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Crop monitoring and forecasting Agrometeorological Crop Forecasting
Agrometeorological Crop Forecasting

Introduction | Inputs | Model | Outputs

Prepared by
R. Gommes, Senior Agrometeorologist
M. Bernardi, Agrometeorologist
and F. Petrassi, Statistical Clerk
Environment and Natural Resources Service (SDRN)
FAO Research, Extension and Training Division

CROP FORECASTING is the art of predicting crop yields (tons/ha) and production before the harvest actually takes place, typically a couple of months in advance. Several techniques can be used. What is described here is the approach used by FAO in national food security systems.

Crop forecasting relies on computer programmes that describe the plant-environment interactions in quantitative terms. Such programmes are called "models", and they attempt to simulate plant-weather-soil interactions. They need, therefore, information and data on the most important factors that affect crop yields - the model inputs. After passing "through" the model, the inputs are converted to a number of outputs, such as maps of crop conditions and yields.

Violent weather factors - cyclones, floods and storms - catch the attention of the public and usually receive wide media coverage. Due to their rare occurrence and unpredictable timing, they are usually extremely difficult to model. It should be stressed, however, that far more agricultural production is lost to "chronic" problems, such as local droughts, and recurrent pest attacks, than to the violent ones.

Over the last 20 years, FAO has developed a crop forecasting "philosophy" through a number of national projects to establish national early warning systems. The FAO approach to crop forecasting philosophy is characterized by the following:

  1. Integration of ground-based agrometeorological information with remotely-sensed (satellite) information, both at the data and at the product, or analysis, level
  2. A modular approach, i.e. crop forecasting tools that are largely independent but can be combined, or "chained" as required by local conditions. The modularity is an essential ingredient of the sustainability of a national crop forecasting system as it facilitates maintenance, training and upgrading of forecasting systems.
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