Long-term Push-pull Cropping System Shifts Rhizosphere and Maize-root Microbiome Diversity Paving Way To Resilient Farming System

doi:10.21203/rs.3.rs-3345474/v1

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Long-term Push-pull Cropping System Shifts Rhizosphere and Maize-root Microbiome Diversity Paving Way To Resilient Farming System

https://doi.org/10.21203/rs.3.rs-3345474/v1

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Background: The rhizosphere biota consists of a complex assembly of microbial communities and other organisms that vary significantly across farming systems and influence soil health and plant productivity. How different cropping systems manipulates soil and plant microbiomes is little understood. In this study, we investigated soil physicochemical properties, rhizosphere and maize-root microbiomes in an agroecological cereal-legume companion cropping system known as push-pull technology (PPT) which has been in use in farmer fields for over two decades for insect-pest management, soil health improvement, and weed control in sub-Saharan Africa. We compared this with the maize-monoculture (Mono) cropping system.

Results: PPT cropping system positively impacted the abundance and diversity of soil and maize-root microbial communities and influenced soil physicochemical characteristics compared to Mono. Specific genera of both bacterial and fungal communities drove the variation in diversity of the microbial communities in these cropping systems. Soil and maize-root from the PPT cropping system had enriched Trichoderma, Mortierella, and Bionectria fungal genera while Streptomyces, RB41, and Nitrospira, among other bacterial genera linked to essential ecosystem services like plant protection, decomposition, carbon utilization, production of bioinsecticides, nitrogen fixation, nematode suppression, phytohormone production, and bioremediation. Bacterial from the genus Bryobacter linked to plant pathogenesis was more abundant in Mono-root. Similarly, fungal genera such as Gibberella, Neocosmospora, and Aspergillus, linked to plant pathogenesis and food contamination, were abundant in Mono. Notable differences were observed in the overall diversity of metabiome functional protein pathways, such as syringate degradation, L-methionine biosynthesis I, and inosine 5'-phosphate degradation.

Conclusion: Push-pull cropping system improves soil physicochemical properties and shifts soil and maize-root microbiomes in farmer fields in favour of microbial communities involved in important ecological services. The current findings add to the diversification of ecosystem services provided by this cropping system where it is practiced and contributes to the system’s resilience and functional redundancy. Underpinning the mechanism through PPT affects the soil and maize-root microbial communities – whether it is through the influence of plant metabolites from the intercrop root exudates or is the alteration of the soil nutritional status which affects microbial enzymatic activities should be the future focus.

Agroecosystem sustainability

Cropping system

Ecosystem services

Soil and maize-root microbiomes

Soil health

To feed the growing world population, agricultural intensification in staple food crop production such as maize, wheat, and rice has been increasing, leading to increased food security [1]–[3]. However, this intensification has also had negative environmental consequences, including increased greenhouse gas emissions, nutrient leaching, soil erosion, and a decline in biodiversity [2], [4], [5]. Ecological diversification that prioritizes environmental quality and preserves beneficial organisms is needed to mitigate these impacts [6]–[8]. Diversification involves agronomic practices that improve productivity while maintaining long-term stability and resilience and supporting ecosystem services [9], [10]. Intercropping, where farmers grow two or more crops together in the field, is one such diversification strategy that has been shown to restore ecosystem services and revitalize soil and biodiversity while improving crop yields [2], [11]–[13].

One such intercropping system that has gained traction in sub-Saharan Africa (SSA) is the push-pull technology (PPT) that has been adopted by thousands of smallholder farmers in East and Southern Africa [14]–[16]. Push-pull is an agroecological companion cropping system where the main crop (maize or sorghum) is intercropped with a leguminous plant (Desmodium spp.) which serves as an insect-repellent (push) and a grass (Napier or Briachiaria) planted as a border crop to attract stemborers and other herbivores away from the main crop (pull) [16]–[18]. The push-pull cropping system being a perennial legume-maize intercrop is likely to impact the soil and maize-root microbial communities strongly. The PPT cropping system utilizes volatile chemical mediated tri-trophic interactions where volatile signals emitted by the leguminous plant create an unfavorable environment for oviposition by insect-pests such as Busseola fusca, Chilo partellus and recently Spodoptera frugiperda [15], [19]–[21]. Desmodium spp. volatiles are also known to recruit the pests’ natural enemies into the cropping system [19], [22]. The trap crop suppresses the larval development of the insect pest upon hatching from the oviposited eggs [16], [19], [23]. Additional ecological benefits of using PPT include reducing the use of chemical pesticides and controlling the parasitic weed (Striga hermonthica) through the allelopathic effects of the root exudates of the Desmodium spp. [16], [22], improved soil health through nitrogen fixation by the leguminous Desmodium spp., carbon sequestration, solubilization of phosphorus, addition of organic matter, reduction of mycotoxins and other plant pathogens in soil and maize [3], [24]–[26]. These changes in the soil properties have had positive plant-soil feedback, contributing to increased yield on farms practicing PPT [27]. Thus, this cropping system has been shown to provide diverse ecosystem services, some of which are immediate and well-pronounced, like insect-pest reduction and yield improvement, and long-term effects like the positive plant-soil feedbacks [27], [28].

Soil microbial communities contribute to plant health and yield through root-mediated mechanisms [29]. Plant-associated microbial communities improve productivity and health by ensuring nutrient availability, stress tolerance, disease resistance, and biodiversity enhancement [30]. The genotype of a plant determines its root-associated microbiota, and the plant can shape the belowground microbiome by supporting or suppressing local microbial populations [2], [5]. Plants use root exudates to actively modify rhizosphere microbial populations favoring beneficial microbes [31], [32]. Legume-based intercropping can improve soil health and optimize the soil's physicochemical properties, including increasing microbial abundance and organic matter, controlling soil erosion, and improving soil structure [30], [33], [34]. Thus, intercropping with legumes positively affects the soil's chemical properties, including the soil organic carbon (SOC) concentration, nutrient content, and cation exchange capacity (CEC) [2], [25]. Legumes can also change the rhizosphere's pH, affecting soil microbe activity [30]. Previous studies have shown that cereal-legume intercropping systems increase soil microbial biomass and nutrient availability, [2] particularly nitrogen, phosphorus, and carbon [33]. Wheat (Triticum aestivum) or maize (Zea mays)-faba bean (Vicia faba), [35] and durum wheat (Triticum turgidum durum)-chickpea (Cicer arietinum) or lentil (Lens culinaris), [5] maize-peanut (Arachis hypogaea), [36] intercropping has been shown to increase overall microbial diversity. The microbial communities of Aspergillus, Gibberalla, and Bryobacter are pathogens to soils and plants in monoculture cropping systems, supporting the hypothesis that healthy intercrop soils have more beneficial microorganisms that improve soil nutrients, plant development and disease management [34], [37]–[39]. In this study, we assessed the impact of the PPT cropping system on the rhizosphere and maize-root microbiome in smallholder farmers’ fields (SHFF) and the influence of soil physicochemical parameters on belowground microbial communities where there are variable climatic and edaphic factors.

