Introduction
Plants show the synthesis of a
larger number of compounds via secondary metabolic pathways. Kössel (1891) was the first who defined the secondary
metabolites and evaluated their role in the adaptability of plants to their
environments. Plant secondary metabolites have no direct role in plant growth
and development, but their presence is necessary and sometimes a specific
secondary metabolite is a distinguishing feature of a plant species to support
its growth and development (Asten et al. 2019). There are two major
classes of secondary compounds, which include nitrogen containing and
non-nitrogen containing metabolites. Among the non-nitrogen containing
secondary metabolites, the synthesis of phenolics, flavonoids, anthocyanins,
and tannins have been regarded as the most important, while among the nitrogen
containing secondary metabolites, alkaloids and saponins are considered
important in ensuring the plant survival in changing climates (Theis and Lerdau 2003; Isah 2019).
Depending upon their
biological roles, the metabolites are placed into two broad categories; primary
and secondary. They exist either as phytoanticipins
or phytoalexins and play their physiological roles in defense against biotic
and abiotic stresses (Moradi 2016; Tiku 2020). The
primary metabolites are low molecular weight compounds and are accumulated as
an early response to growth limiting conditions. Secondary metabolites
typically have a very limited distribution in the plant kingdom but plants
spend quite a bit of energy on their production when required (Siemens et al. 2002). They play very important
roles in plant defense, especially against herbivory and environmental stresses
(Moradi 2016; Scott et al. 2020).
Tolerance to harsh environmental conditions is one of the key factors in the
adaptability of a plant in an area. The accumulation of the secondary
metabolites in high amounts results in better resistance of plants against
abiotic stresses (Mahmood et al.
2014; Alhaithloul et
al. 2020).
Lemongrass [Cymbopogan citratus (DC) Stapf.]
is an important commercial C4 aromatic grass belonging to the family
Poaceae. It
is cosmopolitan in distribution, can thrive in diverse habitats ranging
from the sea to mountains. It has an
average life span of about 5‒6
years (De Boer 2005). It propagates profusely through
rhizome, and produces tillers, which add to plant biomass (Tajidin
et al. 2012).
Lemongrass is known for its lemon like aroma due to having essential oils in it (Joy et al. 2006). The quality
and quantity of essential oils not only vary due to geographical origin and
habitat but also because of agronomic practices and genetic diversity (Khanuja et al. 2005; Negrelle
and Gomes 2007). A number of studies reported that lemongrass has
antioxidant and antimicrobial roles for humans (Francisco et al. 2011; Mirghani et al. 2012). The aroma of lemongrass
leaves also repels insects, especially mosquito (Joy et al. 2006).
Swapping the populations of a cosmopolitan
plant species like lemongrass in diverse environments is a pragmatic approach
to find out novel mechanisms of their adaptability and survival. In earlier
studies, it is established that photosynthetic pigment composition, oxidative
damage parameters and nutrient composition were closely related to the meteorological
condition at both locations (Shaukat et al. 2018a, b). However,
information is lacking regarding the associations of metabolites in modulating
the growth of swapped lemongrass populations elsewhere. Varied accumulation
patterns of both primary and secondary metabolites are an important manifestation
of stress tolerance in plants. The
data regarding swapping lemongrass populations are not reported in
relation to primary and secondary metabolites synthesis and accumulation. It is
predicted that the specific accumulation patterns of different metabolites
during cross-adaptation make the lemongrass apt to grow in a new location. In
this study, lemongrass populations growing in Faisalabad and Quetta were
reciprocally swapped to determine the possible role of the biosynthesis of
different metabolites in the successful survival of the swapped populations in
new locations in comparison to the native counterparts.
Materials
and Methods
Experimental plan
The populations
used in this study were obtained from the Arid Zone Research Institute, Quetta
(QN and FA) and Botanical Garden, University of Agriculture, Faisalabad (FN and
QA). Field experiments were conducted to determine the cross-adaptability of
lemongrass populations native to Faisalabad (FN) and Quetta (QN) in order to
explore the metabolites adjustments in a reciprocal swap arrangement across the
locations. The reciprocally swapped population from Quetta to Faisalabad was
named as Quetta adapted (QA) and that from Faisalabad to Quetta was called as
Faisalabad adapted (FA). The plants from Quetta were grown in the field in
Botanical Garden (Sq. No. 32), University of Agriculture, Faisalabad.
Likewise, the plants from Faisalabad were grown in fields at the Environmental
Protection Agency, Quetta. Both populations were grown under field conditions
at the respective locations in the month of April. The experiments were laid
out in randomized complete block design (RCBD) in three replications. As given
in Shaukat et al. (2018a, b), prior
to planting of populations, the soil samples from both locations were analyzed
for the following physicochemical characteristics: The AB-DTPA extractable P
(mg/g) 1.56 (Qta) and 2.24 (Fsd);
K (mg/g) 184 (Qta) and 162 (Fsd);
nitrate-N 285 (Qta & Fsd);
organic matter (%) 0.532 (Qta) and 1.10 (Fsd); saturation percentage 39.8 (Qta)
and 38.6 (Fsd); pH 8.16 (Qta)
and 8.0 (Fsd); electrical conductivity of extract (dS/m) 1.35 (Qta) and 0.435 (Fsd); Na+ (mg/g) 6.84 (Qta)
and 1.75 (Fsd); bicarbonate (mg/g) 2.95 (Qta) and 2.63 (Fsd); Cl- (mg/g)
5.75 (Qta) and 1.68 (Fsd); Ca+Mg (mg/g) 18.15 (Qta) and 4.18
(Fsd) and sodium adsorption ratio 2.54 (Qta) and 1.17 (Fsd). The
temperature data from Qta and Fsd
during the experimental periods are given in Fig. 1 (adapted from Shaukat et al. 2018a, b).
Fig. 1: Average monthly maximum and
minimum temperatures (oC) in Faisalabad
and Quetta during the experimental years 2015 and 2016
Tissue sampling
For tissue metabolites analysis,
the plants were harvested in June (Jun), July (Jul), August (Aug), September
(Sep), October (Oct), November (Nov) and December (Dec). Shoots were separated
from roots. The shoots were briefly washed to remove the debris while roots
were thoroughly washed to remove the adhering soil and both were blotted dry.
Both shoots and roots were fractioned for fresh and dry analyses. The fraction
for fresh analysis was transferred to the freezer at-40oC until
analyzed while for dry analysis, the plant material was dried in an oven at 65oC
for seven days and preserved until analyzed. The harvested whole of the shoot
and root tissues in each month from different experimental units were subjected
to the metabolite’s analyses.
