ITIKI: bridge between African indigenous knowledge and modern science of drought prediction

  • Muthoni Masinde
  • Antoine Bagula
Keywords: natural disasters, drought, weather forecasting, drought prediction, wireless networks, mobile phones, indigenous knowledge, prototype, illiteracy

Abstract

Droughts are the most common type of natural disaster in Africa and the problem is compounded by their complexity. The agriculture sector still forms the backbone of most economies in Africa, with 70% of output being derived from rain-fed small-scale farming; this sector is the first casualty of droughts. Accurate, timely and relevant drought predication information enables a community to anticipate and prepare for droughts and hence minimize the negative impacts. Current weather forecasts are still alien to African farmers, most of whom live in rural areas and struggle with illiteracy and poor communications infrastructure. However, these farmers hold indigenous knowledge not only on how to predict droughts, but also on unique coping strategies. Adoption of wireless sensor networks and mobile phones to provide a bridge between scientific and indigenous knowledge of weather forecasting methods is one way of ensuring that the content of forecasts and the dissemination formats meet local needs. A framework for achieving this integration is presented in this paper. A system prototype to implement this framework is also presented.

References

Agent Oriented Software Pty Ltd, 2011. JACK: Jack intelligent agent user guide.
Ajibade, L.T. and Shokemi, O., 2003. Indigenous approach to weather forecasting in ASA L.G.A., Kwara State, Nigeria. African Journal of Indigenous Knowledge Systems, 2 (1), 37–44.
Dondeyne, S., Emanuel, L.B., and Deckers, J.A., 2003. Mr Napite’s botanical knowledge: bridging farmers’ and scientists’ insights during participatory research. African Journal of Indigenous
Knowledge Systems, 2 (2), 45–57.
Eiko, Y. and Bacon, J., 2006. A survey of wireless sensor network technologies: research trends and middleware’s role. University of Cambridge Technical Report.
Huschke, R.E., 1959. Glossary of meteorology. Boston, MA: American Meteorological Society.
ISDR, 2006. Developing early warning systems: a checklist. In: Third international conference on early warning: from concept to action, EWC III, pp. 1–13.
ISDR, 2008. 3rd African drought adaptation forum report, Addis Ababa, Ethiopia, 17–19 September 2008. ITU, 2008. Ubiquitous sensor networks (USNs). ITU-T’s Technology Watch Briefing
Report series, No. 4 (February). Available from: www.itu.int/dms_pub/itu-t/oth/23/T230100000 40001PDFE.pdf.
ITU, 2010. The world in 2010 ICT figures: the rise of 3G. ITU-D, ITU World Telecommunication/ICT Indicators database.
Johnson, M., 1992. Lore: capturing traditional environmental knowledge. Ottawa: Dene Cultural Institute and the International Development Research Centre.
KNBS, 2009. The Kenya census 2009: population and housing census highlights. Nairobi: Government Press.
Langhill, S., 1999. Indigenous knowledge: a resource kit for sustainable development researchers in dryland Africa. Ottawa: IDRC.
Loubser, J.A., 2005. Unpacking the expression ‘indigenous knowledge systems’. African Journal of Indigenous Knowledge Systems, 4 (1), 74–86.
Masinde, M. and Bagula, A., 2010. A framework for predicting droughts in developing countries using sensor networks and mobile phones. In: Proceedings of SAICSIT 2010, Bela Bela, South Africa. New York: ACM, 390-393. Available from: http://dl.acm.org/citation.cfm?id=1899551&dl=ACM&coll=DL&CFID=78821470&CFTOKEN=21038012
Masinde, M. and Bagula, A., 2011. The role of ICTs in quantifying the severity and duration of climatic variations – Kenya’s case. ITU Kaleidoscope, 12–14 December.
Masinde, M., Bagula, A., and Murage, V., 2010. MOBIGRID: a middleware for integrating mobile phone and grid computing. In: The 6th international conference on network and service
management, CNSM 2010, 25–29 October. New York: IEEE Communications Society.
Masinde,M., Zeba N., and Bagula, A., 2012. Extending the power of mobile phone using service oriented computing. In: N. Venkatasubramanian et al., eds. Mobilware 2011, LNICST 93. Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 34–44.
Mehdi, S. and Ghorbani, AA., 2004. Application of belief–desire–intention agents in intrusion detection and response. PST, 13–15 October. 181–191.
Mishra, A.K. and Desai, V.R., 2006. Drought forecasting using fee-forward recursive neural network. Ecological Modeling, 198, 128–138.
Mugabe, F.T., et al., 2010. Use of indigenous knowledge systems and scientific methods for climate forecasting in southern Zambia and north western Zimbabwe. Zimbabwe Journal of Technological Sciences, 1 (1).
Njiraine, D., Ocholla, D.N., and Bosire, O.O., 2010. Indigenous knowledge research in Kenya and South Africa: an informetric study. African Journal of Indigenous Knowledge Systems, 9(2), 194–208.
O’Hare, G.M.P. and Jennings, N.R., 1996. Foundations of distributed artificial intelligence. New York: John Wiley & Sons.
Panu, U.S. and Sharma, T.C., 2002. Challenges in drought research: some perspectives and future directions. Hydrological Sciences Journal 47 (August), 19–30.
Red Cross, 2010. The International Federation of Red Cross and Red Crescent Societies. World Disaster report 2010 – Focus on Urban Risk [online]. Available from: http://www.ifrc.org/Global/Publications/disasters/WDR/WDR2010-full.pdf
Roos, V., Shingairai, C., and Niekerk, D.V., 2010. Coping with drought: indigenous knowledge application in rural South Africa. African Journal of Indigenous Knowledge Systems, 9 (1), 1–10.
Rosenberg, N.J., 1979. Drought in the Great Plains – research on impacts and strategies. In:Workshop on research in Great Plains drought management strategies, 26–28 March. Highlands Ranch,
CO: Water Resources Publications.
Steiner, A., 2008. Indigenous knowledge in disaster management in Africa. United Nations Environment Programme (UNEP). Available from: http://www.unep.org/IK/PDF/IndigenousBooklet.pdf
Szöllösi-Nagy, A., 1999. Integrated drought management: lessons for Sub-Saharan Africa. International Hydrological Programme, UNESCO Division of Water Sciences. Available from:
http://www.unesdoc.unesco.org/images/0012/001262/126257e.pdf
Randall, T.E. and Gaudet, B.J., 2008. Implementing priority task management in Jack intelligent agents. SpringSim ’08, 14–17 April. In: Proceedings of the 2008 Spring simulation multiconference. San Diego, CA: Society for Computer Simulation International article 14.
UNEP, 2011. Indigenous knowledge home [online]. Available from: http://Hqweb.Unep.Org/Ik/ [Accessed 3 May 2011].
Warwick, R.A., 1975. Drought hazard in the United States: a research assessment. Nsf/Ra/E-75/004. Boulder, CO: University of Colorado, Institute of Behavioral Science.
Wikipedia, 2010. Zigbee [online]. Available from: http://en.wikipedia.org/wiki/ZigBee [Accessed 3 May 2011].
Wilhite, D.A. and Glantz, M.H., 1985. Understanding the drought phenomenon: the role of definitions. Water International, 10 (3), 111–120.
Yevjevich, V., Hall, W.A., and Salas, J.D., 1977. Drought research needs. Conference on drought research needs, 12–15 December, Colorado State University, Fort Collins, CO.
Ziervogel, G. and Opere, A., 2010. Integrating meteorological and indigenous knowledge-based seasonal climate forecasts for the agricultural sector: lessons from participatory action
research in sub-Saharan Africa. IDRC [online]. Available from: http://web.idrc.ca/uploads/user-S/12882908321CCAA_seasonal_forecasting.pdf
Published
2019-09-06