We hypothesized that the PPT cropping system shifts rhizosphere soil and maize-root microbiome and influences soil physicochemical parameters in favor of ecologically essential groups compared to the maize-monoculture (Mono) cropping system. While the components and aboveground multitrophic interactions of the PPT cropping system and the underlying mechanisms have been studied to a greater extent, the belowground multitrophic interactions, including their impact and interactions with soil and root microbiome, have yet to attract similar attention. Therefore, it is crucial to investigate the impact of this functional cropping system on the soil and maize-root microbiome and the cascading effects that may have on the aboveground tri-trophic interactions.

Sampling site

The study was conducted in three counties in Western Kenya, selected based on the presence of push-pull farms. Rhizospheric soil and maize-root samples were collected from three Counties – Vihiga (S 0° 1' 53.06'' E 34° 34' 0.05''; N 0° 0' 42.85'' E 34° 35' 29.6''), Siaya (N 0° 2' 26.8'' E 34° 18' 19.5''; S 0° 0' 0.23'' E 34° 16' 13.23''), and Bungoma (N 0° 37' 12.8'' E 34° 33' 55.2''; N 0° 35' 01.3'' E 34° 36' 14.4'') (Additional file 1: Fig.S1), respectively, where different maize cropping systems, including push-pull and Mono farm fields, are already established. The samples were collected from 18 SHFF (9 each with PPT and Mono cropping systems) which shared comparable agronomic management practices without the use of pesticides or fertilizers and had minimal cultivation for weed management. The Mono and PPT farms had a similar ground cover, except that Desmodium spp. dominated the PPT farms. The three counties in the study area experience bimodal rainfall, with a long rainy season from March to August and a short rainy season from October to December. The Western Kenya region has a hot and humid climate, abundant sunshine throughout the year, and an average daily temperature of around 25±2 °C. There were altitudinal and rainfall variations among the selected counties, with Vihiga at 1594.28 meters above sea level (masl), and rainfall range of 1800-2000 mm per annum (p.a); Siaya at 1140 masl, and a rainfall range of 1200-1800 mm p.a, and Bungoma at 1385 masl and rainfall range of [15], [40] 1102-1800 mm p.a.

Sample collection

Rhizospheric soil and maize-root samples were collected from 9 farms of each PPT and Mono cropping systems in different SHFF when the maize plants were in the late vegetative development stage (V5-6), about five to six weeks old. Soil sampling was done randomly between rows and pooled. Ten samples were collected per SHFF (about 5-20 cm depth) using a soil auger after cleaning the surface organic matter around the rows of the plants. The collection was halfway between the Desmodium spp. and maize rows on PPT farms and the same distance between maize rows in Mono. Soil samples were placed separately in a 20 mL centrifuge tube (Thermo Fisher Scientific Inc., California, USA), placed in a cool box with ice packs, and immediately transported to the laboratory at the International Centre of Insect Physiology and Ecology (icipe), Nairobi, Kenya where they were stored at -80 °C until needed for DNA extraction. A subset of soil samples was kept in brown paper Khaki bags (Paper Bags LTD, Nairobi, Kenya) and used later for soil physicochemical parameters analysis. Maize-root samples from ten plants located next to the soil cores were collected from the root systems to a depth of approximately 10 cm in each sampled plot. The maize-roots were rinsed to remove soil debris and surface sterilized by immersing them in 70% ethanol and 1% sodium hypochlorite for 1 minute each, followed by washing six times in sterilized distilled water. Sterile blotting paper was used to dry the maize-root samples, prior to storing at -80 °C pending DNA extraction. Soil physicochemical parameters was determined following standard protocols detailed by Chen et al. [41] and Okalebo et al. [42].

Genomic DNA extraction

Based on a method used by Howard et al. [43], high-throughput amplicon sequencing of the V3-V4 region of the 16S rDNA gene regions and internal transcribed spacer (ITS1-ITS2) gene regions were used for bacterial and fungal metagenomics sequencing. PureLink^TM Microbiome DNA purification kit (Thermo Fisher Scientific Inc., California, USA) was utilized for the extraction of soil total genomic deoxyribonucleic acid (gDNA). The manufacturer’s instructions were followed, including extracting each sample twice and mixing supernatants to get more DNA. Power Bead Tubes were filled with 0.25g of soil and vortexed for 10 seconds. The lysis buffer was mixed using a vortex mixer, and the bead tube was then heated for 10 minutes at 65 °C and homogenized by vortexing at 14,000 rpm for 5 minutes. The supernatant was treated using spin columns, and the DNA was extracted using Tris-HCL. The quality of the extracted DNA was analyzed using a Thermo Scientific Nanodrop 2000 (UV-Vis spectrophotometer) and gel electrophoresis. The DNA samples were stored at -80 °C until further processing [41].

The maize-root was lyophilized for 48 h and ball-milled into a fine powder for gDNA extraction. After that, a plant DNA extraction kit ((Isolate II) Thermo Fisher Scientific Inc., California, USA) was used to extract DNA from 0.25g of maize-root (dry weight). Briefly, 0.25g of powdered maize-root sample was put into lysis buffer-filled bead tubes and vortexed for 10 seconds. The mixture was centrifuged for two minutes at 14,000 rpm. After discarding the pellet, the supernatant was centrifuged on a spin column to extract DNA following the manufacturer’s instructions. A Thermo Scientific NanoDrop^TM 2000 (UV-Vis spectrophotometer) and gel electrophoresis were used to assess the quality and size of the extracted DNA. The DNA samples were stored at -80 °C until further processing [44].

PCR, library preparation, and sequencing

Metagenome amplicon sequencing targeting V1-V4 regions of 16s rDNA gene and ITS1-ITS2 regions of ITS gene was carried out at Macrogen Europe in the Netherlands using the MiSeq (Illumina) instrument following the manufacturers instruction. The 16S rRNA (bacterial) gene amplicon library was generated using PCR primers targeting the V1-V2 regions (27F` GAGTTTGATCMTGGCTCAG, 338R` GCTGCCTCCCGTAGGAGT) and V3-V4 regions (341F` CCTACGGGNGGWGCAG and 805R` -GACTACHVGGGTATCTAATCC), while ITS gene (fungal) was amplified using PCR primer pairs targeting the ITS1 and ITS2 regions (ITS1F` CTTGGTCATTTAGAGGAAGTAA, ITS2R` -GCTGCGTTCTTCATCGATGC) and ITS3-ITS4 regions (ITS3F` GCATCGATGAAGAACGCAGC ITS4R` -TCCTCCGCTTATTGATAGC). The libraries were sequenced using a sequencing mode which was set to paired-end two 300-cycle sequencings and a 600-cycle v3 sequencing kit was used.

Statistical analyses

The data for soil physicochemical characteristics were tested for normality using the Shapiro-Wilk test. Because the data was normally distributed, parametric statistical tests were used. One-way analysis of variance (ANOVA) was used to examine the soil characteristics base on cropping systems. Where differences existed, a Tukey HSD test was used to analyze the differences in soil characteristics [41]. A Pearson correlation coefficient analysis detected the relationships between soil conditions by different cropping systems. The soil physicochemical properties' profile was visualized using non-metric multidimensional scaling (NMDS) for the two cropping systems, PPT and Mono. A one-way ANOSIM test was conducted using the Bray-Curtis dissimilarity matrix to compare the differences between these cropping systems. All statistical analyses were carried out using R software (v4.1.2) [45].

