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Biological Control 164 (2021) 104775
Available online 1 October 2021
1049-9644/© 2021 Elsevier Inc. All rights reserved.
Biological management of selected weeds of wheat through co-application
of allelopathic rhizobacteria and sorghum extract
Taqi Raza
a
,
b
,
*
, Muhammad Yahya Khan
a
,
c
,
*
, Sajid Mahmood Nadeem
a
, Shakeel Imran
a
,
Kashif Nazir Qureshi
a
, Muhammad Naeem Mushtaq
a
, Muhammad Sohaib
d
,
Achim Schmalenberger
e
, Neal Samuel Eash
f
a
University of Agriculture, Faisalabad, Sub-Campus Burewala, Vehari 61010, Pakistan
b
Land Resources Research Institute, National Agricultural Research Centre, Islamabad, Pakistan
c
Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
d
Department of Soil Science, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia
e
Department of Biological Sciences, Faculty of Science and Engineering, University of Limerick, Limerick, Ireland
f
Department of Biosystems Engineering & Soil Science, University of Tennessee, USA
HIGHLIGHTS GRAPHICAL ABSTRACT
•Unravelling the role of microorganism
and allelopathic extract for weeds
management.
•Combined application of bacteria and
allelopathic extract reduced the weed
density.
•Highlighted the economic tness of
weed management by biological means.
•Recent developments in bacteria and
allelopathy interactions for better crop
yield.
ARTICLE INFO
Keywords:
Biological management of weeds
Bacterial consortium
Sorghum allelopathic extract
Wheat production
ABSTRACT
Traditional methods of weeds management have caused serious environmental and health concerns. Therefore,
development of alternate strategies for effective management of weeds is becoming indispensable for sustainable
agriculture. In this study, the comparative effectiveness of chemical and bio-herbicides for the management of
weeds in wheat has been assessed under laboratory and eld conditions. Two effective allelopathic rhizobacteria
(6 K and 6) were selected from initial screening experiment which had abilities to suppress the growth of se-
lective weeds as well as had potential to improve the growth of wheat. Based on 16S ribosomal RNA (16S rRNA)
gene sequencing, the selected allelopathic rhizobacteria were identied as Pseudomonas uorescens strain 6 K and
Bacillus sp. strain 6. Further, sorghum allelopathic water extract was also used in combination with selected
allelopathic rhizobacteria as a bio-herbicide. Five treatments used for the laboratory and eld experiments were
control (T1: without herbicide), chemical herbicide (T2: mesosulfuron methyl +idosulfuron methyl; Atlantis®
6WG), sorghum allelopathic extract (T3), consortium of two different allelopathic rhizobacteria (T4) and com-
bined application of allelopathic extract of sorghum and consortium of two allelopathic rhizobacteria (T5 =T3
* Corresponding authors at: University of Agriculture, Faisalabad, Sub-Campus Burewala, Vehari 61010, Pakistan.
E-mail addresses: taqiraza85@gmail.com (T. Raza), yahya.khan@uaf.edu.pk (M. Yahya Khan).
Contents lists available at ScienceDirect
Biological Control
journal homepage: www.elsevier.com/locate/ybcon
https://doi.org/10.1016/j.biocontrol.2021.104775
Received 25 April 2021; Received in revised form 14 September 2021; Accepted 28 September 2021
Biological Control 164 (2021) 104775
2
+T4). Results of laboratory experiment showed that T5 signicantly suppressed the seed germination percentage
of four selected weeds i.e., Anagallis arvensis L., Phalaris minor Retz., Cynodon dactylon L. and Melilotus indicus L.
and the same treatment (T5) also signicantly improved seed germination of wheat as compared to all other
treatments. Further evaluation under eld condition showed that T5 signicantly decreased the weed density
and total weed biomass at 15, 30 and 45 days after sowing (DAS) of all weeds as compared to T1 (control). Field
trial results also indicated that T5 signicantly increased the wheat growth traits including the biological yield
(73%) and grain yield (53%) as compared to T1. Likewise, the economic analysis revealed that T5 improved the
net benets with a higher marginal rate of return than all other treatments. Our ndings indicated that combined
application of allelopathic rhizobacterial consortium and allelopathic extract of sorghum remained more effec-
tive for controlling weeds and improving the growth and yield traits of wheat as compared to their sole appli-
cation. Therefore, co-application of allelopathic rhizobacterial consortium and sorghum allelopathic water
extract could offer an economically viable lever for the biological management of weeds of wheat for sustainable
production.
1. Introduction
Economically, wheat (Triticum aestivum L.) is recognized as one of
the important cereal crops. It is considered as a major source of proteins
and carbohydrates for humans and animals (Biel et al., 2020). Wheat
and wheat related activities largely generate income and jobs, especially
among the rural farming communities (Quinn and Carlisle, 2019).
Globally, the total wheat production was 765 million metric tons from
2019 to 2020 (FAOSTAT, 2020/21). In Pakistan, the area under wheat
cultivation in 2020 was 9.2 million hectares with an annual production
of 27 million tons (FAOSTAT, 2020/21). While wheat production is far
below the potential capabilities of the country. However, attempts to
increase per acre yield has received considerable attention from re-
searchers (Jabran et al., 2011). Yet, the average yield of wheat is very
low (Afelt et al., 2018). Several constraints are associated with low crop
yield (Khan, 2014), nevertheless weed infestation is the key threat to
production and sustainability of the wheat in Pakistan (Khan, 2014;
Shahzad et al., 2016). Weeds compete with wheat for soil nutrients,
light, water and they have been attributed to wheat yield reduction of
about 25–40%, with an annual estimated loss of more than 10 billion
Pakistani rupees (Ahmed et al., 2020; Fahad et al., 2013). Weed infes-
tation imposes 3 to 4 times more uptake of nitrogen (N), potassium (K)
and magnesium (Mg) than wheat crop (Reddy et al., 2003). Weeds also
reduce the grain quality that leads to poor marketability of the main
crop (Guo et al., 2017). Thus, to condense the inconvenience due to
weeds and to increase crop production, it is essential to control and
manage weed infestations (Bromilow, 2004). Agro-chemicals (herbi-
cides and pesticides) have been widely used in Pakistan to control weeds
in several agroecosystem, and it has been predicted that pesticide use
will be increased by 8% from 2019 to 2024 (Farooq et al., 2020). The
cost associated with the chemicals used to control weeds has been
estimated about 349.5 million dollars in 2024 (Farooq et al., 2020). On
the other side, improper and over application of chemical herbicides has
led to serious health and environmental problems (Jabran et al., 2011).
In contrast, low dosage has been attributed to ineffectiveness of herbi-
cides for weed control (Lawrie et al., 2000). Correspondingly, many
weeds which were initially susceptible to herbicides have developed
resistance by changing genetic makeup (Weaver et al., 2016). Therefore,
it has been become necessary to develop methods those could not only
reduce the cost of production but would also improve the growth and
yield of wheat and should be ecologically friendly. The use of growth
suppressing, and growth-promoting activities of plant extracts and
phytotoxic microorganisms (bacteria and fungi) is an ecofriendly and
cost-effective strategy to control weeds (Abbas et al., 2020). Further, it
had also been useful in improving crop growth and yield (Kremer,
2006). Therefore, biological means are becoming the best alternative
ways to control the density of weeds within safer limits (Abbas et al.,
2020). Similarly, allelopathic plants including sorghum release toxic
chemicals (also known as allelochemicals) are used in controlling the
growth rate of specic weeds during the growing cycle of the crop
(Harker and O’Donovan, 2013). Use of allelopathy is a natural and eco-
friendly approach for the control of weeds and ultimately growth and
yield of the crop increased (Mushtaq et al., 2020a). A single spray of
sorghum allelopathic solution suppressed 30–40% of the weeds in wheat
and increased 14% of yield in wheat (Cheng and Cheng, 2015). Thus,
replacing conventional weeds control methods such as use of synthetic
agrochemical with biological methods is a potentially viable solution. It
is also ecofriendly and cost-effective method to achieve objectives of
sustainable agriculture. Rhizobacteria those have inhibitory effect on
the plant growth had been explored and term as allelopathic rhizobac-
teria (Majeed et al., 2018). Allelopathic rhizobacteria, a group of bac-
teria releases phytotoxic metabolic compounds in the rhizosphere that
suppress weeds growth (Kremer, 2006). These bacteria use a host spe-
cic as well as a non-host specic ways to suppress the plant growth.
Such type of allelopathic rhizobacteria exist throughout the life cycle of
weeds in the rhizosphere and impart a stress consistently on the growth
of plant (Kremer, 2006). Ultimately, render the growth of weeds and
unable them to compete with the crop plants. They also produce
different plant-growth regulators (Sarwar and Kremer, 1995) and
phytotoxic metabolites such as hydrogen cyanides (Zeller et al., 2007)
which may suppress the germination as well as the growth of the weedy
plants. Additionally, they may secret lytic agent (Kremer, 2007), anti-
biotics (Compant et al., 2005) and enzymes that degrade the cell
membrane and cell wall of plant cell (Trognitz et al., 2016). In result,
plant susceptibility to diseases and pathogen attack increased (Fre-
drickson and Elliott, 1985). Further, selectivity of allelopathic rhizo-
bacteria to kill the weed plants might be due to competitive root
colonization with non-host plant (Kennedy et al., 2001). The root
colonization of these rhizobacteria may be partial or optimal that
modify or reduce the formation of phytotoxic substances by altering the
availability of required substrates those are essential for phytotoxic
production (Saharan and Nehra, 2011). Similarly, plant species have
differential sensitivity to phytotoxic substance produced by the allelo-
pathic rhizobacteria (Owen and Zdor, 2001). These diverse character-
istics of allelopathic rhizobacteria give a direction to use bacteria for
biological control of pathogens and infestations of weeds in crops
(Flores-Vargas and O’Hara, 2006). Previously, these allelopathic rhizo-
bacteria were evaluated for their potential to produce phytotoxic sub-
stances as well as their supporting effects on the growth of wheat and
tested for the suppression of detritus weeds of wheat (Abbas et al.,
2017).
Many species of microorganism such as bacterial strains have been
isolated that have the potential to inhibit the germination and growth of
plants nonetheless their potential remained very limited due to unfa-
vorable conditions such as nutrient deciency (Li et al., 2003). Nutrients
are one of the key factors which play very important role in proper
functioning of such microorganism. Allelopathic extract of plants is
composed of many types of metabolites and other compounds ( ˙
Zak and
Kosakowska, 2016) which could work as growth regulator or source of
nutrients in very dilute forms (Li et al., 2020). Bacteria can utilize the
water extract of sorghum as a source of nutrients. Combined use of
bacteria and allelopathic water extract may work synergistically to
T. Raza et al.
Biological Control 164 (2021) 104775
3
inhibit the growth of weeds because their individual use has been
proved effective for weed management (Cipollini et al., 2012; Mishra
et al., 2013) but with limited success. Keeping in view these properties of
sorghum allelopathic water extract (Naeem et al., 2018) and role of
allelopathic rhizosphere bacteria (Abbas et al., 2017), application of
bacteria in combination with water extract of allelopathic plants could
offer more sustainable strategy to suppress the growth of weeds.
Moreover, consortia of bacteria have the ability to increase the soil
fertility and to promote the growth of wheat crop (Banowetz et al.,
2008). Therefore, the current study was carried out to explore syner-
gistic relationship between selective allelopathic rhizobacteria and
allelopathic water extract of sorghum. Furthermore, this relationship
was evaluated for biological control for selective but not inclusive weeds
of wheat and its effectiveness for enhancing the growth and yield of
wheat.
2. Materials and methods
This study was planned to assess the comparative effectiveness of
chemical and bio-herbicide for the management of weeds in wheat
under laboratory and eld conditions. A series of experiments was
conducted by following the standard procedures given below. Galaxy-
2013, a government approved variety of wheat was used as a test crop
throughout this study.
2.1. Isolation, characterization, selection, and identication of
allelopathic rhizobacteria
Rhizobacteria were isolated by following the standard procedure of
dilution plate technique from the rhizosphere of wheat and different
weeds (samples were collected from wheat crop grown in different
elds) as described by Abbas et al., (2020). Isolated rhizobacteria were
initially screened by using the standard procedures for their abilities to
produce hydrogen cyanide (Schwyn and Neilands, 1987), indole acetic
acidic (IAA; Sarwar and Kremer, 1995) and exopolysaccharides (Nic-
olaus et al., 1999). Selective isolates were further tested for growth
promotion or inhibition of wheat and selected four weeds (Anagallis
arvensis L., Phalaris minor Retz., Cynodon dactylon L. and Melilotus indicus
L) by growing on agar plates supplement with half strength Hoagland’s
solution. Seed germination and growth (shoot length, root length and
plant dry biomass) of seedlings were observed after 14 days of germi-
nation on agar medium (data not given). One the basis of extensive
screening in laboratory experiments, two rhizobacterial isolates (6 and
6 K) were selected for further evaluation in combination with sorghum
allelopathic water extract. These selected rhizobacterial isolates were
also tested for different properties by following standard techniques like
motility of bacteria (Prescott et al., 1993), siderophores production
(Schwyn and Neilands, 1987), Gram staining (Prescott et al., 1993),
oxidase activity (Steel, 1961) and catalase (MacFaddin, 2000). Further,
method described by Simons et al., 1996 was adopted for observing the
abilities of selected rhizobacterial isolates (6 and 6 K) to colonize the
root system of wheat and selected weeds. Pure colonies of selected
rhizobacterial isolates (6 and 6 K) were sent to Macrogen Inc., Korea for
sequencing of 16S ribosomal RNA (16S rRNA) gene. The sequences of
16S rRNA gene were compared with the known nucleotide sequences by
using BlastN accessed at: http://www.ncbi.nlm.nih.gov/BLAST for
identication of rhizobacterial isolates.
2.2. Preparation of allelopathic water extract of sorghum
Mature sorghum plants were harvested, shade dried, and cut into 2
cm pieces with the help of a fodder cutter. Pieces of sorghum were
soaked in distilled water (1:10 w/v) by following the procedure adopted
by Cheema and Khaliq (2000) for 24 h (25 ±2 ◦C) to prepare sorghum
water extract. Thereafter, sorghum extract was separated by ltering or
sieving the mixture through 10-and 60-mm sieves. This allelopathic
sorghum water extract was used for further experimentation.
2.3. Treatment plan
Five treatments {T1 (Control), T2 (Chemical Herbicide; meso-
sulfuron methyl +idosulfuron methyl: Atlantis 6WG at recommended
dose), T3 (Extract of Sorghum), T4 (Consortium of rhizobacterial iso-
lates 6 and 6 K) and T5 (Combination of T3 and T4)} were used with
three replications of each treatment. Same treatment plan was adopted
for seed germination experiment under laboratory conditions and for
layout of eld experiment.
2.4. Preparation of bacterial consortium
Selected isolates (6 K and 6) of allelopathic rhizobacteria were
cultured in 1 L Erlenmeyer ask containing King’s B broth media and
incubated at 28 ±2 ◦C for 72 h in an orbital shaking incubator at 120
rpm. Bacterial suspension of about 10
3
CFU mL
−1
for each rhizobacterial
isolates were prepared and 1 mL of that suspension from each rhizo-
bacterial isolate was inoculated in another 500 mL Erlenmeyer ask
containing King’s B broth medium. After 72 h incubation population
density of consortium was checked by measuring optical density (OD) at
λ550 nm with spectrophotometer and population counts were adjusted
≈10
6
CFU mL
−1
.
2.5. Formulation of treatment 5 (T5; mixture of allelopathic
rhizobacterial consortium and allelopathic extract of sorghum)
Bioherbicide (Treatment T5) was formulated by culturing selected
rhizobacterial isolates (6 K and 6) in lter sterilized (0.22 µm) allelo-
pathic water extract of sorghum. Survival of each selected rhizobacte-
rium was measured individually as well as in combination. Both isolates
were found able to grow in sorghum water extract by using it as a nu-
trients’ source. Nutrients play an important role in the potential and
performance of the bacteria either as growth promoting or suppressing
agents. Allelopathic extract of plants is a rich source of metabolites and
other nutrients. Keeping in view these properties of allelochemicals, a
bioherbicide was formulated consisting of a combination of allelopathic
rhizobacterial consortium and allelopathic extract of sorghum. Bacterial
consortium of 10 mL (population counts ≈10
6
CFU mL
−1
) was trans-
ferred into 2.5 L Erlenmeyer ask containing one litter of lter sterilized
(0.22 µm) sorghum allelopathic extract under aseptic environment. This
ask was shifted into an orbital shaker for 24 h for maximum growth of
the bacterial culture. Population counts of bacterial suspension in alle-
lopathic water extract of sorghum were adjusted to ≈10
6
CFU mL
−1
.
This formation was used as T5 in the laboratory experiment (seed
germination test; sprayed at the rate of 1 mL per Petri plate) and also in
the eld experiment at the rate of 307 L ha
−1
.
2.6. Seed germination experiment
Seed germination experiment was carried out in the laboratory to
evaluate the herbicidal and growth promoting effects of treatments on
germination of selected weeds and wheat seeds. Petri plates were
autoclaved, and sterilized lter paper was placed at the bottom of each
Patri plate. Ten seeds of each selected weeds and wheat were separately
placed on the wet lter paper in the Petri plates. Seed germination
experiment was performed by following the complete randomized
design (CRD) with three replications. All the treatments were applied by
spraying a mist on the Petri plates containing weeds and wheat seeds. A
piece of similar size of sterilized lter was placed over the seeds in the
Petri plates and wetted with distilled water to maintain the moisture in
the Petri plates. Petri plates were tagged and placed in the incubator for
testing germination. Petri plates were incubated at 28 ±2 ◦C and
germination percentage was recorded for every 48 h till seven days of
placing seeds in the Petri plates (Khan et al., 2013; Al-Barakah and
T. Raza et al.
Biological Control 164 (2021) 104775
4
Sohaib, 2019).
2.7. Field experiment
The experimental site was plowed, harrowed, leveled, ridged, and
then divided into plots with a net size of 3.96 m ×8.25 m. Distance
between each plot was maintained 0.5 m. The experiment was con-
ducted by following the randomized complete block design (RCBD) with
three replicates. A total of ve treatments {T1 (Control), T2 (Chemical
herbicide; Atlantis at recommended dose), T3 (Extract of sorghum), T4
(Consortium of rhizobacteria isolates 6 and 6 K) and T5 (Combination of
T3 and T4; formulated as per procedure given in section 2.5)} were used.
Wheat seeds were sown in rows at recommended plant to plant distance
with help of manual planter. In T1, normal recommended agronomic
practices were followed except weed management. While in T2, along
with all other recommended agronomic practices, chemical-based her-
bicide (Atlantis®) was used by following the recommendations of its
application. In case of T3, allelopathic extract of sorghum was applied in
two different ways; i) by soaking the wheat seeds in the extract for two
hours and ii) by spraying the extract at 7, 15, 30 and 45 DAS. Similarly,
in case of T4, inoculum of selected allelopathic rhizobacteria was also
applied in two different ways; i) by soaking the wheat seeds in the
inoculum (population counts ≈10
6
CFU mL
−1
) for two hours and ii) by
spraying the inoculum (population counts ≈10
6
CFU mL
−1
) at 7, 15, 30
and 45 DAS. Likewise, T5 was also applied in two different ways as T3
and T4 were applied and in this treatment allelopathic rhizobacteria
were co-cultured in sorghum water extract. Wheat seeds were sown by
soaking in this formulated mixture (population counts ≈10
6
CFU mL
−1
)
for two hours and this mixture was also applied by spraying at 7, 15, 30
and 45 DAS. One liter of extract, inoculum, and suspension of allelo-
pathic rhizobacterial consortium was sprayed in respective desired plots
of T3, T4 and T5 having size of 3.96 m ×8.25 m using hand sprayer (the
corresponding volume for each treatment was 307 L per hectare). All
other agronomic practices were kept same. Four selected weeds- Cat
weed (Anagallis arvensis L.), Wild oat (Phalaris minor Retz.), Bermuda
grass (Cynodon dactylon L.) and Sanji (Melilotus indicus L.) were evalu-
ated in response to above mentioned treatments at 15, 30 and 45 DAS.
