Ameliorating biological attributes of agricultural soils is desirable, and one avenue is introducing beneficial microbes via commercial biostimulant products. Although gaining popularity with farmers, scientific evaluation of such products in field-grown crops is often lacking. We tested two microbial products, Soil-Life™ and Nutri-Life Platform®, in a commercial sugarcane crop by profiling bacterial and fungal communities in soil and roots using high throughput phylogenetic marker gene sequencing. The products, one predominantly consisting of Lactobacillus and the other of Trichoderma, were applied as a mixture as per manufacturers’ instructions. Additives included in the formulations were not listed, and plots that did not receive the product mixture were the controls. The compositions of bacterial communities of soil and sugarcane roots, sampled 2, 5 and 25 weeks after application, were unaffected by the products. Soil fungal communities were also unaffected, but sugarcane roots had a greater relative abundance of three unidentified taxa in genera Marasmius, Fusarium and Talaromyces in the treated plots. Sugarcane yield was similar across all treatments that included a 25% lower nitrogen fertiliser rate. Further research must examine if the altered root fungal community is a consistent feature of the tested products, and if it conveys benefits. We conclude that putative biostimulants can be evaluated by analysing the composition of microbial communities. DNA profiling should be complemented by cost-benefit analysis to build a public information base documenting the effects of microbial biostimulants. Such knowledge will assist manufacturers in product development and farmers in making judicious decisions on product selection, to ensure that the anticipated benefits of microbial biostimulants are realised for broad acre cropping.
Soils support critical ecosystem services with the provision of food and clean water (Baveye et al. 2016). To maximise agricultural productivity, soils require significant intervention, which often leads to undesirable environmental impacts and a reduction in soil quality over time (Doran and Zeiss 2000; Doran 2002). While agronomic options are available to alleviate soil physicochemical constraints, managing soil biological properties represents a challenge (Brackin et al. 2017). Soil organisms strongly influence plant health and growth. Beneficial plant-microbe interactions reduce pest and pathogen pressure (so-called biocontrol), mobilise nutrients, improve root architecture and modulate stress responses (Pii et al. 2015). Agronomic methods to improve soil biological properties include (i) crop rotations to reduce the presence of pathogens and pests associated with monoculture cropping, (ii) incorporation of organic materials (crop residues, composts, manures) that stimulate functionally diverse soil biological communities and (iii) inoculating soil or crop with chemical (e.g. phytohormones) and/or microbial (i.e. bacteria and fungi, the focus of this study) biostimulants. The last approach has encouraged the rise of an industry worth $US 2.9 billion in 2017, that is predicted to increase to $US 5.4 billion by 2022 (Research and Market 2017).
The use of plant growth promoting (PGP) microbes has a scientific basis and long-standing practice in the coating of legume seeds with nitrogen (N) fixing Rhizobium and Bradyrhizobium bacteria (Murray 2011; Chen et al. 2015; Vargas et al. 2017). Microbial biostimulants appear justified when considering the benefits of modern legume symbioses that have been optimised with the selection of efficient bacterial strains and efficient delivery to substantially improve N fixation rates and legume yields (Graham and Vance 2003; Batista et al. 2015; Parnell et al. 2016). Commercial products of PGP microbes have been in use since the 1940s, and many crops now receive PGP microbes as soil or plant applications aimed at benefitting shoots or roots (reviewed by Parnell et al. 2016). Sugarcane, for example, is frequently inoculated with diazotrophic Gluconacetobacter diazotrophicus, Herbaspirillum, Azospirillum and Burkholderia in Brazil and elsewhere (Mendes et al. 2007; Pedula et al. 2016), but modes of action are much less well established in sugarcane than in legumes (Robinson et al. 2013; Figueiredo et al. 2017).
PGP microbes can enhance plant performance via three primary modes of action: improved pathogen resistance, access to nutrients and beneficial hormones (Tabassum et al. 2017). The rising interest in microbial biostimulants (also termed biofertilisers, biocontrol agents, biopesticides) is driven by the notion that they can improve crop yield as a green alternative to agrochemicals. Subsequently, commercial products, especially those containing PGP rhizobacteria (PGPR), are increasingly used (Lucy et al. 2004; Berg 2009; Bhattacharyya and Jha 2012). A second type of product aims to improve soil function. For example, Soil-Life™ (tested here) is marketed as a soil activator, claiming to enhance soil quality as well as crop growth and yield. It is often unclear which mechanism a particular microbial product may deliver, and there is agreement in the scientific community that research is warranted (reviewed by Finkel et al. 2017; Parnell et al. 2016). Beyond PGP mechanisms, the complete composition of products is also often lacking from product labels and not outlined in sufficient depth in the scientific literature (Bashan et al. 2016).
