Structural And Functional Changes In Soil Microbes By Foliar Drift Spray of Seaweed Extract As Revealed By Metagenomics

doi:10.21203/rs.3.rs-753007/v1

Kappaphycus alvarezii seaweed extract (KSWE) is known to enhance crop productivity and impart stress tolerance and our preliminary studies showed their biostimulatory effect on soil bacteria also. Close to one quarter of the foliar spray carried out on maize falls on soil either as drift or from leaf as drip. Hence it was hypothesized, it would profoundly influence soil microbes under stress. An experiment was conducted with five treatments, with or without KSWE application at critical stages of maize under soil moisture stress and compared with an irrigated control. Illumina platform was employed for analysis of V3-V4 region of 16S rRNA gene from the soil metagenome. Total of 345,552 operational taxonomic units were generated which were classified into 55 phyla, 152 classes, 240 orders, 305 families and 593 genera. Shannon’s index and Shannon’s equitability indicated increased soil bacterial diversity by multiple KSWE applications under duress. The abundance of Steroidobacter, Balneimonas, Rubrobacter, Bacillus, Alicyclobacillus, Anaerolinea and Nitrospira genera decreased (49-79%) in drought imposed at V5,10, and 15 stages of maize over the irrigated control, while it significantly improved when followed by KSWE application under drought. Nitrosomonas, Nitrosovibrio, Rubrobacter, Flavobacterium genera and several other taxa which are important for plant growth promotion and nutrient cycling were found to be enriched by KSWE application under drought. Treatments having enriched microbial abundance due to KSWE application under stress recorded higher soil enzymatic activities and cob yield, suggesting the contribution of altered soil ecology mediated by KSWE as one of the reasons for yield improvement.

General Biochemistry

Molecular Biology

Molecular Genetics

Biostimulant

Seaweed liquid fertilizer

Metagenomics

Illumina

Microbial diversity

Kappaphycus

Reports on improvement in crop yields and stress alleviation following application of seaweed-based biostimulants are on the rise (Dalal et al., 2019; Garai et al., 2019; Rouphael et al., 2018; Trivedi et al., 2017). Kappaphycus alvarezii seaweed extract (KSWE) is one such tropical seaweed-based biostimulant, the application of which enhances economic yield of many crops such as rice (Pramanick et al. 2014; Sharma et al. 2017; Layek et al. 2018), wheat (Patel et al. 2018), soybean (Rathore et al. 2009), sugarcane (Karthikeyan and Shanmugam 2017; Singh et al. 2018), green-gram (Raverkar et al. 2016), black-gram (Pramanick et al. 2016), potato (Pramanick et al. 2017), and tomato (Zodape et al. 2011). Around 60% of food for the global human population is catered by wheat, rice and maize (Tilman et al. 2002) and to target their higher productivity with low carbon footprint has significance in terms of reducing Global warming potential (Ghosh et al. 2015; Sharma et al. 2017; Singh et al. 2018). In maize, enhancement in grain yield by foliar application of KSWE and the possible underlying physiological, molecular and agronomic basis of improvement have been reported earlier by us (Layek et al. 2015; Mondal et al. 2015; Singh et al. 2016; Kumar et al. 2019). It was also reported that KSWE application partly alleviated soil moisture stress in maize by modulating antioxidant enzymes and differential expression of certain genes (Trivedi et al. 2018a; Trivedi et al. 2018b). However, the effect of foliar spray of KSWE, or rather any seaweed based biostimulant on soil microflora has never been reported. This has been probably neglected because the drift spray and drip from leaves falling onto the soil is seemingly miniscule. However, a careful quantification revealed that on average a quarter of biostimulants applied as foliar spray reaches the soil over the crop cycle (Table 1) and such a significant amount of it may have a direct effect on soil microflora and related soil biochemical processes, which formed the hypothesis of the present work. The study assumes, maintenance of soil fertility and structure is controlled by interactions of a highly assorted and complex web of soil microflora and fauna (Davet 2004). Enzymes released/produced by soil microbes are responsible for organic matter decomposition and nutrient cycling (Quan and Liang 2017). It was also hypothesized that the influence of seaweed based biostimulants on soil bacteria would be manifested more under soil moisture stressed condition as drought profoundly influences the dynamics of the microbial communities of soil (Quan and Liang 2017). Whether or not the seaweed biostimulant influences the soil bacteria positively under abiotic stress condition also formed the basis of present work. Accordingly, the study was conducted with the objective of assessing whether management practice involving the foliar application of KSWE influences the soil culturable and unculturable bacterial community composition beneficially. This was carried out through the amplicon-based sequencing of 16S rRNA genes through nextgen Illumina sequencing. In addition, its effect on the yield of maize crop under normal as well as drought stress conditions was also evaluated.

Table 1

Spray and drift volumes of KSWE at critical growth stages of maize along with leaf area at the time of spraying (numbers presented are mean of three replicates).
Spraying stage	Leaf area (cm² plant^− 1)	Total volume of KSWE sprayed (ml plant^− 1)	Volume retained on plant leaves (ml plant^− 1)	Volume drifted on soil (ml plant^− 1)	% of KSWE drifted on soil
V5	454 ± 57	9.3 ± 1.2	5.06 ± 0.49	4.28 ± 0.75	45.6 ± 3.12
V10	2478 ± 275	19.0 ± 1.7	14.69 ± 1.72	4.31 ± 0.61	22.8 ± 3.36
V15	3259 ± 594	30.0 ± 0.0	25.10 ± 0.13	4.90 ± 0.13	16.3 ± 0.42

2.1. Preparation of KSWE

The extract was prepared in bulk quantity as per the procedure described in Trivedi et al. (2017), stored at 4°C and used as and when required. Details of the composition of KSWE have been described earlier in Singh et al. (2016) and the same batch of the seaweed extract was also used in the present experiment. The total soluble solids (TSS) of the KSWE was in the range of 3–4 %.

2.2. Ethics Statement

There was no specific permission required for sampling in the experimental area of this study. The location is not protected or privately owned in any way, and it is confirmed that our experiment did not involve endangered or protected species.

2.3. Experimental site and design

Two preliminary experiments followed by metagenomic study were conducted in pots at the net house facility (21°44′57.6″N latitude, 72°08′39.3″E longitudes) of CSMCRI, Bhavnagar district, Gujarat, India, during the rabi season (November 2014 – March 2015).

In a preliminary experiment 1, Plant Growth Promoting Rhizobacteria (PGPRs), namely, Bacillus subtilis, Bacillus licheniformis representing Bacillus family and Pseudomonas fluorescens representing Pseudomonas family, were individually tested for bacterial growth enhancement upon KSWE application. For this, two concentrations of KSWE (5 and 10%) were added to a bacterial culture containing initial count of 10⁵-10⁶ colony forming units (CFU) ml^− 1. Nutrient broth medium without added KSWE was treated as control. Final colony counts were recorded through standard plate count technique after 4 h incubation (at 37°C, 120 RPM) of respective treatments along with its control (in triplicate).

In another preliminary experiment (Experiment 2), total bacterial counts in soil were recorded (in triplicate) employing plate count method on 12th day after single KSWE application to soil at 5 and 10% concentrations under normal as well as drought conditions in pots grown with maize. The volume of KSWE applied was 1.5 ml kg^− 1 of soil. The drought was induced by withholding irrigation for the entire duration up to sampling time. The result of these two preliminary experiments formed the basis for further more elaborate metagenomic analysis of soil bacteria.

The metagenomic experiment consisted of 5 treatments viz., T1 – water spray once at V5 stage of maize (variety: Sugar 75, Syngenta) along with transient drought stress (sample code: K1, K2, K3), T2 – KSWE foliar application once at V5 along with transient drought stress (sample code: K4, K5, K6), T3 – three water sprays at V5, V10 and V15 stage with transient drought stress at all the three stages (sample code: K7, K8, K9), T4 – three KSWE foliar applications at V5, V10 and V15 stage with transient drought stress at all the three stages (sample code: K10, K11, K12), T5 – control plants neither with spray of water/KSWE nor with transient drought (sample code: K13, K14, K15). In the metagenomic study, KSWE was applied at 10% concentration which was found to be the optimum dose eliciting a favorable response in maize (Layek et al. 2015, Trivedi et al. 2017). Water spray contained the same amount of preservatives in appropriate concentrations which were present in KSWE. Transient drought condition was generated by withholding watering for 8 days till visible wilting symptoms were observed at some of the important growth stages viz., V5, V10, and V15 identified as critical stages in maize for application of KSWE in our earlier experiments with V5 being the most critical (Trivedi et al. 2017). Except for the drought period, plants were watered at the rate of 2 liters per pot every alternate day in all the experiments. The treatments were distributed in a completely randomized design (CRD) with three replications.

In addition to this, one normally irrigated treatment was kept where KSWE was sprayed daily in between V5 to grain filling stage just to know the detrimental effect of seaweed extract on soil microbes, if at all, when sprayed daily (T6; sample code: K16). However, this treatment was in single replication and not included in any statistical comparison.

All the pots were filled with 32 kg of soil to which chemical fertilizers at the recommended rate of 120:60:40 kg ha^− 1 of N/P₂O₅/K₂O were applied uniformly to all the treatments through urea, single super phosphate, and sulfate of potash, respectively. The initial soil of the experiments was sandy loam in texture, having pH 7.80 and electric conductivity of 0.20 dS m^− 1. Available N, P, and K were 103, 14 and 161 kg ha^− 1, respectively. Organic carbon in soil was 0.51%. Four seeds were sown in each pot, which after successful germination was thinned to single plant per pot. The meteorological data of the experimental site is given in the Appendix S1.

