Indigenous and conventional climate-knowledge for enhanced farmers' adaptation to climate variability in the semi-arid agro-ecologies of Kenya

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Date
2021-12-01
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Elsevier
Abstract
Climate variability is among the main threats to rain-dependent smallholder farming in most sub-Saharan Africa countries. Hence, farmers should make efforts at the local level to utilize indigenous knowledge (IK) combined with conventional knowledge to adapt to climate variability impacts. We assessed; IK used by farmers in climate forecasting, their perceptions of climate variability and adaptation strategies, and their correlation with conventional approaches. We conducted the study in Tharaka South and Kitui Central sub-counties of Kenya. We used the triangulation approach to obtain the quantitative and qualitative data. To select respondents, we used purposive and random sampling strategies combined with the snowballing technique. Observed rainfall and temperature data from 1998 to 2018 were obtained from the Kenya Meteorological Department (KMD). Results showed that there were significant (p<0.05) differences in the use of indigenous indicators such as observation of the behavior of the sky (χ2 = 14.631), moon (χ2 = 7.851), and wind (χ2 = 5.864). The majority of the smallholder farmers (87%) used the change in the behavior of trees as the indigenous indicator in weather forecasting. The most common adaptation strategies (over 80%) used were food storage for future use (88.5%) and change of planting dates (87.5%). The analysis output of conventional data from KMD conformed with the farmers' observations and perception of climate variability over the reference period. Because farmers are still using IK that agrees with conventional knowledge, there is a need to integrate IK with conventional knowledge for use by rain-fed-dependent smallholder farmers in climate forecasting.
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