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Evaluation of Allelopathic Activity Interactions of Some Medicinal Plants Using Fractional Inhibitory Concentration and Isobologram

Somayeh Sadeqifard
Somayeh Mirmostafaee
Mohammad Reza Joharchi
Jaleh Zandavifard
Majid Azizi
1,* and
Yoshiharu Fujii
Department of Horticultural Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad P.O. Box 9716094019, Iran
Institute of Plant Science, Ferdowsi University of Mashhad, Mashhad P.O. Box 9716094019, Iran
Department of International Environmental and Agricultural Sciences, Tokyo University of Agriculture and Technology, Fuchu 183-8509, Tokyo, Japan
Authors to whom correspondence should be addressed.
Agronomy 2022, 12(12), 3001;
Submission received: 6 November 2022 / Revised: 23 November 2022 / Accepted: 24 November 2022 / Published: 29 November 2022
(This article belongs to the Special Issue Chemical Diversity, Yield and Quality of Aromatic Plant)


Allelopathy is a physiological process with an ecological concept and application. Allelopathy is the result of the production of biologically active molecules by growing plants or their remains, which may have a direct effect on the growth and development of individuals of the same species or other species after changing their shape and entering the environment. As regards, the use of natural compounds in the control of weeds and pests is a priority. In this research, the allelopathic activity of 123 specimens of medicinal and aromatic plants were investigated individually by the dish-pack method using lettuce seeds as a model. Then, the strongest inhibitory ones were selected and their allelopathic interaction effects were investigated for the first time by interacting them together. Two methods were used to evaluate allelopathic interaction effects: calculating Fractional Inhibitory Concentration (FIC) and drawing Isobologram diagrams. Lettuce hypocotyl length, root length, germination percentage, and germination rate were investigated. Pelargonium graveolens (leaf) had the greatest inhibitory effect on lettuce radicle growth (EC50 = 5.31 mg/well) and Echinophora platyloba (stem) had the greatest effect on hypocotyl growth inhibition (EC50 = 7.91 mg/well). Also, the lowest lettuce germination percentages were observed in the treatments Lavandula officinalis (flower) and Nepeta binaloudensis (leaf), respectively (23.61, 22.85%). The highest inhibitory effect by considering lettuce germination rate was detected in Salvia ceratophylla (leaf), (12.86 seed/day) and the lowest belonged to Nepeta binaloudensis (leaf) and Lavandula officinalis (flower), respectively (3.60, 3.32 seed/day). According to FIC calculations and isobolograms, two types of interaction, including synergist (Nepeta binaloudensis (leaf) with Trachyspermum ammi (fruit) and Nepeta binaloudensis (leaf) with Lavandula officinalis (flower) and antagonist (Pelargonium graveolens (leaf) with Lavandula officinalis (flower)), were observed significantly among the plants tested in this research. These interactions can be used to prepare more effective natural herbicides and decrease the use of herbicides.

1. Introduction

Weeds and crops have growth interactions and cause high expenses for agricultural systems [1]. This played a significant role during the domestication of crops, so weed control measures are required [2]. In most integrated weed management systems, herbicides are widely used [3,4]. Over the last 40 to 50 years, with the commercial production of more than 200 chemical compounds, significant changes in weed control have begun to develop [5].
Medicinal, aromatic, and spice plants and mushrooms produce a wide range of secondary metabolites and the uses of these metabolites as agrochemicals for the control of pests, diseases, and weeds have been well investigated [6,7,8]. Secondary metabolites are a plentiful source of the natural compounds that are produced in the special structure in medicinal and aromatic plants and their content and composition are affected by plant species, climate, cultural practices, and harvest time [9,10,11]. Some of the secondary metabolites have a good potential use in the development of natural herbicides [12]. They are more environmentally friendly than chemical pesticides and are economically viable and can be easily produced in small industries by farmers [13].
The allelopathic compounds are chemicals produced by some plants (especially medicinal and aromatic plants) that can affect the ecosystem in association with other compounds in collaboration with microorganisms [14,15,16]. In 1999, the International Allelopathy Society offered a precise definition of the allelopathy concept: allelopathy is a science that studies the production of secondary metabolites in plants, algae, bacteria, and fungi and examines their impact on growth in biological and agricultural systems [17].
At the beginning of recognition of plants with allelopathic compounds, due to the lack of rapid methods and knowledge of chemical constituents, it was difficult to describe this important phenomenon [18]. However, in recent years some methods, such as the dish pack method, have be used to detect the allelopathic properties very easily and quickly. In this method, many plant materials can be tested anywhere and in a short period [19].
Although many studies have been conducted on the interaction type (synergistic, antagonistic, and additive potentials) between antimicrobial compounds [20,21,22,23], research on the interaction of allelopathic plants or allelochemicals is very rare [6]. The synergistic interaction is a promising combination which could be used to overcome the resistance to herbicides, insecticides, and microbes. It also could decrease the used herbicide volume and increase the herbicidal efficiency and sustainability.
This research aims to evaluate the allelopathic interaction effects of some medicinal plants by the dish pack method by using seed germination and seedling growth of lettuce as a model plant for allelopathic activity.

