Temporal
Dynamics of Rhizosphere Soil Physicochemical Properties in Sorghum bicolor
Under Salinity and Drought Stress
Vipool Thorat 1, Kundan Narayan Wasnik2,
Riyaz Sayyed1 *
1 Department of
Microbiology, PSGVP Mandal’s, S I Patil Arts, GB Patel Science and
STKVS Commerce
College, Shahada 425409, India; vipoolthorat14@gmail.com
2 CSIR- Central
Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India;
kwasnik.cimap@csir.res.in
*Corresponding
author: sayyedrz@gmail.com
Abstract
This study evaluated the effects of salinity and drought stress on
rhizospheric soil physicochemical properties of sorghum (Sorghum bicolor
L.) across developmental stages to identify the major factors associated with
soil variability during plant growth.Rhizospheric soil samples were collected
from healthy, salinity-stressed, and drought-stressed sorghum plants at
germination, 7, 14, 21, and 35 days after germination (DAG). Soil pH,
electrical conductivity (EC), organic carbon (OC), and available nitrogen (N),
phosphorus (P), and potassium (K) were determined. Temporal variation and
treatment effects were assessed using PERMANOVA, Pearson correlation analysis,
principal component analysis (PCA), and hierarchical clustering.
Temporal profiling revealed stage-dependent changes in nutrient
availability, EC, and OC, whereas soil pH remained comparatively stable.
PERMANOVA identified developmental stage as the primary source of variation in
rhizospheric soil properties, explaining 55.9% of the total variability (p
= 0.024), while treatment effects were not significant. Correlation analysis
showed a significant positive association between pH and available N (r
= 0.615, p = 0.033), whereas most other relationships were weak to moderate.
PCA and hierarchical clustering consistently identified pH, N, EC, OC and K as
major contributors to rhizospheric variability. Coordinated variation among EC,
OC and K and the relatively independent behavior of P were evident across
analyses. Substantial overlap among treatment groups further indicated limited
treatment-specific separation of overall soil physicochemical characteristics.
Developmental-stage-dependent changes were more strongly associated with
rhizospheric soil variability than salinity or drought treatments, which
produced comparatively modest modifications in soil physicochemical properties.
Keywords: Sorghum bicolor; rhizosphere; salinity stress; drought stress; soil physicochemical
properties; PERMANOVA; principal component analysis; hierarchical clustering.
Introduction
Soil health is a key determinant of
agricultural productivity and sustainability, particularly under increasing
environmental constraints such as salinity and drought. These abiotic stresses
can modify soil physicochemical properties, nutrient availability, and
microbial activity, ultimately influencing plant growth and yield. Among cereal
crops, sorghum (Sorghum bicolor L.) is well known for its adaptability
to adverse environments, including water deficit and saline conditions, making
it a valuable model for investigating stress responses in arid and semi-arid
regions (Abuslima et al., 2022; Dewi et al., 2023).
Salinity stress is a widespread
environmental challenge that disrupts soil ionic balance and elevates
electrical conductivity (EC), resulting in osmotic stress and ion toxicity.
High salt concentrations in the rhizosphere interfere with nutrient uptake, particularly
potassium (K⁺), because of competition with sodium (Na⁺), thereby affecting
essential physiological processes in plants (Dourado et al., 2022; Rajabi
Dehnavi et al., 2024). In contrast, drought stress primarily affects soil
biological processes by reducing moisture availability, limiting microbial
activity, and suppressing nutrient mineralization. Consequently, the
availability of essential nutrients, including nitrogen (N) and organic carbon
(OC), may decline, negatively impacting soil fertility and plant performance
(Avila et al., 2023; Silva et al., 2023).
The rhizosphere is a highly dynamic
zone where plant roots, soil microorganisms, and soil physicochemical
properties interact to regulate nutrient cycling and stress adaptation. These
soil–plant–microbe interactions are fundamental for maintaining soil biological
fertility and ecosystem functioning (Bhattacharyya and Furtak, 2022). Recent
studies have shown that salinity and drought can alter rhizosphere microbial
composition and functionality, thereby influencing plant health and resilience
(Qi et al., 2022; Wei et al., 2023).
utrient cycling in soil is governed
by interconnected biogeochemical processes involving microbial activity,
organic matter turnover, and mineral transformations. Nitrogen cycling is
particularly important because it determines the availability of plant-accessible
nitrogen required for growth and productivity (Hayashi, 2022). Furthermore,
soil carbon and nutrient cycles are closely linked with redox reactions and
mineral interactions, which influence nutrient dynamics under environmental
stress (Song et al., 2022; Najdenko et al., 2024; Kunito et al., 2024).
