Journal of Applied Biosciences 213: 22653 – 22674
ISSN 1997-5902
Control of the invasive fall armyworm (Spodoptera frugiperda) in Benin: Analysis of control measures taken by farmers
Fernand A. Sotondji, Kristina Karlsson Green, Elie Dannon, Daniel C. Chougourou
University of Abomey-Calavi, Polytechnique school of Abomey-Calavi, Laboratory of Applied Biological Research at EPAC, Benin
Unit of Chemical Ecology Agriculture, dept of Plant Protection Biology Swedish University of Agricultural Sciences
National University of Science, Technology, Engineering and Mathematics (UNSTIM), Laboratory of Natural Sciences and Applications (LNSA),
Entomology and Plant Protection at the Polytechnique school of Abomey-Calavi (EPAC), Applied Biology Research Laboratory, Benin
Corresponding auteur: fernandsotondji@yahoo.com
Submitted 06/09/2025, Published online on 31/10/2025 in the https://www.m.elewa.org/journals/journal-of-applied-biosciences https://doi.org/10.35759/JABs.213.9
ABSTRACT
Objective: Since its detection in Benin in 2016, fall armyworm (Spodoptera frugiperda) has rapidly spread nationwide, threatening maize production. The aim of this study was to examine the methods used by farmers to prevent and control FAW and their impact on maize production.
Methodology and Results: A field survey across five districts of central and southern Benin was conducted, covering a representative sample of 522 maize producers. The results showed that the dominant control method was the use of chemical pesticides, practiced by 80% of farmers. These farmers reported comparatively lower FAW presence in their fields, particularly among older producers (>60 years), suggesting that pesticides are perceived as the most effective option. However, only 20% of farmers used biopesticides mainly plant extracts (48.15% in N’Dali), ash-water mixtures, bacterial formulations (up to 90% in Bassila), and soap-based foliar applications. Adoption of these agroecological alternatives varied strongly across districts, reflecting cultural and resource differences. Statistical analyses indicated that farmers’ age (p < 0.05) and ethnicity (p < 0.05) significantly influenced FAW management choices.
Conclusion and Application of results: This study findings confirm that while chemical pesticides are currently the most widely used and effective short-term strategy, their ecological and health risks necessitate the promotion of sustainable alternatives. Integrated pest management (IPM), which combines limited pesticide use with crop diversification, organic fertilisers, and locally sourced biopesticides, has been identified as the most effective way to mitigate FAW damage in Benin. This approach also promotes biodiversity conservation and supports the sustainability of farmer livelihoods.
Key words: Farmer surveys, Spodoptera frugiperda, maize, Biological control, Agroecology, sustainable plant protection, Benin.
INTRODUCTION
Cereal crops play an essential role in the daily diet of Africans, accounting for up to 46% of total calorie consumption (Macauley, 2015). Maize (Zea mays L.), for example, is a major staple food crop grown in various agro-ecological zones in sub-Saharan Africa (Macauley, 2015). According to the US Department of Agriculture (USDA, 2020) forecasts, the world maize harvest for 2019-2020 is slightly lower than the previous year, estimated at 1,108 million tonnes, compared with 1,124 million tonnes in 2018-2019. However, maize production is limited by key abiotic and biotic factors, which expose smallholders to food insecurity and vulnerability. The fall armyworm (FAW) Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) is a major pest originating from the Americas, where it’s known preferred host plants are Poaceae, including economically important cereal crops such as corn, millet, sorghum, rice, wheat and sugarcane. FAW damage is also observed on other important crops such as cowpea, groundnut, potato, soybean and cotton (FAO 2017) and has been recorded on more than 353 plant species worldwide (Montezano et al. 2018; CABI 2020). This pest is a new invasive species in Africa (Goergen et al. 2016; Cock et al. 2017; Day et al. 2017; Sisay et al. 2018; Kumela et al. 2019), where it, probably accidentally, was introduced into southwestern Nigeria and Ghana in 2016 and shortly afterwards into Benin, Sao Tomé and Togo (Goergen et al. 2016). In West Africa, the area of maize attacked by FAW is estimated at 39,540,160 hectares, resulting in a probable loss of 41,517,168 tonnes, or 30% of production (Maïga et al. 2017). Recent studies conducted by Centre for Agriculture and Biosciences International in 12 maize producing countries showed that, without control, fall armyworm can cause maize yield losses ranging from 8.3 m to 20.6 m tonnes per year (Day et al., 2017). In Benin, maize damage has been reported on 38,000 hectares in the northern region (Goergen et al. 2016; IPPC 2016). This threatens the main source of calories for rural populations and places millions of people in famine and food insecurity; the need for efficient control is thus urgent. Farmers often resort to repeatedly using synthetic insecticides to save their crops, but this can have serious consequences, such as environmental pollution, negative effects on non-target organisms including beneficial insects, and health risks to farm workers and consumers due to exposure to harmful chemicals (Ansari et al., 2013; Du et al., 2020). Following its first detection on the continent, various strategies were employed to manage the new and highly damaging FAW pest. At the smallholder farmer level, techniques employed included physical and mechanical control (e.g. crushing of egg masses and neonates, placement of sand or wood ash inside plant funnels, and drenching plant funnels with laundry-washing powders), application of extracts from neem (Azadirachta indica) and velvet bean (Mucuna pruriens), application of fish soup, ‘push–pull’, intercropping, and other traditional practices (Hailu et al., 2018 ; Nyamutukwa et al., 2022). In Benin, farmers relied on synthetic insecticides such as deltametrin and chlorpyrifos-ethyl (Allaba-Boni et al. 2016) to protect their maize when the first damage was reported in 2016. Given the history of pesticide use by farmers in Benin, overuse and abuse can be expected (de Bon et al. 2014). Several studies have indicated that S. frugiperda is resistant to several insecticides such