Breeding for stress tolerance
Genomic selection and marker-assisted breeding speed the development of varieties tolerant to drought, heat, salinity and pests, tested in multi-site trials under the conditions farmers face.
GCRI · Food Nexus
Biotechnology in food and agriculture
Biotechnology platforms in food and agriculture include marker-assisted and genomic breeding, gene editing, biological inputs, diagnostics for plant and animal disease, and fermentation-based food production. They widen the options for resilience and raise questions of biosafety, access and governance that regulators must resolve.
01Contents
Biotechnology in food and agriculture in five parts.
02Application
What biotechnology platforms does here.
Biotechnology platforms apply biological systems to produce medicines, diagnostics, materials and agricultural products. They expand response capacity for health threats while introducing dual-use and biosafety concerns. In food and agriculture, trade, resources and supply chains and climate and hazards, biotechnology capacity shapes how quickly countermeasures can be produced in a crisis. Its supply chains and dual-use potential are part of biosecurity planning. The canonical description is on the STEM Commons.
Rapid diagnostics for pests and disease
Molecular and field diagnostics identify plant pathogens and animal diseases quickly, so surveillance can confirm an outbreak and response can begin before it spreads.
Biosafety risk assessment
Case-by-case assessment of food, feed and environmental safety follows the Cartagena Protocol and Codex principles, with data gaps and post-release monitoring stated.
03Monitor
Oversight register.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Oversight register
Food · Biotechnology in food · Run R-3210
Reviews currentGCRI · Food Nexus
Institutional reviews
capacity and oversight only
| Project P-790 | Institutional committee | Level 1 | Q3 | Conditions |
| Project P-211 | Institutional committee | Level 2 | Q1 | Conditions |
| Project P-961 | Institutional committee | Level 1 | Q2 | Approved |
| Project P-399 | Institutional committee | Level 2 | Q1 | Approved |
Training coverage
04Data
What the method consumes.
Datasets and records used by biotechnology platforms on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Genomic and germplasm databases | Trait discovery and breeding | OBSERVED |
| Multi-site trial data | Variety performance | OBSERVED |
| Biosafety Clearing-House records | Regulatory decisions | Published statistics |
| Diagnostic laboratory records | Outbreak confirmation | 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.
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
- 0.54% of a year's output
- Total over direct (multiplier)
- 1.71 times
- Most affected other sector
- Energy
- Its peak inoperability
- 2.4%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Farming | 30.84 | 10.4 |
| Fertiliser and inputs | 1.76 | 15.0 |
| Food processing | 2.01 | 11.8 |
| Transport | 1.83 | 13.5 |
| Wholesale and retail | 1.54 | 24.6 |
| Energy | 2.35 | 24.7 |
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.
Products are evaluated through staged preclinical and clinical testing and manufacturing quality controls. Regulatory review and independent replication check claims.
| Check | How it is done |
|---|---|
| Independent replication | Trait and product claims are replicated across sites, seasons and laboratories |
| Regulatory review | Products are assessed by competent national authorities before release |
| Post-release monitoring | Performance and unintended effects are monitored after adoption |
Scale-up from laboratory to production often fails or takes time. Containment and oversight can lag behind technical capability.
07Sector signature
The sector signature of biotechnology in food and agriculture.
The sectors biotechnology in 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
08Connections
Topics that rely on this method.
09Methods and evidence
How biotechnology in food and agriculture 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 biotechnology in food and agriculture: 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
10Stewardship
Who maintains the method.
Biotechnology and Biosecurity stewards biotechnology platforms for the Consortium and reviews how it is applied across platforms.
11Connected
Where this page leads across the Consortium.
12Questions
Questions and answers.
What are the limits of biotechnology platforms in this field?
Scale-up from laboratory to production often fails or takes time. Containment and oversight can lag behind technical capability.
How are results checked?
Products are evaluated through staged preclinical and clinical testing and manufacturing quality controls. Regulatory review and independent replication check claims.
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.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIFood NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceEvidence Brief
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