# Pre-sowing recurrent inoculation with Pseudomonas fluorescens promotes maize growth

Marine Papin, Amélie Polrot, Marie-Christine Breuil, Sonia Czarnes, Assia Dreux-Zigha, Xavier Le Roux, Ahmed Taibi, Aymé Spor, Laurent Philippot

► To cite this version:

Marine Papin, Amélie Polrot, Marie-Christine Breuil, Sonia Czarnes, Assia Dreux-Zigha, et al. Pre-sowing recurrent inoculation with Pseudomonas fluorescens promotes maize growth. Biology and Fertility of Soils, 2025, 61 (1), pp.125-140. 10.1007/s00374-024-01873-2. hal-04912713

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# Pre-sowing recurrent inoculation with *Pseudomonas fluorescens* promotes maize growth

Marine Papin<fnref n="1" /> · Amélie Polrot<fnref n="1" /> · Marie-Christine Breuil<fnref n="1" /> · Sonia Czarnes<fnref n="2" /> · Assia Dreux-Zigha<fnref n="3" /> · Xavier Le Roux<fnref n="2" /> · Ahmed Taibi<fnref n="3" /> · Aymé Spor<fnref n="1" /> · Laurent Philippot<fnref n="1" />

Received: 1 August 2024 / Revised: 4 October 2024 / Accepted: 7 October 2024
© The Author(s) 2024

## Abstract

Despite the rapid development of microbial inoculants use, their effectiveness still lacks robustness, partly due to our limited understanding of the factors influencing their establishment in soil. Recurrent inoculation can temporarily increase their abundance, but the effect of this inoculation strategy on plant growth and on the resident microbial community is still poorly studied. Here, we investigated maize growth and soil bacterial community responses under recurrent inoculation of the plant-beneficial bacterium *Pseudomonas fluorescens* B177. We further assessed how the effect of recurrent inoculation was modulated by the inoculant dose, the application timing and the soil type. Recurrent inoculation at high dose transiently increased the abundance of *P. fluorescens* B177 and resulted in larger shifts in the resident bacterial community compared to a single inoculation event. Moreover, recurrent inoculation prior to sowing had the strongest effect on maize growth, with increased shoot dry weight by 47.4%, likely due to an indirect effect of the inoculant through early changes in the resident community. Altogether these findings highlight the significance of recurrent pre-sowing inoculations as an alternative strategy for promoting plant growth.

**Keywords** Microbial inoculant · Recurrent inoculation · Impact · Plant growth promotion

## Introduction

Since the Green revolution, modern agriculture relies on the extensive use of agrochemicals to increase crop production and yields. This widespread application of fertilizers and pesticides have raised huge concerns due to their adverse effects on the environment and human health (Prashar and Shah 2016; Kim et al. 2017; Tripathi et al. 2020). With the urgent need to implement more sustainable practices for protecting the environment while maintaining agricultural

production, microbial inoculants are of growing interest as they can reduce the dependence on agrochemicals (Cana- fora et al. 2021). However, despite decades of research into soil microbial inoculants, their benefits for crop yields remain highly variable across soils and climates (Schmidt and Gaudin 2018), which eventually leads to a decline in farmers’ confidence (Khan et al. 2023). This idiosyncratic performance of microbial inoculants is partly due to our limited understanding of the main factors influencing their establishment in soil, which hinders our ability to predict their survival and manage their effectiveness (Albright et al. 2022).

According to invasion biology, the establishment of microbial inoculant is determined by three main processes i.e. environmental filtering, dispersal and biotic interactions (Albright et al. 2020). For instance, inoculant survival depends on abiotic factors, such as nutrient status, pH, temperature, and moisture. These edaphic factors can exert multiple stresses that affect cell viability (Van Veen et al. 1997; da Costa et al. 2020; Jiang et al. 2023). Moreover, the density of the inoculant, the number of applications, their frequency as well as the time between applications

<footnote>Laurent Philippot
Laurent.philippot@inrae.fr</footnote>

<footnote n="1">¹ Univ Bourgogne, INRAE, Institut Agro Dijon, Agroécologie, 17 Rue Sully, Dijon F-21000, France</footnote>

<footnote n="2">² Université Claude Bernard Lyon 1, Microbial Ecology Centre LEM, INRAE, CNRS, VetAgroSup, UMR INRAE 1418, 43 Blvd 11 Novembre 1918, Villeurbanne F-69622, France</footnote>

<footnote n="3">³ GreenCell Biopole Clermont Limagne, St Beauzire F-63360, France</footnote>
are all dispersion-related factors of importance for successful microbial inoculations (Simberloff 2009; Koontz et al. 2018; Dressler et al. 2019). Lastly, the establishment of the inoculant depends on biotic interactions with the resident community as competition, antagonism, cooperation or facilitation may either hinder or foster its survival (Mallon et al. 2015; Jones et al. 2017; Mawarda et al. 2020; Purtschert-Montenegro et al. 2022; Liu and Salles 2024). For example, more diverse communities can act as a biological barrier to invasion due to higher interference and resource competition (Tilman 2004; van Elsas et al. 2012).

