# Maximizing tree diversity in cocoa agroforestry: Taking advantage of planted, spontaneous, and remnant trees

Isaac Kouamé Konan, Anny Estelle N'Guessan, Marie Ruth Dago, Aimé Kouadio Kouassi, Justin Kassi N'Dja, Raphaël Aussenac, Stéphane Guei,

Patrick Jagoret, Soulemane Traore, Bruno Hérault

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## To cite this version:

Isaac Kouamé Konan, Anny Estelle N'Guessan, Marie Ruth Dago, Aimé Kouadio Kouassi, Justin Kassi N'Dja, et al.. Maximizing tree diversity in cocoa agroforestry: Taking advantage of planted, spontaneous, and remnant trees. Agroforestry Systems, 2025, 99, pp.107. 10.1007/s10457-025-01180-1. hal-05183230

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# Maximizing tree diversity in cocoa agroforestry: taking advantage of planted, spontaneous, and remnant trees

Isaac Kouamé Konan · Anny E N'Guessan · Marie R Dago · Aimé Kouassi · Justin K N'dja · Raphaël Aussenac · Stéphane Guei · Patrick Jagoret · Soulemane Traoré · Bruno Hérault

Received: 6 May 2024 / Accepted: 13 March 2025
© The Author(s) 2025

**Abstract** Cocoa production stands as the primary driver of biodiversity loss in West Africa. In response to this challenge, agroforestry emerges as a promising solution to restore tree cover and preserve biodiversity in agricultural landscapes. Despite distributions of millions of trees to farmers through various sustainability initiatives, survival rates of planted trees

have been disappointing in cocoa fields. However, many non-planted trees currently exist in cocoa fields, including trees from spontaneous regeneration and remnants from previous forests. Our study aims to explore the origins of tree biodiversity in current Ivorian cocoa fields and identify the key socio-environmental determinants. Across 150 cocoa fields, 9939 trees were botanically identified, allowing estimation of diversity and complementarity levels between planted, spontaneous, and remnant cohorts, linked to socio-environmental variables within a Bayesian modelling framework. Results revealed that remnant and spontaneous trees exhibited much greater diversity than planted trees, with the latter showing important complementarity with the other cohorts. Moreover, the diversity of each tree cohort was influenced by a distinct set of environmental factors such as climate and soil composition, highlighting the need to consider the unique history and location of each cocoa field to support agroforestry development. Effective biodiversity maximisation thus relies on preserving remnant trees, vital sources of propagules for natural regeneration, while carefully selecting spontaneous trees and complementary introducing planted trees. Furthermore, recognizing the farmer's identity as a key factor shaping observed biodiversity underscores the importance of raising awareness among farmers and offering tree recognition training, particularly for young trees.

I. K. Konan · A. E. N'Guessan · J. K. N'dja
Laboratoire de Botanique, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire

M. R. Dago · A. Kouassi · R. Aussenac · S. Traoré
UMRI SAPT (Sciences Agronomiques et Procédés de Transformation), INPHB (Institut National Polytechnique Félix Houphouët-Boigny), Yamoussoukro, Côte d'Ivoire

R. Aussenac · B. Hérault (✉)
UPR Forêts et Sociétés, CIRAD, Montpellier, France
e-mail: bruno.herault@cirad.fr

R. Aussenac · B. Hérault
Forêts et Sociétés, Université Montpellier, CIRAD, Montpellier, France

S. Guei
Centre d'excellence Africain sur le Changement Climatique la Biodiversité et l'agriculture Durable, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire

P. Jagoret
UMR ABSys, CIRAD, Montpellier, France

S. Traoré
Ministry of Water and Forests, Abidjan, Côte d'Ivoire
**Keywords** Agroforestry · Cocoa farming · Origin of trees · Vegetation diversity · Bayesian modelling

## Introduction

West Africa accounts for approximately 75% of global cocoa production (ICCO 2023), with Ivory Coast and Ghana leading as the top producers, followed by Nigeria and Cameroon. Cocoa is the primary agricultural export in both Ivory Coast and Ghana, playing a vital role in their economies. In Ivory Coast, cocoa production contributes 40% of export earnings (ICCO 2024) and at least 10% of GDP (Tano 2012), depending on market prices and yield, and supports over one million smallholder farmers. The expansion of cocoa cultivation has significantly impacted West Africa's forest cover. Over 80% of the region's forest cover has disappeared in the past century (Aleman et al. 2018), driving biodiversity decline and reducing nature's contributions to society (Norris et al. 2010; Ruf 2011; Vaast and Somarriba 2014). Among the various drivers of deforestation, cash crops, particularly cocoa cultivation, stand out as the primary cause (Quattara et al. 2021; Sabas et al. 2020). Ivory Coast, the world's leading cocoa exporter, has experienced a drastic reduction in forest cover over the last 60 years, from 16 million hectares to 2.97 million hectares today (IFFN 2021). The links between deforestation and cocoa cultivation are well-established. As cocoa fields age, yields decline sharply, pests multiply, and soil mineral resources diminish (Ruf 2011; Tscharntke et al. 2011). Farmers often find it more cost-effective to clear new forest land for cocoa plantations rather than rehabilitate degraded fields (Ruf et al. 2015; Vaast and Somarriba 2014). This extensive deforestation across West Africa has triggered significant regional climate changes, including reduced rainfall during dry seasons and higher ground temperatures, which have made agricultural production increasingly vulnerable (Läderach et al. 2013). Consequently, farming communities face mounting challenges in food production and declining income sources, threatening the long-term sustainability of cocoa-dependent economies.

