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Performance of large-scale irrigation projects in sub-Saharan Africa


After a 30-year hiatus, large-scale irrigation projects have returned to the development agenda in sub-Saharan Africa (SSA). However, the magnitude and drivers of past schemes’ performance remains poorly understood. We quantify the performance, measured as the proportion of proposed irrigated area delivered, of 79 irrigation schemes from across SSA by comparing planning documents with estimates of current scheme size from satellite-derived land-cover maps. We find overwhelming evidence that investments have failed to deliver promised benefits, with schemes supporting a median 16% of proposed area, only 20 (25%) delivering >80% and 16 (20%) completely inactive. Performance has not improved over six decades and we find limited relationships with commonly stated causes of failure such as scheme size and climate. We attribute these findings to political and management frameworks underpinning irrigation development in SSA. First, an emphasis on national food security promotes low-value crops, reducing economic viability. Second, proposals are unrealistically large, driven by optimism bias and political incentives. Finally, centralized bureaucracies lack the technical expertise, local knowledge and financial resources to ensure long-term maintenance. Our findings highlight the need for greater learning from past investments’ outcomes if improvements in agricultural productivity and water security across SSA are to be realized.

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Fig. 1: Geographic distribution of irrigation schemes.
Fig. 2: Percentage of proposed irrigation area delivered.
Fig. 3: Relationship between delivered irrigated percentage and potential explanatory variables.

Data availability

Data used to produce the figures and statistical analysis are available in Supplementary Data 7 and are also available from the corresponding author.

Code availability

Code used to generate Figs. 1–3, Table 1 and Extended Data Figs. 1 and 2 are available in Supplementary Data 1–6 and are also available from the corresponding author.


  1. Hanjra, M. A., Ferede, T. & Gutta, D. G. Reducing poverty in sub-Saharan Africa through investments in water and other priorities. Agric. Water Manag. 96, 1062–1070 (2009).

    Google Scholar 

  2. Moris, J. R. Irrigation Development in Africa: Lessons of Experience (Routledge, 1990).

  3. Aw, D. & Diemer, G. Making a Large Irrigation Scheme Work: A Case Study from Mali (World Bank, 2005).

  4. Bertoncin, M., Pase, A., Quatrida, D. & Turrini, S. At the junction between state, nature and capital: irrigation mega-projects in Sudan. Geoforum 106, 24–37 (2019).

    Google Scholar 

  5. Adams, W. M. Wasting the Rain: Rivers, People and Planning in Africa (Earthscan, 1992).

  6. Chambers, R. & Moris J. R. (eds) Mwea: An Irrigated Rice Settlement in Kenya (Weltforum Verlag, 1973).

  7. Awojobi, O. & Jenkins, G. P. Were the hydro dams financed by the World Bank from 1976 to 2005 worthwhile? Energy Policy 86, 222–232 (2015).

    Google Scholar 

  8. Bazin, F., Hathie, I., Skinner, J. & Koundouno, J. Irrigation, Food Security and Poverty—Lessons from Three Large Dams in West Africa (IIED and IUCN, 2017).

  9. World Commission on Dams Dams and Development: A New Framework for Decision-Making (Earthscan, 2000).

  10. Thomas, D. H. L. & Adams, W. M. Adapting to dams: agrarian change downstream of the Tiga dam, northern Nigeria. World Dev. 27, 919–935 (1999).

    Google Scholar 

  11. Balasubramanian, V., Sie, M., Hijmans, R. J. & Otsuka, K. Increasing rice production in sub-Saharan Africa: challenges and opportunities. Adv. Agron. 94, 55–133 (2007).

    CAS  Google Scholar 

  12. Alam, M. Problems and potential of irrigated agriculture in sub-Saharan Africa. J. Irrig. Drain. Eng. 117, 155–172 (1991).

    Google Scholar 

  13. Mold, A. Will it all end in tears? Infrastructure spending and African development in historical perspective. J. Int. Dev. 24, 237–254 (2012).

    Google Scholar 

  14. Veldwisch, G. J., Bolding, A. & Wester, P. Sand in the engine: the travails of an irrigated rice scheme in Bwanje Valley, Malawi. J. Dev. Stud. 45, 197–226 (2009).

    Google Scholar 

  15. Carney, J. Converting the wetlands, engendering the environment: the intersection of gender with agrarian change in the Gambia. Econ. Geogr. 69, 329–348 (1993).

    Google Scholar 

  16. Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2015).

    Google Scholar 

  17. Woodhouse, P. et al. African farmer-led irrigation development: re-framing agricultural policy and investment? J. Peasant Stud. 44, 213–233 (2017).

    Google Scholar 

  18. Dakar Declaration on Irrigation: Building Resilience and Accelerate Growth in Sahel and West Africa by Boosting Irrigated Agriculture (ICID, 2013).

  19. Merrey, D. J. & Sally, H. Another well-intentioned bad investment in irrigation: the Millennium Challenge Corporation’s ‘compact’ with the Republic of Niger. Water Altern. 10, 195–203 (2017).

    Google Scholar 

  20. Rufin, P. et al. Global-scale patterns and determinants of cropping frequency in irrigation dam command areas. Glob. Environ. Change 50, 110–122 (2018).

    Google Scholar 

  21. Blanc, E. & Strobl, E. Is small better? A comparison of the effect of large and small dams on cropland productivity in South Africa. World Bank Econ. Rev. 28, 545–576 (2014).

    Google Scholar 

  22. Flyvbjerg, B. Policy and planning for large-infrastructure projects: problems, causes, cures. Environ. Plan. B 34, 578–597 (2007).

    Google Scholar 

  23. Ansar, A., Flyvbjerg, B., Budzier, A. & Lunn, D. Should we build more large dams? The actual costs of hydropower megaproject development. Energy Policy 69, 43–56 (2014).

    Google Scholar 

  24. Ika, L. A., Diallo, A. & Thuillier, D. Critical success factors for World Bank projects: an empirical investigation. Int. J. Proj. Manag. 30, 105–116 (2012).

    Google Scholar 

  25. Duponchel, M., Chauvet, L. & Collier, P. What Explains Aid Project Success in Post-Conflict Situations? Policy Research Working Paper Series 5418 (World Bank, 2010).

  26. Flyvbjerg, B. Survival of the unfittest: why the worst infrastructure gets built—and what we can do about it. Oxf. Rev. Econ. Policy 25, 344–367 (2009).

    Google Scholar 

  27. Scott, J. C. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed (Yale Univ. Press, 1998).

  28. Li, T. M. Beyond ‘the state’ and failed schemes. Am. Anthropol. 107, 383–394 (2005).

    Google Scholar 

  29. de Bont, C., Komakech, H. C. & Veldwisch, G. J. Neither modern nor traditional: farmer-led irrigation development in Kilimanjaro region, Tanzania. World Dev. 116, 15–27 (2019).

    Google Scholar 

  30. Li, T. M. The Will to Improve: Governmentality, Development, and the Practice of Politics (Duke Univ. Press, 2007).

  31. Project Performance Audit Report—Chad Lake Chad Polders Project (Credit 592-CD) Report No. 6751 (World Bank, 1987).

  32. Chad: Appraisal of Sategui-Deressia Irrigation Project Report No. 145a-CD (World Bank, 1974).

  33. Adams, W. M. Large scale irrigation in northern Nigeria: performance and ideology. Trans. Inst. Br. Geogr. 16, 287–300 (1991).

    Google Scholar 

  34. Biswas, A. K. Irrigation in Africa. Land Use Policy 3, 269–285 (1986).

    Google Scholar 

  35. Ansar, A., Flyvbjerg, B., Budzier, A. & Lunn, D. in The Oxford Handbook of Megaproject Management (ed. Flyvbjerg, B.) Ch. 4 (Oxford Univ. Press, 2017).

  36. Adams, W. M. The downstream impacts of dam construction: a case study from Nigeria. Trans Inst. Br. Geogr. 10, 292–302 (1985).

    Google Scholar 

  37. Hirschman, A. O. Development Projects Observed (Brookings, 1967).

  38. de Bont, C. The continuous quest for control by African irrigation planners in the face of farmer-led irrigation development: the case of the Lower Moshi area, Tanzania (1935–2017). Water Altern. 11, 893–915 (2018).

    Google Scholar 

  39. Bertoncin, M. & Pase, A. Interpreting mega-development projects as territorial traps: the case of irrigation schemes on the shores of Lake Chad (Borno State, Nigeria). Geogr. Helv. 72, 243–254 (2017).

    Google Scholar 

  40. Schumacher, E. F. Small Is Beautiful: A Study of Economics as If People Mattered (Vintage, 1973).

  41. Jones, B. Desiccation and the West African colonies. Geogr. J. 91, 401–423 (1938).

    Google Scholar 

  42. Adams, W. M. How beautiful is small? Scale, control and success in Kenyan irrigation. World Dev. 18, 1309–1323 (1990).

    Google Scholar 

  43. Ahlers, R., Brandimarte, L., Kleemans, I. & Sadat, S. H. Ambitious development on fragile foundations: criticalities of current large dam construction in Afghanistan. Geoforum 54, 49–58 (2014).

    Google Scholar 

  44. Green, N., Sovacool, B. K. & Hancock, K. Grand designs: assessing the African energy security implications of the Grand Inga dam. Afr. Stud. Rev. 58, 133–158 (2015).

    Google Scholar 

  45. Mbara, C. J., Gadain, H. M. & Muthusi, F. M. Status of Medium to Large Irrigation Schemes in Southern Somalia Technical Report No. W-05 (FAO-SWALIM, 2007).

  46. Flyvbjerg, B. Policy and planning for large-infrastructure projects: problems, causes, cures. Environ. Plan. B 34, 578–597 (2007).

    Google Scholar 

  47. Burney, J., Woltering, L., Burke, M., Naylor, R. & Pasternak, D. Solar-powered drip irrigation enhances food security in the Sudano–Sahel. Proc. Natl Acad. Sci. USA 107, 1848–1853 (2010).

    CAS  Google Scholar 

  48. Higginbottom, T. P., Symeonakis, E., Meyer, H. & van der Linden, S. Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data. ISPRS J. Photogramm. Remote Sens. 139, 88–102 (2018).

    Google Scholar 

  49. Müller, H., Rufin, P., Griffiths, P., Siqueira, A. J. & Hostert, P. Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape. Remote Sens. Environ. 156, 490–499 (2015).

    Google Scholar 

  50. Pal, M. Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26, 217–222 (2005).

    Google Scholar 

  51. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Google Scholar 

  52. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

    Google Scholar 

  53. Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).

    CAS  Google Scholar 

  54. Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 170001 (2017).

    Google Scholar 

  55. Kaufmann, D., Kraay, A. & Mastruzzi, M. The worldwide governance indicators: methodology and analytical issues. Hague J. Rule Law 3, 220–246 (2011).

    Google Scholar 

  56. Walsh, R. P. D. & Lawler, D. M. Rainfall seasonality: description, spatial patterns and change through time. Weather 36, 201–208 (1981).

    Google Scholar 

  57. Wood, S. N. Generalized Additive Models: an Introduction with R 2nd edn (Chapman and Hall/CRC, 2017).

  58. Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).

    Google Scholar 

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This research was funded by the FutureDAMS project (grant code: ES/P011373/1), through the Global Challenges Research Fund from United Kingdom Research and Innovation (UKRI).

Author information

Authors and Affiliations



T.P.H. and T.F. designed the research. T.P.H. collated the data and performed all computations and analyses. T.P.H. and T.F. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Thomas P. Higginbottom.

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The authors declare no competing interests.

Additional information

Peer review information Nature Sustainability thanks Mure Agbonlaho, Atif Ansar and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Modelled relationships from the quasibinomial Generalised Additive Models.

Relationships between delivered irrigated percentage and potential explanatory variables. Delivery percentage were capped at 100%, solid lines are derived from quasibinomial GAMs (multiplied by 100), with shading showing 95% confidence intervals. Vertical dashed lines show the median value of the variable.

Extended Data Fig. 2 Modelled relationships from the binomial Generalised Additive Models.

Binomial models between delivered irrigation scheme status (failed/operational) and potential explanatory variables, solid lines derived from binomial GAMs with shading showing 95% confidence intervals. Rug plot lines show the distribution of scheme status (failure/operational). Vertical dashed lines show the median value of the variable.

Supplementary information

Supplementary Information

Supplementary methods.

Supplementary Data 1

Code to generate Fig. 1.

Supplementary Data 2

Code to generate Fig. 2.

Supplementary Data 3

Code to generate Fig. 3.

Supplementary Data 4

Code to generate Table 1 and summary statistics.

Supplementary Data 5

Code to run the quasibinomial models and create Extended Data Fig. 1.

Supplementary Data 6

Code to run the binomial models and create Extended Data Fig. 2.

Supplementary Data 7

Source data for all analyses and figures.

Supplementary Data 8

Full model outputs relating to Table 2.

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Higginbottom, T.P., Adhikari, R., Dimova, R. et al. Performance of large-scale irrigation projects in sub-Saharan Africa. Nat Sustain 4, 501–508 (2021).

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