While most research has focused on single applications of beneficial microbes (Poppeliers et al. 2023), recent studies suggested the use of recurrent inoculations (Dressler et al. 2019). Indeed, an experimental study has shown that recurrent inoculation can promote inoculant establishment by altering the composition of the resident community, causing its niche to shift away from the invader's resources (Mallon et al. 2018). Although recurrent inoculation was expected to have a stronger impact on the resident community than a single inoculation event (Mawarda et al. 2020; Jack et al. 2021), studies carried out to date have reported only a weak impact on the most abundant members of the resident community (Wang et al. 2023; Papin et al. 2024), or a transient impact with the resident community ultimately exhibiting resilience (Wang et al. 2021). Moreover recurrent inoculation can also improve wheat yield depending on the type of microbial inoculant (Chen et al. 2024). Despite plant-microbe interactions being influenced by the plant growth stage, little is known on the effects of inoculation timing on its effectiveness (Bashan 1986; Trivedi et al. 2021). However, since recurrent inoculations are spanning over a prolonged period during plant development, a better knowledge of the effect of the inoculation timing is needed to exploit the temporal dynamic in plant-microbe interactions, as it can also influence both the survival and performance of the inoculant.

In the present study, we examined the effect of recurrent inoculations, as opposed to a single inoculation, on the survival of the inoculant, morphological and physiological traits of maize plants, as well as on the structure and on the composition of the resident bacterial community. We also assessed how these effects were modulated by the inoculum density and the timing of inoculation in three different arable soils. We hypothesised that (i) recurrent inoculations would transiently increase inoculant abundance and promote rhizosphere colonisation but that its density would remain the main driver and (ii) the timing of inoculation would influence the effectiveness with earlier inoculation having stronger beneficial effect on plant growth.

# Materials and methods

## Plant cultivation

Three arable soils were collected from maize fields in southwest of France: Garlède (43°30′0.32″ N; 0°20′11.51″ W), Ger (43°14′4.125 N; 0°5′28.77″ W) and Meilhan (43°51′0.25″ N; 0°43′21.01″ W). Garlède and Ger soils were both characterized as silty loam while Meilhan was characterized as sandy loam. The physico-chemical properties of each soil are detailed in Supp. Table 1. Greenhouse maize culture was conducted in 10L-pots filled with either one of the three soils previously sieved (1.5 cm) and homogenized. Three maize seeds were sown in each pot (variety "Berlioz", Lidea seeds, France), within a 10 cm diameter area located in the center of the pot and after emergence, one plant per pot was kept. Fertilisation was conducted with 75% of the recommended dose and mixed forms of N (nitrate 5.9%, ammoniac 3.9% and urea 10.2%), P₂O₅ and K₂O. N, P, and K were each applied in equal amounts; 15 Kg.ha⁻¹ at sowing, 30 Kg.ha⁻¹ at 30 days after sowing (DAS), and 68 Kg.ha⁻¹ at 60 DAS. Temperature ranged from 15° to 24°C (night/day) and pots were watered every two days, avoiding inoculation periods to minimise leaching.

## Inoculation

The experiment was set up in a completely randomized design with five replications for each treatment consisting in combinations of three soils, two doses, and three sequences of inoculation (Fig. 1). The plant growth promoting rhizobacteria *Pseudomonas fluorescens* strain B177 (produced by GreenCell, France) was used as bacterial inoculant under a wettable powder formulation. The powder contained the lyophilised bacterial strain (10¹¹ cfu.g⁻¹) along with lactose, which serves as a cryoprotectant agent. Inoculation was carried out by resuspending the powder in water at a final concentration of 100 cfu.mL⁻¹ (thereafter referred to as the low dose i.e. 10 cfu.g⁻¹ dry soil) or 10⁹ cfu.mL⁻¹ (thereafter referred as the high dose i.e. 10⁸ cfu.g⁻¹ dry soil). Then, 100 mL of either of these cell suspensions were inoculated into a 10 cm radius area located in the centre of each pot. Three inoculation sequences were applied at high dose: (A) one inoculation at the sowing day, (B) three inoculations 14 days apart, from the sowing day (C) three inoculation 14 days apart, until the sowing day. Two sequences of inoculation were applied at low dose: (D) one inoculation at sowing day, and (E) three inoculations 14 days apart, from the sowing day. Non-inoculated pots were used as controls.
a. Inoculation treatments

