Next Article in Journal
Exploring Bacterial Communities and Functions in Phytophagous Halyomorpha halys and Predatory Arma chinensis
Previous Article in Journal
Advances and Current Status in the Use of Cuticular Hydrocarbons for Forensic Entomology Applications
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Insects
Volume 16
Issue 2
10.3390/insects16020145
insects-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Seasonal and Spatial Distribution of Fall Armyworm Larvae in Maize Fields: Implications for Integrated Pest Management

by
Karimou Zanzana
1,2,*,
Antonio Sinzogan
1,
Ghislain T. Tepa-Yotto
2,3,*,
Elie Dannon
4,
Georg Goergen
2 and
Manuele Tamò
2
1
Laboratoire d’Entomologie Agricole (LEAg), Faculté des Sciences Agronomiques (FSA), Université d’Abomey-Calavi (UAC), Abomey-Calavi, Cotonou 01 B.P. 526, Benin
2
Biorisk Management Facility (BIMAF), International Institute of Tropical Agriculture (IITA-Benin), Cotonou 08 B.P. 0932, Benin
3
Ecole de Gestion et de Production Végétale et Semencière (EGPVS), Université Nationale d’Agriculture (UNA), Kétou B.P. 43, Benin
4
Laboratoire des Sciences Naturelles et Applications (LNSA), Ecole Normale Supérieure de Natitingou, Université Nationale des Sciences, Technologies, Ingénieries, et de Mathématiques (UNSTIM), Abomey B.P. 486, Benin
*
Authors to whom correspondence should be addressed.
Insects 2025, 16(2), 145; https://doi.org/10.3390/insects16020145
Submission received: 16 October 2024 / Revised: 6 January 2025 / Accepted: 26 January 2025 / Published: 1 February 2025
(This article belongs to the Section Insect Pest and Vector Management)
Browse Figures
Review Reports Versions Notes

Simple Summary

The fall armyworm is a destructive pest that severely impacts maize crops. This study focuses on understanding how this pest spreads in maize fields and how the population fluctuates during both the dry and rainy seasons. We examined maize plants in two different agroecological regions (zones 6 and 8) of southern Benin, tracking the number of larvae, the level of infestation, and the extent of plant damage. Higher larval infestation and damage were observed in zone 8 (fisheries region) during the dry season compared to zone 6 (ferralitic soils region). However, during the rainy season, while more plants were damaged in zone 8, the number of larvae was similar between the two areas. Fall armyworms tend to form small colonies when laying eggs. This behavior influences how they spread and attack crops.

Abstract

The fall armyworm (FAW), Spodoptera frugiperda, a major pest in maize production, was assessed for its temporal and spatial distribution in maize fields during both the dry and rainy seasons of 2021 and 2022 in two agroecological regions in Benin (zone 6 and 8). Zone 6 (AEZ 6) “called zone of terre de barre” (Southern and Central Benin) consisted of ferralitic soils, a Sudano-Guinean climate (two rainy seasons alternating with two dry seasons) with a rainfall ranging between 800 and 1400 mm of rainfall per year; while zone 8 (AEZ 8) called “fisheries region” (Southern Benin” is characterized by coastal gleysols and arenosols with a Sudano-Guinean climate and a rainfall of 900–1400 mm of rainfall per year. In this study, 30 and 50 maize plants were randomly sampled using a “W” pattern during the dry and rainy seasons, respectively. Larval density, larval infestation rates, and damage severity were monitored over time. Taylor’s power law and the mean crowding aggregation index were applied to evaluate the dispersion patterns of the larvae. The results indicate a higher larval infestation rate and larval density in AEZ 8 compared to AEZ 6 during the dry season. In the rainy season, while the percentage of damaged plants was higher in AZE 8, no significant differences in larval density between the two zones were observed. The dispersion analysis revealed moderate aggregation (aggregation index = 1.25) with a basic colony of 2.08 larvae, i.e., an average initial cluster of 2.08 larvae observed per plant, reflecting the aggregation oviposition behavior of FAW. This study provides valuable monitoring data on the FAW’s distribution, offering insights for further research on population dynamics and developing predictive models for integrated pest management strategies.

1. Introduction

In the context of mounting concerns over climate change, the global agricultural landscape is facing a new major challenge, the fall armyworm (FAW), Spodoptera frugiperda J E Smith, (Lepidoptera: Noctuidae). Native to the tropical and subtropical regions of the Americas, this invasive insect pest has rapidly become a great threat to agriculture in Africa [1] and Asia [2,3]. Its voracious larvae and rapid spread across regions have induced heavy damage to several crops such as sorghum and maize [4,5,6]. Estimated annual yield losses due to FAW in African countries range from 8.3 to 20.6 million tons, with losses reaching up to 735,000 tons in Benin [7]. Beyond its voracity, FAW is a polyphagous insect species, infesting over 353 host plant species, with a clear preference for maize [8]. The combination of its remarkable morphological plasticity and polyphagous feeding habits [9,10] is posing a serious threat to agricultural productivity and food security [11], particularly in developing countries [11,12,13,14].
In southern Benin, maize is cultivated during both the rainy and dry seasons and is the major cereal crop in AEZ 6 and 8 (see below detailed description), justifying the choice of these zones for the study. The rainy season extends from April to July, while the dry season spans from November to March. During the dry season, maize cultivation is primarily limited to irrigated areas or valleys with higher water availability. Furthermore, FAW infestations are prevalent in both seasons across AEZ 6 and 8, impacting crop health and yield. To manage FAW, farmers often rely on synthetic pesticides [15]. While these pesticides are known for their effectiveness, economic constraints restrict their use to a limited number of farmers, as not all can afford the costs associated with regular applications.
For a better understanding of the bio-ecology of this invasive pest as the basis for developing sustainable management strategies, the current study focuses on the seasonal fluctuations of its larval population density and the factors that affect its spatial dispersion [16,17]. These fluctuations are driven by a combination of ecological and agroclimatic factors, such as competition, natural enemies, resource availability, temperature, precipitation, and relative humidity [18,19,20,21]. Additionally, the rate of change in the FAW population over time is influenced by its fecundity, speed of development, and survival. Environmental and numerous biotic factors shape all these variables [17,22].
The spatial distribution pattern is an intrinsic factor of species and may reflect their behavioral traits due to the interaction between insects and their environment [23,24]. Thus, effective integrated pest management tactics can benefit from investigations of spatial dispersion data. Several studies have attempted to characterize FAW spatial distribution on maize in Mexico [25,26,27], and in China [28], documenting various distribution patterns for these pest populations. However, the spatial distribution of FAW in maize fields has not yet been investigated in Africa. Aggregation indexes and distribution frequencies can infer distribution types [29,30]. While non-mathematical, these indexes provide an approximate description of population distributions. Based on observed field data, probability distribution models are crucial for establishing sampling and statistical analysis criteria for pest management [27,31].
This study attempts to elucidate the spatiotemporal distribution of FAW associated with the larvae in maize fields in Benin, where maize is one of the staple food security crops. It capitalizes on recent findings by [22,32,33], whereby temperature, relative humidity, and rainfall were considered important agroclimatic factors affecting FAW populations [34].

2. Material and Methods

2.1. Study Area

This study was carried out in the southern region of Benin, more specifically in the two agroecological zones, AZE 6 and AEZ 8 [35]. Zone 6 (AEZ 6) “called zone of terre de barre” (Southern and Central Benin), is characterized by the presence of ferralitic soils, a Sudano-Guinean climate (two rainy seasons alternating with two dry seasons) with an annual rainfall of 800–1400 mm per year and an altitude range of 20 to 250 m, while zone 8 (AEZ 8), called “fisheries region” (Southern Benin), is dominated by coastal gleysols and arenosols with a Sudano-Guinean climate and annual rainfall of 900–1400 mm per year and an altitude range of 0 and 130 m. With a subequatorial climate featuring two rainy (April–July and September–November) and two dry seasons (December–March and August–September) and a growing season of about 240 days, the area encompassing AEZs 6 and 8 spans over 17,920 km2. Fisheries and crop production, with a particular emphasis on maize, are the main drivers of this region’s economy. Sampling in AEZ 8 occurred in the municipalities of Adjohoun (6°42′43″ N/2°29′3″ E), Dangbo (6°34′4″ N/2°29′57″ E), Ouinhi (7°4′5″ N/2° 28′ 5″ E), while in AEZ 6, it was carried out in the municipalities of Zè (6°47′00″ N, 2°18′00″ E), Dogbo (6°49′0″ N/1°46′60″ E), Klouékanmè (6°58′49″ N/1°50′32″ E), and Zakpota (7°13′41″ N/2°12′4″ E) (Figure 1).