Weed density (m
−2
) was observed just before spraying the treatment in
each plot. The assessment of weeds was recorded from each plot by
taking independent sampling from pseudo-replicates through a quad-
rant (made of iron rods) of 1 m
2
. The quadrant was randomly thrown
three times in each plot and observations were taken and averaged to
consider as one replicate. Selected weeds were sampled from each plot
with the help of quadrant and weed density (number of weed plant per
m
2
) was recorded by counting each of selected weed. Entire weed plants
of selected four weeds were uprooted (with some care for minimum
damage to the roots) at soil moisture slightly more than eld capacity
and total biomass (above and below-ground biomass) was taken. The
samples were brought to the laboratory, washed with tap water to
remove adhering soil, shade dried, and total weed biomass (g m
−2
) was
recorded by weighing on electric balance. Further, data regarding
growth and yield contributing attributes of wheat was also recorded at
physiological maturity of wheat. Quadrant of 1 m
2
was randomly
thrown three times in each plot and above ground part of wheat plants in
an area of 1 m
2
were cut and harvested for recording the observations of
the growth and yield contributing attributes of wheat. The observations
of three pseud-replicates of each plot were averaged to consider as one
observation per 1 m
2
. Plant height (cm plant
−1
), number of tillers (m
2
),
spikelet length (cm plant
−1
), number of spikelets per spike, total number
of grains per spike, 1000 grains weight (g), total grain yield (kg ha
−1
),
and biological yield (kg ha
−1
), total dry straw biomass (kg ha
−1
), harvest
index (%) and economic analysis were recorded through standard pro-
cedures. The observations of 1 m
2
were used as a base unit to calculate
per hectare total grain yield, biological yield, and total dry straw
biomass. Harvest index was calculated by the following formula:
Harvest index(%) = Economic yield(kg ha−1)
Biological yield(kg ha−1)×100
2.8. Economic analysis
The aim of economic analysis was to calculate the total traditional
cost of production against input costs of all the treatments. Total cost of
experiment was calculated by economic analysis through determining
the current market prices of variables (Variable cost) and commodities.
Variable cost mean the cost which varied among the treatments
including cost of machinery, labor and purchased inputs etc. (Byerlee
et al., 1988). Similarly, the net benet of each treatment was calculated
through subtracting the total cost of variables from the gross benets of
a treatment. While the MRR (marginal rate of return) was calculated by
dividing the MNB (marginal net benets) by MC (Marginal Cost). The
MRR is expressed by the percentage (%). Similarly, loss of wheat grain
yield was calculated by the following equation:
Yield loss(%) = MY −YT
MY ×100
Where, MY =maximum yield of wheat from treatment and YT =yield
from a particular treatment.
While the marginal rate of return (MRR) was calculated by the
following formula:
2.9. Statistical analysis
Data regarding weed density and total weed biomass were statisti-
cally analyzed with the software R version 4.0.3 (R Core Team, Vienna,
Austria; R Core Team, 2019). Two-way analysis of variance (ANOVA)
was used by following randomized complete block design (RCBD) for
data regarding weed density and total weed biomass at different time
intervals. Data pertaining to seed germination experiment, wheat
growth and yield attributes were analyzed by statistical software (Sta-
tistix 8.1®; Analytical Software, Tallahassee, FL, USA). One-way
ANOVA was used by following a completely randomized design (CRD)
for seed germination experiment. While one-way ANOVA was also
applied with randomized complete block design (RCBD) for wheat data
of eld experiment. Tukey’s honest signicant difference (HSD) test (P
< 0.05) was applied to compare the treatment means. Graphs regarding
weed density and total weed biomass were generating by using software
R version 4.0.3, whereas other graphs were generated by using Microsoft
Excel 2016® (MS Ofce 365, Microsoft Cooperation, USA).
3. Results
In this study, chemical herbicide was compared with various
MRR =Difference between benefits of each treatemnt comapred with control
Difference between costs of each treatemnt comapred with control ×100
T. Raza et al.
Biological Control 164 (2021) 104775
5
biological based herbicides for the management of selected weeds of
wheat. For this purpose, a series of experiments was conducted under
laboratory and eld conditions. The results of these experiments are
given as under.
3.1. Isolation, characterization, screening, root colonization and
identication of allelopathic rhizobacteria
Around 150 rhizobacteria were isolated from rhizosphere of weeds
and wheat. Among them, 58 (38.66%) rhizobacterial isolates were
found able to produce hydrogen cyanide (HCN), auxins as indole acetic
acid (IAA) equivalents and exopolysaccharides. From screening of these
58 rhizobacterial isolates; 20 (34.48%) isolates were recorded as growth
inhibitors of all tested weeds as well as wheat, 10 (17.24%) isolates were
observed as growth promoters for all tested weeds and wheat, 23
(39.65%) isolates were examined as neutral having neither positive nor
negative effect on all tested weeds and wheat. Whilst only 5 (8.6%)
isolates had the ability to suppress the growth of all tested weeds with
growth promoting and/or neutral impact on wheat plants (complete
data not given). From these 5 rhizobacterial isolates, only 2 allelopathic
rhizobacteria (6 K and 6) were selected for further evaluation in labo-
ratory and eld experiment for checking the suppressing impression on
the growth of weeds without being harmful to the wheat. The selected
allelopathic rhizobacteria were further characterized for various traits
(Table 1) and from these, 6 K was computed as Gram negative and 6 as
Gram positive. Both selected allelopathic rhizobacterial isolates were
positive for siderophores production, oxidase activity and for motility
test. Allelopathic rhizobacterium 6 K produced 30.33 ±0.88 µg mL
−1
of
IAA equivalents and rhizobacterium 6 produced 12.67 ±1.20 µg mL
−1
of IAA equivalents. Root colonization potential of allelopathic rhizo-
bacterium 6 was highly variable for different weeds and wheat plants
(Table 1) and it was 10.86 ±0.74 ×10
5
CFU g
−1
for wheat, 12.72 ±
0.99 ×10
5
CFU g
−1
for Anagallis arvensis L., 25.56 ±0.94 ×10
5
CFU g
−1
for Phalaris minor Retz., 16.83 ±0.54 ×10
5
CFU g
−1
for Cynodon
dactylon L. and 14.83 ±0.76 ×10
5
CFU g
−1
for Melilotus indicus L.
Though, root colonization of 6 K was 22.86 ±0.17 ×10
5
CFU g
−1
for
wheat, 7.04 ±0.38 ×10
5
CFU g
−1
for Anagallis arvensis L., 5.56 ±0.9 4
×10
5
CFU g
−1
for Phalaris minor Retz., 06.83 ±0.54 ×10
5
CFU g
−1
for
Cynodon dactylon L. and 04.83 ±0.76 ×10
5
CFU g
−1
for Melilotus
indicus L. Furthermore, selected allelopathic rhizobacteria 6 K and 6
showed more than 98% similarity with Pseudomonas uorescens and
Bacillus sp., respectively, based on 16S ribosomal RNA (16S rRNA) gene
sequencing. Hence, they are identied and named as Pseudomonas u-
orescens strain 6 K and Bacillus sp. strain 6.
3.2. Comparative effect of chemical and bio-herbicides on seed
germination of selected weeds and wheat under laboratory conditions
The effectiveness of treatments on the seed germination of wheat and
weeds is given in the Table 2. The ANOVA showed statically signicant
(p < 0.05) effect of treatments for each of the selected weed and wheat
seeds. Data depicted that highest germination percentage of wheat seed
was recorded in response to T5 (86%) and lowest seed germination was
attributed to T2 (57%). Seed germination of Melilotus indicus L. was
suppressed up to 44% by T5 and T3. In case of Anagallis arvensis L., T5
and T2 were computed statistically at par with each other and showed
maximum reduction in seed germination percentage (up to 45%), and
both treatments were recorded statistically different from T1, T3 and T4.
Seed germination of Cynodon dactylon L. was suppressed by 38% in
response to T5 and it was found statistically signicant as compared to
all other treatments. However, the effect of treatments was noticed
highly variable regarding seed germination of Phalaris minor Retz. as
compared to their effects on other weeds. Maximum suppression in seed
germinations of Phalaris minor Retz. were recorder with T2 and T3 (up to
34%), while 50% seed germination was with the application of T5.
3.3. Bioherbicides decreased the weed density and total weed biomass of
selected weeds of wheat under eld conditions
The comparative effectiveness of bioherbicide treatments and a
chemical herbicide treatment on weeds density of four different selected
weeds at different intervals is given in Fig. 1. Results of two-way ANOVA
indicated that all the treatments applied at certain DAS and their
interaction were statistically signicant, inuencing the weed density of
Anagallis arvensis L., Phalaris minor Retz. and Cynodon dactylon L. except
Melilotus indicus L. where interaction of treatments and DAS was statis-
tically recorded non-signicant (p =0.0546). At 15 DAS, T5 reduced
weed densities of Melilotus indicus L., Anagallis arvensis L., Phalaris minor
Retz. and Cynodon dactylon L. up to 32.44, 64.72, 14.58 and 14.22%,
respectively, compared to T1 (Fig. 1). At 30 DAS, weed densities were
reduced up to 66.90, 73.07, 32.21, and 86.17% of Melilotus indicus L.,
Anagallis arvensis L., Cynodon dactylon L. and Phalaris minor Retz.,
respectively, in response to T5 as compared to T1. At 45 DAS, weed
densities of Melilotus indicus L., Anagallis arvensis L., Phalaris minor Retz.
and Cynodon dactylon L. were reduced up to 76.48, 88.55, 67.37 and
92.69%, respectively, by T5 as compared to T1.
Two-way ANOVA test results showed a highly signicant effect of
treatments (p < 0.001), DAS (p < 0.001) and their interaction (p <
0.001) on the total weed biomass of selected four weeds (Fig. 2). Data
depicted that at 15 DAS, T1, T3, T4 were statistically at par in inu-
encing the total weed biomass, whilst T2 and T5 caused signicant
Table 1
Characterization, root colonization and identication of selected allelopathic
rhizobacteria.
Characteristic Isolate 6 Isolate 6 K
Gram staining +–
Siderophores production + +
Hydrogen cyanide production ++ ++++
Motility + +
Exopolysaccharide production ++ +++++
Oxidase activity + +
Catalase activity – +
IAA production (µg mL
−1
)* 12.67 ±1.20 30.33 ±0.88
Root colonization of wheat (10
5
CFU
g
−1
)*
10.86 ±0.74 22.86 ±0.17
Root colonization of Anagallis arvensis L.
(10
5
CFU g
−1
)*
12.72 ±0.99 07.04 ±0.38
Root colonization of Phalaris minor Retz.
(10
5
CFU g
−1
)*
25.56 ±0.94 05.56 ±0.94
Root colonization of Cynodon dactylon L.
(10
5
CFU g
−1
)*
16.83 ±0.54 06.83 ±0.54
Root colonization of Melilotus indicus L.
(10
5
CFU g
−1
)*
14.83 ±0.76 04.83 ±0.76
Identity based on 16S ribosomal RNA
gene sequencing
Bacillus sp.
strain 6
Pseudomonas uorescens
strain 6 K
*Data are the averages of three
replications ±standard error
Table 2
Comparative impact of chemical and bioherbicide treatments on seed germi-
nation (%) of weeds and wheat.
Treatment Wheat Melilotus
indicus L.
Anagallis
arvensis L.
Cynodon
dactylon L.
Phalaris
minor
Retz.
T1 70C 90 A 90 A 80 A 60B
T2 57 D 67B 45 D 50C 34 D
T3 70C 44C 56C 50C 34 D
T4 78B 67B 67B 63B 84 A
T5 86 A 44C 45 D 38 D 50C
Where, T1 (Control), T2 (Chemical herbicide; Atlantis @ recommended rate), T3
(Extract of sorghum), T4 (Consortium of rhizobacterial isolates 6 and 6 K) and T5
(Combination of T3 and T4). Data are the mean of three replications and means
sharing the same letter(s) in a column do not differ signicantly according to
Tukey’s HSD test (p < 0.05)
T. Raza et al.
Biological Control 164 (2021) 104775
6
reduction in total weed biomass as compared to T1. However, effect of
T2 was observed more prominent and signicant as compared to T5.
Further, at 30 DAS, all the treatments were found signicant in
decreasing the total weed biomass as compared to T1. However, T3 and
T4 were computed statistically at par in their impact, while T2 and T5
were recorded statistically alike. Results showed that at 45 DAS, all the
treatments signicantly decreased the total weed biomass as compared
to T1, though, T2 caused maximum reduction followed by T5, T4 and T3
as compared to T1.
3.4. Impact of bioherbicide treatments on the growth and yield
contributing attributes of wheat
Results of ANOVA given in Table 3 indicated that all the treatments
signicantly inuenced number of tillers (p < 0.001), spike length (p <
0.001), number of grains per spike (p =0.0301), 1000 grain weight (p =
0.0301), total grain yield (p =0.0449), biological yield (p =0.043) and
percent yield loss (p =0.0305). However, the impact of treatments was
recorded insignicant for plant height (p < 0.1079), total dry biomass
(p < 0.1532) and percent harvest index (p < 0.9998). Data regarding
statistically signicant observations was subjected to post-ANOVA
Fig. 1. Comparative effect of chemical
and bioherbicide treatments on the
weed density (number of weed plants
m
−2
) of selected weeds of wheat at 15,
30 and 45 days after sowing (DAS)
under eld conditions. Where, T1 (Con-
trol), T2 (Chemical herbicide; Atlantis @
recommended rate), T3 (Extract of sor-
ghum), T4 (Consortium of rhizobacterial
isolates 6 and 6 K) and T5 (Combination
of T3 and T4). Boxplots show the third
quartile and rst quartile (box edges),
median (middle lines are given for n =3
replicates based on independent eld
plots) and whiskers indicate the inter-
quartile range of the data. p value
resulting from Two-way ANOVA of
treatments, DAS, and interaction of
treatment ×DAS are also given for weed
density of each weed plant.
Fig. 2. Comparative effect of chemical
and bioherbicide treatments on the total
weed biomass (g m
−2
) of selected weeds
of wheat at 15, 30 and 45 days after
sowing (DAS) under eld conditions.
Where, T1 (Control), T2 (Chemical her-
bicide; Atlantis @ recommended rate),
T3 (Extract of sorghum), T4 (Con-
sortium of rhizobacterial isolates 6 and
6 K) and T5 (Combination of T3 and T4).
Boxplots show the third quartile and
rst quartile (box edges), median (mid-
dle lines are given for n =3 replicates
based on independent eld plots) and
whiskers indicate the interquartile range
of the data. p value resulting from Two-
way ANOVA of treatments, DAS, and
interaction of treatment ×DAS are also
given for total weed biomass of weeds.
T. Raza et al.
Biological Control 164 (2021) 104775
7
analysis by applying the Tukey’s honest signicant difference (HSD) test
(p < 0.05) to compare the means.
Maximum spike length (14.1 cm) was linked with T5 shown in
Table 4. However, the minimum (7.8 cm) spike length was recorded
with control treatment (T1). T5 increased up to 51% spike length over
T1. Number of tillers in each plot was counted by 1 m
2
quadrant. Result
regarding the number of tillers indicated that it varied statistically
among the different treatments (Table 4). Their number was signi-
cantly inuenced by the application of treatments and highest numbers
of tillers were recorded in T2 followed by T5, T3 and T4, respectively,
over control (T1). However, the number of tillers in T2 and T5 were
observed statistically at par. Maximum increase in the number of tillers
was examined up to 54% (over T1) in T2 and it was 43% higher in T5
over the control (T1). Date regarding the number of grains per spike are
presented in Table 4. It revealed that the number of grains per spike
were signicantly inuenced by the application of treatments.
Maximum increase in number of grains per spike (up to 20%) was
recorded in T5 over the control (T1). Results pertaining to 1000 grain
weight of wheat are given in Table 4. Results showed that 1000 grains
weight was positively affected by the application of treatments. How-
ever, T5 signicantly enriched the 1000 grains weight of wheat as
compared to T1. The maximum increased in the 1000 grains weight was
recorded up to 56% in T5 as compared to control (T1). Data in Fig. 3
indicated that the total grain yield was also signicantly improved by
the bio-herbicidal treatments. Overall, all the treatments increased the
total grain yield (kg ha
−1
) over control (T1), however, the highest total
grain yield (up to 53% more than T1) was recorded in T5 which was
statically signicant from T1 and T2, but remained at par with T3 and
T4. The highest biological yield was recorded (up to 73% higher than
T1) in T5 (Fig. 4), nonetheless remained statistically at par with all other
treatments.
3.5. Comparative monetary returns of bio-herbicide over other treatments
While comparing the comparative loss of wheat yield due to weeds
infestation under different treatment applications, the lowest yield loss
(18.9%) was recorded in T5 as compared to the other treatments
(Table 5). Similarly, the highest loss in yield (77%) was examined in
control (T1). Economic analysis of wheat production with application of
treatments showed that all the treatments enhanced the net benets
over control (T1). The maximum variable cost was Rs. 3685 ha
−1
, which
was calculated in T2 due to the higher price of chemical pesticides
(Atlantis). Similarly, the maximum net benet was calculated from
treatment T5 (Rs. 840047 ha
−1
) having the cost of variables Rs. 1225
ha
−1
which was lesser than T2 and T4 treatments. Comparative mar-
ginal analysis of wheat production under different treatments showed
Table 3
Analysis of variance (NOVA) of the growth and yield contributing attributes of
wheat under the inuence of chemical and bioherbicide treatments.
Name of parameters DF SS f value p value
Plant height (cm plant
−1
) 4 467.388 2.7 0.1079
Number of tillers (m
2
) 4 11997.7 30.21 0.0001
Spike length (cm) 4 18.6427 16.58 0.0006
Number of grains per spike 4 251.394 4.71 0.0301
1000 grains weigh (g) 4 146.491 4.71 0.0301
Total grain yield (kg h
−1
) 4 26136.5 4.01 0.0449
Biological yield (kg h
−1
) 4 152,793 4.08 0.0430
Total dry straw biomass (kg h
−1
) 4 52,658 2.25 0.1532
Harvest index (%) 4 1.598 0.01 0.9998
Yield loss (%) 4 9101.9 4.68 0.0305
Where DF is the degree of freedom and SS is the sum of square. p value more than 0.05
was considered as non-signicant difference of treatment
Table 4
Comparative effect of chemical and bioherbicide treatments on the number of
tillers (m
2
), 1000 grains weight (g), spike length (cm plant
−1
)) and number of
grains per spike of wheat under eld conditions.
Treatments No. of
tillers
1000 grains
weight
Spike
length
No. of grains per
spike
T1 143.7d 46.7b 7.5b 51.1b
T2 221.7a 51.9ab 7.5b 58.0ab
T3 177.3bc 53.3ab 8.2b 59.8ab
T4 156.0 cd 53.6ab 8.0b 50.2ab
T5 199.0ab 56.1a 10.5a 63.5a
Where T1 (Control), T2 (Chemical herbicide; Atlantis @ recommended rate), T3
(Extract of sorghum), T4 (Consortium of rhizobacterial isolates 6 and 6 K) and T5
(Combination of T3 and T4). Data are the mean of three replications and means
sharing the same letter(s) in a column do not differ signicantly according to
Tukey’s HSD test (p < 0.05)
b
b
ab ab
a
0
5000
10000
15000
20000
25000
30000
T1 T2 T3 T4 T5
Grain yield (kg ha-1)
Treatments
Fig. 3. Comparative effect of chemical and bioherbicide treatments on the
grain yield (kg ha
−1
) of wheat under eld conditions. Where, T1 (Control), T2
(Chemical herbicide; Atlantis @ recommended rate), T3 (Extract of sorghum),
T4 (Consortium of rhizobacterial isolates 6 and 6 K) and T5 (Combination of T3
and T4). Bars represented means of three (n =3) replicates and bars sharing the
same letter(s) do not differ signicantly according to Tukey’s HSD test (p
< 0.05).
b
ab
ab
ab
a
0
100
200
300
400
500
600
700
800
T1 T2 T3 T4 T5
ah gk( dleiy lacigoloiB -1)
Treatments
Fig. 4. Comparative effect of chemical and bioherbicide treatments on the
biological yield (kg ha
−1
) of wheat under eld conditions. Where, T1 (Control),
T2 (Chemical herbicide; Atlantis @ recommended rate), T3 (Extract of sor-
ghum), T4 (Consortium of rhizobacterial isolates 6 and 6 K) and T5 (Combi-
nation of T3 and T4). Bars represented means of three (n =3) replicates and
bars sharing the same letter(s) do not differ signicantly according to Tukey’s
HSD test (p < 0.05).
T. Raza et al.
Biological Control 164 (2021) 104775
8
that T5 was the best and most economical treatment out of all other
treatments with a maximum marginal rate of return (MRR) of 4226.9 %
(Table 6).
4. Discussion
Wheat is the leading grain crop in which yield is often signicantly
reduced by weed infestation and traditionally coped with chemical
herbicides (Qari and Shehawy, 2020; Jahan et al., 2020). Chemical
based herbicides are causing serious problems related to health and
environment (Hasanuzzaman et al., 2020). Therefore, it is needed to
introduce the methods which not only reduce the cost of production but
also prove to be eco-friendly (Mehdizadeh and Mushtaq, 2020). There-
fore, our study is focused on the use of biological ways for the man-
agement of weeds and their potential for improving the growth and yield
of wheat. Hence, efcient allelopathic rhizobacteria and sorghum alle-
lopathic extract were comparatively evaluated as alternative to
chemical-based herbicide for management of selected weeds of wheat.
Melilotus indicus L., Anagallis arvensis L., Phalaris minor Retz. and Cyno-
don dactylon L. are amongst the most dominant weeds of wheat (Pala
et al., 2020; Usman et al., 2020). Biological means such as microor-
ganism mainly use of bacteria and allelopathic extract of plants which
had already been proved as efcient ways (Zuo et al., 2014; Farooq
et al., 2020) are amongst the best strategies to control the weeds. In this
study, several rhizobacteria were isolated from rhizosphere of weeds
and wheat plants. After screening in a series of tests in the laboratory,
two efcient allelopathic rhizobacteria which were effective for sup-
pressing the growth of selective weeds were selected for controlling
weeds of wheat. Further, rhizobacteria were found capable to improve
the seed germination of wheat in laboratory experiment. Kremer (2006)
reported that those allelopathic rhizobacteria inhibited the growth of
weed plants without negatively affecting crop plants can be considered
candidates for further testing as potential biological control agents.
These allelopathic rhizobacteria were effective against the Anagallis
arvensis L., Phalaris minor Retz., Cynodon dactylon L. and Melilotus indi-
cus L. It was observed in seed germination experiment, the roots of
seedlings were signicantly affected as compared to the shoot of
germinated seedlings. Therefore, it can be considered that roots were the
important basepoint to assess the phytotoxic effect of these rhizo-
bacterial strains. Our ndings are in line with the work of Horwath et al.
(1998), who reported that at germination stage, both root and shoot
parameters are important to determine the performance of deleterious
bacteria.
Moreover, efcacy of these allelopathic rhizobacteria was further
increased when they were used in combination with allelopathic sor-
ghum extract which was used as nutrition medium by the same. Appli-
cation of allelopathic rhizobacteria with allelopathic sorghum extract
not only resulted in suppression of weeds in laboratory and eld ex-
periments nevertheless their combination also potentially improved the
growth and yield of wheat under eld conditions. Weed density and
total weed biomass of selected four weeds was signicantly reduced by
the application allelopathic rhizobacteria in combination with allelo-
pathic sorghum extract at 15, 30, 45 DAS. However, at 45 DAS, the
Atlantis (chemical herbicide) maximally controlled the selected four
weeds and their total biomass as compared to control but results of
bioherbicide treatments were also signicant as compared to control.
Therefore, bioherbicide treatments could be considered as ecofriendly
alternative to chemical herbicides for management of weeds.
Suppression of weeds due to combined application of sorghum
allelopathic extract and allelopathic rhizobacteria indicated that this
mixture exhibited phyto-inhibitory effect on weeds plants besides being
neutral or growth promotor for wheat. Differential response of weed
plants and wheat to inoculated rhizobacteria might be linked to the facts
that various plants may also inuence the competitiveness of inocu-
lating bacteria, either directly by changing root colonization, growth,
and physiology, or indirectly by affecting the indigenous rhizosphere
microora that interact with the introduced isolates (Åstr¨
om and Ger-
hardson, 1989). The density of selected weeds was variable and this
could be associated with the differential genomic makeup or natural
variations in the seed bank of different weeds in the soil (Mushtaq et al.,
2020b; Shahbaz et al., 2018). Allelopathic bacteria use different types of
mechanisms to suppress the growth of plants including the over
Table 5
Economic analysis of the various treatments applied for the comparative evaluation of chemical and bioherbicide treatments for management of weeds of wheat under
eld conditions.
Treatments T1 T2 T3 T4 T5 Remarks
Total grain yield 1641.3 2340.5 2204.9 1864.1 2871.1 kg ha
−1
10 percent less 164.13 234.5 220.49 186.41 287.11 kg ha
−1
Adjusted yield 1477.17 2106 1984.41 1677.69 2583.99 To bring at farmers level
Gross income 48746.61 69,498 65485.53 55363.77 85271.67 Rs.33 kg
−1
Herbicide cost 3000 Atlantis expend per hectare: 3000
Cost of water extracts from sorghum 80 40 Rs. 80/10L
Cost of bacterial consortium 1000 500 Rs. 750 ha
−1
Sprayer rent 60 60 60 60 Rs. 60 spray
-1
Spray application cost 625 625 625 625 Rs.50 per sprayer
Cost that varies 3685 765 1685 1225 Rs. ha
−1
Net benet 48,747 65,813 64,721 53,679 84,047 Rs. ha
−1
Where T1 (Control), T2 (Chemical herbicide; Atlantis @ recommended rate), T3 (Extract of sorghum), T4 (Consortium of rhizobacterial isolates 6 and 6 K) and T5 (Combination of T3
and T4). Average exchange rate of USD in 2018 was121.5744 PKR.
Table 6
Marginal analysis of the various treatments applied for the comparative evaluation of chemical and bioherbicide treatments for management of weeds of wheat under
eld conditions.
Treatments Cost that vary (Rs.) Net benet (Rs.) Marginal cost (Rs.) Marginal net benet (Rs.) Marginal rate of return (%)
T1 0 48,747 ———————— ———————— ————————
T3 765 64,721 765 15,987 2089.8
T5 1225 84,047 460 19,444 4226.9
T4 1685 53,679 460 ———————— ————————
T2 3685 65,813 2027 12,124 52.81
Where, T1 (Control), T2 (Chemical herbicide; Atlantis @ recommended rate), T3 (Extract of sorghum), T4 (Consortium of rhizobacterial isolates 6 and 6 K) and T5 (Combination of T3
and T4). Average exchange rate of USD in 2018 was121.5744 PKR.
T. Raza et al.
Biological Control 164 (2021) 104775
9
production of IAA, hydrogen cyanide, ammonia, toxic volatile com-
pounds, dimethyl disulde and some other growth-inhibiting metabolic
compounds (Egamberdieva, 2009; Kim and Rhee, 2012; Popovic et al.,
2013). Several reports have highlighted the use of these inhibiting
mechanisms of bacteria in agriculture for the suppression of the weed
populations without posing a threat to the environment and human
health (Boyette and Hoagland, 2015). The sorghum allelopathic water
extract contains a sorgoleone as an active compound having herbicidal
properties along with presence of other secondary active metabolites
which had highest phytotoxicity (Barbosa et al., 2001). Further, allelo-
pathic rhizobacterial might use the allelopathic sorghum water as a
nutrients source and it is very likely that synergy between allelopathic
rhizobacteria and allelopathic sorghum extract might resulted in sup-
pression of weeds and improvement in wheat growth.
The growth and yield attributes of wheat were positively affected by
sorghum allelopathic water extract and allelopathic rhizobacteria either
applied alone or in mixture. It is very likely that improvement in the
growth and yield attributes of wheat could be due to less density of
weeds in the eld (Shahbaz et al., 2018) because it was suppressed by
the applied treatments. Similar results were disseminated by Abbas et al.
(2017) who reported that antagonistic bacterial strains inoculation
improved the growth, NPK contents and yield of wheat due to weed
suppression trend of deleterious bacteria. The mixture of allelopathic
rhizobacteria grown in allelopathic sorghum extract possessed both
growth suppressing as well as growth supporting activities that might
had an impact on the growth and development activities of weeds and
wheat plants. The ndings of Sturz and Christie (2003) suggested that a
synergistic relationship exists between the allelochemicals of crops and
bacteria. Therefore, it is depicted that the improvement in growth and
yield attributes of wheat might be due to the existence of this synergistic
relationship (Bhattacharya et al., 2010). The ndings of our study are
also in line with the reports of Banowetz et al (2008) who argued that
promoting effect of bacterial consortium improved the growth, devel-
opment and wheat yield (Banowetz et al., 2008). Similarly, the ndings
of Jabran et al. (2011) support our results who concluded that growth
suppressing activities of allele-chemicals reduced the density of weeds
and improved wheat growth alternatively (Jabran et al., 2011). In
another study, Kumar et al. (2016) mentioned that instead of single use
of bacteria, consortium application of bacteria or with any nutrient’s
amendments, improved the efciency of bacteria. The study further
suggested that the combined application consortium of bacteria with the
nutrient amendment is an acceptable and eco-friendly method to
improve plant performance. Thus, our ndings are also in line with Haq
et al. (2010) who reported that the better management of weeds in
wheat improved the growth attributes and ultimately increased the
grain yield (Haq et al., 2010). Likewise, allelopathic rhizobacteria and
sorghum extract combination produced 72.41% and 27.7% higher net
benets as compared with control (no spray) and recommended dose of
synthetic herbicide, respectively. Moreover, the same allelopathic
combination gave 4226.9% marginal rate of return, while synthetic
herbicide resulted in only 52% marginal rate of return. Higher net
benets and marginal rate of return indicate the economic tness of this
weed management strategy, which is very crucial for its possible wide-
spread use by end-users, the farmers. The recommendation of any weed
management strategy for large-scale adoption by farmers should only be
made after critical economic analysis and cost-effectiveness calculation
(CIMMYT, 1988).
5. Conclusion
It can be concluded from the ndings that combined application of
allelopathic rhizobacteria and sorghum allelopathic extract could be
used as an eco-friendly alternative to chemical herbicides for effective
biological management of weeds as well as improvement of wheat
growth and yield. It might be further established that there could be
existence of synergy between allelopathic rhizobacteria and allelopathic
water extract of sorghum which ultimately may be used for the formu-
lation of bioherbicides with multiple bioactive agents.
CRediT authorship contribution statement
Taqi Raza: Conceptualization, Methodology, Software, Writing –
original draft. Muhammad Yahya Khan: Conceptualization, Supervi-
sion, Writing – original draft. Sajid Mahmood Nadeem: Data curation,
Supervision. Shakeel Imran: Visualization, Investigation. Kashif Nazir
Qureshi: Validation. Muhammad Naeem Mushtaq: Visualization,
Investigation, Writing - review & editing. Muhammad Sohaib: Meth-
odology, review & editing. Achim Schmalenberger: Writing - review &
editing. Neal Samuel Eash: Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
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