Limitations to the effectiveness of microbial biostimulant products include that microbe species and strains (‘ecotypes’) can strongly diverge in their plant growth promoting (PGP) effects in response to growth environments, crop species and crop cultivar (Chiarini et al. 1998; do Amaral et al. 2016). Much of the research demonstrating effects and identifying mechanisms of microbial biostimulants has occurred in laboratory and glasshouse, excluding or minimising the effects of competing native soil microbes, unfavourable environmental conditions, transport and storage of the microbial products and field application. The interest of farmers and the rapidly expanding product market is fueled by the potential benefits and the desire for sustainable intensification of cropping, but independent scientific evaluations in real-world settings are rare. Yet this is important since the cost of such products can be considerable and a lack of beneficial effects (or even undesirable effects) jeopardises the usefulness of the technology. Major discrepancies in the efficiency of beneficial bacteria have been reported between laboratory, glasshouse and field-grown plants (Valverde et al. 2006; Chin-A-Woeng and Lugtenberg 2008; Naveed et al. 2014; Etesami et al. 2015; Bashan et al. 2016; Parnell et al. 2016), and government agencies increasingly demand field testing with scientifically valid methodologies to ensure that products deliver on their promises (EPPO 2012a, 2012b, 2012c).
Bacterial taxa often used as microbial biostimulants include members of Acetobacter, Agrobacterium, Azospirillum, Azotobacter, Bacillus, Burkholderia, Enterobacter, Frankia, Pseudomonas, Rhizobia, Serratia and Streptomyces (Saharan and Nehra 2011). Beneficial fungal taxa include Trichoderma and Gliocladium (Raaijmakers et al. 2009), and arbuscular mycorrhizal (AM) fungi (Glomeromycota) (Barea et al. 2005; Bonfante and Genre 2010). While these are generally considered beneficial genera, not all species or strains (ecotypes) are beneficial. Broad-scale categorisation into beneficial and detrimental organisms is unwarranted since the same taxon can benefit or harm different plant hosts (or have no quantifiable effect). For example, while numerous Pseudomonas species are PGP organisms, some negatively impact plants (Mehnaz 2013; Reis and Teixeira 2015). Crop cultivar specificity occurs with Herbaspirillum rubrisubalbicans, which causes mottled stripe disease in some sugarcane cultivars, while other cultivars harbour the organism as an endophytic diazotroph (Baldani et al. 1996; Boddey et al. 2003). Similarly, different strains of a Burkholderia species, a common bacterial genus of soil and roots (Yeoh et al. 2017), can be detrimental or beneficial to different plant hosts (Coenye and Vandamme 2003). In addition to the genetic makeup of the organisms, environmental factors can modulate effects; for example, grown with high amounts of N fertiliser, sugarcane is susceptible to H. rubrisubalbicans as a pathogen (James and Olivares 1997).
The results of microbial product testing in commercial cropping is less commonly reported in the peer-reviewed scientific literature than experiments in controlled settings that demonstrate benefits of particular microbes. Reasons for such underreporting include the high costs of field experimentation, variable or inconclusive results in field settings and, in the absence of regulations that demand proof of efficacy, the practice of extrapolating results from controlled to field settings. PGP microbes have to establish within the biologically complex soil matrix, and compete with native microbes (Dutta and Podile 2010). Product efficacy can be compromised inter alia by an inability of microbes to establish in soil due to unfavourable abiotic conditions, unsuccessful colonisation of host roots and competition from native soil organisms (Egamberdiyeva and Höflich 2003; Lucy et al. 2004; Çakmakçi et al. 2006; Egamberdiyeva 2007).
To address the need for validation of microbial products in broad acre cropping, we tested two bacteria- and fungi-containing products that are used in sugarcane production in tropical Australia. We investigated if (i) manufacturer-specified microbes were present in the products, (ii) application of the products enhances sugarcane yield, (iii) the microbes colonise soil or sugarcane roots/rhizosphere and (iv) the products alter microbial communities. We used next-generation DNA sequencing to characterise bacterial and fungal communities three times over the crop season and determined sugarcane yield to quantify the effects.
Materials and methods
Study site and experimental design
The research was performed at a commercial sugarcane farm in Australia’s Wet Tropics near Ingham (− 18.651844, 146.119511), on a sandy loam soil. The crop was rain-fed (1890 mm annual rainfall) and consisted of plant cane of commercial variety Q250. The block had previously been fallowed (grasses and mixed broadleaf weeds) for 6 months following a former cane crop. Plots within the field were established in a factorial design with each plot consisting of four rows of sugarcane (30 m long, 1.8 m row spacing) and five replicated plots per treatment. The experiment consisted of four treatments that reflect the desire of sugarcane growers to reduce N fertiliser rates and partially achieve this goal with microbial biostimulants. The treatments were (1) recommended N rate (130 kg N ha−1), (2) reduced N rate (75%, 100 kg N ha−1), (3) recommended N rate + microbial products and (4) reduced N rate + microbial products.
Fertiliser was applied at the planting of sugarcane. A di-ammonium phosphate (DAP) starter was used in all plots with 46 kg N, 40 kg P and 15 kg S per hectare. At the same time, a urea and muriate potash mixture was applied as a side dressing. Full N-plots received an additional 85 kg N ha−1 and 105 kg K ha−1 alongside the side dressing. Reduced-N plots received an additional 57 kg ha−1 N and 105 kg ha−1 K. One of the microbial products tested here (Soil-Life™, see below) had been applied in the preceding year but manufacturer instructions stipulate annual re-application. Neither product listed any additives that were included in the formulation, with only the bacteria and fungi included as the composition.
After 14 months of growth (July 2015 to September 2016), sugarcane was hand-harvested by collecting 10 stalks from the middle two rows of all replicate plots of all treatments. Stalks were weighed and processed at the Victoria mill (Wilmar Sugar) to obtain three yield measures: tonnes of cane, sugar yield and commercial cane sugar yield (CCS, recoverable sugar content). Yield per hectare was estimated by multiplying the 10-stalk weight by the total number of stalks in each plot. Wet weight measurement for biomass follows sugarcane industry protocols. Due to the large size of sugarcane (over 4 m tall and 5 cm or more in diameter), stalks would need to be cut for drying which leaks sugar (~ 10% of stalk wet weight) which introduces error. Previous research has shown that wet weights correlate well with dry weights.
Microbial biostimulant products
The commercial products Soil-Life™ (ActivFert 2017) and Nutri-Life Platform® (Nutri-Tech Solutions® 2017) were blended and applied as mixed inoculum, as recommended by the manufacturers. Briefly, 20 L Soil-Life™ was blended with 20 L molasses and 160 L rainwater, and then incubated for 1 month in a shaded 1000-L shuttle prior to use—a procedure referred to by the manufacturers as the extension process. Inoculum was applied at a rate of 185 L per hectare consisting of (i) 20 L of extended Soil-Life™ solution, (ii) 150 g of Nutri-Life Platform® (powder formulation) and (iii) 165 L of water. This was applied with 100 mm deep sub-surface coulters. The inoculum was applied approximately 1 month after fertiliser application when the crop was ~ 1 m tall. We use the term ‘microbial product’ to denote the mixture of Soil-Life™ and Nutri-Life Platform® that was applied to the treated plots.
Sampling of microbial products, soil and roots
Samples of the Soil-Life™, Nutri-Life Platform® and mixed ‘microbial product’ were collected for microbiome analyses. The Nutri-Life Platform® sample was collected immediately after opening the supplier package. The Soil-Life™ sample was collected after the extension process, and the mixed ‘microbial product’ was sampled immediately after the extended Soil-Life™ and freshly opened Nutri-Life Platform® products were combined.
Soil and sugarcane roots were sampled 2, 15 and 25 weeks after application of the microbial product in five replicate plots from the reduced N treatment without (control plots) and with the microbial product applied (treated plots). At each time point, two plants from the centre two rows of each plot were sampled. Root and soil samples were collected from the upper 15 cm of soil at the row shoulder. Sections of thick white roots, as well as darker fibrous roots with adhering soil, were collected to obtain samples of the microbial communities present in roots, rhizoplane and rhizosphere (‘root microbial communities’ in the following). Roots from both plants were pooled into one container for sub-sampling and analysis. Bulk soil was sampled from several places surrounding the soil from which roots were sampled, sieved (2 mm) and pooled into a separate container for analysis of soil microbial communities. Three technical replicates were included for each treatment and time point. Samples were kept cool for transport and stored at − 20 °C prior to DNA extraction.
Analysis of bacterial communities
DNA was extracted from soil and root samples with MP Biomedicals Fast DNA SPIN Kit (MP Biomedicals, LLC, Santa Ana, CA, USA). PCR amplification and sequencing was performed by the Australian Genome Research Facility (AGRF). Bacterial 16S rRNA genes were PCR amplified using the primers 803F (5’-ATTAGATACCCTGGTAGTC-3′) and 1392wR (5’-ACGGGCGGTGWGTRC-3′), which were modified on the 5’end to contain the i5 and i7 Illumina Nextera adaptor sequences, respectively. A PCR was also conducted with no template to act as a negative control. The thermocycling conditions were 95 °C for 3 min, then 30 cycles of 95 °C for 30 s, 55 °C for 45 s, 72 °C for 90 s, then 72 °C for 10 min. Raw sequences were trimmed using Seqtk (version 1.0) and processed using QIIME (version 1.8; Caporaso et al. 2010), USEARCH (version 8.0.1623; Edgar 2010; Edgar et al. 2011) and UPARSE (Edgar et al. 2011) software. Sequences were quality filtered using USEARCH. Full-length duplicate sequences were removed, and sequences were sorted by abundance. Singletons or unique reads in the data set were discarded. Sequences were clustered followed by chimera filtering using the ‘rdp_gold’ database for reference (using the USEARCH software). Reads were mapped back to OTU’s with a minimum identity of 97%. Finally, taxonomy was assigned using QIIME with the Greengenes database (version 13_8 Dated Aug 2013; DeSantis et al. 2006). Relative abundance was calculated and used to illustrate the composition of the commercial products, soil and root microbial communities. The number of reads per sample was rarefied to 10,000 and the numbers of observed (Sobs) and predicted OTUs (Chao1) as well as the Simpson’s Diversity Index were calculated for each sample.
Analysis of fungal communities
DNA was extracted from soil and root samples using MO BIO PowerSoil DNA isolation kits. PCR amplification and sequencing was performed by AGRF. PCR amplicons were generated using the primers ITS1F (5’-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5’-GCTGCGTTCTTCATCGATGC-3′), and the AmpliTaq Gold 360 master mix solution (Life Technologies, Australia). Sequences were modified on the 5′ end to contain the i5 and i7 Illumina Nextera adaptor sequences, respectively. PCR was ran with the thermocycling conditions 95 °C for 7 min, then 35 cycles of 94 °C for 30 s, 55 °C for 45 s, 72 °C for 60 s, then 72 °C for 7 min. A secondary PCR was performed to index amplicons using TaKaRa Taq DNA polymerase (Clontech). Amplicons were measured by fluorometry (Invitrogen Picogreen) and normalised. The equimolar pool was measured by qPCR (KAPA) followed by Illumina MiSeq sequencing (San Diego, CA, USA) with 2 × 300 base pairs paired-end chemistry.
