Adapting to a changing climate: indigenous biotic rainfall forecasting in Western Zambia.

Abstract

Forecasting rainfall at the local scale to inform farm-level decisions is complex and it remains an unresolved problem with dire implications for food security. Here, we examine indigenous knowledge forecasting systems used by smallholder farmers in Maondo Agriculture Camp (MAC) of Sesheke District in the Western Province of Zambia to increase their climate change adaptive capacity at the farm level. We adopted a qualitative approach that uses an exploratory-descriptive design. We then used purposive sampling, a non-probability methodological approach, to choose respondents. We applied semi-structured interviews and questionnaires as data collection tools and examined the data using thematic content analysis. We found that > 50% of small-scale farmers receive forecasts produced by the Zambia Meteorological Department (ZMD) through stakeholders' meetings. Farmers who do not receive ZMD forecasts depend on indigenous knowledge systems. Results further indicate that farmers in the MAC combine several indicators to predict rainfall. Prominent among them include plants, weather-related parameters, and astrological indicators. A cursory inspection of these rainfall predictors revealed several points specifically highlighting three salient thematic contents, i.e. biological, meteorological, and astrological. Results further showed that both conventional science and indigenous knowledge used to forecast rainfall have strengths and weaknesses. We, therefore, conclude that the integration of the two methods has the potential to significantly improve rainfall forecasts and ultimately agricultural productivity at the farm level.