2. Materials and Methods

2.1. Plant Materials

Different parts (leaves, flowers, stems, fruits, roots, flowering branches, and seeds) of 123 specimens belonging to 31 families of volatile and medicinal species were collected from the Research Center for Plant Sciences of Ferdowsi University of Mashhad and Botanical Garden of Mashhad. The plant parts were dried in the oven at 45 °C for 2 days to keep the volatile compounds. After the appropriate drying, the parts were kept in a plastic bag until use.
The effect of the tested plants on radicle and hypocotyl growth of lettuce seeds in comparison with control in different plant families was investigated separately. In each plant family, a comparison was made at two probability levels (p ≤ 0.05; p ≤ 0.01).
They were subjected to the analysis of their allelopathic effects using the dish pack method (Figure 1) and the lettuce seeds (Great Lakes 366) were used as the test plant because of their good reliability in germination, sensitivity to inhibitory and stimulatory chemicals, and their convenience of purchase [6,7,19,24].

2.2. Assessment of Allelopathic Effect on Lettuce Seed and Germination Traits

2.2.1. Dish Pack Method

One of pathways to screen volatile compound secreted from plants is the dish pack method as described by Fujii et al. (2000) [19]. In this method, multi-dishes with six wells (18 mm × 36 mm) (Nunc Company, Tokyo, Japan) were used. The distances from the center of a plant sample well to the center of other wells were 41, 58, 82, and 92 mm, respectively, (Figure 1). Then, 200 mg of dried plant samples were placed in one well (source well), while in the rest of wells no plant sample was added. These wells contained a piece of filter paper that was soaked with 0.7 mL of distilled water. Thereafter, seven lettuce seeds were placed over the filter paper in each well. Five blank dishes were prepared as a control sample according to the above method except that the source well contained no plant. The dishes were sealed with parafilm tape to prevent volatile compound losses and desiccation. Then, they were wrapped in aluminum foil to prevent light penetration and incubated for 3 days at 25 °C [19].

2.2.2. Germination and Seed Traits

The effects of the strongest inhibitory plants in the screening stage on the percentage and rate of germination were investigated. For this reason, the data were recorded every 12 h for a period of 3 days and calculated using the following formulas.
Germination percentage = (Total germinated seeds by the end of experiment/Total seeds) × 100 [25,26].
Germination Rate = (Number of germinated seeds per day/Number of days after planting) × 100 [27].

2.3. Assessment of Medicinal Plants’ Allelopathic Effects on Lettuce Seedling Growth

At the end of the incubation period (3 days), to evaluate the growth of hypocotyl and radicle of lettuce seeds, they were placed on checkered paper and photographed and measured by Image J (version 1.331, August 2022) and Excel software (Version 2210) (Figure 2a–c).

2.4. Statistical Analysis

The inhibition index of plants on lettuce seed germination factors (Criteria) was defined.
According to the results obtained from the average inhibition activity, the mean and standard deviation were calculated, and based on these, the basis of different groupings for the inhibition percentage of plants were defined in 4 levels: (****) Mean + 3 SD, (***) Mean + 2 SD, (**) Mean + 1 SD, (*) Mean < 1 SD. The highest inhibition percentage is related to the group (****), which indicates 3 times the difference with the standard deviation, (***) which indicates 2 times the difference with the standard deviation, (**) which indicates one times the difference with the standard deviation, and (*) which indicates less than 1 times the difference with the standard deviation.
The inhibition index of the samples was calculated and the means of three replications were analyzed statistically on the basis of RCD in each family. The means comparison was performed using Duncan’s multiple-range test at 0.05 level of probability. Minitab (version 21.01.0), Graphpad Prism (Version 9.4.1), and Excel (Version 2210) were used for the statistical analysis and graphing. In addition, the variance analysis was performed separately for plants of each family.

2.5. Headspace Gas Chromatography-Mass Spectrometry (HS-GC-MS)

Headspace GC-MS was performed to investigate the chemical composition of the samples’ volatiles with the most allelopathic effects. For this purpose, 200 mg of solid plant samples were incubated in 20 mL glass vials and stored at 20 °C for one hour. Then, 1000 μL of air over each sample was removed by a 5 mL SGE 5MDR-HSV syringe and injected into the GC-MS (Shimadzu QP 2010, Tokyo, Japan). GC analysis was run on a (30 m × 250 μm × 0.25 μm) column with helium gas as the carrier gas. The temperature of the injection was as follows: the oven temperature was 50–150 °C, with an increase rate of 3 °C/min, kept in this mode for 10 min, then, reached a temperature of 200 °C at 10 °C/min. The components of volatile compounds were determined by the device library (NIST/NBS). Mass spectra were registered at 70 eV with a mass range of 50 to 400 m/z, in comparison to an internal spectral library (NIST and Wiley). They were then validated by comparing the retention times with the valid standards.

2.6. Allelopathic Interaction Effects

After investigating the allelopathic effects of plants, the strongest inhibitory plants were identified. In order to investigate the allelopathic interaction of these plants, the effective concentration was determined first.

2.6.1. Determination of EC50 and EC25

EC50 is the concentration of the substance that causes 50% of the effect in a process. To evaluate EC50, different concentrations (10, 50, 70, 100, 120, 150, and 200 mg) of each allelopathic plant were examined using the dish pack method. The determination of these values for radical and hypocotyl resistance was performed separately. The results were analyzed by GraphPad Prism 8 software and finally EC50 value was calculated. In addition, the values of EC25 were calculated with Quick Calcs online software.

