The now more rampant and severe droughts have become synonymous with Sub-Saharan Africa; they are a major contributor to the acute food insecurity in the Region. Though this scenario may be replicated in other regions in the globe, the uniqueness of the problem in Sub-Saharan Africa is to be found in the ineffectiveness of the drought monitoring and predicting tools in use in these countries. Here, resource-challenged National Meteorological Services are tasked with drought monitoring responsibility. The main form of forecasts is the Seasonal Climate Forecasts whose utilisation by small-scale farmers is below par; they instead consult their Indigenous Knowledge Forecasts. This is partly because the earlier are too supply-driven, too ""coarse"" to have meaning at the local level and their dissemination channels are ineffective. Indigenous Knowledge Forecasts are under serious threat from events such as climate variations and ""modernisation""; blending it with the scientific forecasts can mitigate some of this. Conversely, incorporating Indigenous Knowledge Forecasts into the Seasonal Climate Forecasts will improve its relevance (cultural and local) and acceptability, hence boosting its utilisation among small-scale farmers. The advantages of such a mutual symbiosis relationship between these two forecasting systems can be accelerated using ICTs. This is the thrust of this research: a novel drought-monitoring and predicting solution that is designed to work within the unique context of small-scale farmers in Sub-Saharan Africa. The research started off by designing a novel integration framework that creates the much-needed bridge (itiki) between Indigenous Knowledge Forecasts and Seasonal Climate Forecasts. The Framework was then converted into a sustainable, relevant and acceptable Drought Early Warning System prototype that uses mobile phones as input/output devices and wireless sensor-based weather meters to complement the weather stations. This was then deployed in Mbeere and Bunyore regions in Kenya. The complexity of the resulting system was enormous and to ensure that these myriad parts worked together, artificial intelligence technologies were employed: artificial neural networks to develop forecast models with accuracies of 70% to 98% for lead-times of 1 day to 4 years; fuzzy logic to store and manipulate the holistic indigenous knowledge; and intelligent agents for linking the prototype modules
