Skip to main content

Thank you for visiting nature.com. 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.

The impact of conflict-driven cropland abandonment on food insecurity in South Sudan revealed using satellite remote sensing

An Author Correction to this article was published on 11 January 2022

This article has been updated

Abstract

Armed conflicts often hinder food security through cropland abandonment and restrict the collection of on-the-ground information required for targeted relief distribution. Satellite remote sensing provides a means for gathering information about disruptions during armed conflicts and assessing the food security status in conflict zones. Using ~7,500 multisource satellite images, we implemented a data-driven approach that showed a reduction in cultivated croplands in war-ravaged South Sudan by 16% from 2016 to 2018. Propensity score matching revealed a statistical relationship between cropland abandonment and armed conflicts that contributed to drastic decreases in food supply. Our analysis shows that the abandoned croplands could have supported at least a quarter of the population in the southern states of South Sudan and demonstrates that remote sensing can play a crucial role in the assessment of cropland abandonment in food-insecure regions, thereby improving the basis for timely aid provision.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Land-cover maps.
Fig. 2: Missed crop production per crop type.

Data availability

The intermediate data that support the findings of this study are available at https://doi.org/10.17894/ucph.5e12bdda-b2d7-4835-8ae1-a7c49affb97b. Source data are provided with this paper. Land-cover training and validation data are available on request.

Code availability

Code used for land-cover classification in GEE and propensity score analysis are available at https://github.com/victor-m-olsen/nature-food.

Change history

References

  1. Assessing Cropland Abandonment in Mopti Region with Satellite Imagery (WFP, 2019).

  2. Baumann, M., Radeloff, V. C., Avedian, V. & Kuemmerle, T. Land-use change in the Caucasus during and after the Nagorno-Karabakh conflict. Reg. Environ. Change 15, 1703–1716 (2015).

    Article  Google Scholar 

  3. Hendrix, C. & Brinkman, H.-J. Food insecurity and conflict dynamics: causal linkages and complex feedbacks. Stabil. Int. J. Secur. Dev. 2, 26 (2013).

  4. The State of Food Security and Nutrition in the World 2018: Building Climate Resilience for Food Security and Nutrition (FAO, 2018).

  5. Tong, X. et al. Revisiting the coupling between NDVI trends and cropland changes in the Sahel drylands: a case study in western Niger. Remote Sens. Environ. 191, 286–296 (2017).

    Article  ADS  Google Scholar 

  6. Prishchepov, A. V. Agricultural Land Abandonment (Oxford Bibliographies, 2020).

  7. Witmer, F. D. W. Detecting war‐induced abandoned agricultural land in northeast Bosnia using multispectral, multitemporal Landsat TM imagery. Int. J. Remote Sens. 29, 3805–3831 (2008).

    Article  Google Scholar 

  8. Eklund, L., Degerald, M., Brandt, M., Prishchepov, A. V. & Pilesjö, P. How conflict affects land use: agricultural activity in areas seized by the Islamic State. Environ. Res. Lett. 12, 054004 (2017).

  9. Skakun, S., Justice, C. O., Kussul, N., Shelestov, A. & Lavreniuk, M. Satellite data reveal cropland losses in South-Eastern Ukraine under military conflict. Front. Earth Sci. 7, 305 (2019).

    Article  ADS  Google Scholar 

  10. Yin, H. et al. Monitoring cropland abandonment with Landsat time series. Remote Sens. Environ. 246, 111873 (2020).

    Article  ADS  Google Scholar 

  11. Blair, D., Shackleton, C. M. & Mograbi, P. J. Cropland abandonment in South African smallholder communal lands: land cover change (1950–2010) and farmer perceptions of contributing factors. Land 7, 121 (2018).

  12. Prishchepov, A. V., Schierhorn, F. & Löw, F. Unraveling the diversity of trajectories and drivers of global agricultural land abandonment. Land 10, 97 (2021).

  13. Næss, J. S., Cavalett, O. & Cherubini, F. The land–energy–water nexus of global bioenergy potentials from abandoned cropland. Nat. Sustain. 4, 525–536 (2021).

    Article  Google Scholar 

  14. Raj Khanal, N. & Watanabe, T. Abandonment of agricultural land and its consequences. Mt. Res. Dev. 26, 32–40 (2006).

    Article  Google Scholar 

  15. Baiphethi, M. N. & Jacobs, P. T. The contribution of subsistence farming to food security in South Africa. Agric. Econ. Res. Policy Pract. South Africa 48, 459–482 (2005).

    Google Scholar 

  16. Yin, H. et al. Agricultural abandonment and re-cultivation during and after the Chechen Wars in the northern Caucasus. Global Environ. Change 55, 149–159 (2019).