Primary metabolites analysis
Soluble sugars: To measure the soluble
sugars following the method of Yoshida et
al. (1976), (0.1 g) fresh plant material was boiled in 5 mL distilled water
in water bath at 90şC for 1 h. The extract was filtered and 1 mL of extract was
diluted to 9 mL with distilled water. A 0.5 mL of the diluted extract was taken
and 5 mL of anthrone reagent (prepared by dissolving
Total free amino acids (TFAA): Hamilton and Van
Slyke (1943) method was used to measure TFAA. Fresh plant material (0.1 g) was
extracted in phosphate buffer (pH 7) and 1 mL of the extract was mixed with 1
mL of 2% ninhydrin solution and 1 mL of 10% pyridine solution. Heated the
mixture for 30 min. cooled and diluted up to 25 mL.
Absorbance was taken at 535 nm. Phosphate buffer was used as blank.
Free proline: The free proline
was determined by using the method given by Bates et al. (1973). Fresh plant material (0.1 g) was homogenized in 20
mL 3% aqueous sulfo-salicylic acid and filtered. To 1
mL of the extract in a test tube, 1 mL of acid ninhydrin and 1 mL of glacial
acetic acid were added. Heated the mixture in a water bath at 100oC
for 1 h and terminated the reaction in an ice bath. The reaction mixture was
then extracted with 2 mL of toluene by vigorous vortexing
for 15‒20 sec, aspirated the colored solution
and measured absorbance at 520 nm.
Glycine betaine (GB): The GB was
determined by Grieve and Grattan (1983) method. A 0.5 g dried plant sample was
mechanically shaken in 20 mL of distilled water for 24 h, filtered and frozen.
One mL of the thawed extract was mixed with 1 mL of 2N H2SO4,
and 0.5 mL of this solution was added in 0.2 mL potassium tri-iodide in test
tube and cooled at 4oC for 16 h. The test tubes were centrifuged at
0oC at 10,000 prm for 15 min and aspirated
the supernatant quickly with an aspiration tube. The periodide
crystals in the bottom were dissolved in 9 mL of 1, 2-dichloroethane by vortexing. The tubes were let stand at 25oC for 2.5 h and
measured at 365 nm.
Secondary metabolites determination
Soluble phenolics: Fresh plant material (100 mg) was ground in 1 mL of 80% acetone and
centrifuged at 12000 rpm for 15 min, separated in a microfuge tube and stored
at 20oC until used. A 100 µL of supernatant diluted with
distilled water to 1 mL in a 10 mL capacity test tube was added with 0.5 mL of folin phenol reagent. Shaken the sample vigorously, and
added 2.5 mL of 20% Na2CO3. Volume was made up to 5 mL,
vortexed vigorously for 5–10 sec and waited for 20 min. The absorbance was
measured at 750 nm by setting spectrophotometer background to zero with 80%
acetone. Standard curve was prepared using tannic acid (Julkunen-Tiitto
1985).
Anthocyanins: For anthocyanins determination by Stark and Wray (1989) method, fresh
plant material (0.1 g) was extracted in 2.5 mL of acidified methanol (1% HCl,
v/v); heated at 50oC for 1 h and filtered the extract. The
absorbance of mixture was taken at 535 nm. Acidified methanol was used as
blank.
Flavonoids: Flavonoids were determined following the method of Zhishen
et al. (1999). A 0.1 g fresh plant
material was extracted in 80% acetone (Merck or BDH). Then added 1 mL of
extract in 10 mL of volumetric flask, containing 4 mL distilled water and after
5 min, 0.6 mL of 5% NaNO2 and 0.5 mL of 10% AlCl3 were
mixed. After 1 min, 2 mL of 1 M NaOH was also added. Diluted the reaction
mixture using 2.4 mL of distilled water and absorbance was measured at 510 nm
by using a spectrophotometer, while 80% acetone was used as blank.
Tannins: Fresh plant material (0.1 g) was transferred to 2 mL of
diethyl ether and left for overnight. Then, decanted the solution and 1 mL of
70% acetone was added and kept for overnight. To analyze tannins, 50 µL
of the extract was taken in test tube and the volume was made up to 1 mL. After dilution, 0.5 mL of Folin
Phenol Reagent was added and mixed thoroughly. Then 2.5 mL of 20% Na2CO3
solution was mixed well and kept at room temperature for 40 min. Absorbance was
taken at 725 nm using 70% acetone as blank.
Statistical analysis
The
data recorded from each location for different metabolites were analyzed
statistically using Statistix8.1 online software. The data means were compared
using least significant difference (LSD) test at 5% probability level.
Correlations of maximum and minimum temperature and shoot and root dry weight
with the concentrations of primary and secondary metabolites were also
established to validate their possible role in lemongrass adaptability while
swapped.
Results
Statistical
analysis of data revealed that in the year 2015, there was a significant
difference among the months for all shoot and root parameters of native and
adapted (swapped) populations except shoot flavonoids (SFLA) in Faisalabad and
shoots glycine betaine (SGB) and SFLA in Quetta. Populations, on the other
hand, indicated significant differences in most of the parameters except root
dry weight, shoot free proline (SFP) and SGB, root soluble phenolics (RSP),
SFLA, shoot anthocyanins (SANT) and root tannins (RTAN) in Faisalabad while
root dry weight and root anthocyanins (RANT) in Quetta. The months
× populations interaction was significant for all parameters except root dry
weight in Faisalabad and RANT in Quetta (Table 1). In 2016, months showed
significant differences for all the parameters at both the locations. As for
populations most of the parameters of exhibited significant differences at both
locations excepting shoot total free amino acids (STFAA), SSP and RSP and SFLA
in Faisalabad while SANT and STAN in Quetta. The months × populations
interactions were also significant for all the growth and metabolite attributes
(Table 1).
