Skip to main content

Thank you for visiting 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.

Timescales of transformational climate change adaptation in sub-Saharan African agriculture


Climate change is projected to constitute a significant threat to food security if no adaptation actions are taken1,2. Transformation of agricultural systems, for example switching crop types or moving out of agriculture, is projected to be necessary in some cases3,4,5. However, little attention has been paid to the timing of these transformations. Here, we develop a temporal uncertainty framework using the CMIP5 ensemble to assess when and where cultivation of key crops in sub-Saharan Africa becomes unviable. We report potential transformational changes for all major crops during the twenty-first century, as climates shift and areas become unsuitable. For most crops, however, transformation is limited to small pockets (<15% of area), and only for beans, maize and banana is transformation more widespread (30% area for maize and banana, 60% for beans). We envisage three overlapping adaptation phases to enable projected transformational changes: an incremental adaptation phase focused on improvements to crops and management, a preparatory phase that establishes appropriate policies and enabling environments, and a transformational adaptation phase in which farmers substitute crops, explore alternative livelihoods strategies, or relocate. To best align policies with production triggers for no-regret actions, monitoring capacities to track farming systems as well as climate are needed.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Timing of transformational adaptation.
Figure 2: Extent of transformational adaptation.
Figure 3: Best substitute crops at mean time of crossing for maize for RCP8.5.


  1. 1

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

    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

    Kates, R. W., Travis, W. R. & Wilbanks, T. J. Transformational adaptation when incremental adaptations to climate change are insufficient. Proc. Natl Acad. Sci. USA 109, 7156–7161 (2012).

    CAS  Article  Google Scholar 

  4. 4

    Jones, P. G. & Thornton, P. K. Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change. Environ. Sci. Policy 12, 427–437 (2009).

    Article  Google Scholar 

  5. 5

    Rickards, L. & Howden, S. M. Transformational adaptation: agriculture and climate change. Crop Pasture Sci. 63, 240–250 (2012).

    Article  Google Scholar 

  6. 6

    Porter, J. R. et al. in Climate Change 2014: Impacts, Adaptation and Vulnerability (eds Barros, V. R. et al.) Ch. 7, 485–533 (IPCC, Cambridge Univ. Press, 2014).

    Google Scholar 

  7. 7

    IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Barros, V. R. et al.) (Cambridge Univ. Press, 2014).

  8. 8

    FAO FAOSTAT (2013);

  9. 9

    Vermeulen, S. J. et al. Addressing uncertainty in adaptation planning for agriculture. Proc. Natl Acad. Sci. USA 110, 8357–8362 (2013).

    CAS  Article  Google Scholar 

  10. 10

    Mohamed, H. A., Clark, J. A. & Ong, C. K. Genotypic differences in the temperature responses of tropical crops. J. Exp. Bot. 39, 1121–1128 (1988).

    Article  Google Scholar 

  11. 11

    Thornton, P. K., Jones, P. G., Alagarswamy, G. & Andresen, J. Spatial variation of crop yield response to climate change in East Africa. Glob. Environ. Change 19, 54–65 (2009).

    Article  Google Scholar 

  12. 12

    Park, S. E. et al. Informing adaptation responses to climate change through theories of transformation. Glob. Environ. Change 22, 115–126 (2012).

    Article  Google Scholar 

  13. 13

    Dowd, A.-M. et al. The role of networks in transforming Australian agriculture. Nature Clim. Change 4, 558–563 (2014).

    Article  Google Scholar 

  14. 14

    Araújo, S. S. et al. Abiotic stress responses in legumes? Strategies used to cope with environmental challenges. Crit. Rev. Plant Sci. 34, 237–280 (2015).

    Article  Google Scholar 

  15. 15

    Beyene, Y. et al. Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress. Crop Sci. 55, 154–163 (2015).

    Article  Google Scholar 

  16. 16

    Tittonell, P. & Giller, K. E. When yield gaps are poverty traps: the paradigm of ecological intensification in African smallholder agriculture. F. Crop. Res. 143, 76–90 (2013).

    Article  Google Scholar 

  17. 17

    Chapman, S. C., Chakraborty, S., Dreccer, M. F. & Howden, S. M. Plant adaptation to climate change—opportunities and priorities in breeding. Crop Pasture Sci. 63, 251–268 (2012).

    Article  Google Scholar 

  18. 18

    Lesnikowski, A., Ford, J., Biesbroek, R., Berrang-Ford, L. & Heymann, S. J. National-level progress on adaptation. Nature Clim. Change (2015).

  19. 19

    Byerlee, D. & Heisey, P. W. Africa’s Emerging Maize Revolution 9–22 (Lynne Rienner, 1997).

    Google Scholar 

  20. 20

    Ely, A., Van Zwanenberg, P. & Stirling, A. Broadening out and opening up technology assessment: approaches to enhance international development, co-ordination and democratisation. Res. Policy 43, 505–518 (2014).

    Article  Google Scholar 

  21. 21

    Mapfumo, P. et al. Pathways to transformational change in the face of climate impacts: an analytical framework. Clim. Dev. 1–13 (2015).

  22. 22

    Hassan, R. M. Implications of climate change for agricultural sector performance in Africa: policy challenges and research agenda. J. Afr. Econ. 19, ii77–ii105 (2010).

    Article  Google Scholar 

  23. 23

    Cock, J. et al. Crop management based on field observations: case studies in sugarcane and coffee. Agric. Syst. 104, 755–769 (2011).

    Article  Google Scholar 

  24. 24

    Eakin, H. C., Lemos, M. C. & Nelson, D. R. Differentiating capacities as a means to sustainable climate change adaptation. Glob. Environ. Change 27, 1–8 (2014).

    Article  Google Scholar 

  25. 25

    Shackleton, S., Ziervogel, G., Sallu, S., Gill, T. & Tschakert, P. Why is socially-just climate change adaptation in sub-Saharan Africa so challenging? A review of barriers identified from empirical cases. Wiley Interdiscip. Rev. Clim. Change 6, 321–344 (2015).

    Article  Google Scholar 

  26. 26

    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 

  27. 27

    Lobell, D. B., Banziger, M., Magorokosho, C. & Vivek, B. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nature Clim. Change 1, 42–45 (2011).

    Article  Google Scholar 

  28. 28

    Liu, J. et al. A spatially explicit assessment of current and future hotspots of hunger in sub-Saharan Africa in the context of global change. Glob. Planet. Change 64, 222–235 (2008).

    Article  Google Scholar 

  29. 29

    Ramirez-Villegas, J., Jarvis, A. & Läderach, P. Empirical approaches for assessing impacts of climate change on agriculture: the EcoCrop model and a case study with grain sorghum. Agric. For. Meteorol. 170, 67–78 (2013).

    Article  Google Scholar 

  30. 30

    Jarvis, A., Ramirez-Villegas, J., Herrera Campo, B. V. & Navarro-Racines, C. Is cassava the answer to African climate change adaptation? Trop. Plant Biol. 5, 9–29 (2012).

    Article  Google Scholar 

  31. 31

    Gabriel, L. F. et al. Simulating Cassava growth and yield under potential conditions in Southern Brazil. Agron. J. 106, 1119–1137 (2014).

    Article  Google Scholar 

  32. 32

    Iizumi, T., Tanaka, Y., Sakurai, G., Ishigooka, Y. & Yokozawa, M. Dependency of parameter values of a crop model on the spatial scale of simulation. J. Adv. Model. Earth Syst. 6, 527–540 (2014).

    Article  Google Scholar 

  33. 33

    Ramirez-Villegas, J., Watson, J. & Challinor, A. J. Identifying traits for genotypic adaptation using crop models. J. Exp. Bot. 66, 3451–3462 (2015).

    CAS  Article  Google Scholar 

  34. 34

    You, L., Wood, S. & Wood-Sichra, U. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agric. Syst. 99, 126–140 (2009).

    Article  Google Scholar 

  35. 35

    Hodson, D. P., Martinez-Romero, E., White, J. W., Corbett, J. D. & Bänzinger, M. African Maize Research Atlas Vol. 30 (CIMMYT, 2002).

    Google Scholar 

  36. 36

    Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).

    Article  Google Scholar 

  37. 37

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2011).

    Article  Google Scholar 

  38. 38

    Kirtman, B. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 953–1028 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  39. 39

    Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  40. 40

    Hawkins, E., Osborne, T. M., Ho, C. K. & Challinor, A. J. Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agric. For. Meteorol. 170, 19–31 (2013).

    Article  Google Scholar 

  41. 41

    Hyman, G. et al. Strategic approaches to targeting technology generation: assessing the coincidence of poverty and drought-prone crop production. Agric. Syst. 98, 50–61 (2008).

    Article  Google Scholar 

  42. 42

    Piontek, F. et al. Multisectoral climate impact hotspots in a warming world. Proc. Natl Acad. Sci. USA 111, 3233–3238 (2014).

    CAS  Article  Google Scholar 

  43. 43

    Joshi, M., Hawkins, E., Sutton, R., Lowe, J. & Frame, D. Projections of when temperature change will exceed 2 °C above pre-industrial levels. Nature Clim. Change 1, 407–412 (2011).

    Article  Google Scholar 

  44. 44

    Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8, e66428 (2013).

    CAS  Article  Google Scholar 

Download references


This study was funded by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and by a Young Scientist Innovation Grant from the International Center for Tropical Agriculture (CIAT) awarded to J.R.-V. We thank the crop experts listed in Supplementary Table 3 who kindly provided their feedback on both parameter values and suitability simulations. We thank K. Sonder from the International Maize and Wheat Improvement Center (CIMMYT) for sharing an updated mega-environment data set with us and for providing critical feedback on maize parameterizations. U.R. thanks E. Jones from CIAT for help on scripting some of the analyses. Authors thank C. Navarro and J. Tarapues from CIAT for support with the CMIP5 data, L. P. Moreno (CIAT) for her work on improving the EcoCrop model, and A. K. Koehler, S. Jennings and S. Whitfield from the University of Leeds for insightful comments. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output (models listed in Supplementary Table 5). For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank numerous anonymous reviewers for their insightful feedback.

Author information




J.R.-V. and A.J. conceived the study. U.R., J.R.-V. and A.J. designed the research. U.R. and J.R.-V. performed the analyses and analysed the results. F.M. and L.P. parameterized some of the crops used in the model. U.R., J.R.-V., A.J. and S.J.V. interpreted the results. U.R., J.R.-V., A.J. and S.J.V. wrote the manuscript. All authors discussed results and commented on the manuscript.

Corresponding author

Correspondence to Julian Ramirez-Villegas.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 1695 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rippke, U., Ramirez-Villegas, J., Jarvis, A. et al. Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nature Clim Change 6, 605–609 (2016).

Download citation

Further reading


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