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+ Agricultural Risk Management Team Agriculture and Rural Development Department The World Bank WMO Expert Advisory Group on Financial Risk Transfer (EAG-FRT.

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Presentation on theme: "+ Agricultural Risk Management Team Agriculture and Rural Development Department The World Bank WMO Expert Advisory Group on Financial Risk Transfer (EAG-FRT."— Presentation transcript:

1 + Agricultural Risk Management Team Agriculture and Rural Development Department The World Bank WMO Expert Advisory Group on Financial Risk Transfer (EAG-FRT I) December, 2011 Geneva, Switzerland

2 WB-ARMT Program Areas Agricultural Risk Management Framework Supply Chain Risk Assessments Weather Risk Management Price/Commodity Risk Management Capacity Building and Training in Agricultural Risk Management Forum for Agricultural Risk Management in Development FARM D Support to Sustainable Production Initiatives 2

3 Types of Agricultural Risks Production risks: Drought Flood Pest and disease outbreak Management failure Market risks: Volatility in output price Variability in input price Exchange rate volatility Counterparty risk / default risk Business enabling environment risks: Regulatory risk Crop substitution Infrastructure risk Political risk 3

4 Risk Management Strategies Risk Mitigation: Actions taken to eliminate or reduce events from occurring, or reduce the severity of losses (e.g. water draining infrastructure, crop diversification, extension, etc) Risk Transfer : Actions that will transfer the risk to a willing third party, at a cost. Financial transfer mechanisms will trigger compensation or reduce the losses in the case of a risk generated loss (e.g., insurance, re-insurance, financial hedging tools, etc.) Risk Coping : Actions that will help cope with the losses caused by a risk event (e.g. government assistance to farmers, debt re-structuring, etc.) 4

5 + Weather Data Digitization PROBLEMS WITH WEATHER DATA – Limited coverage of stations – Missing data in the historical records – Short historical record (e.g. recently installed stations) – Quality issues Partly due to backlog in digitizing weather data Weather digitization Projects 1.Training personnel in CLICOM 2.Data digitization 3.Data validation and storage Mozambique To digitize meteorological historical data of the 31 stations regarding the years (3,695,478 observations). Ghana To digitize 5,990,000 weather observations, comprising the period of 1981 to 2010, from 22 Synoptic and 99 Agroclimatic stations throughout Ghana. 5

6 + WRSI for Crop Monitoring 1. Crops are vulnerable to weather risks 2. Weather data as a proxy for production risks A crop, such as maize, is vulnerable to rainfall deficit driven stress to different extents at different stages of its development process. 3. Dollarizing rainfall Knowing the price per kg of a crop, we can then transform mm of rainfall in terms of financial losses Dollarizing Rainfall for Risk Manageme nt Real time assessment of losses in agriculture by observing rainfall -Monitor export losses -Monitor food security -Trigger index insurance For timely DESIGN OF RISK MANAGEMENT MEASURES 6

7 + Weather Risk Mapping for better planning and monitoring Conceptual framework & Workflows USES regional/zonal policy on crop vulnerability inform public and private investments in agriculture land use planning design ag risk mitigation measures design ag. Insurance contracts U Map suitability index for 12 crops 7

8 Solving data problems – synthetic weather PROBLEMS WITH WEATHER DATA – Limited coverage of stations – Missing data in the historical records – Short historical record (e.g. recently installed stations) – Quality issues PRACTICAL SOLUTION: WEATHER DATA GRIDS Gridded datasets generated with a successive correction method (Cressman, 1959) based on two predictors: Primary: Meteorological stations Secondary: The North American Regional Reanalysis (NARR) FROM NOAA* * Other satellite- observed data can be used for Africa. BENEFITS Integration of all existing data in a given day (i.e. partial missing records do not restrict the analysis of meteorological stations) Complete historical records for all the pixels within the domain Simplified association of meteorological fields with spatial features (e.g. assets, political boundaries, topography, land use, etc.) 8

9 + Training on Weather Index Insurance 9

10 Thank You Carlos Arce Agricultural Risk Management Team World Bank 10


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