Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Modelling the productivity of opportunity crops across Africa under climate change in support of the Vision for Adapted Crops and Soils

Nature Plants volume 11, pages 2476–2486 (2025)Cite this article

Subjects

Abstract

Addressing future agricultural challenges requires breeding cultivars with improved tolerance to evolving climatic conditions. Many African traditional and indigenous ‘opportunity crops’ have shown increased resilience to climate hazards, yet have received minimal developmental investment. Here the SIMPLE process-based crop model is used to assess the impact of future climate change on the productivity of 5 staple crops and 19 African opportunity crops under low- and high-emissions scenario projections. Roots and tubers show the highest resiliency, while vegetables are the most vulnerable. Cassava, teff, grass pea, sesame seed and finger millet are projected to have the largest productivity increases, while mung bean, lablab, amaranth, Bambara groundnut and maize productivity are projected to decrease substantially. Soybean and cowpea, important cash crops in Africa, are projected to have comparable losses. Crops grown in the Sahel appear most susceptible to climate change, while crops in East and Central Africa show greater resilience. These findings guide regional investments in opportunity crop development and support their inclusion in adaptation measures.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Detrended maize yield response compared with detrended extreme climate hazards in Africa.
ImageThe alternative text for this image may have been generated using AI.
Fig. 2: Mid-century crop yield responses in Africa.
ImageThe alternative text for this image may have been generated using AI.
Fig. 3: Simulated mid-century crop yield changes for Africa under a projected high-emissions scenario (SSP3-7.0).
ImageThe alternative text for this image may have been generated using AI.
Fig. 4: Best- and worst-performing crops under the high-emission scenario SSP3-7.0.
ImageThe alternative text for this image may have been generated using AI.

Similar content being viewed by others

Data availability

The data that support this study are available from the sources referenced, and the crop model output is available at https://doi.org/10.7910/DVN/SZ9SZW. The GGCMI crop calendar is available at https://doi.org/10.5281/zenodo.5062513 (ref. 63).

Code availability

The SIMPLE crop model code is available from ref. 18. The R code developed to analyse the results and generate the figures is available upon request.

References

  1. Tadele, Z. Orphan crops: their importance and the urgency of improvement. Planta 250, 677–694 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Akinola, R., Pereira, L. M., Mabhaudhi, T., De Bruin, F.-M. & Rusch, L. A review of indigenous food crops in Africa and the implications for more sustainable and healthy food systems. Sustainability 12, 3493 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. van Zonneveld, M. et al. Forgotten food crops in sub-Saharan Africa for healthy diets in a changing climate. Proc. Natl Acad. Sci. USA 120, e2205794120 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  4. McMullin, S. et al. Determining appropriate interventions to mainstream nutritious orphan crops into African food systems. Glob. Food Secur. 28, 100465 (2021).

    Article  Google Scholar 

  5. Hunter, D. et al. The potential of neglected and underutilized species for improving diets and nutrition. Planta 250, 709–729 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Mabhaudhi, T. et al. Prospects of orphan crops in climate change. Planta 250, 695–708 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Talabi, A. O. et al. Orphan crops: a best fit for dietary enrichment and diversification in highly deteriorated marginal environments. Front. Plant Sci. https://doi.org/10.3389/fpls.2022.839704 (2022).

  8. Vinoth, A. & Ravindhran, R. Biofortification in millets: a sustainable approach for nutritional security. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.00029 (2017).

  9. Abebe, B. K. & Alemayehu, M. T. A review of the nutritional use of cowpea (Vigna unguiculata L. Walp) for human and animal diets. J. Agric. Food Res. 10, 100383 (2022).

    CAS  Google Scholar 

  10. Jägermeyr, J. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2, 873–885 (2021).

    Article  PubMed  Google Scholar 

  11. Kerr, R. B. et al. Food, fibre and other ecosystem products. in Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) Ch. 5 (Cambridge Univ. Press, 2022).

