Crop simulation for seasonal yield risk
Process-based crop models are run with observed weather to date and seasonal forecasts for the rest of the season, giving a distribution of yields per district rather than a single estimate.
GCRI · Food Nexus
Agricultural, soil and food science at Food Nexus
Agricultural, soil and food science is the core discipline of this platform. It explains how crops, livestock and fish respond to weather, soil, water, nutrients, pests and management, and how food is preserved and kept safe after harvest. Its models and trials link climate and market shocks to yields, diets and food safety.
01Register
Agricultural, soil and food science at Food Nexus on the record.
Method recordSTEM method
- MethodAgricultural, soil and food science
- StewardFood Nexus
- Applied inFood Nexus
- Data5 inputs
- Checks4 validation steps
- Canonical pageSTEM Commons
02Application
What agricultural, soil and food science does here.
Agricultural science studies crop and livestock production, soil processes and food systems. It explains how weather, pests, soil degradation and markets combine to affect yields and food availability. In food and agriculture, trade, resources and supply chains and climate and hazards, agricultural assessment supports early warning of food shortages, crop insurance design and adaptation planning. It connects climate, water and trade risks to nutrition and price stability. The canonical description is on the STEM Commons.
Yield gap and nutrient limitation analysis
Attainable yields from trials and models are compared with reported yields, following protocols such as those of the Global Yield Gap Atlas, to show whether water, nutrients, pests or management limit output.
Post-harvest quality and safety assessment
Grain moisture, storage conditions and mycotoxin sampling are combined to identify where stored food is at risk of spoilage or contamination, and which handling steps reduce that risk.
03Geography
Growing conditions in the Black Sea region.
Crop greenness, root-zone soil moisture, plant growth and true-colour imagery 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 4
Growing conditions, for this page
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 HLS S30Sources 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 HLS S30 (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
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
04Data
What the method consumes.
Datasets and records used by agricultural, soil and food science on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Multi-site field trials | Model calibration and variety response | OBSERVED |
| Agricultural census and crop-cut surveys | Reported area and yield | Published statistics |
| Soil profiles and laboratory analyses | Soil water and nutrient parameters | OBSERVED |
| Weather stations and ERA5 reanalysis | Crop model forcing | MODELLED |
| Food safety sampling records | Contaminant and residue levels | OBSERVED |
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. 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
- 0.58% of a year's output
- Total over direct (multiplier)
- 1.75 times
- Most affected other sector
- Food processing
- Its peak inoperability
- 3.6%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Farming | 30.79 | 10.8 |
| Fertiliser and inputs | 2.36 | 18.4 |
| Food processing | 3.55 | 24.1 |
| Transport | 0.72 | 12.2 |
| Wholesale and retail | 2.45 | 13.3 |
| Energy | 0.96 | 21.2 |
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
06Validation
How results are checked.
Crop models are compared with observed yields across sites and seasons. Replicated field trials and multi-model intercomparisons test robustness.
| Check | How it is done |
|---|---|
| Multi-model ensembles | Several crop models are run on the same inputs, following AgMIP protocols, so structural uncertainty is visible |
| Out-of-sample yield testing | Models are tested on seasons and sites not used in calibration, and error is reported by region |
| Trial design review | Trials are checked for replication, randomisation and how well they represent farm conditions |
| Survey reconciliation | Modelled and reported yields are compared, and disagreements are reported rather than averaged away |
Models often omit management practices, pests and extreme events that drive actual losses. Smallholder systems are underrepresented in data.
07Twin and cascade simulator
Build the system model. 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
08Connections
Topics that rely on this method.
09Sector signature
The sector signature of Agricultural, soil and food science at Food Nexus.
The sectors Agricultural, soil and food science at Food Nexus 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
10Stewardship
Who maintains the method.
Food Nexus stewards agricultural, soil and food science for the Consortium and reviews how it is applied across platforms.
11Methods and evidence
The model, the data and the uncertainty.
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, soil and food science at Food Nexus: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
12Questions
Questions and answers.
What are the limits of agricultural, soil and food science in this field?
Models often omit management practices, pests and extreme events that drive actual losses. Smallholder systems are underrepresented in data.
How are results checked?
Crop models are compared with observed yields across sites and seasons. Replicated field trials and multi-model intercomparisons test robustness.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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GCRIFood NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceEvidence Brief
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