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Cocoa production for improving biodiversity, rural livelihoods and carbon in the Guinean forests of west Africa

  • Full or part time
  • Application Deadline
    Thursday, January 09, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Summary -This project will quantify the impact of varying levels of shade on biodiversity, carbon and livelihoods in cocoa production, a major land use and economic activity in West Africa. This PhD is co-supervised with Graeme Buchanan and Juliet Vickery from the RSPB.

Project background
The Guinean forests of west Africa are of high conservation importance and are recognized as an Endemic Bird Area and Biodiversity Hotspot. The area is used in the boreal winter by millions of migrant birds which have been suffering severe population declines. The forests also provide ecosystem services supporting carbon storage and human livelihoods. Rapid expansion in human population has led to widespread clearance of forest, losses of biodiversity and stored carbon. Cocoa farming is a major land use in west Africa and shade grown cocoa, whereby cocoa is planted under other trees. Shade grown cocoa could potentially provide connecting corridors that enable biodiversity to move between patches of forest. There is consensus that shade grown cocoa plantations do appear to have benefits for biodiversity compared to non-shade grown cocoa. The relationship between biodiversity and variation in levels of shade is less well known and trade-offs are likely between shade, cocoa yield and carbon storage. This project will quantify the impact of varying levels of shade on biodiversity, carbon storage, cocoa yield and livelihoods.

Possible research questions, but they can change during the first 12 months of the project as the student develops the research.
1 - The project aims to answer five key questions for shade grown cocoa plantations in west Africa:
2 - What level of shade results in biodiversity most similar to that found in uncultivated forest?
3 - Could shade grown cocoa be used as corridors to link patches of natural forests?
4 - How much additional above ground biomass is there in areas of shade gown cocoa compared to standard cultivated areas and uncultivated forest?
5 - How does cocoa productivity, and hence human livelihoods vary with level of shade?
6 - Can we identify a value of shade that maximises biodiversity, carbon and livelihoods?

This project would suit a student with an interest in the interdisciplinary study of conservation, biodiversity and international development. The student will have experience of field work, preferably in remote locations. Additionally the student will be able to work alone in a remote location for extended periods, often in difficult conditions, and organise day to day activities necessary to undertake field work and collect robust data safely.

For more details:
DTP advertisement:
How to apply:

Funding Notes

E4 DTP studentships are fully-funded for a minimum of 3.5 years. They include:
- Stipend based on RCUK minima (currently £15,009 for 2019/2020)
- Fees (Home/EU Fees)
- Research Costs (Standard Research Costs plus, depending on the projects requirements, Additional Research Costs can also be allocated)

The stipend can be extended to up to another 5 months through our two optional schemes, subject to the approval of the Management Board.


- Asare, R., Markussen, B., Asare, R.A., Anim-Kwapong, G., Ræbeld, A. (2018) On-farm cocoa yields increase with canopy cover of shade trees in two agro-ecological zones in Ghana, Climate and Development, 11, 5.

- Luo et al. (2019) Estimating forest aboveground biomass using small-footprint full-waveform airborne LiDAR data, International Journal of Applied Earth Observation and Geoinformation 83.

- Miller et al. (2017) Prevalence, economic contribution, and determinants of trees on farms across Sub-Saharan Africa, Forest Policy and Economics, 47-61.

- Watmough et al. (2019) Socioecologically informed use of remote sensing data to predict rural household poverty, PNAS, 116(4).

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