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

Abstract

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.

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Acknowledgements

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

Authors

Contributions

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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Competing interests

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). https://doi.org/10.1038/s41893-020-00670-7

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