    Article  Google Scholar 

  17. The State of Food Security and Nutrition in the World 2017 (FAO, 2017).

  18. Special Report: Crop and Food Security Assessment Mission to South Sudan, March 2018 (FAO, 2018).

  19. Battersby, J. & Watson, V. Addressing food security in African cities. Nat. Sustain. 1, 153–155 (2018).

    Article  Google Scholar 

  20. Samasse, K., Hanan, N. P., Anchang, J. Y. & Diallo, Y. A high-resolution cropland map for the West African Sahel based on high-density training data, Google Earth Engine, and locally optimized machine learning. Remote Sensing 12, 1436 (2020).

  21. Somanathan, E., Prabhakar, R. & Mehta, B. S. Decentralization for cost-effective conservation. Proc. Natl Acad. Sci. USA 106, 4143–4147 (2009).

    CAS  Article  ADS  Google Scholar 

  22. Rosenbaum, P. R. & Rubin, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983).

  23. FoodData Central (USDA, 06/13/2020); https://fdc.nal.usda.gov/index.html

  24. Baumann, M. & Kuemmerle, T. The impacts of warfare and armed conflict on land systems. J. Land Use Sci. 11, 672–688 (2016).

    Article  Google Scholar 

  25. Bégué, A. et al. Remote sensing and cropping practices: a review. Remote Sensing 10, 99 (2018).

  26. Valero, S. et al. Production of a dynamic cropland mask by processing remote sensing image series at high temporal and spatial resolutions. Remote Sens. 8, 55 (2016).

  27. Carrasco, L., O’Neil, A. W., Daniel Morton, R. & Rowland, C. S. Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sens. 11, 288 (2019).

  28. Moreno-Martínez, Á. et al. Interpolation and gap filling of Landsat reflectance time series. In Proc. IEEE International Geoscience Remote Sensing Symposium 2018, 349–352 (IEEE, 2018).

  29. Special Report: Crop and Food Security Assessment Mission to South Sudan, March 2019 (FAO, 2019).

  30. Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).

    CAS  Article  Google Scholar 

  31. Schleussner, C.-F., Donges, J. F., Donner, R. V. & Schellnhuber, H. J. Armed-conflict risks enhanced by climate-related disasters in ethnically fractionalized countries. Proc. Natl Acad. Sci. USA 113, 9216–9221 (2016).

    CAS  Article  ADS  Google Scholar 

  32. Mach, K. J. et al. Climate as a risk factor for armed conflict. Nature 571, 193–197 (2019).

    CAS  Article  ADS  Google Scholar 

  33. Benayas, J. R., Martins, A., Nicolau, J. M. & Schulz, J. J. Abandonment of agricultural land: an overview of drivers and consequences. CAB Rev. 2, 057 (2007).

  34. Kamp, J. Weighing up reuse of Soviet croplands. Nature 505, 483 (2014).

    CAS  Article  ADS  Google Scholar 

  35. Schiermeier, Q. Soviet Union’s collapse led to massive drop in carbon emissions. Nature https://doi.org/10.1038/d41586-019-02024-6 (2019).

  36. Huang, X., Ziniti, B. & Torbick, N. Assessing conflict driven food security in Rakhine, Myanmar with multisource imagery. Land 8, 95 (2019).

    Article  Google Scholar 

  37. Chaudhary, S. et al. A synopsis of farmland abandonment and its driving factors in Nepal. Land 9, 84 (2020).

    Article  Google Scholar 

  38. Special Report: Crop and Food Security Assessment Mission to South Sudan (FAO, 2017).

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

    Article  ADS  Google Scholar 

  40. Murphy, S. Cloud masking of Sentinel 2 using Google Earth Engine (GitHub, 2018); https://github.com/samsammurphy/cloud-masking-sentinel2

  41. Technical Guide: Cloud Masks (ESA, 2019); https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks

  42. Phalke, A. R. & Özdoğan, M. Large area cropland extent mapping with Landsat data and a generalized classifier. Remote Sens. Environ. 219, 180–195 (2018).

    Article  ADS  Google Scholar 

  43. Inglada, J., Vincent, A., Arias, M. & Marais-Sicre, C. Improved early crop type identification by joint use of high temporal resolution SAR and optical image time series. Remote Sens. 8, 362 (2016).

  44. Van Tricht, K., Gobin, A., Gilliams, S. & Piccard, I. Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: a case study for Belgium. Remote Sens. 10, 1642 (2018).

  45. sklearn.feature_selection.RFECV (SciKit, 2019); https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html

  46. Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).

    Article  ADS  Google Scholar 

  47. Olofsson, P. et al. Mitigating the effects of omission errors on area and area change estimates. Remote Sens. Environ. 236, 111492 (2020).

    Article  ADS  Google Scholar 

  48. Hu, T., Liu, J., Zheng, G., Li, Y. & Xie, B. Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013. Sci. Rep. 8, 7409 (2018).

  49. Zhou, H. et al. Monitoring the change of urban wetland using high spatial resolution remote sensing data. Int. J. Remote Sens. 31, 1717–1731 (2010).

    Article  Google Scholar 

  50. Yuan, F., Sawaya, K. E., Loeffelholz, B. C. & Bauer, M. E. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sens. Environ. 98, 317–328 (2005).

    Article  ADS  Google Scholar 

  51. Prishchepov, A. A., Müller, D., Dubinin, M., Baumann, M. & Radeloff, V. C. Determinants of agricultural land abandonment in post-Soviet European Russia. Land Use Policy 30, 873–884 (2013).

    Article  Google Scholar 

  52. Wilson, S. A. & Wilson, C. O. Modelling the impacts of civil war on land use and land cover change within Kono District, Sierra Leone: a socio-geospatial approach. Geocarto Int. 28, 476–501 (2013).

    Article  Google Scholar 

  53. Sieber, A. et al. Landsat-based mapping of post-Soviet land-use change to assess the effectiveness of the Oksky and Mordovsky protected areas in European Russia. Remote Sens. Environ. 133, 38–51 (2013).

    Article  ADS  Google Scholar 

  54. South Sudan—County Population Estimates—2015–2020 (HDX, 2019); https://data.humdata.org/dataset/south-sudan-county-population-estimates-2015-2020

  55. About ACLED (ACLED, 2019); https://www.acleddata.com/about-acled/

  56. Raleigh, C., Linke, A., Hegre, H. & Karlsen, J. Introducing ACLED: an armed conflict location and event dataset. J. Peace Res. 47, 651–660 (2010).

    Article  Google Scholar 

  57. Donnay, K., Dunford, E. T., McGrath, E. C., Backer, D. & Cunningham, D. E. Integrating conflict event data. J. Conflict Resolut. 63, 1337–1364 (2019).

    Article  Google Scholar 

  58. Eck, K. In data we trust? A comparison of UCDP GED and ACLED conflict events datasets. Coop. Confl. 47, 124–141 (2012).

    Article  Google Scholar 

  59. Raleigh, C. & Dowd, C. Armed Conflict Location and Event Data Project (ACLED) Codebook (ACLED, 2015).

  60. Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).

  61. Gellrich, M., Baur, P., Koch, B. & Zimmermann, N. E. Agricultural land abandonment and natural forest re-growth in the Swiss mountains: a spatially explicit economic analysis. Agric. Ecosyst. Environ. 118, 93–108 (2007).

    Article  Google Scholar 

  62. Ho, D. E., Imai, K., King, G. & Stuart, E. A. MatchIt 804, 495–2027 (2011).

    Google Scholar 

  63. Caliendo, M. & Kopeinig, S. Some practical guidance for the implementation of propensity score matching. J. Econ. Surv. 22, 31–72 (2008).

    Article  Google Scholar 

  64. Special Report: Crop and Food Security Assessment Mission to South Sudan, February 2016 (FAO, 2016).

  65. Special Report: Crop and Food Security Assessment Mission to South Sudan, February 2014 (FAO, 2014).

  66. Special Report: Crop and Food Security Assessment Mission to South Sudan, May 2015 (FAO, 2015).

  67. FAOSTAT. Data. Trade. Crops and Livestock Products (FAO, 2020); http://faostat3.fao.org/browse/T/TP/E

  68. Food and Nutrition Security Assessment in Sudan: Analysis of 2009 National Baseline Household Survey (SIFSIA, 2010).

  69. Buchhorn, M. et al. Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015–2019: Product User Manual (Zenodo, 2020); https://zenodo.org/record/3938963

Download references

Acknowledgements

We acknowledge the support of DFF-Danish ERC Support Program (grant number 116491, 9127-00001B). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We also acknowledge the WFP for providing in situ data for the study. Finally, we acknowledge the contribution of N. Keuler from the University of Wisconsin-Madison for consultation on statistical analysis.

Author information

Authors and Affiliations

Authors

Contributions

V.O. conducted all analyses in this research and drafted the paper. A.P. and R.F. supervised the work, revised the paper and provided technical and thematic insights. Additionally, A.P. conducted the analysis on kilocalorie availability. P.O. and D.D. contributed to the accuracy assessment. R.B. provided field data and revised the paper. V.B. supervised the econometric analysis. D.R. contributed to the food security narrative. All authors edited, reviewed and approved the final manuscript.

Corresponding author

Correspondence to Victor Mackenhauer Olsen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Food thanks Manfred Buchroithner 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.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Tables 1–17.

Source data

Source Data Fig. 2

Calculation on missed crop production and kilocalories.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Olsen, V.M., Fensholt, R., Olofsson, P. et al. The impact of conflict-driven cropland abandonment on food insecurity in South Sudan revealed using satellite remote sensing. Nat Food 2, 990–996 (2021). https://doi.org/10.1038/s43016-021-00417-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43016-021-00417-3

Further reading

Search

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