2.4. Soil sampling

Core soil samples from around 30 cm depth were taken on 12th day after treatment in preliminary experiment 2 and at the time of plant harvesting for metagenomic and biochemical studies. All samples were collected in sterile 50ml flacon tubes and transported immediately to the laboratory in ice. Later they were sieved (< 2 mm) and stored at 4°C until processing. The metagenomic study was carried out from all the treatments, while soil enzyme analysis was carried out only in the two most significant treatments found in the metagenomic study (viz. T3 and T4) from the samples collected at harvest.

Electric conductivity (EC) and pH of the initial soil samples were determined in 1:2.5 slurry of soil: water. Organic carbon was determined by Walkley and Black procedure as described in Nelson and Sommers (1982). Available N and available P were determined according to the procedure described by Maynard et al. (2006) and Olsen’s method (Olsen and Sommers 1982), respectively. K was extracted by neutral normal ammonium acetate (Hanway and Heidel 1952) and determined by using a flame photometer. Ca and Mg were determined by EDTA titrimetric method as described by Estefan et al. (2013). S was extracted with 0.15% CaCl₂ (Williams and Steinbergs 1959) and estimated by turbidimetric method as described by Estefan et al. (2013).

2.5. Drift volume measurements

Drift volume of KSWE solution was measured by placing a polythene cover on the top of soil in the pot during foliar application. A fixed volume of KSWE was sprayed and polythene cover was weighted before and immediately after the spraying. 1g of KSWE equal to 1 ml was used as the basis for calculating the volume. Leaf area was measured using a software called Digimizer (version 5.4.4) (Rangani et al. 2016).

2.6. Crop yield

Fresh cobs were harvested from each plant and were sundried. Dry corn weight with seeds was recorded as crop yield and expressed in g plant^− 1.

2.7. DNA extraction

Bacterial genomic DNA was extracted from soil samples (0.5 g) using the PowerSoil® DNA isolation kit (Mo Bio Laboratories, Carlsbad, CA, USA) as per the manufacturer’s protocol. The initial homogenization was carried out in a FastPrep-24 homogenizer for 30 s at an amplitude of 5.5 m s^− 1. The genomic DNA was tested for its quality and quantity using an Epoch microplate reader and then stored at -20°C.

2.8. Amplicon library construction

PCR amplification of extracted DNA for all the treatments in triplicate was carried out to amplify V3-V4 conserved regions of 16S rRNA gene sequences using the 16S rRNA gene primers (forward primer 5ʼ-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3ʼ and reverse primer 5ʼ-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3ʼ). The PCR library was prepared using 2× KAPA HiFi HotStart Ready-mix PCR Kit (KAPA Biosystems®, U.S.A.) and Nextera® XT DNA library preparation kit (Illumina®, San Diego, California, U.S.A) to add Illumina sequencing adapters and dual index barcodes. First amplicon PCR was done followed by index PCR. For amplicon PCR, each 25 µl of PCR reaction contained 5 ng µl^− 1 (2.5 µl) of genomic DNA template, 12.5 µl of 2× KAPA HiFi HotStart Ready Mix, and 5 µl of each primer (1 µM). The 96 well plate was sealed and PCR reactions were performed in a thermal cycler with an initial denaturation step at 95°C for 3 min followed by 25 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s and ended with an extension step at 72°C for 5 min. One microlitre of the PCR product was run on a Bioanalyzer DNA 1000 chip to verify the size of amplified DNA.

For index PCR, each 50 µl of PCR reaction contained 5 µl of DNA, 5 µl of each Nextera XT Index Primers, 25 µl of 2x KAPA HiFi HotStart ReadyMix and 10 µl of PCR grade water. PCR reactions were performed in a thermal cycler with an initial denaturation step at 95°C for 3 min followed by 8 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s and ended with an extension step at 72°C for 5 min. One microlitre of a 1:50 dilution of the final library was run on a Bioanalyzer DNA 1000 chip to verify the size.

2.9. Illumina sequencing

All samples were subjected to paired-end (250 bp) sequencing (V3-V4 region) using Illumina MiSeq sequencer and analyzed into the MiSeq Reporter on-system software at SciGenom Labs Private Limited, Cochin, Kerala, India. Base quality checking of each cycle for all the samples was carried out and low-quality bases were removed so as to ensure that the majority of the reads with a high-quality score above 30 (Q > 30) were used for further downstream processing. It was found that nearly 75% of the total reads have a Phred score greater than 30 (> Q30, error-probability ≥ 0.001). Base composition and GC content distribution are also summarized in figures but not included in the paper.

All the sample reads were converted to FASTA files and were pooled together. The FASTA were pre-processed using the bioinformatics analysis pipeline. Chimeric sequences were removed using the program UCHIME and all non-chimeric sequences were taken for picking OTUs using the program UCLUST with a threshold of 97% of similarity (Lozupone et al. 2013; D’Argenio et al. 2014).

The Quantitative Insights Into Microbial Ecology (QIIME) program was used for the entire downstream analysis (Caporaso et al. 2010). The representative sequence was identified for each OTU and aligned against the Greengenes core set of sequences using PyNAST program (DeSantis et al. 2006a; DeSantis et al. 2006b). Further alignment was done of these representative sequences against reference chimeric data sets. The read based taxonomy classification was performed using the RDP classifier against Greengenes OTUs database.

Further, heat maps were generated using the QIIME program. The phylum, class, order, family, genus and species distribution for each sample, based on OTUs, were shown as heat maps in the Appendix S2. The taxa other than the top 10 were categorized as “Others” and the sequences that did not have any alignment against the taxonomic database were categorized as “Unknown”. Rare and abundant taxa from the samples were also identified. Rare taxonomy was defined as having frequency < 0.01% and abundant taxonomy as OTUs having > 1% frequency (Galand et al. 2009; Aravindraja et al. 2013).

In order to assess the microbial diversity and distribution within and between the treatments, both α – diversity, and β – diversity were measured.

2.10. Alpha diversity and rarefaction curves

Microbial diversity within the samples was measured using Chao1, Shannon and observed species metrics. The Chao1 metric estimates the species richness while Shannon metric is the measure to estimate observed OTU abundances, and accounts for both richness and evenness. The observed species metric is the count of unique OTUs identified in the sample. The rarefaction curves for each of the metric have been provided in Appendix S3. The metric calculation was performed using QIIME software.

2.11. Beta diversity between samples

Explicit comparisons of bacterial communities between the samples were also performed. The distance matrix was generated using a weighted and unweighted UniFrac approach. Sequence abundances were taken into account in weighted UniFrac for comparing microbial diversity. A jackknife test was performed to construct a consensus UPGMA (Un-weighted Pair Group Method with Arithmetic Mean) tree for all samples in this set. The resulting consensus was taken for UPGMA trees built using a weighted UniFrac distance matrix.

2.12. Accession numbers

Sequencing data of all the 16 samples were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP180107) under the study SRP180107.

2.13. Soil enzyme estimation

Aryl sulphatase, acid-, alkaline- phosphomonoesterase and glucosidase activities in the field moist soil were assayed according to Tabatabai (1982) using 4-nitrophenol as standard. FDA activity was measured according to Schnürer and Rosswall (1982).

2.14. Statistical analysis

All statistical analyse were carried out using MSTAT C software. In the preliminary experiment 1, two-factor Completely Randomized Design was employed for ANOVA, while in the preliminary experiment 2 and metagenomic study one way ANOVA was used. The results were expressed in mean ± standard deviation of three independent replicates. Post hoc comparison of means was carried out using Tukey’s honestly significant differences, Student-Neuman-Keul’s test and Least significant different test at p < 0.05. The principal component analysis (PCA) was conducted using Minitab statistical software to assess the variation among treatments at different taxonomic levels. A Venn diagram was prepared using Ugent tool available on http://bioinformatics.psb. ugent.be/webtools/Venn/ to observe the identified number of unique and shared species present among all the five treatments.

3.1. Bacterial colony counts

In the preliminary experiment 1, It was found that the counts of Bacillus subtilis, Bacillus licheniformis and Pseudomonas fluorescens increased significantly with concomitant increase in the KSWE concentration (5 and 10%) and both the doses recorded higher counts over control (Fig. 1a). Subsequently in preliminary experiment 2, drought significantly reduced the total CFUs in soil while KSWE brought out a significant increase in total bacterial count under both the soil moisture regimes at 10% level (Fig. 1b). Interestingly, the total bacterial CFUs in soil under drought conditions with 10% KSWE applied was similar to that under normally irrigated soil (without KSWE). These results were a prelude to the bacterial metagenomic study for in-depth information on the diversity and ecology of soil due to the influence of KSWE.

3.2. Generation of Illumina reads

In the metagenomic study, targeting the hyper-variable V3-V4 region of 16S rRNA gene amplicons, a total of 4,314,385 OTUs were identified from 6,624,123 reads. 3,968,833 singleton OTUs (OTUs having single read) were identified and removed. After singleton removal, 345,552 distinct OTUs were found which were used for further downstream analysis. These OTUs were classified into 55 phyla, 152 classes, 240 orders, 305 families and 593 genera at a similarity threshold of 0.97.

3.3. Alpha diversity with rarefaction curves of Chao1, Shannon and Observed species

Rarefaction curves depicting alpha diversity (Appendix S3) reached near the plateau, indicating that the sampling depth and sequencing coverage were good. In addition, Shannon’s index (H) and Shannon’s equitability or evenness (E_H) were calculated at phylum level (Table 2) to show the relative bacterial diversity. Shannon’s index revealed that there was maximum bacterial diversity when the soil was subjected to stress with concomitant KSWE application three times (T4) during the growth cycle. This was followed by that in normally irrigated soil (T5), while it was lesser in soil that was subjected to stress thrice but not treated with KSWE (T3). Further, the bacterial communities in soil were found to be more evenly distributed in T4 (E_H = 0.5787) and T5 (E_H = 0.5624) as compared to those in other treatments.