Bioinformatics

Raw reads were processed using nf-core/ampliseq (v2.4.0) (https://github.com/nf-core/ampliseq) metagenomic amplicon pipeline deployed using nextflow (v21.10.3) and singularity (v3.6.3) [46]. Sequence quality control was assessed using FastQC (v0.11.6). Poor reads and primers were trimmed using Cutadapt (v.4.1) [47]. The Divisive Amplicon Denoising Algorithm 2 (DADA2) analysis workflow option of the nf-core/ampliseq pipeline was used to assess soil and maize-root prokaryotic and mycobiome communities. The DADA2 (v1.26.0) analysis workflow was used to denoise, preprocess, infer amplicon sequence variants (ASVs), and assign taxonomy of the clean 16s rRNA and ITS sequence reads [48]. The DADA2 functions parameters used to perform further trimming included truncLen=180, trunc_qmin=25, trunc_rmin=0.75, max_ee=2, min_len=50, and maxN=0. After preprocessing, the reads were dereplicated using the “derepFastq” and “dada” functions, inferring amplicon sequence variants (ASVs) and their counts. The “concatenate_reads,” “sample_inference,” and “removeChimeraDenovo” functions were applied to remove spurious and chimeric ASVs. The “dada_ref_taxonomy” function utilized the “silva=138” database to assign taxonomy to 16s bacterial ASV [49] and “unite-fungi=8.3” to assign taxonomy to ITS fungal ASVs [50]. The ASV abundance tables and ASV taxonomic classification output files obtained from the DADA2 pipeline were used for downstream data exploration, statistical analysis, and visualization in R (v4.2.1). The Basic Rapid Ribosomal RNA Predictor (Barrnap) (v0.9) [51] utilized the ASV nucleotide base sequences to classify them into various categories, which include eukaryotes, archaea, bacteria, chloroplast, and mitochondria [52]. The ASV filtering for bacteria and fungi was done to exclude all archaea, eukaryotic, chloroplast and mitochondria. The soil and maize-root microbial communities were determined by merging the ASV abundance matrix and taxonomy table generated by the DADA2 analysis workflow with the metadata file containing descriptions, treatments, and conditions for each sample collected, forming a phyloseq object. The phyloseq (v1.41) package was used to determine the differential abundance of microbes under different conditions of alpha and beta diversity [46]. The metagMisc (v0.0.4) [53] package was utilized to manipulate the phyloseq object for visualization of the microbiome abundances and percentages in the sample types (soil and maize-root), cropping systems (PPT and Mono), and sampling locations (Bungoma, Siaya, and Vihiga).

Alpha and beta diversity

The Microbiota Process (v1.9.3) function was used to analyze the species richness and diversity of soil and maize-root prokaryotic and mycobiome communities by performing rarefaction analysis on the ASV read counts [54]. Alpha diversity metrics Chao1, Evenness, and Shannon indices were used in this analysis. A Principal Coordinate Analysis (PCoA) was used to identify the microbial contributors and their variation in beta diversity [55]. Venn diagrams were then created to display the shared microbial communities among the sample types, cropping systems, and sampling locations using the Venn Counts function from the limma package [56]. Permutational Multivariate Analysis of Variance (PERMANOVA) was used to compare microbial populations on the different sample types, cropping systems, and locations [55], [57]. The diff_analysis and ggdiffclade functions of the Microbiota Process utilized the phyloseq object to determine biomarkers and visualize the microbes (fungal and bacterial) at the genus level.

Differential expression of soil and maize-root bacterial protein functions in push-pull and maize-monoculture cropping systems

The BIOM file was utilized for the phylogenetic investigation of communities by reconstructing unobserved states (PICRUST2) (v2.5.0) tool to predict microbial functions in the soil and maize-root. The PICRUSt2 pipeline (v2.4.1) was utilized to predict functional abundances based on ASV marker gene sequences. The resulting bacterial abundances were classified using the enzyme commission classification and statistically compared across cropping systems, sample types, and sampling locations. Phylogenetic heat maps of pathways were visualized using the STAMP software (v2.1.3) for further analysis [58], [59].

Soil physicochemical properties from push-pull and maize-monoculture cropping systems

Soil pH, organic carbon, nitrogen, and potassium from PPT soil were significantly different compared to Mono cropping system soil (ANOVA; P < 0.001; Table 1). However, there was no significant difference between PPT and Mono cropping systems among the other soil parameters. The PPT soil texture was sandy loam to sandy clay loam with characteristics ranging from sand, 21.77-74.19%; silt, 10.92-34.38%; and clay, 14.89-43.85%. The soil texture for the Mono cropping system was loamy sand, sandy loam to clay loam with soil characteristics ranging from sand, 36.78-86.53%; silt, 8.48-39.23%; and clay, 3.99-38.43%.

Table 1: Comparison of soil physicochemical properties of push-pull and maize-monoculture cropping systems in smallholder farmer fields

Soil properties	Cropping systems		F Value	P Value
Soil properties	PPT	Mono	^(1,18)	P Value
pH	5.673±0.071^a	4.938±0.099^b	36.580	<0.001
EC	0.081±0.016^a	0.051±0.011^a	2.329	>0.144
P	22.999±5.024^a	16.647±2.233^b	1.335	<0.001
K	91.981±7.631^a	83.324±11.124^a	0.412	>0.412
Na	9.699±0.187^a	9.691±0.253546^a	0.001	>0.980
Ca	808.180±123.026^a	802.239±130.529^a	0.001	>0.974
Mg	137.299±18.709^a	136.470±19.357^a	0.001	>0.976
Fe	110.892±8.929^a	104.926±13.713^a	0.133	>0.721
Mn	208.095±33.248^a	180.074±29.832^a	0.393	>0.538
Cu	2.552±0.429^a	1.978±0.253^a	1.330	>1.332
Mo	0.020±0.001^a	0.020±0.001^a	2.250	>0.151
Zn	3.585±0.649^a	3.665±1.121^a	0.004	>0.954
B	1.425±0.012^b	1.458±0.010^a	4.453	<0.010
S	11.325±1.376^a	8.810±1.069^a	2.083	>0.166
N	0.146±0.009^a	0.068±0.013^b	23.400	<0.001
OC	1.319±0.132^a	0.693±0.146^b	10.170	<0.001
EA	1.180±0.131^a	1.060±0.109^a	0.498	>0.489
ESP	0.972±0.166^a	0.927±0.126^a	0.046	>0.832

PPT, push-pull technology; Mono, maize-monoculture cropping system; pH, potential of hydrogen; EC, electrical conductivity; P, phosphorus; K, potassium; Na, sodium; Ca, calcium; Mg, magnesium; Fe, iron; Mn, manganese; Cu, copper; Zn, zinc; B, boron; Mo, molybdenum; S, sulphur; N, nitrogen; OC, organic carbon; EA, exchangeable acidity; ESP, exchangeable sodium percentage. The different letter indicates a significant difference between the two cropping systems (Significant effects at P < 0.05 are shown in bold, Tukeys honest significance test (HSD)).