<figure><img src="image_2.png" /><figcaption>**Fig. 1** Experimental design with the timeline of the different inoculation treatments (a) at high (10⁸ cfu.g dry soil⁻¹) or low dose (10 cfu.g dry soil⁻¹). Pots filled with soil were inoculated before or after sowing, either once, three times, or not at all. Three different soils were used and each treatment was replicated five times. Maize plants were cultivated 90 days in greenhouse condition, corresponding to the vegetative</figcaption></figure>

stage. Timeline of data measurements (b) with green boxes indicating plant traits measurements and grey boxes indicating bacterial community and inoculant survival measurements. Non-destructive plant traits and bulk soil sampling were measured regularly while destructive sampling was performed at the end of the experiment

Sampling and plant trait measurements

Bulk soil sampling was performed 30 days before sowing (DBS), then at 14, 45, and 90 days after sowing (DAS) with a 25 cm deep corer from each pot, close to the base crown of the plant (Czarnes et al. 2020). The rhizosphere was only collected at the end of the experiment (90 DAS). Briefly, after removing the loosely attached soil by gently shaking the roots, the adhering soil particles were washed with sterile water and collected by centrifugation (4,000 g, 20 min), referred to as rhizosphere samples. Sub-samples were stored at -20 °C until DNA extraction. Non-destructive plant traits were measured at 14, 45, 70, and 90 DAS (i.e. shoot length,

chlorophyll content and stem diameter). Shoot length was measured from the soil surface to the tip of the longest leaf. Chlorophyll content was assessed on the last fully expanded leaf with a SPAD-502Plus, KONICA MINOLTA (Argenta et al. 2004; Rostami 2008). Shoot and root dry biomass were measured at 90 DAS. Three pots with seeds that did not germinate were not harvested (one pot of Ni control, soil Meilhan and two pots of sequence B, soil Meilhan).

DNA extraction

DNA was extracted from the 372 soil samples (285 bulk and 87 rhizosphere) using the Dneasy 96 PowerSoil Pro
kit (Qiagen), according to the manufacturer's instructions. DNA extracts were eluted into 100 µL of TE buffer and quantified using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Invitrogen™, France) following the manufacturer's protocol.

## 16 S rRNA amplicon sequencing

Analysis of the diversity and composition of the bacterial community was performed on 286 DNA extracts (from soil samples collected at 30 DBS, 45, and 90 DAS) using Illumina sequencing of the 16S rRNA amplicons generated from a two steps PCR procedure (Berry et al. 2011). In the first step, the V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified with the fusion primers U341F (5'-CCTACGGGRSGCAGCAG-3') and 805R (5'-GACTA CCAGGGTATCTAAT-3'). First-step PCRs were performed twice and pooled to minimize PCR bias. In the second step, 6 µL of pooled 16 S rRNA amplicons were used as template with Nextera-indexed sequencing primers to add multiplexing index sequences. The PCR products were then purified, and normalized using the SequalPrep Normalisation Plate Kit (Invitrogen) prior pooling. Sequencing was performed on MiSeq (Illumina, 2x250 pb) using the MiSeq reagent kit v2.

## Bioinformatic analysis and bacterial community composition

The sequences obtained were analysed using an in-house developed Python pipeline (https://forgemia.inra.fr/vasa/illuminametabarcoding). Briefly, 16 S rRNA sequences were assembled using PEAR with default settings (Zhang et al. 2014). The QIIME pipeline (version 1.9.1) was only used for quality checks of the raw sequences and removal of sequences shorter than 350 pb (Caporaso et al. 2010). Chimera detection and OTU clustering were performed using VSEARCH (version 2.22.1) (Rognes et al. 2016) and SILVA representative set of sequences (Quast et al. 2013). OTU identity thresholds were set at 94% based on replicate sequencing of a bacterial mock community (Romdhane et al. 2022). This threshold of 94% allows retrieving the expected number of OTUs in the mock community, and was therefore use as an additional quality criterion in order to avoid artificially inflating the number of OTUs in our samples, and hence the diversity indices. The representative sequences for each OTU were aligned and a phylogenetic tree was constructed using INFERNAL (1.1.3). Taxonomy was assigned using BLAST (Altschul et al. 1990) and SILVA reference database. The sequences obtained were assigned to a total of 8897 OTUs and OTUs classified as mitochondria or chloroplasts were removed. α-diversity metrics (Observed

OTUs, Simpson's reciprocal index and Faith's phylogenetic diversity) were calculated on the rarefied I table (> 10,000 sequences per sample). The sequence data were deposited in the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA1140537).