2.6.2. Plants Combinations

The combined effects of these plants were performed based on the combination of EC25 concentrations. The experiments were conducted to investigate the radicle and hypocotyl growth. Based on the FIC formula and isobologram curves, the mutual behaviors of two plants were investigated.
  • Fractional inhibitory concentration (FIC) was calculated using the following equation:
FIC = obtained inhibitory effect in combining two plants expected inhibitory effect in combining two plants
The obtained inhibitory effect of combining two plants indicating the inhibition percentage that was obtained as a result of the combination two plants at a concentration of EC25 in the test.
The expected inhibitory effect of combining two plants indicating the inhibition value of 25% in the concentration of EC25 for each plant, which is naturally expected to be 50% in combination [28].
  • Isobologram curves
To draw the isobologram curves, the inhibitory effect of two plants was considered as base, then the concentrations of each plant A and B that lead to similar inhibition were calculated separately using Quick Calcs software. Therefore, three concentrations (A alone, B alone, and A + B in combination) were used to draw graphs showing the same inhibition percentage in all three modes. This curve can be describe in three different situations: (1) without a curve indicating an additive effect; (2) with an upward curve, meaning an antagonistic effect; and (3) with a downward curve, meaning a synergistic effect (Figure 3) [6].

3. Results

3.1. Allelopathic Effects of Medicinal Plants on Lettuce Seed Growth and Germination Specifications

The results of allelopathic effects of 123 samples selected from 31 plant families are shown in Table 1. In each of the samples, the part with evidence of the presence of the most volatile compounds was selected and analyzed at close distances (41 mm) and the total average (whole wells).
Among these samples, seven species showed a strong inhibitory effect on the germination of lettuce seeds. Some samples also stimulated the germination of lettuce seeds compared to the control.

3.1.1. Radicle Growth (R %)

The most inhibitory effects on radicle growth were observed in the families Lamiaceae (N. binaloudensis leaf); Asteraceae (A. nobilis flower, A. biebersteinii leaf and P. gnaphalodes seed); Apiaceae (E. platyloba stem, C. maculatum leaf, F. vulgare fruit, and T. ammi fruit); Euphorbiaceae (E. serpens leaf, and E. granulata leaf); Solanaceae (L. ruthenicum leaf); Amaranthaceae (A. hypochondriacus leaf); Malvaceae (M. sylvestris leaf) and Hypericaceae (H. perforatum leaf). In the family Verbenaceae, it can be concluded that the individual species had an inhibitory effect on radicle growth (although in some cases small), but no significant difference between them was detected.

3.1.2. Hypocotyl Growth (H %)

The most inhibitory effects on hypocotyl growth were observed in the families Lamiaceae (N. binaloudensis leaf); Asteraceae (P. gnaphalodes seed); Apiaceae (E. platyloba stem, C. maculatum leaf, F. vulgare fruit, and T. ammi fruit); Euphorbiaceae (E. serpens leaf); Solanaceae (L. ruthenicum leaf) and L. depressum leaf); Amaranthaceae (A. hypochondriacus leaf); Malvaceae (M. sylvestris leaf); Verbenaceae (L. montevidensis leaf) and Hypericaceae (H. perforatum leaf and H. helianthemoides root) (Figures S1–S9).

3.1.3. Germination Percentage (G %)

The comparison of the average germination percentage of lettuce seeds in the vicinity of strong inhibitory plants in the period of 3 days of the experiment is shown in Figure 4. In the treatment of S. ceratophylla (leaf), most germinated seeds were observed compared to other treatments, about 72.76%. The highest degree of reduction in lettuce seed germination percentages were observed in the treatment L. officinalis (flower) and N. binaloudensis (leaf), respectively, (23.61, 22.85%).

3.1.4. Germination Rate

As it can be seen in Figure 5, S. ceratophylla (leaf), in comparison to other treatments showed the least effect on reducing the germination rate of lettuce seeds (12.86), while the highest effects belong to N. binaloudensis (leaf) and L. officinalis (flower), respectively, (3.60 and 3.32). The results showed that there is a significant difference between the plants at the probability level of 5%.

3.2. Headspace Gas Chromatography-Mass Spectrometry (HS-GC-MS)

In order to identify the compounds causing allelopathy in the most inhibitory plants (seven specimens), the headspace analysis was performed. The results showed that some phenolic compounds, such as Thymol, Carvacrol, P-Cymene and 1,8-Cineole, were the most common important components in these plants (Table 2).

3.3. The Specification of Effective Concentrations of the Most Inhibitory Plants

The determination of the effective concentration of plants on germination inhibition (EC50 and EC25) was performed on seven plants, including S. lavandulifolia (flowering branch), S. ceratophylla (leaf), E. platyloba (stem), T. ammi (fruit), L. officinalis (flower), N. binaloudensis (leaf), and P. graveolens (leaf). For all these plants, EC50 and EC25, based on the radicle and hypocotyl inhibition, were calculated (Table 3).

3.4. Allelopathic Interaction of the Most Inhibitory Plants

The interaction effect of the strongest inhibitory plants was evaluated separately on radicle and hypocotyl growth based on the screening test results.

3.4.1. Interaction Result

The results showed a different combination of synergistic, additive, and antagonistic effects. As shown in Table 4, 42 results were obtained from 21 combinations, which were investigated on radicle and hypocotyl growth separately. In the investigation of these interactions on radicle inhibition, 15 combinations showed antagonistic interactions, 4 combinations showed additive interactions, and 2 combinations showed synergistic interactions. The combination of P. graveolens (leaf) and L. officinalis (flower) had the most antagonistic interaction (28.13%). The most synergistic interaction (80.00%) was observed in the combination of the T. amm (fruit) and N. binaloudensis (leaf).
For hypocotyl inhibition among 21 combinations, 1 combination showed antagonistic effects, 3 combinations showed additive effects, and most combinations (17 combinations) showed synergistic effects. The combination of P. graveolens (leaf) and L. officinalis (flower) was the only combination to show an antagonistic effect (26.91%). Also, the most synergistic effects (100.00%) were observed in two combinations of N. binaloudensis (leaf) with T. ammi (fruit) or L. officinalis (flower).

3.4.2. Isobologram Curves

Different types of allelopathic effects were shown in the isobologram curves (Figure 6 and Figure 7). In the isobologram curves of root growth inhibition (Figure 3), 11 curves showed antagonistic effects between strong inhibitory plants, 3 curves showed synergistic effects, and 7 combinations showed growth stimulating effects. The highest number of antagonistic effects related to S. ceratophylla (leaf) (with five antagonistic effects) and P. graveolens (leaf) had the highest number of synergistic effects (two synergistic effects) with other plants. The additive effects were not observed in these compounds. In the isobolograms showing hypocotyl inhibition (Figure 4), one combination showed antagonistic effects (L. officinalis (flower) with P. graveolens (leaf)), two curves showed synergistic status between plants, and 18 other curves showed synergistic effects. N. binaloudensis (leaf) and T. ammi (fruit) showed the highest number of synergistic curves.

3.4.3. Comparison of Evaluation Methods of Allelopathic Interactions

In order to compare two evaluation methods (FIC and Isobologram curves), the results are summarized in Table 5.
As you can see in Table 5, the analysis of two methods in more than one case provides similar results. Since in the FIC method, the comparison is based on numbers, it seems that it is more accurate, but more research is needed to ensure this claim.

4. Discussion

The results obtained from this research, in many items, confirm the allelopathic effects of the medicinal plants and their volatile compounds seen in other research. In the lamiaceae family, L. officinalis, S. ceratophylla, S. lavandulifolia, and N. binaloudensis were reported as strong inhibitory plants. In several field observations, Nepeta species prevent the germination of other plant species in their surroundings [29]. In another report on the allelopathic effect of N. binaloudensis, the germination and growth of sunflower seeds had been inhibited by the aqueous extract of its roots and leaves. The inhibitory effect of L. officinalis on the germination of lettuce seeds was observed by the plant box method [30]. Also, a high suppression (83–95%) of radicle elongation was observed in the flowers of Lavandula vera [16]. A study conducted on the allelopathic effects of volatile compounds of different medicinal plants, N. binaloudensis, L. angustifolia, P. graveolens, T. ammi, and salvia species, on factors such as germination percentage, average germination time, radicle and hypocotyl length, vigor index, and dormancy incubation, observed similar results in lettuce seeds [6].
In another report on investigating plant growth inhibitory activities, Salvia officinalis had 100% inhibition of lettuce radicle and hypocotyl growth [7]. In addition, investigating the activity of the essential oil and methanolic extract of E. platyloba showed very strong activity against bacteria [31]. In a study to investigate the antioxidant activity of aqueous and ethanolic extracts of S. lavandulifolia, both types of extracts showed good potential with high phenolic content [32].
The use of plants as phytochemicals has been widely seen in recent years due to the presence of chemicals and the development of cross-resistance to the lack of use of synthetic insecticides [33]. The formulations obtained from different species of the genus Nepeta with a large amount of essential oil and flavonoids showed high antimicrobial, antifungal, and insecticidal properties [34,35]. In another study on the essential oil Nepeta cataria against Spodoptera littoralis larvae, a high insecticidal activity of this plant was seen (LC50) value ≤ 10.0 mL/m3) [36]. In a previously conducted study, the effect of P. graveolens has been investigated as an insecticidal property in killing larvae and preventing egg laying [37,38]. In our study, the leaf of this plant showed many inhibitory effects.
Forasmuch as Thymol, Carvacrol, P-Cymene, and 1,8-Cineole were very high in our headspace experiments, they are probably the main factor responsible for the inhibitory effects. In the report, it was shown that monoterpenes such as 1,8 cineole, thymol, geraniol, menthol, and camphor strongly inhibit the radicle growth of Z. mays L. seedlings [39]. In another study, the effects of insecticides and insect repellants of 1,8 cineole were confirmed [40,41].
Another important point in this experiment was the combination of the most inhibitory plants to check the allelopathic properties that were observed as synergistic, additive, and antagonistic effects. Before this, the synergistic effects in antibacterial and antioxidant activity of the combination of Coriandrum sativum with Cuminum cyminum essential oils was reported [42]. Also, the synergistic antimicrobial effects of volatile compounds, such as eugenol with menthol and linalool and carvacrol with thymol, have been reported [43]. In another study, the synergistic antifungal effects were shown between the essential oils of Mentha spicata with Melaleuca alternifolia and Thymus vulgaris with Cinnamomum verum and Origanum majorana [24].

5. Conclusions

Medicinal plants, especially aromatic ones, have been used in traditional medicine in Iran and have potential allelopathic activity. They are good candidates for finding new allelochemicals to be used in agriculture as bio-herbicides. Among the investigated plants, those with high inhibitory effects were introduced and could be used to control, and even destroy, weeds. In this research, the combination of the N. binaloudensis (leaf) with T. ammi (fruit) and N. binaloudensis (leaf) with L. officinalis (flower) had great synergistic effects. For more accurate investigations in the future, the interaction of the main compounds of these plants can be performed in vitro as well as in field conditions. Also, their interaction effects can provide a new field for the bioherbicide research and such effects can play a significant role in determining the effective dose of each compound. Therefore, applying these compounds in agriculture will help us to reduce the use of chemical pesticides, and ultimately contribute to the health of our community.

Supplementary Materials

The following supporting information can be downloaded at:, Figure S1. Allelopathic effects of plant species of Lamiaceae on radicle and hypocotyl of lettuce. Figure S2. Allelopathic effects of plant species of Asteraceae on radicle and hypocotyl of lettuce. Figure S3. Allelopathic effects of plant species of Apiaceae on radicle and hypocotyl of lettuce. Figure S4. Allelopathic effects of plant species of Euphorbiaceae on radicle and hypocotyl of lettuce. Figure S5. Allelopathic effects of plant species of Solanaceae on radicle and hypocotyl of lettuce. Figure S6. Allelopathic effects of plant species of Amaranthaceae on radicle and hypocotyl of lettuce. Figure S7. Allelopathic effects of plant species of Malvaceae on radicle and hypocotyl of lettuce. Figure S8. Allelopathic effects of plant species of Verbenaceae on radicle and hypocotyl of lettuce. Figure S9. Allelopathic effects of plant species of Hypericaceae on radicle and hypocotyl of lettuce.

Author Contributions

S.S.: Investigation and methodology; S.M.: formal analysis, review, and editing; J.Z.: writing—review and editing; M.R.J.: advisor, research design advising; M.A.: supervision, conceptualization, data curation, manuscript editing and finalizing, funding acquisition; Y.F.: project administration, supervision, conceptualization, data curation, funding acquisition. All authors have read and agreed to the published version of the manuscript.


This research was supported by Ferdowsi University of Mashhad under research grant No. 37847. This study was supported partially by a grant from JST CREST (Grant Number JPMJCR17O2), Japan.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.


The authors appreciate botanists from the Research Center of Plant Science for the identification of plant species.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. The views of multi-well dishes with six wells used in the dish pack method.
Figure 1. The views of multi-well dishes with six wells used in the dish pack method.
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Figure 2. (a) Examination of germinated lettuce seeds after an incubation period, (b) Preparing germinated seeds on checkered paper, and (c) Measuring hypocotyl and radicle growth using Image J software.
Figure 2. (a) Examination of germinated lettuce seeds after an incubation period, (b) Preparing germinated seeds on checkered paper, and (c) Measuring hypocotyl and radicle growth using Image J software.
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Figure 3. Schematic diagram of the isobologram curve to determine allelopathic interactions.
Figure 3. Schematic diagram of the isobologram curve to determine allelopathic interactions.
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Figure 4. Effect of the strongest inhibitory plants on germination percentage of lettuce seeds.
Figure 4. Effect of the strongest inhibitory plants on germination percentage of lettuce seeds.
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Figure 5. Effect of the most inhibitory plants on the germination rate of lettuce seeds. Columns followed by the same letter (a–d) are not significantly different (p ≤ 0.05) by Duncan’s multiple range tests.
Figure 5. Effect of the most inhibitory plants on the germination rate of lettuce seeds. Columns followed by the same letter (a–d) are not significantly different (p ≤ 0.05) by Duncan’s multiple range tests.
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Figure 6. Isobologram curves of allelopathic interaction effects of plants on lettuce radicle growth inhibition.
Figure 6. Isobologram curves of allelopathic interaction effects of plants on lettuce radicle growth inhibition.
Agronomy 12 03001 g006aAgronomy 12 03001 g006b
Figure 7. Isobologram curves of allelopathic interaction effects of plants on lettuce hypocotyl growth inhibition.
Figure 7. Isobologram curves of allelopathic interaction effects of plants on lettuce hypocotyl growth inhibition.
Agronomy 12 03001 g007aAgronomy 12 03001 g007b
Table 1. The results of investigating the inhibitory power of medicinal plants on the growth of lettuce seeds.
Table 1. The results of investigating the inhibitory power of medicinal plants on the growth of lettuce seeds.
NoFamilyPlant Scientific NamePart UsedInhibition Activity (%)Criteria
Average (41 mm)Average (Whole Wells)
1AmaranthaceaeAmaranthus blitoidesFlower30.1236.0830.7040.00**
2Amaranthus hypochondriacusLeaf79.0385.6262.5263.52***
3Atriplex halimusLeaf36.9816.3429.5020.42**
4Kochia prostrataLeaf24.6015.0228.2715.92*
5AmaryllidaceaeAllium sativumLeaf30.0318.0425.2718.25*
6Ungernia trisphaeraLeaf9.72−6.32−21.64−39.28+
7ApiaceaeConium maculatumLeaf82.5785.9366.7073.57***
8Echinophora platylobaStem100.00100.0084.6282.85****
9Ferula szowitsianaLeaf26.9429.225.57−6.90*
10Ferula xylorhachisLeaf11.8517.78−2.080.77+
11Foeniculum vulgareFruit87.0385.2379.9176.73***
12Seseli transcaucasicumFlower12.4735.98−8.70−6.82+
13Seseli transcaucasicumLeaf37.9438.031.61−22.83*
14Seseli transcaucasicumStem33.6329.21−8.06−55.43+
15Seseli transcaucasicumRoot28.0431.6913.911.52*
16Trachyspermum ammiFruit99.41100.0080.2082.73****
17ApocynaceaeVinca minorLeaf77.6280.5552.2351.49**
18AsparagaceaeAsparagus officinalisLeaf78.2586.5466.3572.66***
19AsteraceaeAchillea nobilisFlower94.5697.1082.3979.58***
20Achillea filipendulaLeaf55.2366.3550.8260.69**
21Achillea biebersteiniiLeaf90.0588.3785.7283.40***
22Achillea wilhelmsiiFlower87.9891.6862.1362.93***
23Achillea millefoliumFlower27.9018.0225.8216.74*
24Achillea pachycephalaLeaf70.1765.3660.4953.56***
25Artemisia absinthiumLeaf39.7537.5040.4750.54**
26Artemisia scopariaLeaf40.8135.5635.1627.38**
27Artemisia tournefortianaLeaf11.3616.12−3.64−13.16+
28Calandula officinalisLeaf50.5446.8758.1259.35**
29Centaurea behenLeaf82.1183.8671.3170.17***
30Cousinia raddeanaFlower26.6127.4334.6937.11**
31Codonocephalum peacockianumLeaf36.4020.9130.2730.48**
32Grindelia robustaLeaf31.9031.4648.4944.94**
33Helichrysum italicumLeaf51.9159.1544.6245.42**
34Lactuca persicaLeaf20.4503.04−12.73−9.21+
35Matricaria chamomillaFlower64.7068.1946.5152.23**
36Pseudohandelia umbelliferaLeaf55.1459.7944.5040.47**
37Pulicaria gnaphalodesLeaf28.5530.6916.1113.50*
38Pulicaria gnaphalodesSeed91.0789.7284.6886.79***
39Santolina chamaecyparissusLeaf85.0292.0566.2569.57***
40Tanacetum balsamitaLeaf45.2443.9423.1728.87*
41BerberidaceaeBerberis integerrimaRoot59.0033.9146.2634.12**
42Berberis vulgarisLeaf59.0748.6840.3031.62**
43BoraginaceaeTrichodesma incanumLeaf58.9659.9757.3361.59**
44CannabaceaeCannabis sativaLeaf32.14−4.59−18.57−33.67+
45CapparaceaeCapparis spinosaLeaf87.8692.0257.6968.82**
46CleomaceaeCleome chorassanicaLeaf8.6327.694.5918.94*
47EphedraceaeEphedra majorLeaf−0.86−29.55−14.81−54.96+
48EuphorbiaceaeChrozophora tinctoriaLeaf60.6261.5949.5555.67**
49Ricinus communisFruit56.6749.6520.2627.47*
50Euphorbia petiolataLeaf7.954.61−10.30−18.42+
51Euphorbia serpensLeaf82.7488.2375.2375.29***
52Euphorbia aelleniiLeaf26.9610.1520.2612.25*
53Euphorbia granulataLeaf82.4788.3960.3563.68***
54FabaceaeGenista tinctoriaLeaf41.6937.7734.1222.73**
55FrankeniaceaeFrankenia sppLeaf33.524.7627.6331.92*
56GeraniaceaePelargonium graveolensLeaf100.00100.0083.3588.23****
57HypericaceaeHypericum helianthemoidesRoot51.6859.6237.5248.88**
58Hypericum perforatumLeaf71.8163.7149.2152.58**
59Hypericum scabrumLeaf34.9135.45−11.50−36.67+
60LamiaceaeAcinos graveolensLeaf63.9779.2137.2652.02**
61Ballota nigraLeaf44.9361.8426.1325.00*
62Hyssopus angustifoliusLeaf18.483.639.82−7.34*
63Hyssopus angustifoliusFlower39.8328.1330.4428.13**
64Lavandula officinalisFlower100.00100.0080.2982.49****
65Lavandula officinalisLeaf84.5692.1482.2488.39***
66Melissa officinalisLeaf−12.36−1.2223.6422.37*
67Melissa officinalisRoot−12.37−1.23−13.39−1.50+
68Mentha piperitaLeaf−10.0310.440.6218.36*
69Mentha spicataLeaf18.0629.409.127.31*
70Mentha longifoliaLeaf90.0388.5567.2077.57***
71Nepeta binaloudensisLeaf100.00100.0096.3798.28****
72Nepeta sintenisiiLeaf29.5218.8214.0616.37*
73Origanum majorLeaf57.0775.5416.1617.39*
74Origanum vulgareLeaf64.1292.96−26.70−26.53+
75Perovskia abrotanoidesLeaf95.3289.6581.8177.13***
76Perovskia abrotanoidesSeed31.2120.9420.8116.78*
77Rosmarinus officinalisLeaf90.6791.6380.8685.41***
78Salvia aethiopisLeaf94.6292.5655.3836.90**
79Salvia tebesanaLeaf12.50−22.64−1.00−31.13+
80Salvia nemorosaLeaf71.6871.4563.1665.95***
81Salvia leriifoliaRoot86.2178.9355.8854.49**
82Salvia leriifoliaLeaf67.5465.8985.0683.94***
83Salvia chloroleucaLeaf67.1659.8529.3626.37**
84Salvia ceratophyllaLeaf100.00100.0083.8781.54****
85Salvia macrosiphonLeaf71.5670.5157.7951.84**
86Salvia officinalisLeaf17.866.4911.43−8.16*
87Salvia virgataLeaf32.9935.0040.1741.90**
88Salvia sahendicaLeaf17.8526.0724.5218.59*
89Salvia sclareaLeaf62.2473.5460.1968.31***
90Stachys byzantinaLeaf43.4347.0540.0545.20**
91Stachys lavandulifoliaFlowering branch100.00100.0089.5288.65****
92Thuspeinanta brahuicaLeaf18.18−0.32−10.61−12.72+
93Teucrium chamaedrysLeaf21.367.8417.5713.74*
94MalvaceaeAlthaea officinalisLeaf21.9451.1221.6140.32*
95Malva sylvestrisFlower76.1382.2076.7578.11***
96Malva sylvestrisLeaf49.4154.5816.2810.13*
97MyrtaceaePeganum harmalaLeaf46.2049.0236.2244.65**
98Eucalyptus globulusLeaf100.00100.0080.2982.49***
99NitrariaceaeEucalyptus globulusSeed−12.99−59.002.01−14.22*
100OnagraceaeEpilobium hirsutumLeaf30.0238.0227.2232.99*
101Oenothera biennisLeaf63.0061.0147.5052.41**
102PapaveraceaeCorydalis aitchisoniiLeaf34.4334.8319.2718.55*
103Glaucium flavumLeaf14.6910.1814.85−8.59*
104PlantaginaceaePlantago majorLeaf29.9628.8714.9417.75*
105PolygonaceaePolygonum aviculareLeaf68.7159.0945.1247.84**
106Polygonum patulumLeaf53.9160.0148.0054.02**
107RosaceaeFilipendula ulmariaLeaf16.072.0818.1820.58*
108Rosa foetidaLeaf58.4461.1560.8157.81***
109RutaceaeHaplophyllum furfuraceumLeaf76.9176.1237.1327.99**
110Ruta graveolensLeaf33.5241.4424.4030.36*
111SolanaceaeDatura innoxiaLeaf37.7240.0139.1633.78**
112Datura stramoniumLeaf12.3312.88−3.402.00+
113Lycium depressumLeaf52.6047.9045.9948.99**
114Lycium ruthenicumLeaf55.3755.3559.2461.67***
115Solanum nigrumLeaf38.8336.6032.4228.80**
116UrticaceaeUrtica dioicaLeaf77.6570.9768.3971.68***
117Urtica dioicaRoot−45.3021.10−30.0020.01+
118VerbenaceaeLippia citriodoraLeaf91.3587.3233.0222.83**
119Vitex pseudo-negundoLeaf53.9045.7523.9920.33*
120Vitex pseudo-negundoSeed20.2238.5415.2212.02*
121Lantana montevidensisLeaf19.6715.3329.5036.49**
122ZygophyllaceaeTribulus terrestrisLeaf56.8867.9446.1441.68**
123Zygophyllum fabagoLeaf12.9411.2720.2216.21*
1—The intensity of the inhibitory effect on lettuce seed germination was defined by the standard deviation value in four levels: Criteria (****) Mean + 3 SD; (***) Mean + 2 SD; (**) Mean + 1 SD; (*) Mean < 1 SD. 2—Negative numbers indicate stimulating effects on lettuce seed germination.
Table 2. The main components of the most inhibitory plants based on Headspace Gas Chromatography-Mass Spectrometry (HS-GC-MS) analysis.
Table 2. The main components of the most inhibitory plants based on Headspace Gas Chromatography-Mass Spectrometry (HS-GC-MS) analysis.
SamplePart of UseMain Identified ComponentsRT%Area
Stachys lavandulifoliaFlowering branchThymol20.137.96
Salvia ceratophyllaLeafCarvacrol17.91.42
Thymol acetate20.1311.82
Echinophora platylobaStemThymol acetate20.115.31
Trachyspermum ammiFruitP-Cymene8.820.82
Carvacrol Methyl Ether17.90.77
Lavandula officinalisFlower1,8-Cineole9.114.3
Carvacrol Methyl Ether17.90.42
Bornyl acetate37.40.7
Nepeta binaloudensisLeafp-Cymene8.840.57
Carvacrol Methyl Ether17.920.21
α-iso-methyl ionone20.50.71
Pelargonium graveolensLeafβ-pinene7.171.22
RT: Retention time (min).
Table 3. The effective concentration of the most inhibitory plants on lettuce radicle and hypocotyl by dish pack method.
Table 3. The effective concentration of the most inhibitory plants on lettuce radicle and hypocotyl by dish pack method.
Plant Scientific NamePart of
Radicle InhibitionHypocotyl Inhibition
Stachys lavandulifoliaFlowering branch20.5772.8058.97107.8
Salvia ceratophyllaLeaf36.9683.7092.87130.5
Echinophora platylobaStem2.448.991.977.915
Trachyspermum ammiFruit9.8125.4829.7252.19
Lavandula officinalisFlower7.019.868.6511.21
Nepeta binaloudensisLeaf6.767.852.647.923
Pelargonium graveolensLeaf0.535.312.7213.71
Table 4. Fractional Inhibitory Concentration (FIC) of combinations among the strongest inhibitory plants using EC25 of lettuce radicle and hypocotyl.
Table 4. Fractional Inhibitory Concentration (FIC) of combinations among the strongest inhibitory plants using EC25 of lettuce radicle and hypocotyl.
Plant InteractionsFIC
Echinophora platyloba ×Stachys lavandulifolia0.52 An1.46 S
Salvia ceratophylla0.15 An1.17 Ad
Pelargonium graveolens−0.09 St1.11 Ad
Trachyspermum ammi0.33 An1.61 S
Nepeta binaloudensis0.56 An1.30 S
Lavandula officinalis−0.12 St1.49 S
Stachys lavandulifolia ×Salvia ceratophylla0.27 An1.37 S
Pelargonium graveolens0.73 Ad1.46 S
Trachyspermum ammi0.72 Ad1.28 S
Nepeta binaloudensis−0.53 St1.56 S
Lavandula officinalis0.18 An1.85 S
Salvia ceratophylla ×Pelargonium graveolens0.58 An1.05 Ad
Trachyspermum ammi0.52 An1.24 S
Nepeta binaloudensis0.97 Ad1.22 S
Lavandula officinalis−0.50 St1.36 S
Pelargonium graveolens ×Trachyspermum ammi1.03 Ad1.97 S
Nepeta binaloudensis−0.38 St1.46 S
Lavandula officinalis−0.56 St0.54 An
Trachyspermum ammi ×Nepeta binaloudensis1.60 S2.00 S
Lavandula officinalis−0.20 St1.47 S
Nepeta binaloudensis ×Lavandula officinalis1.50 S2.00 S
FIC values ≤ 0 indicate stimulant effects (St), between 0 and 0.7 indicate antagonistic effects (An), values between 0.7 and 1.2 indicate additive effects (Ad) and values greater than 1.2 indicate synergistic effects (S).
Table 5. Fractional Inhibitory Concentration (FIC) of combinations of the strongest inhibitory plants using EC25 of lettuce radicle and hypocotyl.
Table 5. Fractional Inhibitory Concentration (FIC) of combinations of the strongest inhibitory plants using EC25 of lettuce radicle and hypocotyl.
Plant InteractionsFICIsobologram
Echinophora platyloba ×Stachys lavandulifoliaAnSAnS
Salvia ceratophyllaAnAdAnS
Pelargonium graveolensStAdStS
Trachyspermum ammiAnSAnS
Nepeta binaloudensisAnSAnS
Lavandula officinalisStSStS
Stachys lavandulifolia ×Salvia ceratophyllaAnSAnS
Pelargonium graveolensAdSSS
Trachyspermum ammiAdSAnS
Nepeta binaloudensisStSStS
Lavandula officinalisAnSAnS
Salvia ceratophylla ×Pelargonium graveolensAnAdAnS
Trachyspermum ammiAnSAnS
Nepeta binaloudensisAdSAnS
Lavandula officinalisStSStAd
Pelargonium graveolens ×Trachyspermum ammiAdSSS
Nepeta binaloudensisStSStS
Lavandula officinalisStAnStAn
Trachyspermum ammi ×Nepeta binaloudensisSSSS
Lavandula officinalisStSStS
Nepeta binaloudensis ×Lavandula officinalisSSAnS
(St): stimulant effects, (An): antagonistic effects, (Ad): additive effects, and (S): synergistic effects.
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Sadeqifard, S.; Mirmostafaee, S.; Joharchi, M.R.; Zandavifard, J.; Azizi, M.; Fujii, Y. Evaluation of Allelopathic Activity Interactions of Some Medicinal Plants Using Fractional Inhibitory Concentration and Isobologram. Agronomy 2022, 12, 3001.

AMA Style

Sadeqifard S, Mirmostafaee S, Joharchi MR, Zandavifard J, Azizi M, Fujii Y. Evaluation of Allelopathic Activity Interactions of Some Medicinal Plants Using Fractional Inhibitory Concentration and Isobologram. Agronomy. 2022; 12(12):3001.

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Sadeqifard, Somayeh, Somayeh Mirmostafaee, Mohammad Reza Joharchi, Jaleh Zandavifard, Majid Azizi, and Yoshiharu Fujii. 2022. "Evaluation of Allelopathic Activity Interactions of Some Medicinal Plants Using Fractional Inhibitory Concentration and Isobologram" Agronomy 12, no. 12: 3001.

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