# Farmers' preferences for adoption of service plants in biological control and implications for agricultural system design

Margot Gumbau, Johanna Kupka, Laurent Penet, François Causeret, Jean-Marc Blazy

► To cite this version:

Margot Gumbau, Johanna Kupka, Laurent Penet, François Causeret, Jean-Marc Blazy. Farmers' preferences for adoption of service plants in biological control and implications for agricultural system design. Agricultural Systems, 2026, 231, pp.104535. 10.1016/j.agsy.2025.104535. hal-05501385

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Research Paper

# Farmers' preferences for adoption of service plants in biological control and implications for agricultural system design

Margot Gumbau *, Johanna Kupka, Laurent Penet, François Causeret, Jean-Marc Blazy

INRAE, UR ASTRO, F-97170 Petit-Bourg, Guadeloupe, France

<figure><img src="image_2.png" /></figure>

## HIGHLIGHTS

* This study investigated ex-ante farmers' preferences for biological control.

* Farmers with higher education and knowledge level show greater interest.

* Increased workload, lack of information and economic loss are perceived hurdles.

* Farmers share common preferences of sought service plants' traits.

* Accounting for farmers perception is key for designing cleaner agricultural systems.

## GRAPHICAL ABSTRACT

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Editor: Leonard Rusinamhodzi

**Keywords:**
Agroecology
Innovation
Flower strips
Market gardening
Guadeloupe

## ABSTRACT

**CONTEXT:** Decreasing recourse to chemicals in agriculture is a major challenge in the development of more sustainable agrosystems. Conservation biological control could be key to addressing this challenge by promoting pest regulation using natural enemies.

**OBJECTIVE:** We investigated ex ante perceptions and preferences of farmers about conservation biological control via the use of service plants to attract parasitoids. The aim of this study is to allow for designing systems matching with farmers vision, with higher chances of adoption.

**METHODS:** We interviewed 30 farmers involved in cucumber production in Guadeloupe, and used survey results to build a typology of farms. Farm Types were then assessed for characteristics with a focus on innovation variables. We further assessed how preferences, constraints and opportunities about service plants aiming for biological control differed among farm types. Variable significance was assessed with ANOVA and Chi-square testing, and we processed with post-hoc tests for significantly impacting variables. Finally, we translated these results into recommendations for agricultural systems and policy design.

**RESULTS AND CONCLUSION:** We documented a general interest in innovation but heterogeneity across farm types in terms of farmers' willingness to test and to adopt specific innovation. Knowledge about natural regulation was key for adoption. Workload and cash flow constraints significantly limited adoption of service plants. Despite differences in adoption levels, all farmers shared the same perception of constraints and opportunities for adoption and demonstrated similar preferences regarding service plant traits. Integrating service plants relevant

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: margot.gumbau@inrae.fr, margot.gumbau@gmail.com (M. Gumbau), j.kupka@istom.fr (J. Kupka), laurent.penet@inrae.fr (L. Penet), francois.causeret@inrae.fr (F. Causeret), jean-marc.blazy@inrae.fr (J.-M. Blazy).</footnote>
to biological control in the fields should also consider other benefits and services to farmers, and should be supported by broad minded policies and training accessibility.

**SIGNIFICANCE:** Our results highlight that integrating service plants in innovative cropping schemes should consider farmers preferences and constraints and envision multiple criteria for success, as well as supporting policies for adoption.

# 1. Introduction

Crop pests cause annual losses of approximately 40 % of global agricultural production, estimated at over $222 billion in trade losses (FAO and IPPC Secretariat, 2020). Pesticides are still the main solution worldwide for regulating insect pests because of their efficiency; however, their overuse has translated into many environmental and human health issues. Because of intensive chemical management, about two-thirds of global agricultural land is now at risk of pollution by more than one active ingredient (Tang et al., 2021). Although organic agriculture is on the rise, increasing from 0.3 % in 2020 to 2 % in 2022, pesticides are still heavily used (Willer et al., 2024), representing a significant contribution to the decline in insect populations. As a result, there is an urgent need to improve agricultural systems, make them cleaner, and reduce the use of pesticides, with biological control of pests by auxiliaries being a key step in the process.

Biological control involves the natural regulation of pests by auxiliaries, such as parasitoids (Boivin, 2001; Lahlali et al., 2022). Parasitoid wasps are the first factors influencing pest demographic control (Wang et al., 2019). Because they lay eggs in their hosts, parasitoids actively search for pests *in natura*, and it is possible to modify agriculture landscape to achieve a greater regulation in the fields. Communities of natural parasitoids can be recruited locally with favorable practice: this approach is called conservation biological control (Begg et al., 2017). For example, integrating flowering plants can provide food and shelter for parasitoids (Baudry et al., 2024). These plants (hereafter "service plants"), whose primary purpose is not harvesting, provide biological control services to the crops (Djian-Caporalino and Lavoir, 2024).

In Guadeloupe, cucumber production (Cucumis sativus Linnaeus, 1753) is important for food security and autonomy. Cucumber is indeed highly represented in local cuisine and is cultivated by most market gardeners throughout the year (along with other cucurbits). Cucumber accounts for approximately 10 % of the utilized agricultural area dedicated to market gardening and of the total market gardening production (2750 tons out of 27,055). Production is locally threatened by many pests, but the melon worm (Diaphania hyalinata Linnaeus, 1767) has the highest economic impact on the crop according to farmers and stakeholders. Indeed, *D. hyalinata* is a Lepidopteran species that has been described as a major cucurbit pest in America (Capote Luna et al., 2019; Panthi et al., 2017) and the West Indies (Acosta et al., 1986; Guillaume and Boissot, 2001). Larvae feed mainly on leaves, decreasing photo-synthesis and facilitating the proliferation of pathogens (Gonring et al., 2003). As the population increases, larvae become less discriminatory and feed on all aerial parts (Valles and Capinera, 1992). Under tropical climate, such as Guadeloupe, the high temperatures shorten the life cycle of *D. hyalinata*, leading to increased population and higher pest pressure on crops (Melo et al., 2007; Panthi et al., 2017). Biological control of *D. hyalinata* through parasitoid attraction by service plants appears to be an interesting option. First, previous studies have already identified efficient parasitoid species associated with *D. hyalinata* (da Silva et al., 2015; de Melo et al., 2011; Polanczyk et al., 2011). Moreover, parasitoids are among the insects most actively interacting with flowering plants (Ndakidemi et al., 2022). Several plant species, such as *Cosmos sulphureus*, *Ocimum basilicum* or *Tagetes erecta*, have been shown to indeed attract parasitoid families known to regulate Lepidopteran pests (Aldini et al., 2019; Souza et al., 2019).

Although the concept of service plants is not new, as in the case of cover crops, improved fallows, flower strips, or multifunctional hedges,

practices that have been promoted for several decades, particularly in Africa and Europe, these innovations are not always adopted by farmers in modern schemes (Djian-Caporalino and Lavoir, 2024; Hatt et al., 2018; Kowalska et al., 2022; Schillinger, 2010). This raises the question of what determines farmers' decisions to adopt service plants. This is particularly important when initiating a program to design or redesign cropping systems involving service plants. Identifying the obstacles and motivations for adopting service plants before the design stage will enable these determinants to be integrated, ensuring the systems have the best chances of being adopted by a diverse range of farmers. Assessing farmers' willingness and barriers to adopt should be a key point in the design of new agricultural systems and there is growing recognition that we need to involve future users in more participatory design processes (Prost, 2021), particularly in integrated pest management (Deguine et al., 2021). It is important to involve farmers from the very beginning of the design of an innovation in order to encourage its adoption and dissemination (Le Bellec et al., 2012). Studies on the adoption of service plants are rare in the literature and have confirmed that the adoption of a service plant by a farmer depends on multiple factors and their interaction (Blazy, 2024).

The question of innovation adoption in agriculture has been widely studied since the work of Griliches (1957) on the adoption of hybrid corn varieties in the United States, and that of Feder (1980) on the adoption of Green Revolution innovations. More recently, there is a renewal of interest in adoption factors and farmers' willingness to adopt agro-ecological innovation (Blasch et al., 2021; Montes de Oca et al., 2021; Rosario et al., 2022). Factors can positively or negatively influence adoption (constraints or opportunities). The first entails plant characteristics and especially biological and ecological traits, for example seed size and soil coverage (Martin and Isaac, 2015), and practical aspects, especially when it requires incrementing specific practices (Celette et al., 2024). Service plants performance includes factors such as pest control efficiency, additional labor requirements, input savings, and impact on yield and income (Carlucci et al., 2020). This category is particularly important, as it allows assessing which traits most closely match farmers' expectations and constraints (Blazy, 2024). Second, there is a need to appreciate the farm's natural environment and biophysical constraints and opportunities: soil type, climate, pest pressure, as they must be compatible with expected services and minimal disservices. Third, adoption is also influenced by individual decision-making, i.e. factors directly relating to farmers and the farm structure: human capital and socio-demographic variables (e. g., age, access to information and technical advice), personal attitudes (e.g., sensitivity to environmental problems, ability of farmers to adapt to new situations, attitude toward risk and uncertainty), the farm's material resources (e.g. farm machinery, for sowing and managing service plants) and, finally, the farmer's own objectives for his farm (e.g., maximizing income, minimizing labor, reducing pesticides). Lastly, there are a number of factors exogenous to the farm itself, over which the farmer has little control but on which policy makers can act: regulations (e.g., prohibition of molecules or uses), markets and sectors (e.g., plant seed costs, labelling and production specifications), agricultural incentive policies (e.g., green schemes like in France the Agro-Environmental and Climate Measures) (Blazy, 2024).

In Guadeloupe, little is known about the efficiency of putative service plants in recruiting parasitoids for biological control and farmers' perceptions and preferences about this innovation. In this paper, we present the results of an investigation of farmers' perceptions of service plants
and their potential for adoption, with a focus on how prior knowledge of biological control, perceived benefits, and constraints may lead to increased, or conversely, reduced, adoption of flower strips with service plants in cropping systems. Our goal is to assess and account for farmers' preferences to identify promising service plants based on farmer-valued traits and ultimately, to incorporate these preferences and constraints into the design of innovative agricultural systems that are tailored to farmers' needs for developing effectively conservation biological control in market gardening cropping systems. We first present a method based on a farm survey, followed by a typological analysis of farmers' characteristics and perceptions of integrating flower strips with service plants into their fields. Finally, we discuss the implications for agro-ecological system design and policy adaptation.

## 2. Materials and methods

### 2.1. Study area and farming context

The study was conducted in Guadeloupe, a French Caribbean ar-chipelago in the Lesser Antilles. Situated between the Atlantic and

Caribbean Seas, Guadeloupe has a humid tropical climate with two main seasons: dry (November–July) and rainy (August–October). The average temperature is 27 °C, with higher temperatures during the rainy season. Guadeloupe comprises two main islands separated by a narrow sea channel: Grande-Terre (590 km²) and Basse-Terre (848 km²). In Grande-Terre, agriculture is dominated by sugar cane. In the Eastern region, agriculture is the most diversified industry, with high levels of vegetable gardening, including cucurbits. Agriculture in North Basse-Terre is quite similar to that of Grande-Terre, with sugarcane as the most important crop. The Southeastern region of Basse-Terre is characterized by the production of banana for export. Finally, the Southwestern region of Basse-Terre is characterized by cash crop gardens, orchards, and banananas. Although there is sub-specialization in the various zones, all crops are actually found throughout the territory (Chopin et al., 2015).