as pyrethroids, organophosphorus, and carbamates Yu (1991). Therefore, alternative methods that reduce the application of synthetic pesticides and that use botanicals and natural enemies are recommended in Africa (FAO, 2017). Application of biopesticides such as neem, virus-based, and entomopathogenic fungi-based products may represent such alternatives (Akutse et al 2020 ; Hussain et al.,2021). However, farmer perception of their effectiveness remains a concern to widescale use (Constantine et al., 2020) and information on farmer’ knowledge and management practices are essential for developing appropriate management methods suited to farmers’ need (Mendesil et al., 2008). Today, there are many potential low-cost control options based on local knowledge and ecological principles, which often are most relevant to smallholders who lack the financial resources to purchase chemical pesticides or expensive seeds (Abate et al., 2000; Altieri and Trujillo, 1987; Grzywacz et al., 2014; Orr and Ritchie, 2004; van Huis and Meerman, 1997; Wyckhuys and O’Neil, 2010). Managing fall armyworm infestations requires an integrated approach that combines biological, chemical, and cultural control practices that reduces FAW populations and delay further evolution of resistance against control methods. Importantly, the management should be sustainable, for example based on agroecological principles. Such options could be cultural practices, such as crop rotation, intercropping, and planting resistant plant varieties, play a crucial role in disrupting pest life cycles and minimizing infestations (Ahissou et al., 2022). Another option is biological control, which relies on natural predators and parasitoids, such as birds and beneficial insects, to regulate fall armyworm populations effectively. Although implementing a combination of control strategies may increase production costs, these practices complement one another and provide a sustainable, long term solution for managing S. frugiperda (Kumar et al., 2022). Insect control practices used by farmers, such as the use of synthetic pesticides, biopesticides, natural enemies, host plants and the manual collection of larvae from maize plants, have been reported in the literature by previous studies in certain districts of Benin (Sidol et al 2020). However, information concerning the management of maize crop residues, ploughing techniques, organic or chemical fertilizers used by farmers, weeding methods adopted, the period of application of products to maize plants, the level of infestation of the maize crop and certain cropping practices such as rotation, intercropping and crop association have not yet been studied in Benin before. However, this information is crucial for the development of sustainable control methods against the FAW in the entire country. The aim of the current study was thus to analyse the control measures adopted by farmers in relation to the invasive insect pest S. frugiperda through the cultural habits of farmers in Benin.
MATERIAL AND METHODS
Study Site: The study was conducted across seven distinct administrative districts located in the central-southern region of Benin (Figure 1). These districts include N’Dali and Borgou in the northern part of the study area, Donga and Bassila in the west, Ouèssè in the center, Glazoué (Collines) in the southwest, and Djidja and Zou in the south. The map illustrates the spatial distribution of these administrative zones, intersected by multiple watercourses and connected through a network of main and paved roads. The region’s climate varies along a north-south gradient. The northern districts (N’Dali, Borgou, and Donga) are characterized by a Sudanian climate with distinct rainy and dry seasons (Adomou et al., 2017). The central zone (Bassila, Ouèssè, and Glazoué) experiences a transitional Sudanian-Guinean climate, while the southern part (Djidja and Zou) benefits from a subequatorial climate with two rainy seasons (Yabi & Afouda, 2012). These districts also exhibit significant differences in agricultural production systems. According to Tovignan et al. (2020), large-scale cotton and cereal production dominates the northern regions, whereas central and southern zones are characterized by more diversified agriculture, including subsistence crops (maize, cassava, and yam) and plantations (cashew, oil palm). The average farm size ranges from 5–10 hectares in the north to 1–3 hectares in the south (Honlonkou, 2019). From a socio-economic perspective, population density generally increases from north to south, with the highest concentrations observed in Glazoué and Zou (INSAE, 2023). Access to development infrastructure (health centres, schools, markets) is more limited in the northern districts, particularly in N’Dali and Donga, compared to the better served southern districts (Adégbola et al., 2016). These regional disparities in climate, agricultural systems and socio-economic development are crucial factors to consider when analysing and interpreting the study results.
Figure 1: Map of the five districts studies in Benin (Djidja, Glazoué, Ouèssè, Bassila and N’Dali).
Sampling methods: The first step in the survey was to determine the representative size of the « farmer population » surveyed to be surveyed, based on geographical distribution. In the case of this study, the number of farmer is 522 in all five project districts. This number is known using the formula of Krejcie and Morgan, 1970.
S=(χ^2 NP (1-P))/(d^2 (N-1) +χ^2 P (1-P))
S = sample size
χ^2 = the table value of the chi-square for 1 degree of freedom at the desired confidence level.
N= known population size
P = the proportion of the farming population recognizing the presence of fall armyworm in their fields through damage (assumed to be 0.50 since this would provide the maximum sample size).
d= margin of sampling error (10%)
The sample size thus calculated per districts was distributed proportionally to the size in each selected village. A total of 522 farmers were surveyed in the various study districts: 104 in Djidja, 104 in Glazoué, 104 in Ouèssè, 105 in Bassila and 105 in N’dali. The size here is the number of maize producers recognizing fall armyworm damage to their fields. To determine the parameter p_i alone, we used the fourth General Census of Population and Housing (RGPH-4) carried out in 2013. The following formula enabled us to estimate the farmer population in 2023.
Where
〖Pop〗_n: Population for year n
〖Pop〗_0: population for reference year
t: population growth rate
n: Difference between target year and reference year.
A survey of maize producers and their recognition of the presence of S. frugiperda in their fields were carried out, using digitized questionnaires. The questionnaires were discussed in face-to-face interviews with individual farmers on maize production systems. Farmers were also asked about their perception of fall armyworm and the management practices implemented to control FAW.
Data analysis methods: In the analysis, the response variable was the presence or absence of S. frugiperda in maize fields, while the explanatory variables include socio-demographic factors such as gender, age, ethnicity, religion, marital status, educational level, professional training and association membership. The model was fitted using a logit link function, making it possible to estimate the effect of each explanatory variable on the probability of S. frugiperda presence (Agresti, 2015). The significance of the variables was assessed using a Chi-squared test based on deviance analysis, an approach that compares successive fits of the model and tests the contribution of each explanatory variable (Dobson & Barnett, 2018). The regression results were interpreted using odds ratios, which provide an estimate of the relative effect of each factor on insect presence. An odds ratio greater than 1 indicates that the variable increases the probability of S. frugiperda presence, while an odds ratio less than 1 suggests a protective effect (Peng et al., 2002). This approach is particularly relevant for ecological and agronomic studies, as it enables the identification of potential levers of action for pest management. Data on the management methods used in maize production by farmers to prevent or control FAW infestations on maize in Benin, a frequency analysis of the data was carried out using bar. In addition, to identify factors influencing the implementation of FAW management practices, frequency distributions and percentages were examined using cross-tabulations and graphical representations. The analysis was carried out using R software version 4.3.1 (R Core Team, 2023), in particular using the CA function in the FactoMineR package (Sebastien et al., 2008). The data was analysed with binary logistic regression, a statistical method widely used to model the relationship between a binary dependent variable and several explanatory variables (Hosmer and Lemeshow, 2000). Categorization of the data in the study area, a simple correspondence factor analysis was carried out, followed by a hierarchical ascending classification of Fall Armyworm control strategies, taking into account their adoption frequencies in the different districts. Finally, to identify barriers to the adoption of FAW management measures, we analysed the distributions of reasons provided by farmers using bar charts and tables.
RESULTS
Socio-demographic variations of farmers survey in the different study district of Benin:
Table 1 shows the socio-demographic and occupational characteristics of the agricultural producers surveyed in the various localities studied. The gender breakdown showed a strong male predominance, with men representing the majority of farmers, particularly in Bassila (99.05%) and N’Dali (98.04%), while the proportion of women was relatively higher in Glazoué (35.61%) and Ouèssè (33%). In terms of age, adults of working age are the dominant group in all localities, with proportions ranging from 64.86% in Djidja to 84.76% in Bassila. Young farmers are more represented in Djidja (21.62%) than elsewhere, while older farmers remain a minority. The distribution of farmers by religion varied significantly between localities. In Glazoué, almost all farmers (95.19%) are Christians, while in N’Dali, Islam dominates (90.2%). Djidja has a high proportion of farmers practicing an endogenous religion (43.24%), a phenomenon rarely observed elsewhere. The distribution of social groups shows a high concentration of certain ethnic groups per locality. For example, the Fon are in the majority in Djidja (95.5%), while the Bariba dominate in N’Dali (96.08%) and the Mahi in Ouèssè (96%). Other groups, such as the Idaatcha in Glazoué (84.62%), also illustrate the spatial distribution of ethnic groups farmers’ marital status reflects a high prevalence of marriage, with proportions above 90% in all localities. Single and widowed farmers are rare, while divorced farmers are virtually absent. In terms of level of education, a significant proportion of farmers have received no formal education at all, particularly in Ouèssè (62%) and Djidja (49.55%). Primary education is the second most common level, particularly in N’Dali (53.92%). Access to secondary and higher education remains limited, with rates below 10% in all localities. Access to agricultural training is relatively low, although Djidja (29.73%) and N’Dali (27.45%) have slightly higher rates.
Table 1: Socio-professional characteristics of farmers in different study districts.
