Association of Soil Physicochemical Properties with Bacterial Microbiome Structures in Cowpea Farms from Semiarid Eastern Kenya

Association of Soil Physicochemical Properties with Bacterial Microbiome Structures in Cowpea Farms from Semiarid Eastern Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of Soil Physicochemical Properties with Bacterial Microbiome Structures in Cowpea Farms from Semiarid Eastern Kenya Ahmed Kassem Khater, Ezekiel Mugendi Njeru, Stephen Mwangi Githiri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9264911/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cowpea ( Vigna unguiculata [L.] Walp.) is an important grain legume in semiarid Africa, where it contributes to food security and livelihood resilience in smallholder farming systems. However, information on the soil bacterial microbiome associated with cowpea production in semiarid eastern Kenya remains limited. This study investigated soil physicochemical heterogeneity and bacterial community structure in cowpea farms from Machakos and Kitui Counties via 16S rRNA amplicon next-generation sequencing (NGS), with the aim of improving the understanding of belowground microbial patterns in these production systems. Most measured soil physicochemical properties varied significantly among farms, indicating substantial field-level heterogeneity, whereas county-level differences were less pronounced. Across all the samples, the bacterial communities were dominated by Actinomycetota, followed by Pseudomonadota, Bacillota, Chloroflexota, and Acidobacteriota. Rhizobial genera of agronomic relevance, including Bradyrhizobium , Rhizobium , Ensifer , and Mesorhizobium , were detected across farms, with Bradyrhizobium showing the highest relative abundance. The alpha-diversity indices varied among the samples, while the beta-diversity analysis revealed significant differences in the bacterial community composition between the counties. Correlation analysis further revealed associations between selected soil variables and the distributions of dominant bacterial taxa and rhizobial genera. Cowpea farm soils in semiarid eastern Kenya harbor distinct bacterial communities associated with farm-level edaphic heterogeneity and measurable compositional turnover across sites. The detection of rhizobial genera across farms suggests the potential ecological relevance of native bacterial populations in these systems. This study provides a baseline for understanding the soil bacterial microbiome structure in Kenyan cowpea production systems and supports the need for site specific, microbiome-informed approaches for sustainable soil fertility management. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology Cowpea soil bacterial microbiome 16S rRNA amplicon sequencing soil physicochemical properties rhizobial genera semiarid eastern Kenya next-generation sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Background Cowpea ( Vigna unguiculata [L.] Walp.) is one of the most important grain legumes in sub-Saharan Africa, where it contributes substantially to household nutrition, income generation, and the resilience of smallholder farming systems. Its value is particularly evident in semiarid environments because cowpea is cultivated both as a food and fodder crop and is relatively well adapted to heat, intermittent drought, and low-input conditions [ 1 , 2 ]. In addition to its agronomic and nutritional importance, cowpea can improve soil fertility through symbiotic nitrogen fixation, making it especially relevant in production systems where access to mineral fertilizers is limited [ 2 , 3 ]. In eastern Kenya, where rainfall variability and declining soil fertility constrain crop productivity, cowpea remains an important component of smallholder agriculture and a valuable crop for dryland food security [ 4 ]. Despite its adaptability, cowpea productivity in semiarid regions remains inconsistent because crop performance is influenced not only by water limitation but also by soil fertility constraints and variation in belowground ecological conditions [ 5 ]. Many smallholder fields in eastern Kenya are characterized by nutrient depletion, heterogeneous soil properties, and management differences that may influence plant growth directly and indirectly through their effects on soil microbial communities [ 6 ]. Soil bacteria are integral to organic matter turnover, nutrient cycling, and rhizosphere processes, and their distribution can shape plant health and productivity in agricultural systems. Consequently, variation in the soil bacterial microbiome may represent an important but insufficiently characterized component of cowpea production systems in semiarid environments [ 7 – 9 ]. Previous studies have shown that soil bacterial communities are strongly structured by edaphic factors, particularly soil pH, nutrient availability, and soil organic resource status [ 10 , 11 ]. Lauber et al. (2009) [ 12 ] demonstrated that soil pH was a strong predictor of bacterial community structure across soils, whereas Zhou et al. (2024) [ 13 ] further reported at the global scale that more than 75% of bacterial genera are predicted by soil pH. In agricultural soils, these edaphic effects interact with management-related factors, and Mo et al. (2024) [ 14 ] reported that the fertility source can be a pronounced factor influencing soil microbiome assembly. These controls often operate at relatively fine spatial scales, meaning that neighboring farms may harbor distinct microbial communities even within the same broader agroecological zone [ 15 – 17 ]. Understanding how such variability occurs in cowpea-growing soils is important for interpreting the ecological context within which crop performance is expressed. For legumes such as cowpea, the importance of soil bacterial communities extends beyond general soil processes to include taxa associated with nodulation, biological nitrogen fixation, and rhizosphere functioning. Rhizosphere microorganisms can influence nutrient acquisition, plant growth, and stress tolerance, while host plants can also shape microbial recruitment through root-associated interactions [ 18 ]. In Kenya, previous studies have demonstrated substantial diversity among indigenous cowpea-nodulating bacteria and have shown that native rhizobial populations can vary across locations and differ in symbiotic effectiveness [ 19 ]. Ondieki et al. (2017) [ 20 ] characterized the morphological and genetic diversity of cowpea rhizobia from lower eastern Kenya, including Machakos, Makueni, and Kitui, whereas Muindi et al. (2021) [ 21 ] reported that native rhizobia and associated endophytes from semiarid lower eastern Kenya displayed considerable diversity and variable symbiotic efficiency. Earlier work by Kimiti and Odee (2010) [ 3 ] also revealed that soil fertility management influences the population and effectiveness of indigenous cowpea rhizobia in semiarid eastern Kenya. These findings highlight the agricultural relevance of native microbial populations and suggest that soil biological variation may contribute to differences in cowpea performance across farms. Although earlier work in Kenya characterized rhizobial diversity and symbiotic potential in cowpea systems, microbiome-scale information on whole-soil bacterial communities remains limited, particularly under semiarid smallholder conditions [ 22 ]. In particular, little is known about how farm-level variation in soil physicochemical properties is associated with bacterial microbiome structure in cowpea farms in eastern Kenya. Addressing this gap is important for improving the ecological understanding of these production systems and for informing future microbiome-based approaches to sustainable soil fertility management and the potential use of beneficial native microbial resources. Therefore, this study aimed to assess the associations between soil physicochemical properties and bacterial microbiome structure in cowpea farms in semiarid eastern Kenya via 16S rRNA amplicon sequencing. Specifically, this study sought to characterize the variation in soil physicochemical properties among farms and between counties, describe the composition and diversity of associated bacterial communities, and evaluate associations between selected soil variables and dominant bacterial taxa, including rhizobial genera. By addressing these questions, this study provides baseline information on soil bacterial ecology in Kenyan cowpea systems and contributes to a better understanding of belowground variability in semiarid smallholder agriculture. 2 Methods 2.1 Study design and setting This cross-sectional farm-based study was conducted to assess the associations between soil physicochemical properties and bacterial microbiome structure in cowpea farms located in Machakos and Kitui Counties in semiarid eastern Kenya (Fig. 1). A total of twelve smallholder farms were included, with six farms selected from each county. The farms were purposively selected on the basis of active cowpea cultivation and the absence of commercial rhizobial inoculation. 2.2 Soil sampling and physicochemical analysis Soil sampling was conducted in April 2025. At each farm, soil samples were collected from the 0–20 cm depth via sterile hand shovels. Twenty subsamples were collected across each field and combined into one composite sample to represent the farm. The composite soil samples were transported to the Tissue Culture Laboratory at Kenyatta University for processing and storage until analysis. The soil physicochemical properties were determined via standard laboratory procedures as described by Carter and Gregorich (2007) [ 23 ]. The measured parameters included the soil pH, electrical conductivity, total organic carbon, total nitrogen, available phosphorus, potassium, calcium and magnesium. 