Exploring the integration of indigenous knowledge into modern weather forecasting in pastoral communities in Longido district, Northern Tanzania

This study endeavored to explore the integration of IK in modern weather forecasting in pastoral communities in Longido District, Northern Tanzania. Objectives of the study were to identify and document indigenous weather prediction indicators and approaches in pastoral communities; to assess community perception on indigenous and conventional weather forecasting and climate change; as well as to evaluate institutional role and capacity in enhancing the integration of IK and Scientific weather forecasting. The study was motivated by the quest to understand how indigenous and conventional systems can be synergized on packaging weather information. The study was conducted in Longido District because of recurrent droughts that have been hitting the area, and the sensitivity of pastoral activities to the changing weather and climate. Methods for data collection were household survey, key informant interviews, field observation, and focus group discussions. Quantitative data were analyzed using SPSS version 20.0. Qualitative data were transcribed, classified, summarized and interpreted in the cross tabulation and comparison of different views. Results indicate that 62.0% of household respondents mentioned a combination of stars, animal behaviors and wind direction as the most widely used prediction indicator. About 55.7% acknowledged the reliability of indigenous weather prediction. Also, 77.2% declared to be aware of scientific weather forecasting and only 42.4% acknowledge its reliability. Again, 86.7% acknowledged to access weather information through radio and 13.3% through televisions. Evidences of climate changes impacts in the study area include decreasing of rainfall, increasing of temperature and recurrent drought. The main determinant of the community’s perception on climate change is linked to weather parameters connected to pastoral activities; particularly rainfall variability (17.7%), drought (67.1%), temperature increase (3.2%), and seasonal shift (12.0%). The redness of TMA to integrate IK in conventional system of weather forecasting was noted. However, a number of challenges like a comprehensive documentation and verification of IK indicators on weather prediction appeared to be a prerequisite for the integration process. This study recommends formulation and implementation of policies using the available institutional arrangements to include IK in formal practice of weather forecasts.