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.
The Global Centre for Risk and Innovation (GCRI)
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.
GCRI · Established · service level R0
Agricultural, soil and food science is the study of how crops, livestock and fish respond to weather, soil, water, nutrients, pests and management, and how food is kept safe after harvest. For food risk it links climate and market shocks to yields, diets and food safety through field trials, surveys and crop simulation models.
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.
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.
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.
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.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Crop simulation model | A model that simulates crop growth from weather, soil and management inputs to estimate yield, such as DSSAT or APSIM. |
| Multi-site field trial | An experiment repeated across several locations and seasons to separate a treatment's effect from local conditions. |
| Limiting factor | The resource, such as water or nitrogen, whose shortage most restricts growth at a given time and place. |
Plains, deltas and basins, the corridors that carry food to ports, and how far each division sits from one, shown for East Africa as an example geography for this subject.
Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.
Fig. 01Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.
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 | OFFICIAL |
| 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 |
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.
Food Nexus stewards agricultural, soil and food science for the Consortium and reviews how it is applied across platforms.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.