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Optimization of mixed botanical insecticides from Azadirachta indica and Calophyllum soulattri against Spodoptera frugiperda using response surface methodology

  • Edy Syahputra EMAIL logo , Danar Dono EMAIL logo , Sudarjat , Yusup Hidayat , Lindung Tri Puspasari , Vira Kusuma Dewi , Safri Ishmayana and Neneng Sri Widayani
Published/Copyright: May 26, 2025
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Open Agriculture
From the journal Open Agriculture Volume 10 Issue 1

Abstract

The ratio of ingredients in the mixture of botanical insecticides affects the insecticide activity. This study conducted a single and mixed insecticide test of Azadirachta indica and Callophylum soulattri against Spodoptera frugiperda. Insecticide application used the residue method on leaves with a microsyringe and acetone + methanol solvent (4:1). The test concentration was based on the preliminary tests. Optimization of the ratio between A. indica and C. soulattri was carried out using response surface methods. The test results showed that the LC50 and LC95 values of the single insecticides A. indica and C. soulattri were 0.099, 0.420 and 0.537, 3.481%, respectively. The response surface method optimization showed that the ratio between A. indica and C. soulattri resulted in the highest mortality and the lowest feed consumption at 2.0800:1.1657. The LC50 and LC95 values of the mixed insecticide were 0.030 and 0.224%, respectively. The mixed insecticide of A. indica and C. soulattri has strong synergy at LC50 and weak synergy at LC95. The mixed insecticides of A. indica and C. soulattri can be used as an alternative environmentally friendly control against S. frugiperda in maize fields.

Keywords: botanical insecticide mixture; biological disturbance; synergism; optimize ratio ingredient

1 Introduction

The use and exploration of botanical insecticides as an alternative pest control continues to grow worldwide, including in Indonesia. The literature studies show that from 1993 to 2019, 94 types of plants were used as insecticides, with 6 main types of plants widely reported for pest control: Annona muricata, Azadirachta indica, Nicotiana tabacum, Carica papaya, and Cymbopogon nardus [1]. A. indica oil has been reported to suppress feed consumption with an LC50 value of 0.08% against Spodoptera frugiperda Smith at 16 days after treatment [2] and an LC50 of 0.68% at 12 h after treatment [3]. Other reports also show that it caused mortality of Sitophilus oryzae by 76.5% at a concentration of 10% (10 g/100 g of rice) [4]. This shows the potential of A. indica to control pests in crops and warehouses.

Reports of the effectiveness of A. indica in the form of active ingredients, extracts, and formulations have been widely reported. Azadirachtin (active ingredient of A. indica) has strong feeding and growth inhibitory activity and can interfere with the central nervous system of Drosophila [5,6]. Neem oil (pressed) has toxicity with an LC50 value of 0.039% against S. frugiperda [7], and in the formulation (50% active ingredient), it has an LC50 of 0.8% against Crocodolomia pavonana [8]. The difference in activity between active ingredients, extracts, and formulations differs based on the target pests and materials used [9,10,11]. In addition, the main constituents of commercial formulations of azadirachtin in the world market used for the control of agricultural insect pests are available [12].

Calophyllum soulattri Burm. f. is one of the plants with insecticidal properties that grows in West Kalimantan, Indonesia. C. soulattri was first reported by Syahputra et al. [13] against Crocidolomia pavonana (LC50 0.04%). C. soulattri was confirmed to have compounds from the triterpenoid group as insecticides, and soulattrin compounds that have cytotoxic activity were reported to have antibacterial activity [14,15,16]. Ethanol extract from C. soulattri bark was also reported to have an LC50 value of 0.349% against S. frugiperda [17]. C. soulattri is also reported to have active ingredients calosubellinone and garsubellin B, which have potential activity as an anticancer agent [18,19]. However, reports as an insecticide and effectiveness against other pests are still limited.

S. frugiperda is reported to be an invasive pest and causes quite high losses to maize plants in the Americas, Africa, and Asia. The moth can fly optimally for up to 10 h/night or a flight distance of 63.73 km with a speed of 2.73 km/h, so the spread in coastal areas is higher than in inland areas [20,21]. Plant damage reached 85–100% in East Nusa Tenggara and Lampung provinces and 34–52.7% in West Java, Indonesia [22,23,24]. Losses in maize production due to S. frugiperda reached $2531–6312 million in Africa, and the cost of control using synthetic insecticides reached US$ 1.30 for 100 kg of maize seeds [25,26]. S. frugiperda has 353 host plants from 76 plant families [27]. This causes the survival ability of S. frugiperda to be relatively high, and proper control is needed to reduce the population size and losses caused.

An insecticide mixture is one solution that increases insecticide toxicity. A mixture of C. soulattri with Sesamum indicum shows synergistic properties against S. frugiperda at a ratio of 4:1, but it is antagonistic in its mixture with Piper aduncum at a ratio of 1:2 [17]. A mixture of Piper sarmentosum and A. indica at ratios of 1:9, 2:8, 3:7, and 4:6 is antagonistic, while at a ratio of 5:5, it is addictive. Meanwhile, at ratios of 6:4; 7:3; 8:2 and 9:1, it is synergistic in LC50 values [28]. This shows that the ratio between mixtures determines the properties of the insecticide mixture.