2.2. Sampling of Fall Armyworm Larvae in Maize Fields

Larvae were sampled during the maize growing seasons, from January to February in the dry season and from April to May in the rainy season, in 2021 and 2022. Twelve maize fields were targeted in each AEZ. A total of 384 maize fields at the 2–8 leaf stage (V2–V8) were sampled once over two years during both the dry and rainy seasons, with 96 fields sampled in AEZ 6 and 96 in AEZ.
Maize fields were split into five subplots. Scouting was performed by randomly selecting 6 plants (during the dry season) and 10 plants (during the rainy season) per subplot while moving along a W-shaped pattern [33]. This pattern was used to obtain a more representative assessment of pest incidence and distribution across the entire field. In total, 30 plants and 50 plants were randomly sampled per field during the dry season and rainy seasons, respectively. Inspected maize fields were slightly smaller in size during the dry season (0.5 ha) compared to the rainy season (between 0.5 to 1 ha). The number of larvae and egg masses was recorded per plant. Leaf damage was assessed using a scoring system from 1 to 9, which was first described by [36] and further adjusted by [4] (Table 1). Prior to sampling, farmers were interviewed to identify pesticide-treated maize fields, which were then excluded from the sampling process.

2.3. Parameters

Plants with either FAW egg masses, larvae, or visual damage symptoms were considered as infested. The larval infestation rate was calculated for each maize field, then the rate of larval infested fields per AEZ was calculated by considering the total number of fields sampled over each year. The mean percentages of damaged plants per AEZ were determined by considering the sum of percentages of plants showing visible signs of FAW damage per field and the total number of inspected fields in a given year. Plants showing visible signs of FAW damage were categorized as damaged, regardless of the presence of feeding larvae. The mean numbers of larvae per plant per week were determined. Leaf damage severity was also assessed.

2.4. Meteorological Data

Monthly mean temperatures and rainfall for the study period in the different study locations were obtained from the Agency for Aerial Navigation Safety (Agence pour la Sécurité de la Navigation Aérienne en Afrique et à Madagascar, ASECNA-Bénin). For temperatures, we used data from the weather station of Bohicon, which is closest to the locations of Ouinhi, Zakpota, Klouékanmè, and Dogbo, while for the localities of Adjohoun, Dangbo, and Zè, we used data from the Cotonou station. For rainfall, we used data from the municipalities of Adjohoun, Dangbo, Ouinhi, and Zakpota, and from the Aplahoué and Allada weather stations for the localities of Dogbo, Klouékanmè, and Zè.

2.5. Data Analysis

2.5.1. Larval Dispersion Model Analysis in Maize Fields

The larval dispersion model was analyzed using the Taylor power law and Iwao regression procedure [23,31,37,38,39,40]. Taylor’s power law [40] is expressed as follows:
Si2 = αmiβ
Si2 is the variance at a sampling date i; mi is the mean larval density at sampling date i; α is the sampling factor; and β is the aggregation index. The coefficients α and β were estimated by performing linear regression on the log-transformed values [41] of Si2 (variance sampling date i), and the corresponding log-transformed values of the mean larval density. The aggregation index, as defined by [42] and used by [31,43], and Ref. [23], was estimated to analyze larval dispersion. The following formula defines this index:
mi* = mi + [(Si2/mi) − 1],
where mi* represents the mean of the aggregation index; mi is the mean larval density per plant for sampling date i; and Si2 is the variance of larvae at sampling date i. According to [38], many biological distribution models exhibit a linear relationship between m* and m:
m* = ϒ + δm,
where ϒ is the regression coefficient; m* is the mean of the aggregation index; and m is the larval density. The slope δ represents the rate of aggregation of the larval population, and ϒ indicates the number of individuals constituting the initial colony [43].

2.5.2. Statistical Analysis

Data on larval infestation rate, and larvae number of AEZ were processed by applying the analysis of variance (ANOVA) based on the general linear model (GLM) with SAS software, version 9.3. Means were separated using the Student–Newman–Keuls (SNK). The t-test was used to compare infestation, severity, and mean number of larvae observed during the two years. The percentage of damaged plants was compared using the least square difference (LSD) test.

3. Results

3.1. Larval Infestation of Maize by FAW in Dry and Rainy Seasons

The results of the two-factor ANOVA on the maize larval infestation rate by FAW during the dry and rainy seasons are summarized in Table 2. This analysis reveals that larval infestation rates vary significantly according to zone, season, and year (F1,317 = 4.35; p < 0.03; F1,317 = 48.20; p < 0.0001 and F1,317 = 8.07; p < 0.004). Additionally, significant interaction effects were observed between the three factors zone, season, and year (F1,317 = 2.96; p < 0.05).
Larval infestation rate displayed a significant difference between the two AEZs for the years 2021 and 2022 (F3,129 = 11.69; p < 0.0001). During the dry season, a significantly lower larval infestation rate was observed in 2022 compared to 2021 in each AEZ (Table 3). In the rainy season, the lowest larval infestation rate was observed in AEZ 6 compared to AEZ 8 in 2022 (F3,129 = 3.80; p = 0.01) (Table 2). However, no significant difference was obtained between AEZ 6 and 8 in 2021. Larval infestation rates in AEZ 8 were higher during the rainy season compared to the dry season, regardless of year. However, no significant differences were observed between larval infestation rates between AEZ 6 and AEZ 8, regardless of season.

3.2. Density of FAW Larvae During the Dry and Rainy Season

The results of the two-factor ANOVA on FAW larval density during the dry and rainy seasons are shown in Table 4. The analysis demonstrates that larval density varies highly significantly with zone, season, and year (F1,14752 = 53.01; p < 0.0001; F1,14752 = 235.79; p < 0.0001 and F1,14752 = 23.09; p < 0.0001). The interactions between zone and season on one side, and between zone, season, and year, were significant when considering larval density (F1,14752 = 66.65; p < 0.001 and F1,14752 = 5.59; p < 0.0037).
Dry and rainy season FAW larval densities in 2021 and 2022 are presented in Table 5. During the dry season, significant differences were observed between weeks within each AEZ for the same year. Weekly recorded larval densities decreased significantly over time as well, regardless of AEZs. In the rainy season, the larval density of FAW was highest during sampling at weeks 2 and 3 in 2021 in both AEZs and week 4 in AEZs 6 in 2021 (F5,2995 = 6.38; p < 0.001) and 2022 (F9,5391 = 35.88; p < 0.0001) (Table 3). When comparing the overall means, larval density was higher in AZE 8 compared to AZE 6 in the dry season in 2021, but no significant differences were observed between the AEZs in 2022 during the rainy season for larval density. The between-season comparison shows the highest larval densities in AEZ 8 during the dry season while the lowest larval density was recorded during the rainy season, regardless of agroecological zone and year. The analysis of the interactions revealed that larval density was jointly influenced by both zone and season, as well as by zone, season, and year together.

3.3. Percentage of Damaged Plants During the Dry and Rainy Seasons

Table 6 presents the results of the two-factor ANOVA on the percentage of damaged plants during the dry and rainy seasons. These results indicate that the percentage of damaged plants varies significantly with season and year (F1,317 =173.08; p < 0.0001 and F1,317 = 56.74; p < 0001). The interaction between zone, season, and year was significant when considering the percentage of damaged plants (F1,317 = 6.93; p < 0.0011).
A comparison of the two AEZs revealed a slight difference in the percentage of damaged plants (F3,129 = 8.09; p < 0.0005) (Table 7), regardless of the year.
The percentage of damaged plants was the highest in 2021, regardless of AEZs (F3,129 = 16.61; p < 0.0001) (Table 4). However, in 2022, the highest damage was observed in AEZ 8 compared to zone 6. A comparison between seasons revealed more leaf damage during the dry season in 2022, regardless of the agroecological zone.