Table 2 Relative bacterial diversity as indicated by Shannon’s diversity index and Shannon equitability at phylum level along with rare and abundant species and cob yield of maize plant in normal as well as drought conditions for all the treatments.

Treatments	Shannon’s diversity index (H)	Shannon’s equitability or evenness (E_H)	Rare species	Abundant species	Crop yield (g plant^-1)
Treatments	Shannon’s diversity index (H)	Shannon’s equitability or evenness (E_H)	Total reads		Crop yield (g plant^-1)
T1- V5 water spray	1.927	0.5124	341^b	107234^cd	89.16^c
T2- V5 KSWE	1.929	0.516	471^b	119167^c	97.30^b
T3- V5,10,15 water spray	2.029	0.527	357^b	97812^d	36.33^e
T4- V5,10,15 KSWE	2.240	0.5787	818^a	224435^b	75.66^d
T5- Normal Irrigation	2.233	0.5624	911^a	246370^a	87.03^c
T6- Daily spray till grain filling stage	–	–	–	–	104.33^a

Values are mean of 3 replicates. Values followed by different alphabets in the columns are significantly different at P < 0.05 using Tukey’s HSD.

3.4. Beta diversity with a phylogenetic tree

The bacterial community similarity, as revealed by the weighted UniFrac phylogenetic tree, is depicted in Appendix S4. It revealed that the bacterial community in the T5 (K13, K14, and K15) and T4 (K10, K11, K12) treatments were different from the T1 and T3 (K1, K2, K3, K7, K8), except K9 which clustered with the former group. The samples in these clusters (T1 and T3 vs T4 and T5) were also not close and grouped loosely indicating the difference in bacterial community composition in soil samples due to treatment effect.

3.5. Pairwise multiple comparisons of total OTU means

Significant changes (P ≤ 0.001) in abundance of the 16S bacterial rRNA gene expressed as the number of total OTUs were detected among different treatments (T1-T5) using ANOVA and Tukey’s test (Table 3). The least number of OTUs was observed in soil collected from the treatment receiving moisture stress three times during the life cycle of the crop (T3, 100157). Variation in bacterial abundance due to KSWE was evident by a significant increase in the number of OTUs in soil samples of treatment that was subjected to stress three times and also with KSWE applied (T4, 232029) over the corresponding treatment (T3) where KSWE was not applied (P ≤ 0.001) (Table 3). Moisture stress at one or more stages significantly reduced the OTU abundance in the soil as compared to that of irrigated soil (T5, 254041), except that in case of treatment receiving KSWE three times in which case it was at par.

Table 3 Pairwise Multiple Comparison of total OTUs means (Tukey’s Honestly Significant Test).

Comparison	Diff of Means	P*
T5 vs. T3	153884	<0.001
T5 vs. T1	144704	<0.001
T5 vs. T2	132290	<0.001
T5 vs. T4	22012	0.412
T4 vs. T3	131872	<0.001
T4 vs. T1	122692	<0.001
T4 vs. T2	110278	<0.001
T2 vs. T3	21594	0.43
T2 vs. T1	12414	0.837
T1 vs. T3	9180	0.936

Treatment means of total OTUs: T1: 109337; T2: 121751; T3:100157; T4: 232029; T5: 254041

*P indicates probability value. Comparisons having P < 0.05 were significantly different from each other.

3.6. Impact of drought and KSWE on specific bacterial groups

3.6.1. Distribution of bacterial community at phylum level

Upon taxonomic classification of all the 345,552 distinct OTUs, 9.8% of OTUs (33863 OTUs) were grouped under unclassified bacteria and the rest 90.2% were grouped into different phyla. The top 12 phyla falling within the classified OTUs in different soil samples (T1-T5) are given in Fig. 2. The 12 dominating phyla were Chloroflexi, followed by Proteobacteria, Actinobacteria, Firmicutes, Acidobacteria, Bacteroidetes, Gemmatimonadetes, Planctomycetes, TM7, Nitrospirae, Verrucomicrobia and others (which was a sum of all the rest) having individual abundance less than 0.5%.

There was a striking diversity shift with respect to the relative abundance of phyla Proteobacteria, Chloroflexi and Firmicutes among the treatments T3, T4 and T5 (Fig. 3). Whereas the percentage of OTUs belonging to the Proteobacteria decreased from 26.06% in normal irrigated treatment (T5) to 17.23% due to stress applied three times (T3), application of KSWE increased its corresponding proportion to 24.87 in the stress treatment (T4). Similarly, the proportion of the abundance (OTUs) of microbes in Firmicutes phylum was reduced to 9.99% when subjected to moisture stress three times compared to normally irrigated condition, but upon KSWE application three times under duress, its proportion increased to 19.42%, which was even more than that under normal irrigated treatment (15.88%). It was found that KSWE under moisture stress could bring down the relative proportion of Chloroflexi to an identical level (14.77%) as that under normal irrigated condition (15.9%) from the elevated level of 38.72% due to stress (T3).

The comparison of the relative abundance means of the top 12 enriched bacterial phylum categories (P ≤ 0.01) associated with soil samples collected from T1-T5 is shown in Fig. 4. The soil samples collected from the treatments subjected to stress once or thrice and not sprayed with KSWE (T1, T3) and those subjected to stress and KSWE spray only at V5 stage (T2) had significantly decreased populations of phyla Proteobacteria, Firmicutes, Bacteroidetes, Planctomycetes, and Verrucomicrobia, over the treatment that received stress and KSWE thrice (T4). KSWE applied thrice under stress (T4) showed an increase in the population of Proteobacteria, Firmicutes, Bacteroidetes, Planctomycetes, and Verrucomicrobia in soil samples by 107%, 179%, 162%, 409%, and 408% respectively, over its respective control (T3) (Fig. 4). Notably, the relative abundance of all these soil bacterial phyla in the T4 was found to be statistically similar to that under untreated normal irrigated control soil (T5). The populations of Acidobacteria, TM7, and Nitrospirae were not affected by any of the treatments. The abundance of OTUs belonging to Chloroflexi was not significantly affected by any of the treatments, however, its relative proportion vis-à-vis other phyla within a given treatment varied considerably with its proportion increasing under moisture stress. Compared to the control (T1), mere application of KSWE once in early-stage under stress at the V5 stage (T2) of the crop had no noticeable change in the abundance of any of these top 12 phyla in the soil at harvest (Fig. 4 and Table 3).

The PCA was performed to assess the variation considering 55 known and 1 unknown phylum categories. Based on the factor loadings of these phyla, the four components of PCA explained the total variations as shown in Appendix S5.

The first (PC1) and second (PC2) principal components contributed 73.9% and 10.8% of the total variation, respectively. The members of the dominant phyla including Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes had high loading on PC1 indicating that these vary together in the same direction. In contrast, Chloroflexi had substantial negative loading on PC1.

A biplot of the component scores has been produced indicating the second component plotted against the first component in Appendix S6. Looking at the treatment out by itself to the right, it may be inferred that the KSWE applied thrice under stress (T4) and the normally irrigated control (T5) had very high values for the first component and it is expected that these treatments would have high values for the relative abundance of the bacterial community with which they are strongly correlated, i.e., they move in a similar direction. In agreement, both these treatments had higher values for most of the enriched phylum categories. Both the water sprayed controls as well as the KSWE sprayed once (T1, T2, and T3 respectively) were located extremely left on the spectrum and thus had lower values for the relative abundance of the respective phyla.

3.6.2. Distribution of bacterial community at class, order and family level

Biplots of the distribution of bacterial communities (top ten) at the higher taxonomic levels of class, order and family also exhibited the same pattern wherein the treatments T1, T2 and T3 were distantly located compared to T4 and T5 on the first component (Appendix S6).

With respect to the top 10 enriched classes, KSWE treatment and stress applied thrice (T4) significantly improved the relative abundance of the Alpha-, Beta-, Gamma- and Delta-proteobacteria along with Actinobacteria and Bacilli, when compared to their respective water sprayed stress control (T3). Moreover, the abundance levels of these classes were brought at par to the normally irrigated treatment (T5). Among the top 10 enriched orders, relative abundance of the Actinomycetales, Bacillales, Cytophagales, Myxococcales, Rhizobiales, and Xanthomonadales followed the same trend, while, within the top 10 dominant families, a similar trend was found in Anaerolinaceae, Bacillaceae, Cytophagaceae, Pseudonocardiaceae and Planococcaceae (Table 4).

Table 4

**Relative abundance (total OTUs) of top 10 enriched classes, orders and families as affected by all five treatments.** Values represented are mean of 3 replicates. Values followed by different alphabets in the columns are significantly different at P < 0.05 using Tukey’s HSD test.
Top 10 enriched classes
Treatments	-proteobacteria				Actinobacteria	Thermomicrobia	Clostridia	Bacilli	Anaerolineae	Unknown	Others
Treatments	Alpha-	Beta-	Gamma-	Delta-	Actinobacteria	Thermomicrobia	Clostridia	Bacilli	Anaerolineae	Unknown	Others
V5 water	2663 b	620 b	1071 b	2011 b	5324 bc	2884 a	891 b	2407 b	11091 a	5241 a	9726 b
V5 KSWE	2733 b	665 b	1302 b	2097 b	5087 bc	3520 a	1174 b	2960 b	14689 a	6234 a	12323 b
V5,10,15 water	2874 b	816 b	1390 b	2245 b	4120 c	2747 a	1139 b	3196 b	12463 a	5316 a	11757 b
V5,10,15 KSWE	4582 a	2054 a	2787 a	5653 a	7278 ab	536 a	2995 a	9060 a	7676 a	5916 a	18730 a
Normal irrigation	5316 a	2359 a	3063 a	6573 a	8503 a	703 a	2502 a	8410 a	9005 a	6464 a	21109 a
Top 10 enriched orders
Treatments	Actinomycetales	Bacillales	Clostridiales	Cytophagales	GCA004	Myxococcales	Rhizobiales	SBR1031	Xanthomonadales	Unknown	Others
V5 water	5266 bc	2348 b	640 b	264 b	1600 b	1686 b	916 b	6747 a	508 b	10156 a	13800 b
V5 KSWE	5032 bc	2862 b	861 b	328 b	1575 b	1732 b	941 b	9155 a	603 b	12230 a	17470 ab
V5,10,15 water	4075 c	3111 b	887 b	615 b	1439 b	1799 b	992 b	7053 a	666 b	10741 a	16690 ab
V5,10,15 KSWE	7178 ab	8826 a	2670 a	1929 a	2138 ab	4395 a	1994 a	2634 a	1419 a	12498 a	21600 ab
Normal irrigation	8398 a	8222 a	2149 a	2133 a	2553 a	5159 a	2122 a	3268 a	1435 a	15062 a	23510 a
Top 10 enriched families
Treatments	A4b	Anaerolinaceae	Bacillaceae	Clostridiaceae	Cytophagaceae	Micromonosporaceae	Pseudonocardiaceae	Planococcaceae	Rhodospirillaceae	Unknown	Others
V5 water	4199 ab	30 b	936 b	135 b	212 b	1256 b	504 bc	249 b	525 b	24607 a	11280 b
V5 KSWE	5888 a	19 b	1251 b	158 b	264 b	1156 b	529 bc	354 b	524 b	29168 a	13480 b
V5,10,15 water	3660 ab	182 b	1442 b	209 b	537 b	951 b	366 c	329 b	561 b	26178 a	13650 b
V5,10,15 KSWE	1980 b	904 a	3907 a	928 a	1718 a	1586 ab	620 ab	1104 a	727 ab	31326 a	22480 a
Normal irrigation	2276 ab	873 a	3622 a	685 ab	1916 a	2214 a	761 a	1007 a	941 a	35851 a	23860 a

3.6.3. Distribution of bacterial communities at genus level

Relative distribution of the bacterial communities at the level of genus and species was also assessed. Top 10 enriched genus categories with their corresponding phylum have been shown in Fig. 5.

Among the top 10 genera, three genera (Clostridium, Bacillus, and Alicyclobacillus) belonged to the phylum Firmicutes. The other dominant genera were Steroidobacter and Balneimonas representing Proteobacteria. Actinomadura and Rubrobacter represented Actinobacteria while Anaerolinea and Nitrospira represented Chloroflexi and Nitrospirae phyla, respectively. The average relative abundance of the genera Bacillus, Alicyclobacillus, Steroidobacter, Balneimonas, Rubrobacter, Anaerolinea, and Nitrospira were significantly lower in treatment receiving moisture stress three times (T3) compared to normal irrigated T5 treatment by 59, 71, 57, 49, 63, 79 and 58%, respectively. KSWE treatment applied three times to these stress subjected treatments significantly improved the relative abundance of all these genera by 171, 196, 133, 113, 232, 416 and 126%, respectively over their corresponding control treatment (T3). Further, the normally irrigated treatment (T5) was at par with the treatment receiving stress three times along with KSWE (T4) with respect to a relative abundance of all the aforesaid genera. The relative abundance of other important soil bacterial genera having known or potential role for nitrogen fixation (Anaeromyxobacter, and Methanobacterium) and P solubilization (Flavobacterium) were also assessed. It was found that the KSWE application increased the relative abundance of all of these bacterial genera to that of the normally irrigated treatment. In all the aforesaid genera, the relative abundance in T4 was significantly higher compared to T3 (Appendix S7).

Principal component analysis at genus level of the top 10 and some others involved in N and P cycling also revealed that T1, T2 and T3 have similar associated abundance pattern clustered together towards the left side, while T4 and T5 clustered towards the right of the PC1, which explained 91.3% of the total variation (Appendix S5). The genera which helped distinguish the normally irrigated treatment (T5) from the treatment where KSWE and stress were applied thrice (T4) could be gauged by the influence scores in the PC2, although this component explained 6.1% of the total variation (Appendix S5).

3.6.4. Distribution of bacterial communities at species level

A Venn diagram (Fig. 6) was also prepared to observe the identified number of unique and shared species present among all the five treatments. The total number of different species identified in T1, T2, T3, T4, and T5 was 233, 260, 250, 268 and 248, respectively. The number of species unshared in these treatments were 10, 14, 9, 14 and 17, respectively. The number of species shared between all five treatments was 135. Interestingly, the number of species shared between T4 and T5 was 30, which was the highest among any other paired comparisons.

3.6.5. Abundant and rare species diversity

Both rare (frequency of species with < 0.01% of the total population) and abundant species contributed to the overall bacterial population in all the 5 treatments. Total reads for rare species and abundant species were in the range of 300 to 1000 and 97 thousand to 250,000, respectively in the treatments. Significantly higher rare and abundant species were found in T4 and T5. These treatments were at par for rare species while species abundance was more in T5 (Table 2). T1, T2, and T3 also followed a similar trend as in phylum and genus. They were significantly lower than T4 and T5 and at par with each other except T3 in the case of abundant species which was significantly lower than T2.

3.6.6. Functionally important species

Fourteen unique species found in the treatment T4 (V5,10,15 KSWE) and the top 25 most abundant species that significantly varied with the treatments were classified for their functional roles (Tables 5 and 6). The unique species found in T4 treatment are specifically known for their involvement in the processes like nitrification, denitrification, mineralization of organic compounds and production of enzymatic and non-enzymatic anti-oxidants. T4 showed significantly higher number of OTUs compared to T3 in all the 25 most abundant species shown in Table 6. Most of them were also at par with T5, a normally irrigated treatment. They were also involved in processes such as bioremediation of heavy metals, pollutants, production of antibiotic, antifungal and nematicidal compounds.

Table 5 Summary of unique bacteria identified to the species level from a 16S rRNA gene-based metagenomic study of a soil collected from V5,10,15 KSWE treatment (T4).

Phylum	Class	Order	Family	Genus	Species	OTU^#	%^#	+/-/NA roles from literature
Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Pseudomonas balearica^a	2	0.0029	+
	Gammaproteobacteria	Pseudomonadales	Moraxellaceae	Acinetobacter	Acinetobacter johnsonii^b	2	0.0034	+
	Gammaproteobacteria	Vibrionales	Vibrionaceae	Vibrio	Vibrio rumoiensis^c	1	0.0014	+
	Gammaproteobacteria	Pseudomonadales	Moraxellaceae	Acinetobacter	Acinetobacter schindleri^d	2.5	0.0041	-
	Alphaproteobacteria	Rhizobiales	Methylocystaceae	Pleomorphomonas	Pleomorphomonas oryzae^e	1	0.0014	+
	Alphaproteobacteria	Rhodobacterales	Rhodobacteraceae	Paracoccus	Paracoccus zeaxanthinifaciens^f	1	0.0015	+
Actinobacteria	Actinobacteria	Micrococcales	Microbacteriaceae	Pseudoclavibacter	Pseudoclavibacter bifida^g	1	0.0017	-
Actinobacteria	Actinobacteria	Pseudonocardiales	Pseudonocardiaceae	Actinokineospora	Actinokineospora diospyrosa^h	1	0.0014	+
Firmicutes	Bacilli	Bacillales	Bacillaceae	Lentibacillus	Lentibacillus salisⁱ	1	0.0016	+
Firmicutes	Bacilli	Bacillales	Paenibacillaceae	Paenibacillus	Paenibacillus macerans^j	7	0.0097	+
Euryarchaeota	Halobacteria	Halobacteriales	Halobacteriaceae	Natrialba	Natrialba aegyptia^k	1	0.0017	+
Euryarchaeota	Halobacteria	Natrialbales	Natrialbaceae	Halovivax	Halovivax ruber^l	1	0.0014	+
Bacteroidetes	Cytophagia	Cytophagales	Cytophagaceae	Marinoscillum	Marinoscillum furvescens	1.5	0.0022	NA
Cyanobacteria	Cyanophyceae	Pleurocapsales	Dermocarpellaceae	Stanieria	Stanieria cyanosphaera^m	1	0.0015	+

^{^#values are mean of three replicates. Alphabets written in superscripts are for showing the possible roles of species. +, Positive; -, Negative; NA, No data available}

^aDenitrifying bacterium (Ruan et al., 2020)

^bIncrease phosphate flux by synthesizing and releasing phosphates and polyphosphates (Boswell et al., 2001)

^cPsychrophilic (cold loving) having extraordinary high catalase activity (Yumoto et al., 1999)

^dPotentially pathogenic (Dortet et al., 2006)

^eNitrogen-fixing capacity (Xie and Yokota, 2005)

^fZeaxanthin (a common carotenoid)-producing bacterium (Berry et al., 2003)

^g human pathogen, able to cause pulmonary disease (Oyaert et al., 2013)

^hContribute in recycling organic matter, Positive for having acid and alkaline phosphatase activity, an important soil enzymes for phosphate availability (Tamura et al. 1995)

ⁱCatalase producing, Moderately halophilic bacterium (Lee et al., 2008)

^jPlant growth promoting rhizobacteria (Figueiredo et al., 2008)

^kExtremely halophilic archaea, showed desiccation and gamma radiation resistance property (Shirsalimian et al., 2017), ability to produce an extracellular polymer predominantly composed of glutamic acid (Hezayen et al., 2001)

^lExtremely halophilic Gram-negative archaeon, catalase and oxidase positive (Castillo et al., 2007)

^mHas photosynthetic and nitrogen fixing capacity

Table 6 Top 25 most abundant species that significantly varied across all the samples.

