Bio-herbicidal potential of extracts from allelopathic plants against the emergence of multi-species weed seed bank in Ghana

Abstract

Background:  Growing public concerns about the health and environmental hazards associated with synthetic herbicides have necessitated the pursuit of sustainable weed management alternatives. Allelopathic plant extracts present a potential solution for environmentally friendly weed control. However, little is known about the bioherbicidal potential of allelopathic plant species native to Ghana.

Objective:  This study screened aqueous and ethanol extracts from allelopathic plants in Ghana, to examine their potential as bio-herbicides for sustainable weed management, and also identified the allelocompounds present.

Method:  Laboratory experiments evaluated 27 plant extracts plus a control using a completely randomized design with three replications. Both aqueous and ethanol extraction methods were applied to different plant organs (leaves, roots, bark, stems). Daily emergence of monocot and dicot weeds was monitored over 21 days and analyzed using Generalized Linear Models with control as reference. Using GC-MS analysis, promising extracts were analyzed for bioactive compounds responsible for herbicidal activity.

Results:  Multiple extracts suppressed (p<0.05) cumulative monocot weed emergence over the 21 days compared to the control. Effective aqueous extracts included Chromolaena odorata leaves (ChLE), Gliricidia sepium bark (GBE), Azadirachta indica leaves (NLE), Moringa oleifera leaves (MLE), among others. Ethanol extracts of Senna siamea bark (CBE), Callophyllum inophyllum leaf (CiLE), and Leucaena leucocephala root (LeRE) demonstrated superior performance with sustained suppression effects. GC-MS analysis identified phenolic compounds, alkaloids, and terpenoids, including I-[-]-4-Hydroxy-1-methylproline, 5-Hydroxypipecolic acid, as major bioactive compounds showing potential for commercial bioherbicide development.

Conclusion:  The study identified plant extracts with bioherbicidal activities, with specific plant-solvent combinations showing potential for commercial bioherbicide development.

Keywords:
Weeds, Weed Management; Bio-Herbicides; Plant Extracts; Allelopathy; Bioactive Compounds

1. Introduction

Management of weeds constitutes a key component of crop production across the globe. This is necessary to minimize the direct and indirect effects of the unwanted plants on the growth, development and yield of the crops (Paul et al., 2024). Weeds are known to compete with crops for nutrients, sunlight, moisture and other resources, thereby impeding their proper development (Pesarakloo et al., 2023). Weeds also serve as alternative hosts to many other pests and diseases of crops and thus act as sources of infestation and/or infection for such pests and diseases (Schwarz et al., 2024). Managing weeds can be very costly, and therefore increases the cost of production especially when they are allowed to grow and seed. Altogether, weeds can cause up to 90 per cent losses in crop production (Pesarakloo et al., 2023; Dolianitis et al., 2025).

Modern-day weed management has focused on the use of compounds that can either kill grown weeds, or suppress the germination and subsequent growth of weeds from the weed seed bank (Islam et al., 2024). These compounds, generally, described as herbicides, act by disrupting essential physiological processes and biochemical pathways in the weeds (Mikulka et al., 2024). They include compounds from various chemical families such as phenolics, terpenoids, flavonoids, and alkaloids (Khamare et al., 2022).

The use of herbicides has contributed immensely to the effective management of weeds, considering their ease of use and timely action (Parven et al., 2025). However, the continuous and widespread use of herbicides is believed to be responsible for major health and environmental challenges, which, in recent times, have raised public concerns. Health effects associated with herbicide exposure may be of short-term or long-term and include toxicity, carcinogenicity, dermatological, gastrointestinal, neurological, respiratory, reproductive, and endocrine effects (Dolianitis et al., 2025). Furthermore, the widespread use of synthetic herbicides has resulted in a substantial reduction of biodiversity (Damour et al., 2023), evolution of herbicide-resistant weed biotypes, with over 500 cases reported globally (Heap, 2024).

This necessitated the development of alternative weed management tools with novel modes of action to address the herbicide resistance challenge through integrated weed management strategies. Allelochemicals are plant-based secondary metabolites that suppress the germination and growth of neighboring plants through biochemical reactions. These compounds include phenolics, flavonoids, and terpenoids. They act by disrupting cellular processes, including photosynthesis, mitochondrial respiration, cell division, and hormone signaling in target plants. Studies have shown that some allelochemicals, such as leptospermone from Callistemon citrinus (bottlebrush), have inspired synthetic herbicides including mesotrione, demonstrating that natural compounds can serve as templates for commercial herbicide development (Islam et al., 2024). Despite the general belief that allelochemicals can contribute to weed management, it is unclear which plant extracts can effectively suppress the growth of major weeds in the study area.

Allelochemicals are of different types and forms and may have selective or broad-spectrum effects on weeds depending on the plant species and the part of the plant used (Khamare et al., 2022; Zhang et al., 2025). Ghana harbors numerous plant species with documented allelopathic properties, including Azadirachta indica (neem), Chromolaena odorata (siam weed), and Moringa oleifera (moringa), used in various agricultural and medicinal applications (Kato-Noguchi et al., 2023; Bilali et al., 2024). However, their specific bioherbicidal efficacy against major weed species remains unknown. Earlier studies have reported the presence of potential allelochemicals in these species, but systematic screening for weed suppression capacity has not been conducted. This study was therefore conducted to identify potential plant extracts that could be used as bio-herbicides. Specifically, we sought to screen extracts from allelopathic plant parts for compounds that can effectively inhibit weeds’ emergence, growth, and multiplication, and to identify the specific chemical compounds responsible for the herbicidal activity in the successful candidates.

This preliminary study aimed to initiate the development of plant-based, environmentally friendly, and yet effective herbicides for managing noxious weeds in crop production in southern Ghana. When completed, the identified extracts will go a long way to help manage weeds effectively in a user and environmentally friendly way and ensure minimized impacts on human health.

2. Materials and Methods

2.1 Study Area and Experimental Period

The study, conducted at the AG Carson Technology Centre of the University of Cape Coast from May 2021 to June 2022, consisted of a series of experiments and, Gas Chromatography Mass Spectrometry (GC/MS) analysis of samples. The study area is situated in the Coastal Savanna region of Ghana which has a bimodal rainfall pattern with mean daily temperatures ranging between 28 °C. and 32 °C. The first experiment was conducted to screen various aqueous and ethanol extracts of locally available allelopathic plants for effective suppression of the germination and subsequent growth of weeds in the weed seed bank.

