Cover crop microbiomes affect legume cash crop growth but not consistently through enriching nitrogen-fixing rhizobia

Cover crop microbiomes affect legume cash crop growth but not consistently through enriching nitrogen-fixing rhizobia | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Cover crop microbiomes affect legume cash crop growth but not consistently through enriching nitrogen-fixing rhizobia Kayla M. Clouse , Elizabeth L. Paillan , Cody L. DePew , Jennifer E. Harris , Amanda P. Jason , Kelsey Mercurio , Andy L. Swartley , Ellen Bingham , Liana T. Burghardt doi: https://doi.org/10.1101/2025.10.23.684197 Kayla M. Clouse 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth L. Paillan 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania 2 North Carolina State University, Department of Plant and Microbial Biology , Raleigh, North Carolina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cody L. DePew 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer E. Harris 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania 3 The Pennsylvania State University, Department of Ecosystem Science and Management, University Park , Pennsylvania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amanda P. Jason 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kelsey Mercurio 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andy L. Swartley 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ellen Bingham 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liana T. Burghardt 1 The Pennsylvania State University, Department of Plant Science, University Park , Pennsylvania Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: liana.burghardt{at}gmail.com Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Harnessing plant-microbe interactions offers a promising path toward reducing chemical inputs and enhancing crop resilience in agricultural systems. However, microbial inoculants often fail to persist or function consistently across soils, which limits their broad utility. Here, we explore whether legume cover crops can be used to manipulate and enrich for rhizobial bacteria that improve nodulation and nitrogen fixation in downstream legume cash crops. In a greenhouse experiment, we inoculated cash crops with rhizosphere and nodule microbiomes derived from different legume cover crops. We found that bacterial communities were markedly different between the rhizosphere and nodules of cover crops, indicating strong habitat differentiation. Interestingly, the cover crop inoculum did not substantially alter the taxonomic composition of cash crop nodule communities, suggesting strong partner selectivity by the cash crop host. However, cover crop identity strongly affected nodule formation and, to a lesser extent, total biomass of cash crops. For alfalfa and soybean, a higher abundance of their respective rhizobial partners in cover crop nodule inocula increased total biomass and nodule number for these cash crop species, respectively. These findings emphasize the importance of selecting cover crops that foster the availability of effective microbial partners to support more sustainable legume cropping systems. INTRODUCTION Legumes are valued worldwide not only as nutrient-rich food crops but also for their ability to enhance soil fertility through biological nitrogen fixation. By forming symbiotic relationships with rhizobial bacteria, legumes can reduce reliance on synthetic nitrogen fertilizers and support more sustainable cropping systems ( Herridge et al., 2008 ). The effectiveness of nitrogen fixation, however, depends not only on the presence of legumes but also on the compatibility between the host plant and the rhizobial strains present in the soil or rhizosphere ( Walker et al., 2020 ; Burghardt and diCenzo, 2023 ). Since rhizobial communities associated with one legume species may not be able to form effective symbioses with other legume species, the choice of preceding leguminous crops may influence the success of subsequent legumes. Selecting upstream crops that enrich beneficial rhizobial communities for target cash crops may therefore offer a strategy to improve both crop productivity and sustainable nitrogen management in legume-based systems. Rhizobia occupy multiple, distinct ecological habitats both within and outside of their legume hosts (Denison and Keirs 2011; Burghardt 2020 ). In root nodules, rhizobia fix atmospheric nitrogen into ammonia in exchange for carbon compounds produced by plant photosynthesis. Outside of nodules, rhizobia persist as free-living populations in the soil and rhizosphere, where they form diverse and dynamic communities ( Denison and Kiers, 2004 ; Poole et al., 2018 ). These microbial communities may interact with a wide range of legume hosts and often include hundreds of strains capable of forming symbioses within a single field ( Mutch and Young, 2004 ). In addition, these strains compete for access to legume nodules, in which successful colonization may lead to substantial increases in population size ( Denison and Kiers, 2011 ). Upon nodule senescence, rhizobia are released back into the soil and can persist across seasons, thereby influencing downstream soil and plant microbial community composition and functionality. In agricultural systems, legumes are widely used as both cover crops and cash crops. Cover crops are planted between cash crop seasons to provide ecosystem services like weed suppression, erosion control, and pest management ( Finney et al., 2017 ; Kaye et al., 2019 ). In addition, the nitrogen in cover crop biomass is mineralized by soil microbes after termination, which becomes available to subsequent cash crops. While this nitrogen release from biomass is well established ( Parr et al., 2011 ; Liebman et al., 2018 ), legume cover crops may also have important legacy effects on nitrogen cycling through the rhizobial populations they enrich. The impact of this legacy depends on host partner specificity: legume cover and cash crop species can differ in their specificity for rhizobial partners, in which some legumes form nodules only with a particular bacterial species or subspecies, and others associate with multiple rhizobial genera ( Checcucci et al., 2017 ). For example, fava bean ( Vicia faba ) is highly specific in its rhizobial associations, while cowpea ( Vigna unguiculata L. Walp. ) nodulates with a broader range of bacteria ( Mutch and Young, 2004 ; Simbine et al., 2021 ). The taxonomic identity of resident rhizobia, shaped by prior cover crop use, may therefore determine whether subsequent cash crops can effectively form nodules and fix nitrogen. The association between legumes and rhizobia has been deliberately managed for agricultural benefit for over a century. By improving nitrogen fixation and soil fertility this association can reduce fertilizer needs, however, the application of nitrogen fertilizer remains high due to spatial and temporal variability of nitrogen fixation in leguminous crops ( Galloway et al., 2008 ; Herridge et al., 2008 ). Rhizobial inoculants are increasingly applied in agricultural systems to increase biological nitrogen fixation, but their widespread adoption is constrained by factors such as poor survival and competitiveness of inoculant strains within agricultural soils, as well as variable and context-dependent effects on crop growth ( Kaminsky et al., 2019 ; Batstone et al., 2023 ). Long-term cropping systems studies have shown that differences in legume use can lead to orders-of-magnitude changes in soil rhizobia abundance. For example, rhizobia abundance was 6.8 × 10⁶ cells g dry soil−1 in legume-inclusive rotations compared to 6.1 × 10² cells g dry soil−1 in continuous maize ( Yan et al., 2014 ). Therefore, one underexplored solution to increase cash crop productivity may be to select cover crops that enrich soil rhizobial communities for targeted cash crops. Here we explore how microbial communities, particularly rhizobial communities, shaped by legume cover crops influence nodule microbial communities and nitrogen fixation in downstream legume cash crops. We conducted a greenhouse legacy experiment in which rhizosphere and nodule microbiomes from cover crops were used to inoculate cash crops grown under nitrogen-limiting conditions. Using this framework, we address the following questions: 1) How do bacterial inocula created from the rhizosphere and nodules of cover crop species differ? 2) In the absence of external nitrogen, does the compartment (rhizosphere vs. nodule) or cover crop species of an inoculum influence cash crop growth? 3) What is the taxonomic composition of cash crop nodules, and does it vary with inoculum compartment or cover crop species? 