| Variables | Modalities | Absolute frequency (%) | ||||
| Bassila | Djidja | Glazoué | N’Dali | Ouèssè | ||
| Gender | Female | 1 (95) | 14 (12.61) | 37 (35.61) | 2 (1.96) | 33 (33) |
| Male | 104 (99.05) | 97 (87.39) | 67 (64.42) | 100 (98.04) | 67 (67) | |
| Age | Adults (30-60 years old) | 89 (84.76) | 72 (64.86) | 83 (79.81) | 84 (82.35) | 76 (76) |
| Older (>60 years old) | 8 (7.62) | 15 (13.51) | 10 (9.62) | 11 (10.78) | 10 (10) | |
| Young <30 years old) | 8 (7.62) | 24 (21.62) | 11 (10.58) | 7 (6.86) | 14 (14) | |
| Religion | Christian | 19 (18.1) | 63 (56.76) | 99 (95.19) | 9 (8.82) | 75 (75) |
| Endogenous | 2 (1.9) | 48 (43.24) | 1 (0.96) | 1 (0.98) | 24 (24) | |
| Muslim | 84 (80) | 0 (0) | 4 (3.85) | 92 (90.2) | 1 (1) | |
| Social group | Adja | 0 (0) | 4 (3.6) | 0 (0) | 0 (0) | 0 (0) |
| Idaatcha | 2 (1.9) | 0 (0) | 88 (84.62) | 0 (0) | 0 (0) | |
| Bariba | 0 (0) | 0 (0) | 0 (0) | 98 (96.08) | 0 (0) | |
| Dendi | 0 (0) | 0 (0) | 0 (0) | 1 (0,98) | 0 (0) | |
| Fon | 23 (21.9) | 106 (95.5) | 1 (0.96) | 2 (1.96) | 3 (3) | |
| Mahi | 0 (0) | 0 (0) | 2 (1.92) | 0 (0) | 96 (96) | |
| Nago | 52 (49.52) | 0 (0) | 12 (11.54) | 0 (0) | 0 (0) | |
| Peulh | 1 (0.95) | 0 (0) | 1 (0.96) | 0 (0) | 1 (1) | |
| Yoa-Lokpa | 2 (1.9) | 0 (0) | 0 (0) | 1 (0.98) | 0 (0) | |
| Yoruba | 2 (1.9) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | |
| Ani | 23 (21.9) | 1 (0.9) | 0 (0) | 0 (0) | 0 (0) | |
| Marital status | Single | 5 (4.76) | 6 (5.41) | 5 (4.81) | 7 (6.86) | 4 (4) |
| Divorced | 1 (0.95) | 0 (0) | 2 (1.92) | 0 (0) | 1 (1) | |
| Married | 97 (92.38) | 104 (93.69) | 94 (90.38) | 94 (92.16) | 95 (95) | |
| Widowed | 2 (1.9) | 1 (0.9) | 3 (2.88) | 1 (0.98) | 0 (0) | |
| Registration level | Self-taught | 0 (0) | 4 (3,6) | 0 (0) | 0 (0) | 0 (0) |
| Out of school | 46 (43.81) | 55 (49.55) | 41 (39.42) | 27 (26.47) | 62 (62) | |
| Koranic school | 1 (0.95) | 3 (2.7) | 0 (0) | 1 (0.98) | 0 (0) | |
| Primary | 25 (23.81) | 25 (22.52) | 30 (28.85) | 55 (53.92) | 25 (25) | |
| Secondary 1st cycle | 22 (20.95) | 19 (17.12) | 18 (17.31) | 16 (15.69) | 8 (8) | |
| Secondary 2nd cycle | 9 (8.57) | 3 (2.7) | 7 (6.73) | 1 (0.98) | 5 (5) | |
| Higher | 2 (1.9) | 2 (1.8) | 8 (7.69) | 2 (1.96) | 0 (0) | |
| Formation | No | 86 (81.9) | 78 (70.27) | 103 (99.04) | 74 (72.55) | 79 (79) |
| Farmers training | Yes | 19 (18.1) | 33 (29.73) | 1 (0.96) | 28 (27.45) | 21 (21) |
| Farmers group membership | No | 65 (61.9) | 87 (78.38) | 75 (72.12) | 38 (37.25) | 64 (64) |
| Yes | 40 (38.1) | 24 (21.62) | 29 (27.88) | 64 (62.75) | 36 (36) | |
Factors determining FAW presence in maize fields in the study area districts: According to statistical analysis, various socio-demographic factors have a notable impact on whether S. frugiperda is found in corn fields. Farmer’s age is a key factor (30-60 years old). Ethnicity was also significant (Peulh, Bariba, Nago and Yoruba). In addition, marital status had a notable impact (Married). On the other hand, variables such as gender (Female). Other variables such as religion (p = 0.27), level of education (p = 0.67), vocational training (p = 0.32) and membership of an association (p = 0.98) do not appear to directly influence the presence of fall armyworm (Table 2).
Table 2 : Determining FAW presence in maize fields in the study area in Benin
| Parameters | Df | Deviance | Residu Df | Residu Dev | Pr(>Chi) |
| Model null | 521 | 82.728 | |||
| Gender | 1 | 2.9419 | 520 | 79.786 | 0.086307. |
| Age | 1 | 10.0597 | 519 | 69.727 | 0.001515** |
| Ethnic group | 9 | 25.0817 | 510 | 44.645 | 0.002882** |
| Religion | 2 | 2.5852 | 508 | 42.060 | 0.274556 |
| Marital status | 3 | 11.8462 | 505 | 30.214 | 0.007929** |
| Education level | 6 | 4.0371 | 499 | 26.176 | 0.671650 |
| Training professional | 1 | 1.0024 | 498 | 25.174 | 0.316730 |
| Association membership | 1 | 0.0007 | 497 | 25.173 | 0.978384 |
| Meaning of codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |||||
Significant factors associated with the presence of S. frugiperda in maize fields in the study area: Logistic regression analysis revealed three socio-demographic factors significantly associated with the presence of fall armyworm in maize fields: age, ethnicity and marital status. These factors influence the probability of observing infestations differently, underlining the importance of social and cultural dimensions in pest management dynamics. Farmers’ age emerged as a protective factor against the presence of armyworm (OR = 0.90). In concrete terms, a one-year increase in age was associated with a 10% reduction in the probability of infestation. Ethnicity significantly influences the probability of S. frugiperda infestation. For example, compared to the reference ethnic group, Bariba farmers show an extremely high probability of infestation (OR = 1.70 × 10⁶), which could indicate differences in cultivation practices, maize varieties grown, or access to control measures. In contrast, certain ethnic groups, such as the Peulh (OR ≈ 0.19) and the Yoa-Lokpa (OR ≈ 0.02), appear to be less exposed to infestations.
