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Communication incl. Poster: BibTeX citation key:  Bacci
Bacci, M., Di Vecchia, A., Ndiaye, M., Assane, I. & Genesio, L. 2009. Using rainfall estimate and forecasted precipitation to early detect seeding failures and crops stress areas. Work presented at Third International AMMA Conference, July 20—24, at Ouagadougou, Burkina Faso.
Added by: Devic 2009-09-16 09:19:25
Categories: Environment and Climate Monitoring, Society-Environment-Climate interactions
Keywords: Agriculture, Early warning system
Creators: Assane, Bacci, Di Vecchia, Genesio, Ndiaye
Publisher: African Monsoon Multidisciplinary Analyses (Ouagadougou, Burkina Faso)
Collection: Third International AMMA Conference

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Popularity index:  59.05%
Maturity index:  published

Sahelian countries, due to dependence on rainfed crops, are still facing the food security risks every year at different scales and intensity. In the Sahel, rainy season onset and development is of primary importance for the agricultural yearly production. The recent advancements in Early Warning Systems are based on the prediction of risk phenomena and on the better intercepting their location and intensity.
Remote sensing information, meteorological forecasts and agrometeorological models could potentially provide useful information on crop yield prediction. The demand for information on zones that could present a level of risk for agriculture is becoming a priority for national food security decision makers to prevent the food crises and for farmers to adapt agricultural practices to meteorological events.
This paper presents the integration of meteorological forecasts within the ZAR model, operational in national Meteorological Offices, to provide a forecasting of good conditions for sowing, seeding failures and crop conditions during the growing period. Input data for the model are daily rainfall estimates from MSG (Meteosat Second Generation) and 7 days forecasted precipitation at ground from GFS (Global Forecasting System) downscaled at 8 kilometers. The ZAR model outputs are compared with the field data collected in the 2007 campaign for the Senegal and Niger countries in order to evaluate the performance of prediction.
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