4) Is cash crop performance affected by the abundance of rhizobial partners in the inocula they received? We hypothesized that the effectiveness of nitrogen fixation in cash crops is determined by the degree of bacterial overlap and specificity between cover crop and cash crop hosts. We found that higher abundance of cash crop rhizobial partners in cover crop nodule inocula enhanced the performance of two of the four cash crop species, revealing a previously underexplored role of rhizobial compatibility in shaping downstream symbioses and overall plant performance. METHODS Phase 1: Cover crop enrichment of microbes and inoculant preparation In phase 1, we grew eight replicates of six legume cover crop species in a mixture of field soil, sand, and calcined clay ( Figure 1 ). The field soil was collected from the Russell E. Larson Agricultural Research Center (Pennsylvania Furnace, Pennsylvania), where a rotation of corn, soy, wheat, and diverse cover crops has been in place for the past twelve years ( Cloutier et al., 2020 , Finney et al., 2017 ). The legume cover crop species included bushclover ( Lespedeza cuneata ), cowpea ( Vigna unguiculata ), crimson clover ( Trifolium incarnatum ), field pea ( Pisum sativum subsp. arvense ), white clover ( Trifolium repens ), and yellow sweet clover ( Melilotus officinalis ). These cover crop species were selected due to their association with a diverse span of rhizobial taxa that form nitrogen-fixing associations with common legume cash crops ( Table 1 ). Ten seeds were planted per pot and then thinned to six to seven plants per pot after approximately two weeks of growth. The plants were grown in a randomized block design and fertilized with nitrogen-free Fahräeus solution ( Barker et al., 2006 ) weekly to encourage nodulation. After 18 weeks of growth, microbial inoculants were prepared from the nodules and rhizosphere of each cover crop replicate. Download figure Open in new tab Figure 1. In phase 1, we grew eight pots of six legume cover crop species with six to seven plants per pot (N=48; 6 cover crops, 8 replicates). The legume cover crop species included bush clover, cowpea, crimson clover, field pea, white clover, and yellow sweet clover. After 18 weeks of growth, we assessed the 16S gene profile of the rhizosphere and nodules of each cover crop replicate. In phase 2, we used the rhizosphere and nodules derived from each cover crop species replicate as inocula for four cash crop species that were grown in a randomized block design (N=480; 5 cash crops, 2 compartments, 6 cover crops, 8 replicates). The cash crop species included alfalfa, common bean, fava bean, and soybean. Each block also included one non-inoculated control per cash crop species, which received sterilized DI water instead of inoculum (N=40; 5 cash crops, 8 replicates). To encourage nodulation, all plants received nitrogen-free Fahräeus solution weekly. After 12 weeks of growth, we assessed cash crop performance and the 16S gene profile of cash crop nodules. View this table: View inline View popup Download powerpoint Table 1. Rhizobial partners of cover crops and cash crops. The dominant rhizobial genera associated with the six cover crop species and four cash crop species used in phase 1 and phase 2, respectively. Rhizobial partners are reported at the genus level to be comparable to 16S results presented here. To prepare the nodule inoculum, nodules were collected from approximately five plants per pot and placed in a metal mesh tea strainer. The nodules were surface-sterilized with 15% household bleach for 90 seconds, followed by rinsing with sterile DI water for 60-90 seconds. Sterilized nodules were transferred into a tube containing 80 mL of sterile 0.85 % NaCl solution and homogenized for 90–120 seconds using a tissue homogenizer (Omni International). The homogenate was vortexed and centrifuged at 400 × g for 10 minutes at 10°C. The supernatant was collected and centrifuged at 15,000 × g for two minutes at 10°C. The final supernatant was discarded, and the pellet was resuspended in 40 mL 0.25× phosphate-buffered saline (PBS) for use as the nodule inoculum. To prepare the rhizosphere inoculum, roots from approximately five plants per pot were collected and placed in a sterile 250 mL media bottle. Sterile DI water was added, and the bottle was shaken for one minute to release the rhizosphere microbiota into suspension. The suspension was centrifuged at 400 × g for 10 minutes at 10°C. The resulting supernatant was transferred to a new tube and centrifuged at 15,000 × g for two minutes. The supernatant was discarded, and the resulting pellet was resuspended in 40 mL 0.25x PBS to serve as the rhizosphere inoculum. We used flow cytometry to determine the concentration of the rhizosphere and nodule inoculants. Samples were processed on an Aurora Spectral Analyzer (Cytek Biosciences), which counts cells and records cell flow rate, allowing us to calculate the cells/mL in each sample. Each inoculum sample was diluted 1:15 with sterile 1x PBS with additional dilutions made as necessary to optimize event flow rate. We used SYBR green, a DNA probe (Invitrogen, Ex/Em 498/522 nm, final concentration of 0.5 µM), to distinguish cells from other particulate matter. Samples were incubated with SYBR green for 20 minutes before analyzing to allow time for the dye to incorporate into microbial cells and adhere to DNA. We set a size threshold above 0.2 µm, determined by Flow Cytometry Sub-Micron Particle Size Reference Kit Beads (Invitrogen). This threshold removed small particles and allowed us to focus on microbial cells. Then we removed any plant cells by setting a gate around events that did not have autofluorescence of Chlorophyll at 488/679 (ex/em) or 561/679 (ex/em). In a nested gating strategy, we from our Chlorophyll negative events, we gated for SYBR positive events. We used two positive and negative controls for determining SYBR positive events on the flow cytometer. First, with cultured Sinorhizobium melioti (a known nodule endophyte), we determined the expected size and fluorescence of microbial cells from nodules and drew a gate around it in the 488/525 SYBR and FSC channel around the positive control, cultured S. melioti + SYBR. Then we ran a negative control of heat-killed S. melioti + SYBR to ensure we did not have off-target labeling. To adjust for variation in fluorescence when soil particles were present, we ran a positive control of sterile soil spiked with cultured S. melioiti + SYBR. Once we determined our gate captured the microbial population, we ran a negative control of sterile soil + SYBR and then adjusted our gates to have less than 1% false positives. The same gating strategy was used for nodule and rhizosphere samples. The cell counts for nodule and rhizosphere samples were log-transformed and then analyzed using a linear mixed-effects model with inoculum compartment, cover crop identity, and their interaction as fixed effects and replicate as a random intercept. Phase 2: Cash crop response to nodule and rhizosphere inocula In phase 2, five cash crop species were grown with the nodule and rhizosphere inocula derived from each cover crop species in phase 1 ( Figure 1 ). The cash crop species included alfalfa ( Medicago sativa ), common bean ( Phaseolus vulgaris ), fava bean ( Vicia faba ), peanut ( Arachis hypogaea ), and soybean ( Glycine max ). Due to poor nodulation, peanut was excluded from downstream analyses. Prior to planting, the seeds of each cash crop species were surface-sterilized with 15% household bleach for 15 minutes, followed by five rinses with sterile DI water. Two surface sterilized seeds were planted into Deepots (Stuewe & Sons, Inc.) filled with a sterile 1:1 mixture of sand and calcined clay. Pots were arranged in a randomized block design by cash crop species with eight replicates of each cover crop and inoculum compartment in each block (N = 480; 5 cash crops, 2 compartments, 6 cover crops, 8 replicates). One week after planting, the seedlings were inoculated with 6 mL of nodule or rhizosphere inoculum from each cover crop replicate. In addition, each block included one non-inoculated control per cash crop species, which received 6 mL of sterilized DI water instead of inoculum (N = 40; 5 cash crops, 8 replicates). After two to three weeks of growth, plants were randomly thinned to one plant per pot. To encourage nodulation, all plants received nitrogen-free Fahräeus solution weekly. After 12 weeks of growth, the proportion of flowered plants was recorded for each cash crop species. Plants were then harvested, and nodules were counted and collected. Following nodule collection, roots and shoots were separated and oven-dried for one week at 60°C prior to biomass measurements. Proportion flowered was analyzed for each cash crop species using logistic regression models for binary data. For total biomass, a value of 0.001 was imputed to account for alfalfa biomass samples that were below the minimum load of the scale. Total biomass was evaluated using linear mixed-effects models and two-way ANOVA with Type III sums of squares. Nodule number was square-root transformed and similarly assessed using linear mixed-effects models with two-way ANOVA with Type III sums of squares. In all models, cover crop, inoculum compartment, and their interaction were included as fixed effects, and block was included as a random intercept to account for variation among experimental blocks. Pairwise contrasts between the nodule and rhizosphere inoculum for each plant trait were performed using Dunnett’s post-hoc procedure. To evaluate the effects of inoculum cell count on plant performance, each trait was analyzed using a linear mixed-effects model with log-transformed cell counts as a fixed effect and replicate as a random intercept and then assessed by two-way ANOVAs with Type III sums of squares. After collection, cash crop nodules were stored for approximately one day at 4°C until processing. Processing followed phase 1 methods for inoculant preparation with slight modifications. Nodules from one to eight cash crop plants per cover crop and inoculum compartment were pooled and placed in a metal mesh tea strainer. Nodules were surface-sterilized with 15% bleach for 30 seconds, followed by rinsing with sterile DI water for 60 seconds. Sterilized nodules were transferred into a tube containing approximately 3 mL of autoclaved 0.85 % NaCl solution and homogenized for 90 seconds using a tissue homogenizer (Omni International). The homogenate was vortexed and centrifuged at 400 × g for 10 minutes at 10°C to remove plant debris. The supernatant was then collected and centrifuged at 18,000 × g for five minutes to produce a bacteria-enriched pellet. DNA extraction and 16S rRNA gene amplicon sequencing In phase 1, nodule and rhizosphere samples were pooled from approximately five plants within each cover crop pot. In total, we collected eight replicate nodule and rhizosphere samples per cover crop. In phase 2, nodules from one to eight cash crop plants per cover crop and inoculum compartment were pooled when sufficient nodules were available. Prior to DNA extractions, 1 mL aliquots of the phase 1 nodule and rhizosphere inoculum were pelleted by centrifuging at 15,000 x g for two minutes. DNA was then extracted from phase 1 and phase 2 nodule pellets using the Qiagen DNeasy Plant Pro Kit (Cat. No. 69206) following manufacturer instructions. Rhizosphere DNA was extracted using the Qiagen DNeasy PowerSoil Kit (Cat. No. 12855) also following manufacturer instructions. The 16S rRNA V4 region was then amplified by 515F ( Parada et al., 2016 ) and 806R ( Apprill et al., 2015 ) primers. The Huck Institutes’ Genomics Core Facility performed library preparation and sequencing on an Illumina NextSeq 2000 to produce 300 bp paired-end reads. Sequence data processing Sequence data were processed using established bioinformatic pipelines ( Wagner et al., 2020 ). Raw sequences were processed with Cutadapt ( Martin, 2011 ) to remove adapter primers. We examined read quality profiles using FastQC to assess sequencing quality ( Andrews, 2010 ). We then used DADA2 ( Callahan et al., 2016 ) to quality-filter and truncate reads (truncLen = c(240, 240)), infer amplicon sequence variants (ASVs), and remove chimeras. Truncation lengths were selected based on the FastQC profiles to retain high-quality bases while ensuring sufficient overlap for merging paired-end reads. Taxonomy was assigned to bacterial ASVs using the Ribosomal Database Project (RDP) classifier trained on the RDP training set v. 19 ( Cole et al., 2014 ). ASVs that could not be classified at the kingdom level or were identified as plant sequences were discarded. To ensure reproducibility, we only retained ASVs that were observed at least 25 times in a minimum of three samples. In total, these filtering steps reduced the number of bacterial ASVs from 27,624 to 4,598. The final dataset contained 35,131,484 reads across 136 samples with a mean (±SD) library size of 258,320 ± 74,780 reads per sample. Microbiome analyses All data analyses were performed in R using RStudio version 4.3.2. Alpha diversity metrics (Richness and Shannon diversity indices) were calculated using the phyloseq R package ( McMurdie and Holmes, 2013 ). These metrics were modeled using the lme4 R package ( Bates et al., 2015 ) with compartment, cover crop, and their interaction, as well as sequencing depth, as fixed predictor variables and block as a random-intercept term. We assessed these linear mixed-effect models using ANCOVA then adjusted the p-values for multiple comparisons ( Benjamini and Hochberg, 1995 ). Next, we applied a centered log-ratio (CLR) transformation to the count dataset using the ALDEx2 R package ( Fernandes et al., 2013 ) to account for the compositional nature of microbiome data ( Gloor et al,. 2017 ). A canonical analysis of principal components (CAP) ordination using Atchinson distance was performed on the CLR-transformed bacterial counts and constrained by compartment, cover crop, and their interaction, with sequencing depth partialled out to remove noise. Separate models were run for each compartment to test the effects of cover crop identity with sequencing depth partialled out. We then used an ANOVA-like permutation test for Constrained Correspondence Analysis using the vegan R package (Oksanen et al., 2022) to determine whether the bacterial taxa counts were explained by the constraining variables. Next, we classified ASVs from cash crop nodules as rhizobial partners if they matched known rhizobial taxa for that crop at the genus level. We confirmed their identities by querying the sequences in NCBI BLASTn ( Johnson et al., 2008 ). ASVs were then considered rhizobial partners if they had a relative abundance greater than 1 × 10⁻⁴ in at least two cover crop– inoculum combinations during phase 2. In total, we identified three rhizobial ASVs for alfalfa, seven for common bean, ten for fava bean, and four for soybean. The relative abundance of these rhizobial ASVs was quantified in the rhizosphere and nodules of each cover crop replicate in phase 1 and then square-root transformed to improve normality. Separate linear models were fit for each cash crop and inoculum combination to test whether rhizobia relative abundance predicted plant performance, with total biomass or square-root transformed nodule number as the response variable and square-root transformed rhizobial partner relative abundance as the predictor. Model significance was evaluated using ANOVA on the fitted models. Additional R packages used for data analysis and visualization included tidyverse ( Wickham et al., 2019 ), ggplot2 ( Wickham, 2016 ), lmerTest ( Kuznetsova et al., 2017 ), and genefilter ( Gentleman et al., 2023 ). RESULTS Bacterial communities are distinct within the rhizosphere and nodules of cover crops In phase 1, we assessed the influence of cover crop identity and plant compartment on bacterial community composition by comparing rhizosphere and nodule microbiomes across six cover crops known to have varying associations with rhizobial taxa ( Figure 1 ; Table 1 ). The rhizosphere communities of cover crops exhibited greater diversity, whereas their nodule microbiomes were dominated by a limited number of bacterial genera ( Figure 2A , 2B ). The top 15 genera accounted for 75-95% of the total relative abundance within nodule microbiomes across cover crops. Their relative contributions varied among cover crops; for example, Rhizobium was most abundant in crimson clover (45-99%), field pea (26-97%) and white clover (80-98%), Sinorhizobium was most abundant in yellow sweet clover (55-99%), and Bradyrhizobium contributed substantially to bush clover (73-94%) and cowpea (14-71%). A permutational ANOVA of the CAP ordination confirmed significant effects of compartment (F 1,82 =21.8, p<0.001) and cover crop identity (F 5,82 =4.7, p<0.001) on bacterial community composition, as well as the interaction between compartment and cover crop identity (F 5,82 =2.3, p<0.001) ( Figure 2C ). These differences were also reflected in community diversity, as rhizosphere communities consistently contained more ASVs and exhibited higher Shannon diversity compared to nodules ( Figure 2D ; Supplemental Table 1 ). Cover crop identity significantly influenced bacterial community composition when analyzed separately by compartment. This effect was observed in both the nodule (F 5,41 =2.3, p<0.001) and rhizosphere communities (F 5,40 =3.5, p<0.001). These results indicate that bacterial community composition varied significantly with cover crop identity in both nodules and rhizosphere, while diversity measures were consistently higher in the rhizosphere across all cover crops. Download figure Open in new tab Figure 2. Characterization of bacterial communities in cover crop rhizospheres and nodules used as inoculum for phase 2. (A) Bacterial taxonomic composition of cover crop nodules at the genus level. (B) Bacterial taxonomic composition of cover crop rhizospheres at the class level. (C) Bacterial community composition of the nodule and rhizospheres of each cover crop visualized as a constrained analysis of principal coordinates (CAP) ordination. The CAP model was constrained by compartment (F 1,82 =21.8, p<0.001), cover crop identity (F 5,82 =4.7, p<0.001), and their interaction (F 5,82 =2.3, p<0.001). (D) Bacterial alpha diversity depicted as richness (number of ASVs) and Shannon diversity index for the nodule and rhizospheres communities of each cover crop. Cash crop performance is strongly influenced by cover crop identity but not by inoculum compartment In phase 2, we assessed how the nodule and rhizosphere bacterial communities shaped by cover crops influenced cash crop performance ( Figure 1 ). After 12 weeks of growth, we quantified the number of nodules and biomass for each cash crop. Across cash crop species, the effects of inoculum compartment (rhizosphere vs. nodule) and cover crop identity on plant performance varied considerably. Inoculum compartment had no measurable effect on nodule number for alfalfa, common bean, fava bean, or soybean ( Figure 3A ; Supplemental Table 2 ). For total biomass, inoculum compartment effects were similarly limited with a substantial positive effect only in common bean (F₁,₇₇=19.3, p<0.001), which was driven by an increase in biomass when grown with cover crop rhizosphere inoculum ( Figure 3B ; Supplemental Table 3 ). Cover crop identity significantly influenced nodule number in all cash crop species, with particularly large effects in alfalfa (F₅,₅₉ = 12.1, p<0.001). In contrast, cover crop identity effects on total biomass were detected only in alfalfa (F₅,₆₃=7.6, p<0.001) and fava bean (F₅,₇₂=2.39, p=0.046), suggesting a direct relationship between increased nodulation and plant growth for these two cash crops. A significant interaction between inoculum compartment and cover crop identity for nodule number occurred only in common bean (F₅,₈₄=2.9, p=0.019). In particular, nodule number was greater with rhizosphere inocula from bush clover, cowpea, and field pea, but higher with nodule inocula from crimson clover, white clover, and yellow sweet clover. No interactions were observed for total biomass, highlighting that biomass responses to inoculum and cover crop identity were largely independent. Furthermore, cover crop identity did not influence the proportion of plants that flowered across cash crops. In contrast, inoculum compartment significantly affected flowering in common bean (X 2 1 =8.29, p=0.004), with a higher proportion of plants flowering when receiving rhizosphere inoculum compared to nodule inoculum for all cover crops except cowpea ( Supplemental Figure 1 ; Supplemental Table 6 ). Overall, these results indicate that cover crop identity was the primary driver of nodulation across cash crops, while effects of inoculum compartment were generally minimal and limited to common bean. Download figure Open in new tab Figure 3. Effect of cover crop inocula on cash crop traits in phase 2. (A) Square-root transformed nodule number for each cash crop grown in nodule or rhizosphere inoculum from each cover crop and (B) Total biomass for each cash crop grown in nodule or rhizosphere inoculum from each cover crop. Pairwise contrasts between the nodule and rhizosphere inoculum for nodule number and total biomass were performed using Dunnett’s post-hoc procedure (* = p < 0.05, ** = p < 0.01, *** = p < 0.001). Dashed lines indicate median nodule number or total biomass for non-inoculated control. Cover crop inoculum has minor effects on cash crop nodule microbiome composition To assess the influence of cover crop inoculum on cash crop microbiomes, we characterized the microbiome composition of nodules pooled across replicate plants. However, some cover crop–inoculum combinations yielded an insufficient number of cash crop nodules for microbiome assessment. Among nodulated cash crops, Rhizobium was the dominant bacterial genus in common bean and fava bean when inoculated with cover crop nodule communities ( Figure 4A ). In common bean, Rhizobium relative abundance ranged widely, from 2% under bush clover inoculum to 57% under crimson clover. A similar pattern was observed in fava bean, with the lowest abundance under bush clover (43%) and the highest under crimson clover (90%). In alfalfa, Sinorhizobium was the dominant bacterial genus when grown with cover crop nodule communities, which was consistently high across cover crop inocula (79-92%). In soybean, nodules were dominated by Pseudomonas , but Bradyrhizobium was the most abundant rhizobial genus, contributing 24-33% of the total community across cover crop inocula. When inoculated with cover crop rhizosphere communities, Rhizobium remained dominant in fava bean, but common bean nodules were dominated by Flavobacterium followed by Rhizobium ( Figure 4B ). Rhizobium abundance did not vary much across rhizosphere cover crop inocula for common bean (7-24%) but ranged from 32% under yellow sweet clover to 86% under crimson clover for fava bean. Similar to cover crop nodule inocula , Sinorhizobium abundance was consistently high in alfalfa nodules when grown with cover crop rhizosphere inocula (72-87%). In soybean, Pseudomonas followed by Bradyrhizobium was the most abundant genus, which ranged from 24-36% across rhizosphere cover crop inocula. Together, these patterns indicate that cash crop nodules were consistently colonized by their characteristic rhizobial genera with only minor variation in relative dominance across cover crop inocula. Download figure Open in new tab Figure 4. Relative abundance of cash crop nodule bacterial communities. Top 15 bacterial genera across cash crop nodules that received either (A) nodule or (B) rhizosphere inoculum from each cover crop. Cash crop rhizobial partners were more abundant in cover crop nodule inocula than rhizosphere inocula To assess whether inoculum microbiome composition influenced cash crop performance, we identified ASVs previously reported as rhizobial partners for each cash crop that exceeded a minimal relative abundance threshold in phase 2. We then quantified the relative abundance of these cash crop rhizobia ASVs across cover crop–inoculum combinations in phase 1 and 2. Cash crop partners were consistently more abundant in phase 1 cover crop nodule inocula than rhizosphere inocula ( Figure 5A ). For alfalfa, this enrichment was most pronounced in nodule inocula from yellow sweet clover, while rhizobial partners of common bean and fava bean were highly abundant in nodules from crimson clover, field pea, and white clover. Soybean partners were highly enriched in nodules from bush clover and to a lesser degree in cowpea nodules. Although rhizobia abundance varied across cover crop inoculum compartments, this did not consistently increase the relative abundance of cash crop rhizobial partners in phase 2 ( Figure 5B ). Cash crop nodules generally contained similar rhizobia levels regardless of inoculum compartment, except in common bean, where nodule inocula from crimson clover and white clover increased rhizobia abundance compared to rhizosphere inocula. Interestingly, alfalfa and fava bean enriched a high relative abundance of their nodule rhizobia even when the inoculum contained a very low abundance. By contrast, soybean rhizobial partner abundance was also relatively unaffected by inoculum, but soybean nodules did not achieve majority dominance of rhizobial partners. Download figure Open in new tab Figure 5. Effects of cash crop rhizobial partners in cover crop inocula on cash crop performance. Cash crop rhizobial partner ASV mean relative abundance in (A) phase 1 and (B) phase 2. Cash crop rhizobial partners were identified as those matching known taxonomic classifications at the genus level and exceeding a minimum abundance threshold in at least two cover-crop inoculum combinations in phase 2. Cross marks indicate an insufficient number of nodules for microbiome assessment. The relationship between cash crop rhizobial partner relative abundance in nodule inoculum and (C) nodule number or (D) total biomass. Each dot represents an individual sample and the lines are the regression lines of best fit. The grey shading surrounding the lines represents the 95% confidence interval. Next, we evaluated whether higher rhizobial partner relative abundance in phase 1 nodule inoculum was associated with improved cash crop performance. Across cash crops, we used linear models to test this relationship using square-root–transformed nodule number or total biomass as indicators of performance and square-root–transformed rhizobial partner relative abundance as the predictor. Nodule number was not significantly associated with rhizobia relative abundance in cover crop nodule inocula for alfalfa, common bean, or fava bean ( Figure 5C ; Supplemental Table 4 ). For soybean, however, rhizobial partner relative abundance was positively related to nodule number (F 1,16 =4.28, p=0.055, R²=0.21), with the strongest responses observed in inocula from bush clover and cowpea. Although not significant, regression slopes suggested positive associations in alfalfa and negative associations in common bean and fava bean. Total biomass was not significantly associated with rhizobial partner relative abundance in cover crop nodule inocula for common bean or soybean ( Figure 5D ; Supplemental Table 4 ). For alfalfa, the relationship was marginally significant (F 1,35 =3.57, p=0.067, R 2 =0.093), largely driven by yellow sweet clover. In contrast, fava bean showed a significant negative association between rhizobial partner relative abundance and biomass (F 1,44 =7.62, p=0.008, R 2 =0.148), indicating that higher rhizobial partner abundance in inocula corresponded to reduced plant growth in this species. For rhizosphere cover crop inocula, only alfalfa nodule number was significantly affected by rhizobial partner relative abundance (F 1,35 =10.45, p=0.003, R 2 =0.230) ( Supplemental Figure 2 ; Supplemental Table 5 ). Together, these results suggest that while some cash crops (i.e. alfalfa and soybean) may benefit from greater rhizobial partner abundance in inocula, others such as fava bean may be inhibited, highlighting strong host-specific differences in how cover crop-derived rhizobial partners influence nodulation and plant performance. DISCUSSION Understanding how legume–rhizobium compatibility shapes symbiotic nitrogen fixation is critical for predicting microbial contributions to agricultural productivity ( Herridge et al., 2008 ; Gopalakrishnan et al., 2015 ). Here, we examined how microbial communities, especially rhizobial communities, shaped by legume cover crops influenced nodule microbial communities and nitrogen fixation in subsequent legume cash crops. We found that higher abundances of cash crop rhizobial partners in cover crop nodule inocula corresponded with improved plant performance in two of the four cash crop species. These results reveal a previously underexplored role of rhizobial compatibility between cover crops and cash crops in enhancing downstream symbiotic outcomes for certain cash crop species. Bacterial diversity was much higher in rhizosphere communities than in nodule communities of cover crops, reflecting strong host filtering during cover crop symbiosis. This pattern persisted in the subsequent cash crops, as the identity of rhizobial partners in cash crop nodules remained largely consistent with only slight variation in their relative abundances across different cover crop inocula. Nevertheless, cover crop identity significantly affected nodulation and, to a lesser extent, total biomass of cash crops, suggesting that legume cover crops can leave lasting effects on subsequent plant performance. Patterns of Rhizobium relative abundance in common and fava bean nodules showed subtle variation across cover crop inocula, reflecting differences in the dominant rhizobial partners associated with each cover crop. In both cash crop species, Rhizobium relative abundance in nodules was lowest when plants were grown with bush clover nodule inocula and highest with crimson clover nodule inocula. This pattern likely reflects variation in partner overlap, as bush clover nodules were dominated by Bradyrhizobium , whereas crimson clover nodules were dominated by Rhizobium . A similar pattern was observed in fava bean nodules with rhizosphere inocula, in which Rhizobium relative abundance was enriched under crimson clover compared to the rhizospheres of cover crops that host different rhizobial partners (i.e. bush clover and yellow sweet clover). Common bean is a highly promiscuous host, capable of forming associations with more than 20 Rhizobium species, including R. leguminosarum sv. phaseoli ( Efstathiadou, et al., 2021 ; Segovia et al., 1993 ). In contrast, fava bean is much more selective, primarily associating with R. leguminosarum sv. viciae ( Mendoza-Suárez et al., 2024 ). Since crimson clover nodules harbor R. leguminosarum sv. trifolii ( Smith et al., 1982 ), the observed enrichment of Rhizobium in fava bean and common bean nodules likely reflects recruitment of R. leguminosarum sv. trifolii from crimson clover nodules and rhizosphere. Such cross-inoculation events can occur when closely related R. leguminosarum symbiovars share conserved nodulation genes, allowing limited nodulation beyond their typical host range ( Boivin et al., 2020 ). However, 16S rRNA gene sequencing does not provide sufficient resolution to confirm which symbiovar was enriched in these nodules. Together, these findings suggest that while the specificity of cash crop rhizobial partnerships remains stable, the relative abundance of compatible rhizobial partners can vary among legumes regardless of host promiscuity. Non-rhizobial bacteria were frequently detected in common bean and soybean nodules, supporting the observation that legume nodules often harbor diverse microbes beyond their primary rhizobial partners ( Martinez-Hidalgo and Hirsch, 2017 ; Kosmopoulos et al., 2024 ). In cover crop rhizosphere inocula, common bean nodules were dominated by Flavobacterium rather than their characteristic rhizobial partner, Rhizobium . Flavobacterium has been detected in the nodules of several legume species, including common bean, though the influence of host metabolic or immune traits on its colonization remains unclear ( Yu et al., 2025 ; Rocha et al., 2023 ). Common bean plants grown with cover crop rhizosphere inocula had greater biomass and a higher proportion of flowered plants than those with nodule inocula, suggesting that nodule formation excludes beneficial members of the rhizosphere community important for plant growth. Moreover, the higher relative abundance of Rhizobium in nodules from nodule inocula (2–57%) compared with rhizosphere inocula (7–24%) indicates that nodule inocula may have favored inefficient or parasitic Rhizobium strains, further contributing to reduced plant performance. In soybean, nodules were largely colonized by Pseudomonas , with Bradyrhizobium as the main rhizobial partner. Psuedomonas is also a promiscuous non-rhizobial nodule inhabitant across legume species with species supporting a wide range of functions in association with plants ( Yu et al., 2025 ). However, co-inoculation of Bradyrhizobium japonicum with Pseudomonas brassicacearum was reported to reduce nodule numbers and acetylene reduction activity in soybean ( Chebotar et al., 2001 ). Here, cover crop inocula had only marginal effects on soybean biomass compared to the non-inoculated control, suggesting that Pseudomonas nodule enrichment may have interfered with or displaced rhizobia, limiting their contribution to soybean growth. These findings highlight that non-rhizobial occupants of legume nodules may modulate the outcomes of symbiosis, either by enhancing or disrupting interactions between plants and their rhizobial partners. Only alfalfa exhibited a biomass response proportional to the abundance of its rhizobial partners in cover crop inocula, with yellow sweet clover nodule inocula contributing most strongly. A similar pattern was observed for alfalfa nodule number in yellow sweet clover rhizosphere inocula, suggesting that yellow sweet clover can enrich the soil with rhizobia beneficial to alfalfa (i.e. Sinorhizobium meliloti ). This compatible partner overlap indicates that yellow sweet clover may serve as an effective cover crop for promoting alfalfa-associated rhizobial communities and enhancing plant growth. For soybean, high relative abundance of rhizobial partners in cover crop nodule inocula was associated with increased nodule numbers, but this did not translate into measurable growth benefits. This may be because the Bradyrhizobium enriched by cowpea and bush clover was not an optimal match for soybean ( Bradyrhizobium japonicum vs. other compatible strains). These results demonstrate that nodulation alone is not always a reliable indicator of plant performance, as effective nitrogen fixation and growth benefits also depend on the compatibility and efficiency of the specific rhizobial strains involved. For instance, Bradyrhizobium japonicum strains can differ widely in both nitrogen-fixing capacity and competitiveness for soybean nodule occupancy ( Hungria et al., 1998 ; Smith and Wollum, 1989 ), suggesting that cover crops capable of enriching high-efficiency, compatible Bradyrhizobium strains could substantially enhance soybean performance. However, such targeted cover crop systems have yet to be developed, underscoring the need to identify or engineer cover crops that promote beneficial rhizobial populations for soybean. Cover crop identity was a strong determinant of nodule number and, to a lesser extent, total biomass across cash crop species. This indicates that cash crop growth responses were not solely driven by the presence or abundance of compatible rhizobial partners but may have been influenced by broader microbial community dynamics. Interactions among non-rhizobial bacteria, fungi, and potential plant pathogens may have played important roles in shaping host responses. For instance, co-inoculation of arbuscular mycorrhizal fungi and rhizobia can enhance soybean nutrient uptake, suggesting that shifts in the overall microbial community structure induced by different cover crops could substantially affect plant outcomes ( Meng et al., 2015 ). However, among the cash crops, the strongest effect of cover crop identity on nodulation and growth was observed in alfalfa, likely reflecting the presence or absence of compatible Sinorhizobium partners in the cover crop inocula. Inocula derived from cowpea, crimson clover, field pea, and white clover, cover crops that do not associate with Sinorhizobium , supported little to no alfalfa nodulation or growth. In these treatments, alfalfa performance was comparable to the non-inoculated control, whereas substantial nodulation and biomass accumulation occurred with bush clover and yellow sweet clover inocula. This pattern reinforces the high specificity of the alfalfa– Sinorhizobium symbiosis, in which effective nodulation typically requires the presence of characteristic strains such as S. meliloti or S. medicae . Together, we found that rhizobial compatibility between cover crops and cash crops can shape cash crop symbiotic outcomes. Benefits of partner overlap were most apparent for alfalfa and yellow sweet clover, which share the same rhizobial partner, suggesting that yellow sweet clover could serve as an effective cover crop to enhance alfalfa rhizobial populations and performance outcomes. Extending the duration of cover crop conditioning or monitoring microbial succession across multiple growing seasons could provide deeper insight into how these soil microbial legacies develop and persist. Similarly, refining cover crop–cash crop pairings based on strain-level rhizobial compatibility may reveal stronger and more consistent benefits for cash crop nodulation and nitrogen fixation. Additional mechanisms may also influence cash crop performance following prior cover crop cultivation and warrant further investigation, including shifts in root exudate composition, changes in microbial community structure, and altered nutrient availability ( Seitz et al., 2024 ; Gosh et al., 2024). Overall, these findings highlight the potential to strategically select cover crops that foster effective microbial partnerships in subsequent legumes, improving both crop productivity and sustainable nitrogen management in legume-based systems. Authorship credits L.T.B. conceived the research and designed the experiments. E.L.P. and E.B. executed the phase 1 experiment. E.L.P. and J.E.H. prepared and assessed the microbial inoculants. E.L.P., E.B., A.P.J., A.L.S., and K.M. executed the phase 2 experiment. C.L.D. and A.L.S. processed samples and performed DNA extractions. K.M.C. performed the analyses and drafted the manuscript. All the authors reviewed, edited, and approved the final paper. SUPPLEMENTAL Download figure Open in new tab Supplemental Figure 1. The effect of cover crop inocula on the proportion of flowered cash crops after approximately 12 weeks of growth. Alfalfa is excluded since no plants flowered. Download figure Open in new tab Supplemental Figure 2. The relationship between cash crop rhizobial partner ASV relative abundance in rhizosphere inoculum and (C) nodule number or (D) total biomass. Each dot represents an individual sample and the lines are the regression lines of best fit. The grey shading surrounding the lines represents the 95% confidence interval. Download figure Open in new tab Supplemental Figure 3. The proportion of cash crop rhizobial ASVs present in each replicate cover crop-inoculum combination in phase 1. Cash crop rhizobial ASVs were identified as those matching known taxonomic classifications at the genus level and exceeding a minimum abundance threshold in at least two cover-crop inoculum combinations in phase 2. Cross marks indicate an insufficient number of nodules for microbiome assessment. Download figure Open in new tab Supplemental Figure 4. Flow cytometry was used to quantify rhizosphere and nodule inoculant cell concentrations per replicate and cover crop prior to phase 2 inoculations. Log-transformed counts were analyzed with a linear mixed-effects model, showing significant effects of inoculum compartment (F 1,473 =201.9, p<0.001), cover crop (F 5,473 =170.6, p<0.001), and their interaction (F 5,473 =110.5, p<0.001). View this table: View inline View popup Supplemental Table 1. Shannon and species (ASV) richness diversity indices for the rhizosphere and nodule cover crop communities. P-values were adjusted using the Benjamini-Hochberg method to correct for multiple comparisons. View this table: View inline View popup Download powerpoint Supplemental Table 2. Analysis of variance (ANOVA) was performed on square-root transformed nodule counts using linear models with Type III sums of squares. Significance of model terms was assessed using F-tests. View this table: View inline View popup Download powerpoint Supplemental Table 3. Analysis of variance (ANOVA) was performed on total plant biomass using linear models with Type III sums of squares. Significance of model terms was assessed using F-tests. View this table: View inline View popup Download powerpoint Supplemental Table 4. Analysis of variance (ANOVA) for linear models assessing the effect of square-root–transformed rhizobia relative abundance in phase 1 nodule inoculum on nodule number and total biomass in cash crops. F-tests were used to assess significance and model R² values are reported. View this table: View inline View popup Download powerpoint Supplemental Table 5. Analysis of variance (ANOVA) for linear models assessing the effect of square-root–transformed rhizobia relative abundance in phase 1 rhizosphere inoculum on nodule number and total biomass in cash crops. F-tests were used to assess significance and model R² values are reported. View this table: View inline View popup Supplemental Table 6. Logistic regression analysis of proportion of flowered plants for each cash crop. Alfalfa is excluded since no plants flowered. Likelihood ratio tests were used for significance testing. View this table: View inline View popup Download powerpoint Supplemental Table 7. Analysis of variance (ANOVA) for linear mixed-effects models testing the effect of inoculum cell count on total biomass and nodule number for each cash crop. F-tests were used for significance testing. Acknowledgments We would like to acknowledge the Huck Institutes’ Flow Cytometry Core Facility (RRID:SCR_024460) for use of the BD Fortessa Flow Cytometer, the Penn State Institute for Computational and Data Sciences (RRID:SCR_025154) for providing access to computational infrastructure through the Roar Core Facility (RRID:SCR_026424), and the Huck Institutes’ Genomics Core Facility (RRID:SCR_023645) for library preparation and use of the Illumina NextSeq 2000. We would also like to thank Maria Alejandra Gil-Polo and Patrick Sydow for their help picking nodules, Jason Kaye and Brosi Bradley for the long-term cover crop experiment soil used in phase 1, and Scott Diloreto for greenhouse support and plant care advice. This work was supported by HATCH grant no. 1025611, USDA-NIFA Award # 2022-67013-36860, and Penn State start-up funds to L.T.B. Funder Information Declared USDA-NIFA , 2022-67013-36860 HATCH , 1025611 REFERENCES ↵ Andrews , S. ( 2010 ). FastQC: a quality control tool for high throughput sequence data . Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc ↵ Apprill , A. , McNally , S. , Parsons , R. , & Weber , L . ( 2015 ). Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton . Aquatic Microbial Ecology , 75 ( 2 ), 129 – 137 . doi: 10.3354/ame01753 OpenUrl CrossRef PubMed Ballard , R. A. , Charman , N. , McInnes , A. , & Davidson , J. A . ( 2004 ). Size, symbiotic effectiveness and genetic diversity of field pea rhizobia ( Rhizobium leguminosarum bv. viciae ) populations in South Australian soils . Soil Biology and Biochemistry , 36 ( 8 ), 1347 – 1355 . doi: 10.1016/j.soilbio.2004.04.016 OpenUrl CrossRef ↵ Barker , D. G. , Pfaff , T ., Moreau , D. , Groves , E. , Ruffel , S ., Lepetit , M. , Whitehand , S. , Maillet , F. , Nair , R.M. , Journet , E.P. ( 2006 ). Growing M. truncatula: choice of substrates and growth conditions . The Medicago truncatula handbook . 1 – 28 . ↵ Bates , D. , Mächler , M. , Bolker , B. , & Walker , S . ( 2015 ). Fitting Linear Mixed-Effects Models Using lme4 . Journal of Statistical Software , 67 , 1 – 48 . doi: 10.18637/jss.v067.i01 OpenUrl CrossRef PubMed ↵ Batstone , R. T. , Ibrahim , A. , & MacLean , L. T . ( 2023 ). Microbiomes: Getting to the root of the rhizobial competition problem in agriculture . Current Biology , 33 ( 14 ), R777 – R780 . doi: 10.1016/j.cub.2023.05.053 OpenUrl CrossRef PubMed ↵ Benjamini , Y. , & Hochberg , Y . ( 1995 ). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing . Journal of the Royal Statistical Society. Series B (Methodological ), 57 ( 1 ), 289 – 300 . OpenUrl CrossRef PubMed Web of Science ↵ Boivin , S. , Ait Lahmidi , N. , Sherlock , D. , Bonhomme , M. , Dijon , D. , Heulin-Gotty , K. , Le-Queré , A. , Pervent , M. , Tauzin , M. , Carlsson , G. , Jensen , E. , Journet , E.-P. , Lopez-Bellido , R. , Seidenglanz , M. , Marinkovic , J. , Colella , S. , Brunel , B. , Young , P. , & Lepetit , M . ( 2020 ). Host-specific competitiveness to form nodules in Rhizobium leguminosarum symbiovar viciae . New Phytologist , 226 ( 2 ), 555 – 568 . doi: 10.1111/nph.16392 OpenUrl CrossRef PubMed ↵ Burghardt , L. T . ( 2020 ). Evolving together, evolving apart: Measuring the fitness of rhizobial bacteria in and out of symbiosis with leguminous plants . New Phytologist , 228 ( 1 ), 28 – 34 . doi: 10.1111/nph.16045 OpenUrl CrossRef ↵ Burghardt , L. T. , & diCenzo , G. C . ( 2023 ). The evolutionary ecology of rhizobia: Multiple facets of competition before, during, and after symbiosis with legumes . Current Opinion in Microbiology , 72 , 102281 . doi: 10.1016/j.mib.2023.102281 OpenUrl CrossRef PubMed ↵ Callahan , B. J. , McMurdie , P. J. , Rosen , M. J. , Han , A. W. , Johnson , A. J. A. , & Holmes , S. P . ( 2016 ). DADA2: High-resolution sample inference from Illumina amplicon data . Nature Methods , 13 ( 7 ), 581 – 583 . doi: 10.1038/nmeth.3869 OpenUrl CrossRef PubMed ↵ Chebotar , V. K. , Asis , C. A. , & Akao , S . ( 2001 ). Production of growth-promoting substances and high colonization ability of rhizobacteria enhance the nitrogen fixation of soybean when coinoculated with Bradyrhizobium japonicum . Biology and Fertility of Soils , 34 ( 6 ), 427 – 432 . doi: 10.1007/s00374-001-0426-4 OpenUrl CrossRef ↵ Checcucci , A. , DiCenzo , G. C. , Bazzicalupo , M. , & Mengoni , A . ( 2017 ). Trade, Diplomacy, and Warfare: The Quest for Elite Rhizobia Inoculant Strains . Frontiers in Microbiology , 8 . doi: 10.3389/fmicb.2017.02207 OpenUrl CrossRef ↵ Cloutier , M. L. , Murrell , E. , Barbercheck , M. , Kaye , J. , Finney , D. , García-González , I. , & Bruns , M. A . ( 2020 ). Fungal community shifts in soils with varied cover crop treatments and edaphic properties . Scientific Reports , 10 ( 1 ), 6198 . doi: 10.1038/s41598-020-63173-7 OpenUrl CrossRef ↵ Cole , J. R. , Wang , Q. , Fish , J. A. , Chai , B. , McGarrell , D. M. , Sun , Y. , Brown , C. T. , Porras-Alfaro , A. , Kuske , C. R. , & Tiedje , J. M . ( 2014 ). Ribosomal Database Project: Data and tools for high throughput rRNA analysis . Nucleic Acids Research , 42 (Database issue), D633 – D642 . doi: 10.1093/nar/gkt1244 OpenUrl CrossRef PubMed Web of Science Delestre , C. , Laugraud , A. , Ridgway , H. , Ronson , C. , O’Callaghan , M. , Barrett , B. , Ballard , R. , Griffiths , A ., Young , S. , Blond , C. , Gerard , E. , & Wakelin , S . ( 2015 ). Genome sequence of the clover symbiont Rhizobium leguminosarum bv. trifolii strain CC275e . Standards in Genomic Sciences , 10 ( 1 ), 121 . doi: 10.1186/s40793-015-0110-1 OpenUrl CrossRef ↵ Denison , R. F. , & Kiers , E. T . ( 2004 ). Lifestyle alternatives for rhizobia: Mutualism, parasitism, and forgoing symbiosis . FEMS Microbiology Letters , 237 ( 2 ), 187 – 193 . doi: 10.1111/j.1574-6968.2004.tb09695.x OpenUrl CrossRef PubMed ↵ Denison , R. F. , & Kiers , E. T . ( 2011 ). Life Histories of Symbiotic Rhizobia and Mycorrhizal Fungi . Current Biology , 21 ( 18 ), R775 – R785 . doi: 10.1016/j.cub.2011.06.018 OpenUrl CrossRef PubMed ↵ Efstathiadou , E. , Ntatsi , G. , Savvas , D. , & Tampakaki , A. P . ( 2021 ). Genetic characterization at the species and symbiovar level of indigenous rhizobial isolates nodulating Phaseolus vulgaris in Greece . Scientific Reports , 11 ( 1 ), 8674 . doi: 10.1038/s41598-021-88051-8 OpenUrl CrossRef ↵ Fernandes , A. D. , Macklaim , J. M. , Linn , T. G. , Reid , G. , & Gloor , G. B . ( 2013 ). ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq . PLOS ONE , 8 ( 7 ), e67019 . doi: 10.1371/journal.pone.0067019 OpenUrl CrossRef PubMed ↵ Finney , D. M. , Murrell , E. G. , White , C. M. , Baraibar , B. , Barbercheck , M. E. , Bradley , B. A. , Cornelisse , S. , Hunter , M. C. , Kaye , J. P. , Mortensen , D. A. , Mullen , C. A. , & Schipanski , M. E . ( 2017 ). Ecosystem Services and Disservices Are Bundled in Simple and Diverse Cover Cropping Systems . Agricultural & Environmental Letters , 2 ( 1 ), 170033 . doi: 10.2134/ael2017.09.0033 OpenUrl CrossRef ↵ Galloway , J. N. , Townsend , A. R. , Erisman , J. W. , Bekunda , M. , Cai , Z. , Freney , J. R. , Martinelli , L. A. , Seitzinger , S. P. , & Sutton , M. A . ( 2008 ). Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions . Science , 320 ( 5878 ), 889 – 892 . doi: 10.1126/science.1136674 OpenUrl Abstract / FREE Full Text ↵ Gentleman R , Carey V , Huber W , Hahne F . 2023 . _genefilter: genefilter: methods for filtering genes from high-throughput experiments_ . doi: 10.18129/B9.bioc.genefilter . R package version 1.84.0, https://bioconductor.org/packages/genefilter/ . OpenUrl CrossRef Ghosh , D. , Shi , Y. , Zimmermann , I. M. , Stürzebecher , T. , Holzhauser , K. , von Bergen , M. , Kaster , A.-K. , Spielvogel , S. , Dippold , M. A. , Müller , J. A. , & Jehmlich , N. ( 2024 ). Cover crop monocultures and mixtures enhance bacterial abundance and functionality in the maize root zone . ISME Communications , 4 ( 1 ), ycae132. doi: 10.1093/ismeco/ycae132 OpenUrl CrossRef ↵ Gloor , G. B. , Macklaim , J. M. , Pawlowsky-Glahn , V. , & Egozcue , J. J . ( 2017 ). Microbiome Datasets Are Compositional: And This Is Not Optional . Frontiers in Microbiology , 8 . doi: 10.3389/fmicb.2017.02224 OpenUrl CrossRef PubMed ↵ Gopalakrishnan , S. , Sathya , A. , Vijayabharathi , R. , Varshney , R. K. , Gowda , C. L. L. , & Krishnamurthy , L . ( 2015 ). Plant growth promoting rhizobia: Challenges and opportunities . 3 Biotech , 5 ( 4 ), 355 – 377 . doi: 10.1007/s13205-014-0241-x OpenUrl CrossRef ↵ Herridge , D. F. , Peoples , M. B. , & Boddey , R. M . ( 2008 ). Global inputs of biological nitrogen fixation in agricultural systems . Plant and Soil , 311 ( 1 ), 1 – 18 . doi: 10.1007/s11104-008-9668-3 OpenUrl CrossRef Web of Science Hu , L. , Busby , R. R. , Gebhart , D. L. , & Yannarell , A. C . ( 2014 ). Invasive Lespedeza cuneata and native Lespedeza virginica experience asymmetrical benefits from rhizobial symbionts . Plant and Soil , 384 ( 1 ), 315 – 325 . doi: 10.1007/s11104-014-2213-7 OpenUrl CrossRef Huang , R. , Snedden , W. A. , & diCenzo , G. C . ( 2022 ). Reference nodule transcriptomes for Melilotus officinalis and Medicago sativa cv . Algonquin. Plant Direct , 6 ( 6 ), e408 . doi: 10.1002/pld3.408 OpenUrl CrossRef PubMed ↵ Hungria , M. , Boddey , L. H. , Santos , M. A. , & Vargas , M. A. T . ( 1998 ). Nitrogen fixation capacity and nodule occupancy by Bradyrhizobium japonicum and B. elkanii strains . Biology and Fertility of Soils , 27 ( 4 ), 393 – 399 . doi: 10.1007/s003740050449 OpenUrl CrossRef ↵ Johnson , M. , Zaretskaya , I. , Raytselis , Y. , Merezhuk , Y. , McGinnis , S. , & Madden , T. L . ( 2008 ). NCBI BLAST: A better web interface . Nucleic Acids Research , 36 (suppl_ 2 ), W5 – W9 . doi: 10.1093/nar/gkn201 OpenUrl CrossRef PubMed Web of Science ↵ Kaminsky , L. M. , Trexler , R. V. , Malik , R. J. , Hockett , K. L. , & Bell , T. H . ( 2019 ). The Inherent Conflicts in Developing Soil Microbial Inoculants . Trends in Biotechnology , 37 ( 2 ), 140 – 151 . doi: 10.1016/j.tibtech.2018.11.011 OpenUrl CrossRef PubMed ↵ Kaye , J. , Finney , D. , White , C. , Bradley , B. , Schipanski , M. , Alonso-Ayuso , M. , Hunter , M. , Burgess , M. , & Mejia , C . ( 2019 ). Managing nitrogen through cover crop species selection in the U.S. mid-Atlantic . PLOS ONE , 14 ( 4 ), e0215448 . doi: 10.1371/journal.pone.0215448 OpenUrl CrossRef ↵ Kosmopoulos , J. C. , Batstone-Doyle , R. T. , & Heath , K. D . ( 2024 ). Co-inoculation with novel nodule-inhabiting bacteria reduces the benefits of legume–rhizobium symbiosis . Canadian Journal of Microbiology , 70 ( 7 ), 275 – 288 . doi: 10.1139/cjm-2023-0209 OpenUrl CrossRef PubMed ↵ Kuznetsova , A. , Brockhoff , P. B ., & Christensen , R. H. B. ( 2017 ). lmerTest Package: Tests in Linear Mixed Effects Models . Journal of Statistical Software , 82 , 1 – 26 . doi: 10.18637/jss.v082.i13 OpenUrl CrossRef ↵ Liebman , A. M. , Grossman , J. , Brown , M. , Wells , M. S. , Reberg-Horton , S. C. , & Shi , W . ( 2018 ). Legume Cover Crops and Tillage Impact Nitrogen Dynamics in Organic Corn Production . Agronomy Journal , 110 ( 3 ), 1046 – 1057 . doi: 10.2134/agronj2017.08.0474 OpenUrl CrossRef Liu , X. Y. , Wang , E. T. , Li , Y. , & Chen , W. X . ( 2007 ). Diverse bacteria isolated from root nodules of Trifolium , Crotalaria and Mimosa grown in the subtropical regions of China . Archives of Microbiology , 188 ( 1 ), 1 – 14 . doi: 10.1007/s00203-007-0209-x OpenUrl CrossRef PubMed Web of Science ↵ Martin , M . ( 2011 ). Cutadapt removes adapter sequences from high-throughput sequencing reads . EMBnet.journal 17 : 10 . OpenUrl ↵ Martinez-Hidalgo , P. , & Hirsch , A. M . ( 2017 ). The Nodule Microbiome: N2-Fixing Rhizobia Do Not Live Alone . Phytobiomes Journal , 1 ( 2 ), 70 – 82 . doi: 10.1094/PBIOMES-12-16-0019-RVW OpenUrl CrossRef ↵ McMurdie , P. J. , & Holmes , S . ( 2013 ). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data . PLOS ONE , 8 ( 4 ), e61217 . doi: 10.1371/journal.pone.0061217 OpenUrl CrossRef PubMed ↵ Mendoza-Suárez , M. , Akyol , T. Y. , Nadzieja , M. , & Andersen , S. U . ( 2024 ). Increased diversity of beneficial rhizobia enhances faba bean growth . Nature Communications , 15 ( 1 ), 10673 . doi: 10.1038/s41467-024-54940-5 OpenUrl CrossRef ↵ Meng , L. , Zhang , A. , Wang , F. , Han , X. , Wang , D. , & Li , S . ( 2015 ). Arbuscular mycorrhizal fungi and rhizobium facilitate nitrogen uptake and transfer in soybean/maize intercropping system . Frontiers in Plant Science , 6 , 339 . doi: 10.3389/fpls.2015.00339 OpenUrl CrossRef ↵ Mutch , L. A. , & Young , J. P. W . ( 2004 ). Diversity and specificity of Rhizobium leguminosarum biovar viciae on wild and cultivated legumes . Molecular Ecology , 13 ( 8 ), 2435 – 2444 . doi: 10.1111/j.1365-294X.2004.02259.x OpenUrl CrossRef PubMed Web of Science Oksanen , J. F. , Blanchet , G. , Friendly , M. , Kindt , R. , Legendre , P. , McGlinn , D. , Minchin , P. R. , O’Hara , R. B ., Simpson , G. L. , Solymos , P ., et al. ( 2019 ). Vegan: Community Ecology Package . https://cran.r-project.org/package=vegan ↵ Parada , A. E. , Needham , D. M. , & Fuhrman , J. A . ( 2016 ). Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples . Environmental Microbiology , 18 ( 5 ), 1403 – 1414 . doi: 10.1111/1462-2920.13023 OpenUrl CrossRef PubMed ↵ Parr , M. , Grossman , J. M. , Reberg-Horton , S. C. , Brinton , C. , & Crozier , C . ( 2011 ). Nitrogen Delivery from Legume Cover Crops in No-Till Organic Corn Production . Agronomy Journal , 103 ( 6 ), 1578 – 1590 . doi: 10.2134/agronj2011.0007 OpenUrl CrossRef Peters , N. K. , Frost , J. W. , & Long , S. R . ( 1986 ). A Plant Flavone, Luteolin, Induces Expression of Rhizobium meliloti Nodulation Genes . Science , 233 ( 4767 ), 977 – 980 . doi: 10.1126/science.3738520 OpenUrl Abstract / FREE Full Text ↵ Poole , P. , Ramachandran , V. , & Terpolilli , J . ( 2018 ). Rhizobia: From saprophytes to endosymbionts . Nature Reviews Microbiology , 16 ( 5 ), 291 – 303 . doi: 10.1038/nrmicro.2017.171 OpenUrl CrossRef ↵ Rocha , R. , Lopes , T. , Fidalgo , C. , Alves , A. , Cardoso , P. , & Figueira , E . ( 2023 ). Bacteria Associated with the Roots of Common Bean ( Phaseolus vulgaris L.) at Different Development Stages: Diversity and Plant Growth Promotion . Microorganisms , 11 ( 1 ), 57 . doi: 10.3390/microorganisms11010057 OpenUrl CrossRef Rome , S. , Fernandez , M. P. , Brunel , B. , Normand , P. , & Cleyet-Marel , J.-C . ( 1996 ). Sinorhizobium medicae sp. nov. , Isolated from Annual Medicago spp . International Journal of Systematic and Evolutionary Microbiology , 46 ( 4 ), 972 – 980 . doi: 10.1099/00207713-46-4-972 OpenUrl CrossRef PubMed ↵ Segovia , L. , Young , J. P. W. , & Martinez-Romero , E . ( 1993 ). Reclassification of American Rhizobium leguminosarum Biovar Phaseoli Type I Strains as Rhizobium etli sp. nov . International Journal of Systematic and Evolutionary Microbiology , 43 ( 2 ), 374 – 377 . doi: 10.1099/00207713-43-2-374 OpenUrl CrossRef PubMed ↵ Seitz , V. A. , McGivern , B. B. , Borton , M. A. , Chaparro , J. M. , Schipanski , M. E. , Prenni , J. E. , & Wrighton , K. C . ( 2024 ). Cover crop root exudates impact soil microbiome functional trajectories in agricultural soils . Microbiome , 12 ( 1 ), 183 . doi: 10.1186/s40168-024-01886-x OpenUrl CrossRef Shamseldin , A. , & Velázquez , E . ( 2020 ). The promiscuity of Phaseolus vulgaris L. (common bean) for nodulation with rhizobia: A review . World Journal of Microbiology & Biotechnology , 36 ( 5 ), 63 . doi: 10.1007/s11274-020-02839-w OpenUrl CrossRef ↵ Simbine , M. G. , Mohammed , M. , Jaiswal , S. K. , & Dakora , F. D . ( 2021 ). Functional and genetic diversity of native rhizobial isolates nodulating cowpea ( Vigna unguiculata L . Walp.) in Mozambican soils. Scientific Reports , 11 , 12747 . doi: 10.1038/s41598-021-91889-7 OpenUrl CrossRef ↵ Smith , G. B. , & Wollum , A. G . ( 1989 ). Nodulation of Glycine max by Six Bradyrhizobium japonicum Strains with Different Competitive Abilities . Applied and Environmental Microbiology , 55 ( 8 ), 1957 – 1962 . doi: 10.1128/aem.55.8.1957-1962.1989 OpenUrl Abstract / FREE Full Text ↵ Smith , G. R. , Knight , W. E. , Peterson , H. L. , & Hagedorn , C . ( 1982 ). The Effect of Rhizobium trifolii Strains and Crimson Clover Genotypes on N2 Fixation . Crop Science , 22 ( 5 ), cropsci1982.0011183X002200050017x. doi: 10.2135/cropsci1982.0011183X002200050017x OpenUrl CrossRef Steenkamp , E. T. , Stępkowski , T. , Przymusiak , A. , Botha , W. J. , & Law , I. J . ( 2008 ). Cowpea and peanut in southern Africa are nodulated by diverse Bradyrhizobium strains harboring nodulation genes that belong to the large pantropical clade common in Africa . Molecular Phylogenetics and Evolution , 48 ( 3 ), 1131 – 1144 . doi: 10.1016/j.ympev.2008.04.032 OpenUrl CrossRef PubMed Web of Science Thilakarathna , M. S. , & Raizada , M. N . ( 2017 ). A meta-analysis of the effectiveness of diverse rhizobia inoculants on soybean traits under field conditions . Soil Biology and Biochemistry , 105 , 177 – 196 . doi: 10.1016/j.soilbio.2016.11.022 OpenUrl CrossRef ↵ Wagner , M. R. , Busby , P. E. , & Balint-Kurti , P . ( 2020 ). Analysis of leaf microbiome composition of near-isogenic maize lines differing in broad-spectrum disease resistance . New Phytologist , 225 ( 5 ), 2152 – 2165 . doi: 10.1111/nph.16284 OpenUrl CrossRef PubMed ↵ Walker , L. , Lagunas , B. , & Gifford , M. L. ( 2020 ). Determinants of Host Range Specificity in Legume-Rhizobia Symbiosis . Frontiers in Microbiology , 11 . doi: 10.3389/fmicb.2020.585749 OpenUrl CrossRef ↵ Wickham , H. ( 2016 ). ggplot2: Elegant Graphics for Data Analysis ( 2nd ed.). Springer Publishing Company, Incorporated . ↵ Wickham , H. , Averick , M. , Bryan , J. , Chang , W. , McGowan , L. D. , François , R. , Grolemund , G. , Hayes , A. , Henry , L. , Hester , J. , Kuhn , M. , Pedersen , T. L. , Miller , E. , Bache , S. M. , Müller , K. , Ooms , J. , Robinson , D. , Seidel , D. P. , Spinu , V. , … Yutani , H . ( 2019 ). Welcome to the Tidyverse . Journal of Open Source Software , 4 ( 43 ), 1686 . doi: 10.21105/joss.01686 OpenUrl CrossRef PubMed ↵ Yan , J. , Han , X. Z. , Ji , Z. J. , Li , Y. , Wang , E. T. , Xie , Z. H. , & Chen , W. F . ( 2014 ). Abundance and diversity of soybean-nodulating rhizobia in black soil are impacted by land use and crop management . Applied and Environmental Microbiology , 80 ( 17 ), 5394 – 5402 . doi: 10.1128/AEM.01135-14 OpenUrl Abstract / FREE Full Text Yao , Z. Y. , Kan , F. L. , Wang , E. T. , Wei , G. H. , & Chen , W. X . ( 2002 ). Characterization of rhizobia that nodulate legume species of the genus Lespedeza and description of Bradyrhizobium yuanmingense sp. nov . International Journal of Systematic and Evolutionary Microbiology , 52 ( 6 ), 2219 – 2230 . doi: 10.1099/00207713-52-6-2219 OpenUrl CrossRef PubMed Web of Science ↵ Yu , Y.-H. , Crosbie , D. B. , & Arancibia , M. M . ( 2025 ). Pseudomonas in the spotlight: Emerging roles in the nodule microbiome . Trends in Plant Science , 30 ( 5 ), 461 – 470 . doi: 10.1016/j.tplants.2024.12.002 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted October 24, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Cover crop microbiomes affect legume cash crop growth but not consistently through enriching nitrogen-fixing rhizobia Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Cover crop microbiomes affect legume cash crop growth but not consistently through enriching nitrogen-fixing rhizobia Kayla M. Clouse , Elizabeth L. Paillan , Cody L. DePew , Jennifer E. Harris , Amanda P. Jason , Kelsey Mercurio , Andy L. Swartley , Ellen Bingham , Liana T. Burghardt bioRxiv 2025.10.23.684197; doi: https://doi.org/10.1101/2025.10.23.684197 Share This Article: Copy Citation Tools Cover crop microbiomes affect legume cash crop growth but not consistently through enriching nitrogen-fixing rhizobia Kayla M. Clouse , Elizabeth L. Paillan , Cody L. DePew , Jennifer E. Harris , Amanda P. Jason , Kelsey Mercurio , Andy L. Swartley , Ellen Bingham , Liana T. Burghardt bioRxiv 2025.10.23.684197; doi: https://doi.org/10.1101/2025.10.23.684197 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Plant Biology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17691) Bioengineering (13892) Bioinformatics (41937) Biophysics (21452) Cancer Biology (18588) Cell Biology (25504) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88605) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15156) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)