Agricultural management practices of S. frugiperda by farmers in different study district areas in Benin.: The comprehensive agricultural data collected across five municipalities (Bassila, Djidja, Glazoué, N’Dali, and Ouèssè) reveals significant variations in farming practices and crop preferences within the study region. Regarding crop distribution, maize (corn) emerges as the predominant crop across all districts, with particular dominance in Bassila (54.17%). Bean and peanut cultivation show substantial representation in Djidja, Glazoué, and Ouèssè, while sorghum is notably prevalent in N’Dali (14.88%). Cotton production demonstrates considerable regional specialization, being completely absent in Bassila (0%) while representing a significant cash crop in Djidja (14.72%) and N’Dali (11.42%). Mineral fertilizer application patterns demonstrate near universal adoption of NPK and urea across all districts, with particularly high utilization rates in Ouèssè (100% for both fertilizer types). The intercropping practices data highlights interesting regional adaptations, with groundnut associations being particularly prevalent in Ouèssè (97.87%) and Glazoué (67.27%), while N’Dali farmers show strong preference for cowpea and sorghum associations (both at 75%). Weed control methodologies demonstrate notable regional divergence, with manual weeding before sowing (DM) being almost universal in Bassila (96.19%) but considerably less common in N’Dali (32.35%). Conversely, chemical weeding before sowing (Dchi) shows the opposite pattern, being minimally practiced in Bassila (0.95%) while representing the dominant weed management strategy in N’Dali (97.06%). This stark contrast may reflect differences in labour availability, herbicide accessibility, and possibly different levels of agricultural intensification. The agricultural practice levels data indicates Bassila farmers predominantly operate at high practice levels (71.43%), while Djidja demonstrates the highest proportion of low-level practices (39.64%). Regarding organic fertilizer usage, manure application is particularly significant in Bassila (60.76%), while compost utilization is concentrated in Ouèssè (57.89%) and Djidja (36.84%). Glazoué stands out for its high usage of other organic amendments (66.67%), possibly indicating local innovation or the availability of specific organic resources in this area.
Tableau 3 : Agricultural management practices of S. frugiperda by farmers in different study district areas in Benin
| Variables | Crops (%) | Mineral Fertilizer (%) | Crop Association (%) | Weed Control (%) | Practice adoption (%) | Organic Fertilizer (%) | Chemical pesticides (%) | |||||||||||||||||||||||
| Modalities | Corn | Bean | Sorghum | Millet | Peanut | Cotton | Others | NPK | Urea | Others | Cowpea | Sorghum | Millet | Groundnut | Cotton | Others | DM | Dméc | Dchi | LM | Lméc | LZ | Low | Medium | High | Compost | Manure | Others | No | Yes |
| Bassila | 54.17 | 4.17 | 3.13 | 5.21 | 7.29 | 0 | 26.04 | 86.96 | 79.71 | 5.8 | 30.77 | 23.08 | 17.95 | 33..33 | 0 | 53.85 | 96.19 | 3.81 | 0.95 | 67.62 | 15.24 | 0 | 1.02 | 27.55 | 71.43 | 0 | 60.76 | 0 | 52.04 | 47.96 |
| Djidja | 28.17 | 21.07 | 0.25 | 0.76 | 21.57 | 14.72 | 13.45 | 97.17 | 98.11 | 3.77 | 29.63 | 11.11 | 11.11 | 64.81 | 42.59 | 1.85 | 63.06 | 12.61 | 48.65 | 15.32 | 0 | 0 | 39.64 | 13.51 | 46.85 | 36.84 | 1.27 | 0 | 79.28 | 20.72 |
| Glazoué | 29.13 | 19.33 | 0.84 | 1.4 | 20.17 | 1.4 | 27.73 | 94.05 | 97.62 | 9.52 | 34.55 | 0 | 0 | 67.27 | 0 | 54.55 | 53.85 | 35.58 | 84.62 | 1.92 | 0.96 | 0 | 19.23 | 34.62 | 46.15 | 0 | 7.59 | 66.67 | 62.5 | 37.5 |
| N’DaIi | 34.95 | 11.76 | 14.88 | 3.81 | 0.69 | 11.42 | 22.49 | 98.99 | 96.97 | 1.01 | 75 | 75 | 0 | 0 | 0 | 0 | 32,35 | 0 | 97.06 | 5.88 | 1.96 | 0.98 | 9.9 | 30.69 | 59.41 | 5.26 | 3.8 | 0 | 74.27 | 26.73 |
| Ouéssé | 22.37 | 19.02 | 4.03 | 7.61 | 21.92 | 7.83 | 17.23 | 100 | 100 | 0 | 50 | 2.13 | 13.83 | 97.87 | 2.13 | 31.91 | 70 | 1 | 38 | 88 | 0 | 0 | 26 | 30 | 44 | 57.89 | 26.58 | 33.33 | 75 | 25 |
Note: Percentages indicate the proportion of surveyed farmers reporting the use or adoption of each practice: Crops (%) refers to the share of respondents cultivating key crops; Mineral Fertilizer (%) indicates the proportion of farmers applying synthetic fertilizers such as NPK or urea; Crop Association (%) refers to intercropping or mixed cropping practices involving maize and other crops; Weed Control (%) includes both manual and chemical weed management techniques; Practice Adoption (%) represents the degree to which farmers implement recommended agronomic practices; Organic Fertilizer (%) includes organic soil amendments such as compost, manure, or plant residues; Chemical Pesticides (%) refers to farmers applying commercial insecticides or herbicides to control pests or weeds; DM: Manual weeding before sowing, Dméc: Mechanical weeding before sowing, Dchi, Chemical weeding before sowing, LM: Manual weeding after sowing, Lméc: Mechanical weeding after sowing, LZ: Weeding with herbicide after sowing.
