GCRI · Food Nexus
Agricultural production
Agricultural production is where weather, soil, water, inputs, labour and markets become harvests of crops, livestock products and fish. Output responds to every one of those drivers, and in many countries it is concentrated in a few crops, regions and seasons. This page sets out how production systems respond to stress, how output and its drivers are measured, and what the evidence can tell ministries, producers and insurers before and after a season.
Agricultural production on the record.
Agricultural production: how the risk develops.
Crop, livestock and aquaculture production systems. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
The binding constraint sets yield
Light, temperature and genetics set potential yield; water and nutrients limit it; pests, weeds and disease reduce it. The gap between attainable and actual yield shows which constraint binds in each place.
Timing matters as much as totals
Heat at flowering, a dry spell at grain fill or rain at harvest does damage out of proportion to seasonal totals. A season with normal rainfall can still fail if the rain arrives at the wrong stage.
Inputs tie farms to energy and trade
Nitrogen fertiliser is made from natural gas, pumps and machinery run on diesel or electricity, and feed for livestock and aquaculture is traded globally. A price shock in energy or feed reaches planting and stocking decisions within a season.
Concentration amplifies loss
When a few breadbaskets, varieties or export regions supply most of a commodity, a correlated shock in them moves global availability. Synchronous failure across major producers is the scenario that matters most and the hardest to estimate.
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
- 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
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
- 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
What production depends on in the other four domains.
| Domain | How it couples here | What to watch |
|---|---|---|
| Water | Rain-fed yields follow soil moisture; irrigated yields follow allocation and aquifer levels. See groundwater and aquifers. | Soil moisture in critical crop stages |
| Energy | Fertiliser, diesel and pumping link planting decisions to energy prices. See industrial energy systems. | Fertiliser affordability against crop prices |
| Health | Farm labour productivity falls in heat; zoonotic disease in livestock threatens herds and people. | Heat stress days in the growing season; animal disease notifications |
| Biodiversity | Pollinators, soil biota and pest predators support yields; habitat loss erodes them. See species and genetic diversity. | Pollinator-dependent crop area |
The Agricultural Model Intercomparison and Improvement Project found that crop models disagree most where they matter most, under heat and in their response to carbon dioxide (Rosenzweig and others, 2014). Jägermeyr and colleagues reported in 2021 that the newer generation of models projects climate impacts on maize emerging earlier than previously estimated.
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
Agricultural production · 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 | 2.5 | ▼ 8 | Medium |
| Coastal plain | 3.0 | ▼ 6 | Medium |
| Region North | 0.8 | ▼ 13 | Low |
| Region South | 1.4 | ▼ 1 | Medium |
Field assessment plots
red: crop stress observed
Production evidence in reports, and the data farmers and agencies can offer.
Seasonal production analyses are designed for the Country Quarterly series, and crop model intercomparison for the Methods and Technical series. Each yield series would carry an NS-3 record identifier under the proposed Nexus Standards, so that a published yield-at-risk can be traced to survey or model. Production statistics remain the responsibility of national statistics offices.
- Crop-cut and field trial data with locations and dates.
- Planting date and variety records from cooperatives.
- Ground observations that confirm or contradict satellite crop condition.
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
- 0.64% of a year’s output
- Total over direct (multiplier)
- 1.65 times
- Most affected other sector
- Energy
- Its peak inoperability
- 4.1%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Farming | 30.98 | 15.0 |
| Fertiliser and inputs | 0.20 | 19.1 |
| Food processing | 0.46 | 21.6 |
| Transport | 0.12 | 8.2 |
| Wholesale and retail | 2.89 | 22.9 |
| Energy | 4.12 | 13.1 |
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
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 |
|---|---|---|---|---|
| Crop yield | tonnes per hectare | seasonal | Agricultural surveys, crop cutting | Published statistics |
| Harvested area | hectares | seasonal | Agricultural statistics, satellite mapping | ESTIMATED |
| Vegetation condition anomaly | deviation of NDVI or fAPAR from the long-term mean | dekadal | Satellite optical sensors | OBSERVED |
| Yield gap | percent of attainable yield | multi-year | Crop models against reported yields | MODELLED |
| Livestock population | head; tropical livestock units | annual | Livestock census and surveys | Published statistics |
| Capture fisheries and aquaculture production | tonnes live weight | annual | Fisheries statistics | Published statistics |
| Fertiliser use on cropland | kilograms of nutrient per hectare | annual | Input statistics | Published statistics |
| Crop condition class | categorical scale from poor to exceptional | monthly | Crop monitor consensus assessment | REPORTED |
Model agricultural production 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 |
|---|---|---|
| FAOSTAT production, inputs and land-use domains | Crop, livestock, fertiliser and land-use statistics by country | Free and open, attribution |
| GEOGLAM Crop Monitor | Monthly consensus crop condition for major producers and for countries at risk | Free, attribution |
| FAO FishStat | Capture fisheries and aquaculture production by species and country | Free and open, attribution |
| MapSPAM (IFPRI) | Gridded crop area, yield and production by crop and farming system | Free and open, attribution |
| Copernicus Global Land Service | Vegetation indices, dry matter productivity and phenology | Free and open, attribution |
| USDA Foreign Agricultural Service PSD database | Production, supply and distribution balances by commodity and country | Public domain |
| National agricultural censuses and surveys | Area, yield, holdings and practices under the FAO World Programme for the Census of Agriculture | Public records, terms per statistics office |
The sector signature of agricultural production.