## Quantification of bacterial community and *Pseudomonas fluorescens* abundances

The abundance of *Pseudomonas fluorescens* B177 was assessed by real-time quantitative PCR (qPCR) using the SCAR primers BP1F (5'-TGCCTGACGCTGTGATACT T-3') and BP1R (5'-GCAGAAGAGATGAACCCACC-3') (Papin et al. 2024). Serial dilutions of a linearized plasmid (pGEM-T easy vector, Promega) containing the targeted SCAR gene of *P. fluorescens* B177 were used for as standards (10² to 10⁸ copies per qPCR reaction). Each qPCR reaction consisted of 4.5 ng of DNA template, 7.5 µL of Takyon Low Rox SYBR 2X Master Mix (Eurogentec), 0.5 µM of each Primer, 250 ng of T4 gene 32 (Qbiogene) and Dnase-free water to complete the 15 µL volume. The cycling program included 3 min initial incubation at 95 °C, followed by 40 cycles of 95 °C for 15s, 60 °C for 30 s and 72 °C for 30 s using a ViiA7 (Life Technologies, USA). Two independent runs were performed for each qPCR assay. Melting curves were always checked for amplification specificity. Results were then processed using QuantiStudio software (v1.7.1, Applied Biosystems). The limit of detection was 3.8 × 10⁵ copy.g dry soil⁻¹. *P. fluorescens* genome only contains one copy of the targeted SCAR sequence allowing to consider one gene copy equivalent to one cell. The total abundance of bacterial community was estimated by qPCR using 16 S rRNA primers (Muyzer et al. 1993) with the same DNA template, as described by Lopez-Gutiérrez et al. (2004). Linearised pGEM-T plasmid containing the 16 S rRNA gene was used as standard. PCR efficiencies ranged from 80 to 101%. All samples were tested for inhibition by mixing the control plasmid (PGEM-T easy, Promega) with either soil DNA extracts or water. No inhibition was detected with the amount of DNA used.

## Mineral nitrogen

Soil mineral nitrogen was assessed at the start (30 DBS) and the end (90 DAS) of the experiment. Mineral nitrogen (NH₄⁺ and NO₃⁻) was extracted from 7 g soil with 35 mL of a 0.5 M K₂SO₄ solution, shaken 1 h at 80 rpm and room temperature, filtered and kept frozen until quantification. Quantification was performed by colorimetry on Tecan infi-nite M200 (Life Sciences) according to the ISO standard 14256-2.
## Statistical analysis

Statistical analyses were performed on R software (version 4.2.1). For each plant stage, a linear mixed model was used to predict plants traits with the soil set as random factor (lme4 package) in order to combine the three soils results in one model. For each soil and habitat, differences in the microbial α-diversity were tested with ANOVA followed by Tukey HSD test (p > 0.05). Normality and homogeneity of the residuals' distribution were checked and appropriate transformation were applied when necessary. Differences in compositional data were tested with the multifactorial non-parametric ART anova analysis (Wobbrock et al. 2011). Permutational multivariate analysis of variance (PermANOVA) was carried out on weighted UniFrac distance matrix using adonis2 function implemented in the vegan package (Dixon 2003), using 999 permutations. Filtering was applied on the I table before comparing the differential abundance analysis. Only OTUs with an average relative abundance of 3% across all samples were kept, and only OTUs present in more than 60% of replicates within each treatment or control were kept. These filtering steps reduce the zero counts in the I table which can increase the number of false positives in the differential abundance analysis. Pairwise comparisons were conducted on the resulting 524 OTUs using the following Generalized Linear Mixed Model (GLMM) (Huet et al. 2023). Considering that the abundance Y of each I, in any k replicate of any I treatment, follows a Poisson distribution of parameter Δ as Y~p(Δ), we used the following equation:

$$ \log(\Delta_{ik}) = o_{ik} + \mu + \alpha_{1} + Z_{ik}, Z_{ik} \text{ 1} \le k \le 10 \quad \text{iid} \sim N(0, \sigma^{2}) \quad (1) $$

Here, o represents the offset for each sample calculated as the logarithm of the sample read sum, α is the effect of each treatment and Z is the random sampling effect modelling the data overdispersion. Such model combines a generalised linear model and a mixed model. The generalised linear model allows to infer linear regression from data that does not follow a normal distribution because abundance data typically follow a Poisson distribution. The mixed model includes both fixed effects (treatment effects) and random effects (sampling effects). Each sampling time, soil and habitat were analysed separately. P-values were adjusted with the p adjustable function and the false discovery rate (fdr) method to correct for the high number of OTUs tested. Pairwise comparisons were carried out between each treatment and its corresponding Ni control. Comparisons were considered significant when $p \le 0.05$.

## Results

### Inoculum survival

Recurrent inoculation sequences consisted of three repeated inoculations performed either before or after sowing at two inoculum doses. As expected, qPCR analysis revealed that the inoculation dose affected the abundance of *P. fluorescens* B177 in the bulk soil (Fig. 2). The inoculant was detected in 96% of the high dose inoculated samples at 14 DAS while it was either absent or under the detection limit in the low-dose inoculated samples. At the high inoculation dose, recurrent inoculation resulted in higher abundance of *P. fluorescens* B177, at 14 DAS without differences between inoculation sequences, regardless of the soil type (Fig. 2). However, at 45 DAS the inoculation sequence B led to the highest abundance of *P. fluorescens* B177 in all soils ($p=0.008$). Higher abundances of *P. fluorescens* B177 were also observed for the inoculation sequences C in the Garlède soil as well as for the sequences A and C for the Ger soil.

In both the bulk and rhizosphere soil, *P. fluorescens* B177 was below the detection limit, at 90 DAS for all inoculation treatments and soils. The proportion of *P. fluorescens* within the total bacterial community (Supp. Figure 1) was two times lower in the Meilhan soil than in the Garlède and Ger soils, at 14 DAS.

### Effect of recurrent inoculation on plant development

A higher shoot length, stem diameter and dry shoot biomass were observed in the recurrent inoculation sequence C compared to the NI control, at 90 DAS ($p < 0.05$ with increases by 10.6%, 26.1% and 47.4%, respectively, Fig. 3). The stem diameter was also significantly ($p < 0.05$) larger with inoculation sequences B and D at 90 DAS by 27.3% and 18.2% compared to the control, respectively. No significant effect of the inoculation sequence was observed on the chlorophyll content, the total dry biomass and the dry root biomass regardless of the sampling date (Supp. Figure 2). The soil type had a significant influence on plant traits ($p = 10^{-16}$) with reduced maize growth and chlorophyll content in the Meilhan soil compared to the Garlède and Ger soils. Thus, 90 DAS, dry root biomass was on average 6.2 times lower in Meilhan soil than in Garlède and Ger soils, and shoot length was 1.4 times lower over the 45–90 DAS period. Chlorophyll content was also lower, on average by 13.5% in Meilhan soil over the 45–90 DAS period (data not shown).
<figure><img src="image_3.png" /><figcaption>**Fig. 2** Abundance of the inoculant in the bulk soil compartment, measured by qPCR. Abundance of *Pseudomonas fluorescens* B177 by inoculation sequences and across sampling times (panel columns) and soils (panel lines). Values represent the copy number per gram of dry</figcaption></figure>

soil. Letters above the bars indicate significant differences between sequences and sampling times for each soil according to Tukey's test ($p < 0.05$)

## Effect of recurrent inoculation on the composition and diversity of the resident bacterial community

The dominant bacterial phyla from the three soils consisted of Firmicutes, Actinobacteriota, Proteobacteria and Acidobacteriota representing 80.9%, 83.6% and 86.3% of the total bacterial community in the Garlède, Ger and Meilhan soils respectively (Fig. 4). The recurrent inoculation sequences B and C led to a significant increase in relative abundance of the Actinobacteriota (Supp. Figures 3 and 4) and specifically the Micrococcaceae family in the three bulk soils ($p = 10^{-16}$). The inoculation treatments had no effect on the

richness and the phylogenetic diversity of the bulk soil bacterial community. The evenness, however, was significantly reduced by the high dose treatment in Garlède soil, at 45 DAS, regardless of the inoculation sequence ($p = 10^{-9}$). In particular, the recurrent inoculation sequence B led to the highest decreases in evenness for Garlède and Meilhan soils 45 DAS ($p = 0.002$) and for Garlède soil 90 DAS ($p = 0.002$) (Fig. 5). None of the alpha diversity indices was affected by the inoculation treatments in the rhizosphere regardless of the soil. Analysis of the beta diversity showed that the bacterial communities clustered according to the soil, the compartment (bulk or rhizosphere) but not according to the sampling time (Fig. 6a, Supp. Figure 5). We found that
<figure><img src="image_4.png" /><figcaption>**Fig. 3** Plant traits measured at the 11-leaf stage (90 days after sowing) for each inoculation sequence. Results from the three soils were combined in a linear mixed model to predict the plants traits with the soil</figcaption></figure>

effect set as random factor. Error bars represent the standard error of the mean and letters indicate significant differences between inoculation sequences according to Tukey's test ($p < 0.05$)

the effect of the inoculation sequences was significant but only explained 4.7% of the variance whereas the soil effect explained 63% of the variance. Therefore, we investigated the effect of the inoculation sequence by analysing separately the different soils and compartments, while combining both sampling times (45 DAS and 90 DAS). The effect of the inoculation sequences was significant in all bulk soils

and was most pronounced in Garlède, explaining 25.6% of the variance in the beta diversity in this soil (Fig. 6b). In particular, the bacterial communities after the recurrent inoculation sequences B and C were significantly different from the bacterial community of the NI control in all three soils (PERMANOVA, $R2=0.18$ to $0.38$, $p < 0.03$, Supp. Table 2). The inoculation sequence A also had a significant
<figure><img src="image_5.png" /><figcaption>**Fig. 4** Composition of the bacterial community (phylum level) in the bulk soils 45 and 90 days after sowing (DAS) and in the rhizosphere soil 90 DAS, for each inoculation treatment</figcaption></figure>

effect on the bacterial community compared to the NI control but only in the Meilhan soil (PERMANOVA, R2 = 0.16, *p* = 0.025). Finally, significant differences were observed between single and recurrent inoculations with the resident bacterial community after recurrent inoculation sequence B being different from that of the sequence A in the Ger

and Meilhan soil. In contrast, no effect of the inoculation
sequences was observed on the bacterial community in the
rhizosphere (Fig. 6c). In addition, the bacterial community
in the low dose treatments did not significantly differ from
that in the NI control (PERMANOVA, *p* > 0.05; Supp. Table
2).
<figure><img src="image_6.png" /><figcaption>**Fig. 5** Effect of inoculations on the alpha diversity of the bacterial community in the bulk soil. Three indexes of alpha diversity were calculated per inoculations sequences across sampling times (x-axis) and</figcaption></figure>