Recent reviews have highlighted the
importance of integrated soil health indicators, including EC, pH, organic
carbon, and nutrient availability, for evaluating soil quality and resilience
under changing environmental conditions (Bhaduri et al., 2022; Bekchanova et
al., 2024). However, nutrient cycling processes are highly sensitive to
environmental disturbances, and their disruption can significantly affect
ecosystem functioning and crop productivity (Xing et al., 2022; Drinkwater and
Snapp, 2022). In addition, plant developmental stages influence rhizospheric
processes through changes in root exudation and microbial activity, resulting
in stage-specific shifts in soil properties. Despite this, most studies have
focused on single time-point assessments, limiting understanding of temporal
soil responses throughout plant development (Qi et al., 2022).
Therefore, this study investigated
temporal variations in rhizospheric soil physicochemical properties and
nutrient dynamics of sorghum under salinity and drought stress. By integrating
time-course observations with multivariate statistical analyses, the study
provides insights into soil responses to abiotic stress and contributes to the
development of sustainable crop and soil management strategies.
Materials and
Methods
Experimental Design and Sample Collection
A controlled experiment was conducted
in pots to evaluate the temporal dynamics of rhizospheric soil properties in
sorghum (Sorghum bicolor) under different stress conditions. Three
treatments were considered: healthy (control), salinity stressand drought
stress. Soil samples were collected from the rhizosphere at five developmental
stages, namely germination (baseline), 7 days after germination (7 DAG), 14
DAG, 21 DAG, and 35 DAG. The germination stage represented a common baseline
for all treatments prior to stress imposition.
Soil Physicochemical Analysis
Collected soil samples were analyzed
for key physicochemical parameters following standard protocols. Soil pH was
measured using a digital pH meter in a soil (Systronics, India) water
suspension. Electrical conductivity (EC) was determined using a conductivity
meter (Systronics, India). Organic carbon (OC) were estimated using standard
oxidation methods as per the protocol of Walkley and Black et al., 1934.
Available macronutrients, including nitrogen (N) (Subbiah and Asija et al.,
1956), phosphorus (P)(Olsen et.al.1954; Spectrophotometer, Schimadzu, Japan)and
potassium (K) (Hanway and Heidel et al.,1952; Flame Photometer 1027,
Systronics, India), were quantified using established soil analysis procedures.
Temporal Analysis of Rhizospheric Soil Properties
Temporal variation in rhizospheric
soil physicochemical properties was evaluated across developmental stages of Sorghum
bicolor under healthy, salinity and drought treatments. Soil samples were
collected at germination (baseline), 7, 14, 21 and 35 days after germination
(DAG). The measured variables included soil pH, electrical conductivity (EC),
organic carbon (OC), available nitrogen (N), available phosphorus (P) and
available potassium (K).
Line plots were generated using the ggplot2
package in R to visualize temporal changes in each parameter across treatments.
The germination-stage sample was considered the baseline condition prior to
stress imposition, whereas treatment-specific trends were assessed from 7 DAG
onwards. Temporal patterns were interpreted qualitatively to identify
treatment-dependent shifts in soil physicochemical properties during plant
development.
Permutational Multivariate Analysis of Variance
(PERMANOVA)
Euclidean distance matrices were calculated from the soil
physicochemical data and significance was assessed using 999 permutations. The
independent contributions of treatment and developmental stage were evaluated
using marginal tests. To verify the assumption of homogeneous multivariate
dispersion among groups, a multivariate dispersion analysis (PERMDISP) followed
by a permutation test (999 permutations) was conducted. The germination sample
was excluded from the analysis because it represented the common pre-treatment
baseline.
Pearson Correlation Analysis
Pearson correlation analysis was
performed by excluding the germination stage samples. Pearson correlation
coefficients (r) and their associated significance levels (p-values) were
calculated using the Hmisc package in R. Correlation matrices were visualized
using both correlation plots and heatmaps to facilitate identification of
positive and negative associations among soil variables. Correlation
coefficients were interpreted according to their magnitude and direction and
statistical significance was considered at p < 0.05.
Principal Component Analysis (PCA)
Principal component analysis (PCA)
was performed to explore multivariate variation by retaining germination-stage
sample. Prior to analysis, all variables were cantered and scaled to unit
variance to eliminate differences in measurement units. PCA was conducted using
the prompt function in R. The proportion of variance explained by each
principal component was evaluated using eigenvalues and scree plots. Sample
relationships were visualized through PCA score plots and treatment-specific
confidence ellipses, while variable contributions and associations with sample
groups were examined using PCA biplots.Ninety-five percent confidence ellipses
(ellipse.level = 0.95) assuming a normal distribution were calculated and
overlaid to evaluate group separation.
Hierarchical Clustering and Heatmap Analysis
Hierarchical clustering analysis
(HCA) was performed by retaining germination stage sample. Prior to clustering,
variables were standardized using Z-score normalization to remove differences
in measurement scales. Euclidean distance was used to calculate pairwise
dissimilarities among samples and variables and clustering was performed using
Ward’s minimum variance method (Ward.D2). Results were visualized as a heatmap
with hierarchical dendrograms generated using the pheatmap package in R. Red
colours indicate values above the overall mean, whereas blue colours indicate
values below the mean.
Software Environment
All statistical analyses and
graphical visualizations were performed in R(Version 4.4.2, R Core Team, 2025).
The packages readxl (Wickham and Bryan, 2019), (FactoMineR (Le et al., 2008),
factoextra (Kassambara and Mundt, 2016), pheatmap (Kolde et al.,2019), ggplot2
(Wickham, 2016), Hmisc (Harrell Jr and Dupont, 2020), corrplot (Wei and Simko,
2010), vegan (Oksanen et al., 2001) and RColorBrewer (Neuwirth, 2014). 2025) were
used for multivariate analysis, correlation analysis, PCA visualization,
heatmap generation, graphical customization and hierarchical clustering.
Results
The rhizospheric soil physicochemical
properties measured across developmental stages under healthy, salinity and
drought treatments are presented in Table 1.
|
SAMPLE |
pH |
EC |
OC |
N |
P |
K |
|
G |
7.93 |
0.739 |
0.52 |
222.656 |
34.575 |
206.416 |
|
H_7DAG |
8.01 |
0.735 |
0.3 |
203.84 |
26.385 |
235.872 |
|
H_14DAG |
8.06 |
0.544 |
0.33 |
175.616 |
54.87 |
206.416 |
|
H_21DAG |
8.09 |
0.497 |
0.27 |
222.656 |
41.7225 |
202.048 |
|
H_35DAG |
8.07 |
0.754 |
0.46 |
222.656 |
59.9775 |
198.128 |
|
S_7DAG |
8.03 |
1.244 |
0.41 |
225.792 |
30.0975 |
229.6 |
|
S_14DAG |
7.87 |
1.43 |
0.63 |
163.072 |
39.5175 |
223.552 |
|
S_21DAG |
7.95 |
0.671 |
0.49 |
216.384 |
39.2925 |
198.464 |
|
S_35DAG |
7.86 |
0.651 |
0.46 |
194.432 |
33.1725 |
179.2 |
|
D_7DAG |
8.06 |
0.673 |
0.55 |
222.656 |
55.62 |
229.6 |
|
D_14DAG |
7.93 |
0.586 |
0.41 |
200.704 |
51.72 |
208.656 |
|
D_21DAG |
7.87 |
0.7 |
0.19 |
175.616 |
54.615 |
184.352 |
|
D_35DAG |
8.02 |
0.724 |
0.22 |
194.432 |
28.2525 |
182.224 |
Table 1. Rhizospheric
soil physicochemical properties of Sorghum bicolor under healthy,
salinity, and drought treatments at different developmental stages.
*G = germination (baseline); H = healthy; S = salinity; D = drought; DAG
= days after germination; EC = electrical conductivity (mS cm⁻¹); OC = organic
carbon (%); N = available nitrogen (kg ha⁻¹); P = available phosphorus (kg
ha⁻¹); K = available potassium (kg ha⁻¹). The germination sample was included
as a common baseline reference for all treatments.
Temporal Variation in Physiochemical Parameters
Soil pH remained within a relatively narrow range
throughout the experiment; however, healthy plants showed a generally
increasing pH trend, whereas salinity- and drought-treated plants exhibited
fluctuating responses across developmental stages. Electrical conductivity (EC)
was consistently higher under salinity treatment, while healthy and drought
treatments showed comparatively smaller fluctuations over time (figure S1)
Figure S1.Temporal variation in
rhizospheric soil physicochemical properties of Sorghum bicolor under
healthy, salinity, and drought treatments across developmental stages. Panels
represent (a) pH, (b) electrical conductivity (EC), (c) organic carbon (OC),
(d) organic carbon percentage (OC%), (e) available nitrogen (N), (f) available
phosphorus (P), and (g) available potassium (K). The germination-stage sample
represents the baseline condition prior to stress imposition. Lines connect
measurements across developmental stages (7, 14, 21, and 35 DAG) for each
treatment, illustrating temporal changes in soil nutrient status and
physicochemical characteristics under abiotic stress.