2.2 Soil Collection and Physicochemical Characterization

Soil for the experiment, obtained from the A.G. Carson Technology Centre of the University of Cape Coast, was first crushed and sieved with a 2 mm sieve in order to eliminate clods that could impede germination of weed seeds whiles allowing the majority of the weed seeds to be retained in the soil sample for the experiment. Samples of the soil were then sent to the laboratory for analysis of the physical and chemical properties. Percentage Nitrogen was determined using the Kjeldahl method (AOAC, 1999a); phosphorus with the Ascorbic acid colorimetric method, while potassium was determined by the flame photometer method (Stewart et al., 1974). The soil pH was determined with the aid of a pH meter after mixing 25 mL of distilled water with 10 g of soil and shaking the mixture on a mechanical shaker for 15 min using standard protocols as described by Rowell (1994). Organic carbon determination was conducted following the procedure suggested by Motsara & Roy, (2008). For physical properties, bulk density, moisture contents and particle sizes were determine using standard protocols described by Rowell (1994).

2.3 Assessment of the Weed Seed Bank

An assessment of the weed seed bank was conducted using the seedling emergence method in the Crop Science Laboratory of the University. Soil samples weighing 100 g were spread onto trays measuring 20 cm in diameter and watered to field capacity on daily basis to avoid drying of the samples. There were three replicates. Emergence of weed seedlings was observed on weekly basis and emerged weed seedlings were identified using a reference key (Akobundu, Agyarkwa, 1987), counted and removed after each observation. Seedlings that could not be identified immediately were transferred to another pot and allowed to grow until they could be identified. Observations ended when seedling emergence came to a halt. Data was collected on the relative abundance of various weed species.

2.4 Selection and Collection of Allelopathic Plant Species

Plant species for this experiment (Table 1) were compiled based on documented allelopathic activity in previous literature and indigenous farmers, local availability and abundance in and around the study region, and traditional or ethnobotanical reports of growth-suppressing effects on neighboring plants. Plant materials were harvested at the vegetative maturity stage from three widely separated locations. The fresh-to-dry weight ratio averaged 4:1 across the plant species. For each plant species, different plant parts (leaves, roots, bark, stems) were harvested based on a literature review suggesting differential accumulation of secondary metabolites in specific organs.

Table 1
List of plant species and their parts used in the extraction

Code	Plant Species	Plant part
CBE	Senna siamea	Bark
ChLE	Chromolaena odorata	Leaf
ChRE	Chromolaena odorata	Root
ChSE	Chromolaena odorata	Stem
CiBE	Callophyllum inophyllum	Bark
CiLE	Callophyllum inophyllum	Leaf
CiRE	Callophyllum inophyllum	Root
CLE	Senna siamea	Leaf
GBE	Gliricidia sepium	Bark
GLE	Gliricidia sepium	Leaf
GRE	Gliricidia sepium	Root
LeLE	Leucaena leucocephala	Leaf
LeRE	Leucaena leucocephala	Root
LeSE	Leucaena leucocephala	Stem
LLE	Lantana camara	Leaf
LRE	Lantana camara	Root
LSE	Lantana camara	Stem
MBE	Moringa oleifera	Bark
MiBE	Magnifera indica	Bark
MiLE	Magnifera indica	Leaf
MiRE	Magnifera indica	Root
MLE	Moringa oleifera	Leaf
NBE	Azadirachta indica	Bark
NLE	Azadirachta indica	Leaf
NRE	Azadirachta indica	Root
TLE	Tectonia grandis	Leaf
TRE	Tectonia grandis	Root
WwBE	Salix babylonica	Bark
WwLE	Salix babylonica	Leaf

2.5 Preparation of Plant Materials and Preparation of Extracts

The samples were bulked and then chopped into smaller pieces to allow for easier processing. They were then dried at room temperature (averaging 29 °C) for seven days and then milled into powdered form with the aid of an industrial blender. The powdered materials were stored in hermetically sealed plastic containers to safeguard their dryness and integrity.

Aqueous extracts were obtained by mixing 100 g of the powdered materials with 400 mL of distilled water to obtain 25% w/v concentrations of the extracts. Each mixture was shaken thoroughly and allowed to settle for 24 h. They were then sieved through cheese cloth to rid off solid particles. The obtained solutions were then filtered through Whatman’s No.1 filter papers Final solutions were duly labelled and stored in the refrigerators to maintain their efficacies before application. Ethanol extracts, on the other hand were obtained by first mixing 10 g of each of the dried powered materials with 100 mL of 96% ethanol to obtain 10% w/v concentrations of the extracts. These were filtered through Whatman’s No.1 filter papers and allowed to extract at 4 °C. The mixture was then dispensed into a crucible and heated at 80 °C to evaporate the ethanol.

2.6 Experimental Design and Application of Extracts

Soil for the experiments was obtained from the A.G. Carson Technology Centre of the University of Cape Coast. Samples were collected using an auger from a field infested with a wide range of both monocot and dicot weeds that had been left to seed over a long period, ensuring a rich weed seed bank. The soil samples were bulked, and portions subjected to physicochemical analysis (Table 2).

Table 2
Physico-chemical properties of soil used for the experiment

Soil property	Value
%Nitrogen	0.09
Phosphorus (μgP/g)	5.86
Potassium (cmol/kg)	0.31
% Organic carbon	0.9
pH	6.2
Bulk density (g/cm3)	1.29
Textural class	Sandy loam

Two separate experiments were setup for the screening of the extracts: one for each of the solvents used for the extraction. Both experiments were laid out in completely randomized design with 28 treatments (extracts from the 27 listed plant materials and a control). The treatments were replicated 3 times to obtain a total of 84 experimental units. Each experimental unit consisted of a 30 cm diameter tray filled with 1.5 kg of soil. The soil volume per tray was approximately 1,200 cm³, and 100 mL of extract was applied per tray, equivalent to approximately 67 mL /kg soil. The soils were watered to field capacity for two days, then 100 mL of the extracts was applied as a single application according to the experimental design. The experimental units were monitored for germination of weeds from the weed seed bank, and data were collected on the cumulative number of seedlings that emerged from the soil. Data collected were analyzed using Generalized Linear Models (GLM) with Poisson link function implemented in GenStat 12.0 Edition (VSN International Ltd). Statistical significance was determined at p ≤ 0.05.

2.7 GC–MS Analysis of Selected Extracts

Following the experiment, seven (7) of the best-performing candidate extracts were prepared for GC/MS analysis. The analysis was conducted using a PerkinElmer GC Clarus 580 Gas Chromatograph connected to a PerkinElmer Mass Spectrometer (Clarus SQ 8 S), equipped with a ZB-5HTMS fused capillary column (30 × 0.25 μm ID × 0.25 μm DF) made of 5% diphenyl and 95% dimethyl polysiloxane. The oven temperature was programmed to start at 40 °C (isothermal for 2 min), then increased at a rate of 10 °C min-1 to 250 °C, followed by a 20 °C min-1 increase to 280 °C, where it was held for 10 min. An electron ionization system with an ionization energy of 70 eV was used for GC-MS detection. Helium gas, which is 99.999% pure, was used as the carrier gas, flowing at a steady rate of 1 mL min-1 with 1 μl of injection. The injector temperature was set to 250 °C, and the ion-source temperature was maintained at 220 °C.

Mass spectra were recorded at 70 e V, with a scan interval of 0.5 seconds and fragments ranging from 50 to 4500 Da. The solvent delay was set from 0 to 3 min, and the total running time for GC/MS was 34.5 min. The mass detector used was Turbo-Mass, and mass spectra and chromatograms were analyzed using Turbo-Mass version 6.1.0 software. Interpretation of the mass spectra from the GC-MS was performed using the National Institute of Standards and Technology (NIST) database, which contains over 62,000 patterns.

3. Results and Discussion

Preliminary tests suggested that the soil used for the experiment was deficient in Nitrogen and Phosphorus (Table 2) and this was in agreement with the assertion by Simperegui et al (2025) that Ghanaian soils are deficient in these nutrients. Common weeds identified from the weed seed bank assessment included monocotyledonous weeds, mainly from the Poaceae family, such as Axonopus compressus, Bracharia lata, Cynodon dactlyon, Eleusine indica and Megathyrsus maximus; and dicotyledonous weeds, mainly from the family Asteraceae, such as Euphorbia heterophylla, Trianthema portulacastrum, Amaranthus spinosus, Ageraturm conyzoides and Lantana camara.

The GLM estimates in Tables 3, 4, 5 and 6 shows that most of the extracts suppressed the emergence and subsequent population growth of the weeds, at some point in time, either early or late or through the three weeks period, confirming their bioherbicidal properties. The suppression was obviously due to the presence of allelopathic compounds which adversely affect physiological processes in other plants and either prevented the germination and subsequent emergence of the weed seeds, or killed young shoots of germinated weeds before or just after they emerged. Such compounds have the potential of been harnessed as pre-emergence organic herbicides.

Table 3
Weekly GLM estimates of the cumulative population of monocot weeds after treatment with aqueous extracts

Parameter	GLM estimates ± standard error
	1 WAA	2WAA	3WAA
Constant	1.74 ± 0.24 **	2.71 ± 0.15 **	3.22 ± 0.12 **
CBE2	-9.40 ± 16.40 NS	-1.03 ± 0.29 **	-0.53 ± 0.19 **
ChLE1	-1.45 ± 0.56 **	-0.81 ± 0.27 **	-0.79 ± 0.21 **
ChRE3	-1.22 ± 0.51 *	-1.41 ± 0.34 **	-1.18 ± 0.24 **
ChSE2	-0.27 ± 0.37 NS	-0.22 ± 0.22 NS	0.10 ± 0.16 NS
CiBE2	-0.27 ± 0.37 NS	-0.02 ± 0.21 NS	0.04 ± 0.16 NS
CiLE1	-1.04 ± 0.48 *	-0.44 ± 0.24 NS	-0.21 ± 0.17 NS
CiRE3	-9.40 ± 16.40 NS	-8.40 ± 6.03 NS	-2.37 ± 0.40 **
CLE1	-0.44 ± 0.39 NS	-0.09 ± 0.22 NS	-0.29 ± 0.18 NS
GBE2	-1.45 ± 0.56 **	-1.17 ± 0.31 **	-0.82 ± 0.21 **
GLE1	-0.44 ± 0.39 NS	-0.20 ± 0.22 NS	-0.19 ± 0.17 NS
LeLE1	-0.75 ± 0.43 NS	-0.47 ± 0.24 *	-0.04 ± 0.17 NS
LeRE3	-0.19 ± 0.36 NS	0.25 ± 0.20 NS	0.46 ± 0.15 **
LeSE2	-0.64 ± 0.41 NS	-0.41 ± 0.24 NS	-0.49 ± 0.19 **
LLE1	-1.22 ± 0.51 *	-0.63 ± 0.25 *	-0.76 ± 0.21 **
LRE3	-1.22 ± 0.51 *	-1.03 ± 0.29 **	-0.92 ± 0.22 **
LSE2	-1.74 ± 0.63 **	-1.32 ± 0.33 **	-1.83 ± 0.31 **
MBE2	-0.53 ± 0.40 NS	-0.37 ± 0.23 NS	-0.37 ± 0.18 *
MiBE2	-0.27 ± 0.37 NS	0.30 ± 0.20 NS	0.62 ± 0.14 **
MiLE1	-1.74 ± 0.63 **	-0.97 ± 0.29 **	-0.63 ± 0.20 **
MLE1	-1.45 ± 0.56 **	-0.86 ± 0.27 **	-0.51 ± 0.19 **
NBE2	-0.89 ± 0.45 *	-0.55 ± 0.25 *	-0.37 ± 0.18 *
NLE1	-2.14 ± 0.75 **	-2.20 ± 0.47 **	-2.71 ± 0.46 **
NRE3	-0.75 ± 0.43 NS	-0.31 ± 0.23 NS	0.09 ± 0.16 NS
TLE1	-0.89 ± 0.45 *	-0.37 ± 0.23 NS	-0.17 ± 0.17 NS
TRE3	-0.89 ± 0.45 *	-0.51 ± 0.24 *	-0.06 ± 0.17 NS
WwBE2	-2.14 ± 0.75 **	-1.41 ± 0.34 **	-1.10 ± 0.23 **
WwLE1	-0.64 ± 0.41 NS	-0.17 ± 0.22 NS	0.05 ± 0.16 NS