**Effect of the recurrent inoculations on the relative abundance of dominant OTUs**

Differential abundance analysis was performed to identify the OTUs affected by the inoculation treatment at 45 and 90 DAS. Overall, the inoculation treatments had minor effects with a maximum of 4.2% of the dominant OTUs exhibiting significant shifts in relative abundances. Nevertheless, the inoculation sequence B and to a lesser extent the inoculation sequence C affected the greatest numbers of OTUs in all soils both at 45 and 90 DAS (Fig. 7). The mean log-fold change in OTU relative abundances was also more important for the inoculation sequence B. In most cases, the inoculation sequence led to increases rather than decreases of the relative abundances of the affected OTUs. In both bulk and rhizosphere compartments, inoculations affected

soils (column panels). Letters above the boxplot indicate significant differences between inoculation sequences and were tested for each sampling time and soil according to Tukey's test ($p \le 0.002$)

more bacterial OTUs in the Garlède soil than in the Ger and Meilhan soils. In the rhizosphere, less OTUs were impacted (maximum 20 OTUs), which is consistent with the weak effect previously observed on the alpha and beta diversities. Consistently with beta-diversity analysis, the number of OTUs significantly impacted by a given inoculation treatment was similar between sampling times regardless of the compartment. These impacted OTUs were distributed across most of the dominant phyla without any phylum-specific pattern of increases or decreases in relative abundance (Fig. 7, Supp. Figure 6). Finally, inoculation with *P. fluorescens* B.177 at high dose also had a significant impact on some of the most abundant OTUs in the three different soils.
<figure><img src="image_7.png" /><figcaption>**Fig. 6** Principal coordinates analysis (PCoA) of the bacterial community based on weighted unifrac dissimilarity matrix for the total bacterial community (a), the bulk community of the three soils (b) and the rhizosphere community (c). In panel a, colours represent the three soils</figcaption></figure>

and shape associated to ellipse represents the bulk and rhizosphere compartments. In panels b and c, colours represent the inoculation sequences and the variance explained (R2) by the sequences within each soil community is indicated. “n.s.” means not significant

## Effect of the recurrent inoculations on the mineral nitrogen pool

Nitrate and ammonium were measured at the beginning (30 DBS) and the end (90 DAS) of the experiment. The

nitrate content decreased in the Garlède and Ger soils over the course of the experiment, while no clear dynamic was observed in Meilhan soil due to a high variability (Supp. Figure 7). The inoculation sequences only affected the nitrate content in soil Meilhan with sequence E resulting in
<figure><img src="image_8.png" /><figcaption>**Fig. 7** Differential abundance analysis showing OTUs significantly affected by inoculation in the bulk soil. Heatmap colour intensity indicate log2 fold change with increases in red and decreases in blue for each inoculation sequence compared to the non-inoculated control. Soils and sampling times are divided into panel column. The right bar chart indicates the mean relative abundance of each OTU in the corresponding soil. Bottom bar chart indicates the mean log2 fold for each</figcaption></figure>

inoculation sequence compared to the non-inoculated control. Only OTUs with at least one significant abundance change are displayed among the 524 dominant OTUs included in the analysis (*p* ≤ 0.05). The number of significant abundances change per inoculation sequence is indicated in the blue-shaded bottom panel. “Nb sign. ch.” means number of significant changes
lower nitrate content compared to sequence B. In contrast,
the ammonium content tended to increase with time, espe-
cially in the low dose inoculation sequences E and D.

**Discussion**

Microbial inoculants products are often intended for use on
a broad spectrum of crops growing under various environ-
mental conditions. While this nonspecific application aims
for simplicity, their efficiency is undermined by multifacto-
rial effects such as soil type, local climate, nutrient avail-
ability, and resident soil community leading to a lack of
reproducibility (Mallon et al. 2015; Kaminsky et al. 2019;
Jiang et al. 2023). Thus, a better understanding of the fac-
tors governing the survival of inoculants in soil is needed in
order to adjust the inoculation strategy accordingly. In many
cases, the abundance of the inoculant declines following a
single application (Bashan et al. 2014; O’Callaghan et al.
2022), which can hamper colonization and efficiency. Some
studies suggested that recurrent applications of inoculant
could improve its survival in soil through niche displace-
ment of the resident community (Mallon et al. 2018; Wang
et al. 2021) or promote crop yield due to modification of
the rhizobacterial community (Chen et al. 2024). However,
studies assessing simultaneously the survival of the intro-
duced strain after recurrent inoculation, as well as its impact
on plant growth and the resident community, are lacking.