  12. Reynolds, M. et al. Addressing research bottlenecks to crop productivity. Trends Plant Sci. 26, 607–630 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Wu, A., Hammer, G. L., Doherty, A., von Caemmerer, S. & Farquhar, G. D. Quantifying impacts of enhancing photosynthesis on crop yield. Nat. Plants 5, 380–388 (2019).

    Article  PubMed  Google Scholar 

  14. Zabel, F. et al. Large potential for crop production adaptation depends on available future varieties. Glob. Change Biol. 27, 3870–3882 (2021).

    Article  CAS  Google Scholar 

  15. Guarin, J. R. & Asseng, S. in Achieving Sustainable Cultivation of Wheat: Cultivation Techniques Vol. 2 (ed. Langridge, P.) Ch. 6 (Burleigh Dodds Science Publishing, 2017).

  16. Alimagham, S. et al. Climate change impact and adaptation of rainfed cereal crops in sub-Saharan Africa. Eur. J. Agron. 155, 127137 (2024).

    Article  Google Scholar 

  17. Rosenzweig, C. et al. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agric. Meteorol. 170, 166–182 (2013).

    Article  Google Scholar 

  18. Zhao, C. et al. A SIMPLE crop model. Eur. J. Agron. 104, 97–106 (2019).

    Article  Google Scholar 

  19. Yang, M. et al. Climate-crop models to support opportunity crop adaptation in Africa. Nat. Commun. https://doi.org/10.1038/s41467-025-66180-2 (2025).

  20. O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  21. IPCC Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  22. Ranasinghe, R., Ruane, A. C. & Vautard, R. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) Ch. 12 (Cambridge Univ. Press, 2021).

  23. Trisos, C. H., Adelekan, I. O. & Totin, E. in Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) Ch. 9 (Cambridge Univ. Press, 2022).

  24. IPCC Summary for Policymakers. In Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).

  25. Gerik, T., Bean, B. W. & Vanderlip, R. Sorghum Growth and Development (Texas A&M AgriLife Extension Service, 2003); https://hdl.handle.net/1969.1/87184

  26. Jones, C. A. & Kiniry, J. R. CERES-Maize: A Simulation Model of Maize Growth and Development (Texas A&M Univ. Press, 1986).

  27. Singh, P. et al. An assessment of yield gains under climate change due to genetic modification of pearl millet. Sci. Total Environ. 601-602, 1226–1237 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chemura, A., Gleixner, S. & Gornott, C. Dataset of the suitability of major food crops in Africa under climate change. Sci. Data 11, 294 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Martre, P. et al. Multimodel ensembles of wheat growth: many models are better than one. Glob. Change Biol. 21, 911–925 (2015).

    Article  Google Scholar 

  30. Ruane, A. C. et al. An AgMIP framework for improved agricultural representation in integrated assessment models. Environ. Res. Lett. 12, 125003 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Alimagham, S. et al. Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments. Agric. Syst. 228, 104367 (2025).

    Article  Google Scholar 

  32. Omotayo, A. O., Omotoso, A. B. & Asong, J. A. Leveraging Africa’s underutilized crops to combat climate change, water scarcity, and food insecurity in South Africa. Sci. Rep. 15, 19404 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lambein, F., Travella, S., Kuo, Y.-H., Van Montagu, M. & Heijde, M. Grass pea (Lathyrus sativus L.): orphan crop, nutraceutical or just plain food?. Planta 250, 821–838 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Carducci, B. et al. Anticipating climate impacts on nutrition through climate–crop nutrient modelling. Nat. Clim. Change 15, 1165–1172 (2025).

    Article  Google Scholar 

  35. Fredenberg, E. et al. Vision for Adapted Crops and Soils (VACS) Research in Action: Opportunity Crops for Africa (Columbia Univ., Columbia Academic Commons, 2024).

  36. You, L. et al. What is the irrigation potential for Africa? A combined biophysical and socioeconomic approach IFPRI Discussion Paper 00993 (International Food Policy Research Institute, 2010).

  37. Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832 (2013).

    Article  CAS  Google Scholar 

  38. Chapagain, R. et al. Decomposing crop model uncertainty: a systematic review. Field Crops Res. 279, 12 (2022).

    Article  Google Scholar 

  39. IPCC Climate Change 2023: Synthesis Report (eds Core Writing Team, Lee, H. & Romero, J.) (IPCC, 2023).

  40. Wang, B. et al. Australian wheat production expected to decrease by the late 21st century. Glob. Change Biol. 24, 2403–2415 (2018).

    Article  Google Scholar 

  41. Ruane, A. C. et al. The climatic impact-driver framework for assessment of risk-relevant climate information. Earth’s Future 10, e2022EF002803 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12, 3055–3070 (2019).

    Article  Google Scholar 

  43. Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Change 9, 102–110 (2019).

    Article  Google Scholar 

  44. van Vuuren, D. P. et al. Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Clim. Change 81, 119–159 (2007).

    Article  CAS  Google Scholar 

  45. RCP Database version 2.0.5 (IIASA, accessed 30 April 2024); https://tntcat.iiasa.ac.at/RcpDb/

  46. Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).

    Article  CAS  Google Scholar 

  47. Jiang, T. et al. Identifying sources of uncertainty in wheat production projections with consideration of crop climatic suitability under future climate. Agric. Meteorol. 319, 108933 (2022).

    Article  Google Scholar 

  48. Shangguan, W., Dai, Y. J., Duan, Q. Y., Liu, B. Y. & Yuan, H. A global soil data set for earth system modeling. J. Adv. Model. Earth Syst. 6, 249–263 (2014).

    Article  Google Scholar 

  49. Nachtergaele, F. et al. Harmonized World Soil Database v1.2 (FAO, IIASA, ISRIC, ISSCAS, JRC, 2012); https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/

  50. Dai, Y. et al. A review of the global soil property maps for Earth system models. SOIL 5, 137–158 (2019).

    Article  CAS  Google Scholar 

  51. Leenaars, J. G. B. et al. Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa. Geoderma 324, 18–36 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Woli, P., Jones, J. W., Ingram, K. T. & Fraisse, C. W. Agricultural Reference Index for Drought (ARID). Agron. J. 104, 287–300 (2012).

    Article  Google Scholar 

  54. Herrero, M. et al. Farming and the geography of nutrient production for human use: a transdisciplinary analysis. Lancet Planet. Health 1, e33–e42 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Tang, F. H. M. et al. CROPGRIDS: a global geo-referenced dataset of 173 crops. Sci. Data 11, 413 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles https://doi.org/10.1029/2008GB003435 (2010).

  57. Wolf, J., Ouattara, K. & Supit, I. Sowing rules for estimating rainfed yield potential of sorghum and maize in Burkina Faso. Agric. Meteorol. 214–215, 208–218 (2015).

    Article  Google Scholar 

  58. International Food Policy Research Institute & HarvestChoice Agro-Ecological Zones for Africa South of the Sahara (Harvard Dataverse, 2015); https://doi.org/10.7910/DVN/M7XIUB

  59. Fertilizer Use By Crop - FUBC Crop Calendars by Country (International Fertilizer Association, accessed 6 September 2024); https://www.ifastat.org/consumption/fertilizer-use-by-crop

  60. Malherbe, S. & Marais, D. Economics, yield and ecology: a case study from the South African tomato industry. Outlook Agric. 44, 37–47 (2015).

    Article  Google Scholar 

  61. Robinson, E. J. Z. & Kolavalli, S. L. The Case of Tomato in Ghana: Productivity. Ghana Strategy Support Program Working Papers 19 (International Food Policy Research Institute, 2010).

  62. Kamga, R. T., Some, S., Tenkouano, A., Issaka, Y. B. & Ndoye, O. Assessment of traditional African vegetable production in Burkina Faso. J. Agric. Ext. Rural Dev. 8, 141–150 (2016).

    Article  Google Scholar 

  63. Jägermeyr, J., Müller, C., Minoli, S., Ray, D. & Siebert, S. GGCMI phase 3 crop calendar. Zenodo https://doi.org/10.5281/zenodo.5062513 (2021).

Download references

Acknowledgements

This work is funded by the Rockefeller Foundation (grant number 2023 FCW 004) and is conducted in support of the US Department of State VACS, which aims to highlight nutritionally important opportunity crops across Africa, examine future climate change impacts on these crops, and catalyse climate change adaptation efforts across the continent. J.R.G. thanks C. Zhao and S. Asseng for sharing the SIMPLE model code and for providing initial model guidance. J.R.G., M.Y. and K.K. thank C. Kruse and The Plotline by Earth Genome for creating the interactive online figures. J.R.G., M.Y., K.K., J.J., A.C.R. and C.R. were enabled by the NASA Earth Sciences Division’s support of the NASA GISS Climate Impacts Group.

Author information

Author notes

  1. Jose Rafael Guarin

    Present address: Autonomic Integra LLC, Bethesda, MD, USA

  2. These authors contributed equally: Jose Rafael Guarin, Meijian Yang.

Authors and Affiliations

  1. Center for Climate Systems Research, Columbia Climate School, Columbia University, New York, NY, USA

    Jose Rafael Guarin, Meijian Yang, Kevin Karl, Jonas Jägermeyr & Elena Mendez Leal

  2. NASA Goddard Institute for Space Studies, New York, NY, USA

    Jose Rafael Guarin, Meijian Yang, Kevin Karl, Jonas Jägermeyr, Alex C. Ruane & Cynthia Rosenzweig

  3. Soil and Irrigation Research Centre, School of Agriculture, College of Basic and Applied Sciences, University of Ghana, Kpong, Ghana

    Dilys S. MacCarthy, Bright S. Freduah, Gershom O. Wesley & Stephen Narh

  4. Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany

    Jonas Jägermeyr

  5. Autonomic Integra LLC, Bethesda, MD, USA

    Andres Castellano

Authors
  1. Jose Rafael Guarin
    View author publications

    Search author on:PubMed Google Scholar

  2. Meijian Yang
    View author publications

    Search author on:PubMed Google Scholar

  3. Dilys S. MacCarthy
    View author publications

    Search author on:PubMed Google Scholar

  4. Kevin Karl
    View author publications

    Search author on:PubMed Google Scholar

  5. Jonas Jägermeyr
    View author publications

    Search author on:PubMed Google Scholar

  6. Alex C. Ruane
    View author publications

    Search author on:PubMed Google Scholar

  7. Andres Castellano
    View author publications

    Search author on:PubMed Google Scholar

  8. Bright S. Freduah
    View author publications

    Search author on:PubMed Google Scholar

  9. Gershom O. Wesley
    View author publications

    Search author on:PubMed Google Scholar

  10. Stephen Narh
    View author publications

    Search author on:PubMed Google Scholar

  11. Elena Mendez Leal
    View author publications

    Search author on:PubMed Google Scholar

  12. Cynthia Rosenzweig
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.R.G. and M.Y. conducted the crop model simulations and data analysis and developed the paper and figures. D.S.M., K.K. and C.R. conceived the idea and coordinated integration with VACS. A.C.R. and C.R. coordinated integration with AgMIP. J.J. provided global modelling guidance and soil input datasets. A.C.R. and A.C. provided climate input datasets. D.S.M., B.S.F., G.O.W. and S.N. conducted crop model calibration and assisted with regional crop information. K.K. and E.M.L. provided crop profile background information and data. All coauthors supported the writing of the paper.

Corresponding authors

Correspondence to Jose Rafael Guarin or Meijian Yang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Christine Foyer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guarin, J.R., Yang, M., MacCarthy, D.S. et al. Modelling the productivity of opportunity crops across Africa under climate change in support of the Vision for Adapted Crops and Soils. Nat. Plants 11, 2476–2486 (2025). https://doi.org/10.1038/s41477-025-02157-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41477-025-02157-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing