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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.

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.

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.

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.

A rainfall season across the Sahel, read against the points where drought cover would start and stop paying.Synthetic season and design terms; no cover is implied.
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.

DataRoleLabel
Agricultural census and yield statisticsBaseline production and yieldsPublished statistics
Satellite vegetation indicesCrop condition and index designMODELLED
Soil surveysSuitability and degradationOBSERVED
Weather station recordsCrop model forcing and index triggersOBSERVED
FAOSTAT prices and tradeMarket context for incomesPublished 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

Circle size: exposure value · exampleN

Latest results

S-37Projects10.77Above
S-37Safeguard8.99Near
S-18Disbursement1.48Near
S-36Adaptation1.69Below

Trend at selected site

Field sampling: the screen's structure, terms and states for Development finance.Example names and illustrative values; a design, not a record or a live system.
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.

CheckHow it is done
Crop model comparisonSimulated yields are compared with observed yields across sites and seasons before use in appraisal
Basis risk testingIndex payouts are compared with recorded farm losses in past seasons
Smallholder representationData 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
SectorPeak inoperability (%)Output weight (%)
Farming30.7317.2
Fertiliser and inputs2.2115.3
Food processing2.8723.4
Transport2.7313.4
Wholesale and retail1.0921.5
Energy1.489.2

Methods

  1. Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
  2. Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
  3. Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
  4. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. 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
Indirect losses along the value chain. Illustrative teaching model: it runs on the values set by the controls, not on measured data. 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).
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)

Controls

  • NS-0 Constitution and principles
  • NS-3 Risk record UNIs (grammar, legacy crosswalk, allocation authority)
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NS-14 Conformance and crosswalk to external standards
  • NRS-F8 Parametric and index-based risk transfer
  • NRS-F7 Disaster risk finance and pre-arranged finance
  • NRS-H113 Hazard family: Financial-system fragility (NHS-1 soc.financial-system)
  • NRS-H114 Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)
  • NRS-D1 Risk-informed development and project screening
  • NRS-H28 Hazard 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
Teaching values for a typical city-region, not measurements for any place. Coupling is the summed strength of the relations in play, as a share of all fifteen.
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.

Channel structure after the Basel Committee's work on climate-related risk drivers and transmission channels; links shown are typical paths, not estimated exposures.
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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