Creating Agricultural Risk Models for Developing Countries

January 29, 2015

Every year, farmers in developing countries alter the crop exposure landscape by choosing to plant particular crops. These choices are guided, for the most part, by individual households' annual consumption needs, the availability of on-farm and off-farm employment, and an expectation of the profit that can be had at harvest time from the portion of the crops that will be sold.

Technological improvements have increased the ability of crops to recover from growing seasons that get off to a bad start, and still produce average yields. The accumulated effect of adverse weather during the growing season will not be known with certainty until harvest time, once the crop is harvested, weighed, and marketed. Because losses in agricultural production areas are spatially correlated, catastrophic weather events, such as droughts or floods, will trigger widespread losses to farmers in the affected region. Eventually, if the adverse weather conditions persist, crops will fail entirely and governments and donors will have to allocate resources for humanitarian aid and recovery.

There has been a great amount of fieldwork done in several developing countries to understand the impact that adverse weather has on the well-being of farmers and to study mechanisms by which risk can be spread outside the farming sector.

To quantify weather risk to crop portfolios in developing countries, data is required to fit the yield loss models. The AIR multiple peril crop insurance (MPCI) models for the United States and China currently use the following data layers:

  • Weather information-precipitation, temperature
  • Soil information-soil classification, land use, irrigation, crop moisture index
  • Crop information-crop types, phenological stages, production practices
  • Price information-farm level prices, market prices, production costs
  • Production information-area planted, area harvested, yields
  • Loss information-according to type of weather perils, insured losses, non-insured losses; and
  • Socio-economic information-farm typology, farmer characteristics

This information is available on a global scale at different levels of resolution (most commonly the equivalents of county and/or state/province).

AIR's agricultural modeling team has collected data on hundreds of historical drought, flood, and typhoon events around the world. AIR relies on information from agencies that gather original daily temperature and precipitation data, radar data, actual wind records from weather service stations, and wind reports. This information is then coupled with historical crop phenological data, as well as soil, terrain/elevation, and land use/land cover data, to determine the extent of these events.

For developing countries, the weather and yield loss models are based on historical data from a variety of sources such as the China Meteorological Administration (CMA), the Climate Prediction Center (NOAA/CPC), the Africa Soil Information Service (AfSIS), the Japan Meteorological Agency (JMA), the National Bureau of Statistics of China, the Shanghai Typhoon Institute (STI), and the Tropical Rainfall Measuring Mission (TRMM). Additional sources of data are available from international research centers, NGOs, and universities conducting field research in developing countries.

Agricultural risk modeling can be used as a planning tool to anticipate the likelihood and severity of potential future weather-based catastrophes, ultimately permitting farmers, governments, policy makers, and the donor community to better prepare for the financial impact of natural disasters affecting the agricultural sector. The inclusion of agricultural risk modeling output (agricultural loss scenarios) as part of the information disseminated by market information systems will benefit farmers, policymakers, and other end users of the information.

For more information, see Agricultural Risk Modelling to Improve Market Information Systems in Developing Countries.

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