GCRI · Food Nexus
Food, water and energy
Food production draws on water and energy at every stage: irrigation and pumping, fertiliser manufacture, machinery, processing, cooling and cooking. Agriculture also shares rivers with hydropower and power-plant cooling, and supplies bioenergy feedstocks. Heat and drought squeeze all three at once. This page sets out how the demands couple, how they are measured, and what the coupled evidence serves for those who allocate water, power and land.
Food, water and energy on the record.
Food, water and energy: how the risk develops.
Water and energy demand in production. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Irrigation links food to water and power
Irrigated land produces a large share of food from a small share of cropland. Pumped groundwater and lifted surface water make that output depend on electricity or diesel, and on aquifers that may be falling.
Fertiliser is embodied energy
Ammonia synthesis for nitrogen fertiliser runs mainly on natural gas. Gas price spikes raise fertiliser prices, farmers apply less, and yields respond in the following season.
Subsidies shape demand
Free or flat-rate electricity for farm pumps removes the incentive to save water and power, deepens aquifer depletion and strains utility finances. Reform is a food, water and energy decision at once.
Heat stacks the demands
In a hot, dry season irrigation, cooling and power demand peak together while river flows and hydropower fall. The coupling turns one hazard into a compound one for farms, utilities and grids.
Water now in the Black Sea region.
Root-zone soil moisture, night lights and crop greenness 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
Water now, for this page
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
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
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
- 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 (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. 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
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.
Water now, 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 SMAP · 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
- 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 Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. 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
- 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 SMAP. 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).
- 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 (14 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
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 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
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
The water-energy-food nexus, link by link.
| Link | Mechanism | Measure |
|---|---|---|
| Energy into food | Nitrogen fertiliser is synthesised mainly from natural gas; mechanisation, irrigation pumping, processing and cooling use fuel and power | Energy per tonne of product; gas price passed through to fertiliser price |
| Water into food | Irrigation withdrawals and green water from rainfall | Cubic metres per tonne; water productivity |
| Food into energy | Biofuel feedstocks and residues used for energy | Share of crop output used for fuel |
| Water into energy | Cooling and hydropower; see water-energy interdependence | Cubic metres per megawatt-hour |
The term water-energy-food nexus entered policy through the Bonn 2011 conference background paper by Hoff and FAO’s 2014 framing; FAO’s issue paper of 2011 on energy-smart food documented the energy intensity of food systems. The IPBES Nexus Assessment of 2024 extended the frame to biodiversity and health.
Crop and harvest monitor.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Crop and harvest monitor
Food, water and energy · Main season · Crop stage: grain fill
Crop calendar
- Land preparationdone
- Plantingdone
- Emergencedone
- Vegetativedone
- Grain fillnow
- Harvestnext
- Post-harvest
Vegetation index, season to date
Yield outlook by zone
| Highland zone | 2.4 | ▲ 1 | Medium |
| Coastal plain | 2.2 | ▼ 4 | Medium |
| Region North | 0.9 | ▼ 29 | Low |
| Region South | 1.3 | ▼ 6 | Medium |
Field assessment plots
red: crop stress observed
Where nexus findings are designed to go.
Joint analyses of the three systems are designed for the Scenario and Stress-Test series, where an energy price shock is followed through fertiliser to the next harvest. Coefficients are averages; local practice can differ widely, and studies say so. Contributions that help: farm energy and water use records, and cases where an energy shock changed planting decisions.
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. 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
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 7%
- Assets still working
- 39%
- Amplification of the initial damage
- 8.3 times
- Steps to settle
- 2
Numbers, methods and sources
| Step | Water working (%) | Power working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 83 | 100 | 94 |
| 1 | 50 | 50 | 56 |
| 2 | 33 | 50 | 33 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- 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:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
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
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 |
|---|---|---|---|---|
| Water use efficiency in agriculture (SDG 6.4.1) | value added per cubic metre withdrawn | annual | National water and economic accounts | Published statistics |
| Crop water productivity | kilograms per cubic metre of evapotranspiration | seasonal | Satellite evapotranspiration and biomass | MODELLED |
| Area equipped for irrigation and area actually irrigated | hectares | annual | Irrigation statistics, satellite mapping | ESTIMATED |
| Energy use in agriculture and food processing | terajoules | annual | National energy balances | Published statistics |
| Irrigation pumping electricity demand | gigawatt hours | seasonal | Utility feeder metering | OBSERVED |
| Fertiliser-to-crop price ratio | ratio | monthly | Commodity price series | Published statistics |
| Groundwater level in irrigated districts | metres below ground | monthly | Observation wells | OBSERVED |
Model food, water and energy 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
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 AQUASTAT | Agricultural water withdrawal, irrigated area and water use efficiency by country | Free and open, attribution |
| FAO WaPOR | Satellite evapotranspiration, biomass and water productivity for Africa and the Near East | Free and open, attribution |
| FAOSTAT energy use and fertiliser domains | Energy consumed in agriculture and fertiliser use by country | Free and open, attribution |
| World Bank Commodity Price Data (Pink Sheet) | Monthly prices for fertilisers, energy and crops | Free and open, attribution |
| IEA World Energy Balances | Energy supply and use by sector, agriculture included | Licensed, with some free indicators |
| Global Map of Irrigation Areas (FAO and University of Bonn) | Gridded area equipped for irrigation | Free, attribution |
| National utility and water agency records | Feeder-level pumping demand and well levels | By agreement with each agency |
The sector signature of food, water and energy.