2.3 DNA extraction, amplification, and sequencing Total genomic DNA was extracted from the soil samples via the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The DNA concentration was quantified via a VICTOR Nivo™ system (PerkinElmer) with PicoGreen reagents. Bacterial community profiling targeted the V3–V4 region of the 16S rRNA gene. The sequencing libraries were prepared according to the Illumina 16S Metagenomic Sequencing Library Preparation protocol using the 341F/805R primer pair, which has been widely applied and validated for amplicon-based bacterial diversity studies [ 24 ]. For library preparation, the following locus-specific primers were used with Illumina adapter overhang sequences: forward primer 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′ and reverse primer 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′. For the first PCR, 5 ng of genomic DNA was amplified in a reaction containing 5× reaction buffer, 1 mM dNTP mixture, 500 nM of each primer, and Herculase II Fusion DNA Polymerase (Agilent Technologies, Santa Clara, CA, USA). The thermal cycling conditions consisted of initial denaturation at 95°C for 3 min, followed by 25 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 30 s, with a final extension at 72°C for 5 min. The first PCR products were purified via AMPure beads (Agencourt Bioscience, Beverly, MA, USA). Following purification, 10 µL of the first PCR product was used as a template in a second PCR for index attachment via Nextera XT Indexed Primers. The second PCR was performed under the same thermal conditions as the first PCR, except that amplification was carried out for 10 cycles. Indexed PCR products were purified again using AMPure beads. The final purified libraries were quantified via the VICTOR Nivo™ system with PicoGreen reagents and assessed for fragment quality via the TapeStation D1000 ScreenTape system (Agilent Technologies, Waldbronn, Germany). Libraries were then normalized, pooled, and quantified via qPCR via the KAPA Library Quantification Kit for Illumina Sequencing Platforms. Sequencing was performed on the Illumina MiSeq i100 platform (Illumina, San Diego, CA, USA). 2.4 Sequence processing and taxonomic assignment Raw paired-end reads were processed in R via the DADA2 pipeline. Low-quality reads were filtered and trimmed, sequencing error rates were determined from the data, identical reads were dereplicated, amplicon sequence variants (ASVs) were inferred, paired reads were merged, and chimeric sequences were removed. The DADA2 pipeline enables exact sequence inference from Illumina amplicon data and resolves biological variation at single-nucleotide resolution [ 25 ]. Because ASVs provide greater taxonomic resolution and reproducibility than traditional operational taxonomic units (OTUs), they are now widely recommended for marker‒gene studies [ 26 ]. Taxonomic assignment was performed via the SILVA ribosomal RNA gene reference database [ 27 ]. After taxonomic classification, sequences assigned to chloroplasts, mitochondria, and other nonbacterial taxa were removed prior to downstream analyses. A phyloseq object was then constructed to integrate the ASV table, taxonomy table, and sample metadata for community-level analysis [ 28 ]. 2.5 Microbial diversity and community composition analysis Alpha diversity was quantified via the observed richness, Chao1, Shannon, and Simpson indices. Beta diversity was evaluated via Bray–Curtis dissimilarity, a widely used ecological distance metric for community composition data [ 29 ]. Differences in bacterial community structure were visualized via principal coordinate analysis (PCoA), and variation in community composition between counties was tested via permutational multivariate analysis of variance (PERMANOVA), a robust nonparametric approach for multivariate ecological datasets [ 30 ]. The bacterial community composition was summarized at the phylum and genus levels, and the dominant taxa were visualized via relative abundance plots and clustered heatmaps. Microbiome data integration, management, and visualization were performed via the phyloseq framework [ 28 ]. 2.6 Environmental associations between soil properties and bacterial taxa Relationships between soil physicochemical properties and bacterial taxa were assessed via Spearman correlation analysis. Correlation matrices were constructed to evaluate associations between selected soil variables and the relative abundances of the top 10 bacterial phyla, as well as between soil variables and selected rhizobial genera. These relationships were visualized as clustered heatmaps to highlight patterns of positive and negative associations between soil physicochemical variables and bacterial groups across the sampled farms. 2.7 Statistical analysis and visualization Soil physicochemical data and microbial diversity analyses were performed via R software. Differences in soil physicochemical properties among farms were evaluated via one-way analysis of variance, followed by Tukey’s honestly significant difference test for mean separation where appropriate. County-level comparisons were performed via the appropriate test according to the structure of the data. Alpha diversity indices were calculated for each sample, and differences in bacterial community composition were assessed via Bray–Curtis dissimilarity and permutational multivariate analysis of variance. Associations between soil variables and bacterial taxa were examined via Spearman’s rank correlation coefficient. Statistical significance was considered at P < 0.05. 3 Results 3.1 Soil physicochemical properties The soil physicochemical properties differed among the sampled cowpea farms in Kitui and Machakos Counties (Table 1 ). The soil pH ranged from 5.82 ± 0.10 in KF1 to 6.67 ± 0.07 in MF6. Significant farm-level differences were observed for pH, electrical conductivity, total nitrogen, phosphorus, potassium, magnesium, and calcium, whereas total organic carbon did not differ significantly among the farms. At the county level, most soil variables, including pH, electrical conductivity, total organic carbon, total nitrogen, phosphorus, potassium, and calcium, did not differ significantly between Kitui and Machakos (Table 2 ). In contrast, magnesium was significantly greater in Kitui (0.33 ± 0.03 mg kg⁻¹) than in Machakos (0.19 ± 0.03 mg kg⁻¹; P = 0.0036). Principal component analysis revealed variation in the soil physicochemical properties among the sampled farms in Kitui and Machakos Counties (Fig. 2). Table 1 Soil physicochemical properties of the sampled cowpea farms Farm pH EC (dS/m) TOC (%) N (%) P (mg kg⁻¹) K (mg kg⁻¹) Mg (mg kg⁻¹) Ca (mg kg⁻¹) KF1 5.82 ± 0.10 d 0.08 ± 0.00 de 0.68 ± 0.09 a 0.03 ± 0.00 j 8.35 ± 0.12 f 26.93 ± 0.23 i 0.20 ± 0.00 g 3.43 ± 0.06 f KF2 5.90 ± 0.08 cd 0.04 ± 0.00 h 0.96 ± 0.10 a 0.09 ± 0.00 g 13.53 ± 0.06 d 38.97 ± 0.32 g 0.26 ± 0.00 ef 3.64 ± 0.02 ef KF3 6.16 ± 0.04 bcd 0.39 ± 0.01 a 0.66 ± 0.15 a 0.07 ± 0.00 h 6.73 ± 0.06 g 23.23 ± 0.34 j 0.11 ± 0.00 h 3.82 ± 0.06 de KF4 6.63 ± 0.12 a 0.08 ± 0.00 e 0.94 ± 0.08 a 0.12 ± 0.00 e 16.50 ± 0.22 c 64.67 ± 0.91 c 0.24 ± 0.00 f 4.96 ± 0.04 b KF5 6.50 ± 0.10 ab 0.06 ± 0.00 g 0.66 ± 0.11 a 0.12 ± 0.00 e 16.18 ± 0.17 c 59.04 ± 0.49 d 0.21 ± 0.00 g 4.77 ± 0.10 b KF6 5.87 ± 0.09 d 0.07 ± 0.00 f 0.91 ± 0.15 a 0.14 ± 0.00 d 12.84 ± 0.15 d 54.27 ± 0.51 e 0.10 ± 0.00 h 4.03 ± 0.06 d MF1 6.33 ± 0.08 ab 0.10 ± 0.00 c 0.85 ± 0.10 a 0.17 ± 0.00 b 6.57 ± 0.08 g 74.67 ± 0.82 b 0.37 ± 0.01 b 2.50 ± 0.03 g MF2 6.29 ± 0.07 abc 0.04 ± 0.00 h 0.67 ± 0.13 a 0.15 ± 0.00 c 4.85 ± 0.07 h 52.64 ± 0.53 e 0.26 ± 0.00 de 2.12 ± 0.05 h MF3 6.32 ± 0.08 ab 0.04 ± 0.00 h 0.76 ± 0.11 a 0.30 ± 0.00 a 17.47 ± 0.37 b 63.27 ± 0.40 c 0.41 ± 0.00 a 4.43 ± 0.04 c MF4 6.43 ± 0.06 ab 0.09 ± 0.00 d 0.66 ± 0.11 a 0.10 ± 0.00 f 24.68 ± 0.26 a 78.28 ± 1.17 a 0.41 ± 0.01 a 4.03 ± 0.03 d MF5 6.60 ± 0.09 a 0.12 ± 0.00 b 0.72 ± 0.08 a 0.16 ± 0.00 c 6.31 ± 0.07 g 42.55 ± 0.42 f 0.28 ± 0.01 c 6.05 ± 0.06 a MF6 6.67 ± 0.07 a 0.03 ± 0.00 i 0.60 ± 0.12 a 0.05 ± 0.00 i 9.42 ± 0.09 e 34.19 ± 0.34 h 0.28 ± 0.00 cd 4.46 ± 0.06 c P-value < 0.0001 < 0.0001 0.3614 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 The values represent mean ± standard error (n = 5). Different lowercase letters within a column indicate significant differences among farms according to Tukey’s honestly significant difference (HSD) test at P < 0.05. The p-values shown at the bottom of each column represent the overall one-way ANOVA for farm effects. Table 2 County-level comparison of soil physicochemical properties Variable Kitui Machakos P_value pH 6.44 ± 0.07 6.15 ± 0.14 0.1034 EC (dS/m) 0.07 ± 0.02 0.12 ± 0.05 0.4035 TOC (%) 0.71 ± 0.04 0.81 ± 0.06 0.2348 N (%) 0.16 ± 0.03 0.10 ± 0.02 0.1534 P (mg kg⁻¹) 11.55 ± 3.21 12.35 ± 1.64 0.829 K (mg kg⁻¹) 57.60 ± 7.19 44.52 ± 7.08 0.224 Mg (mg kg⁻¹) 0.33 ± 0.03 0.19 ± 0.03 0.0036 Ca (mg kg⁻¹) 3.93 ± 0.59 4.11 ± 0.25 0.7909 The values represent means ± standard errors of the farms means (n = 6 farms per county). P values were obtained from t tests comparing county means. 3.2 Sequencing output and ASV recovery Amplicon sequencing generated substantial read numbers across all the soil samples (Table S1 ). The number of input reads ranged from 83,144 in KF4 to 157,037 in KF1. After filtering, denoising, merging, and chimera removal, the number of nonchimeric reads ranged from 32,650 to 147,791 per sample. The final number of retained reads ranged from 32,620 in KF4 to 147,742 in KF1 (Table S1 ; Fig. S1 ). All the samples retained sufficient sequencing depth to support downstream diversity and community composition analyses. 3.3 Bacterial community composition The bacterial communities across all the cowpea farm soils were dominated by five major phyla (Fig. 3). Actinomycetota was the most abundant phylum (46.23%), followed by Pseudomonadota (15.87%), Bacillota (12.11%), Chloroflexota (10.16%), and Acidobacteriota (6.97%). Additional phyla, including Myxococcota, Bacteroidota, and Gemmatimonadota, Planctomycetota and Verrucomicrobiota were consistently detected but occurred at relatively low relative abundances. Relative abundances varied among farms, although the dominant phyla were shared across both counties. Among the detected rhizobial genera, Bradyrhizobium was the most abundant and occurred across all the sampled farms (Fig. 4). Rhizobium , Ensifer , and Mesorhizobium were also detected but at lower relative abundances and with greater variation among farms. No consistent county-level pattern was observed for these genera. The genus-level community structure was further resolved by hierarchical clustering of the dominant taxa (Fig. 5). The heatmap showed marked variation in the abundance of dominant genera among farms, with clustering patterns reflecting both within-county and between-county differences in bacterial composition. 3.4 Bacterial diversity and richness 3.4.1 Alpha diversity The alpha diversity varied among the samples and between the counties (Table 3 ). The Chao1 richness ranged from 1381.36 in KF1 to 2231.46 in MF4, the Shannon diversity ranged from 6.29 in MF5 to 6.93 in KF5, and the Simpson diversity ranged from 0.9942 in MF5 to 0.9982 in KF5. The mean Chao1 richness was greater in Machakos (1974.4 ± 83.0) than in Kitui (1812.1 ± 134.1). In contrast, the Shannon (6.83 ± 0.03) and Simpson (0.9978 ± 0.0002) values were greater in Kitui than in Machakos (6.53 ± 0.07 and 0.9945 ± 0.0006, respectively). Table 3 Alpha diversity indices of the soil bacterial communities SampleID Chao1 Shannon Simpson KF1 1381.27 6.75 0.998 KF2 1453.08 6.87 0.998 KF3 1981.68 6.75 0.997 KF4 1840.81 6.83 0.997 KF5 2209.27 6.92 0.997 KF6 2006.27 6.85 0.997 MF1 1788.85 6.38 0.993 MF2 2216.25 6.65 0.994 MF3 1967.05 6.51 0.994 MF4 2232.07 6.63 0.994 MF5 1815.60 6.29 0.992 MF6 1826.35 6.70 0.996 3.4.2 Beta diversity and community structure Beta diversity analysis revealed significant differences in bacterial community composition between Kitui County and Machakos County (Fig. 6; Table S2). PERMANOVA based on Bray–Curtis dissimilarity revealed a significant effect of county type on community structure ( F = 2.076, R ² = 0.1719, P = 0.004), indicating that county type explained 17.2% of the total variation in bacterial community composition. Despite the shared dominance of major phyla across counties, ordination based on Bray–Curtis dissimilarity revealed separation in the overall bacterial community structure at the multivariate level (Fig. 6). 3.5 Associations between soil physicochemical properties and bacterial taxa Spearman correlation analysis revealed variable associations between the soil physicochemical properties and the dominant bacterial phyla (Fig. 7). Positive correlations were observed between several nutrient-related variables and dominant phyla, whereas negative correlations were observed for other variables. In particular, Bacillota was positively associated with pH and magnesium, whereas Chloroflexota was negatively associated with several nutrient-related variables. Hierarchical clustering grouped the soil variables and bacterial phyla according to similarity in their correlation patterns. Correlation analysis between the soil variables and rhizobial genera also revealed distinct association patterns (Fig. 8). Bradyrhizobium and Rhizobium were positively associated with nitrogen, phosphorus, and potassium, whereas Ensifer and Mesorhizobium were negatively associated with several of these variables. Contrasting relationships were also observed with magnesium and pH among the rhizobial genera. 4 Discussion The present study provides a farm-scale view of how soil physicochemical heterogeneity is associated with bacterial microbiome structure in cowpea farms in semiarid eastern Kenya. The significant variation observed in most soil properties among farms, in contrast with the relatively limited county-level differences, indicates that local edaphic conditions are likely stronger determinants of soil heterogeneity than county identity alone. This interpretation is consistent with broader soil microbiome literature showing that bacterial community structure is often shaped more strongly by local environmental filters than by geographic distance per se. Lauber et al. (2009) [ 12 ] demonstrated that soil pH is a major predictor of bacterial community structure across broad spatial scales, whereas Fierer and Jackson (2006) [ 31 ] similarly reported that edaphic variation, especially pH, strongly influences bacterial biogeography. At the global scale, Bahram et al. (2018) [ 32 ] reported that microbial gene composition varies more strongly with environmental variables than with geographic distance, reinforcing the idea that local soil conditions can outweigh coarse spatial grouping in structuring microbiomes. Although the county-level contrasts in the measured soil properties were modest, the Bray–Curtis PERMANOVA results indicated significant separation in the bacterial community composition between Machakos and Kitui. This suggests that county-level microbiome differentiation may reflect the combined influence of subtle edaphic gradients, environmental variables not captured in the present dataset, and differences in field history or management. This pattern is ecologically plausible because bacterial beta diversity often responds to the interaction of multiple drivers rather than to a single soil variable. Fierer (2017) [ 33 ] emphasized that soil microbiomes are shaped by a complex combination of abiotic and biotic filters rather than by simple geographic proximity. Similarly, Delgado-Baquerizo et al. (2018) [ 34 ] reported that relatively few bacterial taxa dominate globally, but their ecological distributions are strongly associated with environmental conditions, whereas Mo et al. (2024) [ 14 ] recently demonstrated that agricultural practices, particularly fertility sources, are pronounced drivers of microbiome assembly in managed soils. At the phylum level, the dominance of Actinomycetota, followed by Pseudomonadota, Bacillota, Chloroflexota, and Acidobacteriota, is consistent with the environmental context of semiarid agricultural soils. Actinomycetota are frequently associated with drought-prone and nutrient-variable soils, where their broad metabolic capacity and stress tolerance may confer an ecological advantage. Barnard et al. (2013) [ 35 ] reported that bacterial communities, including abundant Actinobacteria, respond strongly to drying–rewetting cycles, whereas Ebrahimi-Zarandi et al. (2023) [ 36 ] highlighted the ecological importance of Actinobacteria under drought stress and their persistence in water-limited environments. Similar phylum-level patterns have also been reported in arid and semiarid soils, including in recent work from Botswana, where Actinomycetota, Pseudomonadota, and Acidobacteriota were among the dominant bacterial groups [ 37 ]. These observations support the view that the present microbiome composition reflects a dryland edaphic setting rather than an anomalous community structure. The genus-level heatmap further revealed considerable farm-level heterogeneity, suggesting that bacterial assembly in these cowpea soils may be influenced by local environmental filtering. This is expected in smallholder systems, where neighboring farms can differ in nutrient inputs, residue return, tillage intensity, crop sequence, and organic matter management. Hartmann et al. (2015) [ 38 ] demonstrated that long-term farming system differences reshape soil microbial diversity and community structure, whereas Bier et al. (2024) [ 8 ] reported that the fertility source and soil depth are important determinants of the soil microbiome composition in agricultural systems. Peralta et al. (2018) [ 39 ] likewise reported that crop rotation history influences the soil microbiome and disease-suppressive potential. Taken together, these studies support the interpretation that the microbiome patterns observed here likely reflect cumulative field-level ecological histories rather than simple spatial proximity. One of the most agriculturally relevant findings of this study was the detection of Bradyrhizobium, Rhizobium, Ensifer , and Mesorhizobium across the sampled farms. These genera include important legume-associated taxa involved in nodulation and biological nitrogen fixation, and their occurrence indicates that the studied soils harbor native bacterial groups with potential relevance to cowpea nutrition. The relatively greater abundance of Bradyrhizobium is biologically credible because this genus is widely associated with cowpea nodulation in African soils and frequently dominates symbiotic associations under stressful field conditions. Zahran (1999) [ 40 ] reviewed the importance of rhizobial adaptation in harsh edaphic environments, whereas Broughton et al. (2000) [ 41 ] emphasized the central role of rhizobial compatibility in legume symbiosis. In eastern Kenya specifically, Kimiti and Odee (2010) [ 3 ] demonstrated that integrated soil fertility management affects the population and effectiveness of indigenous cowpea rhizobia. Ondieki et al. (2017) [ 20 ] documented substantial morphological and genetic diversity among cowpea rhizobia from Machakos, Makueni, and Kitui, and Muindi et al. (2021) [ 21 ] reported that the symbiotic efficiency of native rhizobia and associated endophytes from semiarid Kenya varies. The present study extends those isolate-based findings by showing that rhizobial genera are also detectable in whole-soil community profiles across farms. However, this taxonomic signal should still be interpreted cautiously because 16S rRNA amplicon sequencing does not confirm the effectiveness of nodulation or nitrogen fixation under field conditions. The alpha diversity results provide additional, albeit more limited, ecological insight. Descriptively, Machakos presented higher mean Chao1 richness, whereas Kitui presented slightly higher Shannon and Simpson values. These contrasting patterns may indicate differences in the balance between richness and evenness. The stronger and statistically supported result is the significant county-level difference in beta diversity, indicating that the two counties differed in community composition rather than necessarily in overall diversity magnitude. This distinction is important because compositional turnover can occur without large shifts in richness alone. Bahram et al. (2018) [ 32 ] and Delgado-Baquerizo et al. (2018) [ 34 ] both reported that large-scale microbiome differentiation often reflects ecological turnover among taxa more than simple richness changes do, whereas Hartmann et al. (2015) [ 38 ] reported that management can shift evenness and dispersion in ways not fully captured by a single alpha-diversity metric. The correlation analyses provided preliminary insight into the soil variables potentially associated with the observed taxonomic patterns. At the phylum level, the positive associations of Bacillota with pH and magnesium and the more negative associations of Chloroflexota with several nutrient-related variables are consistent with broader evidence that edaphic gradients influence major bacterial groups differently. Lauber et al. (2009) [ 12 ] reported that bacterial community composition closely tracks soil pH across landscapes, and Rousk et al. (2010) [ 42 ] reported that in an arable soil system, bacterial communities are strongly influenced by pH across a liming gradient. More recently, Xiong et al. (2024) [ 43 ] reported that soil pH amendment can alter microbial abundance, diversity, and