Arbuscular Mycorrhizal Fungi and Rhizobium Improve Nutrient Uptake and Microbial Diversity Relative to Dryland Site-Specific Soil Conditions

Abstract

Arbuscular mycorrhizal fungi (AMF) and rhizobium play a significant role in plant symbiosis. However, their influence on the rhizosphere soil microbiome associated with nutrient acquisition and soil health is not well defined in the drylands of Montana (MT), USA. This study investigated the effect of microbial inoculants as seed treatment on pea yield, nutrient uptake, potential microbial functions, and rhizosphere soil microbial communities using high-throughput sequencing of 16S and ITS rRNA genes. The experiment was conducted under two contrasting dryland conditions with four treatments: control, single inoculation with AMF or Rhizobium, and dual inoculations of AMF and Rhizobium (AMF+Rhizobium). Our findings revealed that microbial inoculation efficacy was site-specific. AMF+Rhizobium synergistically increased grain yield at Sidney dryland field site (DFS) 2, while at Froid site, DFS 1, AMF improved plant resilience to acidic soil but contributed a marginal yield under non-nutrient limiting conditions. Across dryland sites, the plants' microbial dependency on AMF+Rhizobium (12%) was higher than single inoculations of AMF (8%) or Rhizobium (4%) alone. Variations in microbial community structure and composition indicate a site-specific response to AMF and AMF+Rhizobium inoculants. Overall, site-specific factors significantly influenced plant nutrient uptake, microbial community dynamics, and functional potential. It underscores the need for tailored management strategies that consider site-specific characteristics to optimize benefits from microbial inoculation.

Keywords: Rhizobium; agroecosystem; arbuscular mycorrhizal fungi (AMF); drylands; field pea; microbial inoculants; microbiome; root symbiosis.

Figure 1

The influence of microbial inoculants on (a) plant grain yield and (b) microbial dependency (%) based on plant biomass and grain yield at contrasting dryland site conditions. Error bars indicate standard error of the mean from five replications. Bars with common letters are not significantly different based on Wilcoxon tests at 0.05% probability level.

Figure 2

The effect of microbial inoculants on grain nutrient content (%) and plant nutrient uptake (kg/ha): carbon (a,b), nitrogen (c,d), and phosphorus (e,f) at two dryland field sites. The vertical bars in the least square means denote confidence intervals. Lines with common letters are not significantly different based on LSD tests at 0.05% probability level. Asterisk indicates dryland field site with significantly higher grain nutrient content and plant nutrient uptake, ** denotes significance level at p ≤ 0.001.

Figure 3

Effect of microbial inoculants on nutrient dynamics at contrasting dryland sites. Comparison of the initial soil nutrients vs. the effect of microbial inoculants on soil nutrient residuals after pea cropping between sites: soil N (a) and soil P residuals (b). Comparison of the plant nutrient uptake vs. the soil nutrient residuals across sites: plant N uptake vs. soil N residual (c) and grain P uptake vs. soil P residual (d). The vertical bars in the least square means denote confidence intervals. Lines with common letters are not significantly different based on LSD tests at 0.05% probability level. Asterisk indicates dryland field site or comparison between plant nutrient uptake and soil nutrient residual with significantly high nutrient levels, ** denotes significance level at p ≤ 0.001.

Figure 4

Site-specific effect response to microbial inoculation on microbial diversity. Alpha-diversity of bacterial (a) and fungal communities (b) was calculated as observed number of species per sample and visualized using box-plots. Beta-diversity of microbial communities among treatments at the two sites for the bacterial (c) and fungal communities (d), and between site comparisons for bacterial (e) and fungal communities (f). Beta diversity was calculated using the Bray–Curtis index and visualized using principal coordinate analysis (PCoA) ordination plots. The different groups are highlighted by ellipses showing a 95% confidence range and colored areas correspond to the bacterial and fungal community structure of the different treatments and sites.

Figure 5

Taxabar plots showing the microbial community profiles of bacterial (a) and fungal communities (b) at the phylum level of the different treatments. Heatmap showing the microbial community pattern at the phylum and order taxonomic level composition of the bacterial (c) and fungal communities (d) at two dryland field conditions.

Figure 6

The heat tree showing the microbial communities of AMF and AMF+Rhizobium associated with increased crop performance in DFS 1 and DFS 2, respectively. The taxonomic differences between AMF-treated bacterial (a) and fungal communities vs. the control (b) in DFS 1; and AMF+Rhizobium-treated bacterial (c) and fungal communities vs. the control (d) in DFS 2. The heat tree analysis leverages the hierarchical structure of taxonomic classifications quantitatively using the median abundance and statistically using the non-parametric Wilcoxon Rank Sum test [56]. The indicated taxa with red nodes were significantly abundant in the microbial-treated plants, while green and blue nodes were significantly sparse in the bacterial and fungal communities of the microbial-treated plants compared to the untreated control.

Figure 7

Impact of microbial inoculants on the relative abundance of potential microbial functions: functional profile of bacterial communities relative to C, N, and P nutrient cycling genes (a) predicted using Tax4Fun2 based on the 16S rRNA genes according to the KEGG Ortholog groups (KOs). Ecophysiological functions of fungal communities (b) relative to nutrient cycling, plant-microbe interaction, and soil health based on the FungalTraits database. Asterisk indicates microbial function with signficant difference between sites. * and ** denote significance levels at p ≤ 0.05 and p ≤ 0.001, respectively.