3.4. Leaf Damage Severity During the Dry and Rainy Season

The results of the two-factor ANOVA on leaf damage severity during the dry and rainy seasons are presented in Table 8. Damage severity varies highly significantly with season and year (F1,14753 = 453.04; p < 0.0001 and F1,14753 = 86.55; p < 0.0001). The interaction between zone and season was significant (F1,14753 = 13.42; p < 0.003). Likewise, the interaction between zone, season, and year was significant for damage severity (F1,14753 = 182.58; p < 0.0001).
Table 9 shows the severity of FAW damage on maize leaves during the dry and rainy seasons of 2021–2022. During the dry season, in 2021, the highest damage was observed in the third week-sampling in AEZ 8 compared to AEZ 6 (F5,1795 = 8.60; p < 0.0001). But in 2022, even significant differences occurred between weekly samples (F9,3231 = 12.91; p < 0.0001), with the highest plant damage obtained in the fourth week in AEZ 6 and from the second to the fifth week in AEZ 8. When comparing leaf damage severity within the same AEZ between seasons, mean values obtained were higher in the dry season compared to the rainy season, regardless of the agroecological zone in 2021 (F3,5157 = 197.99; p < 0.0001). In 2022, leaf damage severity was the lowest in AEZ 8 during the rainy season (F3,899 = 13.53, p < 0.0001). The interaction between zone and season, as well as between zone, season, and year, demonstrated that leaf damage severity was jointly influenced by zone and season within years.

3.5. Dispersion Pattern of FAW Larvae

The linear relationship between the logarithm of Si2 and the logarithm of mi is depicted in Figure 2 with a determination coefficient (R2) of 0.801 (p < 0.0001). The slope β of 1.32, exceeding 1, indicates an aggregation trend of larvae. The larval dispersion pattern was found to follow a negative binomial distribution. Figure 3 shows the linear relationship between the aggregation index and the mean larval density per plant, with a determination coefficient (R2) of 0.463. The slope δ of the regression line, equal to 1.25, suggests a moderate aggregation of larvae, while the intercept ϒ, equal to 2.08, indicates that FAW larvae were grouped in basic colonies [38,43].

3.6. Temperature and Precipitation During the Survey

The dry season had higher monthly mean temperatures than the rainy season, and there were significant variations in rainfall between the two seasons (Figure 4). The monthly mean temperatures at AEZ 6 in 2021 varied from 29.39 to 30.38 °C during the dry season, and 27.48 to 29.84 °C during the rainy season. Monthly mean rainfall varied from 0.25 to 5.12 mm in the dry season, while during the rainy season, it varied from 39.75 to 123.32 mm. For the same area (AEZ 6), temperature variations were observed in 2022, ranging from 26.63 to 29.8 °C in the dry season and from 26.79 to 30.12 °C during the rainy season. Conversely, in the dry season, rainfall varied from 0 to 3.1 mm, whereas during the rainy season, it ranged from 35.95 to 181.62 mm.
Monthly mean temperatures during the dry season in AEZ 8 for 2021 varied from 29.39 to 30.78 °C, while rainy season temperatures ranged from 27.49 to 29.84 °C. Monthly mean rainfall ranged from 4.5 to 5.53 mm during the dry season, while it ranged from 78.06 to 198.03 mm during the rainy season. Temperatures in AEZ 8 ranged from 26.48 to 29.7 °C during the dry season and from 26.79 to 30.12 °C during the rainy season in 2022. Rainfall ranged from 82.73 to 222.9 mm in the wet season and 0 to 0.56 mm in the dry season.

4. Discussion

Our study revealed significant variability in FAW larval infestation rates and the percentage of damaged plants across seasons, years, and AEZs, likely influenced by the pest’s life cycle and maize phenology [33,44]. Younger maize growth stages (VE–V3) were particularly susceptible to FAW damage, as evidenced by higher damage scores during early plant development [45,46,47]. Larval infestations were lower in AEZ 6 than in AEZ 8 during the rainy season. Such variability may be due to differences in physical conditions and biotic factors such as food and natural enemies [18].
Interestingly, larval densities during the dry season were much higher than in the rainy season, despite a larger number of maize plants being sampled in the latter. This discrepancy could be attributed to weather factors, including precipitation, temperature, and humidity [18]. Although there was a slight difference in temperatures, substantial differences in rainfall between seasons (Figure 4) likely played a pivotal role in shaping FAW population dynamics. Heavy rainfall may have a lethal effect on larvae, particularly during the 2nd–4th instar stages, as they were frequently observed drowning in the whorls. These findings support the hypothesis that rainfall significantly reduces larval density and plant damage in maize fields, as also reported by [33,48,49]. Seasonal differences in infestation further align with the well-documented impacts of temperature and rainfall on insect life cycles [50,51]. Variations in these climatic factors, exacerbated by climate change, may intensify FAW damage and facilitate its spread to new regions. In Africa, for instance, FAW may exploit migrations from climatically favorable zones to colonize previously unsuitable areas. These findings emphasize the importance of ongoing surveillance and the development of adaptive pest management strategies [52].
Our findings indicate that FAW population density is higher in the dry season than in the rainy season, consistent with studies conducted in other regions of Africa and Latin America. For example, Ref. [33] reported significant seasonal variations in FAW infestations in Uganda and Mozambique, where higher pest densities occurred during the rainy season, mirroring our observations in Southern Benin. Similarly, research from Latin America, where FAW is endemic, demonstrates that rainfall and temperature strongly influence pest dynamics, with warm and humid conditions favoring higher infestation levels [46]. In contrast, a study from Ghana reported an opposite trend, where the rainy season positively influenced FAW populations in maize fields [22]. These contrasting patterns underscore the complexity of pest population dynamics, which are shaped by interacting factors, including pest biology, behavior, and highly variable climatic conditions [18].
Our study further revealed inter-annual variability in larval density and damage severity in AEZ 6 and AEZ 8 during the rainy season. Both larval densities and damage severity were lower in 2022 compared to 2021, likely due to higher rainfall levels recorded in 2022. Heavy rainfall may have drowned many larvae in the whorls, thereby reducing damage to the plants. In contrast, during the dry season, significant variation in FAW larval densities was observed, with lower densities recorded in 2021. This reduction could be attributed to sporadic rains that drowned larvae or the migration of FAW adults driven by unfavorable environmental conditions.
Other important factors that can regulate the FAW population in the field are biotic factors, such as natural enemies, maize varieties, and agricultural practices. These factors significantly shape FAW dynamics [48,53,54].
Larval mortality is influenced by multiple factors, including entomopathogenic fungi, bacterial infections, parasitoids, and predatory insects. For example, Telenomus remus Nixon (Hymenoptera; Platygastridae), Chelonus bifoveolatus Szépligeti, Coccygidium luteum (Brullé), Cotesia icipe Fernandez-Triana and Fiaboe (Hymenoptera; Braconidae), Pristomerus pallidus (Kriechbaumer), Charops sp. (Hymenoptera; Ichneumonidae), and Drinoquadri zonula (Thomson) (Diptera; Tachinidae) have been reported as important parasitoids that contribute to the mortality of FAW populations in Benin [55]. Additionally, entomopathogenic fungi such as Metarhizium sp., and nematodes such as Hexamermis sp., have also been shown to infect FAW larvae [56]. Furthermore, predatory ants have been identified as significant natural enemies, with seven species recorded by [57]. Conservation of natural enemies is crucial for sustainable FAW management [58]. Integrated pest management (IPM) strategies should therefore prioritize practices that enhance the effectiveness of these biocontrol agents.
The population dynamics of FAW in maize fields are strongly influenced by the choice of maize varieties. Conventional varieties are often more susceptible to FAW infestations due to the absence of genetic resistance, leading to higher larval densities and damage levels [59]. In contrast, hybrids and Bt maize varieties exhibit moderate to high resistance, reducing pest survival and oviposition rates [4,60]. For instance, studies have shown that Bt maize containing Cry proteins significantly lowers larval densities by disrupting their feeding [60]. Differences observed in larval densities might have been caused by factors other than variety as the accessibility of resistant varieties remains limited in Benin due to socioeconomic and regulatory challenges, such as the high cost of seeds and restrictions on the adoption of genetically modified crops.
Agricultural practices and cropping systems are factors influencing FAW dynamics, as they directly affect pest habitat and interactions with natural enemies [61]. Fertilizer application can have varying effects on crop damage, depending on the type and quantity of fertilizer used. Inconsistent results may stem from differences in fertilizer application methods. For example, Ref. [62] recently observed that split applications of NPK fertilizers reduced both the incidence and damage caused by FAW. On the other hand, nitrogen-based fertilizers can alter the carbon-to-nitrogen (C/N) ratio in plants, potentially making them more vulnerable to FAW damage [63].
In Benin, smallholder farmers often rotate maize with other crops and intercrop it with cassava and legumes such as cowpea, groundnut, and pigeon pea. Intercropping has been shown to reduce FAW oviposition and feeding activity by disrupting pest habitats and promoting natural enemy populations [64]. Ref. [65] found that intercropping maize with leguminous crops significantly reduced the incidence of both stem borers and FAW. When appropriately designed, diverse cropping systems can effectively maintain FAW populations at manageable levels by combining reduced pest pressure with enhanced natural regulation [66].
Our analysis of the aggregation index and the mean number of larvae per plant revealed a slope of δ = 1.25, indicating a moderate larval aggregation. This aggregation reflects known larval behaviors, such as ballooning during early instars and self-thinning through cannibalism at later stages [67,68]. Environmental factors, including plant morphology, food availability, and microclimatic conditions, also influence larval clustering. These behaviors and environmental interactions align with observations in Mexico [25,27], America [69], and Brazil [26,70]. These findings were similar to previous observations documenting more pronounced intraspecific aggregation patterns in similar pest species [37,71,72]. Furthermore, the intercept of γ = 2.08 supports that FAW larvae evolve in colonies of at least two larvae before they disperse.
Understanding this aggregation pattern is essential for developing effective control strategies, developing effective sampling plans, and predicting pest damage, as this pattern could affect the dispersion of FAW larvae. For instance, probability distribution models based on larval clustering can improve sampling designs and guide site-specific control measures, reducing costs and environmental impacts [73]. Behavioral patterns and environmental characteristics play a key role in determining the spatial distribution of individuals within a population in a given ecosystem. It has been demonstrated that different plant varieties also impact the spatial distribution of insects [74]. Therefore, further investigations are required to validate the spatial distribution of this pest in maize growth stages. The current study did not explore the range of alternative host plants of FAW. Therefore, there is a need to examine the influence of these host plants on the dispersion and population dynamics of this pest insect. The larval density and aggregation patterns of FAW identified in this study can inform action threshold determination for targeted interventions by providing critical data on population dynamics that help identify when pest populations exceed economically damaging levels, allowing for timely and precise management strategies.
The current study provides valuable insights into the seasonal dynamics and key factors influencing FAW infestations in maize crops across two AEZs in Benin. The findings confirm that FAW is present year-round, with larval populations peaking during the rainy season and declining during the dry season, likely due to temperature and rainfall variations. These results underscore the need for tailored management strategies that account for seasonal patterns. Biological control could be optimized by employing natural enemies and entomopathogens adapted to rainy season conditions, reducing FAW’s impact while minimizing reliance on chemical inputs. Early detection and control measures during the rainy season are critical, particularly in zones with consistently high infestation rates. This study provides valuable baseline data on the spatiotemporal dynamics of FAW in southern Benin, contributing to a better understanding of its behavior and distribution patterns. The findings lay the groundwork for further research on integrated pest management strategies, particularly in similar agroecological zones in sub-Saharan Africa and beyond.