Biopesticides used by farmers in the districts studied area of Benin: The different types of biopesticides used in five regions: Bassila, Djidja, Glazoué, N’dali and Ouèssè. The results of the analyses reveal distinct patterns of biopesticide use in these regions. Fungal biopesticides are used mostly in Djidja (70%), Bassila (20%), and N’dali (10%) while viral biopesticides are not used in any of the regions. Bacterial biopesticides are used extensively in Bassila (90%), and only to a limited extent in Djidja (10%). Plant extract-based biopesticides are the most widely used, with N’dali in the lead (48.15%), followed by Ouèssè (24.07%), Djidja (16.67%) and Glazoué (11.11%). Other biopesticides are mainly used in N’dali (52.38%), Glazoué and Ouèssè (23.81% each). Notably, Bassila relies heavily on bacterial biopesticides but does not use other types, while N’dali and Ouèssè concentrate on plant-based and other biopesticides.
Table 4: Biopesticides used by producers in the study area
| Biopesticides | Bassila | Djidja | Glazoué | N’dali | Ouèssè |
| Fungal-based biopesticides | 20 | 70 | 0 | 10 | 0 |
| Viral biopesticides | 0 | 0 | 0 | 0 | 0 |
| Bacterial biopesticides | 90 | 10 | 0 | 0 | 0 |
| Plant-based biopesticides | 0 | 16,67 | 11,11 | 48,15 | 24,07 |
| Other biopesticides | 0 | 0 | 23,81 | 52,38 | 23,81 |
Others biopesticides: Neem oil, Plant extracts (including quinine), Neem leaf mixtures (often combined with soap), Soap and crushed pepper mixtures, Tabacco leaf mixtures, Urea fertilizer, Ash and water solutions, Commercial detergents (like Omo), Salt-based mixtures, Combinations of neem, tobacco, soap, salt, Charcoal crushed with table salt and crushed pepper
Identify factors influencing the implementation of fall armyworm (Spodoptera frugiperda) management practices in Benin.
Choice of number of axes: The eigenvalues obtained from the factorial correspondence analysis revealed that with three (03) axes, we control around 95.43% of the information, which is sufficient to guarantee a good interpretation of the results. This led us to select the first three axes.
Table 5: Percentage of variance explained by axes
| Dim 1 | Dim 2 | Dim 3 | Dim 4 | |
| Valeurs propres | 0.21 | 0.09 | 0.04 | 0.02 |
| % of variables | 58.69 | 24.54 | 12.2 | 4.57 |
| Cumulative % of variables | 58.69 | 83.23 | 95.43 | 100 |
Partial contributions of municipalities and strategies to axis representation: For a point to be considered as having a good contribution to the formation of an axis, its contribution must be greater than k= 100/ (number of municipalities). With k =100/5 =20. We note that the districts of Bassila is well represented on axis 1, the districts of Glazoué and N’Dali are well represented on axis 2, while the districts of Ouèssè is well represented on axis 3 (Table 7). For a point to be considered as having a good contribution to the formation of an axis, its contribution must be greater than k= 100/ (number of districts). With k =100/5 =20. We note that the district of Bassila is well represented on axis 1, the district of Glazoué and N’Dali are well represented on axis 2, while the districts of Ouèssè is well represented on axis 3 (Table 7).
Table 6: Contribution of districts to axis representation
| Districts | Dim 1 | Dim 2 | Dim 3 |
| Bassila | 40.79 | 0.03 | 39.78 |
| Djidja | 1.81 | 1.22 | 6.89 |
| Glazoué | 11.37 | 57.65 | 1.21 |
| N’Dali | 34.59 | 36.45 | 9.33 |
| Ouèssè | 11.44 | 4.65 | 42.79 |
Table 7: Contribution of strategies to axis representation
| Control methods | Dim 1 | Dim 2 | Dim 3 |
| Association culture | 2.92 | 8.3 | 29.26 |
| Mineral fertilizers | 3.84 | 0.02 | 3.14 |
| Organic fertilizers | 13.3 | 0.2 | 6.67 |
| Crop rotation | 7.18 | 33.29 | 3.18 |
| Manual weeding | 4.71 | 1.79 | 8.62 |
| Mechanical weeding | 2.9 | 45.6 | 0.77 |
| Chemical weeding | 31.12 | 0.14 | 1.12 |
| Manual ploughing | 26.38 | 5.24 | 2.39 |
| Mechanical ploughing | 4.48 | 0.09 | 34.21 |
| Zero tillage | 0.44 | 1.11 | 0.57 |
| Chemical pesticides | 0.73 | 0.44 | 8.17 |
| Biological pesticides | 2.01 | 3.78 | 1.89 |
Correspondence factor analysis carried out on data relating to control methods against S. frugiperda in different districts revealed three main axes explaining 95.43% of total variability. The first axis contrasts the districts of Bassila, strongly associated with the use of organic fertilizers, chemical weeding and manual ploughing, with the districts of Glazoué and N’Dali, more oriented towards agricultural practices such as crop association. The second axis mainly differentiates the districts of Glazoué and N’Dali, where there is a high use of organic pesticides, from the districts of Bassila and Djidja, which give greater preference to manual weeding and organic fertilizers. Finally, the third axis contrasts the Ouèssè districts, characterized by the use of chemical pesticides and crop combinations, with the districts of Djidja and Bassila, where agricultural practices such as crop rotation and mechanical weeding are used.
