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.

Anticipating drought-related food security changes

Abstract

Food insecurity early warning can provide time to mitigate unfolding crises; however, drought remains a large source of uncertainty. The challenge is to filter unclear or conflicting signals from various climatic and socio-economic variables and link them to food security outcomes. Integrating lag-1 autocorrelation diagnostics into remotely sensed observations from the Soil Moisture Active Passive (SMAP) mission and food prices, we found dramatic improvement in anticipating the timing and intensity of food crises, except in conflict settings. We analysed drought-induced food crises globally in the SMAP record (since 2015; approximately five per year). The change in soil moisture autocorrelation, which we term the Soil Moisture Auto-Regressive Threshold (SMART), signalled an accurate food security transition for all cases studied here (P < 0.05; n = 212), including lead time of up to three to six months for every case. The SMART trigger anticipates the timing of the transition and the magnitude of the food security change among small to large transitions, both into and out of crises (R2 = 0.80–0.83). While we do not evaluate out-of-sample forecast accuracy using our model, our findings suggest a significant advancement in the capabilities of food security early-warning diagnostics and could save lives and resources.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: SMART uses lag-1 autocorrelation of remotely sensed soil moisture and food prices to anticipate food security transitions.
Fig. 2: Data visualization dashboard showing how food security transitions are detected by the SMART.
Fig. 3: Sustained periods of SMART values are indicative of a potential food security state shift.
Fig. 4: The three-month median SMART values forecast the size of the transition for both crises and exits (P < 0.05).

Data availability

All data used in this study are publicly available through the NASA National Snow and Ice Data Center (NSIDC) website (https://nsidc.org/data/smap/smap-data.html), the FEWS NET Data Portal (https://fews.net/fews-data/333) and the FAO Food Price Monitoring and Analysis (FPMA) Tool (https://www.fao.org/giews/food-prices/price-tool/en/).

Code availability

All code used in this study is available upon request or at https://github.com/Krishna2609.

References

  1. Dakos, V., Carpenter, S. R., van Nes, E. H. & Scheffer, M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Philos. Trans. R. Soc. B 370, 20130263 (2015).

    Article  Google Scholar 

  2. Berg, A. & Sheffield, J. Climate change and drought: the soil moisture perspective. Curr. Clim. Change Rep. 4, 180–191 (2018).

    Article  Google Scholar 

  3. Benton, T. et al. Environmental Tipping Points and Food System Dynamics: Main Report (Global Food Security Programme UK, 2017).

  4. Mathys, E. Trigger Indicators and Early Warning and Response Systems in Multi‐Year Title II Assistance Programs (Food and Nutrition Technical Assistance Project 20, 2007).

  5. Wilkinson, E. et al. Forecasting Hazards, Averting Disasters: Implementing Forecast-Based Early Action at Scale (Overseas Development Institute, 2018).

  6. Krishnamurthy, P. K., Choularton, R. J. & Kareiva, P. Dealing with uncertainty in famine predictions: how complex events affect food security early warning skill in the Greater Horn of Africa. Glob. Food Secur. 26, 100374 (2020).

    Article  Google Scholar 

  7. Sepulcre-Canto, G., Horion, S. M. A. F., Singleton, A., Carrao, H. & Vogt, J. Development of a combined drought indicator to detect agricultural drought in Europe. Nat. Hazards Earth Syst. Sci. 12, 3519–3531 (2012).

    Article  Google Scholar 

  8. Svoboda, M. et al. The drought monitor. Bull. Am. Meteorol. Soc. 83, 1181–1190 (2002).

    Article  Google Scholar 

  9. Beneke, C., Chartrand, R., Kontgis, C. & Rich, D. in Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI (International Society for Optics and Photonics, 2019), 62–72, https://doi.org/10.1117/12.2532840

  10. Rembold, F. et al. ASAP: a new global early warning system to detect anomaly hot spots of agricultural production for food security analysis. Agric. Syst. 168, 247–257 (2019).

    Article  Google Scholar 

  11. Ogallo, L. et al. Adapting to climate variability and change: the Climate Outlook Forum process. Bull. World Meteorol. Organ. 57, 93–102 (2008).

    Google Scholar 

  12. de la Casa, A., Ovando, G. & Díaz, G. Linking data of ENSO, NDVI-MODIS and crops yield as a base of an early warning system for agriculture in Córdoba, Argentina. Remote Sens. Appl.: Soc. Environ. 22, 100480 (2021).

    Google Scholar 

  13. Becker-Reshef, I., Vermote, E., Lindeman, M. & Justice, C. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens. Environ. 114, 1312–1323 (2010).

    Article  Google Scholar 

  14. Husak, G. J., Funk, C., Michaelsen, J., Magadzire, T. & Goldsberry, K. P. Developing seasonal rainfall scenarios for food security early warning. Theor. Appl. Climatol. 114, 291–302 (2013).

    Article  Google Scholar 

  15. Funk, C. & Shukla, S. Drought Early Warning and Forecasting: Theory and Practice (Elsevier, 2020).

  16. Walker, P. Famine Early Warning Systems: Victims and Destitution (Routledge, 2013).

  17. Kalkuhl, M., Von Braun, J. & Torero, M. Food Price Volatility and Its Implications for Food Security and Policy (Springer, 2016).

  18. Lentz, E. C., Michelson, H., Baylis, K. & Zhou, Y. A data-driven approach improves food insecurity crisis prediction. World Dev. 122, 399–409 (2019).

    Article  Google Scholar 

  19. Maxwell, D. Famine Early Warning and Information Systems in Conflict Settings: Challenges for Humanitarian Metrics and Response (LSE/Conflict Research Programme 2019).

  20. Zhu, Q. et al. Satellite soil moisture for agricultural drought monitoring: assessment of SMAP-derived soil water deficit index in Xiang River Basin, China. Remote Sens. 11, 362 (2019).

    Article  Google Scholar 

  21. Sadri, S. et al. Wood. A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP. Remote Sens. Environ. 246, 111864 (2020).

    Article  Google Scholar 

  22. Bolten, J. D., Crow, W. T., Zhan, X., Jackson, T. J. & Reynolds, C. A. Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 3, 57–66 (2009).

    Article  Google Scholar 

  23. Cleverly, J. et al. Soil moisture controls on phenology and productivity in a semi‐arid critical zone. Sci. Total Environ. 568, 1227–1237 (2016).

    Article  CAS  Google Scholar 

  24. Krishnamurthy R, P. K., Fisher, J. B., Schimel, D. S. & Kareiva, P. M. Applying tipping point theory to remote sensing science to improve early warning drought signals for food security. Earth’s Future 8, e2019EF001456 (2020).

    Article  Google Scholar 

  25. Lenton, T. M. Early warning of climate tipping points. Nat. Clim. Change 1, 201–209 (2011).

    Article  Google Scholar 

  26. Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23, (2018).

  27. Integrated Food Security Phase Classification Manual Ver. 3.0 (IPC Global Partners, 2019).

  28. Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).

    Article  Google Scholar 

  29. Baro, M. & Deubel, T. F. Persistent hunger: perspectives on vulnerability, famine, and food security in sub-Saharan Africa. Annu. Rev. Anthropol. 35, 521–538 (2006).

    Article  Google Scholar 

  30. Kenya—Key Message Update September 2017 (FEWS NET Kenya, 2017).

  31. Verification Products: Greater Horn of Africa Climate Outlook Forum (ICPAC, 2019).

  32. Walker, D. P. et al. Skill of dynamical and GHACOF consensus seasonal forecasts of East African rainfall. Clim. Dyn. 53, 4911–4935 (2019).

    Article  Google Scholar 

  33. Jjemba, E. W., Mwebaze, B. K., Arrighi, J., de Perez, E. C. & Bailey, M. in Resilience (eds Zommers, Z. & Alverson, K.) 237–242 (Elsevier, 2018).

  34. He, X. et al. Integrated approaches to understanding and reducing drought impact on food security across scales. Curr. Opin. Environ. Sustainability 40, 43–54 (2019).

    Article  Google Scholar 

  35. Lowder, S. K., Bertini, R. & Croppenstedt, A. Poverty, social protection and agriculture: levels and trends in data. Glob. Food Sec. 15, 94–107 (2017).

    Article  Google Scholar 

  36. Hazell, P. B. in Agriculture & Food Systems to 2050: Global Trends, Challenges and Opportunities, Vol. 4 (eds Serraj, R. & Pingali, P.) 37–160 (World Scientific, 2019).

  37. Dithmer, J. & Abdulai, A. Does trade openness contribute to food security? A dynamic panel analysis. Food Policy 69, 218–230 (2017).

    Article  Google Scholar 

  38. Funk, C. et al. Recognizing the Famine Early Warning Systems Network: over 30 years of drought early warning science advances and partnerships promoting global food security. Bull. Am. Meteorol. Soc. 100, 1011–1027 (2019).

    Article  Google Scholar 

  39. Martin-Shields, C. P. & Stojetz, W. Food security and conflict: empirical challenges and future opportunities for research and policy making on food security and conflict. World Dev. 119, 50–164 (2019).

    Article  Google Scholar 

  40. Schauberger, B., Jägermeyr, J. & Gornott, C. A systematic review of local to regional yield forecasting approaches and frequently used data resources. Eur. J. Agron. 120, 126153 (2020).

    Article  Google Scholar 

  41. Schwalbert, R. et al. Mid‐season county‐level corn yield forecast for US corn belt integrating satellite imagery and weather variables. Crop Sci. 60, 739–750 (2020).

    Article  Google Scholar 

  42. Boogaard, H., van der Wijngaart, R., van Kraalingen, D., Meroni, M. & Rembold, F. ASAP Water Satisfaction Index (EU-JRC, 2018).

  43. FAO, IFAD, UNICEF, WFP & WHO The State of Food Insecurity 2019: Safeguarding Against Economic Slowdowns and Downturns (FAO, 2019).

  44. Reichle, R. H. et al. Assessment of the SMAP level-4 surface and root-zone soil moisture product using in situ measurements. J. Hydrometeorol. 18, 2621–2645 (2017).

    Article  Google Scholar 

  45. Skofronick-Jackson, G. et al. The Global Precipitation Measurement (GPM) mission for science and society. Bull. Am. Meteorol. Soc. 98, 1679–1695 (2017).

    Article  Google Scholar 

  46. MODIS and VIIRS Land Products Global Subsetting and Visualization Tool (ORNL DAAC, 2020).

  47. Lemoine, J. M., Bourgogne, S., Biancale, R. & Gégout, P. The new GRGS-RL04 series of mass variations modelled with GRACE data. In (ed.) EGU General Assembly 20th EGU General Assembly Conference Abstracts 18624 (EGU, 2018).

  48. Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. P. Natl Acad. Sci. USA 105, 14308–14312 (2008).

    Article  CAS  Google Scholar 

  49. Takimoto, G. Early warning signals of demographic regime shifts in invading populations. Popul. Ecol. 51, 419–426 (2009).

    Article  Google Scholar 

  50. Golledge, N. R. et al. Antarctic climate and ice‐sheet configuration during the early Pliocene interglacial at 4.23 Ma. Clim. Past 13, 959–975 (2017).

    Article  Google Scholar 

  51. Wieczorek, S., Ashwin, P., Luke, C. M. & Cox, P. M. Excitability in ramped systems: the compost‐bomb instability. Philos. Trans. R. Soc. A 467, 1243–1269 (2011).

    Google Scholar 

  52. Dakos, V., van Nes, E. H., d’Odorico, P. & Scheffer, M. Robustness of variance and autocorrelation as indicators of critical slowing down. Ecology 93, 264–271 (2012).

    Article  Google Scholar 

  53. Dakos, V. et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PloS ONE 7, (2012).

  54. Kalkuhl, M., von Braun, J. & Torero, M. in Food Price Volatility and Its Implications for Food Security and Policy (eds Kalkuhl, M., von Braun, J. & Torero, M.) 3–31 (Springer, 2016).

Download references

Acknowledgements

This research was carried out, in part, at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA) and funded through the Internal Strategic University Research Partnerships (SURP) programme. P.K.K.R. was supported, in part, by SURP and UCLA’s Institute of the Environment and Sustainability. J.B.F. was supported, in part, by NASA programmes Science Utilization of SMAP (SUSMAP) and NASA Climate Indicators and Data Products for Future National Climate Assessments (INCA). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. A. Barreca, M. Gebremichael and T. Gillespie provided useful feedback to earlier drafts of this paper.

Author information

Authors and Affiliations

Authors

Contributions

P.K.K.R. first conceptualized the study, conducted all data analysis, performed all statistical tests and generated the figures; P.K.K.R., J.B.F. and P.M.K. contributed to designing the methodology. All authors wrote and edited the manuscript.

Corresponding author

Correspondence to P. Krishna Krishnamurthy R.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Sustainability thanks David MacLeod and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Results, Discussion, Figs. 1–12 and Tables 1–3.

Reporting Summary

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Krishnamurthy R, P.K., Fisher, J.B., Choularton, R.J. et al. Anticipating drought-related food security changes. Nat Sustain 5, 956–964 (2022). https://doi.org/10.1038/s41893-022-00962-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41893-022-00962-0

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