The Sahel region is known for the high vulnerability of its agriculture to climate variability. Early warning systems that make use of agrometerological forecasts are one of the coping strategies developed by policy makers. However, the predictive quality of the tools and methods used needs improvement. In order to address some of these challenges, we conducted agronomic trials and on-farm surveys to adapt the SARRAH (Syst`eme d’Analyse R´egionale des Risques Agroclimatiques, version H) crop simulation model, and also evaluated it in farmers’ field conditions. The farmers’ practices such as sowing dates and densities, fertilizer use and yields potentials of the millet and sorghum crops were characterized under different climatic conditions.
The objective of this study was to assess the performance and predictive uncertainty of the Soil and Water Assessment Tool (SWAT) model on the Bani River Basin, at catchment and subcatchment levels. The SWAT model was calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE) approach. Potential Evapotranspiration (PET) and biomass were considered in theverificationofmodeloutputsaccuracy. GlobalSensitivityAnalysis(GSA)wasusedforidentifying important model parameters. Results indicated a good performance of the global model at daily as well as monthly time steps with adequate predictive uncertainty. PET was found to be overestimated but biomass was better predicted in agricultural land and forest. Surface runoff represents the dominant process on streamflow generation in that region. Individual calibration at subcatchment scale yielded better performance than when the global parameter sets were applied. These results are very useful and provide a support to further studies on regionalization to make prediction in ungauged basins.