GCRI · Food Nexus
Food system shocks
Food systems are hit by shocks that start in different places: drought and flood, pests such as desert locust and fall armyworm, plant and animal disease, conflict, economic crises and disruption of trade or energy. The damage depends less on the shock itself than on what it meets: concentrated supply, low stocks, high import dependence, indebted households and weak services. This page sets out how food-system shocks propagate and compound, how they are monitored, and who acts on the evidence.
How the work runs.
Fig.Market and harvest early warning: example names and illustrative values, not a record or a live system.
Food system shocks: how the risk develops.
Disease, drought, conflict and supply disruption. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Weather shocks hit output and income
Drought, heat and flood reduce harvests, kill livestock and destroy stores, and they cut farm labour income at the same time. Rural households lose their food and the money to buy it in the same season.
Transboundary pests and disease
Desert locust swarms, fall armyworm, wheat rusts, African swine fever and avian influenza spread with wind, trade and animal movement. Control depends on surveillance and rapid response in countries that may lack both.
Conflict breaks every link
Conflict displaces farmers, blocks markets and aid, destroys infrastructure and drives up prices. It is a leading driver of the most severe food crises and often coincides with weather and economic shocks.
Shocks compound and cascade
A drought that coincides with a currency crisis, a fertiliser price spike and an export ban causes far more hunger than any one alone. Recovery is slow because assets sold to cope are not quickly rebuilt.
Growing conditions in the Black Sea region.
Crop greenness, root-zone soil moisture and plant growth over the Black Sea region, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Growing conditions, for this page
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra NDVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite greenness index over the 8 days to the date shown; an indicator of crop and pasture condition, not a yield estimate or a food security warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled
- Source
- SMAP Level 4 (NASA NSIDC DAAC)
- Provider
- NASA NSIDC DAAC
- Licence
- NASA open data
- Vintage
- daily; Model with satellite data assimilated
- Newest at build
- 2026-10-11
- Resolution
- about 2 km grid
- Uncertainty
- Model with satellite data assimilated. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest 8-day period GIBS publishes)
- Source
- MODIS Aqua GPP (MYD17) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- 8-day; Satellite-driven model; latest 8-day period GIBS publishes
- Newest at build
- 2026-09-30
- Resolution
- about 500 m grid
- Uncertainty
- Model estimate of carbon taken up by plants over the 8-day period shown; an indicator of growth, not harvest or production.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Growing conditions, framed on the Black Sea region.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- SMAP Level 4 soil moisture and snow mass, NASA Global Modeling and Assimilation Office; distributed by the NASA National Snow and Ice Data Center DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (13 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- Gross primary production, 8-day total: MODIS Aqua GPP (MYD17), NASA LP DAAC · 30 Sep 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
How a food shock is born in, and returns to, other systems.
| Domain | How it couples here | What to watch |
|---|---|---|
| Water | Synchronous drought across breadbaskets is the shock that matters most. See drought and flood. | Concurrent drought across major producing regions |
| Energy | An energy price spike raises input costs and can prompt export restrictions on fertiliser. | Fertiliser export restrictions |
| Health | Animal disease (for example African swine fever or avian influenza) removes supply and threatens people. See One Health. | WOAH notifications in major producers |
| Biodiversity | Pests and invasive species spread with warming and trade. | Plant pest outbreak reports under the IPPC |
Gaupp and colleagues (2020) found that the risk of simultaneous low yields across major breadbaskets rises with warming. Kornhuber and colleagues (2023) showed that climate and crop models underestimate the atmospheric patterns that cause synchronised low yields, so model-based estimates of joint failure are likely too low.
A shock through an input-output economy.
A loss of output in one sector propagates through the technical coefficients to its suppliers and customers, and recovers at the rate you set; the coefficients are teaching values, not national accounts. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Black Sea region; the model itself is not tied to a place.
Controls
- Sector hit first (position in the list)
- 0
- Size of the shock
- 30% of its output
- Duration of the shock
- 30 days
- Recovery rate
- 0.08 per day
- Days simulated
- 180 days
Readouts
- Output lost over the run
- 0.89% of a year’s output
- Total over direct (multiplier)
- 1.86 times
- Most affected other sector
- Fertiliser and inputs
- Its peak inoperability
- 3.5%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Farming | 30.13 | 22.5 |
| Fertiliser and inputs | 3.49 | 15.6 |
| Food processing | 1.70 | 23.4 |
| Transport | 2.03 | 15.4 |
| Wholesale and retail | 2.78 | 14.3 |
| Energy | 3.02 | 8.6 |
Methods
- Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
- Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
- Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Seaports Natural Earth
- Maritime chokepoints Nexus (named straits and canals at their published coordinates)
- Railways Natural Earth
- Major roads Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Food Nexus in Foundry
- Tested at Food Nexus in Universe
- Codified in Water and Food Systems in Standards
- Published in Food Nexus in Reports
- Taught in Faculty of Earth and Environmental Sciences in Academy
Shock scenarios and contributions that test them.
Compound food shock scenarios are designed for the Scenario and Stress-Test series and the Foresight Outlook series, always labelled as scenario evidence. Famine early warning remains with FEWS NET, the IPC and national systems. Scenario models would carry NS-12 cards under the proposed Nexus Standards. Useful contributions: historical cases of compound food shocks with sources, and critiques of scenario assumptions.
Model food system shocks as a system. Run the cascade.
Assembled from the Nexus Standards area profile Food security and nutrition risk: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Famine
- Mass displacement (set off downstream)
- Health-system overload (set off downstream)
- Chronic malnutrition (set off downstream)
- Acute malnutrition surge (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H57Hazard family: Humanitarian crises (NHS-1 soc.humanitarian)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H81Hazard family: Malnutrition and micronutrient deficiency (NHS-1 health.nutrition)NRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)NRS-N3Food and agriculture risk and resilienceNRS-N7Water-energy-food-health-biodiversity-infrastructure nexus coupling
Observation
- IPC acute food insecurity analyses (listed for reference)
- FEWS NET outlooks and data (listed for reference)
- FAO GIEWS Food Price Monitoring and Analysis tool (listed for reference)
- CHIRPS quasi-global gridded precipitation (listed for reference)
- Prevalence of undernourishment
- Prevalence of moderate or severe food insecurity
- Prevalence of stunting among children under 5 years of age
- Prevalence of malnutrition among children under 5 years of age
Example geography
- The Ganges and Brahmaputra Basin: 178 hexagonal cells
- Surface soil moisture (SMAP), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Seasonal rainfall anomaly | percent of the long-term mean | dekadal | Satellite and gauge rainfall | ESTIMATED |
| Agricultural stress index | percent of cropland affected by water stress | dekadal | Satellite vegetation and temperature indices | MODELLED |
| Desert locust situation level | warning level from calm to danger | weekly | FAO Desert Locust Information Service | Published statistics |
| Animal disease events notified | count by disease and country | weekly | National veterinary notifications | Published statistics |
| Conflict events in agricultural areas | events per administrative unit | weekly | Event coding from media and partner reports | REPORTED |
| Projected population in IPC Phase 3 or above | persons | per analysis | IPC and Cadre Harmonisé projections | FORECAST |
| Direct agricultural loss from disasters (Sendai indicator C-2) | monetary value at constant prices | per event | Post-disaster needs assessments | ESTIMATED |
The sector signature of food system shocks.
The sectors food system shocks touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Food and its partners
Food gives nutrition to health (0.7), land conversion to biodiversity (0.6) and withdrawals and runoff to water (0.3).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
- Food → Health · nutrition0.7
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| FAO Global Information and Early Warning System (GIEWS) | Country briefs, agricultural stress index and crop prospects | Free and open, attribution |
| FAO Desert Locust Watch and Locust Hub | Locust situation bulletins, survey and control records | Free and open, attribution |
| WOAH World Animal Health Information System (WAHIS) | Notifications by national veterinary services of animal disease events | Public, terms per WOAH |
| ACLED | Coded conflict and protest events with location and date | Free with registration, terms per ACLED |
| EM-DAT (CRED) | Disaster events and recorded impacts by country | Free for non-commercial use, registration |
| CHIRPS rainfall | Gauge-blended satellite rainfall for drought monitoring | Public domain |
| Global Report on Food Crises | Annual synthesis of acute food insecurity and its drivers by country | Free, attribution |
How food system shocks is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in food system shocks: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Who decides, and what the evidence serves.
