Indigenous Knowledge Systems for Rainfall Prediction
Meteorological Indigenous Knowledge Systems for Prediction of Rainfall in the Chimanimani District of Zimbabwe and Potential for Community Disaster Preparedness. Pindai M. Sithole1, Moses Chundu2.
1Academic Department, Africa Leadership and Management Academy (ALMA), Harare, Zimbabwe
2Faculty of Business Management Sciences and Economics, University of Zimbabwe, Harare, Zimbabwe
ABSTRACT: The paper focuses on indigenous knowledge systems (IKS) which people of Chimanimani District of Zimbabwe use to predict rainfall patterns. This is based on a 2019 socio-ecological study conducted in the district. The main objective of the study was to establish the extent of use and efficacy of meteorological IKS for the prediction of rainfall in Chimanimani and to explore the potential contribution of the meteorological IKS in local community disaster preparedness. The study came at the backdrop of a trail of unprecedented human and environmental destruction that occurred in the district following the devastating Cyclone Idai of April of 2019. Despite the existence of IKS and modern weather forecasting tools, the Cyclone seemed to have caught both the community and government unprepared, prompting this study. In depth-interviews and storytelling were conducted with traditional leaders and community elders. A qualitative approach was applied through the use of detailed interviews and focus group discussions to collect meteorological IKS from three communities in the district. Thematic analysis anchored in the interpretive and ontological paradigms was utilized within the realism theory. The meteorological IKS found in the study were classified into five interpretive categories namely vegetation, animals, birds, insects, and cosmology. The study concluded that meteorological IKS are no longer widely used due to a number of factors including inferioritisation and limited documentation. It was noted that the IKS has the potential for incorporation into the community disaster preparedness frameworks.
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