The sectors food, water and energy touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Water, Energy and Food
Water gives energy cooling water and hydropower (0.6); water gives food irrigation (0.8): the strongest relations between the sectors this subject joins.
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Energy → Infrastructure · power for transport and telecoms0.8
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Infrastructure → Water · pipes, dams and treatment works0.6
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 |
|---|---|---|
| Agriculture, water and energy ministries together | How much irrigation a basin and a grid can carry in a dry year, and who bears the shortfall | Coupled scenarios with each sector’s loss stated separately |
| Electricity regulators and distribution utilities | How to reform farm power tariffs or supply schedules without cutting food output | Pumping demand by feeder, groundwater trends and crop water productivity |
| Agriculture and finance ministries setting input policy | Whether to buffer farmers against a fertiliser price spike, and how | Fertiliser-to-crop price ratios and estimated application response |
| Irrigation agencies and water user associations | Which schemes to modernise and where water savings are real rather than reallocated | Basin accounts that separate consumption from return flows |
Outputs this topic produces
- Water and energy footprints of production by crop and district
- Compound heat and drought scenarios across irrigation, power and supply
- Fertiliser and energy price transmission traces
- Allocation options with food, water and energy losses stated apart
How food, water and energy 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, water and energy: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
What experts ask.
Shortages in water or energy rarely stay in one sector. When a reservoir runs low or fuel prices rise, irrigation, processing, transport and storage are affected together, and the costs reach farmers and consumers at the same time. Experts therefore analyse the three systems jointly rather than one at a time.
- How much water does a production system use, from which sources, and is that use sustainable over time?
- How do energy prices and supply reliability affect irrigation, fertiliser, processing and cold storage?
- Where do food, water and energy compete for the same resource, for example river flow for irrigation and hydropower?
- Which options, such as efficient irrigation, solar pumping or crop changes, reduce combined risk without shifting it elsewhere?
Drivers and processes.
The three systems are coupled through physical flows, prices and policy.
Irrigation and groundwater
Groundwater pumping supports production in many dry regions but can deplete aquifers. Efficiency gains can raise total use if irrigated area expands. See Water Nexus.
Power and fertiliser
Nitrogen fertiliser manufacture is energy-intensive, so energy shocks become fertiliser shocks. Irrigation and cold chains need reliable power. See Energy Nexus.
Subsidies and incentives
Subsidised electricity or water can encourage over-pumping; removing subsidies abruptly can hurt farm incomes.
How the evidence is built, and its limits.
- 01
Water accounting
Basin water balances built with hydrology and water science track supply and use. Groundwater data are often sparse.
- 02
Energy and input data
Fuel, power and fertiliser prices and use show exposure; farm-level energy data are limited.
- 03
Integrated models
Linked water, energy and land models built with water and environmental engineering and optimisation and operations research explore trade-offs. They are sensitive to assumptions about behaviour and policy.
- 04
Satellite evapotranspiration
Estimates crop water use over large areas, with errors that vary by method and season.
How it is studied, and what the methods cannot say.
- 01Hydrology and water scienceIrrigation supply and demand · Agricultural drought monitoring · Flood exposure of croplandApplied here
- 02Optimisation and operations researchHumanitarian supply and prepositioning · Strategic reserve designApplied here
- 03EconometricsPrice transmission and market integration · Effects of trade policyApplied here
- 04Earth observation and remote sensingCrop condition monitoring · Crop type and area mapping · Irrigation and water productivityApplied here
- 05Water and environmental engineeringIrrigation and drainage design · Safe wastewater reuse in agricultureApplied 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.
- Energy-smart food for people and climate, issue paper (FAO, 2011)
- IPBES Nexus Assessment on biodiversity, water, food and health (IPBES, 2024)
- FAO AQUASTAT and the SDG 6.4.2 water stress method (FAO)
- Macknick and others, Operational water consumption and withdrawal factors for electricity generating technologies, Environmental Research Letters 7 (2012)
Questions and answers.
Does efficient irrigation always save water?
Why do gas prices affect harvests?
What is the water, energy and food nexus approach?
Is the water–energy–food nexus approach a planning method?
Does more efficient irrigation always save water?
Who works on water and energy themselves?
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