Table 4
Weekly GLM estimates of the cumulative population of dicot weeds after treatment with aqueous extracts

Parameter	GLM estimates ± standard error
	1 WAA	2WAA	3WAA
Constant	0.98 ± 0.35 **	2.23 ± 0.19 **	3.05 ± 0.00 **
CBE2	-1.39 ± 0.79 NS	-0.93 ± 0.36 **	-0.31 ± 0.11 NS
ChLE1	-2.08 ± 1.06 NS	-1.14 ± 0.38 **	-1.50 ± 0.00 **
ChRE3	0.22 ± 0.47 NS	0.52 ± 0.24 *	0.49 ± 0.00 **
ChSE2	-1.39 ± 0.79 NS	-0.62 ± 0.32 NS	-0.56 ± 0.01 **
CiBE2	-0.29 ± 0.54 NS	-0.07 ± 0.27 NS	0.00 ± 1.00 NS
CiLE1	0.32 ± 0.47 NS	0.04 ± 0.27 NS	-0.27 ± 0.16 NS
CiRE3	0.75 ± 0.43 NS	0.81 ± 0.23 **	0.40 ± 0.01 *
CLE1	0.63 ± 0.44 NS	0.60 ± 0.24 *	0.43 ± 0.01 **
GBE2	0.32 ± 0.47 NS	0.28 ± 0.25 NS	0.33 ± 0.04 *
GLE1	0.32 ± 0.47 NS	0.47 ± 0.24 *	0.29 ± 0.08 NS
LeLE1	-2.08 ± 1.06 NS	-1.72 ± 0.49 **	-1.84 ± 0.00 **
LeRE3	0.22 ± 0.47 NS	0.19 ± 0.26 NS	-0.14 ± 0.46 NS
LeSE2	-8.70 ± 16.40 NS	-8.93 ± 9.95 NS	-1.84 ± 0.00 **
LLE1	0.22 ± 0.47 NS	0.25 ± 0.25 NS	0.28 ± 0.10 NS
LRE3	-1.39 ± 0.79 NS	-0.93 ± 0.36 **	-0.92 ± 0.00 **
LSE2	-0.47 ± 0.57 NS	-0.62 ± 0.32 NS	-0.38 ± 0.05 NS
MBE2	-0.29 ± 0.54 NS	-0.24 ± 0.29 NS	-0.56 ± 0.01 **
MiBE2	-0.29 ± 0.54 NS	-0.77 ± 0.34 *	-0.97 ± 0.00 **
MiLE1	-0.98 ± 0.68 NS	-0.34 ± 0.29 NS	-0.65 ± 0.00 **
MLE1	-1.39 ± 0.79 NS	-1.03 ± 0.37 **	-0.56 ± 0.01 **
NBE2	0.63 ± 0.44 NS	0.47 ± 0.24 *	0.39 ± 0.02 *
NLE1	-0.98 ± 0.68 NS	-0.77 ± 0.34 *	-0.78 ± 0.00 **
NRE3	0.12 ± 0.49 NS	0.22 ± 0.25 NS	0.25 ± 0.14 NS
TLE1	0.00 ± 0.50 NS	0.00 ± 0.27 NS	-0.10 ± 0.58 NS
TRE3	-0.69 ± 0.61 NS	-0.24 ± 0.29 NS	-0.56 ± 0.01 **
WwBE2	-0.29 ± 0.61 NS	0.02 ± 0.30 NS	-0.24 ± 0.26 NS
WwLE1	-0.69 ± 0.61 NS	-0.44 ± 0.30 NS	-0.38 ± 0.05 NS

Table 5
Weekly GLM estimates of the cumulative population of dicot weeds after treatment with ethanol extracts

Parameter	GLM estimates ± standard error
	1 WAA	2WAA	3WAA
Constant	1.47 ± 0.28 **	3.19 ± 0.12 **	4.00 ± 0.08 **
CBE2	-0.08 ± 0.40 NS	-0.55 ± 0.19 **	-0.74 ± 0.14 **
ChLE1	-1.47 ± 0.64 *	-1.52 ± 0.28 **	-1.57 ± 0.19 **
ChRE3	0.00 ± 0.39 NS	-0.34 ± 0.18 NS	-0.77 ± 0.14 **
ChSE2	0.93 ± 0.33 **	0.25 ± 0.16 NS	-0.14 ± 0.12 NS
CiBE2	0.77 ± 0.34 *	0.24 ± 0.16 NS	-0.15 ± 0.12 NS
CiLE1	-0.17 ± 0.41 NS	-0.48 ± 0.19 *	-0.87 ± 0.14 **
CiRE3	-0.26 ± 0.42 NS	-0.51 ± 0.19 **	-0.52 ± 0.13 **
CLE1	0.27 ± 0.37 NS	-0.09 ± 0.17 NS	-0.36 ± 0.12 **
GBE2	-10.20 ± 27.00 NS	-2.50 ± 0.43 **	-1.92 ± 0.22 **
GLE1	-1.18 ± 0.57 *	-0.83 ± 0.21 **	-0.99 ± 0.15 **
LeLE1	-0.17 ± 0.41 NS	-0.07 ± 0.17 NS	-0.35 ± 0.12 **
LeRE3	0.27 ± 0.37 NS	-0.40 ± 0.19 *	-0.82 ± 0.14 **
LeSE2	-10.20 ± 27.00 NS	-1.40 ± 0.26 **	-1.06 ± 0.15 **
LLE1	-0.37 ± 0.43 NS	-0.96 ± 0.22 **	-1.15 ± 0.16 **
LRE3	-0.49 ± 0.45 NS	-0.46 ± 0.19 *	-0.59 ± 0.13 **
LSE2	0.27 ± 0.37 NS	-0.38 ± 0.18 *	-0.40 ± 0.12 **
MBE2	0.84 ± 0.33 *	0.24 ± 0.16 NS	0.01 ± 0.11 NS
MiBE2	-1.18 ± 0.57 *	-1.00 ± 0.23 **	-0.71 ± 0.14 **
MiLE1	-10.20 ± 27.00 NS	-1.07 ± 0.23 **	-1.09 ± 0.16 **
MLE1	-1.47 ± 0.64 *	-1.35 ± 0.26 **	-1.34 ± 0.17 **
NBE2	0.21 ± 0.37 NS	-0.23 ± 0.18 NS	-0.56 ± 0.13 **
NLE1	0.27 ± 0.37 NS	-0.20 ± 0.17 NS	-0.26 ± 0.12 *
NRE3	-0.37 ± 0.43 NS	-0.74 ± 0.21 **	-0.76 ± 0.14 **
TLE1	0.33 ± 0.36 NS	0.00 ± 0.17 NS	-0.30 ± 0.12 *
TRE3	0.43 ± 0.36 NS	0.05 ± 0.16 NS	-0.03 ± 0.11 NS
WwBE2	-10.20 ± 27.00 NS	-1.11 ± 0.24 **	-1.11 ± 0.16 **
WwLE1	-10.20 ± 27.00 NS	-2.34 ± 0.39 **	-1.67 ± 0.20 **

