Organic vs. conventional cocoa farming management: what is the relationship between agrobiodiversity, farming practices and cocoa tree health in Côte d'Ivoire?

Organic vs. conventional cocoa farming management: what is the relationship between agrobiodiversity, farming practices and cocoa tree health in Côte d'Ivoire? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Organic vs. conventional cocoa farming management: what is the relationship between agrobiodiversity, farming practices and cocoa tree health in Côte d'Ivoire? Marie Thérèse Alloua Morrisson, Stéphane De Tourdonnet, Laciné Fofana, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6295383/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The most widespread plant health management approach is pathogen-focused and relies on systematic, often excessive, use of chemical inputs. Conversely, agroecology is gaining ground alongside the emergence of new salutogenic approaches based on an understanding of the factors that promote biodiversity-based plant health. To apply these approaches, it is thus essential to gain further insight into the role of agrobiodiversity, particularly its structure and composition, on plant health. Our study was conducted in cocoa-based agroforestry systems in Côte d'Ivoire, characterized by an agrobiodiversity gradient, varying in terms of species diversity, plant density and spatial distribution. These systems are managed by farmers using diverse agricultural practices based on their knowledge and experience, while also being influenced by the socioeconomic environment. We hypothesized that cocoa tree health could be enhanced via efficient agrobiodiversity management and hence that organic farming systems would be more advanced in this respect than conventional systems due to their closer alignment with biodiversity-based practices. We tested this by comparing management practices and shade tree diversity in both organic and conventional farming systems in a network of 84 cocoa plots. We then performed statistical tests on a subset of 37 plots to assess the relationship between agrobiodiversity and farming practices. Our study revealed that farming practices and their annual intervention frequency varied little between organic and conventional farming situations. However, the organic farmers relied on substitution strategy, replacing chemical inputs with organic inputs. Furthermore, our results indicated that the species richness could have a significant impact on cocoa tree health, particularly in organic farming systems. This raises fundamental questions about how agricultural practices influence biodiversity-health interactions and calls for further research to deepen our understanding of these dynamics. plant health farming practices agrobiodiversity farmers’ perception Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Cultivated plant health is primarily threatened by the combined effects of diseases, pests and abiotic factors such as drought, extreme temperatures and poor soil quality (Agrios 2005 ; Makundi 2006 ). These factors markedly reduce agricultural yields, thereby affecting the food security and economy of growing human populations (Agrios 2005 ; Makundi 2006 ; Oerke 2006 ; Rizzo et al. 2021 ). Moreover, these threats are exacerbated by climate change, which can alter the biological cycles of diseases and pests and increase cultivated plant susceptibility to both biotic and abiotic stresses (Elad and Pertot 2014 ). Since the 1960s, plant health management has primarily relied on chemical inputs, including biocides and fertilizers, which has led to their widespread systematic application in agroecosystems. This scenario is in line with the conventional agriculture model, where productivity constraints (limiting and reducing factors) are mostly compensated by chemical and organic (Duru et al. 2015a ). This model is now being challenged due to the negative externalities it produces and the technical deadlocks encountered in controlling certain pests and diseases. Biodiversity-based Agriculture is an alternative by compensating for these limiting factors through the leveraging of ecosystem services, such as soil fertility and biological regulation, which are primarily driven by the associated biodiversity within the agroecosystem (Deguine et al. 2023 ). To ensure a smooth shift from conventional agriculture inputs to biodiversity-based agriculture, it is essential to have clear insight into the role of agrobiodiversity (Duru et al. 2015b )—in both its composition and spatial structure aspects— in the ecological processes underlying access to resources (water, nutrients, light), as well as disease and pest regulation. The conventional plant health management approach involves a so-called pathogenic paradigm, focusing on identifying adversities—e.g. diseases, weeds, pests or limiting abiotic factors—that threaten cultivated plant health (Vega et al. 2020 ). Subsequently, interventions aim to restore plant health by overcoming a succession of factors that keep plants from achieving their full genetic potential as crops (Cook 2000 ). The use of inputs provides an immediate and seemingly effective way to overcome these adversities. In this context, a ‘healthy plant’ refers to a state where the plant is free of pathogens or symptoms associated with these adversities (Döring et al. 2012 ). Given the impacts of current plant health management strategies, it is crucial to explore new paradigms for assessing and designing sustainable cropping systems that do not rely on inputs exclusively. The salutogenic approach—originally developed for human health—provides a holistic analytical framework that may be applied to plant health (Antonovsky 1980 ; Döring et al. 2012 ). Vega et al. ( 2020 ) proposed to tailor Antonovsky's (1980) human health principles to plant health. This approach advocates for a holistic view of plant health, considering the biological, economic, and social environments in which the cropping system operates, as well as farmers’ knowledge, vision and goals. Health is thus regarded as a continuum, i.e. a process geared towards achieving good health. The ability to maintain and enhance progress towards health depends on the extent to which the whole situation and the ability to use available resources to solve the problem is understood (Mittelmark et al. 2017 ). In salutogenesis, the focus is on the individual's ability to use internal and external resources available to manage ubiquitous stressful situations. This model contrasts with the standard pathogenic model focused on risk factors and mechanisms involved in disease onset, and instead addresses the question: why do some individuals, despite stressful situations and difficulties, remain healthy? The health concept in crop protection—viewed from an agroecological perspective—can build on this salutogenic model. The ability to maintain and improve the health process hence depends not only on the intrinsic properties of plants (induced defense system, phenotypic plasticity, symbiotic interactions, functional traits) that enable them to respond to stress, but also on the grower's capacity—as a key agroecosystem player and manager—to leverage the resources available in the immediate environment. Through appropriate agrobiodiversity management practices, farmers can optimize ecological function that enhance ecosystem services that are essential to insure accessible yields. Consequently, management strategies should be approached at the agroecosystem level rather than solely at the individual plant scale, as plant health is intrinsically linked to the overall functioning of the agroecosystem. Plant health should therefore be assessed from a systemic standpoint (Döring et al. 2012 ) as it is the result of complex interactions between: (i) biophysical factors such as the plant system, pests and diseases, soil, associated biodiversity, climate, and (ii) agricultural practices which are dependent on the socioeconomic conditions of the concerned farming households. To design ‘healthy’ agroecosystems, the dynamics of direct and indirect interactions between the system’s biophysical components must be clearly understood (Durand-Bessart et al. 2020), especially considering that farming practices influence these interactions (Durand et al. 2019). From an agroecological perspective, managing these practices requires continuous adaptation to system dynamics. In this context, identifying appropriate combinations of farming practices is essential to effectively steer biological regulations (Meynard 2017 ). The mechanisms and pathways underlying plant resistance and tolerance in the presence of pests and diseases, while maintaining them at non-harmful levels, should be investigated to achieve a systemic understanding of the process. This can be achieved through biodiversity management practices that enhance ecological processes supporting plant health. In this study, we hypothesized that the organization of biodiversity—i.e. its composition and vertical and horizontal structure—plays a pivotal role in maintaining ‘healthy agroecosystems’. In such agroecosystems, pests and diseases would not be an economically unacceptable yield-reducing issue for growers We argue that addressing these challenges requires a paradigm shift in crop protection. Instead of relying on chemical or biological pesticides, crop protection strategies should focus on leveraging biodiversity, which is crucial for designing healthy sustainable agroecosystems. From this standpoint, the following key questions should be addressed to enhance knowledge on the mechanisms involved: What ecosystem pathways and functions underpin the maintenance of healthy crop plants? How can biodiversity be organized to optimize these pathways? What biodiversity management practices ensure the maintenance of plant and agroecosystem health? What indicators can be used to assess the health of cultivated plants and agroecosystems? Furthermore, integrating farmers' perspectives and knowledge is essential in this process. Farmers are both actors and decision makers in daily agroecosystem management. These farmers have developed a high level of expertise based on their experience, history, habits and accumulated knowledge. The empirical knowledge they acquire can contribute to the development of sustainable context-specific cropping systems that take socioeconomic constraints into account (Doré et al., 2011). In this study, we focus on cocoa-based agroforestry systems in Côte d'Ivoire, i.e. the world's leading cocoa producing country, to explore these issues. Cocoa cultivation plays a crucial role in the Ivorian economy, accounting for 15% of the GDP, 40% of exports, and serving as a primary source of income for nearly eight million people. Cocoa-based agroforestry systems exhibit heterogeneity in terms of plant species composition and the spatial structure of the agrobiodiversity—both vertical and horizontal—associated with cocoa trees. Moreover, we assume that this plot organization is partly the result of farmers’ management conditions. The cocoa sector currently faces challenging issues such as declining soil fertility, the proliferation of pests and diseases, changing rainfall patterns, and the loss of forest resources, all of which are exacerbated by climate change (Stocker et al. 2013 ; Cilas and Bastide 2020 ; Mensah et al. 2024 ). Agrobiodiversity is thus a key pillar in addressing these environmental challenges, particularly due to the biological regulation services it provides. In this setting, we hypothesize that agrobiodiversity management is a key lever for improving cocoa tree health. Moreover, we compare two distinct farming methods: organic and conventional cocoa agroforestry, while assuming that the organic systems are closer to biodiversity-based agriculture, and we expect that certain characteristics of the associated biodiversity must play a role in cocoa tree health, more than in conventional agriculture systems (Fig. 2 ). 2. Materials and Methods 2.1. Study area The study took place in the Agnéby-Tiassa region, specifically at N’zianouan, in southeastern Côte d’Ivoire. The study area lies between latitudes 6.02°N and 6.10°N and longitudes 4.82°W and 4.76°W (Fig. 3 ). It is under a sub-equatorial climate regime characterized by four distinct seasons: a major rainy season from March to June, followed by a short dry season from July to August. There is also a minor rainy season from September to October, followed by a major dry season from November to February (Gnangouin et al., 2023 ). Annual rainfall in this area ranges from 1,200 to 1,600 mm, with average annual temperatures of 25°C to 30°C. This area is characterized by a ferruginous soil with a sandy texture, low clay content and thus a very friable structure. (Dabin et al. 1960 ; Perraud 1967 ; Blaud et al. 2016 ). 2.2. Cocoa plot sampling strategy The first step involved a preliminary random survey of cocoa plantations to select cocoa plots with contrasted characteristics in terms of agrobiodiversity associated with the cocoa trees and agricultural management intensity, while also reflecting the production system diversity in the study area. In each plot, data were collected on species associated with cocoa trees, including their vernacular and scientific names, abundance, and annual agricultural practice intervention frequency—this information was collected in the initial semi-structured interview with farmers. The assessed agrobiodiversity encompassed all remnant, spontaneous and cultivated plant species that are managed by the farmer. This agrobiodiversity included food crops, timber and fruit trees exceeding 1.5 m in height, with the exception of weeds. This sampling phase lasted from October 2022 to March 2023, while the data were collected on 84 plots. In order to represent a range of agricultural situations, 40 plots were selected according to associated agrobiodiversity and management intensity gradients. These plots included 20 that were managed under organic farming practices and 20 under conventional farming practices. To represent the agrobiodiversity gradient, we constructed a biodiversity-density (BD) index that served as the underlying metric, incorporating both species richness, as a measure of species diversity, and plant density, reflecting abundance and spatial distribution. The BD index was calculated using the following formula: $$\:\text{B}\text{D}\:\text{i}\text{n}\text{d}\text{e}\text{x}=\:\frac{{d}_{pi}-{d}_{min}}{{d}_{max}-{d}_{min}}+\:\frac{{S}_{pi}-{S}_{min}}{{S}_{max}-{S}_{min}}$$ The management intensity gradient was then developed based on the intervention frequencies of the different agricultural practices, including manual weeding, chemical weeding, cocoa tree pruning, sanitary harvesting, fertilizer application and pesticide application. The following formula was used to calculate this index: $$\:management\:intensity\:index=\:\sum\:\frac{{Pn}_{Pi}-{Pn}_{min}}{{Pn}_{max}-{Pn}_{min}}$$ These two indices relied on the methods developed by Karr ( 1991 ) and O’Connell et al. ( 2000 ), which were subsequently applied by Mas and Dietsch ( 2003 ) to characterize management intensities in coffee plots. The index construction method consisted of assigning an equal weight to each variable on a 0 to 1 scale, where 0 represents a system with a low management intensity or low agrobiodiversity diversity, and 1 represents a system with a high management intensity or high agrobiodiversity diversity. The overall index for each plot is calculated as the sum of the indices for all of the considered variables. The present study was successfully conducted on only 37 of the 40 selected plots. 2.3. Defining healthy and less healthy zones Individual semi-structured interviews were conducted between June 2023 and August 2023 with each of the 37 selected farmers to gather their perceptions of cocoa tree health. During these interviews, farmers were free to respond as they wished, mentioning as many health evaluation criteria as they deemed relevant. Based on these criteria, two distinct zones were identified within each plot: one where cocoa trees were generally considered to be in good long-term health (the ‘healthy zone’), and another where trees seemed to be in poorer health overall (the ‘less healthy zone’). These zones varied in size because they were defined according to the farmer interviews. 2.4. Variables measured for agrobiodiversity characterization The agrobiodiversity structure was characterized by conducting a detailed floristic inventory, whereby all plants contributing to agrobiodiversity, as previously defined, were catalogued within the 37 selected plots. The agrobiodiversity structure was defined as the spatial, vertical and horizontal, and quantitative organization of plants associated with cash crops, especially cocoa trees, in cocoa agroforestry systems, according to their physical characteristics and spatial arrangement (Ngo Bieng 2007 ). To characterize the vertical stratification of agrobiodiversity, each plant was classified into a height stratum (Table 1 ) and the trunk diameter at breast height (DBH) was measured to calculate the basal area. Moreover, the geographic position of each tree was determined using a GARMIN 64S GPS device to map the spatial arrangement of agrobiodiversity associated with the cocoa trees. The agrobiodiversity associated with each plot and within each healthy and less healthy zone was characterized based on two distinct components: (1) the species composition, representing the abundance of each associated species, and (2) the agrobiodiversity structure. The following variables, as detailed in Table 1 , were used to describe the agrobiodiversity structure: the associated agrobiodiversity density, species richness, mean height stratum, basal area, and spatial arrangement. Differentiating the species abundance from the species richness provided insights into the distribution of individual species within the sample. The species constituting the agrobiodiversity were categorized based on the socioeconomic services they offered to farmers. These categories are: Tubers , including yam ( Dioscorea spp.) and cassava ( Manihot esculenta ) Banana plants , including all Musaceae, while plantain ( Musa paradisiaca ) and sweet banana ( Musa spp. ) were classified in a distinct category due to their high abundance Commercially valuable species , including woody cash crops and plants producing fruits intended for both domestic consumption and marketing, e.g. citrus fruits ( Citrus spp. ), avocado ( Persea americana ), oil palm ( Elaeis guineensis ), mango ( Mangifera indica ), kola ( Cola spp. ), coconut ( Cocos nucifera ), guava ( Psidium guajava ) and papaya ( Carica papaya ), as well as cash crops like coffee ( Coffea arabica ), rubber ( Hevea brasiliensis ) and cashew ( Anacardium occidentale ). Other woody species , including species used for medicinal purposes and timber production (long-term economic use). Table 1 Variables measured for all agrobiodiversity plants in the 37 selected plots. Variables Units Local name - Scientific name of plant species - Plant species abundance Number of plants (n) Species richness Number of associated species/plots Associated plant density Number of plants/ha (plants ha-1 ) Basal area m² ha-1 Stratum Four levels of strata considered: Stratum 1: 0 to 5 m Stratum 2: 5 to 10 m Stratum 3: 10 to 20 m Stratum 4: above 20 m Spatial layout Regular, Random, Aggregate 2.5. Statistical analysis We used multivariate statistical methods to determine cocoa plantation typologies at the whole plot scale and the healthy and less healthy zone scale and thereby gain insight into the relationships between the different variables. Specifically, a multiple factor analysis (MFA) followed by clustering using the hierarchical classification on principal components (HCPC) method were conducted to obtain a cocoa plantation typology based on variables describing the associated agrobiodiversity structure at the 37 cocoa plantation scale and at the smaller healthy and less healthy zone scale. We then performed correspondence analyses using Fisher’s test to assess the links between the typology based on the agrobiodiversity structure, the management type (organic or conventional) at the plot scale, and the health status of cocoa trees in the identified healthy and less healthy zones. In addition, a non-parametric Kruskal-Wallis test was used to compare the different clusters based on a given variable. Statistical analyses were performed using R Studio software (version 2023.12.1). PCA and HCA analyses were performed with the FactoMineR version 2.9 (Lê et al., 2008 ) and ade4 version 1.7–22 (Dray et al. , 2007), respectively. The agrobiodiversity spatial layout was characterized using Ripley’s K function (Ripley, 1977), as implemented in the R package spatstat.exploreversion 3.2-5 (Baddeley and Turner 2005 ). 3. Results & Discussion 3.1. Overview of agrobiodiversity and farming practices This preliminary stage allowed us to identify 84 cocoa plots with diverse characteristics. These plots were randomly selected and represented according to the agrobiodiversity and management intensity gradient (Fig. 4 ). This sample consisted of 44 plots managed under organic farming and 40 plots under conventional farming, with average areas of 2.4 ± 1.6 ha and 2.2 ± 1.2 ha, respectively. Agrobiodiversity dominated by plants offering production services The floristic inventory identified 72 species distributed across 30 botanical families (Supplementary material S1). This number is relatively close to the 84 species documented by Konan et al. (2023) in the western Côte d'Ivoire. The average species richness was 21 species/plot in our sample, regardless of plot size. The average overall agrobiodiversity density associated with cocoa trees was 130 individuals ha − 1 (Supplementary material S2). This was lower than the values recorded in other tropical regions, such as Cameroon with 190 individuals ha − 1 (Jagoret et al. 2017 ) and Costa Rica with 350 individuals ha − 1 (Deheuvels et al. 2012 ). An examination of the agrobiodiversity composition revealed that it was dominated by Musa sp., with an average density of 50 individuals ha − 1 . This was higher than the densities of other species categories present. We recorded 30 individuals ha − 1 for commercially valuable species, 28 individuals ha − 1 for other woody species, and 29 individuals ha − 1 for tubers, Discorea sp. (yam) and Manihot esculenta (cassava) (Supplementary material S1). Furthermore, when comparing the two farming methods, the plant category densities were similar for commercially valuable species, with 30 individuals ha − 1 under conventional farming vs 28 individuals ha − 1 in organic farming plots, while for Musa sp. we obtained a density of 52 individuals ha − 1 in organic farming conditions vs 46 individuals ha − 1 under conventional farming. In contrast, for tubers and other woody species, the densities in organic farming plots were 50 individuals ha⁻¹ and 44 individuals ha⁻¹, respectively, compared to only 6 individuals ha⁻¹ and 11 individuals ha⁻¹ under conventional farming. The total agrobiodiversity density associated with cocoa trees was 164 individuals ha − 1 in organic farming plots vs 92 individuals ha − 1 under conventional farming (Supplementary material S2). Studies have shown that agrobiodiversity associated with cocoa trees is often dominated by Musa sp., followed by economically valuable species, including fruit and cash crops (Sonwa et al. 2007 , 2017 ; Deheuvels et al. 2012 ; Notaro et al. 2021 ). These latter species play a key role in increasing household income while also serving as subsistence crops. Musa sp. are frequently used to provide shade for young cocoa trees (Opoku-Ameyaw et al. 2012 ; Adejobi et al. 2022 ), thereby fostering their rapid growth and production. In areas of the plots where there was sufficient sunlight once the cocoa trees had reached maturity, some of these banana trees were maintained, which could also explain their predominance. Description of farming practices 91% of the farmers in this sample did not apply fertilizers, while 71% did not apply herbicides as they managed weeds manually and mechanically. Moreover, all farmers practiced manual weeding at least once yearly, and 70% did so thrice a year. 11% of the farmers did not use phytosanitary products for disease and pest management, whereas 54% conducted chemical treatments twice yearly. Cocoa tree pruning was performed by 84% of the farmers. The frequency of implementing these practices was lower than the recommendations in technical manuals in Côte d'Ivoire (Conseil du Café-Cacao 2015). However, this pattern was consistent with observations of Assiri et al. (2009), Siapo Yao et al. (2018) and Ano et al. (2018) who also reported implementation frequencies below the technical recommendations. Socioeconomic context as a driver of agrobiodiversity and farming practices The predominance of food and cash crops in cocoa plantations reflects an income diversification strategy adopted by producers to optimize their plantation profitability (Malézieux and Moustier 2005 ), particularly since the cocoa sector crisis in the late 1970s. In agroforestry systems, in addition to generating supplementary income, these species can also provide ecosystem services that promote the growth and productivity of cocoa trees in the stand of cocoa trees. Moreover, the preference for fruit tree species can help mitigate conflicts over tree ownership, particularly in Côte d’Ivoire, where tensions frequently arise between cocoa producers and timber loggers over the control and exploitation of valuable trees. As Sanial ( 2018 ) points out, fruit trees are generally of little interest to loggers and are not targeted for timber extraction, making them less prone to appropriation. Their integration into cocoa-based agroforestry systems thus represents a socially acceptable compromise for farmers wishing to plant trees while avoiding the risk of losing them to external exploitation. Consequently, the selection of associated species is shaped not only by economic incentives, but also by socio-political and land tenure considerations. In agroforestry systems, these species not only generate supplementary income but also contribute ecosystem services that support the growth and productivity of cocoa trees. Moreover, the preference for fruit tree species may help reduce conflicts over tree ownership, especially in contexts like Côte d’Ivoire, where tensions often arise between cocoa producers and forestry authorities. Because fruit trees are generally of little interest to forest officials, their integration into cocoa-based systems can act as a socially acceptable compromise. The choice of associated species is thus shaped by both economic and socio-political factors. We consider it relevant to asses the implications of these crop choices on cocoa tree health. To what extent does the structure and composition of agrobiodiversity associated with cocoa trees influence their health? Although shade trees are often studied with regard to their impact on cocoa tree performance indicators, it is also essential to integrate food and cash crops in the analysis in order to broaden the understanding of biological interactions in cocoa production systems. This analysis aims to broaden the understanding of biological interactions within cocoa production systems. Given the complexity of these interactions, a systemic approach is needed. This approach must consider not only the potential agronomic benefits, but also the technical and organizational constraints faced by producers. These constraints are further reinforced by the need to meet market standards, particularly those imposed by technical institutes and private certification schemes. Ruf et al. ( 2013 ) showed that the technical lessons promoted through certification are primarily based on input use, with an emphasis on facilitating access—either through subsidized credit, or by donating inputs as a reward or incentive. This approach tends to standardize farming practices and may discourage the adoption of more context-specific or agroecological alternatives. However, these technical prescriptions, often designed without genuine consultation with the producers, clash with the realities on the ground. Indeed, our results showed that the recommendations put forward by these organizations are not followed by farmers in the field, as noted by Ruf et al. ( 2013 ) and Gboko ( 2020 ). This reflects a gap between institutional requirements and the practical constraints faced by farmers. This questions the role given to farmers in technical innovation or in the promotion of sustainable agricultural practices. The effective adoption of these practices depends not only on their agronomic and economic relevance but also on their compatibility with production situations. Better integration of local knowledge and use of a participatory approach could facilitate the development of more suitable agroecological technical solutions that are adapted to the realities of cocoa farming systems. 3.2. Organic vs conventional management at the cocoa farm level The general description of the 84 screened plots revealed a total agrobiodiversity density of 164 individuals ha -1 in the organic plots, compared to 92 individuals ha -1 in the conventional plots. The selection (Fig. 4 ) aimed to minimize the differences between the plots by grouping organic and conventional farming plots with similar management intensity levels and agrobiodiversity gradients. The objective was to have comparable plots in terms of management and agrobiodiversity associated with cocoa trees. The results presented in this section revealed a small difference in terms of agrobiodiversity density associated with cocoa trees, but a significant difference between plots managed by these two strategies in terms of species richness. This suggests that the agrobiodiversity species diversity had a greater impact. Relationship between the agrobiodiversity structure and the farming methods For our 37 plots subsample, a second floristic inventory was conducted that excluded food crops such as Dioscorea sp. (yam) and Manihot esculenta (cassava), with 63 species distributed across 27 botanical families identified. These cocoa plantations contained 86% of the species richness of the initial sample. Musa sp. and commercially valuable species were dominant in this sample, with an average density of 57 individuals ha -1 and 45 individuals ha -1 , respectively. Other woody species had a density of 27 individuals ha -1 . Several significant differences were noted when we compared the organic and conventional farming plots (Supplementary material S3). The species richness was significantly higher in the organic plots than in the conventional plots, with values of 26 (± 10) and 17 (± 7), respectively, indicating that there was greater plant species diversity in organic agroforestry systems than in conventional ones. Similarly, the average height stratum of agrobiodiversity was significantly higher in the organic plots, i.e. 2.01 (± 0.38) as compared to conventional plots with 1.54 (± 0.31). Furthermore, the woody species density was significantly higher in organic plots, i.e. averaging 37 individuals ha -1 (± 30), compared to 16 individuals ha -1 (± 21) in the conventional plots. These two differences may be explained by the fact that in organic agriculture conditions farmers are encouraged to maintain timber species in cocoa plantations in order to promote agroforestry systems and enhance the agronomic services these species provide to cocoa trees (Hatimy 2024 ). Conversely, the conventional farmers in our sample were not affiliated with any cooperative and received little technical support. They were also not subject to the same requirements as organic farmers regarding the number of associated trees ha -1 , as they had not undergone any certification. Otherwise, there were no significant differences between the two management modes regarding other variables, such as the total agrobiodiversity density associated with cocoa trees or the density of economically valuable species. This suggests that all farmers were equally inclined to diversify their income sources, regardless of their chosen cocoa farming method, as has been observed in different cocoa-producing countries (Jagoret et al. 2009 ; Notaro et al. 2020 ). Three clusters of plots emerged from the HCPC analysis (Table 2 ). The S1-Low group, consisting of 10 plots (6 in conventional farming), was characterized by relatively low species richness compared to the other groups. The plants in this group were typically smaller and thinner, with a basal area of 4.9 m² ha -1 and a less developed vegetation stratum (on average 1.64). Their spatial arrangement was entirely random throughout all plots. Conversely, the S2-med group, consisting of 15 plots (9 in conventional farming), had average species richness and basal area values of 20 and 6.2 m² ha -1 , respectively. The plants had an intermediate stratum height of 1.68 and they had an aggregated spatial arrangement. Finally, the S3-high group, consisting of 12 plots (10 in organic farming), was characterized by a significantly higher species richness of 32 and a basal area of 14.2 m² ha -1 . Plants in this group were taller and occupied a higher vegetation stratum, with an average stratum height of 2.08. 