In this context, restoring a minimum forest cover, promoting trees in post-forest landscapes, conserving biodiversity, and reinstating key

ecosystem services are major priorities for ensuring sustainable agricultural production, including cocoa cultivation (FAO 2019). It is within this framework that agroforestry has been promoted over the past decade as a win-win solution, capable of securing the future of agricultural production and providing the timber industry with new resources (Zo-Bi and Hérault 2023). Practically, developing agroforestry in existing cocoa fields involves progressively enriching the field with trees of interest to the farmer. In this way, agroforestry may contribute to diversifying farmers' income sources and, ultimately, their social well-being (Batsi et al. 2020; Rendón-Sandoval et al. 2020). On the policy front, several certifications and initiatives, e.g. Rainforest Alliance, UTZ Certified or ARS 1000, have emerged to encourage adequate forest cover in cocoa plantations while ensuring cocoa production (Carimentrand 2020; Kemper et al. 2023). Additionally, the world's two largest cocoa producers, Ivory Coast and Ghana, along with major global cocoa industry players, have formed a partnership known as the Cocoa Forest Initiative. This initiative aims to improve tree diversity levels in cocoa fields in order to ensure the sustainability of cocoa systems and to contribute to the restoration of the biodiversity lost due to deforestation (Amani et al. 2021). Consequently, this initiative has promoted the distribution of millions of tree seedlings in cocoa fields. However, it has largely failed, as very few trees have survived and developed properly (Kouassi et al. 2023). Paradoxically, the majority of forest tree species found in cocoa fields today come from natural regeneration, selected by farmers without any technical support program (Kouassi et al. 2023).

Recent studies (Sanial et al. 2023; Kouassi et al. 2023) have indeed identified three distinct cohorts of trees associated with cocoa plantations: (1) trees spared during forest clearing (remnants), (2) planted or transplanted trees (planted), and (3) spontaneously trees (spontaneous) recruited from natural regeneration after or during the creation of the cocoa plantation. These three cohorts contribute differently to ecosystem service provision (Sanial et al. 2023). Remnant trees serve as significant carbon stocks (Andreotti et al. 2018; Bastin et al. 2018; Sanial et al. 2023), planted trees provide various provisioning services (food, firewood), and spontaneous trees represent the primary future source of timber (Doua-Bi et al. 2021; Kouassi et al. 2023; Elogne
et al. 2023; Lykke et al. 2025). However, despite the stated role of cocoa fields in conserving/restoring West African biodiversity, few studies have examined the relative contribution of these three cohorts in shaping plant diversity, let alone the explanatory factors behind this diversity. Among the explanatory factors, four main types of variables are often cited to explain tree community structure in post-forest West African landscapes (Amani et al. 2021, 2022) (i) system-related variables (field), in particular their spatial location (within forest or non-forest landscapes), and the temporal aspects that condition their intrinsic regeneration potential (N'Guessan et al. 2019), (ii) climate-related variables (climate) that strongly influence tree performance (Amani et al. 2022), (iii) soil-related variables (soil) which structure the major vegetation types in West Africa (Traoré et al. 2024), and (iv) variables related to the farmer (social), as cocoa fields are systems under constant human management (Sanial et al. 2023).

The objective of this study is to understand the origin of tree diversity in Ivorian cocoa plantations and identify its main determinants. Specifically, we address the following questions: (1) What is the local biodiversity level (alpha diversity) of the 3 tree cohorts (planted, spontaneous, and remnant) present in Ivorian cocoa plantations? (2) What is the level of complementarity (beta diversity) between these cohorts? (3) Do the factors (field, climate, soil, social) structuring alpha diversity differ between cohorts? (4) If so, what are the main explanatory factors by cohort?

## Materials and methods

This study was conducted in the southern half of Ivory Coast, which corresponds to the former forest zone where cocoa cultivation takes place (Fig. 1). The climate is of equatorial humid type, characterized by temperatures ranging from 25 to 30 °C and precipitation varying from 1100 to 1900 mm.

### Data collection

This study was conducted across 150 cocoa plots covering a total area of 240.4 hectares. These plots are distributed across 15 sites spread throughout the cocoa production area (Fig. 1), with 10 plots per site.

Plot sizes range from 0.5 to 5 hectares, encompassing the entirety of a given farmer's field. The selection of these cocoa plots was aimed to maximize the variability of three key variables within each site: cocoa plantation age, cocoa yield, and associated tree density (details in Kouassi et al. 2023) in order to capture the full socio-economic and environmental variability of Ivorian cocoa cultivation. Cocoa production in Ivory Coast is primarily undertaken by migratory groups, notably the Baoulés and foreign nationals, particularly Burkinabés from Burkina Faso, who represent the main foreign workforce. Consequently, rural populations exhibit a blend of local and foreign cultures and traditions, shaping diverse cocoa farming practices. However, exceptions exist, such as Agnibilékrou in the east, along with Adzopé and Azaguié in the south, where indigenous populations dominate and share common cultural customs.

Before starting the data collection, the objectives of this study were presented to each village chief, and permission was requested and granted to conduct surveys in their respective villages. Before each interview, the study's objectives and ethical guidelines for data handling were explained to cocoa farmers and their families. This ensured they understood that they had (i) the right to withdraw from the study at any time, (ii) the freedom to refuse to answer any question without providing justification, and (iii) the ability to pause the fieldwork as needed. Farmers who verbally confirmed their consent and understanding then signed a declaration outlining the ethical guidelines and authorizing the use of personal data (e.g., knowledge, ownership) and survey responses. All collected data were later anonymized following a pre-established data anonymization procedure. Interviews were conducted with ten cocoa farmers in each of the fifteen sites, covering various aspects of cocoa cultivation and personal background. Farmers were questioned on previous land use (forest or non-forest), education level (schooling or no schooling), origin (born in Ivory Coast or foreign), and an evaluation of their botanical knowledge. Tree inventory and measurements were primarily conducted in French by the study's lead author, with assistance from one or two interpreters for the local language. Each fieldwork session with a producer lasted an average of six hours, allowing for in-depth data collection on both environmental and socio-economic variables.
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Location of the 15 study sites along in Ivory Coast</figcaption></figure>

Between June 2021 and December 2022, a comprehensive inventory of all woody individuals with a diameter at breast height (DBH) ≥ 10 cm present on each plot was conducted. The following information was collected: (i) botanical identification (with herbarium collection if identification was not possible in the field), (ii) cohort membership among planted, spontaneous, and remnant trees. Species names were updated using the R package BIOMASS (Réjou-Méchain et al. 2017).

Twelve socio-environmental variables were recorded for each plot (Table 1):

* Three sociological variables including farmer education level (schooling or no schooling), cocoa farmer's botanical knowledge level and origin

(born in the country or foreigner), were obtained by interviews with farmers. The evaluation of the farmers' botanical knowledge was carried out using a digital tablet equipped with an image-based questionnaire. This questionnaire presented three images for each of the 23 most common tree species found in the Ivorian cocoa fields (Sanial 2019). Semi-quantitative scores were assigned based on recognition levels: a score of 0 if the species was not recognized, 1 if the species was recognized but not named in either French or a local language, and 2 if the species was recognized and correctly named in either French or a local language. The scores were then summed to provide an overall assessment of the farmer's botanical knowledge.

<figure><img src="image_4.png" /></figure>
<table><caption>**Table 1** List of the studied socio-environmental variables</caption><thead><tr><th></th><th>Name</th><th>Description</th><th>Unit</th><th>Source</th></tr></thead><tbody><tr><td rowspan="3">Social</td><td>Farmer's education level</td><td>No schooling (0) or schooling (1)</td><td>Binary</td><td>Interviews with farmers</td></tr><tr><td>Farmer's tree knowledge</td><td>Recognition test of the 23 most common species in agroforestry</td><td>%</td><td>Interviews with farmers</td></tr><tr><td>Farmer's origin</td><td>Cocoa farmer's origin (indigenous 0 or foreign 1)</td><td>Binary</td><td>Interviews with farmers</td></tr><tr><td rowspan="2">Field</td><td>Previous land-use</td><td>Previous land-use (forest 1 or not 0)</td><td>Binary</td><td>Interviews with farmers</td></tr><tr><td>Cocoa density</td><td>Cocoa tree density</td><td>Ind/ha</td><td>Measured on 1000 m<sup>2</sup> square</td></tr><tr><td rowspan="4">Soil</td><td>Forest proximity</td><td>Distance from the nearest forest</td><td>m</td><td>2015 national land-use map</td></tr><tr><td>Bulk density</td><td>Bulk density from a depth of 30 to 60 cm</td><td>Kg/dm<sup>3</sup></td><td>https://soilgrids.org/</td></tr><tr><td>Soil organic carbon content</td><td>Soil organic carbon content (fine soil fraction)</td><td>g/kg</td><td>https://soilgrids.org/</td></tr><tr><td>Clay content</td><td>Clay content (0–2 microns) mass fraction</td><td>%</td><td>https://soilgrids.org/</td></tr><tr><td rowspan="3">Climate</td><td>Precipitation seasonality</td><td>Precipitation seasonality from 1979 to 2013 at 1 km resolution</td><td>% (Coefficient of Variation)</td><td>https://chelsa-climate.org/</td></tr><tr><td>Annual precipitation</td><td>Annual precipitation from 1979 to 2013 at 1 km resolution</td><td>mm</td><td>https://chelsa-climate.org/</td></tr><tr><td>Temperature seasonality</td><td>Temperature seasonality from 1979 to 2013 at 1 km resolution</td><td>°C (standard deviation *100)</td><td>https://chelsa-climate.org/</td></tr></tbody></table>

* Three field-related variables including Previous land-use (forest or non-forest), cocoa tree density and proximity to the forest edge. Previous land-use was obtained by interviews with farmers, cocoa tree density was measured on 1000 m² square and proximity to forest edge was estimated from the 2015 national land-use map (Traoré et al. 2024).

* Three soil-related variables including bulk density, organic carbon content, and soil clay content compiled from existing databases (https://soilgrids.org/).

* Three climate-related variables including precipitation seasonality, annual precipitation and temperature seasonality compiled from existing databases (https://chelsa-climate.org/)

Diversity estimators

Two diversity estimators were used:

* Shannon's Alpha diversity is defined as the asymptotic diversity of a given local community (Marcon et al. 2012). We expressed it in Hill numbers for each cohort.

* Shannon's Beta diversity estimates the compositional divergence between two groups of observations (Marcon et al. 2012). It is also expressed in Hill numbers and equals 1 if the species composition of the two groups is strictly identical, greater than 1 if the species composition of the two groups differs and equals 2 if the species composition of the two groups is strictly different. We estimated this diversity for each pair of cohorts in each plot.

All diversities were calculated using unbiased estimators, as implemented in the entropart package (Marcon & Hérault 2015) in the R software version 4.2.2.
## Diversity modeling

We assessed the effects of twelve socio-environmental variables (Table 1) on the alpha diversity *DIV* of each cohort *c* of associated trees. The model was given by:

$$DIV_c \sim LN\left[\log\left(\theta_c * e^{\left(\sum_{se=1}^{12} \theta_{X_{se,c}} * X_{se}\right)}\right), \sigma^2\right]$$

with $\theta_c$ the predicted average diversity of each cohort *c*.

$\theta_{X_{se,c}}$: the effect of each socio-environmental variable *se* on the Hill value of a cohort *c*.

$X_{se,c}$: the normalized (centered-reduced) distribution of each socio-environmental variable *se*.

$\sigma^2$: the variance of the lognormal law used to infer the model.

We used a log-normal likelihood model since alpha diversity values are defined on $\mathbb{R}^+$ (Maurer et al. 2023; Dago et al. 2023). The model was inferred using the Stan language within a Bayesian framework and employing Markov Chain Monte Carlo methods (Carpenter et al. 2017).

## Results

In total, 9939 woody individuals were inventoried. These individuals are distributed among 278 species, 175 genera, and 50 families. Among them, 2878 trees were planted (29%), 4277 are spontaneous (43%), and 2784 are remnants (28%). Details on the most common species by cohort are provided in Table 2.

### Alpha diversity

The Hill number of Alpha diversity of trees in cocoa plots varies from 1 to 71.53 with a median of 18.94. Remnant and spontaneous trees both show a higher diversity (median of 9) than planted trees (median of 3.97; Fig. 2).

### Beta diversity

The Hill value densities of Beta diversities obtained per pair of cohorts (Fig. 3) demonstrate a significant

complementarity of diversity between planted trees and remnant and/or spontaneous trees. The medi-ans obtained for these Beta diversities are 2. For the remnant-spontaneous cohort pair, the Beta diversity is lower (median of 1.7).

## Structure of diversity

The previous land use (forest), the botanical knowl-edge level of the farmer and the seasonality of pre-cipitation positively influence associated tree diver-sity regardless of cohort. However, cocoa tree density consistently has a negative effect on associated tree diversity (Fig. 4).

One factor shows no effect on associated tree diversity (Fig. 4): soil bulk density.

Seven factors differentially structure planted, spontaneous, and remnant tree diversity (Fig. 4). Two social factors are identified: the farmer's education level and the farmer's origin (foreign). The farmer's education level (schooling) negatively impacts planted tree diversity but positively affects spontaneous and remnant tree diversity. The farmer's origin (foreign) negatively influences planted tree diversity, positively impacts remnant tree diversity, and has no effect on spontaneous tree diversity. Additionally, two soil-related factors are identified: the organic carbon content and the soil clay content. Organic carbon content has no effect on planted tree diversity, negatively affects remnant tree diversity, and positively influences spontaneous tree diversity. Soil clay content negatively impacts planted and spontaneous tree diversity but positively influences remnant tree diversity. Two climate-related factors are also identified: annual precipitation and temperature seasonality. Annual precipitation does not affect planted tree diversity but positively influences remnant and spontaneous tree diversity. Temperature seasonality negatively impacts planted tree diversity, positively influences remnant tree diversity, and has no effect on spontaneous tree diversity.

### Important factors by cohort

Out of twelve socio-environmental factors stud-ied, three have significant effects on the diversity of planted trees. They all exhibit negative effects. These include temperature seasonality ($\theta = -0.15 \pm 0.13$),
<table><caption>**Table 2** Distribution by tree cohort (planted, spontaneous and remnant) of the 30 most abundant species found in cocoa farms</caption><thead><tr><th>Species name</th><th>Family</th><th>Abundance</th><th>% Planted</th><th>% Remnant</th><th>% Spontaneous</th></tr></thead><tbody><tr><td>Elaeis guineensis Jacq</td><td>Arecaceae</td><td>1275</td><td>4</td><td>17.96</td><td>78.04</td></tr><tr><td>Persea americana Mill</td><td>Lauraceae</td><td>868</td><td>83.06</td><td>4.03</td><td>12.9</td></tr><tr><td>Citrus sinensis (L.) Osbeck</td><td>Rutaceae</td><td>460</td><td>86.52</td><td>2.83</td><td>10.65</td></tr><tr><td>Cola nitida (Vent.) Schott & Endl</td><td>Malvaceae</td><td>435</td><td>57.7</td><td>26.21</td><td>16.09</td></tr><tr><td>Mangifera indica L</td><td>Anacardiaceae</td><td>417</td><td>67.39</td><td>15.59</td><td>17.03</td></tr><tr><td>Ficus exasperata Vahl</td><td>Moraceae</td><td>373</td><td>0.27</td><td>30.56</td><td>69.17</td></tr><tr><td>Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg</td><td>Euphorbiaceae</td><td>305</td><td>92.46</td><td>0</td><td>7.54</td></tr><tr><td>Terminalia superba Engl. & Diels</td><td>Combretaceae</td><td>238</td><td>49.58</td><td>17.23</td><td>33.19</td></tr><tr><td>Morinda lucida Benth</td><td>Rubiaceae</td><td>212</td><td>1.89</td><td>37.26</td><td>60.85</td></tr><tr><td>Anacardium occidentale L</td><td>Anacardiaceae</td><td>176</td><td>90.34</td><td>0.57</td><td>9.09</td></tr><tr><td>Ficus sur Forssk</td><td>Moraceae</td><td>169</td><td>0</td><td>22.49</td><td>77.51</td></tr><tr><td>Spondias mombin L</td><td>Anacardiaceae</td><td>165</td><td>18.79</td><td>8.48</td><td>72.73</td></tr><tr><td>Cocos nucifera L</td><td>Arecaceae</td><td>164</td><td>84.76</td><td>12.8</td><td>2.44</td></tr><tr><td>Antiaris toxicaria (J.F.Gmel.) Lesch</td><td>Moraceae</td><td>161</td><td>0.62</td><td>41.61</td><td>57.76</td></tr><tr><td>Milicia excelsa (Welw.) C.C.Berg</td><td>Moraceae</td><td>156</td><td>5.77</td><td>49.36</td><td>44.87</td></tr><tr><td>Rauvolfia vomitoria Wennberg</td><td>Apocynaceae</td><td>155</td><td>0</td><td>24.52</td><td>75.48</td></tr><tr><td>Pycnanthus angolensis (Welw.) Warb</td><td>Myristicaceae</td><td>149</td><td>0</td><td>51.68</td><td>48.32</td></tr><tr><td>Carica papaya L</td><td>Caricaceae</td><td>146</td><td>5.48</td><td>0</td><td>94.52</td></tr><tr><td>Ceiba pentandra (L.) Gaertn</td><td>Malvaceae</td><td>144</td><td>1.39</td><td>65.97</td><td>32.64</td></tr><tr><td>Albizia zygia (DC.) J.F.Macbr</td><td>Fabaceae</td><td>140</td><td>0</td><td>40</td><td>60</td></tr><tr><td>Cecropia peltata L</td><td>Urticaceae</td><td>133</td><td>0.75</td><td>25.56</td><td>73.68</td></tr><tr><td>Sterculia tragacantha Lindl</td><td>Malvaceae</td><td>126</td><td>0.79</td><td>46.83</td><td>52.38</td></tr><tr><td>Amphimas pterocarpoides Harms</td><td>Fabaceae</td><td>123</td><td>0</td><td>50.41</td><td>49.59</td></tr><tr><td>Ricinodendron heudelotii (Baill.) Pierre ex Heckel</td><td>Euphorbiaceae</td><td>120</td><td>15.83</td><td>45.83</td><td>38.33</td></tr><tr><td>Alstonia boonei De Will</td><td>Apocynaceae</td><td>116</td><td>0.86</td><td>56.9</td><td>42.24</td></tr><tr><td>Entandrophragma angolense C.DC</td><td>Meliaceae</td><td>108</td><td>0</td><td>51.85</td><td>48.15</td></tr><tr><td>Tectona grandis L.f</td><td>Lamiaceae</td><td>104</td><td>88.46</td><td>8.65</td><td>2.88</td></tr><tr><td>Albizia adianthifolia (Schumach.) W.Wight</td><td>Fabaceae</td><td>92</td><td>0</td><td>47.83</td><td>52.17</td></tr><tr><td>Psidium guajava L</td><td>Myrtaceae</td><td>85</td><td>57.65</td><td>5.88</td><td>36.47</td></tr><tr><td>Funtumia africana (Benth.) Stapf</td><td>Apocynaceae</td><td>79</td><td>0</td><td>40.51</td><td>59.49</td></tr></tbody></table>

