# Short-term ecological and economic performance of transitioning from conventional practices to organic pest management in cotton-based cropping systems

Détondji Lucien Tomagnankodo, Gbèblonoudo Anicet Dassou, Pierrot Lionel

Yemadje, Saturnin Azonkpin, Douro Kpindou, Pablo Tittonell, Ana Lopez

Llandres

► To cite this version:

Détondji Lucien Tomagnankodo, Gbèblonoudo Anicet Dassou, Pierrot Lionel Yemadje, Saturnin Azonkpin, Douro Kpindou, et al.. Short-term ecological and economic performance of transitioning from conventional practices to organic pest management in cotton-based cropping systems. Agriculture, Ecosystems & Environment, 2026, 402, pp.110299. 10.1016/j.agee.2026.110299. hal-05526384

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# Short-term ecological and economic performance of transitioning from conventional practices to organic pest management in cotton-based cropping systems

Dètondji Lucien Tomagnankodo<sup>a,g</sup>, Anicet Gbèblonoudo Dassou<sup>a,g,*</sup>, Pierrot Lionel Yemadje<sup>b,c,d</sup>, Saturnin Azonkpin<sup>b</sup>, Douro Kpindou<sup>e</sup>, Pablo Tittonell<sup>c,d,f</sup>, Ana L. Llandres<sup>b,c,d,*</sup>

## ARTICLE INFO

### Keywords:

Legume-intercropping

Biopesticides

Transition to organic farming

Cotton pests

Biological control

Crop yield

Profitability

## ABSTRACT

Conventional cotton cultivation relies heavily on synthetic inputs, driving environmental degradation, biodiversity loss, serious human health impacts, and widespread pest resistance. Transitioning to organic systems integrating agroecological practices offers a promising pathway toward more sustainable production. This study assessed short-term pest management performance and economic viability of five cotton-based management systems during the first year of conversion in Benin, West Africa: a conventional farmer-practice system and four systems transitioning from conventional to organic production combining sole-cotton cropping or cotton-soybean intercropping, with or without biopesticide applications. Across sites, pest species composition and abundance varied markedly, yet plant injury metrics showed consistent patterns: pesticide use (synthetic or biopesticides) reduced crop damage and increased the proportion of healthy cotton. In central Benin, where bollworm pressure was lower, biopesticide-treated intercrops reduced key bollworms (*Earias* spp. and endocarpics) but increased the sucking pest *Bemisia tabaci*, while in northern Benin conventional systems reduced *Earias* spp. and *Diparopsis watersi* but slightly increased *B. tabaci*. Transition-to-organic systems generally supported higher abundances of natural enemies than conventional ones, although diversification effects were site-dependent. Biopesticides did not suppress predator communities, indicating their potential to sustain pest regulation without disrupting ecological functions. Despite lower cotton yields, intercropping improved overall profitability relative to sole-crops in transition to organic and, in several cases, performed similarly to or better than conventional cotton, mainly due to additional soybean production. These findings demonstrate that biopesticide-treated cotton-soybean intercropping can enhance pest regulation and farm profitability during the first year of transition to organic cotton in West Africa.

## 1. Introduction

The environmental and health problems associated with conventional cotton cultivation are well acknowledged nowadays (Greenberg et al., 2012; Silvie et al., 2017; Janvier et al., 2022; Koussé et al., 2023;

Eclou and Glèlè Kakaï, 2024). Historically, cotton has been one of the crops more heavily treated with pesticides worldwide, receiving more pesticide-based control than most other arable crops (Delate et al., 2021; Malinga and Laing, 2024; Eclou and Glèlè Kakaï, 2024). Many of these insecticides have broad activity against pests and their natural enemies,

<footnote><sup>a</sup> National School of Applied Biosciences and Biotechnology (ENSBBA), Dassa-Zoumé, Benin</footnote>

<footnote><sup>b</sup> Institute of research on Cotton, Cotonou, Benin</footnote>

<footnote><sup>c</sup> AIDA, Univ Montpellier, CIRAD, Montpellier, France</footnote>

<footnote><sup>d</sup> CIRAD, UPR AIDA, Montpellier F-34398, France</footnote>

<footnote><sup>e</sup> International Institute of Tropical Agriculture, 08 BP 0932 Tri Postal, Cotonou, Benin</footnote>

<footnote><sup>f</sup> Groningen Institute of Evolutionary Life Sciences, Groningen University, PO Box 11103, Groningen 9700 12 CC, the Netherlands</footnote>

<footnote><sup>g</sup> National University of Sciences, Technologies, Engineering and Mathematics (UNSTIM), Dassa-Zoumé, Benin</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author at: Institute of research on Cotton, Cotonou, Benin.</footnote>

<footnote>E-mail addresses: dassoua5@gmail.com (A.G. Dassou), ana.llandres_lopez@cirad.fr (A.L. Llandres).</footnote>
posing risks to the environment and human health and promoting the evolution of pest resistance (Deguine et al., 2009; Bakker et al., 2020; Klingelhöfer et al., 2022; Gul et al., 2025). Efforts to tackle the problems inherent in conventional cotton cultivation have mainly focused on reducing pesticide resistance in insects, developing genetically engineered insect-resistant cultivars or minimizing the environmental impacts of chemicals, rather than eliminating chemical dependency through alternative production systems (Ghaffar et al., 2020; Quan and Wu, 2023; Luneja and Mkindi, 2025).

Organic agriculture offers a holistic alternative that aims to promote and sustain the health of soils, ecosystems, and human well-being compared with conventional agriculture (IFOAM, 2008; Lori et al., 2024; Feliziani et al., 2025). It relies on ecological processes, biodiversity-enhancing practices and locally adapted nutrient cycles rather than synthetic inputs, which are prohibited under organic regulations (Leifert, 2022; Chopin et al., 2023). In practice, the implementation of organic systems varies widely, ranging from input-substitution approaches to redesigned, highly diversified agroecosystems (Petit and Aubry, 2016; Kamau et al., 2018; Chopin et al., 2023; Dagoudo et al., 2024).

Because synthetic pesticides and fertilizers are not permitted, organic cotton systems depend on diversification strategies, such as crop rotation, intercropping, mulching and organic nutrient sources, to regulate pests and sustain soil fertility (Rodriguez et al., 2021; Gamage et al., 2023; Keller et al., 2024). Previous studies have reported that the performance of these systems depends on crop choices, organic matter availability, locally adapted varieties, and farmer knowledge and training (Myers and Stolton, 1999; Delate et al., 2021; Rodriguez et al., 2021; Gamage et al., 2023). However, ecological and economic evaluations of organic cotton systems remain limited, particularly during the first years of transition, a period characterized by unstable production, income uncertainty, and high agronomic and economic vulnerability for smallholder farmers (Myers and Stolton, 1999; Delate et al., 2021).

Intercropping is a widely used diversification strategy that can regulate pests through bottom-up and top-down mechanisms (Dassou et al., 2015, 2024; Xiang et al., 2021; Dambolachepa et al., 2023; Yousefi et al., 2024). Intercrops can create favourable living conditions by providing additional food resources and habitats for natural enemies, which can in turn contribute to better management of pests (Wilkinson and Landis, 2005; Rakotomalala et al., 2023; Lamzira et al., 2025). Moreover, intercrops may act as physical or chemical barriers that reduce pest colonization by disrupting movement, orientation, and host-finding through concealment of the main crop (Schader et al., 2005; Lv et al., 2023). Legume intercrops offer additional benefits through nitrogen fixation, improved nutrient cycling, and livelihood diversification (Yemadje et al., 2025a; Kokkini et al., 2025; Akchaya et al., 2025).

Cotton is characterized by a highly diverse and damaging pest complex, making the early years of organic transition especially challenging. Diversification alone, may therefore be insufficient, and combining such practices with biopesticides can be necessary to achieve effective pest regulation. Entomopathogenic fungi, bacterial formulations and botanical extracts have demonstrated efficacy against major cotton pests (Dhawan, 2016; Togbé et al., 2015; Malinga and Laing, 2022; Mantzoukas et al., 2023), including aphids (Aphis gossypii Glover, 1877), whiteflies (Bemisia tabaci Gennadius, 1889), thrips (Thrips tabaci Lindemann, 1889), leafhoppers (Jacobiella facialis Jacobi, 1912) and the cotton bollworm (Helicoverpa armigera Hübner, 1808) (Kubicek and Druzhinina, 2007; Sullivan et al., 2022; Malinga and Laing, 2022; Shanker et al., 2023).

The aim of this study was to evaluate, during the first year of transition from conventional to organic cotton production in Benin, how different cotton-based management systems affect both ecological pest-management parameters and economic performance. We compared five systems: a conventional farmer-practice system representing current local practices, and four systems transitioning from conventional to

organic management combining two cropping patterns (cotton-sole cropping or cotton-soybean intercropping) with or without biopesticide applications. Specifically, we assessed (i) how pest populations, natural enemy communities and plant injury metrics responded to these contrasting systems, and (ii) how the systems differed in cotton and soybean yields and in key economic indicators (gross margin, benefit-cost ratio, labour productivity and return to labour). By jointly evaluating ecological and economic outcomes, our objective was to determine whether cotton-soybean intercropping and biopesticide use can serve as viable agroecological entry points for farmers beginning the transition away from synthetic inputs.