Table 6
Weekly GLM estimates of the cumulative population of monocot weeds after treatment with ethanol extracts

Parameter	GLM estimates ± standard error
	1 WAA	2WAA	3WAA
Constant	2.49 ± 0.17 **	2.94 ± 0.13 **	3.16 ± 0.12 **
CBE2	-1.97 ± 0.48 **	-0.82 ± 0.24 **	-0.65 ± 0.20 **
ChLE1	-11.20 ± 27.00 NS	-8.64 ± 6.03 NS	-8.86 ± 6.03 NS
ChRE3	-0.45 ± 0.27 NS	-0.17 ± 0.20 NS	-0.27 ± 0.18 NS
ChSE2	-0.88 ± 0.31 **	-0.33 ± 0.21 NS	-0.09 ± 0.17 NS
CiBE2	-1.10 ± 0.33 **	-0.31 ± 0.20 NS	0.01 ± 0.17 NS
CiLE1	-3.58 ± 1.01 **	-2.25 ± 0.43 **	-1.56 ± 0.28 **
CiRE3	-0.37 ± 0.26 NS	0.07 ± 0.18 NS	0.25 ± 0.16 NS
CLE1	-1.39 ± 0.37 **	-0.58 ± 0.22 **	-0.48 ± 0.19 *
GBE2	-0.81 ± 0.30 **	-0.09 ± 0.19 NS	-0.01 ± 0.17 NS
GLE1	-2.49 ± 0.60 **	-1.27 ± 0.28 **	-0.83 ± 0.22 **
LeLE1	-0.69 ± 0.29 *	-0.49 ± 0.22 *	-0.71 ± 0.21 **
LeRE3	-2.20 ± 0.53 **	-1.74 ± 0.34 **	-1.96 ± 0.34 **
LeSE2	-11.20 ± 27.00 NS	-3.35 ± 0.72 **	-2.18 ± 0.37 **
LLE1	-1.79 ± 0.44 **	-1.34 ± 0.29 **	-0.97 ± 0.23 **
LRE3	-1.50 ± 0.39 **	-0.58 ± 0.22 **	-0.24 ± 0.18 NS
LSE2	-2.20 ± 0.53 **	-1.48 ± 0.31 **	-1.32 ± 0.26 **
MBE2	-0.64 ± 0.28 *	-0.46 ± 0.21 *	-0.27 ± 0.18 NS
MiBE2	-11.20 ± 27.00 NS	-1.74 ± 0.34 **	-0.93 ± 0.22 **
MiLE1	-11.20 ± 27.00 NS	-3.35 ± 0.72 **	-1.87 ± 0.32 **
MLE1	-1.28 ± 0.36 **	-0.46 ± 0.21 *	-0.27 ± 0.18 NS
NBE2	-1.19 ± 0.35 **	-0.75 ± 0.23 **	-0.97 ± 0.23 **
NLE1	-1.02 ± 0.32 **	-0.79 ± 0.24 **	-0.63 ± 0.20 **
NRE3	-11.20 ± 27.00 NS	-3.35 ± 0.72 **	-1.62 ± 0.29 **
TLE1	-1.50 ± 0.39 **	-0.82 ± 0.24 **	-0.26 ± 0.18 NS
TRE3	-1.97 ± 0.48 **	-1.56 ± 0.32 **	-1.78 ± 0.31 **
WwBE2	-11.20 ± 27.00 NS	-2.66 ± 0.52 **	-1.04 ± 0.23 **
WwLE1	-11.20 ± 27.00 NS	-2.94 ± 0.59 **	-3.16 ± 0.59 **

The period of activity is of prime importance for potential extracts; extracts with longer periods of activity are preferred for the reason that the frequency of application will be minimized. Thus extracts such as ChLE, GBE and NLE that showed significant suppression throughout the three week period in suppressing monocot and dicot weed growth were deemed superior to those that showed effectiveness in only one or two out of the three weeks. The superior performance of these extracts may be attributed to the presence of allelochemicals or phytotoxic compounds with low volatility, which can lead to persistent suppression possibly mediated by soil-binding or gradual microbial breakdown processes, resulting in prolonged bioavailability (Pardo-Muras et al., 2022; Kyaw et al., 2022). In contrast, the consistent lack of significant inhibitory effect of extracts like ChSE, CiBE, GLE, and CLE across the evaluation period likely confirms either low concentrations of potent allelochemicals, volatilization or early degradation of the active compounds shortly after application. Pardo-Muras et al. (2022) emphasized that field inefficiency of allelopathic compounds is due to early degeneration by microorganisms or their adsorption to colloidal particles, and these characterizes their lack of significant inhibitory effects.

The allelopathic effects of extracts sourced from different plant organs (leaves, roots, and bark) of the same plant species indicate their respective herbicidal potential on weed population dynamics (Zhang et al., 2025). Extracts from different organs of the same plant species varied in their effects on the weed populations. Despite originating from the same plant species, the aqueous extract Gliricida Leaf Extract (GLE) remained insignificant in suppressing monocot weed population throughout the study period whereas the aqueous extract of the bark of the same species (GBE) significantly reduced monocot weed population throughout the study period. This is indicative of the fact that allelopathic compounds are not evenly distributed in the plant but may better accumulate in certain organs than others. This undermines the concept of universal allelopathic efficacy among plant organs and necessitates chemical profiling to ascertain the actual distribution and concentration of allelochemicals in plant parts (Zhang et al., 2025).

Again, the results revealed that, extraction solvent plays a significant role in the effectiveness of plants extracts. Comparably, the ethanol extracts generally showed significant suppression, particularly for WwLE, GLE, and MLE, where substantial negative GLM estimates persisted throughout the data collection period. The superior efficacy of the ethanol extracts in several instances indicates that solvent polarity is essential in extracting a broader spectrum of allelochemicals, encompassing less polar and more lipophilic compounds recognised for their significant phytotoxicity (Krumsri et al., 2024). The delayed yet amplified effects observed for many ethanol extracts, including CiLE and ChLE, further support the hypothesis of cumulative or soil-mediated action.

A large percentage of treatments revealed significant suppression effects on monocot weed populations, with several extracts showing prolonged phytotoxicity over the three-week post-application period (Table 3). The aqueous extracts of NLE, LSE, MLE, and WwBE showed persistent and significant negative estimates (p < 0.01) against monocot weeds across all weeks (-2.71, -1.83, -0.51, and -1.10, respectively, at 3 WAA), indicating both acute phytotoxicity and prolonged suppressive potential (Table 2) while ethanol extracts of CBE, CiLE, CLE, GLE, LeLE, LeRE, LLE, LSE, NBE, NLE and TRE also significantly suppressed the monocot weed population. Specifically, NLE demonstrated mean monocot weed counts of 0.33 ± 0.12 seedlings per tray at 3 WAA compared to 4.67 ± 0.28 in control treatments, representing 93% suppression. The observed allelopathic efficacy could be due to the bioactive compounds identified through the GC-MS analysis (Table 7). For instance, Chromolaena odorata leaf extract (ChLE), which showed consistent monocot suppression, contained I-[-]-4-Hydroxy-1-methylproline (67.21% relative abundance) and 5-Hydroxypipecolic acid, compounds reported to poses bioherbicidal potential in target plants. Similarly, Azadirachta indica leaf extract (NLE), the most effective treatment, contained Thiourea, N,N’-diethyl- (30.94%), which is known to inhibit urease activity and disrupt nitrogen metabolism. The presence of these compounds explains the sustained suppression observed, as they target fundamental metabolic processes essential for seedling establishment and growth (Kato-Noguchi, & Kato 2023).

Table 7
Qualitative report of GCMS for the selected extracts and their corresponding likely active compounds based on library search

Extract	RT	SCAN	HEIGHT	AREA	AREA%	NORM%	Likely active compounds based on library search
ChLE	11.699	420	38,234,480	48,459,580	67.721	100	i. I-[-]-4-Hydroxy-1-methylproline ii. 5-Hydroxypipecolic acid iii. Formamide, N-[1-[(1-cyano-2-methylopropyl)hydroxyamino]-2-methylpropyl]-
ChRE	10.122	334	5,682,654	7,004,274.0	40.716	100	i. I-[-]-4-Hydroxy-1-methylproline ii. 5-Hydroxypipecolic acid iii. 4-Hydroxy-2-methylpyrrolidine-2-carboxylic acid
GLE	12.763	478	146,072,128	141,392,432.0	63.517	100	i. 3-O-Methyl-d-glucose ii. ẚ-d-Mannofuranoside, methyl iii. Myo-Inositol, 4-C-methyl-
LRE	10.489	354	2,683,848	549,950.1	26.843	100	i. Nitrous acid, butyl ester ii. Hydrazine, 1,2-dimethyl
	10.691	365	2,299,663	436,699.8	21.315	79.41	i. Propanamide, N-(aminocarbonyl)- ii. RS-2,3-hexanediol iii. Formic acid, neopentyl ester
NLE	20.519	901	114,329	2,097	30.940	100	i. Thiourea, N,N’-diethyl- ii. 1,3-Oxathiane, 4,6-dimethyl-, cis-
WwBE	12.066	440	15,971,558	8,972,056.0	93.826	100	i. Hexanoic acid, 2-methyl- ii. Myo-Inositol, 4-C-methyl iii. Butane, 2-methoxy-2-methyl
WwLE	15.825	645	16,348,171	2,150,891.5	81.357	100	i. Platyphylline ii. Succinic acid, di(5-fluoro-2-nitrophenyl) ester

Unlike the monocot responses, greater variability in trend was observed for the dicot weed population. The significant cumulative suppression of dicot weed population, specifically 2 weeks after application of the aqueous extracts such as ChLE, MLE, LeLE, and CBE posits that specific plant taxa exhibit a broader allelochemical spectrum with efficacy across several plant functional groups, a trait desired in the selection of allelopathic agents for mixed-weed ecosystems. While some treatments produced a higher magnitude of allelopathy, the positive GLM estimate yielded by a substantial number of aqueous extracts, including CLE, CiRE, and GLE, indicates the potential hermetic effects, where low concentrations of active ingredients in allelopathic plant extracts stimulate instead of suppressing the emergence and growth of weeds (Belz, Cedergreen, 2010; Scavo et al., 2018). This phenomenon, although not rare, is significantly under-explored in the weed science field, despite its relevance in comprehending sublethal allelopathic interactions and extract concentration effects, as investigated by Belz and Cedergreen (2010). The ethanol extracts were comparatively more effective against the dicot weeds, with treatments including TRE, LeRE, WwLE, and MiLE showing appreciable and prolonged suppression effects on weed population densities. Interestingly, some treatments that were ineffective in aqueous extracts such as ChLE and WwBE exhibited significant suppressive effects when administered as ethanol extracts. The superior efficacy of ethanol extracts was supported by our GC-MS analysis, which revealed distinct compound profiles between extraction methods. Gliricidia sepium leaf ethanol extract (GLE) yielded 3-O-Methyl-d-glucose (63.52%), α-d-Mannofuranoside, and Myo-Inositol, 4-Cmethyl-, compounds with low water solubility that were likely under-represented or absent in aqueous extracts. The lipophilic nature of these compounds correlates with membrane permeability and cellular uptake, explaining their superior phytotoxic effects as many potent allelochemicals demonstrates high solubility in ethanol-based extraction systems compared to aqueous (Azmir et al., 2013). The phenolic compounds, particularly flavonoids and tannins, which are among the most potent allelochemicals, often exist in plant tissues as glycosidic complexes or bound to cell wall components, making them more accessible through ethanol extraction (Cheynier et al., 2013). Moreover, many allelochemicals are susceptible to oxidative degradation, enzymatic breakdown, or hydrolytic decomposition in aqueous environments (Inderjit, Duke, 2003). The antioxidant properties of ethanol and its ability to denature plant enzymes during extraction can preserve labile compounds that would otherwise degrade in water-based extraction systems (Dai, Mumper, 2010).

Furthermore, the differential suppression profiles observed on monocot and dicot species demand further scrutiny. Treatments like NLE, LSE, and MLE demonstrated dual efficacy, suggesting that their allelochemical profiles may include both broad-spectrum and functionally targeted phytotoxins, resulting in their interference with hormonal pathways, photosynthesis, or mitosis targets. This cross-functional suppression is of significant interest for effective weed management approaches, which require tools capable of managing diverse weed floras without fostering rapid resistance development. Similar finding was reported by Hickman et al. (2021), who stated that natural phytotoxins have different modes of action and displayed multi-kingdom effect of allelochemicals. However, the emergence of neutral and stimulatory responses in some treatments highlights a critical need for dose-response validation and allelochemical characterization (Ramesh, Krishnan, 2023).

The GC-MS results revealed a wide range of bioactive compounds present in the extracts (Table 7). Among the predominant compounds identified, Chromolaena odorata extracts (both ChLE and ChRE) showed high concentrations of hydroxyproline, which have been previously reported as potent allelochemicals affecting protein synthesis in target plants (Kato-Noguchi et al., 2023). Leucaena leucocephala root extract (LeRE) contained Nitrous acid, butyl ester (26.84%) and Hydrazine, 1,2- dimethyl, compounds known for their phytotoxic properties through oxidative stress induction while Salix babylonica leaf extract (WwLE) was characterized by Platyphylline (81.36%), an alkaloid reported for allelopathic activity affecting mitochondrial respiration in germinating seeds (Kato-Noguchi, & Kato 2023). The similar shapes of Figures 1A and 1B are indicative of their common origin, Chromolaena odorata. This suggests common compounds in the leaves and the roots of the plant species (Table 6). However, it is also clear from the results that the concentrations of the major bioactive compounds, as indicated by the peaks, differ in the two plant organs. This confirms our earlier statement that such compounds are not evenly distributed in the plant. A similar trend was observed in Figures 1F and 1G, which also originates from the same plant species.

Figure 1
Chromatogram plots from GC-MS for ChLE (a), ChRE (b), GLE (c), LRE (d), NLE (e), WwBE (f) and WwLE (g)

4. Conclusions

This study demonstrated significant bioherbicidal potential of various plant extracts against both weed types. Among monocot suppressors, NLE exhibited the strongest inhibition (93% suppression at 3 WAA), followed by LSE with 83% suppression. For dicot weeds, ChLE exhibited superior performance with 89% suppression, while LeLE achieved 87% suppression. The study also revealed that the extraction method substantially impacted bioactive compound recovery, with ethanol extracts outperforming aqueous ones, particularly for dicot suppression, where ethanol extracts recorded 40% greater efficacy on average. The GC-MS analysis identified bioactive compounds, including hydroxyproline (67.21% in ChLE), Thiourea, N,N’-diethyl- (30.94% in NLE), and Platyphylline (81.36% in WwLE), which explained the observed allelopathic activities. Interestingly, NLE, LSE, and MLE displayed broad-spectrum allelochemical profiles revealing dual suppression efficacy against both monocot and dicot weeds. The study identified a potential hermetic effect in CLE, CiRE, and GLE, demonstrating the importance of dose-response optimization in developing sustainable bioherbicide. However, further studies should probe into field validation trials under different agroecological zones to assess efficacy under natural weed pressure and environmental variability, phytotoxicity assessments on major crop species to establish selectivity indices and safety margins, and soil persistence studies to determine allelochemical degradation rates and residual effects on crops.

Data Availability Statement

Data may be shared upon reasonable request.

• Funding
  This research was funded by The Directorate of Research, Innovation and Consultancy of The University of Cape Coast.

References

• Akobundu IO, Agyakwa CW. A handbook of West African weeds. Ibadan: International Institute of Tropical Agriculture; 1987.
• Association of Official Analytical Chemists – AOAC. AOAC Method 930.15: moisture in animal feed, loss on drying at 135 °C for 2 h. In: Association of Official Analytical Chemists, editor. Official Methods of Analysis. 16th ed. Gaithersburg: Association of Official Analytical Chemists; 1999.
• Azmir J, Zaidul ISM, Rahman MM, Sharif KM, Mohamed A, Sahena F et al. Techniques for extraction of bioactive compounds from plant materials: a review. J Food Eng. 2013;117(4):426-36. Available from: https://doi.org/10.1016/j.jfoodeng.2013.01.014
  » https://doi.org/10.1016/j.jfoodeng.2013.01.014
• Belz RG, Cedergreen N. Parthenin hormesis in plants depends on growth conditions. Environ Exp Bot. 2010;69(3):293-301. Available from: https://doi.org/10.1016/j.envexpbot.2010.04.010
  » https://doi.org/10.1016/j.envexpbot.2010.04.010
• Bilali H, Guimbo I, Nanema R, Falalou H, Kiebre Z, Rokka V et al. Research on moringa (Moringa oleifera Lam.) in Africa. Plants. 2024;13(12):1-44. Available from: https://doi.org/10.3390/plants13121613
  » https://doi.org/10.3390/plants13121613
• Cheynier V, Comte G, Davies KM, Lattanzio V, Martens S. Plant phenolics: recent advances on their biosynthesis, genetics, and ecophysiology. Plant Physiol. 2013;162(3):1382-400. Available from: https://doi.org/10.1016/j.plaphy.2013.05.009
  » https://doi.org/10.1016/j.plaphy.2013.05.009
• Dai J, Mumper RJ. Plant phenolics: extraction, analysis and their antioxidant and anticancer properties. Molecules. 2010;15(10):7313-52. Available from: https://doi.org/10.3390/molecules15107313
  » https://doi.org/10.3390/molecules15107313
• Damour G, Tixier P, Coulis M. Glyphosate reduces the biodiversity of soil macrofauna and benefits exotic over native species in a tropical agroecosystem. Basic Appl Ecol. 2023;73:18-26. Available from: https://doi.org/10.1016/j.baae.2023.10.001
  » https://doi.org/10.1016/j.baae.2023.10.001
• Dolianitis BM, Frescura VDS, Furtado GDF, Tres MV, Zabot GL. Plant-based bioherbicides: review of eco-friendly strategies for weed control in organic bean and corn farming. AgriEngineering. 2025;7(9):1-25. Available from: https://doi.org/10.3390/agriengineering7090288
  » https://doi.org/10.3390/agriengineering7090288
• Heap I. The international herbicide-resistant weed database. Weedscience. 2024[access Oct 31, 2025]. Available from: http://www.weedscience.org
  » http://www.weedscience.org
• Hickman DT, Rasmussen A, Ritz K, Birkett MA, Neve P. Allelochemicals as multi-kingdom plant defence compounds: towards an integrated approach. Pest Manag Sci. 2021;77(3):1121-31. Available from: https://doi.org/10.1002/ps.6076
  » https://doi.org/10.1002/ps.6076
• Inderjit, Duke SO. Ecophysiological aspects of allelopathy. Planta. 2003;217(4):529-39. Available from: https://doi.org/10.1007/s00425-003-1054-z
  » https://doi.org/10.1007/s00425-003-1054-z
• Islam AM, Karim SMR, Kheya SA, Yeasmin S. Unlocking the potential of bioherbicides for sustainable and environment friendly weed management. Heliyon. 2024;10(16):1-13. Available from: https://doi.org/10.1016/j.heliyon.2024.e36088.
  » https://doi.org/10.1016/j.heliyon.2024.e36088.
• Kato-Noguchi H, Kato M. Evolution of the secondary metabolites in invasive plant species Chromolaena odorata for the defense and allelopathic functions. Plants. 2023;12(3):1-20. Available from: https://doi.org/10.3390/plants12030521
  » https://doi.org/10.3390/plants12030521
• Khamare Y, Chen J, Marble SC. Allelopathy and its application as a weed management tool: a review. Front Plant Sci. 2022;13:1-17. Available from: https://doi.org/10.3389/fpls.2022.1034649
  » https://doi.org/10.3389/fpls.2022.1034649
• Krumsri R, Kato-Noguchi H, Poonpaiboonpipat T. Allelopathic effects of sugarcane leaves: optimal extraction solvent, partial separation of allelopathic active fractions, and herbicidal activities. Plants. 2024;13(15):1-18. Available from: https://doi.org/10.3390/plants13152085
  » https://doi.org/10.3390/plants13152085
• Kyaw EH, Iwasaki A, Suenaga K, Kato-Noguchi H. Allelopathy of the medicinal plant Dregea volubilis (L.f.) Benth. ex Hook. f. and its phytotoxic substances with allelopathic activity. Agronomy. 2022;12(2):1-13. Available from: https://doi.org/10.3390/agronomy12020303
  » https://doi.org/10.3390/agronomy12020303
• Mikulka J, Sen MK, Košnarová P, Hamouz P, Hamouzová K, Sur VP et al. Molecular mechanisms of resistance against psii-inhibiting herbicides in Amaranthus retroflexus from the Czech Republic. Genes. 2024;15(7):1-13. Available from: https://doi.org/10.3390/genes15070904
  » https://doi.org/10.3390/genes15070904
• Pardo-Muras M, Puig CG, Pedrol N. Complex synergistic interactions among volatile and phenolic compounds underlie the effectiveness of allelopathic residues added to the soil for weed control. Plants. 2022;11(9):1-14. Available from: https://doi.org/10.3390/plants11091114
  » https://doi.org/10.3390/plants11091114
• Parven A, Meftaul IM, Venkateswarlu K, Megharaj M. Herbicides in modern sustainable agriculture: environmental fate, ecological implications, and human health concerns. Int J Environ Sci Technol. 2025;22(2):1181–202. Available from: https://doi.org/10.1007/s13762-024-05818-y
  » https://doi.org/10.1007/s13762-024-05818-y
• Paul SK, Mazumder S, Naidu R. Herbicidal weed management practices: history and future prospects of nanotechnology in an eco-friendly crop production system. Heliyon. 2024;10(5):1-18. Available from: https://doi.org/10.1016/j.heliyon.2024.e26527
  » https://doi.org/10.1016/j.heliyon.2024.e26527
• Pesarakloo M, Alamdari EG, Avarseji Z, Moghaddam AN. The critical periods of weed control on the weed species composition, morphological, physiological, and biochemical characteristics responses of black seed (Nigella sativa L.). Biochem Syst Ecol. 2023;108. Available from: https://doi.org/10.1016/j.bse.2023.104645
  » https://doi.org/10.1016/j.bse.2023.104645
• Ramesh V, Krishnan J. A unified approach to dissecting biphasic responses in cell signaling. eLife. 2023;12:1-32. Available from: https://doi.org/10.7554/eLife.86520
  » https://doi.org/10.7554/eLife.86520
• Rowell DL. Soil science: methods and applications. Harlow: Longman; 1994.
• Scavo A, Restuccia A, Pandino G, Onofri A, Mauromicale G. Allelopathic effects of Cynara cardunculus L. leaf aqueous extracts on seed germination of some Mediterranean weed species. Ital J Agron. 2018;13(2):1-7. Available from: https://doi.org/10.4081/ija.2018.1021
  » https://doi.org/10.4081/ija.2018.1021
• Schwarz T, Jennings K, Gorny A. Evaluation of weed species for host status to the root-knot nematodes Meloidogyne enterolobii and M. incognita race 4. J Nematol. 2024;56(1):1-9. Available from: https://doi.org/10.2478/jofnem-2024-0017
  » https://doi.org/10.2478/jofnem-2024-0017
• Simperegui KB, Kouame AK, Kwesie B, Bindraban PS, Adzawla W, Asamoah E et al. Digital mapping of Ghana’s soil properties and nutrients: performance of spline and weighted average approaches. Geoderma. 2025;459:1-18. Available from: https://doi.org/10.1016/j.geoderma.2025.117365
  » https://doi.org/10.1016/j.geoderma.2025.117365
• Zhang TJ, Guo W, Tian X, Lv YZ, Feng KF, Zhang C. Allelopathic effects of Borreria latifolia on weed germination and identification of allelochemicals. J Sci Food Agric. 2025;105(1):626–34 Available from: https://doi.org/10.1002/jsfa.13859
  » https://doi.org/10.1002/jsfa.13859

Edited by

• Editor in Chief:
  Carol Ann Mallory-Smith

• Associate Editor:
  Ricardo Alcántara-de la Cruz

Publication Dates

• Publication in this collection
  22 June 2026
• Date of issue
  2026

History

• Received
  16 Sept 2025
• Accepted
  24 Jan 2026