Table 1: Analysis of variance (mean
squares) of sampling months, lemongrass populations and their interactions wheat
flag leaf and grain characteristics under seed priming and foliar spray
treatments at two locations in Faisalabad in the year 2015 and 2016
Parameters |
2015 |
2016 |
||||||
Months (M) (df = 6) |
Populations (P) (df = 1) |
M × P (df = 6) |
EMS (df = 70) |
Months (M) (df = 6) |
Populations (P) (df = 1) |
M × P (df = 6) |
EMS (df = 70) |
|
Faisalabad |
|
|
|
|
|
|
|
|
Shoot dry weight |
47702.80** |
2433.80** |
1902.00** |
80.30 |
69971.50** |
16.00ns |
209.60* |
70.60 |
Root dry weight |
775.14** |
20.20ns |
14.54ns |
7.56 |
891.74** |
493.99** |
50.19** |
5.90 |
Shoot soluble sugars |
71.21** |
15.63** |
181.67** |
1.35 |
14.62* |
84.53** |
48.71** |
5.47 |
Root soluble sugars |
90.02** |
8.80** |
139.81** |
1.16 |
37.61** |
55.18** |
91.10** |
3.42 |
Shoot total free amino acids |
1405.40** |
13661.30** |
4857.00** |
73.00 |
6900.90** |
224.40ns |
14674.10** |
492.00 |
Root total free amino acids |
596.91** |
222.39* |
2972.46** |
46.23 |
3915.30** |
18540.30** |
10236.80** |
173.30 |
Shoot free proline |
184.87** |
0.019ns |
561.53** |
11.37 |
443.31** |
1770.66** |
566.82** |
44.67 |
Root free proline |
174.89** |
845.68** |
949.56** |
5.59 |
413.35** |
1486.68** |
560.80** |
20.79 |
Shoot glycine betaine |
28.06** |
0.65ns |
51.59** |
1.60 |
36.79** |
15.92** |
59.86** |
1.23 |
Root glycine betaine |
8.15** |
271.30** |
23.28** |
0.42 |
29.08** |
11.93** |
39.05** |
1.11 |
Shoot soluble phenolics |
138.53** |
29.82** |
553.91** |
10.79 |
245.09** |
0.02ns |
471.22** |
16.55 |
Root soluble phenolics |
115.02** |
13.00ns |
235.73** |
10.76 |
57.07** |
8.45ns |
168.47** |
13.21 |
Shoot flavonoids |
0.55ns |
0.03ns |
17.87** |
0.32 |
1.29** |
0.02ns |
8.12** |
0.20 |
Root flavonoids |
2.51** |
8.29** |
16.94** |
0.15 |
1.70** |
3.49** |
7.70** |
0.15 |
Shoot anthocyanins |
0.09** |
0.03ns |
0.20** |
0.003 |
0.04** |
1.06** |
0.51** |
0.003 |
Root anthocyanins |
0.11** |
0.16** |
0.34** |
0.003 |
0.04** |
0.56** |
0.32** |
0.003 |
Shoot tannins |
1102.86** |
1402.22** |
2316.94** |
38.84 |
77.63** |
1990.12** |
448.12** |
34.83 |
Root tannins |
193.70** |
54.07ns |
1313.59** |
19.98 |
153.34** |
408.68** |
613.22** |
51.32 |
Quetta |
|
|
|
|
|
|
|
|
Shoot dry weight |
78.24.58** |
1867.20** |
42.48ns |
43.87 |
35587.70** |
69073.70** |
7376.90** |
95.70 |
Root dry weight |
498.13** |
10.36ns |
18.03** |
3.99 |
490.86** |
496.46** |
52.97** |
5.98 |
Shoot soluble sugars |
665.68** |
44.43** |
43.33** |
3.77 |
50.53** |
361.56** |
138.71** |
2.90 |
Root soluble sugars |
128.46** |
100.28** |
28.26** |
3.75 |
29.86** |
181.65** |
39.69** |
2.45 |
Shoot total free amino acids |
1775.11** |
3216.35** |
5629.63** |
143.82 |
1970.00** |
11345.00** |
6375.40** |
353.20 |
Root total free amino acids |
6741.95** |
429.27** |
1741.64** |
61.53 |
2518.80** |
11466.60** |
3673.80** |
221.80 |
Shoot free proline |
9.23** |
268.78** |
416.05** |
11.14 |
186.24** |
270.68** |
721.34** |
26.20 |
Root free proline |
1281.57** |
206.62** |
399.40** |
13.46 |
176.26** |
77.92** |
372.03** |
12.22 |
Shoot glycine betaine |
0.99ns |
31.70** |
53.62** |
1.18 |
42.04** |
520.21** |
121.65** |
3.24 |
Root glycine betaine |
5.56** |
19.31** |
44.15** |
0.66 |
34.05** |
136.16** |
135.91** |
1.58 |
Shoot soluble phenolics |
24.99** |
383.14** |
153.50** |
12.04 |
156.00** |
263.10** |
463.00** |
17.97 |
Root soluble phenolics |
292.32** |
149.48** |
297.14** |
5.53 |
105.76** |
35.37* |
412.47** |
9.90 |
Shoot flavonoids |
0.33ns |
5.24** |
10.08** |
0.21 |
1.82** |
44.06** |
6.75** |
0.23 |
Root flavonoids |
0.62* |
0.43* |
9.18** |
0.20 |
2.24** |
5.26** |
7.85** |
0.19 |
Shoot anthocyanins |
0.03* |
0.07** |
0.15** |
0.004 |
0.10** |
0.00ns |
0.35** |
0.005 |
Root anthocyanins |
0.06** |
0.01ns |
0.10** |
0.003 |
0.05** |
0.08** |
0.13** |
0.004 |
Shoot tannins |
9.81** |
69.30** |
1907.91** |
32.51 |
205.04** |
3.88ns |
1396.77** |
44.07 |
Root tannins |
685.17** |
195.43** |
1193.92** |
27.21 |
285.34** |
272.16** |
954.37** |
31.81 |
df, degree of freedom
EMS, Error mean square
Significant at: *, P<0.05; **, P<0.01 and ns,
P>0.05
Plant biomass
Irrespective
of the populations, the dry mass of shoot and root increased with the plant
age. At both the locations and in all swapped populations, the both the shoot
and root dry weight was relatively lower in 2015 as compared to 2016. FN
population displayed the highest shoot and root dry mass followed by FA while these
parts exhibited the lowest dry mass in QN (Fig. 2). This may be assigned to
relatively more adverse prevailing temperature in the years 2015.
Primary metabolites accumulation
pattern
Overall,
the levels of primary metabolites in the shoot (SSS, STFAA, SFP and SGB) were
higher than the root (RSS, RTFAA, RFP and RGB) in both the years (Fig. 3). The SSS
and RSS contents were higher in FN and QA population as compared to QN and FA
populations. In FN population highest SSS and RSS contents were observed from
Nov to Dec. However, in FA population maximum SSS contents were detected in the
months of Jun and Jul that gradually decreased thereafter. As far as QN (Quetta
native) population was concerned SSS content increased from Jun to Aug.
Furthermore, in QA population higher SSS and RSS contents were ascertained in
the month of Dec (Fig. 3). Considering STFAA and RTFAA and SFP and RFP, it was
observed that both these osmoprotectants were
significantly higher during Oct to Dec in FN population while in FA population
subsequently decreased from Jul to Dec. On the other hand, in QN population,
maximum STFAA and SFP content was detected during Jul and Aug followed by RTFAA
and RFP. Conversely, in QA population STFAA and SFP content was higher in the
winter season followed by RTFAA and RFP (Fig. 3). Higher SGB and RGB content
was observed in the winter season in FN population, while in FA population maximum
SGB and RGB was detected in the summer season during 2015 and 2016. Conversely,
in QN population higher SGB contents followed by RGB were observed in months of
Jun to Aug; however, in QA population higher SGB followed by RGB contents were
detected in the month of Dec. On the other hand, in QN population higher SGB
and RGB contents were analyzed in the summer season. In QA population higher
SGB and RGB content was confirmed in the winter season (Fig. 3).
Fig. 2: Monthly changes in the shoot and
root dry weight in native and reciprocally swapped and adapted lemongrass
populations grown in Faisalabad and Quetta during 2015 and 2016
Fig. 3: Monthly changes in the shoot and
root levels of primary metabolites in native and reciprocally swapped and
adapted lemongrass populations grown in Faisalabad and Quetta during 2015 and
2016
Secondary metabolites
accumulation pattern
Data
regarding secondary metabolites in shoot (SSP, SFLA, SANT, STAN) and root (RSP,
RFLA, RANT, RTAN) revealed that, with few exceptions, SSP and RSP indicated
similar trend of accumulation in FN and QA populations being higher during
Oct-Dec in both the years, while in FA and QN populations their accumulation
was greater during Jul-Sep (Fig. 4). In FN and QA populations, the shoot and
root levels of flavonoids kept low Jun–Sep but began to accumulate later and
attained the highest level in Dec. However, in FA and QN populations, the SFLA
and RFLA contents were higher in Aug–Sep of both the years (Fig. 4). The SANT
accumulation in FN population was quite exaggerated but such a trend was not
seen in 2016 in FN and 2015 and 2016 when the anthocyanins declined from
Jun–Sep. On the other hand, in FA and QN populations, the accumulation of SANT
and RANT was the highest during Jul–Sep in both the experimental years (Fig.
4). In the STAN and RTAN of FN and QA populations during both the years
declined from Jun–Oct but depicted a substantial gain from Oct–Dec. Contrarily,
FA and QN lemongrass populations indicated much higher levels of STAN and RTAN
from Jul–Aug in 2015 than 2016 (Fig. 4). Overall, the results revealed that the
accumulation of the studied secondary metabolites was relatively higher in the
shoot than in the root tissue, except for flavonoids, which were comparable in
both these tissues (Fig. 4).
Fig. 4: Monthly changes in the shoot and
root levels of secondary metabolites in native and reciprocally swapped and
adapted lemongrass populations growing in Faisalabad and Quetta during 2015 and
2016
Correlations
Metabolites association with maximum and minimum temperature: In view of the fact that
prevailing temperature is a major factor affecting the growth and metabolism of
lemongrass populations, the correlations of the levels of primary and secondary
metabolites were established with maximum and minimum temperatures based on
their changes during Jun–Dec of 2015 and 2016 in the native (FN, QN) and
swapped (FA, QA) populations (Table 2). Among the primary metabolites, shoot
soluble sugars (SSS) in FN population were negatively correlated with maximum
and minimum temperature in both the years; FA population showed positive
correlation with minimum temperature in both the years; QN population exhibited
a positive correlation with minimum temperature during 2016 while QA population
indicated negative correlation with both maximum and minimum temperatures in
both the years for SSS. For RSS, FN population indicated negative correlation
with maximum and minimum temperatures in 2016 only, while for FA population, a
positive correlation was noted with maximum temperatures in 2015 only and with
both the temperatures in 2016. QN showed no association with any temperatures
in both the years while QA indicated negative correlation with maximum and
minimum temperatures in 2015 only for RSS. For STFAA, FN population revealed
negative correlation with maximum and minimum temperatures in 2015 only, while
FA population showed positive correlation with both the temperatures in both
the years except with maximum temperature in 2015. QN exhibited positive
correlation with maximum and minimum temperatures in 2016 only while QA indicated
negative correlation of both the temperatures in 2015 only for STFAA. RTFAA in
FN population was negatively correlated with maximum and minimum temperatures
in 2015; while for FA this attribute was positively correlated with both the
temperatures in both the years excepting no correlation of maximum temperature
in 2015. In QN population, the RTFAA was positively correlated with both the
temperatures in 2016 while no correlation of this attribute was noted with
temperatures in both the years in QA population (Table 2).
The SFP accumulation was not correlated with temperatures and years in
FN population while in FA positive correlations of maximum and minimum
temperatures were noted with SFP in both the years except with maximum
temperature in 2015. QN indicated positive correlation of SFP with both the
temperatures in 2015 while QA manifested negative correlation of SFP with both
the temperatures in 2016. For RFP, the FN population indicated negative
correlation with both the temperatures in 2015 only, while FA indicated
positive correlation with both the temperatures in both the years except with
maximum temperature in 2015. QN showed positive correlation with both the
temperatures in 2016 only, whereas QA indicated only negative correlation
minimum temperature with RFP in 2015 only. Data revealed that SGB in FN
population was negatively correlated with maximum and minimum temperature in
2016; was not correlated with both the temperatures in 2015 or 2016 in FA
population; was positively correlated with both the temperatures in QN
population in 2015 only, while QA population was negatively correlated with
maximum and minimum temperatures in QA population in 2016. The RGB was
negatively correlated with both the temperatures in FN population in 2015; was
positively correlated with minimum temperature in FA population in 2015 and
2016; was positively correlated with both the temperatures and years in QN
population with the exception of minimum temperature in 2015 but negatively
correlated with both the temperatures and years in QA population except minimum
temperature in 2016 for this attribute (Table 2).
As regards secondary metabolites, SSP in FN population revealed
negative correlation with maximum and minimum temperatures in both the years
except no correlation of minimum temperature with this attribute. FA population
revealed no correlation of SSP with maximum or minimum temperature in 2015 and
2016 except significant correlation of minimum temperature with SSP in 2015.
SSP in QN population indicated no correlation with both maximum and minimum
temperatures in both the years except a positive correlation of minimum
temperature, while QA population indicated no relationship with SSP maximum and
minimum temperatures in both the years. The RSP of FN population indicated
negative correlation of both the maximum and minimum temperature in both the
years except no relationship with minimum temperature in 2016, while reverse of
it was true for FA population. QN population indicated positive while QA
population showed negative correlation with maximum and minimum temperatures in
2015 as well as 2016. The SFLA in FN population exhibited negative correlation
with maximum and minimum temperatures while FA population revealed positive
correlation of this attribute with minimum temperature in both the years. QN
population indicated positive while QA population showed negative correlation
with maximum and minimum temperatures in 2015 as well as 2016. AS regards RFLA,
FN population indicated negative correlation with both the temperatures in both
the years while FN population showed positive correlation with minimum
temperature only in both the years. The QN population manifested positive while
QA population indicated negative correlation with temperatures in the years for
RFLA (Table 2).
Table 2: Correlation of changes in
secondary metabolites in the shoot and root tissues of lemongrass populations
over different sampling months during 2015 and 2016 (Faisalabad and Quetta)
with maximum and minimum temperatures (n = 7)
Parameter |
Lemongrass population |
2015 |
2016 |
||
Max Temp |
Min
Temp |
Max
Temp |
Min
Temp |
||
Shoot
soluble sugars |
Fsd Native |
-0.802* |
-0.789* |
-0.772* |
-0.843* |
Fsd Adapted |
0.717ns |
0.765* |
0.678ns |
0.823* |
|
Qta Native |
0.563ns |
0.479ns |
0.750ns |
0.858* |
|
Qta Adapted |
-0.780* |
-0.849* |
-0.928** |
-0.833* |
|
Root
soluble sugars |
Fsd Native |
-0.579ns |
-0.725ns |
-0.838* |
-0.952** |
Fsd Adapted |
0.826* |
0.683ns |
0.845* |
0.831* |
|
Qta Native |
0.312ns |
0.422ns |
0.034ns |
0.128ns |
|
Qta Adapted |
-0.783* |
-0.834* |
-0.666ns |
-0.589ns |
|
Shoot
total free amino acids |
Fsd Native |
-0.841* |
-0.872** |
-0.543ns |
-0.484ns |
Fsd Adapted |
0.699ns |
0.891** |
0.879** |
0.972** |
|
Qta Native |
0.445ns |
0.377 |
0.821* |
0.854* |
|
Qta Adapted |
-0.816* |
-0.864* |
-0.717ns |
-0.598ns |
|
Root
total free amino acids |
Fsd Native |
-0.883** |
-0.940** |
-0.578ns |
-0.665ns |
Fsd Adapted |
0.772ns |
0.825* |
0.801* |
0.947** |
|
Qta Native |
0.749ns |
0.723ns |
0.786* |
0.853* |
|
Qta Adapted |
0.116ns |
-0.212ns |
-0.610ns |
-0.464ns |
|
Shoot
free proline |
Fsd Native |
-0.672ns |
-0.580ns |
-0.554ns |
-0.644ns |
Fsd Adapted |
0.670ns |
0.958** |
0.850* |
0.783* |
|
Qta Native |
0.690ns |
0.625ns |
0.780* |
0.871* |
|
Qta Adapted |
-0.687ns |
-0.734ns |
-0.760* |
-0.675ns |
|
Root
free proline |
Fsd Native |
-0.866* |
-0.947** |
-0.741ns |
0.745ns |
Fsd Adapted |
0.729ns |
0.848* |
0.843* |
0.768* |
|
Qta Native |
0.792ns |
0.742ns |
0.911** |
0.949** |
|
Qta Adapted |
0.735ns |
-0.772* |
-0.597ns |
-0.447ns |
|
Shoot
glycine betaine |
Fsd Native |
-0.440ns |
-0.421ns |
-0.847* |
-0.838* |
Fsd Adapted |
0.574ns |
0.836* |
0.523ns |
0.735ns |
|
Qta Native |
0.955** |
0.957** |
0.630ns |
0.643ns |
|
Qta Adapted |
-0.418ns |
-0.462ns |
-0.980** |
-0.973** |
|
Root
glycine betaine |
Fsd Native |
-0.956** |
-0.871* |
-0.716ns |
-0.669ns |
Fsd Adapted |
0.713ns |
0.866* |
0.584ns |
0.786* |
|
Qta Native |
0.787* |
0.703ns |
0.790* |
0.840* |
|
Qta Adapted |
-0.831* |
-0.866* |
-0.884** |
-0.938** |
|
Shoot
soluble phenolics |
Fsd Native |
-0.763* |
-0.841* |
-0.832* |
-0.745ns |
Fsd Adapted |
0.610ns |
0.912** |
0.494ns |
0.720ns |
|
Qta Native |
0.308ns |
0.339ns |
0.747ns |
0.818* |
|
Qta Adapted |
-0.686ns |
-0.698ns |
-0.745ns |
-0.669ns |
|
Root
soluble phenolics |
Fsd Native |
-0.950** |
-0.894** |
-0.839* |
-0.730ns |
Fsd Adapted |
0.197ns |
0.537ns |
0.681ns |
0.834* |
|
Qta Native |
0.828* |
0.783* |
0.774* |
0.843* |
|
Qta Adapted |
-0.780* |
-0.826* |
-0.816* |
-0.773* |
|
Shoot
flavonoids |
Fsd Native |
-0.755* |
-0.963** |
-0.833* |
-0.935** |
Fsd Adapted |
0.508ns |
0.872* |
0.601ns |
0.790* |
|
Qta Native |
0.820* |
0.770* |
0.811* |
0.800* |
|
Qta Adapted |
-0.765* |
-0.779* |
-0.884** |
-0.925** |
|
Root
flavonoids |
Fsd Native |
-0.975* |
-0.955** |
-0.756* |
-0.863* |
Fsd Adapted |
0.639ns |
0.882** |
0.746ns |
0.902** |
|
Qta Native |
0.861* |
0.861* |
0.761* |
0.790* |
|
Qta Adapted |
-0.921** |
-0.919** |
-0.922** |
-0.910** |
|
Shoot anthocyanins |
Fsd Native |
-0.774* |
-0.771* |
-0.901** |
-0.965** |
Fsd Adapted |
0.757* |
0.941** |
0.827* |
0.919** |
|
Qta Native |
0.572ns |
0.445ns |
0.763* |
0.806* |
|
Qta Adapted |
-0.822* |
-0.864* |
-0.838* |
-0.803* |
|
Root anthocyanins |
Fsd Native |
-0.878** |
-0.978** |
-0.898** |
0.928** |
Fsd Adapted |
0.250ns |
0.628ns |
0.872* |
0.952** |
|
Qta Native |
0.838* |
0.780* |
0.723ns |
0.806* |
|
Qta Adapted |
-0.734ns |
-0.744ns |
-0.838* |
-0.803* |
|
Shoot
tannins |
Fsd Native |
-0.706ns |
-0.636ns |
-0.755* |
-0.892** |
Fsd Adapted |
0.680ns |
0.924** |
0.714ns |
0.875** |
|
Qta Native |
0.891** |
0.889** |
0.824* |
0.898** |
|
Qta Adapted |
-0.882** |
-0.900** |
-0.731ns |
-0.741ns |
|
Root
tannins |
Fsd Native |
-0.873* |
-0.904** |
-0.916** |
-0.891** |
Fsd Adapted |
0.800* |
0.946** |
0.761* |
0.876** |
|
Qta Native |
0.879** |
0.862* |
0.878* |
0.945** |
|
Qta Adapted |
