Second Generation

Food security is still the main problem that Sahelian populations have to face. In the Sahel, agriculture is primarily based on rainfed crops and it is often structurally inadequate to manage the climatic variability. Latest Early Warning approaches insist on two action levels: prevision and prevention. In its biophysical aspect, prevision is mainly based on tools and models utilizing satellite data to monitor the growing season. Agrometeorological models have a central role in this chain because they transform meteorological data into levels of risk for agriculture. On the other hand, prevention, aiming to reduce the risks, is actually based on meteorological forecasts. Nowadays, quantitative meteorological forecasts allow early warning systems providing critical information to farmers, in order to reduce risks related to meteorological phenomena. This paper presents the integration of meteorological forecasts with classical agrometeorological monitoring achieved by ZAR model. Input data are Rainfall Estimate provided by Meteosat Second Generation and forecasts from GFS (Global Operation Forecast) model, Precipitation at ground, at 7 days, downscaled at 8 kilometres. Such integration allows the production of information, as prevision of good conditions for sowing, of crops onset in sowed areas and of crop conditions during the growing period. ZAR is used b