## 2. Methods

### 2.1. Study area

The present study was conducted during the 2022–2023 cotton season, from July to January, at the Permanent Experimental Center (PEC) of Gobé (Central-South Benin: 2° 25' 6" E, 8° 00' 149" N), in the commune of Savè, and at Angaradébou (North Benin: 3° 02' 26" E, 11° 19' 44" N) in the commune of Kandi. Gobé is located in central Benin, its climate is Sudanian, with annual rainfall varying between 1000 and 1300 mm. The region of Angaradébou has a dry tropical climate with a single growing season per year from May to September (Atakoun et al., 2023). The average annual rainfall ranges from 900 to 1300 mm (Peel et al., 2007).

The soil in both experimental sites is classified as ferruginous tropical type in the French soil classification system, which corresponds to Acrisols or Lixisols according to the World Reference Base (Baxter, 2007). Basic soil chemical characteristics for each site, measured in 2020 prior to the establishment of the experiment, are presented in Supplementary Table S1 to provide context for fertility status and nutrient availability.

### 2.2. Experimental design

The experimental design consisted of a randomized complete block design (RCBD) with five treatments replicated five times. Each experimental plot (elementary unit) consisted of 9 cotton rows, each 9 m in length. Adjacent plots within the same block were spaced 2 m apart, and a 2-meter buffer separated individual blocks. The total experimental area, including a 2-meter buffer zone isolating the trial from neighbouring experiments, encompassed 2600 m².

To evaluate the effects of the different management systems, we compared all five systems: the conventional farmer-practice (CF) and four systems transitioning from conventional to organic production managed without synthetic inputs (OF_Sole_NT, OF_Sole_Biop, OF_Inter_NT, OF_Inter_Biop). This approach allowed us to assess pest abundance, plant damage, natural enemy communities and crop yield across complete management systems rather than isolating individual factors in a factorial design. Because the experiment corresponded to the first year of transition toward organic management, the planned crop rotation had not yet been fully implemented. In Benin, cotton is normally grown within a rotation that includes cereals such as maize or sorghum. Accordingly, both the sole-cotton and cotton-soybean intercropping treatments in this study represent only the cotton phase of a typical cotton-cereal rotation. The sole-cropping treatments therefore reflect the seasonal cotton component of the rotation and should not be interpreted as stand-alone monocultures.

The systems transitioning from conventional to organic production shared a common baseline management (including PCAS and polyhalite amendments, see Crop management section) but differed in the introduction of agroecological practices: intercropping with soybean and/or biopesticide application. The CF system differed more broadly, representing the local reference model with synthetic fertilizer and chemical pesticide use. It was included not as an experimental control, but as a
<table><caption>**Table 1**

Characteristics of the five cropping systems including crop pattern and the type of inputs applied (organic or conventional).</caption><thead><tr><th>System label</th><th>Crop pattern</th><th>Input type</th><th>Mineral N fertilizer</th><th>Organic amendments</th><th>Synthetic insecticides</th><th>Biopesticide</th></tr></thead><tbody><tr><td>CF</td><td>Sole-cotton</td><td>Conventional</td><td>Yes*</td><td>No</td><td>Yes (Thalis®, Pyro FTE 672®, Cotonix 328 EC®, Belt expert 480 SC)</td><td>No</td></tr><tr><td>OF_Sole_NT</td><td>Sole-cotton</td><td>Organic</td><td>No</td><td>Yes (PCaS + Polyhalite)</td><td>No</td><td>No</td></tr><tr><td>OF_Sole_Biop</td><td>Sole-Cotton</td><td>Organic</td><td>No</td><td>Yes (PCaS + Polyhalite)</td><td>No</td><td>Yes (Metarhizium; Beauveria, Spinosad)</td></tr><tr><td>OF_Inter_NT</td><td>Cotton + soybean</td><td>Organic</td><td>No</td><td>Yes (PCaS + Polyhalite)</td><td>No</td><td>No</td></tr><tr><td>OF_Inter_Biop</td><td>Cotton + soybean</td><td>Organic</td><td>No</td><td>Yes (PCaS + Polyhalite)</td><td>No</td><td>Yes (Metarhizium, Beauveria, Spinosad)</td></tr></tbody></table>

$$ ^{*} 28 \text{ kg N ha}^{-1} + 23 \text{ kg N ha}^{-1} $$

reference reflecting local farmer practice, enabling agronomic (yield-based), ecological (pests management metrics) and economic comparisons.

Each treatment was thus considered a distinct cropping system, defined as a combination of crop pattern (sole cropping or intercropping) and type of inputs applied, including fertilization and pest management strategies. A summary of all systems and their management components is provided in Table 1.

## 2.3. Crop management

The planting material consisted of a cotton variety (OKP768, 180-day vegetative cycle) and a soybean variety (TGX 1830 - 20E, 130-140 days). Soybean was selected as a companion crop for cotton due to its economic importance to farmers in Benin, its wide acceptance among organic producers, and its agronomic benefits as a nitrogen-fixing legume that enhances soil fertility. For all treatments, flat ploughing to a depth of 20 cm was carried with a tiller equipped with a share plow. Cotton seeds were sown using a sowing wheel on July 25th, 2022 in Gobé and on June 17th, 2022 in Angaradébou, the same dates as for soybean. For cotton, five seeds were placed in each planting station (i.e., one sowing hole), and seedlings were later thinned to retain a single plant per station on August 12th, 2022 in Gobé and July 1st, 2022 in Angaradébou. Across all five systems, cotton row spacing was 0.8 m with 0.2 m between planting stations along the row. Each sole cropping treatment (CF, OF_Sole_NT and OF_Sole_Biop) comprised 9 cotton rows, numbered 1-9. In the intercropping treatments (OF_Inter_NT and OF_Inter_Biop), cotton rows were spaced 0.8 m apart. Between each pair of consecutive cotton rows, we planted two soybean rows (i.e., cotton - soybean - soybean - cotton) (see Supplementary Fig S1). Soybean rows were positioned 0.20 m from the adjacent cotton row and 0.40 m apart from each other. Each intercropping plot contained 27 total rows (numbered 1-27), consisting of 9 cotton rows and 18 soybean rows. For the CF treatment, 200 kg ha⁻¹ of N₁₄P₁₈K₁₈S₆B₁ was applied on August 12th, 2022 in Gobé and on July 30th, 2022 in Angaradébou and then 50 kg ha⁻¹ of urea (46 %N) at hoeing-ridging, which equivalent to 51 kg N ha⁻¹, 36 kg P ha⁻¹, 36 kg K ha⁻¹, 12 kg S ha⁻¹, and 2 kg B ha⁻¹. For plots transitioning from conventional to organic production, amendments for organic farming were applied to cotton rows (OF_Sole_NT, OF_Sole_Biop, OF_Inter NT and OF_Inter Biop) at a rate of 200 kg ha⁻¹ of phosphocalcic amendment (PCaS) and 200 kg ha⁻¹ of Polyhalite (KSCaMg) on the same dates. The phosphocalcic amendment used was a phosphate rock-based amendment. In this fertilizer, phosphorus is primarily present as apatite (Ca₅(PO₄)₃(F,OH,Cl)), which is a sparingly soluble form of calcium phosphate. Polyhalite (K₂CaMg (SO₄)·2H₂O) contains: Potassium (K⁺) as part of the sulfate salt (K₂SO₄ component), Calcium (Ca²⁺) as part of the sulfate salt (CaSO₄), Magnesium (Mg²⁺) as part of the sulfate salt (MgSO₄). All these nutrients are supplied in water-soluble sulfate forms, which are gradually available to plants. Total quantities of each element represent: 7.2 kg Mg ha⁻¹,

26 kg P ha⁻¹, 23.2 kg K ha⁻¹, 73 kg Ca ha⁻¹, and 38 kg S ha⁻¹. No N was applied to these treatments transitioning from conventional to organic production.

Three biopesticides were used to treat the OF_Sole_Biop and OF_Inter_Biop plots: two entomopathogenic fungi, *Bauveria bassiana* (strain Bb 11) and *Metarhizium anisopliae* (strain Met 31), provided by IITA (International Institute of Tropical Agriculture in Benin) and Spinosad®, a biopesticide derived from the soil bacterium *Saccharopolyspora spinosa*. The entomopathogenic fungi were used at a rate of 50 g ha⁻¹ or 0.324 g per 64.8 m² plot, and Spinosad at 100 mL ha⁻¹ or 0.648 L per 64.8 m² plot. Given that the experimental design included five blocks and two plots to be treated per block (OF_Sole_Biop and OF_Inter_Biop), the dose of each biopesticide for each treatment was 3.24 g for entomopathogenic fungi and 6.48 mL for Spinosad. The volume of water used was 100 L ha⁻¹, i.e. 0.648 L per 64.8 m² plot, i.e. 6.48 L for the ten plots for each treatment. So, for each Spinosad treatment, a mixture of 6.48 L of water and 6.48 mL of Spinosad was mixed in the sprayer for the ten plots. For each entomopathogenic fungus treatment, 3.24 g of entomopathogenic fungus were used in 6.48 L of water, plus 0.05 % Tween 80 water equivalent to 3.24 mL. Tween 80 was used to allow the biopesticide to mix with water. After each formulation, the mixture was shaken well to homogenize it before application. Given the small quantity of water to be used and the number of plots to be covered, tests were first carried out with simple water to control the application, before the actual application to ensure good coverage of all plots.

For the CF treatment, the synthetic pesticides used were Thalis® (24 g l⁻¹ Emamectin benzoate, and 32 g l⁻¹ Acetamiprid), Pyro FTE 672® (72 g l⁻¹ Cypermethrin, and 600 g l⁻¹ Chlorpyrifos-ethyl), Cotonix 328 EC® (12 g l⁻¹ de deltaméthrine, 300 g l⁻¹ de chlorpyriphos-ethyl, and 16 g l⁻¹ d'acétamipride) and Belt expert 480 SC (240 g l⁻¹ Flubendiamide and 240 g l⁻¹ Thiacloprid). Treatments OF_Sole_Biop and OF_Inter_Biop were treated exclusively with biopesticides, applied based on threshold interventions: two larvae of *Helicoverpa armigera*, or four larvae of other bollworm species for every 16 cotton plants observed (Silvie et al., 2013); 5 plants attacked by aphids, jassids or whiteflies; one outbreak of mites or 3 plants attacked by phyllophagous caterpillars (*Syllepte derogata*, *Anomis flava*, *Spodoptera littoralis*). Biopesticide applications were alternated among Metarhizium-, Beauveria-, and Spinosad-based products following a sequential rotation throughout the season. Threshold treatments were carried out on the plot where the threshold had been reached as well as on the other plots corresponding to the same treatment, on the same day or the day after observation. Observations for threshold treatments were made weekly on 3 cotton rows per plot for plots treated with biopesticides (rows 4, 5 and 6 for OF_Sole_Biop and 11, 14 and 17 for OF_Inter Biop), on 5 plants per row (15 plants observed per plot in total). The phytosanitary treatments applied to each treated system, along with their application frequencies, are summarized in Supplementary Table S2. Weed control was carried out as follows: in the CF treatment, an application of glyphosate was made on July 02nd, 2022 in Gobé. An application of pre-emergent
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Abundance of major bollworm species under different management systems. Mean abundance of bollworms (± 95 % CI) observed in a total of 15 plants per plot for the whole cropping season for each cropping system and experimental site. Left (A, C, E and G) and right (B, D, F and H) panels show bollworm abundances in Gobé and Angaradébou experimental site respectively. Earias: *Earias* spp, Endocarpic bollworms: *Cryptophlebia leucotreta* and *Pectinophora gossypiella* Diparopsis: *Diparopsis watersi*; Helicoverpa: *Helicoverva armigera*; and Spodoptera: *Spodoptera littoralis*. CF: Conventional farmer-practice; OF_Sole NT: Transition-to-organic sole-cotton farming not treated with pesticides; OF_Sole Biop: Transition-to-organic sole-cotton farming treated with biopesticides; OF_Inter NT: Transition-to-organic cotton + legume intercropping not treated with pesticides; OF_Inter Biop: Transition-to-organic cotton + legume intercropping treated with biopesticides.</figcaption></figure>

control (250 g l⁻¹ of Fluometuron, 250 g l⁻¹ of Prometryn, and 60 g l⁻¹ of Glyphosate) herbicide was used the day after sowing. A post-emergent control (Trifloxysulfuron- sodium 11 g l⁻¹) was applied on August 31th, 2022 in Gobé and July 08th, 2022 in Angaradébou. Plots transitioning from conventional to organic management were managed solely by manual weeding. Hilling was performed on 12 September 2022 in Gobé and 12 August 2022 in Angaradébou for sole-cotton treatments, while intercropping treatments underwent weeding at the same dates.

## 2.4. Data collection

### 2.4.1. Assessment of pest presence, damage and natural enemies on plants
Observations were conducted weekly and focused on pest surveys

(pest population density and plant damage), auxiliary fauna, proportion of rotten cotton collected from harvested bolls and seed cotton production. Pest surveys were divided into three types of observations: bollworm occurrence and 2 types of plant damage. For bollworm occurrence, we counted the number of *Helicoverpa armigera*, *Diparopsis watersi*, *Earias* spp., *Spodoptera littoralis*, and the endocarpic bollworms *Cryptophlebia leucotreta* and *Pectinophora gossypiella* in 5 plants per row. For plant damage, there were two types of observations: (i) the proportion of plants attacked (number of plants attacked divided by the total number of plants observed) by sucking insects—including the cotton aphid (*Aphis gossypii*), the whitefly (*Bemisia tabaci*), and the leafhoppers or jassids (*Jacobiella facialis* and *Amrasca biguttula*)—and by the cotton leafworm (*Syllepte derogata*); and (ii) the proportion of injured
<caption>**Table 2**</caption>

Effect of cropping systems (CF, OF_Sole_NT, OF_Sole_Biop, OF_Inter_NT and OF_Inter_Biop), experimental site (Gobé and Angaradébou) and their interaction on abundances of bollworms (Earias: *Earias* spp, Helicoverpa: *Helicoverva armigera*, Spodoptera: *Spodoptera littoralis*, Endocarpic bollworms: *Cryptophlebia leucotreta* and *Pectinophora gossypiella*, and Diparopsis: *Diparopsis watersi*).

<table><thead><tr><th>Variables</th><th>Chisq</th><th>Df</th><th>P-Value</th></tr></thead><tbody><tr><td>(Intercept)</td><td colspan="3">Earias (2 sites)</td></tr><tr><td>Cropping system</td><td>70.67</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>13.65</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>Cropping system × Site</td><td>6.59</td><td>1</td><td>0.01</td></tr><tr><td></td><td>8.56</td><td>4</td><td>0.07</td></tr><tr><td>(Intercept)</td><td colspan="3">Helicoverpa (2 sites)</td></tr><tr><td>Cropping system</td><td>17.34</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>4.98</td><td>4</td><td>0.28</td></tr><tr><td>Cropping system × Site</td><td>7.1</td><td>1</td><td>&lt; 0.01</td></tr><tr><td></td><td>0.59</td><td>4</td><td>0.96</td></tr><tr><td>(Intercept)</td><td colspan="3">Spodoptera (2 sites)</td></tr><tr><td>Cropping system</td><td>0.01</td><td>1</td><td>0.92</td></tr><tr><td>Site</td><td>2.47</td><td>4</td><td>0.64</td></tr><tr><td>Cropping system × Site</td><td>0</td><td>1</td><td>0.99</td></tr><tr><td></td><td>0.3</td><td>4</td><td>0.98</td></tr><tr><td>(Intercept)</td><td colspan="3">Endocarpic bollworms (Gobé)</td></tr><tr><td>Cropping system</td><td>7.56</td><td>1</td><td>&lt; 0.01</td></tr><tr><td></td><td>8.34</td><td>4</td><td>0.05</td></tr><tr><td>(Intercept)</td><td colspan="3">Diparopsis (Angaradébou)</td></tr><tr><td>Cropping system</td><td>4.8</td><td>1</td><td>0.02</td></tr><tr><td></td><td>9.6</td><td>4</td><td>0.04</td></tr></tbody></table>

shed organs, defined as naturally abscised fruiting structures (including flower buds and bolls, hereafter called squares and bolls) collected from the soil surface between two cotton rows. Collection of shed organs started at the onset of flowering and continued weekly. Organs were collected from designated inter-rows (3–4 and 6–7 for sole crops; 8–11 and 17–20 for intercrops) and classified as healthy or injured based on visible pest damage.

Pest surveys were carried out in all plots and started on the eve of the first pesticide treatment for the conventional farmer-practice system (CF). These observations were conducted on 15 plants per elementary plot, taken consecutively using the diagonal method (Brévault et al., 2009; Sarwar and Sattar, 2016) in groups of 5 plants on the 3 cotton rows located in the middle of the plot (rows 4, 5 and 6 for sole crops and rows 11, 14 and 17 for intercrops). For intercropped cotton-soybean plots (OF_Inter_NT and OF_Inter_Biop), pest surveys were also conducted on 15 soybean plants in the middle legume rows (rows 13, 15 and 16), but the data are not presented in this paper because we hardly observed any pests in soybean plants throughout the whole cropping season, except for whiteflies.

To facilitate interpretation of pest pressure relative to regional management benchmarks, action thresholds were adapted from threshold-based control programs developed for West African cotton systems (Silvie et al., 2013). Because pest monitoring in this study was conducted on five plants per row, threshold values were scaled proportionally to match the sampling unit. A reference threshold of 0.5 larva per five plants (equivalent to approximately three larvae per 30 plants) was used for bollworms (*H. armigera*, *Earias* spp., *D. watersi* and endocarpic species). For sucking pests - aphids, whiteflies and jassids - and for the cotton leafworm (*S. derogata*), an action threshold of one attacked plant out of five observed was applied, corresponding to approximately 20 % plant infestation. These values fall within the range of thresholds commonly used in West African cotton systems and provide a conservative reference for interpreting pest pressure (Silvie et al., 2013). Thresholds are shown graphically in the Supplementary Material (Supplementary Fig S2 and S3).