-0.889** |
-0.923** |
-0.704ns |
-0.670ns |
Significant at: *, P<0.05; **, P<0.01 and ns,
P>0.05
As for SANT, FN population indicated negative while FA showed positive
relationship with maximum and minimum temperature in both the years. FA
population indicated positive correlation for SANT in 2016 only while QA
population exhibited negative relationship with this attribute for both the
temperatures and years. The RANT of FN population showed negative correlation
with both temperatures and years while FA population showed positive
correlation with both temperatures with this character in 2016 only. Likewise,
RANT of QN population showed positive correlation with both the temperatures in
both the years while RANT of QA population exhibited negative correlation with
maximum and minimum temperature in 2016 only. As regards STAN, there was no correlation
of this attribute with maximum and minimum temperatures in 2015 but significant
ones with both the temperatures in 2016. The FA population indicated positive
correlations of STAN with minimum temperatures in both the years. The STAN of
QN population manifested positive correlation with both maximum and minimum
temperatures in both the years while QA population showed a negative
correlation of this attribute with both the temperatures in 2015 only. As for
RTAN, both maximum and minimum temperatures showed negative correlations for FN
population but positive for FA and QN population in both the years while for QA
population, negative correlations of maximum and minimum temperatures were
noted in the year 2015 only (Table 2).
Metabolites association with plant
biomass: The correlations were established to substantiate the role of
metabolites accumulation in the dry biomass production of the respective parts
in both the years (Table 3). As regards SSS and RSS contents, FN population
indicated positive and FA showed negative correlation with shoot dry weight
while FA population revealed negative correlation for root dry weight. However,
QN and QA populations showed no correlation with shoot or root dry weight with
SSS or RSS respectively in 2015. On the other hand in 2016, both shoot and root
dry weight held no association with SSS and RSS. FN and FA populations
indicated positive and negative correlations, respectively of shoot dry weight
with STFAA while FN indicated positive correlation with RTFAA in 2015 and STFAA
with shoot dry weight in 2016. However, QN and QA manifested no correlation of
STFAA and RTFAA with shoot and root dry weight in 2016. SFP revealed no
correlation with shoot dry weight of FN, QN and QA populations but showed a
negative correlation with this attribute of QN in 2015. RFP of FN population
indicated positive while those of FA and QN populations indicated negative
correlations while QA revealed no association with root dry weight in 2015.
However, in 2016 except for a negative correlation of shoot and RFP of FA, none
of the population indicated any correlation with RFP of the respective parts.
SGB showed no correlation with shoot dry weight of FN, QN and QA populations
but displayed a negative correlation with this parameter of QN in 2015. Root
dry weight, on the other hand, showed positive correlation with RGB in FN,
negative correlation in FA and QN while no correlation with QA in 2015. In the
year 2016, SGB was not correlated with shoot dry weight of any population. RGB
indicated no correlation with root dry weight of FN and FA populations while it
was positive correlated with dry weight of QN and negatively with QA
populations in 2016 (Table 3).
Table 3: Correlation of changes in shoot
and root secondary metabolites with shoot and root dry weight (n = 7) of
lemongrass populations over different sampling months during 2015 to 2016
(Quetta & Faisalabad)
Parameter |
Lemongrass Population |
Shoot dry weight |
Root
dry weight |
|
||||||
2015 |
2016 |
2015 |
2016 |
|||||||
Soluble sugars |
Fsd Native |
0.754* |
0.517ns |
0.544ns |
0.687ns |
|
||||
Fsd Adapted |
-0.930** |
-0.675ns |
-0.785* |
-0.582ns |
|
|||||
Qta Native |
-0.270ns |
-0.727ns |
-0.314ns |
-0.220ns |
|
|||||
Qta Adapted |
0.657ns |
0.362ns |
0.623ns |
0.639ns |
|
|||||
Total free amino acids |
Fsd Native |
0.945** |
0.259ns |
0.964** |
0.286ns |
|
||||
Fsd Adapted |
-0.817* |
-0.759* |
-0.594ns |
-0.604ns |
|
|||||
Qta Native |
-0.236ns |
-0.366ns |
-0.539ns |
-0.521ns |
|
|||||
Qta Adapted |
0.619ns |
0.546ns |
0.222ns |
0.0679ns |
|
|||||
Free proline |
Fsd Native |
0.480ns |
0.241ns |
0.885** |
0.433ns |
|
||||
Fsd Adapted |
-0.827* |
-0.892** |
-0.937** |
-0.876** |
|
|||||
Qta Native |
-0.747ns |
-0.467ns |
-0.810* |
-0.721ns |
|
|||||
Qta Adapted |
0.392ns |
0.044ns |
0.384ns |
0.641ns |
|
|||||
Glycine betaine |
Fsd Native |
0.275ns |
0.651ns |
0.823* |
0.439ns |
|
||||
Fsd Adapted |
-0.746ns |
-0.494ns |
-0.938** |
-0.392ns |
|
|||||
Qta Native |
-0.858* |
-0.640ns |
-0.760* |
-0.893** |
|
|||||
Qta Adapted |
0.071ns |
0.324ns |
0.573ns |
0.794* |
|
|||||
Soluble phenolics |
Fsd Native |
0.857* |
0.617ns |
0.891** |
0.677ns |
|
||||
Fsd Adapted |
-0.878** |
-0.275ns |
-0.394ns |
-0.335ns |
|
|||||
Qta Native |
-0.148ns |
-0.363ns |
-0.694ns |
-0.596ns |
|
|||||
Qta Adapted |
0.428ns |
0.181ns |
0.581ns |
0.807* |
|
|||||
Flavonoids |
Fsd Native |
0.867* |
0.685ns |
0.821* |
0.584ns |
|
||||
Fsd Adapted |
-0.677ns |
-0.401ns |
-0.822* |
-0.530ns |
|
|||||
Qta Native |
-0.691ns |
-0.468ns |
-0.725ns |
-0.516ns |
|
|||||
Qta Adapted |
0.613ns |
0.062ns |
0.670ns |
0.874** |
|
|||||
Anthocyanins |
Fsd Native |
0.785* |
0.732ns |
0.948** |
0.698ns |
|
||||
Fsd Adapted |
-0.833* |
-0.678ns |
-0.320ns |
-0.663ns |
|
|||||
Qta Native |
-0.436ns |
-0.453ns |
-0.713ns |
-0.564ns |
|
|||||
Qta Adapted |
0.581ns |
0.001ns |
0.367ns |
0.794* |
|
|||||
Tannins |
Fsd Native |
0.607ns |
0.577ns |
0.875** |
0.784* |
|
||||
Fsd Adapted |
-0.869* |
-0.581ns |
-0.938** |
-0.515ns |
|
|||||
Qta Native |
-0.668ns |
-0.574ns |
-0.754* |
-0.785* |
|
|||||
Qta Adapted |
0.650ns |
0.373ns |
0.636ns |
0.623ns |
|
|||||
Significant at: *, P<0.05; **, P<0.01 and ns,
P>0.05
Among the plant
secondary metabolites, SSP of FN population indicated positive correlation, FA
population showed negative correlation whereas QN and QA populations indicated
no correlation with shoot dry weight in 2015. RSP of FN population indicated
positive correlation while those of FA, QN and QA indicated no correlations
with root dry weight in 2015. In the year 2016, SSP and RSP of none of the
populations indicated any correlation with shoot and root dry weight except a
positive correlation of RSP with root dry weight. In 2015, among the population
SFLA of FA population only indicated positive correlation with shoot dry
weight. However, for RFLA, the FN population showed positive and FN population
showed negative correlation with root dry weight whereas QN and QA populations
showed no correlation. In 2016, SFLA and RFLA of none of the populations
indicated no associations with shoot and root dry weight except a positive
correlation of RFLA with root dry weight. For SANT, FN and FA indicated
positive and negative correlation with shoot dry weight in 2015 while RFLA
indicated a positive correlation with root dry weight in FN only in 2015.
However, in 2016, SANT and RANT of any populations indicated no association
with shoot and root dry weight except a positive correlation of RANT with root
dry weight. The STAN of FA population exhibited negative while that of QN
showed positive correlation with shoot dry weight in 2015. The RTAN indicated
positive correlation with root dry weight while rest of the populations
indicated no relationship of RTAN with this attribute in 2015. However, in the
year 2016, although shoot dry weight was not correlated with STAN of any
population, the RTAN was positively correlated with root dry weight of FN and
negatively with that of QN (Table 3).
Discussion
The prevailing temperature of an
area majorly determines the success of any species; the species with inherent
ability to synthesize the stress-resistance compounds are on an advantage (Raza
et al. 2019). The statistical
analysis of two years data from all populations from both the locations
revealed that although the months × populations interactions were significant
for all the parameters, the behavior of lemongrass populations at Faisalabad
was relatively less-specific than at Quetta. Lemongrass is a C4
tropical plant species and needs a relatively higher optimal temperature for
growth. A relatively higher shoot and root dry mass of the lemongrass
populations (with C4 photosynthetic pathway) growing in Faisalabad than in
Quetta can be assigned to a more favorable sub-tropical condition of Faisalabad
(Fig. 2). A more specific behavior of the accumulation of metabolites in
lemongrass populations in Faisalabad and Quetta can be attributed to
differences in the temperate semi-arid type climate of Quetta and sub-tropical
climate of Faisalabad.
De novo synthesis of primary and secondary phytochemicals is
important enabling the plants to respond successfully to varied environmental
conditions (Murakeözy et al. 2003; Wahid 2007; Moradi 2016). The major biological roles
of primary metabolites are to act as osmoprotectants
and maintain the cytoplasmic water balance to sustain cell life, which is
pivotal to withstand suboptimal conditions (Papageorgiou
and Murata 1995; Slama et al. 2015). Their role is perceivable since under adverse
conditions, the ensured availability of water is pre-requisite for hydration
and sustained functioning of cytoplasmic and organelle membranes (Slama et al.
2015). Among the major osmoprotectants, low molecular
weight sugars are accumulated in a major bulk, while the accumulation of FP and
GB in the shoot and root specifically takes place under the conditions of
drought, salinity and high temperature (Wahid 2007; Nahar et al. 2016). The results of the current study on the shoot and
root accumulation of soluble sugars, total free amino acids, free proline, and glycinebetaine revealed that native Quetta population
adapted in Faisalabad and Faisalabad population adapted in Quetta displayed a
greater accumulation of all these primary metabolites in both shoot and root of
lemongrass (Fig. 3) when the temperature was high enough in Faisalabad and
chilling to freezing in Quetta (Fig. 1). The trend was similar in both
experimental years with some exceptional fluctuations. These results,
therefore, clearly showed that, as already reported, enhanced free proline and
glycine betaine accumulation help the plants to withstand under environmental
adversaries such as water stress (Yamada et
al. 2005), heat stress (Wahid 2007) and heavy metal stress (Roy and Bera 2003). The lemongrass was able to sustain growth under
prevailing sub- or supra-optimal conditions with the adjustment of primary
metabolites which acted as cytosolutes in shoot and
root. Furthermore, the rate of primary metabolites accumulation was similar
during both the years.