Food Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Disaster management and agriculture ministries | When to activate contingency plans, release reserves or request assistance | Early-warning indicators agreeing across sources; projected acute food insecurity phases |
| Plant protection and veterinary services | Where to survey, treat, vaccinate or restrict movement | Pest and disease notifications, habitat conditions and animal and trade movements |
| Humanitarian agencies and anticipatory action financiers | Whether forecast triggers justify acting before the shock peaks | Trigger skill from hindcasts, lead time, and the cost of acting and of not acting |
| Sovereign risk pools and agricultural insurers | Whether a drought index pays when losses occur, and how basis risk is managed | Index performance against recorded losses by season and area |
Outputs this topic produces
- Compound shock scenarios across weather, pests, conflict, prices and trade
- Cascade traces from shock to production, markets and access, labelled at each step
- Anticipatory action trigger designs with their historical performance
- Prepositioning and response options under alternative shocks
What experts ask.
History shows that the most severe food crises seldom have one cause. They arise when a production shock meets weak buffers, high prices and constrained public finances, often during conflict. Experts therefore study shocks as combinations and sequences, and ask how preparation and early action can break the chain before harm peaks.
- Which shocks are most likely in a given food system, and which could coincide?
- How do shocks propagate from production to prices to household consumption, and over what time frames?
- Which early signals give useful warning, and how reliable are they?
- What preparedness and anticipatory measures reduce harm, and what evidence supports them?
Drivers and processes.
Shocks compound when buffers are thin and when they strike several links at once.
Weather and climate
Droughts, floods and heat cause the largest production losses. Climate change alters their frequency and severity. See Climate and Hazards.
Pests and disease
Outbreaks such as locusts, wheat rusts or animal diseases can spread quickly across borders.
Conflict and policy
Conflict destroys assets and blocks access; trade restrictions and economic crises reduce supply and purchasing power.
How the evidence is built, and its limits.
- 01
Early warning indicators
Rainfall, vegetation, price and pest signals, analysed with forecasting and time-series analysis, give lead time. False alarms and missed events both occur.
- 02
Scenario analysis
Explores compound shocks and cascades. Scenarios are explorations, not forecasts.
- 03
Historical analysis
Past crises show patterns of propagation and response, though conditions change.
- 04
Anticipatory action evidence
Evaluations of pre-agreed actions taken before a shock peaks are growing but remain limited for some hazards.
How it is studied, and what the methods cannot say.
- 01Forecasting and time-series analysisSeasonal production outlooks · Staple price forecasting · Anticipatory action triggersApplied here
- 02Earth observation and remote sensingCrop condition monitoring · Crop type and area mapping · Irrigation and water productivityApplied here
- 03Ecology and biodiversity sciencePollination and natural pest control · Fisheries and aquatic food websApplied here
- 04EconometricsPrice transmission and market integration · Effects of trade policyApplied here
- 05Optimisation and operations researchHumanitarian supply and prepositioning · Strategic reserve designApplied here
- 06Hydrology and water scienceIrrigation supply and demand · Agricultural drought monitoring · Flood exposure of croplandApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
- Gaupp and others, Changing risks of simultaneous global breadbasket failure, Nature Climate Change 10 (2020)
- Kornhuber and others, Risks of synchronised low yields are underestimated in climate and crop model projections, Nature Communications 14 (2023)
- Global Report on Food Crises 2025 (Global Network Against Food Crises and FSIN, 2025)
- FEWS NET Famine Early Warning Systems Network
- NS-12, Software, model and AI/ML artefacts, a proposed standard
Questions and answers.
What is anticipatory action?
Why is conflict so central to food crises?
Are pest and disease outbreaks predictable?
Who declares a famine?
Does Food Nexus issue early warnings?
Can shocks be ranked by importance?
How does climate change affect food system shocks?
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