Author Contributions

Conceptualization, K.Z.; methodology, K.Z., A.S., G.G., G.T.T.-Y., M.T. and E.D.; data collection and analysis, K.Z. and E.D.; writing—original draft preparation, K.Z.; writing—review and editing, A.S., G.G., G.T.T.-Y. and M.T. All authors have read and agreed to the published version of the manuscript.

Funding

The authors greatly acknowledge the financial support provided by the International Development Association (IDA) of the World Bank to projects aimed at Accelerating Impacts of CGIAR Climate Research for Africa (P173398, AICCRA-Ghana). The IDA helps the world’s poorest countries by providing grants and low- to zero-interest loans for projects and programs that boost economic growth, reduce poverty, and improve poor people’s lives. The IDA is one of the largest sources of assistance for the world’s 76 poorest countries, 39 of which are in Africa. Annual IDA commitments have averaged about USD 21 billion over circa 2017–2020, with approximately 61 percent going to Africa.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

We acknowledge the assistance of Jonas Gnanmi, Borghero Dahoueto, and Dieudonné Hounkanri from the Institute of Tropical Agriculture in the fieldwork. We would also like to thank the maize farmers of each municipality for their collaboration.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Goergen, G.; Kumar, P.L.; Sankung, S.B.; Togola, A.; Tamò, M. First Report of Outbreaks of the Fall Armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a New Alien Invasive Pest in West and Central Africa. PLoS ONE 2016, 11, 0165632. [Google Scholar] [CrossRef]
  2. CABI. Spodoptera frugiperda (Fall Armyworm). Available online: https://www.cabi.org/isc/datasheet/29810 (accessed on 14 July 2024).
  3. Tao, W.C.; Zhang, X.Y.; Zhang, Y.; Deng, X.Y.; Zhang, H.L.; Zhang, Z.H.; Li, Q.; Jiang, C.X. Effects of the Host Plants of the Maize-Based Intercropping Systems on the Growth, Development and Preference of Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae). Insects 2024, 15, 26. [Google Scholar] [CrossRef]
  4. Prasanna, B.M.; Huesing, J.E.; Eddy, R.; Peschke, V.M.; Prasanna, B.M.; Huesing, J.E.; Eddy, R.; Peschke, V.M. Fall Armyworm in Africa: A Guide for Integrated Pest Management, 1st ed.; CIMMYT: Mexico City, Mexico, 2018. [Google Scholar]
  5. Rwomushana, I.; Bateman, M.; Beale, T.; Beseh, P.; Cameron, K.; Chiluba, M.; Clottey, V.; Davis, T.; Day, R.; Early, R.; et al. Fall Armyworm: Impacts and Implications for Africa. CABI Evidence Note Update; CABI: Oxfordshire, UK, 2018. [Google Scholar]
  6. Toepfer, S.; Fallet, P.; Kajuga, J.; Bazagwira, D.; Mukundwa, I.P.; Szalai, M.; Turlings, T.C.J. Streamlining Leaf Damage Rating Scales for the Fall Armyworm on Maize. J. Pest. Sci. 2021, 94, 1075–1089. [Google Scholar] [CrossRef]
  7. Day, R.; Abrahams, P.; Bateman, M.; Beale, T.; Clottey, V.; Cock, M.; Colmenarez, Y.; Corniani, N.; Early, R.; Godwin, J.; et al. Fall Armyworm: Impacts and Implications for Africa. Outlooks Pest Manag. 2017, 28, 196–201. [Google Scholar] [CrossRef]
  8. Montezano, D.G.; Specht, A.; Sosa-Gómez, D.R.; Roque-Specht, V.F.; Sousa-Silva, J.C.; Paula-Moraes, S.V.; Peterson, J.A.; Hunt, T.E. Host Plants of Spodoptera frugiperda (Lepidoptera: Noctuidae) in the Americas. Afr. Entomol. 2018, 26, 286–300. [Google Scholar] [CrossRef]
  9. Hafeez, M.; Li, X.W.; Zhang, J.M.; Zhang, Z.J.; Huang, J.; Wang, L.K.; Khan, M.M.; Shah, S.; Fernández-Grandon, G.M.; Lu, Y. Bin Role of Digestive Protease Enzymes and Related Genes in Host Plant Adaptation of a Polyphagous Pest, Spodoptera frugiperda. Insect Sci. 2021, 28, 611–626. [Google Scholar] [CrossRef]
  10. Hafeez, M.; Li, X.; Chen, L.; Ullah, F.; Huang, J.; Zhang, Z.; Zhang, J.; Siddiqui, J.A.; Zhou, S.X.; Ren, X.Y.; et al. Molecular Characterization and Functional Analysis of Cytochrome P450-Mediated Detoxification CYP302A1 Gene Involved in Host Plant Adaptation in Spodoptera frugieprda. Front. Plant. Sci. 2023, 13, 1079442. [Google Scholar] [CrossRef]
  11. FAO. The Global Action for Fall Armyworm Control: Action Framework 2020–2022: Working Together to Tame the Global Threat; FAO: Rome, Italy, 2020. [Google Scholar]
  12. Han, S.-P.; Zhou, Y.-Y.; Wang, D.; Qin, Q.-J.; Song, P.; He, Y.-Z. Impact of Host Plants on Biological Characteristics and Vg/VgR Expression of Spodoptera frugiperda. J. Pest. Sci. 2022, 96, 1569–1577. [Google Scholar] [CrossRef]
  13. Koffi, D.; Kyerematen, R.; Eziah, V.Y.; Osei-Mensah, Y.O.; Afreh-Nuamah, K.; Aboagye, E.; Osae, M.; Meagher, R.L. Assessment of Impacts of Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) on Maize Production in Ghana. J. Integr. Pest. Manag. 2021, 11. [Google Scholar] [CrossRef]
  14. Suby, S.B.; Soujanya, P.L.; Yadava, P.; Patil, J.; Subaharan, K.; Prasad, G.S.; Babu, K.S.; Jat, S.L.; Yathish, K.R.; Vadassery, J.; et al. Invasion of Fall Armyworm (Spodoptera frugiperda) in India: Nature, Distribution, Management and Potential Impact. Curr. Sci. 2020, 119, 44–51. [Google Scholar] [CrossRef]
  15. Houngbo, S.; Zannou, A.; Aoudji, A.; Sossou, H.C.; Sinzogan, A.; Sikirou, R.; Zossou, E.; Totin Vodounon, H.S.; Adomou, A.; Ahanchédé, A. Farmers’ Knowledge and Management Practices of Fall Armyworm, Spodoptera frugiperda (J.E. Smith) in Benin, West Africa. Agriculture 2020, 10, 430. [Google Scholar] [CrossRef]
  16. Kogan, M.; Herzog, D.C. Sampling Methods in Soybean Entomology; Springer Science & Business Media: New York, NY, USA, 1980. [Google Scholar]
  17. Rahmathull, V.K.; Sathyanara, K.; Angadi, B.S. Influence of Abiotic Factors on Population Dynamics of Major Insect Pests of Mulberry. Pak. J. Biol. Sci. 2015, 18, 215–223. [Google Scholar] [CrossRef]
  18. Cammell, M.E.; Knight, J.D. Effects of Climatic Change on the Population Dynamics of Crop Pests. Adv. Ecol. Res. 1992, 22, 117–162. [Google Scholar]
  19. Hamby, K.A.; Bellamy, D.E.; Chiu, J.C.; Lee, J.C.; Walton, V.M.; Wiman, N.G.; York, R.M.; Biondi, A. Biotic and Abiotic Factors Impacting Development, Behavior, Phenology, and Reproductive Biology of Drosophila suzukii. J. Pest. Sci. 2016, 89, 605–619. [Google Scholar] [CrossRef]