Critical perusal of
the results revealed that the levels of the primary metabolites were especially
higher in the shoot and lesser in the root. This substantiated their osmoprotective role in the sustained growth of all
populations under relatively suboptimal conditions (Hare et al. 1998; Chalker-Scott 1999; Wahid 2007). Under heat stress,
manifold increase in free proline and soluble sugars contents was reported in Cicer arietinum (Khetarpal et al. 2009; Arunkumar
et al. 2012). Smallwood and Bowles (2002) reported that during cold acclimation, primary
metabolites such as proline and sugars
accumulate in high amounts in different plants. It is also important to
notice that, although in lower amounts, the accumulation of all these primary
metabolites was observed in the root from the native or adapted populations in
both locations (Fig. 3). This indicated that after the perception of the stress
signal, the roots also synthesized such important primary metabolite, which
sustained the root functions concerned with the absorption of water and
nutrients. The stress induced modulations in the root temperature are
considered of great significance in stress tolerance (Koevoets
et al. 2016). The trend of
accumulation of primary metabolites revealed that irrespective of their types,
their levels declined when the favorable conditions prevailed. This indicated
that their accumulation is only prompted once the cell perceives signals
regarding a change in the ambient environment (Ramakrishna and Ravishankar
2011; Shaukat et al. 2018). As
reported above, the production of reactive oxygen species (ROS) is the initial
cellular response, which damages the cellular membranes (Wang et al. 2006; Königshofer et al. 2008; Shaukat et al. 2018a). So, protection from ROS damage is an important
cellular strategy to withstand stress conditions.
In the current
experiments, it was noted that during the summer months in Faisalabad and in
the winter months in Quetta, the concentration of studied secondary metabolites
was higher, which indicated their specific role in tolerance to adverse
conditions of high temperature (Faisalabad) and chilling (Quetta) in the
adapted and non-adapted populations (Fig. 4). Increased phenolics accumulation
during the months of Jul to Sep and reduced during May was reported in Camellia sinensis (Anesini et al. 2008).
However, Garmesh (2005) reported that chilling stress and plant maturity increased the
concentration of flavonoids and phenolics during the winter months in barley (Hordeum vulgare L.). A highest accumulation of
phenolics was observed during Nov in Glycyrriza
glabra (Aires et al. 2011). Anthocyanin and phenolic
content in blueberries (Cyanococcus
sp.) were found to be significantly affected with maturity; however, different
locations did not affect its accumulation (Prior et al. 1998), as also noted here. Anthocyanins entail an essential
role in the adaptability of plants to environmental stresses by acting as UV
screen and having an osmoregulatory role (Chalker-Scott 1999; Wahid 2007).
The secondary
metabolites have more of the defensive roles against environmental
perturbations by acting as phytoalexins (Moradi 2016; Yang et al. 2018). The accumulation of secondary metabolites with the
incidence of stress conditions is a slow adaptive strategy of lemongrass, which
appeared to act as a second line of defense to a new location. The results
revealed that there was a greater accumulation of soluble phenolics,
anthocyanins, and flavonoids, while tannins were accumulated to a lesser
extent, both in the shoot and root (Fig. 4). The compounds except tannins are
usually found in the soluble phase; therefore, an increase in their
concentration under more adverse climatic conditions is known (Wahid 2007; Tiku 2020). Soluble
phenolics act as non-enzymatic antioxidant due to having a phenol ring in their
structure, which confers on them important physiological properties (Van Sumere 1989). Anthocyanins act as UV-screens when they
accumulate in the epidermal cells and protect the underlying more
physiologically important mesophyll tissues from the damaging effects of
harmful, especially UV, radiations (Chalker-Scott 1999; Moradi 2016). Likewise,
flavonoids are also soluble in nature and act as antioxidants (Agati et al. 2007) and protect the cytoplasmic
membranes from the adverse effects of stressful conditions (Winkel-Shirley 2002). Tannins are of two types;
condensed and hydrolysable. The condensed tannins are not much important
physiologically because of being complex and insoluble, but hydrolysable
tannins play an important role in the plant growth and development under
adverse conditions (Tiku 2020). In this study we
noted that the accumulation of tannins was greater during the summer months in
the FA lemongrass populations and i the winter season in QA population (Fig.
4). These findings again speak of the role of these metabolites in tolerance to
relatively sub-optimal environmental conditions.
The establishment of
correlations of maximum and minimum temperatures with the shoot and root
accumulation of primary (soluble sugars, total free amino acids, free proline
and glycine betaine) and secondary (soluble phenolics, flavonoids, anthocyanins
and tannins) metabolites was specific to locations. Here the secondary
metabolites were relatively more closely associated to the minimum and maximum
temperature especially in QN and FA populations suggesting their perceived
defense role in abiotic stress tolerance (Chalker-Scott 1999; Wahid 2007; Isah 2019) by acting as phytoalexins (Yang et al. 2018). These findings further
revealed that the swapping had little effect on changing the inherent tendency
of the populations to accumulate metabolites in response to temperature
fluctuations, although the swapped populations tended to show the similar
metabolites accumulation patterns to their native counterparts (Table 2).
In addition to the
specific accumulation pattern of metabolites in response to prevailing
temperatures, significant correlations of metabolites levels were detected with
shoot and root dry weight of native and swapped populations (Table 3). The role
of metabolites accumulation in improved growth and performance of plants by
improving water status and reduced ROS production under relatively subversive
conditions has been documented (Arbona et al. 2013; Isah
2019). As the results revealed, secondary metabolites showed tighter
associations than the primary ones thus substantiating their greater role as
defense arsenal in enabling the native and swapped (adapted) populations in
their original or new locations, although the swapped populations behaved alike
their native counterparts.
Conclusion
As revealed from their
correlation drawn with maximum and minimum temperatures and shoot and root dry
weight, the roles of both the primary and secondary metabolites were devoted in
adapting the swapped population to new locations mainly by acting as
phytoalexins. Primary metabolites played a major role in adjusting the swapped
populations to a new environment, primarily by improved cellular water balance,
which is pivotal under all conditions. The changes in secondary metabolites
were not much different during both the years. They indicated delayed but
consistent accumulation although their concentration varied greatly from
metabolite to metabolite and population to population. Nonetheless, the
secondary metabolites improved tolerance to suboptimal conditions and appeared
to support later growth of the respective lemongrass populations adapting to
new locations for a longer time.
Acknowledgments
This paper is part of PhD thesis of first author, who is thankful to
the Higher Education Commission of Pakistan, Islamabad for financial support
under 5000 indigenous PhD program.
Author Contributions
KS and AW designed the
experiment; NZ helped in data analysis and preparation of initial draft and
SMAB finalized the script for submission
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