annual precipitation (θ = -0.17 ± 0.10) and farmer's education level (θ = -0.18 ± 0.16) (Fig. 4).

Six factors have significant effects on the diversity of spontaneous trees. Three of these factors have positive effects: annual precipitation (θ = 0.13 ± 0.09), farmer's education level (θ = 0.26 ± 0.16) and the previous land-use (θ = 0.20 ± 0.17). Three of these factors have negative effects: forest proximity (θ = -0.14 ± 0.08), cocoa density (θ = -0.16 ± 0.09) and the clay content (θ = -0.18 ± 0.09) (Fig. 4).

Six factors have significant effects on the diversity of remnant trees. Five have positive effects. These include temperature seasonality (θ = 0.39 ± 0.13), annual precipitation (θ = 0.19 ± 0.10),

farmer's origin (θ = 0.28 ± 0.21), farmer's education level (θ = 0.17 ± 0.17) and the previous land-use (θ = 0.42 ± 0.18) (Fig. 4).

Discussion

The objective of this study was to determine the origin of the tree biodiversity in West African cocoa fields. Clearly, Alpha diversity in these fields comes from spontaneous and remnant trees, while planted trees exhibit very low Alpha diversity. However, since planted trees show high complementarity (resulting in high Beta diversity) with spontaneous and remnant
<figure><img src="image_5.png" /><figcaption>**Fig. 2** Density distribution of Hill numbers for Shannon Alpha diversity</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3** Density distribution of Hill numbers for Shannon Beta diversity by cohort pairs. The blue line represents Beta diversity between remnant and spontaneous trees (re_sp), green between planted and spontaneous trees (pl_sp), and red between planted and remnant trees (pl_re)</figcaption></figure>

trees, they contribute to an additional gain in plant diversity. Several socio-environmental factors explain these different levels of observed diversity. These factors exhibit different effects by cohort, highlighting the importance of cohort-based methodological

approaches (Hérault & Piponiot 2018), which provides better insights into the underlying factors influencing observed biodiversity levels.

The plant diversity in cocoa fields originates from remnant and spontaneous trees

Remnant and spontaneous trees are the most diversified in cocoa fields. (i) During forest clearing by farmers, remnant trees are spared to provide shade for young cocoa plants (Dawson et al. 2013; Smith Dumont et al. 2014) and also to camouflage the plantation from administration (Ouattara et al. 2021). These trees thus reflect the level of diversity of the forest preceding the cocoa plantation, a level that is very high (Haggar et al. 2019; Maney et al. 2022). (ii) Spontaneous trees mainly come from propagules from remnant trees (Amani et al. 2021; Herrera & García 2009) and sometimes from propagules of planted trees. These spontaneous trees demonstrate the strong capacity for natural regeneration in cocoa fields, especially under conditions of recent deforestation in most of these fields (Zo-Bi and Hérault 2023). This recent, massive deforestation creates conditions conducive to forest regeneration with abundant seed banks still present in the soil, the survival of fauna dispersing seeds, and recent soil disturbances (Arroyo-Rodríguez et al. 2017; Hérault and Hiernaux 2004). This can be linked to the high capacity of tropical forests to regenerate after a period of conversion to agriculture (Poorter et al. 2021; Amani et al. 2022). However, it seems evident that in a heavily modified environment, this restoration will be incomplete as it relies solely on propagules provided by the diversity of remnant trees (Chazdon 2003)(Poorter et al. 2024).

The diversity of planted trees is very low in cocoa fields (Fig. 2). Planted trees are typically fruit trees such as *Citrus sinensis* (L.) Osbeck, *Persea americana* Mill., *Cola nitida* (Vent.) Schott & Endl., *Mangifera indica* L., *Cocos nucifera* Linn. (Atangana et al. 2021; Laird et al. 2007). Occasionally, commercial timber species such as *Terminalia superba* Engl. & Diels, *Cedrela odorata* L., and *Terminalia ivorensis* A. Chev are found (Kouassi et al. 2023; Sanial et al. 2023).

Finally, it is worth noting that the diversity of remnant trees asymptotically will approach zero over time. Indeed, these trees are a legacy of the forest and are inevitably destined to die either through natural
<figure><img src="image_7.png" /><figcaption>**Fig. 4** Effects of social, field, soil and climate factors on Shannon Alpha diversity among planted, remnant, and spontaneous trees. All covariates have been standardized; their relative effects are directly comparable</figcaption></figure>

senescence (Franklin et al. 1987) or through gradual elimination by cocoa farmers (Laird et al. 2007). In a few decades, alpha diversity in these fields will therefore be supported only by spontaneous and planted trees.

Planted trees exhibit high complementarity with other trees

Our results have shown a very significant complementarity of diversity between planted trees and remnant/spontaneous trees (Fig. 3). This has been previously observed (Sanial et al. 2023) and can be explained by the exotic or non-indigenous nature of many planted species (Gao et al. 2023; Asase and Tetteh 2010), particularly fruit trees (Ordonez et al. 2014). For food purposes and/or to add commercial value to the cocoa plantation, farmers introduce fruit trees such as *Citrus sinensis*, *Persea americana Mill.*, *Mangifera indica L.* and *Psidium guajava Linn.*, which are exotic species. Some exotic tree species are also introduced to provide shade or wood (*Gmelina arborea Roxb.*, *Cedrela odorata L.*) (van der Meersch et al. 2021) and improve soil fertility (*Gliricidia sepium* (Jacq.)Walp.).

The complementarity of diversity between planted and spontaneous trees is slightly less pronounced compared to that between planted and remnant trees (Fig. 3). This is because spontaneous trees also consist of exotic species that regenerate naturally in cocoa farms. For instance, *Persea americana Mill.*, *Mangifera indica L.*, *Citrus sinensis* (L.) Osbeck and *Psidium guajava Linn.*, are exotic species that can be found in natural regeneration (Table 2) and are also selected for food and economic reasons.

The complementarity of diversity between remnant and spontaneous trees is not negligible; on the contrary, it is quite significant (Fig. 3). Indeed, the remnant tree cohort is generally richer and more diverse than the spontaneous tree cohort (Fig. 2). Spontaneous trees consist of three types of tree species: pioneer species, regeneration from residual remnants, and regeneration from planted species. (i) Pioneer species are often the first to colonize cocoa farms spontaneously (Kouman et al. 2022). These species complement remnant trees because, although they are present in low densities in the original forest, they are not conserved during clearing by
cocoa farmers who prefer timber species (Kouassi et al. 2023). (ii) Regeneration from remnant trees is not fully representative of the diversity of remnant trees found on the plot. Indeed, the completely different environmental conditions of the cocoa farm understory (high light intensity, low relative humidity, abundant litter) likely prevent many purely forest species from regenerating (Amani et al. 2021; Mirabel et al. 2019). (iii) Regeneration from planted trees, although limited and highly species-dependent, provides spontaneous trees that are highly complementary to remnant trees. All of these factors contribute to explaining the surprising level of complementarity observed between remnant and spontaneous trees.

Diversity of planted, spontaneous, and remnant trees depends on different factors

In cocoa farms, tree performance relies heavily on tree age and life history (Ordonez et al. 2014). Remnant trees have experienced, at the beginning of their life, a competitive forest environment (Pillet et al. 2018), while spontaneously growing trees have faced much less competition. In contrast, planted trees have distinct life histories, starting from human intervention rather than natural regeneration. Therefore, factors affecting tree performance may vary significantly among these different tree cohorts.

For planted trees, temperature seasonality is one of factors leading to a low level of diversity. Planted trees are generally more vulnerable to climatic extremes (Payn et al. 2015) as they come from seedlings grown in nurseries (Kouassi et al. 2023). After a period of root inactivity due to the displacement from their initial growth environment to another, they resume growth but develop a shallower root system, which hampers them during periods of high temperatures and/or pronounced water deficit (Zhang et al. 2021). This results in an increased risk of mortality (Hérault et al. 2020; Louppe et al. 2021). Another factor that leading to a low level of diversity of planted trees in cocoa farms is the farmer's level of education (schooling). The negative relationship between species diversity of planted trees and level of education has been observed before (Allendorf et al. 2006; Sah and Heinen 2001; Sanou et al. 2019). Educated cocoa farmers mainly plant trees in their cocoa farms for economic reasons (to maximize

cocoa farm profitability, for example by associating cashew or rubber trees), preferring a low number of species with high returns on investment (Kaba et al. 2020; Dago et al. 2025). In contrast, less educated cocoa farmers are more likely to prioritize other uses such as traditional medicine (Laird et al. 2007). The farmer's origin (foreign) is also leading to the low level of diversity of planted trees in cocoa farms. A foreign cocoa farmer generally plants less diversified trees (Fig. 4), which is consistent with their general knowledge level of the flora of the host country (Laird et al. 2007). Consequently, they often plant two or three widely known and distributed tree species in West Africa (such as mango, avocado, or cola trees, for example). Indigenous cocoa farmers, on the other hand, tend to diversify their food and income sources more, linked to their better understanding of local markets (Laird et al. 2007).