PEAR (version 0.9.5; Zhang et al. 2014) was used to assemble paired-end reads by aligning the forward and reverse reads. Sequences were trimmed from the raw reads using Seqtk (version 1.0) and processed using QIIME (version 1.8; Caporaso et al. 2010), USEARCH (version 8.0.1623; Edgar 2010; Edgar et al. 2011) and UPARSE (Edgar et al. 2011) software. Sequences were quality filtered using USEARCH. Full-length duplicate sequences were removed, and sequences were sorted by abundance. Singletons or unique reads in the data set were also discarded. Sequences were then clustered followed by chimera filtering using Unite database for reference (using the USEARCH software). Reads were mapped back to OTU’s with a minimum identity of 97%. Taxonomy was assigned using QIIME with the Unite database (version 7; Kõljalg et al. 2005). The number of reads per sample was rarefied to 40,000 and the numbers of observed (Sobs) and predicted OTUs (Chao1) as well as the Simpson’s Diversity Index were calculated for each sample.
To determine whether the addition of N fertiliser and microbial products significantly influenced sugarcane yield and the alpha diversity indices, we used two-way analysis of variance (ANOVA). The effects of these parameters on community composition were assessed using Permutational Multivariate Analysis of Variance (PERMANOVA; Anderson 2017). All PERMANOVA analyses were performed using Hellinger transformed OTU abundances (Legendre and Gallagher 2001). All analyses were implemented using R 3.5.1 (R Core Team 2013) and the ‘vegan’ R package (Philip 2003).
Sugarcane biomass yield was similar with approximately 120 t/ha in all treatments irrespective of N fertiliser rate or microbial product application (ANOVA and Tukey HSD, P > 0.05, Fig. 1). Similarly, sugar yield (17 t/ha) and CCS (14) were unaffected by the microbial product with no statistically significant differences across treatments.
Composition of the microbial products
According to the manufacturer, Soil-Life™ comprises three bacterial groups (lactic acid bacteria (i.e. Lactobacillus), Actinomycetes and photosynthetic bacteria), and fungi including yeasts and unspecified fungi (Table 1). Phylogenetic marker gene sequencing indicated that Lactobacillus represented 99.9% of the bacteria in Soil-Life™. In addition, yeasts, non-specified fungi, Actinomycetes and photosynthetic bacteria (Rhodosporillaceae, Chloroflexaceae, Cyanophyta, Heliobacteria) were detected albeit at < 1% relative abundance (Figs. 2 and 3).
According to the manufacturer, Nutri-Life Platform® contains AM fungi (Glomus intraradices, Glomus etunicatum, Glomus aggregatum and Glomus mosseae), four strains of Trichoderma, as well as Azospirillum sp., Bacillus sp., Pseudomonas sp. and Streptomyces cellulosae bacteria. Phylogenetic marker gene sequencing indicated that Hypocrea was the most abundant genera of fungi in the Nutri-Life Platform® (85.8%, Fig. 3; note Hypocrea in the sexual stage of the life cycle with its anamorph (asexual stage) Trichoderma (Kubicek et al. 2008) which is the claimed primary constituent of the Nutri-Life Platform® product). The fungal genera with the highest relative abundances in the product included members of the Hypocrea/Trichoderma, Thermoascus, Talaromyces and Candida (relative abundances of 86, 9.3, 2.4 and 2.3% respectively). No AM fungi (Glomeromycota) were detected.
Response of bacterial communities to microbial product application
Bacterial communities in soil and roots differed significantly (P < 0.05) (Fig. 2). Soil communities were dominated by Gaiellaceae, Bacillus and Streptomyces (7–9%, 4–6% and 4–6% across sampling times) while roots were dominated by Streptomyces (10–40% across sampling times, Fig. 2). Treating soil with the microbial product (i.e. the mixture of Soil-Life™ and Nutri-Life Platform®) did not result in a significant difference in the composition of bacterial communities in soil or roots (Fig. 2).
Response of fungal communities to microbial product application
Fungal communities of soil and roots differed significantly (P < 0.05). Similar to bacteria, plants can shape their rhizosphere microbiome by attracting particular fungi from the bulk soil (Houlden et al. 2008; Zhang et al. 2008; Paungfoo-Lonhienne et al. 2015; Zhou et al. 2018; Huang et al. 2019). Soil fungal communities were dominated by Capnodiales (relative abundance 20–30% across sampling times) while root communities were dominated by members of the Nectriaceae and Marasmius (relative abundances 10–40% and 10–80%, respectively, across sampling times) (Fig. 3). Inoculation did not affect the composition of soil fungal communities, while root fungal communities differed significantly (P < 0.05). The plots treated with the microbial product had larger relative abundances of Marasmius, Fusarium and Talaromyces populations compared to roots in the untreated control. We detected OTUs identified to the Glomeromycota phylum in soil and root samples; however, they were in very small relative abundance and were unaffected by microbial product application.