Phylum	Class	Order	Family	Genus	Species name	T1 (V5 water)	T2 (V5 KSWE)	T3 (V5,10,15 water)	T4 (V5,10,15 KSWE)	T5 (Normal Irrigation)	+/-/NA roles from literature
Actinobacteria	Actinobacteria	Streptosporangiales	Thermomonosporaceae	Actinomadura	Actinomadura vinacea¹	129^bc	136^bc	97^c	222^ab	265^a	-
	Actinobacteria	Actinomycetales	Micromonosporaceae	Virgisporangium	Virgisporangium ochraceum²	57^bc	55^bc	45^c	81^b	136^a	+
	Actinobacteria	Actinomycetales	Nocardiaceae	Rhodococcus	Rhodococcus ruber³	17^bc	15^c	11^c	24^b	34^a	+
	Actinobacteria	Streptosporangiales	Streptosporangiaceae	Microbispora	Microbispora rosea⁴	8^b	4^b	5^b	17^a	20^a	+
Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	Bacillus foraminis⁵	85^b	127^b	156^b	258^a	255^a	+
	Bacilli	Bacillales	Bacillaceae	Bacillus	Bacillus selenatarsenatis⁶	16^b	25^b	31^b	55^a	53^a	+
	Bacilli	Bacillales	Bacillaceae	Bacillus	Bacillus badius⁷	26^d	33^cd	42^c	82^a	61^b	+
	Bacilli	Bacillales	Bacillaceae	Bacillus	Bacillus humi⁸	22^c	44^b	40^bc	70^a	67^a	+
	Bacilli	Bacillales	Bacillaceae	Oceanobacillus	Oceanobacillus chironomi⁹	15^b	12^b	18^b	29^a	30^a	+
	Bacilli	Bacillales	Planococcaceae	Sporosarcina	Sporosarcina ginsengi¹⁰	9^b	12^b	16^b	50^a	61^a	+
	Bacilli	Bacillales	Bacillaceae	Bacillus	Bacillus endophyticus¹¹	12^b	18^b	18^b	41^a	35^a	+
	Bacilli	Bacillales	Bacillaceae	Gracilibacillus	Gracilibacillus dipsosauri¹²	6^c	15^bc	9^c	35^ab	57^a	+
	Clostridia	Clostridiales	Peptostreptococcaceae	Clostridioides	Clostridium difficile¹³	10^c	8^c	10^c	29^a	20^b	-
	Bacilli	Bacillales	Bacillaceae	Bacillus	Bacillus firmus¹⁴	8^b	11^b	13^b	26^a	26^a	+
	Bacilli	Bacillales	Bacillaceae	Bacillus	Bacillus flexus¹⁵	8^b	10^b	10^b	46^a	39^a	-
	Bacilli	Bacillales	Bacillaceae	Lysinibacillus	Lysinibacillus massiliensis¹⁶	3^b	3^b	4^b	15^a	16^a	-
Proteobacteria	Betaproteobacteria	Nitrosomonadales	Nitrosomonadaceae	Nitrosovibrio	Nitrosovibrio tenuis¹⁷	15^c	20^bc	43^b	116^a	98^a	+
	Delta Proteobacteria	Myxococcales	Polyangiaceae	Sorangium	Sorangium cellulosum¹⁸	15^c	10^c	12^c	25^b	35^a	+
	Betaproteobacteria	Nitrosomonadales	Nitrosomonadaceae	Nitrosomonas	Nitrosomonas nitrosa¹⁹	7^b	9^b	17^b	42^a	38^a	+
	Deltaproteobacteria	Syntrophobacterales	Syntrophobacteraceae	Desulfovirga	Desulfovirga adipica²⁰	2^c	6^c	7^c	30^a	17^b	+
	Gammaproteobacteria	Xanthomonadales	Xanthomonadaceae	Lysobacter	Lysobacter brunescens²¹	8^c	5^c	5^c	21^a	15^b	+
	Deltaproteobacteria	Bdellovibrionales	Bacteriovoracaceae	Peredibacter	Peredibacter starrii	1^c	3^c	5^c	17^a	11^b	NA
Cyanobacteria	Cyanophyceae	Oscillatoriales	Oscillatoriaceae	Oscillatoria	Oscillatoria acuminata²²	1^b	6^b	1^b	25^a	13^ab	-
Bacteroidetes	Sphingobacteriia	Sphingobacteriales	Chitinophagaceae	Parasegetibacter	Parasegitibacter luojiensis²³	2^b	1^b	1^b	18^a	21^a	+
Euryarchaeota	Methanomicrobia	Methanosarcinales	Methanosarcinacaea	Methanosarcina	Methanosarcina mazei²⁴	0^b	0^b	1^b	11^a	10^a	+

Values are mean of 3 replicates. Values followed by different alphabets in the rows are significantly different at P < 0.05 using Least Significant Different (LSD) test. Numbers written in superscripts form are for showing the possible roles of species. +, Positive; -, Negative; NA, No data available

¹ Possess pathogenic properties, found from nonhealing cutaneous wound in a cat (Wells et al., 2018)

² Can convert nitrate to nitrite, can hydrolyse starch (Tamura et al., 2001)

³ Can produce cell-wall-degrading enzymes (like β-1,3-Glucanase), for the management of crop diseases (like Pythium) (El-Tarabily, 2006).

⁴ Ability to reduce nitrate to nitrite, has salt tolerance capacity (upto 3 % NaCl) (Tiago et al., 2006)

⁵possess properties for biodegradation and bioremediation of pollutants. It can also degrade polyethylene (Guevara et al., 2019; Orr et al., 2004)

⁶a selenate- and arsenate-reducing bacterium (Yamamura et al., 2007)

⁷has the ability to oxidize NO₂^- (Nitrite) to NO₃^- (Nitrate) –important role in nitrification and denitrification process (Whalen and Sampedro, 2009)

⁸possess plant growth promoting activity (Wang et al., 2016)

⁹endospore-forming, halotolerant bacteria, able to reduce nitrate to nitrite and nitrite to N₂, also catalase and oxidase positive (Raats and Halpern, 2007)

¹⁰possess the arsenic tolerant capacity and able to remediate arsenic from the contaminated soil (Achal et al., 2012)

¹¹industrially important bacterium due to production of antibiotics such as fosfomycin and bacitracin. Also considered plant growth-promoting rhizobacterium (Lekota et al., 2018)

¹²have a positive effect on control of root-knot nematode (Podestá et al., 2013)

¹³ Human pathogenic bacterium (Cautivo et al., 2020)

¹⁴shows excellent control of Plant-parasitic nematodes and has been produced as a commercial nematicide (Geng et al., 2016)

¹⁵ Has bioremediation capacity, can transform arsenic from arsenic contaminated soil (Jebeli et al., 2017)

¹⁶ Contains pathogenic property (Jin et al., 2017)

¹⁷an ammonia oxidizing bacteria, comes under nitrifying bacteria. Has monooxygenase activity (Harms et al., 1976)

¹⁸ producer of secondary fungicides and bactericides that reduce competition in soil environments. Produce compounds that are antifungal, antibacterial, and antibiotic resistant (Pradella et al., 2002)

¹⁹ an ammonia oxidizing bacteria (Garrity and others, 2005)

²⁰ an adipate-degrading, sulfate-reducing bacterium (Tanaka et al., 2000)

²¹ possess antibacterial property (Ling et al., 2019)

²² Can produce saxitoxin, a neurotoxin (Mohamed and Al-Shehri, 2015)

²³ Positive for having acid and alkaline phosphatase activity, an important soil enzymes for phosphate availability (Zhang et al., 2009)

²⁴ has an ability to digest organic waste (Tatton et al., 1989)

3.7. Effect of KSWE on soil enzymes

The influence of KSWE on five different soil enzymes at the harvest of maize crop is shown in Table 7. Compared to the control (T3), alkaline and acid phosphomonoesterases, aryl sulphatase, glycosidase, and FDA hydrolysis had significantly higher activities due to the application of KSWE (T4).

Table 7

**Influence of KSWE on soil enzymatic properties applied at V5,10,15 growth stages of maize plant under drought at same stages.** Values are mean of 3 replicates. Values followed by different alphabets in the columns are significantly different at P < 0.05 at Tukey’s HSD.
Treatments	Aryl sulphatase (µg of nitrophenol released h^− 1 g^− 1 dry soil)	Fluorescein Di-acetate (FDA) (µg of fluorescein g^− 1 dry soil)	Alkaline phosphatase	Acid phosphatase	Glycosidase
Treatments			µg of nitrophenol released h^− 1 g^− 1 dry soil
T3- V5,10,15 water spray	7.71 ± 1.01^b	54.53 ± 11.14^b	327.51 ± 34.58^b	311.15 ± 31.61^b	266.76 ± 38.14^b
T4- V5,10,15 KSWE	12.04 ± 1.01^a	80.95 ± 9.30^a	484.96 ± 15.84^a	408.90 ± 46.63^a	387.40 ± 57.21^a

3.8. Effect on crop yield

The cob yield of maize plants was significantly altered due to the treatments (Table 2). Significantly higher cob yield was observed in the treatments where KSWE was applied once or thrice along with drought stress compared to their respective controls. The highest cob yield was observed under the treatment receiving normal irrigation along with the KSWE application on a daily basis.

From the results of the initial studies on a few PGPRs and total soil bacterial CFUs under different soil moisture regimes, it was apparent that KSWE is likely to have a positive influence on soil bacteria under drought conditions. Further, our study showed that on an average, 23% of the spray volume of KSWE falls to the ground either as a drift or from the leaves as a drip (excess spray accumulating on the leaf surface drips to the ground), considering foliar spraying at V5, V10 and V15 stages of maize (Table 1). Hence KSWE was conjectured to have a considerable effect on soil bacterial community structure and function. In addition to direct effect, KSWE may also be indirectly contributing to growth and productivity of crop by modulating the soil ecology by effecting a desirable bacterial shift under stress conditions, which formed the hypothesis of this study. The study showed that KSWE helps the soil bacteria to recover from the negative impact of drought by rebuilding the affected soil microbial population, especially the species having functional importance in soil nutrient transformation and cycling.

This study characterized the effect of week-long drought stress subjected once or thrice during the critical stages of maize crop with or without the use of KSWE on soil bacterial community and compared it with the bacterial community present in normally irrigated soil. A detailed investigation carried out on soil collected at harvest revealed propensity towards a major shift in soil bacterial communities due to drought when compared to normally irrigated soil. This tendency to restructure the microbiome by drought was thwarted by KSWE application when applied three times at the critical crop stages. It was evident that soil moisture stress, when subjected early at the V5 stage, continued to have a detrimental effect on the relative abundance of soil bacteria till harvest in spite of resuming normal watering thereafter. Application of KSWE once at V5 stage probably did not help the abundance to recover till the end of the cropping season, which happened in a case where KSWE was applied thrice at V5, 10 and 15 stages. Bacteria adopt various strategies to ward off stress, for example by producing stress-resistant spores (some members of Firmicutes, Actinobacteria), biofilm formation, osmoprotectant production (Santos-Medellin et al. 2017), alterations in gene expression and protein activity (Boor 2006) leading to quiescence or dormancy. From the results, it is conjectured that repeated KSWE application three times helped such bacteria to break quiescence/dormancy and regain their growth potential. Notably, KSWE was found to contain quaternary ammonium compounds like carnitine (unpublished) and betaines (Mondal et al. 2015) which have been reported for their osmoprotectant and growth rate stimulating activity in some microorganisms (Kleber 1997). The effect of these compounds in KSWE might explain the improvement in the number of OTUs upon its application under drought stress. The restructured microbial community could also have been due to the crop response to soil moisture stress that could have modulated diverse plant processes such as quality and quantity of root exudates (Song et al. 2012), leaf fall on the ground and their subsequent decomposition, or changes in the microhabitat due to differential above- and below-ground plant growth to which the native microbiome reacted. Further, a significant role of antioxidant activity in leaves for achieving high productivity of maize and drought resistance was earlier reported in our findings (Trivedi et al. 2018a); the same was also found true in the present experiment (data not shown here as not in the scope of the present manuscript). In brief, KSWE significantly enhanced the activity of the stress enzymes in leaves, namely, catalase (CAT), ascorbate peroxidase (APX) and glutathione reductase (GR) when applied at V5,10,15 (T4), compared to its controls under drought.

The results of our present study also demonstrated that the KSWE application had no detrimental effect on the dominant soil bacterial microbiome, except in the case of Chloroflexi, even when applied on a daily basis under normally irrigated conditions. Chuah et al. (2017) also reported zero inhibition in bacterial growth of some species upon application of the crude extract of K. alvarezii. Our results are also in conformity to the reported observation that the relative abundance of Chloroflexi increases under moisture stress conditions (Santos-Medellin et al. 2017). Chloroflexi is composed of monoderm bacteria with thick cell walls that have better soil moisture stress resisting abilities (Lennon et al. 2012) because of which they would continue to thrive better compared to others and would be in a greater proportion under moisture stressed soils as was also observed in this study. The observation that the nearly doubled relative abundance of Chloroflexi in soil subjected to stress (T3) was brought down to the level present in the normal irrigated soil by KSWE application, supported the role of KSWE in eliciting stress response (Fig. 3).

The overall response of the bacterial community to soil moisture stress and KSWE was more or less consistent across all the taxonomic levels. At phylum level, the response to KSWE under stress (T4), when compared to its corresponding control (T3) was primarily driven by a significant enrichment of multiple phyla such as Actinobacteria, Proteobacteria, Firmicutes, Planctomycetes, Bacteroidetes and Verrucomicrobia (Fig. 4), all of whose abundances were diminished under stress when compared to normally irrigated conditions. The enriched relative abundance of these phyla brought about by KSWE under stress was at par to that under the normal irrigated condition and this observation was confirmed by beta diversity analysis depicted by a phylogenetic tree and PCA analysis, wherein T4 and T5 clustered more or less together, while T3 clustered separately from these two treatments. Within the Firmicutes, the most enriched OTUs in T4 compared to its control (T3) were mainly from the genera Clostridium, Bacillus, and Alicyclobacillus, all of which were endospore-forming bacteria. The higher relative abundance of several OTUs classified as Proteobacteria due to T4 treatment (compared to T3) was mainly due to the genus Steriodobacter and Balneimonas falling under Gamma- and Alpha -proteobacteria classes, respectively. The genera most affected by soil moisture stress were Anaerolinea and Nitrospira falling under the phylum Chloroflexi and Nitrospirae, respectively. The relative abundance of both these genera under drought was increased due to KSWE when applied three times. Many other genera playing an important role in nutrient cycling in soil were also found to be beneficially influenced by KSWE when applied three times under stress. For example, compared to T3, the relative abundance of the genus Rubrobacter under the phylum Actinobacteria—a thermophilic bacteria capable of degrading xylan, chitin, cellulose and hemicellulose and having an important role in organic matter turnover and C cycling— was significantly higher and may thus influence nutrient availability to plants for better growth and yield. The phylum Actinobacteria is also associated with the production of bioactive compounds and plant growth promoters which might influence the growth and yield of crops. Similarly, various genera instrumental in ammonia oxidation (Nitrosomonas, Nitrosovibrio, and Nitrospira) also were beneficially increased by KSWE under severe stress conditions compared to its respective control and their abundance levels were raised at par to that under normal irrigated condition (Appendix S7). The other taxons important for N cycling such as the phyla Chlorobi and those important for potassium solubilization such as Paenibacillus genus also followed the same trend (data not shown). The microbial communities are expected to play a crucial role in biochemical activities in the soil and thus the soil enzymes are crucial in C, P and S cycling. They can be used as soil quality indicators since they have more sensitivity to the changes in soil properties (Anand et al. 2015). The enrichment of species having functional roles such as in nutrient transformation and heavy metal tolerance (table 5 and 6) clearly brings out the beneficial role KSWE plays in soil for the benefit of crop growth and productivity. In agreement, the activities of various soil enzymes involved in P (acid and alkaline phosphatases), S (aryl sulphatase) and C (glycosidase) cycling were higher in T4 compared to the T3 treatment.

The observed KSWE-mediated changes in the soil bacterial communities and soil enzyme activities were also associated with higher crop yields under soil moisture stress conditions. Thus, this study provides a comprehensive understanding of how the KSWE could impart stress tolerance not only to the host plants but also was beneficial in providing a conducive soil bacterial microbiome. Further elaborate field studies are warranted to understand soil-plant-microbe interactions.

This study provided a comprehensive understanding of how the KSWE could impart stress tolerance not only to the host plants but also to the extent to which it enriched the soil bacterial microbiome elucidated to species level under stress conditions. The essence of the study was also to show that the KSWE application had no deleterious effect on the soil bacterial community and that the consequential beneficial effects may contribute towards improvement in the crop yields by improving nutrient cycling in soil.

Acknowledgment

KT and DK would like to thank CSIR and AcSIR-CSMCRI for the award of senior research fellowship (SRF) and enrollment in a Ph.D. program. The authors also wish to thank CSMCRI MARS Mandapam Camp, Tamil Nadu, India, especially Dr. K Eswaran and Dr. Vaibhav Mantri for useful discussion and support. Dr Shruti Chatterjee is acknowledged for providing bacterial strains. SciGenom Labs Private Limited, Cochin, Kerala is duly acknowledged for help in sequencing analysis. This manuscript bears CSIR-CSMCRI communication no. 132/2019.

Funding: This work was supported by the council of scientific and industrial research (CSIR) under the project MLP 0016.

Conflicts of interest/Competing interests: The authors have no conflicts of interest to declare that are relevant to the content of this article.

Availability of data and material: The datasets generated during and/or analysed during the current study are available in the National Center for Biotechnology Information (NCBI) repository, https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP180107

Code availability: Not applicable

Authors' contributions: KT performed experimental work, contributed to data acquisition, its analysis and interpretation. RK, GB and DK contributed during data acquisition and drafting of the article. KGV contributed to data acquisition, drafting of manuscript and reviewed the manuscript critically for important intellectual content. AG designed the experiment, coordinated the work, interpreted data and critically reviewed the manuscript. All the authors have read and approved the final manuscript.

Achal V, Pan X, Fu Q, Zhang D (2012) Biomineralization based remediation of As (III) contaminated soil by Sporosarcina ginsengisoli. J Hazard Mater 201:178–184
Anand KGV, Kubavat D, Trivedi K, Agarwal PK, Wheeler C, Ghosh A (2015) Long-term application of Jatropha press cake promotes seed yield by enhanced soil organic carbon accumulation, microbial biomass and enzymatic activities in soils of semi-arid tropical wastelands. Eur J Soil Biol 69:57–65
Aravindraja C, Viszwapriya D, Pandian SK (2013) Ultradeep 16S rRNA sequencing analysis of geographically similar but diverse unexplored marine samples reveal varied bacterial community composition. PLoS One 8:e76724
Berry A, Janssens D, Hümbelin M, Jore JPM, Hoste B, Cleenwerck I, Vancanneyt M et al (2003) Paracoccus zeaxanthinifaciens sp. nov., a zeaxanthin-producing bacterium. Int J Syst Evol Microbiol 53:231–238
Boor KJ (2006) Bacterial stress responses: what doesn’t kill them can make them stronger. PLoS Biol 4:e23
Boswell CD, Dick RE, Eccles H, Macaskie LE (2001) Phosphate uptake and release by Acinetobacter johnsonii in continuous culture and coupling of phosphate release to heavy metal accumulation. J Ind Microbiol Biotechnol 26:333–340
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335
Castillo AM, Gutiérrez MC, Kamekura M, Xue Y, Ma Y, Cowan DA, Jones BE et al (2007) Halovivax ruber sp. nov., an extremely halophilic archaeon isolated from Lake Xilinhot, Inner Mongolia, China. Int J Syst Evol Microbiol 57:1024–1027
Cautivo K, Riley T (2020) Prevalence, Molecular Epidemiology and Biogeographic Distribution of Clostridium difficile in Soils of Western Australia, 19. International Congress on Infectious Diseases, & others ). th
Chuah XQ, Mun W, Teo SS (2017) Comparison study of anti-microbial activity between crude extract of Kappaphycus alvarezii and Andrographis paniculata. Asian Pac J Trop Biomed 7:729–731
D’Argenio V, Casaburi G, Precone V, Salvatore F (2014) Comparative metagenomic analysis of human gut microbiome composition using two different bioinformatic pipelines. Biomed Res. Int. 2014
Dalal A, Bourstein R, Haish N, Shenhar I, Wallach R, Carpentier SC (2019) Dynamic physiological phenotyping of drought-Stressed pepper plants treated with “productivity-enhancing” and “survivability-enhancing” biostimulants. Front Plant Sci 10:905
Davet P (2004) Microbial ecology of soil and plant growth. CRC Press
DeSantis TZ, Hugenholtz P, Keller K, Brodie EL, Larsen N, Piceno YM, Phan R et al (2006a) NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids Res 34:W394–W399
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T et al (2006b) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072
Dortet L, Legrand P, Soussy CJ, Cattoir V (2006) Bacterial identification, clinical significance, and antimicrobial susceptibilities of Acinetobacter ursingii and Acinetobacter schindleri, two frequently misidentified opportunistic pathogens. J Clin Microbiol 44:4471–4478
El-Tarabily KA (2006) Rhizosphere-competent isolates of streptomycete and non-streptomycete actinomycetes capable of producing cell-wall-degrading enzymes to control Pythium aphanidermatum damping-off disease of cucumber. Botany 84:211–222
Estefan G, Sommer R, Ryan J (2013) Methods of soil, plant, and water analysis. A Man. West Asia North Africa Reg. 170–6
Figueiredo MVB, Martinez CR, Burity HA, Chanway CP (2008) Plant growth-promoting rhizobacteria for improving nodulation and nitrogen fixation in the common bean (Phaseolus vulgaris L.). World J Microbiol Biotechnol 24:1187–1193
Galand PE, Casamayor EO, Kirchman DL, Lovejoy C (2009) Ecology of the rare microbial biosphere of the Arctic Ocean. Proc. Natl. Acad. Sci. 106:22427–32
Garai S, Brahmachari K, Sarkar S, Kundu R, Pal M, Pramanick B (2019) Crop growth and productivity of rainy maize-garden pea copping sequence as influenced by Kappaphycus and Gracilaria saps at alluvial soil of West Bengal, India. Curr. J. Appl. Sci. Technol. 1–11
Garrity GM & others (2005) Systematic bacteriology. Proteobacteria, Part C, Alpha-, Beta-, Delta-, Epsilonproteobacteria, Bergey’s Man. Trust. Dep. Microbiol. Mol. Genet. 2
Geng C, Nie X, Tang Z, Zhang Y, Lin J, Sun M, Peng D (2016) A novel serine protease, Sep1, from Bacillus firmus DS-1 has nematicidal activity and degrades multiple intestinal-associated nematode proteins. Sci Rep 6:1–12
Ghosh A, Vijay Anand KG, Seth A (2015) Life cycle impact assessment of seaweed based biostimulant production from onshore cultivated Kappaphycus alvarezii (Doty) Doty ex Silva-Is it environmentally sustainable? Algal Res 12:513–521
Guevara G, Lopez MC, Alonso S, Perera J, Navarro-Llorens JM (2019) New insights into the genome of Rhodococcus ruber strain Chol-4. BMC Genomics 20:332
Hanway JJ, Heidel H (1952) Soil analysis methods as used in Iowa state college soil testing laboratory. Iowa Agric 57:1–31
Harms H, Koops HP, Wehrmann H (1976) An ammonia-oxidizing bacterium, Nitrosovibrio tenuis nov. gen. nov. sp. Arch Microbiol 108:105–111
Hezayen FF, Rehm BH, Tindall BJ, Steinbüchel A (2001) Transfer of Natrialba asiatica B1T to Natrialba taiwanensis sp. nov. and description of Natrialba aegyptiaca sp. nov., a novel extremely halophilic, aerobic, non-pigmented member of the Archaea from Egypt that produces extracellular poly (glutamic acid). Int J Syst Evol Microbiol 51:1133–1142
Jebeli MA, Maleki A, Amoozegar MA, Kalantar E, Izanloo H, Gharibi F (2017) Bacillus flexus strain As-12, a new arsenic transformer bacterium isolated from contaminated water resources. Chemosphere 169:636–641
Jin JJ, Keith PJ, Cummins NW, Kane SV, Pritt BS, Sanchez JL (2017) Lysinibacillus massiliensis panniculitis masquerading as erythema nodosum: A Case Report. In Open Forum Infectious Diseases. p. ofx072
Karthikeyan K, Shanmugam M (2017) The effect of potassium-rich biostimulant from seaweed Kappaphycus alvarezii on yield and quality of cane and cane juice of sugarcane var. Co 86032 under plantation and ratoon crops. J Appl Phycol 29:3245–3252
Kleber HP (1997) Bacterial carnitine metabolism. FEMS Microbiol Lett 147:1–9
Kumar R, Trivedi K, Vijay Anand K, Ghosh A (2019) Science behind biostimulant action of seaweed extract on growth and crop yield: Insights into transcriptional changes in roots of maize treated with Kappaphycus alvarezii seaweed extract under soil moisture stressed conditions. J Appl Phycol 32:599–613
Layek J, Das A, Idapuganti RG, Sarkar D, Ghosh A, Zodape ST, Lal R et al (2018) Seaweed extract as organic bio-stimulant improves productivity and quality of rice in eastern Himalayas. J Appl Phycol 30:547–558
Layek J, Das A, Ramkrushna GI, Trivedi K, Yesuraj D, Chandramohan M, Kubavat D et al (2015) Seaweed sap: a sustainable way to improve productivity of maize in North-East India. Int J Environ Stud 72:305–315
Lee JC, Li WJ, Xu LH, Jiang CL, Kim CJ (2008) Lentibacillus salis sp. nov., a moderately halophilic bacterium isolated from a salt lake. Int J Syst Evol Microbiol 58:1838–1843
Lekota KE, Bezuidt OKI, Mafofo J, Rees J, Muchadeyi FC, Madoroba E, Van Heerden H (2018) Whole genome sequencing and identification of Bacillus endophyticus and B. anthracis isolated from anthrax outbreaks in South Africa. BMC Microbiol 18:67
Lennon JT, Aanderud ZT, Lehmkuhl BK, Schoolmaster DR Jr (2012) Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology 93:1867–1879
Ling J, Zhu R, Laborda P, Jiang T, Jia Y, Zhao Y, Liu F (2019) LbDSF, the Lysobacter brunescens quorum sensing system diffusible signalling factor, regulates anti-xanthomonas XSAC biosynthesis, colony morphology, and surface motility. Front Microbiol 10:1230
Lozupone CA, Stombaugh J, Gonzalez A, Ackermann G, Wendel D, Vázquez-Baeza Y, Jansson JK et al (2013) Meta-analyses of studies of the human microbiota. Genome Res 23:1704–1714
Maynard D, Kalra Y, Crumbaugh J (2006) Soil sampling and methods of analysis. In: Carter M, Gregorich E (eds) [Eds.], 2nd edn. CRC Press, Taylor & Francis Group
Mohamed ZA, Al-Shehri AM (2015) Biodiversity and toxin production of cyanobacteria in mangrove swamps in the Red Sea off the southern coast of Saudi Arabia. Bot Mar 58:23–34
Mondal D, Ghosh A, Prasad K, Singh S, Bhatt N, Zodape ST, Chaudhary JP et al (2015) Elimination of gibberellin from Kappaphycus alvarezii seaweed sap foliar spray enhances corn stover production without compromising the grain yield advantage. Plant Growth Regul 75:657–666
Nelson DW, Sommers L (1982) Total carbon, organic carbon, and organic matter 1. Methods soil Anal. Part 2. Chem. Microbiol. Prop. 539–79
Olsen S, Sommers L (1982) Phosphorus. In: Page A (ed) [Ed.] Methods of Soil Analysis, Part 2: Chemical and Microbial Properties. Soil Science Society of America, Madison, pp 421–422
Orr IG, Hadar Y, Sivan A (2004) Colonization, biofilm formation and biodegradation of polyethylene by a strain of Rhodococcus ruber. Appl Microbiol Biotechnol 65:97–104
Oyaert M, De Baere T, Breyne J, De Laere E, Mariën S, Waets P, Laffut W (2013) First case of Pseudoclavibacter bifida bacteremia in an immunocompromised host with chronic obstructive pulmonary disease (COPD). J Clin Microbiol 51:1973–1976
Patel K, Agarwal P, Agarwal PK (2018) Kappaphycus alvarezii sap mitigates abiotic-induced stress in Triticum durum by modulating metabolic coordination and improves growth and yield. J Appl Phycol 30:2659–2673
Podestá GS de, Freitas LG de, Dallemole-Giaretta R, Zooca RJF, Caixeta L, de B, Ferraz S (2013) Meloidogyne javanica control by Pochonia chlamydosporia, Gracilibacillus dipsosauri and soil conditioner in tomato. Summa Phytopathol. 39:122–5
Pradella S, Hans A, Spröer C, Reichenbach H, Gerth K, Beyer S (2002) Characterisation, genome size and genetic manipulation of the myxobacterium Sorangium cellulosum So ce56. Arch Microbiol 178:484–492
Pramanick B, Brahmachari K, Ghosh A, Zodape ST (2014) Effect of seaweed saps on growth and yield improvement of transplanted rice in old alluvial soil of West Bengal. Bangladesh J Bot 43:53–58
Pramanick B, Brahmachari K, Ghosh A, Zodape ST (2016) Effect of seaweed saps derived from two marine algae Kappaphycus and Gracilaria on growth and yield improvement of blackgram. Indian J. Geo-Marine Sci. 45
Pramanick B, Brahmachari K, Mahapatra BS, Ghosh A, Ghosh D, Kar S (2017) Growth, yield and quality improvement of potato tubers through the application of seaweed sap derived from the marine alga Kappaphycus alvarezii. J Appl Phycol 29:3253–3260
Quan M, Liang J (2017) The influences of four types of soil on the growth, physiological and biochemical characteristics of Lycoris aurea. (L’Her.) Herb Sci Rep 7:43284
Raats D, Halpern M (2007) Oceanobacillus chironomi sp. nov., a halotolerant and facultatively alkaliphilic species isolated from a chironomid egg mass. Int J Syst Evol Microbiol 57:255–259
Rangani J, Parida AK, Panda A, Kumari A (2016) Coordinated changes in antioxidative enzymes protect the photosynthetic machinery from salinity induced oxidative damage and confer salt tolerance in an extreme halophyte Salvadora persica L. Front plant science 7:50
Rathore SS, Chaudhary DR, Boricha GN, Ghosh A, Bhatt BP, Zodape ST, Patolia JS (2009) Effect of seaweed extract on the growth, yield and nutrient uptake of soybean (Glycine max) under rainfed conditions. South African J Bot 75:351–355
Raverkar KP, Pareek N, Chandra R, Chauhan S, Zodape ST, Ghosh A (2016) Impact of foliar application of seaweed saps on yield, nodulation and nutritional quality in green gram (Vigna radiata L). Legum Res 39:315–318
Rouphael Y, Giordano M, Cardarelli M, Cozzolino E, Mori M, Kyriacou M, Bonini P et al (2018) Plant-and seaweed-based extracts increase yield but differentially modulate nutritional quality of greenhouse spinach through biostimulant action. Agronomy 8:126
Ruan Y, Taherzadeh MJ, Kong D, Lu H, Zhao H, Xu X, Liu Y et al (2020) Nitrogen removal performance and metabolic pathways analysis of a novel aerobic denitrifying halotolerant Pseudomonas balearica strain RAD-17. Microorganisms 8:72
Santos-Medellin C, Edwards J, Liechty Z, Nguyen B, Sundaresan V (2017) Drought stress results in a compartment-specific restructuring of the rice root-associated microbiomes. MBio 8:e00764–e00717
Schnürer J, Rosswall T (1982) Fluorescein diacetate hydrolysis as a measure of total microbial activity in soil and litter. Appl Environ Microbiol 43:1256–1261
Sharma L, Banerjee M, Malik GC, Vijayanand KG, Zodape ST, Ghosh A (2017) Sustainable agro-technology for enhancement of rice production in the red and lateritic soils using seaweed based biostimulants. J Clean Prod 149:968–975
Shirsalimian MS, Amoozegar MA, Sepahy AA, Kalantar SM, Dabbagh R (2017) Isolation of extremely halophilic Archaea from a saline river in the Lut Desert of Iran, moderately resistant to desiccation and gamma radiation. Microbiology 86:403–411
Singh I, Vijayanand KG, Solomon S, Shukla SK, Rai R, Zodape ST, Ghosh A (2018) Can we not mitigate climate change using seaweed based biostimulant: A case study with sugarcane cultivation in India. J Clean Prod 204:992–1003
Singh S, Singh MK, Pal SK, Trivedi K, Yesuraj D, Singh CS, Vijayanand KGV et al (2016) Sustainable enhancement in yield and quality of rain-fed maize through Gracilaria edulis and Kappaphycus alvarezii seaweed sap. J Appl Phycol 28:2099–2112
Song F, Han X, Zhu X, Herbert SJ (2012) Response to water stress of soil enzymes and root exudates from drought and non-drought tolerant corn hybrids at different growth stages. Can J Soil Sci 92:501–507
Tabatabai M (1982) Soil enzymes. In: Page AL (ed) [Ed.] Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties. American Society of Agronomy, Soil Science Society of America, pp 903–947
Tamura T, Hayakawa M, Hatano K (2001) A new genus of the order Actinomycetales, Virgosporangium gen. nov., with descriptions of Virgosporangium ochraceum sp. nov. and Virgosporangium aurantiacum sp. nov. Int J Syst Evol Microbiol 51:1809–1816
Tanaka K, Stackebrandt E, Tohyama S, Eguchi T (2000) Desulfovirga adipica gen. nov., sp. nov., an adipate-degrading, gram-negative, sulfate-reducing bacterium. Int J Syst Evol Microbiol 50:639–644
Tatton MJ, Archer DB, Powell GE, Parker ML (1989) Methanogenesis from ethanol by defined mixed continuous cultures. Appl Environ Microbiol 55:440–445
Tiago I, Pires C, Mendes V, Morais PV, Da Costa MS, Verissimo A (2006) Bacillus foraminis sp. nov., isolated from a non-saline alkaline groundwater. Int J Syst Evol Microbiol 56:2571–2574
Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S (2002) Agricultural sustainability and intensive production practices. Nature. 418
Trivedi K, Vijayanand KG, Kubavat D, Kumar R (2017) Crop stage selection is vital to elicit optimal response of maize to seaweed bio-stimulant application. J Appl Phycol 29:2135–2144
Trivedi K, Vijayanand KG, Kubavat D, Patidar R, Ghosh A (2018a) Drought alleviatory potential of Kappaphycus seaweed extract and the role of the quaternary ammonium compounds as its constituents towards imparting drought tolerance in Zea mays L. J Appl Phycol 30:2001–2015
Trivedi K, Vijayanand KG, Vaghela P, Ghosh A (2018b) Differential growth, yield and biochemical responses of maize to the exogenous application of Kappaphycus alvarezii seaweed extract, at grain-filling stage under normal and drought conditions. Algal Res 35:236–244
Wang J, Liu B, Liu G, Pan Z, Xiao R, Chen M, Chen D (2016) Draft genome sequence of Bacillus humi LMG 22167T (DSM 16318), an endospore-forming bacterium isolated from soil. Genome Announc. 4
Wells B, Burnum AL, Armstrong J, Sanchez S, Stanton JB, Camus MS (2018) Actinomadura vinacea isolated from a nonhealing cutaneous wound in a cat. Vet Clin Pathol 47:638–642
Whalen JK, Sampedro L (2009) Soil Ecology and Management CABI
Williams CH, Steinbergs A (1959) Soil sulphur fractions as chemical indices of available sulphur in some Australian soils. Aust J Agric Res 10:340–352
Xie CH, Yokota A (2005) Pleomorphomonas oryzae gen. nov., sp. nov., a nitrogen-fixing bacterium isolated from paddy soil of Oryza sativa. Int J Syst Evol Microbiol 55:1233–1237
Yamamura S, Yamashita M, Fujimoto N, Kuroda M, Kashiwa M, Sei K, Fujita M et al (2007) Bacillus selenatarsenatis sp. nov., a selenate-and arsenate-reducing bacterium isolated from the effluent drain of a glass-manufacturing plant. Int J Syst Evol Microbiol 57:1060–1064
Yumoto I, Iwata H, Sawabe T, Ueno K, Ichise N, Matsuyama H, Okuyama H et al (1999) Characterization of a facultatively psychrophilic bacterium, Vibrio rumoiensis sp. nov., that exhibits high catalase activity. Appl Environ Microbiol 65:67–72
Zhang K, Tang Y, Zhang L, Dai J, Wang Y, Luo X, Liu M et al (2009) Parasegetibacter luojiensis gen. nov., sp. nov., a member of the phylum Bacteroidetes isolated from a forest soil. Int J Syst Evol Microbiol 59:3058–3062
Zodape ST, Gupta A, Bhandari SC, Rawat US, Chaudhary DR, Eswaran K, Chikara J (2011) Foliar application of seaweed sap as biostimulant for enhancement of yield and quality of tomato (Lycopersicon esculentum Mill.). J Sci Ind Res 70:215–219