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Building social resilience in North Korea can mitigate the impacts of climate change on food security

Nature Food volume 3, pages 499–511 (2022)Cite this article

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Abstract

Adaptation based on social resilience is proposed as an effective measure to mitigate hunger and avoid food shocks caused by climate change. But these have not been investigated comprehensively in climate-sensitive regions. North Korea (NK) and its neighbours, South Korea and China, represent three economic levels that provide us with examples for examining climatic risk and quantifying the contribution of social resilience to rice production. Here our data-driven estimates show that climatic factors determined rice biomass changes in NK from 2000 to 2017, and climate extremes triggered reductions in production in 2000 and 2007. If no action is taken, NK will face a higher climatic risk (with continuous high-temperature heatwaves and precipitation extremes) by the 2080s under a high-emissions scenario, when rice biomass and production are expected to decrease by 20.2% and 14.4%, respectively, thereby potentially increasing hunger in NK. Social resilience (agricultural inputs and population development for South Korea; resource use for China) mitigated climate shocks in the past 20 years (2000–2019), even transforming adverse effects into benefits. However, this effect was not significant in NK. Moreover, the contribution of social resilience to food production in the undeveloped region (15.2%) was far below the contribution observed in the developed and developing regions (83.0% and 86.1%, respectively). These findings highlight the importance of social resilience to mitigate the adverse effects of climate change on food security and human hunger and provide necessary quantitative information.

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Fig. 1: Spatial patterns of geographical and climatic distribution across NK, SK, and Liaoning and Jilin provinces of China.
Fig. 2: Simulating rice biomass and attributing the contribution of climatic variables on the basis of RF modelling in NK.
Fig. 3: Projected rice biomass and production losses under four future scenarios in NK.
Fig. 4: Social resilience mitigating climate shocks for rice production in NK, SK and China.
Fig. 5: The contribution of vulnerability indexes for social resilience to rice production variability.

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Data availability

All original MODIS reflectance and other MODIS products that NASA LP DAAC provided at the USGS EROS Center in this study are freely accessible on the GEE platform at https://developers.google.cn/earth-engine/datasets. The observational, Digital Elevation Model and reanalysis data are publicly available from the following sources: the EC data are at http://www.cnern.org.cn/index.jsp, the ERA5 reanalysis is at https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators and the Digital Elevation Model data are at https://srtm.csi.cgiar.org/. The 27 downscaled GCMs of CMIP6 were provided by D.L.L. and H. Zuo57, who downscaled them on the basis of the original CMIP6 at https://esgf-node.llnl.gov/projects/cmip6/. The statistical data are freely available from the following sources: data on rice production, rice imports and exports, fertilizer application, and population from FAO are at http://www.fao.org/faostat/en/; data on GDP from the United Nations Statistics Division are at https://unstats.un.org/unsd/snaama/Basic; and data on population ages 0–14, population ages 15–64, rural population, energy use, access to electricity, school enrolment, patent applications and net ODA received per capita from the World Bank are at https://data.worldbank.org/. Source data are provided with this paper.

Code availability

The first and corresponding authors are prepared to respond to reasonable requests for code.

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Acknowledgements

This study was supported by the collaboration project jointly founded by the NSFC (grant no. 41961124006) and the NSF (grant no. 1903722) for Innovations at the Nexus of Food, Energy, and Water Systems (INFEWS: US–China). H.T. acknowledges funding support from the Andrew Carnegie Fellow Program (award no. G-F-19-56910).

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Authors and Affiliations

  1. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, China

    Yu Shi, Yajie Zhang, Bingyan Wu, Linchao Li, Xiaoliang Lu, Nufang Fang, Chao Yue, Jianqiang He, Hao Feng & Qiang Yu

  2. College of Natural Resources and Environment, Northwest A&F University, Yangling, China

    Yu Shi, Bingyan Wu & Linchao Li

  3. New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, New South Wales, Australia

    Bin Wang & De Li Liu

  4. International Center for Climate and Global Change Research, College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL, USA

    Hao Shi & Shufen Pan

  5. Department of Resources and Environment, Shanxi Institute of Energy, Jinzhong, China

    Ning Jin

  6. Department of Agricultural Economics and Rural Sociology, Auburn University, Auburn, AL, USA

    Ruiqing Miao

  7. College of Earth Science and Technology, Zhengzhou University, Zhengzhou, China

    Qingling Geng

  8. Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA

    Chaoqun Lu

  9. National Meteorological Center, Beijing, China

    Liang He

  10. Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA, USA

    Hanqin Tian

  11. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Qiang Yu

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Contributions

Q.Y. and H.T. designed the study. Y.S. analysed the modelling results, created the figures and wrote the draft of the paper. Y.Z., B. Wu, H.S., L.L. and N.J. provided technology services and analysed the data. L.L. and B. Wang were responsible for technical support and manuscript editing of the partial dependence analysis. D.L.L. provided the downscaled GCM data of CMIP6. R.M. provided edits and suggestions for the social–economic analysis. X.L., Q.G., C.L., L.H., N.F., C.Y., J.H., H.F. and S.P. contributed to the interpretation of the results and the writing of the manuscript.

Corresponding authors

Correspondence to Yu Shi, Hanqin Tian or Qiang Yu.

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Nature Food thanks Matthew Cooper, Sujong Jeong and Tao Lin for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1–18, Tables 1–10 and Methods.

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Source Data Fig. 2

Statistical source data for the scatter and bar plots.

Source Data Fig. 3

Statistical source data for the box plots.

Source Data Fig. 4

Statistical source data for the bar plots.

Source Data Fig. 5

Statistical source data for the bar and scatter plots.

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Shi, Y., Zhang, Y., Wu, B. et al. Building social resilience in North Korea can mitigate the impacts of climate change on food security. Nat Food 3, 499–511 (2022). https://doi.org/10.1038/s43016-022-00551-6

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