For spontaneous trees, cocoa tree density is an important factor limiting the reconstitution of tree diversity. A dense cocoa field with developed self-shading results in low light transmission under cocoa trees, ultimately hindering the germination and/or survival of natural regeneration (Somarriba et al. 2018). The previous land-use of the cocoa farm is also important, as forests provide a rich floristic legacy preserved in the soil seed banks (Amani et al. 2022). This is particularly true for commercial forest species that can survive a period without forest cover if it is transitional (Doua-Bi et al. 2021). Lastly, the level of education of the farmer is also an important factor, as educated cocoa farmers tend to promote the regeneration of a high diversity of tree species, linked to their propensity to better understand and implement new issues (carbon sequestration, biodiversity conservation) surrounding cocoa agroforestry systems (Kaba 2020; Mwangi and Kariuki 2015).

For remnant trees, the origin of the cocoa farmer is an important factor explaining the diversity of remnant trees. It has been observed that foreign cocoa farmers spare more tree species during forest clearing, which can be linked to their somewhat illegal status in many regions: sparing trees during clearing allows them to go unnoticed by authorities (Ouattara et al. 2021). On the other hand, cocoa farmers in Ivory Coast are mainly Baoulé people who often plant yam before planting cocoa trees (Budelman and Zander 1990). Yam cultivation requires a shade-free area, resulting in almost total
forest clearing when establishing the cocoa farm. The previous land-use of the plot is also a factor explaining the diversity of remnant trees. We have observed, as have others, higher species diversity of remnant trees in cocoa farms when the previous land-use was a forest (Rendón-Sandoval et al. 2020). Given that natural forests are the most diversified systems in terms of plant species, compared to fallows or abandoned old plantations, forests offer a greater choice of species to spare. Finally, the farmer's education level also positively influences the diversity of remnant trees. Like with spontaneous trees, educated cocoa farmers have a better understanding of cocoa cultivation sustainability concepts (Graefe et al. 2017; Wartenberg et al. 2018). As for uneducated cocoa farmers, many still often believe that forest trees are harmful to cocoa trees (Kaba et al. 2020; Laird et al. 2007).

## Implications for the management of plant diversity in cocoa agroforestry systems

The results obtained in this study highlight the importance of simultaneously considering planted, spontaneous, and remnant trees for optimal management of plant diversity in West African cocoa fields. Among the three tree cohorts, spontaneous and remnant trees are by far the most diversified. However, additional plant diversity complementarity is provided by the cohort of planted trees, which, although less diverse, consists of different species. Effective biodiversity management in cocoa fields should thus be based on (i) maintaining remnant trees in the system for as long as possible, as they are the main sources of propagules for natural regeneration, (ii) the potential future contribution of spontaneous trees, selected from natural regeneration based on the farmer's aspirations, and (iii) the potential for diversity complementarity and services to farmers offered by planted trees. Additionally, our results show that the identity of the farmer (educational level, tree recognition) is a major structuring variable of observed biodiversity. This suggests that (i) strategies to increase awareness among farmers about the importance of trees in cocoa fields and (ii) training strategies for cocoa farmers in seedling species recognition could mechanically lead to an

increase in tree biodiversity in fields. However, other environmental factors (climate, soil, field) are also structuring, so the unique history and location of each field must therefore be considered to implement agroforestry strategies that must therefore be personalized and contextualized to be effective. This requires a complete paradigm shift from a strategy based on large-scale top-down tree distribution campaigns to bottom-up strategies where technical support services and research are there to enlighten the desirable future of the field for the farmer in a co-construction approach.

**Acknowledgements** This study was conducted within the framework of the Cocoa4Future (C4F) project, which is funded by the European DeSIRA Initiative under grant agreement No. FOOD/2019/412-132 and by the French Development Agency.

**Author contributions** Konan Isaac Kouamé: conceptualization, methodology, data supply, formal analysis, investigation, data curation, writing—original draft, visualization→N'Guessan Anny Estelle: conceptualization, methodology, validation, writing—original draft, visualization, supervision→Dago Marie Ruth, Kouassi Aimé, Guei Stéphane, Traoré Soulemane: data supply, review & editing→N'Dja Justin Kassi: conceptualization, methodology, validation, review & editing→Aussenac Raphaël: methodology, review & editing→Jagoret Patrick: project administration, funding acquisition, writing—review & editing→Bruno Hérault: conceptualization, methodology, validation, formal analysis, investigation, data curation, writing—original draft, visualization, supervision.

**Funding** Open access funding provided by CIRAD.

**Data availability** Data will be deposited in Zenodo upon acceptance.

## Declarations

**Conflict of interest** The authors declare no competing interests.

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