Within the PPT field, several soil properties such as sulphur, nitrogen, organic carbon, exchangeable acidity, pH, electrical conductivity, potassium, and iron exhibited positive correlations. They displayed distinct characteristics within this cropping system. These findings suggest that PPT significantly influences these soil properties and remains advantageous for farm mechanization, while the Mono cropping system does not impact them. Conversely, in the Mono field soil properties like boron, molybdenum, calcium, and exchangeable sodium percentage were more abundant (Fig. 1A). Notably, PC1 and PC2, accounting for 34% and 17.6% of the total variance, respectively, played a vital role in elucidating the interactions between the selected soil properties.

The Correlograms (Additional file 1: Fig. S2) showed the relationships and correlations between the different soil physicochemical properties in the PPT and Mono cropping systems.

The non-metric multidimensional scaling (NMDS) plot using the Bray-Curtis similarity index revealed no significant difference between the soil physicochemical properties tested in PPT and Mono (One-way ANOSIM: P = 0.9591, R = -0.078; Stress value = 0.1894) (Fig. 1B) cropping systems. However, the analysis of similarities (ANOSIM) showed that certain soil physicochemical properties contributed to most of the differences between the PPT and Mono cropping systems. Interestingly, the properties that contributed positively to the differences in the PPT cropping system compared to Mono are as follows: calcium (59.20%); manganese (16.34%); magnesium (9.59%); iron (6.02%); potassium (5.18%); phosphorus (1.87%); sulphur (0.69%); zinc (0.40%); copper (0.16%); sodium (0.11%); pH, (0.11); electrical conductivity (0.11%); and organic carbon (0.11%) (Fig. 1C). These properties collectively accounted for the observed differences between the two cropping systems (Fig. 1A-C).

Soil and maize-root microbiome composition and abundance in push-pull and maize-monoculture cropping system fields

Profiling bacterial community yielded 5,714,532 high-quality sequences, averaging 150,382.4 per sample and a range of 237,684 to 80,956. A total of 5,466,660 high-quality sequences were obtained for the fungal sequences, with a mean of 165,656.4, and each sample generated a read between 233,381 and 123,338. However, after the rerafaction of the sequences and removal of non-fungal and non-bacterial sequences, we found 3,953 fungal and 7,556 bacterial amplicon sequence variants (ASVs) in all soil and maize-root samples.

Taxonomic profiles of belowground fungal and bacterial communities

The differences between the maize-root and soil bacterial and fungal genera were evident in the ASVs across all samples. We found the 30 most abundant bacterial genera communities in PPT and Mono cropping systems (Additional file 1: Figs. S3 and S4). The order of abundance in PPT (soil and maize-root) compared to the Mono (soil and maize-root) cropping system was as follows Enterobacter>, Sphingomonas>, Candidatus Udaeobacter>, Sphingobium>, RB41>, Stenotrophomonas>, Streptomyces>, Nitrospira> and >Mitsuaria (Fig. 2; Additional file 1: Table S1). Pseudomonas>, Bryobacter>, Conexibacter>, Acidothermus> and >Pantoea were the most abundant genera in the Mono cropping system compared to PPT. However, the interaction between sample type (soil and maize-root), and PPT and Mono cropping system (PPT soil and PPT maize-root, and Mono soil and Mono maize-root) had varying effects on the impact of the bacterial genera communities (Fig. 2; Additional file 1: Table S2). Regarding study counties, the most enriched bacterial genera in Bungoma in the order of abundant were Pseudomonas>, Flavobacterium> and >Nocardioides compared to Siaya and Vihiga. Streptomyces>, Bacillus>, Sphingobium> and >RB41 were the most abundant genera in Siaya compared to the other two counties. Candidatus Udaeobacter>, Bradyrhizobium>, Enterobacter>, Sphingomonas>, Nitrospira> and >Stenotrophomonas were more abundant in Vihiga compared to Bungoma and Siaya counties (Additional file 1: Fig. S5; Table S3). However, PPT positively impacted bacterial genera in different sample types between the cropping system and study locations. Burkholderia-Caballeronia-Paraburkholderia were more abundant in Siaya PPT maize-root compared to the other cropping system, sample types, and study locations. Acidothermus> and >Pantoea were more abundant in Siaya Mono maize-root, and Conexibacter enriched in Siaya Mono soil (Fig. 2; Additional file 1: Table S4). Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium was more abundant in Vihiga PPT maize-root compared to the other cropping system, sample type, and study location. Enterobacter> and >Streptomyces were more abundant in Vihiga PPT soil. Bradyrhizobium> and >Stenotrophomonas were more abundant in Vihiga Mono maize-root. Arthrobacter> and >Nitrospira were more enriched in Vihiga Mono soil. Dyella> and >Ralstonia were more abundant in Bungoma PPT maize-root compared to the other cropping system, sample types, and study locations. Gaiella>, Nocardioides>, and >Sphingobium were more abundant in Bungoma PPT soil. Bryobacter> and >Sphingomonas were more abundant in Bungoma Mono soil. Pseudomonas were more abundant in Bungoma Mono maize-root compared to the other cropping system, sample type, and study location.

The most highly abundant species in PPT (soil and maize-root) compared to Mono (soil and maize-root) cropping system in the order of abundant were Rhizobium phaseoli>, Bacillus flexus>, Bradyrhizobium elkanii>, Paraburkholderia vietnamiensis>, Dyella marensis>, Enterobacter hormaechei>, Herbaspirillum seropedicae>, Pseudomonas nitroreducens>, Ralstonia pickettii>, Sphingomonas paucimobilis>, Stenotrophomonas maltophilia>, and >Variovorax paradoxus.

In fungal genera communities, the most abundant genera in PPT (soil and maize-root) in the order of abundant were Mortierella>, Spiromyces>, Bionectria>, Clitopilus>, Marasmius>, Trichoderma>, and >Ramicandelaber compared to Mono (soil and maize-root) cropping system. Gibberella>, Similiphoma>, Neocosmospora>, Aspergillus>, and >Psathyrella were more abundant in the Mono cropping system compared to PPT (Fig. 3; Additional file 1: Table S5). Candenascus, Xepicula, and Chloridium had the same abundance in PPT and Mono cropping systems. However, the interaction between cropping systems (PPT and Mono) and sample type (PPT soil and PPT maize-root, and Mono soil and Mono maize-root) had varying effects on the impact of the fungal genera communities. Arachnion>, Bionectria>, Spiromyces>, and >Trichoderma were more enriched in PPT soil, while Exophiala>, Marasmius>, and >Poaceascoma were the most abundant in PPT maize-root compared to Mono soil and Mono maize-root (Fig. 3; Additional file 1: Table S6). However, Aspergillus>, Condenasus>, Neocosmosphora>, Parafabraea> and >Xepicula were more abundant in Mono soil. At the same time, Curvularia>, Psathyrella>, and >Similiphoma were more abundant in Mono maize-root compared to PPT soil and PPT maize-root. The three study counties had varying effects on the impact of fungal genera communities (Additional file 1: Fig. S6; Table S7). However, the impact of the fungal genera was felt differently when PPT and Mono cropping systems interacted with the sample types, study locations, and cropping system (Fig. 3; Additional file 1: Table S8).

