The now more rampant and severe droughts have become synonymous with SubSaharan
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 droughtmonitoring
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.