Fig 2: Correspondence factor analysis of variables studied in the study areas
Identification of groups of control strategies for FAW management: In order to carry out the hierarchical classification of control strategies against S. frugiperda, we decided to use an initial level index comprising three (03) distinct classes. The results of variable grouping are strongly influenced by the chosen linking method and distance measure, which was achieved using R software. Thanks to this Hierarchical Ascending Classification (HAC), we have identified three (03) groups of farmers distinguished by the strategies adopted, as illustrated in the following. The latter, a dendrogram, reveals three distinct groups of farmers, each adopting specific approaches. The first group favors mechanical and organic cultivation practices, opting for environmentally-friendly methods. The second group emphasizes fertilization and crop rotation, combining traditional and modern practices. Finally, the third group combines crops and chemical control, adopting a more conventional approach.
Figure 3: Classification of fall armyworm control strategies in study district area in Benin
Discrimination against Spodoptera frugiperda control measures taking by farmers: Discriminant analysis revealed three distinct groups of climate change adaptation practices. The first group is characterized by a relatively low adoption of diversified strategies, particularly with regard to soil improvement and crop rotation. The districts of Bassila, Djidja and Ouèssè are typical of this profile. The second group shows an intermediate level of adoption of adaptation strategies, with a preference for practices aimed at improving soil quality and diversifying crops. Bassila and Ouèssè are also represented in this group. Finally, the third group is characterized by a very high and varied adoption of adaptation strategies, implementing agricultural practices such as crop rotation, the use of biological pesticides and soil conservation.
Table 8: Variables discrimination table by strategy class in the studies areas
| Classes | Characteristics | V test | Category average | Overall average | P value |
| Class 1 | Glazoué | -1,986 | 7,853 | 26,786 | 4,70e-02 |
| Bassila | -2,427 | 4,705 | 28,405 | 1,52e-02 | |
| Djidja | -2,629 | 3,753 | 30,052 | 8,56e-03 | |
| Ouèssè | -3,201 | 2,500 | 38,571 | 0,001 | |
| Class 2 | Bassila | 2,690 | 58,924 | 28,405 | 7e-03 |
| Ouèssè | 2,553 | 72,000 | 38,571 | 1e-02 | |
| Class 3 | N’dali | 3,399 | 89,460 | 26,419 | 7e-04 |
| Glazoué | 1,976 | 58,013 | 26,786 | 5e-02 |
DISCUSSION
Practices used by farmers to manage FAW: Results from field surveys of farmers revealed that the fall armyworm is present everywhere in the different agro-ecological zones of Benin. This result confirms the studies of Goergen et al (2016) since the detection of the fall armyworm by stating that in Africa, the FAW seems to have established itself in cereal-based agroecosystems. In the present study, to improve soil structure and increase crop yields, most farmers use mineral fertilizers. These results are close to those of (Sidol et al 2020) and show that more than half of the farmers applied mineral fertilizers to the maize plants, on average 134.6 kg/ha of NPK (Nitrogen, Phosphorus, and Potassium) and 75.4 kg/ha of urea. This management are not in line with agroecological approaches, which are based on the sustainable management of soil fertility through the use of organic fertilizers to improve crop health and protect biodiversity (Altieri and Nicholls, 2003). Although mineral fertilizers increase crop yields, they can have a number of negative consequences for soil and animals (Yuan et al.,2017). For example, excessive use of mineral fertilizers impoverishes the soil’s natural structure, reducing its capacity to retain water and nutrients (Marton et al., 2025). A minority farmers’ representing 19% of farmers do, however, use organic fertilizers. These results suggest that the use of organic fertilizers is not widespread among the farmers surveyed. The results of (Harrison et al., 2019) showed that unbalanced plant nutrition through use of inorganic fertilizers on poor soils can lead to increased pest damage. Manure application can induce changes in the biogeochemical cycles by elevating soil C and N contents, consequently microbial biomass and even pH value (Dambreville et al., 2008; Zhai et al., 2011). Spreading manure can lead to an increase in N2O emissions in the short term, while mineral fertilization has a longer-term effect on soil depletion. (Dambreville et al., 2008). Overall, our study found great variability in the choice of organic fertilizers between districts, reflecting different farming practices or resource availability in each region. Emphasis should be placed on the district of Bassila and N’dali. The combination of mineral and organic fertilizer can enhance the microbial activity of soils, thus influencing CO2 emission (Guo et al., 2019). Furthermore, this study found variation in intercropping strategies where some practices are absent in certain districts, as indicated by the 0% frequencies. These results corroborate the studies by (Houngbo et al 2020) which showed that farmers’ two main cropping systems were used for maize production single-cropping in rotation with other crops (cotton, cowpea, cassava, soybean, and groundnut) and intercropping with cassava, groundnut, cowpea, and sorghum. Overall, single-cropping was the most common cropping system in our study (67.6%). The data suggests that farmers in these regions employ a range of intercropping strategies, potentially to optimize land use, control pests or improve soil fertility. The practice of intercropping is an ancient practice used in traditional agricultural systems in Africa (Kafara, 2007), one of the advantages of which is to break the life cycle of pests, including insects.
Factors influencing the implementation of FAW management: Interestingly, we observed that socio-economic characteristics such as, age, ethnic group and marital status have a significant effect on FAW management while level of education, professional experience and membership of farmers organizations did not have any effect on FAW management. While in previous studies, level of education and membership in a farmers’ group had a significant influence on FAW management, this means that awareness of the best methods of FAW management must be strengthened in the study areas. The positive influence of age was previously also demonstrated by Mango et al.(2017). We observed that socio-economic characteristics such as age, experience in maize production, farmer’s organization membership, level of education and income level of the farmer significantly determine the type of control method used against fall armyworm (Houndété et al., 2023). Farmers with high acreage prefer chemical control to any other control method. Farmers’ age emerged as a protective factor against the presence of armyworm. In concrete terms, a one-year increase in age was associated with a 10% reduction in the probability of infestation. The effect of ethnicity on FAW management may reflect distinct farming systems adopt crop diversification practices or pastoral systems that disrupt the life cycle of insect pests. These results suggest that taking into account local knowledge and traditional practices could be an important lever for adapting control strategies against S. frugiperda. Farmers’ marital status also influenced the probability of S. frugiperda presence compared to single farmers, with married farmers showing a significantly reduced probability of infestation (OR = 2.23 × 10-⁸). This reduction could be explained by a better division of tasks within the household, enabling more regular crop monitoring and more systematic application of control measures. Conversely, divorced (OR = 4.05 × 10-²⁵) and widowed (OR = 1.77 × 10-⁴⁷) farmers had almost zero infestation probabilities, which could reflect a reduction in cultivated area or a change in farming activity after a change in family status. These results highlight the complexity of interactions between family structure and crop management. Preferably farmers awareness of good FAW management practices together with sustainable agricultural management, such as organic fertilisers and biopesticides, needs to be prioritised in farmers’ organizations in the study areas.
Control strategies for FAW: The choice of fall armyworm control methods is also influenced by contextual factors specific to each district, such as pedoclimatic and bioclimatic characteristics, economic constraints, cropping habits and local agricultural policies. An integrated approach that combines different control methods while taking into account the interactions between crops and different pests as well as beneficial organisms may be the most sustainable option forward. The control method most used by maize farmers is, however, chemical control and conducted with chemical families containing active ingredients such as Acetamiprid, Emamectin benzoate, Lambda-cyhalothrin, Cypermethrin, Pacha, Diméthoate and Chlorpyriphos-ethyl. These active ingredients that are dangerous for the ecosystem and humans (Chimweta et al., 2020; Kansiime et al., 2019; Davis et al., 2018). These results are close to those of (Abrahams et al., 2017) where the main management methods used in America against S. frugiperda are synthetic pesticides. This may be explained by the fact that biopesticides are not well known in Benin, are even more expensive, and do not have special incentives (Houndoté et al., 2023). Data collected from farmers revealed regional variations in the use of biopesticides. This regional variation in biopesticide use probably reflects differences in local farming practices, insect pest pressures, environmental conditions and, potentially, access to different types of biopesticides. The present study has shown variation in knowledge and perceptions of subsistence farmers’ to fall Armyworm management in Benin. Preferably farmers awareness of good fall armyworm management practices together with sustainable agricultural management, such as organic fertilizers and biopesticides, needs to be prioritised in farmers’ organizations in the study areas. We also found that older farmers are the ones that mainly prefers pesticides and they also have low FAW presence. Even though this may imply that pesticides are the most efficient control method against FAW, we need to develop and implement alternatives as we know that chemical pesticides are detrimental for biodiversity and human health. Our study furthermore underline the diversity of adaptation practices in the different study areas, and the need to adapt support policies to local specificities for maize production in Benin, i.e. to develop agroecological pest management strategies while taking into account the needs and priorities of farmers.
CONCLUSION AND APPLICATIONOF RESULTS
This study highlights the diversity of strategies used by subsistence farmers in Benin to manage the fall armyworm (FAW), ranging from chemical pesticides to biopesticides, crop associations, crop rotation, and soil fertility management practices. Among these, chemical pesticides remain the most widely used method and were associated with lower FAW presence in the fields, particularly among older farmers. However, while pesticides appear effective in the short term, their negative impacts on biodiversity, human health, and long-term soil quality underline the urgent need for sustainable alternatives. Our findings suggest that integrated agroecological approaches such as the use of organic fertilizers, intercropping, crop rotation, and biopesticides based on plant extracts, ash-water mixtures, or microbial agentsoffer promising complementary solutions for FAW control. These practices not only disrupt pest life cycles but also enhance soil health and resilience of maize systems. Nevertheless, adoption rates for such alternatives remain low, mainly due to limited awareness, availability, and institutional support. We therefore recommend that future policies and extension programs prioritize the dissemination and accessibility of agroecological pest management practices, while strengthening farmer training and knowledge-sharing platforms. Tailored interventions should reflect the socio-cultural and agroecological specificities of each district, as age, ethnicity, and household structures were shown to shape management choices. A gradual shift toward integrated pest management (IPM), combining targeted pesticide use with biocontrol and ecological practices, would allow farmers to reduce dependence on harmful chemicals while ensuring food security and safeguarding ecosystem services.
Disclosure statement
The author declares no conflict of interest regarding the publication of this paper.
Funding
This study was supported by the EKHAGASTIFTELSEN foundation under ref: Grant 2023_122.
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