Integrating Indigenous And Conventional Knowledge-Based Climate Forecast For Farmers’ Enhanced Adaptation To Climate Variability In Tharaka-Nithi And Kitui Counties

ABSTRACT

Climate variability has a negative impact on crop productivity and has affected many small-holder farmers in the arid and semi-arid lands (ASALs). Small-holder farmers in the study area are faced with the constraint of climate variability and have consequently made effort at the local level to utilize indigenous knowledge (IK) in addition to conventional knowledge to mitigate the impacts of the variability. However, documentation of the IK indicators is limited. This study was therefore undertaken with the aim of identifying the IK and preparedness techniques employed in coping with climate variability by small-holder farmers, integrate the indigenous and conventional knowledge of climate forecasting and assess how the household’s socio-economic factors influence the level of adaptation to climate variability. The study was conducted in Tharaka and Kitui-Central Sub-Counties in Tharaka-Nithi and Kitui counties, respectively and used both primary and secondary data. Data collected included: (i) Indigenous and conventional knowledge of climate forecasting employed by small holder farmers, (ii) Household demographic and socio-economic characteristics, (iii) Farmers’ adaptation strategies to cope with climate variability, (iv) Rainfall and temperature data from Kenya Meteorological Department (KMD). Research design involved a triangulation approach to simultaneously collect both quantitative and qualitative data. Primary data was specifically gathered through the use of a survey. Sampling strategy involved random sampling and also a purposive sampling in combination with snow balling technique. Data was analyzed using descriptive statistics, multinomial and binary logistic regression, using variables produced through Principal Component Analysis (PCA). Results showed that there were significant differences in the use of indigenous strategies such as change in the sky (2=14.631), moon (2=7.851) and wind (2=5.864) at p