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The unrealized potential of agroforestry for an emissions-intensive agricultural commodity

Nature Sustainability volume 8, pages 994–1003 (2025)Cite this article

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Abstract

Reconciling agricultural production with climate change mitigation is a formidable sustainability problem. Retaining trees in agricultural systems is one proposed solution, but the magnitude of the current and future potential benefit that trees contribute to climate change mitigation remains uncertain. Here we help to resolve these issues across a West African region that produces ~60% of the world’s cocoa, a crop contributing one of the highest carbon footprints of all foods. Using machine learning, we mapped shade-tree cover and carbon stocks across the region and found that the existing average shade-tree cover is low (~13%) and poorly aligned with climate threats. Yet, increasing shade-tree cover to a minimum of 30% could sequester an additional 307 MtCO2e, enough to offset ~167% of contemporary cocoa-related emissions in Ghana and Côte d’Ivoire—without reducing production. Our approach is transferable to other shade-grown crops and aligns with emerging carbon market and sustainability reporting frameworks.

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Fig. 1: Map of shade-tree cover across cocoa-growing areas in Côte d’Ivoire and Ghana for 2022.
Fig. 2: Map of AGBD across Côte d’Ivoire and Ghana for 2022.
Fig. 3: Current aboveground carbon stocks and climate mitigation potential of cocoa systems across Ghana and Côte d’Ivoire.

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Data availability

All key datasets used in this study are publicly available. The final maps of shade-tree cover, canopy height and AGBD can be explored interactively and downloaded via Google Earth Engine at https://albecker.users.earthengine.app/view/agroforestry. The drone-derived ground-truth data for shade-tree cover are available via UQ eSpace at https://doi.org/10.48610/dda018c (ref. 80). Sentinel-2 satellite imagery is available from the Copernicus Open Access Hub at https://sentinels.copernicus.eu. Aboveground biomass reference data were obtained from NASA GEDI L4A (2022), available at https://doi.org/10.3334/ORNLDAAC/2056. Canopy height data were generated using a previous model29. The cocoa-growing area mask used in this study was obtained from a previous study17 and used as provided. Forest classifications were based on the European Commission’s Joint Research Centre (JRC) Tropical Moist Forests (TMF) map at https://forobs.jrc.ec.europa.eu/TMF/. Climatic variables were derived from the CHIRTS-daily temperature dataset and the CHIRPS v2.0 precipitation dataset at https://www.chc.ucsb.edu/data. Potential evapotranspiration (PET) data were obtained from the Global Aridity and PET Database at https://cgiarcsi.community/data/global-aridity-and-pet-database/. Administrative boundaries were sourced from GADM v4.1 at https://gadm.org. The greenhouse gas emissions estimates for cocoa production were provided by Quantis using the World Food LCA Database, which is not publicly available; however, the exact values used in this study are reported in the paper. Farm boundary polygons used to guide drone flight planning were provided by cocoa trading companies and are not publicly available owing to confidentiality agreements.

Code availability

The code used to estimate shade-tree cover and AGBD is available via GitHub at https://github.com/prs-eth/agroforestry. This repository includes code for pre-processing satellite imagery, training the gradient boosting and deep learning models (implemented in scikit-learn and PyTorch), and generating the final maps. The code used to calculate the climate change mitigation scenarios is available via UQ eSpace at https://doi.org/10.48610/dda018c (ref. 80).

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Acknowledgements

We thank R. Tetteh, J. Afele, E. Nimo, M. Yombu, V. Agumenu and Hammond for their exceptional efforts in visiting hundreds of cocoa farms to collect the ground-truth data in Ghana. Our thanks also extend to B. Callebaut and Olam Food Ingredients for their invaluable support in making the ground campaign possible. Special thanks to M. Gilmour, S. Ankamah, C. Parra Paitan, O. Nkuah, E. Prempeh, R. Seidu, B. Karibu, S. Adusei, S. Dodzie, E. Obiri Yeboah, S. Larbie and D. Forson and their ground-level field teams for their support in helping us gain access to recently mapped cocoa farms. We are grateful to the German Development Agency (GIZ) for their collaboration, which enabled us to extend our ground-truth sampling to Côte d’Ivoire. Special thanks to H. Walz, A. Bio, P. Ripplinger and M. Pallauf. We also appreciate the support of P. Kouakou in processing high-resolution drone images for Côte d’Ivoire. Thanks to A. Ernstoff, V. Rossi, C. Guignard and T. Levova from Quantis for their assistance with analysis of annual carbon emissions from cocoa production. This project received funding from the Lindt Cocoa Foundation (W.J.B.-H., S.P.H, J.D.W.); the 2019–2020 BiodivERsA joint call for research proposals under the BiodivClim ERA-Net COFUND programme, with the funding organization of the Swiss National Science Foundation (FNRS under grant number PINT MULTI/BEJ—R.8002.20; R.D.G., C.B., J.D.W. and W.J.B.-H.); the Joint Cocoa Research Fund of CAOBISCO and ECA (W.J.B.-H., S.P.H., J.D.W.); and the Queensland Government under the Women’s Research Assistance Program (WRAP194-2019RD1; W.J.B.-H.). We thank the European Space Agency’s Copernicus programme for its commitment to open data access, which made this study possible.

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  1. These authors contributed equally: Simon P. Hart, Wilma J. Blaser-Hart.

Authors and Affiliations

  1. Photogrammetry and Remote Sensing, ETH Zurich, Zurich, Switzerland

    Alexander Becker & Konrad Schindler

  2. EcoVision Lab, Department of Mathematical Modeling and Machine Learning, University of Zurich, Zurich, Switzerland

    Jan D. Wegner

  3. Department of Agroforestry, Faculty of Renewable Natural Resources, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Evans Dawoe

  4. Nature-based Solutions Initiative, Department of Biology and Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK

    William J. Thompson

  5. International Center for Tropical Agriculture, Cali, Colombia

    Christian Bunn & Fabio Castro-Llanos

  6. Conservation and Development Lab, Department of Geography and Conservation Research Institute, University of Cambridge, Cambridge, UK

    Rachael D. Garrett

  7. School of the Environment and Centre for Biodiversity and Conservation Science (CBCS), The University of Queensland, Brisbane, Queensland, Australia

    Simon P. Hart & Wilma J. Blaser-Hart

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Contributions

W.J.B.-H., S.P.H. and J.D.W. conceived the research question and designed the study. W.J.B.-H. planned the fieldwork, with W.J.B.-H. and E.D. overseeing its execution. A.B. developed the code with guidance from J.D.W. and K.S. W.J.B.-H., A.B. and S.P.H. analysed the results. C.B. and F.C.-L. created the maps of agroclimatic zones. W.J.B.-H. and S.P.H. led the writing, with contributions from all authors.

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Correspondence to Wilma J. Blaser-Hart.

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Extended data

Extended Data Fig. 1 Agro-climatic classifications and shade levels across cocoa-growing regions of Côte d’Ivoire and Ghana.

Different colors represent various climatic types determined from climatic data spanning 1990-2020. Areas in dark grey (mixed) show classification uncertainty, while light grey areas (limitations) are likely unsuitable for cocoa cultivation but with high uncertainty, and white areas are unsuitable for cocoa growth. Black dots indicate cocoa communities visited during fieldwork for ground truth data collection. Sub-panels (a-e) display the shade levels in each agro-climatic zone based on our map of shade-tree cover (Fig. 1). Basemap boundaries from GADM v4.1 (https://gadm.org).

Extended Data Fig. 2 Shade levels in a) cocoa-growing districts of Côte d’Ivoire and b) cocoa-growing regions of Ghana.

The map in panel c) displays the distribution of administrative districts/regions within cocoa-growing areas, shaded in turquoise. Mean shade levels (%) are shown for each district/region, with error bars representing ± one standard deviation across all pixel-level values. The unit of analysis is individual 10 × 10m pixels, with a total of n = 679, 593, 915 pixels included across all district/region. Sample sizes (n) per region correspond to the number of pixels within each administrative unit and vary with cocoa-growing area. Bubble sizes correspond to the total cocoa-growing area in each region, ranging from 78,382 to 1,190,000 hectares. Only administrative units comprising more than 2% of the total cocoa-growing area in each country were included in the plot. All data represent biological replicates based on independent spatial observations. Cocoa-growing areas data in Panel c are adapted from ref. 17 under a Creative Commons license. Basemap boundaries from GADM v4.1 (https://gadm.org).

Extended Data Fig. 3 Area, carbon densities, and carbon stocks of cocoa-growing areas, disturbed forests (dist. forest), and undisturbed forests (forest) across Ghana and Côte d’Ivoire.

The values in (a) represent the total mapped extent of each land use class, for which there are no error estimates. The values in (b) are means ± standard deviations, because we are interested in variation among land use class in carbon density. The values in (c) are total carbon ± 95 % confidence intervals, reflecting uncertainty in the estimates of total carbon stocks. All values were calculated at the resolution of the AGBD map (50 × 50 m). The unit of analysis is individual pixels. Sample sizes (n) correspond to the number of pixels per land cover class: n = 34, 284, 584 for cocoa, n = 7, 704, 695 for disturbed forest, and n = 9, 185, 882 for undisturbed forest. Due to these large sample sizes for pixel-level estimates of total carbon, the confidence intervals in panel (c) are too small to be visually distinguishable. Reported 95% confidence intervals are: cocoa = ± 1.2 × 10−5 million tonnes C; disturbed forest = ± 4.1 × 10−6 million tonnes C; undisturbed forest = ± 2.5 × 10−6 million tonnes C. All data represent biological replicates in the form of independent spatial observations.

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Becker, A., Wegner, J.D., Dawoe, E. et al. The unrealized potential of agroforestry for an emissions-intensive agricultural commodity. Nat Sustain 8, 994–1003 (2025). https://doi.org/10.1038/s41893-025-01608-7

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  • DOI: https://doi.org/10.1038/s41893-025-01608-7

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