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Potential adaptive strategies for 29 sub-Saharan crops under future climate change


Climate change is expected to severely impact cultivated plants and consequently human livelihoods1,2,3, especially in sub-Saharan Africa (SSA)4,5,6. Increasing agricultural plant diversity (agrobiodiversity) could overcome this global challenge7,8,9 given more information on the climatic tolerance of crops and their wild relatives. Using >200,000 worldwide occurrence records for 29 major crops and 778 of their wild relative species, we assess, for each crop, how future climatic conditions are expected to change in SSA and whether populations of the same crop from other continents, wild relatives around the world or other crops from SSA are better adapted to expected future climatic conditions in the region. We show that climate conditions not currently experienced by the 29 crops in SSA are predicted to become widespread, increasing production insecurity, especially for yams. However, crops such as potato, squash and finger millet may be maintained by using wild relatives or non-African crop populations with climatic niches more suited to future conditions. Crop insecurity increases over time and with rising GHG emissions, but the potential for using agrobiodiversity for resilience is less altered. Climate change will therefore affect sub-Saharan agriculture but agrobiodiversity can provide resilient solutions in the short and medium term.

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Fig. 1: Analytical framework.
Fig. 2: Potential security and adaptive strategies for 29 sub-Saharan crops under future climate change by 2070.
Fig. 3: Potential security and adaptive strategies for 29 sub-Saharan crops under different scenarios of future climate change.
Fig. 4: Details of the three potential adaptive strategies under future climate change by 2070.

Data availability

The data that support the findings of this study are available from several databases listed in the Methods of the manuscript. Data are available from the authors on reasonable request and following data restrictions from these databases.

Code availability

The main R functions and packages used in this study are provided in the Methods. Full R scripts are available from the authors on reasonable request.


  1. 1.

    Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).

    CAS  Article  Google Scholar 

  2. 2.

    Wheeler, T. & von Braun, J. Climate change impacts on global food security. Science 341, 508–513 (2013).

    CAS  Article  Google Scholar 

  3. 3.

    Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).

    Article  Google Scholar 

  4. 4.

    Morton, J. F. The impact of climate change on smallholder and subsistence agriculture. Proc. Natl Acad. Sci. USA 104, 19680–19685 (2007).

    CAS  Article  Google Scholar 

  5. 5.

    Muller, C., Cramer, W., Hare, W. L. & Lotze-Campen, H. Climate change risks for African agriculture. Proc. Natl Acad. Sci. USA 108, 4313–4315 (2011).

    Article  Google Scholar 

  6. 6.

    Kurukulasuriya, P. et al. Will African agriculture survive climate change? World Bank Econ. Rev. 20, 367–388 (2006).

    Article  Google Scholar 

  7. 7.

    Dwivedi, S., Sahrawat, K., Upadhyaya, H. & Ortiz, R. Food, nutrition and agrobiodiversity under global climate change. Adv. Agron. 120, 1–128 (2013).

    CAS  Article  Google Scholar 

  8. 8.

    Ramankutty, N. et al. Trends in global agricultural land use: implications for environmental health and food security. Annu. Rev. Plant Biol. 69, 789–815 (2018).

    CAS  Article  Google Scholar 

  9. 9.

    Waha, K. et al. Agricultural diversification as an important strategy for achieving food security in Africa. Glob. Change Biol. 24, 3390–3400 (2018).

    Article  Google Scholar 

  10. 10.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2014).

  11. 11.

    Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).

    Article  Google Scholar 

  12. 12.

    Tester, M. & Langridge, P. Breeding technologies to increase crop production in a changing world. Science 327, 818–822 (2010).

    CAS  Article  Google Scholar 

  13. 13.

    Zhang, H., Mittal, N., Leamy, L. J., Barazani, O. & Song, B.-H. Back into the wild—apply untapped genetic diversity of wild relatives for crop improvement. Evol. Appl. 10, 5–24 (2017).

    Article  Google Scholar 

  14. 14.

    Castañeda-Álvarez, N. P. et al. Global conservation priorities for crop wild relatives. Nat. Plants 2, 16022 (2016).

    Article  Google Scholar 

  15. 15.

    Dempewolf, H. et al. Adapting agriculture to climate change: a global initiative to collect, conserve and use crop wild relatives. Agroecol. Sustain. Food Syst. 38, 369–377 (2014).

    Article  Google Scholar 

  16. 16.

    FAOSTAT (FAO, 2019);

  17. 17.

    Adoption of the Paris Agreement FCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015).

  18. 18.

    Siebert, S. et al. Development and validation of the global map of irrigation areas. Hydrol. Earth Syst. Sci. Discuss. 2, 1299–1327 (2005).

    Article  Google Scholar 

  19. 19.

    Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

    CAS  Article  Google Scholar 

  20. 20.

    Atwater, D. Z., Ervine, C. & Barney, J. N. Climatic niche shifts are common in introduced plants. Nat. Ecol. Evol. 2, 34–43 (2018).

    Article  Google Scholar 

  21. 21.

    Rippke, U. et al. Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nat. Clim. Change 6, 605–609 (2016).

    Article  Google Scholar 

  22. 22.

    Ortiz, R., Jarvis, A., Fox, P., Aggarwal, P. K. & Campbell, B. M. Plant Genetic Engineering, Climate Change and Food Security (CGIAR and CCAFS, 2014).

  23. 23.

    Ramírez-Villegas, J. et al. Climate Analogues: Finding Tomorrow’s Agriculture Today (CGIAR and CCAFS, 2011).

  24. 24.

    Burke, M. B., Lobell, D. B. & Guarino, L. Shifts in African crop climates by 2050, and the implications for crop improvement and genetic resources conservation. Glob. Environ. Change 19, 317–325 (2009).

    Article  Google Scholar 

  25. 25.

    Stokstad, E. The new potato. Science 363, 574–577 (2019).

    CAS  Article  Google Scholar 

  26. 26.

    Longo, R. M. J. Information transfer and the adoption of agricultural innovations. J. Am. Soc. Inf. Sci. 41, 1–9 (1990).

    Article  Google Scholar 

  27. 27.

    Borrell, J. S. et al. Enset in Ethiopia: a poorly characterized but resilient starch staple. Ann. Bot. 123, 747–766(2019).

    Article  Google Scholar 

  28. 28.

    Hoffman, A. L., Kemanian, A. R. & Forest, C. E. Analysis of climate signals in the crop yield record of sub-Saharan Africa. Glob. Change Biol. 24, 143–157 (2018).

    Article  Google Scholar 

  29. 29.

    Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).

    CAS  Article  Google Scholar 

  30. 30.

    The International Treaty on Plant Genetic Resources for Food and Agriculture (Food and Agriculture Organization, 2001).

  31. 31.

    Harlan, J. R. & de Wet, J. M. J. Toward a rational classification of cultivated plants. Taxon 20, 509 (1971).

    Article  Google Scholar 

  32. 32.

    Maxted, N., Ford-Lloyd, B. V., Jury, S., Kell, S. & Scholten, M. Towards a definition of a crop wild relative. Biodivers. Conserv. 15, 2673–2685 (2006).

    Article  Google Scholar 

  33. 33.

    Dauby, G. et al. RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 2016, 1–18 (2016).

    Google Scholar 

  34. 34.

    Waha, K., Zipf, B., Kurukulasuriya, P. & Hassan, R. M. An agricultural survey for more than 9,500 African households. Sci. Data 3, 160020 (2016).

    Article  Google Scholar 

  35. 35.

    Hijmans, R. J., Phillips, S., Leathwick, J. R. & Elith, J. dismo: Species distribution modeling. R package version 1.1-4 (2017).

  36. 36.

    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).

  37. 37.

    Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).

    Article  Google Scholar 

  38. 38.

    Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Article  Google Scholar 

  39. 39.

    Pironon, S. et al. The ‘Hutchinsonian niche’ as an assemblage of demographic niches: implications for species geographic ranges. Ecography 41, 1103–1113 (2018).

    Article  Google Scholar 

  40. 40.

    Hijmans, R. J. et al. raster: Geographic data analysis and modeling. R package version 3.0-2 (2019).

  41. 41.

    Reside, A. E., Watson, I., VanDerWal, J. & Kutt, A. S. Incorporating low-resolution historic species location data decreases performance of distribution models. Ecol. Model. 222, 3444–3448 (2011).

    Article  Google Scholar 

  42. 42.

    Guisan, A., Graham, C. H., Elith, J. & Huettmann, F. and the NCEAS Species Distribution Modelling Group Sensitivity of predictive species distribution models to change in grain size. Divers. Distrib. 13, 332–340 (2007).

    Article  Google Scholar 

  43. 43.

    Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).

    Article  Google Scholar 

  44. 44.

    Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).

    Article  Google Scholar 

  45. 45.

    Knox, J., Hess, T., Daccache, A. & Wheeler, T. Climate change impacts on crop productivity in Africa and South Asia. Environ. Res. Lett. 7, 034032 (2012).

    Article  Google Scholar 

  46. 46.

    Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Glob. Change Biol. 16, 1145–1157 (2010).

    Article  Google Scholar 

  47. 47.

    Knutti, R., Masson, D. & Gettelman, A. Climate model genealogy: generation CMIP5 and how we got there. Geophys. Res. Lett. 40, 1194–1199 (2013).

    Article  Google Scholar 

  48. 48.

    Danielson, J. J. & Gesch, D. B. Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) (USGS, 2011).

  49. 49.

    Dray, S. & Dufour, A.-B. The ade4 Package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).

    Article  Google Scholar 

  50. 50.

    R Core Team and contributors worldwide. grDevices: The R graphics devices and support for colours and fonts. R package version 3.4.1 (2019).

  51. 51.

    Pebesma, E. et al. sp: Classes and methods for spatial data. R package version 1.3-1 (2018).

  52. 52.

    Blonder, B. Hypervolume concepts in niche- and trait-based ecology. Ecography 41, 1441–1455 (2017).

    Article  Google Scholar 

  53. 53.

    Qiao, H., Soberón, J. & Peterson, A. T. No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation. Methods Ecol. Evol. 6, 1126–1136 (2015).

    Article  Google Scholar 

  54. 54.

    Merow, C. et al. What do we gain from simplicity versus complexity in species distribution models? Ecography 37, 1267–1281 (2014).

    Article  Google Scholar 

  55. 55.

    Etherington, T. R. Mahalanobis distances and ecological niche modelling: correcting a chi-squared probability error. PeerJ 7, e6678 (2019).

    Article  Google Scholar 

  56. 56.

    Giraudoux, P., Antonietti, J.-P., Beale, C., Pleydell, D. & Treglia, M. pgirmess: Spatial analysis and data mining for field ecologists. R package version 1.6.9 (2018).

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We acknowledge the financial support of the UK Natural Environment Research Council (NERC) for the Belmont Forum project FICESSA (Food Security Impacts of Industrial Crop Expansion in Sub-Sahara Africa) NE/M021351/1, as well as the Norwegian Ministry of Foreign Affairs and the Global Crop Diversity Trust for the ‘Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives’ project. Funders had no role in the preparation of this manuscript. We thank G. Dauby and T. Couvreur for providing unpublished data from the Rainbio database, N. Castañeda-Álvarez for her help with the Crop Wild Relatives Atlas, O. Romero for his help with extracting data from the Genesys database, A. Gasparatos, B. Siddighi Balde and M. Jarzebski for providing data from the agricultural surveys conducted for the FICESSA project, G. von Maltitz for helping selecting crop species and E. Hammond Hunt and M. Soto Gomez for comments on the manuscript and technical assistance.

Author information




S.P., T.R.E., M.M.-F. and K.J.W. designed the study with help from all co-authors. S.P., N.K. and J.S.B. collected data. S.P. analysed the data with help from I.O. S.P. wrote the manuscript, with substantial help from all co-authors.

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Correspondence to Samuel Pironon.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Luigi Guarino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Pironon, S., Etherington, T.R., Borrell, J.S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Chang. 9, 758–763 (2019).

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