# Mapping the extent and exploring the drivers of cocoa agroforestry in Nigeria, insights into trends for climate change adaptation

Anita Koralewicz · Jan Vlcek · Imma Oliveras Menor · Mark Hirons · Akinyemi Akinyugha · Olalekan Sikiru Olowoyo · Mercy Ajayi-Ebenezer · Oliver Owen

Received: 29 June 2023 / Accepted: 28 October 2024 / Published online: 3 February 2025
© The Author(s) 2025

**Abstract** Climate change threatens West Africa's cocoa sector due to rising temperatures and unpredictable rainfall, exacerbating concerns for environmental degradation and socio-economic challenges. In Nigeria, modernization efforts promoting full-sun cocoa have been linked to deforestation and

biodiversity loss. The promotion of traditional cocoa agroforestry methods are regaining interest as an approach to climate adaptation and forest restoration. This case study on Ekiti State, Nigeria, aims to understand the physical extent to which full-sun and agroforestry cocoa practices have been employed, while exploring the complex and interlinked dynamics informing land use decision-making in the area. Remote sensing leveraging tasseled cap indices for Sentinel 2 data were used to delineate cocoa agroforestry from full-sun systems. Interviews with policy-makers and local cocoa producers across 15 out of 16

Anita Koralewicz and Jan Vlcek have contributed equally to this work.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10457-024-01126-z.

A. Koralewicz (✉) · J. Vlcek · I. O. Menor · M. Hirons
School of Geography and the Environment, Environmental Change Institute, University of Oxford, Oxford, UK
e-mail: anitakoralewicz@gmail.com

J. Vlcek
e-mail: johnvlcek@gmail.com

I. O. Menor
e-mail: imma.oliveras@ouce.ox.ac.uk

M. Hirons
e-mail: mark.hirons@ouce.ox.ac.uk

I. O. Menor
AMAP (Botanique et Modélisation de L'Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France

M. Hirons
Leverhulme Centre for Nature Recovery, School of Geography and the Environment, University of Oxford, Oxford, UK

A. Akinyugha · O. Owen
Ekiti State Forestry Commission, Ministry of Environment and Natural Resources, Ado Ekiti, Ekiti State, Nigeria
e-mail: yemi.akinyugha@gmail.com

O. S. Olowoyo
Federal University Oye-Ekiti, Oye-Are Road, Oye-Ekiti, Ekiti State, Nigeria
e-mail: olowoyoolalekan43@gmail.com

M. Ajayi-Ebenezer
Federal University of Technology Akure, Akure, Ondo State, Nigeria
e-mail: mercyogunmolu@gmail.com

O. Owen
School of Anthropology and Museum Ethnography, University of Oxford, Oxford, UK
e-mail: oliver.owen@anthro.ox.ac.uk
local government areas were analyzed through the-
matic analysis and descriptive statistics. Agroforestry
constituted 18% of Ekiti land while full-sun cocoa
covered 13%. Thus, 57% of cocoa cover in Ekiti State
was agroforestry. The classification had overall spatial
differentiation accuracy of 73.1% with a kappa statis-
tic of 68% indicating substantial agreement strength
between the classification and the collected validation
data. Interviews were similarly aligned, with 74% of
respondents using agroforestry or mixed methods.
The continued use, despite government promotion of
full-sun methods, suggests limited policy uptake and
the enduring value of agroforestry for farmers. This
research can contribute to improved monitoring of
cocoa-driven tree loss and provide important context
for policy and program design to enhance climate
change adaptation in similar cocoa producing regions.

**Keywords** Cocoa agroforestry · Climate change · Remote sensing · Land classification · Land management · Policy

**Introduction**

Cocoa (*Theobroma cacao* L) is one of West Africa's largest agricultural commodities, supplying the world with popular products such as chocolate, cosmetics, and pharmaceuticals (Torres and van Seters 2016; Kim et al. 2011). More than 70% of the global cocoa supply originates from the West African Cocoa belt, stretching from the coast of Sierra Leone to Cameroon (Wessel and Quist-Wessel 2015). However, cocoa is intertwined with environmental and economic challenges such as deforestation for agricultural purposes and persistent poverty (Sassen et al. 2022; Vliet et al. 2021). Simultaneously, the suitable range for cocoa production is expected to decrease due to challenges such as drought and hotter temperatures brought about by climate change (Ariza-Salamanca et al. 2023; Schroth et al. 2016).

As Nigeria's largest agricultural commodity,
bringing in over ₦ 200 billion (~$500 million USD)
in 2021 (Nigeria Bureau of Statistics 2022), cocoa is
an important contributor to the country's economy.
However, climate change could impact Nigeria's
ambitions to increase its cocoa output and diversify
away from its heavy reliance on oil exports (Schroth
et al. 2016; Awoyemi and Aderinoye-Abdulwahab

2019). Nigeria has already experienced an average
temperature increase of 0.19 °C per decade over the
past 30 years (World Bank 2021) and continued cli-
mate changes are expected to further challenge the
country's development objectives (Okon et al. 2021).
As a largely smallholder-grown crop, cocoa is also
important for livelihoods and local land management,
lending it strong social and cultural significance
(Adejare 2021).

In 2020, Ekiti State produced nearly 8,000 tonnes
of graded cocoa (Ekiti State Government 2021a).
Ekiti State together with Ogun, Ondo, Osun, and Edo
states produces 80% of Nigeria's cocoa output (Awoy-
emi and Aderinoye-Abdulwahab 2019). Predicted cli-
matic changes are likely to decrease the suitability
of Ekiti State for cocoa farming by 10–20% by 2050
(Schroth et al. 2016). Without adoption of appropri-
ate adaptation measures, the future viability of the
cocoa sector in Ekiti State could be at risk. Recom-
mended climate adaptation strategies for cocoa in the
region include microclimate protection (e.g., through
shading), farm diversification, adoption of heat and
drought-resistant crops, and improved farm manage-
ment (Schroth et al. 2017).

Nigeria's and Ekiti State's plans for agricultural
development and cocoa revitalization historically
encouraged land clearing and industrialization to
maximize short-term returns (Ojong and Anam 2018;
Lokpobiri 2019; Ekiti State Government 2020a, b).
Agriculture has contributed to around a third of all
deforestation in the region around Ekiti (Olorun-
femi et al. 2020). Since the 1980s, the creation and
distribution of hybrid cocoa varieties across much of
West Africa promoted the use of full-sun monocrop
cocoa plantations (World Agroforestry 2021), push-
ing a strong narrative of modernization with the ulti-
mate aim of increasing yields (Kolavalli and Vigneri
2011). This shift away from traditional shaded cocoa
agroforestry systems, which integrate trees with
cocoa crops has been observed to a large extent on the
ground (Ruf 2011; Hutchins et al. 2015). However, in
Nigeria, some researchers such as Lokpobiri (2019)
noted a failed translation of federal policy to state-
level implementation due to issues such as a lack of
commitment, consistency, and falsification (Ojong
and Anam 2018; Lokpobiri 2019).

Despite some studies showing that full-sun cocoa
systems can boost yields under ideal conditions (Bla-
ser et al. 2017; Kolavalli and Vigneri 2011), increased
output in the long term is uncertain. Full-sun cocoa plantations can degrade soil fertility and biodiversity which impacts crop health and decreases resilience (Ariza-Salamanca et al. 2023; Tondoh et al. 2015; Bennett et al. 2021). Full-sun systems have also been found to stop producing cocoa at a younger age (Andres et al. 2016; Wessel and Quist-Wessel 2015). Agroforestry has therefore become a topic of interest as both a climate change mitigation measure through its retention of trees as well as an adaptation measure through regulation of micro-climates (e.g., shading cocoa from direct sunlight) and provision of diversified income streams (Jagoret et al. 2019). With proper management, agroforestry systems can have 10 times the total system output (when non-cocoa harvests are included) compared to their full-sun counterparts, potentially contributing to greater food and income security (Niether et al. 2020; Jeezeer et al. 2017).

Globally, with greater prioritization of sustainability, there has been a movement amongst national governments, NGOs and agricultural research organizations towards climate resilient agriculture, including a returning interest in agroforestry practices (Sanial et al. 2020). In Nigeria, agroforestry practices have recently been promoted by the federal-level Forest Research Institute of Nigeria (FRIN)<fnref n="1" /> and in Ekiti State through the Forestry Commission (2021a). However, a return to traditional cocoa practices is likely to be complex given the country's previous push for full-sun modernization. For example, in Ghana, the reintroduction of oversimplified climate-smart agriculture (CSA) practices through private sector partnership has not sufficiently addressed issues such as a lack of tenure rights, potential rebound effects leading to deforestation, and agro-chemical dependence, resulting in some smallholder farmers being left with unequitable outcomes (Nasser et al. 2020). These swings in research and policy interests highlight the longstanding privileged role of external knowledge sources that have often devalued traditional knowledge and practice, only to later recognize their value when aligned with modern narratives (Ajani et al. 2013; Makinde and Shorunke 2013).

In Ekiti, there is a lack of understanding of how contradicting government policy dynamics for modernization and sustainability interact with local knowledge and influence farmer decision-making for cocoa production on the ground. Agriculture and land use policy creation in the State has historically been diffuse, with programs largely influenced by the direction of external donors. This has made it difficult for central coordination and tracking of progress. The State has recently defined ambitions for forestry and land use, signaling the emergence of more formal direction setting and greater prioritization of land use topics. However, it notes that there is a significant need to bridge the gap between agricultural and forestry agendas (Ekiti State Forestry Commission 2021a).

Spatial mapping and understanding of land use across Nigeria have been extremely limited, with only a handful of academic studies (e.g., Olorunfemi et al. 2020; Fasona et al. 2020; Abbas et al. 2018; Akinyemi 2013; Koranteng et al. 2016), and two national land cover surveys conducted since 1976 (Fasona et al. 2020). Cocoa farms have rarely been differentiated from other land covers in Nigeria due to their similar spatial signature to open canopy forests and a high chance of misclassification (Ashiagbor et al. 2020; Benefoh et al. 2018). When this study was conducted, only three studies in West Africa (Ghana and Cameroon) had been able to distinguish agroforestry cocoa systems from their full-sun counterparts (Ashiagbor et al. 2020; Numbisi et al. 2019; Benefoh et al. 2018). While of great scientific relevance, the methods used in those studies have limited spatial accuracy or public accessibility due to the use of commercial satellite imagery. This has resulted in limited applicability for cocoa land use planning (Ashiagbor et al. 2020), and understanding of the current level of climate change adaptation in Nigeria. Since this study was conducted, there have been some further developments in this area (such as Moraiti et al. 2024; Kanmegne Tamga et al. 2023). That said, mapping in Nigeria has continued to be limited. A recent mapping exercise conducted by CHUSSDEV (2023) in Cross River State, has emphasized the importance of accurate mapping to address challenges such as forest and biodiversity loss faced by the sector.

This case study aims to provide insight into the extent and drivers of cocoa agroforestry practices in Ekiti State, Nigeria. It uses a mixed-methods

<footnote n="1">¹ Personal communication with a researcher at the Forest Research Institute of Nigeria on August 10, 2021.</footnote>
approach to investigate the hypothesis that the major-
ity of cocoa production in Ekiti State has been car-
ried out using full-sun plantations due to histori-
cal policy guidance. It validates the use of optical
remote sensing data to differentiate agroforestry and
full-sun cocoa in Nigeria, utilizing tasseled cap indi-
ces for Sentinel 2 data to delineate cocoa systems. It
then grounds this spatial understanding in the socio-
political context by exploring the potential trends and
influencing drivers for the use of cocoa agroforestry
practices contributing to this outcome. These meth-
ods could help provide more accurate and nuanced
data, increasing robustness in land use planning and
management.

This research contributes to a greater understand-
ing of cocoa production in Nigeria, which has gar-
nered little attention compared to more heavily stud-
ied areas in West Africa. While the cocoa sectors in
the largest producing countries of Ghana and Cote
d'Ivoire have been linked to substantial deforestation

(Kalischek et al. 2023), Nigeria’s extensive cocoa production is yet to reach the same extent (Awoyemi and Aderinoye-Abdulwahab 2019; FAO 2023). This presents a unique opportunity to chart a new course for cocoa sector growth, avoiding the pitfalls experienced by neighboring countries. Local understandings surrounding climate resilient practices, such as those observed in this study around agroforestry in Ekiti State, can enhance the design of future policies and programs to reduce deforestation and increase adaptive capacity towards climate change.

**Materials and methods**

Study area

Ekiti State (7°17′ to 8°07′ North and 4°52′ to 5°48′ East; see Fig. 1), is a state in Southwestern Nigeria with an area of approximately 5,888 km² (Salau et al.

<figure><img src="image_1.png" /><figcaption>Fig. 1 Map of Ekiti State illustrating the study area (Google Earth Pro, 2024)</figcaption></figure>

<figure><img src="image_2.png" /></figure>
2016). Its elevation is between 250 and 540 m above sea level (Olorunfemi et al. 2020), with a terrain that is generally undulating (Talabi and Tijani 2013). The region is underlain by metamorphic rock with the most common soils of Egbeda (loamy sand and Iwo (sandy loam) series) (Olorunfemi et al. 2018). Some of Ekiti's soils were sampled for quality for cocoa production and were all classified as marginally suitable for that purpose (three out of five suitability classes; Omotoso et al. 2007). There are two main climatological seasons. A rainy season that runs from April until October and a dry season from November until March (Olorunfemi and Fasinmirin 2017). The mean temperature ranges between 21 and 28 °C (Ekiti State Government 2021b) while peak temperatures have reached between 37 and 39 °C over the last decade (World Weather Online 2021). Tropical rainforests are present in the South while the Nigeria Guinea Savanna is found in the North (Olorunfemi et al. 2020). The state is divided into 16 Local Government Areas (LGAs), with Ado-Ekiti serving as the State capital. Data was collected during the period of June to August 2021.

## Remote sensing methodology

### Datasets utilized

Three satellite imagery datasets from the google earth engine (GEE) repository for 2020 were utilized for this study. Sentinel 2 satellite mission imagery from the European Space Agency Copernicus Mission provided data from 13 spectral bands of its Multispectral Instrument at 10 m, 20 m, and 60 m spatial resolution with a five-day revisit temporal frequency (European Space Agency 2015). The top of atmosphere (ToA) level 1C product, as well as the surface reflectance level 2A product were utilized. The ToA datasets were utilized for some of the variable sets because coefficients for tasseled cap vegetation indices have been defined only for ToA. The third dataset utilized was the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) with 30 m spatial resolution.

Three variable sets (V1, V2, and V3) were created in order to compare the accuracy of the different approaches employed. The V1 variable set comprised only of Sentinel 2 spectral bands and functioned as a baseline to establish the utility of vegetation indices

across sets. The V2 variable set employed an equivalent methodology to an optical remote sensing dataset utilized by Ashiagbor et al. (2020) to validate the methodology for use in Nigeria. Finally, the V3 variable set used tasseled cap indices to extend the previously used methodology with Sentinel 2 data. The surface reflectance dataset was utilized for variable sets V1 and V2 while the ToA dataset was utilized for V3 because coefficients for tasseled cap vegetation indices have been defined only for ToA (Shi and Xu 2019). Variables based on the DEM were added to variable sets V2 and V3.

A cloud mask was applied to both Sentinel datasets utilizing the Sentinel 2 Cloud Probability asset. Pixels with a cloud probability of above 20% were masked out. All water bodies in the state were masked out using the Scene Classification Map water class inbuilt in the Sentinel 2-2A product. The Ekiti State shapefile was manually created using 112 points to delineate Ekiti State boundaries along the boundaries on the base layer map. The final area comprised 5,818 km² constituting over 98% of the previously recorded area of Ekiti State by Salau et al. (2016).

## Variable creation

Several vegetation indices (VIs) were calculated for classifications V2 and V3 (see Table 1). The following indices together with the original spectral bands of Sentinel 2 comprised the classification V2 variables: Normalised Difference Vegetation Index (NDVI), Green Normalised Difference Vegetation Index (GNDVI), plant senescing reflectance index (PSRI), red-edge chlorophyll index (CI), inverted red-edge chlorophyll index (IRECI), and Sentinel 2 Red-Edge Position (S2REP) (Zhang et al. 2018; Frampton et al. 2013). These indices help to assess vegetation condition, canopy cover, and structure, as well as gross primary productivity.

Tasseled cap (TC) indices were used to create the classification of V3 variables. Tasseled cap indices compress and orthogonally transform multiple spectral bands into three uncorrelated bands (Baig et al. 2014). The three resulting bands represent greenness, brightness, and wetness (Benefoh et al. 2018). Tasseled cap indices have been widely used for Landsat imagery. However, tasseled cap coefficients from one sensor cannot be directly transferred for use on another sensor because they are dependent on the
number, wavelength, and spectral response of the bands present (Shi and Xu 2019). Shi and Xu (2019) have developed such coefficients for the Sentinel 2 ToA dataset which are utilized here. Finally, slope and elevation variables were derived from the DEM dataset.

## Field data collection and analysis

GPS points were collected during July and August 2021 across all 16 Local Government Areas in Ekiti State, Nigeria. GPS points were collected using the GPS Dragon mobile app with an accuracy of three meters. The land-use categories were based on Ashiagbor et al. (2020) research and included built-up area/bare land, agricultural land, agroforestry cocoa, full-sun cocoa, plantations, primary forest, and secondary forest (see Supplementary Information 1, Table 2 for category descriptions). The points were classified by researchers' on-the-ground empirical assessment in accordance with the category descriptions (e.g., Agroforestry cocoa: Cocoa farmland on which cocoa is shaded by trees. The level of shade provided may vary). While agroforestry systems experienced varying amounts of tree densities, a full-sun system was classified when there were no trees present amongst cocoa, or where limited trees were present which were not providing shading due to their placement. At locations where farmers were interviewed, their insights also served as a reference. Points that did not clearly fall into one or another category (e.g., cocoa trees in a park in a built up area) were excluded from the analysis.

All points were transcribed into an Excel spreadsheet and verified on a map using GEE. For each individual location (e.g., a cocoa farm), up to eight individual points were collected with examples of land classified in Fig. 2.

## Land-use classification

Five hundred and four (79.5% of total) data points were used for classification training and 130 (20.5% of total) data points for validation. The 80% to 20% ratio between training and validation points was adopted instead of the more common 70% to 30% in order to ensure a sufficient number of training points were available for each category. The lowest number of training points per category was 35 (agricultural

land) and the maximum number was capped at 100 to avoid skewing the classification due to a large category imbalance between the number of points used (See Supplementary Information 1, Table 3 for details).

The Random Forest (RF) classifier available in GEE was used to perform the classification in accordance with the data flow in Fig. 3. Five hundred random trees were chosen to be used because values beyond this number have a very small impact on the classification results (Wang et al. 2018). The land-use classification was then performed using the three pre-defined variable sets V1, V2, and V3 as inputs and was optimized by varying inclusion of SRTM DEM model variables, time period for the assessment (full-year/dry season), and aggregator used (min/median/max), and the combination that yielded the highest overall accuracy for any one of the variable sets was used. Accuracy was assessed for all three classification algorithms, using overall, producer and user accuracies as well as kappa statistic.

## Interview methodology

To understand the perspectives, policies, and programs that are in place to inform land-based decision-making in Ekiti, a range of policy-influencing stakeholders were interviewed. Due to Covid-19 restrictions, interviews were conducted through audio or video calls. A list of actors working in cocoa agriculture and forestry was created, including government officials, research institutions, NGOs, producer associations, and commodity traders. Relevant local contacts were purposely sampled on the basis of their knowledge of the cocoa sector, and further participants were identified through snowball sampling. A total of 13 key informants were interviewed. Interviews were semi-structured following a set of 15 questions (Supplementary Information 2, Text 3) to allow for more fluid discussions of the topics and covered relevant policies and programs, perspectives on the agriculture-forestry nexus, as well as the challenges and opportunities for cocoa production and agroforestry. Interviews were conducted in English. As there is a paucity in official policy documentation, these conversations were used to gauge guidance for land use, agriculture, and cocoa production in Ekiti State and more broadly in Nigeria.
<figure><img src="image_3.png" /><figcaption>**Fig. 2** Illustrations of the land use categories used for the classification: a agricultural land, b plantation, c built up/bare land, d primary forest, e agroforestry cocoa, f full-sun cocoa, and g secondary forest</figcaption></figure>

Interviews were conducted with 32 cocoa farmers in Ekiti State on their land to understand their perception and use of cocoa agroforestry methods, production challenges, and sources of information. Farmers were identified through discussions with local villagers. A set of 27 pre-determined questions (Supplementary Information 2, Text 2) were posed in Ekiti Yoruba dialect while audio, videos, and field notes were taken on the property. Interviews were carried out in 15 of the 16 LGAs in Ekiti State. In each LGA, the ambition was to sample two villages and hold one

interview in each. Due to accessibility and farmer availability, between one and four interviews were held in each LGA. No interviews were conducted in Moba LGA as it had limited cocoa farm presence. See Supplementary Information 2, Table 5 for an overview of interviews held.

Field interviews were translated to English from Ekiti Yoruba dialect, and remotely conducted interviews were transcribed from their original audio recordings. Data was anonymized and thematic analysis was performed following usual protocols
<figure><img src="image_4.png" /><figcaption>**Fig. 3** Research data flow illustrating the process of developing the final three classification maps and their accuracy and category area calculation. On the dataset level, S2 = senti- nel 2 data, SR = surface reflectance, ToA = top of the atmosphere, SRTM DEM = shuttle radar topog- raphy mission digital eleva- tion model. Under SR bands 1–13 vegetation indices spectral math, see Table 1 for indice overview</figcaption></figure>

<figure><img src="image_5.png" /></figure>
<table><caption>**Table 1** Overview of vegetation indices utilized for the construction of V2 and V3 classification variables</caption><thead><tr><th>Vegetation Index</th><th>Formula</th><th>Bands used</th></tr></thead><tbody><tr><td>NDVI</td><td>(NIR - Red)/(NIR + Red)</td><td>B8, B4</td></tr><tr><td>CI</td><td>((Rededge 3)/(Rededge 1)) - 1</td><td>B7, B5</td></tr><tr><td>PSRI</td><td>(Red-Blue)/(Rededge 2)</td><td>B4, B2, B6</td></tr><tr><td>GNDVI</td><td>(NIR - Green)/(NIR + Green)</td><td>B8, B3,</td></tr><tr><td>S2REP</td><td>705 + 35* [((NIR + Red)/2) - Rededge1]/(Rededge2 - Rededge1)]</td><td>B8, B4, B5, B6</td></tr><tr><td>IRECI</td><td>NIR - Red/(Rededge1/Rededge2)</td><td>B8, B4, B6, B5</td></tr><tr><td>Tass_B</td><td>0.3510 Blue + 0.3813 Green + 0.3437 Red + 0.7196 NIR 1 + 0.2396 MIR 1 + 0.1949 MIR 2</td><td>B2, B3, B4, B8, B11, B12</td></tr><tr><td>Tass_G</td><td>-0.3599 Blue - 0.3533 Green - 0.4734 Red + 0.6633 NIR 1 + 0.0087 MIR 1 - 0.2856 MIR 2</td><td>B2, B3, B4, B8, B11, B12</td></tr><tr><td>Tass_W</td><td>0.2578 Blue + 0.2305 Green + 0.0883 Red + 0.1071 NIR 1 - 0.7611 MIR 1 - 0.5308 MIR 2</td><td>B2, B3, B4, B8, B11, B12</td></tr></tbody></table>

(Miles and Huberman 1994; Punch 2013), coding the responses to identify key themes and trends. We also report some descriptive statistics in the form of percentages, but these should be interpreted as indicative given the small and non-random sample.

## Results and Discussion

### Extent of cocoa agroforestry

### Cocoa agroforestry in Ekiti

In this study, cocoa agroforestry was defined as cocoa grown with trees, including those used for timber, fruit and other tree products, shading, and other regulating services. In Ekiti, many cocoa agroforestry system types were observed such as parklands (scattered non-crop trees throughout the land), using plantain/banana trees for cocoa seed establishment, and inter cropping with non-cocoa tree crops. These are consistent with the systems found in previous Ekiti cocoa agroforestry studies (Ajayi et al. 2020; Awoyemi and Aderinoye-Abdulwahab 2019; Ilesanmi and Afolabi 2020). The density of shading varied from site to site, with large variations in what was classified as cocoa agroforestry. One cocoa farmer that was interviewed shared guidance provided by their buyer association, defining cocoa agroforestry as a system with at least 15 trees per hectare. This is similar to the definition of 18 trees per hectare provided by a recent Nigerian land mapping study (CHUSSDEV 2023). The majority of cocoa agroforestry systems observed in this

study had greater tree densities. A more recent Ekiti study quantified tree density in cocoa agroforestry systems, finding between 160 and 960 non-cocoa trees per hectare (Brown et al. 2023). Other forms of cocoa agroforestry including cocoa intercropping with non-tree crops were not assessed.

In Ekiti, cocoa production was the largest land use category making up 31% of the total land cover. This is in line with the results of Dada and Hahn (2020) who found that cocoa made up nearly one-third of the total area in a neighboring cocoa-leading Ondo state. In Ekiti, agroforestry cocoa made up the majority of cocoa cover (18% of total land cover), compared to full-sun cocoa (13% of total land cover). Plantations (i.e., teak—*Tectona grandis*, gmelina—*Gmelina arborea*, and oil palm—*Elaeis guineensis*) were the second-largest land cover type comprising 25%. Next, the built-up/bare area made up 15%, secondary forest 11%, agricultural land 10%, and primary forest 9% (see Fig. 4).

The above results are based on the V3 classification that was selected due to its high accuracy (see Supplementary Information 1, Fig. 5 and 6 for results based on V1 and V2 classifications). The overall accuracy of the V1, V2, and V3 classification was 51.5%, 69.2%, and 73.1%, respectively. For agroforestry and full-sun cocoa, producer accuracy reached 80% and 85% respectively for V3. The kappa statistic compares the classification accuracy to random assignment and can range from -1 to 1, with 1 representing perfect alignment. The kappa statistic was 42.3%, 63.5%, and 68%, respectively, with V1 having a “moderate” strength of agreement and V2
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Ekiti State resulting classification using V3 variables set</figcaption></figure>

and V3 falling into the “substantial” agreement category (Landis and Koch 1977) between the classification and the collected validation data. Challenges remained in terms of classification of the secondary forest category, confirming previous findings on challenges differentiating agroforestry and secondary forest due to their similar spectral signatures (Benefoh et al. 2018; Ashiagbor et al. 2020). In Ekiti, accuracy for secondary forests was 40% and at the same time, 33% of validation points were erroneously classified as agroforestry cocoa, leading to possible overreporting of the extent of agroforestry cocoa.

The overall accuracy found in this study is slightly lower than those found in two of the three Western African cocoa full-sun and agroforestry differentiation studies on which the methodology references (73.1% accuracy vs. 83% (Benefoh et al. 2018) and

89.8% (Ashiagbor et al. 2020)). This may be due to the significantly larger dataset employed by Benefoh et al. (2018), as well as their inclusion of normalized difference vegetation index (NDVI) and the land surface water index (LSWI) alongside the use of tasseled cap indices. Ashiagbor et al. (2020) utilized object-oriented approaches which take into account contextual information from pixels by grouping them into more homogeneous objects which integrate a range of geometrical features into the classification (Whiteside et al. 2011). Further research which explores these approaches could provide additional insights into the land cover of Ekiti State and its implications for potential adaptation to climate change.

The classification analysis revealed that 57% of cocoa in Ekiti State is grown in agroforestry systems. This was also reflected in the on-the-ground

<figure><img src="image_7.png" /></figure>
interviews with cocoa farmers. Of the farmers inter-
viewed, 74% practiced either agroforestry or both
agroforestry and full-sun cocoa methods. This find-
ing appears to contradict previous research in Nige-
ria and neighboring countries that postulated that
farmers prefer to grow full-sun varieties because of
faster maturity and translation into profits (Ruf 2011;
Niether et al. 2020; Hutchins et al. 2015; Wessel and
Quist-Wessel 2015). Due to a lack of temporal trends,
it was unclear whether (1) full-sun systems were
increasingly replacing agroforestry systems, despite
the current agroforestry system majority, or whether;
(2) full-sun cocoa systems were increasingly being
converted back into agroforestry systems. Regular
monitoring of cocoa spatial cover would lend insights
into the rate and ways in which cocoa practices are
changing in Ekiti State. Nevertheless, this study was
able to investigate current and historical socio-politi-
cal considerations on the ground to better understand
the potential direction and drivers behind agroforestry
trends in Ekiti State.

Policy-level drivers for cocoa production practices

Conflicting cocoa-related policies

Conversations conducted with government and policy
influencing stakeholders in Ekiti State revealed that
there was limited formalization and central coordi-
nation of land use policy. Internal and external pro-
grams played a key role in direction setting. While
there have been efforts to provide central guidance
and tracking for forestry in the State (Ekiti Forestry
Commission 2021a), this has been largely seen as
aspirational,<fnref n="2" /> and lacking alignment with any formal-
ized agricultural land use guidance. When it came to
tree retention on cocoa production systems in Ekiti,
there was often conflicting messaging to farmers
coming from agriculture versus forest related policy.<fnref n="3" />

From the side of agricultural policy, government
officials from the Ekiti Ministry of Agriculture and

Food Security (MoA) shared that full-sun cocoa has
been actively promoted to farmers through agricul-
tural initiatives.<fnref n="4" /> One such initiative is the subsidi-
zation of improved cocoa seeds (Ekiti State Govern-
ment 2024a). The State has been actively promoting
the use of “Agric cocoa”, a local name for a series of
hybrid cocoa varieties, which was introduced in Ekiti
around 20 years ago. In 2010, the Cocoa Research
Institute of Nigeria (CRIN) released eight new high-
yielding, low-input, early-maturing cocoa hybrids
(CRIN TC 1–8) to improve the productivity and sus-
tainability of Nigerian cocoa farms (CRIN 2016).
These varieties have been introduced in Ekiti State,
with a goal of 65% adoption for example in 2017
(ADP 2017). A program manager of the Agricultural
Development Programme (ADP), which provides
government extension services on the ground, con-
firmed that they have been distributing Agric cocoa
with accompanying guidance on the adoption of full-
sun growing methods. Extension workers have been
directly advising farmers to clear trees from their
lands claiming that the Agric varieties do poorly in
overly shaded conditions. Extension guidance is often
delivered to farmers verbally and is based on collec-
tive local experiences as well as local and foreign
government program guidance. In Ekiti, training for
extension workers on the production of hybrid cocoa
has been based on the good agricultural practices
(GAP) provided by the German federal develop-
ment agency Deutsche Gesellschaft für Internation-
ale Zusammenarbeit (GIZ)'s sustainable smallholder
agri-business programme (SSAB) (ADP 2017). That
said, without access to the training material provided
to ADP, this study was unable to assess how much
GIZ-SSAB guidance has influenced extension advice
provided to farmers on the ground versus advice that
has been based on local experience or other informa-
tion sources.

From the side of forest policy, the reintegration
of agroforestry methods has recently been promoted
by the Ekiti State Forestry Commission operat-
ing under the Ministry of Environment and Natural
Resources. Initiatives such as the World Bank-backed
Nigeria Erosion and Watershed Management Project

<footnote n="2">² Personal communication with an official from the Ekiti State Government on May 14, 2023.</footnote>

<footnote n="3">³ Personal communication with officials from the Ekiti State Forestry Commission and Ekiti State Ministry of Agriculture and Food Security on July 27, 2021 and August 11, 2021.</footnote>

<footnote n="4">⁴ Personal communication with officials from The Agricultural Development Programme (ADP) and the Youth Commer-</footnote>

<footnote>Footnote 4 (continued)

Cial Agriculture Development (YCAD) of the Ekiti State Gov-
ernment on August 10 and 11, 2021.</footnote>
(NEWMAP) erosion control project and the Road-
map for Sustainable Management of Ekiti For-
ests 2021–2035 project have been encouraging the
reestablishment of trees in forest reserves and on
farmland (Ekiti NEWMAP 2024, Ekiti State For-
stry Commission 2021a). Farmers have been given
incentives such as free seedlings to plant fruit (such
as mango—*Mangifera indica*, moringa—*Moringa
oleifera*, and various guava—*Psidium* spp. and cit-
rus species) and timber trees (such as teak—*Tectona
grandis* and gmelina—*Gmelina arborea*) on their
properties to encourage aforestation while provid-
ing benefits such as on-farm water management and
income diversification (Ekiti State Forestry Commis-
sion 2021b, Ekiti NEWMAP 2024). As many of these
projects were fairly new at the time of investigation,
they are unlikely to be reflected in this study’s spatial
mapping exercise due to the time lag in tree maturity
and establishment of canopy cover. Further investiga-
tion to understand the projects’ suitability to current
cocoa systems could reveal expected adoption rates in
the future.

From a policy perspective, as farm-level forest
policy has only more recently developed, it could be
inferred that the promotion of full-sun cocoa pro-
duction methods has likely had a dominating effect
against the reintroduction of agroforestry methods in
Ekiti State. With historically conflicting priorities and
minimal collaboration between the Ministry of Agri-
culture and the Forestry Commission, tree conserva-
tion has been deprioritized and treated as incompat-
ible with State priorities of economic development
and food security.<fnref n="5" /> That said, there was belief from
the Forestry officer that with increased awareness,
especially in light of climate change, the importance
of forest and tree regeneration would become an
important consideration for food security.

With the increasing need to address multifaceted
issues of economic development and climate change
in the land use sector, better alignment between gov-
ernment bodies could provide a step in the right direc-
tion. Further exploration of the compatibility of tree
promotion and improved cocoa development could
simultaneously support both agriculture and forestry

priorities in Ekiti State. Despite policy promot-
ing full-sun cocoa production, spatial mapping and
farmer interviews reveal a gap between policy and
on-the-ground practices. Through the promotion of
compatible cocoa agroforestry methods, the govern-
ment would not have to expend significant resources
on educating and convincing farmers to transition
to a new full-sun system. Instead, it could focus on
enabling the farmers to develop existing and neces-
sary toolkits to carry out agroforestry practices effec-
tively. Government efforts could prioritize actions
such as providing access to capital for essential farm-
ing inputs, identified by interviewed farmers as their
top obstacle, or training on optimizing complex agro-
forestry systems through practices like pruning and
regular diseased pod removal (Armengot et al. 2020).

Lack of government support

Despite policy ambition for cocoa development in
the State, negative relationships between farmers and
agricultural extension workers and a lack of govern-
ment presence have likely prevented the implemen-
tation of cocoa support policies on the ground. In
half of the interviews with farmers, it was revealed
that there was a general distrust in the government’s
intentions and capacity to support farmers and man-
age resources. A local NGO leader reported that
non-agricultural institutions are often tasked with
the distribution of agricultural funds. However, they
often fail to grasp the complexity and seasonality of
agricultural systems, resulting in funds being improper-
ly channeled and wasted.<fnref n="6" /> This has contributed to
the negative perception that the government is out of
touch with what farmers need on the ground.

The majority of interviewed farmers did not
believe that the government provided them with
adequate support to achieve the objectives set out by
the administration. Of the farmers interviewed, more
than half had never encountered government exten-
sion workers, and those who had, often purposefully
avoided them due to their negative reputation. Many
farmers referred to past incidents of deceit and the
failed delivery of promised agricultural inputs and
subsidies. These findings are in line with a study

<figure><img src="image_8.png" /></figure>

<footnote n="5">⁵ Personal communication with officials from the Ekiti State Forestry Commission and Ekiti State Ministry of Agriculture and Food Security on July 27, 2021 and August 11, 2021.</footnote>

<footnote n="6">⁶ Personal communication with a researcher at the Interna-
tional Institute of Tropical Agriculture (IITA) on July 22, 2021.</footnote>
conducted by Oladosu and Yekinni (2008) which found that over 95% of surveyed Ekiti farmers saw the low availability of extension agents as a constraint while 95% saw a lack of input supply through extension services as a challenge to their operations. The Ministry of Agriculture and Food Security has established farmers’ associations across Ekiti State to facilitate extension work and disperse information about new technologies and methods. However, many farmers revealed that after initially joining, they left these groups due to membership fees and limited benefits. As full-sun cocoa practices are being promoted through extension services in Ekiti, it is possible that a lack of government presence and their negative reputation have prevented information regarding these systems from reaching farmers at anticipated levels. The lack of policy effectiveness may have therefore contributed to the continued use of agroforestry practices on the ground.

## Farm-level drivers for cocoa production practices

### *Cocoa stand structure*

The decision of whether to use agroforestry or full-sun systems was found to be closely tied to the variety of cocoa grown. The majority of farms grew both hybrid and local cocoa varieties. Older cocoa stands, more than 20 years old, were largely local cocoa varieties, whereas newer stands were most often hybrid varieties. There were some cases where local cocoa was still being bred and grown on-farm, however the majority of new seedlings were purchased hybrids. In line with government cocoa-growing guidance, agricultural officers and the majority of farmers claimed that local cocoa was suited to agroforestry, while hybrid varieties were suited to full-sun practices. The majority of those that practiced full-sun claimed that it was easier to manage their cocoa, they experienced faster maturity rates, and a reduced presence of diseases such as black pod. In both this study and Brown et al. (2023)'s study, farmers shared that agroforestry systems required active management, as too much shade increased pests and diseases presence during the rainy season and increased fire during dry season. This may suggest why full-sun methods are often seen by both extension and farmers as a production improvement measures against these challenges. If hybrid cocoa, with the current associated full-sun

guidance, continues to be promoted and implemented, it can be expected that agroforestry practice will decline.

The age of cocoa trees also revealed potential trends toward the future share of hybrid cocoa in Ekiti. Most farms had cocoa trees between 20 and 30 years old. This is significant because the estimated productive lifespan of a cocoa tree is ca. 25–30 years after which productivity declines (Babalola et al. 2017). In order to increase the productivity of the aging local stand, it is expected that many will be replaced. As local cocoa is no longer distributed in the State, any replacement trees are likely to be hybrid cocoa, thus increasing full-sun practices with the current guidance. That said, productivity of cocoa stands could continue to be low despite an increased uptake of hybrid cocoa, without the adoption of climate adaptation measures such as the use of agroforestry (Schroth et al. 2017). Socio-economic factors such as the cost of replanting stands, lack of understanding around new varieties, or the continued desire to use traditional methods, may lead to farmers keeping local trees. Regardless of the variety of cocoa planted, productivity is also highly dependent on access to capital, inputs, and reliable information and training around cocoa management.

### *Availability of capital*

The most often discussed challenge during farmer interviews was a lack of sufficient funds to maintain and improve their systems. Many farmers expressed that the profits gained from a cocoa harvest one year often had to be reinvested into inputs the following year, leaving farmers with little flexibility to expand or change their systems. Only 38% of the interviewees had ever received any external funding, none of which had come from the government, but from private organizations and NGOs. Support often came from buyers in the form of loans at the start of the growing season and were to be repaid after harvest. Of those interviewed, 94% of farmers had other sources of income besides cocoa to maintain their operations. Brown et al. (2023) found that for cocoa farmers in Ekiti, income per hectare ranged between ₦191,188 and ₦1,945,000, including both cocoa and non-cocoa harvests. It found that cocoa made up between 31% and 98% of their annual income with significant shares also coming from other food
crops and timber. Many cocoa farmers in the State, especially those with smaller operations, earn less than the minimum wage (Ogunjinmi et al. 2018), likely linking to the experienced financial inflexibility. Research from neighboring Oyo state, which has a high uptake of hybrid cocoa, found that the most significant constraint for the adoption of hybrid cocoa was the high cost of agrochemicals associated with the varieties (Akinwale and Folarin 2018). Farmers currently using local varieties with traditional agroforestry methods may therefore find it challenging to purchase new hybrid cocoa and adopt new full-sun practices.

The financial burden associated with hybrid cocoa and full-sun adoption have likely resulted in slow agroforestry conversion. Switching from agroforestry to a full-sun system often requires upfront costs for new trees and inputs. Additionally, when establishing a new cocoa plantation, full-sun cocoa faces a greater risk of crop failure during the establishment phase as hybrid seedlings are less resilient to high temperatures than the local variety.<fnref n="7" /> A local extension agent shared that a few years ago, many young farmers had invested in new hybrid cocoa crops. However, due to temperature shocks, many lost all of their crops and were forced to abandon cocoa production entirely.<fnref n="8" /> The State-wide ambition to convert to full-sun production systems could have been therefore slowed by insufficient financial security on the farm level. That said, in other countries, there has been a transition toward full-sun production in the past two decades even when farmers did not have the necessary resources to provide all inputs required in such a system (Niether et al. 2020; Hutchins et al. 2015; Ruf 2011; Wessel and Quist-Wessel 2015). This reinforces the multicausal effect for the continued use of agroforestry in Ekiti State.

A lack of capital also had the counter effect of increasing tree harvesting on cocoa farms. Some cocoa farms were cleared of shade trees in order to generate additional revenue. The majority of interviewed farmers harvested timber for cash, aligned

with similar findings from a more recent study (Brown et al. 2023). Oftentimes, the trees that were cut had been on the property from before the land was converted for agricultural production, implying that the trees were not grown by the farmers. Many farmers that used agroforestry expressed that despite recognizing the benefits that trees provided to their production, the trees were cut and sold when they were struggling financially and were often not replaced. Many, especially older farmers, expressed that they did not see the benefit of planting new trees due to the long maturity time before harvest. These findings imply that having trees on cocoa farms can provide important financial security for farmers during challenging times. It also emphasizes the need for permanent sources of diversified income to act as insurance in case of cocoa failure. Instead of harvesting timber, the establishment of fruit-bearing trees can act as a long-term income source as well as provide additional benefits to cocoa such as shading and nutrient cycling (Jagoret et al. 2019). This insight has been captured through some of the Forestry Commission's planned projects such as the NEWMAP aforestation program (Ekiti NEWMAP 2024) and could serve as an effective tree retention strategy on farms. That said, to see increased adoption, the planting and retention of trees on cocoa farms would need to be compatible and aligned with cocoa production and agroforestry guidance.

## Sources of information

Where and how farmers receive information about cocoa growing can contribute to their decision on which practices to adopt. Of the farmers interviewed, around 70% had inherited their cocoa farms, and a quarter of farmers relied solely on heritage information to maintain their systems. Heritage knowledge in the region has mostly been built around the use of the local cocoa variety using agroforestry methods. These figures reveal the legacy and importance of traditional agroforestry and heritage knowledge in cocoa production in Ekiti. The rest of the farmers received their information from multiple sources including radio/tel-evision (78%), government extension services (31%), the local cocoa farming community (22%), and buyer and export associations/organizations (9%). Farmers often received conflicting guidance on best practices. For example, while extension agents advise full-sun

<figure><img src="image_9.png" /></figure>

<footnote n="7">⁷ Personal communication with an official from The Agricultural Development Programme (ADP) of the Ekiti State Government on August 10, 2021.</footnote>

<footnote n="8">⁸ Personal communication with an official from The Agricultural Development Programme (ADP) of the Ekiti State Government on August 10, 2021.</footnote>
practices, some export organizations and NGOs oper-
ating in the area promote and train farmers on how to
use agroforestry with hybrid cocoa. One such farmer
that had received information from the export organi-
zation Olam disputed the claim that hybrid cocoa was
incompatible with agroforestry. This dispute was also
supported by research at the Cocoa Research Institute
Nigeria (CRIN), which provides occasional training
and seminar events in the region.<fnref n="9" /> Although some
provided guidance supports agroforestry with hybrid
cocoa production, as the interviews revealed, many
of these information sources currently have a limited
reach, further being inaccessible to isolated farm-
ers across the State. These findings warrant further
investigation surrounding the compatibility of hybrid
cocoa with agroforestry methods. With hybrid cocoa
becoming increasingly widespread, compatibility and
proper guidance will likely be a key determining fac-
tor in the continued use and readoption of agrofor-
stry methods in the State.

Tenure

The lack of tenure security was revealed to have a
negative influence on tree planting and retention on
cocoa farms. With the Land Use Act of 1978 and the
nationalization of all Nigerian land, farmers have lim-
ited ownership and control over their lands (Isikhue-
men 2014). Farmers expressed that a lack of tenure
security and uncertainty over how long they will
remain on the land has often restricted long-term
decision-making, such as retaining trees for the ben-
efits they provide. This is a common theme in other
cocoa producing areas of West Africa (Bosselmann
et al. 2024). One farmer that was interviewed recently
had their property seized by the government for an
airport construction project and feared that they
would not be properly compensated for the eviction,
leading them to harvest all the trees on the prop-
erty to secure savings. Additionally, some farmers
also expressed that the risk of illegal loggers enter-
ing their properties was a large incentive to harvest
trees as soon as they were mature. Law enforcement
for illegal land-use practices in Ekiti State has histori-
cally been weak due to a lack of capacity and fear of

conflict with perpetrators (Kayode 2011). This issue
is more recently being approached by the Ekiti Gov-
ernment with the deployment of greater efforts to
safeguard forest resources and protect farms (Ekiti
State Government 2024b). However, if there contin-
ues to be a fear of asset loss, this may contribute to
the continued harvest and sale of agroforestry trees.

Farmers are also disincentivized from planting
new trees due to a lack of tree ownership rights. Nota-
bly, farmers referred to the policy of “you are not the
owner of what you plant” whereby the government
is the owner of all lands and thus the trees on those
lands (Ekiti State Government 2023; 2016). This pol-
icy requires farmers to pay a timber fee when harvest-
ing, which was found to be a demotivating factor in
tree planting on cocoa farms. Although some studies,
such as that conducted by Amalu et al. (2016) in Boki
LGA in Cross-River State found that effective collec-
tion and government reinvestment of forest royalties
could be beneficial to outcomes of community devel-
opment and forest resources, this was often not per-
ceived to be the case in Ekiti State. Oso and Babalola
(2021), noted that many farmers felt that forest offic-
ers unfairly profited from the timber tariffs leading to
farmers being unwilling to invest in new seedlings. A
researcher at the Forest Association of Nigeria (FAN)
as well as some of the interviewed farmers noted that
a relaxation of these regulations would likely encour-
age greater tree planting on farms.<fnref n="10" /> That said, these
regulations may also be preventing greater timber
harvest rates in Ekiti, as they limit financial returns to
the farmer. The benefits to farmers of retaining trees,
if the right varieties were planted and provided addi-
tional sources of income, could outweigh the finan-
cial gain from timber sales. This could imply that
the planting of fruit trees may provide greater long-
term income and encourage retention in comparison
to timber trees planted for harvest. These insights
demonstrate some of the conflicting and intersecting
factors that influence farmer decision-making on the
ground in regard to cocoa agroforestry use.

<footnote n="9">⁹ Personal communication with an agricultural officer from Ondo State Government on August 6, 2021.</footnote>

<footnote n="10">¹⁰ Personal communication with a researcher at the Forest Association of Nigeria (FAN) on August 10, 2021.</footnote>
## Conclusion

Cocoa agroforestry systems persist as the dominant (57% of total cocoa cover) form of cocoa production in Ekiti State, despite a policy push for cocoa modernization through hybrid cocoa use and full-sun production methods. From interviews with cocoa farmers and policy-influencing stakeholders in Ekiti, it was revealed that cocoa agroforestry has not been converted into full-sun systems as fast as anticipated due to intersecting factors such as conflicting government priorities, continued use of local cocoa varieties, limited reach of extension support, a lack of farmer financing, and complexities with tenure security. Further, agroforestry systems are still highly valued by local cocoa farmers for the shading and moisture retention benefits that non-cocoa trees provide as well as the provision of diversified income streams through timber and tree crops. Without stronger support for farmers to maintain or adopt agroforestry, full-sun systems are likely to surpass agroforestry systems, posing a risk to the sector's long-term productivity, adaptive capacity to climate change and halt of cocoa-led deforestation.

Leveraging the remote sensing methodology employed in this study, long-term monitoring of land use would be valuable to understand trends in cocoa practices in Ekiti State. The findings also warrant further investigation into how State-level agencies could work more collaboratively towards joint land-use priorities such as long-term food security and climate change. This study was not able to assess the compatibility of hybrid cocoa and agroforestry methods. Given the growing prominence of hybrid cocoa varieties in the State, research in this area would be a crucial step in assessing suitability of the practice. Simultaneously, best practices on maintaining and increasing productivity of existing agroforestry stands could be established. To effectively support farmers, this study revealed the importance of rebuilding trust with government and extension services, or leveraging trusted actors from NGOs or the private sector.

The methodology constitutes a first step towards developing a more comprehensive remote sensing system that could provide researchers and local actors with reliable near-real time information about the extent of full-sun and agroforestry cocoa and cocoa-led deforestation.

Our results emphasize that policy inefficacy has been a factor contributing to tree retention and environmental protection in Ekiti State. Findings regarding the drivers for the use of cocoa agroforestry are pertinent in wider global-scale discussions surrounding the growing interest and policy creation for sustainable, climate-smart, and deforestation-free cocoa. They reveal complexities in cocoa system transformation, offering grounded insights into local decision-making. There is opportunity to build on this understanding to safeguard existing agroforestry systems and prevent further and more rapid tree loss. These considerations could be used in the design of more informed and effective policies for local land-use management.

**Author contribution** Conceptualization: all authors; Methodology: all authors; Data collection: Olalekan Sikiru Olowoyo, Mercy Oluyemi, Anita Koralewicz, Jan Vlcek; Formal analysis and investigation: Anita Koralewicz, Jan Vlcek; Writing—original draft preparation: Anita Koralewicz, Jan Vlcek; Review and editing: all authors; Funding acquisition: Anita Koralewicz, Jan Vlcek; Supervision: Imma Oliveras Menor, Mark Hirons, Akinyemi Akinyugha, Oliver Owen. All authors have read and approved the final manuscript.

**Funding** Environmental Change Institute, University of Oxford, Christ Church College, University of Oxford.

## Declarations

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

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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