Organic carbon (OC) displayed treatment-dependent
variation. Drought-treated samples showed a progressive decline in OC from 7 to
35 DAG, whereas healthy and salinity-treated samples exhibited fluctuating
patterns across developmental stages. Available nitrogen (N) and phosphorus (P)
showed variable responses throughout plant development with no consistent
increasing or decreasing trend across treatments. In contrast, available
potassium (K) increased from 7 to 21 DAG in all treatments, followed by a decline
at 35 DAG.
PERMANOVA and Multivariate Dispersion Analysis
PERMANOVA
revealed that developmental stage significantly influenced the multivariate
rhizospheric soil physicochemical profile, explaining 55.907% of the total
variation (R² = 0.55907; F = 2.941; p = 0.024; Table 2). In contrast, treatment
accounted for only 6.077% of the variation and did not significantly affect the
overall soil physicochemical composition (R² = 0.06077; F = 0.4795; p = 0.829).
Residual variation accounted for 38.02% of the total variance.
The
multivariate dispersion analysis indicated no significant differences in
within-group dispersion among treatments (PERMDISP: F = 0.087, p = 0.917). This
result was further confirmed by permutation testing (p = 0.911), demonstrating
that group dispersions were comparable and that the PERMANOVA results were not
influenced by unequal variability among treatments (Table S1, S2 and S3).
|
||||||||
|
||||||||
|
Df |
Sum
of Sqs |
R2 |
F-value |
p-value |
||||
|
Treatment |
2 |
660.3 |
0.06077 |
0.4795 |
0.829 |
|||
|
Time |
3 |
6075.2 |
0.55907 |
2.9413 |
0.024
* |
|||
|
Residual |
6 |
4131 |
0.38016 |
- |
- |
|||
|
Total |
11 |
10866.6 |
1 |
- |
- |
|||
|
Table
2. Marginal
PERMANOVA assessing the independent contributions of treatment and
developmental stage to variation in rhizospheric soil physicochemical
properties of Sorghum bicolor.
|
||||||||
*PERMANOVA was performed
using Euclidean distances with 999 permutations using the marginal testing
approach (adonis2, by = "margin"). R² values represent the proportion
of total variation explained independently by each factor. Germination-stage
samples were excluded prior to analysis. Significant effects are indicated by p
< 0.05.
|
Source |
Df |
Sum of Squares |
R² |
F-value |
p-value |
|
Model |
5 |
6735.56 |
0.62 |
1.957 |
0.088 |
|
Residual |
6 |
4131.04 |
0.38 |
– |
– |
|
Total |
11 |
10866.6 |
1 |
– |
– |
|
Table
S1.
Permutational multivariate analysis of variance (PERMANOVA) evaluating the
overall effects of treatment and developmental stage on rhizospheric soil
physicochemical properties of Sorghum bicolor.
|
*PERMANOVA was performed using Euclidean distances. Significance was
assessed using 999 permutations. R² represents the proportion of total
multivariate variation explained by the model. Germination-stage samples were
excluded prior to analysis.
|
Source |
Df |
Sum of Squares |
Mean Square |
F-value |
p-value |
|
Groups |
2 |
45.79 |
22.89 |
0.087 |
0.917 |
|
Residuals |
9 |
2365.67 |
262.85 |
– |
– |
Table S2. Multivariate dispersion analysis (PERMDISP)
*Homogeneity of multivariate dispersions among
treatment groups was assessed using Euclidean distances.
|
Source |
Df |
Sum of Squares |
Mean Square |
F-value |
Permutations |
p-value |
|
Groups |
2 |
45.79 |
22.89 |
0.087 |
999 |
0.911 |
|
Residuals |
9 |
2365.67 |
262.85 |
– |
– |
– |
Table S3. Permutation test for multivariate dispersion
*Significance was assessed using 999 unrestricted permutations.
Correlation Analysis
Pearson correlation analysis revealed
generally weak to moderate associations among rhizospheric soil properties
(Table 3; Fig. 1). Among all pairwise relationships, only the correlation
between pH and available nitrogen was statistically significant, exhibiting a
moderate positive association (r = 0.615, p = 0.033). This indicates that
higher soil pH values were associated with increased nitrogen availability
across treatments and developmental stages.
|
Physiochemical Parameters |
pH |
EC |
OC |
N |
P |
K |
|
|
pH |
- |
-0.28544 |
-0.19391 |
0.615018 |
0.140373 |
0.307672 |
|
|
EC |
0.368485 |
- |
0.485178 |
-0.22069 |
-0.3213 |
0.442245 |
|
|
OC |
0.545931 |
0.109856 |
- |
0.072083 |
0.132642 |
0.395974 |
|
|
N |
0.033305 |
0.490651 |
0.823832 |
- |
-0.04079 |
0.218736 |
|
|
P |
0.663468 |
0.308513 |
0.681114 |
0.89983 |
- |
-0.10608 |
|
|
K |
0.330623 |
0.149992 |
0.202588 |
0.494599 |
0.742827 |
- |
|
Table 3. Pearson correlation coefficients (above diagonal)
and p-values (below diagonal) among rhizospheric soil physicochemical
properties
*EC = electrical conductivity (mS cm⁻¹); OC = organic
carbon (%); N = available nitrogen (kg ha⁻¹); P = available phosphorus (kg
ha⁻¹); K = available potassium (kg ha⁻¹). Significant correlations criteria
used (p< 0.05) .
Figure 1. Pearson correlation heatmap showing pairwise
relationships among rhizospheric soil physicochemical properties. Cell values
represent correlation coefficients (r), with red and blue colors indicating
positive and negative correlations, respectively.
Electrical conductivity showed
moderate positive correlations with organic carbon (r = 0.485, p = 0.1098) and
potassium (r = 0.442, p = 0.1499) and moderate negative correlations with pH (r
=-0.285, p = 0.368), nitrogen (r = -0.2206, p = 0.4906) and phosphorus (r =
-0.321, p = 0.3085), although these relationships were not statistically
significant. Organic carbon exhibited weak positive associations with nitrogen
(r = 0.0720, p = 0.8238), phosphorus (r = 0.1326, p = 0.6811) and potassium (r
= 0.3959, p = 0.2025), suggesting a potential linkage between soil carbon
status and nutrient availability. Phosphorus displayed consistently weak
correlations with all other measured variables, indicating relative
independence from the measured physicochemical properties (Supplementary fig.
S2)
Figure S2.Pearson correlation
heatmap showing pairwise relationships among rhizospheric soil physicochemical
properties. Color intensity and cell values represent the magnitude and
direction of Pearson correlation coefficients (r).
The correlation heatmap and matrix
further confirmed the predominance of weak-to-moderate associations among
variables with the strongest positive relationship observed between pH and
nitrogen and the strongest negative relationship observed between EC and
phosphorus. Ultimately, the correlation structure suggestsdirect links between
most soil properties appear limited and pH may be the primary variable
influencing nitrogen availability in the sorghum rhizosphere.
Principal
Component Analysis
Principal component analysis revealed that the first
two principal components explained 61.37% of the total variation in
rhizospheric soil physicochemical properties, with PC1 accounting for 32.43%
and PC2 accounting for 28.94% of the variance. Inclusion of PC3 increased the
cumulative explained variance to 79.19%, indicating that the first three
components adequately represented the major patterns in the dataset. The scree
plot showed a marked decline in variance explained after PC3, suggesting that
subsequent components contributed relatively little additional information
(Table 4; Fig.2, Supplementary Fig. S3, S4)
|
|
PC1 |
PC2 |
PC3 |
PC4 |
PC5 |
PC6 |
|
Standard deviation |
1.3949 |
1.3177 |
1.0341 |
0.8633 |
0.56112 |
0.43396 |
|
Proportion of
Variance |
0.3243 |
0.2894 |
0.1782 |
0.1242 |
0.05248 |
0.03139 |
|
Cumulative
Proportion |
0.3243 |
0.6137 |
0.7919 |
0.9161 |
0.96861 |
1 |
Table
4.
Variance explained by principal components derived from PCA of rhizospheric
soil physicochemical properties.
*PCA
was conducted using pH, EC, OC, N, P, and K. The proportion of variance shows
the contribution of each principal component to total variability, while the
cumulative proportion indicates the total variance explained by successive
components. The first three principal components explained 79.19% of the total
variation.