  20. Omoregie, M.E.; Enobakhare, D.A.; Omoregie, A.O. Population Dynamics of the Fall Armyworm, Spodoptera frugiperda J. E. Smith (Lepidoptera: Noctuidae) on Early and Late Season Maize. Anim. Res. Int. 2023, 20, 4734–4740. [Google Scholar]
  21. Schowalter, T.D. Population Systems. In Insect Ecology: An Ecosystem Approach; Academic Press: Cambridge, MA, USA, 2011; pp. 129–156. [Google Scholar]
  22. Nboyine, J.A.; Kusi, F.; Abudulai, M.; Badii, B.K.; Zakaria, M.; Adu, G.B.; Haruna, A.; Seidu, A.; Osei, V.; Alhassan, S.; et al. A New Pest, Spodoptera frugiperda (J.E. Smith), in Tropical Africa: Its Seasonal Dynamics and Damage in Maize Fields in Northern Ghana. Crop Prot. 2020, 127, 104960. [Google Scholar] [CrossRef]
  23. Kuno, E. Sampling and Analysis of Insect Populations. Annu. Rev. Entomol. 1991, 36, 285–304. [Google Scholar] [CrossRef]
  24. Afshari, A.; Soleiman-Negadian, E.; Shishebor, P. Population Density and Spatial Distribution of Aphis gossypii Glover (Homoptera: Aphididae) on Cotton in Gorgan. J. Agric. Sci. Technol 2009, 11, 27–38. [Google Scholar]
  25. Báez, M.S.; Ibarra, J.E.; Villanueva, F. Distribución Espacial y Tamaño de Muestra de Los Gusanos: Cogollero Spodoptera frugiperda (Smith) y Elotero Heliothis Zea (Boddie) En Cultivo de Maíz. Folia Entomol. Mex. 1980, 45, 58–59. [Google Scholar]
  26. Farias, P.R.S.; Barbosa, J.C.; Busoli, A.C. Distribuição Espacial Da Lagarta-Do-Cartucho, Spodoptera frugiperda (J.E.Smith) (Lepidoptera: Noctuidae), Na Cultura Do Milho. Neotrop. Entomol. 2001, 30, 681–689. [Google Scholar] [CrossRef]
  27. Hernández-Mendoza, J.L.; López-Barbosa, E.C.; Garza-González, E.; Mayek-Pérez, N. Spatial Distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in Maize Landraces Grown in Colima, Mexico. Int. J. Trop. Insect. Sci. 2008, 28, 126–129. [Google Scholar] [CrossRef]
  28. He, Y.; Wang, K.; Du, G.; Zhang, Q.; Li, B.; Zhao, L.; He, P.; Chen, B. Temporal and Spatial Distribution Patterns of Spodoptera frugiperda in Mountain Maize Fields in China. Insects 2022, 13, 938. [Google Scholar] [CrossRef]
  29. Elliott, N.C.; Kieckhefer, R.W.; Walgenbach, D.D. Binomial Sequential Sampling Methods for Cereal Aphids in Small Grains. J. Eco. Entomol. 1990, 83, 1381–1387. [Google Scholar] [CrossRef]
  30. Taylor, L.R.; Woiwod, I.P.; Perry, J.N. The Negative Binomial as a Dynamic Ecological Model for Aggregation, and the Density Dependence of k. J. Anim. Ecol. 1979, 48, 289–304. [Google Scholar] [CrossRef]
  31. Taylor, L.R. Assessing and Interpreting the Spatial Distributions of Insect Populations. Annu. Rev. Entomol. 1984, 29, 321–358. [Google Scholar] [CrossRef]
  32. Anandhi, S.; Saminathan, V.R.; Yasodha, P.; Roseleen, S.S.J.; Sharavanan, P.T.; Rajanbabu, V. Correlation of Fall Armyworm Spodoptera frugiperda (J.E. Smith) with Weather Parameters in Maize Ecosystem. Int. J. Curr. Microbiol. Appl. Sci. 2020, 9, 1213–1218. [Google Scholar] [CrossRef]
  33. Caniço, A.; Mexia, A.; Santos, L. Seasonal Dynamics of the Alien Invasive Insect Pest. Insects 2020, 11, 512. [Google Scholar] [CrossRef]
  34. Ramasamy, M.; Das, B.; Ramesh, R. Predicting Climate Change Impacts on Potential Worldwide Distribution of Fall Armyworm Based on CMIP6 Projections. J. Pest. Sci. 2021, 95, 841–854. [Google Scholar] [CrossRef]
  35. Aholoukpè, H.S.N.; Amadji, G.L.; Koussihouede, H.K.I. Stocks de Carbone Dans Les Sols Des Zones Agro-Écologiques Du Bénin. In Carbon des Sols en Afrique: Impact des Usages des Sols et des Pratiques Agricoles. Carbon des Sols en Afrique: Impact des Usages des Sols et des Pratiques Agricoles; Chevallier, T., Razafimbelo, T.M., Chapuis-Lardy, L., Brossard, M., Eds.; IRD Éditions; Food and Agriculture Organization of the United Nations: Rome, Italy, 2020; pp. 101–122. [Google Scholar]
  36. Davis, F.M.; Ng, S.S.; Williams, W.P. Visual Rating Scales for Screening Whorl-Stage Corn for Resistance to Fall Armyworm. In Technical bulletin (Mississippi Agricultural and Forestry Experiment Station); Department of Information Services, Division of Agriculture, Forestry, and Veterinary Medicine, Mississippi State University: Starkville, MI, USA, 1992; pp. 1–9. [Google Scholar]
  37. Atachi, P.; Dannon, E.A.; Arodokoun, Y.D.; Tamò, M. Distribution and Sampling of Maruca vitrata (Fabricius) (Lep., Pyralidae) Larvae on Lonchocarpus sericeus (Poir) H.B. and K. J. Appl. Entomol. 2002, 126, 188–193. [Google Scholar] [CrossRef]
  38. Iwao, S. A New Regression Method for Analyzing the Aggregation Pattern of Animal Populations. Res. Popul. Ecol. 1968, 10, 1–20. [Google Scholar] [CrossRef]
  39. Ruesink, W.G. Introduction to Sampling Theory. In Sampling Methods in Soybean Entomology; Springer: New York, NY, USA, 1980; pp. 61–78. [Google Scholar]
  40. Taylor, L.R. Aggregation, Variance and the Mean. Nature 1961, 189, 732–735. [Google Scholar] [CrossRef]
  41. Southwood, T.R.E. Ecological Methods; Chapman & Hall: London, UK, 1978; p. 524. [Google Scholar]
  42. Lloyd, M. Mean Crowding. J. Anim. Ecol. 1967, 36, 1–30. [Google Scholar] [CrossRef]
  43. Zahner, P.J.B.; Delucchi, V.; Graf, B. Distribution and Sampling of Winter Populations of Panonychus ulmi Koch (Acarina: Tetranychidae) on Apple Tree. Oecol. Appl. 1985, 6, 99–110. [Google Scholar]
  44. Sharanabasappa, S.D.; Kalleshwaraswamy, C.M.; Maruthi, M.S.; Pavithra, H.B. Biology of Invasive Fall Army Worm Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) on Maize. Indian J. Entomol. 2018, 80, 540. [Google Scholar] [CrossRef]
  45. Bakry, M.M.S.; Abdel-Baky, N.F. Population Density of the Fall Armyworm, Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae) and Its Response to Some Ecological Phenomena in Maize Crop. Braz. J. Biol. 2023, 83, 1–17. [Google Scholar] [CrossRef]
  46. Murúa, G.; Molina-Ochoa, J.; Coviella, C. Population Dynamics of the Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) and Its Parasitoids in Northwestern Argentina. Fla. Entomol. 2006, 89, 175–182. [Google Scholar] [CrossRef]