Beneficial arthropods were recorded on the same plants and rows used for pest observations. Each week, observers examined five cotton plants per row (three rows per plot) and counted all visible individuals of the main predator groups (ladybirds, syrphids, lacewings, predatory

bugs, earwigs, spiders, praying mantises and ants). For ladybirds and syrphids, both adults and larvae were counted. For lacewings, predatory bugs, earwigs, spiders and praying mantises, all visible mobile stages (adults and nymphs/juveniles) were recorded. Ants were counted only as adults, as immature stages occur inside the nests and are generally not observable on plants. Eggs were not included for any predator group. Although ants can also engage in mutualistic relationships with honeydew-producing pests such as aphids, many ant species in West African cropping systems also act as generalist predators and can influence pest dynamics (Dassou et al., 2015). For this reason, ants were included among the recorded natural enemies.

At harvest (December/January in Gobé and November 2022 in Angaradébou), data on the rotten cotton from harvested bolls was collected to estimate cotton plant damage at the maturity stage on two rows per plot (rows 3 and 7 for sole crop plots and rows 8 and 20 for intercropping plots). We recorded the number of healthy (non-attacked) and attacked (mummified, pierced/holed and rotten) ripe bolls. The weight of healthy and rotten cotton was also recorded to calculate the proportion of healthy white cotton fiber at harvest to get an estimation of fiber quality.

### 2.4.2. Yield and economic analysis

Seed cotton yields were estimated by harvesting mature cotton from two designated rows per treatment (rows 2 and 8 for sole cropping, and rows 5 and 23 for intercropping plots). In intercropping treatments, two corresponding soybean rows (rows 4 and 24 for intercropping plots) were also harvested to assess soybean yield. Cotton harvesting was performed in two rounds: the first when approximately 70 % of the bolls had opened, and the second when all remaining bolls had opened.

For the economic analyses, operating costs were estimated by factoring in land preparation (ploughing), seeds, organic amendments, fertilizers, biopesticides, phytosanitary products (herbicides, insecticides) and temporary salary costs (external labour) allocated to crop management (tillage, weeding, fertilizer and amendments application, herbicides, biopesticides, insecticides application, harvest) (Supplementary Table S3). To emulate family farming systems, where farmers seldom pay taxes on their agricultural activities and there are almost no structural expenses (with the exception of farms with permanent personnel and/or substantial machineries such as a tractor or tiller), no fixed expenses have been estimated.

The gross income (GI) was determined from yield (kg ha⁻¹) and the national selling prices of seed-cotton and soybean as follows (Eq. 1):

$$GI \left(\frac{US\$}{ha}\right) = Yield \left(\frac{kg}{ha}\right) \times Selling \ price \left(\frac{US\$}{kg}\right) \quad (1)$$

Economic calculations were performed using both conventional and organic market price scenarios for cotton and soybean. Conventional prices (300 FCFA·kg⁻¹ for cotton; 275 FCFA·kg⁻¹ for soybean; US$1 = 600 FCFA) were used to reflect OBEPAB's (Beninese Organisation for the Promotion of Organic Agriculture) indication that products during the three-year conversion period are normally sold as conventional. Organic price scenarios (cotton: 360 FCFA·kg⁻¹; soybean: 300 FCFA·kg⁻¹) were also included to assess the sensitivity of profitability outcomes to potential price premiums. To better interpret profitability within intercropping systems, Supplementary Table S4 presents cotton and soybean gross margins separately for each intercropping treatment.

The gross margin (GM) was calculated by deducting the operating costs from the gross income (Penot et al., 2021). The benefit-cost ratio (BCR) was determined by dividing the gross margin by the operating costs, as shown in Eq. 2.

$$BCR = \frac{\text{Gross margin}}{\text{operating costs}} \quad (2)$$

The labour productivity is measured as kg of seed-cotton and soybean grains per day of labour following Eq. (3) (Hunt, 2000).
<figure><img src="image_4.png" /><figcaption>Fig. 2. Proportion of plants attacked by different pest species under different management systems. Mean proportion (± 95 % CI) of plants attacked by the different pest species for a total of 15 plants per plot for the whole cropping season for each cropping system and experimental site. Left (A, C, E and G) and right (B, D, F and H) panels show injury plant levels in Gobé and Angaradébou experimental site respectively. Aphis: Aphis gossypii; Bemisia: Bemisia tabaci; Jassids: Jacobiella fascialis and Amrasca biguttula; Syllepte: Syllepte derogate. CF: Conventional farmer-practice; OF_Sole_NT: Transition-to-organic sole-cotton farming not treated with pesticides; OF_Sole_Biop: Transition-to-organic sole-cotton farming treated with biopesticides; OF_Inter NT: Transition-to-organic cotton + legume intercropping not treated with pesticides; OF_Inter_Biop: Transition-to-organic cotton + legume intercropping treated with biopesticides.</figcaption></figure>

$$ \text{Labour productivity} \left( \frac{\text{kg}}{\text{person - day}} \right) = \frac{\text{Yield} \left( \frac{\text{Kg}}{\text{ha}} \right)}{\text{Working time} \left( \frac{\text{person-day}}{\text{ha}} \right)} \quad (3) $$

The return to labour was determined as the ratio of the gross margin by the working time as in Eq. 4.

$$ \text{Return to labour} \left( \frac{\text{US$}}{\text{person - day}} \right) = \frac{\text{Gross margin} \left( \frac{\text{US$}}{\text{ha}} \right)}{\text{Working time} \left( \frac{\text{person-day}}{\text{ha}} \right)} \quad (4) $$

## 2.5. Statistical analysis

All statistical analyses were performed using R software version 4.1.3. To test the effect of cropping systems on abundance data (abundance of bollworms and abundance of natural enemies directly observed in plants), we used a generalized linear mixed-effects model (glmmer function from the lme4 package) (Douglas et al., 2015) fitted with a Poisson family distribution for count data. For data on proportions of (i)
<caption>**Table 3**</caption>

Effect of cropping system (CF, OF_Sole NT, OF_Sole Biop, OF_Inter NT and OF_Inter Biop), experimental site (Gobé and Angaradébou) and their interaction on the proportion of cotton plants attacked by the different pest species (Aphis: Aphis gossypii; Bemisia: Bemisia tabaci; Jassids: Jacobiella fascialis and Amrasca biguttula; Syllepte: Syllepte derogate).

<table><caption>**Table 4**</caption><thead><tr><th>Variables</th><th>Chisq</th><th>Df</th><th>P-Value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>Aphis</td><td></td><td></td></tr><tr><td>Cropping system</td><td>287.78</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>2.14</td><td>4</td><td>0.70</td></tr><tr><td>Cropping system × Site</td><td>0.04</td><td>1</td><td>0.84</td></tr><tr><td></td><td>7.73</td><td>4</td><td>0.10</td></tr><tr><td>(Intercept)</td><td>Bemisia</td><td></td><td></td></tr><tr><td>Cropping system</td><td>331.72</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>13.34</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>Cropping system × Site</td><td>26.88</td><td>1</td><td>&lt; 0.01</td></tr><tr><td></td><td>68.24</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>(Intercept)</td><td>Jassids</td><td></td><td></td></tr><tr><td>Cropping system</td><td>96.44</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>1.29</td><td>4</td><td>0.86</td></tr><tr><td>Cropping system × Site</td><td>8.14</td><td>1</td><td>&lt; 0.01</td></tr><tr><td></td><td>34.06</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>(Intercept)</td><td>Syllepte</td><td></td><td></td></tr><tr><td>Cropping system</td><td>85.05</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>17.56</td><td>4</td><td>0.01</td></tr><tr><td>Cropping system × Site</td><td>0.37</td><td>1</td><td>0.54</td></tr><tr><td></td><td>13.51</td><td>4</td><td>0.01</td></tr></tbody></table>

Effect of cropping system (CF, OF_Sole NT, OF_Sole Biop, OF_Inter NT and OF_Inter Biop), experimental site (Gobé and Angaradébou) and their interaction on the proportion of attacked shed organs, attacked ripe bolls and healthy white cotton at harvest.

<table><thead><tr><th>Variables</th><th>Chisq</th><th>Df</th><th>P-Value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>Shed organs</td><td></td><td></td></tr><tr><td>Cropping system</td><td>170.41</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>264.41</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>Cropping system × Site</td><td>3.07</td><td>1</td><td>0.07</td></tr><tr><td></td><td>1.65</td><td>4</td><td>0.79</td></tr><tr><td>(Intercept)</td><td>Ripe bolls</td><td></td><td></td></tr><tr><td>Cropping system</td><td>31.2</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>213.15</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>Cropping system × Site</td><td>252.54</td><td>1</td><td>&lt; 0.01</td></tr><tr><td></td><td>124.78</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>(Intercept)</td><td>Healthy cotton</td><td></td><td></td></tr><tr><td>Cropping system</td><td>5.99</td><td>1</td><td>0.01</td></tr><tr><td>Site</td><td>527.66</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>Cropping system × Site</td><td>1332.57</td><td>1</td><td>&lt; 0.01</td></tr><tr><td></td><td>430.94</td><td>4</td><td>&lt; 0.01</td></tr></tbody></table>

plants attacked by pests, (ii) injured fruiting shed organs, (iii) injured ripe bolls collected at harvest and (iv) healthy white cotton collected from harvested bolls we used a binomial family distribution for binary data. The data on abundance (of pest or natural enemies) was the variable to be explained, while the cropping system (CF, OF_Sole NT, OF_Sole Biop, OF_Inter NT and OF_Inter Biop), the site (Angaradébou and Gobé) and the interaction between cropping system and site were the explanatory fixed variables. Block was considered as the random effect variable. Note that for the explanatory variables for which we had only data in one experimental site (endocarpic bollworms - *Cryptophlebia leucotreta* and *Pectinophora gossypiella* - were only present in Gobé and *Diparosis watersi* in Angaradébou), the same model was applied but the fixed factors site and the interaction cropping system and site were omitted from the model. Since some of the explanatory variables collected through time contained many zeros, for each each explanatory variable measured through time (bollworm and predator abundances and shedding) we calculated the sum of each variable collected in a total of 15 plants per plot for each observation date, cropping system and experimental site.

To evaluate overall system productivity, we calculated cumulative yield for each plot by summing cotton and soybean yields. Cumulative yield was analysed using a linear mixed-effects model with cropping system, site, and their interaction as fixed effects and block as a random effect. After testing the models, we used the 'lsmeans' function in the 'emmeans' package to perform post-hoc Tukey multiple comparison tests to compare the cropping systems whose effects were significantly different from one another and the letters showing significant differences between cropping systems for each site were plotted in the figures.

## 3. Results

### 3.1. Abundance of cotton insect pests and plant damage

Pest bollworm communities differed between sites: *Earias* spp., *H. armigera* and *S. littoralis* occurred at both locations, whereas endocarpic bollworms (*C. leucotreta*, *P. gossypiella*) were recorded only in Gobé and *D. watersi* only in Angaradébou (Fig. 1). Several species (*Earias* spp., *D. watersi* and endocarpic bollworms) exceeded action thresholds at least once during the season (Supplementary Fig S2). Cropping system significantly influenced the abundance of *Earias* spp., endocarpic bollworms and *D. watersi*, with a strong site effect for *Earias* spp. (Table 2). Two broad patterns emerged: (i) the transition-to-organic sole-cotton crops without biopesticides (OF_Sole NT) frequently showed the highest pest abundance, especially for *Earias* spp.; and (ii) the treated systems, namely the biopesticide-treated intercrop (OF_Inter Biop) in Gobé and the conventional system (CF) in Angaradébou, tended to show the lowest abundances of key bollworms (*Earias* spp. and endocarpic species in Gobé; *Earias* spp. and *D. watersi* in Angaradébou). In contrast, *S. littoralis* and *H. armigera* did not differ significantly among treatments.

Plant injury patterns differed by pest species and site (Fig. 2; Table 3), although all monitored pests (*A. gossypii*, *B. tabaci*, jassids and *S. derogata*) exceeded action thresholds at least once during the season (Supplementary Fig S3). *Syllepte derogata* injury was almost absent in Angaradébou but showed clear treatment differences in Gobé, where pesticide-treated systems (notably CF and OF_Inter Biop) consistently exhibited the lowest damage, and the untreated transition-to-organic systems (OF_Sole NT and OF_Inter NT) the highest. Injury from sucking pests (*A. gossypii*, *B. tabaci* and jassids) showed more variable patterns. In Gobé, jassid injury was highest in the conventional system, while all transition-to-organic systems showed lower levels. In contrast, *B. tabaci* injury in Gobé was highest in the transition-to-organic intercrops (OF_Inter NT and OF_Inter Biop), intermediate in CF, and lowest in sole cotton untreated systems transitioning from conventional to organic production (OF_Sole NT). In Angaradébou, *B. tabaci* injury tended to be higher in CF than in most transition-to-organic systems, though differences were smaller.

Overall, pest responses were strongly species- and site-specific, but two consistent tendencies emerged: untreated sole-cotton systems transitioning from conventional to organic production often showed the highest pest pressure, while biopesticide-treated systems frequently reduced key bollworm and foliar injuries such as those from *S. derogata*. In contrast, although conventional management reduced certain bollworms, it tended to exacerbate injury from some sucking pests, particularly jassids in Gobé and *B. tabaci* in Angaradébou.

### 3.2. Injured shed fruiting organs, ripe bolls and healthy white cotton

The proportion of shed injured fruiting organs, injured ripe bolls as well as healthy white cotton collected at harvest varied significantly across cropping systems (Table 4). Site and site × system interaction also showed significant effects for the injured ripe bolls as well as the healthy white cotton. For the two experimental sites, the conventional system (CF) showed the lowest injury to shed organs, followed by the biopesticide-treated plots in both crop patterns (OF_Inter Biop and OF_Sole Biop; Fig. 3A-B). Gobé presented higher ripe boll injury
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Shedding, ripe boll damage and fiber quality indicators under different management systems. Mean proportion (± 95 % CI) of total i) attacked shed organs (shedding) (A and B), ii) attacked ripe bolls (C and D) and iii) healthy white cotton (E and F) collected for each cropping system and experimental site. Left (A, C, E and G) and right (B, D, F and H) panels show data collected in Gobé and Angaradébou experimental site respectively. The shedding data represents the mean proportion of the total attacked shed organs collected between two inter-rows of cotton per plot for the whole cropping season. CF: Conventional farmer-practice; OF_Sole_NT: Transition-to-organic sole-cotton farming not treated with pesticides; OF_Sole_Biop: Transition-to-organic sole-cotton farming treated with biopesticides; OF_Inter_NT: Transition-to-organic cotton + legume intercropping not treated with pesticides; OF_Inter_Biop: Transition-to-organic cotton + legume intercropping treated with biopesticides.</figcaption></figure>

compared to Angaradébou (Fig. 3C–D). In Gobé, CF had the lowest ripe boll damage, followed by the biopesticide-treated intercrop (OF_Inter_Biop), whereas non-treated plots (OF_Sole_NT and OF_Inter_NT) showed the highest injury. In Angaradébou, ripe boll damage was lowest in OF_Inter_Biop and CF and highest in the non-treated plots. The CF systems showed the lowest injury levels of ripe bolls in Gobé followed by the biopesticide treated intercropped plots (OF_Inter_Biop), while injury levels were highest in the transition-to-organic non-treated plots (OF_Sole_NT and OF_Inter_NT). Finally, the proportion of healthy white cotton was higher in Angaradébou compared to Gobé (Fig. 3 E–F). In Gobé, CF showed higher proportion of healthy cotton followed by the OF_Inter_Biop. In Angaradébou, OF_Inter_Biop showed higher proportion of healthy cotton followed by CF and biopesticide-treated sole crops (OF_Sole_Biop). Non-treated sole crop plots (OF_Sole_NT) showed the lowest proportion of healthy cotton at both sites.

Overall, biopesticide-treated systems showed the most favourable plant injury outcomes among the transition treatments and, in several cases, approached the performance of the conventional system.

### 3.3. Abundance of predators

Cropping system significantly affected overall predator abundance (Table 5; Fig. 4A–B; Supplementary Fig S4). Ants, spiders and ladybugs

were the dominant predator groups in both sites, while other predators (earwigs, syrphids, predatory bugs, Chrysomela) were too rare for separate analysis. Predator responses were strongly site-dependent. In Gobé, abundances were highest in sole-cotton systems treated with biopesticides transitioning from conventional to organic production (OF_Sole_Biop) and lowest in the conventional system (CF) and the biopesticide-treated intercrop (OF_Inter_Biop). In contrast, Angaradébou showed the highest overall predator abundances in the transition-to-organic intercrops (OF_Inter_Biop and OF_Inter_NT), with the lowest values in CF.

Patterns also differed among predator groups (Fig. 4C–J). Spiders were more abundant in Angaradébou than Gobé and were consistently suppressed in CF at both sites. They reached their highest levels in OF_Inter_NT (Angaradébou) and OF_Sole_NT (Gobé). Ladybugs showed a site-specific response: no differences among treatments in Angaradébou but markedly higher abundances in OF_Sole_Biop in Gobé. Praying mantises peaked in the biopesticide-treated intercrop (OF_Inter_Biop) in Angaradébou. Ants, the most abundant predator group, responded differently across sites: they dominated in OF_Sole_Biop and OF_Inter_NT in Gobé, while in Angaradébou they were highest in OF_Inter_NT and lowest in OF_Sole_NT.

Overall, conventional management consistently supported the lowest predator abundances, whereas transition-to-organic systems
<caption>**Table 5**</caption>

Effect of cropping system (CF, OF_Sole_NT, OF_Sole_Biop, OF_Inter_NT and OF_Inter_Biop), experimental site (Gobé and Angaradébou) and their interaction on the abundance of predators.

<table><thead><tr><th>Variables</th><th>Chisq</th><th>Df</th><th>P-Value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>Total natural enemies (2 sites)</td><td></td><td></td></tr><tr><td>Cropping system</td><td>213.01</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>19.26</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>Cropping system × Site</td><td>2.75</td><td>1</td><td>0.09</td></tr><tr><td></td><td>100.18</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>(Intercept)</td><td>Spiders (2 sites)</td><td></td><td></td></tr><tr><td>Cropping system</td><td>208.76</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>10.36</td><td>4</td><td>0.03</td></tr><tr><td>Cropping system × Site</td><td>21.35</td><td>1</td><td>&lt; 0.01</td></tr><tr><td></td><td>7.89</td><td>4</td><td>0.09</td></tr><tr><td>(Intercept)</td><td>Ladybugs (2 sites)</td><td></td><td></td></tr><tr><td>Cropping system</td><td>1.82</td><td>1</td><td>0.17</td></tr><tr><td>Site</td><td>5.22</td><td>4</td><td>0.26</td></tr><tr><td>Cropping system × Site</td><td>5.83</td><td>1</td><td>0.01</td></tr><tr><td></td><td>10.06</td><td>4</td><td>0.03</td></tr><tr><td>(Intercept)</td><td>Mantises (2 sites)</td><td></td><td></td></tr><tr><td>Cropping system</td><td>0.00</td><td>1</td><td>0.97</td></tr><tr><td>Site</td><td>16.70</td><td>4</td><td>&lt; 0.01</td></tr><tr><td>Cropping system × Site</td><td>2.10</td><td>1</td><td>0.14</td></tr><tr><td></td><td>4.64</td><td>4</td><td>0.32</td></tr><tr><td>(Intercept)</td><td>Ants (2 sites)</td><td></td><td></td></tr><tr><td>Cropping system</td><td>58.64</td><td>1</td><td>&lt; 0.01</td></tr><tr><td>Site</td><td>11.42</td><td>4</td><td>0.02</td></tr><tr><td>Cropping system × Site</td><td>0.00</td><td>1</td><td>0.96</td></tr><tr><td></td><td>53.86</td><td>4</td><td>&lt; 0.01</td></tr></tbody></table>

harboured more abundant predator communities.

### 3.4. Crop yield and economic indicators

Cotton yields were consistently highest in the conventional system (CF) at both sites (Fig. 5). Sole-cotton systems transitioning from conventional to organic production (OF_Sole_NT and OF_Sole_Biop) showed substantial yield penalties relative to CF, while the intercropping systems (OF_Inter_NT and OF_Inter_Biop) produced lower cotton yields but substantial soybean yields that compensated for cotton losses. In Gobé, soybean yields in the intercrops were approximately 1600 kg ha⁻¹, whereas in Angaradébou they ranged from 1582 kg ha⁻¹ (OF_Inter_Biop) to 2313 kg ha⁻¹ (OF_Inter_NT). As a result, cumulative system productivity (cotton + soybean) differed significantly among cropping systems at both sites (Fig. 5). In Gobé, cumulative yields were significantly higher in all three systems CF, OF_Inter_NT, and OF_Inter_Biop than in sole-cotton systems transitioning from conventional to organic production, with no differences among the three higher-performing systems. In Angaradébou, cumulative yields increased from sole-cotton in transition-to-organic treatments to CF, and from CF to the intercropping systems, with OF_Inter_Biop achieving the highest productivity. Thus, intercropping consistently improved total system yield relative to transition sole crops and matched or exceeded the productivity of conventional cotton.

Under both conventional and organic price scenarios, the cropping system significantly affected gross margin, benefit-cost ratio, labour productivity and return to labour at both sites (p < 0.0001; Table 6). Across price scenarios, the intercropping systems (OF_Inter_NT and OF_Inter_Biop) consistently outperformed the sole cotton systems transitioning from conventional to organic production and, in several instances, performed similarly to or better than CF.

Under conventional prices, the relevant scenario for the conversion period, intercropping markedly improved economic outcomes relative to untreated transition sole cotton crops (Table 6). At Gobé, gross margins in OF_Inter_NT and OF_Inter_Biop were 372 ± 55 and 365 ± 92 US$ ha⁻¹, respectively, compared with -59 ± 31 US$ ha⁻¹ in OF_Sole_NT, -109 ± 33 US$ ha⁻¹ in OF_Sole_Biop, and 264 ± 42 US$ ha⁻¹ in

CF. At Angaradébou, gross margins reached 852 ± 163 and 944 ± 67 US$ ha⁻¹ for OF_Inter_NT and OF_Inter_Biop, respectively, compared with 47 ± 56 US$ ha⁻¹ in OF_Sole_NT, 187 ± 108 US$ ha⁻¹ in OF_Sole_Biop and 617 ± 107 US$ ha⁻¹ in CF. Benefit-cost ratio, labour productivity and return to labour followed the same pattern: intercropping consistently outperformed transition sole-cotton crops and in several cases matched or exceeded CF.

Soybean contributed the majority of gross margins in most intercropping treatments (Supplementary Table S4), with its relative contribution ranging from 54% to over 100%, depending on site and treatment. Overall, although cotton yields were reduced in the systems transitioning from conventional to organic management, the addition of soybean enabled intercropping to maintain or improve economic performance relative to CF- consistently across both organic and conventional price scenarios.

### 4. Discussion

Our study evaluated cotton production systems in Benin under conventional management and during the first year of transition from conventional to organic production, examining both pest regulation and economic outcomes. Pest species composition and abundance differed markedly between sites, yet plant injury metrics showed consistent trends: the use of pesticides - whether synthetic or biopesticides - generally reduced damage to ripe bolls and shed organs while increasing the proportion of healthy fibre. In central Benin, biopesticide-treated transition-to-organic intercrops reduced key bollworms, including *Earias* spp. (cropping system effect, p < 0.01) and endocarpic species (p = 0.05; Table 2; Fig. 1), but increased *B. tabaci* (p < 0.01; Table 3; Fig. 2), whereas in northern Benin the conventional system reduced *Earias* spp. (p < 0.01) and *D. watersi* (p = 0.04; Table 2; Fig. 1) but slightly increased *B. tabaci* (p < 0.01; Table 3; Fig. 2). Across systems, plots transitioning from conventional to organic management supported higher natural enemy abundances than conventional plots (cropping system effect, p < 0.01; Table 5; Fig. 4), although diversification effects on beneficial arthropods were not consistent across the two sites. Importantly, biopesticides did not suppress predator communities, underscoring their potential to maintain ecological regulation while contributing to pest control.

Despite reduced cotton yields in the systems transitioning from conventional to organic production, cotton-soybean intercropping improved economic viability relative to untreated sole cotton and, in several cases, performed as well as or better than conventional cotton - even when evaluated using conventional cotton and soybean prices (cropping system effect, p < 0.01; Table 6). These results suggest that intercropping can buffer farmers against income losses during early conversion to organic production while delivering partial ecological benefits for pest management.

#### 4.1. Pest abundance and plant injury metrics

Despite clear differences in pest species composition between sites - endocarpic bollworms occurring only in central Benin and exocarpic species being more abundant in the north (Supplementary Fig S2) - plant injury patterns showed consistent tendencies. Across Benin, major endocarpic and exocarpic bollworms remain widespread, and recent outbreaks of the invasive jassid *A. biguttula* pose an increasing threat to cotton production (Djihinto et al., 2017; Bonni et al., 2018; Legros et al., 2023). Both, synthetic pesticides and biopesticides, reduced damage to ripe bolls and shed organs and increased the proportion of healthy cotton (cropping system effect, p < 0.01; Table 4; Fig. 3), confirming their effectiveness across contrasting pest pressures. However, differences in healthy white cotton and ripe boll damage between sites - particularly the lower ripe boll damage and higher healthy cotton in the north - were not fully explained by the monitored pests. Lint discoloration is strongly influenced by two groups: (i) endocarpic bollworms,
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Abundance of natural enemies across management systems. Mean abundance (± 95 % CI) of natural enemies observed in a total of 15 plants per plot for the whole cropping season for each cropping system and experimental site. Left (A, C, E, G and I) and right (B, D, F, H and J) panels show predator abundances in Gobé and Angaradébou experimental site respectively. CF: Conventional farmer-practice; OF_Sole NT: Transition-to-organic sole-cotton farming not treated with pesticides; OF_Sole Biop: Transition-to-organic sole-cotton farming treated with biopesticides; OF Inter_NT: Transition-to-organic cotton + legume intercropping not treated with pesticides; OF_Inter_Biop: Transition-to-organic cotton + legume intercropping treated with biopesticides.</figcaption></figure>

which damage bolls internally and promote lint staining, and (ii) late-season sucking pests, especially cotton stainers (*Dysdercus* spp.) and honeydew-producers (aphids and whiteflies). Endocarpic bollworms occurred only in central Benin, consistent with both the lower healthy cotton and the higher ripe boll damage in this site. Moreover, because *Dysdercus* spp. were not monitored and pest surveys ended before harvest, peak late-season sucking-pest pressure was thus missed.

The observed pest patterns reflect both the benefits and trade-offs of diversification. Soybean intercropping can suppress some pests by disrupting host-finding or supporting natural enemies, but it may also favour pests such as *B. tabaci* and *D. watersi*, which can exploit the companion crop when it serves as an alternative host plant (Schader et al., 2005; Mansaray and Sundufu, 2009; Lv et al., 2023). Biopesticide efficacy also depended on pest biology and application timing: although *Metarhizium*, *Beauveria* and spinosad formulations are effective against several cotton pests (Faria and Wraight, 2001; Zafar et al., 2016; Malinga and Laing, 2022), their impact on *B. tabaci* and *D. watersi* appeared limited under our conditions and may have been constrained by pest susceptibility and timing of application (Tang et al., 2010).