composition, further reinforcing the importance of pH as a structuring variable. The present genus-level correlations, particularly the positive associations of Bradyrhizobium and Rhizobium with nitrogen, phosphorus, and potassium, are ecologically plausible, but they should not be interpreted causally because correlation analysis reflects covariation rather than mechanistic control. Future constrained ordination or variation-partitioning analyses would be valuable for disentangling the relative contributions of individual soil variables more rigorously. From an applied perspective, the results suggest that cowpea farms in semiarid eastern Kenya should not be regarded as microbiologically uniform. The coexistence of considerable farm-level soil heterogeneity, recurring dominant phyla, and variable rhizobial distributions suggests that local soil conditions shape the ecological background within which plant–microbe interactions operate. This information is relevant for future efforts aimed at improving soil fertility and developing microbial inoculants. Van der Heijden et al. (2008) [ 44 ] reported that soil microbes are major drivers of plant productivity, whereas Wagg et al. (2014) [ 45 ] reported that soil biodiversity and community composition influence ecosystem multifunctionality. Mendes et al. (2011) [ 46 ] and Luo et al. (2025) [ 18 ] further reported that specific rhizosphere microbiome configurations can be functionally important for plant health. In that context, the present findings support the idea that site specific, microbiome-informed approaches may be more appropriate than uniform recommendations when designing future interventions for semiarid cowpea systems. While this study provides valuable insight into the associations between soil physicochemical properties and bacterial microbiome structure in cowpea farms in semiarid eastern Kenya, the findings should be interpreted within the context of the study design. As with all observational studies, the relationships identified between soil variables and bacterial community composition reflect statistical associations derived from Spearman rank correlation and PERMANOVA analyses; establishing causal directionality would require future experimental approaches such as controlled soil amendment trials with concurrent microbiome profiling, which represent a natural and productive extension of the present baseline work. The exploratory nature of the study, which included twelve purposively selected farms across two counties, was appropriate for generating initial ecological insight into an understudied system, though future studies incorporating larger and randomly sampled farm networks would further strengthen the generalisability of these findings across the broader semiarid smallholder farming landscape of eastern Kenya. Sampling at a single time point in April 2025 allowed a focused characterisation of the bacterial microbiome under active cropping conditions, and longitudinal studies spanning multiple seasons would complement the present cross-sectional baseline by capturing temporal dynamics in soil and microbiome properties. The 16S rRNA amplicon sequencing approach employed here provided robust and high-resolution taxonomic profiling of the bacterial community across all farms; future integration with functional metagenomics or symbiotic effectiveness assays would build on these community-level findings to evaluate the agronomic significance of the detected rhizobial genera, including Bradyrhizobium , Rhizobium , Ensifer , and Mesorhizobium . As is inherent to marker-gene approaches, the V3–V4 region offers broad taxonomic coverage with well-established sensitivity, though species-level resolution and potential PCR amplification biases represent boundaries that are common to all amplicon-based studies of this type. Taken together, these considerations do not diminish the value of the present findings but rather define the scope of this study and point toward a clear and productive research agenda for understanding the ecology and agronomic potential of soil bacterial communities in cowpea-based systems under semiarid Kenyan conditions. 5 Conclusion This study revealed that cowpea farms in semiarid eastern Kenya harbor distinct soil bacterial microbiomes associated with substantial variation in soil physicochemical properties. The bacterial communities were dominated by Actinomycetota, Pseudomonadota, Bacillota, Chloroflexota, and Acidobacteriota, while agriculturally important rhizobial genera, including Bradyrhizobium , Rhizobium , Ensifer , and Mesorhizobium , were detected across farms. The significant compositional differentiation observed between counties further suggests that both local edaphic heterogeneity and broader spatial variation may contribute to microbiome structure. Overall, this study provides baseline evidence associating soil physicochemical properties with bacterial microbiome composition in cowpea-based systems under semiarid Kenyan conditions and suggests the potential value of microbiome-informed, site-specific approaches for future soil fertility management and sustainable cowpea production. Declarations Availability of data and material The datasets generated and/or analysed during the current study are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.31920030. All data are publicly available. Competing interests The authors declare that they have no competing interests. Funding Open access funding was provided by the Science, Technology & Innovation Funding Authority (STDF) in cooperation with the Egyptian Knowledge Bank (EKB). Authors' contributions AKK conceived the study, performed the experiments, analyzed the data, and wrote the manuscript. EMN supervised the research and revised the manuscript. SMG provided resources, supervised the research, and edited the manuscript. All the authors read and approved the final manuscript. Acknowledgments We express our sincere gratitude to Pan African University, Institute of Science, Technology, and Innovation (PAUSTI) , for supporting this study. Special thanks to laboratory staff from Microbiology and Molecular Biology, Kenyatta University, for their participation, cooperation, time, and efforts during the collection of molecular data and analysis. Additionally, the authors wish to thank the farmers from Kitui and Machakos Counties who participated in the fieldwork. References Boukar, O. et al. Cowpea [Vigna unguiculata (L.) Walp.] breeding, in Advances in Plant Breeding Strategies: Legumes: Volume 7 p. 201–243 (Springer, 2019). 10.1007/978-3-030-23400-3_6 Kim, D. K. et al. Cowpea (Vigna unguiculata L.) production, genetic resources and strategic breeding priorities for sustainable food security: a review. Front. 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Soil pH amendment alters the abundance, diversity, and composition of microbial communities in two contrasting agricultural soils. Microbiol. Spectr. 12 (8), e04165–e04123. https://doi.org/10.1128/spectrum.04165-23 (2024). Van Der Heijden, M. G., Bardgett, R. D. & Van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11 (3), 296–310. https://doi.org/10.1111/j.1461-0248.2008.01199.x (2008). Wagg, C. et al. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proceedings of the National Academy of Sciences, 111(14): pp. 5266–5270. (2014). https://doi.org/10.1073/pnas.1320054111 Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332 (6033), 1097–1100. https://doi.org/10.1126/science.1203980 (2011). Additional Declarations No competing interests reported. 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Background","content":"\u003cp\u003eCowpea (\u003cem\u003eVigna unguiculata\u003c/em\u003e [L.] Walp.) is one of the most important grain legumes in sub-Saharan Africa, where it contributes substantially to household nutrition, income generation, and the resilience of smallholder farming systems. Its value is particularly evident in semiarid environments because cowpea is cultivated both as a food and fodder crop and is relatively well adapted to heat, intermittent drought, and low-input conditions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition to its agronomic and nutritional importance, cowpea can improve soil fertility through symbiotic nitrogen fixation, making it especially relevant in production systems where access to mineral fertilizers is limited [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In eastern Kenya, where rainfall variability and declining soil fertility constrain crop productivity, cowpea remains an important component of smallholder agriculture and a valuable crop for dryland food security [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite its adaptability, cowpea productivity in semiarid regions remains inconsistent because crop performance is influenced not only by water limitation but also by soil fertility constraints and variation in belowground ecological conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Many smallholder fields in eastern Kenya are characterized by nutrient depletion, heterogeneous soil properties, and management differences that may influence plant growth directly and indirectly through their effects on soil microbial communities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Soil bacteria are integral to organic matter turnover, nutrient cycling, and rhizosphere processes, and their distribution can shape plant health and productivity in agricultural systems. Consequently, variation in the soil bacterial microbiome may represent an important but insufficiently characterized component of cowpea production systems in semiarid environments [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have shown that soil bacterial communities are strongly structured by edaphic factors, particularly soil pH, nutrient availability, and soil organic resource status [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Lauber et al. (2009) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated that soil pH was a strong predictor of bacterial community structure across soils, whereas Zhou et al. (2024) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] further reported at the global scale that more than 75% of bacterial genera are predicted by soil pH. In agricultural soils, these edaphic effects interact with management-related factors, and Mo et al. (2024) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] reported that the fertility source can be a pronounced factor influencing soil microbiome assembly. These controls often operate at relatively fine spatial scales, meaning that neighboring farms may harbor distinct microbial communities even within the same broader agroecological zone [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Understanding how such variability occurs in cowpea-growing soils is important for interpreting the ecological context within which crop performance is expressed.\u003c/p\u003e \u003cp\u003eFor legumes such as cowpea, the importance of soil bacterial communities extends beyond general soil processes to include taxa associated with nodulation, biological nitrogen fixation, and rhizosphere functioning. Rhizosphere microorganisms can influence nutrient acquisition, plant growth, and stress tolerance, while host plants can also shape microbial recruitment through root-associated interactions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In Kenya, previous studies have demonstrated substantial diversity among indigenous cowpea-nodulating bacteria and have shown that native rhizobial populations can vary across locations and differ in symbiotic effectiveness [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Ondieki et al. (2017) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] characterized the morphological and genetic diversity of cowpea rhizobia from lower eastern Kenya, including Machakos, Makueni, and Kitui, whereas Muindi et al. (2021) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported that native rhizobia and associated endophytes from semiarid lower eastern Kenya displayed considerable diversity and variable symbiotic efficiency. Earlier work by Kimiti and Odee (2010) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] also revealed that soil fertility management influences the population and effectiveness of indigenous cowpea rhizobia in semiarid eastern Kenya. These findings highlight the agricultural relevance of native microbial populations and suggest that soil biological variation may contribute to differences in cowpea performance across farms.\u003c/p\u003e \u003cp\u003eAlthough earlier work in Kenya characterized rhizobial diversity and symbiotic potential in cowpea systems, microbiome-scale information on whole-soil bacterial communities remains limited, particularly under semiarid smallholder conditions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In particular, little is known about how farm-level variation in soil physicochemical properties is associated with bacterial microbiome structure in cowpea farms in eastern Kenya. Addressing this gap is important for improving the ecological understanding of these production systems and for informing future microbiome-based approaches to sustainable soil fertility management and the potential use of beneficial native microbial resources.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to assess the associations between soil physicochemical properties and bacterial microbiome structure in cowpea farms in semiarid eastern Kenya via 16S rRNA amplicon sequencing. Specifically, this study sought to characterize the variation in soil physicochemical properties among farms and between counties, describe the composition and diversity of associated bacterial communities, and evaluate associations between selected soil variables and dominant bacterial taxa, including rhizobial genera. By addressing these questions, this study provides baseline information on soil bacterial ecology in Kenyan cowpea systems and contributes to a better understanding of belowground variability in semiarid smallholder agriculture.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and setting\u003c/h2\u003e \u003cp\u003eThis cross-sectional farm-based study was conducted to assess the associations between soil physicochemical properties and bacterial microbiome structure in cowpea farms located in Machakos and Kitui Counties in semiarid eastern Kenya (Fig.\u0026nbsp;1). A total of twelve smallholder farms were included, with six farms selected from each county. The farms were purposively selected on the basis of active cowpea cultivation and the absence of commercial rhizobial inoculation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Soil sampling and physicochemical analysis\u003c/h2\u003e \u003cp\u003eSoil sampling was conducted in April 2025. At each farm, soil samples were collected from the 0\u0026ndash;20 cm depth via sterile hand shovels. Twenty subsamples were collected across each field and combined into one composite sample to represent the farm. The composite soil samples were transported to the Tissue Culture Laboratory at Kenyatta University for processing and storage until analysis. The soil physicochemical properties were determined via standard laboratory procedures as described by Carter and Gregorich (2007) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The measured parameters included the soil pH, electrical conductivity, total organic carbon, total nitrogen, available phosphorus, potassium, calcium and magnesium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 DNA extraction, amplification, and sequencing\u003c/h2\u003e \u003cp\u003eTotal genomic DNA was extracted from the soil samples via the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions. The DNA concentration was quantified via a VICTOR Nivo\u0026trade; system (PerkinElmer) with PicoGreen reagents.\u003c/p\u003e \u003cp\u003eBacterial community profiling targeted the V3\u0026ndash;V4 region of the \u003cem\u003e16S rRNA\u003c/em\u003e gene. The sequencing libraries were prepared according to the Illumina 16S Metagenomic Sequencing Library Preparation protocol using the 341F/805R primer pair, which has been widely applied and validated for amplicon-based bacterial diversity studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For library preparation, the following locus-specific primers were used with Illumina adapter overhang sequences: forward primer 5\u0026prime;-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3\u0026prime; and reverse primer 5\u0026prime;-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3\u0026prime;.\u003c/p\u003e \u003cp\u003eFor the first PCR, 5 ng of genomic DNA was amplified in a reaction containing 5\u0026times; reaction buffer, 1 mM dNTP mixture, 500 nM of each primer, and Herculase II Fusion DNA Polymerase (Agilent Technologies, Santa Clara, CA, USA). The thermal cycling conditions consisted of initial denaturation at 95\u0026deg;C for 3 min, followed by 25 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 55\u0026deg;C for 30 s, and extension at 72\u0026deg;C for 30 s, with a final extension at 72\u0026deg;C for 5 min.\u003c/p\u003e \u003cp\u003eThe first PCR products were purified via AMPure beads (Agencourt Bioscience, Beverly, MA, USA). Following purification, 10 \u0026micro;L of the first PCR product was used as a template in a second PCR for index attachment via Nextera XT Indexed Primers. The second PCR was performed under the same thermal conditions as the first PCR, except that amplification was carried out for 10 cycles. Indexed PCR products were purified again using AMPure beads.\u003c/p\u003e \u003cp\u003eThe final purified libraries were quantified via the VICTOR Nivo\u0026trade; system with PicoGreen reagents and assessed for fragment quality via the TapeStation D1000 ScreenTape system (Agilent Technologies, Waldbronn, Germany). Libraries were then normalized, pooled, and quantified via qPCR via the KAPA Library Quantification Kit for Illumina Sequencing Platforms. Sequencing was performed on the Illumina MiSeq i100 platform (Illumina, San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sequence processing and taxonomic assignment\u003c/h2\u003e \u003cp\u003eRaw paired-end reads were processed in R via the DADA2 pipeline. Low-quality reads were filtered and trimmed, sequencing error rates were determined from the data, identical reads were dereplicated, amplicon sequence variants (ASVs) were inferred, paired reads were merged, and chimeric sequences were removed. The DADA2 pipeline enables exact sequence inference from Illumina amplicon data and resolves biological variation at single-nucleotide resolution [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Because ASVs provide greater taxonomic resolution and reproducibility than traditional operational taxonomic units (OTUs), they are now widely recommended for marker‒gene studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaxonomic assignment was performed via the SILVA ribosomal RNA gene reference database [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. After taxonomic classification, sequences assigned to chloroplasts, mitochondria, and other nonbacterial taxa were removed prior to downstream analyses. A phyloseq object was then constructed to integrate the ASV table, taxonomy table, and sample metadata for community-level analysis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Microbial diversity and community composition analysis\u003c/h2\u003e \u003cp\u003eAlpha diversity was quantified via the observed richness, Chao1, Shannon, and Simpson indices. Beta diversity was evaluated via Bray\u0026ndash;Curtis dissimilarity, a widely used ecological distance metric for community composition data [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Differences in bacterial community structure were visualized via principal coordinate analysis (PCoA), and variation in community composition between counties was tested via permutational multivariate analysis of variance (PERMANOVA), a robust nonparametric approach for multivariate ecological datasets [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe bacterial community composition was summarized at the phylum and genus levels, and the dominant taxa were visualized via relative abundance plots and clustered heatmaps. Microbiome data integration, management, and visualization were performed via the phyloseq framework [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Environmental associations between soil properties and bacterial taxa\u003c/h2\u003e \u003cp\u003eRelationships between soil physicochemical properties and bacterial taxa were assessed via Spearman correlation analysis. Correlation matrices were constructed to evaluate associations between selected soil variables and the relative abundances of the top 10 bacterial phyla, as well as between soil variables and selected rhizobial genera. These relationships were visualized as clustered heatmaps to highlight patterns of positive and negative associations between soil physicochemical variables and bacterial groups across the sampled farms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis and visualization\u003c/h2\u003e \u003cp\u003eSoil physicochemical data and microbial diversity analyses were performed via R software. Differences in soil physicochemical properties among farms were evaluated via one-way analysis of variance, followed by Tukey\u0026rsquo;s honestly significant difference test for mean separation where appropriate. County-level comparisons were performed via the appropriate test according to the structure of the data. Alpha diversity indices were calculated for each sample, and differences in bacterial community composition were assessed via Bray\u0026ndash;Curtis dissimilarity and permutational multivariate analysis of variance. Associations between soil variables and bacterial taxa were examined via Spearman\u0026rsquo;s rank correlation coefficient. Statistical significance was considered at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Soil physicochemical properties\u003c/h2\u003e \u003cp\u003eThe soil physicochemical properties differed among the sampled cowpea farms in Kitui and Machakos Counties (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The soil pH ranged from 5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 in KF1 to 6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 in MF6. Significant farm-level differences were observed for pH, electrical conductivity, total nitrogen, phosphorus, potassium, magnesium, and calcium, whereas total organic carbon did not differ significantly among the farms. At the county level, most soil variables, including pH, electrical conductivity, total organic carbon, total nitrogen, phosphorus, potassium, and calcium, did not differ significantly between Kitui and Machakos (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, magnesium was significantly greater in Kitui (0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mg kg⁻\u0026sup1;) than in Machakos (0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mg kg⁻\u0026sup1;; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0036). Principal component analysis revealed variation in the soil physicochemical properties among the sampled farms in Kitui and Machakos Counties (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoil physicochemical properties of the sampled cowpea farms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003cp\u003e(dS/m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTOC\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003e(mg kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003c/p\u003e \u003cp\u003e(mg kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003cp\u003e(mg kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003cp\u003e(mg kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 j\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 f\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 ef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 bcd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 j\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 de\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 g\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 abc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.3614\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eThe values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (n\u0026thinsp;=\u0026thinsp;5). Different lowercase letters within a column indicate significant differences among farms according to Tukey\u0026rsquo;s honestly significant difference (HSD) test at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The p-values shown at the bottom of each column represent the overall one-way ANOVA for farm effects.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCounty-level comparison of soil physicochemical properties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKitui\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachakos\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP_value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC (dS/m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOC (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP (mg kg⁻\u0026sup1;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eK (mg kg⁻\u0026sup1;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.52\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMg (mg kg⁻\u0026sup1;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa (mg kg⁻\u0026sup1;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe values represent means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard errors of the farms means (n\u0026thinsp;=\u0026thinsp;6 farms per county). P values were obtained from t tests comparing county means.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sequencing output and ASV recovery\u003c/h2\u003e \u003cp\u003eAmplicon sequencing generated substantial read numbers across all the soil samples (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The number of input reads ranged from 83,144 in KF4 to 157,037 in KF1. After filtering, denoising, merging, and chimera removal, the number of nonchimeric reads ranged from 32,650 to 147,791 per sample. The final number of retained reads ranged from 32,620 in KF4 to 147,742 in KF1 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All the samples retained sufficient sequencing depth to support downstream diversity and community composition analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Bacterial community composition\u003c/h2\u003e \u003cp\u003eThe bacterial communities across all the cowpea farm soils were dominated by five major phyla (Fig.\u0026nbsp;3). Actinomycetota was the most abundant phylum (46.23%), followed by Pseudomonadota (15.87%), Bacillota (12.11%), Chloroflexota (10.16%), and Acidobacteriota (6.97%). Additional phyla, including Myxococcota, Bacteroidota, and Gemmatimonadota, Planctomycetota and Verrucomicrobiota were consistently detected but occurred at relatively low relative abundances. Relative abundances varied among farms, although the dominant phyla were shared across both counties.\u003c/p\u003e \u003cp\u003eAmong the detected rhizobial genera, \u003cem\u003eBradyrhizobium\u003c/em\u003e was the most abundant and occurred across all the sampled farms (Fig.\u0026nbsp;4). \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eEnsifer\u003c/em\u003e, and \u003cem\u003eMesorhizobium\u003c/em\u003e were also detected but at lower relative abundances and with greater variation among farms. No consistent county-level pattern was observed for these genera.\u003c/p\u003e \u003cp\u003eThe genus-level community structure was further resolved by hierarchical clustering of the dominant taxa (Fig.\u0026nbsp;5). The heatmap showed marked variation in the abundance of dominant genera among farms, with clustering patterns reflecting both within-county and between-county differences in bacterial composition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Bacterial diversity and richness\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Alpha diversity\u003c/h2\u003e \u003cp\u003eThe alpha diversity varied among the samples and between the counties (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Chao1 richness ranged from 1381.36 in KF1 to 2231.46 in MF4, the Shannon diversity ranged from 6.29 in MF5 to 6.93 in KF5, and the Simpson diversity ranged from 0.9942 in MF5 to 0.9982 in KF5. The mean Chao1 richness was greater in Machakos (1974.4\u0026thinsp;\u0026plusmn;\u0026thinsp;83.0) than in Kitui (1812.1\u0026thinsp;\u0026plusmn;\u0026thinsp;134.1). In contrast, the Shannon (6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03) and Simpson (0.9978\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0002) values were greater in Kitui than in Machakos (6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 and 0.9945\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0006, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlpha diversity indices of the soil bacterial communities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampleID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1381.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1453.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1981.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1840.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2209.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2006.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1788.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2216.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1967.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2232.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1815.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1826.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Beta diversity and community structure\u003c/h2\u003e \u003cp\u003eBeta diversity analysis revealed significant differences in bacterial community composition between Kitui County and Machakos County (Fig.\u0026nbsp;6; Table S2). PERMANOVA based on Bray\u0026ndash;Curtis dissimilarity revealed a significant effect of county type on community structure (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.076, \u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.1719, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), indicating that county type explained 17.2% of the total variation in bacterial community composition. Despite the shared dominance of major phyla across counties, ordination based on Bray\u0026ndash;Curtis dissimilarity revealed separation in the overall bacterial community structure at the multivariate level (Fig.\u0026nbsp;6).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Associations between soil physicochemical properties and bacterial taxa\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis revealed variable associations between the soil physicochemical properties and the dominant bacterial phyla (Fig.\u0026nbsp;7). Positive correlations were observed between several nutrient-related variables and dominant phyla, whereas negative correlations were observed for other variables. In particular, Bacillota was positively associated with pH and magnesium, whereas Chloroflexota was negatively associated with several nutrient-related variables. Hierarchical clustering grouped the soil variables and bacterial phyla according to similarity in their correlation patterns.\u003c/p\u003e \u003cp\u003eCorrelation analysis between the soil variables and rhizobial genera also revealed distinct association patterns (Fig.\u0026nbsp;8). \u003cem\u003eBradyrhizobium\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e were positively associated with nitrogen, phosphorus, and potassium, whereas \u003cem\u003eEnsifer\u003c/em\u003e and \u003cem\u003eMesorhizobium\u003c/em\u003e were negatively associated with several of these variables. Contrasting relationships were also observed with magnesium and pH among the rhizobial genera.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study provides a farm-scale view of how soil physicochemical heterogeneity is associated with bacterial microbiome structure in cowpea farms in semiarid eastern Kenya. The significant variation observed in most soil properties among farms, in contrast with the relatively limited county-level differences, indicates that local edaphic conditions are likely stronger determinants of soil heterogeneity than county identity alone. This interpretation is consistent with broader soil microbiome literature showing that bacterial community structure is often shaped more strongly by local environmental filters than by geographic distance per se. Lauber et al. (2009) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated that soil pH is a major predictor of bacterial community structure across broad spatial scales, whereas Fierer and Jackson (2006) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] similarly reported that edaphic variation, especially pH, strongly influences bacterial biogeography. At the global scale, Bahram et al. (2018) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] reported that microbial gene composition varies more strongly with environmental variables than with geographic distance, reinforcing the idea that local soil conditions can outweigh coarse spatial grouping in structuring microbiomes.\u003c/p\u003e \u003cp\u003eAlthough the county-level contrasts in the measured soil properties were modest, the Bray\u0026ndash;Curtis PERMANOVA results indicated significant separation in the bacterial community composition between Machakos and Kitui. This suggests that county-level microbiome differentiation may reflect the combined influence of subtle edaphic gradients, environmental variables not captured in the present dataset, and differences in field history or management. This pattern is ecologically plausible because bacterial beta diversity often responds to the interaction of multiple drivers rather than to a single soil variable. Fierer (2017) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] emphasized that soil microbiomes are shaped by a complex combination of abiotic and biotic filters rather than by simple geographic proximity. Similarly, Delgado-Baquerizo et al. (2018) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] reported that relatively few bacterial taxa dominate globally, but their ecological distributions are strongly associated with environmental conditions, whereas Mo et al. (2024) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] recently demonstrated that agricultural practices, particularly fertility sources, are pronounced drivers of microbiome assembly in managed soils.\u003c/p\u003e \u003cp\u003eAt the phylum level, the dominance of Actinomycetota, followed by Pseudomonadota, Bacillota, Chloroflexota, and Acidobacteriota, is consistent with the environmental context of semiarid agricultural soils. Actinomycetota are frequently associated with drought-prone and nutrient-variable soils, where their broad metabolic capacity and stress tolerance may confer an ecological advantage. Barnard et al. (2013) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] reported that bacterial communities, including abundant Actinobacteria, respond strongly to drying\u0026ndash;rewetting cycles, whereas Ebrahimi-Zarandi et al. (2023) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] highlighted the ecological importance of Actinobacteria under drought stress and their persistence in water-limited environments. Similar phylum-level patterns have also been reported in arid and semiarid soils, including in recent work from Botswana, where Actinomycetota, Pseudomonadota, and Acidobacteriota were among the dominant bacterial groups [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These observations support the view that the present microbiome composition reflects a dryland edaphic setting rather than an anomalous community structure.\u003c/p\u003e \u003cp\u003eThe genus-level heatmap further revealed considerable farm-level heterogeneity, suggesting that bacterial assembly in these cowpea soils may be influenced by local environmental filtering. This is expected in smallholder systems, where neighboring farms can differ in nutrient inputs, residue return, tillage intensity, crop sequence, and organic matter management. Hartmann et al. (2015) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] demonstrated that long-term farming system differences reshape soil microbial diversity and community structure, whereas Bier et al. (2024) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported that the fertility source and soil depth are important determinants of the soil microbiome composition in agricultural systems. Peralta et al. (2018) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] likewise reported that crop rotation history influences the soil microbiome and disease-suppressive potential. Taken together, these studies support the interpretation that the microbiome patterns observed here likely reflect cumulative field-level ecological histories rather than simple spatial proximity.\u003c/p\u003e \u003cp\u003eOne of the most agriculturally relevant findings of this study was the detection of \u003cem\u003eBradyrhizobium, Rhizobium, Ensifer\u003c/em\u003e, and \u003cem\u003eMesorhizobium\u003c/em\u003e across the sampled farms. These genera include important legume-associated taxa involved in nodulation and biological nitrogen fixation, and their occurrence indicates that the studied soils harbor native bacterial groups with potential relevance to cowpea nutrition. The relatively greater abundance of \u003cem\u003eBradyrhizobium\u003c/em\u003e is biologically credible because this genus is widely associated with cowpea nodulation in African soils and frequently dominates symbiotic associations under stressful field conditions. Zahran (1999) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] reviewed the importance of rhizobial adaptation in harsh edaphic environments, whereas Broughton et al. (2000) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] emphasized the central role of rhizobial compatibility in legume symbiosis. In eastern Kenya specifically, Kimiti and Odee (2010) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] demonstrated that integrated soil fertility management affects the population and effectiveness of indigenous cowpea rhizobia. Ondieki et al. (2017) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] documented substantial morphological and genetic diversity among cowpea rhizobia from Machakos, Makueni, and Kitui, and Muindi et al. (2021) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported that the symbiotic efficiency of native rhizobia and associated endophytes from semiarid Kenya varies. The present study extends those isolate-based findings by showing that rhizobial genera are also detectable in whole-soil community profiles across farms. However, this taxonomic signal should still be interpreted cautiously because 16S rRNA amplicon sequencing does not confirm the effectiveness of nodulation or nitrogen fixation under field conditions.\u003c/p\u003e \u003cp\u003eThe alpha diversity results provide additional, albeit more limited, ecological insight. Descriptively, Machakos presented higher mean Chao1 richness, whereas Kitui presented slightly higher Shannon and Simpson values. These contrasting patterns may indicate differences in the balance between richness and evenness. The stronger and statistically supported result is the significant county-level difference in beta diversity, indicating that the two counties differed in community composition rather than necessarily in overall diversity magnitude. This distinction is important because compositional turnover can occur without large shifts in richness alone. Bahram et al. (2018) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and Delgado-Baquerizo et al. (2018) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] both reported that large-scale microbiome differentiation often reflects ecological turnover among taxa more than simple richness changes do, whereas Hartmann et al. (2015) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] reported that management can shift evenness and dispersion in ways not fully captured by a single alpha-diversity metric.\u003c/p\u003e \u003cp\u003eThe correlation analyses provided preliminary insight into the soil variables potentially associated with the observed taxonomic patterns. At the phylum level, the positive associations of Bacillota with pH and magnesium and the more negative associations of Chloroflexota with several nutrient-related variables are consistent with broader evidence that edaphic gradients influence major bacterial groups differently. Lauber et al. (2009) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] reported that bacterial community composition closely tracks soil pH across landscapes, and Rousk et al. (2010) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] reported that in an arable soil system, bacterial communities are strongly influenced by pH across a liming gradient. More recently, Xiong et al. (2024) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] reported that soil pH amendment can alter microbial abundance, diversity, and composition, further reinforcing the importance of pH as a structuring variable. The present genus-level correlations, particularly the positive associations of \u003cem\u003eBradyrhizobium\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e with nitrogen, phosphorus, and potassium, are ecologically plausible, but they should not be interpreted causally because correlation analysis reflects covariation rather than mechanistic control. Future constrained ordination or variation-partitioning analyses would be valuable for disentangling the relative contributions of individual soil variables more rigorously.\u003c/p\u003e \u003cp\u003eFrom an applied perspective, the results suggest that cowpea farms in semiarid eastern Kenya should not be regarded as microbiologically uniform. The coexistence of considerable farm-level soil heterogeneity, recurring dominant phyla, and variable rhizobial distributions suggests that local soil conditions shape the ecological background within which plant\u0026ndash;microbe interactions operate. This information is relevant for future efforts aimed at improving soil fertility and developing microbial inoculants. Van der Heijden et al. (2008) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] reported that soil microbes are major drivers of plant productivity, whereas Wagg et al. (2014) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] reported that soil biodiversity and community composition influence ecosystem multifunctionality. Mendes et al. (2011) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and Luo et al. (2025) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] further reported that specific rhizosphere microbiome configurations can be functionally important for plant health. In that context, the present findings support the idea that site specific, microbiome-informed approaches may be more appropriate than uniform recommendations when designing future interventions for semiarid cowpea systems.\u003c/p\u003e \u003cp\u003eWhile this study provides valuable insight into the associations between soil physicochemical properties and bacterial microbiome structure in cowpea farms in semiarid eastern Kenya, the findings should be interpreted within the context of the study design. As with all observational studies, the relationships identified between soil variables and bacterial community composition reflect statistical associations derived from Spearman rank correlation and PERMANOVA analyses; establishing causal directionality would require future experimental approaches such as controlled soil amendment trials with concurrent microbiome profiling, which represent a natural and productive extension of the present baseline work. The exploratory nature of the study, which included twelve purposively selected farms across two counties, was appropriate for generating initial ecological insight into an understudied system, though future studies incorporating larger and randomly sampled farm networks would further strengthen the generalisability of these findings across the broader semiarid smallholder farming landscape of eastern Kenya. Sampling at a single time point in April 2025 allowed a focused characterisation of the bacterial microbiome under active cropping conditions, and longitudinal studies spanning multiple seasons would complement the present cross-sectional baseline by capturing temporal dynamics in soil and microbiome properties. The 16S rRNA amplicon sequencing approach employed here provided robust and high-resolution taxonomic profiling of the bacterial community across all farms; future integration with functional metagenomics or symbiotic effectiveness assays would build on these community-level findings to evaluate the agronomic significance of the detected rhizobial genera, including \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eEnsifer\u003c/em\u003e, and \u003cem\u003eMesorhizobium\u003c/em\u003e. As is inherent to marker-gene approaches, the V3\u0026ndash;V4 region offers broad taxonomic coverage with well-established sensitivity, though species-level resolution and potential PCR amplification biases represent boundaries that are common to all amplicon-based studies of this type. Taken together, these considerations do not diminish the value of the present findings but rather define the scope of this study and point toward a clear and productive research agenda for understanding the ecology and agronomic potential of soil bacterial communities in cowpea-based systems under semiarid Kenyan conditions.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study revealed that cowpea farms in semiarid eastern Kenya harbor distinct soil bacterial microbiomes associated with substantial variation in soil physicochemical properties. The bacterial communities were dominated by Actinomycetota, Pseudomonadota, Bacillota, Chloroflexota, and Acidobacteriota, while agriculturally important rhizobial genera, including \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eEnsifer\u003c/em\u003e, and \u003cem\u003eMesorhizobium\u003c/em\u003e, were detected across farms. The significant compositional differentiation observed between counties further suggests that both local edaphic heterogeneity and broader spatial variation may contribute to microbiome structure. Overall, this study provides baseline evidence associating soil physicochemical properties with bacterial microbiome composition in cowpea-based systems under semiarid Kenyan conditions and suggests the potential value of microbiome-informed, site-specific approaches for future soil fertility management and sustainable cowpea production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.31920030. All data are publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen access funding was provided by the Science, Technology \u0026amp; Innovation Funding Authority (STDF) in cooperation with the Egyptian Knowledge Bank (EKB).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAKK conceived the study, performed the experiments, analyzed the data, and wrote the manuscript. EMN supervised the research and revised the manuscript. SMG provided resources, supervised the research, and edited the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to Pan African University, Institute of Science, Technology, and Innovation (PAUSTI)\u003cins cite=\"mailto:Editor%202\" datetime=\"2026-03-13T04:46\"\u003e,\u003c/ins\u003e for supporting this study. Special thanks to laboratory staff from Microbiology and Molecular Biology, Kenyatta University, for their participation, cooperation, time, and efforts during the collection of molecular data and analysis. Additionally, the authors wish to thank the farmers from Kitui and Machakos Counties who participated in the fieldwork.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoukar, O. et al. \u003cem\u003eCowpea [Vigna unguiculata (L.) 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Deciphering the rhizosphere microbiome for disease-suppressive bacteria. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e332\u003c/b\u003e (6033), 1097\u0026ndash;1100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1203980\u003c/span\u003e\u003cspan address=\"10.1126/science.1203980\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cowpea, soil bacterial microbiome, 16S rRNA amplicon sequencing, soil physicochemical properties, rhizobial genera, semiarid eastern Kenya, next-generation sequencing","lastPublishedDoi":"10.21203/rs.3.rs-9264911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9264911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCowpea (\u003cem\u003eVigna unguiculata\u003c/em\u003e [L.] Walp.) is an important grain legume in semiarid Africa, where it contributes to food security and livelihood resilience in smallholder farming systems. However, information on the soil bacterial microbiome associated with cowpea production in semiarid eastern Kenya remains limited. This study investigated soil physicochemical heterogeneity and bacterial community structure in cowpea farms from Machakos and Kitui Counties via 16S rRNA amplicon next-generation sequencing (NGS), with the aim of improving the understanding of belowground microbial patterns in these production systems. Most measured soil physicochemical properties varied significantly among farms, indicating substantial field-level heterogeneity, whereas county-level differences were less pronounced. Across all the samples, the bacterial communities were dominated by Actinomycetota, followed by Pseudomonadota, Bacillota, Chloroflexota, and Acidobacteriota. Rhizobial genera of agronomic relevance, including \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eEnsifer\u003c/em\u003e, and \u003cem\u003eMesorhizobium\u003c/em\u003e, were detected across farms, with \u003cem\u003eBradyrhizobium\u003c/em\u003e showing the highest relative abundance. The alpha-diversity indices varied among the samples, while the beta-diversity analysis revealed significant differences in the bacterial community composition between the counties. Correlation analysis further revealed associations between selected soil variables and the distributions of dominant bacterial taxa and rhizobial genera. Cowpea farm soils in semiarid eastern Kenya harbor distinct bacterial communities associated with farm-level edaphic heterogeneity and measurable compositional turnover across sites. The detection of rhizobial genera across farms suggests the potential ecological relevance of native bacterial populations in these systems. This study provides a baseline for understanding the soil bacterial microbiome structure in Kenyan cowpea production systems and supports the need for site specific, microbiome-informed approaches for sustainable soil fertility management.\u003c/p\u003e","manuscriptTitle":"Association of Soil Physicochemical Properties with Bacterial Microbiome Structures in Cowpea Farms from Semiarid Eastern Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 05:17:46","doi":"10.21203/rs.3.rs-9264911/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d09bf8a-5e96-4cd6-b145-71b58b3c37bb","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65570062,"name":"Biological sciences/Ecology"},{"id":65570063,"name":"Earth and environmental sciences/Ecology"},{"id":65570064,"name":"Earth and environmental sciences/Environmental sciences"},{"id":65570065,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-06T12:43:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 05:17:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9264911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9264911","identity":"rs-9264911","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}