The majority of farms in Guadeloupe grow cucumbers, which can be grown year-round because of the short growth cycle that enables farmers to generate cash flow quickly. Farmers grow local varieties (soleil) and imported varieties (eureka) by sowing them in trays. The seedlings are then transplanted onto open ground on ridges, usually without mulching or trellising. Throughout the growing cycle, farmers

<figure><img src="image_4.png" /><figcaption>Fig. 1. Map of farmers surveyed (n = 30).</figcaption></figure>
use few herbicides and rely primarily on hand weeding. Irrigation is
performed by drip. Soil fertilization and pest treatments can be chemi-
cal, organic, or natural. After harvesting, cucumbers are sold at local
markets, cooperatives, and supermarkets. The main problem facing
cucumber growers is the presence of defoliating caterpillars
(*D. hyalinata*), which are treated using chemical (Decis Protech) and
biological (DiPel® DF) inputs.

## 2.2. Study sample

In total, 30 farmers were surveyed in three geographical areas: the
two main market gardening areas (East Grande-Terre from Anse-
Bertrand to Saint-François and South Basse-Terre from Bouillante to
Capesterre-Belle-Eau) and the central island region (North Basse-Terre/
West Grande-Terre from Sainte-Rose to Abymes) (Fig. 1). At the scale of
the territory, 473 farms are specialized in market gardening and horti-
culture (Agreste, 2022). Cucumber gardeners account for approximately
a quarter of these farms (around 120). Our sample therefore includes
around 25 % of cucumber growers in the region.

The diversity of pedoclimatic conditions found in Guadeloupe was included to yield a sample representative of Guadeloupean agriculture, including the diversity of cucumber farming practices. We tried to survey a similar number of farms in these subregions to cover the diversity of agropedoclimatic situations. We surveyed both conventional and organic farmers in each zone, as preferences may differ across these two types of farmers. Farmers were sampled via previous surveys and contacts from extension officers. Prior to the surveys, we conducted interviews with various agricultural stakeholders (advisory services, producer groups, interbranch organizations, technical and research institutes). These interviews highlighted *D. hyalinata* on cucumber as a major constraint for the development of cucumber market gardening in Guadeloupe. All the farmers were then contacted and asked whether they would agree to contribute in the survey. Appointments were then scheduled to conduct the interviews on farms. Eventually, we directly asked the farmers if they knew other farmers who might fit the criteria (snowball method).

Among the 30 farmers surveyed 7 % were women and 93 % were men. In Guadeloupe, the agricultural sector in mainly composed by men (80 %) (Agreste, 2022), we therefore have a slightly lower representation of women in our surveys. The average age of the sample was 52.8 years, which is consistent with the overall farming population in Guadeloupe, where more than 65 % of farmers are over 50 years old. The average utilized agricultural area (UAA) was 15.1 ha, with an average of 26 % of farmland dedicated to market gardening. A third of the farms were certified organic.

## 2.3. Questionnaire design

The interviews were conducted face-to-face directly on the farm from
May to July 2023 using a semi structured questionnaire (Horton et al.,
2004). Each interview lasted an average of 2 h. The questionnaire
consisted of six parts, including both open and closed questions. The first
part focused on the technical management of cucumbers (cropping
system). Its aim was to understand (1) the benefits of growing cucum-
bers, (2) how the system was conducted (variety, rotation, associations,
irrigation, fertilization, and use of chemicals), and (3) the market
strategy and importance of the crop to farmers' income. This part
enabled us to assess "on-farm" diversity ("crop diversity" and "market
gardening crop diversity") based on the assumption that highly diver-
sified farmers are more likely to integrate service plants. We assumed
that the same applies to a tailored practice of intercropping within farm
plots ("crop association"), which may correlate with increased odds of
adopting service plants and flower strips. We also asked farmers indi-
rectly about mechanization via the presence or absence of a tractor
("mechanized tillage") on the farm, which, on the contrary, may reflect
an increased difficulty in integrating innovation, as tilling a large surface

might be the practice.

The second part focused on the local hazards associated with cu-
cumber crops. We aimed to understand (1) the major problems
encountered in growing cucumber crops, especially its main pests; and
(2) the economic impact of *Diaphania hyalinata* and how farmers manage
it (frequency of treatments, use of chemical or biological inputs, and
preventive or curative treatments). The impact of *D. hyalinata* and spe-
cific treatments against this pest were assessed using closed questions.

The third part assessed farmers' knowledge about conservation
biological control, perceptions about pesticide use, and interest and
capacity for developing biological control on the farm. We checked
knowledge about several key concepts—service plants, biological con-
trol, auxiliaries, and parasitoid wasps—using binary answers (yes/no).
We developed two indices, namely, “knowledge of service plants” and
“knowledge of biological control,” each measured on a scale ranging
from 0 to 4: 0—no mention or knowledge of the concept; 1—has already
heard about the concept; 2—can provide a simple definition; 3—can
provide a definition and an example of service provided by plants or
biological control application; and 4—can give a complete definition
and several examples of services provided by plants or types of biolog-
ical control. “Adoption variables,” which encompassed farmers’ will-
ingness to adopt, were divided into three categories: “interest in
experimentation,” assessed as a closed question (yes/no); “innovation
interest,” evaluated on a scale from 0 to 4 with the question, “Would you
be interested in integrating service plants for pest regulation on your
farm?”; and “innovation adoptability,” assessed on a scale from 0 to 4
with the question, “In view of your current constraints, would you be
prepared to currently adopt the innovation on at least 50% of your own
fields ?”. These variables first evaluated hypothetical interest in inno-
vation and, in contrast, the potential for actual adoption. To assess the
interest rate of different types of farmers, we considered the percentage
of responses ≥3 on the choice scale.

The fourth part aimed to inform preferences in the design of the
flower strip innovation by asking farmers which service plant traits they
sought, which species they preferred among a selection of six species,
and how they would best fit in the plot (central flower strip, lateral strip,
close association, or patchy integration). These species were local plants
from five different botanical families, selected to represent the diversity
of flowering plants commonly found in local agro-ecosystems: *Bidens
pilosa* L. 1753, “black-jack” (Asterales: Asteraceae), *Canavalia ensiformis*
(L.) DC. 1825, “Jack bean” (Fabales: Fabaceae), *Cosmos sulphureus* Cav.
1791, “Yellow cosmos” (Asterales: Asteraceae), *Lippia alba* (Mill.) N.E.
Br. Ex Britton & P. Wilson 1925, “Brushy lippia” (Lamiales: Verbena-
ceae), *Ocimum basilicum* L. 1753, “Basil” (Lamiales: Lamiaceae). This
part also evaluated farmers’ constraints and perceived opportunities for
adoption. We asked the questions in a purely open-ended fashion,
leaving farmers free to express themselves. The answers were then
grouped into standardized categories: service plant preferences (ease of
management, profitability, auxiliaries’ attraction and pest regulation,
ornamental, and multiservice), constraints (workload increase, lack of
knowledge, risk of economic losses, aversion to change, and access to
seed), and opportunities (access to knowledge and training, service plant
traits, access to economic incentive, access to seeds, and access to
manpower).

The fifth and sixth parts covered general farm characteristics (size,
organic certification, land tenure, labor workforce, and cash flow) and
farmer demographics (age, gender, education, time spent on farming,
education level, and cash flow problems). These variables are commonly
used in agricultural surveys to assess the adoption levels of various in-
novations (Knowler & Bradshow, 2006). These parts aimed to help re-
searchers understand which structural variables may play a role in
adoption. In addition, they characterized the study sample. All the
variables used in the statistical treatments derived from this question-
naire and are presented in Table 1.
<caption>**Table 1**</caption>

Typology of cucumber farming in Guadeloupe. ANOVA and Chi-square tests indicate significance: * significant at the 10 % level; ** significant at the 5 % level; *** significant at the 1 % level. Letters (a, b, c, d) indicate significant differences between types based on Tukey and pairwise Fisher post-hoc tests.

<table><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Descriptive variables</td><td>Definition</td><td>Pr &gt; |t| (ANOVA)</td><td>Type 1 (n = 6)</td><td>Type 2 (n = 11)</td><td>Type 3 (n = 6)</td><td>Type 4 (n = 7)</td></tr><tr><td>Age</td><td>Age of farmers (years)</td><td>0.000***</td><td>45</td><td>58</td><td>62</td><td>44</td></tr><tr><td>Farming time (%)</td><td>Percentage of time devoted to farming (%)</td><td>&lt;0.0001***</td><td>97 %</td><td>95 %</td><td>90 %</td><td>49 %</td></tr><tr><td>Farm size (ha)</td><td>Utilized agricultural area (UAA) total (ha)</td><td>0.060*</td><td>9</td><td>12</td><td>31</td><td>11</td></tr><tr><td></td><td>Number of crops (banana, sugarcane, market gardening, tubers, fruits trees, ananas, grassland, etc.)</td><td>0.040**</td><td>4.5</td><td>3.6</td><td>2.5</td><td>4.0</td></tr><tr><td>Crop diversity</td><td>Percentage of farms devoted to market gardening (%)</td><td>0.439</td><td>21 %</td><td>20 %</td><td>36 %</td><td>30 %</td></tr><tr><td>Specialization in market gardening diversity</td><td>Number of market gardening crops (cucumber, melon, watermelon, tomato, salad, chili pepper, etc.)</td><td>0.165</td><td>7.3</td><td>4.7</td><td>4.7</td><td>5.4</td></tr><tr><td>Market gardening diversity</td><td>Mean of percentages of land owned by the farmers of the type (%)</td><td>0.104</td><td>18 %</td><td>14 %</td><td>66 %</td><td>36 %</td></tr><tr><td>Land tenure (%)</td><td></td><td>P-value (Khi-2)</td><td>Proportions</td><td>Proportions</td><td>Proportions</td><td>Proportions</td></tr><tr><td>Higher education</td><td>Yes/No: Yes if the farmer has higher education</td><td>0.081*</td><td>67 %</td><td>9 %</td><td>50%</td><td>29 %</td></tr><tr><td>Organic farming certification</td><td>Yes/No: Yes if farming system is organic certified</td><td>0.021**</td><td>83 %</td><td>9 %</td><td>33 %</td><td>29 %</td></tr><tr><td>Cash flow problems</td><td>Yes/No: Yes if the farmer is frequently constrained by cash flow problems</td><td>0.796</td><td>50%</td><td>27 %</td><td>33 %</td><td>43 %</td></tr><tr><td>Crop associations</td><td>Yes/No: Yes if crop association is practiced on at least two-third of their market gardening plots</td><td>0.002***</td><td>100 %</td><td>18 %</td><td>17 %</td><td>14 %</td></tr><tr><td>Mechanized tillage</td><td>Yes/No: Yes if the farmer uses mechanized tillage</td><td>0.439</td><td>50%</td><td>82 %</td><td>50 %</td><td>71 %</td></tr><tr><td>Impact of D. hyalinata</td><td>Yes/No: Yes if the farmer has significant damage caused by D. hyalinata (infestation, defoliation, yield losses)</td><td>0.186</td><td>50%</td><td>82 %</td><td>33 %</td><td>43 %</td></tr><tr><td>Insecticide treatments</td><td>D. hyalinata</td><td>0.034**</td><td>0%</td><td>73 %</td><td>33 %</td><td>43 %</td></tr><tr><td>Knowledge and attitude</td><td>of farmers toward innovation</td><td>Pr &gt; |t| (ANOVA)</td><td>Average</td><td>Average</td><td>Average</td><td>Average</td></tr><tr><td>Knowledge of service plants</td><td>Level from 0 (doesn't know) to 4 (knows the concept and cites examples)</td><td>&lt;0.0001***</td><td>4.0</td><td>1.2</td><td>3.5</td><td>1.4</td></tr><tr><td>Knowledge of biological control</td><td>Level from 0 (doesn't know) to 4 (knows the concept and cites examples)</td><td>&lt;0.0001***</td><td>3.2</td><td>0.6</td><td>2.8</td><td>0.7</td></tr><tr><td></td><td></td><td>P-value (Khi-2)</td><td>Proportions</td><td>Proportions</td><td>Proportions</td><td>Proportions</td></tr><tr><td>Interest in experimentation</td><td>Yes/No: Yes if the farmer is interested in having experiments on his farm</td><td>0.751</td><td>83 %</td><td>82 %</td><td>100 %</td><td>86 %</td></tr><tr><td>Innovation interest</td><td>Yes/No: Yes if the farmer is interested in adopting the innovation for pest regulation</td><td>0.296</td><td>83 %</td><td>55 %</td><td>83 %</td><td>43 %</td></tr><tr><td>Innovation adoptability</td><td>Yes/No: Yes if the farmer declares he is able to adopt service plants for pest regulation on at least 50% of the area of cucumber fields</td><td>0.098*</td><td>50%</td><td>18 %</td><td>50 %</td><td>0%</td></tr></tbody></table>

## 2.4. Data analysis and typology

First, we developed a typology of the study farms, to assess interest and adoption levels based on farm and farmer characteristics (Bellon et al., 2001; Blazy et al., 2009; Maseda et al., 2004). A principal component analysis (PCA) was performed using quantitative variables, and a multiple correspondence analysis was performed using qualitative variables (MCA). Based on the results, we performed an ascending hierarchical classification (AHC) using the Ward method and a four-class truncation to obtain more balanced size classes. The analyses were conducted using XLSTAT version 2018.1.1 (Addinsoft, 2018). Each farm and farmer were then assigned to one of the four types. The resulting farm types were further described based on farm and farmer characteristics and preferences regarding flower strip innovation. We performed ANOVA and Chi-square tests to identify the most important variables (see Table 1). To assess significant differences between types, we conducted post hoc Tukey and pairwise Fisher tests using R (version 4.4.0) (R Core Team, 2023). Finally, we analyzed constraints and opportunities to facilitate adoption and farmers' preferences for service plants traits and species similarly.

## 2.5. Adapting the crop management system to farmers' preferences

In order to adapt the system to farmers' constraints and expectations, we reviewed the structure of the cucumber cropping system at various level (service plants, field). The goal was to have a cropping scheme compatible with farmers decision-making models and effective in terms of both biocontrol and yield (Blazy, 2024; Blazy et al., 2009). First, we needed to identify service plant with traits compatible with farmers' preferences. For example, for farmers looking for minimal work load, it was necessary to find species requiring little work for sowing and maintenance. Second, we needed to consider the integration of service plants according to a spatial structure that was compatible with farmers expectations (Celette et al., 2024). Several patterns for integrating service plants are indeed possible and we had to choose the pattern that most fits farmers resources: in mixed crops, in flower strips, in crop rotation or patches. Finally, the integration of service plants would require adjusting to practices for managing cash crops, such as tillage strategy, sowing date, and planting density. An important point with such adjustments is to ensure overall consistency within the system, which for the farmer is often a holistic take reflecting both technical
consistency (practices are compatible with each other) and strategic consistency (the system is consistent with the farmer's objectives). A workshop with interviewed farmers and local stakeholders was organized to present the results of our diagnosis and discuss the adaptations to be made to the system to best fit farmers.

# 3. Results

## 3.1. Farmer typology

The results of our analysis produced a typology with four farm types: Types 1 and 3 each included 6 farmers, Type 2 included 11 farmers, and Type 4 included 7 farmers (Table 1).

Type 1 accounted for most organic farmers (83 %). Most Type 1 farmers were interested in the flower strip innovation and were also keen to participate in the experiments (83 %). Half of them were currently in a position to implement innovation directly on the farm, given their current constraints. Innovation adoptability was marginally significant (P = 0.098; Table 1). These farmers had the highest knowledge of service plants and biological control, both highly significant variables (P < 0.0001 Table 1). Type 1 included younger farmers, with an average of 45 years. Most of these farmers had a formal higher education level (67 %) and were farming full-time. The education and farming time variables were marginally and highly significant, respectively. Only a small proportion of land are owned by farmers (18 %), and half of them declared cash flow issues. The average farm size was approximately 9 ha, of which an average of 21 % was used for the market garden. Farm diversity was highest in this group, with an average of 4.5 crop types and 7.3 market gardening crops. All crop associations were practiced, and half of the farmers used mechanized tillage. Half of these farms experienced a high impact of *D. hyalinata*, but no chemicals were used for control.

Type 2 included mainly conventional farmers (91 %). Only half of these farmers were interested in flower strip innovation (55 %), but a majority were interested in experimenting with it (82 %). Type 2 farmers had a lower capacity to implement the solution directly (Table 1) and had significantly less knowledge about service plants and biological control. They were also significantly older, with an average age of 58 years. Type 2 farmers also had a lower level of education overall and had fewer cash flow issues, with only 27 % reporting them. Type 2 farms were generally larger (12 ha) but had a much lower level of diversification, with an average of 3.6 crop types and 4.7 market gardening crops. Farmers in this group practiced significantly less crop association (18 %) and had a higher level of mechanized tillage (83 %). The majority of these farmers experienced a high impact of *D. hyalinata* (83 %) and used chemicals for control (73 %).

Type 3 includes large farms with an average area of 31 ha, with 36 % dedicated to market gardening. Farmers in this group had a higher percentage of land ownership in terms of land tenure (66 %) than other types and a moderate level of cash flow problems (33 %). The majority were highly interested in innovation (83 %) and its experimentation (100 %). Considering their current constraints, half of the farmers claimed to have the capacity to implement innovation. Type 3 exhibited levels of service plants and biological control knowledge similar to those of Type 1, with scores of 3.5 and 2.8, respectively (scale from 0 to 4). Type 3 farmers were about the same age as Type 2 farmers, with an average age of 62 years. Half of Type 3 farmers had a higher education level, making them intermediate between Types 1 and 2. Despite the large farm size, Type 3 farms had a low level of diversification, with an average of only 2.5 crop types and 4.7 market gardening crops. One-third of the farmers had organic certification, again placing them at an intermediary level between Types 1 and 2, as was also the case for crop associations. Type 3 farmers were less affected by *D. hyalinata*, with only 33 % being highly impacted by the pest. In line with this result, only one-third of these farmers used pesticides for control, which is the lowest rate after Type 1.

Type 4 included individuals who had multiple jobs in addition to farming. They differed from the other types by spending significantly less time farming, with an average of only 49 % (Table 1). Less than half of the farmers were interested in innovation (43 %), but a majority were interested in experimenting on farms (86 %). Type 4 farmers showed a complete inability to implement flower strip innovation because of current constraints. They also had significantly lower knowledge of service plants and biological control, with scores of approximately 1.4 and 0.7, respectively, which were similar to those of Type 2. Type 4 farmers were generally young, with an average age of 44 years. In this group, 29 % had attained a higher education level, placing the group at an intermediate level. One-third of the farmers owned their farms (36 %), which was half the ownership rate of Type 3 but still higher than that of Types 1 and 2. They reported cash flow issues (43 %) less frequently than did Type 1 but more frequently than did the other types. The average farm size was approximately 11 ha, with approximately 30 % of the land used for market gardening. Type 4 farms had a good level of diversification, with an average of 4 crops types, similar to Type 2, and 5.4 market gardening crops. One-third of the farmers had organic certification. The crop association level (14 %) was similar between Types 2 and 3 and showed high use of mechanized tillage (71 %). Half of the farmers were highly impacted by *D. hyalinata* (43 %) and used chemical treatments against caterpillars (43 %).

## 3.2. Constraints and opportunities

We assessed the frequency at which factors were cited as constraints to adoption by farmers (Fig. 2A). The main constraint was the increase in workload due to adoption, which was mentioned by 77 % of farmers. The second major constraint (for 67 % of interviewees) was the lack of confidence in innovation, which includes both a lack of practical knowledge and insufficient proof of efficiency. The third constraint was the risk of economic losses (60 % of farmers), reflecting concerns about risk aversion and cash flows. The fourth factor was aversion to change, which was cited by less than half of the farmers (43 %). Finally, access to service plant seeds was the least-mentioned constraint, mentioned by only 17 % of farmers.

Among the potential opportunities to facilitate adoption, “access to knowledge and training” was relevant to 70 % of farmers (Fig. 2B). “Service plant traits” was the second most important factor for adoption (30 %), as farmers indicated that their level of interest varied depending on the available plant species. “Access to economic incentive” was mentioned by 27 % of farmers. In line with expressed constraints, “access to seed” was a concern for 17 % of farmers. In contrast to constraints, “access to manpower” was mentioned by only 10 % of farmers. We also evaluated whether constraints and means of action differed according to farmer type. None of the Chi-square tests were significant for any of these factors, indicating that perceptions of constraints and opportunities did not differ significantly across farmers. Despite the lack of significant differences between farmers, we highlight trends in our sample: Type 3 farmers had a lower aversion to change (17 %) than the other groups (approximately 50 %). However, they had a higher concern for “access to seed” (33 % vs. 15 %). Type 2 farmers were more interested in economic incentives (45 % vs. 17 % vs. 14 %). Finally, facilitating access to manpower was not considered a means of action by Type 3 and Type 4 farmers (0 % of claims).

## 3.3. Farmers' preferences for plant traits

We assessed the plant traits that farmers cited as beneficial (Fig. 3). The main trait sought by farmers was the “ease of management” of service plants (57 %). This trait refers to both “ease of sowing” and having a “non-competitive/non-invasive plant.” This trait was more important for Type 1 farmers (83 %). The next most important plant traits were related to profitability and included plants that were “cheap,” “remunerative,” and “profitable” (47 %). This preference was
<figure><img src="image_5.png" /><figcaption>Fig. 2. Constraints (A) and opportunities (B) identified by farmers and percentage of farmers agreeing (n = 30).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Service plant traits sought by farmers (n = 30).</figcaption></figure>

most exhibited by Type 4 farmers (86 %). Farmers also sought plants that could attract auxiliaries, mainly bees, and had the capacity to regulate pests (40 %). This concern was most expressed by Type 2 (55 %) and Type 3 (50 %) farmers compared with Types 1 (17 %) and Type 4 (29 %) farmers. At a much lower level of interest, 20 % of farmers also looked for ornamental plants and 13 % for multiservice plants. Pairwise Fisher tests did not yield any significant differences between the four farmer types, suggesting strong homogeneity in farmers' expectations and needs regarding plants. Among service plants species suggested to farmers, *Ocimum basilicum* was the species that interested farmers the most (70 %), closely followed by *C. sulphureus* (Fig. 4). *C. ensiformis* and *B. pilosa* were the plant species that pleased farmers the least (37 % and

33 % respectively).

3.4. Design of the new crop management system with service plants

Taking into account farmers’ constraints to adoption of service plants and preferences, we designed a system with a mix of service plants that best meets the agricultural challenges and expectations of farmers. Farmers clearly prefer integration of servicer plants as a flower strip and not as intercrop. The designed strip consists of a central line of *C. sulphureus* surrounded by a mixture of *B. pilosa* and *O. basilicum*, and casual patches of *L. alba* (Fig. 5). All of these plants exhibit a perennial growth habit, allowing the flower strips to establish sustainably and requiring only a single installation step. The species have the advantage of self-sowing, propagating by cuttings or layering, they are growing quickly, and require minimal maintenance once established. These species are all known for their ability to recruit parasitoid or enhance parasitoid fitness (Chau et al., 2019; Laffon et al., 2022; Ndakidemi et al., 2022; de Venâncio et al., 2016). They offer a good fit with farmers preferences: they are all melliferous, *C. sulphureus* is ornamental, *L. alba* is used for medicinal purposes, and *O. basilicum* is an aromatic plant – the two latter species might thus provide additional income. Although *B. pilosa* is the least interesting species for farmers, it is already present in agroecosystems and possesses the most promising agronomic traits for parasitoids recruitment. Cucumber management system was thus adapted to make integrating the flower strip possible and easier, and thus enhance parasitoid attraction: sowing density was increased, cumcumber plantation date was postponed and pesticide use was removed (Table 2). This innovation is currently tested on station and on-farm with volunteer farmers, with the aim of acquiring data on performance and help with wider dissemination locally to market gardeners.

<figure><img src="image_7.png" /><figcaption>Fig. 4. Service plant species preferences by farmers (n = 30).</figcaption></figure>
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Design of a cucumber crop management system with service plants for biological control of *D. hyalinata* tailored to farmers’ preferences and constraints. A) conceptual schema of the system. B) photo of the designed system on an experimental plot at the research station (right), and of farmers visiting the setup (left).</figcaption></figure>

**4. Discussion**

Our results highlighted four farmer profiles among cucumber pro-
ducers, differing along a continuum of on-farm crop diversity, educa-
tion, knowledge of auxiliaries and service plants, production system
structure, and perception of innovation. Moreover, we documented an
important difference between interest in innovation and adoption.
Indeed, even if a majority were interested in integrating service plants
for the natural regulation of *Diaphania hyalinata* (43 %–83 % depending
on the type), only a small percentage claimed the ability to permanently
adopt a flower strip innovation (from 0 % to 50 % depending on farmer
type). The difference between interest and adoption feasibility depen-
ded on both farm and farmer characteristics. Specifically, prior knowl-
edge of biological control may be a key driver of adoption by farmers.
Our results also demonstrate that even if farmers have different profiles,

they exhibit common perceptions of constraints and opportunities for
adoption and preferences regarding service plant traits. A transition
toward sustainable cucumber production systems in the Antilles relies
on providing efficient information diffusion and training on biological
control and answering issues pertaining to production constraints (low
economic profitability and difficult access to agricultural workforce).
We further discuss the relevance of the four profiles in relation to flower
strip innovation, then address the issue of plant traits and how they
could affect adoption, before suggesting ways to design cropping sys-
tems that support farm transition while gaining better acceptance from
farmers.

Farmers naturally have different attitudes toward innovative crop-
ping systems (Rizzo et al., 2024), and our results confirmed this pattern.
In general, integrating innovative practices is associated with specific
factors, such as age ((Baffoe-Asare et al., 2013; Blazy et al., 2011; Li
<table><caption>**Table 2**
system adaptations tailored to farmers preferences.</caption><thead><tr><th rowspan="2">Farmers' preferences</th><th colspan="2">System adaptations</th></tr><tr><th>Service plant level</th><th>Field level</th></tr></thead><tbody><tr><td>Ease of management</td><td><ul><li>easy-to-sow and easy-to-remove species</li><li>perennials species</li><li>local and abundant species</li></ul></td><td><ul><li>central flower strip</li><li>system suited to mechanization</li></ul></td></tr><tr><td>Maintain of farm income</td><td><ul><li>low risk for competition with cucumber</li><li>supplementary income (honey production, ornamental landscaping for the development of educational farms, medicinal and edible plants)</li></ul></td><td><ul><li>increase in cucumber planting density</li><li>stop or reduction of pesticide use</li></ul></td></tr></tbody></table>

et al., 2023)), education level (Despotović et al., 2019), technical expertise (Allahyari et al., 2016; Timprasert et al., 2014), and farm wealth (Anderson et al., 2005; Luis et al., 2023). The last factor is more difficult to assess because it is linked to both economic capacity for innovation and greater resilience and buffering ability to risks. As a result, it is sometimes estimated using independent and weak proxies (Roussy et al., 2015). Our results align with expectations from the literature because younger farmers (associated with Type 1) scored higher in innovation adoption, as did groups with greater knowledge of service plants and conservation biological control (Types 1 and 3). Although Type 3 farmers were generally older in our sample, they operated larger, mostly conventional farms that were relatively financially stable.

However, farmers had very different perceptions of how biological control could benefit agriculture. Types 1 and 3 farmers were the least interested in flower strip innovation, but for different reasons. Type 2 farmers were both fundamentally conventional in practice (heavily reliant on chemical control) and older (associated with a shorter planning horizon) (see Prokopy et al., 2008), and they likely see no reason to change. Meanwhile, Type 4 farmers are heavily constrained by cash flows from farming and depend on external sources of income. These constraints make implementing innovations more time-consuming and therefore beyond reach (Abdulai and Huffman, 2014; Luis and Aubert, 2023). This type of farmer may thus be more easily convinced by policy and financial incentives (Zhang et al., 2018). In summary, while the transition to cleaner production systems requires political commitment, financial policies would quickly help Type 1 farmers, who are already convinced of the ecological benefits of conservation control and ecosystem services by service plants, and possibly Type 4 farmers, who face the greatest challenges in cash flow and labor availability. Type 3 farmers may adopt these practices when integrated into on-farm experimental frameworks and pilot farms because they most need scientific evidence.

In our sample, service plants received contrasting levels of appreciation, with excellent knowledge among farmers most inclined to adopt innovation. Meanwhile, farmers did not significantly differ in their perception of constraints, benefits, and preferences for specific plant species characteristics. The most important constraint to expanding the reach of innovative agro-ecological systems is managing the potential increase in workload, service plant traits, and labor costs. Farmers primarily seek plants that are easy to manage. They prefer local plants that are readily available, easy to sow, adapted to local pedoclimatic conditions, capable of reseeding behaviors, and noncompetitive and noninvasive (Fig. 3). Surprisingly, although facilitating access to manpower may seem an important means of transition, it is seldom mentioned. Farmers do not consider access to labor an opportunity for adoption because they see it as “expensive,” “poorly qualified,” or “beyond reach.” Similarly, conservation biological control was regarded in a highly contrasting manner and mostly suffered from a lack of formal

understanding. Therefore, information, training, and field demonstrations should be improved to increase the use of auxiliaries in the context of pesticide reduction. Farmers need concrete evidence of the actual benefits of integrating service plants, including the benefits gained and the services provided. For farmers, the landscape is not a source of auxiliaries, but rather of pests (Brévault and Clouvel, 2019), and this perception can be corrected (Mkenda et al., 2020). Ultimately, information flows must address common misperceptions and assess the unintended effects of service plants, such as the increase in pests and diseases (Lu et al., 2014).

Our results highlighted that both constraints and opportunities regarding service plant use were closely linked to the crucial need for information and technical support for systems that were neither time-consuming nor labor-intensive (Fig. 2). We documented that integrating service plants goes hand in hand with ease of management and that profitability comes first before ecosystem services, including attracting auxiliaries (Fig. 3). As a general rule, the first step is to choose plants that pose little risk in terms of compromising cash crop yields so that, in the event of a negative experience, the “service plant” innovation is not rejected outright. An important step would be to select plants that do not host polyphagous pests (Celette et al., 2024) or serve as reservoirs for pathogens (Dentika et al., 2022) and that can be quickly destroyed, allowing farmers to test them without jeopardizing future harvests. Even if not frequently mentioned in our sample, redesigning systems with plants that provide multiple services is likely the best guiding principle (auxiliary attraction, pollinator attraction, or even melliferous resource provision, green manure, wind break, aromatic sales, and even esthetic ornamentals). The more services a plant provides, the better its chances of being adopted by a diverse population of farmers. Farmers are more inclined to adopt innovations that match their personal preferences and goals (Huber et al., 2024; Kuria et al., 2024).

At a larger scale, structuring the market gardening sector would enable us to better meet farmers’ expectations and ease constraints on adoption. Aversion to change, expressed by nearly half of the farmers, is largely correlated with the hurdles mentioned (Fig. 2). Even if some farmers are genuinely averse to change because of their age or beliefs, the majority are simply reluctant to redesign their systems. A sustained agricultural extension sector could provide farmers with better access to knowledge by pooling information, producing technical support, providing training, offering administrative assistance for existing subsidy schemes and creating resources such as a service plant seed bank with a formal distribution scheme (Yu et al., 2023). As stated by (Duru et al., 2014), the profound ecological modernization of agriculture requires the implementation of both agronomic innovations rooted in components of local bio- and agro-diversity, as well as new ways of coordinating and learning among stakeholders in the diverse sectors and territories.

Institutional and policy tools can help promote the adoption of service plant practices (Day et al., 2022). First, service plants need to be made economically attractive. Regulatory measures like pesticide restrictions can also trigger broader adoption, while financial incentives and eco-labelling can offset costs and add market value through advertised environmental benefits (Lefebvre et al., 2015). Last, remunerating farmers for environmental services (e.g., biodiversity conservation, sparing water resources), as a rewards for on farm environmental services, is another economic tool to encourage the adoption of service plants (Mota et al., 2023). This transition could also be supported by behavioral nudges, which are non-monetary incentives guiding farmers toward eco-friendly practices by appealing to social image and peer influence (Bollinger et al., 2022). Simple measures like informative farm signs could promote farmers using service plants over chemical inputs. Finally, creating a socio-economic and institutional ecosystem favorable to the adoption of service plants is essential to transition toward sustainable agriculture. This involves adapting supply chains to inform farmers and provide access to resources enhancing their skills in
choosing and managing systems with service plants (personalized advice, technical training, tools to help choose service plants). This involves also adapting upstream and downstream agricultural production to provide access to the required inputs (seeds in particular) and equipment, as well as to enhance the economic value of production from systems with service plants. Local farmer networking, driven by pioneering and innovative farmers to exchange seeds and knowledge, could be a lever to strengthen and ease the spread of service plant innovations (Asprooth et al., 2023; Lin et al., 2021).

## 5. Conclusion

This study assessed, ex ante, the willingness of market gardening farmers to adopt service plants integrated into plots as flower strips in the specific context of involving the natural regulation of the melon worm, *D. hyalinata*, by recruiting local parasitoids wasps. First, our study highlights differences among farm types in terms of interest in innovation and adoption. Farmers with higher education and knowledge levels demonstrate greater interest and adoptability, in contrast to those with a conventional approach to agriculture or with cash flow issues, who are more reluctant to integrate service plants. However, even though farmers differ in adoption willingness, they share common perceptions of hurdles: increased workload, lack of information, and putative economic loss. Suitable service plants could help overcome these constraints, as farmers are focused on plants that are easy to manage and have profitable traits. Facilitating the adoption of service plants associated with increased levels of natural regulation across the territory requires a communication effort and improved information access. This study laid the foundation for designing agro-ecological systems tailored farmers' needs and constraints, and we defined a self-sustaining flower strip with increased natural regulation potential and putative side income. Our new system is currently being tested experimentally.

### CRediT authorship contribution statement

**Margot Gumbau:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Johanna Kupka:** Software, Methodology, Investigation, Formal analysis, Data curation. **Laurent Penet:** Writing – review & editing, Supervision, Funding acquisition. **François Causeret:** Supervision, Investigation, Conceptualization. **Jean-Marc Blazy:** Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### 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 work was supported by the National Institute for Food, Agriculture and Environment (INRAE) SumCrop metaprogram (MP), Plant Health and Environment (SPE) INRAE department, and Region Guadeloupe (PhD grant of Margot Gumbau). The authors gratefully acknowledge the farmers of Guadeloupe who participated in this project. The authors would also like to acknowledge the members of the work team who participated in the field surveys (Aymeric Beugnon, Tim Hogge, Eric Bandou, and Marie Nazaire Robin).

### Data availability

Data will be made available on request.

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