  47. Morrill, W.L.; Greene, G.L. Distribution of Fall Armyworm Larvae. 1. Regions of Field Corn Plants Infested by Larvae 1. Environ. Entomol. 1973, 2, 195–198. [Google Scholar] [CrossRef]
  48. Early, R.; González-Moreno, P.; Murphy, S.T.; Day, R. Forecasting the Global Extent of Invasion of the Cereal Pest Spodoptera frugiperda, the Fall Armyworm. NeoBiota 2018, 50, 25–50. [Google Scholar] [CrossRef]
  49. van Huis, A. Integrated Pest Management in the Small Farmer’s Maize Crop in Nicaragua. Ph.D. Thesis, Wageningen University and Research, Wageningen, The Netherlands, 1981. [Google Scholar]
  50. Deutsch, C.A.; Tewksbury, J.J.; Tigchelaar, M.; Battisti, D.S.; Merrill, S.C.; Huey, R.B.; Naylor, R.L. Climate Change Increase in Crop Losses to Insect Pests in a Warming Climate. Science 2018, 361, 31. [Google Scholar] [CrossRef]
  51. Vicente, J.R.; Vaz, A.S.Q.; Isabel, A.; Buchadas, A.R.; Guisan, A.; Kueffer, C.; Marchante, E.; Marchante, H.; Cabral, J.A.; Nesper, M.; et al. Alien Plant Species: Environmental Risks in Agricultural and Agro-Forest Landscapes Under Climate Change. In Climate Change-Resilient Agriculture and Agroforestry: Ecosystem Services and Sustainability; Springer International Publishing: Berlin/Heidelberg, Germany, 2019; pp. 215–234. [Google Scholar]
  52. Tepa-Yotto, G.T.; Tonnang, H.E.Z.; Goergen, G.; Subramanian, S.; Kimathi, E.; Abdel-Rahman, E.M.; Flø, D.; 532 Thunes, K.H.; Fiaboe, K.K.M.; Niassy, S.; et al. Global Habitat Suitability of Spodoptera frugiperda (JE Smith) 533 (Lepidoptera, Noctuidae): Key Parasitoids Considered for Its Biological Control. Insects 2021, 12, 273. [Google Scholar] [CrossRef] [PubMed]
  53. Vilarinho, E.C.; Fernandes, O.A.; Hunt, T.E.; Caixeta, D.F. Movement of “Spodoptera frugiperda” Adults (Lepidoptera: Noctuidae) in Maize in Brazil. Fla. Entomol. 2011, 94, 480–488. [Google Scholar] [CrossRef]
  54. Ahmad, S.; Arslan, M. Influence of Meteorological Factors on Population Dynamics of Fall Armyworm, Spodoptera frugiperda, Lepidoptera: Noctuidae and Its Varietal Susceptiblilty to FAW. In Proceedings of the 1st International Electronic Conference on Entomology, Virtual, 1–15 July 2021; pp. 1–15. [Google Scholar]
  55. Winsou, J.K.; Tepa-Yotto, G.T.; Thunes, K.H.; Meadow, R.; Tamò, M.; Sæthre, M.G. Seasonal Variations of Spodoptera frugiperda Host Plant Diversity and Parasitoid Complex in Southern and Central Benin. Insects 2022, 13, 491. [Google Scholar] [CrossRef] [PubMed]
  56. Boukari, S.A.; Sinzogan, A.A.C.; Sikirou, R.; Deguenon, J.M.; Amagnidé, G.A.Y.G.; Zossou, N.; Vodounon, H.S.T.; Adomou, A.C.; Ahanchédé, A. Influence of Agricultural Practices on Spodoptera frugiperda (JE Smith) Infestation, Natural Enemies and Biocontrol in Maize. J. Agric. Crop. Res. 2022, 10, 113–130. [Google Scholar]
  57. Dassou, A.G.; Idohou, R.; Azandémè-Hounmalon, G.Y.; Sabi-Sabi, A.; Houndété, J.; Silvie, P.; Dansi, A. Fall Armyworm, Spodoptera frugiperda (J.E. Smith) in Maize Cropping Systems in Benin: Abundance, Damage, Predatory Ants and Potential Control. Int. J. Trop. Insect. Sci. 2021, 41, 2627–2636. [Google Scholar] [CrossRef]
  58. Meagher, R.L.; Nuessly, G.S.; Nagoshi, R.N.; Hay-Roe, M.M. Parasitoids Attacking Fall Armyworm (Lepidoptera: Noctuidae) in Sweet Corn Habitats. Biol. Control 2016, 95, 66–72. [Google Scholar] [CrossRef]
  59. Kasoma, C.; Shimelis, H.; Laing, M.D. Fall Armyworm Invasion in Africa: Implications for Maize Production and Breeding. J. Crop. Improv. 2021, 35, 111–146. [Google Scholar] [CrossRef]
  60. Viana, P.A.; Guimaraes, P.D.O.; Goncalves, I.D.S.; Magalhaes, C.D.S. Resistência Nativa de Híbridos Experimentais de Milho à Spodoptera frugiperda. In Proceedings of the Congresso Nacional de Milho e Sorgo, Simpósio Sobre Lepdópteros Comuns a Milho, Soja e Algodão, 1., 2014, Salvador, Brazil, 25 September 2016. [Google Scholar]
  61. Kennedy, G.G.S.N.P. Life Systems of Polyphagous Arthropod Pests in Temporally Unstable Cropping Systems. Annu. Rev. Entomol. 2000, 45, 467–493. [Google Scholar] [CrossRef]
  62. Fiaboe, K.R.; Agboka, K.; Agnamba, A.O.; Teyo, K.L.; Amegah, A.L.; Koffi, D.; Kpadonou, G.E.; Agboka, K.M.; Gwokyalya, R.; Fening, K.O.; et al. Fertilizer-Bioinsecticide Synergy Improves Maize Resilience to Spodoptera frugiperda Infestation. Crop. Prot. 2024, 177, 106548. [Google Scholar] [CrossRef]
  63. Altieri, M.A.; Nicholls, C.I. Soil Fertility Management and Insect Pests: Harmonizing Soil and Plant Health in Agroecosystems. Soil Tillage Res 2003, 72, 203–211. [Google Scholar] [CrossRef]
  64. FAO. Integrated Management of the Fall Armyworm on Maize: A Guide for Farmer Field Schools in Africa. Available online: http://www.fao.org/family-farming/detail/en/c/1112643/ (accessed on 14 July 2024).
  65. Hailu, G.; Niassy, S.; Zeyaur, K.R.; Ochatum, N.; Subramanian, S. Maize–Legume Intercropping and Push–Pull for Management of Fall Armyworm, Stemborers, and Striga in Uganda. Agron. J. 2018, 110, 2513–2522. [Google Scholar] [CrossRef]
  66. Wyckhuys, K.A.O.R.J. Population Dynamics of Spodoptera frugiperda Smith (Lepidoptera: Noctuidae) and Associated Arthropod Natural Enemies in Honduran Subsistence Maize. Crop. Prot. 2006, 25, 1180–1190. [Google Scholar] [CrossRef]
  67. Barbosa, J.C.; Perecin, D. Modelos Probabilísticos Para Distribuicão de Lagartas de Spodoptera frugiperda (JE Smith 1797) Na Cultura Do Milho. Cientifica 1982, 10, 181–191. [Google Scholar]
  68. Price, P.W. Insect Ecology, 3rd ed.; John Wiley & Sons: New York, NY, USA, 1997. [Google Scholar]
  69. Mitchell, F.L. Natural Control and Spatial Distribution of Fall Armyworm (Spodoptera frugiperda) Within Louisiana Corn Fields; Louisiana State University and Agricultural & Mechanical College: Baton Rouge, LA, USA, 1985. [Google Scholar]
