Irrigation scheme appraisal
Crop water requirements, soil suitability and yield response are combined with water availability to test whether a scheme's projected yields and incomes hold under dry years.
GRA · Development Finance Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Agricultural science for development finance
Agricultural, soil and food science inform development finance for farming, irrigation, agribusiness and food security. They explain how weather, soils, pests and markets shape yields and incomes, and so the repayment capacity of farmers, the viability of irrigation schemes and the design of crop insurance.
01Contents
Agricultural science for development finance in five parts.
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 development finance, critical infrastructure and economic development, 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.
Index insurance and credit design
Yield records and satellite vegetation indices are used to design indices for agricultural insurance and to check basis risk, the gap between index payouts and farmers' actual losses.
Soil and land degradation screening
Soil surveys and erosion assessments identify where land degradation threatens yields and where restoration belongs in a project's design and safeguards.
03Spatial finance
A drought index against its attachment point.
How a dry season is read against the point where drought cover starts to pay. Synthetic season and design terms.
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 |
|---|---|---|
| Agricultural census and yield statistics | Baseline production and yields | Published statistics |
| Satellite vegetation indices | Crop condition and index design | MODELLED |
| Soil surveys | Suitability and degradation | OBSERVED |
| Weather station records | Crop model forcing and index triggers | OBSERVED |
| FAOSTAT prices and trade | Market context for incomes | Published statistics |
05Monitor
Field sampling.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Field sampling
Development finance · Agricultural science · Run R-2474
Three sites overdueGRA · Development Finance Nexus
Sampling sites
red: result above guideline
Latest results
| S-37 | Projects | 10.7 | 7 | Above |
| S-37 | Safeguard | 8.9 | 9 | Near |
| S-18 | Disbursement | 1.4 | 8 | Near |
| S-36 | Adaptation | 1.6 | 9 | Below |
Trend at selected site
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 |
|---|---|
| Crop model comparison | Simulated yields are compared with observed yields across sites and seasons before use in appraisal |
| Basis risk testing | Index payouts are compared with recorded farm losses in past seasons |
| Smallholder representation | Data are checked for coverage of smallholder systems before conclusions are drawn for them |
Models often omit management practices, pests and extreme events that drive actual losses. Smallholder systems are underrepresented in data.
07Exposure model
Indirect losses along the value chain.
How much output, and so revenue, is lost beyond the sector hit first, the indirect exposure a lender or investor carries through suppliers and customers. Teaching model with stated illustrative values, not a prediction. The map on this page shows Southern Africa; 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
- 0.76% of a year's output
- Total over direct (multiplier)
- 1.81 times
- Most affected other sector
- Food processing
- Its peak inoperability
- 2.9%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Farming | 30.73 | 17.2 |
| Fertiliser and inputs | 2.21 | 15.3 |
| Food processing | 2.87 | 23.4 |
| Transport | 2.73 | 13.4 |
| Wholesale and retail | 1.09 | 21.5 |
| Energy | 1.48 | 9.2 |
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 Development Finance Nexus in Foundry
- Tested at Development Finance Nexus in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Development Finance Nexus in Reports
- Taught in Faculty of Finance and Economics in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Trace a shock in agricultural science for development finance to the balance sheet.
Assembled from the Nexus Standards area profile Disaster and climate risk finance: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Tropical cyclone
- Earthquake
- Ice-jam flood
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Aftershock sequence (set off downstream)
- Fault surface rupture (set off downstream)
- Soil liquefaction (set off downstream)
- Slope slide (set off downstream)
- Rockfall (set off downstream)
- Tsunami (set off downstream)
- Building collapse (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)NS-14Conformance and crosswalk to external standardsNRS-F8Parametric and index-based risk transferNRS-F7Disaster risk finance and pre-arranged financeNRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H114Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)NRS-D1Risk-informed development and project screeningNRS-H28Hazard family: Climate-state shifts (NHS-1 eco.climate-state)
Observation
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- CHIRPS quasi-global gridded precipitation (listed for reference)
- USGS earthquake GeoJSON summary feeds (listed for reference)
- Total public international support for national disaster risk reduction actions (Sendai indicator F-1)
- Direct economic loss attributed to disasters in relation to global GDP
Example geography
- Western Europe: 152 hexagonal cells
- Rain rate, last half hour (IMERG), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
10Stewardship
Who maintains the method.
Food Nexus stewards agricultural, soil and food science for the Consortium and reviews how it is applied across platforms.
11Sector signature
Where exposure in agricultural science for development finance travels.
The sectors that carry exposure in agricultural science for development finance and how a loss in one passes to the others, across portfolios and public finances.
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
12Connected
Where this page leads across the Consortium.
13Exposure, loss and capital
What agricultural science for development finance means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Transmission channels
How a shock in agricultural science for development finance reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
14Questions
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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