Recurrent inoculation improved the inoculant
survival

Propagule pressure, which encompasses the density (dose)
and pattern of the arrival (frequency) of invasive individuals
(Albright et al. 2022), is widely considered a primary deter-
minant of invasion success (Catford et al. 2009; Simberloff
2009). Accordingly, we found that the inoculant was never
detected in the low dose treatments while in the high dose
treatments, the abundance of the inoculant was higher at 45
DAS in the bulk soil after recurrent inoculations compared
to the single inoculation. This aligns with previous studies
reporting a greater success of invasion following repeated
inoculations over time compared to a single inoculation
event (Koontz et al. 2018; Chen et al. 2024; Papin et al.
2024). While the abundance of *Pseudomonas fluorescens* is
known to decline steadily after inoculation in a bulk soil
(van Elsas et al. 1986; Araujo et al. 1994), colonisation of
maize rhizosphere was observed up to 90 DAS by different
strains of *P. fluorescens* (Troxler et al. 1997; Humphris et
al. 2005; Von Felten et al. 2010; Mosimann et al. 2017).
In contrast, in our study, *P. fluorescens* B177 was neither
detected at 90 DAS in the bulk soil nor in the rhizosphere

compartment, regardless of the inoculation sequence. Our
results do not allow us to decipher whether *P. fluorescens*
B177 simply failed to establish, colonised the maize rhi-
zosphere but at a density below the qPCR detection limit,
or adopted an endophytic lifestyle (Mercado-Blanco et al.
2016). In addition, the inoculation timing did not affect the
persistence of *P. fluorescens* in the bulk soil because similar
inoculant abundances were observed for both sequences B
and C, two weeks after the last inoculation.

Our findings also showed that the survival of the inocu-
lant was modulated by soil type. The relative abundance of
the inoculant was around two times lower in Meilhan soil
compared to Garlède and Ger soils. Soil type is known to
influence the survival of the inoculant, with both abiotic fac-
tor such as soil pH or nutrient availability (Mosimann et al.
2017) and biotic factor such as the diversity and composition
of the native community (Eisenhauer et al. 2013; Spor et al.
2020; Gravuer and Scow 2021) being important. According
to the often reported inverse relationship between the diver-
sity of a community and its invasibility, the higher bacte-
rial diversity observed in the Meilhan could contribute to
explain the weaker inoculant survival (van Elsas et al. 2012;
Vivant et al. 2013). However, in our experiment, it is not
possible to distinguish the effect of the soil abiotic proper-
ties from that of the resident community.

Recurrent inoculation had a major impact on the
resident bacterial community

PERMANOVA results based on Weighted Unifrac dissimi-
larity matrix community as well as differential abundance
analysis showed a stronger impact of recurrent inoculation
on the resident bacterial communities compared to the sin-
gle inoculation at high dose. This supports previous stud-
ies suggesting that even unsuccessful bacterial invasion can
generate a legacy effect on the resident community (Yao et
al. 2014; Mallon et al. 2018), resulting in changes in the
dynamic of the resident community succession by repeated
inoculations (Wang et al. 2023). On the opposite, recent
experimental studies showed that additional inoculation
events had only minor impact on the resident community
compared to the initial inoculation (Wang et al. 2021; Papin
et al. 2024). The observed shifts in the composition of the
resident community can be attributed to direct effects caused
by the inoculant or to indirect effects resulting from changes
in plant morphology or physiology induced by the inocu-
lant (Vacheron et al. 2013; Rozier 2016; Kandaswamy et al.
2019). This supports previous studies showing that inocu-
lants can modify the resident soil microbial communities in
the short or long term through various mechanisms such as
resource competition, antagonism or cooperation (Mallon
et al. 2015; Ke et al. 2019). In addition, no resilience of
the resident bacterial community was observed between 45 DAS and 90 DAS.

The extent of the effect of recurrent inoculation on the resident community was dependent on soil type and compartment. The bacterial community of Garlède soil was more strongly impacted by the inoculations compared to Ger and Meilhan. Previous studies showed that nutrient availability was a major driver of community succession upon inoculation (Jiang et al. 2023; Garrido-Sanz et al. 2023). Accordingly, the organic carbon content was lower in Garlède that in the Ger and Meilhan soils (Supp. Table 1). In contrast to the bulk soil, no effect of the inoculation sequence was observed on the α and β diversity of the resident bacterial community in the rhizosphere at 90 DAS. This is likely due to the overriding influence of plants on the soil microbiome, either directly or indirectly through changes in soil properties (Philippot et al. 2013). This much weaker effect of the inoculation sequences is also consistent with the fact that the inoculant was not detected in the rhizosphere compartment.

