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Anticipating drought-related food security changes


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.

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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 (, the FEWS NET Data Portal ( and the FAO Food Price Monitoring and Analysis (FPMA) Tool (

Code availability

All code used in this study is available upon request or at


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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.

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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.

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Correspondence to P. Krishna Krishnamurthy R.

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

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Krishnamurthy R, P.K., Fisher, J.B., Choularton, R.J. et al. Anticipating drought-related food security changes. Nat Sustain 5, 956–964 (2022).

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