  70. Melo, E.P.; Fernandes, M.G.; Degrande, P.E.; Cessa, R.M.A.; Salomão, J.L.; Nogueira, R.F. Distribuição Espacial de Plantas Infestadas Por Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) Na Cultura Do Milho. Neotrop. Entomol. 2006, 35, 689–697. [Google Scholar] [CrossRef] [PubMed]
  71. Sétamou, M.; Schulthess, F.; Poehling, H.M.; Borgemeister, C. Spatial Distribution of and Sampling Plans for Mussidia Nigrivenella (Lepidoptera: Pyralidae) on Cultivated and Wild Host Plants in Benin. Environ. Entomol. 2000, 29, 1216–1225. [Google Scholar] [CrossRef]
  72. Moradi Vajargah, M.; Golizadeh, A.; Dastjerdi, H.R.; Zalucki, M.P.; Hassanpour, M.; Naseri, B. Population Density and Spatial Distribution Pattern of Hypera postica (Coleoptera: Curculionidae) in Ardabil, Iran. Not. Bot. Horti. Agrobot. Cluj Napoca. 2011, 39, 42–48. [Google Scholar] [CrossRef]
  73. Guimapi, R.A.; Niassy, S.; Mudereri, B.T.; Abdel-Rahman, E.M.; Tepa-Yotto, G.T.; Subramanian, S.; Mohamed, S.A.; Thunes, K.H.; Kimathi, E.; Agboka, K.M.; et al. Harnessing Data Science to Improve Integrated Management of Invasive Pest Species across Africa: An Application to Fall Armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae). Glob. Ecol. Conserv. 2022, 35, e02056. [Google Scholar] [CrossRef]
  74. Sedaratian, A.; Fathipour, Y.; Talebi, A.A.; Farahani, S. Population Density and Spatial Distribution Pattern of Thrips tabaci (Thysanoptera: Thripidae) on Different Soybean Varieties. J. Agric. Sci. Technol. 2010, 12, 275–288. [Google Scholar]
Insects 16 00145 g001
Figure 1. Study municipalities in southern Benin.
Figure 1. Study municipalities in southern Benin.
Insects 16 00145 g001
Insects 16 00145 g002
Figure 2. Relationship between the log-transformed values of Si2, variance for the plant at each sampling date, and the corresponding log-transformed values (mi) of larval mean density per plant.
Figure 2. Relationship between the log-transformed values of Si2, variance for the plant at each sampling date, and the corresponding log-transformed values (mi) of larval mean density per plant.
Insects 16 00145 g002
Insects 16 00145 g003
Figure 3. Relationship between the index of aggregation (mi*) and the larval mean density per plant (mi).
Figure 3. Relationship between the index of aggregation (mi*) and the larval mean density per plant (mi).
Insects 16 00145 g003
Insects 16 00145 g004
Figure 4. Monthly mean temperatures (°C) and mean rainfall (mm) in the AEZ in the dry season (January to February) and in the rainy season (March to June).
Figure 4. Monthly mean temperatures (°C) and mean rainfall (mm) in the AEZ in the dry season (January to February) and in the rainy season (March to June).
Insects 16 00145 g004
Table 1. Scale for assessment of leaf damage due to FAW in maize [4] adjusted from [36].
Table 1. Scale for assessment of leaf damage due to FAW in maize [4] adjusted from [36].
ScoreDescription
1No visible leaf-feeding damage
2Few pinholes on 1–2 older leaves
3Several shot-hole injuries on a few leaves (<5 leaves) and small circular hole damage to leaves
4Several shot-hole injuries on several leaves (6–8 leaves) or small lesions/pinholes, small circular lesions, and a few small elongated (rectangular-shaped) lesions up to 1.3 cm in length on whorl and furl leaves
5Elongated lesions (>2.5 cm long) on 8–10 leaves, plus a few small- to mid-sized uniform to irregular-shaped holes (with basement membrane consumed) eaten from the whorl and/or furl leaves
6Several large, elongated lesions on several whorl and furl leaves and/or several large uniform- to irregular-shaped holes eaten from furl and whorl leaves
7Many elongated lesions of all sizes on several whorl and furl leaves plus several large uniform- to irregular-shaped holes eaten from the whorl and furl leaves
8Many elongated lesions of all sizes on most whorl and furl leaves plus many mid- to large-sized uniform- to irregular-shaped holes eaten from the whorl and furl leaves
9Whorl and furl leaves almost totally destroyed, with the plant dying as a result of extensive foliar damage
Table 2. Analysis of variance (Fisher’s value) results for larval infestation of maize by FAW in dry and rainy seasons.
Table 2. Analysis of variance (Fisher’s value) results for larval infestation of maize by FAW in dry and rainy seasons.
SourceDFMean SquareF Valuep
Zone10.154.350.0378
Replicate590.041.210.1546
Season11.6748.200.0001
Year10.288.070.0048
Zone*Season10.113.360.0677
Zone*Year10.000.190.6637
Zone*Season*Year20.102.960.0531
Table 3. Larval infestation rate in the AEZ 6 and 8 during the dry and rainy seasons.
Table 3. Larval infestation rate in the AEZ 6 and 8 during the dry and rainy seasons.
SeasonAgroecological ZoneLarval Infestation Rate (%) (Mean ± SE)
Year 2021Year 2022
Dry seasonAEZ 636.94 ± 2.61 bB29.97 ± 2.25 aCF3,129 = 11.69;
p < 0.0001
AEZ 836.50 ± 2.00 bB29.87 ± 2.27 aC
Rainy seasonAEZ 642.40 ± 2.90 aB41.44 ± 2.32 aBF3,129 = 3.80;
p < 0.01
AEZ 847.96 ± 2.87 abA50.94 ± 1.90 bA
F3,10 = 3.54; p < 0.01F3,17 = 21.49; p < 0.0001
AEZ = agroecological zone; SE = standard error. Means ± SE followed by the same uppercase letter in the column are not significantly different. Mean ±SE followed by the same letter are not significantly different at the 5% threshold (p < 0.05). Uppercase letters refer to the comparison between seasons within the same year while lowercase letters refer to the comparison of agroecological zones and years for the same season.
Table 4. Analysis of variance (Fisher’s value) results for the density of FAW larvae during the dry and rainy seasons.
Table 4. Analysis of variance (Fisher’s value) results for the density of FAW larvae during the dry and rainy seasons.
SourceDFMean SquareF Valuep
Zone160.7753.010.0001
Replicate5991.110.970.6681
Season1270.33235.790.0001
Year126.4723.090.0001
Zone*Season176.4166.650.0001
Zone*Year12.111.840.1744
Zone*Season*Year26.415.590.0037
Table 5. Weekly larval density of FAW in the two AEZs during the dry and rainy seasons.
Table 5. Weekly larval density of FAW in the two AEZs during the dry and rainy seasons.
SeasonAgroecological ZoneSampling
Week		Larval Density (Mean ± SE)