In 90% of the sample ASVs, the most prevalent and distinguishing genera were present, with a 75% prevalence in each ASV. A Venn diagram showed that PPT had one unique bacterial genus compared to the Mono cropping system. The two cropping system shared three overlapping bacteria genera. When comparing cropping systems, and sample type interaction, PPT soil and PPT maize-root had 18 and eight unique bacteria, respectively, compared to Mono soil and Mono maize-root, which had 11 and one bacterium taxon (Fig. 4). In terms of the studied counties, Bungoma and Vihiga had two different unique bacteria genera. In contrast, Siaya had only one unique bacteria genus. PPT had seven individual taxa in fungal genera communities, while the Mono cropping system had six. Push-pull and Mono cropping systems shared 11 overlapping fungal genera. Between sample and soil type, and cropping systems interaction, PPT soil and PPT maize-root had eight and one unique genus, respectively, compared to Mono soil and Mono maize-root, which had five and one genus (Fig. 5). In study locations, Vihiga had six unique fungal genera compared to Bungoma and Siaya.

Alpha diversity of rhizosphere and maize-root microbiomes in push-pull and maize-monoculture cropping system fields

There was no significant difference in bacterial genera communities between PPT (soil + maize-root) and Mono (soil + maize-root) cropping systems in richness (Chao1 estimator, P = 0.820) and evenness (Shannon index, P = 0.390) (Fig. 6). However, a significant differenace was found in richness and evenness between cropping system, sample, and soil type interaction (Choa1 estimator, PPT-soil vs Mono-root P < 0.001; PPT-soil vs PPT-root P < 0.001; Mono-soil vs Mono-root P < 0.001; Shannon index, PPT-soil vs Mono-root P < 0.001; PPT-soil vs PPT-root P < 0.001; Mono-soil vs Mono-root P < 0.001). There was no significant difference in richness and evenness between PPT and Mono-soil (Choa1 estimator, P = 0.730; Shannon index, P = 0.140). There was no significant difference between the studied locations in richness and evenness (Chao1 estimator, Bungoma vs Vihiga P = 0.340; Bungoma vs Siaya P = 0.490; Vihiga vs Siaya P = 0.690; Shannon index, Bungoma vs Vihiga P = 0.18; Bungoma vs Siaya P = 0.570; Vihiga vs Siaya P = 0.370).

The fungal genera communities did not differ in richness (Chao1 estimator, P = 0.930) and evenness (Shannon index, P = 0.71) between PPT (soil + maize-root) and Mono (soil + maize-root) cropping systems (Fig. 7). However, there was a significant difference in the fungal community in richness and evenness between cropping systems sample and soil type interaction (Choa1 estimator, PPT-soil vs PPT-root P < 0.001; PPT-soil vs Mono-root P < 0.001; Mono-soil vs Mono-root P < 0.001; Shannon index, PPT-soil vs PPT-root P < 0.001; PPT-soil vs Mono-root P < 0.002; Mono-soil vs Mono-root P < 0.003). There was no significant difference between the cropping system, sample, and soil type interaction in richness (Choa1 estimator, PPT-soil vs Mono-soil P = 0.160 and Shannon index, PPT-soil vs Mono-soil P = 0.073). There was no significant effect in richness between studied locations (Choa1 estimator, Bungoma vs Siaya P = 0.180; Vihiga vs Siaya P = 0.560; except Bungoma vs Vihiga P = 0.034). There was a significant effect in evenness between studied locations (Shannon index, Bungoma vs Vihiga P = 0.056; Bungoma vs Siaya P = 0.050; except Vihiga vs Siaya P = 0.940).

Beta diversity and the influence of push-pull and maize-monoculture cropping systems on soil and maize-root microbial communities

The β-diversity of fungal and bacterial communities was compared between PPT and Mono cropping systems through visualization and quantification of soil and maize-root microbiomes community (Bacterial and fungal) clustered by cropping systems (Figs. 8 and 9). The differences in microbial composition between the different sample types and studied locations that were conditioned by the two cropping systems were found to be statistically significant (PERMANOVA comparisons: sample types and cropping systems of bacteria, R² = 0.337, P < 0.001 and fungi R²= 0.160, P < 0.001; locations, sample types and cropping systems of bacteria R² = 0.524, P < 0.001 and fungi R² = 0.352, P < 0.001; study locations of bacteria R² = 0.071, P > 0.05 and fungi R² = 0.991, P < 0.001; Additional file 1: Tables S9 (bacteria) and S10 (fungi)). However, there was no significant difference in the PPT and Mono cropping systems (PERMANOVA comparisons: bacteria R² = 0.018, P > 0.05 and fungi R² = 0.032, P > 0.05; Additional file 1: Tables S9 (bacteria) and S10 (fungi)). At the genus level, no differences in the abundance of taxa were found between bacteria and fungi microbial communities of PPT and Mono cropping systems. The soil and maize-root bacterial communities of the sample type cropping systems were distinctly separated along axis 1, and we observed a subtle clustering by PPT and Mono cropping system and sample type along axis 2. The soil bacterial communities tended to group themselves through the intensity of sample-type interaction (Fig. 8). A comparable trend was observed in the soil and maize-root fungal communities (Fig. 9).

Differential expression of bacterial protein functions in soil and maize-root

The prediction of sequences associated with significant functional metabolic pathways in PPT and Mono cropping systems across different sample types and locations revealed the following six most abundant pathways: PWY-5695 (inosine 5-phosphate degradation), theocat-PWY (superpathway of L-threonine metabolism), gallate-degradation-I-PWY (gallate degradation II), P221-PWY (Octane oxidation), 3-hydroxyphenyllacetate-degradation, and biotin-biosynthesis PWY. Based on hierarchical clustering, the different cropping systems, samples, and locations were grouped into two main clades. In the first clade, Bungoma push-pull soil (BPS), Siaya maize-monoculture soil (SMS), and Siaya push-pull soil (SPS) clustered together. Similarly, Bungoma maize-monoculture soil (BMS), Vihiga maize-monoculture soil (VMS), and Vihiga push-pull soil (VPS) formed another cluster. In the second clade, Bungoma maize-monoculture root (BMR) clustered separately, while Siaya maize-monoculture root (SMR) and Siaya push-pull root (SPR) clustered together. Vihiga push-pull root (VPR), Bungoma push-pull root (BPR), and Vihiga maize-monoculture root (VMR) were also grouped (Fig. 10).

Based on the prediction of sequences associated with major functional metabolic pathways, the ten most abundant pathways were identified in the two cropping systems: PWY-5695 (inosine 5'-phosphate degradation), theocat-PWY (superpathway of L-threonine metabolism), gallate-degradation-I-PWY (gallate degradation II), lipasyn-PWY (phospholipases), P161-PWY (acetylene degradation (anaerobic)), PWY-5088 (L-glutamate degradation VIII), lactosecat-PWY (lactose degradation I), homoser-metsyn-PWR (L-methionine biosynthesis I), PWY-6386 (syringate degradation), and P221-PWY (Octane oxidation). Regarding the cropping systems, two hierarchical clustering clades were observed. The first clade consisted of push-pull soil (PS) and maize-monoculture soil (MS), while the second clade consisted of push-pull root (PR) and maize-monoculture root (MR) (Fig. 11).

