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. 2023 Mar 13:14:1135693.
doi: 10.3389/fmicb.2023.1135693. eCollection 2023.

Cold-tolerant phosphate-solubilizing Pseudomonas strains promote wheat growth and yield by improving soil phosphorous (P) nutrition status

Hemant Dasila  1   2 , V K Sah  3 , Vandana Jaggi  4 , Arun Kumar  5 , Lakshmi Tewari  2 , Gohar Taj  6 , Sumit Chaturvedi  3 , Kahkashan Perveen  7 , Najat A Bukhari  7 , Tan Ching Siang  8 , Manvika Sahgal  2
Affiliations

Cold-tolerant phosphate-solubilizing Pseudomonas strains promote wheat growth and yield by improving soil phosphorous (P) nutrition status

Hemant Dasila et al. Front Microbiol. .

Abstract

It is well-known that phosphate-solubilizing bacteria (PSB) promote crop growth and yield. The information regarding characterization of PSB isolated from agroforestry systems and their impact on wheat crops under field conditions is rarely known. In the present study, we aim to develop psychrotroph-based P biofertilizers, and for that, four PSB strains (Pseudomonas sp. L3, Pseudomonas sp. P2, Streptomyces sp. T3, and Streptococcus sp. T4) previously isolated from three different agroforestry zones and already screened for wheat growth under pot trial conditions were evaluated on wheat crop under field conditions. Two field experiments were employed; set 1 includes PSB + recommended dose of fertilizers (RDF) and set 2 includes PSB - RDF. In both field experiments, the response of the PSB-treated wheat crop was significantly higher compared to the uninoculated control. In field set 1, an increase of 22% in grain yield (GY), 16% in biological yield (BY), and 10% in grain per spike (GPS) was observed in consortia (CNS, L3 + P2) treatment, followed by L3 and P2 treatments. Inoculation of PSB mitigates soil P deficiency as it positively influences soil alkaline phosphatase (AP) and soil acid phosphatase (AcP) activity which positively correlated with grain NPK %. The highest grain NPK % was reported in CNS-treated wheat with RDF (N-0.26%, P-0.18%, and K-1.66%) and without RDF (N-0.27, P-0.26, and K-1.46%), respectively. All parameters, including soil enzyme activities, plant agronomic data, and yield data were analyzed by principal component analysis (PCA), resulting in the selection of two PSB strains. The conditions for optimal P solubilization, in L3 (temperature-18.46, pH-5.2, and glucose concentration-0.8%) and P2 (temperature-17°C, pH-5.0, and glucose concentration-0.89%), were obtained through response surface methodology (RSM) modeling. The P solubilizing potential of selected strains at <20°C makes them a suitable candidate for the development of psychrotroph-based P biofertilizers. Low-temperature P solubilization of the PSB strains from agroforestry systems makes them potential biofertilizers for winter crops.

Keywords: PSB; fertilizer; principal component analysis; psychrotroph; response surface methodology.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The detail of treatment in the field lay out showing two experiment sets, Experiment set 1 (Treatment + RDF) and Experiment set 2 (Treatment – RDF).
Figure 2
Figure 2
PSI (A) and ZSI (B) of L3, P2, T3 and T4 PSB, forming a clear halozone around the bacterial colony.
Figure 3
Figure 3
(A) Quantitative soluble P release along with pH drop (r = −0.52) in L3, P2, T3, and T4 PSB with correlation value. (B) FTIR analysis of PSB strains showing carboxylic group peak.
Figure 4
Figure 4
Variation among the treatment during sampling of field set 1 (A) and field set 2 (B).
Figure 5
Figure 5
PCA (A) and loading plot (B) of plant vigor PSB treatments of type field set 1, PCA plot (C) and loading plot (D) of plant vigor PSB treatments in the field set 2, and hierarchal cluster analysis of PSB treatment in the field set 1 (E) and in the field set 2 (F).
Figure 6
Figure 6
Grain per spike (GPS) variation in different PSB treatments in the field set 1 (PSB + RDF) (A) and field set 2 (PSB – RDF) (B).
Figure 7
Figure 7
Soil enzyme variation includes FDA, AP, and AcP in response to PSB inoculation under field conditions in (A) Set I and (B) Set II after final harvesting.
Figure 8
Figure 8
α-diversity indices of the microbial population with different media of field type 1 (A) and S.H.E indices of the field set 1 (B). α-diversity (C) indices and S.H.E indices (D) of the field set 2.
Figure 9
Figure 9
Nutrient analysis (N, P, and K) in plant sample (A) field set 1 (PSB + RDF), (B) field set 2 (PSB – RDF).
Figure 10
Figure 10
Chord analysis of chlorophyll showing response percent sharing of PSB treatments in the field set 1 (A) and field set 2 (B).
Figure 11
Figure 11
ESI (Electrospray ionization)-MS results of L3, P2, and CNS for the detection of organic acid.
Figure 12
Figure 12
RSM plots for optimizing P quantification of L3 (A–C) and P2 (D–F) PSB treatment including the interaction of three variables, e.g., temperature, pH, and sugar concentration.
See this image and copyright information in PMC

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