100% of the plots in this group had an aggregated spatial arrangement. Table 2 Clusters of 37 cocoa plots based on agrobiodiversity structure variables Variables S1-Low S2-Med S3-High Total Significance (N = 10) (N = 15) (N = 12) (N = 37) Mean ± SD Mean ± SD Mean ± SD Mean ± SD Management mode Organic 4 (11%) 6 (16%) 10 (27%) 20 (54%) 0.058 • Conventional 6 (16%) 9 (24%) 2 (0,05%) 17 (46%) Total density 114 ± 88 NS 114 ± 49 NS 171 ± 72 NS 133 ± 72 0.066 • Species richness 14 ± 8 c 20 ± 5 b 32 ± 8 a 22 ± 10 < 0.001*** Average stratum height 1.61 ± 0.48 b 1.68 ± 0.31 bc 2.08 ± 0.35 a 1.79 ± 0.42 0.007** Basal area 4.9 ± 4.0 b 6.2 ± 2.6 bc 14.2 ± 4.8 a 8.5 ± 5.5 < 0.001*** Spatial layout Aggregate 0 (0%) 15 (100%) 12 (100%) 27 (73%) 0.1. • for p < 0.1 (trend toward significance); p < 0.05*; **p < 0.01; ***p < 0.001. The Fisher's test correspondence analysis highlighted a marginal significant correlation (p = 0.058) between the management mode and the biodiversity structure associated with cocoa trees. Cocoa plots under conventional management were primarily found in the S1-Low and S2-Med clusters. In contrast, the S3-High cluster was predominantly composed of plots under organic management. The S3-High cluster had greater agrobiodiversity, featuring a species richness of 32, taller trees with broader trunks. Note that it has been demonstrated that organic agriculture promotes biodiversity in agroecosystems (Bengtsson et al. 2005 ; Jacobi et al. 2015 ; Reganold and Wachter 2016 ) and that this trend more marked when organic agriculture is combined with agroforestry (Rosati et al. 2021 ). Indeed, our results indicated that cocoa plantations managed under organic agriculture had richer agrobiodiversity with a higher vegetation stratum, as also observed by Jacobi et al. (2015). Furthermore, Ratnadass et al. ( 2012 ) demonstrated that agrobiodiversity in agroecosystems enhances disease and pest regulation. For example, some observations have suggested that higher species richness may be associated with increased cocoa productivity (Jagoret et al. 2017 ; Saj et al. 2019 ), while the cocoa pod fungal disease intensity tends to decrease as the agrobiodiversity richness increases (Gidoin et al. 2014 ). It is therefore likely that healthy cocoa trees would prevail in plots with characteristics close to those found in the S3-High cluster. Otherwise, Notaro et al. ( 2021 ) and Saj et al. ( 2023 ) further investigated this prospect by analysing the effects of agrobiodiversity through its spatial organization by taking the tree height and proximity to cocoa trees, as well as the interplay between distance and species composition, into account. Our findings open up new avenues for exploring relationships between agrobiodiversity organization, cocoa tree health and farming practices. The present study is an initial step towards understanding the combined effects of these factors on tree health. Further in-depth analysis of individual agroecosystem components and their interactions would be essential to identify key drivers of plant health and gain further insight into the complexity of the role of agrobiodiversity in biological regulation (Mortimer, Saj & David, 2018 ). Regarding agricultural practices, the conventional farming plots differed very little from the organic farming plots (Supplementary material S3). Organic fertilizer application was practiced by two conventional farmers and seven organic farmers. No significant differences in fertilizer use (application frequency and fertilizer type) were noted between organic and conventional management. Among these farmers, three practiced composting, three applied biostimulants, and one solely applied decomposed cocoa pod husks—a method that was also used by two conventional farmers. However, only two conventional farmers used chemical fertilizers, whereas twelve organic farmers and thirteen conventional farmers did not use any fertilizers. The cocoa boom in Côte d’Ivoire took place following extensive forest clearings starting in the 1930s, which resulted in the release of nutrients that enabled fertilizer-free cocoa production. Cocoa cultivation initially emerged in the eastern part of the country before expanding westward, forming a ‘new cocoa belt’. Unlike these older eastern production areas, fertilizer applications were more common in the west, where highly leached and acidic ferrallitic soils made fertilization essential once the forest rent has been exhausted. In contrast, the richer eastern soils enabled sustainable fertilizer-free cocoa production for 30–35 years (Ruf 2015 ). However, as reported by Assiri et al. ( 2009 ), fertilizer adoption remained marginal in the eastern and central-western regions, which suggests that the low fertilizer adoption rate observed in our study area could be explained by these historical dynamics. Indeed, our sampled plots had an average cultivation age of 22 years, and 31 of them had been set up on former forest lands. Regarding pesticide usage, eight organic farmers used organic pesticides, compared to six conventional farmers who opted for chemical pesticides. The usage frequency was similar between the two management methods, although the pesticide types differed significantly. Organic farmers used biopesticides as required under organic farming regulations, whereas conventional farmers used chemical pesticides. Two types of biopesticides were allowed in organic farming conditions, i.e. bio-inputs produced in cooperatives or by farmers, and certified manufactured biopesticides. The cooperative-recommended application frequency was three times yearly. In organic farming, manual weeding was the only observed weed control method, and this practice was also adopted by conventional farmers. Some of the latter farmers, however, combined manual and chemical weeding, particularly for managing weeds along plot borders and in uncultivated areas within the plantation. There was no significant difference between the two management modes in terms of manual weeding frequency, which was 3.5 times/year on average. The implementation frequency of agricultural practices was generally in line with previously observed trends in Côte d’Ivoire, regardless of the management mode. The main differences concerned the types of products used, particularly with regard to chemical weeding and pesticide applications. We did not observe any significant differences between the two farming systems regarding the pesticide application frequency, although organic farmers only used authorized organic pesticides. We initially expected that organic farmers would implement biodiversity-based practices to reduce external input usage. This suggests a substitution rationale, where chemical pesticides are substituted by organic alternatives. For example, an organic cocoa farmer who produce his own pesticides might apply them at least once a month, i.e. roughly 12 applications/year. The similarities we observed between organic and conventional farming systems may be attributed to the fact that the technical recommendations for both systems were predominantly based on input usage. Farming practices are often promoted with emphasis on productivity and market compliance, particularly through technical support programs developed by cocoa trading companies over the years to ensure compliance with certification standards (Gboko 2020 ). Organic farming support programs, for instance, aim to encourage farmers to substitute chemical inputs by organic alternatives through the set up of biofactories in cooperatives to produce organic inputs that can be directly marketed to cooperative members at preferential prices. Alternatively, phytosanitary products certified for organic farming are also promoted. Regardless of the farming method, chemical or organic inputs are promoted as the primary solution. Table 3 Clusters of 39 healthy and less healthy zones identified in organic cocoa plots based on agrobiodiversity structure variables Variables Cluster Sb1 Cluster Sb2 TOTAL Significance (N = 22) (N = 17) (N = 39) Mean ± SD Mean ± SD Mean ± SD Number of healthy zones 0.053 • Healthy zone 8 (20%) 12 (31%) 20 (51%) Less healthy zone 14 (36%) 5 (13%) 19 (49%) Total density 71 ± 45 165 ± 61 112 ± 70 < 0.001*** Species richness 4.3 ± 2.3 14,9 ± 5,0 8.9 ± 6.4 < 0,001*** Average stratum height 1.86 ± 0.51 2.07 ± 0,33 1.95 ± 0.45 0.036* Basal area 6 ± 5 14 ± 9 9 ± 8 < 0.001*** Spatial layout 0.029* Aggregate 0 (0%) 4 (24%) 4 (10%) Random 22 (100%) 13 (76%) 35 (90%) The level of significance: • for p < 0.1 (trend toward significance); p < 0.05*; **p < 0.01; ***p < 0.001. 3.3. Cocoa tree health zones and relationship with agrobiodiversity Farmers’ perception of cocoa tree health: a visual assessment Interviews with farmers in our sample revealed that they assessed cocoa tree health based on visual criteria (Fig. 5 ). Satisfactory cocoa bean production was the primary indicator they used to evaluate tree health. Indeed, for 65% of the farmers (24 out of 37), “a healthy cocoa tree is one that produces.” However, the notion of ‘good production’ varied among farmers, with each having their own perception of acceptable cocoa yield. Moreover, 57% of cocoa farmers associated healthy cocoa trees with the ‘canopy conditions’, explaining that “the cocoa tree leaves must be bright green” or that “there should be many leaves.” The canopy quality was assessed based on leaf quantity and color, which reflected the tree nutritional status and soil quality according to the farmers (Wartenberg et al. 2018 ). 54% of the farmers also indicated that the absence of diseases and pests was a key cocoa tree health criterion, stating that “a healthy cocoa tree is one that is not attacked”. Farmers' perception of health was sometimes apophatic (based on negation), i.e. the absence of negative symptoms rather than the presence of specific characteristics. This was also the case for other characteristics, such as the absence of green moss for 30% of the farmers. The ‘absence of green moss’ was considered important because otherwise it could inhibit flower development and thereby impact production. As with the ‘good production’ and ‘canopy conditions’ criteria, farmers sometimes also had a cataphatic attitude, i.e. taking into account the positive attributes of healthy cocoa trees. In the same vein, the ‘presence of flowers’ on the trunk was a criterion mentioned by 30% of the farmers. However, the ‘presence of flowers’ is considered to be an unreliable production indicator because of the natural flower drop phenomenon. Moreover, 27% of the farmers mentioned that the presence of mature and healthy cocoa pods on the trunk was a sign of good tree health. This criterion was associated with both high productivity and the absence of pod diseases and pests. Moreover, 24% of the farmers considered that the absence of weeds within or around the plot was beneficial, i.e. they viewed weeds as being pest shelters. The ‘absence of trunk rot and/or cracks’ was mentioned by 22% of the farmers. Furthermore, 14% of the farmers mentioned the ‘absence of parasitic Loranthus’ ( Loranthus gabonensis ) as an indicator of cocoa tree health. Lastly, the ‘condition of cherelles’ was considered a tree health criterion by only 3% of farmers. Since cherelles can be impacted by pest infestations that hinder their development, their condition serves as an indicator of pest presence within the plot. Farmers assessed cocoa tree health mainly on the basis of visual, qualitative and subjective criteria, relying on direct observations rather than measurable indicators, as also reported by Toffolini et al. ( 2016 ). These tangible signs required no specialized tools and allowed farmers to monitor temporal changes in their cropping systems. Yet their interpretation was highly context-dependent, shaped by individual experience and environmental conditions. Moreover, the perception of tree health varied spatially within and across farms, thereby reflecting the complex interactions that prevail between cocoa trees and their surrounding environment. Factors such as soil fertility, microclimatic conditions and local pest dynamics likely also influence how farmers identify and prioritize these indicators. This spatial heterogeneity suggests that farmers may not apply homogeneous management practices throughout their plots. Instead, they could focus and adjust their interventions based on perceived differences in tree health within their individual farms. However, this variability in management strategies was not fully captured by our agronomic assessment, thus highlighting the need for researchers to tailor their methods for documenting agricultural practices, while ensuring they account for potential intra-plot variations. It is essential that agronomists adopt more granular and adaptive approaches to enhance detection and assessment of the nuances of farmer decision making and intervention strategies at finer spatial scales. Enhanced diversity in healthy organic farming zones Based on the criteria provided by each farmer, 70 zones were identified: 39 under organic farming and 31 under conventional farming. Among these, 35 zones were classified as having healthy cocoa trees, and 35 as having less healthy trees over the long term. These zones have an average surface area of 0.29 ± 0.19 ha. Their only differed significantly (p = 0.018) in terms of species richness. Healthy zones generally exhibited higher species richness (mean: 9 ± 6) compared to less healthy zones (mean: 6 ± 5) (Supplementary material S4). This difference in species richness likely contributed to enhancing biological regulation in the cocoa farms. The dilution effect plays a key role in diversified farming systems, i.e. when a greater variety of plant species is present, pests and pathogens find fewer suitable host plants concentrated in one place, hence reducing their capacity to spread and cause significant damage (Ratnadass et al. 2012 ). Studies have revealed that organic farming tends to amplify the dilution effect by contributing to increased species diversity within cropping systems (Muneret et al. 2018 ). This process interrupts pest life cycles, increases competition among species, and attracts natural enemies that help regulate pest populations, thereby leading to a natural reduction in pest and disease pressure (Gidoin et al. 2014 ). In our study, farmers who assessed tree health in terms of the visible absence pests and diseases, likely perceived these zones as being healthier because they experienced fewer pest and pathogen outbreaks. These farmers’ identification of healthy zones based on this criterion was in line with the hypothesis that higher species richness contributes to greater ecosystem stability and resilience. This indicated that agrobiodiversity played a key role in sustaining tree health in the long term. Among cocoa farms managed under organic agriculture, as described in Table 3 , two clusters of zones emerged from the HCPC analysis based on agrobiodiversity structure. We observed a marginal significant correlation relationship between the zone health status and the structure of the agrobiodiversity associated with cocoa trees (p = 0.053). These clusters differed significantly across all of the structural variables considered, i.e. total density, species richness, average canopy height, basal area and spatial arrangement. Compared to the Sb1 group, which was characterized by a lower biodiversity density (71 plants/ha), lower species richness (4.3), shorter canopy (mean stratum height of 1.86), and a smaller basal area (6 m²/ha), the Sb2 group showed significantly higher biodiversity density (165 plants/ha), greater species richness (14.9), taller canopy (mean stratum height of 2.07), and a larger basal area (14 m²/ha). While all zones in Sb1 displayed a random spatial arrangement of the associated agrobiodiversity, this pattern was only observed in 76% of the Sb2 zones, suggesting potential differences in structural organization between the two groups. Furthermore, most of the Sb1 zones (14 out of 22) were classified as less healthy by farmers, whereas most of the Sb2 zones (12 out of 17) were perceived as being healthy, indicating a potential link between higher biodiversity and improved tree health. Table 4: Comparison of associated biodiversity structures between clusters of healthy organic farming zones Our comparison of healthy and less healthy zones in organic cocoa farms revealed that species richness was the only variable that significantly differentiated the two types of zones (p = 0.044; Supplementary material S5). Indeed, the healthy zones were more diversified, with a species richness of 11, compared to 6.8 in the less healthy zones. Otherwise, there was no significant link between the health status and the associated agrobiodiversity composition and structure in the conventional cocoa farms. These results provide insights into the relationships between agrobiodiversity characteristics and cocoa tree health, while highlighting promising avenues for further research to better understand the salutogenic determinants of cocoa tree health, particularly the links between farmer-defined health indicators and associated agrobiodiversity. Our findings suggest that biodiversity may play a key role in shaping tree health, as perceived by farmers, yet the exact mechanisms underlying these relationships have yet to be elucidated. Future studies should strive to gain further insight into this trend by implementing approaches able to capture the dynamics of farmers’ plant health indicators, including temporal variations in yield, as well as tree vigor, as reflected in the canopy characteristics, and the disease incidence. Moreover, a more detailed characterization of associated agrobiodiversity is essential, while moving beyond species richness to explore functional traits and ecosystem services provided by associated plants. Several researchers have already begun working in this direction by analyzing various agrobiodiversity characteristics associated with cocoa trees (Bisseleua D. Hervé B. and Vidal 2008; Deheuvels et al. 2012 ; Sonwa et al. 2017 , 2019 ). This includes assessing their roles in pest regulation, nutrient cycling and microclimate modulation, while also considering the soil environment and its biological activity as key components of tree health. Future research should be focused on enhancing plant health assessment frameworks by integrating these multiple dimensions, thereby bridging farmer knowledge with ecological and agronomic science to develop more holistic indicators of sustainable cocoa production. 4. Conclusion This study provides new insights into the role of agrobiodiversity in supporting cocoa tree health within agroforestry systems in Côte d’Ivoire. Our results demonstrate that species richness and structural complexity—particularly in organically managed plots—are positively associated with zones perceived by farmers as healthy. These findings support the idea that diversified agroecosystems may enhance ecological regulation and plant resilience, notably through mechanisms such as the dilution effect. However, despite higher biodiversity levels, organic farming systems did not fundamentally differ from conventional ones in terms of intervention frequency, indicating a widespread reliance on input substitution rather than functional biodiversity management. This highlights a disconnect between the principles of agroecology and current practices on the ground, where ecosystem services remain underutilized. To bridge this gap, future research should integrate long-term, multidimensional assessments combining ecological indicators (biodiversity, soil functioning, pest dynamics) with farmers' knowledge and decision-making processes. Developing such frameworks will be key to designing truly biodiversity-based crop protection strategies that are both ecologically sound and socio-economically viable for cocoa producers. Declarations Acknowledgements We thank our local partner, Société Coopérative Equitable du Bandama de M'Brimbo (SCEB.SCOOPS). Special thanks go to all the farmers and their families for their trust, ease access to their cocoa farms, and their involvement in the project. We thank David Manley for English revision. Authors ‘contributions Conceptualization, MT.A.M., S.D.T., M.N. and C.A.; Methodology, MT.A.M., S.D.T., M.N. and C.A.; Investigation, MT.A.M., L.F., T.J.K., and M.N.; Writing – Original Draft, MT.A.M.; Writing –Review & Editing, MT.A.M., S.D.T., M.N. and C.A.; Funding Acquisition, M.N. and C.A.; Resources, M.N. and C.A.; Supervision, S.D.T., M.N. and C.A. Funding This study was funded by the project Health of plants in their socio-ecological environment founded by Agropolis Foundation Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author on a reasonable request. Code availability Code is available from the corresponding author on a reasonable request. Ethics approval Not applicable. Consent to participate Not applicable. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6295383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437488461,"identity":"0fafa561-d9d2-45ab-afbc-7eabd46f9342","order_by":0,"name":"Marie Thérèse Alloua Morrisson","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0006-6229-4288","institution":"Centre de Coopération Internationale en Recherche Agronomique pour le Développement: CIRAD","correspondingAuthor":true,"prefix":"","firstName":"Marie","middleName":"Thérèse Alloua","lastName":"Morrisson","suffix":""},{"id":437488462,"identity":"86995abb-10a8-4738-b0b2-0c4c1ea5ef32","order_by":1,"name":"Stéphane De Tourdonnet","email":"","orcid":"https://orcid.org/0000-0002-9693-6449","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Stéphane","middleName":"","lastName":"De Tourdonnet","suffix":""},{"id":437488463,"identity":"ea38dd3c-af90-4f87-978e-72c8b4849e0f","order_by":2,"name":"Laciné Fofana","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Laciné","middleName":"","lastName":"Fofana","suffix":""},{"id":437488464,"identity":"fa4ff3df-6956-4957-985f-c9f03f5a2ab8","order_by":3,"name":"Tidiane Jacques Kobenan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tidiane","middleName":"Jacques","lastName":"Kobenan","suffix":""},{"id":437488465,"identity":"7833d256-9eef-43b8-99e5-26fdff550a0a","order_by":4,"name":"Clémentine Allinne","email":"","orcid":"https://orcid.org/0000-0002-8147-5977","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Clémentine","middleName":"","lastName":"Allinne","suffix":""},{"id":437488466,"identity":"ce797d5b-b1f3-4358-a919-a8afd5a7a201","order_by":5,"name":"Martin Notaro","email":"","orcid":"https://orcid.org/0000-0002-8504-682X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Notaro","suffix":""}],"badges":[],"createdAt":"2025-03-24 12:35:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6295383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6295383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81603406,"identity":"dfe35a06-a45e-4193-9786-d9962d942fc8","added_by":"auto","created_at":"2025-04-29 04:56:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185312,"visible":true,"origin":"","legend":"\u003cp\u003eThese photos of two cocoa plots illustrate the heterogeneity of the cocoa tree structure and agrobiodiversity both between and within plots. Photo A shows a vertical structure with two levels of strata with a low agrobiodiversity density associated with cocoa trees. The lower photo shows a more complex structure, with different strata levels, species diversity and a higher agrobiodiversity density.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/3ec89c06bef1c6157eec1887.jpg"},{"id":81603405,"identity":"401873fd-49bc-432a-bae9-cf0025886b88","added_by":"auto","created_at":"2025-04-29 04:56:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48554,"visible":true,"origin":"","legend":"\u003cp\u003eThis diagram represents the formulated hypotheses. Farmers adopt three strategies to manage cocoa tree health: relying solely on agrobiodiversity, particularly in organic farming systems; combining agrobiodiversity services with chemical or organic inputs; or exclusively using chemical inputs. The choice of practices is influenced by their socioeconomic environment, experience, habits and history.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/37ce4a9c048bb268db03c5e2.jpg"},{"id":81604977,"identity":"9a3bbc50-7db6-49be-b566-60dc53bea867","added_by":"auto","created_at":"2025-04-29 05:28:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107387,"visible":true,"origin":"","legend":"\u003cp\u003eThis map of the study area was generated using QGIS software. It represents the plot outlines determined using a Garmin GPS (GPS MAP 64S). The plots are differentiated according to whether they are farmed organically or conventionally.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/4c0591df7309a393c3e50563.jpg"},{"id":81603408,"identity":"905ee03a-d063-44e3-8793-def020956e49","added_by":"auto","created_at":"2025-04-29 04:56:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74803,"visible":true,"origin":"","legend":"\u003cp\u003eCocoa plots according to the associated biodiversity and management intensity\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/e272501ec80381bae9f5ca44.jpg"},{"id":81603601,"identity":"b9240070-aa86-4f58-aac4-1baa48d13808","added_by":"auto","created_at":"2025-04-29 05:04:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54144,"visible":true,"origin":"","legend":"\u003cp\u003eFarmers’ plant health assessment criteria.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/7d197f2c8ffc91e6b97d614d.jpg"},{"id":83841118,"identity":"d3b5c39f-004a-4628-9145-c54098c8547e","added_by":"auto","created_at":"2025-06-03 14:15:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1683816,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/818914a6-d82b-47e8-b314-ff53b7929222.pdf"},{"id":81603424,"identity":"d7fba9f3-3b07-4e9a-aed5-308732391ba0","added_by":"auto","created_at":"2025-04-29 04:56:53","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":22958,"visible":true,"origin":"","legend":"","description":"","filename":"S1SupplementarymaterialsMMTA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/3fdb4385c3ff5f38020fa79a.docx"},{"id":81603425,"identity":"fb6a8d32-f543-4736-bcb8-86da2842576d","added_by":"auto","created_at":"2025-04-29 04:56:54","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":13580,"visible":true,"origin":"","legend":"","description":"","filename":"S2SupplementarymaterialsMMTA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/a95d23e86d3e4bca06022e7f.docx"},{"id":81603417,"identity":"a20790d7-d182-42fc-9a30-9a2aa1d40710","added_by":"auto","created_at":"2025-04-29 04:56:53","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":17993,"visible":true,"origin":"","legend":"","description":"","filename":"S3SupplementarymaterialsMMTA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/941b9e06b931080ceb6dfc71.docx"},{"id":81603423,"identity":"74f392d8-9543-431c-aca8-d97034da6472","added_by":"auto","created_at":"2025-04-29 04:56:53","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":15021,"visible":true,"origin":"","legend":"","description":"","filename":"S4SupplementarymaterialsMMTA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/5effde112fb1e1caae253ef4.docx"},{"id":81603419,"identity":"9f392084-a6d9-46b0-a55d-44f04d493a06","added_by":"auto","created_at":"2025-04-29 04:56:53","extension":"docx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":15149,"visible":true,"origin":"","legend":"","description":"","filename":"S5SupplementarymaterialsMMTA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6295383/v1/5152e43de001365f2ab5ddc8.docx"}],"financialInterests":"","formattedTitle":"Organic vs. conventional cocoa farming management: what is the relationship between agrobiodiversity, farming practices and cocoa tree health in Côte d'Ivoire?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCultivated plant health is primarily threatened by the combined effects of diseases, pests and abiotic factors such as drought, extreme temperatures and poor soil quality (Agrios \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Makundi \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These factors markedly reduce agricultural yields, thereby affecting the food security and economy of growing human populations (Agrios \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Makundi \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Oerke \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rizzo et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, these threats are exacerbated by climate change, which can alter the biological cycles of diseases and pests and increase cultivated plant susceptibility to both biotic and abiotic stresses (Elad and Pertot \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the 1960s, plant health management has primarily relied on chemical inputs, including biocides and fertilizers, which has led to their widespread systematic application in agroecosystems. This scenario is in line with the conventional agriculture model, where productivity constraints (limiting and reducing factors) are mostly compensated by chemical and organic (Duru et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e). This model is now being challenged due to the negative externalities it produces and the technical deadlocks encountered in controlling certain pests and diseases. Biodiversity-based Agriculture is an alternative by compensating for these limiting factors through the leveraging of ecosystem services, such as soil fertility and biological regulation, which are primarily driven by the associated biodiversity within the agroecosystem (Deguine et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To ensure a smooth shift from conventional agriculture inputs to biodiversity-based agriculture, it is essential to have clear insight into the role of agrobiodiversity (Duru et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e)\u0026mdash;in both its composition and spatial structure aspects\u0026mdash; in the ecological processes underlying access to resources (water, nutrients, light), as well as disease and pest regulation.\u003c/p\u003e \u003cp\u003eThe conventional plant health management approach involves a so-called pathogenic paradigm, focusing on identifying adversities\u0026mdash;e.g. diseases, weeds, pests or limiting abiotic factors\u0026mdash;that threaten cultivated plant health (Vega et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Subsequently, interventions aim to restore plant health by overcoming a succession of factors that keep plants from achieving their full genetic potential as crops (Cook \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The use of inputs provides an immediate and seemingly effective way to overcome these adversities. In this context, a \u0026lsquo;healthy plant\u0026rsquo; refers to a state where the plant is free of pathogens or symptoms associated with these adversities (D\u0026ouml;ring et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the impacts of current plant health management strategies, it is crucial to explore new paradigms for assessing and designing sustainable cropping systems that do not rely on inputs exclusively. The salutogenic approach\u0026mdash;originally developed for human health\u0026mdash;provides a holistic analytical framework that may be applied to plant health (Antonovsky \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; D\u0026ouml;ring et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Vega et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) proposed to tailor Antonovsky's (1980) human health principles to plant health. This approach advocates for a holistic view of plant health, considering the biological, economic, and social environments in which the cropping system operates, as well as farmers\u0026rsquo; knowledge, vision and goals.\u003c/p\u003e \u003cp\u003eHealth is thus regarded as a continuum, i.e. a process geared towards achieving good health. The ability to maintain and enhance progress towards health depends on the extent to which the whole situation and the ability to use available resources to solve the problem is understood (Mittelmark et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In salutogenesis, the focus is on the individual's ability to use internal and external resources available to manage ubiquitous stressful situations. This model contrasts with the standard pathogenic model focused on risk factors and mechanisms involved in disease onset, and instead addresses the question: why do some individuals, despite stressful situations and difficulties, remain healthy?\u003c/p\u003e \u003cp\u003eThe health concept in crop protection\u0026mdash;viewed from an agroecological perspective\u0026mdash;can build on this salutogenic model. The ability to maintain and improve the health process hence depends not only on the intrinsic properties of plants (induced defense system, phenotypic plasticity, symbiotic interactions, functional traits) that enable them to respond to stress, but also on the grower's capacity\u0026mdash;as a key agroecosystem player and manager\u0026mdash;to leverage the resources available in the immediate environment. Through appropriate agrobiodiversity management practices, farmers can optimize ecological function that enhance ecosystem services that are essential to insure accessible yields. Consequently, management strategies should be approached at the agroecosystem level rather than solely at the individual plant scale, as plant health is intrinsically linked to the overall functioning of the agroecosystem.\u003c/p\u003e \u003cp\u003ePlant health should therefore be assessed from a systemic standpoint (D\u0026ouml;ring et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) as it is the result of complex interactions between: (i) biophysical factors such as the plant system, pests and diseases, soil, associated biodiversity, climate, and (ii) agricultural practices which are dependent on the socioeconomic conditions of the concerned farming households. To design \u0026lsquo;healthy\u0026rsquo; agroecosystems, the dynamics of direct and indirect interactions between the system\u0026rsquo;s biophysical components must be clearly understood (Durand-Bessart et al. 2020), especially considering that farming practices influence these interactions (Durand et al. 2019). From an agroecological perspective, managing these practices requires continuous adaptation to system dynamics. In this context, identifying appropriate combinations of farming practices is essential to effectively steer biological regulations (Meynard \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mechanisms and pathways underlying plant resistance and tolerance in the presence of pests and diseases, while maintaining them at non-harmful levels, should be investigated to achieve a systemic understanding of the process. This can be achieved through biodiversity management practices that enhance ecological processes supporting plant health. In this study, we hypothesized that the organization of biodiversity\u0026mdash;i.e. its composition and vertical and horizontal structure\u0026mdash;plays a pivotal role in maintaining \u0026lsquo;healthy agroecosystems\u0026rsquo;. In such agroecosystems, pests and diseases would not be an economically unacceptable yield-reducing issue for growers\u003c/p\u003e \u003cp\u003eWe argue that addressing these challenges requires a paradigm shift in crop protection. Instead of relying on chemical or biological pesticides, crop protection strategies should focus on leveraging biodiversity, which is crucial for designing healthy sustainable agroecosystems. From this standpoint, the following key questions should be addressed to enhance knowledge on the mechanisms involved: What ecosystem pathways and functions underpin the maintenance of healthy crop plants? How can biodiversity be organized to optimize these pathways? What biodiversity management practices ensure the maintenance of plant and agroecosystem health? What indicators can be used to assess the health of cultivated plants and agroecosystems?\u003c/p\u003e \u003cp\u003eFurthermore, integrating farmers' perspectives and knowledge is essential in this process. Farmers are both actors and decision makers in daily agroecosystem management. These farmers have developed a high level of expertise based on their experience, history, habits and accumulated knowledge. The empirical knowledge they acquire can contribute to the development of sustainable context-specific cropping systems that take socioeconomic constraints into account (Dor\u0026eacute; et al., 2011).\u003c/p\u003e \u003cp\u003eIn this study, we focus on cocoa-based agroforestry systems in C\u0026ocirc;te d'Ivoire, i.e. the world's leading cocoa producing country, to explore these issues. Cocoa cultivation plays a crucial role in the Ivorian economy, accounting for 15% of the GDP, 40% of exports, and serving as a primary source of income for nearly eight million people. Cocoa-based agroforestry systems exhibit heterogeneity in terms of plant species composition and the spatial structure of the agrobiodiversity\u0026mdash;both vertical and horizontal\u0026mdash;associated with cocoa trees. Moreover, we assume that this plot organization is partly the result of farmers\u0026rsquo; management conditions.\u003c/p\u003e \u003cp\u003eThe cocoa sector currently faces challenging issues such as declining soil fertility, the proliferation of pests and diseases, changing rainfall patterns, and the loss of forest resources, all of which are exacerbated by climate change (Stocker et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cilas and Bastide \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mensah et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Agrobiodiversity is thus a key pillar in addressing these environmental challenges, particularly due to the biological regulation services it provides.\u003c/p\u003e \u003cp\u003eIn this setting, we hypothesize that agrobiodiversity management is a key lever for improving cocoa tree health. Moreover, we compare two distinct farming methods: organic and conventional cocoa agroforestry, while assuming that the organic systems are closer to biodiversity-based agriculture, and we expect that certain characteristics of the associated biodiversity must play a role in cocoa tree health, more than in conventional agriculture systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e "},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThe study took place in the Agn\u0026eacute;by-Tiassa region, specifically at N\u0026rsquo;zianouan, in southeastern C\u0026ocirc;te d\u0026rsquo;Ivoire. The study area lies between latitudes 6.02\u0026deg;N and 6.10\u0026deg;N and longitudes 4.82\u0026deg;W and 4.76\u0026deg;W (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It is under a sub-equatorial climate regime characterized by four distinct seasons: a major rainy season from March to June, followed by a short dry season from July to August. There is also a minor rainy season from September to October, followed by a major dry season from November to February (Gnangouin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Annual rainfall in this area ranges from 1,200 to 1,600 mm, with average annual temperatures of 25\u0026deg;C to 30\u0026deg;C. This area is characterized by a ferruginous soil with a sandy texture, low clay content and thus a very friable structure. (Dabin et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1960\u003c/span\u003e; Perraud \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Blaud et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Cocoa plot sampling strategy\u003c/h2\u003e \u003cp\u003eThe first step involved a preliminary random survey of cocoa plantations to select cocoa plots with contrasted characteristics in terms of agrobiodiversity associated with the cocoa trees and agricultural management intensity, while also reflecting the production system diversity in the study area. In each plot, data were collected on species associated with cocoa trees, including their vernacular and scientific names, abundance, and annual agricultural practice intervention frequency\u0026mdash;this information was collected in the initial semi-structured interview with farmers. The assessed agrobiodiversity encompassed all remnant, spontaneous and cultivated plant species that are managed by the farmer. This agrobiodiversity included food crops, timber and fruit trees exceeding 1.5 m in height, with the exception of weeds.\u003c/p\u003e \u003cp\u003eThis sampling phase lasted from October 2022 to March 2023, while the data were collected on 84 plots. In order to represent a range of agricultural situations, 40 plots were selected according to associated agrobiodiversity and management intensity gradients. These plots included 20 that were managed under organic farming practices and 20 under conventional farming practices.\u003c/p\u003e \u003cp\u003eTo represent the agrobiodiversity gradient, we constructed a biodiversity-density (BD) index that served as the underlying metric, incorporating both species richness, as a measure of species diversity, and plant density, reflecting abundance and spatial distribution.\u003c/p\u003e \u003cp\u003eThe BD index was calculated using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{B}\\text{D}\\:\\text{i}\\text{n}\\text{d}\\text{e}\\text{x}=\\:\\frac{{d}_{pi}-{d}_{min}}{{d}_{max}-{d}_{min}}+\\:\\frac{{S}_{pi}-{S}_{min}}{{S}_{max}-{S}_{min}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \n\u003cp\u003e\u003cimg 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\" width=\"606\" height=\"138\"\u003e\u003c/p\u003e\u003cp\u003eThe management intensity gradient was then developed based on the intervention frequencies of the different agricultural practices, including manual weeding, chemical weeding, cocoa tree pruning, sanitary harvesting, fertilizer application and pesticide application. The following formula was used to calculate this index:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:management\\:intensity\\:index=\\:\\sum\\:\\frac{{Pn}_{Pi}-{Pn}_{min}}{{Pn}_{max}-{Pn}_{min}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"507\" height=\"99\"\u003e\u003c/p\u003e \u003cp\u003eThese two indices relied on the methods developed by Karr (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and O\u0026rsquo;Connell et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), which were subsequently applied by Mas and Dietsch (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) to characterize management intensities in coffee plots. The index construction method consisted of assigning an equal weight to each variable on a 0 to 1 scale, where 0 represents a system with a low management intensity or low agrobiodiversity diversity, and 1 represents a system with a high management intensity or high agrobiodiversity diversity. The overall index for each plot is calculated as the sum of the indices for all of the considered variables.\u003c/p\u003e \u003cp\u003eThe present study was successfully conducted on only 37 of the 40 selected plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Defining healthy and less healthy zones\u003c/h2\u003e \u003cp\u003eIndividual semi-structured interviews were conducted between June 2023 and August 2023 with each of the 37 selected farmers to gather their perceptions of cocoa tree health. During these interviews, farmers were free to respond as they wished, mentioning as many health evaluation criteria as they deemed relevant. Based on these criteria, two distinct zones were identified within each plot: one where cocoa trees were generally considered to be in good long-term health (the \u0026lsquo;healthy zone\u0026rsquo;), and another where trees seemed to be in poorer health overall (the \u0026lsquo;less healthy zone\u0026rsquo;). These zones varied in size because they were defined according to the farmer interviews.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Variables measured for agrobiodiversity characterization\u003c/h2\u003e \u003cp\u003eThe agrobiodiversity structure was characterized by conducting a detailed floristic inventory, whereby all plants contributing to agrobiodiversity, as previously defined, were catalogued within the 37 selected plots. The agrobiodiversity structure was defined as the spatial, vertical and horizontal, and quantitative organization of plants associated with cash crops, especially cocoa trees, in cocoa agroforestry systems, according to their physical characteristics and spatial arrangement (Ngo Bieng \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To characterize the vertical stratification of agrobiodiversity, each plant was classified into a height stratum (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the trunk diameter at breast height (DBH) was measured to calculate the basal area. Moreover, the geographic position of each tree was determined using a GARMIN 64S GPS device to map the spatial arrangement of agrobiodiversity associated with the cocoa trees. The agrobiodiversity associated with each plot and within each healthy and less healthy zone was characterized based on two distinct components: (1) the species composition, representing the abundance of each associated species, and (2) the agrobiodiversity structure.\u003c/p\u003e\u003cp\u003eThe following variables, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, were used to describe the agrobiodiversity structure: the associated agrobiodiversity density, species richness, mean height stratum, basal area, and spatial arrangement. Differentiating the species abundance from the species richness provided insights into the distribution of individual species within the sample.\u003c/p\u003e \u003cp\u003eThe species constituting the agrobiodiversity were categorized based on the socioeconomic services they offered to farmers. These categories are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTubers\u003c/b\u003e, including yam (\u003cem\u003eDioscorea\u003c/em\u003e spp.) and cassava (\u003cem\u003eManihot esculenta\u003c/em\u003e)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBanana plants\u003c/b\u003e, including all Musaceae, while plantain (\u003cem\u003eMusa paradisiaca\u003c/em\u003e) and sweet banana (\u003cem\u003eMusa spp.\u003c/em\u003e) were classified in a distinct category due to their high abundance\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCommercially valuable species\u003c/b\u003e, including woody cash crops and plants producing fruits intended for both domestic consumption and marketing, e.g. citrus fruits (\u003cem\u003eCitrus spp.\u003c/em\u003e), avocado (\u003cem\u003ePersea americana\u003c/em\u003e), oil palm (\u003cem\u003eElaeis guineensis\u003c/em\u003e), mango (\u003cem\u003eMangifera indica\u003c/em\u003e), kola (\u003cem\u003eCola spp.\u003c/em\u003e), coconut (\u003cem\u003eCocos nucifera\u003c/em\u003e), guava (\u003cem\u003ePsidium guajava\u003c/em\u003e) and papaya (\u003cem\u003eCarica papaya\u003c/em\u003e), as well as cash crops like coffee (\u003cem\u003eCoffea arabica\u003c/em\u003e), rubber (\u003cem\u003eHevea brasiliensis\u003c/em\u003e) and cashew (\u003cem\u003eAnacardium occidentale\u003c/em\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOther woody species\u003c/b\u003e, including species used for medicinal purposes and timber production (long-term economic use).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables measured for all agrobiodiversity plants in the 37 selected plots.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScientific name of plant species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant species abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of plants (n)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of associated species/plots\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociated plant density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of plants/ha (plants ha-1 )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u0026sup2; ha-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eStratum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFour levels of strata considered:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStratum 1: 0 to 5 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStratum 2: 5 to 10 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStratum 3: 10 to 20 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStratum 4: above 20 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSpatial layout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAggregate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eWe used multivariate statistical methods to determine cocoa plantation typologies at the whole plot scale and the healthy and less healthy zone scale and thereby gain insight into the relationships between the different variables. Specifically, a multiple factor analysis (MFA) followed by clustering using the hierarchical classification on principal components (HCPC) method were conducted to obtain a cocoa plantation typology based on variables describing the associated agrobiodiversity structure at the 37 cocoa plantation scale and at the smaller healthy and less healthy zone scale. We then performed correspondence analyses using Fisher\u0026rsquo;s test to assess the links between the typology based on the agrobiodiversity structure, the management type (organic or conventional) at the plot scale, and the health status of cocoa trees in the identified healthy and less healthy zones. In addition, a non-parametric Kruskal-Wallis test was used to compare the different clusters based on a given variable. Statistical analyses were performed using R Studio software (version 2023.12.1). PCA and HCA analyses were performed with the FactoMineR version 2.9 (L\u0026ecirc; et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and \u003cem\u003eade4\u003c/em\u003e version 1.7\u0026ndash;22 (Dray \u003cem\u003eet al.\u003c/em\u003e, 2007), respectively.\u003c/p\u003e \u003cp\u003eThe agrobiodiversity spatial layout was characterized using Ripley\u0026rsquo;s K function (Ripley, 1977), as implemented in the R package spatstat.exploreversion 3.2-5 (Baddeley and Turner \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results \u0026 Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Overview of agrobiodiversity and farming practices\u003c/h2\u003e \u003cp\u003eThis preliminary stage allowed us to identify 84 cocoa plots with diverse characteristics. These plots were randomly selected and represented according to the agrobiodiversity and management intensity gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This sample consisted of 44 plots managed under organic farming and 40 plots under conventional farming, with average areas of 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 ha and 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 ha, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAgrobiodiversity dominated by plants offering production services\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe floristic inventory identified 72 species distributed across 30 botanical families (Supplementary material S1). This number is relatively close to the 84 species documented by Konan \u003cem\u003eet al.\u003c/em\u003e(2023) in the western C\u0026ocirc;te d'Ivoire. The average species richness was 21 species/plot in our sample, regardless of plot size.\u003c/p\u003e \u003cp\u003eThe average overall agrobiodiversity density associated with cocoa trees was 130 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Supplementary material S2). This was lower than the values recorded in other tropical regions, such as Cameroon with 190 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Jagoret et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Costa Rica with 350 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Deheuvels et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). An examination of the agrobiodiversity composition revealed that it was dominated by \u003cem\u003eMusa\u003c/em\u003e sp., with an average density of 50 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This was higher than the densities of other species categories present. We recorded 30 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for commercially valuable species, 28 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for other woody species, and 29 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for tubers, \u003cem\u003eDiscorea\u003c/em\u003e sp. (yam) and \u003cem\u003eManihot esculenta\u003c/em\u003e (cassava) (Supplementary material S1).\u003c/p\u003e \u003cp\u003eFurthermore, when comparing the two farming methods, the plant category densities were similar for commercially valuable species, with 30 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under conventional farming vs 28 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in organic farming plots, while for \u003cem\u003eMusa\u003c/em\u003e sp. we obtained a density of 52 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in organic farming conditions vs 46 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under conventional farming. In contrast, for tubers and other woody species, the densities in organic farming plots were 50 individuals ha⁻\u0026sup1; and 44 individuals ha⁻\u0026sup1;, respectively, compared to only 6 individuals ha⁻\u0026sup1; and 11 individuals ha⁻\u0026sup1; under conventional farming. The total agrobiodiversity density associated with cocoa trees was 164 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in organic farming plots vs 92 individuals ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under conventional farming (Supplementary material S2).\u003c/p\u003e \u003cp\u003eStudies have shown that agrobiodiversity associated with cocoa trees is often dominated by \u003cem\u003eMusa\u003c/em\u003e sp., followed by economically valuable species, including fruit and cash crops (Sonwa et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Deheuvels et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Notaro et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These latter species play a key role in increasing household income while also serving as subsistence crops. \u003cem\u003eMusa\u003c/em\u003e sp. are frequently used to provide shade for young cocoa trees (Opoku-Ameyaw et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Adejobi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), thereby fostering their rapid growth and production. In areas of the plots where there was sufficient sunlight once the cocoa trees had reached maturity, some of these banana trees were maintained, which could also explain their predominance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDescription of farming practices\u003c/b\u003e \u003c/p\u003e \u003cp\u003e91% of the farmers in this sample did not apply fertilizers, while 71% did not apply herbicides as they managed weeds manually and mechanically. Moreover, all farmers practiced manual weeding at least once yearly, and 70% did so thrice a year. 11% of the farmers did not use phytosanitary products for disease and pest management, whereas 54% conducted chemical treatments twice yearly. Cocoa tree pruning was performed by 84% of the farmers. The frequency of implementing these practices was lower than the recommendations in technical manuals in C\u0026ocirc;te d'Ivoire (Conseil du Caf\u0026eacute;-Cacao 2015). However, this pattern was consistent with observations of Assiri \u003cem\u003eet al.\u003c/em\u003e(2009), Siapo Yao \u003cem\u003eet al.\u003c/em\u003e(2018) and Ano \u003cem\u003eet al.\u003c/em\u003e(2018) who also reported implementation frequencies below the technical recommendations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSocioeconomic context as a driver of agrobiodiversity and farming practices\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe predominance of food and cash crops in cocoa plantations reflects an income diversification strategy adopted by producers to optimize their plantation profitability (Mal\u0026eacute;zieux and Moustier \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), particularly since the cocoa sector crisis in the late 1970s. In agroforestry systems, in addition to generating supplementary income, these species can also provide ecosystem services that promote the growth and productivity of cocoa trees in the stand of cocoa trees.\u003c/p\u003e \u003cp\u003eMoreover, the preference for fruit tree species can help mitigate conflicts over tree ownership, particularly in C\u0026ocirc;te d\u0026rsquo;Ivoire, where tensions frequently arise between cocoa producers and timber loggers over the control and exploitation of valuable trees. As Sanial (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) points out, fruit trees are generally of little interest to loggers and are not targeted for timber extraction, making them less prone to appropriation. Their integration into cocoa-based agroforestry systems thus represents a socially acceptable compromise for farmers wishing to plant trees while avoiding the risk of losing them to external exploitation. Consequently, the selection of associated species is shaped not only by economic incentives, but also by socio-political and land tenure considerations.\u003c/p\u003e \u003cp\u003eIn agroforestry systems, these species not only generate supplementary income but also contribute ecosystem services that support the growth and productivity of cocoa trees. Moreover, the preference for fruit tree species may help reduce conflicts over tree ownership, especially in contexts like C\u0026ocirc;te d\u0026rsquo;Ivoire, where tensions often arise between cocoa producers and forestry authorities. Because fruit trees are generally of little interest to forest officials, their integration into cocoa-based systems can act as a socially acceptable compromise. The choice of associated species is thus shaped by both economic and socio-political factors.\u003c/p\u003e \u003cp\u003eWe consider it relevant to asses the implications of these crop choices on cocoa tree health. To what extent does the structure and composition of agrobiodiversity associated with cocoa trees influence their health? Although shade trees are often studied with regard to their impact on cocoa tree performance indicators, it is also essential to integrate food and cash crops in the analysis in order to broaden the understanding of biological interactions in cocoa production systems.\u003c/p\u003e \u003cp\u003eThis analysis aims to broaden the understanding of biological interactions within cocoa production systems. Given the complexity of these interactions, a systemic approach is needed. This approach must consider not only the potential agronomic benefits, but also the technical and organizational constraints faced by producers. These constraints are further reinforced by the need to meet market standards, particularly those imposed by technical institutes and private certification schemes. Ruf et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) showed that the technical lessons promoted through certification are primarily based on input use, with an emphasis on facilitating access\u0026mdash;either through subsidized credit, or by donating inputs as a reward or incentive. This approach tends to standardize farming practices and may discourage the adoption of more context-specific or agroecological alternatives.\u003c/p\u003e \u003cp\u003eHowever, these technical prescriptions, often designed without genuine consultation with the producers, clash with the realities on the ground. Indeed, our results showed that the recommendations put forward by these organizations are not followed by farmers in the field, as noted by Ruf et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Gboko (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This reflects a gap between institutional requirements and the practical constraints faced by farmers. This questions the role given to farmers in technical innovation or in the promotion of sustainable agricultural practices. The effective adoption of these practices depends not only on their agronomic and economic relevance but also on their compatibility with production situations. Better integration of local knowledge and use of a participatory approach could facilitate the development of more suitable agroecological technical solutions that are adapted to the realities of cocoa farming systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Organic vs conventional management at the cocoa farm level\u003c/h2\u003e \u003cp\u003eThe general description of the 84 screened plots revealed a total agrobiodiversity density of 164 individuals ha\u003csup\u003e-1\u003c/sup\u003e in the organic plots, compared to 92 individuals ha\u003csup\u003e-1\u003c/sup\u003e in the conventional plots. The selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) aimed to minimize the differences between the plots by grouping organic and conventional farming plots with similar management intensity levels and agrobiodiversity gradients. The objective was to have comparable plots in terms of management and agrobiodiversity associated with cocoa trees. The results presented in this section revealed a small difference in terms of agrobiodiversity density associated with cocoa trees, but a significant difference between plots managed by these two strategies in terms of species richness. This suggests that the agrobiodiversity species diversity had a greater impact.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationship between the agrobiodiversity structure and the farming methods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor our 37 plots subsample, a second floristic inventory was conducted that excluded food crops such as \u003cem\u003eDioscorea\u003c/em\u003e sp. (yam) and \u003cem\u003eManihot esculenta\u003c/em\u003e (cassava), with 63 species distributed across 27 botanical families identified. These cocoa plantations contained 86% of the species richness of the initial sample. \u003cem\u003eMusa\u003c/em\u003e sp. and commercially valuable species were dominant in this sample, with an average density of 57 individuals ha\u003csup\u003e-1\u003c/sup\u003e and 45 individuals ha\u003csup\u003e-1\u003c/sup\u003e, respectively. Other woody species had a density of 27 individuals ha\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral significant differences were noted when we compared the organic and conventional farming plots (Supplementary material S3). The species richness was significantly higher in the organic plots than in the conventional plots, with values of 26 (\u0026plusmn;\u0026thinsp;10) and 17 (\u0026plusmn;\u0026thinsp;7), respectively, indicating that there was greater plant species diversity in organic agroforestry systems than in conventional ones. Similarly, the average height stratum of agrobiodiversity was significantly higher in the organic plots, i.e. 2.01 (\u0026plusmn;\u0026thinsp;0.38) as compared to conventional plots with 1.54 (\u0026plusmn;\u0026thinsp;0.31). Furthermore, the woody species density was significantly higher in organic plots, i.e. averaging 37 individuals ha\u003csup\u003e-1\u003c/sup\u003e (\u0026plusmn;\u0026thinsp;30), compared to 16 individuals ha\u003csup\u003e-1\u003c/sup\u003e (\u0026plusmn;\u0026thinsp;21) in the conventional plots. These two differences may be explained by the fact that in organic agriculture conditions farmers are encouraged to maintain timber species in cocoa plantations in order to promote agroforestry systems and enhance the agronomic services these species provide to cocoa trees (Hatimy \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, the conventional farmers in our sample were not affiliated with any cooperative and received little technical support. They were also not subject to the same requirements as organic farmers regarding the number of associated trees ha\u003csup\u003e-1\u003c/sup\u003e, as they had not undergone any certification. Otherwise, there were no significant differences between the two management modes regarding other variables, such as the total agrobiodiversity density associated with cocoa trees or the density of economically valuable species. This suggests that all farmers were equally inclined to diversify their income sources, regardless of their chosen cocoa farming method, as has been observed in different cocoa-producing countries (Jagoret et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Notaro et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThree clusters of plots emerged from the HCPC analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The S1-Low group, consisting of 10 plots (6 in conventional farming), was characterized by relatively low species richness compared to the other groups. The plants in this group were typically smaller and thinner, with a basal area of 4.9 m\u0026sup2; ha\u003csup\u003e-1\u003c/sup\u003e and a less developed vegetation stratum (on average 1.64). Their spatial arrangement was entirely random throughout all plots. Conversely, the S2-med group, consisting of 15 plots (9 in conventional farming), had average species richness and basal area values of 20 and 6.2 m\u0026sup2; ha\u003csup\u003e-1\u003c/sup\u003e, respectively. The plants had an intermediate stratum height of 1.68 and they had an aggregated spatial arrangement. Finally, the S3-high group, consisting of 12 plots (10 in organic farming), was characterized by a significantly higher species richness of 32 and a basal area of 14.2 m\u0026sup2; ha\u003csup\u003e-1\u003c/sup\u003e. Plants in this group were taller and occupied a higher vegetation stratum, with an average stratum height of 2.08. 100% of the plots in this group had an aggregated spatial arrangement.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClusters of 37 cocoa plots based on agrobiodiversity structure variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1-Low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eS2-Med\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS3-High\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagement mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.058 \u0026bull;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (0,05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17 (46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e114\u0026thinsp;\u0026plusmn;\u0026thinsp;88 NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e114\u0026thinsp;\u0026plusmn;\u0026thinsp;49 NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e171\u0026thinsp;\u0026plusmn;\u0026thinsp;72 NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.066 \u0026bull;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;8 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;5 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;8 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage stratum height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatial layout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggregate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe level of significance: NS at p\u0026thinsp;\u0026gt;\u0026thinsp;0.1. \u0026bull; for p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (trend toward significance); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Fisher's test correspondence analysis highlighted a marginal significant correlation (p\u0026thinsp;=\u0026thinsp;0.058) between the management mode and the biodiversity structure associated with cocoa trees. Cocoa plots under conventional management were primarily found in the S1-Low and S2-Med clusters. In contrast, the S3-High cluster was predominantly composed of plots under organic management. The S3-High cluster had greater agrobiodiversity, featuring a species richness of 32, taller trees with broader trunks. Note that it has been demonstrated that organic agriculture promotes biodiversity in agroecosystems (Bengtsson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jacobi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Reganold and Wachter \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and that this trend more marked when organic agriculture is combined with agroforestry (Rosati et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, our results indicated that cocoa plantations managed under organic agriculture had richer agrobiodiversity with a higher vegetation stratum, as also observed by Jacobi \u003cem\u003eet al.\u003c/em\u003e(2015). Furthermore, Ratnadass et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) demonstrated that agrobiodiversity in agroecosystems enhances disease and pest regulation. For example, some observations have suggested that higher species richness may be associated with increased cocoa productivity (Jagoret et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saj et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while the cocoa pod fungal disease intensity tends to decrease as the agrobiodiversity richness increases (Gidoin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It is therefore likely that healthy cocoa trees would prevail in plots with characteristics close to those found in the S3-High cluster. Otherwise, Notaro et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Saj et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further investigated this prospect by analysing the effects of agrobiodiversity through its spatial organization by taking the tree height and proximity to cocoa trees, as well as the interplay between distance and species composition, into account. Our findings open up new avenues for exploring relationships between agrobiodiversity organization, cocoa tree health and farming practices. The present study is an initial step towards understanding the combined effects of these factors on tree health. Further in-depth analysis of individual agroecosystem components and their interactions would be essential to identify key drivers of plant health and gain further insight into the complexity of the role of agrobiodiversity in biological regulation (Mortimer, Saj \u0026amp; David, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding agricultural practices, the conventional farming plots differed very little from the organic farming plots (Supplementary material S3). Organic fertilizer application was practiced by two conventional farmers and seven organic farmers. No significant differences in fertilizer use (application frequency and fertilizer type) were noted between organic and conventional management. Among these farmers, three practiced composting, three applied biostimulants, and one solely applied decomposed cocoa pod husks\u0026mdash;a method that was also used by two conventional farmers. However, only two conventional farmers used chemical fertilizers, whereas twelve organic farmers and thirteen conventional farmers did not use any fertilizers.\u003c/p\u003e \u003cp\u003eThe cocoa boom in C\u0026ocirc;te d\u0026rsquo;Ivoire took place following extensive forest clearings starting in the 1930s, which resulted in the release of nutrients that enabled fertilizer-free cocoa production. Cocoa cultivation initially emerged in the eastern part of the country before expanding westward, forming a \u0026lsquo;new cocoa belt\u0026rsquo;. Unlike these older eastern production areas, fertilizer applications were more common in the west, where highly leached and acidic ferrallitic soils made fertilization essential once the forest rent has been exhausted. In contrast, the richer eastern soils enabled sustainable fertilizer-free cocoa production for 30\u0026ndash;35 years (Ruf \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, as reported by Assiri et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), fertilizer adoption remained marginal in the eastern and central-western regions, which suggests that the low fertilizer adoption rate observed in our study area could be explained by these historical dynamics. Indeed, our sampled plots had an average cultivation age of 22 years, and 31 of them had been set up on former forest lands.\u003c/p\u003e \u003cp\u003eRegarding pesticide usage, eight organic farmers used organic pesticides, compared to six conventional farmers who opted for chemical pesticides. The usage frequency was similar between the two management methods, although the pesticide types differed significantly. Organic farmers used biopesticides as required under organic farming regulations, whereas conventional farmers used chemical pesticides. Two types of biopesticides were allowed in organic farming conditions, i.e. bio-inputs produced in cooperatives or by farmers, and certified manufactured biopesticides. The cooperative-recommended application frequency was three times yearly. In organic farming, manual weeding was the only observed weed control method, and this practice was also adopted by conventional farmers. Some of the latter farmers, however, combined manual and chemical weeding, particularly for managing weeds along plot borders and in uncultivated areas within the plantation. There was no significant difference between the two management modes in terms of manual weeding frequency, which was 3.5 times/year on average.\u003c/p\u003e \u003cp\u003eThe implementation frequency of agricultural practices was generally in line with previously observed trends in C\u0026ocirc;te d\u0026rsquo;Ivoire, regardless of the management mode. The main differences concerned the types of products used, particularly with regard to chemical weeding and pesticide applications.\u003c/p\u003e \u003cp\u003e We did not observe any significant differences between the two farming systems regarding the pesticide application frequency, although organic farmers only used authorized organic pesticides. We initially expected that organic farmers would implement biodiversity-based practices to reduce external input usage. This suggests a substitution rationale, where chemical pesticides are substituted by organic alternatives. For example, an organic cocoa farmer who produce his own pesticides might apply them at least once a month, i.e. roughly 12 applications/year. The similarities we observed between organic and conventional farming systems may be attributed to the fact that the technical recommendations for both systems were predominantly based on input usage. Farming practices are often promoted with emphasis on productivity and market compliance, particularly through technical support programs developed by cocoa trading companies over the years to ensure compliance with certification standards (Gboko \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Organic farming support programs, for instance, aim to encourage farmers to substitute chemical inputs by organic alternatives through the set up of biofactories in cooperatives to produce organic inputs that can be directly marketed to cooperative members at preferential prices. Alternatively, phytosanitary products certified for organic farming are also promoted. Regardless of the farming method, chemical or organic inputs are promoted as the primary solution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClusters of 39 healthy and less healthy zones identified in organic cocoa plots based on agrobiodiversity structure variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster Sb1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster Sb2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of healthy zones\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.053 \u0026bull;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHealthy zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLess healthy zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u0026thinsp;\u0026plusmn;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165\u0026thinsp;\u0026plusmn;\u0026thinsp;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112\u0026thinsp;\u0026plusmn;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpecies richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,9\u0026thinsp;\u0026plusmn;\u0026thinsp;5,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage stratum height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasal area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpatial layout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.029*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAggregate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eThe level of significance: \u0026bull; for p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (trend toward significance); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Cocoa tree health zones and relationship with agrobiodiversity\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFarmers\u0026rsquo; perception of cocoa tree health: a visual assessment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInterviews with farmers in our sample revealed that they assessed cocoa tree health based on visual criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Satisfactory cocoa bean production was the primary indicator they used to evaluate tree health. Indeed, for 65% of the farmers (24 out of 37), \u0026ldquo;a healthy cocoa tree is one that produces.\u0026rdquo; However, the notion of \u0026lsquo;good production\u0026rsquo; varied among farmers, with each having their own perception of acceptable cocoa yield. Moreover, 57% of cocoa farmers associated healthy cocoa trees with the \u0026lsquo;canopy conditions\u0026rsquo;, explaining that \u0026ldquo;the cocoa tree leaves must be bright green\u0026rdquo; or that \u0026ldquo;there should be many leaves.\u0026rdquo; The canopy quality was assessed based on leaf quantity and color, which reflected the tree nutritional status and soil quality according to the farmers (Wartenberg et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). 