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Food and agriculture

Food system risk is the chance that production shocks, trade restrictions, input prices and household incomes combine so people cannot obtain enough safe, nutritious food. It can happen even when global supply looks adequate.

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01Contents

Food and agriculture in six parts.

In this dossier6 parts
  1. 01Three mandatesThe field, seen three ways
  2. 02The systemic questionSynchronous failure and the price channel
  3. 03FrameworksHow food insecurity is measured and governed
  4. 04CouplingsThe links that make food a systemic field
  5. 05GroundingThe methods behind the field
  6. 06SourcesWhere this page’s references lead
02Three mandates

The field, seen three ways.

GCRI

Science and evidence

Food Nexus, Biotechnology and Biosecurity.

GRF

Governance and dialogue

Climate and Nature Governance.

GRA

Financial understanding

No platform yet in this field.

03Geography

Growing conditions worldwide.

Crop greenness, root-zone soil moisture and plant growth across the world, 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
Observed (persistent cloud still leaves gaps)
Source
VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
8-day; Satellite 8-day composite; persistent cloud still leaves gaps
Newest at build
2026-09-14
Resolution
about 1 km grid
Uncertainty
Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map

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 world.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
  • 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
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026

04The systemic question

Synchronous failure and the price channel.

The global food system concentrates production of staple grains in a few breadbaskets and export in a few corridors. A concurrent harvest shortfall, an export restriction or a fertiliser price spike can travel to import-dependent countries faster than domestic policy can respond. Access, not availability, is where acute food insecurity is decided: price transmission into local markets, household purchasing power and social protection.

  • Breadbasket synchrony: climate modes such as El Nino-Southern Oscillation correlate yields across regions.
  • Chokepoints: a small number of straits and ports carry a large share of grain trade.
  • Input dependence: nitrogen fertiliser production is tied to natural gas prices.
  • Policy contagion: one country’s export restriction raises the incentive for others to follow.
05Simulator

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.

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
0.98% of a year’s output
Total over direct (multiplier)
1.94 times
Most affected other sector
Energy
Its peak inoperability
5.7%
Numbers, methods and sources
SectorPeak inoperability (%)Output weight (%)
Farming34.1522.0
Fertiliser and inputs0.109.0
Food processing2.4010.2
Transport0.5016.4
Wholesale and retail0.3022.6
Energy5.6719.8

Methods

  1. Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
  2. Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
  3. Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
  4. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. 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
A shock through an input-output economy. Illustrative teaching model: it runs on the values set by the controls, not on measured data. 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).

Across the six pillars, led by Nexus Consortium

06Frameworks

How food insecurity is measured and governed.

Framework or bodyWhat it governs or supplies
Integrated Food Security Phase Classification (IPC)Five-phase severity scale for acute and chronic food insecurity, with consensus analysis
FAO food balance sheets and FAOSTATProduction, trade and utilisation by commodity and country
AMIS (G20 market information system)Market transparency on wheat, maize, rice and soybeans
GEOGLAM Crop MonitorSatellite-informed crop condition assessments
Committee on World Food SecurityVoluntary guidelines including those on the right to food and on tenure
07Twin and cascade simulator

Model food and agriculture as a system. Run the cascade.

Assembled from the Nexus Standards area profile Food and agriculture risk standards: 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.

Controls

  • NS-0 Constitution and principles
  • NS-1 The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)
  • NS-3 Risk record UNIs (grammar, legacy crosswalk, allocation authority)
  • NS-3b Dataset and product metadata profile (STAC, DCAT, PROV-O)
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NRS-H60 Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)
  • NRS-H81 Hazard family: Malnutrition and micronutrient deficiency (NHS-1 health.nutrition)
  • NRS-H43 Hazard family: Livestock and aquaculture epizootics (NHS-1 health.animal-health)
  • NRS-N3 Food and agriculture risk and resilience
  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-H44 Hazard family: Plant disease and pests (NHS-1 health.plant-health)

Observation

  • IPC acute food insecurity analyses (listed for reference)
  • FEWS NET outlooks and data (listed for reference)
  • CHIRPS quasi-global gridded precipitation (listed for reference)
  • FAO GIEWS Food Price Monitoring and Analysis tool (listed for reference)
  • ESA WorldCover 10 m land cover (listed for reference)
  • Prevalence of undernourishment
  • Prevalence of moderate or severe food insecurity (FIES)
  • Indicator of food price anomalies
  • Direct agricultural loss attributed to disasters

Example geography

  • The Ganges and Brahmaputra Basin: 178 hexagonal cells
  • Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
  • Rain rate, last half hour (IMERG), NASA GIBS
  • Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
08Couplings

The links that make food a systemic field.

  • Water: irrigated land produces a large share of global food output from a smaller share of cropland.
  • Energy: fertiliser, diesel and cold chains price energy into food.
  • Trade: corridors, export policy and freight rates set import bills.
  • Health: undernutrition raises disease burden, and animal disease affects protein supply.

The science sits on Food Nexus; methods start from agricultural science.

09Sector signature

The sector signature of food and agriculture.

The sectors food and agriculture touches and how strongly they depend on each other: what a study has to observe and model together.

Select corners · hover lines6 / 15 relations

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
Teaching values for a typical city-region, not measurements for any place. Coupling is the summed strength of the relations in play, as a share of all fifteen.
10Grounding

The methods behind the field.

11Sources

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.

12Questions

Questions and answers.

Why does GCRI host field gateways?
GCRI hosts the shared references of the Consortium, including the STEM Commons and the field gateways, so each field has one place that links all three institutions’ work.
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