Of most interest to agricultural producers is if microbial products benefit their crops, manifested as improved yields and/or reduced need for inputs such as fertilisers and pesticides. We quantified sugarcane tonnage and sugar yield in replicate plots in a commercial crop, and found that the application of microbial product at full or reduced N fertiliser rates had no measurable effect on these yield parameters. Application of the microbial product was, however, associated with a shift in three fungal OTUs in roots, suggesting indirect effects of the product application that did not translate into detectable yield increases in our study. For a comprehensive analysis of benefits, multiyear yield experimentation is needed, and is indeed a requirement in countries that demand proof of efficacy prior to registering commercial products (EPPO 2012c). A multiyear analysis was beyond the scope of our study, and interpretation focusses therefore on the methodology of microbial community characterisation as a tool to substantiate potential effects of microbial products.
Composition of the microbial products
The organisms present in both products broadly aligned with manufacturer product labels. All microbes claimed to be present were identified in the Soil-Life™ product. However, Lactobacillus was the dominant bacterial taxon, accounting for approximately 99%, with all other taxa present at very low relative abundance (< 1%). An identified problem with small populations of target microbes is that they may be unable to establish, outcompete native microbes and survive when applied to soil (Parnell et al. 2016). A second issue is that some microbial groups present in the product are extremely diverse which makes it difficult to gauge potential benefits. For example, ‘photosynthetic bacteria’ contain many taxa. Soil-Life™ also lists ‘unspecified fungi’ as an ingredient, which is uninformative given that there are ~ 400,000 known fungal species in 18,500 genera (Bensch 2016), and an estimated 5 million fungal species globally. Fungi are prevalent soil organisms, associate with roots, and range from beneficial to highly pathogenic to crops. Our analysis detected 440 fungal genera with 45 OTUs with > 1% relative abundance (Fig. 3).
Similarly to fungi, the diversity of bacteria is immense with only 5000 bacteria species described so far out of an estimated 1 billion species (Nielsen et al. 2015). With such broad classification, it is difficult to ascertain potential mechanisms as species and strains can be beneficial, neutral and/or pathogenic. For example, Burkholderia species/strains isolated from soil can benefit plant growth, while others are primary plant pathogens, and some are beneficial for one plant host but pathogenic for another (Coenye and Vandamme 2003; Paungfoo-Lonhienne et al. 2016).
We identified all microbes claimed to be present in the Nutri-Life Platform® product except AM fungi (Glomeromycota) although Glomeromycota were detected in root and soil. Whether the lack of detection in the product is indeed due to an absence of mycorrhizal spores in the product requires further investigation. It is not trivial to generate AM spores for microbial products (Singh et al. 2014), and even if mycorrhizal fungi are present in a product and associate with a target crop, their abundance in roots or soil does not necessarily increase in response to product application. Insufficient quantities of propagules and soil conditions that are unfavourable to the AM symbioses (e.g. high soil N and phosphorus levels; Treseder 2004) can prevent an increase in the presence of AM fungi.
More generally, DNA analysis of organisms has to be accompanied by biological tests, since the presence of DNA may not translate into viable organisms. There is additionally the consideration that different marker genes and classification databases can yield different results as outlined by Xue et al. (2019). The UNITE database, which we used here, is considered best for identifying the highest number of taxa but also has the highest number of unclassified OTUs, denoting AM fungi may have been present that were not classified. An argument against this notion would be that AM fungi have received considerable attention, including DNA-based identification. Our study highlights that product quality should be confirmed in each batch, and that quantitative information on each type of organism on the product label will assist consumer decision making. The onus should be on manufacturers to ensure presence and viability of the claimed microbes, and to communicate suitable methods of storage and handling to customers.
Effects of microbial product application on the composition of bacterial communities
The application of microbial products to soil did not directly or indirectly influence the composition of soil or root bacterial communities. Microbes compete for niches, for example by being efficient root colonisers (e.g. biofilm formers), producing antibiotic compounds that directly impact the growth of other microbes, or by depleting resources that are essential for other microbes, and thereby indirectly reducing the presence of native bacteria and fungi (Gamalero and Glick 2011). Many microbial biostimulant products include only one or very few taxa, especially in regulated markets such as EU countries. Other products, including the products tested here, have a mixed-culture fermentation step (so-called extension). These microbial products aim to deliver a rich mixture of microbes (‘effective microorganisms’), commonly including Lactobacillus, photosynthetic bacteria, yeasts and Actinomycetes (Calvo et al. 2014; Lamont et al. 2017), all of which are included in Soil-Life™. Similarly, Lamont et al. (2017) found that Lactobacillus commonly dominates fermented cultures. Microbes are often fermented with organic materials and molasses so that microbes enhance the efficiency of mineral and organic fertilisers (Calvo et al. 2014). Multiyear trials testing these ‘effective microorganism products’ have detected variable effects and inconsistent impacts on yield in temperate crops (Calvo et al. 2014; Lamont et al. 2017), confirming that there is limited evidence and understanding of the mechanisms through which crop growth is enhanced with such products (Cóndor Golec et al. 2007). Evidence supporting the importance of each individual microbial component to the efficacy of the overall product is also lacking for most mixed-culture products.
Lactobacillus dominated the Soil-Life™ and (mixed) microbial products but this did not translate to an increased abundance in soil or roots. This is in line with manufacturer claims that Lactobacillus does not directly enhance plant growth but delivers indirect benefits by stimulating other microorganisms. Our analysis detected no evidence that Lactobacillus enhanced the populations of other bacteria within the taxonomic resolution of our study, but may have contributed to the observed increase in three fungal taxa in the root microbial community. While certain Lactobacillus strains possess antifungal activity (Kim 2005; Gerbaldo et al. 2012), there is currently no published record of direct stimulation of root fungal communities by Lactobacillus.
Lactobacillus species produce lactic acid from monosaccharides, inhabit the human gut, are used in food preservation (König and Fröhlich 2009; Lamont et al. 2017), commonly occur in composts and silage and decompose organic materials (Lamont et al. 2017). While there is little evidence that Lactobacillus, when applied directly to plants, enhances growth, it may enhance nutrient cycling or stimulate other soil microbes. Inoculation of composts with microbial consortia dominated by Lactobacillus can increase yield and nutrient uptake of different crops (Lamont et al. 2017), although mechanisms remain unclear.
Actinomycete species, a minor component in Soil-Life™, are abundant in soil and have been studied for their PGP potential (Franco-Correa et al. 2010). There is evidence that members of this bacterial order can enhance P mobilization, fix N2, produce PGP hormones (such as IAA), release siderophores, hydrolytic enzymes and antimicrobial compounds to boost plant growth and protect against pathogens (Jog et al. 2016). Many commercial microbial products include Actinomycetes with a wide array of potential benefits reported, including biocontrol (Doumbou et al. 2002). Actinomycetes had relative abundances ranging from 20 to 30% and 40 to 60% in soil and roots, respectively, which were unaffected by the products.
Photosynthetic bacteria are a third bacterial group present in Soil-Life™. These organisms include Cyanobacteria, Rhodosporillaceae and Chloroflexaceae which can perform incomplete photosynthesis anaerobically and grow well in mixed liquid cultures with other fermenting bacteria and yeasts (Higa and Parr 1994). These organisms are often included in mixed fermentation biostimulant products due to their ability to fix atmospheric CO2 and N2, and their hydrogen metabolism (Kobayashi and Haque 1971; Higa and Parr 1994; Bothe et al. 2010; Hallenbeck and Liu 2016). Photosynthetic bacteria can detoxify soils by metabolising toxic compounds such as hydrogen sulphide or amines, and improve soil fertility (Kobayashi and Haque 1971; Higa and Parr 1994). In our study, the collective relative abundance of all microbes that classify as photosynthetic bacteria was < 5% across soil and root samples of all treatments. Whether this is a sufficiently high abundance to deliver benefits to soil and crops is unknown.
The Nutri-Life Platform® contains a number of bacterial genera including Azospirillum, Bacillus, Pseudomonas and Streptomyces. These are well-known PGP genera with demonstrated potential to enhance plant vigour and growth, and are common ingredients of microbial products (Glick 2012; Cote et al. 2015). These organisms encompass a wide range of PGP traits to deliver benefits via hormone production, nutrient mobilisation and pathogen resistance (van Loon 2007; Dutta and Podile 2010; Trabelsi and Mhamdi 2013). Members of these genera were identified in all soils and roots (Table 1), with no detectable change in abundance in response to product application.
Effects of microbial product application on the composition of fungal communities
There was no quantifiable effect on soil fungal communities, but root-associated populations of Marasmius, Fusarium and Talaromyces increased in product-treated plots (discussed below). Fungi applied with the products include four Trichoderma strains (the main fungus present in Nutri-Life Platform™), a genus reported to convey benefits to several crops including tomato, mustard and bean (Ghorbanpour et al. 2018). Various species of Trichoderma have modes of action that include out-competing pathogens for space and nutrients, parasitising pathogenic fungi, antimicrobial compounds and inducing pathogen resistance in plants (Ghorbanpour et al. 2018). Application of the product to soil did not, however, significantly change the relative abundance of Trichoderma (< 3% relative abundance in roots and soil). Arbuscular mycorrhizas (AM) are desirable plant symbiotic fungi and were detected in soil and roots in low relative abundance (< 0.1%) without discernible increase following product treatment. Possible reasons include insufficient or no viable AM spores in Nutri-Life Platform® or unfavourable conditions for AM fungi. AM spores can become nonviable in high temperatures which may occur during storage, compromising carrier materials and microbe viability (Bashan et al. 2014). Yeasts were a component of Soil-Life™ and are commonly included in ‘effective microbe’ formulations due to several PGP benefits (Higa and Parr 1994; Nutaratat et al. 2014; Fu et al. 2016; Sarabia et al. 2018). Yeasts can mobilise nutrients (P, siderophores) (Sarabia et al. 2018), regulate hormones (IAA production, 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity) and protect from pathogens by producing hydrogen cyanide, cell-wall degrading enzymes, catalase and ammonia (Nutaratat et al. 2014). While yeasts were identified in soil and roots, their relative abundance was small (< 1%). Marasmius was present in soil of all plots (< 1% relative abundance), and roots in the treated plots had an increased relative abundance in a specific population of Marasmius (OTU_32, Fig. 3) while roots in control plots had an increased abundance of a different Marasmius population (OTU_30, Fig. 3). These populations are not annotated in the database and interactions with sugarcane are unknown. Marasmius contains decomposer species but also crop pathogens (Ferreira Gregorio et al. 2006) with Marasmius sacchari the cause of root rot disease in sugarcane (Singh et al. 2017).
Similarly, it is unclear whether Fusarium OTU_21 that increased statistically significantly from 12 to 17% relative abundance in roots of treated plots are beneficial genera, neutral and/or pathogenic organisms. Fusarium wilt disease, for example, is caused by soilborne pathogenic Fusarium oxysporum strains that invade roots and vascular tissue of economically important crops (including tomato, banana, brassicas, cucurbits), causing wilting and eventually death (Fravel et al. 2003). F. oxysporum is widespread in soils and nonpathogenic strains compete with, and act as, biocontrol agents against pathogenic F. oxysporum strains (Mandeel and Baker 1991). Whether the Fusarium OTU_21 includes biocontrol strains remains to be investigated.
At week 25, the largest abundance change in response to product application occurred in Talaromyces (OTU_15) which increased in relative abundance from 5 to 17% from week 2 to 25, but in control plots remained at 5% throughout. Talaromyces represents the sexual reproductive stage (teleomorphic) of Penicillium (Yilmaz et al. 2014). The genus Talaromyces contains PGP species including some that emit volatiles that promote plant growth or act as strong biocontrol agents against fungal pathogens including Verticillium, Rhizoctonia and Sclerotinia (Yamagiwa et al. 2011; Yilmaz et al. 2014).
Future research could verify or refute efficacy and mechanisms of the three fungal OTU’s that increased in abundance in response to product application. Taken together, our findings suggest that the effects of the microbial product on root fungal communities are indirect since the fungal genera that increased in abundance in roots were not primary constituents of the products. Further research should examine if the altered fungal OTUs have PGP or biocontrol function for sugarcane. The comparatively large increase in the abundance of Talaromyces makes this taxon a priority for further investigation.
Considerations for product efficacy
The claimed benefits of the tested microbial products include improving soil physical and biological health, stimulating beneficial microbial populations, increasing crop yield, root growth, plant establishment, drought tolerance and reducing fertiliser requirements. We did not test all claims but focussed on microbial communities in soil and roots, and quantified sugarcane yield. As outlined above, despite changes in root fungal communities, no quantifiable benefit on yield was detected, but longer-term and multi-site research is needed for a full evaluation of the microbial products to quantify effects. That sugarcane yields were similar with full and a 25% reduced N rate is perhaps unsurprising considering that the full N fertiliser rate exceeds crop N needs and factors in N losses from soil that are inevitable with current fertilisers. Similar to our previous research (Yeoh et al. 2015), abundance of N fixing bacteria did not increase in soil or roots with lower N fertiliser supply. The Nutri-Life Platform® product claims to improve N fixation with Azospirillum, a genus that includes free-living N fixing species (Zhang et al. 1997; Steenhoudt and Vandereyden 2000) but this was not substantiated here. Many studies in Brazil have shown that inoculation with Azospirillum can significantly enhance crop growth and grain yield, primarily for cereal crops; however, this was reliant on seed inoculation and stringent seed storage procedures (Garcia et al. 2017; Díaz-Zorita et al. 2015). Limitations of our study include that identification of microbes was restricted to genus level, which is a current restriction of the 16S rRNA marker gene sequencing technique. The 16S rRNA method has been widely adopted to obtain a snapshot of microbial community composition and is useful for taxonomic classification of organisms, but is insufficiently sensitive to characterise microbial assemblages at a functional level (Hamady and Knight 2009). Meta-genomic analysis requiring longer DNA-reads characterises the function of microbial communities, but this technique is much more costly. High-throughput methods, such as pyrosequencing, offer an affordable and rapid approach to obtain a snapshot of bacterial and fungal communities. Sequencing with Illumina MiSeq (used here) has a low error rate and a high resolution so that microbes that occur in very small relative abundances, as low as 0.00001% in our study, are detected (Loman et al. 2012). It is possible that some microorganisms present in the sampled soil and roots were not identified with this method, but they would have been present in minute abundance relative to the quantified microbial populations. However, microbes occurring in very small abundances can impact plant health and growth. For example, as few as seven propagules of F. oxysporum f. sp. apii per gram of soil were required to cause vascular discolouration in celery (Elmer 1987), which is a minor proportion of the soil microbial community with 108 bacterial cells g−1 soil reported in temperate soils (Raynaud and Nunan 2014). A recent study by Wei et al. (2019) also showed that rare taxa that exist in low relative abundances can play pivotal roles in the mineralisation of P under P-limiting conditions.
Our experiment was limited to 1 year, which may have been insufficient time to stimulate changes in soil biology and manifest quantifiable benefits, although the product manufacturers do not stipulate a timeline for effects. Longer-term interactions between microbial products, lower N fertiliser use and a range of yield and soil parameters have to be investigated to conclusively evaluate microbial biostimulant products. Previous reports have shown that application of microbial suspensions to soil can shift native microbial communities for short periods, but that the community composition often returns to its original state within several weeks postinoculation (Bauoin et al. 2009; Qiao et al. 2017). A glasshouse study using non-sterilised natural soils showed that inoculation with PGPR can change native microbial community composition in soil or roots for several months (Thokchom et al. 2017). As outlined above, however, there is a dearth of field studies that examine root, rhizosphere and soil communities in longer-term field experimentation.
The Nutri-Life Platform® product sheet lists 143 crops that benefit from inoculation, sugarcane included, but, to the best of our knowledge, no scientific evidence for those claims is publicly available. In light of research demonstrating that individual microbes often have very specific interactions with plant hosts and benefit only a subset of crops (Alves et al. 2015; do Amaral et al. 2016), as well as plant genotype influencing PGP effects of microbes, such claims may overstate the effects of microbial products. For example, of 21 Herbaspirillum strains tested on two maize genotypes, only one strain promoted the growth of both maize genotypes under field conditions (Alves et al. 2015). Aside from plant genotype, abiotic factors influence PGPR success, making product efficacy difficult to maintain across different soils and climates (Tabassum et al. 2017). As a result, industry stakeholders are often sceptical about microbial biostimulants including their effect as biocontrol agents. For example, four out of five commercial microbial products tested for their ability to control soilborne plant pathogens in glasshouse conditions showed large variability in efficacy (Koch 1999). Most field trials evaluating commercial microbial products in Australia have examined Rhizobium products for legumes and advanced the selection and manufacture of effective microbial products. In contrast, evaluation of commercial biostimulants beyond Rhizobium has not had much attention past small-scale testing in pot trials with most studies occurring in India and South America (Figueiredo et al. 2017). More generally, we have often observed that in field trials, two variables are changed simultaneously, e.g. crops grown with 25% less fertiliser with added microbial product are compared to a fully fertilised crop. Such experimentation does not distinguish between the effects of a lower fertiliser dose and microbial product, and can lead to false claims. Overall, we find little information available in Australia about the tests that have been performed on various crops and the outcomes of such tests, which may be a feature of the microbial biostimulant market more generally (Parnell et al. 2016). The responsibility of product testing and confirmation of efficacy should fall to the manufacturers. Insufficient regulation on product efficacy in unregulated markets often prevents rigorous scientific evaluation and optimisation of biostimulant products for particular crops and farming systems. EU countries are addressing this by legislating that the efficacy of products has to be demonstrated in scientifically validated field trials (EPPO 2012c). In regulated markets, commercial products are often formulated with a single microbial strain for a particular context (i.e. crop, soil, climate). In contrast, in unregulated markets such as Australia and the USA, products are often mixtures of numerous microbial taxa and recommended for many crops. We anticipate that in the future the commercial microbial biostimulant industry will consolidate the product market with effective products for particular crops and growth situations. There is evidence that single-microbial strain products may not establish and survive well under field conditions (Tabassum et al. 2017), and that mixed-culture products enable a pre-established synergistic microbial community that enhances competitive ability (Trabelsi and Mhamdi 2013; Burmølle et al. 2014). For both types of products, thorough classification of each active constituent should be a priority to reduce the risk of inadvertent pathogen release. We found that product labels are often not informative, lacking detail on microbial composition (including on sub-species/strain), recommended storage and application rates (Bashan et al. 2016). If microbial biostimulants are to contribute to the sustainable intensification of farming, collaboration between regulators, manufacturers, scientists and end-users is an obvious next step.
Microbial biostimulants and biocontrol agents are receiving much attention with promising discoveries over recent decades and a desire to improve agricultural practices to reduce the environmental footprint of farming. Fuelled by the accelerating ability to analyse microbial DNA in environmental samples, it is now possible to characterise microbial communities taxonomically and functionally. Our study was motivated by the bottleneck that has limited the translation of fundamental research into field-effective products, as identified by sugarcane industry stakeholders. Multiple factors need to be considered for product efficacy. These include (i) do microbes benefit the target crop directly (host specificity) or indirectly (improved soil function including soil biology), (ii) are the introduced microbes viable at the time of application (suitable transport, storage, formulation), (iii) is the application technique appropriate (seeds coating, liquid sub/surface application to soil, timing) to deliver viable microbes and (vi) do introduced microbes effectively compete with native microbes and survive soil biotic and abiotic conditions? Many farmers see microbial products as a tool to improve identified soil constraints, but there has to be clarity on what such products can deliver. We agree with others that microbial products have potential to complement current agronomic practices, but that that the aims must be realistic and backed up by science to ensure quantifiable benefits. Our study confirms the potential of microbial community profiling to ascertain if biological inoculants influence native microbial communities directly and indirectly. Next steps require longer-term field evaluations together with an analysis of functional changes of the microbial communities. Metagenomics now allow closer examination of microbial function and will benefit understanding of the mechanisms. Research and development of beneficial microbes can then integrate into comprehensive analyses of soil and crop health, connecting physical, chemical and biological attributes of crop systems.
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We thank Terrain Natural Resource Management for funding this project, as well as Melissa Royle and Minka Ibanez for sample collection. Shelby Berg gratefully acknowledges financial support from Sugar Research Australia (PhD top-up stipend and operating funds) and the Australian Government Research Training Program.
Data availability statement
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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The authors declare that they have no conflict of interest.
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Berg, S., Dennis, P.G., Paungfoo-Lonhienne, C. et al. Effects of commercial microbial biostimulants on soil and root microbial communities and sugarcane yield. Biol Fertil Soils 56, 565–580 (2020). https://doi.org/10.1007/s00374-019-01412-4
- Microbial biostimulants
- Crop probiotics
- Beneficial microbes
- Root microbial communities
- Soil microbial communities