Overall, intercropping effects were not uniform across pests or sites, whereas biopesticide applications consistently improved plant injury metrics, sometimes approaching levels observed under conventional

management. These findings highlight the need for well-timed biopesticide applications targeted to key pests under plant diversification during the early years of transition, when cotton remains highly vulnerable due to its complex pest community. Such tailored strategies are essential for sustainable management of the cotton pest complex in West Africa (Brévault et al., 2009; Chi et al., 2021).

## 4.2. Effects on natural enemies

Systems transitioning from conventional to organic production generally supported higher natural enemy abundances than conventional systems, although responses varied by site and predator group (cropping system effect, *p* < 0.01; Table 5; Fig. 4). In northern Benin, intercropping favoured spiders and praying mantises, likely due to increased habitat complexity and alternative resources (Rezende et al., 2014; Llandres et al., 2019; Chi et al., 2021). In central Benin, ladybugs and ants were more abundant in biopesticide-treated sole-cotton crops, probably reflecting ant-aphid mutualisms that subsequently attracted ladybugs (Fischer and Shingleton, 2001; Supplementary Fig S4). These site-specific patterns align with previous findings that diversification can enhance predator communities (Ramalho et al., 2012; Thompson et al., 2025), while outcomes remain dependent on local pest pressure,
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Cumulative cotton and soybean yield under different systems. Mean cumulative yields of cotton and soybean in the two study sites (A: Gobé; B: Angaradébou). CF: Conventional farmer-practice; OF_Sole NT: Transition-to-organic sole-cotton farming not treated with pesticides; OF_Sole Biop: Transition-to-organic sole-cotton farming treated with biopesticides; OF Inter NT: Transition-to-organic cotton + legume intercropping not treated with pesticides; OF Inter Biop: Transition-to-organic cotton + legume intercropping treated with biopesticides. Bars represent means ± standard error.</figcaption></figure>

baseline predator populations and landscape context (Costamagna, 2015; Karp, 2018; Galloway et al., 2021).

Four complementary mechanisms may explain the contrasting responses between sites: (i) predator assemblages tracked prey dynamics, with aphid outbreaks structuring predator communities in central Benin (Ceron et al., 2022); (ii) soybean intercrops in the north created favourable microclimates and alternative prey (Rakotomalala et al., 2023), supporting spiders and mantises; (iii) baseline predator pools differed strongly between sites, with more spiders and mantises in the north and more ants and ladybugs in the south; and (iv) landscape and farming-system structure differed markedly between sites: the northern cotton basin is a simplified, cotton-dominated landscape with extensive deforestation (Kouta and Imorou, 2019), whereas the central region contains diversified cropping systems-including mixed food crops and agroforestry (Assogba et al., 2023) - thereby providing more stable refuges and resources for predators.

Despite variation, one consistent pattern emerged: conventional pesticide use markedly suppressed natural enemies, reflecting the detrimental effects of broad-spectrum insecticides on beneficial arthropods (Jenkins et al., 2013; Yarou et al., 2017; Ankit et al., 2020). In contrast, biopesticide applications did not reduce predator populations, suggesting that these products can support pest control without disrupting ecological regulation. Predator suppression in conventional systems likely weakened natural biological control, contributing to higher injury from some sucking pests, such as jassids in central Benin and *B. tabaci* in the north. These trade-offs highlight how reliance on broad-spectrum insecticides may exacerbate pest outbreaks and

undermine long-term ecological resilience (Wilson et al., 1998; Hill et al., 2017).

These patterns are consistent with evidence from a meta-analysis in sub-Saharan Africa showing that biological control interventions, including intercropping and botanical pesticides, can maintain pest suppression while significantly increasing natural enemy abundance compared with synthetic insecticides (Ratto et al., 2022). Although most studies included in that synthesis were not cotton-specific, the general trend supports our finding that reduced reliance on broad-spectrum insecticides favours natural enemy communities without compromising pest regulation.

Similar outcomes have been reported across cotton-growing regions in China, the United States, India, Pakistan, Egypt, Brazil and Europe, where habitat diversification practices - such as intercropping and cover crops - enhanced natural enemy communities and reduced pest pressure without yield penalties, although responses varied with crop combinations, pest taxa and local context (Chi et al., 2021 and references therein; Bowers et al., 2020). More broadly, a global meta-analysis across multiple cropping systems confirmed that intercropping significantly increases the abundance, density and richness of beneficial arthropods while reducing pest abundance relative to monocultures, particularly in cereal-legume systems (Rakotomalala et al., 2023).

Together, these studies reinforce our results by indicating that both reduced pesticide reliance and increased crop diversification consistently promote biological control across agroecological contexts, while the magnitude of benefits remains strongly context dependent.

### 4.3. System performance, limitations and recommendations

Our results show that cotton-soybean intercropping can substantially improve farm profitability during the first year of transition to organic production, mainly because soybean income compensates for reduced cotton yields. This economic buffer is particularly important given the strong nitrogen limitation and degraded soil fertility typically found in fields emerging from long-term conventional cotton production (Yemadje et al., 2025b). Under such conditions, cotton responds poorly to the absence of N inputs, and competition from soybean - sown simultaneously and at high density between cotton rows - likely intensified resource limitation in intercrops. These agronomic constraints, rather than pest pressure alone, explain much of the yield gap observed between conventional cotton systems and systems in the first year of transition from conventional to organic management in our experiment.

Despite lower cotton yields, intercropping consistently improved profitability relative to the transition-to-organic sole-cotton crops and, in several cases, performed similarly to or better than conventional cotton, even under conservative (conventional) price assumptions (cropping system effect, *p* < 0.01; Table 6). This pattern is consistent with evidence from cotton-based and other crop intercropping studies conducted across diverse agroecological contexts in Asia, Africa and the United States, showing that diversified systems can enhance total system productivity, stabilize farm income and reduce financial risk despite partial yield trade-offs in the main crop (Schader et al., 2005; Himmelstein et al., 2017; Amin et al., 2018; Chi et al., 2019, 2021; Atique-ur-Rehman et al., 2020; Li et al., 2023; Raza et al., 2025). However, in our study, soybean contributed the majority of gross margins in most intercropping treatments (Supplementary Table S4), indicating that improvements in cotton performance will be necessary for the system to remain attractive beyond the transition period. Although sole soybean could generate higher short-term returns - and soybean cultivation has expanded rapidly in Benin in recent years, reflecting growing market demand - cotton remains central to farmer livelihoods due to its well-established purchasing networks, guaranteed buyers, and structured institutional support, including for organic cotton through NGOs such as the Beninese Organisation for the Promotion of Organic Agriculture, OBEPAB. In this context, intercropping provides a pragmatic diversification strategy: it allows farmers to benefit from soybean
<table><caption>**Table 6**
Gross margin, benefit-cost ratio, labour productivity and return to labour under conventional and organic prices.</caption><thead><tr><th>Price Scenario</th><th>Site</th><th>Cropping system</th><th>Gross margin (US$·ha<sup>-1</sup>)</th><th>Benefit-cost ratio</th><th>Labour productivity (Kg.day<sup>-1</sup>)</th><th>Return to labour (US$·day<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="10">Organic</td><td rowspan="5">Gobé</td><td>CF</td><td>265 ± 40 ab</td><td>0.5 ± 0.4 ab</td><td>25 ± 3 ab</td><td>4 ± 1 ab</td></tr><tr><td>OF_Sole_NT</td><td>16 ± 10 a</td><td>0.03 ± 0.1 a</td><td>17 ± 1 a</td><td>0.3 ± 1 a</td></tr><tr><td>OF_Sole_Biop</td><td>-25 ± 38 a</td><td>0.0 ± 3 a</td><td>16 ± 2 a</td><td>-0.6 ± 1 a</td></tr><tr><td>OF_Inter_NT</td><td>471 ± 57 b</td><td>0.9 ± 0.1 b</td><td>33 ± 3 b</td><td>8 ± 1 b</td></tr><tr><td>OF_Inter_Biop</td><td>473 ± 34 b</td><td>0.8 ± 0.2 b</td><td>32 ± 4 b</td><td>8 ± 1 b</td></tr><tr><td rowspan="5">Anagaradébou</td><td>Pr > F</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td></tr><tr><td>CF</td><td>617 ± 98 b</td><td>1.1 ± 0.1 b</td><td>35 ± 3 b</td><td>10 ± 1 a</td></tr><tr><td>OF_Sole_NT</td><td>144 ± 39 a</td><td>0.3 ± 0.1 a</td><td>22 ± 2 a</td><td>3 ± 1 a</td></tr><tr><td>OF_Sole_Biop</td><td>325 ± 124 ab</td><td>0.7 ± 0.1 ab</td><td>28 ± 4 ab</td><td>6 ± 2 a</td></tr><tr><td>OF_Inter_NT</td><td>1010 ± 138c</td><td>1.9 ± 0.1c</td><td>49 ± 6c</td><td>17 ± 3 b</td></tr><tr><td rowspan="10">Conventional</td><td rowspan="5">Gobé</td><td>OF_Inter_Biop</td><td>1169 ± 72c</td><td>2.0 ± 0.2c</td><td>51 ± 2c</td><td>18 ± 1 b</td></tr><tr><td>Pr > F</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td></tr><tr><td>CF</td><td>264 ± 42 bc</td><td>0.4 ± 0.1 bc</td><td>25 ± 1 ab</td><td>4 ± 1 bc</td></tr><tr><td>OF_Sole_NT</td><td>-59 ± 31 ab</td><td>0.0 ± 0.1ab</td><td>17 ± 1 a</td><td>-1 ± 1 a</td></tr><tr><td>OF_Sole_Biop</td><td>-109 ± 33 a</td><td>0.0 ± 0.1 a</td><td>16 ± 1 a</td><td>-2 ± 1 ab</td></tr><tr><td rowspan="5">Anagaradébou</td><td>OF_Inter_NT</td><td>372 ± 55c</td><td>0.7 ± 0.1c</td><td>33 ± 2 b</td><td>6 ± 1c</td></tr><tr><td>OF_Inter_Biop</td><td>365 ± 92c</td><td>0.6 ± 0.2c</td><td>32 ± 3 b</td><td>6 ± 1c</td></tr><tr><td>Pr > F</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td></tr><tr><td>CF</td><td>617 ± 107 b</td><td>1.1 ± 0.2 b</td><td>36 ± 3 b</td><td>10 ± 2 bc</td></tr><tr><td>OF_Sole_NT</td><td>47 ± 56 a</td><td>0.1 ± 0.1 a</td><td>22 ± 2 a</td><td>1 ± 1 ab</td></tr><tr><td rowspan="5">Anagaradébou</td><td>OF_Sole_Biop</td><td>187 ± 108 a</td><td>0.4 ± 0.2 a</td><td>28 ± 4 ab</td><td>4 ± 2 a</td></tr><tr><td>OF_Inter_NT</td><td>852 ± 163 b</td><td>1.6 ± 0.3 b</td><td>50 ± 6c</td><td>14 ± 2 c</td></tr><tr><td>OF_Inter_Biop</td><td>944 ± 67 b</td><td>1.6 ± 0.1 b</td><td>51 ± 2 c</td><td>15 ± 1 c</td></tr><tr><td>Pr > F</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td></tr></tbody></table>

CF: Conventional farmer-practice; OF_Sole_NT: transition-to-organic sole-cotton farming not treated with pesticides; OF_Sole_Biop: transition-to-organic sole-cotton farming treated with biopesticides; OF_Inter_NT: transition-to-organic cotton + legume intercropping not treated with pesticides; OF_Inter_Biop: transition-to-organic cotton + legume intercropping treated with biopesticides. Different letters indicate significant differences among cropping systems based on post-hoc Tukey multiple comparison tests (p < 0.05).

income while retaining access to reliable cotton markets, rather than requiring a full shift away from cotton.

Several limitations of this first-year study must be acknowledged. Soil fertility does not recover automatically after ceasing synthetic inputs; transition-to-organic systems require deliberate incorporation of organic N sources such as compost, manure, green manures or biofertilizers. Integrating these amendments, along with optimising planting geometry and soybean density, will be essential to reduce crop competition and enhance cotton performance in organic intercrops. Weed management was also more labour-intensive in intercropping systems, highlighting the need for complementary practices such as mulching or mechanical weeding. Further research is required to refine biopesticide efficacy, including optimal timing and dosage, and to target key pests such as *B. tabaci*, *D. watersi* and jassids.

Interpretation of arthropod data should also be cautious. Because the experiment used small, adjacent plots, insecticide drift from conventional to nearby transition-to-organic plots may have influenced both pest and predator populations, potentially reducing observable treatment differences. Such cross-treatment interference is a known limitation of small-plot entomological studies. In addition, our monitoring focused on visible predators and did not include parasitoids, which may be more sensitive to pesticides and biopesticides and could reveal different patterns. Larger or farm-scale trials allowing greater spatial isolation of treatments and including parasitoid assessments would provide a more complete understanding of natural enemy responses.

Finally, participatory co-design with farmers will be essential to tailor intercropping arrangements, input strategies and pest management to local contexts, and to ensure the long-term adoption and resilience of cotton-based cropping systems transitioning from conventional to organic production.

## 5. Conclusions

This study shows that cotton cropping systems transitioning from conventional to organic management support substantially higher natural enemy abundances than conventional cotton, while biopesticides

maintained predator communities without the suppressive effects observed under synthetic pesticides. These findings highlight the ecological advantages of organic-compatible pest management for conserving beneficial arthropods.

Across sites, pesticide use (synthetic or biopesticides) reduced plant injury and increased the proportion of healthy cotton, but these differences could not account for the large yield gap between conventional systems and those transitioning from conventional to organic management. Yield reductions were instead largely driven by severe nitrogen limitation in all transition plots and by crop competition within the intercrops.

Despite reduced cotton yields, cotton-soybean intercropping substantially improved farm profitability relative to sole-cotton in systems transitioning from conventional to organic production, and in several cases performed similarly to or better than conventional cotton. This economic benefit was mainly driven by soybean, which contributed most of the total gross margin. Although a sole soybean crop could be more profitable in some contexts, cotton remains central to smallholder livelihoods in Benin and other African cotton-producing countries due to its structured markets and institutional support. Intercropping therefore provides a realistic and adoptable diversification strategy during the transition period.

In the longer term, however, cotton-soybean intercropping alone is insufficient. Embedding these systems within broader, more diverse crop rotations - including legumes, cereals and cover crops - and organic soil amendments will be essential to rebuild soil fertility, enhance biodiversity and maintain ecological pest regulation in organic cotton systems. Future work should assess multi-year soil and pest dynamics under improved nutrient management and optimised intercropping designs.

## CRediT authorship contribution statement

Detondji Lucien Tomagnankodo: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Anicet Gbèblonoudo Dassou: Writing – review &
editing, Supervision, Formal analysis, Conceptualization. **Pierrot Lionel Yemadje:** Writing – review & editing, Visualization, Supervision, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Saturnin Azonkpin:** Writing – review & editing, Resources, Investigation, Funding acquisition, Conceptualization. **Douro Kpindou:** Writing – review & editing, Resources, Methodology. **Pablo Tittonell:** Writing – review & editing, Supervision, Conceptualization. **Ana L Llandres:** Writing – review & editing, Visualization, Supervision, Project administration, Formal analysis, Data curation, Conceptualization.

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process

The author(s) used ChatGPT (OpenAI, GPT-5) to improve the clarity, grammar, and style of parts of the manuscript. After using this tool, the author(s) reviewed and edited the content as needed and take full responsibility for the final version of the manuscript.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Acknowledgments

This research was funded by the Benin Cotton Research Institute (IRC), Cotton Interprofessional Association (AIC), and the TAZCO2 project (Transition Agroécologique des Zones Cotonnières du Bénin), which is funded by the Republic of Benin and the French Development Agency (AFD). Pablo Tittonell would like to thank the EU Marie Sklodowska-Curie Program for financial support through the project ACROPICS (P. 101131263). We thank Professor Simplice Vodohoué (OBEPAB Coordinator) for providing key information.

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agee.2026.110299.

Data availability

Data will be made available on request.

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Further reading

Dannon, H.F., Dannon, A.E., Douro-Kpindou, O.K., Zinsou, A.V., Houndete, A.T., Toffa-Mehinto, J., Elegbede, I.A.T.M., Olou, B.D., Tamò, M., 2020. Toward the efficient use of Beauveria bassiana in integrated cotton insect pest management. J. Cotton Res. 3, 24. https://doi.org/10.1186/s42397-020-00061-5.

El-Sayed, M.H., Ibrahim, M.M., Elsobki, A.E., Aioub, A.A., 2023. Enhancing the toxicity of cypermethrin and spinosad against Spodoptera littoralis (Lepidoptera: Noctuidae) by inhibition of detoxification enzymes. Toxics 11, 215. https://doi.org/10.3390/toxics11030215.

Fernandes, F.S., Ramalho, F.S., Nascimento, A.R.B., Malaquias, J.B., Silva, C.D., Zanuncio, J.C., 2012. Within-plant distribution of cotton aphid (Hemiptera: Aphididae), in cotton with colored fibers and cotton-fennel intercropping system. Ann. Entomol. Soc. Am. 105, 599–607. https://doi.org/10.1603/AN12018.

Goulson, D., 2013. REVIEW: An overview of the environmental risks posed by neonicotinoid insecticides. J. Appl. Ecol. 50, 977–987. https://doi.org/10.1111/1365-2664.12111.

Rusch, A., Chaplin-Kramer, R., Gardiner, M.M., Hawro, V., Holland, J., Landis, D., Thies, C., Tscharntke, T., Weisser, W.W., Winqvist, C., 2016. Agricultural landscape simplification reduces natural pest control: A quantitative synthesis. Agric. Ecosyst. Environ. 221, 198–204. https://doi.org/10.1016/j.agee.2016.01.039.

Symondson, W.O.C., Sunderland, K.D., Greenstone, M.H., 2002. Can Generalist Predators be Effective Biocontrol Agents? Annu. Rev. Entomol. 47, 561–594. https://doi.org/10.1146/annurev.ento.47.091201.145240.