The sectors agricultural production 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
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 ministries and extension services | Which districts, crops and herds face the largest production risk this season, and where to direct inputs and advice | Crop condition against the seasonal outlook; yield-at-risk by district with its label |
| Crop and livestock insurers, and index insurance programmes | Whether a loss index tracks actual farm losses closely enough to pay fairly | Satellite and survey yields compared for the same units and seasons, with basis risk stated |
| Food reserve and import planning agencies | How large the domestic shortfall is likely to be, and how early imports must be contracted | Production outlooks with ranges; supply balances with stocks and trade |
| Producer organisations and cooperatives | Which varieties, planting dates and practices reduce exposure to the stresses now most frequent | Trial and survey evidence on varieties and practices, with the conditions each was tested under |
Outputs this topic produces
- Production profiles by region with the concentration of output made explicit
- Seasonal yield-at-risk layers with forecast skill stated
- Input dependency maps linking fertiliser, feed and fuel to production
- Synchronous-failure scenarios across major producing regions
How agricultural production 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 agricultural production: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
What experts ask.
Production is where most food-system risk begins. Weather, pests and input shortages act first on fields, herds and ponds, and the size of the loss there sets the scale of everything that follows downstream.
- How sensitive are the main crops, livestock and farmed fish in a region to temperature, rainfall timing and extreme events?
- Where is production concentrated in a few crops, varieties or breeds, and what does that concentration mean for exposure to a single pest or disease?
- How much of the gap between actual and attainable yields comes from water, nutrients, pests or management?
- Which adaptation options, such as changed planting dates, tolerant varieties, irrigation or diversification, work in which conditions, and for whom?
Drivers and processes.
Production risk arises where biological thresholds meet weather, markets and management.
Heat, water and timing
Crops are most sensitive during flowering and grain filling; heat and water stress at those stages can sharply cut yields. Livestock lose productivity under heat stress, and warmer water lowers oxygen in aquaculture.
Pests and disease
Pathogens and pests spread through trade, wind and changing climate envelopes. Genetic uniformity raises the chance that one outbreak affects a wide area.
Seed, feed, fertiliser and labour
Availability and price of inputs determine whether producers plant, feed and harvest at full capacity in a given season.
How the evidence is built, and its limits.
- 01
Field and station data
Agronomic trials, farm surveys and veterinary surveillance give direct evidence but cover limited places and years.
- 02
Earth observation
Satellite indicators of vegetation, soil moisture and crop area, applied through Earth observation and remote sensing, give wide coverage but need ground truth to separate crop types and stress causes.
- 03
Crop and livestock models
Process models simulate growth under weather and management scenarios. Their skill varies by crop and region, and they often represent pests, disease and extremes poorly.
- 04
Statistics
Yield statistics from national systems are essential for trends but can be revised, aggregated or delayed, which limits their use in a fast-moving season.
How it is studied, and what the methods cannot say.
- 01Agricultural, soil and food scienceCrop simulation for seasonal yield risk · Yield gap and nutrient limitation analysisApplied here
- 02Earth observation and remote sensingCrop condition monitoring · Crop type and area mapping · Irrigation and water productivityApplied here
- 03Forecasting and time-series analysisSeasonal production outlooks · Staple price forecasting · Anticipatory action triggersApplied here
- 04Spatial statistics and geostatisticsSmall-area estimation of food insecurity · Digital soil mappingApplied here
- 05Ecology and biodiversity sciencePollination and natural pest control · Fisheries and aquatic food websApplied 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.
- Rosenzweig and others, Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison, PNAS 111 (2014)
- Jägermeyr and others, Climate impacts on global agriculture emerge earlier in new generation of climate and crop models, Nature Food 2 (2021)
- GEOGLAM Crop Monitor (Group on Earth Observations Global Agricultural Monitoring)
- FAOSTAT (Food and Agriculture Organization of the United Nations)
- NS-3, NXS-GRX (canonical data, provenance and risk) record UNIs, a proposed standard
Questions and answers.
What is a yield gap?
Can satellites measure yields?
Why do synchronous failures matter so much?
Are livestock and fish covered?
Does Food Nexus forecast harvests?
Is aquaculture included?
Are smallholder systems covered?
Why does genetic diversity matter for production risk?
Contribute evidence from this page.
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