Figure
2. Principal component analysis (PCA) biplot of
rhizospheric soil physicochemical properties in Sorghum bicolor under
healthy, salinity, and drought treatments. Samples represent developmental
stages, while arrows indicate the direction and magnitude of variable
contributions to sample separation.Principal components were
calculated using standardized soil physicochemical variables. The percentages
shown on the axes indicate the proportion of total variance explained by each
principal component. Samples positioned closer together exhibit greater similarity
in their overall soil physicochemical characteristics, whereas variables with
longer vectors contribute more strongly to sample separation.
Figure S3.Scree plot showing the
percentage of variance explained by the first six principal components derived
from rhizospheric soil physicochemical properties of Sorghum bicolor.
Bars represent the variance explained by individual principal components, while
the line illustrates the cumulative decline in explained variance across
successive components.
The PCA score plot demonstrated distinct
treatment-dependent variation in rhizospheric soil properties (Supplementary
Fig. S4).
Figure S4.PCA individuals (score)
plot of rhizospheric soil samples of Sorghum bicolor. Samples are projected
onto PC1 (32.4%) and PC2 (28.9%), with ellipses indicating treatment-wise
clustering patterns and multivariate variation among developmental stages and
stress treatments.
The germination-stage sample occupied a central
position, reflecting the common baseline condition prior to stress exposure.
Healthy samples clustered closely together on the positive side of PC1,
indicating comparatively stable soil physicochemical characteristics throughout
plant development. Drought-treated samples exhibited moderate dispersion and
progressive separation across developmental stages, whereas salinity-treated
samples displayed the widest distribution, particularly along PC1, indicating
greater heterogeneity in rhizospheric responses under salt stress
(Supplementary Fig. S5).
Figure S5. Labelled principal
component analysis (PCA) score plot of rhizospheric soil physicochemical
properties under healthy, salinity, and drought treatments. Each point
represents an individual soil sample and is labelled according to treatment and
developmental stage. Samples are coloured by treatment group and ellipses
represent the 95% confidence intervals of each group. The plot illustrates the
distribution and similarity of samples based on soil pH, electrical
conductivity (EC), organic carbon (OC) and available nitrogen (N), phosphorus
(P) and potassium (K). Healthy samples clustered relatively closely, whereas
salinity-treated samples showed greater dispersion. Drought-treated samples
exhibited intermediate variability. The substantial overlap among treatment
ellipses indicates limited treatment-specific separation of overall
rhizospheric soil physicochemical characteristics.
The PCA biplot showed that pH and
available nitrogen (N) were positively associated, as indicated by their
similar vector orientation toward the positive side of both PC1 and PC2 (Fig.
2). In contrast, electrical conductivity (EC) was oriented in the opposite
direction, suggesting an inverse relationship with pH and N. Organic carbon
(OC) and potassium (K) were aligned toward the upper-left quadrant, indicating
coordinated variation between these variables, whereas phosphorus (P) was
primarily associated with variation along PC1. The distribution of samples
showed substantial overlap among treatment groups, indicating limited
treatment-specific separation in overall rhizospheric soil physicochemical
properties(Supplementary
Fig. S5).
Hierarchical
Clustering Analysis
The hierarchical clustering heatmap revealed
distinct patterns of similarity among rhizospheric soil samples and
physicochemical variables. Sample clustering did not occur strictly according
to treatment, indicating substantial overlap in overall soil physicochemical
profiles among healthy, salinity and drought conditions. Nevertheless, several
treatment-specific subclusters were evident. Salinity-treated samples,
particularly S_7DAG and S_14DAG, clustered closely together and were
characterized by elevated EC, OC, and K values. In contrast, healthy samples
from later developmental stages formed a separate cluster associated with
comparatively higher pH and phosphorus levels (Fig. 3).
Figure 3. Hierarchical clustering
heatmap showing patterns of variation in rhizospheric soil physicochemical
properties under healthy, salinity, and drought treatments of Sorghum
bicolor. Rows represent individual samples and columns represent soil
variables. Samples and variables were clustered using Euclidean distance and
Ward’s linkage method. Color intensity indicates the relative standardized
abundance of each parameter, with red representing higher values and blue
representing lower values.
The variable dendrogram identified two major groups
of soil properties. EC, OC and K clustered together, indicating coordinated
variation among soil ionic status, organic carbon content and potassium
availability. A second cluster comprised pH, N and P, may suggesting close
relationships among nutrient availability and soil chemical balance. The
clustering pattern indicates that variables within each group responded
similarly across treatments and developmental stages.
The heatmap demonstrates that rhizospheric soil
physicochemical properties were structured by complex interactions among
nutrient availability, soil ionic status and carbon dynamics rather than by
treatment alone.
Discussion:
Temporal
Variation in Physiochemical Parameters
The contrasting temporal responses of individual soil
properties indicate that abiotic stress influenced specific components of the
rhizospheric environment rather than producing uniform changes across all
measured variables. The consistently higher EC observed under salinity
treatment reflects the direct effect of salt addition on soil ionic status
(Liu, 2015), whereas the relatively stable pH across treatments suggests that
soil acidity-alkalinity was less sensitive to the imposed stresses.
The progressive decline in organic carbon under
drought indicates a sustained reduction in soil carbon status during plant
development, while the more variable responses under healthy and salinity
conditions suggest greater temporal fluctuation in carbon availability. In
contrast, the absence of clear trends for nitrogen and phosphorus implies that
their availability was influenced by multiple factors operating across
developmental stages rather than by treatment alone.
The similar potassium pattern observed across all
treatments, characterized by an increase up to 21 DAG followed by a decline at
35 DAG, suggests a stronger association with plant developmental stage than
with stress treatment. The observed temporal patterns support the view that
developmental-stage-dependent changes contributed substantially to variation in
rhizospheric soil physicochemical properties, while salinity and drought
primarily affected specific variables rather than the overall soil profile.
PERMANOVA
The PERMANOVA results indicate that developmental
stage was the primary factor shaping rhizospheric soil physicochemical
properties, whereas salinity and drought treatments had comparatively limited
effects on the overall multivariate soil profile. The significant contribution
of developmental stage suggests that changes associated with plant growth and
rhizosphere development exerted a stronger influence on soil chemistry than the
imposed stress treatments. As sorghum progressed from early vegetative growth
to later developmental stages, alterations in nutrient uptake, root exudation
patterns, microbial activity and rhizosphere nutrient transformations likely
contributed to the observed temporal variation (Philippot et al., 2013; Prashar
et al., 2014).
This finding suggests that treatment-induced changes
were more subtle than the temporal shifts associated with plant development.
The non-significant PERMDISP results further strengthen this interpretation by
confirming that the observed multivariate patterns were not artifacts of
unequal dispersion among treatment groups.
Correlation Analysis
The significant positive correlation between pH and
available nitrogen suggests that soil chemical conditions played an important
role in regulating nitrogen availability within the sorghosphere. In alkaline
soils, slight variations in pH can influence microbial mineralization processes
and nitrogen transformation pathways, thereby affecting the pool of
plant-available nitrogen (Marschner, 2012; Paul, 2015). The positive
association observed in this study indicates that higher pH conditions may have
favoured nitrogen availability during plant development.
Although not statistically significant, the moderate
positive relationships between EC, organic carbon and potassium indicate
potential interactions among soil ionic status, carbon dynamics and nutrient
availability across developmental stages and stress treatments. The positive
association between EC and potassium is biologically plausible because
potassium contributes to osmotic regulation and ionic balance, particularly
under stress conditions (Wang et al., 2013; Hasanuzzaman et al., 2018).
Similarly, the association between organic carbon and nutrient variables may
reflect the role of soil organic matter in nutrient storage and rhizosphere
functioning (Magdoff and Weil, 2004).
Phosphorus exhibited weak correlations with all
measured soil properties, suggesting that its availability was influenced by
factors not directly captured by the analyzed physicochemical variables. In
alkaline soils, phosphorus availability is often governed by precipitation
reactions, adsorption processes and localized rhizosphere interactions rather
than broad changes in soil chemistry (Marschner, 2012; Brady and Weil, 2016).
The correlation analysis indicates that the measured
soil properties were largely independent of one another with pH representing
the most influential factor associated with nitrogen availability. These
findings suggest that abiotic stress may influence rhizospheric nutrient
dynamics through complex and multifactorial processes rather than through
strong direct interactions among individual soil physicochemical parameters.
Principal
Component Analysis
Principal component analysis revealed that variation
in rhizospheric soil physicochemical properties was primarily associated with
relationships among soil nutrients, ionic status and carbon dynamics rather
than with distinct treatment-specific separation. The close alignment of pH and
available nitrogen vectors indicates a strong positive association between
these variables, consistent with the correlation analysis. This relationship
likely reflects the influence of soil chemical conditions on nitrogen availability
and nitrogen transformation processes (Marschner, 2012; Paul, 2015).
