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Communication incl. Poster: BibTeX citation key:  Nicklin
Nicklin, K., Challinor, A. & Tompkins, A. M. 2009. Choice and calibration of crop models for seasonal forecasting of yield in the Sahel. Work presented at Third International AMMA Conference, July 20—24, at Ouagadougou, Burkina Faso.
Added by: roussot 2009-11-23 19:46:51
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Categories: Monsoon system and its variability, Society-Environment-Climate interactions, Weather to Climatic modelling and forecasting
Keywords: Agriculture, Modelling, Seasonal cycle, Vegetation
Creators: Challinor, Nicklin, Tompkins
Publisher: African Monsoon Multidisciplinary Analyses (Ouagadougou, Burkina Faso)
Collection: Third International AMMA Conference

Number of views:  1040
Popularity index:  55.03%
Maturity index:  published

 
Abstract
Farming systems in the Sahel are heavily dependent on the West African Monsoon. Large variability in the monsoon rains leads to large variability in crop yields, thus threatening food security. This dependency, coupled with the inherent predictability in monsoon systems, suggests that a crop yield forecasting system for the region may be both skillful and useful. Such a system would give advanced warning of crop failures, thus informing planning. It could also allow a range of crop management options to be assessed, enabling adaptation to emergent climate change. Accordingly, a suite of crop models is being developed that operates on spatial scales commensurate with the grids used by seasonal weather forecasts. The models are based on the groundnut version of the General Large Area Model for annual crops (GLAM).
Potential limitations to the skill of a yield forecasting system include the model error, the availability of calibration data, and the ability of the model to reproduce the impact of extreme events, such as droughts and floods. The present study critically assesses the ability of three crop models, and of a number of calibration methods for GLAM, to reproduce observed interannual variability in yield. One of these methods has a reduced soils data requirement. The ability of offline and fully coupled crop-climate models to reproduce observed yields is also discussed.
Added by: roussot