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Substantial impacts of climate shocks in African smallholder agriculture

Nature Sustainability (2024)Cite this article

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Abstract

Climate change is affecting the frequency and severity of extreme weather events, such as droughts or floods, which result in loss and damage to people, crops and infrastructure. Global data on loss and damage used in research, policy and media primarily come from macrostatistics based on disaster inventories. Here, we propose a different approach, based on survey microdata. We harmonize data from 120,000 agricultural fields in six African countries for a period from 2008 to 2019 and quantify crop production losses related to climate shocks. We find substantial damages which affect around 35% of plots and reduce national crop production by 29% on average. The economic impacts are greater than the global disaster data suggest. The economic losses resulting from droughts and flood alone are US$5.1 billion higher than reported in disaster inventories, affecting between 145 and 170 million people. The difference stems mostly from smaller and less severe but frequent adverse events that go under-reported or undetected in disaster inventories and therefore elude macrostatistics and reporting. The findings have implications for measurement and policies related to loss and damage and disaster risk reduction.

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Fig. 1: Frequency of crop losses due to adverse events, value lost and aggregate production loss.
Fig. 2: Most common climatic shocks by administrative unit, selected countries and years.
Fig. 3: Comparison of shock prevalence and impact between EM-DAT and LSMS-ISA data.

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Data availability

The raw data used in this study as well as the full questionnaires are publicly available through the World Bank online resources. LSMS-ISA datasets, questionnaires and documentation are publicly accessible through the following link: https://www.worldbank.org/en/programs/lsms/initiatives/lsms-ISA. Links to the various datasets used in this study are provided in the ‘dissemination’ tabs. More specifically, the dataset includes data from the Ethiopian Social Survey (waves 1 to 4), Malawi’s Integrated Household Panel Survey (waves 1 to 4), Mali’s Enquête Agricole de Conjoncture Intégrée (waves 1 and 2), Niger’s Enquête National sur les Conditions de Vie des Ménages et Agriculture (waves 1 and 2), Nigeria’s General Household Survey (wave 4) and Tanzania’s National Panel Survey (waves 1 to 5). The EM-DAT data were downloaded for free on the following public website: https://public.emdat.be/. Data were downloaded on 29 November 2023. The following data filters were applied—Classification: Natural; Countries: Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania; Time range: 2008–2020. The analysis dataset is available from Zenodo at https://doi.org/10.5281/zenodo.12667754 (ref. 61). Shapefiles and other raw geodata required to produce Fig. 2 were downloaded from the GADM database, which can be accessed for free at https://gadm.org/download_country.html. Population and other aggregate statistics were downloaded from the World Bank Development Indicators Database, directly into Stata using the wbopendata package. Source data are provided with this paper.

Code availability

The code for the analysis is available from Zenodo at https://doi.org/10.5281/zenodo.12667754 (ref. 61).

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Acknowledgements

This study received funding support from the 50 × 2030 Initiative to Close the Agricultural Data Gap (P.W., Y.M., T.B. and G.P.) and the World Bank Research Support Budget grant ‘On the Measurement of Agricultural Productivity Trends in Africa’ (P.W.). The authors are grateful to D. Gollin, E. Bulte, G. Carletto, R. Hill, S. Hallegatte, T. Kilic, T. Lybbert and participants of the CSAE 2023 Conference, the ICAS IX Conference, the EAAE 2023 Congress and at the World Bank for their comments and feedback.

Author information

Authors and Affiliations

  1. The World Bank, Living Standards Measurement Study, Development Economics Data Group, Rome, Italy

    Philip Wollburg, Yannick Markhof, Thomas Bentze & Giulia Ponzini

  2. Wageningen University, Development Economics Group, Wageningen, the Netherlands

    Philip Wollburg

  3. United Nations University, UNU-MERIT, Maastricht, the Netherlands

    Yannick Markhof

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Contributions

P.W., Y.M. and G.P. conceived the ideas. P.W., Y.M. and T.B. designed the methodology. T.B. and Y.M. curated and analysed the data. P.W., Y.M. and T.B. wrote the original draft. P.W., G.P., Y.M. and T.B. edited and reviewed the draft.

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Correspondence to Philip Wollburg.

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Nature Sustainability thanks Mook Bangalore, Guy Jackson and Sandy Tubeuf for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Mean fraction of potential harvest lost at the plot level, by country-year
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Extended Data Table 2 Total fraction of aggregate potential harvest lost, by country-year
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Extended Data Table 3 Heterogeneity in climate shock exposure within the same clusters
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Extended Data Table 4 Heterogeneity in climate shock exposure by plot characteristics
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Extended Data Table 5 Heterogeneity in loss size by plot characteristics
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Extended Data Table 6 Heterogeneity in climate shock exposure by plot manager characteristics
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Extended Data Table 7 Heterogeneity in loss size by plot manager characteristics
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Extended Data Table 8 Loss incidence and size by wealth deciles
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Extended Data Table 9 Estimated combined impacts of drought or flood events captured in LSMS-ISA
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Extended Data Table 10 Comparison of micro-data with disaster inventories
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Supplementary information

Supplementary Information

Supplementary text, Tables 1–16 and references.

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Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

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Wollburg, P., Markhof, Y., Bentze, T. et al. Substantial impacts of climate shocks in African smallholder agriculture. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01411-w

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