Electrical conductivity was oriented in the opposite
direction to pH and nitrogen, suggesting that increased soil ionic content was
associated with changes in nutrient dynamics. This pattern is biologically
reasonable under salinity stress, where the accumulation of soluble salts can
alter nutrient availability and rhizosphere chemical balance (Rietz and Haynes,
2003; Wang et al., 2013). Organic carbon and potassium exhibited similar vector
orientations, indicating coordinated variation between soil carbon status and
potassium availability. Potassium plays a critical role in osmotic regulation
and stress adaptation while soil organic carbon influences nutrient retention
and rhizosphere functioning, potentially explaining their association (Wang et
al., 2013; Hasanuzzaman et al., 2018; Magdoff and Weil, 2004).
Phosphorus showed a distinct orientation along the
first principal component, indicating that its variation was partly independent
of the patterns observed for nitrogen, potassium and organic carbon. This
suggests that phosphorus dynamics were regulated by additional soil processes,
including adsorption-desorption reactions and pH-dependent availability
(Marschner, 2012; Brady and Weil, 2016).
Despite some dispersion among treatment groups,
substantial overlap of the confidence ellipses was observed, indicating limited
separation of healthy, drought and salinity treatments based on overall soil
physicochemical characteristics. This interpretation is supported by PERMANOVA
which identified developmental stage as the primary source of multivariate
variation, whereas treatment effects were not statistically significant.
Together, the PCA results suggest that rhizospheric soil properties were governed
predominantly by temporal changes associated with plant growth and rhizosphere
development, while salinity and drought induced comparatively modest
modifications to the overall physicochemical profile (Philippot et al., 2013).
Heatmap
and Hierarchical Clustering Analysis
Hierarchical clustering revealed coordinated
responses among specific groups of soil physicochemical properties. The close
association of EC, OC, and K suggests that changes in soil ionic status were
accompanied by alterations in carbon-related processes and potassium
availability, particularly under salinity stress. Potassium is a key osmotic
regulator in plants and its clustering with EC may reflect adaptive responses
to osmotic and ionic stress conditions (Wang et al., 2013; Hasanuzzaman et al.,
2018).
The clustering of pH, nitrogen and phosphorus
indicates that nutrient availability and soil chemical balance remained closely
linked across samples. Soil pH strongly influences nutrient solubility and can
affect microbial-mediated nutrient transformations, which may explain its
association with nitrogen and phosphorus dynamics (Marschner, 2012; Paul,
2015). The absence of complete treatment-specific clustering suggests that
salinity and drought modified particular soil properties without fundamentally
restructuring the overall rhizospheric physicochemical profile.
The observed clustering patterns support the
correlation and PCA analyses which identified EC, nutrient availability and
carbon-related properties as major contributors to variation in the
rhizosphere. Collectively, the results suggest that interactions among soil
ionic status, nutrient availability and carbon-related processes were important
contributors to rhizospheric soil variability in sorghum.
Conclusion
Developmental stage was the primary factor associated
with variation in rhizospheric soil physicochemical properties of sorghum,
explaining substantially more variability than salinity or drought treatments.
Temporal changes in nutrient availability, electrical conductivity and organic
carbon were evident throughout plant development, whereas treatment effects on
the overall soil physicochemical profile were comparatively modest.
Multivariate analyses identified pH, available nitrogen, electrical
conductivity, organic carbon and potassium as major contributors to
rhizospheric variability with a positive association between pH and nitrogen
and coordinated variation among EC, OC and K. Phosphorus exhibited relatively
independent variation. The findings indicate that developmental-stage-dependent
changes were more strongly associated with rhizospheric soil variability than
the imposed abiotic stress treatments.
Acknowledgments
The authors gratefully
acknowledge Principal and Head, Department of Microbiology, PSGVP Mandal’s, S I
Patil Arts, GB Patel Science andSTKVS Commerce College, Shahada and CSIR-Central
Institute of Medicinal and Aromatic Plants, Lucknow, India for providing necessaryfacilities during this
study.
Author contributions
Methodology,
Investigation and Writing-Original draft; Formal analysis, Writing-Review and
editing: VT; Methodology, Investigation; KNW.
Methodology
and investigation, Conceptualization, Supervision, Project administration
andWriting- Review and Editing; RS. All the authors reviewed and approved the
final version of the paper.
Funding
Nil
Competing interests
The
authors declare no competing interests.
Ethics approval and consent to
participate
Not
applicable
Consent for publication
Not
applicable.
Data Availability
All the
data is available in the manuscript and its supplementary files.
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