Optimization of the mixture ratio between insecticide ingredients can be done using the response surface method (RSM). RSM is a set of mathematical and statistical techniques used to model and analyze problems where the desired response is influenced by multiple variables, aiming to optimize that response [29]. The use of RSM to determine the optimization of botanical insecticides is still limited. One of the uses of RSM was reported to optimize the conditions of neem seed oil, emulsifier, and water to reduce the weight reduction of pests and optimize the pesticide activity of Chrysanthemum coronarium and A. indica in the form of nanosuspension [30,31]. The mixture of C. soulattri and A. indica against S. frugiperda has not been reported yet.

This study tested botanical insecticides A. indica and C. soulattri and their mixtures against S. frugiperda. The mixture of A. indica and C. soulattri from the results of RSM optimization is expected to be an alternative for environmentally friendly control of S. fruiperda in maize plants.

2 Materials and methods

2.1 Test insect and botanical insecticide

Spodoptera frugiperda as an insect test was from the Laboratory of Pesticide and Environment Toxicology (Department of Plant Pest and Diseases, Universitas Padjadjaran, West Java, Indonesia) and reared with baby maize (pesticide-free) for larvae and 10% honey solution for adults. Environment conditions for rearing and experiment were conducted at a temperature of 27–32°C and humidity of 65–80%. Leave maize plant (Zea mays; a variety of Talenta) was used for the experiment as an insect fed for 48 h after application and replaced with baby maize until S. frugiperda became pupa.

Azadirachta indica seed (obtained from Situbondo, East Java Province, Indonesia) was pressed using a seed press machine (MKS-J05) and oil-filtered with filter paper (Whatman no. 41). C. soulattri sap (obtained from Teluk Melano District, Ketapang Regency, West Kalimantan Province, Indonesia) is cleaned from dirt with acetone, and then the solvent is removed by evaporation in Laboratory of Pesticide (Department of Agrotechnology, Universitas Tanjungpura, West Kalimantan, Indonesia). The oil and sap are put in bottles and stored at −4°C.

2.2 Experiment

2.2.1 Determination of the ratio of the insecticide mixture

A. indica and C. soulattri materials were tested using the RSM with central composite design (CCD) to obtain the optimum response. The RSM-CCD design was created using Minitab 20 software. The RSM design shows the ratio between the A. indica and C. soulattri materials. From the modeling, 13 experimental units were obtained with different ratios of each A. indica and C. soulattri material in each mixture tested (the test design is presented in Table 1). All treatments were carried out at a concentration of 0.5%. Bioassay was conducted using the residue method on the surface of the feed leaves by Widayani et al. [17]. Maize leaves (4 cm × 4 cm in size) are treated with insecticide according to treatment and concentration. Each leaf surface was smeared with 100 μl of test insecticide solution with a microsyringe. The leaves are then air-dried, and two leaves are given to the second instar larvae, S. frugiperda, in a Petri dish (9 cm in diameter) lined with tissue. The Petri dishes were stored on a table in the laboratory (temperature of 27–32°C and humidity of 65–80%). After 2 days of treatment, each live larva was placed in a plastic glass (50 ml) individually. The observation was made by counting the number of dead larvae and the feed weight consumed.

Table 1

Mixed insecticide test design of A. indica and C. soulattri

No. A. indica C. soulattri
1 1.20000 0.60000
2 2.00000 0.20000
3 1.20000 1.16569
4 1.20000 0.03431
5 1.20000 0.60000
6 2.33137 0.60000
7 2.00000 1.00000
8 1.20000 0.60000
9 1.20000 0.60000
10 0.06863 0.60000
11 0.40000 1.00000
12 1.20000 0.60000
13 0.40000 0.20000

Note: The comparative values of the lower and upper limits are as follows: A. indica: 0.4–2; C. soulattri: 0.2–1.

The data obtained were then processed using Minitab-20 to obtain an equation showing the relationship between the influence of research variables on the desired response. Then, the data were processed using ANOVA, and the relationship between responses was viewed using a contour plot to obtain the most optimum comparison. RSM verification of observation variables used a model verification test under optimum conditions with three repetitions. The optimization results were then compared with the control.

2.2.2 Botanical insecticide test

The optimization results obtained in the RSM test were then used as a mixture test insecticide. Then, the single and mixture extract tests were carried out again using the residue method on the surface of the feed leaves, as explained in the comparison test of the mixture insecticide. The test concentration used was from preliminary tests, which caused the range of 0% mortality < × < 100% insect test. The research used a randomized block design with five concentration ranges and controls, or six treatments repeated four times.

Observation parameters in the study of determining the toxicity of single and mixed insecticides include the mortality of test insect equation (1), development time, weight of feed consumption equation (2), and pupa weight.

(1) Mortality ( % ) = Σ dead S . frugiperda Σ S . frugiperda tested × 100 % .

The observation of larvae development time started 2 days after treatment (from larva instar II) until instar VI larvae at a 24-h observation interval. The time for the larvae to instars III, IV, V, and VI was recorded.

Feed consumption weight is calculated based on weight loss in the leaves used as feed. As a correction factor, the calculation uses dry and wet weights based on the average test results of five initial feed leaf sample weights. Dry weight is obtained by baking the feed leaves at 90°C for 48 h. Furthermore, the weight data are used to calculate the proportion of dry weight and obtain the initial dry weight data. Forty-eight hours after the treatment feed is given, the leaves are oven-baked at 90°C for 48 h and weighed as the final dry weight. Feed consumption weight is calculated based on equation (2):

(2) Feed consumption = dryweight before treatment – dryweight after treatment .

The pupae’s weight was observed using an analytical scale. The data obtained from observation were analyzed using the variance analysis. If the results were significant, the data were analyzed using the Duncan test using the SPSS 26 program. The probit was analyzed using the Polo Plus program 1.1.

The insecticide mixture activity is analyzed using different joint action models by calculating the combination index (CI) at the LC50 and LC95 levels [32] in equation (3):

(3) CI = LC x 1 ( cm ) LC x 1 + LC x 2 ( cm ) LC x 2 + LC x 1 ( cm ) LC x 1 × LC x 2 ( cm ) LC x 2 ,

Here, LC x 1 and LC x 2 are LC x insecticide 1 and insecticide 2 in separate tests, while LC x 1(cm) and LC x 2(cm) are the respective LC components of insecticide 1 and insecticide 2 in the mixture resulting in mortality (ex 50 and 95%). The LC value was obtained by multiplying the LC x of the mixture with the proportion of the concentration of component insecticide 1 and insecticide 2.

The categories of mixed activity properties:

CI < 0.5: strong synergies,

CI 0.5–0.77: weak synergies,

CI > 0.77–1.43: additive,

CI > 1.43: antagonistic.

3 Results

3.1 Single toxicity of A. indica and C. soulattri

The test results showed that C. soulattri sap had a rapid mortality effect on the test insects. The mortality of test insects occurred at the observation’s beginning (2–4 DAT). The probit regression analysis of C. soulattri on the mortality showed that the LC50 and LC95 values were 0.537 and 3.481% (Table 2). The results of the A. indica insecticide test showed that the mortality of the test insects occurred from the beginning of instar II until the larvae reached instar VI. The LC50 and LC95 values at the end of the observation or from S. frugiperda larva instars II–VI were 0.099 and 0.420%, respectively (Table 3).

Table 2

Probit regression parameters of the relationship between C. soulattri sap concentration and S. frugiperda mortality

Observation time a ± SE b ± SE LC50 CL95 LC95 CL95
2 DAT 0.471 ± 0.135 1.982 ± 0.233 0.579 0.287–1.413 3.910 1.544–78.586
4 DAT 0.547 ± 0.138 2.026 ± 0.235 0.537 0.297–1.078 3.481 1.537–30.272
8 DAT 0.547 ± 0.138 2.026 ± 0.235 0.537 0.297–1.078 3.481 1.537–30.272
16 DAT 0.547 ± 0.138 2.026 ± 0.235 0.537 0.297–1.078 3.481 1.537–30.272

a: intercept; b: slope; SE: standard error; LC: lethal concentration (%); CL: confidence level; DAT: day after treatment.

Table 3

Probit regression parameters of the relationship between A. indica concentration and S. frugiperda mortality

Instar time a ± SE b ± SE LC50 CL95 LC95 CL95
II–III −0.397 ± 0.284 1.258 ± 0.393 2.068 0.828−73.724 42.017 5.593−∞
II–IV 1.619 ± 0.138 2.052 ± 0.297 0.162 0.110−0.243 1.029 0.530−5.632
II–V 2.610 ± 0.326 2.735 ± 0.348 0.111 0.091−0.132 0.444 0.331−0.699
II–VI 2.630 ± 0.331 2.617 ± 0.348 0.099 0.080−0.119 0.420 0.312−0.676

a: intercept; b: slope;.

SE: standard error; LC: lethal concentration (%); CL: confidence level.

A single C. soulattri sap treatment decreased feed consumption or inhibited feed consumption by 92.67% (Table 4), prolonged larval development time from instars II–VI to 13 days or 1.12 days longer than the control (Table 5) but did not significantly decrease pupal weight (Table 6). These results indicate that in addition to causing the mortality of test insects, C. soulattri insecticides also disrupt the biological activity of pests.

Table 4

Effect of C. soulattri sap treatment on the weight of S. frugiperda feed consumption

Treatment Consumption weight (mg) X ± SE (%) Inhibition percentage (%)
C. soulattri (2.5%) 1.563 ± 0.0718a 92.67
C. soulattri (0.94%) 2.871 ± 0.3436a 86.53
C. soulattri (0.35%) 4.490 ± 0.3169b 78.93
C. soulattri (0.13%) 6.153 ± 0.2726b 71.12
C. soulattri (0.05%) 7.753 ± 0.1537c 63.61
Control 21.307 ± 0.3316d 0.00

Values followed by the same letter indicate no significant difference according to Duncan’s test at the 5% level. X: mean; SE: standard error.

Table 5

Effect of C. soulattri sap treatment on the development time of S. frugiperda larvae

Treatment X ± SE
N Instars II–II N Instars II–IV N Instars II–V N Instars II–VI
C. soulattri (2.5%) 1 4 ± − 1 7.00 ± − 1 10.00 ± − 1 13.00 ± −
C. soulattri (0.94%) 17 3.29 ± 0.111 15 6.13 ± 0.088 15 9.27 ± 0.114 15 12.40 ± 0.126
C. soulattri (0.35%) 28 3.46 ± 0.094 28 6.21 ± 0.078 28 9.36 ± 0.091 28 12.39 ± 0.105
C. soulattri (0.13%) 36 3.11 ± 0.094 36 5.86 ± 0.080 36 9.28 ± 0.075 36 12.53 ± 0.083
C. soulattri (0.05%) 38 3.42 ± 0.109 38 5.66 ± 0.100 38 9.37 ± 0.078 38 12.32 ± 0.075
Control 40 2.45±0.100 40 5.48±0.079 40 8.75 ± 0.116 40 11.88 ± 0.052

X: mean; SE: standard error; and N: number of larvae.

Table 6

Effect of C. soulattri sap treatment on S. frugiperda pupae weight

Treatment n Pupae weight X ± SE (g)
C. soulattri (2.5%) 1 0.2102 ± 0.0000
C. soulattri (0.94%) 15 0.1934 ± 0.0041
C. soulattri (0.35%) 26 0.1885 ± 0.0045
C. soulattri (0.13%) 35 0.1871 ± 0.0051
C. soulattri (0.05%) 37 0.1861 ± 0.0032
Control 40 0.2111 ± 0.0018

X: mean; SE: standard error; and N: number of pupae.

3.2 Mix ratio optimization using RSM

This study used the RSM factor and CCD. For one numerical factor, CCD has five levels (−α, −1, 0, +1, +α). Based on 13 treatments tested from the 2-factor RSM-CCD design (Table 7), the observation parameters of the mortality of test insects and the weight of feed consumption were obtained. Test insects’ mortality was observed for optimization analysis on the 7th day after treatment. This is based on analyzing the most visible differences in response compared to the observation day before or after the 7th day (Figure 1). At the beginning of the observation, an increase in the mortality of test insects was still seen. In contrast, in the observation, after 7 DAT, several treatments showed 100% mortality, so the data were not used for RSM analysis.

Table 7

Results of RSM test of a mixture of A. indica and C. soulattri at various comparison ratios

Treatment Mortality percentage on 7 DAT (%) Weight larva consumption (mg)
No. A. indica C. soulattri
1 1.2000 0.6000 65 3.341
2 2.0000 0.2000 55 0.531
3 1.2000 1.1657 85 0.906
4 1.2000 0.0343 75 4.075
5 1.2000 0.6000 60 1.678
6 2.3314 0.6000 90 9.388
7 2.0000 1.0000 100 1.106
8 1.2000 0.6000 65 8.997
9 1.2000 0.6000 55 4.941
10 0.0686 0.6000 100 6.819
11 0.4000 1.0000 100 7.653
12 1.2000 0.6000 80 4.731
13 0.4000 0.2000 100 2.028
Figure 1 Mortality graph of S. frugiperda in the mixed RSM test of A. indica and C. soulattri.
Figure 1

Mortality graph of S. frugiperda in the mixed RSM test of A. indica and C. soulattri.

Based on the research, test insects’ mortality against various comparisons of A. indica and C. soulattri were obtained (Table 8). The analysis showed that equation (4) can be used to predict the mortality response of test insects. In addition, the results of the ANOVA showed that single A. indica and C. soulattri did not significantly affect the mortality of test insects, with a P value > 0.05. As for the interaction between A. indica * A. indica and A. indica * C. soulattri, it showed a significant effect where P < 0.05. The results of the ANOVA showed that the P value of the lack-of-fit test was 0.508, which was greater than the degree of significance of 0.05, which means that the model formed was acceptable. The results of the contour plot analysis also showed that the number of comparisons of A. indica was 0.5 and the number of C. soulattri was between 0.2 and 0.4, or the number of comparisons of A. indica was less than < 0.5, and the number of C. soulattri was greater than 1 to obtain the optimum response of mortality of test insects (Figure 2).

(4) Larvae mortality ( 7 DAT ) ( % ) = 142,3 − 87,8 A . i n d i c a − 82.3 C . s o u l a t t r i + 23.93 A . i n d i c a ⁎ A . i n d i c a + 48.8 C . s o u l a t t r i ⁎ C . s o u l a t t r i + 35.2 A . i n d i c a ⁎ C . s o u l a t t r i

Table 8

Results of ANOVA of the RSM mixture of A. indica and C. soulattri based on the mortality of S. frugiperda

Source DF Adj SS Adj MS F-value P-value
Model 5 3251.1 650.23 7.70 0.009
Linear 2 874.4 437.22 5.18 0.042
A. indica 1 437.2 437.22 5.18 0.057
C. soulattri 1 437.2 437.22 5.18 0.057
Square 2 1870.4 935.22 11.07 0.007
A. indica*A. indica 1 1631.1 1631.11 19.31 0.003
C. soulattri*C. soulattri 1 424.6 424.59 5.03 0.060
2-Way interaction 1 506.3 506.25 5.99 0.044
A. indica*C. soulattri 1 506.3 506.25 5.99 0.044
Error 7 591.2 84.45
Lack-of-fit 3 241.2 80.39 0.92 0.508
Pure error 4 350.0 87.50
Total 12 3842.3
Figure 2 Contour plot between A. indica and C. soulattri on the mortality of S. frugiperda.
Figure 2

Contour plot between A. indica and C. soulattri on the mortality of S. frugiperda.

Consumption weight is another parameter observed to determine the level of plant damage after the test insecticide treatment. The lower the consumption weight, the lower the possibility of plant damage due to pest attacks. In this study, the values of feed consumption weight for various comparisons of A. indica and C. soulattri, equation (5), were obtained, which showed the relationship between the mixture of A. indica and C. soulattri and the response of feed consumption weight. The results of the ANOVA test showed that the amount of the A. indica and C. soulattri mixture did not significantly affect the feed consumption weight because the P value of both parameters was >0.05%. However, the results of the ANOVA test showed that the lack-of-fit value was 0.261, which was greater than the significance level of 0.05, which means that the model formed was acceptable (Table 9). The response from the equation obtained can be plotted with a contour plot to see the RSM shape of the experimental points more clearly. From these results, it can be predicted that the optimum concentration of the ratio of A. indica and C. soulattri to obtain a feed consumption weight of less than 1.5 mg, then the ratio of A. indica is 0.5–1.5 and the ratio of C. soulattri is less than 0.2 (Figure 3).

(5) Weight consumption ( mg ) = 0,88 − 2,32 A . i n d i c a + 18.3 C . s o u l a t t r i + 1.67 A . i n d i c a ⁎ A . i n d i c a − 10.88 C . s o u l a t t r i ⁎ C . s o u l a t t r i − 3.95 A . i n d i c a ⁎ C . s o u l a t t r i

Table 9

Results of variance analysis of RSM A. indica and C. soulattri based on weight consumption

Source DF Adj SS Adj MS F-value P-value
Model 5 42.076 8.4152 0.81 0.579
 Linear 2 2.801 1.4007 0.13 0.876
  A. indica 1 2.432 2.4321 0.23 0.644
  C. soulattri 1 0.369 0.3692 0.04 0.856
 Square 2 32.899 16.4494 1.58 0.272
  A. indica*A. indica 1 7.905 7.9053 0.76 0.413
  C. soulattri*C. soulattri 1 21.067 21.0672 2.02 0.198
 2-Way interaction 1 6.376 6.3756 0.61 0.460
  A. indica*C. soulattri 1 6.376 6.3756 0.61 0.460
Error 7 72.972 10.4246
 Lack-of-fit 3 43.478 14.4926 1.97 0.261
 Pure error 4 29.495 7.3737
Total 12 115.048
Figure 3 Contour plot between A. indica and C. soulattri on weight consumption of S. frugiperda.
Figure 3

Contour plot between A. indica and C. soulattri on weight consumption of S. frugiperda.

The next step in RSM is ratio optimization. Optimization is carried out to obtain the optimal value of the model. In optimizing the mortality of test insects and consumption weight, the optimized two responses are analyzed, namely the maximum mortality condition of test insects and the minimum consumption weight. Based on the RSM optimization to obtain the highest mortality of 100% and a minimum weight of 0.2802 mg, the number of comparisons of C. soulattri is 1.1657 and A. indica is 2.3314 (Figure 4). The validation results of the optimization comparison show that the mortality of test insects at 7 DAT is 95%, and the consumption weight is 1.49 mg (Table 10). These results indicate that the results of the mortality of test insects and consumption weight are close to the optimization results of RSM.

Figure 4 Optimization of A. indica and C. soulattri mixture.
Figure 4

Optimization of A. indica and C. soulattri mixture.

Table 10

Results of insecticide mixture verification based on optimization results

No. Insecticide ingredient Ratio Mortality from prediction (%) Mortality from validation (%) (X ± SE) Weight consumption from prediction (mg) Weight consumption from validation (mg) (X ± SE)
1 A. indica C. soulattri 2.0800:1.1657 100 95 ± 0.250 0.2802 1.493 ± 0.634
2 Control 0 ± 0.000 23.274 ± 1.774

3.2.1 Insecticide mixture of A. indica and C. soulattri

The mortality of test insects in the A. indica and C. soulattri insecticide mixture (ratio 2.0800:1.1657) caused an increase in mortality from 2 to 12 DAT, and there was no increase in mortality after 13 DAT at the highest test concentration. At lower test concentrations, the mortality of test insects occurred at 2–7 DAT, and there was no significant increase in mortality after that (Figure 5). The results of the probit analysis showed that the LC50 and LC95 values at 12–16 DAT were 0.030 and 0.224%, respectively (Table 11). The LC value of the mixture of A. indica and C. soulattri insecticides (2.0800:1.1657) was lower than that of every single insecticide. Based on the calculation of the value of the mixture properties, at the beginning of the observation of 2–8 DAT, the mixture of insecticides had strong synergies in LC50 and LC95. At 12–16 DAT, insecticides have strong synergies at LC50 and weak synergies at LC95. This may be because, at the beginning of the observation, the mortality of the test insects was faster due to the role of C. soulattri, while at the end of the observation, the mortality of the test insects was likely only influenced by A. indica (Table 12).

Figure 5 Mortality graph of S. frugiperda in the mixed treatment of A. indica and C. soulattri.
Figure 5

Mortality graph of S. frugiperda in the mixed treatment of A. indica and C. soulattri.

Table 11

Probit regression parameters of the relationship between C. soulattri sap and A. indica oil mixture concentration against S. frugiperda mortality

Time observation a ± SE b ± SE LC50 CL95 LC95 CL95
2 DAT 1.641 ± 0.276 1.485 ± 0.189 0.079 0.045–0.178 1.006 0.351–12.268
4 DAT 1.926 ± 0.288 1.601 ± 0.193 0.063 0.031–0.183 0.667 0.214–22.879
8 DAT 2.498 ± 0.340 1.688 ± 0.209 0.033 0.16–0.078 0.313 0.114–7.871
12 DAT 2.866 ± 0.386 1.879 ± 0.233 0.030 0.016–0.066 0.224 0.089–4.075
16 DAT 2.866 ± 0.386 1.879 ± 0.233 0.030 0.016–0.066 0.224 0.089–4.075

a: intercept;b: slope; SE: standard error; LC: lethal concentration (%); CL: confidence level; DAT: day after treatment.

Table 12

Results of the analysis of mixed insecticidal properties

Treatment LC50 and LC95
2 DAT (II–III) 8 DAT (II–IV) 12 DAT (II–V) 16 DAT (II–VI)
LC50 LC95 LC50 LC95 LC50 LC95 LC50 LC95
A 2.068 42.017 0.162 1.029 0.111 0.444 0.099 0.420
C 0.579 3.910 0.537 3.481 0.537 3.481 0.537 3.481
AC 0.079 1.006 0.033 0.313 0.030 0.224 0.030 0.224
Combination index value
Treatment 2 DAT (II–III) 8 DAT (II–IV) 12 DAT (II–V) 16 DAT (II–VI)
LC50 LC95 LC50 LC95 LC50 LC95 LC50 LC95
AC 0.18 0.29 0.28 0.40 0.34 0.60 0.38 0.63
Combination index value
Treatment 2 DAT (II–III) 8 DAT (II–IV) 12 DAT (II–V) 16 DAT (II–VI)
LC50 LC95 LC50 LC95 LC50 LC95 LC50 LC95
AC Strong synergies Strong synergies Strong synergies Strong synergies Strong synergies Weak synergies Strong synergies Weak synergies

A: A. indica; C: C. soulatttri; and AC: A. indica + C. soulattri.

The mixture of A. indica and C. soulattri insecticides also inhibited feed consumption weight by up to 93.02% at a concentration of 0.3%. At lower concentrations (0.0015–0.080%), the inhibition of feed consumption was 46.05–91.97% (Table 13). Other observation parameters showed that the treatment of mixed insecticides had an effect by reducing the weight of S. frugiperda pupae (Table 14) and extending the development time of larvae from instar II to instar VI (Table 15). It is shown that the mixture of A. indica and C. soulattri may cause physiological disturbances in larvae’s growth and metabolic processes.

Table 13

Effect of the A. indica and C. soulattri mixture on feed consumption of S. frugiperda

Treatment Weight consumption (mg) ± SE Inhibition (%)
AC 0.3% 1.979 ± 0.6784a 93.02
AC 0.080% 2.279 ± 0.8577a 91.97
AC 0.021% 7.293 ± 1.0210ab 74.29
AC 0.0056% 9.493 ± 2.4353b 66.53
AC 0.0015% 15.301 ± 1.0495c 46.05
Control 28.363 ± 1.8126d —

Values followed by the same letter indicate no significant difference according to Duncan’s test at a 5% level. X: mean; SE: standard error; and AC: A. indica + C. soulattri.

Table 14

Effect of the A. indica and C. soulattri mixture on the weight of S. frugiperda pupae

Treatment n Mean of pupae weight (g) ± SE
AC 0.3% 1 0.2073 ± 0.00000
AC 0.080% 10 0.2113 ± 0.00576
AC 0.021% 21 0.2166 ± 0.00465
AC 0.0056% 30 0.2168 ± 0.00538
AC 0.0015% 37 0.2174 ± 0.00538
Control 40 0.2177 ± 0.00249

X: mean; n: number of pupae; SE: standard error; and AC: A. indica + C. soulattri.

Table 15

Effect of the A. indica and C. soulattri mixture on the development of S. frugiperda larvae

Treatment Mean duration of larval development ± SE
N Instars II–II N Instars II–IV N Instars II–V N Instars II–VI
AC 0.3% 9 3.56 ± 0.166 8 7.75 ± 0.153 3 10.67 ± 0.544 1 14.00 ± 0.000
AC 0.080% 18 2.83 ± 0.142 13 6.38 ± 0.135 10 8.70 ± 0.202 10 11.80 ± 0.237
AC 0.021% 31 2.48 ± 0.111 24 5.75 ± 0.159 21 8.62 ± 0.142 21 11.19 ± 0.209
AC 0.0056% 35 2.63 ± 0.082 31 5.61 ± 0.118 31 8.35 ± 0.086 30 10.93 ± 0.176
AC 0.0015% 39 2.49 ± 0.080 37 5.11 ± 0.113 37 7.62 ± 0.080 37 10.86 ± 0.122
Control 40 2.18 ± 0.060 40 4.55 ± 0.079 40 6.90 ± 0.131 40 10.43 ± 0.078

n: number of larvae; SE: standard error; and AC: A. indica + C. soulattri.

4 Discussion

Botanical insecticides A. indica and C. soulattri caused the mortality of S. frugiperda with LC50 and LC95 values higher than the mixture. This shows that mixing both insecticides can increase toxicity. Mixing insecticides A. indica and C. soulattri can reduce the test concentration and reduce the need for the required materials. This also shows that the RSM optimization method effectively determines the ratio optimization in insecticide mixtures. In this study, optimization uses two responses: the mortality of test insects and the weight of feed consumption. Each response provides a mathematical equation to predict the response that will be produced. The optimization ratio in this study was 2.0800:1.1657 between A. indica and C. soulattri.

Determination of the ratio between insecticide ingredients using RSM is still rarely reported. In this study, we confirm that using RSM makes it easier to design research to determine the effects of mixing on mortality and feed consumption parameters. In conventional ratio determination, such as determining the properties of mixed insecticides at various ratios, all single and mixed insecticides must have known LC or toxicity values to determine the properties of the mixed insecticide [33,34]. In RSM, the optimum ratio can be determined using one experimental unit. As stated by Sai et al. [35], RSM can be used for experimental and numerical response estimates.

The mortality time of test insects also differed in the single treatment of A. indica and C. soulattri and their mixtures. The mortality of the test insects in the C. soulattri treatment was faster or occurred at the beginning of the observation. Meanwhile, in the A. indica treatment, the mortality of the test insects occurred from the beginning to the end of the observation. Syahputra et al. [14] reported that the way C. soulattri works on the active fraction is faster. A. indica causes inhibition of cuticle turnover due to disruption of the endocrine system, prevents ecdysis, apolysis, cuticle secretion, and inhibition of the eclosion process and disruption of the central nervous system [6,36,37]. In the mixture of A. indica and C. soulattri, the mortality of the test insects was high at the beginning of observation (2–5 DAT), but there was still an increase in mortality in subsequent observations. C. soulattri is reported to have neurotoxin properties, so it provides a response at the beginning of observation, while A. indica has hormonal toxins that cause developmental disorders, and mortality of test insects can occur until the end of larval development. This was confirmed by Widayani et al. [17], who showed that in a mixture of C. soulattri and S. indicum (4:1) and a mixture of C. soulattri and P. aduncum (1:2), the mortality of test insects occurred at the beginning of the observation period (2–4 DAT). This also shows that the type of insecticide mixture will affect the mechanism of action of the mixture, including the time of mortality of the test insects.

A synergistic effect is obtained when two materials are mixed to produce a combination index value of ≤0.5. Synergism will be obtained at the right ratio between materials, so determining the ratio between mixed materials is important. In this study, the results obtained below the optimum ratio of the RSM test of the A. indica and C. soulattri insecticide mixture can increase the toxicity of the insecticide mixture and have strong synergistic properties at LC50 based on the combination index. This shows that each mode of action of A. indica and C. soulattri can work together so that the mixed effect is better than either alone. The phenomenon of the mechanism of action of the combination of two active ingredients, one of which increases penetration while the other inhibits the detoxification mechanism (synergistic component). Another phenomenon can be that both components are toxic, whose targets can be the same or different. In this study, the two ingredients used were not pure components but were still in the form of extracts, both of which have adverse effects on insects. Azadirachtin, which is an active compound from the A. indica plant, can interfere with insect development/hormones, although it has also been reported to interfere with the reproductive system. Research on azadirachtin, especially in their physiological and biological activities and their applications in agriculture, has brought a lot of progress, but the exact mechanism of action, especially at the molecular level, is not fully understood [38,39]. On the other hand, based on the observations of symptoms of C. soulattri extract poisoning in insects, there were no symptoms of hormonal disorders [40]. Efforts to find active components from this plant extract using chromatography techniques have been carried out by Syahputra (2004) in his dissertation research. After separating the materials into fractions, the results obtained were active fractions that crystallized (indicating relatively pure components), indicating that the insecticidal toxicity of the active fraction decreased, and finally, the search for active compounds was stopped. This indicates that the insecticidal activity shown by the C. soulattri extract is likely to work synergistically.

Two or more mixed ingredients will cause synergistic and antagonistic effects. The mixture of two products can be synergistic if the effect shows more of their 1 + 1 effect or increases their effectiveness. Then, antagonism reduces the effectiveness of two compounds or the effect less than the expected sum of two individual effects [41,42]. The research results of Levchenko and Silivanova [43] show that interaction patterns (synergistic or antagonistic) in the insecticide mixtures can depend on both the combination of insecticides and their ratio. A synergistic interaction was considered to occur when the combination of the sublethal doses of two compounds resulted in a significantly higher lethality relative to the individual effects of each other [44]. The addition of synergistic ingredients can also increase the toxicity of insecticides. Synergies ingredients are non-toxic ingredients, and the mode of action is to block the metabolic systems (like detoxification of insecticides) that would otherwise break down insecticide molecules [45]. In this study, a mixture of two insecticide ingredients was used against S. frugiperda. Each ingredient has a different mode of action and is expected to work together to increase the toxicity and effectiveness of the mixture.

Insecticide treatment of C. soulattri and its mixture also affects feed consumption, larval development time, and pupal weight. Ethanol extract from C. soulattri bark can suppress feed consumption, extend larval time, and reduce the pupal weight of S. frugiperda [17]. Physiological disorders in test larvae can be caused by the inhibition of invertase and protease enzyme activity of C. pavonana and provide the effect of refusing to eat and affecting digestion and absorption of food [46]. This affects the growth and development process of test insects. A. indica is reported to contain limonoids, quadinoids, meliantriol, and other active ingredients that function as repellents and feeding inhibitors and reduce the number of circulating cells and the activity of lysozyme-like enzymes [47,48,49]. These effects are combined with the work of C. soulattri, which results in cytotoxic effects [50]. Specific reports regarding the active ingredients of C. soulattri insecticide against insect pests are still limited. Both botanical insecticides have also been reported to suppress acetylcholine esterase activity [51,52]. This shows that the synergistic effect is obtained due to the existence of a mode of action or influence that both supports and other influences that increase the activity of the mixed insecticide.

5 Conclusions

The test results showed that the LC50 and LC95 values of single insecticides A. indica and C. soulattri were higher than those of the mixture at 2.0800:1.1657. In this experiment, RSM optimization showed that the estimated optimum response in determining the mixture ratio could increase the insecticide activity. The mixture of insecticides A. indica and C. soulattri had strong synergy at LC50 and weak synergy at LC95. The mixture of insecticides A. indica and C. soulattri also affected feed consumption, larval development time, and pupal weight of S. frugiperda. The mixture of insecticides A. indica and C. soulattri can be used to control S. frugiperda pests in maize plants in an environmentally friendly way. The obtained results indicate that mixed insecticides can increase toxicity and effects on tested insects. Determination of the mixture ratio can use RSM to save research time and energy and predict the response of the treatment used.

  1. Funding information: This research was funded by the DIPA Scheme Universitas Tanjungpura in 2022 (No. 2788/UN22.3/PT.01.03/2022) and the Internal Grant Universitas Padjadjaran with the Academic Leadership Grant scheme in 2023/2024 (No. 1430/UN6.3.1/PT.00/2024).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. E.S.; conceptual, data curation, formal analysis, funding acquation, investigation, methodology, resources, writing – review & editing, validation, supervision, and project administration. D.D.; conceptual, data curation, formal analysis, funding acquation, investigation, methodology, resources, writing – review & editing, validation, supervision. Sudarjat; investigation, project administration, supervision, validation. Y.H.; data curation, formal analysis, investigation, methodology, resources, writing – review & editing, supervision. L.T.P.; data curation, formal analysis, investigation, project administration, resources, writing – original draft, supervision. V.K.D.; data curation, formal analysis, investigation, project administration. S.I.; data curation, formal analysis, investigation, methodology, resources, software writing – review & editing, validation and N.S.W.; data curation, investigation, project administration, writing – original draft.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets are stored in the repository of Universitas Padjadjaran (https://repository.unpad.ac.id/home) and included in this published article.

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Received: 2024-11-25
Revised: 2025-04-24
Accepted: 2025-04-24
Published Online: 2025-05-26

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/opag-2025-0445
Keywords for this article
botanical insecticide mixture; biological disturbance; synergism; optimize ratio ingredient
Creative Commons
BY 4.0

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