The use of push-pull cropping system by small-scale farmers positively impacted soil physicochemical properties, rhizosphere, and maize-root microbial communities. The positive association of PPT was demonstrated by the enhancement of soil OC, pH, P, N, and B, and the presence of ecologically important below-ground microbial groups involved in the improvement of soil fertility, decomposition, siderophore production, high carbon sequestration, nutrient cycling, and plant protection in comparison to the Mono cropping system. These findings can be linked to agroecosystem functions and other ecosystem services, including soil health and maize yield. We discuss our results in light of PPT’s belowground ecosystem services provision including soil physicochemical properties, microbiome abundance and diversity, and system’s role and function in sustainable maize production.

Effect of soil physicochemical properties on soil health and maize-root microbiome

The health of rhizosphere soil is crucial for maintaining the stability of microbial communities. Previously, multiple cropping systems have been shown to influence soil characteristics like pH, soil organic carbon content, and nitrogen compared to Mono [41]. In this study, we found higher significant levels of pH, OC, N, and P from PPT soil in comparison to Mono soil cropping systems. A pH below 5.5 can negatively affect plants and significantly threaten the agroecosystem [60]. Given that pH in PPT was higher than in Mono, we infer that PPT is a good cropping system in terms of soil health [25], [30]. Frac et al. [61]reported that biotic and abiotic factors, such as soil pH, structure, and nutrient levels influence soil microbes' diversity and activity. Our findings on the possible influence of cropping systems on above and belowground abiotic and biotic factors are consistent with the predictions by Bennett et al. [3], [5], [60], [62]–[65] that crop diversification has a significant impact on belowground microbiomes, plant and soil health as well as production.

Effect and role of push-pull cropping system on soil and maize-root bacterial abundance

There is growing evidence suggesting that plant diversification can have an impact on below-ground microbiomes [6], [13], [41], [66], [67]. This study found that PPT cropping systems led to a higher diversity of rhizosphere soil fungal and bacterial communities compared to Mono cropping system soil. Notably, the high abundances of beneficial microbes like Sphingomonas, Bacillus, Enterobacter, RB41, Herbaspirillum, Nocardioides, Mitsuaria, Gaiella, Nitrospira, Burkholderia-Caballeronia-Paraburkholderia, Dyella, Enterobacter, Nitrospira and Conexibacter in both the soil and maize-root of PPT systemsindicates that PPT soil systems favor the proliferation of beneficial bacteria which improve crop performance and possibly contribute to pest management. Herbaspirillum is a nitrogen-fixing endophytic bacterium that colonizes plant roots and has been shown to positively impact plant growth, crop yields, and nitrogen content of plants [68]. Additionally, Bacillus and Enterobacter bacterial genera are potential biofertilizer agents due to their ability to perform functions such as solubilizing inorganic phosphate through the production of low molecular weight organic acids, nitrogen fixation, enhancing biological control, bioremediation, and plant growth promotion [69], [70]. Bacteria in the genera Sphingomonas, Gaiella, and Dyella play a vital role in promoting plant growth by producing phytohormones and/or inducing changes in phytohormone signaling through volatile organic compound (VOCs), decomposing lignocellulose, bioremediation of soil hydrocarbon-contaminated and contribute to nutrient cycling in agroecosystem fields [71]–[74]. Additionally, Sphingomonas possess distinctive capabilities, including the degradation of persistent contaminants, acting as bacterial antagonists to phytopathogenic fungi, and secreting highly beneficial gellan exopolysaccharides [75]. On the other hand, RB41 plays a critical role in regulating the soil carbon cycle and is involved in processing the metabolism of both organic and inorganic nitrogen sources [35], [41], [70]. Furthermore, according to Huang et al. [76], Burkholderia and Mitsuaria genera have a beneficial impact on drought resistance in plants. These bacteria accomplish this by reducing the levels of ethylene, a plant hormone, and producing 1-aminocyclopropane-1-carboxylic acid. Brewer et al. [77] stated that Candidatus Udaeobacter contributes to global hydrogen cycling by utilizing H₂. Lazcano et al. [78]found that Nocardioides spp. can act as biocontrol agents for bacterial leaf spots and promote plant growth. Similar observations in the enrichment of bacteria genera have been made on long-term intercropping systems; PPT experimental plots, multiple cropping systems, crop rotation, and cover cropping [7], [8], [79].

Soil and maize-root from PPT had a greater abundance of Streptomyces and Stenotrophomonas, which possess broad biotechnological potential, such as the ability to promote plant growth, production of bioactive secondary metabolites, VOCs and are promising candidates for biocontrol of phytopathogenic microbes [80], [81]. These characteristics may be attributed to their multiplication rate, ability to produce antibiotics and siderophores, controlled gene expression quorum detection, and synthesis of lipase, chitinase, cellulases, phytohormones, β-1,3-glucanase, and amino acids [82]. Streptomyces spp. can colonize plant root surfaces, survive in various soil types, and produce spores that allow them to persist in extreme conditions. Stenotrophomonas can absorb iron from siderophores and is a potential biocontrol agent against Ralstonia [83], [84]. The presence of these bacteria genera in PPT soils and maize-roots implies that PPT positively influences below-ground microbial populations compared to Mono. Similar findings have been observed in various other cropping systems, including long-term intercropping systems, push-pull experimental plots, multiple cropping systems, crop rotation, and cover cropping [7], [8], [31], [79]. Nitrospira were found more in PPT, and these types of bacteria are capable of carrying out nitrification, which involves the oxidation of ammonia in a single organism [3], [22], [25], [85]. This is different from other nitrifying bacteria, which require two different organisms to complete the process [7]. These findings imply that PPT influences maize-root microbial populations compared to Mono-root and affects maize-root and rhizosphere microbial communities. We also found that the presence of companion crops in a push-pull cropping system had a greater impact on PPT maize-root microbiota such as Streptomyces, Herbasoirillum, Stenotrophomonas, Sphingomonas, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium,and Dyella compared to Mono root cropping system. The presence of these beneficial bacteria in the push-pull maize-root may positively contribute to an increase in nitrogen nutrients, carbon sequestration, and biocontrol agent against plant pathogens. This, in turn, results in improved plant growth due to plant growth-promoting rhizobacterial (PGPR) and siderophores availability, which facilitates iron content in soil and plants from the PPT field. This may lead to higher yields in PPT fields compared to Mono fields. To better understand the role of different bacterial and fungal species, including those within the same genus, in this cropping system, it is necessary to perform species-level characterization. Conversely, the finding that Bryobacter, a disease-causing bacterial genus, was more abundant in Mono than in the PPT cropping system shows that Mono cropping systems potentially predispose crops to disease-causing agents.

The high abundance of beneficial bacterial species, including Rhizobium phaseoli, Bacillus flexus, Bradyrhizobium elkanii, Paraburkholderia vietnamiensis, Dyella marensis, Enterobacter hormaechei, Herbaspirillum seropedicae, Pseudomonas nitroreducens, Ralstonia pickettii, Sphingomonas paucimobilis, Stenotrophomonas maltophilia, and Variovorax paradoxus, in both the soil and maize-roots within the push-pull cropping systems indicates that this system promotes the proliferation of bacteria that enhance crop performance, improve soil health, water purification, and plant growth, and potentially contribute to insect-pests and disease management. Interestingly, Bacillus flexus possesses the ability to solubilize tricalcium phosphate and hydroxyapatite, making it valuable for biodegradation processes [86], [87]. Bradyrhizobium elkanii produces rhizobitoxine, which acts as a defense mechanism against stress-induced ethylene, and plays a significant role in nitrogen fixation [88]. ariovorax paradoxus and Pseudomonas aeruginosa have the ability to degrade and/or metabolize N-acyl-homoserine lactones (AHLs) as a carbon source [89]. Additionally, the study by Chen et al. [90] demonstrated the importance of the complete ethylene signal transduction pathway was essential for enhancing Arabidopsis thaliana growth through the PGPR, Variovorax paradoxus, underscoring the significance of ethylene signaling in the activity of different PGPRs. Stenotrophomonas maltophilia contributes to bioremediation and nitrogen fixation processes. Interestingly, it contributes to the sulfur cycle and promotes plant growth and health in ecosystems [84], [91]. Sphingomonas paucimobilis enhances antioxidant activity, promotes plant growth, and exhibits biodegradation capabilities [79], [92], [93]. Ralstonia pickettii demonstrates biodegradative abilities through siderophore production, while Pseudomonas nitroreducens produce biosurfactants and solubilizes phosphate [94]–[96]. Herbaspirillum seropedicae, an endophytic diazotrophic PGPR, colonizes various crops (rice, maize, sorghum, and sugarcane) and exhibits beneficial traits such as solubilize minerals, producing phytohormones and siderophores, and fixing atmospheric nitrogen [97], [98]. Enterobacter hormaechei has been identified as a potassium solubilizing microbe, showing potential for plant growth and controlling harmful algal blooms [99]–[103]. Dyella marensis produces biosurfactants and siderophores, while Paraburkholderia vietnamiensis and Rhizobium phaseoli have shown promise as nitrogen-fixing fertilizers for plant growth [104]–[106].

Abundance, effect,and role of push-pull cropping system on soil and maize-root mycobiome

Push-pull cropping system decreased the number of harmful fungal genera. Still, it increased the presence and abundance of beneficial belowground fungal such as Mortieralla, Exophiala, Paraboeremia, Bionectria, Clitopilus, Marasmius, Pyrenochaetopsis and Trichoderma compared to the Mono cropping system. The findings of these studies are in line with previous studies, which indicated that beneficial fungi have a positive impact on agroecosystem [31], [107]–[109]. For example, Mortierella spp. has been shown to solubilize phosphate, improve nutrient uptake, and impact soil microbiota. These endophytic fungi are effective in synthesizing phytohormones, which promote plant growth and support defense mechanisms in plants [71], [86]. Mortierella and Pyrenochaetopsis spp. which were more enriched in PPT, has been identified as important indicators of the soil-root microbiome continuum and predictors of tobacco [17], [31]. Khan et al. [17] observed that Exophiala spp. produces phytohormones and enzymes, promoting plant shoot growth under drought and salinity. Paraboeremia spp. has been demonstrated to increase plant biomass and glycyrrhizin content in Liquorice plants [110] and can parasitize eggs of the rice root-knot nematode, Meloidogyne graminicola, in in-vitro assays [32], [111]. Bionectria spp. has been shown to decompose plant debris, improve rhizosphere soil health, and act as biological control agents against insect-pests [107], [112]. The volatile antimicrobial compounds produced by this fungus have been reported to suppress plant pathogens and could be used as an effective biofumigant [113], [114]. Clitopilus spp. produces a biologically active compound, pleuromutilin, with strong and potent antimicrobial activity and increases plant growth through facilitative potassium uptake [115], [116]. We found Trichoderma spp. which are associated with colonizing the rhizoplane, rhizosphere, and plant roots, and produces metabolites with antimicrobial (volatile and non-volatile compounds, cellulose/lignin/cell wall degrading enzymes and antibiotics) and biostimulating properties (phytohormones and phytoregulators) [108], [117]. This fungus has direct and indirect biocontrol potential against soil phytopathogens, increases nutrient uptake solubility, and contributes to plant protection yield, and biofertilization production [118], [119]. Fungal spp. belongs to Ramicandelaber and Robillarda have been reported as decomposers, with Robillarda producing β-1,3/1,4-glucans that respond to disease resistance in plants [120], [121]. Therefore, beneficial fungal communities can be improved and harnessed through PPT.

Harmful fungal genera like Aspergillus, Gibberalla, Neocosmopora, and Curvularia were found more enriched in the Mono cropping system compared to PPT. Zearalenone is a powerful estrogenic mycotoxin that is produced by Gibberella spp. and is known to cause Gibberella ear rot (GER) in cereal crops like maize, oats, wheat, sorghum, rice, and barley [38], [122]. Neocosmospora has been identified by [100], [101] as a phytopathogen that causes stem rot in various crops and negatively affects potato growth and yield quality. In addition, this pathogen is a significant emerging disease that causes considerable economic losses for farmers due to stunted growth, leaf yellowing, and grayish-black stems [101], [123]. Fungal spp. belonging to Curvularia has been shown to be a devastating disease on cereal crops in the Poaceae family, causing Curvularia leaf spots in maize, which is an economically overwhelming disease [124], [125]. Similarly, Aspergillus has been reported as a dangerous mycotoxin that infects many fruits, cereal, and vegetable plants, causing several disorders in various crop and plant products, reducing seed germination, and root and shoot elongation [37], [126].

Soil and maize-root microbiome diversity influenced by push-pull and maize-monoculture cropping systems

While annual legume intercropping may temporarily affect belowground microbiome profiles, the impact of companion crop intercrops (Desmodium spp.) is expected to be stronger and more resilient, contributing to increased soil and maize-root microbial diversity [3], [79], [127]. Hence, we argue that the higher beta diversity of microbial communities in long-term push-pull compared to Mono cropping systems for both soil and maize-root bacterial and fungal populations could result from the baseline differences in the two cropping systems. Similar trends have been reported in other studies investigating cereal and legume intercropping systems, such as wheat-soybean, millet-mung bean, and maize/wheat-faba bean; push-pull experimental plots intercropping [7], [12], [63], [128], [129]. Diversification, including the use of two or more plants as intercropping systems, was primarily implemented for enhancing food security, improving soil fertility, and/or controlling insect-pests through push-pull strategies or maize-legume intercropping systems [130]. These practices were originally designed to combat stem-borers of maize and sorghum, and these systems have gradually demonstrated additional ecosystem services, including the suppression of parasitic weeds like Striga spp. increased soil nitrogen and carbon content, and even a reduction in mycotoxin incidence in maize [24], [26], [88], [131]. The current study contributes to these benefits by describing a diversification of soil and maize-root microbial communities, particularly highlighting a significant shift in bacterial and fungal genera composition. Ecosystem diversity is generally recognized to enhance stability, resilience, and productivity, primarily due to resource complementarity and functional redundancy. The study also highlights a concerning finding regarding monoculture farms, which tend to harbor a high burden of pathogenic microbes known to contribute to crop loss. These findings collectively emphasize the importance of promoting diverse cropping systems, such as push-pull, to foster a balanced and resilient beneficial microbiome in agricultural ecosystems and mitigate the risks associated with Mono cropping.

Microbiome functional protein pathways

The study focused on the differential expression of microbial protein function in cropping systems, specifically examining belowground microbial proliferation and buildup in push-pull cropping systems. Various critical pathways have been identified, including inosine 5'-phosphate degradation (PWY-5695), theocat-PWY (L-threonine metabolism), gallate-degradation-I-PWY (gallate degradation II), lipasyn-PWY (phospholipases), P161-PWY (acetylene degradation, anaerobic), PWY-5088 (L-glutamate degradation VIII), lactosecat-PWY (lactose degradation I), homoser-metsy n-PWR (L-methionine biosynthesis I), PWY-6386 (syringate degradation), catechol degradation II (meta-cleavage pathway), and P221-PWY (octane oxidation). These pathways play a role in soil-plant biochemical processes, plant growth, nitrogen fixation, stress, and disease resistance, climate change effects, and root architecture modulation [69], [132]–[134].

The study highlights the significance of nitrogenase, which converts atmospheric nitrogen into ammonia, meeting the plant's nitrogen requirements and promoting plant growth through nitrogen fixation. In the process of nitrogen fixation, the inosine 5'-phosphate degradation pathways are involved, and the enzyme nitrogenase catalyzes convert atmospheric nitrogen to ammonia, which plants then utilize for their metabolic nitrogen [135]. Bacteria generate ammonia, which is transformed into L-glutamine, an amino acid, and incorporated into IMP, a purine synthesized de novo during nitrogen fixation in plant roots [136]. Zahid et al. [69] reported that aerobic bacteria can utilize acetylene for plant growth and nitrogen fixation. The gallate degradation II pathways are widely present in nature and play a role in the breakdown of plant lignin and tannins, essential carbon cycle components [134]. Phospholipases and their derived products are crucial second messengers in signal transduction during plant growth, development, and stress responses [137]. The meta-cleavage pathway is essential for the degradation of aromatic compounds and has been observed in bacterial genera like Azotobacter, Ralstonia, and Pseudomonas [132], [133]. Pseudomonas simiae WCS417r in plant roots triggers induced systemic resistance (ISR) against leaf-chewing insects by stimulating the expression of jasmonic acid and ethylene-dependent ORA59-branch [138]. Non-pathogenic bacteria (biocontrol strains) like Pseudomonas fluorescens SS101 induce resistance against tomato pathogens like Phytophithora infestans [139].

Additionally, Bacillus subtilis S499 provides ISR-mediated protection to tomato plants against Botrytis cinerea[140].The L-glutamate degradation VIII pathway plays a role in nutrient foraging and shaping root architecture in soil environments. Like the plant growth regulator auxin (indole-3-acetic acid, IAA), L-glutamate acts as an environmental signal that modifies root growth and branching, serving to communicate between plants and microbes [141]. Intense responses were observed in the roots of Arabidopsis thaliana upon exposure to L-glutamate, highlighting its significance as a modifier for root growth and branching in soil environments [141]–[143]. Notably, L-methionine biosynthesis positively influences the growth and development of maize and tomato plants, contributing to plant growth processes [144]. The syringate degradation pathway enables microbes to utilize lignin-derived compounds. Lignin is broken down into biaryl and monoaryl compounds such as Biphenyl, ferulate, vanillate, and syringate. Microbes like Sphingomonas spp. SYK6 can use guaiacyl and syringyl moieties derived from lignin to degrade them into vanillate and syringate [145]. Further research is necessary to investigate the influence of soil microorganisms on soil physicochemical properties, plant mineral nutrients, and bacterial protein activities. Additionally, the role of belowground bacteria and fungi in perennial intercropping scenarios should be explored in future studies.

The study demonstrated that the PPT cropping system greatly influenced soil characteristics such as pH, P, N, and soil organic carbon content compared to the Mono cropping system. The study has revealed that the PPT cropping system shifts belowground soil and maize-root microbiome composition compared to the Mono cropping system. The microbial communities enriched in both soil and maize-root by the PPT belong to genera and species associated with essential ecosystem services, including soil fertility enhancement, organic matter decomposition, carbon sequestration, plant protection, and human safety. This enrichment of ecologically important microbial communities in the soil and maize-root by the PPT cropping system contributes to the diversification of ecosystem services provided by this cropping system in farmer fields. This ecosystem services diversification enhances the system’s resilience and functional redundancy. Further research is needed to assess which specific soil and maize-root microorganisms are strongly impacted by the cropping system, the role of altered microorganisms in aboveground tri-trophic interactions, and their influence on the enzymatic activity and nutrient accessibility in fields where this cropping system is practiced.

Acknowledgements

We are grateful to smallholder farmers in Vihiga, Siaya and Bungoma counties, Kenya for allowing us to collect rhizospheric soil and maize-root samples from their farms. We thank Kentosse Gutu Ouma, Basilio Njiru, Catherine Awuoche, and Mbita push-pull team for field work and samples collection assistance; Levi Ombura, Maureen Ong’ong’e and Evalyne Wambui Ndotono for assistance in laboratory processing of samples, bioinformatics unit through Nehemaih Ongeso and Dr. Caleb Kibet at icipe for metagenomic data analysis advice.

Funding

The authors gratefully acknowledge the financial support for this research by the following organizations and agencies: Integrated pest management strategy to counter the threat of invasive FAW to food security in eastern Africa (FAW-IPM) (Grant No. DCI-FOOD/2017/) ﬁnanced by European Union. We are grateful to the Swiss Agency for Development and Cooperation (SDC); Swedish International Development Cooperation Agency (Sida); Federal Democratic Republic of Ethiopia; and the Government of the Republic of Kenya for core support to icipe.

Availability of data and materials

The unprocessed sequencing datasets generated during the current study have been deposited in GenBank, NCBI under BioProject PRJNA1015669. The 16S (V1-V2, V3-V4) metagenome data were registered as Biosamples SAMN37384180 – SAMN37384217 and the sequences assigned SRA accessions SRR26087688 – SRR26087719. The ITS (ITS1-ITS2) metagenome data were registered as Biosamples SAMN37384218 – SAMN37384250 and the sequences assigned SRA accessions SRR26087651 – SRR26087687. We also provide the R scripts for data analysis along with all the necessary input files as additional files 2A and 2B. The data for soil physicochemical properties and GPS coordinates were provided as additional files 3 and 4.

Authors’ contributions

DMM and SS conceived the idea, AAJ, DMM, FK, and AAY designed the study; AAJ collected data; AAJ, DMM, FK, and AAY analysed data; AAJ and DMM led the drafting of the manuscript; SS supervised the work and provided resources. All authors critically reviewed and approved the final version.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that hey have no competing interests.

Authors’ information

¹International Centre of Insect Physiology and Ecology, P.O. Box 30772-00100 Nairobi, Kenya. ²Department of Zoology and Entomology, University of Pretoria, Private Bag x20 Hatfield, South Africa. ³Forestry and Agricultural Biotechnology Institute, University of Pretoria, Private Bag x20 Hatfield, South Africa. ⁴Department of Biochemistry, Microbiology and Biotechnology, Kenyatta University, P.O. Box 43844-00100 Nairobi, Kenya.

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No competing interests reported.

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Long-term Push-pull Cropping System Shifts Rhizosphere and Maize-root Microbiome Diversity Paving Way To Resilient Farming System

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