Inoculation timing also, to a lesser extent, modulated the impact of inoculations on the resident bacterial community. In all three soils, the inoculation sequence B (prior sowing) led to larger shifts in the resident bacterial community of the bulk soil than the inoculation sequence C (after sowing), resulting in a greater reduction of evenness and stronger impacts on the dominant OTUs. According to invasion experiments, the persistence on the invader influences the magnitude of the shift in community composition (Xing et al. 2021; Liu and Salles 2024). However, in our experiment, the inoculum persistence was not affected by the timing of inoculation and, therefore is unlikely to explain the observed shift in the bacterial community composition between pre- and post-sowing inoculation. instead, the greater effect of post-sowing inoculations compared to pre-sowing inoculations may be linked to the presence of young seedlings. Germination and seedling development are known to release sugars, amino acids and organic acids, which are shaping bacterial communities (Nelson 2018). Thus, the resident communities thriving at pre- and post-sowing inoculation were likely different, leading to distinct responses to the inoculant. Finally, as previously observed by Wang et al. (2023), this difference might be due to a possible bacterial community's resilience to inoculation, with sequence B exhibiting a delayed recovery compared to sequence C since inoculation in sequence B ended four weeks after that in sequence C.

## Pre-sowing recurrent inoculation enhanced maize growth

Our results show that not only inoculum density and recurrent inoculation but also inoculation timing was important

for plant growth promotion. Thus, sequence C was the only inoculation treatment that induced higher shoot length and biomass, as well as stem diameter. For example, pre-sowing recurrent inoculation significantly increased shoot dry weight by 47.4%, slightly exceeding the average reported increase (30%) observed in other studies using *Pseudomonas fluorescens* strains on maize (Noumavo et al. 2013; Di Salvo et al. 2018; Pereira et al. 2020). However, there is considerable variability in the inoculant efficiency among studies using different strains and soil types, with increases in dry weight ranging from 3 to 96%, (Nezarat and Gholami 2008; Vyas and Gulati 2009; Mosimann et al. 2017). Plants are reported to be receptive to colonization by microbial inoculants mostly during the first week after germination (Aleklett et al. 2022). During this short period, the microbial community assembly in the seedling rhizosphere is mostly driven by the surrounding soil community (Rochefort et al. 2021). As such, microbes are often inoculated at sowing to increase their chance to be recruited during rhizosphere community assembly (Berninger et al. 2018; O’Callaghan et al. 2022). However, in our study, the importance of the inoculation timing is unlikely due to a direct effect of the inoculant as both treatments B and C included an inoculation event at sowing without significant differences in the abundance of *P. fluorescens* B177 two weeks later. Instead, our results suggest that indirect effects of inoculation timing through early shifts in the resident community prior sowing may play an important role in plant growth promotion. In line with our findings, Hu et al. (2021) showed that plant growth promotion was best explained by changes in the resident microbiome rather than by the introduction of plant-beneficial traits carried by the inoculant.

## Conclusions

The present study showed that recurrent inoculation promoted plant growth, especially aerial biomass only when performed before sowing and at high dose. Conversely, recurrent inoculation performed after sowing at high dose led to a smaller effect on plant growth and had a stronger impact on the resident bacterial community. Altogether, our results demonstrate the importance of inoculation timing and the potential of inoculation legacy in promoting plant growth. This work provides new avenues for microbial inoculation, suggesting that the combination of dose, frequency, and timing of application should be simultaneously considered to enhance inoculant establishment and efficiency.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s00374-024-01873-2.
**Acknowledgements** We are grateful to Baptiste Cuny, Eleonore Attard and Joana Falcao Salles for supplying soil and helping prepare the experiment; Elise Lacroix and Aurélie Miomandre for their help in the greenhouse facility. This work benefited from the "Serre et Chambres Climatiques" platform, supported by the *BioEEnVis* Research Federation. This work was funded by the Agence Nationale de la Recherche (ANR) - project number: ANR-20-CE02-0014.

**Authors contribution** X. Le Roux, L. Philippot, A. Spor, A. Dreux-Zigha and M. Papin designed the study. Material preparation and experiment were conducted by M. Papin. Data collection was performed by M. Papin, A. Polrot and S. Czarnes. Analysis was performed by M. Papin and M.C Breuil. The first draft of the manuscript was written by M. Papin and L. Philippot. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

**Data availability** Data is available upon request and the sequence data is available in the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA1140537).

## Declarations

**Competing interests** A.Z. Dreux is R&D manager and A. Taibi is R&D engineer at the GreenCell company, which markets the *Pseudomonas fluorescens* B177 strain protected under BUDAPEST treaty at Pasteur Institute (CNCM I-4015), as a component of the 'CERES' product. The experiment, analyses and manuscript strictly follow the ethical standards.

**Open Access** This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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