmentCropping Pattern
CRR(CK)Chinese milk vetch–early rice–late rice
CRIChinese milk vetch–early rice–sweet potato || late soybean
RRRRapeseed–early rice–late rice (RRR), rapeseed–early rice–sweet potato || late soybean
RRIRapeseed–early rice–sweet potato || late soybean
PRRPotato–early rice–late rice
Note ‘–’ means stubble, ‘||’ means inter-cropping.
Table 4. Details of different management practices used during study.
Table 4. Details of different management practices used during study.
CropVarietySeeding and Harvest TimeFertilizing Amount
Chinese milk vetchYujiang big leaf seed2.10.2020~6.04.2021
1.10.2021~7.04.2022		No fertilizer
rapeseedZhongyou8214.12.2020~6.04.2021
10.11.2021~7.04.2022		No fertilizer
early riceZhongjia early172.05.2021~22.07.2021
2.05.2022~23.7.2022		N 180 kg/hm2, P2O590 kg/hm2, K2O 120 kg/hm2
late riceTianyou Huazhan29.07.2021~24.10.2021
30.07.2022~30.10.2022		N 180 kg/hm2, P2O590 kg/hm2, K2O 120 kg/hm2
soybeanFengyuan No.113.08.2021~8.11.2021
4.08.2022~18.11.2022		N 150 kg/hm2, P2O5150 kg/hm2, K2O 375 kg/hm2
sweet potatoGuangshu 8713.08.2021~8.11.2021
4.08.2022~18.11.2022		N 80 kg/hm2, P2O5375 kg/hm2, K2O 80 kg/hm2
potatoDongnong 3036.12.2020~10.04.2021
1.12.2021~7.04.2022		No fertilizer
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tang, H.; Zhou, J.; Liu, N.; Huang, Y.; Liu, Q.; Altihani, F.A.; Yang, B. Response of Soil Microbial Diversity to Triple-Cropping System in Paddy Fields in Middle Reaches of Yangtze River. Plants 2025, 14, 1292. https://doi.org/10.3390/plants14091292

AMA Style

Tang H, Zhou J, Liu N, Huang Y, Liu Q, Altihani FA, Yang B. Response of Soil Microbial Diversity to Triple-Cropping System in Paddy Fields in Middle Reaches of Yangtze River. Plants. 2025; 14(9):1292. https://doi.org/10.3390/plants14091292

Chicago/Turabian Style

Tang, Haiying, Junlin Zhou, Ning Liu, Yao Huang, Qin Liu, Faizah Amer Altihani, and Binjuan Yang. 2025. "Response of Soil Microbial Diversity to Triple-Cropping System in Paddy Fields in Middle Reaches of Yangtze River" Plants 14, no. 9: 1292. https://doi.org/10.3390/plants14091292

APA Style

Tang, H., Zhou, J., Liu, N., Huang, Y., Liu, Q., Altihani, F. A., & Yang, B. (2025). Response of Soil Microbial Diversity to Triple-Cropping System in Paddy Fields in Middle Reaches of Yangtze River. Plants, 14(9), 1292. https://doi.org/10.3390/plants14091292

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop