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. 2026 May 15;18:e70360. doi: 10.1111/1758-2229.70360

Elucidating the Functional and Taxonomic Diversity of Soil Microbial Communities From Three Commercial Soybean Farms in South Africa

Khumbudzo Ndhlovu 1,2, Adeola Salawu‐Rotimi 3, Francina L Bopape 1, Prudence N Mtsweni 1, Olubukola Oluranti Babalola 2,4,✉, Ahmed Idris Hassen 1,5,✉
PMCID: PMC13178142  PMID: 42138445

ABSTRACT

Prior to the introduction of the exotic inoculant strain of Bradyrhizobium, South African soils lacked the rhizobia that nodulate soybean. Five decades of soybean inoculation practice resulted in the establishment of the Bradyrhizobium population in many soybean growing fields. However, there is no record of the magnitude of this establishment and its impact on the taxonomic and functional abundance of other microbes. Here we use a shotgun metagenomics approach to elucidate the taxonomic and functional profiles of the soil microbes from selected commercial soybean farms in South Africa. Metagenomics of the total sequences revealed that Proteobacteria, Actinobacteria, Firmicutes, Acidobacteria and Bacteroitedes are the prevalent phyla which differed in their relative abundance. Bradyrhizobium was the predominant genus at all three locations. Predicted functions detected genes essential for nitrogen metabolism, including nitrogen fixation, which have been unveiled in this study at a higher rate in all locations investigated. This study uncovers the microbial communities associated with soybean soils in South Africa. The study also generated vital information on the establishment of Bradyrhizobium spp. in the soils of soybean farms, providing a clue on whether inoculation of soya beans is always necessary. The findings, however, warrant further field investigations before any recommendations are rendered.

Keywords: Bradyrhizobium, DNA, functional genes, microbes, plant‐ soil‐ interaction, shotgun metagenomics, soils, soybean, taxonomic abundance

Prior to inoculation of soybeans, South African soils contain no Bradyhizobia that nodulate and fix nitrogen in soybeans. Soybean cultivation with prolonged inoculation resulted in the establishment and dominance of Bradyrhizobia in three soybean farms in South Africa as revealed by a microbial community study using shotgun metagenomics.

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1. Introduction

Soil microorganisms are very diverse and play several essential roles in maintaining the health status of the soil through metabolic, nutrient cycling and organic matter breakdown processes amid a range of environmental factors (Dai et al. 2019). The interaction between plants and soil microorganisms has been thoroughly investigated for biogeochemical cycles, plant growth promotion, biocontrol, and other interactions most of which are of critical importance for sustainable soil and plant health (Wallenstein 2017; Ajiboye et al. 2022; Dlamini et al. 2022).

Plant‐microbe interactions in the rhizosphere of leguminous plants are fascinating and complex phenomena that are vital for both soil health and plant growth. Studies have shown that the legume‐rhizobia symbiotic association involves nodulation of the roots induced by the secretion of certain flavonoids by the host legumes' roots which has been investigated intensively since the discovery of the rhizobia (Shanmugam and Kingery 2018). The rhizosphere of several plant species, including legumes, is very rich in its content of microbial communities. Some of these microbes can be cultured under standard laboratory conditions and identified through molecular processes, while a greater proportion of these microbial communities contain unculturable microbes. Through the development of Next Generation Sequencing (NGS) technology, metagenomic sequencing was introduced which provides a clear insight into soil and other environmental microbes which goes far beyond the culturable microorganisms, providing a better understanding of their phenotypes, metabolic functions, and taxonomic abundance (Cai et al. 2016).

In terms of the legume‐rhizobium symbiosis of soybean ( Glycine max L.) in South Africa, the exotic Bradyrhizobium diazoefficiens WB74 strain, introduced and released as a commercial inoculant in 1998 into South African soils, is very effective in its nodulation and nitrogen fixation capacity on different soybean cultivars (Bloem 1998). Nevertheless, there is no record of the impact of its introduction into the South African soils on soil nitrogen and fertility status, as well as on the taxonomic abundance and functional diversity of the native soil microbial community. Most of the studies conducted on the legume microbe interactions in the past focused on the isolation, screening, characterization, and inoculation of rhizobia for high nitrogen fixation, plant growth, and yield. It is however worth noting that other microorganisms also play several crucial roles, including increasing nitrogen availability, soil fertility, and plant growth promotion. Many of these studies thus overlooked the overall microbial diversity associated with soybean rhizosphere in South Africa.

As there is little information on the microbial community of the soybean rhizosphere, the current study is designed to explore the soil microbial diversity, including that of the rhizobia complex at three different soybean producing fields. It is hypothesized that the microbial diversity, in particular the diversity of the rhizobia complex in the three soybean fields will be similar due to decades‐long inoculation of soybeans with an exotic strain of Bradyrhizobium spp. The study will be conducted on the rhizosphere soils in three commercial soybean farms in Mpumalanga and the Free State provinces of South Africa. This broader perspective can provide insights to help manage the soybean rhizosphere for enhanced soybean growth, increased productivity, and support agricultural sustainability practices.

2. Materials and Methods

2.1. Soil Sample Collection

Soil samples were collected from three different soybean growing fields in South Africa viz. Lothair (262450.9S latitude, 301926.3E longitude) and Standerton (27135152S latitude, 29448120E longitude) in the Mpumalanga province and Bothaville (−27.534286S latitude, 26.524414E longitude) in the Free State province. The soil samples were collected during the soybean planting season from November/December 2021. Approximately 1 kg of the top 6 to 8 in. of the rhizosphere soil was collected and transferred to sterile plastic bags and stored in a cooler box containing ice blocks. In total, 6 soil samples were randomly collected from different sites at each of the three soybean farms in each location. The samples were transported to the Biological Nitrogen Fixation laboratory of the Agricultural Research Council, Pretoria, South Africa. For soil physicochemical analysis, 500 g of the 6 pooled soil samples were sent for physiochemical analysis to the Agricultural Research Council‐ Institute of Soil, Climate and Water (ARC‐ISCW), Pretoria, South Africa. Another 10 g of pooled soil samples were used for shotgun metagenomics.

2.2. Shotgun Metagenomics

DNA extraction and sequencing were conducted following Hassen et al. (2020). Briefly, the total DNA of the soil samples was extracted from a 250 mg subsample taken randomly from the rhizosphere soil using the ZymBIOMICS DNA/RNA extraction kit. Approximately 50 ng of the DNA for each sample was used to construct the library sequence. The quality of the DNA was quantified using a Qubit dsDNA kit. DNA was subsequently randomly fragmented with a Covaris sonicator into the appropriate size range before library preparation, with the size of the DNA ranging between 300 and 500 bp for each sample. The quality of the library was assessed after the circularization of the DNA on the TruSeq Nano DNA using a high throughput preparation kit (Illumina) before sequencing on HiSeq 2500 (Illumina).

Raw FASTQ sequence files have been pre‐processed using Kraken (Wood and Salzberg 2014) and the quality of the raw data was checked using FastQC (version 0.23.1) (Andrews 2010). De novo assembly was performed with MetaSpades (Nurk et al. 2017). The quality of contigs was evaluated with Metaquast (Mikheenko et al. 2016). After preprocessing, the sequences were imported into R/RStudio version 4 (R Core Team 2024) and the (.tsv) were used for downstream analysis in R using the tidyverse, vegan, and ggplot2 packages. Taxonomic classification of the metagenomic reads was performed using Kaiju (Menzel et al. 2016), a protein‐level taxonomic classifier that assigns reads to taxa based on translated sequence similarity searches against reference microbial databases. The Kaiju output files in table separated values (.tsv) were generated for phylum, family, genus, and species levels for each site that is, Bothaville, Lothair, and Standerton.

2.3. Functional Profile Analysis Including Major Nitrogen Metabolism

For the functional profile analysis, contigs were annotated using Prodigal (Hyatt et al. 2010). The Evolutionary Genealogy of Genes: Non‐supervised Orthologous Groups (eggNOG) was used for functional annotation of genes generated from Prodigal. The FAMA functional profile analysis was also used to capture the genetic potential of the soil microbial communities with particular interest in nitrogen metabolism including nitrogen fixation, ammonification, nitrate assimilation, denitrification, and urease activity. The functional profile was generated using the FAMA computational tool for shotgun metagenomics data functional profiling on KBase (Arkin et al. 2018). The functional profiles between the three‐soya bean rhizosphere soil samples were compared in the EFPKG (the normalization metric for paired‐end libraries). The generated results were determined as the number of fragments per kb of effective gene length per genome equivalent (EFPKG).

2.4. Diversity Analysis

Alpha diversity indices (Shannon (H′), Simpson (D), and Evenness) were calculated using the vegan package to assess within‐sample diversity for each location and taxonomic level. Each site had a single representative Kaiju dataset, and alpha diversity indices were summarized per site. For beta diversity, the Bray–Curtis dissimilarity matrices were computed to assess compositional differences between sites. The dissimilarity matrices were visualized using NMDS ordination plots and heatmaps to show relative similarity among samples. All plots were generated in R/Rstudio, and formatted tables summarizing alpha diversity indices and Bray–Curtis distances.

3. Results

3.1. Analysis of Soil Physicochemical Properties

The soil physicochemical analysis results were indicated as a (Table S1). Soils from the soya bean growing farm, Lothair, are largely loamy, whereas those of Bothaville and Standerton were mainly sandy and clay in nature, respectively. While the soils in the Bothaville site were slightly acidic, ranging from 6.28–6.43, the soils in Lothair fall from slightly acidic to near neutral (6.31–6.94). Standerton soils have slightly acidic pH within the range of 6.43–6.54. It is also observed that Bothaville soils have a very low level of organic carbon, whereas in Standerton and Lothair, the organic carbon content was medium to high. On the other hand, the phosphorus levels were consistently high in all the soil samples. For the exchangeable cations, the level of Ca was very moderate in both Lothair and Standerton, while in Bothaville it was low. It was also observed that the level of Na was low in all three locations, while potassium and magnesium were very high. The analysis also shows the presence of a higher level of the CEC in all three locations.

3.2. Metagenome Sequence Analysis

The Genome assembly metrics that show the genome size, the number of contigs, the N50/L50 values and the GC content for metagenome sequences of all three locations is provided in Table S2. The total number of base pairs between the samples varied greatly in which the sequence base pairs were 54,168,517 bp for Bothaville, 44,738,967 bp for Lothair, and 53,806,625 bp for Standerton. After carrying out quality control, no sequences failed the quality control (QC) in all the samples. The predicted number of known functional gene sequences for the three locations was 2,670,017 (Bothaville), 1,371,560 (Lothair), and 2,095,656 (Standerton). The sequence showed a G + C percentage of 63.71 for Bothaville, 62.81 for Lothair, and 56.22 for Standerton.

3.3. Diversity of Microbial Community

Kaiju classification revealed that reads were successfully assigned across multiple taxonomic levels including phylum, family, genus, and species. At the phylum level, the bacterial community was dominated by members of Proteobacteria, Actinobacteria, and Acidobacteria followed by Bacteriodetes and Firmicutes at all the three locations but differed in each site by relative abundance (Figure 1A,B). At the genus level, Streptomyces, Sphingomonas, and Bradyrhizobium are the topmost abundant genera in Standerton, but in Lothair and Bothaville the genus Bradyrhizobium is outnumbered by the genera Mycobacterium and Rburobacter, leaving Bradyrhizobium as the fifth most abundant genus in these locations (Figure 2A,B). Standerton soils contain the highest proportion of Bradyrhizobium at the genus level, almost twice the abundance recorded in Lothair and Bothaville. In addition to the genus Bradyrhizobium, the genus Rhizobium that contains the other root nodulating microsymbionts in several legumes was detected in all the three locations but at a much lower rate than the genus Bradyrhizobium.

FIGURE 1.

FIGURE 1

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Relative abundance of top phyla with a bar chart (A) and stacked bar (B) for the three locations of soybean rhizospheres (Bothaville, Lothair, and Standerton).

FIGURE 2.

FIGURE 2

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Relative abundance of top Genera of the soil microbial communities of the soybean rhizosphere in the three locations shown with a bar chart (A) and stacked bar (B).

The relative abundance of the top class is dominated by Actinobacteria, followed by Alphaproteobacteria, Betaprotepbacteria, Gamaproteobacteria and Deltaproteobacteria as the topmost five classes in all the three soils of the soybean farms (Figure S4A). Bothaville soils have the highest relative abundance of Actinobacteria (28%) followed by Alphaproteobacteria (10%). whereas Lothair and Standerton soils contain the highest proportion of Betaproteobacteria, the class that contain the emerging root nodulating microsymbionts, the Burkholderia and Paraburkholderia genera. The relative abundance of the ubiquitous Bacilli in the soils of all three locations is much below 10%. Streptomycetaceae, Sphingomonadaceae and Bradyrhizobiaceae are the topmost abundant families at all the three soybean farms included in this study. The relative abundance of Bradyrhizobiaceae, that contains the root nodulating microsymbionts of soybeans, the Bradyrhizobia spp. is highest at Standerton soils but the lowest for Bothaville (Figure S4B). Despite the presence of a higher proportion of the Bradyrhizobiaceae that contains the genus Bradyrhizobium, the nitrogen fixing symbionts of soybeans, Lothair soils have generally the lowest relative abundance at both class and family level (Figure S4A,B). Alpha diversity metrics (Shannon, Simpson, and Evenness) were calculated to evaluate within‐site diversity (Figure 3). The results indicated that Standerton had slightly higher diversity and evenness compared with Bothaville and Lothair. Although PERMANOVA could not be computed due to a single replicate per site, Bray–Curtis dissimilarity heatmaps (Figure 4) revealed modest community differences, with Standerton showing greater dissimilarity from the other two sites.

FIGURE 3.

FIGURE 3

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Alpha diversity indices (Shannon, Simpson, Evenness) of the soybean rhizosphere microbial communities in the three locations.

FIGURE 4.

FIGURE 4

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Beta diversity of the microbial communities shown by the Nonmetric multidimensional scaling ordination (NMDS) (A) and the Bray–Curtis Dissimilarity metric (B) showing how the soils in the three locations differ in species composition.

3.4. Predicted Metabolic Functions Using eggNOG

The eggNOG highlights the prevalence of gene functions, functional composition and key biological processes and pathways represented in the data and identifies genes that share ancestry across species. Bar plots generated in R were used to provide a visual summary of the most frequent functional annotations from the eggNOG dataset for Bothaville, Lothair and Standerton sites. The y‐axis lists the top 10 descriptions of the genes or proteins in each dataset. These descriptions are derived from the eggNOG annotations and provide a summary of the predicted function of each gene or protein (Figures S1–S3). In Bothaville soils, transposase showed the highest count followed by the DDE superfamily endonuclease enzyme. Winged helix‐turn helix and SPTR A7NFQ2 transposase and inactivated derivatives enzyme showed a count of 14 as indicated (Figure S1). Other function annotations observed in Bothaville samples with the same count were sequence‐specific DNA binding, PFAM transposase, IS4 family protein, PFAM transposase, PFAM transposase IS116 IS110 IS902 family protein, MacB‐like periplasmic core domain, COG NOG 1460 non‐supervised Orthologs group, and binding protein‐dependent transport system inner membrane system component. In Lothair soils, transposase appeared to be high, but a little bit lower compared with Bothaville (Figures S1 and S2). Transposase activity, and reverse transcriptase belonging to the phage integrase family showed the same read count. Other functional annotations were transposase DDE domain group, transpose DDE domain, transposase and inactivated derivatives, transposase (IS116 IS110 IS902 family) pyridoxamine 5′‐phosphate oxidase and PFAM transposase. Transposase in both Lothair and Bothaville was high whereas, in the Standerton sample, it shows that it is the same as other enzymes (Figure S3). Phage portal protein shows the highest read count. Reverse transcriptase and HNH endonuclease belonging to DegT DNrJ Erycl family showed the same read count.

3.5. Nitrogen Metabolism Functional Genes

This study also investigated nitrogen metabolism genes including nitrogen fixation, ammonification, ammonium oxidation, denitrification, nitrate assimilatory reduction, nitrate assimilation, and urease that improve the availability of nitrogen the plants need. The comparison of selected FAMA functional profiles between the soya bean soils in the three different locations is indicated in Tables 1 and 2. Ammonium oxidation, nitrate assimilatory reduction, nitrate assimilation, and urease were found to be high in Bothaville compared with Lothair and Standerton in terms of raw sequence count (Table 1). In Lothair, raw reads sequence count for nitrogen fixation and ammonification was high (192) in contrast to Bothaville (28) and Standerton (36) with an amino acid count of 90.74% in Lothair, 90.55% in Bothaville and 88.58% in Standerton (Table 2). Denitrification was high in Standerton with a raw sequence count of 4604. Fama functional profile identified functional enzymes such as nitrogenase reductase (nifH with a raw sequence count of 41), Nitrogenase alpha chain (nifD with a raw sequence count 58), Nitrogenase beta chain (nifK with a raw sequence count 48), and nitrogen fixation proteins nifB with a raw sequence count 45 which are involved in the nitrogen cycle with the taxonomy of proteobacteria found to be higher in Lothair than in Bothaville and Standerton (Table 2). Genes like Nitrate reductase (narG‐NxrA linked with the taxonomy of Actinobacteria), Nitrate reductase (nirK with a raw sequence count of 1589) were found to be high in Standerton compared with other locations and are associated with Proteobacteria and Chloroflexota. Nitrate Reductase (nirD with a raw sequence count of 587) are involved in denitrification processes and found to be high in Bothaville being highly associated with Burkholderia, and Solibacter species (Table 2). Hydroxylamine dehydrogenase (hao) and Ammonia monooxygenase subunit A (amoa) are involved in nitrification which differed in location based on raw sequence reads. Other nitrogen metabolism genes were also identified.

TABLE 1.

Functional profiles of nitrogen metabolism of soil microbial communities from the three soya bean farms based on the FAMA functional analysis.

Function category Lothair Bothaville Standerton
Raw sequence count EPFKG a Amino acid identity (%) Raw sequence count EPFKG Amino acid identity (%) Raw sequence count EPFKG Amino acid identity (%)
Nitrogen fixation 192 0.27794 90.74 28 0.03410 90.55 36 0.05340 88.58
Ammonification 1158 1.87266 76.64 871 1.32238 76.97 689 1.24838 77.21
Ammonium oxidation 271 0.56714 87.1 489 0.93170 85.55 192 0.44621 90.41
Denitrification 4246 4.71150 77.89 2607 2.71569 77.41 4604 5.70185 78.48
Nitrate assimilatory reduction 3798 3.69007 77.44 4182 3.303390 77.49 3935 3.76634 85.61
Nitrate assimilation 4131 4.52261 90.74 4837 5.07001 78.84 3919 5.04758 78.14
Urease 3133 5.35362 86.41 3213 5.10504 86.62 2749 5.42779 87.07
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a

The number of fragments per kb of effective gene length per genome‐equivalent.

TABLE 2.

FAMA functional report on nitrogen metabolism of the soil microbial communities from the rhizosphere of the soya bean farms in Lothair, Bothaville and Standerton and representative taxonomic groups with the highest rates of the designated metabolism.

Function/Genes Description of functions Lothair Standerton Bothaville Taxonomy with a greater number of fragments of the gene length across all soil samples
efpkg Raw sequence count efpkg Raw sequence count efpkg Raw sequence count
NifB Nitrogen fixation protein NifB 0.071 45 0.018 13 0.009 9 Proteobacteria, Beta Proteobacteria
NifD_Anf_VnfD Nitrogenase alpha chain 0.073 58 0.014 10 0.009 8 Proteobacteria, Desulfuromonas, Rhizobiales
NifH_AnfH_VnfH Nitrogenase reductase and maturation protein 0.074 41 0.008 4 0.008 5 α‐Proteobacteria, Rhizobiales, Bradyrhizobiaceae
NifK_AnfK_VnfK Nitrogenase beta chain 0.059 48 0.012 9 0.007 6 Proteobacteria, Desulfuromonadels, Desulfuromonas
NasA Assimilatory nitrate reductase large subunit 3.208 3935 3.233 3552 2.739 3683 Proteobacteria, unclassified Acidobacteria, Solibacterales, Actinobacteria, beta proteobacteria
AmoA_PmoA Methane/ammonia monooxygenase subunit A 0.162 75 0.128 54 0.314 158 Proteobacteria, Archaea, Nitrospira
AmoC_PmoC Methane/ammonia monooxygenase subunit C 0.256 119 0.202 81 0.409 205 Beta Proteobacteria, Nitrosomonadales, Actinobacteria, Nitrospirae, Archaea
AmoB_AmoB Methane/ammonia monooxygenase subunit A 0.149 77 0.116 57 0.209 126 Archaea, alpha and beta proteobacteria, Nitrosomonadales
NarG_NxrA Nitrate reductase 0.869 1526 1.011 1592 0.677 1290 Proteobacteria, unclassified Proteobacteria, Actinobacteria, Nitrospirae
NirA Ferredoxin‐nitrate reductase 0.854 784 0.829 679 1.174 1148 Proteobacteria, Alphaproteobacteria, Rhizobiales, Unclassified bacteria
NirB Nitrate reductase (NADH) large subunit 1.956 2471 2.162 2412 2.095 2867 Unclassified bacteria, Proteobacteria, Alphaproteobacteria
NirD Nitrate reductase (NADH) small subunit 1.366 578 1.667 527 1.549 587 Proteobacteria, Burkholderiales, Actinobacteria, Solibacters
NirK Nitrate reductase (NO‐ forming) 1.918 1456 2.253 1539 1.122 917 Chloroflexi, Unclasified Proteobacteria
UreA Urease subunit gamma 1.536 549 1.667 520 1.471 561 Alphaproteobacterial, Actinobacteria, Unclassified Alphaproteobacteria
UreB Urease subunit beta 1.617 591 1.619 513 1.523 585 Alphaproteobacterial, Rhizobiales, Actinobacteria, Unclassified Proteobacteria
Function/Genes Description of functions Lothair Standerton Bothaville Examples of taxonomic groups with highest functions across all soya bean soil samples
efpkg Raw sequence count efpkg Raw sequence count efpkg Raw sequence count
UreC Urease subunit alpha 2.199 1993 2.141 1716 2.111 2067 Proteobacteria, Unclassified bacteria
HAO Hydroxylamine dehydrogenase 0.057 52 0.046 37 0.061 59 Betaproteobacteria, Nitorsomonadales, Nitrospora
NapA Periplasmic nitrate reductase precursor 0.533 661 0.609 672 0.353 475 Alphaproteobacteria, Rhizobiales, Unclassified Proteobacteria
NapB Periplasmic nitrate reductase cytochrome 0.308 112 0.399 127 0.227 90 Betaproteobacteria, Alphaproteobacteria, Rhizobiales, unclassified Proteobacteria
NapC Cytochrome c‐type protein 0.526 217 0.667 245 0.227 104 Unclassified Betaproteobacteria
NapD Periplasmic nitrate reductase component 0.108 33 0.244 64 0.087 27 Proteobacteria
NrfA Nitrate reductase (cytochrome c‐552) 1.121 882 0.761 531 0.775 657 Unclassified Bacteria, Proteobacteria, Deltaproteobacteria
NrfH Cytochrome c nitrite reductase small subunit 0.726 265 0.477 154 0.541 211 Deltaproteobacteria, Myxococales, desulfuromonadales, Unclassified bacteria
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4. Discussion

This report presents how the taxonomic abundance and functional diversity of rhizosphere soils from three soybean growing fields in South Africa vary based on shotgun metagenomics study. Our study reveals that soil physicochemical properties as well as cropping history influence the taxonomic abundance and functional diversity of rhizosphere microorganisms at the three sites. It is evident from other studies that physicochemical properties including texture and associated properties significantly influence the microbial diversity and composition of a given soil rhizosphere (Xia et al. 2020). For instance, when we categorically look into the microsymbionts of soybean, the highest abundance of the genus Bradyrhizobium was detected in the soybean rhizosphere soil of Standerton, which is characterized by high organic carbon and phosphorous (P), less available soil nitrogen (NO3), and a higher percentage of cation exchange capacity (CEC) including Na, K, Ca and Mg (Table S1). At all three locations, the rhizosphere soil contains sufficient phosphorous which could probably be because of established rhizobia that stimulate the host legumes to release more root exudates, which can mobilize phosphorous and increase its availability in the rhizosphere (Richardson et al. 2009; Maseko and Dakora 2013; Jaiswal et al. 2021). Compared with the relatively higher sandy textures of Bothaville and Lothair soils, the clay loam soils of Standerton are not easily flushed away and contain high levels of organic carbon which allow microorganisms to multiply at a higher rate resulting in higher microbial diversity especially during the rainy seasons (Witzgall et al. 2021). Hence, we have observed in this study that the soils in Standerton, with the intermediate texture of clay loam, are more favourable for the survival and proliferation of the root nodulating Bradyrhizobium which are more abundant at the genus level than in the other two locations. On the other hand, the taxonomic abundance of Bradyrhizobium decreased at genus level at both Bothaville and Lothair farms which have a higher proportion of sandy textured soils, which can easily dry out. These results are concurrent with the review report by Zahran (1999) that the population of rhizobia decreased in a sandy soil where the moisture level decreases and the size of the rhizobia population increased as the moisture level returns to normal.

It is very interesting, though, to observe the genus Bradyrhizobium as the only Alphaproteobacteria represented in the top five abundant genera at all three locations. Bradyrhizobium, being one of the dominant genera in all the three rhizosphere soils, helps maintain healthy soils due to its ability to fix nitrogen and to survive under abiotic stress conditions (Delmont et al. 2018; Omotayo and Babalola 2021; Omotayo et al. 2021). The dominance of Bradyrhizobium at all sites in this study is possibly because of the introduction of exotic rhizobia into South African soils since the early 1960s that resulted in the establishment and persistence of Bradyrhizobium population in major soybean farms (Figure 2A,B).

Microorganisms belonging to the phylum Proteobacteria are very common in several soils occupying the highest species richness. In this study, Proteobacteria was the most dominant phylum (Figures 1A,B) and the results concur with many other studies that reported the predominance of this phylum in the maize and soybean rhizosphere (Omotayo et al. 2022; Ajiboye et al. 2022). Although, the most abundant phylum in all the three soybean rhizospheres soils is Proteobacteria, four of the topmost abundant genera viz. Streptomyces, Sphingomonas, Mycobacterium and Actinoplanes belong to phylum Actinomycetota (formerly Actinobacteria). Bacteria belonging to these predominant genera often thrive in soil niches and become persistent due to their unique ecological specializations including breakdown of complex organic matter, antibiotic production, degradation of polymers and aromatic compounds (Brett et al. 2023; Jo‐Anne et al. 2024; Shuyun et al. 2025).

Actinobacteria is the second largest phylum detected in the soybean rhizosphere soils in this study. Several members of Actinobacteria are involved in improving soil health by colonizing the soil near the plant's roots, thereby creating a protective environment that promotes healthy plant growth. Some Actinobacteria groups were found in soybean nodules as endophytic partners during isolation in this study, including Pseudonocardia, Rhodococcus, Mycobacterium, Microbacterium, Nocardia, Arthrobacter, Actinomycetes, and Archangium. These genera can also be found in the rhizosphere of several other crops such as tomatoes, wheat, peas, and chickpeas, and many others (El‐tarabily 2008; Yu et al. 2020; AbdElgawad et al. 2020) and are known for their ability to solubilize phosphorus, produce siderophores, Aminocyclopropane carboxylic acid (ACC), and phytohormones (Lasudee et al. 2021).

The role of certain members of the Firmicutes that constitute Bacillus species and related genera, such as Paenibacillus and Priesta in contributing to soil fertility and act as biofertilizers for sustainable agriculture is well documented (Nikolic et al. 2025; Ichahashi et al. 2020; Omotayo et al. 2021). A recent study by Ng et al. (2025) revealed that Bacillus based biofertilizers enhance soil health by influencing the soil microbiome, resulting in efficient nutrient cycling and plant growth. Firmicutes have been detected frequently from all the three soybean rhizosphere soils at all locations in this study and are the fifth topmost abundant phylum. Bacteria of the genus Gemmatirosa within the phylum Gemmatimonadetes are not easily obtainable through culturing methods and were detected at almost similar abundance levels from all the three rhizosphere soils of soybean. Most of the bacteria in this group are not culturable and are mostly known from environmental DNA, and the first reported species Gemmatimonas aurantiaca strain T‐27 T was isolated from activated sludge (Zhang et al. 2003). The detection of this group of bacteria in the current study from the rhizosphere soils of soybeans is supported by a previous study (Liu et al. 2020) using metagenomics sequencing technology which confirmed their availability in the soils and their involvement in organic carbon cycling. Other groups such as Acidobacteria play a role in carbon cycling, iron cycling, and the ability to photosynthesize (Ward et al. 2009), while the processes of denitrification are performed by members of the Bacteroidetes (Chaparro et al. 2014). In addition, Chloroflexi phyla contain photosynthetic bacteria (Singh et al. 2022) that were present at higher proportions in Lothair soils.

Nitrogen is an essential element needed for plant growth, but despite its abundant presence in the atmosphere as N2, plants cannot use it easily because of its strong triple bond. However, leguminous plants such as soybeans can fix this atmospheric nitrogen by forming a symbiotic association with the root nodule bacteria, the rhizobia with a special preference for Bradyrhizobium species. It has previously been reported (Bloem 1998) that South African soils lack the specific Bradyrhizobium strains that can fix nitrogen with soybeans. After the introduction of exotic strains of Bradyrhizobia with which several soybean fields were inoculated since the 1960s, populations of Bradyrhizobium spp. established in several soybean‐producing fields in South Africa (Botha et al. 2004). One good observation that supports this argument is the case of the Lothair farm in this study which was last inoculated with rhizobia products 8 years ago. However, a higher microbial abundance of the nitrogen‐fixing rhizobia, mainly Bradyrhizobium, was observed in this location indicating the establishment and persistence of the introduced strains after several years without further inoculation. In essence, the presence of Bradyrhizobium enhances the nodulation efficiency in soybeans, particularly under favourable environmental conditions. It is well known that Bradyrhizobium species are the major symbionts of soybean and different soybean genotypes interact at varying compatibility rates with different Bradyrhizobium strains for nodulation and nitrogen fixation (Zheng et al. 2023). That means, not all soybean genotypes can be nodulated by a given Bradyrhizobium species but show preference or compatibility for different Bradyrhizobium species or strains. For instance, some cultivars nodulate efficiently with Bradyrhizobium japonicum , while others may form more effective symbiosis with B. elkani, and many of such specificity between soybean cultivars and Bradyrhizobium species has a genetic basis (Omari et al. 2022; Sarao et al. 2025).

While Bothaville and Lothair soils have more taxonomic abundance than Standerton soils at the genus level, such notable genera as Rhizobium, Mesorhizobium, Arthrobacter, Pseudomonas, and Sphingomonas were detected at a higher proportion. Other genera like Devosia, Agrobacterium, Microvirga, Burkholderia, and Rhodococcus detected in the soybean rhizosphere of at least one of the soybean farms are capable of inducing nodules in other legume hosts like Sesbania, Neptunia, Papillionoid, Mimosoid, and Lotus (Rivas et al. 2002; Cummings et al. 2009; Ampomah and Huss‐Danell 2011; Youseif et al. 2014; Dobritsa and Samadpour 2016). Another genus, which contributes to the nitrogen cycle, is Nitrospira, which was detected to be dominant in each location.

There is a high predominance of bacterial community than fungi at all the three soybean rhizospheres in the current study. The most predominantly detected fungal phyla were the Ascomycota followed by Basidiomycota, but with very low relative abundance of less than 2% at all the three soybean farms. Although there are no reports on the diversity of fungi in the soybean growing fields in South Africa, it is generally believed that the soybean rhizosphere secretes root exudates that are more readily metabolized by bacteria than by fungi (Cheng et al. 2025). Moreover, judging by the fact that the soybean farms included in this study have been inoculated with rhizobia for decades, the rhizobia establish a strong mutualistic association with the soybeans, which also changes the root physiology and signalling (Ren et al. 2025). These properties favour bacterial colonization over fungi, resulting in the relative abundance of the fungal communities in the soybean rhizosphere being much lower than that of the bacteria.

Alpha diversity metrics (Shannon, Simpson, and Evenness) for the three locations indicated that Standerton soil had slightly higher diversity and evenness compared with Bothaville and Lothair. The Bray–Curtis dissimilarity heatmaps also revealed modest community differences, with Standerton showing greater dissimilarity from the other two sites (Figure 2B). The differences in relative abundance of the taxa between the three locations reflect the variations in soil physicochemical characteristics that significantly influence both alpha and beta diversity of rhizosphere microbial communities. Such properties shape the ecological niches available to microbes, which in turn affect the composition and richness of the microbial community.

The current study also analysed the functional diversity of the soil samples collected from all the three soybean producing farms. As indicated in Figures S1–S3, the Bothaville soils show a very high transposase function. Live inoculants may have led to the exchange of genes with native microorganisms in the soil, causing changes in their entire genome due to the presence of transposase that influences the spread of genetic materials, thus increasing diversity regulated by the host (Mahillon and Chandler 1998; Moran and Plague 2014; Vigil‐Stenman et al. 2017). As the transposase enzyme is the most ubiquitous, it is highly controlled by the prokaryotes group. Study by Akinola et al. (2021) shows that each microbial species displays varying functions based on the acquired genes in each location.

Functional annotations using the EggNOG datasets revealed a higher distribution of transposases in the microbiomes of Lothair and Bothaville rhizosphere soils (Figures S1 and S2). This observation is crucial, particularly for the soya bean rhizosphere in Lothair farm, where inoculation with rhizobia commercial products has been disconnected for almost a decade. The occurrence of higher distribution of transposases in these soils could have resulted in the transfer of symbiotic (nod, nif and fix) genes from the Bradyrhizobium inoculant strain to the free‐living rhizobia through horizontal gene transfer (HGT) (Lemaire et al. 2015). In Standerton soils, the microbiome has a higher distribution of Phage portal proteins and HNH endonuclease both of which are also involved in facilitating horizontal gene transfer among the soil microbial communities. HNH endonuclease may assist in HGT by facilitating DNA cleavage and recombination during conjugation. Whereas phage portal proteins carry genes from one bacterium to another. Some genes spread through the soil by phage portal proteins include nitrogen‐fixation and phosphate solubilization genes. Reports in earlier studies for instance showed that, in nitrogen fixing bacteria such as rhizobia, phage portal proteins act as indispensable structural sensors that facilitate horizontal gene transfer (HGT) through transduction (Finan et al. 1984; Buchanan‐Wollaston (1979)). The introduction of a highly effective nitrogen fixing bacterium in the soil could reshape the native soil microbial community by spreading new traits through HGT including metabolic pathways (such as nitrogen fixation) and stress tolerance. This in turn can change both the composition and functional structure of the soil microbial communities with implications for soil health, plant productivity and ecosystem resilience (Hong et al. 2024; Maheshwari et al. 2017; Macedo et al. 2022).

When we look a bit deeper into the predicted nitrogen metabolism functions using the FAMA functional profile, Lothair soils have the largest raw sequence counts related to nitrogen fixation (192), in comparison to that of Bothaville (28) and Standerton (36). Likewise, the number of nitrogen fixation gene fragments per kb of effective gene length per genome equivalent (epfkg) is the highest for Lothair (0.27794) but several folds less for Bothaville (0.03410) and Standerton (0.05340) (Table 1). With particular emphasis on nitrogen fixation metabolism genes, Bothaville soils have the least raw sequence counts for NifB (Nitrogen fixation protein B), Nif D (Nitrogen fixation alpha chain), Nif H (Nitrogen reductase and maturation protein) and NifK (Nitrogenase beta chain) (Table 2). The analysis also revealed the abundance of nifB, nifH, nifD and several other nitrogen metabolism functional genes in the three locations, which once have been devoid of the rhizobia that fix atmospheric nitrogen on soybean.

Once the exotic Bradyrhizobium strains were introduced into soils in South Africa which never harbour rhizobia that nodulate soybeans, continuous soybean cropping gives a selective advantage allowing the rhizobia to persist for years. In doing so, even native soil bacteria acquire nitrogen fixation genes (Epstein and Tiffin 2021), resulting in an increased abundance of the nif genes. These results show the potential to increase nitrogen metabolism genes, and hence nitrogen fixation in the soil to improve legume yield and quality without having to apply synthetic fertilizers, but through prolonged legume cropping and inoculation with rhizobia.

5. Conclusion

Although South African soils were previously reported to lack the rhizobia strains that colonize and nodulate soybean roots, this study revealed for the first time that prolonged inoculation of exotic strains resulted in the establishment and persistence of Bradyrhizobium strains associated with soybean nodulation and nitrogen fixation. Another important finding of this study is that metagenomics sequence analysis revealed the predominance of bacterial groups belonging to Proteobacteria and Actinobacterial phyla. We have also observed that the relatively more fertile and nutrient‐rich soils of Standerton resulted in the highest abundance of Bradyrhizobium population and microbial diversity compared with the Lothair and Bothaville soils. It is evident that in all three locations, a higher proportion of Bradyrhizobium species was observed despite the variations in percentages due to several environmental factors including nutrient availability and soil properties.

The presence of a large proportion of Bradyrhizobium spp. capable of nodulating and fixing atmospheric nitrogen in the soybean rhizosphere is essential for sustainable soybean production, as it not only improves nitrogen fixation, growth, and yield but also reduces the dependency on external inputs such as chemical fertilizers. Through prolonged inoculation of effective nitrogen fixing strains in soils that once lacked them, a substantial establishment and persistence of the rhizobia could occur through time. The case of the Lothair farm is a perfect example in this regard, in which after prolonged inoculation, farmers discontinued the application of rhizobia inoculants since 2014. Neither did the farmers use chemical fertilizer inputs but were able to harvest as much as 3 tons per hectare due to the already established effective nitrogen fixing strains. However, given the sandy soil nature and nutrient depleted soils of Bothaville farms, it is advisable to use rhizobia inoculant products every time soybeans are planted. Generally, information on the general properties of the soil that include soil microbial abundance, functional diversity, and physicochemical characteristics is crucial in promoting sustainable agriculture, and the data generated in this study are very valuable when it comes to soybean cultivation in South Africa.

Author Contributions

Khumbudzo Ndhlovu: investigation, methodology, writing – review and editing, data curation, writing – original draft. Adeola Salawu‐Rotimi: writing – review and editing, software, data curation, formal analysis. Francina L. Bopape: investigation, writing – review and editing, resources. Prudence N. Mtsweni: investigation, resources. Olubukola Oluranti Babalola: supervision, validation, writing – review and editing, investigation. Ahmed Idris Hassen: conceptualization, funding acquisition, validation, supervision, methodology.

Funding

This work was supported by the National Research Foundation (NRF), South Africa, 135456.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Distribution of the top 10 functional annotations of the metagenome in Bothaville analysed using the EggNOG Dataset.

Figure S2: Distribution of the top 10 functional annotations of the metagenome in Lothair analysed using the EggNOG Dataset.

Figure S3: Distribution of the top 10 functional annotations of the metagenome in Standerton analysed using the EggNOG Dataset.

EMI4-18-e70360-s002.pdf (105.8KB, pdf)

Figure S4: comparison of the relative abundance of the top class (A) and family (B) of the soybean rhizosphere microbial communities between all the three locations.

EMI4-18-e70360-s001.pdf (75.3KB, pdf)

Table S1: Physicochemical properties of the soybean rhizosphere soils from three locations.

Table S2: Genome Assembly Metrics for Bothaville, Lothair and Standerton.

EMI4-18-e70360-s003.pdf (99.5KB, pdf)

Acknowledgements

The National Research Foundation (NRF) of South Africa is duly acknowledged for funding this project through the Research and Technology Fund (RTF) program, grant number 135456.

Contributor Information

Olubukola Oluranti Babalola, Email: olubukola.babalola@nwu.ac.za.

Ahmed Idris Hassen, Email: hassena@arc.agric.za.

Data Availability Statement

The metagenome sequence data were deposited at the NCBI database library under the Bio‐project number PRJNA1273861. The individual sample metadata are available under Biosample accession numbers [SAMN48959320—SAMN48959322].

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: Distribution of the top 10 functional annotations of the metagenome in Bothaville analysed using the EggNOG Dataset.

Figure S2: Distribution of the top 10 functional annotations of the metagenome in Lothair analysed using the EggNOG Dataset.

Figure S3: Distribution of the top 10 functional annotations of the metagenome in Standerton analysed using the EggNOG Dataset.

EMI4-18-e70360-s002.pdf (105.8KB, pdf)

Figure S4: comparison of the relative abundance of the top class (A) and family (B) of the soybean rhizosphere microbial communities between all the three locations.

EMI4-18-e70360-s001.pdf (75.3KB, pdf)

Table S1: Physicochemical properties of the soybean rhizosphere soils from three locations.

Table S2: Genome Assembly Metrics for Bothaville, Lothair and Standerton.

EMI4-18-e70360-s003.pdf (99.5KB, pdf)

Data Availability Statement

The metagenome sequence data were deposited at the NCBI database library under the Bio‐project number PRJNA1273861. The individual sample metadata are available under Biosample accession numbers [SAMN48959320—SAMN48959322].

Articles from Environmental Microbiology Reports are provided here courtesy of Wiley

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