54% of the farmers also indicated that the absence of diseases and pests was a key cocoa tree health criterion, stating that \u0026ldquo;a healthy cocoa tree is one that is not attacked\u0026rdquo;. Farmers' perception of health was sometimes apophatic (based on negation), i.e. the absence of negative symptoms rather than the presence of specific characteristics. This was also the case for other characteristics, such as the absence of green moss for 30% of the farmers. The \u0026lsquo;absence of green moss\u0026rsquo; was considered important because otherwise it could inhibit flower development and thereby impact production. As with the \u0026lsquo;good production\u0026rsquo; and \u0026lsquo;canopy conditions\u0026rsquo; criteria, farmers sometimes also had a cataphatic attitude, i.e. taking into account the positive attributes of healthy cocoa trees. In the same vein, the \u0026lsquo;presence of flowers\u0026rsquo; on the trunk was a criterion mentioned by 30% of the farmers. However, the \u0026lsquo;presence of flowers\u0026rsquo; is considered to be an unreliable production indicator because of the natural flower drop phenomenon. Moreover, 27% of the farmers mentioned that the presence of mature and healthy cocoa pods on the trunk was a sign of good tree health. This criterion was associated with both high productivity and the absence of pod diseases and pests. Moreover, 24% of the farmers considered that the absence of weeds within or around the plot was beneficial, i.e. they viewed weeds as being pest shelters. The \u0026lsquo;absence of trunk rot and/or cracks\u0026rsquo; was mentioned by 22% of the farmers. Furthermore, 14% of the farmers mentioned the \u0026lsquo;absence of parasitic Loranthus\u0026rsquo; (\u003cem\u003eLoranthus gabonensis\u003c/em\u003e) as an indicator of cocoa tree health. Lastly, the \u0026lsquo;condition of cherelles\u0026rsquo; was considered a tree health criterion by only 3% of farmers. Since cherelles can be impacted by pest infestations that hinder their development, their condition serves as an indicator of pest presence within the plot.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFarmers assessed cocoa tree health mainly on the basis of visual, qualitative and subjective criteria, relying on direct observations rather than measurable indicators, as also reported by Toffolini et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These tangible signs required no specialized tools and allowed farmers to monitor temporal changes in their cropping systems.\u003c/p\u003e \u003cp\u003eYet their interpretation was highly context-dependent, shaped by individual experience and environmental conditions. Moreover, the perception of tree health varied spatially within and across farms, thereby reflecting the complex interactions that prevail between cocoa trees and their surrounding environment. Factors such as soil fertility, microclimatic conditions and local pest dynamics likely also influence how farmers identify and prioritize these indicators. This spatial heterogeneity suggests that farmers may not apply homogeneous management practices throughout their plots. Instead, they could focus and adjust their interventions based on perceived differences in tree health within their individual farms. However, this variability in management strategies was not fully captured by our agronomic assessment, thus highlighting the need for researchers to tailor their methods for documenting agricultural practices, while ensuring they account for potential intra-plot variations. It is essential that agronomists adopt more granular and adaptive approaches to enhance detection and assessment of the nuances of farmer decision making and intervention strategies at finer spatial scales.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnhanced diversity in healthy organic farming zones\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the criteria provided by each farmer, 70 zones were identified: 39 under organic farming and 31 under conventional farming. Among these, 35 zones were classified as having healthy cocoa trees, and 35 as having less healthy trees over the long term. These zones have an average surface area of 0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 ha. Their only differed significantly (p\u0026thinsp;=\u0026thinsp;0.018) in terms of species richness. Healthy zones generally exhibited higher species richness (mean: 9\u0026thinsp;\u0026plusmn;\u0026thinsp;6) compared to less healthy zones (mean: 6\u0026thinsp;\u0026plusmn;\u0026thinsp;5) (Supplementary material S4). This difference in species richness likely contributed to enhancing biological regulation in the cocoa farms.\u003c/p\u003e \u003cp\u003eThe dilution effect plays a key role in diversified farming systems, i.e. when a greater variety of plant species is present, pests and pathogens find fewer suitable host plants concentrated in one place, hence reducing their capacity to spread and cause significant damage (Ratnadass et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies have revealed that organic farming tends to amplify the dilution effect by contributing to increased species diversity within cropping systems (Muneret et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This process interrupts pest life cycles, increases competition among species, and attracts natural enemies that help regulate pest populations, thereby leading to a natural reduction in pest and disease pressure (Gidoin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In our study, farmers who assessed tree health in terms of the visible absence pests and diseases, likely perceived these zones as being healthier because they experienced fewer pest and pathogen outbreaks. These farmers\u0026rsquo; identification of healthy zones based on this criterion was in line with the hypothesis that higher species richness contributes to greater ecosystem stability and resilience. This indicated that agrobiodiversity played a key role in sustaining tree health in the long term.\u003c/p\u003e \u003cp\u003eAmong cocoa farms managed under organic agriculture, as described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, two clusters of zones emerged from the HCPC analysis based on agrobiodiversity structure. We observed a marginal significant correlation relationship between the zone health status and the structure of the agrobiodiversity associated with cocoa trees (p\u0026thinsp;=\u0026thinsp;0.053). These clusters differed significantly across all of the structural variables considered, i.e. total density, species richness, average canopy height, basal area and spatial arrangement. Compared to the Sb1 group, which was characterized by a lower biodiversity density (71 plants/ha), lower species richness (4.3), shorter canopy (mean stratum height of 1.86), and a smaller basal area (6 m\u0026sup2;/ha), the Sb2 group showed significantly higher biodiversity density (165 plants/ha), greater species richness (14.9), taller canopy (mean stratum height of 2.07), and a larger basal area (14 m\u0026sup2;/ha). While all zones in Sb1 displayed a random spatial arrangement of the associated agrobiodiversity, this pattern was only observed in 76% of the Sb2 zones, suggesting potential differences in structural organization between the two groups. Furthermore, most of the Sb1 zones (14 out of 22) were classified as less healthy by farmers, whereas most of the Sb2 zones (12 out of 17) were perceived as being healthy, indicating a potential link between higher biodiversity and improved tree health.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTable\u0026nbsp;4: Comparison of associated biodiversity structures between clusters of healthy organic farming zones\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOur comparison of healthy and less healthy zones in organic cocoa farms revealed that species richness was the only variable that significantly differentiated the two types of zones (p\u0026thinsp;=\u0026thinsp;0.044; Supplementary material S5). Indeed, the healthy zones were more diversified, with a species richness of 11, compared to 6.8 in the less healthy zones. Otherwise, there was no significant link between the health status and the associated agrobiodiversity composition and structure in the conventional cocoa farms. These results provide insights into the relationships between agrobiodiversity characteristics and cocoa tree health, while highlighting promising avenues for further research to better understand the salutogenic determinants of cocoa tree health, particularly the links between farmer-defined health indicators and associated agrobiodiversity. Our findings suggest that biodiversity may play a key role in shaping tree health, as perceived by farmers, yet the exact mechanisms underlying these relationships have yet to be elucidated.\u003c/p\u003e \u003cp\u003eFuture studies should strive to gain further insight into this trend by implementing approaches able to capture the dynamics of farmers\u0026rsquo; plant health indicators, including temporal variations in yield, as well as tree vigor, as reflected in the canopy characteristics, and the disease incidence. Moreover, a more detailed characterization of associated agrobiodiversity is essential, while moving beyond species richness to explore functional traits and ecosystem services provided by associated plants. Several researchers have already begun working in this direction by analyzing various agrobiodiversity characteristics associated with cocoa trees (Bisseleua D. Herv\u0026eacute; B. and Vidal 2008; Deheuvels et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sonwa et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This includes assessing their roles in pest regulation, nutrient cycling and microclimate modulation, while also considering the soil environment and its biological activity as key components of tree health.\u003c/p\u003e \u003cp\u003eFuture research should be focused on enhancing plant health assessment frameworks by integrating these multiple dimensions, thereby bridging farmer knowledge with ecological and agronomic science to develop more holistic indicators of sustainable cocoa production.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study provides new insights into the role of agrobiodiversity in supporting cocoa tree health within agroforestry systems in C\u0026ocirc;te d\u0026rsquo;Ivoire. Our results demonstrate that species richness and structural complexity\u0026mdash;particularly in organically managed plots\u0026mdash;are positively associated with zones perceived by farmers as healthy. These findings support the idea that diversified agroecosystems may enhance ecological regulation and plant resilience, notably through mechanisms such as the dilution effect.\u003c/p\u003e \u003cp\u003eHowever, despite higher biodiversity levels, organic farming systems did not fundamentally differ from conventional ones in terms of intervention frequency, indicating a widespread reliance on input substitution rather than functional biodiversity management. This highlights a disconnect between the principles of agroecology and current practices on the ground, where ecosystem services remain underutilized.\u003c/p\u003e \u003cp\u003eTo bridge this gap, future research should integrate long-term, multidimensional assessments combining ecological indicators (biodiversity, soil functioning, pest dynamics) with farmers' knowledge and decision-making processes. Developing such frameworks will be key to designing truly biodiversity-based crop protection strategies that are both ecologically sound and socio-economically viable for cocoa producers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e We thank our local partner, Société Coopérative Equitable du Bandama de M'Brimbo (SCEB.SCOOPS). Special thanks go to all the farmers and their families for their trust, ease access to their cocoa farms, and their involvement in the project. We thank David Manley for English revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors ‘contributions\u003c/strong\u003e Conceptualization, MT.A.M., S.D.T., M.N. and C.A.; Methodology, MT.A.M., S.D.T., M.N. and C.A.; Investigation, MT.A.M., L.F., T.J.K., and M.N.; Writing – Original Draft, MT.A.M.; Writing –Review \u0026amp; Editing, MT.A.M., S.D.T., M.N. and C.A.; Funding Acquisition, M.N. and C.A.; Resources, M.N. and C.A.; Supervision, S.D.T., M.N. and C.A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was funded by the project \u003cstrong\u003eHealth of plants in their socio-ecological environment founded by Agropolis Foundation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets generated and/or analyzed during the current study are available from the corresponding author on a reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e Code is available from the corresponding author on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e Not applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Not applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e All authors consent to the publication. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdejobi KB, Famaye AO, Adeosun SA, Edibo GO (2022) Growth and Establishment of Cacao Seedlings under Intercrop with Plantain at Different Transplanting Positions. 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Agron Sustain Dev 36:np. https://doi.org/10.1007/s13593-015-0340-z\u003c/li\u003e\n\u003cli\u003eVega D, Gazzano Santos MI, Salas-Zapata W, Poggio SL (2020) Revising the concept of crop health from an agroecological perspective. Agroecol Sustain Food Syst 44:215\u0026ndash;237. https://doi.org/10.1080/21683565.2019.1643436\u003c/li\u003e\n\u003cli\u003eWartenberg A, Blaser W, Janudianto K, et al (2018) Farmer perceptions of plant\u0026ndash;soil interactions can affect adoption of sustainable management practices in cocoa agroforests: a case study from Southeast Sulawesi. Ecol Soc 23:. https://doi.org/10.5751/ES-09921-230118\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 4","content":"\u003cp\u003eTable 4 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"plant health, farming practices, agrobiodiversity, farmers’ perception","lastPublishedDoi":"10.21203/rs.3.rs-6295383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6295383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe most widespread plant health management approach is pathogen-focused and relies on systematic, often excessive, use of chemical inputs. Conversely, agroecology is gaining ground alongside the emergence of new salutogenic approaches based on an understanding of the factors that promote biodiversity-based plant health. To apply these approaches, it is thus essential to gain further insight into the role of agrobiodiversity, particularly its structure and composition, on plant health.\u003c/p\u003e \u003cp\u003eOur study was conducted in cocoa-based agroforestry systems in C\u0026ocirc;te d'Ivoire, characterized by an agrobiodiversity gradient, varying in terms of species diversity, plant density and spatial distribution. These systems are managed by farmers using diverse agricultural practices based on their knowledge and experience, while also being influenced by the socioeconomic environment. We hypothesized that cocoa tree health could be enhanced via efficient agrobiodiversity management and hence that organic farming systems would be more advanced in this respect than conventional systems due to their closer alignment with biodiversity-based practices. We tested this by comparing management practices and shade tree diversity in both organic and conventional farming systems in a network of 84 cocoa plots. We then performed statistical tests on a subset of 37 plots to assess the relationship between agrobiodiversity and farming practices. Our study revealed that farming practices and their annual intervention frequency varied little between organic and conventional farming situations. However, the organic farmers relied on substitution strategy, replacing chemical inputs with organic inputs. Furthermore, our results indicated that the species richness could have a significant impact on cocoa tree health, particularly in organic farming systems. This raises fundamental questions about how agricultural practices influence biodiversity-health interactions and calls for further research to deepen our understanding of these dynamics.\u003c/p\u003e","manuscriptTitle":"Organic vs. conventional cocoa farming management: what is the relationship between agrobiodiversity, farming practices and cocoa tree health in Côte d'Ivoire?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 04:56:48","doi":"10.21203/rs.3.rs-6295383/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50a44242-8178-416a-9b0b-97cc01e48beb","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-03T14:07:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 04:56:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6295383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6295383","identity":"rs-6295383","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}