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Crop productivity in southern Africa is stagnant despite moderate climate trends

Nature Food volume 6, pages 762–765 (2025)Cite this article

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Abstract

Southern Africa faces high food insecurity and projected declines in agroclimatic conditions. Multiple satellite measures indicate that cropland productivity has stagnated for most of the region except South Africa in the past 20 years, in contrast to what official crop statistics suggest. Climate trends do not explain this stagnation, with the region experiencing more rainfall and less warming than most climate model projections. A change of course is needed before climate impacts accelerate.

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Fig. 1: Estimated changes in crop productivity, 2003–2022.
Fig. 2: The net impact of climate trends on crop productivity.

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Data availability

Data used to generate all figures as well as scripts used to process and analyse satellite data are available via Zenodo at https://zenodo.org/records/15492669 (ref. 32).

Code availability

All replication code for this study is available via Zenodo at https://zenodo.org/records/15492669 (ref. 32) and via GitHub at https://github.com/LobellLab/southern-africa-trends.

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Acknowledgements

This work was supported by the NASA Harvest Consortium (NASA Applied Sciences grant number 80NSSC17K0652, sub-award 54308-Z6059203 to D.B.L).

Author information

Authors and Affiliations

  1. Department of Earth System Science, Stanford University, Stanford, CA, USA

    David B. Lobell & Richard J. Lee

  2. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA

    David B. Lobell & Richard J. Lee

Authors
  1. David B. Lobell
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  2. Richard J. Lee
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Contributions

D.B.L. conceived and designed the study. R.J.L. performed data acquisition, data processing and statistical analysis. Both authors analysed results. D.B.L. wrote the paper and R.J.L. generated figures.

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Correspondence to David B. Lobell.

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The authors declare no competing interests.

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Nature Food thanks Ayala Wineman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Co-occurrence of stunting prevalence and projected deterioration of agroclimatic conditions.

The expected change in annual mean surface soil moisture from the latest IPCC models20 plotted against current rates of childhood stunting21. Each dot represents a single country, with countries that have fewer than 10M people excluded for clarity. Countries in southern Africa that are the focus of the current study, highlighted in green, are among the most food insecure out of countries with projected moisture declines, and among the countries with the largest projected moisture declines out of food insecure countries.

Extended Data Fig. 2 Total harvested area (top) and total harvested calories per ha (bottom) for maize vs. all grain crops or top five crops in each country.

Yields were converted to total calories for each crop to facilitate comparison between different crops. Maize is the majority of grain area in all five countries, although non-grain crops such as cassava and sugarcane are also important. Yields of maize show very similar trends and variation to those from the top five crops, although levels differ because of the higher calorie yield of cassava and sugarcane. The one case where maize and other crops deviate significantly is in Zambia since 2015, where reported cassava yields more than doubled in a span of a few years. We consider this more likely to be an artefact in the data than a true increase in cassava productivity.

Extended Data Fig. 3 Producer prices of maize in each country.

(source: FAOstat; https://www.fao.org/faostat/en/#data/PP).

Extended Data Fig. 4 Observed climate trends compared to model projections for 2003–2022.

Boxplots in each panel show the distribution of simulated climate trends for (a) precipitation, (b) average daily minimum temperatures and (c) average daily maximum temperature across 35 general circulation models used in the IPCC 6th assessment report, which account for historical anthropogenic forcings up until 2014 and projected forcings under the SSP5-8.5 scenario from 2015 onward. We use the Jan-Mar period for 2003–2022 in each country. Boxes indicate 25th–75th percentile, vertical lines show minimum and maximum, and horizontal line shows median. Dashed lines show the observed trend in each country according to the two observational datasets used in the study. Observed trends were generally at the cool and wet end of climate model simulations for the study period.

Extended Data Fig. 5 The sensitivity of crop productivity to temperature and precipitation.

Coefficients describing response of MODIS peak greenness to an increase in average daily maximum and minimum temperatures (left) and increased precipitation (right), based on regression equation (2). Error bars indicate 95% confidence interval based on clustered standard errors at the state level.

Extended Data Fig. 6 Productivity trends and field sizes in South Africa and Lesotho.

(a) Trends in peak greenness from MODIS over the study period in the area centred on Lesotho. (b) Average field sizes in the region according to the GeoWiki dataset19.

Supplementary information

Supplementary Information

Online methods, Supplementary Table 1.

Reporting Summary

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Lobell, D.B., Lee, R.J. Crop productivity in southern Africa is stagnant despite moderate climate trends. Nat Food 6, 762–765 (2025). https://doi.org/10.1038/s43016-025-01203-1

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