Year 2021Year 2022
Dry seasonAEZ 610.88 ± 0.08 abc0.96 ± 0.09 abc
20.58 ± 0.05 d0.68 ± 0.05 cde
30.71 ± 0.06 cd0.67 ± 0.06 cde
4-0.56 ± 0.05 ed
5-0.43 ± 0.04 e
Overall mean0.73 ± 0.03 B0.66 ± 0.02 B
AEZ 811.11 ± 0.09 a1.07 ± 0.11 ab
20.82 ± 0.07 bc1.20 ± 0.09 a
30.98 ± 0.70 ab0.95 ± 0.07 abc
4-0.87 ± 0.08 bc
5-0.82 ± 0.07 bcd
Overall mean0.98 ± 0.04 A0.99 ± 0.03 A
F5,1795 = 7.15; p < 0.0001F9,3230 = 10.42; p < 0.0001
Rainy seasonAEZ 610.44 ± 0.03 b0.18 ± 0.03 d
20.62 ± 0.04 a0.42 ± 0.03 c
30.69 ± 0.04 a0.36 ± 0.03 c
4-0.38 ± 0.03 c
5-0.77 ± 0.04 a
Overall mean0.59 ± 0.02 C0.43 ± 0.01 C
AEZ 810.48 ± 0.04 b0.37 ± 0.03 c
20.56 ± 0.04 a0.21 ± 0.02 d
30.63 ± 0.04 a0.39 ± 0.03 c
4-0.45 ± 0.03 c
5-0.67 ± 0.03 b
Overall mean0.56 ± 0.02 C0.42 ± 0.01 C
F5,2995 = 6.38; p < 0.0001F9,5391 = 35.88; p < 0.0001
AEZ = agroecological zone; SE = standard error. Means ± SE followed by the same uppercase letter in the column are not significantly different. Mean ± SE followed by the same letter in the column is not significantly different at the 5% threshold (p < 0.05). Uppercase letters refer to the comparison of the overall mean between seasons within the same year and lowercase letters refer to the comparison of agroecological zones within the same year regardless of the season.
Table 6. Analysis of variance (Fisher’s value) results for the percentage of damaged plants during the dry and rainy seasons.
Table 6. Analysis of variance (Fisher’s value) results for the percentage of damaged plants during the dry and rainy seasons.
SourceDFMean SquareF Valuep
Zone10.072.710.1010
Replicate590.031.160.2095
Season14.68173.080.0001
Year11.5356.740.0001
Zone*Season10.083.050.0819
Zone*Year10.093.360.0679
Zone*Season*Year20.186.930.0011
Table 7. Percentage of damaged plants by FAW in the two AEZs during the dry and rainy seasons.
Table 7. Percentage of damaged plants by FAW in the two AEZs during the dry and rainy seasons.
SeasonAgroecological ZonesPercentage of Damaged Plants
(Mean ± SE)
Year 2021Year 2022
Dry seasonAEZ 658.33 ± 1.51 bB51.53 ± 2.31 aCF3,129 = 8.09;
p < 0.0005
AEZ 855.72 ± 1.82 abB53.73 ± 2.35 aC
Rainy seasonAEZ 680.92 ± 1.94 cA63.44 ± 2.06 aBF3,129 = 16.61;
p < 0.0001
AEZ 882.22 ± 2.36 cA72.05 ± 1.86 bA
F3,10 = 66.42; p < 0.0001F3,17 = 19.91; p < 0.0001
AEZ = agroecological zone; SE = standard error. Means ± (SE) followed by the same uppercase letter in the column are not significantly different. SE = standard error. Mean ± (SE) followed by the same letter in the column are not significantly different at the 5% threshold (p < 0.05). Uppercase letters refer to the comparison of the overall mean between seasons within the same year and lowercase letters refer to the comparison of agroecological zones within the same year regardless of the season.
Table 8. Analysis of variance (Fisher’s value) results in leaf damage severity during the dry and rainy seasons.
Table 8. Analysis of variance (Fisher’s value) results in leaf damage severity during the dry and rainy seasons.
SourceDFMean SquareF Valuep
Zone14.061.710.1913
Replicate5992.491.050.1981
Season11077.71453.040.0001
Year1205.8886.550.0001
Zone*Season131.9113.420.0003
Zone*Year12.861.200.2724
Zone*Season*Year2434.31182.580.0001
Table 9. Weekly leaf damage severity in the two AEZs during the dry and rainy seasons.
Table 9. Weekly leaf damage severity in the two AEZs during the dry and rainy seasons.
SeasonAgroecological ZoneSampling WeekPlant Damage Score Per AEZ (Scale 1–9) (Mean ± SE)
Year 2021Year 2022
Dry seasonAEZ 613.36 ± 0.10 b2.58 ± 0.07 b
23.17 ± 0.09 bc2.25 ± 0.06 c
33.30 ± 0.08 bc2.30 ± 0.06 c
4-2.73 ± 0.07 ab
5-2.24 ± 0.07 c
Overall mean3.28 ± 0.05 A2.42 ± 0.03 BC
AEZ 812.95 ± 0.08 c2.32 ± 0.07 c
23.13 ± 0.08 bc2.63 ± 0.07 ab
33.82 ± 0.15 a2.63 ± 0.06 ab
4-2.89 ± 0.07 a
5-2.84 ± 0.07 a
Overall mean3.30 ± 0.06 A2.66 ± 0.03 A
F5,1795 = 8.60; p < 0.0001F9,3231 = 12.91; p < 0.0001
Rainy seasonAEZ 612.03 ± 0.05 b1.55 ± 0.04 f
22.10 ± 0.05 b2.56 ± 0.07 b
32.32 ± 0.05 a2.70 ± 0.08 b
4-1.98 ± 0.05 e
5-3.48 ± 0.08 a
Overall mean2.16 ± 0.02 B2.46 ± 0.03 B
AEZ 812.00 ± 0.05 b1.84 ± 0.04 e
22.10 ± 0.05 b1.79 ± 0.05 e
32.32 ± 0.05 a2.45 ± 0.07 c
4-2.23 ± 0.05 d
5-3.41 ± 0.08 a
Overall mean2.14 ± 0.03 B2.35 ± 0.02 C
F5,2995 = 8.22; p < 0.0001F9,5391 = 110.90; p < 0.0001
AEZ = agroecological zone; SE = standard error. Means ± (SE) followed by the same uppercase letter in the column are not significantly different. Mean ± (SE) followed by the same letter in the column are not significantly different at the 5% threshold (p < 0.05). Uppercase letters refer to the comparison of the overall mean between seasons within the same year and lowercase letters refer to the comparison of agroecological zones within the same year regardless of the season.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zanzana, K.; Sinzogan, A.; Tepa-Yotto, G.T.; Dannon, E.; Goergen, G.; Tamò, M. Seasonal and Spatial Distribution of Fall Armyworm Larvae in Maize Fields: Implications for Integrated Pest Management. Insects 2025, 16, 145. https://doi.org/10.3390/insects16020145

AMA Style

Zanzana K, Sinzogan A, Tepa-Yotto GT, Dannon E, Goergen G, Tamò M. Seasonal and Spatial Distribution of Fall Armyworm Larvae in Maize Fields: Implications for Integrated Pest Management. Insects. 2025; 16(2):145. https://doi.org/10.3390/insects16020145

Chicago/Turabian Style

Zanzana, Karimou, Antonio Sinzogan, Ghislain T. Tepa-Yotto, Elie Dannon, Georg Goergen, and Manuele Tamò. 2025. "Seasonal and Spatial Distribution of Fall Armyworm Larvae in Maize Fields: Implications for Integrated Pest Management" Insects 16, no. 2: 145. https://doi.org/10.3390/insects16020145

APA Style

Zanzana, K., Sinzogan, A., Tepa-Yotto, G. T., Dannon, E., Goergen, G., & Tamò, M. (2025). Seasonal and Spatial Distribution of Fall Armyworm Larvae in Maize Fields: Implications for Integrated Pest Management. Insects, 16(2), 145. https://doi.org/10.3390/insects16020145

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop